Split (1)

train · 18.2k rows

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CRF	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/xl/cxlj66yhbj6pddjs6ct6bbzhrptsw5opnb7fqx3xemmnc2kvezgp.py # Topologically Sorted Source Nodes: [add_1, max_1], Original ATen: [aten.add, aten.max] # Source node to ATen node mapping: # add_1 => add_1 # max_1 => max_1 # Graph fragment: # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze_2, %unsqueeze_1), kwargs = {}) # %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.dim](args = (%add_1, 1), kwargs = {}) triton_poi_fused_add_max_0 = async_compile.triton('triton_poi_fused_add_max_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i64', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_max_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_max_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (16x1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (0)) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (1 + (16x1)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (1)) tmp8 = tl.broadcast_to(tmp7, [XBLOCK]) tmp10 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last') tmp13 = tl.load(in_ptr0 + (2 + (16x1)), xmask, eviction_policy='evict_last') tmp14 = tl.load(in_ptr1 + (2)) tmp15 = tl.broadcast_to(tmp14, [XBLOCK]) tmp17 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last') tmp20 = tl.load(in_ptr0 + (3 + (16x1)), xmask, eviction_policy='evict_last') tmp21 = tl.load(in_ptr1 + (3)) tmp22 = tl.broadcast_to(tmp21, [XBLOCK]) tmp24 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last') tmp3 = tmp0 + tmp2 tmp5 = tmp3 + tmp4 tmp9 = tmp6 + tmp8 tmp11 = tmp9 + tmp10 tmp12 = triton_helpers.maximum(tmp5, tmp11) tmp16 = tmp13 + tmp15 tmp18 = tmp16 + tmp17 tmp19 = triton_helpers.maximum(tmp12, tmp18) tmp23 = tmp20 + tmp22 tmp25 = tmp23 + tmp24 tmp26 = triton_helpers.maximum(tmp19, tmp25) tmp27 = tmp5 > tmp11 tmp28 = tmp5 == tmp11 tmp29 = tmp5 != tmp5 tmp30 = tmp11 != tmp11 tmp31 = tmp29 > tmp30 tmp32 = tmp27 \| tmp31 tmp33 = tmp29 & tmp30 tmp34 = tmp28 \| tmp33 tmp35 = tl.full([1], 0, tl.int64) tmp36 = tl.full([1], 1, tl.int64) tmp37 = tmp35 < tmp36 tmp38 = tmp34 & tmp37 tmp39 = tmp32 \| tmp38 tmp40 = tl.where(tmp39, tmp5, tmp11) tmp41 = tl.where(tmp39, tmp35, tmp36) tmp42 = tmp40 > tmp18 tmp43 = tmp40 == tmp18 tmp44 = tmp40 != tmp40 tmp45 = tmp18 != tmp18 tmp46 = tmp44 > tmp45 tmp47 = tmp42 \| tmp46 tmp48 = tmp44 & tmp45 tmp49 = tmp43 \| tmp48 tmp50 = tl.full([1], 2, tl.int64) tmp51 = tmp41 < tmp50 tmp52 = tmp49 & tmp51 tmp53 = tmp47 \| tmp52 tmp54 = tl.where(tmp53, tmp40, tmp18) tmp55 = tl.where(tmp53, tmp41, tmp50) tmp56 = tmp54 > tmp25 tmp57 = tmp54 == tmp25 tmp58 = tmp54 != tmp54 tmp59 = tmp25 != tmp25 tmp60 = tmp58 > tmp59 tmp61 = tmp56 \| tmp60 tmp62 = tmp58 & tmp59 tmp63 = tmp57 \| tmp62 tmp64 = tl.full([1], 3, tl.int64) tmp65 = tmp55 < tmp64 tmp66 = tmp63 & tmp65 tmp67 = tmp61 \| tmp66 tmp68 = tl.where(tmp67, tmp54, tmp25) tmp69 = tl.where(tmp67, tmp55, tmp64) tl.store(out_ptr0 + (x2), tmp26, xmask) tl.store(out_ptr1 + (x2), tmp69, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/y6/cy6zns6lydgnhxyuiflkxztnecejlxnrq4tudu5ep65cuidnsrnl.py # Topologically Sorted Source Nodes: [add_3, max_2], Original ATen: [aten.add, aten.max] # Source node to ATen node mapping: # add_3 => add_3 # max_2 => max_2 # Graph fragment: # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze_3, %unsqueeze_1), kwargs = {}) # %max_2 : [num_users=2] = call_function[target=torch.ops.aten.max.dim](args = (%add_3, 1), kwargs = {}) triton_poi_fused_add_max_1 = async_compile.triton('triton_poi_fused_add_max_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i64', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_max_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_max_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4 + (16x1)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr1 + (5 + (16x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (6 + (16x1)), xmask, eviction_policy='evict_last') tmp14 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last') tmp17 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp18 = tl.load(in_ptr1 + (7 + (16x1)), xmask, eviction_policy='evict_last') tmp20 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp7 = tmp5 + tmp6 tmp9 = tmp7 + tmp8 tmp10 = triton_helpers.maximum(tmp4, tmp9) tmp13 = tmp11 + tmp12 tmp15 = tmp13 + tmp14 tmp16 = triton_helpers.maximum(tmp10, tmp15) tmp19 = tmp17 + tmp18 tmp21 = tmp19 + tmp20 tmp22 = triton_helpers.maximum(tmp16, tmp21) tmp23 = tmp4 > tmp9 tmp24 = tmp4 == tmp9 tmp25 = tmp4 != tmp4 tmp26 = tmp9 != tmp9 tmp27 = tmp25 > tmp26 tmp28 = tmp23 \| tmp27 tmp29 = tmp25 & tmp26 tmp30 = tmp24 \| tmp29 tmp31 = tl.full([1], 0, tl.int64) tmp32 = tl.full([1], 1, tl.int64) tmp33 = tmp31 < tmp32 tmp34 = tmp30 & tmp33 tmp35 = tmp28 \| tmp34 tmp36 = tl.where(tmp35, tmp4, tmp9) tmp37 = tl.where(tmp35, tmp31, tmp32) tmp38 = tmp36 > tmp15 tmp39 = tmp36 == tmp15 tmp40 = tmp36 != tmp36 tmp41 = tmp15 != tmp15 tmp42 = tmp40 > tmp41 tmp43 = tmp38 \| tmp42 tmp44 = tmp40 & tmp41 tmp45 = tmp39 \| tmp44 tmp46 = tl.full([1], 2, tl.int64) tmp47 = tmp37 < tmp46 tmp48 = tmp45 & tmp47 tmp49 = tmp43 \| tmp48 tmp50 = tl.where(tmp49, tmp36, tmp15) tmp51 = tl.where(tmp49, tmp37, tmp46) tmp52 = tmp50 > tmp21 tmp53 = tmp50 == tmp21 tmp54 = tmp50 != tmp50 tmp55 = tmp21 != tmp21 tmp56 = tmp54 > tmp55 tmp57 = tmp52 \| tmp56 tmp58 = tmp54 & tmp55 tmp59 = tmp53 \| tmp58 tmp60 = tl.full([1], 3, tl.int64) tmp61 = tmp51 < tmp60 tmp62 = tmp59 & tmp61 tmp63 = tmp57 \| tmp62 tmp64 = tl.where(tmp63, tmp50, tmp21) tmp65 = tl.where(tmp63, tmp51, tmp60) tl.store(out_ptr0 + (x2), tmp22, xmask) tl.store(out_ptr1 + (x2), tmp65, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/zk/czkkpjfsouwtzbomvb3pdvson7uz2wx2kly4u3glmaj6qbd3ivs7.py # Topologically Sorted Source Nodes: [add_5, max_3], Original ATen: [aten.add, aten.max] # Source node to ATen node mapping: # add_5 => add_5 # max_3 => max_3 # Graph fragment: # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze_4, %unsqueeze_1), kwargs = {}) # %max_3 : [num_users=2] = call_function[target=torch.ops.aten.max.dim](args = (%add_5, 1), kwargs = {}) triton_poi_fused_add_max_2 = async_compile.triton('triton_poi_fused_add_max_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i64', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_max_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_max_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (8 + (16x1)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr1 + (9 + (16x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (10 + (16x1)), xmask, eviction_policy='evict_last') tmp14 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last') tmp17 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp18 = tl.load(in_ptr1 + (11 + (16x1)), xmask, eviction_policy='evict_last') tmp20 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp7 = tmp5 + tmp6 tmp9 = tmp7 + tmp8 tmp10 = triton_helpers.maximum(tmp4, tmp9) tmp13 = tmp11 + tmp12 tmp15 = tmp13 + tmp14 tmp16 = triton_helpers.maximum(tmp10, tmp15) tmp19 = tmp17 + tmp18 tmp21 = tmp19 + tmp20 tmp22 = triton_helpers.maximum(tmp16, tmp21) tmp23 = tmp4 > tmp9 tmp24 = tmp4 == tmp9 tmp25 = tmp4 != tmp4 tmp26 = tmp9 != tmp9 tmp27 = tmp25 > tmp26 tmp28 = tmp23 \| tmp27 tmp29 = tmp25 & tmp26 tmp30 = tmp24 \| tmp29 tmp31 = tl.full([1], 0, tl.int64) tmp32 = tl.full([1], 1, tl.int64) tmp33 = tmp31 < tmp32 tmp34 = tmp30 & tmp33 tmp35 = tmp28 \| tmp34 tmp36 = tl.where(tmp35, tmp4, tmp9) tmp37 = tl.where(tmp35, tmp31, tmp32) tmp38 = tmp36 > tmp15 tmp39 = tmp36 == tmp15 tmp40 = tmp36 != tmp36 tmp41 = tmp15 != tmp15 tmp42 = tmp40 > tmp41 tmp43 = tmp38 \| tmp42 tmp44 = tmp40 & tmp41 tmp45 = tmp39 \| tmp44 tmp46 = tl.full([1], 2, tl.int64) tmp47 = tmp37 < tmp46 tmp48 = tmp45 & tmp47 tmp49 = tmp43 \| tmp48 tmp50 = tl.where(tmp49, tmp36, tmp15) tmp51 = tl.where(tmp49, tmp37, tmp46) tmp52 = tmp50 > tmp21 tmp53 = tmp50 == tmp21 tmp54 = tmp50 != tmp50 tmp55 = tmp21 != tmp21 tmp56 = tmp54 > tmp55 tmp57 = tmp52 \| tmp56 tmp58 = tmp54 & tmp55 tmp59 = tmp53 \| tmp58 tmp60 = tl.full([1], 3, tl.int64) tmp61 = tmp51 < tmp60 tmp62 = tmp59 & tmp61 tmp63 = tmp57 \| tmp62 tmp64 = tl.where(tmp63, tmp50, tmp21) tmp65 = tl.where(tmp63, tmp51, tmp60) tl.store(out_ptr0 + (x2), tmp22, xmask) tl.store(out_ptr1 + (x2), tmp65, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/mk/cmksqduwcd5tloln2etlzpluekcgaronfgkbvxfvpa7fahyhlyd2.py # Topologically Sorted Source Nodes: [v_6, add_7, max_4, tag_1, tag_2, tag_3], Original ATen: [aten.add, aten.max, aten.gather] # Source node to ATen node mapping: # add_7 => add_7 # max_4 => max_4 # tag_1 => gather # tag_2 => gather_1 # tag_3 => gather_2 # v_6 => add_6 # Graph fragment: # %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, %select_3), kwargs = {}) # %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_6, %unsqueeze_5), kwargs = {}) # %max_4 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%add_7, 1, True), kwargs = {}) # %gather : [num_users=2] = call_function[target=torch.ops.aten.gather.default](args = (%getitem_5, 1, %getitem_7), kwargs = {}) # %gather_1 : [num_users=2] = call_function[target=torch.ops.aten.gather.default](args = (%getitem_3, 1, %gather), kwargs = {}) # %gather_2 : [num_users=1] = call_function[target=torch.ops.aten.gather.default](args = (%getitem_1, 1, %gather_1), kwargs = {}) triton_poi_fused_add_gather_max_3 = async_compile.triton('triton_poi_fused_add_gather_max_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i64', 4: 'i64', 5: 'i64', 6: 'i64', 7: 'i64', 8: 'i64', 9: 'i64', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_gather_max_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_gather_max_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr): xnumel = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (12 + (16x0)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (0)) tmp4 = tl.broadcast_to(tmp3, [XBLOCK]) tmp6 = tl.load(in_ptr0 + (1 + (4x0)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (13 + (16x0)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (1)) tmp10 = tl.broadcast_to(tmp9, [XBLOCK]) tmp27 = tl.load(in_ptr0 + (2 + (4x0)), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr1 + (14 + (16x0)), xmask, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + (2)) tmp31 = tl.broadcast_to(tmp30, [XBLOCK]) tmp47 = tl.load(in_ptr0 + (3 + (4x0)), xmask, eviction_policy='evict_last') tmp48 = tl.load(in_ptr1 + (15 + (16x0)), xmask, eviction_policy='evict_last') tmp50 = tl.load(in_ptr2 + (3)) tmp51 = tl.broadcast_to(tmp50, [XBLOCK]) tmp2 = tmp0 + tmp1 tmp5 = tmp2 + tmp4 tmp8 = tmp6 + tmp7 tmp11 = tmp8 + tmp10 tmp12 = tmp5 > tmp11 tmp13 = tmp5 == tmp11 tmp14 = tmp5 != tmp5 tmp15 = tmp11 != tmp11 tmp16 = tmp14 > tmp15 tmp17 = tmp12 \| tmp16 tmp18 = tmp14 & tmp15 tmp19 = tmp13 \| tmp18 tmp20 = tl.full([1], 0, tl.int64) tmp21 = tl.full([1], 1, tl.int64) tmp22 = tmp20 < tmp21 tmp23 = tmp19 & tmp22 tmp24 = tmp17 \| tmp23 tmp25 = tl.where(tmp24, tmp5, tmp11) tmp26 = tl.where(tmp24, tmp20, tmp21) tmp29 = tmp27 + tmp28 tmp32 = tmp29 + tmp31 tmp33 = tmp25 > tmp32 tmp34 = tmp25 == tmp32 tmp35 = tmp25 != tmp25 tmp36 = tmp32 != tmp32 tmp37 = tmp35 > tmp36 tmp38 = tmp33 \| tmp37 tmp39 = tmp35 & tmp36 tmp40 = tmp34 \| tmp39 tmp41 = tl.full([1], 2, tl.int64) tmp42 = tmp26 < tmp41 tmp43 = tmp40 & tmp42 tmp44 = tmp38 \| tmp43 tmp45 = tl.where(tmp44, tmp25, tmp32) tmp46 = tl.where(tmp44, tmp26, tmp41) tmp49 = tmp47 + tmp48 tmp52 = tmp49 + tmp51 tmp53 = tmp45 > tmp52 tmp54 = tmp45 == tmp52 tmp55 = tmp45 != tmp45 tmp56 = tmp52 != tmp52 tmp57 = tmp55 > tmp56 tmp58 = tmp53 \| tmp57 tmp59 = tmp55 & tmp56 tmp60 = tmp54 \| tmp59 tmp61 = tl.full([1], 3, tl.int64) tmp62 = tmp46 < tmp61 tmp63 = tmp60 & tmp62 tmp64 = tmp58 \| tmp63 tmp65 = tl.where(tmp64, tmp45, tmp52) tmp66 = tl.where(tmp64, tmp46, tmp61) tmp67 = tl.full([XBLOCK], 4, tl.int32) tmp68 = tmp66 + tmp67 tmp69 = tmp66 < 0 tmp70 = tl.where(tmp69, tmp68, tmp66) tl.device_assert(((0 <= tmp70) & (tmp70 < 4)) \| ~(xmask), "index out of bounds: 0 <= tmp70 < 4") tmp72 = tl.load(in_ptr3 + (tmp70 + (4x0)), xmask, eviction_policy='evict_last') tmp73 = tmp72 + tmp67 tmp74 = tmp72 < 0 tmp75 = tl.where(tmp74, tmp73, tmp72) tl.device_assert(((0 <= tmp75) & (tmp75 < 4)) \| ~(xmask), "index out of bounds: 0 <= tmp75 < 4") tmp77 = tl.load(in_ptr4 + (tmp75 + (4x0)), xmask, eviction_policy='evict_last') tmp78 = tmp77 + tmp67 tmp79 = tmp77 < 0 tmp80 = tl.where(tmp79, tmp78, tmp77) tl.device_assert(((0 <= tmp80) & (tmp80 < 4)) \| ~(xmask), "index out of bounds: 0 <= tmp80 < 4") tmp82 = tl.load(in_ptr5 + (tmp80 + (4x0)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (4x0), tmp66, xmask) tl.store(out_ptr1 + (4x0), tmp82, xmask) tl.store(out_ptr2 + (4x0), tmp77, xmask) tl.store(out_ptr3 + (4*x0), tmp72, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1, arg3_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(arg1_1, (4, ), (1, )) assert_size_stride(arg2_1, (4, 4), (4, 1)) assert_size_stride(arg3_1, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf1 = empty_strided_cuda((4, 4), (4, 1), torch.int64) # Topologically Sorted Source Nodes: [add_1, max_1], Original ATen: [aten.add, aten.max] stream0 = get_raw_stream(0) triton_poi_fused_add_max_0.run(arg0_1, arg1_1, arg2_1, buf0, buf1, 16, grid=grid(16), stream=stream0) del arg1_1 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.int64) # Topologically Sorted Source Nodes: [add_3, max_2], Original ATen: [aten.add, aten.max] triton_poi_fused_add_max_1.run(buf0, arg0_1, arg2_1, buf2, buf3, 16, grid=grid(16), stream=stream0) buf4 = buf0; del buf0 # reuse buf5 = empty_strided_cuda((4, 4), (4, 1), torch.int64) # Topologically Sorted Source Nodes: [add_5, max_3], Original ATen: [aten.add, aten.max] triton_poi_fused_add_max_2.run(buf2, arg0_1, arg2_1, buf4, buf5, 16, grid=grid(16), stream=stream0) del arg2_1 del buf2 buf10 = empty_strided_cuda((4, 4), (4, 1), torch.int64) buf6 = reinterpret_tensor(buf10, (4, 1), (4, 1), 3) # alias buf7 = reinterpret_tensor(buf10, (4, 1), (4, 1), 0) # alias buf8 = reinterpret_tensor(buf10, (4, 1), (4, 1), 1) # alias buf9 = reinterpret_tensor(buf10, (4, 1), (4, 1), 2) # alias # Topologically Sorted Source Nodes: [v_6, add_7, max_4, tag_1, tag_2, tag_3], Original ATen: [aten.add, aten.max, aten.gather] triton_poi_fused_add_gather_max_3.run(buf4, arg0_1, arg3_1, buf5, buf3, buf1, buf6, buf7, buf8, buf9, 4, grid=grid(4), stream=stream0) del arg0_1 del arg3_1 del buf1 del buf3 del buf4 del buf5 return (buf10, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) arg3_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class CRF(nn.Module): """ Implements Conditional Random Fields that can be trained via backpropagation. """ def __init__(self, num_tags): super(CRF, self).__init__() self.num_tags = num_tags self.transitions = nn.Parameter(torch.Tensor(num_tags, num_tags), requires_grad=True) self.start_transitions = nn.Parameter(torch.randn(num_tags), requires_grad=True) self.stop_transitions = nn.Parameter(torch.randn(num_tags), requires_grad=True) nn.init.xavier_normal_(self.transitions) self.params = {'transitions': self.transitions, 'start_transitions': self.start_transitions, 'stop_transitions': self.stop_transitions} def forward(self, feats, params=None): if params is None: params = self.params if len(feats.shape) != 3: raise ValueError('feats must be 3-d got {}-d'.format(feats.shape)) return self._viterbi(feats) def loss(self, feats, tags, params=None): """ Computes negative log likelihood between features and tags. Essentially difference between individual sequence scores and sum of all possible sequence scores (partition function) Parameters: feats: Input features [batch size, sequence length, number of tags] tags: Target tag indices [batch size, sequence length]. Should be between 0 and num_tags Returns: Negative log likelihood [a scalar] """ if params is not None: self.params = params else: params = self.params if len(feats.shape) != 3: raise ValueError('feats must be 3-d got {}-d'.format(feats.shape)) if len(tags.shape) != 2: raise ValueError('tags must be 2-d but got {}-d'.format(tags.shape) ) if feats.shape[:2] != tags.shape: raise ValueError( 'First two dimensions of feats and tags must match ', feats .shape, tags.shape) sequence_score = self._sequence_score(feats, tags, params) partition_function = self._partition_function(feats, params) log_probability = sequence_score - partition_function return -log_probability.mean() def _sequence_score(self, feats, tags, params): """ Parameters: feats: Input features [batch size, sequence length, number of tags] tags: Target tag indices [batch size, sequence length]. Should be between 0 and num_tags Returns: Sequence score of shape [batch size] """ feat_score = feats.gather(2, tags.unsqueeze(-1)).squeeze(-1).sum(dim=-1 ) tags_pairs = tags.unfold(1, 2, 1) indices = tags_pairs.permute(2, 0, 1).chunk(2) self.transitions = params['transitions'] self.start_transitions = params['start_transitions'] self.stop_transitions = params['stop_transitions'] trans_score = self.transitions[indices].squeeze(0).sum(dim=-1) start_score = self.start_transitions[tags[:, 0]] stop_score = self.stop_transitions[tags[:, -1]] return feat_score + start_score + trans_score + stop_score def _partition_function(self, feats, params): """ Computes the partitition function for CRF using the forward algorithm. Basically calculate scores for all possible tag sequences for the given feature vector sequence Parameters: feats: Input features [batch size, sequence length, number of tags] Returns: Total scores of shape [batch size] """ _, seq_size, num_tags = feats.shape if self.num_tags != num_tags: raise ValueError('num_tags should be {} but got {}'.format(self .num_tags, num_tags)) self.transitions = params['transitions'] self.start_transitions = params['start_transitions'] self.stop_transitions = params['stop_transitions'] a = feats[:, 0] + self.start_transitions.unsqueeze(0) transitions = self.transitions.unsqueeze(0) for i in range(1, seq_size): feat = feats[:, i].unsqueeze(1) a = self._log_sum_exp(a.unsqueeze(-1) + transitions + feat, 1) return self._log_sum_exp(a + self.stop_transitions.unsqueeze(0), 1) def _viterbi(self, feats): """ Uses Viterbi algorithm to predict the best sequence Parameters: feats: Input features [batch size, sequence length, number of tags] Returns: Best tag sequence [batch size, sequence length] """ _, seq_size, num_tags = feats.shape if self.num_tags != num_tags: raise ValueError('num_tags should be {} but got {}'.format(self .num_tags, num_tags)) v = feats[:, 0] + self.start_transitions.unsqueeze(0) transitions = self.transitions.unsqueeze(0) paths = [] for i in range(1, seq_size): feat = feats[:, i] v, idx = (v.unsqueeze(-1) + transitions).max(1) paths.append(idx) v = v + feat v, tag = (v + self.stop_transitions.unsqueeze(0)).max(1, True) tags = [tag] for idx in reversed(paths): tag = idx.gather(1, tag) tags.append(tag) tags.reverse() return torch.cat(tags, 1) def _log_sum_exp(self, logits, dim): """ Computes log-sum-exp in a stable way """ max_val, _ = logits.max(dim) return max_val + (logits - max_val.unsqueeze(dim)).exp().sum(dim).log() def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'num_tags': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_add_max_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + 16 * x1, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (1 + 16 * x1), xmask, eviction_policy='evict_last' ) tmp7 = tl.load(in_ptr1 + 1) tmp8 = tl.broadcast_to(tmp7, [XBLOCK]) tmp10 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last') tmp13 = tl.load(in_ptr0 + (2 + 16 * x1), xmask, eviction_policy= 'evict_last') tmp14 = tl.load(in_ptr1 + 2) tmp15 = tl.broadcast_to(tmp14, [XBLOCK]) tmp17 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last') tmp20 = tl.load(in_ptr0 + (3 + 16 * x1), xmask, eviction_policy= 'evict_last') tmp21 = tl.load(in_ptr1 + 3) tmp22 = tl.broadcast_to(tmp21, [XBLOCK]) tmp24 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last') tmp3 = tmp0 + tmp2 tmp5 = tmp3 + tmp4 tmp9 = tmp6 + tmp8 tmp11 = tmp9 + tmp10 tmp12 = triton_helpers.maximum(tmp5, tmp11) tmp16 = tmp13 + tmp15 tmp18 = tmp16 + tmp17 tmp19 = triton_helpers.maximum(tmp12, tmp18) tmp23 = tmp20 + tmp22 tmp25 = tmp23 + tmp24 tmp26 = triton_helpers.maximum(tmp19, tmp25) tmp27 = tmp5 > tmp11 tmp28 = tmp5 == tmp11 tmp29 = tmp5 != tmp5 tmp30 = tmp11 != tmp11 tmp31 = tmp29 > tmp30 tmp32 = tmp27 \| tmp31 tmp33 = tmp29 & tmp30 tmp34 = tmp28 \| tmp33 tmp35 = tl.full([1], 0, tl.int64) tmp36 = tl.full([1], 1, tl.int64) tmp37 = tmp35 < tmp36 tmp38 = tmp34 & tmp37 tmp39 = tmp32 \| tmp38 tmp40 = tl.where(tmp39, tmp5, tmp11) tmp41 = tl.where(tmp39, tmp35, tmp36) tmp42 = tmp40 > tmp18 tmp43 = tmp40 == tmp18 tmp44 = tmp40 != tmp40 tmp45 = tmp18 != tmp18 tmp46 = tmp44 > tmp45 tmp47 = tmp42 \| tmp46 tmp48 = tmp44 & tmp45 tmp49 = tmp43 \| tmp48 tmp50 = tl.full([1], 2, tl.int64) tmp51 = tmp41 < tmp50 tmp52 = tmp49 & tmp51 tmp53 = tmp47 \| tmp52 tmp54 = tl.where(tmp53, tmp40, tmp18) tmp55 = tl.where(tmp53, tmp41, tmp50) tmp56 = tmp54 > tmp25 tmp57 = tmp54 == tmp25 tmp58 = tmp54 != tmp54 tmp59 = tmp25 != tmp25 tmp60 = tmp58 > tmp59 tmp61 = tmp56 \| tmp60 tmp62 = tmp58 & tmp59 tmp63 = tmp57 \| tmp62 tmp64 = tl.full([1], 3, tl.int64) tmp65 = tmp55 < tmp64 tmp66 = tmp63 & tmp65 tmp67 = tmp61 \| tmp66 tl.where(tmp67, tmp54, tmp25) tmp69 = tl.where(tmp67, tmp55, tmp64) tl.store(out_ptr0 + x2, tmp26, xmask) tl.store(out_ptr1 + x2, tmp69, xmask) @triton.jit def triton_poi_fused_add_max_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4 + 16 * x1), xmask, eviction_policy='evict_last' ) tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr1 + (5 + 16 * x1), xmask, eviction_policy='evict_last' ) tmp8 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp12 = tl.load(in_ptr1 + (6 + 16 * x1), xmask, eviction_policy= 'evict_last') tmp14 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last') tmp17 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp18 = tl.load(in_ptr1 + (7 + 16 * x1), xmask, eviction_policy= 'evict_last') tmp20 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp7 = tmp5 + tmp6 tmp9 = tmp7 + tmp8 tmp10 = triton_helpers.maximum(tmp4, tmp9) tmp13 = tmp11 + tmp12 tmp15 = tmp13 + tmp14 tmp16 = triton_helpers.maximum(tmp10, tmp15) tmp19 = tmp17 + tmp18 tmp21 = tmp19 + tmp20 tmp22 = triton_helpers.maximum(tmp16, tmp21) tmp23 = tmp4 > tmp9 tmp24 = tmp4 == tmp9 tmp25 = tmp4 != tmp4 tmp26 = tmp9 != tmp9 tmp27 = tmp25 > tmp26 tmp28 = tmp23 \| tmp27 tmp29 = tmp25 & tmp26 tmp30 = tmp24 \| tmp29 tmp31 = tl.full([1], 0, tl.int64) tmp32 = tl.full([1], 1, tl.int64) tmp33 = tmp31 < tmp32 tmp34 = tmp30 & tmp33 tmp35 = tmp28 \| tmp34 tmp36 = tl.where(tmp35, tmp4, tmp9) tmp37 = tl.where(tmp35, tmp31, tmp32) tmp38 = tmp36 > tmp15 tmp39 = tmp36 == tmp15 tmp40 = tmp36 != tmp36 tmp41 = tmp15 != tmp15 tmp42 = tmp40 > tmp41 tmp43 = tmp38 \| tmp42 tmp44 = tmp40 & tmp41 tmp45 = tmp39 \| tmp44 tmp46 = tl.full([1], 2, tl.int64) tmp47 = tmp37 < tmp46 tmp48 = tmp45 & tmp47 tmp49 = tmp43 \| tmp48 tmp50 = tl.where(tmp49, tmp36, tmp15) tmp51 = tl.where(tmp49, tmp37, tmp46) tmp52 = tmp50 > tmp21 tmp53 = tmp50 == tmp21 tmp54 = tmp50 != tmp50 tmp55 = tmp21 != tmp21 tmp56 = tmp54 > tmp55 tmp57 = tmp52 \| tmp56 tmp58 = tmp54 & tmp55 tmp59 = tmp53 \| tmp58 tmp60 = tl.full([1], 3, tl.int64) tmp61 = tmp51 < tmp60 tmp62 = tmp59 & tmp61 tmp63 = tmp57 \| tmp62 tl.where(tmp63, tmp50, tmp21) tmp65 = tl.where(tmp63, tmp51, tmp60) tl.store(out_ptr0 + x2, tmp22, xmask) tl.store(out_ptr1 + x2, tmp65, xmask) @triton.jit def triton_poi_fused_add_max_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (8 + 16 * x1), xmask, eviction_policy='evict_last' ) tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr1 + (9 + 16 * x1), xmask, eviction_policy='evict_last' ) tmp8 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp12 = tl.load(in_ptr1 + (10 + 16 * x1), xmask, eviction_policy= 'evict_last') tmp14 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last') tmp17 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp18 = tl.load(in_ptr1 + (11 + 16 * x1), xmask, eviction_policy= 'evict_last') tmp20 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp7 = tmp5 + tmp6 tmp9 = tmp7 + tmp8 tmp10 = triton_helpers.maximum(tmp4, tmp9) tmp13 = tmp11 + tmp12 tmp15 = tmp13 + tmp14 tmp16 = triton_helpers.maximum(tmp10, tmp15) tmp19 = tmp17 + tmp18 tmp21 = tmp19 + tmp20 tmp22 = triton_helpers.maximum(tmp16, tmp21) tmp23 = tmp4 > tmp9 tmp24 = tmp4 == tmp9 tmp25 = tmp4 != tmp4 tmp26 = tmp9 != tmp9 tmp27 = tmp25 > tmp26 tmp28 = tmp23 \| tmp27 tmp29 = tmp25 & tmp26 tmp30 = tmp24 \| tmp29 tmp31 = tl.full([1], 0, tl.int64) tmp32 = tl.full([1], 1, tl.int64) tmp33 = tmp31 < tmp32 tmp34 = tmp30 & tmp33 tmp35 = tmp28 \| tmp34 tmp36 = tl.where(tmp35, tmp4, tmp9) tmp37 = tl.where(tmp35, tmp31, tmp32) tmp38 = tmp36 > tmp15 tmp39 = tmp36 == tmp15 tmp40 = tmp36 != tmp36 tmp41 = tmp15 != tmp15 tmp42 = tmp40 > tmp41 tmp43 = tmp38 \| tmp42 tmp44 = tmp40 & tmp41 tmp45 = tmp39 \| tmp44 tmp46 = tl.full([1], 2, tl.int64) tmp47 = tmp37 < tmp46 tmp48 = tmp45 & tmp47 tmp49 = tmp43 \| tmp48 tmp50 = tl.where(tmp49, tmp36, tmp15) tmp51 = tl.where(tmp49, tmp37, tmp46) tmp52 = tmp50 > tmp21 tmp53 = tmp50 == tmp21 tmp54 = tmp50 != tmp50 tmp55 = tmp21 != tmp21 tmp56 = tmp54 > tmp55 tmp57 = tmp52 \| tmp56 tmp58 = tmp54 & tmp55 tmp59 = tmp53 \| tmp58 tmp60 = tl.full([1], 3, tl.int64) tmp61 = tmp51 < tmp60 tmp62 = tmp59 & tmp61 tmp63 = tmp57 \| tmp62 tl.where(tmp63, tmp50, tmp21) tmp65 = tl.where(tmp63, tmp51, tmp60) tl.store(out_ptr0 + x2, tmp22, xmask) tl.store(out_ptr1 + x2, tmp65, xmask) @triton.jit def triton_poi_fused_add_gather_max_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr): xnumel = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (12 + 16 * x0), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr2 + 0) tmp4 = tl.broadcast_to(tmp3, [XBLOCK]) tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (13 + 16 * x0), xmask, eviction_policy= 'evict_last') tmp9 = tl.load(in_ptr2 + 1) tmp10 = tl.broadcast_to(tmp9, [XBLOCK]) tmp27 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp28 = tl.load(in_ptr1 + (14 + 16 * x0), xmask, eviction_policy= 'evict_last') tmp30 = tl.load(in_ptr2 + 2) tmp31 = tl.broadcast_to(tmp30, [XBLOCK]) tmp47 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp48 = tl.load(in_ptr1 + (15 + 16 * x0), xmask, eviction_policy= 'evict_last') tmp50 = tl.load(in_ptr2 + 3) tmp51 = tl.broadcast_to(tmp50, [XBLOCK]) tmp2 = tmp0 + tmp1 tmp5 = tmp2 + tmp4 tmp8 = tmp6 + tmp7 tmp11 = tmp8 + tmp10 tmp12 = tmp5 > tmp11 tmp13 = tmp5 == tmp11 tmp14 = tmp5 != tmp5 tmp15 = tmp11 != tmp11 tmp16 = tmp14 > tmp15 tmp17 = tmp12 \| tmp16 tmp18 = tmp14 & tmp15 tmp19 = tmp13 \| tmp18 tmp20 = tl.full([1], 0, tl.int64) tmp21 = tl.full([1], 1, tl.int64) tmp22 = tmp20 < tmp21 tmp23 = tmp19 & tmp22 tmp24 = tmp17 \| tmp23 tmp25 = tl.where(tmp24, tmp5, tmp11) tmp26 = tl.where(tmp24, tmp20, tmp21) tmp29 = tmp27 + tmp28 tmp32 = tmp29 + tmp31 tmp33 = tmp25 > tmp32 tmp34 = tmp25 == tmp32 tmp35 = tmp25 != tmp25 tmp36 = tmp32 != tmp32 tmp37 = tmp35 > tmp36 tmp38 = tmp33 \| tmp37 tmp39 = tmp35 & tmp36 tmp40 = tmp34 \| tmp39 tmp41 = tl.full([1], 2, tl.int64) tmp42 = tmp26 < tmp41 tmp43 = tmp40 & tmp42 tmp44 = tmp38 \| tmp43 tmp45 = tl.where(tmp44, tmp25, tmp32) tmp46 = tl.where(tmp44, tmp26, tmp41) tmp49 = tmp47 + tmp48 tmp52 = tmp49 + tmp51 tmp53 = tmp45 > tmp52 tmp54 = tmp45 == tmp52 tmp55 = tmp45 != tmp45 tmp56 = tmp52 != tmp52 tmp57 = tmp55 > tmp56 tmp58 = tmp53 \| tmp57 tmp59 = tmp55 & tmp56 tmp60 = tmp54 \| tmp59 tmp61 = tl.full([1], 3, tl.int64) tmp62 = tmp46 < tmp61 tmp63 = tmp60 & tmp62 tmp64 = tmp58 \| tmp63 tl.where(tmp64, tmp45, tmp52) tmp66 = tl.where(tmp64, tmp46, tmp61) tmp67 = tl.full([XBLOCK], 4, tl.int32) tmp68 = tmp66 + tmp67 tmp69 = tmp66 < 0 tmp70 = tl.where(tmp69, tmp68, tmp66) tl.device_assert((0 <= tmp70) & (tmp70 < 4) \| ~xmask, 'index out of bounds: 0 <= tmp70 < 4') tmp72 = tl.load(in_ptr3 + (tmp70 + 4 * x0), xmask, eviction_policy= 'evict_last') tmp73 = tmp72 + tmp67 tmp74 = tmp72 < 0 tmp75 = tl.where(tmp74, tmp73, tmp72) tl.device_assert((0 <= tmp75) & (tmp75 < 4) \| ~xmask, 'index out of bounds: 0 <= tmp75 < 4') tmp77 = tl.load(in_ptr4 + (tmp75 + 4 * x0), xmask, eviction_policy= 'evict_last') tmp78 = tmp77 + tmp67 tmp79 = tmp77 < 0 tmp80 = tl.where(tmp79, tmp78, tmp77) tl.device_assert((0 <= tmp80) & (tmp80 < 4) \| ~xmask, 'index out of bounds: 0 <= tmp80 < 4') tmp82 = tl.load(in_ptr5 + (tmp80 + 4 * x0), xmask, eviction_policy= 'evict_last') tl.store(out_ptr0 + 4 * x0, tmp66, xmask) tl.store(out_ptr1 + 4 * x0, tmp82, xmask) tl.store(out_ptr2 + 4 * x0, tmp77, xmask) tl.store(out_ptr3 + 4 * x0, tmp72, xmask) def call(args): arg0_1, arg1_1, arg2_1, arg3_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(arg1_1, (4,), (1,)) assert_size_stride(arg2_1, (4, 4), (4, 1)) assert_size_stride(arg3_1, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf1 = empty_strided_cuda((4, 4), (4, 1), torch.int64) get_raw_stream(0) triton_poi_fused_add_max_0[grid(16)](arg0_1, arg1_1, arg2_1, buf0, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1) del arg1_1 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.int64) triton_poi_fused_add_max_1[grid(16)](buf0, arg0_1, arg2_1, buf2, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) buf4 = buf0 del buf0 buf5 = empty_strided_cuda((4, 4), (4, 1), torch.int64) triton_poi_fused_add_max_2[grid(16)](buf2, arg0_1, arg2_1, buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1) del arg2_1 del buf2 buf10 = empty_strided_cuda((4, 4), (4, 1), torch.int64) buf6 = reinterpret_tensor(buf10, (4, 1), (4, 1), 3) buf7 = reinterpret_tensor(buf10, (4, 1), (4, 1), 0) buf8 = reinterpret_tensor(buf10, (4, 1), (4, 1), 1) buf9 = reinterpret_tensor(buf10, (4, 1), (4, 1), 2) triton_poi_fused_add_gather_max_3[grid(4)](buf4, arg0_1, arg3_1, buf5, buf3, buf1, buf6, buf7, buf8, buf9, 4, XBLOCK=4, num_warps=1, num_stages=1) del arg0_1 del arg3_1 del buf1 del buf3 del buf4 del buf5 return buf10, class CRFNew(nn.Module): """ Implements Conditional Random Fields that can be trained via backpropagation. """ def __init__(self, num_tags): super(CRFNew, self).__init__() self.num_tags = num_tags self.transitions = nn.Parameter(torch.Tensor(num_tags, num_tags), requires_grad=True) self.start_transitions = nn.Parameter(torch.randn(num_tags), requires_grad=True) self.stop_transitions = nn.Parameter(torch.randn(num_tags), requires_grad=True) nn.init.xavier_normal_(self.transitions) self.params = {'transitions': self.transitions, 'start_transitions': self.start_transitions, 'stop_transitions': self.stop_transitions} def loss(self, feats, tags, params=None): """ Computes negative log likelihood between features and tags. Essentially difference between individual sequence scores and sum of all possible sequence scores (partition function) Parameters: feats: Input features [batch size, sequence length, number of tags] tags: Target tag indices [batch size, sequence length]. Should be between 0 and num_tags Returns: Negative log likelihood [a scalar] """ if params is not None: self.params = params else: params = self.params if len(feats.shape) != 3: raise ValueError('feats must be 3-d got {}-d'.format(feats.shape)) if len(tags.shape) != 2: raise ValueError('tags must be 2-d but got {}-d'.format(tags.shape) ) if feats.shape[:2] != tags.shape: raise ValueError( 'First two dimensions of feats and tags must match ', feats .shape, tags.shape) sequence_score = self._sequence_score(feats, tags, params) partition_function = self._partition_function(feats, params) log_probability = sequence_score - partition_function return -log_probability.mean() def _sequence_score(self, feats, tags, params): """ Parameters: feats: Input features [batch size, sequence length, number of tags] tags: Target tag indices [batch size, sequence length]. Should be between 0 and num_tags Returns: Sequence score of shape [batch size] """ feat_score = feats.gather(2, tags.unsqueeze(-1)).squeeze(-1).sum(dim=-1 ) tags_pairs = tags.unfold(1, 2, 1) indices = tags_pairs.permute(2, 0, 1).chunk(2) self.transitions = params['transitions'] self.start_transitions = params['start_transitions'] self.stop_transitions = params['stop_transitions'] trans_score = self.transitions[indices].squeeze(0).sum(dim=-1) start_score = self.start_transitions[tags[:, 0]] stop_score = self.stop_transitions[tags[:, -1]] return feat_score + start_score + trans_score + stop_score def _partition_function(self, feats, params): """ Computes the partitition function for CRF using the forward algorithm. Basically calculate scores for all possible tag sequences for the given feature vector sequence Parameters: feats: Input features [batch size, sequence length, number of tags] Returns: Total scores of shape [batch size] """ _, seq_size, num_tags = feats.shape if self.num_tags != num_tags: raise ValueError('num_tags should be {} but got {}'.format(self .num_tags, num_tags)) self.transitions = params['transitions'] self.start_transitions = params['start_transitions'] self.stop_transitions = params['stop_transitions'] a = feats[:, 0] + self.start_transitions.unsqueeze(0) transitions = self.transitions.unsqueeze(0) for i in range(1, seq_size): feat = feats[:, i].unsqueeze(1) a = self._log_sum_exp(a.unsqueeze(-1) + transitions + feat, 1) return self._log_sum_exp(a + self.stop_transitions.unsqueeze(0), 1) def _viterbi(self, feats): """ Uses Viterbi algorithm to predict the best sequence Parameters: feats: Input features [batch size, sequence length, number of tags] Returns: Best tag sequence [batch size, sequence length] """ _, seq_size, num_tags = feats.shape if self.num_tags != num_tags: raise ValueError('num_tags should be {} but got {}'.format(self .num_tags, num_tags)) v = feats[:, 0] + self.start_transitions.unsqueeze(0) transitions = self.transitions.unsqueeze(0) paths = [] for i in range(1, seq_size): feat = feats[:, i] v, idx = (v.unsqueeze(-1) + transitions).max(1) paths.append(idx) v = v + feat v, tag = (v + self.stop_transitions.unsqueeze(0)).max(1, True) tags = [tag] for idx in reversed(paths): tag = idx.gather(1, tag) tags.append(tag) tags.reverse() return torch.cat(tags, 1) def _log_sum_exp(self, logits, dim): """ Computes log-sum-exp in a stable way """ max_val, _ = logits.max(dim) return max_val + (logits - max_val.unsqueeze(dim)).exp().sum(dim).log() def forward(self, input_0): arg2_1 = self.transitions arg1_1 = self.start_transitions arg3_1 = self.stop_transitions arg0_1 = input_0 output = call([arg0_1, arg1_1, arg2_1, arg3_1]) return output[0]	Franck-Dernoncourt/meta_cross_nlu_qa	CRF	false	8,146	[ "MIT" ]	14	98f0af07988f24d9c7827030765246c6f67a0f4d	https://github.com/Franck-Dernoncourt/meta_cross_nlu_qa/tree/98f0af07988f24d9c7827030765246c6f67a0f4d
Model	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/zb/czbbllwh7sfg4givymrrkjxzw23nmw5ismoyqt4debm22krcdkqc.py # Topologically Sorted Source Nodes: [h_0], Original ATen: [aten.zero] # Source node to ATen node mapping: # h_0 => full # Graph fragment: # %full : [num_users=3] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) triton_poi_fused_zero_0 = async_compile.triton('triton_poi_fused_zero_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_zero_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_zero_0(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = 0.0 tl.store(out_ptr0 + (x0), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gu/cgumvpbq5erg3rnqxec3fd4jo7ptcsj3kczlww57elnxgqgsibht.py # Topologically Sorted Source Nodes: [sigmoid, mul, sigmoid_1, tanh, mul_1, c_1, sigmoid_2, tanh_1, h_1], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward] # Source node to ATen node mapping: # c_1 => add_1 # h_1 => mul_2 # mul => mul # mul_1 => mul_1 # sigmoid => sigmoid # sigmoid_1 => sigmoid_1 # sigmoid_2 => sigmoid_2 # tanh => tanh # tanh_1 => tanh_1 # Graph fragment: # %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %full), kwargs = {}) # %sigmoid_1 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_1,), kwargs = {}) # %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_3,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %tanh), kwargs = {}) # %add_1 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {}) # %sigmoid_2 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_2,), kwargs = {}) # %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {}) # %mul_2 : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %tanh_1), kwargs = {}) # %sub_19 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {}) # %mul_73 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %sub_19), kwargs = {}) triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (4 + x0 + (16x1)), xmask) tmp1 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (4 + x0 + (16x1)), xmask) tmp6 = tl.load(in_ptr0 + (12 + x0 + (16x1)), xmask) tmp7 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (12 + x0 + (16x1)), xmask) tmp12 = tl.load(in_ptr0 + (x0 + (16x1)), xmask) tmp13 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (x0 + (16x1)), xmask) tmp25 = tl.load(in_ptr0 + (8 + x0 + (16x1)), xmask) tmp26 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr2 + (8 + x0 + (16x1)), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = libdevice.tanh(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = tl.sigmoid(tmp16) tmp18 = 0.0 tmp19 = tmp17 * tmp18 tmp20 = tmp5 * tmp11 tmp21 = tmp19 + tmp20 tmp22 = 1.0 tmp23 = tmp22 - tmp17 tmp24 = tmp17 * tmp23 tmp27 = tmp25 + tmp26 tmp29 = tmp27 + tmp28 tmp30 = tl.sigmoid(tmp29) tmp31 = libdevice.tanh(tmp21) tmp32 = tmp30 * tmp31 tl.store(out_ptr0 + (x2), tmp5, xmask) tl.store(out_ptr1 + (x2), tmp11, xmask) tl.store(out_ptr2 + (x2), tmp21, xmask) tl.store(out_ptr3 + (x2), tmp24, xmask) tl.store(out_ptr4 + (x2), tmp30, xmask) tl.store(out_ptr5 + (x2), tmp32, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gq/cgqwwez7hgnau2ufccutodv4pz77aowb3qzpfc3ym7ivb7snhuqp.py # Topologically Sorted Source Nodes: [sigmoid_3, mul_3, sigmoid_4, tanh_2, mul_4, c_2, sigmoid_5, tanh_3, h_2], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] # Source node to ATen node mapping: # c_2 => add_3 # h_2 => mul_5 # mul_3 => mul_3 # mul_4 => mul_4 # sigmoid_3 => sigmoid_3 # sigmoid_4 => sigmoid_4 # sigmoid_5 => sigmoid_5 # tanh_2 => tanh_2 # tanh_3 => tanh_3 # Graph fragment: # %sigmoid_3 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_4,), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_3, %add_1), kwargs = {}) # %sigmoid_4 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_5,), kwargs = {}) # %tanh_2 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_7,), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_4, %tanh_2), kwargs = {}) # %add_3 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %mul_4), kwargs = {}) # %sigmoid_5 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_6,), kwargs = {}) # %tanh_3 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_3,), kwargs = {}) # %mul_5 : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_5, %tanh_3), kwargs = {}) triton_poi_fused_add_mul_sigmoid_tanh_2 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_tanh_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'fp32', 9: 'fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_tanh_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 13, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16x1)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0 + (16x1)), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + (16x1)), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (4 + x0 + (16x1)), xmask) tmp12 = tl.load(in_ptr0 + (12 + x0 + (16x1)), xmask) tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (12 + x0 + (16x1)), xmask) tmp18 = tl.load(in_ptr3 + (x2), xmask) tmp22 = tl.load(in_ptr0 + (8 + x0 + (16x1)), xmask) tmp23 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp25 = tl.load(in_ptr2 + (8 + x0 + (16x1)), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = tl.sigmoid(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = libdevice.tanh(tmp16) tmp19 = tmp5 * tmp18 tmp20 = tmp11 * tmp17 tmp21 = tmp19 + tmp20 tmp24 = tmp22 + tmp23 tmp26 = tmp24 + tmp25 tmp27 = tl.sigmoid(tmp26) tmp28 = libdevice.tanh(tmp21) tmp29 = tmp27 * tmp28 tl.store(out_ptr0 + (x2), tmp5, xmask) tl.store(out_ptr1 + (x2), tmp11, xmask) tl.store(out_ptr2 + (x2), tmp17, xmask) tl.store(out_ptr3 + (x2), tmp21, xmask) tl.store(out_ptr4 + (x2), tmp27, xmask) tl.store(out_ptr5 + (x2), tmp29, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/p4/cp4hc27y5z2efbcvfuh3f2ytub6r2x3mg2tag432n5zqm32vjxl6.py # Topologically Sorted Source Nodes: [sigmoid_9, mul_9, sigmoid_10, tanh_6, mul_10, c_4, tanh_7], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] # Source node to ATen node mapping: # c_4 => add_7 # mul_10 => mul_10 # mul_9 => mul_9 # sigmoid_10 => sigmoid_10 # sigmoid_9 => sigmoid_9 # tanh_6 => tanh_6 # tanh_7 => tanh_7 # Graph fragment: # %sigmoid_9 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_12,), kwargs = {}) # %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_9, %add_5), kwargs = {}) # %sigmoid_10 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_13,), kwargs = {}) # %tanh_6 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_15,), kwargs = {}) # %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_10, %tanh_6), kwargs = {}) # %add_7 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_9, %mul_10), kwargs = {}) # %tanh_7 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_7,), kwargs = {}) triton_poi_fused_add_mul_sigmoid_tanh_3 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_tanh_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_tanh_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_sigmoid_tanh_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16x1)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0 + (16x1)), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + (16x1)), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (4 + x0 + (16x1)), xmask) tmp12 = tl.load(in_ptr0 + (12 + x0 + (16x1)), xmask) tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (12 + x0 + (16x1)), xmask) tmp18 = tl.load(in_ptr3 + (x2), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = tl.sigmoid(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = libdevice.tanh(tmp16) tmp19 = tmp5 * tmp18 tmp20 = tmp11 * tmp17 tmp21 = tmp19 + tmp20 tmp22 = libdevice.tanh(tmp21) tl.store(out_ptr0 + (x2), tmp5, xmask) tl.store(out_ptr1 + (x2), tmp11, xmask) tl.store(out_ptr2 + (x2), tmp17, xmask) tl.store(out_ptr3 + (x2), tmp22, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/7n/c7ndokn6ji4llxnabzyjlads6rh4x5ey476tt2px3zc4d23m2qgt.py # Topologically Sorted Source Nodes: [sigmoid_11], Original ATen: [aten.sigmoid] # Source node to ATen node mapping: # sigmoid_11 => sigmoid_11 # Graph fragment: # %sigmoid_11 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_14,), kwargs = {}) triton_poi_fused_sigmoid_4 = async_compile.triton('triton_poi_fused_sigmoid_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_sigmoid_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (8 + x0 + (16x1)), xmask) tmp1 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (8 + x0 + (16x1)), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tl.store(out_ptr0 + (x2), tmp5, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yv/cyv52nqg474urnhaqatxvbklhih33ignejyc4cwxxhp2jytcbskj.py # Topologically Sorted Source Nodes: [c_n], Original ATen: [aten.stack] # Source node to ATen node mapping: # c_n => cat_1 # Graph fragment: # %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%add_1, %add_3, %add_5, %add_7],), kwargs = {}) triton_poi_fused_stack_5 = async_compile.triton('triton_poi_fused_stack_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_stack_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x0 = xindex % 4 x2 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (4x1)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + (4((-4) + x1))), tmp9 & xmask, other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + (4((-8) + x1))), tmp14 & xmask, other=0.0) tmp16 = tmp0 >= tmp12 tmp17 = tl.full([1], 16, tl.int64) tmp18 = tmp0 < tmp17 tmp19 = tl.load(in_ptr3 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp20 = tl.load(in_ptr2 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp21 = tmp19 tmp20 tmp22 = tl.load(in_ptr4 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp23 = tl.load(in_ptr5 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp24 = tmp22 * tmp23 tmp25 = tmp21 + tmp24 tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype) tmp27 = tl.where(tmp16, tmp25, tmp26) tmp28 = tl.where(tmp14, tmp15, tmp27) tmp29 = tl.where(tmp9, tmp10, tmp28) tmp30 = tl.where(tmp4, tmp5, tmp29) tl.store(out_ptr0 + (x2), tmp30, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ai/cai7gbb6ayhln7jiow4pkqlopuq3kycdei7elu6sopif4qdkhilf.py # Topologically Sorted Source Nodes: [h_n], Original ATen: [aten.stack] # Source node to ATen node mapping: # h_n => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul_2, %mul_5, %mul_8, %mul_11],), kwargs = {}) triton_poi_fused_stack_6 = async_compile.triton('triton_poi_fused_stack_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_stack_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x0 = xindex % 4 x2 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (4x1)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + (4((-4) + x1))), tmp9 & xmask, other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + (4((-8) + x1))), tmp14 & xmask, other=0.0) tmp16 = tmp0 >= tmp12 tmp17 = tl.full([1], 16, tl.int64) tmp18 = tmp0 < tmp17 tmp19 = tl.load(in_ptr3 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp20 = tl.load(in_ptr4 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp21 = tmp19 tmp20 tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype) tmp23 = tl.where(tmp16, tmp21, tmp22) tmp24 = tl.where(tmp14, tmp15, tmp23) tmp25 = tl.where(tmp9, tmp10, tmp24) tmp26 = tl.where(tmp4, tmp5, tmp25) tl.store(out_ptr0 + (x2), tmp26, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (16, ), (1, )) assert_size_stride(primals_3, (4, 16), (16, 1)) assert_size_stride(primals_4, (4, 16), (16, 1)) assert_size_stride(primals_5, (1, 4), (4, 1)) assert_size_stride(primals_6, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [h_0], Original ATen: [aten.zero] stream0 = get_raw_stream(0) triton_poi_fused_zero_0.run(buf0, 16, grid=grid(16), stream=stream0) buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf0, primals_3, out=buf1) buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32) # Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 0), primals_4, out=buf2) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf35 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid, mul, sigmoid_1, tanh, mul_1, c_1, sigmoid_2, tanh_1, h_1], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward] triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1.run(buf1, primals_2, buf2, buf3, buf4, buf5, buf35, buf6, buf7, 16, grid=grid(16), stream=stream0) buf8 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf7, primals_3, out=buf8) buf9 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [mm_1], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 4), primals_4, out=buf9) buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid_3, mul_3, sigmoid_4, tanh_2, mul_4, c_2, sigmoid_5, tanh_3, h_2], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] triton_poi_fused_add_mul_sigmoid_tanh_2.run(buf8, primals_2, buf9, buf5, buf10, buf11, buf12, buf13, buf14, buf15, 16, grid=grid(16), stream=stream0) buf16 = buf9; del buf9 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf15, primals_3, out=buf16) buf17 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [mm_2], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 8), primals_4, out=buf17) buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf20 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf21 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf22 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf23 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid_6, mul_6, sigmoid_7, tanh_4, mul_7, c_3, sigmoid_8, tanh_5, h_3], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] triton_poi_fused_add_mul_sigmoid_tanh_2.run(buf16, primals_2, buf17, buf13, buf18, buf19, buf20, buf21, buf22, buf23, 16, grid=grid(16), stream=stream0) buf24 = buf17; del buf17 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf23, primals_3, out=buf24) buf25 = buf16; del buf16 # reuse # Topologically Sorted Source Nodes: [mm_3], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 12), primals_4, out=buf25) del primals_4 buf26 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf27 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf28 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid_9, mul_9, sigmoid_10, tanh_6, mul_10, c_4, tanh_7], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] triton_poi_fused_add_mul_sigmoid_tanh_3.run(buf24, primals_2, buf25, buf21, buf26, buf27, buf28, buf30, 16, grid=grid(16), stream=stream0) buf29 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid_11], Original ATen: [aten.sigmoid] triton_poi_fused_sigmoid_4.run(buf24, primals_2, buf25, buf29, 16, grid=grid(16), stream=stream0) del primals_2 buf31 = reinterpret_tensor(buf25, (16, 4), (4, 1), 0); del buf25 # reuse # Topologically Sorted Source Nodes: [c_n], Original ATen: [aten.stack] triton_poi_fused_stack_5.run(buf5, buf13, buf21, buf26, buf27, buf28, buf31, 64, grid=grid(64), stream=stream0) buf32 = reinterpret_tensor(buf24, (16, 4), (4, 1), 0); del buf24 # reuse # Topologically Sorted Source Nodes: [h_n], Original ATen: [aten.stack] triton_poi_fused_stack_6.run(buf7, buf15, buf23, buf29, buf30, buf32, 64, grid=grid(64), stream=stream0) buf34 = empty_strided_cuda((4, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.addmm] extern_kernels.addmm(primals_6, reinterpret_tensor(buf32, (4, 4), (4, 1), 48), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf34) del primals_6 return (buf34, reinterpret_tensor(buf32, (4, 4, 4), (4, 16, 1), 0), reinterpret_tensor(buf31, (4, 4, 4), (4, 16, 1), 0), buf0, buf3, buf4, buf5, buf6, buf10, buf11, buf12, buf13, buf14, buf18, buf19, buf20, buf21, buf22, buf26, buf27, buf28, buf29, buf30, reinterpret_tensor(buf32, (4, 4), (4, 1), 48), primals_5, reinterpret_tensor(primals_1, (4, 4), (1, 16), 12), reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), reinterpret_tensor(buf23, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 16), 8), reinterpret_tensor(buf15, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 16), 4), reinterpret_tensor(buf7, (4, 4), (1, 4), 0), buf35, reinterpret_tensor(primals_1, (4, 4), (1, 16), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from typing import Tuple import torch.nn as nn class LSTM(nn.Module): """Implementation of the standard LSTM. TODO: Include ref and LaTeX equations Parameters ---------- input_size : int Number of input features hidden_size : int Number of hidden/memory cells. batch_first : bool, optional If True, expects the batch inputs to be of shape [batch, seq, features] otherwise, the shape has to be [seq, batch, features], by default True. initial_forget_bias : int, optional Value of the initial forget gate bias, by default 0 """ def __init__(self, input_size: 'int', hidden_size: 'int', batch_first: 'bool'=True, initial_forget_bias: 'int'=0): super(LSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.batch_first = batch_first self.initial_forget_bias = initial_forget_bias self.weight_ih = nn.Parameter(torch.FloatTensor(input_size, 4 * hidden_size)) self.weight_hh = nn.Parameter(torch.FloatTensor(hidden_size, 4 * hidden_size)) self.bias = nn.Parameter(torch.FloatTensor(4 * hidden_size)) self.reset_parameters() def reset_parameters(self): """Initialize all learnable parameters of the LSTM""" nn.init.orthogonal_(self.weight_ih.data) weight_hh_data = torch.eye(self.hidden_size) weight_hh_data = weight_hh_data.repeat(1, 4) self.weight_hh.data = weight_hh_data nn.init.constant_(self.bias.data, val=0) if self.initial_forget_bias != 0: self.bias.data[:self.hidden_size] = self.initial_forget_bias def forward(self, x: 'torch.Tensor') ->Tuple[torch.Tensor, torch.Tensor]: """[summary] Parameters ---------- x : torch.Tensor Tensor, containing a batch of input sequences. Format must match the specified format, defined by the batch_first agrument. Returns ------- h_n : torch.Tensor The hidden states of each time step of each sample in the batch. c_n : torch.Tensor] The cell states of each time step of each sample in the batch. """ if self.batch_first: x = x.transpose(0, 1) seq_len, batch_size, _ = x.size() h_0 = x.data.new(batch_size, self.hidden_size).zero_() c_0 = x.data.new(batch_size, self.hidden_size).zero_() h_x = h_0, c_0 h_n, c_n = [], [] bias_batch = self.bias.unsqueeze(0).expand(batch_size, self.bias. size()) for t in range(seq_len): h_0, c_0 = h_x gates = torch.addmm(bias_batch, h_0, self.weight_hh) + torch.mm(x [t], self.weight_ih) f, i, o, g = gates.chunk(4, 1) c_1 = torch.sigmoid(f) c_0 + torch.sigmoid(i) * torch.tanh(g) h_1 = torch.sigmoid(o) * torch.tanh(c_1) h_n.append(h_1) c_n.append(c_1) h_x = h_1, c_1 h_n = torch.stack(h_n, 0) c_n = torch.stack(c_n, 0) if self.batch_first: h_n = h_n.transpose(0, 1) c_n = c_n.transpose(0, 1) return h_n, c_n class Model(nn.Module): """Wrapper class that connects LSTM/EA-LSTM with fully connceted layer""" def __init__(self, input_size_dyn: 'int', hidden_size: 'int', initial_forget_bias: 'int'=5, dropout: 'float'=0.0, concat_static: 'bool'=False, no_static: 'bool'=False): """Initialize model. Parameters ---------- input_size_dyn: int Number of dynamic input features. hidden_size: int Number of LSTM cells/hidden units. initial_forget_bias: int Value of the initial forget gate bias. (default: 5) dropout: float Dropout probability in range(0,1). (default: 0.0) concat_static: bool If True, uses standard LSTM otherwise uses EA-LSTM no_static: bool If True, runs standard LSTM """ super(Model, self).__init__() self.input_size_dyn = input_size_dyn self.hidden_size = hidden_size self.initial_forget_bias = initial_forget_bias self.dropout_rate = dropout self.concat_static = concat_static self.no_static = no_static self.lstm = LSTM(input_size=input_size_dyn, hidden_size=hidden_size, initial_forget_bias=initial_forget_bias) self.dropout = nn.Dropout(p=dropout) self.fc = nn.Linear(hidden_size, 1) def forward(self, x_d: 'torch.Tensor') ->Tuple[torch.Tensor, torch. Tensor, torch.Tensor]: """Run forward pass through the model. Parameters ---------- x_d : torch.Tensor Tensor containing the dynamic input features of shape [batch, seq_length, n_features] Returns ------- out : torch.Tensor Tensor containing the network predictions h_n : torch.Tensor Tensor containing the hidden states of each time step c_n : torch,Tensor Tensor containing the cell states of each time step """ h_n, c_n = self.lstm(x_d) last_h = self.dropout(h_n[:, -1, :]) out = self.fc(last_h) return out, h_n, c_n def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'input_size_dyn': 4, 'hidden_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice from typing import Tuple import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_zero_0(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = 0.0 tl.store(out_ptr0 + x0, tmp0, xmask) @triton.jit def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask) tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask) tmp7 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask) tmp12 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask) tmp13 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask) tmp25 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask) tmp26 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = libdevice.tanh(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = tl.sigmoid(tmp16) tmp18 = 0.0 tmp19 = tmp17 * tmp18 tmp20 = tmp5 * tmp11 tmp21 = tmp19 + tmp20 tmp22 = 1.0 tmp23 = tmp22 - tmp17 tmp24 = tmp17 * tmp23 tmp27 = tmp25 + tmp26 tmp29 = tmp27 + tmp28 tmp30 = tl.sigmoid(tmp29) tmp31 = libdevice.tanh(tmp21) tmp32 = tmp30 * tmp31 tl.store(out_ptr0 + x2, tmp5, xmask) tl.store(out_ptr1 + x2, tmp11, xmask) tl.store(out_ptr2 + x2, tmp21, xmask) tl.store(out_ptr3 + x2, tmp24, xmask) tl.store(out_ptr4 + x2, tmp30, xmask) tl.store(out_ptr5 + x2, tmp32, xmask) @triton.jit def triton_poi_fused_add_mul_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask) tmp12 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask) tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask) tmp18 = tl.load(in_ptr3 + x2, xmask) tmp22 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask) tmp23 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp25 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = tl.sigmoid(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = libdevice.tanh(tmp16) tmp19 = tmp5 * tmp18 tmp20 = tmp11 * tmp17 tmp21 = tmp19 + tmp20 tmp24 = tmp22 + tmp23 tmp26 = tmp24 + tmp25 tmp27 = tl.sigmoid(tmp26) tmp28 = libdevice.tanh(tmp21) tmp29 = tmp27 * tmp28 tl.store(out_ptr0 + x2, tmp5, xmask) tl.store(out_ptr1 + x2, tmp11, xmask) tl.store(out_ptr2 + x2, tmp17, xmask) tl.store(out_ptr3 + x2, tmp21, xmask) tl.store(out_ptr4 + x2, tmp27, xmask) tl.store(out_ptr5 + x2, tmp29, xmask) @triton.jit def triton_poi_fused_add_mul_sigmoid_tanh_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl. constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask) tmp12 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask) tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask) tmp18 = tl.load(in_ptr3 + x2, xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = tl.sigmoid(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = libdevice.tanh(tmp16) tmp19 = tmp5 * tmp18 tmp20 = tmp11 * tmp17 tmp21 = tmp19 + tmp20 tmp22 = libdevice.tanh(tmp21) tl.store(out_ptr0 + x2, tmp5, xmask) tl.store(out_ptr1 + x2, tmp11, xmask) tl.store(out_ptr2 + x2, tmp17, xmask) tl.store(out_ptr3 + x2, tmp22, xmask) @triton.jit def triton_poi_fused_sigmoid_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tl.store(out_ptr0 + x2, tmp5, xmask) @triton.jit def triton_poi_fused_stack_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x0 = xindex % 4 x2 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1)), tmp9 & xmask, other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + 4 * (-8 + x1)), tmp14 & xmask, other=0.0) tmp16 = tmp0 >= tmp12 tl.full([1], 16, tl.int64) tmp19 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp20 = tl.load(in_ptr2 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp21 = tmp19 * tmp20 tmp22 = tl.load(in_ptr4 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp23 = tl.load(in_ptr5 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp24 = tmp22 * tmp23 tmp25 = tmp21 + tmp24 tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype) tmp27 = tl.where(tmp16, tmp25, tmp26) tmp28 = tl.where(tmp14, tmp15, tmp27) tmp29 = tl.where(tmp9, tmp10, tmp28) tmp30 = tl.where(tmp4, tmp5, tmp29) tl.store(out_ptr0 + x2, tmp30, xmask) @triton.jit def triton_poi_fused_stack_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x0 = xindex % 4 x2 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1)), tmp9 & xmask, other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + 4 * (-8 + x1)), tmp14 & xmask, other=0.0) tmp16 = tmp0 >= tmp12 tl.full([1], 16, tl.int64) tmp19 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp20 = tl.load(in_ptr4 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp21 = tmp19 * tmp20 tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype) tmp23 = tl.where(tmp16, tmp21, tmp22) tmp24 = tl.where(tmp14, tmp15, tmp23) tmp25 = tl.where(tmp9, tmp10, tmp24) tmp26 = tl.where(tmp4, tmp5, tmp25) tl.store(out_ptr0 + x2, tmp26, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (16,), (1,)) assert_size_stride(primals_3, (4, 16), (16, 1)) assert_size_stride(primals_4, (4, 16), (16, 1)) assert_size_stride(primals_5, (1, 4), (4, 1)) assert_size_stride(primals_6, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_zero_0[grid(16)](buf0, 16, XBLOCK=16, num_warps=1, num_stages=1) buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32) extern_kernels.mm(buf0, primals_3, out=buf1) buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 0), primals_4, out=buf2) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf35 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1[grid(16)](buf1 , primals_2, buf2, buf3, buf4, buf5, buf35, buf6, buf7, 16, XBLOCK=16, num_warps=1, num_stages=1) buf8 = buf2 del buf2 extern_kernels.mm(buf7, primals_3, out=buf8) buf9 = buf1 del buf1 extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 4), primals_4, out=buf9) buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_mul_sigmoid_tanh_2[grid(16)](buf8, primals_2, buf9, buf5, buf10, buf11, buf12, buf13, buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1) buf16 = buf9 del buf9 extern_kernels.mm(buf15, primals_3, out=buf16) buf17 = buf8 del buf8 extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 8), primals_4, out=buf17) buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf20 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf21 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf22 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf23 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_mul_sigmoid_tanh_2[grid(16)](buf16, primals_2, buf17, buf13, buf18, buf19, buf20, buf21, buf22, buf23, 16, XBLOCK=16, num_warps=1, num_stages=1) buf24 = buf17 del buf17 extern_kernels.mm(buf23, primals_3, out=buf24) buf25 = buf16 del buf16 extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 12 ), primals_4, out=buf25) del primals_4 buf26 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf27 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf28 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_mul_sigmoid_tanh_3[grid(16)](buf24, primals_2, buf25, buf21, buf26, buf27, buf28, buf30, 16, XBLOCK=16, num_warps=1, num_stages=1) buf29 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_sigmoid_4[grid(16)](buf24, primals_2, buf25, buf29, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_2 buf31 = reinterpret_tensor(buf25, (16, 4), (4, 1), 0) del buf25 triton_poi_fused_stack_5[grid(64)](buf5, buf13, buf21, buf26, buf27, buf28, buf31, 64, XBLOCK=64, num_warps=1, num_stages=1) buf32 = reinterpret_tensor(buf24, (16, 4), (4, 1), 0) del buf24 triton_poi_fused_stack_6[grid(64)](buf7, buf15, buf23, buf29, buf30, buf32, 64, XBLOCK=64, num_warps=1, num_stages=1) buf34 = empty_strided_cuda((4, 1), (1, 1), torch.float32) extern_kernels.addmm(primals_6, reinterpret_tensor(buf32, (4, 4), ( 4, 1), 48), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf34) del primals_6 return (buf34, reinterpret_tensor(buf32, (4, 4, 4), (4, 16, 1), 0), reinterpret_tensor(buf31, (4, 4, 4), (4, 16, 1), 0), buf0, buf3, buf4, buf5, buf6, buf10, buf11, buf12, buf13, buf14, buf18, buf19, buf20, buf21, buf22, buf26, buf27, buf28, buf29, buf30, reinterpret_tensor(buf32, (4, 4), (4, 1), 48), primals_5, reinterpret_tensor(primals_1, (4, 4), (1, 16), 12), reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), reinterpret_tensor(buf23, (4, 4), (1, 4), 0), reinterpret_tensor( primals_1, (4, 4), (1, 16), 8), reinterpret_tensor(buf15, (4, 4), ( 1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 16), 4), reinterpret_tensor(buf7, (4, 4), (1, 4), 0), buf35, reinterpret_tensor(primals_1, (4, 4), (1, 16), 0)) class LSTM(nn.Module): """Implementation of the standard LSTM. TODO: Include ref and LaTeX equations Parameters ---------- input_size : int Number of input features hidden_size : int Number of hidden/memory cells. batch_first : bool, optional If True, expects the batch inputs to be of shape [batch, seq, features] otherwise, the shape has to be [seq, batch, features], by default True. initial_forget_bias : int, optional Value of the initial forget gate bias, by default 0 """ def __init__(self, input_size: 'int', hidden_size: 'int', batch_first: 'bool'=True, initial_forget_bias: 'int'=0): super(LSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.batch_first = batch_first self.initial_forget_bias = initial_forget_bias self.weight_ih = nn.Parameter(torch.FloatTensor(input_size, 4 * hidden_size)) self.weight_hh = nn.Parameter(torch.FloatTensor(hidden_size, 4 * hidden_size)) self.bias = nn.Parameter(torch.FloatTensor(4 * hidden_size)) self.reset_parameters() def reset_parameters(self): """Initialize all learnable parameters of the LSTM""" nn.init.orthogonal_(self.weight_ih.data) weight_hh_data = torch.eye(self.hidden_size) weight_hh_data = weight_hh_data.repeat(1, 4) self.weight_hh.data = weight_hh_data nn.init.constant_(self.bias.data, val=0) if self.initial_forget_bias != 0: self.bias.data[:self.hidden_size] = self.initial_forget_bias def forward(self, x: 'torch.Tensor') ->Tuple[torch.Tensor, torch.Tensor]: """[summary] Parameters ---------- x : torch.Tensor Tensor, containing a batch of input sequences. Format must match the specified format, defined by the batch_first agrument. Returns ------- h_n : torch.Tensor The hidden states of each time step of each sample in the batch. c_n : torch.Tensor] The cell states of each time step of each sample in the batch. """ if self.batch_first: x = x.transpose(0, 1) seq_len, batch_size, _ = x.size() h_0 = x.data.new(batch_size, self.hidden_size).zero_() c_0 = x.data.new(batch_size, self.hidden_size).zero_() h_x = h_0, c_0 h_n, c_n = [], [] bias_batch = self.bias.unsqueeze(0).expand(batch_size, self.bias. size()) for t in range(seq_len): h_0, c_0 = h_x gates = torch.addmm(bias_batch, h_0, self.weight_hh) + torch.mm(x [t], self.weight_ih) f, i, o, g = gates.chunk(4, 1) c_1 = torch.sigmoid(f) c_0 + torch.sigmoid(i) * torch.tanh(g) h_1 = torch.sigmoid(o) * torch.tanh(c_1) h_n.append(h_1) c_n.append(c_1) h_x = h_1, c_1 h_n = torch.stack(h_n, 0) c_n = torch.stack(c_n, 0) if self.batch_first: h_n = h_n.transpose(0, 1) c_n = c_n.transpose(0, 1) return h_n, c_n class ModelNew(nn.Module): """Wrapper class that connects LSTM/EA-LSTM with fully connceted layer""" def __init__(self, input_size_dyn: 'int', hidden_size: 'int', initial_forget_bias: 'int'=5, dropout: 'float'=0.0, concat_static: 'bool'=False, no_static: 'bool'=False): """Initialize model. Parameters ---------- input_size_dyn: int Number of dynamic input features. hidden_size: int Number of LSTM cells/hidden units. initial_forget_bias: int Value of the initial forget gate bias. (default: 5) dropout: float Dropout probability in range(0,1). (default: 0.0) concat_static: bool If True, uses standard LSTM otherwise uses EA-LSTM no_static: bool If True, runs standard LSTM """ super(ModelNew, self).__init__() self.input_size_dyn = input_size_dyn self.hidden_size = hidden_size self.initial_forget_bias = initial_forget_bias self.dropout_rate = dropout self.concat_static = concat_static self.no_static = no_static self.lstm = LSTM(input_size=input_size_dyn, hidden_size=hidden_size, initial_forget_bias=initial_forget_bias) self.dropout = nn.Dropout(p=dropout) self.fc = nn.Linear(hidden_size, 1) def forward(self, input_0): primals_3 = self.lstm.weight_ih primals_4 = self.lstm.weight_hh primals_2 = self.lstm.bias primals_5 = self.fc.weight primals_6 = self.fc.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]) return output[0], output[1], output[2]	Flash-Of-Thunder/testing	Model	false	8,147	[ "Apache-2.0" ]	18	36366e2cd32756fb07abc533ecbb7672a4738bc6	https://github.com/Flash-Of-Thunder/testing/tree/36366e2cd32756fb07abc533ecbb7672a4738bc6
SmallDecoder3_16x	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/s5/cs5cyzetyp2bdsm3i6hqlu4lg4ybgcxgjqmjyuoxibnvbimxi3yn.py # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] # Source node to ATen node mapping: # pad => _unsafe_index, _unsafe_index_1 # Graph fragment: # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 9216 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 6 x1 = (xindex // 6) % 6 x2 = (xindex // 36) x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16x2)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (x3), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ki/ckii6ld2pjui2kyh33ieyagpaxjso55jvdwg3kttj5omjlrhkys6.py # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_1 => add, add_1, convert_element_type, convert_element_type_1, iota_2, mul, mul_1 # Graph fragment: # %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {}) # %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_1 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gv/cgvu2fbgbn3bbvn62mgfltq7he63vndwqyqf2cmythsrcq7vv3mf.py # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d => convolution # pad_1 => _unsafe_index_3, _unsafe_index_4 # y => relu # y_1 => _unsafe_index_2 # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {}) # %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 12800 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 10) % 10 x0 = xindex % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 32 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (4tmp4) + (16x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/qj/cqj5pcot4hdonby4qcbt3hfanieuutwfhpjngt3csorxdmtvodat.py # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # pad_2 => _unsafe_index_5, _unsafe_index_6 # y_2 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_5, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 12800 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 10 x1 = (xindex // 10) % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 32 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/7m/c7m636s67xk4qnqesb4ybcmhmpkfe6tt3f776lqq7o47rf4vm2oo.py # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.arange] # Source node to ATen node mapping: # y_4 => iota_8 # Graph fragment: # %iota_8 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) triton_poi_fused_arange_4 = async_compile.triton('triton_poi_fused_arange_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_arange_4(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + (x0), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/b4/cb45juu5nfh3qqk3tymmpltlxxn65rerwss2drltolfburgqjtmy.py # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_4 => add_4, add_5, convert_element_type_4, convert_element_type_5, mul_4, mul_5 # Graph fragment: # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_8, 1), kwargs = {}) # %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, 0), kwargs = {}) # %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_4, torch.float32), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.5), kwargs = {}) # %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_5 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_5(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/u6/cu6yzxaafuwvb5r4jgk2oo3owdipkikoyzwegz76tp3ehqi4ox3a.py # Topologically Sorted Source Nodes: [conv2d_2, y_3, y_4, pad_3], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_2 => convolution_2 # pad_3 => _unsafe_index_8, _unsafe_index_9 # y_3 => relu_2 # y_4 => _unsafe_index_7 # Graph fragment: # %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_6, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {}) # %_unsafe_index_7 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_2, [None, None, %unsqueeze_1, %convert_element_type_5]), kwargs = {}) # %_unsafe_index_8 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_7, [None, None, %sub_13, None]), kwargs = {}) # %_unsafe_index_9 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_8, [None, None, None, %sub_13]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 20736 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 18) % 18 x0 = xindex % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 16 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (8tmp4) + (64x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/i6/ci66azakate4plibmcfdtuespzftw3qbq2exsco24yqfpx34hn6w.py # Topologically Sorted Source Nodes: [conv2d_3, y_5, pad_4], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # pad_4 => _unsafe_index_10, _unsafe_index_11 # y_5 => relu_3 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %_unsafe_index_10 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_3, [None, None, %sub_13, None]), kwargs = {}) # %_unsafe_index_11 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_10, [None, None, None, %sub_13]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_7 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 20736 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 18 x1 = (xindex // 18) % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 16 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/3i/c3ifzmviwxyk6ji2r47wzy7lmeq3gzxnckyibz2jslvmoqiylsxi.py # Topologically Sorted Source Nodes: [conv2d_4, y_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # y_6 => relu_4 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_11, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_8 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_8', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_8(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 3072 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 256) % 3 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, xmask) tl.store(out_ptr0 + (x3), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/rc/crc3kuas5pan3t4otuy46uww66omqjitecff6ddcnmigizh4z57l.py # Topologically Sorted Source Nodes: [conv2d_3, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # y_5 => relu_3 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %le_19 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_9 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_9', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 256) % 16 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/p7/cp7sy6hrsogtwjblgxmlkp3boq6wmj3wkf2m4dpz43fnz3bcpebe.py # Topologically Sorted Source Nodes: [conv2d_2, y_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_2 => convolution_2 # y_3 => relu_2 # Graph fragment: # %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_6, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {}) # %le_38 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_10 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_10', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_10(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 16 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/fl/cfltyvyo4xumt3eb3gx27sapwpj3jxbd47s5mqkw7s5qqkqvf7gg.py # Topologically Sorted Source Nodes: [conv2d_1, y_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # y_2 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %le_57 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_11 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_11', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8192 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 32 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/2n/c2nwyqodiggiuyd3g6uygflvkj5ewhn6i2734irrbxziouo2n5ei.py # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d => convolution # y => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le_76 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_12 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_12', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_12(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 2048 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 32 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (4, 64, 4, 4), (1024, 16, 4, 1)) assert_size_stride(primals_2, (32, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_3, (32, ), (1, )) assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_5, (32, ), (1, )) assert_size_stride(primals_6, (16, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_7, (16, ), (1, )) assert_size_stride(primals_8, (16, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_9, (16, ), (1, )) assert_size_stride(primals_10, (3, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_11, (3, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] stream0 = get_raw_stream(0) triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 9216, grid=grid(9216), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 32, 4, 4), (512, 16, 4, 1)) buf2 = empty_strided_cuda((8, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_1.run(buf2, 8, grid=grid(8), stream=stream0) buf3 = empty_strided_cuda((4, 32, 10, 10), (3200, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2.run(buf2, buf1, primals_3, buf3, 12800, grid=grid(12800), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 32, 8, 8), (2048, 64, 8, 1)) buf5 = empty_strided_cuda((4, 32, 10, 10), (3200, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf4, primals_5, buf5, 12800, grid=grid(12800), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 16, 8, 8), (1024, 64, 8, 1)) buf7 = empty_strided_cuda((16, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.arange] triton_poi_fused_arange_4.run(buf7, 16, grid=grid(16), stream=stream0) buf8 = empty_strided_cuda((16, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_5.run(buf8, 16, grid=grid(16), stream=stream0) buf9 = empty_strided_cuda((4, 16, 18, 18), (5184, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_2, y_3, y_4, pad_3], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6.run(buf8, buf6, primals_7, buf9, 20736, grid=grid(20736), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 16, 16, 16), (4096, 256, 16, 1)) buf11 = empty_strided_cuda((4, 16, 18, 18), (5184, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_3, y_5, pad_4], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_7.run(buf10, primals_9, buf11, 20736, grid=grid(20736), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution] buf12 = extern_kernels.convolution(buf11, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 3, 16, 16), (768, 256, 16, 1)) buf13 = buf12; del buf12 # reuse buf14 = empty_strided_cuda((4, 3, 16, 16), (768, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_4, y_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_8.run(buf13, primals_11, buf14, 3072, grid=grid(3072), stream=stream0) del primals_11 buf15 = empty_strided_cuda((4, 16, 16, 16), (4096, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_3, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_9.run(buf10, primals_9, buf15, 16384, grid=grid(16384), stream=stream0) del buf10 del primals_9 buf16 = empty_strided_cuda((4, 16, 8, 8), (1024, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_2, y_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_10.run(buf6, primals_7, buf16, 4096, grid=grid(4096), stream=stream0) del buf6 del primals_7 buf17 = empty_strided_cuda((4, 32, 8, 8), (2048, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, y_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_11.run(buf4, primals_5, buf17, 8192, grid=grid(8192), stream=stream0) del buf4 del primals_5 buf18 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_12.run(buf1, primals_3, buf18, 2048, grid=grid(2048), stream=stream0) del buf1 del primals_3 return (buf13, primals_2, primals_4, primals_6, primals_8, primals_10, buf0, buf2, buf3, buf5, buf7, buf8, buf9, buf11, buf14, buf15, buf16, buf17, buf18, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((16, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((3, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class SmallDecoder3_16x(nn.Module): def __init__(self, model=None, fixed=False): super(SmallDecoder3_16x, self).__init__() self.fixed = fixed self.conv31 = nn.Conv2d(64, 32, 3, 1, 0) self.conv22 = nn.Conv2d(32, 32, 3, 1, 0) self.conv21 = nn.Conv2d(32, 16, 3, 1, 0) self.conv12 = nn.Conv2d(16, 16, 3, 1, 0, dilation=1) self.conv11 = nn.Conv2d(16, 3, 3, 1, 0, dilation=1) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.unpool_pwct = nn.MaxUnpool2d(kernel_size=2, stride=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: weights = torch.load(model, map_location=lambda storage, location: storage) if 'model' in weights: self.load_state_dict(weights['model']) else: self.load_state_dict(weights) None if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, y): y = self.relu(self.conv31(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv22(self.pad(y))) y = self.relu(self.conv21(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv12(self.pad(y))) y = self.relu(self.conv11(self.pad(y))) return y def forward_pwct(self, x, pool1_idx=None, pool1_size=None, pool2_idx= None, pool2_size=None, pool3_idx=None, pool3_size=None): out31 = self.relu(self.conv31(self.pad(x))) out31 = self.unpool_pwct(out31, pool2_idx, output_size=pool2_size) out22 = self.relu(self.conv22(self.pad(out31))) out21 = self.relu(self.conv21(self.pad(out22))) out21 = self.unpool_pwct(out21, pool1_idx, output_size=pool1_size) out12 = self.relu(self.conv12(self.pad(out21))) out11 = self.conv11(self.pad(out12)) return out11 def get_inputs(): return [torch.rand([4, 64, 4, 4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 9216 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 6 x1 = xindex // 6 % 6 x2 = xindex // 36 x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 + x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + x3, tmp0, xmask) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 12800 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 10 % 10 x0 = xindex % 10 x4 = xindex // 100 x2 = xindex // 100 % 32 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1 ))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0 ))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 4 * tmp4 + 16 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 12800 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 10 x1 = xindex // 10 % 10 x4 = xindex // 100 x2 = xindex // 100 % 32 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_arange_4(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + x0, tmp0, xmask) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_5(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 20736 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 18 % 18 x0 = xindex % 18 x4 = xindex // 324 x2 = xindex // 324 % 16 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x1))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 8 * tmp4 + 64 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 20736 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 18 x1 = xindex // 18 % 18 x4 = xindex // 324 x2 = xindex // 324 % 16 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_8(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 3072 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 256 % 3 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, xmask) tl.store(out_ptr0 + x3, tmp6, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 256 % 16 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_10(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 16 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 32 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_12(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 32 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (4, 64, 4, 4), (1024, 16, 4, 1)) assert_size_stride(primals_2, (32, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_3, (32,), (1,)) assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_5, (32,), (1,)) assert_size_stride(primals_6, (16, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_7, (16,), (1,)) assert_size_stride(primals_8, (16, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_9, (16,), (1,)) assert_size_stride(primals_10, (3, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_11, (3,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch. float32) get_raw_stream(0) triton_poi_fused_reflection_pad2d_0[grid(9216)](primals_1, buf0, 9216, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 32, 4, 4), (512, 16, 4, 1)) buf2 = empty_strided_cuda((8,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK =8, num_warps=1, num_stages=1) buf3 = empty_strided_cuda((4, 32, 10, 10), (3200, 100, 10, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid (12800)](buf2, buf1, primals_3, buf3, 12800, XBLOCK=128, num_warps=4, num_stages=1) buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 32, 8, 8), (2048, 64, 8, 1)) buf5 = empty_strided_cuda((4, 32, 10, 10), (3200, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(12800)](buf4, primals_5, buf5, 12800, XBLOCK=256, num_warps=4, num_stages=1) buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 16, 8, 8), (1024, 64, 8, 1)) buf7 = empty_strided_cuda((16,), (1,), torch.int64) triton_poi_fused_arange_4[grid(16)](buf7, 16, XBLOCK=16, num_warps= 1, num_stages=1) buf8 = empty_strided_cuda((16,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_5[grid(16)](buf8, 16, XBLOCK=16, num_warps=1, num_stages=1) buf9 = empty_strided_cuda((4, 16, 18, 18), (5184, 324, 18, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6[grid (20736)](buf8, buf6, primals_7, buf9, 20736, XBLOCK=256, num_warps=4, num_stages=1) buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 16, 16, 16), (4096, 256, 16, 1)) buf11 = empty_strided_cuda((4, 16, 18, 18), (5184, 324, 18, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_7[grid(20736)](buf10 , primals_9, buf11, 20736, XBLOCK=256, num_warps=4, num_stages=1) buf12 = extern_kernels.convolution(buf11, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 3, 16, 16), (768, 256, 16, 1)) buf13 = buf12 del buf12 buf14 = empty_strided_cuda((4, 3, 16, 16), (768, 256, 16, 1), torch .bool) triton_poi_fused_convolution_relu_threshold_backward_8[grid(3072)]( buf13, primals_11, buf14, 3072, XBLOCK=128, num_warps=4, num_stages=1) del primals_11 buf15 = empty_strided_cuda((4, 16, 16, 16), (4096, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_9[grid(16384)]( buf10, primals_9, buf15, 16384, XBLOCK=256, num_warps=4, num_stages=1) del buf10 del primals_9 buf16 = empty_strided_cuda((4, 16, 8, 8), (1024, 64, 8, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_10[grid(4096)]( buf6, primals_7, buf16, 4096, XBLOCK=256, num_warps=4, num_stages=1 ) del buf6 del primals_7 buf17 = empty_strided_cuda((4, 32, 8, 8), (2048, 64, 8, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_11[grid(8192)]( buf4, primals_5, buf17, 8192, XBLOCK=256, num_warps=4, num_stages=1 ) del buf4 del primals_5 buf18 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_12[grid(2048)]( buf1, primals_3, buf18, 2048, XBLOCK=128, num_warps=4, num_stages=1 ) del buf1 del primals_3 return (buf13, primals_2, primals_4, primals_6, primals_8, primals_10, buf0, buf2, buf3, buf5, buf7, buf8, buf9, buf11, buf14, buf15, buf16, buf17, buf18) class SmallDecoder3_16xNew(nn.Module): def __init__(self, model=None, fixed=False): super(SmallDecoder3_16xNew, self).__init__() self.fixed = fixed self.conv31 = nn.Conv2d(64, 32, 3, 1, 0) self.conv22 = nn.Conv2d(32, 32, 3, 1, 0) self.conv21 = nn.Conv2d(32, 16, 3, 1, 0) self.conv12 = nn.Conv2d(16, 16, 3, 1, 0, dilation=1) self.conv11 = nn.Conv2d(16, 3, 3, 1, 0, dilation=1) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.unpool_pwct = nn.MaxUnpool2d(kernel_size=2, stride=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: weights = torch.load(model, map_location=lambda storage, location: storage) if 'model' in weights: self.load_state_dict(weights['model']) else: self.load_state_dict(weights) None if fixed: for param in self.parameters(): param.requires_grad = False def forward_pwct(self, x, pool1_idx=None, pool1_size=None, pool2_idx= None, pool2_size=None, pool3_idx=None, pool3_size=None): out31 = self.relu(self.conv31(self.pad(x))) out31 = self.unpool_pwct(out31, pool2_idx, output_size=pool2_size) out22 = self.relu(self.conv22(self.pad(out31))) out21 = self.relu(self.conv21(self.pad(out22))) out21 = self.unpool_pwct(out21, pool1_idx, output_size=pool1_size) out12 = self.relu(self.conv12(self.pad(out21))) out11 = self.conv11(self.pad(out12)) return out11 def forward(self, input_0): primals_2 = self.conv31.weight primals_3 = self.conv31.bias primals_4 = self.conv22.weight primals_5 = self.conv22.bias primals_6 = self.conv21.weight primals_7 = self.conv21.bias primals_8 = self.conv12.weight primals_9 = self.conv12.bias primals_10 = self.conv11.weight primals_11 = self.conv11.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0]	EndyWon/Texture-Reformer	SmallDecoder3_16x	false	8,148	[ "MIT" ]	11	f84f95accb3574c7b759a7f03c0b0b4e150314b5	https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5
LayerNorm	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ja/cjaucday56qysdhw4ckc6reur4gs4gzurnemt4fkal25vhle5a7b.py # Topologically Sorted Source Nodes: [mean, std, sub, mul, add, truediv, add_1], Original ATen: [aten.mean, aten.std, aten.sub, aten.mul, aten.add, aten.div] # Source node to ATen node mapping: # add => add # add_1 => add_1 # mean => mean # mul => mul # std => sqrt, var # sub => sub # truediv => div # Graph fragment: # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [-1], True), kwargs = {}) # %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%primals_1, [-1]), kwargs = {correction: 1.0, keepdim: True}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %sub), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-05), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %primals_3), kwargs = {}) triton_poi_fused_add_div_mean_mul_std_sub_0 = async_compile.triton('triton_poi_fused_add_div_mean_mul_std_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_std_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_mean_mul_std_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask) tmp2 = tl.load(in_ptr1 + (4x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tmp9 = 4.0 tmp10 = tmp8 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp0 * tmp11 tmp13 = tmp2 - tmp10 tmp14 = tmp13 * tmp13 tmp15 = tmp3 - tmp10 tmp16 = tmp15 * tmp15 tmp17 = tmp14 + tmp16 tmp18 = tmp5 - tmp10 tmp19 = tmp18 * tmp18 tmp20 = tmp17 + tmp19 tmp21 = tmp7 - tmp10 tmp22 = tmp21 * tmp21 tmp23 = tmp20 + tmp22 tmp24 = 3.0 tmp25 = tmp23 / tmp24 tmp26 = libdevice.sqrt(tmp25) tmp27 = 1e-05 tmp28 = tmp26 + tmp27 tmp29 = tmp12 / tmp28 tmp31 = tmp29 + tmp30 tl.store(out_ptr0 + (x2), tmp31, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mean, std, sub, mul, add, truediv, add_1], Original ATen: [aten.mean, aten.std, aten.sub, aten.mul, aten.add, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_add_div_mean_mul_std_sub_0.run(primals_2, primals_1, primals_3, buf0, 256, grid=grid(256), stream=stream0) del primals_2 del primals_3 return (buf0, primals_1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class LayerNorm(nn.Module): """ Layer Normalization (https://arxiv.org/abs/1607.06450) """ def __init__(self, normalized_shape, eps=1e-05): super(LayerNorm, self).__init__() self.gamma = nn.Parameter(torch.ones(normalized_shape)) self.beta = nn.Parameter(torch.zeros(normalized_shape)) self.eps = eps def forward(self, x): mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) return self.gamma * (x - mean) / (std + self.eps) + self.beta def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'normalized_shape': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_div_mean_mul_std_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask) tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tmp9 = 4.0 tmp10 = tmp8 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp0 * tmp11 tmp13 = tmp2 - tmp10 tmp14 = tmp13 * tmp13 tmp15 = tmp3 - tmp10 tmp16 = tmp15 * tmp15 tmp17 = tmp14 + tmp16 tmp18 = tmp5 - tmp10 tmp19 = tmp18 * tmp18 tmp20 = tmp17 + tmp19 tmp21 = tmp7 - tmp10 tmp22 = tmp21 * tmp21 tmp23 = tmp20 + tmp22 tmp24 = 3.0 tmp25 = tmp23 / tmp24 tmp26 = libdevice.sqrt(tmp25) tmp27 = 1e-05 tmp28 = tmp26 + tmp27 tmp29 = tmp12 / tmp28 tmp31 = tmp29 + tmp30 tl.store(out_ptr0 + x2, tmp31, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_div_mean_mul_std_sub_0[grid(256)](primals_2, primals_1, primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 del primals_3 return buf0, primals_1 class LayerNormNew(nn.Module): """ Layer Normalization (https://arxiv.org/abs/1607.06450) """ def __init__(self, normalized_shape, eps=1e-05): super(LayerNormNew, self).__init__() self.gamma = nn.Parameter(torch.ones(normalized_shape)) self.beta = nn.Parameter(torch.zeros(normalized_shape)) self.eps = eps def forward(self, input_0): primals_2 = self.gamma primals_3 = self.beta primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	GMDennis/claf	LayerNorm	false	8,149	[ "MIT" ]	10	d1e064e593127e5d654f000f5506c5ae1caab5ce	https://github.com/GMDennis/claf/tree/d1e064e593127e5d654f000f5506c5ae1caab5ce
GeLU	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/jb/cjbxzli6v7rwzt24mjtja7cu7a6zbkabsw5two6m5axucpijmjfr.py # Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu] # Source node to ATen node mapping: # gelu => add, erf, mul, mul_1, mul_2 # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.5), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.7071067811865476), kwargs = {}) # %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_1,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {}) triton_poi_fused_gelu_0 = async_compile.triton('triton_poi_fused_gelu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gelu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_gelu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = 0.7071067811865476 tmp4 = tmp0 * tmp3 tmp5 = libdevice.erf(tmp4) tmp6 = 1.0 tmp7 = tmp5 + tmp6 tmp8 = tmp2 * tmp7 tl.store(out_ptr0 + (x0), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu] stream0 = get_raw_stream(0) triton_poi_fused_gelu_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn import torch.jit import torch.nn.functional import torch.nn from torch.nn.functional import gelu class GeLU(nn.Module): def forward(self, x): return gelu(x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice from torch import nn import torch.jit import torch.nn.functional import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_gelu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = 0.7071067811865476 tmp4 = tmp0 * tmp3 tmp5 = libdevice.erf(tmp4) tmp6 = 1.0 tmp7 = tmp5 + tmp6 tmp8 = tmp2 * tmp7 tl.store(out_ptr0 + x0, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_gelu_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) del arg0_1 return buf0, class GeLUNew(nn.Module): def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	Gitsamshi/nnUNet-1	GeLU	false	8,150	[ "Apache-2.0" ]	28	5341684211e6d91dab6ad76a7595a95addff23be	https://github.com/Gitsamshi/nnUNet-1/tree/5341684211e6d91dab6ad76a7595a95addff23be
PositionwiseFeedForward	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/4g/c4guhk7x6skkidedvs2gxz2kcu6gb76l3ig5crjjvjtzvnjlhlte.py # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x_2 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4, ), (1, )) assert_size_stride(primals_5, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), primals_3, out=buf0) del primals_3 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf3, 256, grid=grid(256), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.addmm] extern_kernels.addmm(primals_4, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), primals_5, alpha=1, beta=1, out=buf2) del primals_4 return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), reinterpret_tensor(buf1, (4, 64), (1, 4), 0), buf3, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class PointwiseConv(nn.Module): """ Pointwise Convolution (1x1 Conv) Convolution 1 Dimension (Faster version) (cf. https://github.com/huggingface/pytorch-openai-transformer-lm/blob/ eafc28abdfadfa0732f03a0fc65805c5bfb2ffe7/model_pytorch.py#L45) * Args: input_size: the number of input tensor's dimension num_filters: the number of convolution filter """ def __init__(self, input_size, num_filters): super(PointwiseConv, self).__init__() self.kernel_size = 1 self.num_filters = num_filters weight = torch.empty(input_size, num_filters) nn.init.normal_(weight, std=0.02) self.weight = nn.Parameter(weight) self.bias = nn.Parameter(torch.zeros(num_filters)) def forward(self, x): size_out = x.size()[:-1] + (self.num_filters,) x = torch.addmm(self.bias, x.contiguous().view(-1, x.size(-1)), self.weight) x = x.view(size_out) return x class PositionwiseFeedForward(nn.Module): """ Pointwise Feed-Forward Layer Args: input_size: the number of input size hidden_size: the number of hidden size * Kwargs: dropout: the probability of dropout """ def __init__(self, input_size, hidden_size, dropout=0.1): super(PositionwiseFeedForward, self).__init__() self.pointwise_conv1 = PointwiseConv(input_size=input_size, num_filters=hidden_size) self.pointwise_conv2 = PointwiseConv(input_size=hidden_size, num_filters=input_size) self.activation_fn = F.relu self.dropout = nn.Dropout(p=dropout) def forward(self, x): x = self.pointwise_conv1(x) x = self.activation_fn(x) x = self.pointwise_conv2(x) x = self.dropout(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'input_size': 4, 'hidden_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.nn.functional as F assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4,), (1,)) assert_size_stride(primals_5, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), primals_3, out=buf0) del primals_3 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf0 buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, primals_2, buf3, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_4, reinterpret_tensor(buf1, (64, 4), ( 4, 1), 0), primals_5, alpha=1, beta=1, out=buf2) del primals_4 return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0 ), reinterpret_tensor(buf1, (4, 64), (1, 4), 0 ), buf3, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0) class PointwiseConv(nn.Module): """ Pointwise Convolution (1x1 Conv) Convolution 1 Dimension (Faster version) (cf. https://github.com/huggingface/pytorch-openai-transformer-lm/blob/ eafc28abdfadfa0732f03a0fc65805c5bfb2ffe7/model_pytorch.py#L45) * Args: input_size: the number of input tensor's dimension num_filters: the number of convolution filter """ def __init__(self, input_size, num_filters): super(PointwiseConv, self).__init__() self.kernel_size = 1 self.num_filters = num_filters weight = torch.empty(input_size, num_filters) nn.init.normal_(weight, std=0.02) self.weight = nn.Parameter(weight) self.bias = nn.Parameter(torch.zeros(num_filters)) def forward(self, x): size_out = x.size()[:-1] + (self.num_filters,) x = torch.addmm(self.bias, x.contiguous().view(-1, x.size(-1)), self.weight) x = x.view(size_out) return x class PositionwiseFeedForwardNew(nn.Module): """ Pointwise Feed-Forward Layer Args: input_size: the number of input size hidden_size: the number of hidden size * Kwargs: dropout: the probability of dropout """ def __init__(self, input_size, hidden_size, dropout=0.1): super(PositionwiseFeedForwardNew, self).__init__() self.pointwise_conv1 = PointwiseConv(input_size=input_size, num_filters=hidden_size) self.pointwise_conv2 = PointwiseConv(input_size=hidden_size, num_filters=input_size) self.activation_fn = F.relu self.dropout = nn.Dropout(p=dropout) def forward(self, input_0): primals_3 = self.pointwise_conv1.weight primals_2 = self.pointwise_conv1.bias primals_5 = self.pointwise_conv2.weight primals_4 = self.pointwise_conv2.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	GMDennis/claf	PositionwiseFeedForward	false	8,151	[ "MIT" ]	10	d1e064e593127e5d654f000f5506c5ae1caab5ce	https://github.com/GMDennis/claf/tree/d1e064e593127e5d654f000f5506c5ae1caab5ce
Decoder1	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/s5/cs5cyzetyp2bdsm3i6hqlu4lg4ybgcxgjqmjyuoxibnvbimxi3yn.py # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] # Source node to ATen node mapping: # pad => _unsafe_index, _unsafe_index_1 # Graph fragment: # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 9216 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 6 x1 = (xindex // 6) % 6 x2 = (xindex // 36) x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16x2)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (x3), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/fu/cfuuep44fhcx57ojm3wf2viyzgg4t27zgsmj7twdoartbh2mrdao.py # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d => convolution # y => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 3 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, xmask) tl.store(out_ptr0 + (x3), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 64, 4, 4), (1024, 16, 4, 1)) assert_size_stride(primals_2, (3, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_3, (3, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] stream0 = get_raw_stream(0) triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 9216, grid=grid(9216), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 3, 4, 4), (48, 16, 4, 1)) buf2 = buf1; del buf1 # reuse buf3 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_1.run(buf2, primals_3, buf3, 192, grid=grid(192), stream=stream0) del primals_3 return (buf2, primals_2, buf0, buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((3, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class Decoder1(nn.Module): def __init__(self, model=None, fixed=False): super(Decoder1, self).__init__() self.fixed = fixed self.conv11 = nn.Conv2d(64, 3, 3, 1, 0, dilation=1) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input): y = self.relu(self.conv11(self.pad(input))) return y def get_inputs(): return [torch.rand([4, 64, 4, 4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 9216 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 6 x1 = xindex // 6 % 6 x2 = xindex // 36 x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 + x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + x3, tmp0, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 3 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, xmask) tl.store(out_ptr0 + x3, tmp6, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 64, 4, 4), (1024, 16, 4, 1)) assert_size_stride(primals_2, (3, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_3, (3,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch. float32) get_raw_stream(0) triton_poi_fused_reflection_pad2d_0[grid(9216)](primals_1, buf0, 9216, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 3, 4, 4), (48, 16, 4, 1)) buf2 = buf1 del buf1 buf3 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_1[grid(192)](buf2, primals_3, buf3, 192, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 return buf2, primals_2, buf0, buf3 class Decoder1New(nn.Module): def __init__(self, model=None, fixed=False): super(Decoder1New, self).__init__() self.fixed = fixed self.conv11 = nn.Conv2d(64, 3, 3, 1, 0, dilation=1) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input_0): primals_2 = self.conv11.weight primals_3 = self.conv11.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	EndyWon/Texture-Reformer	Decoder1	false	8,152	[ "MIT" ]	11	f84f95accb3574c7b759a7f03c0b0b4e150314b5	https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5
SeqAttnMatch	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/qz/cqzfoapfwkjofuduqagxh74kcw5gflirqo2hl244lxyxn7izx5tk.py # Topologically Sorted Source Nodes: [x_proj_1, y_proj_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x_proj_1 => relu # y_proj_1 => relu_1 # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_out_ptr1 + (x2), xmask) tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = tmp5 + tmp1 tmp7 = triton_helpers.maximum(tmp3, tmp6) tmp8 = 0.0 tmp9 = tmp7 <= tmp8 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(in_out_ptr1 + (x2), tmp7, xmask) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/zk/czktdnavknlx6gnvx3zkuaydlukavpk4m6nxhhjkarz6noapvzgm.py # Topologically Sorted Source Nodes: [alpha_flat], Original ATen: [aten._softmax] # Source node to ATen node mapping: # alpha_flat => amax, exp, sub, sum_1 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_4, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_4, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4(x0 // 4)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (4x0), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (1 + (4(x0 // 4))), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (1 + (4x0)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (2 + (4(x0 // 4))), xmask, eviction_policy='evict_last') tmp13 = tl.load(in_ptr1 + (2 + (4x0)), xmask, eviction_policy='evict_last') tmp16 = tl.load(in_ptr0 + (3 + (4(x0 // 4))), xmask, eviction_policy='evict_last') tmp18 = tl.load(in_ptr1 + (3 + (4x0)), xmask, eviction_policy='evict_last') tmp1 = 0.0 tmp2 = tmp0 == tmp1 tmp4 = -1.0000000150474662e+30 tmp5 = tl.where(tmp2, tmp4, tmp3) tmp7 = tmp6 == tmp1 tmp9 = tl.where(tmp7, tmp4, tmp8) tmp10 = triton_helpers.maximum(tmp5, tmp9) tmp12 = tmp11 == tmp1 tmp14 = tl.where(tmp12, tmp4, tmp13) tmp15 = triton_helpers.maximum(tmp10, tmp14) tmp17 = tmp16 == tmp1 tmp19 = tl.where(tmp17, tmp4, tmp18) tmp20 = triton_helpers.maximum(tmp15, tmp19) tmp21 = tmp5 - tmp20 tmp22 = tl_math.exp(tmp21) tmp23 = tmp9 - tmp20 tmp24 = tl_math.exp(tmp23) tmp25 = tmp22 + tmp24 tmp26 = tmp14 - tmp20 tmp27 = tl_math.exp(tmp26) tmp28 = tmp25 + tmp27 tmp29 = tmp19 - tmp20 tmp30 = tl_math.exp(tmp29) tmp31 = tmp28 + tmp30 tl.store(out_ptr0 + (x0), tmp20, xmask) tl.store(out_ptr1 + (x0), tmp31, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/dm/cdmmiygirtq5aevxowdbkte7q62vvwwukw4lrlk4z2pcjmktyhzm.py # Topologically Sorted Source Nodes: [alpha_flat], Original ATen: [aten._softmax] # Source node to ATen node mapping: # alpha_flat => amax, div, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_4, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_4, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (4*(x1 // 4))), xmask) tmp3 = tl.load(in_ptr1 + (x2), xmask) tmp6 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last') tmp1 = 0.0 tmp2 = tmp0 == tmp1 tmp4 = -1.0000000150474662e+30 tmp5 = tl.where(tmp2, tmp4, tmp3) tmp7 = tmp5 - tmp6 tmp8 = tl_math.exp(tmp7) tmp10 = tmp8 / tmp9 tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf2) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0); del buf0 # reuse buf3 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0); del buf2 # reuse buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_proj_1, y_proj_1], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, buf3, primals_3, buf9, 64, grid=grid(64), stream=stream0) del primals_3 buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [scores], Original ATen: [aten.bmm] extern_kernels.bmm(buf1, reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0), out=buf4) buf5 = empty_strided_cuda((16, 1), (1, 16), torch.float32) buf6 = empty_strided_cuda((16, 1), (1, 16), torch.float32) # Topologically Sorted Source Nodes: [alpha_flat], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(primals_5, buf4, buf5, buf6, 16, grid=grid(16), stream=stream0) buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [alpha_flat], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(primals_5, buf4, buf5, buf6, buf7, 64, grid=grid(64), stream=stream0) del buf5 del buf6 buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [matched_seq], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0), primals_4, out=buf8) del buf7 return (buf8, primals_4, primals_5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf1, buf4, buf3, buf9, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class SeqAttnMatch(nn.Module): """ Given sequences X and Y, match sequence Y to each element in X. * o_i = sum(alpha_j * y_j) for i in X * alpha_j = softmax(y_j * x_i) """ def __init__(self, embed_dim, identity=False): super(SeqAttnMatch, self).__init__() if not identity: self.linear = nn.Linear(embed_dim, embed_dim) else: self.linear = None def forward(self, x, y, y_mask): if self.linear: x_proj = self.linear(x.view(-1, x.size(2))).view(x.size()) x_proj = F.relu(x_proj) y_proj = self.linear(y.view(-1, y.size(2))).view(y.size()) y_proj = F.relu(y_proj) else: x_proj = x y_proj = y scores = x_proj.bmm(y_proj.transpose(2, 1)) y_mask = y_mask.unsqueeze(1).expand(scores.size()) scores = scores.masked_fill(y_mask == 0, -1e+30) alpha_flat = F.softmax(scores.view(-1, y.size(1)), -1) alpha = alpha_flat.view(-1, x.size(1), y.size(1)) matched_seq = alpha.bmm(y) return matched_seq def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {'embed_dim': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_out_ptr1 + x2, xmask) tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = tmp5 + tmp1 tmp7 = triton_helpers.maximum(tmp3, tmp6) tmp8 = 0.0 tmp9 = tmp7 <= tmp8 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(in_out_ptr1 + x2, tmp7, xmask) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * (x0 // 4), xmask, eviction_policy='evict_last' ) tmp3 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (1 + 4 * (x0 // 4)), xmask, eviction_policy= 'evict_last') tmp8 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (2 + 4 * (x0 // 4)), xmask, eviction_policy= 'evict_last') tmp13 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp16 = tl.load(in_ptr0 + (3 + 4 * (x0 // 4)), xmask, eviction_policy= 'evict_last') tmp18 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp1 = 0.0 tmp2 = tmp0 == tmp1 tmp4 = -1.0000000150474662e+30 tmp5 = tl.where(tmp2, tmp4, tmp3) tmp7 = tmp6 == tmp1 tmp9 = tl.where(tmp7, tmp4, tmp8) tmp10 = triton_helpers.maximum(tmp5, tmp9) tmp12 = tmp11 == tmp1 tmp14 = tl.where(tmp12, tmp4, tmp13) tmp15 = triton_helpers.maximum(tmp10, tmp14) tmp17 = tmp16 == tmp1 tmp19 = tl.where(tmp17, tmp4, tmp18) tmp20 = triton_helpers.maximum(tmp15, tmp19) tmp21 = tmp5 - tmp20 tmp22 = tl_math.exp(tmp21) tmp23 = tmp9 - tmp20 tmp24 = tl_math.exp(tmp23) tmp25 = tmp22 + tmp24 tmp26 = tmp14 - tmp20 tmp27 = tl_math.exp(tmp26) tmp28 = tmp25 + tmp27 tmp29 = tmp19 - tmp20 tmp30 = tl_math.exp(tmp29) tmp31 = tmp28 + tmp30 tl.store(out_ptr0 + x0, tmp20, xmask) tl.store(out_ptr1 + x0, tmp31, xmask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 4 * (x1 // 4)), xmask) tmp3 = tl.load(in_ptr1 + x2, xmask) tmp6 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last') tmp1 = 0.0 tmp2 = tmp0 == tmp1 tmp4 = -1.0000000150474662e+30 tmp5 = tl.where(tmp2, tmp4, tmp3) tmp7 = tmp5 - tmp6 tmp8 = tl_math.exp(tmp7) tmp10 = tmp8 / tmp9 tl.store(out_ptr0 + x2, tmp10, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf2) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0) del buf0 buf3 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0) del buf2 buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(64)](buf1, buf3, primals_3, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_3 buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(buf1, reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0), out=buf4) buf5 = empty_strided_cuda((16, 1), (1, 16), torch.float32) buf6 = empty_strided_cuda((16, 1), (1, 16), torch.float32) triton_poi_fused__softmax_1[grid(16)](primals_5, buf4, buf5, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1) buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32) triton_poi_fused__softmax_2[grid(64)](primals_5, buf4, buf5, buf6, buf7, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf5 del buf6 buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0), primals_4, out=buf8) del buf7 return buf8, primals_4, primals_5, reinterpret_tensor(primals_1, (16, 4 ), (4, 1), 0), buf1, buf4, buf3, buf9 class SeqAttnMatchNew(nn.Module): """ Given sequences X and Y, match sequence Y to each element in X. * o_i = sum(alpha_j * y_j) for i in X * alpha_j = softmax(y_j * x_i) """ def __init__(self, embed_dim, identity=False): super(SeqAttnMatchNew, self).__init__() if not identity: self.linear = nn.Linear(embed_dim, embed_dim) else: self.linear = None def forward(self, input_0, input_1, input_2): primals_2 = self.linear.weight primals_3 = self.linear.bias primals_1 = input_0 primals_4 = input_1 primals_5 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	GMDennis/claf	SeqAttnMatch	false	8,153	[ "MIT" ]	10	d1e064e593127e5d654f000f5506c5ae1caab5ce	https://github.com/GMDennis/claf/tree/d1e064e593127e5d654f000f5506c5ae1caab5ce
NonnegativeLinear	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/53/c53h27i2t66vt65fmkoq7ahn4vondesndqnm4m3wlrusali7nthh.py # Topologically Sorted Source Nodes: [clamp_], Original ATen: [aten.clamp] # Source node to ATen node mapping: # clamp_ => clamp_min # Graph fragment: # %clamp_min : [num_users=2] = call_function[target=torch.ops.aten.clamp_min.default](args = (%primals_1, 0.0), kwargs = {}) triton_poi_fused_clamp_0 = async_compile.triton('triton_poi_fused_clamp_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clamp_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 0.0 tmp2 = triton_helpers.maximum(tmp0, tmp1) tl.store(out_ptr0 + (x0), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [clamp_], Original ATen: [aten.clamp] stream0 = get_raw_stream(0) triton_poi_fused_clamp_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0) del primals_1 buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm] extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_2 return (buf0, reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import numpy as np import torch.nn as nn import torch.nn.functional as F class NonnegativeLinear(nn.Linear): def reset_parameters(self): nn.init.xavier_uniform_(self.weight) self.weight.data.abs_() if self.bias is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / np.sqrt(fan_in) nn.init.uniform_(self.bias, -bound, bound) def forward(self, input): self.weight.data.clamp_(0.0) return F.linear(input, self.weight, self.bias) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_features': 4, 'out_features': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import numpy as np import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clamp_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 0.0 tmp2 = triton_helpers.maximum(tmp0, tmp1) tl.store(out_ptr0 + x0, tmp2, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clamp_0[grid(16)](primals_1, buf0, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_1 buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_2 return buf0, reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0) class NonnegativeLinearNew(nn.Linear): def reset_parameters(self): nn.init.xavier_uniform_(self.weight) self.weight.data.abs_() if self.bias is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / np.sqrt(fan_in) nn.init.uniform_(self.bias, -bound, bound) def forward(self, input_0): primals_1 = self.weight primals_2 = self.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	GlenHGHUANG/STRODE	NonnegativeLinear	false	8,155	[ "MIT" ]	11	91565275dffd4f08738c8a0e5b6c9ad89344623e	https://github.com/GlenHGHUANG/STRODE/tree/91565275dffd4f08738c8a0e5b6c9ad89344623e
TimeEncoding	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/cj/ccjrbiivtgszjm4ak34pn3m4vzzp72pz7gxawg6fztwj7ljqvt3r.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.add] # Source node to ATen node mapping: # x => add # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg2_1, %unsqueeze_3), kwargs = {}) triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 4 x7 = (xindex // 64) x4 = (xindex // 256) % 4 x5 = (xindex // 1024) x6 = (xindex // 4) % 16 x2 = (xindex // 16) % 4 x3 = (xindex // 64) % 4 x1 = (xindex // 4) % 4 x9 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (4x7)), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x6 + (16x5) + (64x4)), None, eviction_policy='evict_last') tmp4 = tl.load(in_ptr2 + (x2 + (4x5) + (16x4) + (64x3)), None, eviction_policy='evict_last') tmp2 = 1.0 tmp3 = tmp1 * tmp2 tmp5 = tmp4 - tmp2 tmp6 = tmp3 / tmp5 tmp7 = x1 tmp8 = tmp7.to(tl.float32) tmp9 = tmp6 * tmp8 tmp10 = tmp0 + tmp9 tl.store(out_ptr0 + (x9), tmp10, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4, 4, 4), (4, 16, 1024, 256, 64, 1), torch.float32) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.add] stream0 = get_raw_stream(0) triton_poi_fused_add_0.run(arg2_1, arg0_1, arg1_1, buf0, 4096, grid=grid(4096), stream=stream0) del arg0_1 del arg1_1 del arg2_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn class TimeEncoding(nn.Module): def __init__(self, d_model, dropout=0.1, max_len=5000): super(TimeEncoding, self).__init__() self.dropout = nn.Dropout(p=dropout) def forward(self, x, mask, lengths): time = mask * 1 / (lengths[..., None] - 1) time = time[:, None] * torch.arange(time.shape[1], device=x.device)[ None, :] time = time[:, 0].T x = x + time[..., None] return self.dropout(x) def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {'d_model': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 4 x7 = xindex // 64 x4 = xindex // 256 % 4 x5 = xindex // 1024 x6 = xindex // 4 % 16 x2 = xindex // 16 % 4 x3 = xindex // 64 % 4 x1 = xindex // 4 % 4 x9 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 4 * x7), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x6 + 16 * x5 + 64 * x4), None, eviction_policy='evict_last') tmp4 = tl.load(in_ptr2 + (x2 + 4 * x5 + 16 * x4 + 64 * x3), None, eviction_policy='evict_last') tmp2 = 1.0 tmp3 = tmp1 * tmp2 tmp5 = tmp4 - tmp2 tmp6 = tmp3 / tmp5 tmp7 = x1 tmp8 = tmp7.to(tl.float32) tmp9 = tmp6 * tmp8 tmp10 = tmp0 + tmp9 tl.store(out_ptr0 + x9, tmp10, None) def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4, 4, 4), (4, 16, 1024, 256, 64, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_0[grid(4096)](arg2_1, arg0_1, arg1_1, buf0, 4096, XBLOCK=128, num_warps=4, num_stages=1) del arg0_1 del arg1_1 del arg2_1 return buf0, class TimeEncodingNew(nn.Module): def __init__(self, d_model, dropout=0.1, max_len=5000): super(TimeEncodingNew, self).__init__() self.dropout = nn.Dropout(p=dropout) def forward(self, input_0, input_1, input_2): arg0_1 = input_0 arg1_1 = input_1 arg2_1 = input_2 output = call([arg0_1, arg1_1, arg2_1]) return output[0]	GuyTevet/MotionCLIP	TimeEncoding	false	8,156	[ "MIT" ]	45	c2b9f40b0e721e42981f3e8b58133a1c51fde715	https://github.com/GuyTevet/MotionCLIP/tree/c2b9f40b0e721e42981f3e8b58133a1c51fde715
Encoder3	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/cn/ccn3wvv7hvajmiytvd4cxr5ymsooreymqjbzit5wvlzzy3qc5dck.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4096], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 12 xnumel = 4096 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1) x2 = xindex y3 = yindex y0 = yindex % 3 y1 = (yindex // 3) tmp0 = tl.load(in_ptr0 + (x2 + (4096y3)), ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (3x2) + (12288y1)), tmp0, ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/r4/cr42tvpznzo4jto3kd2nndggpyldbuuskklxhoqx7cipetnq44st.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 192 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 3 y1 = (yindex // 3) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (3x2) + (27y1)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/5o/c5obv3n3z3nigs3ufrll6zerfbkjuaxny47v7qwuboce4vc6t226.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 4096 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = (yindex // 64) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (64x2) + (576y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/z6/cz6baxmxj4vnpfhcx7vlebdugozcmmuhwadqps4sypq4ddnvvvvh.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_3 = async_compile.triton('triton_poi_fused_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 8192 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = (yindex // 64) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (64x2) + (576y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/db/cdb2sjzbytfspypoa6pp2oxhbkctfipulwxnhjr5sqo5qqjfizvu.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_4 = async_compile.triton('triton_poi_fused_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16384 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = (yindex // 128) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (128x2) + (1152y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gd/cgdnlfwbslhdmfriewbfv2omlx6rnligyuanv24xvvvpftjpu6rp.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_5 = async_compile.triton('triton_poi_fused_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 32768 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = (yindex // 128) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (128x2) + (1152y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/3j/c3j3qzfnvqieirxuwovs6lpn2xbedxfiht4chp3m3zhsaho4llfa.py # Topologically Sorted Source Nodes: [y, pad], Original ATen: [aten.convolution, aten.reflection_pad2d] # Source node to ATen node mapping: # pad => _unsafe_index, _unsafe_index_1 # y => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_6 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 52272 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 3 x1 = (xindex // 3) % 66 x2 = (xindex // 198) % 66 x3 = (xindex // 13068) x4 = xindex tmp0 = tl.load(in_ptr0 + (12285 + x0 + ((-192)(tl_math.abs((-63) + (tl_math.abs((-1) + x2))))) + ((-3)(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (12288x3)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x4), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/p6/cp6ygtph36rzfctjp5l7uav44c3xpnadnsfuzqxxn7xp6nzjfqop.py # Topologically Sorted Source Nodes: [conv2d_1, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # pad_1 => _unsafe_index_2, _unsafe_index_3 # y_1 => relu # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_7 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2097152], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = (xindex // 64) % 66 x2 = (xindex // 4224) % 66 x3 = (xindex // 278784) x4 = xindex tmp0 = tl.load(in_ptr0 + (262080 + x0 + ((-4096)(tl_math.abs((-63) + (tl_math.abs((-1) + x2))))) + ((-64)(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x4), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/vx/cvxifl3gnbvntf56zhjqgdxs4s5366socumbrzrds5uezjvtgsqa.py # Topologically Sorted Source Nodes: [conv2d_2, y_2], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_2 => convolution_2 # y_2 => relu_1 # Graph fragment: # %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_3, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {}) triton_poi_fused_convolution_relu_8 = async_compile.triton('triton_poi_fused_convolution_relu_8', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1048576 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/io/cio2c7vwuairhtoupwkrfakew5mwjb56w3ga5tiwxzxvdzgo3bgd.py # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_3 => getitem_1 # Graph fragment: # %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_9 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_9', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i8', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_9(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 64 x1 = (xindex // 64) % 32 x2 = (xindex // 2048) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (128x1) + (8192x2)), None) tmp1 = tl.load(in_ptr0 + (64 + x0 + (128x1) + (8192x2)), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + (128x1) + (8192x2)), None) tmp12 = tl.load(in_ptr0 + (4160 + x0 + (128x1) + (8192x2)), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) tmp16 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ij/cijpoyvzxdab4srjcw2qwgjf4qstod6wbrntbdzmwobtc2oacwi2.py # Topologically Sorted Source Nodes: [y_3, pad_2], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] # Source node to ATen node mapping: # pad_2 => _unsafe_index_4, _unsafe_index_5 # y_3 => _low_memory_max_pool2d_with_offsets # Graph fragment: # %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {}) # %_unsafe_index_4 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem, [None, None, %sub_9, None]), kwargs = {}) # %_unsafe_index_5 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_4, [None, None, None, %sub_9]), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_10 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_10', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_10(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = (xindex // 64) % 34 x2 = (xindex // 2176) % 34 x3 = (xindex // 73984) x4 = xindex tmp0 = tl.load(in_ptr0 + (257920 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp1 = tl.load(in_ptr0 + (257984 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp3 = tl.load(in_ptr0 + (262016 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp5 = tl.load(in_ptr0 + (262080 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + (x4), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/vm/cvmvr7w35r2lxnbmqhzlcep6kmv7rcdxyvoelxdewm4vtbw7wfsp.py # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # pad_3 => _unsafe_index_6, _unsafe_index_7 # y_4 => relu_2 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %_unsafe_index_6 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_2, [None, None, %sub_9, None]), kwargs = {}) # %_unsafe_index_7 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_6, [None, None, None, %sub_9]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_11 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_11', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 591872 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = (xindex // 128) % 34 x2 = (xindex // 4352) % 34 x3 = (xindex // 147968) x4 = xindex tmp0 = tl.load(in_ptr0 + (130944 + x0 + ((-4096)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x4), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ix/cixrx66mflznr4bcbxojzfbyl3us2fzoabuwrjggzikvbkxjznpi.py # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # y_5 => relu_3 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_7, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) triton_poi_fused_convolution_relu_12 = async_compile.triton('triton_poi_fused_convolution_relu_12', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_12', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 524288 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/sq/csqcoyr2n5qahyclmz7o5hlpuba4g6ttsnpiqcn44ousikr5qjl5.py # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_6 => getitem_3 # Graph fragment: # %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_13 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_13', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i8', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_13(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = (xindex // 128) % 16 x2 = (xindex // 2048) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (256x1) + (8192x2)), None) tmp1 = tl.load(in_ptr0 + (128 + x0 + (256x1) + (8192x2)), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + (256x1) + (8192x2)), None) tmp12 = tl.load(in_ptr0 + (4224 + x0 + (256x1) + (8192x2)), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) tmp16 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/fm/cfmeppq3maxq2jjcqdebl7zorx3hp6kwoa2354fjkgb6dktalfxp.py # Topologically Sorted Source Nodes: [y_6, pad_4], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] # Source node to ATen node mapping: # pad_4 => _unsafe_index_8, _unsafe_index_9 # y_6 => _low_memory_max_pool2d_with_offsets_1 # Graph fragment: # %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_3, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {}) # %_unsafe_index_8 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem_2, [None, None, %sub_17, None]), kwargs = {}) # %_unsafe_index_9 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_8, [None, None, None, %sub_17]), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_14 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_14', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_14(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 165888 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = (xindex // 128) % 18 x2 = (xindex // 2304) % 18 x3 = (xindex // 41472) x4 = xindex tmp0 = tl.load(in_ptr0 + (126720 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp1 = tl.load(in_ptr0 + (126848 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp3 = tl.load(in_ptr0 + (130816 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp5 = tl.load(in_ptr0 + (130944 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + (x4), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/3k/c3kazg6vyzcmhfmapvjmntxniydytlyyegq7db4xf67cadyltufj.py # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_5 => convolution_5 # y_7 => relu_4 # Graph fragment: # %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_15 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_15', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024, 256], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i1', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 1024 xnumel = 256 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 256 y1 = (yindex // 256) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (256x2) + (65536y1)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (y0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1, 1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2 + (256y3)), tmp4, xmask) tl.store(out_ptr1 + (y0 + (256x2) + (65536y1)), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/sr/csrwirygpelse4xk3xe4pso6xv3wkv5vj3e2rjq5ovmggcglevbq.py # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # y_4 => relu_2 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %le_38 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_16', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 524288 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_ptr0 + (x2), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/cn/ccn4tubgymkswsces45v2yukhs27ns4crivqopa7fhmrtyevuwnq.py # Topologically Sorted Source Nodes: [conv2d_1, y_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # y_1 => relu # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %le_76 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_17 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_17', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1048576 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_ptr0 + (x2), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args args.clear() assert_size_stride(primals_1, (3, 3, 1, 1), (3, 1, 1, 1)) assert_size_stride(primals_2, (3, ), (1, )) assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1)) assert_size_stride(primals_4, (64, 3, 3, 3), (27, 9, 3, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64, ), (1, )) assert_size_stride(primals_8, (128, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_9, (128, ), (1, )) assert_size_stride(primals_10, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_11, (128, ), (1, )) assert_size_stride(primals_12, (256, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_13, (256, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] stream0 = get_raw_stream(0) triton_poi_fused_0.run(primals_3, buf0, 12, 4096, grid=grid(12, 4096), stream=stream0) del primals_3 buf1 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_1.run(primals_4, buf1, 192, 9, grid=grid(192, 9), stream=stream0) del primals_4 buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_6, buf2, 4096, 9, grid=grid(4096, 9), stream=stream0) del primals_6 buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_3.run(primals_8, buf3, 8192, 9, grid=grid(8192, 9), stream=stream0) del primals_8 buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_4.run(primals_10, buf4, 16384, 9, grid=grid(16384, 9), stream=stream0) del primals_10 buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_5.run(primals_12, buf5, 32768, 9, grid=grid(32768, 9), stream=stream0) del primals_12 # Topologically Sorted Source Nodes: [y], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 3, 64, 64), (12288, 1, 192, 3)) buf7 = empty_strided_cuda((4, 3, 66, 66), (13068, 1, 198, 3), torch.float32) # Topologically Sorted Source Nodes: [y, pad], Original ATen: [aten.convolution, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_6.run(buf6, primals_2, buf7, 52272, grid=grid(52272), stream=stream0) del buf6 del primals_2 # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf9 = empty_strided_cuda((4, 64, 66, 66), (278784, 1, 4224, 64), torch.float32) # Topologically Sorted Source Nodes: [conv2d_1, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_7.run(buf8, primals_5, buf9, 1115136, grid=grid(1115136), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf11 = buf10; del buf10 # reuse # Topologically Sorted Source Nodes: [conv2d_2, y_2], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_8.run(buf11, primals_7, 1048576, grid=grid(1048576), stream=stream0) del primals_7 buf12 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64), torch.int8) # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_9.run(buf11, buf12, 262144, grid=grid(262144), stream=stream0) buf13 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.float32) # Topologically Sorted Source Nodes: [y_3, pad_2], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_10.run(buf11, buf13, 295936, grid=grid(295936), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf14 = extern_kernels.convolution(buf13, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf15 = empty_strided_cuda((4, 128, 34, 34), (147968, 1, 4352, 128), torch.float32) # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_11.run(buf14, primals_9, buf15, 591872, grid=grid(591872), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution] buf16 = extern_kernels.convolution(buf15, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf17 = buf16; del buf16 # reuse # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_12.run(buf17, primals_11, 524288, grid=grid(524288), stream=stream0) del primals_11 buf18 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128), torch.int8) # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_13.run(buf17, buf18, 131072, grid=grid(131072), stream=stream0) buf19 = empty_strided_cuda((4, 128, 18, 18), (41472, 1, 2304, 128), torch.float32) # Topologically Sorted Source Nodes: [y_6, pad_4], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_14.run(buf17, buf19, 165888, grid=grid(165888), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution] buf20 = extern_kernels.convolution(buf19, buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf21 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.float32) buf22 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_15.run(buf20, primals_13, buf21, buf22, 1024, 256, grid=grid(1024, 256), stream=stream0) del buf20 del primals_13 buf23 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128), torch.bool) # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_16.run(buf14, primals_9, buf23, 524288, grid=grid(524288), stream=stream0) del buf14 del primals_9 buf24 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, y_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_17.run(buf8, primals_5, buf24, 1048576, grid=grid(1048576), stream=stream0) del buf8 del primals_5 return (buf21, primals_1, buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf9, buf11, buf12, buf13, buf15, buf17, buf18, buf19, buf22, buf23, buf24, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class Encoder3(nn.Module): def __init__(self, model=None, fixed=False): super(Encoder3, self).__init__() self.fixed = fixed self.conv0 = nn.Conv2d(3, 3, 1, 1, 0) self.conv11 = nn.Conv2d(3, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv21 = nn.Conv2d(64, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv31 = nn.Conv2d(128, 256, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.pool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=False) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input): y = self.conv0(input) y = self.relu(self.conv11(self.pad(y))) y = self.relu(self.conv12(self.pad(y))) y = self.pool(y) y = self.relu(self.conv21(self.pad(y))) y = self.relu(self.conv22(self.pad(y))) y = self.pool(y) y = self.relu(self.conv31(self.pad(y))) return y def get_inputs(): return [torch.rand([4, 3, 64, 64])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 12 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, YBLOCK], True, tl.int1) x2 = xindex y3 = yindex y0 = yindex % 3 y1 = yindex // 3 tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy= 'evict_last') tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 192 xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 3 y1 = yindex // 3 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy= 'evict_last') tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = yindex // 64 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = yindex // 64 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = yindex // 128 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = yindex // 128 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 52272 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 3 x1 = xindex // 3 % 66 x2 = xindex // 198 % 66 x3 = xindex // 13068 x4 = xindex tmp0 = tl.load(in_ptr0 + (12285 + x0 + -192 * tl_math.abs(-63 + tl_math .abs(-1 + x2)) + -3 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 12288 * x3), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x4, tmp2, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = xindex // 64 % 66 x2 = xindex // 4224 % 66 x3 = xindex // 278784 x4 = xindex tmp0 = tl.load(in_ptr0 + (262080 + x0 + -4096 * tl_math.abs(-63 + tl_math.abs(-1 + x2)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1 )) + 262144 * x3), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x4, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 64 x1 = xindex // 64 % 32 x2 = xindex // 2048 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 8192 * x2), None) tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 8192 * x2), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + 128 * x1 + 8192 * x2), None) tmp12 = tl.load(in_ptr0 + (4160 + x0 + 128 * x1 + 8192 * x2), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_10(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = xindex // 64 % 34 x2 = xindex // 2176 % 34 x3 = xindex // 73984 x4 = xindex tmp0 = tl.load(in_ptr0 + (257920 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp1 = tl.load(in_ptr0 + (257984 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp3 = tl.load(in_ptr0 + (262016 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp5 = tl.load(in_ptr0 + (262080 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + x4, tmp6, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = xindex // 128 % 34 x2 = xindex // 4352 % 34 x3 = xindex // 147968 x4 = xindex tmp0 = tl.load(in_ptr0 + (130944 + x0 + -4096 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x4, tmp4, None) @triton.jit def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_13(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = xindex // 128 % 16 x2 = xindex // 2048 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 8192 * x2), None) tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 8192 * x2), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + 256 * x1 + 8192 * x2), None) tmp12 = tl.load(in_ptr0 + (4224 + x0 + 256 * x1 + 8192 * x2), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_14(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = xindex // 128 % 18 x2 = xindex // 2304 % 18 x3 = xindex // 41472 x4 = xindex tmp0 = tl.load(in_ptr0 + (126720 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp1 = tl.load(in_ptr0 + (126848 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp3 = tl.load(in_ptr0 + (130816 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp5 = tl.load(in_ptr0 + (130944 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + x4, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): xnumel = 256 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 256 y1 = yindex // 256 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 256 * x2 + 65536 * y1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1, 1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2 + 256 * y3), tmp4, xmask) tl.store(out_ptr1 + (y0 + 256 * x2 + 65536 * y1), tmp6, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_ptr0 + x2, None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_ptr0 + x2, None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13) = args args.clear() assert_size_stride(primals_1, (3, 3, 1, 1), (3, 1, 1, 1)) assert_size_stride(primals_2, (3,), (1,)) assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1)) assert_size_stride(primals_4, (64, 3, 3, 3), (27, 9, 3, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64,), (1,)) assert_size_stride(primals_8, (128, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_9, (128,), (1,)) assert_size_stride(primals_10, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_11, (128,), (1,)) assert_size_stride(primals_12, (256, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_13, (256,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch .float32) get_raw_stream(0) triton_poi_fused_0[grid(12, 4096)](primals_3, buf0, 12, 4096, XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1) del primals_3 buf1 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32) triton_poi_fused_1[grid(192, 9)](primals_4, buf1, 192, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_4 buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch. float32) triton_poi_fused_2[grid(4096, 9)](primals_6, buf2, 4096, 9, XBLOCK= 16, YBLOCK=64, num_warps=4, num_stages=1) del primals_6 buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch .float32) triton_poi_fused_3[grid(8192, 9)](primals_8, buf3, 8192, 9, XBLOCK= 16, YBLOCK=64, num_warps=4, num_stages=1) del primals_8 buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32) triton_poi_fused_4[grid(16384, 9)](primals_10, buf4, 16384, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_10 buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32) triton_poi_fused_5[grid(32768, 9)](primals_12, buf5, 32768, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_12 buf6 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 3, 64, 64), (12288, 1, 192, 3)) buf7 = empty_strided_cuda((4, 3, 66, 66), (13068, 1, 198, 3), torch .float32) triton_poi_fused_convolution_reflection_pad2d_6[grid(52272)](buf6, primals_2, buf7, 52272, XBLOCK=256, num_warps=4, num_stages=1) del buf6 del primals_2 buf8 = extern_kernels.convolution(buf7, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf9 = empty_strided_cuda((4, 64, 66, 66), (278784, 1, 4224, 64), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_7[grid(1115136)]( buf8, primals_5, buf9, 1115136, XBLOCK=1024, num_warps=4, num_stages=1) buf10 = extern_kernels.convolution(buf9, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf11 = buf10 del buf10 triton_poi_fused_convolution_relu_8[grid(1048576)](buf11, primals_7, 1048576, XBLOCK=1024, num_warps=4, num_stages=1) del primals_7 buf12 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64), torch.int8) triton_poi_fused_max_pool2d_with_indices_9[grid(262144)](buf11, buf12, 262144, XBLOCK=1024, num_warps=4, num_stages=1) buf13 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.float32) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_10[grid( 295936)](buf11, buf13, 295936, XBLOCK=1024, num_warps=4, num_stages=1) buf14 = extern_kernels.convolution(buf13, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf15 = empty_strided_cuda((4, 128, 34, 34), (147968, 1, 4352, 128), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_11[grid(591872)]( buf14, primals_9, buf15, 591872, XBLOCK=1024, num_warps=4, num_stages=1) buf16 = extern_kernels.convolution(buf15, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf17 = buf16 del buf16 triton_poi_fused_convolution_relu_12[grid(524288)](buf17, primals_11, 524288, XBLOCK=1024, num_warps=4, num_stages=1) del primals_11 buf18 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128), torch.int8) triton_poi_fused_max_pool2d_with_indices_13[grid(131072)](buf17, buf18, 131072, XBLOCK=1024, num_warps=4, num_stages=1) buf19 = empty_strided_cuda((4, 128, 18, 18), (41472, 1, 2304, 128), torch.float32) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_14[grid( 165888)](buf17, buf19, 165888, XBLOCK=512, num_warps=8, num_stages=1) buf20 = extern_kernels.convolution(buf19, buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf21 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.float32) buf22 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_15[grid(1024, 256) ](buf20, primals_13, buf21, buf22, 1024, 256, XBLOCK=32, YBLOCK =32, num_warps=4, num_stages=1) del buf20 del primals_13 buf23 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_16[grid(524288)]( buf14, primals_9, buf23, 524288, XBLOCK=1024, num_warps=4, num_stages=1) del buf14 del primals_9 buf24 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_17[grid(1048576)]( buf8, primals_5, buf24, 1048576, XBLOCK=1024, num_warps=4, num_stages=1) del buf8 del primals_5 return (buf21, primals_1, buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf9, buf11, buf12, buf13, buf15, buf17, buf18, buf19, buf22, buf23, buf24) class Encoder3New(nn.Module): def __init__(self, model=None, fixed=False): super(Encoder3New, self).__init__() self.fixed = fixed self.conv0 = nn.Conv2d(3, 3, 1, 1, 0) self.conv11 = nn.Conv2d(3, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv21 = nn.Conv2d(64, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv31 = nn.Conv2d(128, 256, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.pool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=False) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input_0): primals_1 = self.conv0.weight primals_2 = self.conv0.bias primals_4 = self.conv11.weight primals_5 = self.conv11.bias primals_6 = self.conv12.weight primals_7 = self.conv12.bias primals_8 = self.conv21.weight primals_9 = self.conv21.bias primals_10 = self.conv22.weight primals_11 = self.conv22.bias primals_12 = self.conv31.weight primals_13 = self.conv31.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13]) return output[0]	EndyWon/Texture-Reformer	Encoder3	false	8,158	[ "MIT" ]	11	f84f95accb3574c7b759a7f03c0b0b4e150314b5	https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5
LSTM	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/zb/czbbllwh7sfg4givymrrkjxzw23nmw5ismoyqt4debm22krcdkqc.py # Topologically Sorted Source Nodes: [h_0], Original ATen: [aten.zero] # Source node to ATen node mapping: # h_0 => full # Graph fragment: # %full : [num_users=3] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) triton_poi_fused_zero_0 = async_compile.triton('triton_poi_fused_zero_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_zero_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_zero_0(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = 0.0 tl.store(out_ptr0 + (x0), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gu/cgumvpbq5erg3rnqxec3fd4jo7ptcsj3kczlww57elnxgqgsibht.py # Topologically Sorted Source Nodes: [sigmoid, mul, sigmoid_1, tanh, mul_1, c_1, sigmoid_2, tanh_1, h_1], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward] # Source node to ATen node mapping: # c_1 => add_1 # h_1 => mul_2 # mul => mul # mul_1 => mul_1 # sigmoid => sigmoid # sigmoid_1 => sigmoid_1 # sigmoid_2 => sigmoid_2 # tanh => tanh # tanh_1 => tanh_1 # Graph fragment: # %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %full), kwargs = {}) # %sigmoid_1 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_1,), kwargs = {}) # %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_3,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %tanh), kwargs = {}) # %add_1 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {}) # %sigmoid_2 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_2,), kwargs = {}) # %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {}) # %mul_2 : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %tanh_1), kwargs = {}) # %sub_19 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {}) # %mul_73 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %sub_19), kwargs = {}) triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (4 + x0 + (16x1)), xmask) tmp1 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (4 + x0 + (16x1)), xmask) tmp6 = tl.load(in_ptr0 + (12 + x0 + (16x1)), xmask) tmp7 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (12 + x0 + (16x1)), xmask) tmp12 = tl.load(in_ptr0 + (x0 + (16x1)), xmask) tmp13 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (x0 + (16x1)), xmask) tmp25 = tl.load(in_ptr0 + (8 + x0 + (16x1)), xmask) tmp26 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr2 + (8 + x0 + (16x1)), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = libdevice.tanh(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = tl.sigmoid(tmp16) tmp18 = 0.0 tmp19 = tmp17 * tmp18 tmp20 = tmp5 * tmp11 tmp21 = tmp19 + tmp20 tmp22 = 1.0 tmp23 = tmp22 - tmp17 tmp24 = tmp17 * tmp23 tmp27 = tmp25 + tmp26 tmp29 = tmp27 + tmp28 tmp30 = tl.sigmoid(tmp29) tmp31 = libdevice.tanh(tmp21) tmp32 = tmp30 * tmp31 tl.store(out_ptr0 + (x2), tmp5, xmask) tl.store(out_ptr1 + (x2), tmp11, xmask) tl.store(out_ptr2 + (x2), tmp21, xmask) tl.store(out_ptr3 + (x2), tmp24, xmask) tl.store(out_ptr4 + (x2), tmp30, xmask) tl.store(out_ptr5 + (x2), tmp32, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gq/cgqwwez7hgnau2ufccutodv4pz77aowb3qzpfc3ym7ivb7snhuqp.py # Topologically Sorted Source Nodes: [sigmoid_3, mul_3, sigmoid_4, tanh_2, mul_4, c_2, sigmoid_5, tanh_3, h_2], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] # Source node to ATen node mapping: # c_2 => add_3 # h_2 => mul_5 # mul_3 => mul_3 # mul_4 => mul_4 # sigmoid_3 => sigmoid_3 # sigmoid_4 => sigmoid_4 # sigmoid_5 => sigmoid_5 # tanh_2 => tanh_2 # tanh_3 => tanh_3 # Graph fragment: # %sigmoid_3 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_4,), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_3, %add_1), kwargs = {}) # %sigmoid_4 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_5,), kwargs = {}) # %tanh_2 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_7,), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_4, %tanh_2), kwargs = {}) # %add_3 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %mul_4), kwargs = {}) # %sigmoid_5 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_6,), kwargs = {}) # %tanh_3 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_3,), kwargs = {}) # %mul_5 : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_5, %tanh_3), kwargs = {}) triton_poi_fused_add_mul_sigmoid_tanh_2 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_tanh_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'fp32', 9: 'fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_tanh_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 13, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16x1)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0 + (16x1)), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + (16x1)), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (4 + x0 + (16x1)), xmask) tmp12 = tl.load(in_ptr0 + (12 + x0 + (16x1)), xmask) tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (12 + x0 + (16x1)), xmask) tmp18 = tl.load(in_ptr3 + (x2), xmask) tmp22 = tl.load(in_ptr0 + (8 + x0 + (16x1)), xmask) tmp23 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp25 = tl.load(in_ptr2 + (8 + x0 + (16x1)), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = tl.sigmoid(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = libdevice.tanh(tmp16) tmp19 = tmp5 * tmp18 tmp20 = tmp11 * tmp17 tmp21 = tmp19 + tmp20 tmp24 = tmp22 + tmp23 tmp26 = tmp24 + tmp25 tmp27 = tl.sigmoid(tmp26) tmp28 = libdevice.tanh(tmp21) tmp29 = tmp27 * tmp28 tl.store(out_ptr0 + (x2), tmp5, xmask) tl.store(out_ptr1 + (x2), tmp11, xmask) tl.store(out_ptr2 + (x2), tmp17, xmask) tl.store(out_ptr3 + (x2), tmp21, xmask) tl.store(out_ptr4 + (x2), tmp27, xmask) tl.store(out_ptr5 + (x2), tmp29, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/p4/cp4hc27y5z2efbcvfuh3f2ytub6r2x3mg2tag432n5zqm32vjxl6.py # Topologically Sorted Source Nodes: [sigmoid_9, mul_9, sigmoid_10, tanh_6, mul_10, c_4, tanh_7], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] # Source node to ATen node mapping: # c_4 => add_7 # mul_10 => mul_10 # mul_9 => mul_9 # sigmoid_10 => sigmoid_10 # sigmoid_9 => sigmoid_9 # tanh_6 => tanh_6 # tanh_7 => tanh_7 # Graph fragment: # %sigmoid_9 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_12,), kwargs = {}) # %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_9, %add_5), kwargs = {}) # %sigmoid_10 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_13,), kwargs = {}) # %tanh_6 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_15,), kwargs = {}) # %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_10, %tanh_6), kwargs = {}) # %add_7 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_9, %mul_10), kwargs = {}) # %tanh_7 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_7,), kwargs = {}) triton_poi_fused_add_mul_sigmoid_tanh_3 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_tanh_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_tanh_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_sigmoid_tanh_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16x1)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0 + (16x1)), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + (16x1)), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (4 + x0 + (16x1)), xmask) tmp12 = tl.load(in_ptr0 + (12 + x0 + (16x1)), xmask) tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (12 + x0 + (16x1)), xmask) tmp18 = tl.load(in_ptr3 + (x2), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = tl.sigmoid(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = libdevice.tanh(tmp16) tmp19 = tmp5 * tmp18 tmp20 = tmp11 * tmp17 tmp21 = tmp19 + tmp20 tmp22 = libdevice.tanh(tmp21) tl.store(out_ptr0 + (x2), tmp5, xmask) tl.store(out_ptr1 + (x2), tmp11, xmask) tl.store(out_ptr2 + (x2), tmp17, xmask) tl.store(out_ptr3 + (x2), tmp22, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/7n/c7ndokn6ji4llxnabzyjlads6rh4x5ey476tt2px3zc4d23m2qgt.py # Topologically Sorted Source Nodes: [sigmoid_11], Original ATen: [aten.sigmoid] # Source node to ATen node mapping: # sigmoid_11 => sigmoid_11 # Graph fragment: # %sigmoid_11 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_14,), kwargs = {}) triton_poi_fused_sigmoid_4 = async_compile.triton('triton_poi_fused_sigmoid_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_sigmoid_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (8 + x0 + (16x1)), xmask) tmp1 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (8 + x0 + (16x1)), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tl.store(out_ptr0 + (x2), tmp5, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yv/cyv52nqg474urnhaqatxvbklhih33ignejyc4cwxxhp2jytcbskj.py # Topologically Sorted Source Nodes: [c_n], Original ATen: [aten.stack] # Source node to ATen node mapping: # c_n => cat_1 # Graph fragment: # %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%add_1, %add_3, %add_5, %add_7],), kwargs = {}) triton_poi_fused_stack_5 = async_compile.triton('triton_poi_fused_stack_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_stack_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x0 = xindex % 4 x2 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (4x1)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + (4((-4) + x1))), tmp9 & xmask, other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + (4((-8) + x1))), tmp14 & xmask, other=0.0) tmp16 = tmp0 >= tmp12 tmp17 = tl.full([1], 16, tl.int64) tmp18 = tmp0 < tmp17 tmp19 = tl.load(in_ptr3 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp20 = tl.load(in_ptr2 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp21 = tmp19 tmp20 tmp22 = tl.load(in_ptr4 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp23 = tl.load(in_ptr5 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp24 = tmp22 * tmp23 tmp25 = tmp21 + tmp24 tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype) tmp27 = tl.where(tmp16, tmp25, tmp26) tmp28 = tl.where(tmp14, tmp15, tmp27) tmp29 = tl.where(tmp9, tmp10, tmp28) tmp30 = tl.where(tmp4, tmp5, tmp29) tl.store(out_ptr0 + (x2), tmp30, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ai/cai7gbb6ayhln7jiow4pkqlopuq3kycdei7elu6sopif4qdkhilf.py # Topologically Sorted Source Nodes: [h_n], Original ATen: [aten.stack] # Source node to ATen node mapping: # h_n => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul_2, %mul_5, %mul_8, %mul_11],), kwargs = {}) triton_poi_fused_stack_6 = async_compile.triton('triton_poi_fused_stack_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_stack_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x0 = xindex % 4 x2 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (4x1)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + (4((-4) + x1))), tmp9 & xmask, other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + (4((-8) + x1))), tmp14 & xmask, other=0.0) tmp16 = tmp0 >= tmp12 tmp17 = tl.full([1], 16, tl.int64) tmp18 = tmp0 < tmp17 tmp19 = tl.load(in_ptr3 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp20 = tl.load(in_ptr4 + (x0 + (4((-12) + x1))), tmp16 & xmask, other=0.0) tmp21 = tmp19 tmp20 tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype) tmp23 = tl.where(tmp16, tmp21, tmp22) tmp24 = tl.where(tmp14, tmp15, tmp23) tmp25 = tl.where(tmp9, tmp10, tmp24) tmp26 = tl.where(tmp4, tmp5, tmp25) tl.store(out_ptr0 + (x2), tmp26, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (16, ), (1, )) assert_size_stride(primals_3, (4, 16), (16, 1)) assert_size_stride(primals_4, (4, 16), (16, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [h_0], Original ATen: [aten.zero] stream0 = get_raw_stream(0) triton_poi_fused_zero_0.run(buf0, 16, grid=grid(16), stream=stream0) buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf0, primals_3, out=buf1) buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32) # Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 0), primals_4, out=buf2) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf33 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid, mul, sigmoid_1, tanh, mul_1, c_1, sigmoid_2, tanh_1, h_1], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward] triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1.run(buf1, primals_2, buf2, buf3, buf4, buf5, buf33, buf6, buf7, 16, grid=grid(16), stream=stream0) buf8 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf7, primals_3, out=buf8) buf9 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [mm_1], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 4), primals_4, out=buf9) buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid_3, mul_3, sigmoid_4, tanh_2, mul_4, c_2, sigmoid_5, tanh_3, h_2], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] triton_poi_fused_add_mul_sigmoid_tanh_2.run(buf8, primals_2, buf9, buf5, buf10, buf11, buf12, buf13, buf14, buf15, 16, grid=grid(16), stream=stream0) buf16 = buf9; del buf9 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf15, primals_3, out=buf16) buf17 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [mm_2], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 8), primals_4, out=buf17) buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf20 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf21 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf22 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf23 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid_6, mul_6, sigmoid_7, tanh_4, mul_7, c_3, sigmoid_8, tanh_5, h_3], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] triton_poi_fused_add_mul_sigmoid_tanh_2.run(buf16, primals_2, buf17, buf13, buf18, buf19, buf20, buf21, buf22, buf23, 16, grid=grid(16), stream=stream0) buf24 = buf17; del buf17 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf23, primals_3, out=buf24) buf25 = buf16; del buf16 # reuse # Topologically Sorted Source Nodes: [mm_3], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 12), primals_4, out=buf25) del primals_4 buf26 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf27 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf28 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid_9, mul_9, sigmoid_10, tanh_6, mul_10, c_4, tanh_7], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add] triton_poi_fused_add_mul_sigmoid_tanh_3.run(buf24, primals_2, buf25, buf21, buf26, buf27, buf28, buf30, 16, grid=grid(16), stream=stream0) buf29 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid_11], Original ATen: [aten.sigmoid] triton_poi_fused_sigmoid_4.run(buf24, primals_2, buf25, buf29, 16, grid=grid(16), stream=stream0) del primals_2 buf31 = reinterpret_tensor(buf25, (16, 4), (4, 1), 0); del buf25 # reuse # Topologically Sorted Source Nodes: [c_n], Original ATen: [aten.stack] triton_poi_fused_stack_5.run(buf5, buf13, buf21, buf26, buf27, buf28, buf31, 64, grid=grid(64), stream=stream0) buf32 = reinterpret_tensor(buf24, (16, 4), (4, 1), 0); del buf24 # reuse # Topologically Sorted Source Nodes: [h_n], Original ATen: [aten.stack] triton_poi_fused_stack_6.run(buf7, buf15, buf23, buf29, buf30, buf32, 64, grid=grid(64), stream=stream0) return (reinterpret_tensor(buf32, (4, 4, 4), (4, 16, 1), 0), reinterpret_tensor(buf31, (4, 4, 4), (4, 16, 1), 0), buf0, buf3, buf4, buf5, buf6, buf10, buf11, buf12, buf13, buf14, buf18, buf19, buf20, buf21, buf22, buf26, buf27, buf28, buf29, buf30, reinterpret_tensor(primals_1, (4, 4), (1, 16), 12), reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), reinterpret_tensor(buf23, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 16), 8), reinterpret_tensor(buf15, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 16), 4), reinterpret_tensor(buf7, (4, 4), (1, 4), 0), buf33, reinterpret_tensor(primals_1, (4, 4), (1, 16), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from typing import Tuple import torch.nn as nn class LSTM(nn.Module): """Implementation of the standard LSTM. TODO: Include ref and LaTeX equations Parameters ---------- input_size : int Number of input features hidden_size : int Number of hidden/memory cells. batch_first : bool, optional If True, expects the batch inputs to be of shape [batch, seq, features] otherwise, the shape has to be [seq, batch, features], by default True. initial_forget_bias : int, optional Value of the initial forget gate bias, by default 0 """ def __init__(self, input_size: 'int', hidden_size: 'int', batch_first: 'bool'=True, initial_forget_bias: 'int'=0): super(LSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.batch_first = batch_first self.initial_forget_bias = initial_forget_bias self.weight_ih = nn.Parameter(torch.FloatTensor(input_size, 4 * hidden_size)) self.weight_hh = nn.Parameter(torch.FloatTensor(hidden_size, 4 * hidden_size)) self.bias = nn.Parameter(torch.FloatTensor(4 * hidden_size)) self.reset_parameters() def reset_parameters(self): """Initialize all learnable parameters of the LSTM""" nn.init.orthogonal_(self.weight_ih.data) weight_hh_data = torch.eye(self.hidden_size) weight_hh_data = weight_hh_data.repeat(1, 4) self.weight_hh.data = weight_hh_data nn.init.constant_(self.bias.data, val=0) if self.initial_forget_bias != 0: self.bias.data[:self.hidden_size] = self.initial_forget_bias def forward(self, x: 'torch.Tensor') ->Tuple[torch.Tensor, torch.Tensor]: """[summary] Parameters ---------- x : torch.Tensor Tensor, containing a batch of input sequences. Format must match the specified format, defined by the batch_first agrument. Returns ------- h_n : torch.Tensor The hidden states of each time step of each sample in the batch. c_n : torch.Tensor] The cell states of each time step of each sample in the batch. """ if self.batch_first: x = x.transpose(0, 1) seq_len, batch_size, _ = x.size() h_0 = x.data.new(batch_size, self.hidden_size).zero_() c_0 = x.data.new(batch_size, self.hidden_size).zero_() h_x = h_0, c_0 h_n, c_n = [], [] bias_batch = self.bias.unsqueeze(0).expand(batch_size, self.bias. size()) for t in range(seq_len): h_0, c_0 = h_x gates = torch.addmm(bias_batch, h_0, self.weight_hh) + torch.mm(x [t], self.weight_ih) f, i, o, g = gates.chunk(4, 1) c_1 = torch.sigmoid(f) c_0 + torch.sigmoid(i) * torch.tanh(g) h_1 = torch.sigmoid(o) * torch.tanh(c_1) h_n.append(h_1) c_n.append(c_1) h_x = h_1, c_1 h_n = torch.stack(h_n, 0) c_n = torch.stack(c_n, 0) if self.batch_first: h_n = h_n.transpose(0, 1) c_n = c_n.transpose(0, 1) return h_n, c_n def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'input_size': 4, 'hidden_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_zero_0(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = 0.0 tl.store(out_ptr0 + x0, tmp0, xmask) @triton.jit def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask) tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask) tmp7 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask) tmp12 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask) tmp13 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask) tmp25 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask) tmp26 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = libdevice.tanh(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = tl.sigmoid(tmp16) tmp18 = 0.0 tmp19 = tmp17 * tmp18 tmp20 = tmp5 * tmp11 tmp21 = tmp19 + tmp20 tmp22 = 1.0 tmp23 = tmp22 - tmp17 tmp24 = tmp17 * tmp23 tmp27 = tmp25 + tmp26 tmp29 = tmp27 + tmp28 tmp30 = tl.sigmoid(tmp29) tmp31 = libdevice.tanh(tmp21) tmp32 = tmp30 * tmp31 tl.store(out_ptr0 + x2, tmp5, xmask) tl.store(out_ptr1 + x2, tmp11, xmask) tl.store(out_ptr2 + x2, tmp21, xmask) tl.store(out_ptr3 + x2, tmp24, xmask) tl.store(out_ptr4 + x2, tmp30, xmask) tl.store(out_ptr5 + x2, tmp32, xmask) @triton.jit def triton_poi_fused_add_mul_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask) tmp12 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask) tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask) tmp18 = tl.load(in_ptr3 + x2, xmask) tmp22 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask) tmp23 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp25 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = tl.sigmoid(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = libdevice.tanh(tmp16) tmp19 = tmp5 * tmp18 tmp20 = tmp11 * tmp17 tmp21 = tmp19 + tmp20 tmp24 = tmp22 + tmp23 tmp26 = tmp24 + tmp25 tmp27 = tl.sigmoid(tmp26) tmp28 = libdevice.tanh(tmp21) tmp29 = tmp27 * tmp28 tl.store(out_ptr0 + x2, tmp5, xmask) tl.store(out_ptr1 + x2, tmp11, xmask) tl.store(out_ptr2 + x2, tmp17, xmask) tl.store(out_ptr3 + x2, tmp21, xmask) tl.store(out_ptr4 + x2, tmp27, xmask) tl.store(out_ptr5 + x2, tmp29, xmask) @triton.jit def triton_poi_fused_add_mul_sigmoid_tanh_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl. constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask) tmp12 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask) tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask) tmp18 = tl.load(in_ptr3 + x2, xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tmp8 = tmp6 + tmp7 tmp10 = tmp8 + tmp9 tmp11 = tl.sigmoid(tmp10) tmp14 = tmp12 + tmp13 tmp16 = tmp14 + tmp15 tmp17 = libdevice.tanh(tmp16) tmp19 = tmp5 * tmp18 tmp20 = tmp11 * tmp17 tmp21 = tmp19 + tmp20 tmp22 = libdevice.tanh(tmp21) tl.store(out_ptr0 + x2, tmp5, xmask) tl.store(out_ptr1 + x2, tmp11, xmask) tl.store(out_ptr2 + x2, tmp17, xmask) tl.store(out_ptr3 + x2, tmp22, xmask) @triton.jit def triton_poi_fused_sigmoid_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.sigmoid(tmp4) tl.store(out_ptr0 + x2, tmp5, xmask) @triton.jit def triton_poi_fused_stack_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x0 = xindex % 4 x2 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1)), tmp9 & xmask, other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + 4 * (-8 + x1)), tmp14 & xmask, other=0.0) tmp16 = tmp0 >= tmp12 tl.full([1], 16, tl.int64) tmp19 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp20 = tl.load(in_ptr2 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp21 = tmp19 * tmp20 tmp22 = tl.load(in_ptr4 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp23 = tl.load(in_ptr5 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp24 = tmp22 * tmp23 tmp25 = tmp21 + tmp24 tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype) tmp27 = tl.where(tmp16, tmp25, tmp26) tmp28 = tl.where(tmp14, tmp15, tmp27) tmp29 = tl.where(tmp9, tmp10, tmp28) tmp30 = tl.where(tmp4, tmp5, tmp29) tl.store(out_ptr0 + x2, tmp30, xmask) @triton.jit def triton_poi_fused_stack_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x0 = xindex % 4 x2 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1)), tmp9 & xmask, other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + 4 * (-8 + x1)), tmp14 & xmask, other=0.0) tmp16 = tmp0 >= tmp12 tl.full([1], 16, tl.int64) tmp19 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp20 = tl.load(in_ptr4 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0) tmp21 = tmp19 * tmp20 tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype) tmp23 = tl.where(tmp16, tmp21, tmp22) tmp24 = tl.where(tmp14, tmp15, tmp23) tmp25 = tl.where(tmp9, tmp10, tmp24) tmp26 = tl.where(tmp4, tmp5, tmp25) tl.store(out_ptr0 + x2, tmp26, xmask) def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (16,), (1,)) assert_size_stride(primals_3, (4, 16), (16, 1)) assert_size_stride(primals_4, (4, 16), (16, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_zero_0[grid(16)](buf0, 16, XBLOCK=16, num_warps=1, num_stages=1) buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32) extern_kernels.mm(buf0, primals_3, out=buf1) buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 0), primals_4, out=buf2) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf33 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1[grid(16)](buf1 , primals_2, buf2, buf3, buf4, buf5, buf33, buf6, buf7, 16, XBLOCK=16, num_warps=1, num_stages=1) buf8 = buf2 del buf2 extern_kernels.mm(buf7, primals_3, out=buf8) buf9 = buf1 del buf1 extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 4), primals_4, out=buf9) buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_mul_sigmoid_tanh_2[grid(16)](buf8, primals_2, buf9, buf5, buf10, buf11, buf12, buf13, buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1) buf16 = buf9 del buf9 extern_kernels.mm(buf15, primals_3, out=buf16) buf17 = buf8 del buf8 extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 8), primals_4, out=buf17) buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf20 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf21 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf22 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf23 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_mul_sigmoid_tanh_2[grid(16)](buf16, primals_2, buf17, buf13, buf18, buf19, buf20, buf21, buf22, buf23, 16, XBLOCK=16, num_warps=1, num_stages=1) buf24 = buf17 del buf17 extern_kernels.mm(buf23, primals_3, out=buf24) buf25 = buf16 del buf16 extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 12 ), primals_4, out=buf25) del primals_4 buf26 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf27 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf28 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_mul_sigmoid_tanh_3[grid(16)](buf24, primals_2, buf25, buf21, buf26, buf27, buf28, buf30, 16, XBLOCK=16, num_warps=1, num_stages=1) buf29 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_sigmoid_4[grid(16)](buf24, primals_2, buf25, buf29, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_2 buf31 = reinterpret_tensor(buf25, (16, 4), (4, 1), 0) del buf25 triton_poi_fused_stack_5[grid(64)](buf5, buf13, buf21, buf26, buf27, buf28, buf31, 64, XBLOCK=64, num_warps=1, num_stages=1) buf32 = reinterpret_tensor(buf24, (16, 4), (4, 1), 0) del buf24 triton_poi_fused_stack_6[grid(64)](buf7, buf15, buf23, buf29, buf30, buf32, 64, XBLOCK=64, num_warps=1, num_stages=1) return (reinterpret_tensor(buf32, (4, 4, 4), (4, 16, 1), 0), reinterpret_tensor(buf31, (4, 4, 4), (4, 16, 1), 0), buf0, buf3, buf4, buf5, buf6, buf10, buf11, buf12, buf13, buf14, buf18, buf19, buf20, buf21, buf22, buf26, buf27, buf28, buf29, buf30, reinterpret_tensor(primals_1, (4, 4), (1, 16), 12), reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), reinterpret_tensor(buf23, (4, 4), (1, 4), 0), reinterpret_tensor( primals_1, (4, 4), (1, 16), 8), reinterpret_tensor(buf15, (4, 4), ( 1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 16), 4), reinterpret_tensor(buf7, (4, 4), (1, 4), 0), buf33, reinterpret_tensor(primals_1, (4, 4), (1, 16), 0)) class LSTMNew(nn.Module): """Implementation of the standard LSTM. TODO: Include ref and LaTeX equations Parameters ---------- input_size : int Number of input features hidden_size : int Number of hidden/memory cells. batch_first : bool, optional If True, expects the batch inputs to be of shape [batch, seq, features] otherwise, the shape has to be [seq, batch, features], by default True. initial_forget_bias : int, optional Value of the initial forget gate bias, by default 0 """ def __init__(self, input_size: 'int', hidden_size: 'int', batch_first: 'bool'=True, initial_forget_bias: 'int'=0): super(LSTMNew, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.batch_first = batch_first self.initial_forget_bias = initial_forget_bias self.weight_ih = nn.Parameter(torch.FloatTensor(input_size, 4 * hidden_size)) self.weight_hh = nn.Parameter(torch.FloatTensor(hidden_size, 4 * hidden_size)) self.bias = nn.Parameter(torch.FloatTensor(4 * hidden_size)) self.reset_parameters() def reset_parameters(self): """Initialize all learnable parameters of the LSTM""" nn.init.orthogonal_(self.weight_ih.data) weight_hh_data = torch.eye(self.hidden_size) weight_hh_data = weight_hh_data.repeat(1, 4) self.weight_hh.data = weight_hh_data nn.init.constant_(self.bias.data, val=0) if self.initial_forget_bias != 0: self.bias.data[:self.hidden_size] = self.initial_forget_bias def forward(self, input_0): primals_3 = self.weight_ih primals_4 = self.weight_hh primals_2 = self.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4]) return output[0], output[1]	Flash-Of-Thunder/testing	LSTM	false	8,159	[ "Apache-2.0" ]	18	36366e2cd32756fb07abc533ecbb7672a4738bc6	https://github.com/Flash-Of-Thunder/testing/tree/36366e2cd32756fb07abc533ecbb7672a4738bc6
TVLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/pk/cpk4fxkha5p2i5gkbm5kyfvoyqn3fhzajspzwiol6f6vzv3u6zoc.py # Topologically Sorted Source Nodes: [sub, pow_1, h_tv, sub_1, pow_2, w_tv, add], Original ATen: [aten.sub, aten.pow, aten.sum, aten.add] # Source node to ATen node mapping: # add => add # h_tv => sum_1 # pow_1 => pow_1 # pow_2 => pow_2 # sub => sub # sub_1 => sub_1 # w_tv => sum_2 # Graph fragment: # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_3, %slice_7), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_12, %slice_16), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {}) # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_2,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {}) triton_per_fused_add_pow_sub_sum_0 = async_compile.triton('triton_per_fused_add_pow_sub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 1 rnumel = 192 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = rindex < rnumel r0 = rindex % 12 r1 = (rindex // 12) r2 = rindex % 3 r3 = (rindex // 3) tmp0 = tl.load(in_ptr0 + (4 + r0 + (16r1)), rmask, other=0.0) tmp1 = tl.load(in_ptr0 + (r0 + (16r1)), rmask, other=0.0) tmp8 = tl.load(in_ptr0 + (1 + r2 + (4r3)), rmask, other=0.0) tmp9 = tl.load(in_ptr0 + (r2 + (4r3)), rmask, other=0.0) tmp2 = tmp0 - tmp1 tmp3 = tmp2 * tmp2 tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK]) tmp6 = tl.where(rmask, tmp4, 0) tmp7 = tl.sum(tmp6, 1)[:, None] tmp10 = tmp8 - tmp9 tmp11 = tmp10 * tmp10 tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK]) tmp14 = tl.where(rmask, tmp12, 0) tmp15 = tl.sum(tmp14, 1)[:, None] tmp16 = tmp7 + tmp15 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp16, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf2 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [sub, pow_1, h_tv, sub_1, pow_2, w_tv, add], Original ATen: [aten.sub, aten.pow, aten.sum, aten.add] stream0 = get_raw_stream(0) triton_per_fused_add_pow_sub_sum_0.run(buf2, arg0_1, 1, 192, grid=grid(1), stream=stream0) del arg0_1 return (buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.utils.data import torch.nn as nn class TVLoss(nn.Module): def __init__(self): super(TVLoss, self).__init__() def forward(self, x): x.size()[0] h_x = x.size()[2] w_x = x.size()[3] self._tensor_size(x[:, :, 1:, :]) self._tensor_size(x[:, :, :, 1:]) h_tv = torch.pow(x[:, :, 1:, :] - x[:, :, :h_x - 1, :], 2).sum() w_tv = torch.pow(x[:, :, :, 1:] - x[:, :, :, :w_x - 1], 2).sum() return h_tv + w_tv def _tensor_size(self, t): return t.size()[1] * t.size()[2] * t.size()[3] def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.utils.data import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr): rnumel = 192 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] rmask = rindex < rnumel r0 = rindex % 12 r1 = rindex // 12 r2 = rindex % 3 r3 = rindex // 3 tmp0 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask, other=0.0) tmp1 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, other=0.0) tmp8 = tl.load(in_ptr0 + (1 + r2 + 4 * r3), rmask, other=0.0) tmp9 = tl.load(in_ptr0 + (r2 + 4 * r3), rmask, other=0.0) tmp2 = tmp0 - tmp1 tmp3 = tmp2 * tmp2 tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK]) tmp6 = tl.where(rmask, tmp4, 0) tmp7 = tl.sum(tmp6, 1)[:, None] tmp10 = tmp8 - tmp9 tmp11 = tmp10 * tmp10 tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK]) tmp14 = tl.where(rmask, tmp12, 0) tmp15 = tl.sum(tmp14, 1)[:, None] tmp16 = tmp7 + tmp15 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf2 = buf0 del buf0 get_raw_stream(0) triton_per_fused_add_pow_sub_sum_0[grid(1)](buf2, arg0_1, 1, 192, XBLOCK=1, num_warps=2, num_stages=1) del arg0_1 return buf2, class TVLossNew(nn.Module): def __init__(self): super(TVLossNew, self).__init__() def _tensor_size(self, t): return t.size()[1] * t.size()[2] * t.size()[3] def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	GuoShi28/GCP-Net	TVLoss	false	8,160	[ "Apache-2.0" ]	24	cef7513fa242343055af64e612429e4384d3c1d7	https://github.com/GuoShi28/GCP-Net/tree/cef7513fa242343055af64e612429e4384d3c1d7
SmallDecoder4_16x	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/qf/cqfmtaclwwokanuqklrcfbfjoodyqdqhbvsmbqe3rvai4vt4jakp.py # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] # Source node to ATen node mapping: # pad => _unsafe_index, _unsafe_index_1 # Graph fragment: # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 18432 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 6 x1 = (xindex // 6) % 6 x2 = (xindex // 36) x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16x2)), None, eviction_policy='evict_last') tl.store(out_ptr0 + (x3), tmp0, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ki/ckii6ld2pjui2kyh33ieyagpaxjso55jvdwg3kttj5omjlrhkys6.py # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_1 => add, add_1, convert_element_type, convert_element_type_1, iota_2, mul, mul_1 # Graph fragment: # %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {}) # %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_1 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/mk/cmkh5sjr647lcubldqpy6e5li5z5wx5mcm6huu6afcqt3rdju3tb.py # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d => convolution # pad_1 => _unsafe_index_3, _unsafe_index_4 # y => relu # y_1 => _unsafe_index_2 # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {}) # %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 25600 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 10) % 10 x0 = xindex % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 64 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (4tmp4) + (16x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/jd/cjdeicwf7lgv5yjg3dpsu2qr2dwh2mpczvh4lt5bgjirqrezvxra.py # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # pad_2 => _unsafe_index_5, _unsafe_index_6 # y_2 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_5, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 25600 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 10 x1 = (xindex // 10) % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 64 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/7m/c7m636s67xk4qnqesb4ybcmhmpkfe6tt3f776lqq7o47rf4vm2oo.py # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange] # Source node to ATen node mapping: # y_6 => iota_12 # Graph fragment: # %iota_12 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) triton_poi_fused_arange_4 = async_compile.triton('triton_poi_fused_arange_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_arange_4(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + (x0), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/b4/cb45juu5nfh3qqk3tymmpltlxxn65rerwss2drltolfburgqjtmy.py # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_6 => add_4, add_5, convert_element_type_4, convert_element_type_5, mul_4, mul_5 # Graph fragment: # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_12, 1), kwargs = {}) # %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, 0), kwargs = {}) # %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_4, torch.float32), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.5), kwargs = {}) # %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_5 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_5(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ca/ccalcowwb3h4zm24oe3hiia75yw4uofvwi3qc47ibr6ebf4fx4ys.py # Topologically Sorted Source Nodes: [conv2d_4, y_5, y_6, pad_5], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # pad_5 => _unsafe_index_12, _unsafe_index_13 # y_5 => relu_4 # y_6 => _unsafe_index_11 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) # %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_4, [None, None, %unsqueeze_1, %convert_element_type_5]), kwargs = {}) # %_unsafe_index_12 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_11, [None, None, %sub_21, None]), kwargs = {}) # %_unsafe_index_13 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_12, [None, None, None, %sub_21]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 41472 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 18) % 18 x0 = xindex % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 32 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (8tmp4) + (64x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/lk/clk35ksgn6op6xiattqi2roqz7umkuq7t3ecc22eixljr3kbkt7t.py # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_5 => convolution_5 # pad_6 => _unsafe_index_14, _unsafe_index_15 # y_7 => relu_5 # Graph fragment: # %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {}) # %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_5, [None, None, %sub_21, None]), kwargs = {}) # %_unsafe_index_15 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_14, [None, None, None, %sub_21]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_7 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 41472 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 18 x1 = (xindex // 18) % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 32 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/vc/cvcde7szgs7fi3bcwd5ydohr5tlt4cvg4kaykp2ramesoolh5pnh.py # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.arange] # Source node to ATen node mapping: # y_9 => iota_18 # Graph fragment: # %iota_18 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) triton_poi_fused_arange_8 = async_compile.triton('triton_poi_fused_arange_8', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_arange_8(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + (x0), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/h4/ch4gssewmpqybmcvtudsjhftm6vkmgi7k4ihwtwyd4gbwkx5cpae.py # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_9 => add_8, add_9, convert_element_type_8, convert_element_type_9, mul_8, mul_9 # Graph fragment: # %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_18, 1), kwargs = {}) # %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, 0), kwargs = {}) # %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_8, torch.float32), kwargs = {}) # %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_8, 0.0), kwargs = {}) # %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_9, 0.5), kwargs = {}) # %convert_element_type_9 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_9, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_9 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_9', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_9(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/cx/ccxabofigctovjnxcerfkyob5qxahwm3427hrct67qe53hi6yy5b.py # Topologically Sorted Source Nodes: [conv2d_6, y_8, y_9, pad_7], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_6 => convolution_6 # pad_7 => _unsafe_index_17, _unsafe_index_18 # y_8 => relu_6 # y_9 => _unsafe_index_16 # Graph fragment: # %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {}) # %_unsafe_index_16 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_6, [None, None, %unsqueeze_2, %convert_element_type_9]), kwargs = {}) # %_unsafe_index_17 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_16, [None, None, %sub_29, None]), kwargs = {}) # %_unsafe_index_18 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_17, [None, None, None, %sub_29]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 73984 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 34) % 34 x0 = xindex % 34 x4 = (xindex // 1156) x2 = (xindex // 1156) % 16 x7 = xindex tmp0 = tl.load(in_ptr0 + (31 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (31 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 16, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (16tmp4) + (256x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/n5/cn5bwzqhur7hqdclvtldqleoqnxhq4vlaaomuzq54ram2lkvfati.py # Topologically Sorted Source Nodes: [conv2d_7, y_10, pad_8], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_7 => convolution_7 # pad_8 => _unsafe_index_19, _unsafe_index_20 # y_10 => relu_7 # Graph fragment: # %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_18, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {}) # %_unsafe_index_19 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_7, [None, None, %sub_29, None]), kwargs = {}) # %_unsafe_index_20 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_19, [None, None, None, %sub_29]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_11 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_11', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 73984 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 34 x1 = (xindex // 34) % 34 x4 = (xindex // 1156) x2 = (xindex // 1156) % 16 x5 = xindex tmp0 = tl.load(in_ptr0 + (1023 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x0))))) + ((-32)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (1024x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/nv/cnvs3bdrsnvp5q2rklqyjpnuqc65wbpn47ybls5s7yykmz7w23t4.py # Topologically Sorted Source Nodes: [conv2d_8, y_11], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_8 => convolution_8 # y_11 => relu_8 # Graph fragment: # %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_20, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_8, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_12 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_12', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_12', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_12(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 12288 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 1024) % 3 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, None) tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/nz/cnz7in33vb6ow72qy4sl4ixzmd3j3zbgejev7tgxhvbnplql632o.py # Topologically Sorted Source Nodes: [conv2d_7, y_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_7 => convolution_7 # y_10 => relu_7 # Graph fragment: # %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_18, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {}) # %le_19 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_7, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_13 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_13', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 65536 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 1024) % 16 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/cl/cclv5ye3fxxpo74qen5cjgxbjvpvnq63kvfqgya7aubqltm6nw2k.py # Topologically Sorted Source Nodes: [conv2d_6, y_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_6 => convolution_6 # y_8 => relu_6 # Graph fragment: # %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {}) # %le_38 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_14 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_14', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 256) % 16 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/tz/ctzrrrapq5gw5q2x4w57kjkm4yrjcprfwlquw4c5zjlyo5dd324x.py # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_5 => convolution_5 # y_7 => relu_5 # Graph fragment: # %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {}) # %le_57 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_5, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_15 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_15', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32768 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 256) % 32 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ln/clnwyio7qdqnfw2sjdaamtbly6dcdjpdi2c4qggmoxcno7nfeapx.py # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # y_5 => relu_4 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) # %le_76 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_16', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8192 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 32 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/57/c57huoprebsacwhgxa2qv7go6gg55covtzyqh6v6cg6poqmway2z.py # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # y_4 => relu_3 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_8, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %le_95 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_17 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_17', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 64 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/5s/c5siqfatinwonhbss7e35dppr3bd3y74krkgacwdftc3hhteeezp.py # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d => convolution # y => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le_152 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_18 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_18', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 64 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19 = args args.clear() assert_size_stride(primals_1, (4, 128, 4, 4), (2048, 16, 4, 1)) assert_size_stride(primals_2, (64, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_3, (64, ), (1, )) assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64, ), (1, )) assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_9, (64, ), (1, )) assert_size_stride(primals_10, (32, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_11, (32, ), (1, )) assert_size_stride(primals_12, (32, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_13, (32, ), (1, )) assert_size_stride(primals_14, (16, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_15, (16, ), (1, )) assert_size_stride(primals_16, (16, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_17, (16, ), (1, )) assert_size_stride(primals_18, (3, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_19, (3, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 128, 6, 6), (4608, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] stream0 = get_raw_stream(0) triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 18432, grid=grid(18432), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 64, 4, 4), (1024, 16, 4, 1)) buf2 = empty_strided_cuda((8, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_1.run(buf2, 8, grid=grid(8), stream=stream0) buf3 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2.run(buf2, buf1, primals_3, buf3, 25600, grid=grid(25600), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 64, 8, 8), (4096, 64, 8, 1)) buf5 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf4, primals_5, buf5, 25600, grid=grid(25600), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 64, 8, 8), (4096, 64, 8, 1)) buf7 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_2, y_3, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf6, primals_7, buf7, 25600, grid=grid(25600), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 64, 8, 8), (4096, 64, 8, 1)) buf9 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_4], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf8, primals_9, buf9, 25600, grid=grid(25600), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 32, 8, 8), (2048, 64, 8, 1)) buf11 = empty_strided_cuda((16, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange] triton_poi_fused_arange_4.run(buf11, 16, grid=grid(16), stream=stream0) buf12 = empty_strided_cuda((16, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_5.run(buf12, 16, grid=grid(16), stream=stream0) buf13 = empty_strided_cuda((4, 32, 18, 18), (10368, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_4, y_5, y_6, pad_5], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6.run(buf12, buf10, primals_11, buf13, 41472, grid=grid(41472), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution] buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 32, 16, 16), (8192, 256, 16, 1)) buf15 = empty_strided_cuda((4, 32, 18, 18), (10368, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_7.run(buf14, primals_13, buf15, 41472, grid=grid(41472), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution] buf16 = extern_kernels.convolution(buf15, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 16, 16, 16), (4096, 256, 16, 1)) buf17 = empty_strided_cuda((32, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.arange] triton_poi_fused_arange_8.run(buf17, 32, grid=grid(32), stream=stream0) buf18 = empty_strided_cuda((32, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_9.run(buf18, 32, grid=grid(32), stream=stream0) buf19 = empty_strided_cuda((4, 16, 34, 34), (18496, 1156, 34, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_6, y_8, y_9, pad_7], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10.run(buf18, buf16, primals_15, buf19, 73984, grid=grid(73984), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution] buf20 = extern_kernels.convolution(buf19, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 16, 32, 32), (16384, 1024, 32, 1)) buf21 = empty_strided_cuda((4, 16, 34, 34), (18496, 1156, 34, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_7, y_10, pad_8], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_11.run(buf20, primals_17, buf21, 73984, grid=grid(73984), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution] buf22 = extern_kernels.convolution(buf21, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf22, (4, 3, 32, 32), (3072, 1024, 32, 1)) buf23 = buf22; del buf22 # reuse buf24 = empty_strided_cuda((4, 3, 32, 32), (3072, 1024, 32, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_8, y_11], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_12.run(buf23, primals_19, buf24, 12288, grid=grid(12288), stream=stream0) del primals_19 buf25 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_7, y_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_13.run(buf20, primals_17, buf25, 65536, grid=grid(65536), stream=stream0) del buf20 del primals_17 buf26 = empty_strided_cuda((4, 16, 16, 16), (4096, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_6, y_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_14.run(buf16, primals_15, buf26, 16384, grid=grid(16384), stream=stream0) del buf16 del primals_15 buf27 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_15.run(buf14, primals_13, buf27, 32768, grid=grid(32768), stream=stream0) del buf14 del primals_13 buf28 = empty_strided_cuda((4, 32, 8, 8), (2048, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_16.run(buf10, primals_11, buf28, 8192, grid=grid(8192), stream=stream0) del buf10 del primals_11 buf29 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_17.run(buf8, primals_9, buf29, 16384, grid=grid(16384), stream=stream0) del buf8 del primals_9 buf30 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_2, y_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_17.run(buf6, primals_7, buf30, 16384, grid=grid(16384), stream=stream0) del buf6 del primals_7 buf31 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, y_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_17.run(buf4, primals_5, buf31, 16384, grid=grid(16384), stream=stream0) del buf4 del primals_5 buf32 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_18.run(buf1, primals_3, buf32, 4096, grid=grid(4096), stream=stream0) del buf1 del primals_3 return (buf23, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf13, buf15, buf17, buf18, buf19, buf21, buf24, buf25, buf26, buf27, buf28, buf29, buf30, buf31, buf32, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 128, 4, 4), (2048, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((16, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((3, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_19 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class SmallDecoder4_16x(nn.Module): def __init__(self, model=None, fixed=False): super(SmallDecoder4_16x, self).__init__() self.fixed = fixed self.conv41 = nn.Conv2d(128, 64, 3, 1, 0) self.conv34 = nn.Conv2d(64, 64, 3, 1, 0) self.conv33 = nn.Conv2d(64, 64, 3, 1, 0) self.conv32 = nn.Conv2d(64, 64, 3, 1, 0) self.conv31 = nn.Conv2d(64, 32, 3, 1, 0) self.conv22 = nn.Conv2d(32, 32, 3, 1, 0, dilation=1) self.conv21 = nn.Conv2d(32, 16, 3, 1, 0, dilation=1) self.conv12 = nn.Conv2d(16, 16, 3, 1, 0, dilation=1) self.conv11 = nn.Conv2d(16, 3, 3, 1, 0, dilation=1) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.unpool_pwct = nn.MaxUnpool2d(kernel_size=2, stride=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: weights = torch.load(model, map_location=lambda storage, location: storage) if 'model' in weights: self.load_state_dict(weights['model']) else: self.load_state_dict(weights) None if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, y): y = self.relu(self.conv41(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv34(self.pad(y))) y = self.relu(self.conv33(self.pad(y))) y = self.relu(self.conv32(self.pad(y))) y = self.relu(self.conv31(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv22(self.pad(y))) y = self.relu(self.conv21(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv12(self.pad(y))) y = self.relu(self.conv11(self.pad(y))) return y def forward_pwct(self, x, pool1_idx=None, pool1_size=None, pool2_idx= None, pool2_size=None, pool3_idx=None, pool3_size=None): out41 = self.relu(self.conv41(self.pad(x))) out41 = self.unpool_pwct(out41, pool3_idx, output_size=pool3_size) out34 = self.relu(self.conv34(self.pad(out41))) out33 = self.relu(self.conv33(self.pad(out34))) out32 = self.relu(self.conv32(self.pad(out33))) out31 = self.relu(self.conv31(self.pad(out32))) out31 = self.unpool_pwct(out31, pool2_idx, output_size=pool2_size) out22 = self.relu(self.conv22(self.pad(out31))) out21 = self.relu(self.conv21(self.pad(out22))) out21 = self.unpool_pwct(out21, pool1_idx, output_size=pool1_size) out12 = self.relu(self.conv12(self.pad(out21))) out11 = self.conv11(self.pad(out12)) return out11 def get_inputs(): return [torch.rand([4, 128, 4, 4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 6 x1 = xindex // 6 % 6 x2 = xindex // 36 x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 + x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None, eviction_policy='evict_last') tl.store(out_ptr0 + x3, tmp0, None) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 25600 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 10 % 10 x0 = xindex % 10 x4 = xindex // 100 x2 = xindex // 100 % 64 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1 ))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0 ))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 4 * tmp4 + 16 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 25600 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 10 x1 = xindex // 10 % 10 x4 = xindex // 100 x2 = xindex // 100 % 64 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_arange_4(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + x0, tmp0, xmask) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_5(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 41472 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 18 % 18 x0 = xindex % 18 x4 = xindex // 324 x2 = xindex // 324 % 32 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x1))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 8 * tmp4 + 64 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 41472 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 18 x1 = xindex // 18 % 18 x4 = xindex // 324 x2 = xindex // 324 % 32 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_arange_8(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + x0, tmp0, xmask) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_9(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10(in_ptr0 , in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 73984 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 34 % 34 x0 = xindex % 34 x4 = xindex // 1156 x2 = xindex // 1156 % 16 x7 = xindex tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x1))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x0))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 16, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 16 * tmp4 + 256 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 73984 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 34 x1 = xindex // 34 % 34 x4 = xindex // 1156 x2 = xindex // 1156 % 16 x5 = xindex tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_12(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 1024 % 3 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, None) tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 1024 % 16 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 256 % 16 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 256 % 32 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 32 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 64 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 64 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19) = args args.clear() assert_size_stride(primals_1, (4, 128, 4, 4), (2048, 16, 4, 1)) assert_size_stride(primals_2, (64, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_3, (64,), (1,)) assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64,), (1,)) assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_9, (64,), (1,)) assert_size_stride(primals_10, (32, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_11, (32,), (1,)) assert_size_stride(primals_12, (32, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_13, (32,), (1,)) assert_size_stride(primals_14, (16, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_15, (16,), (1,)) assert_size_stride(primals_16, (16, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_17, (16,), (1,)) assert_size_stride(primals_18, (3, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_19, (3,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 128, 6, 6), (4608, 36, 6, 1), torch. float32) get_raw_stream(0) triton_poi_fused_reflection_pad2d_0[grid(18432)](primals_1, buf0, 18432, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 64, 4, 4), (1024, 16, 4, 1)) buf2 = empty_strided_cuda((8,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK =8, num_warps=1, num_stages=1) buf3 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid (25600)](buf2, buf1, primals_3, buf3, 25600, XBLOCK=256, num_warps=4, num_stages=1) buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 64, 8, 8), (4096, 64, 8, 1)) buf5 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(25600)](buf4, primals_5, buf5, 25600, XBLOCK=128, num_warps=4, num_stages=1) buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 64, 8, 8), (4096, 64, 8, 1)) buf7 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(25600)](buf6, primals_7, buf7, 25600, XBLOCK=128, num_warps=4, num_stages=1) buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 64, 8, 8), (4096, 64, 8, 1)) buf9 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(25600)](buf8, primals_9, buf9, 25600, XBLOCK=128, num_warps=4, num_stages=1) buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 32, 8, 8), (2048, 64, 8, 1)) buf11 = empty_strided_cuda((16,), (1,), torch.int64) triton_poi_fused_arange_4[grid(16)](buf11, 16, XBLOCK=16, num_warps =1, num_stages=1) buf12 = empty_strided_cuda((16,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_5[grid(16)](buf12, 16, XBLOCK=16, num_warps=1, num_stages=1) buf13 = empty_strided_cuda((4, 32, 18, 18), (10368, 324, 18, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6[grid (41472)](buf12, buf10, primals_11, buf13, 41472, XBLOCK=256, num_warps=4, num_stages=1) buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 32, 16, 16), (8192, 256, 16, 1)) buf15 = empty_strided_cuda((4, 32, 18, 18), (10368, 324, 18, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_7[grid(41472)](buf14 , primals_13, buf15, 41472, XBLOCK=512, num_warps=4, num_stages=1) buf16 = extern_kernels.convolution(buf15, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 16, 16, 16), (4096, 256, 16, 1)) buf17 = empty_strided_cuda((32,), (1,), torch.int64) triton_poi_fused_arange_8[grid(32)](buf17, 32, XBLOCK=32, num_warps =1, num_stages=1) buf18 = empty_strided_cuda((32,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_9[grid(32)](buf18, 32, XBLOCK=32, num_warps=1, num_stages=1) buf19 = empty_strided_cuda((4, 16, 34, 34), (18496, 1156, 34, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10[ grid(73984)](buf18, buf16, primals_15, buf19, 73984, XBLOCK=512, num_warps=8, num_stages=1) buf20 = extern_kernels.convolution(buf19, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 16, 32, 32), (16384, 1024, 32, 1)) buf21 = empty_strided_cuda((4, 16, 34, 34), (18496, 1156, 34, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_11[grid(73984)]( buf20, primals_17, buf21, 73984, XBLOCK=512, num_warps=8, num_stages=1) buf22 = extern_kernels.convolution(buf21, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf22, (4, 3, 32, 32), (3072, 1024, 32, 1)) buf23 = buf22 del buf22 buf24 = empty_strided_cuda((4, 3, 32, 32), (3072, 1024, 32, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_12[grid(12288)]( buf23, primals_19, buf24, 12288, XBLOCK=128, num_warps=4, num_stages=1) del primals_19 buf25 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_13[grid(65536)]( buf20, primals_17, buf25, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf20 del primals_17 buf26 = empty_strided_cuda((4, 16, 16, 16), (4096, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_14[grid(16384)]( buf16, primals_15, buf26, 16384, XBLOCK=256, num_warps=4, num_stages=1) del buf16 del primals_15 buf27 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_15[grid(32768)]( buf14, primals_13, buf27, 32768, XBLOCK=128, num_warps=4, num_stages=1) del buf14 del primals_13 buf28 = empty_strided_cuda((4, 32, 8, 8), (2048, 64, 8, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_16[grid(8192)]( buf10, primals_11, buf28, 8192, XBLOCK=256, num_warps=4, num_stages=1) del buf10 del primals_11 buf29 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_17[grid(16384)]( buf8, primals_9, buf29, 16384, XBLOCK=128, num_warps=4, num_stages=1) del buf8 del primals_9 buf30 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_17[grid(16384)]( buf6, primals_7, buf30, 16384, XBLOCK=128, num_warps=4, num_stages=1) del buf6 del primals_7 buf31 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_17[grid(16384)]( buf4, primals_5, buf31, 16384, XBLOCK=128, num_warps=4, num_stages=1) del buf4 del primals_5 buf32 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_18[grid(4096)]( buf1, primals_3, buf32, 4096, XBLOCK=128, num_warps=4, num_stages=1 ) del buf1 del primals_3 return (buf23, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf13, buf15, buf17, buf18, buf19, buf21, buf24, buf25, buf26, buf27, buf28, buf29, buf30, buf31, buf32) class SmallDecoder4_16xNew(nn.Module): def __init__(self, model=None, fixed=False): super(SmallDecoder4_16xNew, self).__init__() self.fixed = fixed self.conv41 = nn.Conv2d(128, 64, 3, 1, 0) self.conv34 = nn.Conv2d(64, 64, 3, 1, 0) self.conv33 = nn.Conv2d(64, 64, 3, 1, 0) self.conv32 = nn.Conv2d(64, 64, 3, 1, 0) self.conv31 = nn.Conv2d(64, 32, 3, 1, 0) self.conv22 = nn.Conv2d(32, 32, 3, 1, 0, dilation=1) self.conv21 = nn.Conv2d(32, 16, 3, 1, 0, dilation=1) self.conv12 = nn.Conv2d(16, 16, 3, 1, 0, dilation=1) self.conv11 = nn.Conv2d(16, 3, 3, 1, 0, dilation=1) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.unpool_pwct = nn.MaxUnpool2d(kernel_size=2, stride=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: weights = torch.load(model, map_location=lambda storage, location: storage) if 'model' in weights: self.load_state_dict(weights['model']) else: self.load_state_dict(weights) None if fixed: for param in self.parameters(): param.requires_grad = False def forward_pwct(self, x, pool1_idx=None, pool1_size=None, pool2_idx= None, pool2_size=None, pool3_idx=None, pool3_size=None): out41 = self.relu(self.conv41(self.pad(x))) out41 = self.unpool_pwct(out41, pool3_idx, output_size=pool3_size) out34 = self.relu(self.conv34(self.pad(out41))) out33 = self.relu(self.conv33(self.pad(out34))) out32 = self.relu(self.conv32(self.pad(out33))) out31 = self.relu(self.conv31(self.pad(out32))) out31 = self.unpool_pwct(out31, pool2_idx, output_size=pool2_size) out22 = self.relu(self.conv22(self.pad(out31))) out21 = self.relu(self.conv21(self.pad(out22))) out21 = self.unpool_pwct(out21, pool1_idx, output_size=pool1_size) out12 = self.relu(self.conv12(self.pad(out21))) out11 = self.conv11(self.pad(out12)) return out11 def forward(self, input_0): primals_2 = self.conv41.weight primals_3 = self.conv41.bias primals_4 = self.conv34.weight primals_5 = self.conv34.bias primals_6 = self.conv33.weight primals_7 = self.conv33.bias primals_8 = self.conv32.weight primals_9 = self.conv32.bias primals_10 = self.conv31.weight primals_11 = self.conv31.bias primals_12 = self.conv22.weight primals_13 = self.conv22.bias primals_14 = self.conv21.weight primals_15 = self.conv21.bias primals_16 = self.conv12.weight primals_17 = self.conv12.bias primals_18 = self.conv11.weight primals_19 = self.conv11.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19]) return output[0]	EndyWon/Texture-Reformer	SmallDecoder4_16x	false	8,161	[ "MIT" ]	11	f84f95accb3574c7b759a7f03c0b0b4e150314b5	https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5
CharbonnierLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ld/cldtt4ebftddfj4rfkhdg5qms3hjdmulchilkr5fnpjj7n4w67nv.py # Topologically Sorted Source Nodes: [diff, mul, add, sqrt, loss], Original ATen: [aten.sub, aten.mul, aten.add, aten.sqrt, aten.sum] # Source node to ATen node mapping: # add => add # diff => sub # loss => sum_1 # mul => mul # sqrt => sqrt # Graph fragment: # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %sub), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 1e-06), kwargs = {}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sqrt,), kwargs = {}) triton_per_fused_add_mul_sqrt_sub_sum_0 = async_compile.triton('triton_per_fused_add_mul_sqrt_sub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_sqrt_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_mul_sqrt_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp1 = tl.load(in_ptr1 + (r0), None) tmp2 = tmp0 - tmp1 tmp3 = tmp2 * tmp2 tmp4 = 1e-06 tmp5 = tmp3 + tmp4 tmp6 = libdevice.sqrt(tmp5) tmp7 = tl.broadcast_to(tmp6, [RBLOCK]) tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0)) tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp9, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) # Topologically Sorted Source Nodes: [diff, mul, add, sqrt, loss], Original ATen: [aten.sub, aten.mul, aten.add, aten.sqrt, aten.sum] stream0 = get_raw_stream(0) triton_per_fused_add_mul_sqrt_sub_sum_0.run(arg0_1, arg1_1, buf0, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.utils.data import torch.nn as nn class CharbonnierLoss(nn.Module): """Charbonnier Loss (L1)""" def __init__(self, eps=1e-06): super(CharbonnierLoss, self).__init__() self.eps = eps def forward(self, x, y): diff = x - y loss = torch.sum(torch.sqrt(diff * diff + self.eps)) return loss def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import torch.utils.data import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_mul_sqrt_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp1 = tl.load(in_ptr1 + r0, None) tmp2 = tmp0 - tmp1 tmp3 = tmp2 * tmp2 tmp4 = 1e-06 tmp5 = tmp3 + tmp4 tmp6 = libdevice.sqrt(tmp5) tmp7 = tl.broadcast_to(tmp6, [RBLOCK]) tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0)) tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp9, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) get_raw_stream(0) triton_per_fused_add_mul_sqrt_sub_sum_0[grid(1)](arg0_1, arg1_1, buf0, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf0, class CharbonnierLossNew(nn.Module): """Charbonnier Loss (L1)""" def __init__(self, eps=1e-06): super(CharbonnierLossNew, self).__init__() self.eps = eps def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	GuoShi28/GCP-Net	CharbonnierLoss	false	8,162	[ "Apache-2.0" ]	24	cef7513fa242343055af64e612429e4384d3c1d7	https://github.com/GuoShi28/GCP-Net/tree/cef7513fa242343055af64e612429e4384d3c1d7
SmallDecoder5_16x	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/qf/cqfmtaclwwokanuqklrcfbfjoodyqdqhbvsmbqe3rvai4vt4jakp.py # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] # Source node to ATen node mapping: # pad => _unsafe_index, _unsafe_index_1 # Graph fragment: # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 18432 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 6 x1 = (xindex // 6) % 6 x2 = (xindex // 36) x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16x2)), None, eviction_policy='evict_last') tl.store(out_ptr0 + (x3), tmp0, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ki/ckii6ld2pjui2kyh33ieyagpaxjso55jvdwg3kttj5omjlrhkys6.py # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_1 => add, add_1, convert_element_type, convert_element_type_1, iota_2, mul, mul_1 # Graph fragment: # %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {}) # %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_1 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/uw/cuw2bwjzi7yls2s6pmwc256upm7wakbnbnnmg4kzboptcqkagykr.py # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d => convolution # pad_1 => _unsafe_index_3, _unsafe_index_4 # y => relu # y_1 => _unsafe_index_2 # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {}) # %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 51200 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x1 = (xindex // 10) % 10 x0 = xindex % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 128 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (4tmp4) + (16x4)), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/fk/cfk52xdhjt42cxoynmeuwzri2n3lcch6jizrtctuiqmsv6a2mkr3.py # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # pad_2 => _unsafe_index_5, _unsafe_index_6 # y_2 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_5, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 51200 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 10 x1 = (xindex // 10) % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 128 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64x4)), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/7m/c7m636s67xk4qnqesb4ybcmhmpkfe6tt3f776lqq7o47rf4vm2oo.py # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange] # Source node to ATen node mapping: # y_6 => iota_12 # Graph fragment: # %iota_12 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) triton_poi_fused_arange_4 = async_compile.triton('triton_poi_fused_arange_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_arange_4(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + (x0), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/b4/cb45juu5nfh3qqk3tymmpltlxxn65rerwss2drltolfburgqjtmy.py # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_6 => add_4, add_5, convert_element_type_4, convert_element_type_5, mul_4, mul_5 # Graph fragment: # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_12, 1), kwargs = {}) # %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, 0), kwargs = {}) # %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_4, torch.float32), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.5), kwargs = {}) # %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_5 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_5(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/iw/ciw3ol6yhqo77rufiqbsxm2mpwbja6y32t76rjsjihma7p5lz3ap.py # Topologically Sorted Source Nodes: [conv2d_4, y_5, y_6, pad_5], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # pad_5 => _unsafe_index_12, _unsafe_index_13 # y_5 => relu_4 # y_6 => _unsafe_index_11 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) # %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_4, [None, None, %unsqueeze_1, %convert_element_type_5]), kwargs = {}) # %_unsafe_index_12 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_11, [None, None, %sub_21, None]), kwargs = {}) # %_unsafe_index_13 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_12, [None, None, None, %sub_21]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 82944 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 18) % 18 x0 = xindex % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 64 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (8tmp4) + (64x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/3j/c3jl6ra5o5ocn2p47tnm3gczpjcfqsbiqa77ry2qzk6z2iaipyq3.py # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_5 => convolution_5 # pad_6 => _unsafe_index_14, _unsafe_index_15 # y_7 => relu_5 # Graph fragment: # %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {}) # %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_5, [None, None, %sub_21, None]), kwargs = {}) # %_unsafe_index_15 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_14, [None, None, None, %sub_21]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_7 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 82944 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 18 x1 = (xindex // 18) % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 64 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/vc/cvcde7szgs7fi3bcwd5ydohr5tlt4cvg4kaykp2ramesoolh5pnh.py # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.arange] # Source node to ATen node mapping: # y_11 => iota_22 # Graph fragment: # %iota_22 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) triton_poi_fused_arange_8 = async_compile.triton('triton_poi_fused_arange_8', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_arange_8(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + (x0), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/h4/ch4gssewmpqybmcvtudsjhftm6vkmgi7k4ihwtwyd4gbwkx5cpae.py # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_11 => add_8, add_9, convert_element_type_8, convert_element_type_9, mul_8, mul_9 # Graph fragment: # %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_22, 1), kwargs = {}) # %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, 0), kwargs = {}) # %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_8, torch.float32), kwargs = {}) # %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_8, 0.0), kwargs = {}) # %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_9, 0.5), kwargs = {}) # %convert_element_type_9 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_9, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_9 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_9', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_9(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/lv/clvpr5qet52g4l6gbrpcst5c4rkdmxjrpo4u3zimrlx42oxrbjmq.py # Topologically Sorted Source Nodes: [conv2d_8, y_10, y_11, pad_9], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_8 => convolution_8 # pad_9 => _unsafe_index_21, _unsafe_index_22 # y_10 => relu_8 # y_11 => _unsafe_index_20 # Graph fragment: # %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_19, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {}) # %_unsafe_index_20 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_8, [None, None, %unsqueeze_2, %convert_element_type_9]), kwargs = {}) # %_unsafe_index_21 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_20, [None, None, %sub_37, None]), kwargs = {}) # %_unsafe_index_22 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_21, [None, None, None, %sub_37]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 147968 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 34) % 34 x0 = xindex % 34 x4 = (xindex // 1156) x2 = (xindex // 1156) % 32 x7 = xindex tmp0 = tl.load(in_ptr0 + (31 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (31 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 16, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (16tmp4) + (256x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/jo/cjoxudpxyx7zvaakjlfwkolwordnxnm7kvgd2hjlgflgtnbtqjqe.py # Topologically Sorted Source Nodes: [conv2d_9, y_12, pad_10], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_9 => convolution_9 # pad_10 => _unsafe_index_23, _unsafe_index_24 # y_12 => relu_9 # Graph fragment: # %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_22, %primals_20, %primals_21, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {}) # %_unsafe_index_23 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [None, None, %sub_37, None]), kwargs = {}) # %_unsafe_index_24 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_23, [None, None, None, %sub_37]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_11 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_11', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 147968 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 34 x1 = (xindex // 34) % 34 x4 = (xindex // 1156) x2 = (xindex // 1156) % 32 x5 = xindex tmp0 = tl.load(in_ptr0 + (1023 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x0))))) + ((-32)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (1024x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/tn/ctnbrinmzk76ajgbcbtpwnprwmu55bckgbhhtzpeowj4ed2wx344.py # Topologically Sorted Source Nodes: [y_14], Original ATen: [aten.arange] # Source node to ATen node mapping: # y_14 => iota_28 # Graph fragment: # %iota_28 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (64,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) triton_poi_fused_arange_12 = async_compile.triton('triton_poi_fused_arange_12', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_arange_12(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + (x0), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/au/caua7s3wcffpgp2fqat3qxat3k3ro3kdge6fxezxfwnmyjfik5zi.py # Topologically Sorted Source Nodes: [y_14], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_14 => add_12, add_13, convert_element_type_12, convert_element_type_13, mul_12, mul_13 # Graph fragment: # %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_28, 1), kwargs = {}) # %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_12, 0), kwargs = {}) # %convert_element_type_12 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_12, torch.float32), kwargs = {}) # %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_12, 0.0), kwargs = {}) # %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_13, 0.5), kwargs = {}) # %convert_element_type_13 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_13, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_13 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_13', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_13(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/u3/cu3na56mbv7a5j6wen7y5sn7odfrd5izj7sh5hgugag5f6rne2d4.py # Topologically Sorted Source Nodes: [conv2d_10, y_13, y_14, pad_11], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_10 => convolution_10 # pad_11 => _unsafe_index_26, _unsafe_index_27 # y_13 => relu_10 # y_14 => _unsafe_index_25 # Graph fragment: # %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_24, %primals_22, %primals_23, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {}) # %_unsafe_index_25 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_10, [None, None, %unsqueeze_3, %convert_element_type_13]), kwargs = {}) # %_unsafe_index_26 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_25, [None, None, %sub_45, None]), kwargs = {}) # %_unsafe_index_27 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_26, [None, None, None, %sub_45]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_14 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_14', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_14(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 278784 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 66) % 66 x0 = xindex % 66 x4 = (xindex // 4356) x2 = (xindex // 4356) % 16 x7 = xindex tmp0 = tl.load(in_ptr0 + (63 + ((-1)(tl_math.abs((-63) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (63 + ((-1)(tl_math.abs((-63) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 32, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (32tmp4) + (1024x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/vw/cvw6uhjlxy7dpvdvmzm4smkzp33ikjqktjfo46vko7b6nkjdv2zb.py # Topologically Sorted Source Nodes: [conv2d_11, y_15, pad_12], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_11 => convolution_11 # pad_12 => _unsafe_index_28, _unsafe_index_29 # y_15 => relu_11 # Graph fragment: # %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_27, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {}) # %_unsafe_index_28 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_11, [None, None, %sub_45, None]), kwargs = {}) # %_unsafe_index_29 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_28, [None, None, None, %sub_45]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_15 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_15', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 278784 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 66 x1 = (xindex // 66) % 66 x4 = (xindex // 4356) x2 = (xindex // 4356) % 16 x5 = xindex tmp0 = tl.load(in_ptr0 + (4095 + ((-1)(tl_math.abs((-63) + (tl_math.abs((-1) + x0))))) + ((-64)(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (4096x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/iq/ciqyten22j3jgysb3rkgdifnyzmpsxsqgxpw7lrmo35apnsi5kex.py # Topologically Sorted Source Nodes: [conv2d_12, y_16], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_12 => convolution_12 # y_16 => relu_12 # Graph fragment: # %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_29, %primals_26, %primals_27, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_12 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_12,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_12, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_16', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_16', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 49152 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 4096) % 3 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, None) tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/pk/cpk4cy5r76sbeqhtmomxx7bmcbc7kwgq667rvxxfyvgsvz3xwxvk.py # Topologically Sorted Source Nodes: [conv2d_11, y_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_11 => convolution_11 # y_15 => relu_11 # Graph fragment: # %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_27, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {}) # %le_19 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_11, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_17 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_17', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 4096) % 16 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/l3/cl3slqcqwmshko4pmjuhoahmkvaivkj7r74lbbc7pxcktecuw2q6.py # Topologically Sorted Source Nodes: [conv2d_10, y_13], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_10 => convolution_10 # y_13 => relu_10 # Graph fragment: # %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_24, %primals_22, %primals_23, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {}) # %le_38 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_10, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_18 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_18', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 65536 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 1024) % 16 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/rh/crhkabwpqbgeqa63ryrpe6ebf4x6kl25tihzmfsbl6rw7je3vg7a.py # Topologically Sorted Source Nodes: [conv2d_9, y_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_9 => convolution_9 # y_12 => relu_9 # Graph fragment: # %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_22, %primals_20, %primals_21, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {}) # %le_57 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_9, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_19 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_19', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_19(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 1024) % 32 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/vc/cvcaxzmyfl7243z34slamupicm6sdzdot3ldo436oojxt7ondbvd.py # Topologically Sorted Source Nodes: [conv2d_8, y_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_8 => convolution_8 # y_10 => relu_8 # Graph fragment: # %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_19, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {}) # %le_76 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_8, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_20 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_20', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_20(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32768 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 256) % 32 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/i3/ci3cdbgxsgpqews5yd5pr5u3b2cnkxuwhqbywxsgk3hd752bky4v.py # Topologically Sorted Source Nodes: [conv2d_7, y_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_7 => convolution_7 # y_9 => relu_7 # Graph fragment: # %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_17, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {}) # %le_95 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_7, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_21 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_21', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 65536 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 256) % 64 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/3m/c3mvfvidl6gth2dqfdlb6nwdwpi6ka4ul2dnlsgngqhrtdygjfr2.py # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # y_5 => relu_4 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) # %le_152 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_22 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_22', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_22', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 64 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/46/c46laf5guogl3dci5iqvvaujiidwqtayi7fi7syc6367jjsvyltj.py # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # y_4 => relu_3 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_8, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %le_171 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_23 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_23', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_23', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_23(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32768 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 128 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/dt/cdty4hij46mqqfkraevjsga77qhqgxit4sp2n2qobrjdbnjiglfb.py # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d => convolution # y => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le_228 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_24 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_24', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_24', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_24(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8192 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 128 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27 = args args.clear() assert_size_stride(primals_1, (4, 128, 4, 4), (2048, 16, 4, 1)) assert_size_stride(primals_2, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_3, (128, ), (1, )) assert_size_stride(primals_4, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_5, (128, ), (1, )) assert_size_stride(primals_6, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_7, (128, ), (1, )) assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_9, (128, ), (1, )) assert_size_stride(primals_10, (64, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_11, (64, ), (1, )) assert_size_stride(primals_12, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_13, (64, ), (1, )) assert_size_stride(primals_14, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_15, (64, ), (1, )) assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_17, (64, ), (1, )) assert_size_stride(primals_18, (32, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_19, (32, ), (1, )) assert_size_stride(primals_20, (32, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_21, (32, ), (1, )) assert_size_stride(primals_22, (16, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_23, (16, ), (1, )) assert_size_stride(primals_24, (16, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_25, (16, ), (1, )) assert_size_stride(primals_26, (3, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_27, (3, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 128, 6, 6), (4608, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] stream0 = get_raw_stream(0) triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 18432, grid=grid(18432), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 128, 4, 4), (2048, 16, 4, 1)) buf2 = empty_strided_cuda((8, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_1.run(buf2, 8, grid=grid(8), stream=stream0) buf3 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2.run(buf2, buf1, primals_3, buf3, 51200, grid=grid(51200), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 128, 8, 8), (8192, 64, 8, 1)) buf5 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf4, primals_5, buf5, 51200, grid=grid(51200), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 128, 8, 8), (8192, 64, 8, 1)) buf7 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_2, y_3, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf6, primals_7, buf7, 51200, grid=grid(51200), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 128, 8, 8), (8192, 64, 8, 1)) buf9 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_4], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf8, primals_9, buf9, 51200, grid=grid(51200), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 64, 8, 8), (4096, 64, 8, 1)) buf11 = empty_strided_cuda((16, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange] triton_poi_fused_arange_4.run(buf11, 16, grid=grid(16), stream=stream0) buf12 = empty_strided_cuda((16, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_5.run(buf12, 16, grid=grid(16), stream=stream0) buf13 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_4, y_5, y_6, pad_5], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6.run(buf12, buf10, primals_11, buf13, 82944, grid=grid(82944), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution] buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 64, 16, 16), (16384, 256, 16, 1)) buf15 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_7.run(buf14, primals_13, buf15, 82944, grid=grid(82944), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution] buf16 = extern_kernels.convolution(buf15, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 64, 16, 16), (16384, 256, 16, 1)) buf17 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_6, y_8, pad_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_7.run(buf16, primals_15, buf17, 82944, grid=grid(82944), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution] buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 64, 16, 16), (16384, 256, 16, 1)) buf19 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_7, y_9, pad_8], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_7.run(buf18, primals_17, buf19, 82944, grid=grid(82944), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution] buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 32, 16, 16), (8192, 256, 16, 1)) buf21 = empty_strided_cuda((32, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.arange] triton_poi_fused_arange_8.run(buf21, 32, grid=grid(32), stream=stream0) buf22 = empty_strided_cuda((32, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_9.run(buf22, 32, grid=grid(32), stream=stream0) buf23 = empty_strided_cuda((4, 32, 34, 34), (36992, 1156, 34, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_8, y_10, y_11, pad_9], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10.run(buf22, buf20, primals_19, buf23, 147968, grid=grid(147968), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution] buf24 = extern_kernels.convolution(buf23, primals_20, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 32, 32, 32), (32768, 1024, 32, 1)) buf25 = empty_strided_cuda((4, 32, 34, 34), (36992, 1156, 34, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_9, y_12, pad_10], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_11.run(buf24, primals_21, buf25, 147968, grid=grid(147968), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution] buf26 = extern_kernels.convolution(buf25, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf26, (4, 16, 32, 32), (16384, 1024, 32, 1)) buf27 = empty_strided_cuda((64, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_14], Original ATen: [aten.arange] triton_poi_fused_arange_12.run(buf27, 64, grid=grid(64), stream=stream0) buf28 = empty_strided_cuda((64, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_14], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_13.run(buf28, 64, grid=grid(64), stream=stream0) buf29 = empty_strided_cuda((4, 16, 66, 66), (69696, 4356, 66, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_10, y_13, y_14, pad_11], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_14.run(buf28, buf26, primals_23, buf29, 278784, grid=grid(278784), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution] buf30 = extern_kernels.convolution(buf29, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 16, 64, 64), (65536, 4096, 64, 1)) buf31 = empty_strided_cuda((4, 16, 66, 66), (69696, 4356, 66, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_11, y_15, pad_12], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_15.run(buf30, primals_25, buf31, 278784, grid=grid(278784), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution] buf32 = extern_kernels.convolution(buf31, primals_26, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf32, (4, 3, 64, 64), (12288, 4096, 64, 1)) buf33 = buf32; del buf32 # reuse buf34 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_12, y_16], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_16.run(buf33, primals_27, buf34, 49152, grid=grid(49152), stream=stream0) del primals_27 buf35 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_11, y_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_17.run(buf30, primals_25, buf35, 262144, grid=grid(262144), stream=stream0) del buf30 del primals_25 buf36 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_10, y_13], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_18.run(buf26, primals_23, buf36, 65536, grid=grid(65536), stream=stream0) del buf26 del primals_23 buf37 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_9, y_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_19.run(buf24, primals_21, buf37, 131072, grid=grid(131072), stream=stream0) del buf24 del primals_21 buf38 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_8, y_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_20.run(buf20, primals_19, buf38, 32768, grid=grid(32768), stream=stream0) del buf20 del primals_19 buf39 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_7, y_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_21.run(buf18, primals_17, buf39, 65536, grid=grid(65536), stream=stream0) del buf18 del primals_17 buf40 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_6, y_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_21.run(buf16, primals_15, buf40, 65536, grid=grid(65536), stream=stream0) del buf16 del primals_15 buf41 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_21.run(buf14, primals_13, buf41, 65536, grid=grid(65536), stream=stream0) del buf14 del primals_13 buf42 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_22.run(buf10, primals_11, buf42, 16384, grid=grid(16384), stream=stream0) del buf10 del primals_11 buf43 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_23.run(buf8, primals_9, buf43, 32768, grid=grid(32768), stream=stream0) del buf8 del primals_9 buf44 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_2, y_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_23.run(buf6, primals_7, buf44, 32768, grid=grid(32768), stream=stream0) del buf6 del primals_7 buf45 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, y_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_23.run(buf4, primals_5, buf45, 32768, grid=grid(32768), stream=stream0) del buf4 del primals_5 buf46 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_24.run(buf1, primals_3, buf46, 8192, grid=grid(8192), stream=stream0) del buf1 del primals_3 return (buf33, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf13, buf15, buf17, buf19, buf21, buf22, buf23, buf25, buf27, buf28, buf29, buf31, buf34, buf35, buf36, buf37, buf38, buf39, buf40, buf41, buf42, buf43, buf44, buf45, buf46, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 128, 4, 4), (2048, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_19 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_20 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_21 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_22 = rand_strided((16, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_23 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_24 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_25 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_26 = rand_strided((3, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_27 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class SmallDecoder5_16x(nn.Module): def __init__(self, model=None, fixed=False): super(SmallDecoder5_16x, self).__init__() self.fixed = fixed self.conv51 = nn.Conv2d(128, 128, 3, 1, 0) self.conv44 = nn.Conv2d(128, 128, 3, 1, 0) self.conv43 = nn.Conv2d(128, 128, 3, 1, 0) self.conv42 = nn.Conv2d(128, 128, 3, 1, 0) self.conv41 = nn.Conv2d(128, 64, 3, 1, 0) self.conv34 = nn.Conv2d(64, 64, 3, 1, 0, dilation=1) self.conv33 = nn.Conv2d(64, 64, 3, 1, 0, dilation=1) self.conv32 = nn.Conv2d(64, 64, 3, 1, 0, dilation=1) self.conv31 = nn.Conv2d(64, 32, 3, 1, 0, dilation=1) self.conv22 = nn.Conv2d(32, 32, 3, 1, 0, dilation=1) self.conv21 = nn.Conv2d(32, 16, 3, 1, 0, dilation=1) self.conv12 = nn.Conv2d(16, 16, 3, 1, 0, dilation=1) self.conv11 = nn.Conv2d(16, 3, 3, 1, 0, dilation=1) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: weights = torch.load(model, map_location=lambda storage, location: storage) if 'model' in weights: self.load_state_dict(weights['model']) else: self.load_state_dict(weights) None if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, y): y = self.relu(self.conv51(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv44(self.pad(y))) y = self.relu(self.conv43(self.pad(y))) y = self.relu(self.conv42(self.pad(y))) y = self.relu(self.conv41(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv34(self.pad(y))) y = self.relu(self.conv33(self.pad(y))) y = self.relu(self.conv32(self.pad(y))) y = self.relu(self.conv31(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv22(self.pad(y))) y = self.relu(self.conv21(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv12(self.pad(y))) y = self.relu(self.conv11(self.pad(y))) return y def forward_branch(self, input): out51 = self.relu(self.conv51(self.pad(input))) out51 = self.unpool(out51) out44 = self.relu(self.conv44(self.pad(out51))) out43 = self.relu(self.conv43(self.pad(out44))) out42 = self.relu(self.conv42(self.pad(out43))) out41 = self.relu(self.conv41(self.pad(out42))) out41 = self.unpool(out41) out34 = self.relu(self.conv34(self.pad(out41))) out33 = self.relu(self.conv33(self.pad(out34))) out32 = self.relu(self.conv32(self.pad(out33))) out31 = self.relu(self.conv31(self.pad(out32))) out31 = self.unpool(out31) out22 = self.relu(self.conv22(self.pad(out31))) out21 = self.relu(self.conv21(self.pad(out22))) out21 = self.unpool(out21) out12 = self.relu(self.conv12(self.pad(out21))) out11 = self.relu(self.conv11(self.pad(out12))) return out11 def get_inputs(): return [torch.rand([4, 128, 4, 4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 6 x1 = xindex // 6 % 6 x2 = xindex // 36 x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 + x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None, eviction_policy='evict_last') tl.store(out_ptr0 + x3, tmp0, None) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x1 = xindex // 10 % 10 x0 = xindex % 10 x4 = xindex // 100 x2 = xindex // 100 % 128 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1 ))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0 ))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 4 * tmp4 + 16 * x4), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, None) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 10 x1 = xindex // 10 % 10 x4 = xindex // 100 x2 = xindex // 100 % 128 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, None) @triton.jit def triton_poi_fused_arange_4(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + x0, tmp0, xmask) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_5(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 82944 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 18 % 18 x0 = xindex % 18 x4 = xindex // 324 x2 = xindex // 324 % 64 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x1))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 8 * tmp4 + 64 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 82944 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 18 x1 = xindex // 18 % 18 x4 = xindex // 324 x2 = xindex // 324 % 64 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_arange_8(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + x0, tmp0, xmask) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_9(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10(in_ptr0 , in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 147968 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 34 % 34 x0 = xindex % 34 x4 = xindex // 1156 x2 = xindex // 1156 % 32 x7 = xindex tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x1))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x0))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 16, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 16 * tmp4 + 256 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 147968 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 34 x1 = xindex // 34 % 34 x4 = xindex // 1156 x2 = xindex // 1156 % 32 x5 = xindex tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_arange_12(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + x0, tmp0, xmask) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_13(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_14(in_ptr0 , in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 278784 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 66 % 66 x0 = xindex % 66 x4 = xindex // 4356 x2 = xindex // 4356 % 16 x7 = xindex tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 + x1))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 + x0))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 32, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 32 * tmp4 + 1024 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 278784 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 66 x1 = xindex // 66 % 66 x4 = xindex // 4356 x2 = xindex // 4356 % 16 x5 = xindex tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-1 + x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 4096 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 4096 % 3 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, None) tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 4096 % 16 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 1024 % 16 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_19(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 1024 % 32 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_20(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 256 % 32 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 256 % 64 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 64 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_23(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 128 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_24(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 128 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27) = args args.clear() assert_size_stride(primals_1, (4, 128, 4, 4), (2048, 16, 4, 1)) assert_size_stride(primals_2, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_3, (128,), (1,)) assert_size_stride(primals_4, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_5, (128,), (1,)) assert_size_stride(primals_6, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_7, (128,), (1,)) assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_9, (128,), (1,)) assert_size_stride(primals_10, (64, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_11, (64,), (1,)) assert_size_stride(primals_12, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_13, (64,), (1,)) assert_size_stride(primals_14, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_15, (64,), (1,)) assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_17, (64,), (1,)) assert_size_stride(primals_18, (32, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_19, (32,), (1,)) assert_size_stride(primals_20, (32, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_21, (32,), (1,)) assert_size_stride(primals_22, (16, 32, 3, 3), (288, 9, 3, 1)) assert_size_stride(primals_23, (16,), (1,)) assert_size_stride(primals_24, (16, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_25, (16,), (1,)) assert_size_stride(primals_26, (3, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_27, (3,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 128, 6, 6), (4608, 36, 6, 1), torch. float32) get_raw_stream(0) triton_poi_fused_reflection_pad2d_0[grid(18432)](primals_1, buf0, 18432, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 128, 4, 4), (2048, 16, 4, 1)) buf2 = empty_strided_cuda((8,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK =8, num_warps=1, num_stages=1) buf3 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid (51200)](buf2, buf1, primals_3, buf3, 51200, XBLOCK=256, num_warps=4, num_stages=1) buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 128, 8, 8), (8192, 64, 8, 1)) buf5 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(51200)](buf4, primals_5, buf5, 51200, XBLOCK=256, num_warps=4, num_stages=1) buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 128, 8, 8), (8192, 64, 8, 1)) buf7 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(51200)](buf6, primals_7, buf7, 51200, XBLOCK=256, num_warps=4, num_stages=1) buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 128, 8, 8), (8192, 64, 8, 1)) buf9 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(51200)](buf8, primals_9, buf9, 51200, XBLOCK=256, num_warps=4, num_stages=1) buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 64, 8, 8), (4096, 64, 8, 1)) buf11 = empty_strided_cuda((16,), (1,), torch.int64) triton_poi_fused_arange_4[grid(16)](buf11, 16, XBLOCK=16, num_warps =1, num_stages=1) buf12 = empty_strided_cuda((16,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_5[grid(16)](buf12, 16, XBLOCK=16, num_warps=1, num_stages=1) buf13 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_6[grid (82944)](buf12, buf10, primals_11, buf13, 82944, XBLOCK=512, num_warps=8, num_stages=1) buf14 = extern_kernels.convolution(buf13, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 64, 16, 16), (16384, 256, 16, 1)) buf15 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_7[grid(82944)](buf14 , primals_13, buf15, 82944, XBLOCK=512, num_warps=8, num_stages=1) buf16 = extern_kernels.convolution(buf15, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 64, 16, 16), (16384, 256, 16, 1)) buf17 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_7[grid(82944)](buf16 , primals_15, buf17, 82944, XBLOCK=512, num_warps=8, num_stages=1) buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 64, 16, 16), (16384, 256, 16, 1)) buf19 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_7[grid(82944)](buf18 , primals_17, buf19, 82944, XBLOCK=512, num_warps=8, num_stages=1) buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 32, 16, 16), (8192, 256, 16, 1)) buf21 = empty_strided_cuda((32,), (1,), torch.int64) triton_poi_fused_arange_8[grid(32)](buf21, 32, XBLOCK=32, num_warps =1, num_stages=1) buf22 = empty_strided_cuda((32,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_9[grid(32)](buf22, 32, XBLOCK=32, num_warps=1, num_stages=1) buf23 = empty_strided_cuda((4, 32, 34, 34), (36992, 1156, 34, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_10[ grid(147968)](buf22, buf20, primals_19, buf23, 147968, XBLOCK= 512, num_warps=8, num_stages=1) buf24 = extern_kernels.convolution(buf23, primals_20, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 32, 32, 32), (32768, 1024, 32, 1)) buf25 = empty_strided_cuda((4, 32, 34, 34), (36992, 1156, 34, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_11[grid(147968)]( buf24, primals_21, buf25, 147968, XBLOCK=1024, num_warps=4, num_stages=1) buf26 = extern_kernels.convolution(buf25, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf26, (4, 16, 32, 32), (16384, 1024, 32, 1)) buf27 = empty_strided_cuda((64,), (1,), torch.int64) triton_poi_fused_arange_12[grid(64)](buf27, 64, XBLOCK=64, num_warps=1, num_stages=1) buf28 = empty_strided_cuda((64,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_13[grid(64)](buf28, 64, XBLOCK=64, num_warps=1, num_stages=1) buf29 = empty_strided_cuda((4, 16, 66, 66), (69696, 4356, 66, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_14[ grid(278784)](buf28, buf26, primals_23, buf29, 278784, XBLOCK= 512, num_warps=8, num_stages=1) buf30 = extern_kernels.convolution(buf29, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 16, 64, 64), (65536, 4096, 64, 1)) buf31 = empty_strided_cuda((4, 16, 66, 66), (69696, 4356, 66, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_15[grid(278784)]( buf30, primals_25, buf31, 278784, XBLOCK=1024, num_warps=4, num_stages=1) buf32 = extern_kernels.convolution(buf31, primals_26, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf32, (4, 3, 64, 64), (12288, 4096, 64, 1)) buf33 = buf32 del buf32 buf34 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_16[grid(49152)]( buf33, primals_27, buf34, 49152, XBLOCK=512, num_warps=4, num_stages=1) del primals_27 buf35 = empty_strided_cuda((4, 16, 64, 64), (65536, 4096, 64, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_17[grid(262144)]( buf30, primals_25, buf35, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf30 del primals_25 buf36 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_18[grid(65536)]( buf26, primals_23, buf36, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf26 del primals_23 buf37 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_19[grid(131072)]( buf24, primals_21, buf37, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del buf24 del primals_21 buf38 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_20[grid(32768)]( buf20, primals_19, buf38, 32768, XBLOCK=128, num_warps=4, num_stages=1) del buf20 del primals_19 buf39 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_21[grid(65536)]( buf18, primals_17, buf39, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf18 del primals_17 buf40 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_21[grid(65536)]( buf16, primals_15, buf40, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf16 del primals_15 buf41 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_21[grid(65536)]( buf14, primals_13, buf41, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf14 del primals_13 buf42 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_22[grid(16384)]( buf10, primals_11, buf42, 16384, XBLOCK=256, num_warps=4, num_stages=1) del buf10 del primals_11 buf43 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool ) triton_poi_fused_convolution_relu_threshold_backward_23[grid(32768)]( buf8, primals_9, buf43, 32768, XBLOCK=128, num_warps=4, num_stages=1) del buf8 del primals_9 buf44 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool ) triton_poi_fused_convolution_relu_threshold_backward_23[grid(32768)]( buf6, primals_7, buf44, 32768, XBLOCK=128, num_warps=4, num_stages=1) del buf6 del primals_7 buf45 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool ) triton_poi_fused_convolution_relu_threshold_backward_23[grid(32768)]( buf4, primals_5, buf45, 32768, XBLOCK=128, num_warps=4, num_stages=1) del buf4 del primals_5 buf46 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.bool ) triton_poi_fused_convolution_relu_threshold_backward_24[grid(8192)]( buf1, primals_3, buf46, 8192, XBLOCK=128, num_warps=4, num_stages=1 ) del buf1 del primals_3 return (buf33, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf13, buf15, buf17, buf19, buf21, buf22, buf23, buf25, buf27, buf28, buf29, buf31, buf34, buf35, buf36, buf37, buf38, buf39, buf40, buf41, buf42, buf43, buf44, buf45, buf46) class SmallDecoder5_16xNew(nn.Module): def __init__(self, model=None, fixed=False): super(SmallDecoder5_16xNew, self).__init__() self.fixed = fixed self.conv51 = nn.Conv2d(128, 128, 3, 1, 0) self.conv44 = nn.Conv2d(128, 128, 3, 1, 0) self.conv43 = nn.Conv2d(128, 128, 3, 1, 0) self.conv42 = nn.Conv2d(128, 128, 3, 1, 0) self.conv41 = nn.Conv2d(128, 64, 3, 1, 0) self.conv34 = nn.Conv2d(64, 64, 3, 1, 0, dilation=1) self.conv33 = nn.Conv2d(64, 64, 3, 1, 0, dilation=1) self.conv32 = nn.Conv2d(64, 64, 3, 1, 0, dilation=1) self.conv31 = nn.Conv2d(64, 32, 3, 1, 0, dilation=1) self.conv22 = nn.Conv2d(32, 32, 3, 1, 0, dilation=1) self.conv21 = nn.Conv2d(32, 16, 3, 1, 0, dilation=1) self.conv12 = nn.Conv2d(16, 16, 3, 1, 0, dilation=1) self.conv11 = nn.Conv2d(16, 3, 3, 1, 0, dilation=1) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: weights = torch.load(model, map_location=lambda storage, location: storage) if 'model' in weights: self.load_state_dict(weights['model']) else: self.load_state_dict(weights) None if fixed: for param in self.parameters(): param.requires_grad = False def forward_branch(self, input): out51 = self.relu(self.conv51(self.pad(input))) out51 = self.unpool(out51) out44 = self.relu(self.conv44(self.pad(out51))) out43 = self.relu(self.conv43(self.pad(out44))) out42 = self.relu(self.conv42(self.pad(out43))) out41 = self.relu(self.conv41(self.pad(out42))) out41 = self.unpool(out41) out34 = self.relu(self.conv34(self.pad(out41))) out33 = self.relu(self.conv33(self.pad(out34))) out32 = self.relu(self.conv32(self.pad(out33))) out31 = self.relu(self.conv31(self.pad(out32))) out31 = self.unpool(out31) out22 = self.relu(self.conv22(self.pad(out31))) out21 = self.relu(self.conv21(self.pad(out22))) out21 = self.unpool(out21) out12 = self.relu(self.conv12(self.pad(out21))) out11 = self.relu(self.conv11(self.pad(out12))) return out11 def forward(self, input_0): primals_2 = self.conv51.weight primals_3 = self.conv51.bias primals_4 = self.conv44.weight primals_5 = self.conv44.bias primals_6 = self.conv43.weight primals_7 = self.conv43.bias primals_8 = self.conv42.weight primals_9 = self.conv42.bias primals_10 = self.conv41.weight primals_11 = self.conv41.bias primals_12 = self.conv34.weight primals_13 = self.conv34.bias primals_14 = self.conv33.weight primals_15 = self.conv33.bias primals_16 = self.conv32.weight primals_17 = self.conv32.bias primals_18 = self.conv31.weight primals_19 = self.conv31.bias primals_20 = self.conv22.weight primals_21 = self.conv22.bias primals_22 = self.conv21.weight primals_23 = self.conv21.bias primals_24 = self.conv12.weight primals_25 = self.conv12.bias primals_26 = self.conv11.weight primals_27 = self.conv11.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27]) return output[0]	EndyWon/Texture-Reformer	SmallDecoder5_16x	false	8,163	[ "MIT" ]	11	f84f95accb3574c7b759a7f03c0b0b4e150314b5	https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5
SageLayer	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/zm/czm6acrrgjryz6xi3wza7npycjuiqsdsygpfdo3lbzaquecrmeuj.py # Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat] # Source node to ATen node mapping: # combined => cat # Graph fragment: # %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + ((4x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/xo/cxoy25xst5lr7sipjfpjv7a72jjvrc7bpzabd73irv466cnlaugy.py # Topologically Sorted Source Nodes: [combined_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # combined_1 => relu # Graph fragment: # %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_4), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, 8), (8, 1)) assert_size_stride(primals_4, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32) # Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 32, grid=grid(32), stream=stream0) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1) del primals_3 buf2 = buf1; del buf1 # reuse buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool) # Topologically Sorted Source Nodes: [combined_1], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_1.run(buf2, primals_4, buf3, 16, grid=grid(16), stream=stream0) del primals_4 return (buf2, buf0, buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class SageLayer(nn.Module): """ Encodes a node's using 'convolutional' GraphSage approach """ def __init__(self, input_size, out_size): super(SageLayer, self).__init__() self.input_size = input_size self.out_size = out_size self.linear = nn.Linear(self.input_size * 2, self.out_size) def forward(self, self_feats, aggregate_feats, neighs=None): """ Generates embeddings for a batch of nodes. nodes -- list of nodes """ combined = torch.cat([self_feats, aggregate_feats], dim=1) combined = F.relu(self.linear(combined)) return combined def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {'input_size': 4, 'out_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x2, tmp10, xmask) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, 8), (8, 1)) assert_size_stride(primals_4, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32, XBLOCK=32, num_warps=1, num_stages=1) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 4), (1, 8 ), 0), out=buf1) del primals_3 buf2 = buf1 del buf1 buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_1[grid(16)](buf2, primals_4, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_4 return buf2, buf0, buf3 class SageLayerNew(nn.Module): """ Encodes a node's using 'convolutional' GraphSage approach """ def __init__(self, input_size, out_size): super(SageLayerNew, self).__init__() self.input_size = input_size self.out_size = out_size self.linear = nn.Linear(self.input_size * 2, self.out_size) def forward(self, input_0, input_1): primals_3 = self.linear.weight primals_4 = self.linear.bias primals_1 = input_0 primals_2 = input_1 output = call([primals_1, primals_2, primals_3, primals_4]) return output[0]	HKUST-KnowComp/CSKB-Population	SageLayer	false	8,164	[ "MIT" ]	13	7b1b2d25fbd0095b0cf009b933cfd5a62feadd58	https://github.com/HKUST-KnowComp/CSKB-Population/tree/7b1b2d25fbd0095b0cf009b933cfd5a62feadd58
Decoder4	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/g3/cg3ccnqa6wyzxanbi6fgd6vkyhgnnoo3va4nix3ohszhmyutqgep.py # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] # Source node to ATen node mapping: # pad => _unsafe_index, _unsafe_index_1 # Graph fragment: # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 73728 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 6 x1 = (xindex // 6) % 6 x2 = (xindex // 36) x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16x2)), None, eviction_policy='evict_last') tl.store(out_ptr0 + (x3), tmp0, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ki/ckii6ld2pjui2kyh33ieyagpaxjso55jvdwg3kttj5omjlrhkys6.py # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_1 => add, add_1, convert_element_type, convert_element_type_1, iota_2, mul, mul_1 # Graph fragment: # %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {}) # %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_1 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/iw/ciwouyfqxfi5suuzcagn3bbdbaulgyw4bpqnsdjlizmkc2oz2muq.py # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d => convolution # pad_1 => _unsafe_index_3, _unsafe_index_4 # y => relu # y_1 => _unsafe_index_2 # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {}) # %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 102400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x1 = (xindex // 10) % 10 x0 = xindex % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 256 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (4tmp4) + (16x4)), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/x6/cx6kpvwny3lwf4b75yjzmpojqlofwsrfkk23uf3lterlix2wg3ah.py # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # pad_2 => _unsafe_index_5, _unsafe_index_6 # y_2 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_5, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 102400 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 10 x1 = (xindex // 10) % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 256 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64x4)), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/od/codkvvxpur7vfcixyfgg6ayyzatao2cw5of6bxy53exotyuzofwb.py # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_6 => add_4, add_5, convert_element_type_4, convert_element_type_5, iota_12, mul_4, mul_5 # Graph fragment: # %iota_12 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_12, 1), kwargs = {}) # %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, 0), kwargs = {}) # %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_4, torch.float32), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.5), kwargs = {}) # %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_4 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/u4/cu42vug3hzqlursadot6js3gerux56tz7joz5blmrj6a5xowpyd6.py # Topologically Sorted Source Nodes: [conv2d_4, y_5, y_6, pad_5], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # pad_5 => _unsafe_index_12, _unsafe_index_13 # y_5 => relu_4 # y_6 => _unsafe_index_11 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) # %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_4, [None, None, %unsqueeze_1, %convert_element_type_5]), kwargs = {}) # %_unsafe_index_12 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_11, [None, None, %sub_21, None]), kwargs = {}) # %_unsafe_index_13 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_12, [None, None, None, %sub_21]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 165888 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x1 = (xindex // 18) % 18 x0 = xindex % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 128 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (8tmp4) + (64x4)), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/lz/clznsh6fdhksacfsunvntibvsjaoxwmok2tqs5avvjtlb7uojj5w.py # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_5 => convolution_5 # pad_6 => _unsafe_index_14, _unsafe_index_15 # y_7 => relu_5 # Graph fragment: # %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {}) # %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_5, [None, None, %sub_21, None]), kwargs = {}) # %_unsafe_index_15 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_14, [None, None, None, %sub_21]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 165888 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 18 x1 = (xindex // 18) % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 128 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256x4)), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/6w/c6wyyvxovkkzfk6k64bfon5db5xfxiywvm7oionlfmjjhdvc6ugb.py # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_9 => add_8, add_9, convert_element_type_8, convert_element_type_9, iota_18, mul_8, mul_9 # Graph fragment: # %iota_18 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_18, 1), kwargs = {}) # %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, 0), kwargs = {}) # %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_8, torch.float32), kwargs = {}) # %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_8, 0.0), kwargs = {}) # %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_9, 0.5), kwargs = {}) # %convert_element_type_9 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_9, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_7 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/74/c74eexru54jzdd7so47hr6vfjhuchik3ect6ywzjtozle2ccoanv.py # Topologically Sorted Source Nodes: [conv2d_6, y_8, y_9, pad_7], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_6 => convolution_6 # pad_7 => _unsafe_index_17, _unsafe_index_18 # y_8 => relu_6 # y_9 => _unsafe_index_16 # Graph fragment: # %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {}) # %_unsafe_index_16 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_6, [None, None, %unsqueeze_2, %convert_element_type_9]), kwargs = {}) # %_unsafe_index_17 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_16, [None, None, %sub_29, None]), kwargs = {}) # %_unsafe_index_18 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_17, [None, None, None, %sub_29]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 34) % 34 x0 = xindex % 34 x4 = (xindex // 1156) x2 = (xindex // 1156) % 64 x7 = xindex tmp0 = tl.load(in_ptr0 + (31 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (31 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 16, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (16tmp4) + (256x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/hz/chz63phj47p2gjm5xtlsmszcrnfpp4xsj7dae5glkrxk5zs7hyto.py # Topologically Sorted Source Nodes: [conv2d_7, y_10, pad_8], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_7 => convolution_7 # pad_8 => _unsafe_index_19, _unsafe_index_20 # y_10 => relu_7 # Graph fragment: # %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_18, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {}) # %_unsafe_index_19 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_7, [None, None, %sub_29, None]), kwargs = {}) # %_unsafe_index_20 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_19, [None, None, None, %sub_29]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_9 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_9', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 34 x1 = (xindex // 34) % 34 x4 = (xindex // 1156) x2 = (xindex // 1156) % 64 x5 = xindex tmp0 = tl.load(in_ptr0 + (1023 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x0))))) + ((-32)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (1024x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/zt/czt6mxt4ehrpko5jtksfx3dfly73tudxibe7dubc45qxrhv5umkx.py # Topologically Sorted Source Nodes: [conv2d_8, y_11], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_8 => convolution_8 # y_11 => relu_8 # Graph fragment: # %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_20, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_8, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_10 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_10', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_10(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 12288 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 1024) % 3 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, None) tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/5o/c5ojoadwtfg74b52gh7y53jwhw2ktpi5q7bj2iprlawfjiexyy3r.py # Topologically Sorted Source Nodes: [conv2d_7, y_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_7 => convolution_7 # y_10 => relu_7 # Graph fragment: # %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_18, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {}) # %le_19 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_7, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_11 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_11', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 1024) % 64 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/aa/caa4f5re2hltkmtlkj3n4yzdxyuaw5kf6spnc6g2phrpe6faiy4x.py # Topologically Sorted Source Nodes: [conv2d_6, y_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_6 => convolution_6 # y_8 => relu_6 # Graph fragment: # %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {}) # %le_38 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_12 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_12', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_12(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 65536 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 256) % 64 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/jr/cjrwyczsgjgyy6qosgum4wgnyb2w3ihpqvja6fteyhda2zxoh5ot.py # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_5 => convolution_5 # y_7 => relu_5 # Graph fragment: # %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {}) # %le_57 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_5, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_13 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_13', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 256) % 128 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/sz/cszsc7yevnmc6ugclb5yovyvzqbxqknvq6uzgneygczjmkvgtczc.py # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # y_5 => relu_4 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) # %le_76 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_14 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_14', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32768 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 128 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/rf/crftfxpfxphanidbjn5jncdcv65uaywlzsfsv5jzcsmdxwvjvf2c.py # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # y_4 => relu_3 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_8, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %le_95 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_15 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_15', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 65536 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 256 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ia/cianovbanealq7vsk3q7ox66wiuiaoeqjzpupak5lcrtiilg2ewl.py # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d => convolution # y => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le_152 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_16', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 256 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19 = args args.clear() assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1)) assert_size_stride(primals_2, (256, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_3, (256, ), (1, )) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256, ), (1, )) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256, ), (1, )) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256, ), (1, )) assert_size_stride(primals_10, (128, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (128, ), (1, )) assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_13, (128, ), (1, )) assert_size_stride(primals_14, (64, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_15, (64, ), (1, )) assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_17, (64, ), (1, )) assert_size_stride(primals_18, (3, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_19, (3, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] stream0 = get_raw_stream(0) triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 73728, grid=grid(73728), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 256, 4, 4), (4096, 16, 4, 1)) buf2 = empty_strided_cuda((8, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_1.run(buf2, 8, grid=grid(8), stream=stream0) buf3 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2.run(buf2, buf1, primals_3, buf3, 102400, grid=grid(102400), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 256, 8, 8), (16384, 64, 8, 1)) buf5 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf4, primals_5, buf5, 102400, grid=grid(102400), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 8, 8), (16384, 64, 8, 1)) buf7 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_2, y_3, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf6, primals_7, buf7, 102400, grid=grid(102400), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 256, 8, 8), (16384, 64, 8, 1)) buf9 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_4], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf8, primals_9, buf9, 102400, grid=grid(102400), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 128, 8, 8), (8192, 64, 8, 1)) buf11 = empty_strided_cuda((16, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_4.run(buf11, 16, grid=grid(16), stream=stream0) buf12 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_4, y_5, y_6, pad_5], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5.run(buf11, buf10, primals_11, buf12, 165888, grid=grid(165888), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution] buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf13, (4, 128, 16, 16), (32768, 256, 16, 1)) buf14 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf13, primals_13, buf14, 165888, grid=grid(165888), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution] buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf15, (4, 64, 16, 16), (16384, 256, 16, 1)) buf16 = empty_strided_cuda((32, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_7.run(buf16, 32, grid=grid(32), stream=stream0) buf17 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_6, y_8, y_9, pad_7], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8.run(buf16, buf15, primals_15, buf17, 295936, grid=grid(295936), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution] buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 64, 32, 32), (65536, 1024, 32, 1)) buf19 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_7, y_10, pad_8], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_9.run(buf18, primals_17, buf19, 295936, grid=grid(295936), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution] buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 3, 32, 32), (3072, 1024, 32, 1)) buf21 = buf20; del buf20 # reuse buf22 = empty_strided_cuda((4, 3, 32, 32), (3072, 1024, 32, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_8, y_11], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_10.run(buf21, primals_19, buf22, 12288, grid=grid(12288), stream=stream0) del primals_19 buf23 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_7, y_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_11.run(buf18, primals_17, buf23, 262144, grid=grid(262144), stream=stream0) del buf18 del primals_17 buf24 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_6, y_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_12.run(buf15, primals_15, buf24, 65536, grid=grid(65536), stream=stream0) del buf15 del primals_15 buf25 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_13.run(buf13, primals_13, buf25, 131072, grid=grid(131072), stream=stream0) del buf13 del primals_13 buf26 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_14.run(buf10, primals_11, buf26, 32768, grid=grid(32768), stream=stream0) del buf10 del primals_11 buf27 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_15.run(buf8, primals_9, buf27, 65536, grid=grid(65536), stream=stream0) del buf8 del primals_9 buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_2, y_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_15.run(buf6, primals_7, buf28, 65536, grid=grid(65536), stream=stream0) del buf6 del primals_7 buf29 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, y_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_15.run(buf4, primals_5, buf29, 65536, grid=grid(65536), stream=stream0) del buf4 del primals_5 buf30 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_16.run(buf1, primals_3, buf30, 16384, grid=grid(16384), stream=stream0) del buf1 del primals_3 return (buf21, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf14, buf16, buf17, buf19, buf22, buf23, buf24, buf25, buf26, buf27, buf28, buf29, buf30, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 512, 4, 4), (8192, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((3, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_19 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class Decoder4(nn.Module): def __init__(self, model=None, fixed=False): super(Decoder4, self).__init__() self.fixed = fixed self.conv41 = nn.Conv2d(512, 256, 3, 1, 0) self.conv34 = nn.Conv2d(256, 256, 3, 1, 0) self.conv33 = nn.Conv2d(256, 256, 3, 1, 0) self.conv32 = nn.Conv2d(256, 256, 3, 1, 0) self.conv31 = nn.Conv2d(256, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv21 = nn.Conv2d(128, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv11 = nn.Conv2d(64, 3, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input): y = self.relu(self.conv41(self.pad(input))) y = self.unpool(y) y = self.relu(self.conv34(self.pad(y))) y = self.relu(self.conv33(self.pad(y))) y = self.relu(self.conv32(self.pad(y))) y = self.relu(self.conv31(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv22(self.pad(y))) y = self.relu(self.conv21(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv12(self.pad(y))) y = self.relu(self.conv11(self.pad(y))) return y def get_inputs(): return [torch.rand([4, 512, 4, 4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 6 x1 = xindex // 6 % 6 x2 = xindex // 36 x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 + x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None, eviction_policy='evict_last') tl.store(out_ptr0 + x3, tmp0, None) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x1 = xindex // 10 % 10 x0 = xindex % 10 x4 = xindex // 100 x2 = xindex // 100 % 256 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1 ))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0 ))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 4 * tmp4 + 16 * x4), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, None) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 10 x1 = xindex // 10 % 10 x4 = xindex // 100 x2 = xindex // 100 % 256 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, None) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x1 = xindex // 18 % 18 x0 = xindex % 18 x4 = xindex // 324 x2 = xindex // 324 % 128 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x1))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 8 * tmp4 + 64 * x4), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, None) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 18 x1 = xindex // 18 % 18 x4 = xindex // 324 x2 = xindex // 324 % 128 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, None) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 34 % 34 x0 = xindex % 34 x4 = xindex // 1156 x2 = xindex // 1156 % 64 x7 = xindex tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x1))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x0))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 16, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 16 * tmp4 + 256 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 34 x1 = xindex // 34 % 34 x4 = xindex // 1156 x2 = xindex // 1156 % 64 x5 = xindex tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_10(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 1024 % 3 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, None) tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 1024 % 64 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_12(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 256 % 64 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 256 % 128 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 128 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 256 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 256 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19) = args args.clear() assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1)) assert_size_stride(primals_2, (256, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_3, (256,), (1,)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256,), (1,)) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256,), (1,)) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256,), (1,)) assert_size_stride(primals_10, (128, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (128,), (1,)) assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_13, (128,), (1,)) assert_size_stride(primals_14, (64, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_15, (64,), (1,)) assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_17, (64,), (1,)) assert_size_stride(primals_18, (3, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_19, (3,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch. float32) get_raw_stream(0) triton_poi_fused_reflection_pad2d_0[grid(73728)](primals_1, buf0, 73728, XBLOCK=1024, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 256, 4, 4), (4096, 16, 4, 1)) buf2 = empty_strided_cuda((8,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK =8, num_warps=1, num_stages=1) buf3 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid (102400)](buf2, buf1, primals_3, buf3, 102400, XBLOCK=512, num_warps=8, num_stages=1) buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 256, 8, 8), (16384, 64, 8, 1)) buf5 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(102400)](buf4 , primals_5, buf5, 102400, XBLOCK=512, num_warps=8, num_stages=1) buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 8, 8), (16384, 64, 8, 1)) buf7 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(102400)](buf6 , primals_7, buf7, 102400, XBLOCK=512, num_warps=8, num_stages=1) buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 256, 8, 8), (16384, 64, 8, 1)) buf9 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(102400)](buf8 , primals_9, buf9, 102400, XBLOCK=512, num_warps=8, num_stages=1) buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 128, 8, 8), (8192, 64, 8, 1)) buf11 = empty_strided_cuda((16,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_4[grid(16)](buf11, 16, XBLOCK=16, num_warps=1, num_stages=1) buf12 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5[grid (165888)](buf11, buf10, primals_11, buf12, 165888, XBLOCK=512, num_warps=8, num_stages=1) buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf13, (4, 128, 16, 16), (32768, 256, 16, 1)) buf14 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(165888)]( buf13, primals_13, buf14, 165888, XBLOCK=1024, num_warps=4, num_stages=1) buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf15, (4, 64, 16, 16), (16384, 256, 16, 1)) buf16 = empty_strided_cuda((32,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_7[grid(32)](buf16, 32, XBLOCK=32, num_warps=1, num_stages=1) buf17 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8[grid (295936)](buf16, buf15, primals_15, buf17, 295936, XBLOCK=512, num_warps=8, num_stages=1) buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 64, 32, 32), (65536, 1024, 32, 1)) buf19 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_9[grid(295936)]( buf18, primals_17, buf19, 295936, XBLOCK=1024, num_warps=4, num_stages=1) buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 3, 32, 32), (3072, 1024, 32, 1)) buf21 = buf20 del buf20 buf22 = empty_strided_cuda((4, 3, 32, 32), (3072, 1024, 32, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_10[grid(12288)]( buf21, primals_19, buf22, 12288, XBLOCK=256, num_warps=4, num_stages=1) del primals_19 buf23 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_11[grid(262144)]( buf18, primals_17, buf23, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf18 del primals_17 buf24 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_12[grid(65536)]( buf15, primals_15, buf24, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf15 del primals_15 buf25 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_13[grid(131072)]( buf13, primals_13, buf25, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del buf13 del primals_13 buf26 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool ) triton_poi_fused_convolution_relu_threshold_backward_14[grid(32768)]( buf10, primals_11, buf26, 32768, XBLOCK=128, num_warps=4, num_stages=1) del buf10 del primals_11 buf27 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch .bool) triton_poi_fused_convolution_relu_threshold_backward_15[grid(65536)]( buf8, primals_9, buf27, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf8 del primals_9 buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch .bool) triton_poi_fused_convolution_relu_threshold_backward_15[grid(65536)]( buf6, primals_7, buf28, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf6 del primals_7 buf29 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch .bool) triton_poi_fused_convolution_relu_threshold_backward_15[grid(65536)]( buf4, primals_5, buf29, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf4 del primals_5 buf30 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool ) triton_poi_fused_convolution_relu_threshold_backward_16[grid(16384)]( buf1, primals_3, buf30, 16384, XBLOCK=256, num_warps=4, num_stages=1) del buf1 del primals_3 return (buf21, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf14, buf16, buf17, buf19, buf22, buf23, buf24, buf25, buf26, buf27, buf28, buf29, buf30) class Decoder4New(nn.Module): def __init__(self, model=None, fixed=False): super(Decoder4New, self).__init__() self.fixed = fixed self.conv41 = nn.Conv2d(512, 256, 3, 1, 0) self.conv34 = nn.Conv2d(256, 256, 3, 1, 0) self.conv33 = nn.Conv2d(256, 256, 3, 1, 0) self.conv32 = nn.Conv2d(256, 256, 3, 1, 0) self.conv31 = nn.Conv2d(256, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv21 = nn.Conv2d(128, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv11 = nn.Conv2d(64, 3, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input_0): primals_2 = self.conv41.weight primals_3 = self.conv41.bias primals_4 = self.conv34.weight primals_5 = self.conv34.bias primals_6 = self.conv33.weight primals_7 = self.conv33.bias primals_8 = self.conv32.weight primals_9 = self.conv32.bias primals_10 = self.conv31.weight primals_11 = self.conv31.bias primals_12 = self.conv22.weight primals_13 = self.conv22.bias primals_14 = self.conv21.weight primals_15 = self.conv21.bias primals_16 = self.conv12.weight primals_17 = self.conv12.bias primals_18 = self.conv11.weight primals_19 = self.conv11.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19]) return output[0]	EndyWon/Texture-Reformer	Decoder4	false	8,165	[ "MIT" ]	11	f84f95accb3574c7b759a7f03c0b0b4e150314b5	https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5
GlobalAttention	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/wz/cwzlgmghy6nxuchbiog4puo46i4tq7yhd3qu6ftkgjf3gwib6hxn.py # Topologically Sorted Source Nodes: [align_vectors], Original ATen: [aten._softmax] # Source node to ATen node mapping: # align_vectors => amax, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yh/cyhf6bhaqimi2pucos5fnrpvhrt4vuaetbxnooyr5pvgjt7s6fgo.py # Topologically Sorted Source Nodes: [align_vectors], Original ATen: [aten._softmax] # Source node to ATen node mapping: # align_vectors => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ph/cph7lssg6ok5pn3rbmbprurourgd72q6n57v7b4qu2gqs3wcxbt7.py # Topologically Sorted Source Nodes: [concat_c], Original ATen: [aten.cat] # Source node to ATen node mapping: # concat_c => cat # Graph fragment: # %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%bmm_1, %primals_1], 2), kwargs = {}) triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[128], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + ((4x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/5n/c5nzoru25qhqsfq2ae2hsnhwroe2und6kjr6h7td35tqeg7qekch.py # Topologically Sorted Source Nodes: [attn_h_2], Original ATen: [aten.clone] # Source node to ATen node mapping: # attn_h_2 => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) % 4 x2 = (xindex // 16) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (4x2) + (16x1)), xmask) tmp1 = libdevice.tanh(tmp0) tl.store(out_ptr0 + (x3), tmp1, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/sn/csndnn6ybbxc3prcysl54ujl2vi4k3dieym7whmpedcdmkufsuwc.py # Topologically Sorted Source Nodes: [concat_c_1], Original ATen: [aten.clone] # Source node to ATen node mapping: # concat_c_1 => clone_1 # Graph fragment: # %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_3,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[128], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) % 4 x2 = (xindex // 32) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (8x2) + (32x1)), xmask) tl.store(out_ptr0 + (x3), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/pa/cpahb65yvviyt4hyf5skq57svkrrspumwbmnkf3vcjwrdwhaedaq.py # Topologically Sorted Source Nodes: [align_vectors_2], Original ATen: [aten.clone] # Source node to ATen node mapping: # align_vectors_2 => clone_2 # Graph fragment: # %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_4,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_5 = async_compile.triton('triton_poi_fused_clone_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) % 4 x2 = (xindex // 16) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (4x2) + (16x1)), xmask) tl.store(out_ptr0 + (x3), tmp0, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 8), (8, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [align], Original ATen: [aten.bmm] extern_kernels.bmm(primals_1, reinterpret_tensor(primals_2, (4, 4, 4), (16, 1, 4), 0), out=buf0) buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [align_vectors], Original ATen: [aten._softmax] stream0 = get_raw_stream(0) triton_poi_fused__softmax_0.run(buf0, buf1, 64, grid=grid(64), stream=stream0) buf2 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [align_vectors], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf1, buf2, 64, grid=grid(64), stream=stream0) buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0); del buf1 # reuse # Topologically Sorted Source Nodes: [c], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0), primals_2, out=buf3) del primals_2 buf4 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [concat_c], Original ATen: [aten.cat] triton_poi_fused_cat_2.run(buf3, primals_1, buf4, 128, grid=grid(128), stream=stream0) del primals_1 buf5 = reinterpret_tensor(buf3, (16, 4), (4, 1), 0); del buf3 # reuse # Topologically Sorted Source Nodes: [attn_h], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf4, (16, 8), (8, 1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf5) del primals_3 buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_h_2], Original ATen: [aten.clone] triton_poi_fused_clone_3.run(buf5, buf6, 64, grid=grid(64), stream=stream0) buf7 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [concat_c_1], Original ATen: [aten.clone] triton_poi_fused_clone_4.run(buf4, buf7, 128, grid=grid(128), stream=stream0) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [align_vectors_2], Original ATen: [aten.clone] triton_poi_fused_clone_5.run(buf2, buf8, 64, grid=grid(64), stream=stream0) del buf2 return (buf6, buf8, buf7, reinterpret_tensor(buf4, (16, 8), (8, 1), 0), buf5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F def aeq(args): """ Assert all arguments have the same value """ arguments = (arg for arg in args) first = next(arguments) assert all(arg == first for arg in arguments ), 'Not all arguments have the same value: ' + str(args) class Bottle(nn.Module): def forward(self, input): if len(input.size()) <= 2: return super(Bottle, self).forward(input) size = input.size()[:2] out = super(Bottle, self).forward(input.view(size[0] size[1], -1)) return out.contiguous().view(size[0], size[1], -1) class BottleLinear(Bottle, nn.Linear): pass class GlobalAttention(nn.Module): """ Luong Attention. Global attention takes a matrix and a query vector. It then computes a parameterized convex combination of the matrix based on the input query. H_1 H_2 H_3 ... H_n q q q q \| \| \| \| \\ \| \| / ..... \\ \| / a Constructs a unit mapping. $$(H_1 + H_n, q) => (a)$$ Where H is of `batch x n x dim` and q is of `batch x dim`. Luong Attention (dot, general): The full function is $$ anh(W_2 [(softmax((W_1 q + b_1) H) H), q] + b_2)$$. * dot: $$score(h_t,{\\overline{h}}_s) = h_t^T{\\overline{h}}_s$$ * general: $$score(h_t,{\\overline{h}}_s) = h_t^T W_a {\\overline{h}}_s$$ Bahdanau Attention (mlp): $$c = \\sum_{j=1}^{SeqLength}_jh_j$$. The Alignment-function $$a$$ computes an alignment as: $$a_j = softmax(v_a^T anh(W_a q + U_a h_j) )$$. """ def __init__(self, dim, is_transform_out, attn_type='dot', attn_hidden= 0, context_size=None): super(GlobalAttention, self).__init__() self.dim = dim self.attn_type = attn_type self.attn_hidden = attn_hidden if context_size is None: self.context_size = dim else: self.context_size = context_size assert self.attn_type in ['dot', 'general', 'mlp' ], 'Please select a valid attention type.' if attn_hidden > 0: self.transform_in = nn.Sequential(nn.Linear(dim, attn_hidden), nn.ELU(0.1)) if self.attn_type == 'general': d = attn_hidden if attn_hidden > 0 else dim self.linear_in = nn.Linear(d, d, bias=False) elif self.attn_type == 'mlp': self.linear_context = BottleLinear(self.context_size, dim, bias =False) self.linear_query = nn.Linear(dim, dim, bias=True) self.v = BottleLinear(dim, 1, bias=False) out_bias = self.attn_type == 'mlp' if is_transform_out: self.linear_out = nn.Linear(dim + self.context_size, dim, bias= out_bias) else: self.linear_out = None self.sm = nn.Softmax(dim=-1) self.tanh = nn.Tanh() self.mask = None self.ignore_small = 0 def applyMask(self, mask): self.mask = mask def applyMaskBySeqBatch(self, q): self.applyMask(q.eq(table.IO.PAD).t().contiguous().unsqueeze(0)) def score(self, h_t, h_s): """ h_t (FloatTensor): batch x tgt_len x dim h_s (FloatTensor): batch x src_len x dim returns scores (FloatTensor): batch x tgt_len x src_len: raw attention scores for each src index """ src_batch, src_len, _src_dim = h_s.size() tgt_batch, tgt_len, _tgt_dim = h_t.size() aeq(src_batch, tgt_batch) if self.attn_type in ['general', 'dot']: if self.attn_hidden > 0: h_t = self.transform_in(h_t) h_s = self.transform_in(h_s) if self.attn_type == 'general': h_t = self.linear_in(h_t) h_s_ = h_s.transpose(1, 2) return torch.bmm(h_t, h_s_) else: dim = self.dim wq = self.linear_query(h_t.view(-1, dim)) wq = wq.view(tgt_batch, tgt_len, 1, dim) wq = wq.expand(tgt_batch, tgt_len, src_len, dim) uh = self.linear_context(h_s.contiguous().view(-1, self. context_size)) uh = uh.view(src_batch, 1, src_len, dim) uh = uh.expand(src_batch, tgt_len, src_len, dim) wquh = self.tanh(wq + uh) return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len) def forward(self, input, context): """ input (FloatTensor): batch x tgt_len x dim: decoder's rnn's output. context (FloatTensor): batch x src_len x dim: src hidden states Returns: (attn_h, align_vectors, concat_c) attn_h (FloatTensor): (tgt_len,batch,input_dim) linear and activte concat_c align_vectors (FloatTensor): (tgt_len,batch,src_len) probability concat_c (FloatTensor): (tgt_len,batch,src_dim+input_dim) Concat input and context vector or corresponding tgt_len=1 and squeeze """ if input.dim() == 2: one_step = True input = input.unsqueeze(1) else: one_step = False batch, sourceL, _dim = context.size() batch_, targetL, dim_ = input.size() aeq(batch, batch_) if self.mask is not None: beam_, batch_, sourceL_ = self.mask.size() aeq(batch, batch_ * beam_) aeq(sourceL, sourceL_) align = self.score(input, context) if self.mask is not None: mask_ = self.mask.view(batch, 1, sourceL) align.masked_fill_(mask_, -float('inf')) align_vectors = self.sm(align.view(batch * targetL, sourceL)) align_vectors = align_vectors.view(batch, targetL, sourceL) if self.ignore_small > 0: align_vectors = F.threshold(align_vectors, self.ignore_small, 0) c = torch.bmm(align_vectors, context) concat_c = torch.cat([c, input], 2) if self.linear_out is None: attn_h = concat_c else: attn_h = self.linear_out(concat_c) if self.attn_type in ['general', 'dot']: attn_h = self.tanh(attn_h) if one_step: attn_h = attn_h.squeeze(1) if self.linear_out is not None: concat_c = concat_c.squeeze(1) align_vectors = align_vectors.squeeze(1) else: attn_h = attn_h.transpose(0, 1).contiguous() if self.linear_out is not None: concat_c = concat_c.transpose(0, 1).contiguous() align_vectors = align_vectors.transpose(0, 1).contiguous() return attn_h, align_vectors, concat_c def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'dim': 4, 'is_transform_out': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x2, tmp10, xmask) @triton.jit def triton_poi_fused_clone_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 % 4 x2 = xindex // 16 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask) tmp1 = libdevice.tanh(tmp0) tl.store(out_ptr0 + x3, tmp1, xmask) @triton.jit def triton_poi_fused_clone_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 % 4 x2 = xindex // 32 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 8 * x2 + 32 * x1), xmask) tl.store(out_ptr0 + x3, tmp0, xmask) @triton.jit def triton_poi_fused_clone_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 % 4 x2 = xindex // 16 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask) tl.store(out_ptr0 + x3, tmp0, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 8), (8, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(primals_1, reinterpret_tensor(primals_2, (4, 4, 4), (16, 1, 4), 0), out=buf0) buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__softmax_0[grid(64)](buf0, buf1, 64, XBLOCK=64, num_warps=1, num_stages=1) buf2 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0) del buf0 triton_poi_fused__softmax_1[grid(64)](buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1) buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0) del buf1 extern_kernels.bmm(reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0), primals_2, out=buf3) del primals_2 buf4 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) triton_poi_fused_cat_2[grid(128)](buf3, primals_1, buf4, 128, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 buf5 = reinterpret_tensor(buf3, (16, 4), (4, 1), 0) del buf3 extern_kernels.mm(reinterpret_tensor(buf4, (16, 8), (8, 1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf5) del primals_3 buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_clone_3[grid(64)](buf5, buf6, 64, XBLOCK=64, num_warps=1, num_stages=1) buf7 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) triton_poi_fused_clone_4[grid(128)](buf4, buf7, 128, XBLOCK=128, num_warps=4, num_stages=1) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_clone_5[grid(64)](buf2, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf2 return buf6, buf8, buf7, reinterpret_tensor(buf4, (16, 8), (8, 1), 0), buf5 def aeq(args): """ Assert all arguments have the same value """ arguments = (arg for arg in args) first = next(arguments) assert all(arg == first for arg in arguments ), 'Not all arguments have the same value: ' + str(args) class Bottle(nn.Module): def forward(self, input): if len(input.size()) <= 2: return super(Bottle, self).forward(input) size = input.size()[:2] out = super(Bottle, self).forward(input.view(size[0] size[1], -1)) return out.contiguous().view(size[0], size[1], -1) class BottleLinear(Bottle, nn.Linear): pass class GlobalAttentionNew(nn.Module): """ Luong Attention. Global attention takes a matrix and a query vector. It then computes a parameterized convex combination of the matrix based on the input query. H_1 H_2 H_3 ... H_n q q q q \| \| \| \| \\ \| \| / ..... \\ \| / a Constructs a unit mapping. $$(H_1 + H_n, q) => (a)$$ Where H is of `batch x n x dim` and q is of `batch x dim`. Luong Attention (dot, general): The full function is $$ anh(W_2 [(softmax((W_1 q + b_1) H) H), q] + b_2)$$. * dot: $$score(h_t,{\\overline{h}}_s) = h_t^T{\\overline{h}}_s$$ * general: $$score(h_t,{\\overline{h}}_s) = h_t^T W_a {\\overline{h}}_s$$ Bahdanau Attention (mlp): $$c = \\sum_{j=1}^{SeqLength}_jh_j$$. The Alignment-function $$a$$ computes an alignment as: $$a_j = softmax(v_a^T anh(W_a q + U_a h_j) )$$. """ def __init__(self, dim, is_transform_out, attn_type='dot', attn_hidden= 0, context_size=None): super(GlobalAttentionNew, self).__init__() self.dim = dim self.attn_type = attn_type self.attn_hidden = attn_hidden if context_size is None: self.context_size = dim else: self.context_size = context_size assert self.attn_type in ['dot', 'general', 'mlp' ], 'Please select a valid attention type.' if attn_hidden > 0: self.transform_in = nn.Sequential(nn.Linear(dim, attn_hidden), nn.ELU(0.1)) if self.attn_type == 'general': d = attn_hidden if attn_hidden > 0 else dim self.linear_in = nn.Linear(d, d, bias=False) elif self.attn_type == 'mlp': self.linear_context = BottleLinear(self.context_size, dim, bias =False) self.linear_query = nn.Linear(dim, dim, bias=True) self.v = BottleLinear(dim, 1, bias=False) out_bias = self.attn_type == 'mlp' if is_transform_out: self.linear_out = nn.Linear(dim + self.context_size, dim, bias= out_bias) else: self.linear_out = None self.sm = nn.Softmax(dim=-1) self.tanh = nn.Tanh() self.mask = None self.ignore_small = 0 def applyMask(self, mask): self.mask = mask def applyMaskBySeqBatch(self, q): self.applyMask(q.eq(table.IO.PAD).t().contiguous().unsqueeze(0)) def score(self, h_t, h_s): """ h_t (FloatTensor): batch x tgt_len x dim h_s (FloatTensor): batch x src_len x dim returns scores (FloatTensor): batch x tgt_len x src_len: raw attention scores for each src index """ src_batch, src_len, _src_dim = h_s.size() tgt_batch, tgt_len, _tgt_dim = h_t.size() aeq(src_batch, tgt_batch) if self.attn_type in ['general', 'dot']: if self.attn_hidden > 0: h_t = self.transform_in(h_t) h_s = self.transform_in(h_s) if self.attn_type == 'general': h_t = self.linear_in(h_t) h_s_ = h_s.transpose(1, 2) return torch.bmm(h_t, h_s_) else: dim = self.dim wq = self.linear_query(h_t.view(-1, dim)) wq = wq.view(tgt_batch, tgt_len, 1, dim) wq = wq.expand(tgt_batch, tgt_len, src_len, dim) uh = self.linear_context(h_s.contiguous().view(-1, self. context_size)) uh = uh.view(src_batch, 1, src_len, dim) uh = uh.expand(src_batch, tgt_len, src_len, dim) wquh = self.tanh(wq + uh) return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len) def forward(self, input_0, input_1): primals_3 = self.linear_out.weight primals_1 = input_0 primals_2 = input_1 output = call([primals_1, primals_2, primals_3]) return output[0], output[1], output[2]	GT-SALT/Disfluency-Generation-and-Detection	GlobalAttention	false	8,166	[ "MIT" ]	11	72126172b466aa74277f3cf0f73b915e5dbeefbb	https://github.com/GT-SALT/Disfluency-Generation-and-Detection/tree/72126172b466aa74277f3cf0f73b915e5dbeefbb
MultiHeadedAttention	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/wa/cwaiybphswtmaspdhe4zyk6oz5vihcszja6ki3t57pocvhhcwxt2.py # Topologically Sorted Source Nodes: [q_2, scores], Original ATen: [aten.div, aten.clone] # Source node to ATen node mapping: # q_2 => div # scores => clone # Graph fragment: # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%permute_5, 1.0), kwargs = {}) # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_div_0 = async_compile.triton('triton_poi_fused_clone_div_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 16], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_div_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4x2) + (64y1)), xmask & ymask) tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tl.store(out_ptr0 + (x2 + (16y3)), tmp4, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/n2/cn26k6uy3k56o6xygmxysijxph7soufdw3x7n52ij3kcef3c5psp.py # Topologically Sorted Source Nodes: [scores], Original ATen: [aten.clone] # Source node to ATen node mapping: # scores => clone_1 # Graph fragment: # %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 16], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4x2) + (64y1)), xmask & ymask) tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + (16y3)), tmp2, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/la/claeh2obujpnm3noljofpxfgv3lz36hqergikbja3ayg2zoo7agn.py # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attention => amax, div_1, exp, sub, sum_1 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_11, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_11, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_per_fused__softmax_2 = async_compile.triton('triton_per_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[256, 16], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 256 rnumel = 16 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + (16x0)), xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, float("-inf")) tmp4 = triton_helpers.max2(tmp3, 1)[:, None] tmp5 = tmp0 - tmp4 tmp6 = tl_math.exp(tmp5) tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = tl.where(xmask, tmp7, 0) tmp10 = tl.sum(tmp9, 1)[:, None] tmp11 = tmp6 / tmp10 tl.store(out_ptr2 + (r1 + (16x0)), tmp11, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/44/c44udt7qyfjbhr63snwdnvww3k3vzms334kz5lteswpyl475z5jc.py # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] # Source node to ATen node mapping: # contiguous => clone_4 # Graph fragment: # %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 64 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = (yindex // 16) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (16x2) + (64y1)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4, ), (1, )) assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1) del primals_4 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2) del primals_7 buf3 = empty_strided_cuda((4, 4, 16, 1), (64, 16, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [q_2, scores], Original ATen: [aten.div, aten.clone] stream0 = get_raw_stream(0) triton_poi_fused_clone_div_0.run(buf2, primals_8, buf3, 16, 16, grid=grid(16, 16), stream=stream0) del primals_8 buf4 = reinterpret_tensor(buf2, (4, 4, 1, 16), (64, 16, 16, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [scores], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf0, primals_3, buf4, 16, 16, grid=grid(16, 16), stream=stream0) del primals_3 buf5 = empty_strided_cuda((16, 16, 16), (256, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [scores], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 16), (16, 0, 1), 0), out=buf5) buf8 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax] triton_per_fused__softmax_2.run(buf5, buf8, 256, 16, grid=grid(256), stream=stream0) del buf5 buf9 = reinterpret_tensor(buf0, (4, 4, 16, 1), (64, 16, 1, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [context], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf1, primals_5, buf9, 16, 16, grid=grid(16, 16), stream=stream0) del primals_5 buf10 = reinterpret_tensor(buf1, (16, 16, 1), (16, 1, 1), 0); del buf1 # reuse # Topologically Sorted Source Nodes: [context], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf8, (16, 16, 16), (256, 16, 1), 0), reinterpret_tensor(buf9, (16, 16, 1), (16, 1, 0), 0), out=buf10) buf11 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] triton_poi_fused_clone_3.run(buf10, buf11, 64, 4, grid=grid(64, 4), stream=stream0) buf12 = reinterpret_tensor(buf10, (64, 4), (4, 1), 0); del buf10 # reuse # Topologically Sorted Source Nodes: [output], Original ATen: [aten.addmm] extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf12) del primals_11 return (reinterpret_tensor(buf12, (4, 16, 4), (64, 4, 1), 0), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), buf8, reinterpret_tensor(buf11, (64, 4), (4, 1), 0), primals_10, reinterpret_tensor(buf9, (16, 1, 16), (16, 1, 1), 0), reinterpret_tensor(buf3, (16, 1, 16), (16, 1, 1), 0), reinterpret_tensor(buf4, (16, 16, 1), (16, 1, 16), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import math import torch from torch import Tensor from torch import nn class MultiHeadedAttention(nn.Module): """ Multi-Head Attention module from "Attention is All You Need" Implementation modified from OpenNMT-py. https://github.com/OpenNMT/OpenNMT-py """ def __init__(self, num_heads: 'int', size: 'int', dropout: 'float'=0.1): """ Create a multi-headed attention layer. :param num_heads: the number of heads :param size: model size (must be divisible by num_heads) :param dropout: probability of dropping a unit """ super().__init__() assert size % num_heads == 0 self.head_size = head_size = size // num_heads self.model_size = size self.num_heads = num_heads self.k_layer = nn.Linear(size, num_heads * head_size) self.v_layer = nn.Linear(size, num_heads * head_size) self.q_layer = nn.Linear(size, num_heads * head_size) self.output_layer = nn.Linear(size, size) self.softmax = nn.Softmax(dim=-1) self.dropout = nn.Dropout(dropout) def forward(self, k: 'Tensor', v: 'Tensor', q: 'Tensor', mask: 'Tensor' =None): """ Computes multi-headed attention. :param k: keys [B, M, D] with M being the sentence length. :param v: values [B, M, D] :param q: query [B, M, D] :param mask: optional mask [B, 1, M] or [B, M, M] :return: """ batch_size = k.size(0) num_heads = self.num_heads k = self.k_layer(k) v = self.v_layer(v) q = self.q_layer(q) k = k.view(batch_size, -1, num_heads, self.head_size).transpose(1, 2) v = v.view(batch_size, -1, num_heads, self.head_size).transpose(1, 2) q = q.view(batch_size, -1, num_heads, self.head_size).transpose(1, 2) q = q / math.sqrt(self.head_size) scores = torch.matmul(q, k.transpose(2, 3)) if mask is not None: scores = scores.masked_fill(~mask.unsqueeze(1), float('-inf')) attention = self.softmax(scores) attention = self.dropout(attention) context = torch.matmul(attention, v) context = context.transpose(1, 2).contiguous().view(batch_size, -1, num_heads * self.head_size) output = self.output_layer(context) return output def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_heads': 4, 'size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clone_div_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask) tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tl.store(out_ptr0 + (x2 + 16 * y3), tmp4, xmask & ymask) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask) tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask & ymask) @triton.jit def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 256 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, float('-inf')) tmp4 = triton_helpers.max2(tmp3, 1)[:, None] tmp5 = tmp0 - tmp4 tmp6 = tl_math.exp(tmp5) tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = tl.where(xmask, tmp7, 0) tmp10 = tl.sum(tmp9, 1)[:, None] tmp11 = tmp6 / tmp10 tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask) @triton.jit def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 64 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = yindex // 16 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4,), (1,)) assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1) del primals_4 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2) del primals_7 buf3 = empty_strided_cuda((4, 4, 16, 1), (64, 16, 1, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clone_div_0[grid(16, 16)](buf2, primals_8, buf3, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1) del primals_8 buf4 = reinterpret_tensor(buf2, (4, 4, 1, 16), (64, 16, 16, 1), 0) del buf2 triton_poi_fused_clone_1[grid(16, 16)](buf0, primals_3, buf4, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1) del primals_3 buf5 = empty_strided_cuda((16, 16, 16), (256, 16, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 16), (16, 0, 1), 0), out=buf5) buf8 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch .float32) triton_per_fused__softmax_2[grid(256)](buf5, buf8, 256, 16, XBLOCK= 32, num_warps=4, num_stages=1) del buf5 buf9 = reinterpret_tensor(buf0, (4, 4, 16, 1), (64, 16, 1, 1), 0) del buf0 triton_poi_fused_clone_1[grid(16, 16)](buf1, primals_5, buf9, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1) del primals_5 buf10 = reinterpret_tensor(buf1, (16, 16, 1), (16, 1, 1), 0) del buf1 extern_kernels.bmm(reinterpret_tensor(buf8, (16, 16, 16), (256, 16, 1), 0), reinterpret_tensor(buf9, (16, 16, 1), (16, 1, 0), 0), out=buf10) buf11 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32) triton_poi_fused_clone_3[grid(64, 4)](buf10, buf11, 64, 4, XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1) buf12 = reinterpret_tensor(buf10, (64, 4), (4, 1), 0) del buf10 extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf12) del primals_11 return reinterpret_tensor(buf12, (4, 16, 4), (64, 4, 1), 0 ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0 ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0 ), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0 ), buf8, reinterpret_tensor(buf11, (64, 4), (4, 1), 0 ), primals_10, reinterpret_tensor(buf9, (16, 1, 16), (16, 1, 1), 0 ), reinterpret_tensor(buf3, (16, 1, 16), (16, 1, 1), 0 ), reinterpret_tensor(buf4, (16, 16, 1), (16, 1, 16), 0) class MultiHeadedAttentionNew(nn.Module): """ Multi-Head Attention module from "Attention is All You Need" Implementation modified from OpenNMT-py. https://github.com/OpenNMT/OpenNMT-py """ def __init__(self, num_heads: 'int', size: 'int', dropout: 'float'=0.1): """ Create a multi-headed attention layer. :param num_heads: the number of heads :param size: model size (must be divisible by num_heads) :param dropout: probability of dropping a unit """ super().__init__() assert size % num_heads == 0 self.head_size = head_size = size // num_heads self.model_size = size self.num_heads = num_heads self.k_layer = nn.Linear(size, num_heads * head_size) self.v_layer = nn.Linear(size, num_heads * head_size) self.q_layer = nn.Linear(size, num_heads * head_size) self.output_layer = nn.Linear(size, size) self.softmax = nn.Softmax(dim=-1) self.dropout = nn.Dropout(dropout) def forward(self, input_0, input_1, input_2): primals_2 = self.k_layer.weight primals_3 = self.k_layer.bias primals_4 = self.v_layer.weight primals_5 = self.v_layer.bias primals_7 = self.q_layer.weight primals_8 = self.q_layer.bias primals_10 = self.output_layer.weight primals_11 = self.output_layer.bias primals_1 = input_0 primals_6 = input_1 primals_9 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0]	GuyTevet/MotionCLIP	MultiHeadedAttention	false	8,167	[ "MIT" ]	45	c2b9f40b0e721e42981f3e8b58133a1c51fde715	https://github.com/GuyTevet/MotionCLIP/tree/c2b9f40b0e721e42981f3e8b58133a1c51fde715
AttenHead	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/qg/cqg7jnitjjsooef4r3sgc2773gaxdkjgfwij5wn7m5hkph3xfrvx.py # Topologically Sorted Source Nodes: [f], Original ATen: [aten.cat] # Source node to ATen node mapping: # f => cat # Graph fragment: # %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %squeeze],), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x0 = xindex % 4 x2 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (4x1)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + (x0 + (4((-4) + x1))), tmp6 & xmask, other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/vz/cvzsqmfnbleek3sfszevs67zok5gi5aarihh7viz3c66es5o6qz5.py # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] # Source node to ATen node mapping: # softmax => exp # Graph fragment: # %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm, 1), kwargs = {}) # %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [2], True), kwargs = {}) # %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {}) # %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 2.0), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp3 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 0.5 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + (x2), tmp17, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/wp/cwpr2jp7bpqwkbaxgttcl2ekjdtkf6lezuspoiunutzd3j2qgeyk.py # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] # Source node to ATen node mapping: # softmax => div_1, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {}) # %div_1 : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yu/cyuzxauq4nah7ud6435i24w6yd6ewxycliv4z4mfmcdzhfi5xajm.py # Topologically Sorted Source Nodes: [fa], Original ATen: [aten.cat] # Source node to ATen node mapping: # fa => cat_1 # Graph fragment: # %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%bmm_1, %slice_2], 2), kwargs = {}) triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + ((4x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/5q/c5qeq6iyvcwyvhd7amrdkaagr4z322cxibi5u6tolvnuvv3tkai2.py # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # relu => relu # Graph fragment: # %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_6), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {}) # %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_4 = async_compile.triton('triton_poi_fused_relu_threshold_backward_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_4(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/6i/c6iyadtb7x6uv5jaz2qc2kwdriohsflybg4dbmhfg4ec66amijhu.py # Topologically Sorted Source Nodes: [add, fx_1], Original ATen: [aten.add, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # add => add # fx_1 => relu_1 # Graph fragment: # %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_8), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %add_tensor), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_add_relu_threshold_backward_5 = async_compile.triton('triton_poi_fused_add_relu_threshold_backward_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_relu_threshold_backward_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_relu_threshold_backward_5(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_out_ptr0 + (x2), xmask) tmp2 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp4 = tmp0 + tmp3 tmp5 = tl.full([1], 0, tl.int32) tmp6 = triton_helpers.maximum(tmp5, tmp4) tmp7 = 0.0 tmp8 = tmp6 <= tmp7 tl.store(in_out_ptr0 + (x2), tmp6, xmask) tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4, ), (1, )) assert_size_stride(primals_5, (4, 8), (8, 1)) assert_size_stride(primals_6, (4, ), (1, )) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((8, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [f], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(primals_2, primals_1, buf0, 32, grid=grid(32), stream=stream0) del primals_1 buf1 = empty_strided_cuda((8, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm] extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_3 del primals_4 buf2 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf1, (1, 4, 4), (32, 4, 1), 0), reinterpret_tensor(buf1, (1, 4, 4), (32, 1, 4), 16), out=buf2) buf3 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf2, buf3, 16, grid=grid(16), stream=stream0) buf4 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf3, buf4, 16, grid=grid(16), stream=stream0) buf5 = buf3; del buf3 # reuse # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.bmm] extern_kernels.bmm(buf4, reinterpret_tensor(buf1, (1, 4, 4), (32, 4, 1), 16), out=buf5) buf6 = empty_strided_cuda((1, 4, 8), (32, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [fa], Original ATen: [aten.cat] triton_poi_fused_cat_3.run(buf5, buf1, buf6, 32, grid=grid(32), stream=stream0) buf7 = reinterpret_tensor(buf5, (4, 4), (4, 1), 0); del buf5 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf6, (4, 8), (8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf7) buf8 = buf7; del buf7 # reuse buf12 = empty_strided_cuda((4, 4), (4, 1), torch.bool) # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_4.run(buf8, primals_6, buf12, 16, grid=grid(16), stream=stream0) del primals_6 buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf8, reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf9) buf10 = buf9; del buf9 # reuse buf11 = empty_strided_cuda((4, 4), (4, 1), torch.bool) # Topologically Sorted Source Nodes: [add, fx_1], Original ATen: [aten.add, aten.relu, aten.threshold_backward] triton_poi_fused_add_relu_threshold_backward_5.run(buf10, primals_2, primals_8, buf11, 16, grid=grid(16), stream=stream0) del primals_2 del primals_8 return (buf10, reinterpret_tensor(buf4, (4, 1, 4), (4, 16, 1), 0), buf0, buf1, buf4, reinterpret_tensor(buf6, (4, 8), (8, 1), 0), buf8, buf11, primals_7, buf12, primals_5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import math import torch from torch.nn import functional as F from torch import nn class AttenHead(nn.Module): def __init__(self, fdim, num_heads=1): super().__init__() self.num_heads = num_heads self.fatt = fdim // num_heads for i in range(num_heads): setattr(self, f'embd{i}', nn.Linear(fdim, self.fatt)) for i in range(num_heads): setattr(self, f'fc{i}', nn.Linear(2 * self.fatt, self.fatt)) self.fc = nn.Linear(self.fatt * num_heads, fdim) self.dropout = nn.Dropout(0.1) def forward(self, fx_in, fp_in): fp_in = fp_in.squeeze(0) d = math.sqrt(self.fatt) Nx = len(fx_in) f = torch.cat([fx_in, fp_in]) f = torch.stack([getattr(self, f'embd{i}')(f) for i in range(self. num_heads)]) fx, fp = f[:, :Nx], f[:, Nx:] w = self.dropout(F.softmax(torch.matmul(fx, torch.transpose(fp, 1, 2)) / d, dim=2)) fa = torch.cat([torch.matmul(w, fp), fx], dim=2) fa = torch.stack([F.relu(getattr(self, f'fc{i}')(fa[i])) for i in range(self.num_heads)]) fa = torch.transpose(fa, 0, 1).reshape(Nx, -1) fx = F.relu(fx_in + self.fc(fa)) w = torch.transpose(w, 0, 1) return fx, w def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {'fdim': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x0 = xindex % 4 x2 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1)), tmp6 & xmask, other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x2, tmp10, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 0.5 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + x2, tmp17, xmask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x2, tmp10, xmask) @triton.jit def triton_poi_fused_relu_threshold_backward_4(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) @triton.jit def triton_poi_fused_add_relu_threshold_backward_5(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_out_ptr0 + x2, xmask) tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp4 = tmp0 + tmp3 tmp5 = tl.full([1], 0, tl.int32) tmp6 = triton_helpers.maximum(tmp5, tmp4) tmp7 = 0.0 tmp8 = tmp6 <= tmp7 tl.store(in_out_ptr0 + x2, tmp6, xmask) tl.store(out_ptr0 + x2, tmp8, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4,), (1,)) assert_size_stride(primals_5, (4, 8), (8, 1)) assert_size_stride(primals_6, (4,), (1,)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((8, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(32)](primals_2, primals_1, buf0, 32, XBLOCK=32, num_warps=1, num_stages=1) del primals_1 buf1 = empty_strided_cuda((8, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_3 del primals_4 buf2 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf1, (1, 4, 4), (32, 4, 1), 0), reinterpret_tensor(buf1, (1, 4, 4), (32, 1, 4), 16), out=buf2) buf3 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_1[grid(16)](buf2, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) buf4 = buf2 del buf2 triton_poi_fused__softmax_2[grid(16)](buf3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1) buf5 = buf3 del buf3 extern_kernels.bmm(buf4, reinterpret_tensor(buf1, (1, 4, 4), (32, 4, 1), 16), out=buf5) buf6 = empty_strided_cuda((1, 4, 8), (32, 8, 1), torch.float32) triton_poi_fused_cat_3[grid(32)](buf5, buf1, buf6, 32, XBLOCK=32, num_warps=1, num_stages=1) buf7 = reinterpret_tensor(buf5, (4, 4), (4, 1), 0) del buf5 extern_kernels.mm(reinterpret_tensor(buf6, (4, 8), (8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf7) buf8 = buf7 del buf7 buf12 = empty_strided_cuda((4, 4), (4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_4[grid(16)](buf8, primals_6, buf12, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_6 buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(buf8, reinterpret_tensor(primals_7, (4, 4), (1, 4 ), 0), out=buf9) buf10 = buf9 del buf9 buf11 = empty_strided_cuda((4, 4), (4, 1), torch.bool) triton_poi_fused_add_relu_threshold_backward_5[grid(16)](buf10, primals_2, primals_8, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_2 del primals_8 return buf10, reinterpret_tensor(buf4, (4, 1, 4), (4, 16, 1), 0 ), buf0, buf1, buf4, reinterpret_tensor(buf6, (4, 8), (8, 1), 0 ), buf8, buf11, primals_7, buf12, primals_5 class AttenHeadNew(nn.Module): def __init__(self, fdim, num_heads=1): super().__init__() self.num_heads = num_heads self.fatt = fdim // num_heads for i in range(num_heads): setattr(self, f'embd{i}', nn.Linear(fdim, self.fatt)) for i in range(num_heads): setattr(self, f'fc{i}', nn.Linear(2 * self.fatt, self.fatt)) self.fc = nn.Linear(self.fatt * num_heads, fdim) self.dropout = nn.Dropout(0.1) def forward(self, input_0, input_1): primals_1 = self.embd0.weight primals_4 = self.embd0.bias primals_5 = self.fc0.weight primals_6 = self.fc0.bias primals_2 = self.fc.weight primals_8 = self.fc.bias primals_3 = input_0 primals_7 = input_1 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8]) return output[0], output[1]	GT-RIPL/FeatMatch	AttenHead	false	8,168	[ "MIT" ]	41	03e16af82d8c94f7bbbbf5eab1334dc1fc9b93cb	https://github.com/GT-RIPL/FeatMatch/tree/03e16af82d8c94f7bbbbf5eab1334dc1fc9b93cb
PartialBCELoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/i2/ci2zd7ln7hvskg2fehzeiwwebpykrj7bbtalrcrhgdzv4oxskdqz.py # Topologically Sorted Source Nodes: [neg, log_sigmoid, pos_vals, log_sigmoid_1, neg_1, neg_2, sub, neg_vals, vals, mul_2, losses, mean], Original ATen: [aten.neg, aten.log_sigmoid_forward, aten.mul, aten.rsub, aten.add, aten.sum, aten.mean] # Source node to ATen node mapping: # log_sigmoid => abs_1, exp, full_default, log1p, minimum, neg_1, sub # log_sigmoid_1 => abs_2, exp_1, full_default_1, log1p_1, minimum_1, neg_3, sub_1 # losses => sum_1 # mean => mean # mul_2 => mul_2 # neg => neg # neg_1 => neg_2 # neg_2 => neg_4 # neg_vals => mul_1 # pos_vals => mul # sub => sub_2 # vals => add # Graph fragment: # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg0_1,), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default, %arg1_1), kwargs = {}) # %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg1_1,), kwargs = {}) # %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {}) # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {}) # %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %sub), kwargs = {}) # %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %neg_2 : [num_users=2] = call_function[target=torch.ops.aten.neg.default](args = (%arg1_1,), kwargs = {}) # %minimum_1 : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default_1, %neg_2), kwargs = {}) # %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%neg_2,), kwargs = {}) # %neg_3 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_2,), kwargs = {}) # %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_3,), kwargs = {}) # %log1p_1 : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp_1,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum_1, %log1p_1), kwargs = {}) # %neg_4 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sub_1,), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg_4, %sub_2), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %arg2_1), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [-1]), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_1,), kwargs = {}) triton_per_fused_add_log_sigmoid_forward_mean_mul_neg_rsub_sum_0 = async_compile.triton('triton_per_fused_add_log_sigmoid_forward_mean_mul_neg_rsub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 64], reduction_hint=ReductionHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_log_sigmoid_forward_mean_mul_neg_rsub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_log_sigmoid_forward_mean_mul_neg_rsub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 1 rnumel = 64 RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (4r0), None, eviction_policy='evict_last') tmp2 = tl.load(in_ptr1 + (4r0), None, eviction_policy='evict_last') tmp23 = tl.load(in_ptr2 + (4r0), None, eviction_policy='evict_last') tmp25 = tl.load(in_ptr0 + (1 + (4r0)), None, eviction_policy='evict_last') tmp27 = tl.load(in_ptr1 + (1 + (4r0)), None, eviction_policy='evict_last') tmp46 = tl.load(in_ptr2 + (1 + (4r0)), None, eviction_policy='evict_last') tmp49 = tl.load(in_ptr0 + (2 + (4r0)), None, eviction_policy='evict_last') tmp51 = tl.load(in_ptr1 + (2 + (4r0)), None, eviction_policy='evict_last') tmp70 = tl.load(in_ptr2 + (2 + (4r0)), None, eviction_policy='evict_last') tmp73 = tl.load(in_ptr0 + (3 + (4r0)), None, eviction_policy='evict_last') tmp75 = tl.load(in_ptr1 + (3 + (4r0)), None, eviction_policy='evict_last') tmp94 = tl.load(in_ptr2 + (3 + (4r0)), None, eviction_policy='evict_last') tmp1 = -tmp0 tmp3 = 0.0 tmp4 = triton_helpers.minimum(tmp3, tmp2) tmp5 = tl_math.abs(tmp2) tmp6 = -tmp5 tmp7 = tl_math.exp(tmp6) tmp8 = libdevice.log1p(tmp7) tmp9 = tmp4 - tmp8 tmp10 = tmp1 * tmp9 tmp11 = -tmp2 tmp12 = triton_helpers.minimum(tmp3, tmp11) tmp13 = tl_math.abs(tmp11) tmp14 = -tmp13 tmp15 = tl_math.exp(tmp14) tmp16 = libdevice.log1p(tmp15) tmp17 = tmp12 - tmp16 tmp18 = -tmp17 tmp19 = 1.0 tmp20 = tmp19 - tmp0 tmp21 = tmp18 * tmp20 tmp22 = tmp10 + tmp21 tmp24 = tmp22 * tmp23 tmp26 = -tmp25 tmp28 = triton_helpers.minimum(tmp3, tmp27) tmp29 = tl_math.abs(tmp27) tmp30 = -tmp29 tmp31 = tl_math.exp(tmp30) tmp32 = libdevice.log1p(tmp31) tmp33 = tmp28 - tmp32 tmp34 = tmp26 * tmp33 tmp35 = -tmp27 tmp36 = triton_helpers.minimum(tmp3, tmp35) tmp37 = tl_math.abs(tmp35) tmp38 = -tmp37 tmp39 = tl_math.exp(tmp38) tmp40 = libdevice.log1p(tmp39) tmp41 = tmp36 - tmp40 tmp42 = -tmp41 tmp43 = tmp19 - tmp25 tmp44 = tmp42 * tmp43 tmp45 = tmp34 + tmp44 tmp47 = tmp45 * tmp46 tmp48 = tmp24 + tmp47 tmp50 = -tmp49 tmp52 = triton_helpers.minimum(tmp3, tmp51) tmp53 = tl_math.abs(tmp51) tmp54 = -tmp53 tmp55 = tl_math.exp(tmp54) tmp56 = libdevice.log1p(tmp55) tmp57 = tmp52 - tmp56 tmp58 = tmp50 * tmp57 tmp59 = -tmp51 tmp60 = triton_helpers.minimum(tmp3, tmp59) tmp61 = tl_math.abs(tmp59) tmp62 = -tmp61 tmp63 = tl_math.exp(tmp62) tmp64 = libdevice.log1p(tmp63) tmp65 = tmp60 - tmp64 tmp66 = -tmp65 tmp67 = tmp19 - tmp49 tmp68 = tmp66 * tmp67 tmp69 = tmp58 + tmp68 tmp71 = tmp69 * tmp70 tmp72 = tmp48 + tmp71 tmp74 = -tmp73 tmp76 = triton_helpers.minimum(tmp3, tmp75) tmp77 = tl_math.abs(tmp75) tmp78 = -tmp77 tmp79 = tl_math.exp(tmp78) tmp80 = libdevice.log1p(tmp79) tmp81 = tmp76 - tmp80 tmp82 = tmp74 * tmp81 tmp83 = -tmp75 tmp84 = triton_helpers.minimum(tmp3, tmp83) tmp85 = tl_math.abs(tmp83) tmp86 = -tmp85 tmp87 = tl_math.exp(tmp86) tmp88 = libdevice.log1p(tmp87) tmp89 = tmp84 - tmp88 tmp90 = -tmp89 tmp91 = tmp19 - tmp73 tmp92 = tmp90 * tmp91 tmp93 = tmp82 + tmp92 tmp95 = tmp93 * tmp94 tmp96 = tmp72 + tmp95 tmp97 = tl.broadcast_to(tmp96, [XBLOCK, RBLOCK]) tmp99 = tl.sum(tmp97, 1)[:, None] tmp100 = 64.0 tmp101 = tmp99 / tmp100 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp101, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf1 = empty_strided_cuda((), (), torch.float32) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [neg, log_sigmoid, pos_vals, log_sigmoid_1, neg_1, neg_2, sub, neg_vals, vals, mul_2, losses, mean], Original ATen: [aten.neg, aten.log_sigmoid_forward, aten.mul, aten.rsub, aten.add, aten.sum, aten.mean] stream0 = get_raw_stream(0) triton_per_fused_add_log_sigmoid_forward_mean_mul_neg_rsub_sum_0.run(buf2, arg0_1, arg1_1, arg2_1, 1, 64, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 del arg2_1 return (buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch class PartialBCELoss(torch.nn.Module): def __init__(self): super(PartialBCELoss, self).__init__() self.log_sigmoid = torch.nn.LogSigmoid() def forward(self, logits, targets, targets_mask, weights=None): pos_vals = -targets * self.log_sigmoid(logits) neg_vals = -self.log_sigmoid(-logits) * (1 - targets) vals = pos_vals + neg_vals losses = torch.sum(vals * targets_mask, dim=-1) if weights is not None: losses *= weights return torch.mean(losses) def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_log_sigmoid_forward_mean_mul_neg_rsub_sum_0( in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl. constexpr): RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last') tmp2 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last') tmp23 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last') tmp25 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last') tmp27 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last') tmp46 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last') tmp49 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last') tmp51 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last') tmp70 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last') tmp73 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last') tmp75 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last') tmp94 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last') tmp1 = -tmp0 tmp3 = 0.0 tmp4 = triton_helpers.minimum(tmp3, tmp2) tmp5 = tl_math.abs(tmp2) tmp6 = -tmp5 tmp7 = tl_math.exp(tmp6) tmp8 = libdevice.log1p(tmp7) tmp9 = tmp4 - tmp8 tmp10 = tmp1 * tmp9 tmp11 = -tmp2 tmp12 = triton_helpers.minimum(tmp3, tmp11) tmp13 = tl_math.abs(tmp11) tmp14 = -tmp13 tmp15 = tl_math.exp(tmp14) tmp16 = libdevice.log1p(tmp15) tmp17 = tmp12 - tmp16 tmp18 = -tmp17 tmp19 = 1.0 tmp20 = tmp19 - tmp0 tmp21 = tmp18 * tmp20 tmp22 = tmp10 + tmp21 tmp24 = tmp22 * tmp23 tmp26 = -tmp25 tmp28 = triton_helpers.minimum(tmp3, tmp27) tmp29 = tl_math.abs(tmp27) tmp30 = -tmp29 tmp31 = tl_math.exp(tmp30) tmp32 = libdevice.log1p(tmp31) tmp33 = tmp28 - tmp32 tmp34 = tmp26 * tmp33 tmp35 = -tmp27 tmp36 = triton_helpers.minimum(tmp3, tmp35) tmp37 = tl_math.abs(tmp35) tmp38 = -tmp37 tmp39 = tl_math.exp(tmp38) tmp40 = libdevice.log1p(tmp39) tmp41 = tmp36 - tmp40 tmp42 = -tmp41 tmp43 = tmp19 - tmp25 tmp44 = tmp42 * tmp43 tmp45 = tmp34 + tmp44 tmp47 = tmp45 * tmp46 tmp48 = tmp24 + tmp47 tmp50 = -tmp49 tmp52 = triton_helpers.minimum(tmp3, tmp51) tmp53 = tl_math.abs(tmp51) tmp54 = -tmp53 tmp55 = tl_math.exp(tmp54) tmp56 = libdevice.log1p(tmp55) tmp57 = tmp52 - tmp56 tmp58 = tmp50 * tmp57 tmp59 = -tmp51 tmp60 = triton_helpers.minimum(tmp3, tmp59) tmp61 = tl_math.abs(tmp59) tmp62 = -tmp61 tmp63 = tl_math.exp(tmp62) tmp64 = libdevice.log1p(tmp63) tmp65 = tmp60 - tmp64 tmp66 = -tmp65 tmp67 = tmp19 - tmp49 tmp68 = tmp66 * tmp67 tmp69 = tmp58 + tmp68 tmp71 = tmp69 * tmp70 tmp72 = tmp48 + tmp71 tmp74 = -tmp73 tmp76 = triton_helpers.minimum(tmp3, tmp75) tmp77 = tl_math.abs(tmp75) tmp78 = -tmp77 tmp79 = tl_math.exp(tmp78) tmp80 = libdevice.log1p(tmp79) tmp81 = tmp76 - tmp80 tmp82 = tmp74 * tmp81 tmp83 = -tmp75 tmp84 = triton_helpers.minimum(tmp3, tmp83) tmp85 = tl_math.abs(tmp83) tmp86 = -tmp85 tmp87 = tl_math.exp(tmp86) tmp88 = libdevice.log1p(tmp87) tmp89 = tmp84 - tmp88 tmp90 = -tmp89 tmp91 = tmp19 - tmp73 tmp92 = tmp90 * tmp91 tmp93 = tmp82 + tmp92 tmp95 = tmp93 * tmp94 tmp96 = tmp72 + tmp95 tmp97 = tl.broadcast_to(tmp96, [XBLOCK, RBLOCK]) tmp99 = tl.sum(tmp97, 1)[:, None] tmp100 = 64.0 tmp101 = tmp99 / tmp100 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp101, None) def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf1 = empty_strided_cuda((), (), torch.float32) buf2 = buf1 del buf1 get_raw_stream(0) triton_per_fused_add_log_sigmoid_forward_mean_mul_neg_rsub_sum_0[grid (1)](buf2, arg0_1, arg1_1, arg2_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1) del arg0_1 del arg1_1 del arg2_1 return buf2, class PartialBCELossNew(torch.nn.Module): def __init__(self): super(PartialBCELossNew, self).__init__() self.log_sigmoid = torch.nn.LogSigmoid() def forward(self, input_0, input_1, input_2): arg0_1 = input_0 arg1_1 = input_1 arg2_1 = input_2 output = call([arg0_1, arg1_1, arg2_1]) return output[0]	HKUST-KnowComp/MLMET	PartialBCELoss	false	8,169	[ "MIT" ]	10	ae1188a929a5ca6a8e087bb091853b328ea2c7e7	https://github.com/HKUST-KnowComp/MLMET/tree/ae1188a929a5ca6a8e087bb091853b328ea2c7e7
Gaussian_Distance	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/cc/cccj5t34lj3k5oiecwqlh7evwud5hb2jk74m4sjau2e6pfd537gr.py # Topologically Sorted Source Nodes: [sub, pow_1, add, div, mul, exp_2, add_1, sqrt, div_1, vaa, sub_2, pow_3, add_4, div_4, mul_2, exp_4, add_5, sqrt_2, div_5, vbb, add_6, sub_1, pow_2, add_2, div_2, mul_1, exp_3, add_3, sqrt_1, div_3, vab, mul_3, loss], Original ATen: [aten.sub, aten.pow, aten.add, aten.div, aten.mul, aten.exp, aten.sqrt, aten.sum] # Source node to ATen node mapping: # add => add # add_1 => add_1 # add_2 => add_2 # add_3 => add_3 # add_4 => add_4 # add_5 => add_5 # add_6 => add_6 # div => div_2 # div_1 => div_3 # div_2 => div_4 # div_3 => div_5 # div_4 => div_6 # div_5 => div_7 # exp_2 => exp_2 # exp_3 => exp_3 # exp_4 => exp_4 # loss => sub_3 # mul => mul # mul_1 => mul_1 # mul_2 => mul_2 # mul_3 => mul_3 # pow_1 => pow_1 # pow_2 => pow_2 # pow_3 => pow_3 # sqrt => sqrt # sqrt_1 => sqrt_1 # sqrt_2 => sqrt_2 # sub => sub # sub_1 => sub_1 # sub_2 => sub_2 # vaa => sum_1 # vab => sum_2 # vbb => sum_3 # Graph fragment: # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %view_1), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %view_3), kwargs = {}) # %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%pow_1, %add), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_2, -0.5), kwargs = {}) # %exp_2 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul,), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %view_3), kwargs = {}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_1,), kwargs = {}) # %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_2, %sqrt), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%div_3,), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_4, %view_5), kwargs = {}) # %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {}) # %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_6, %view_7), kwargs = {}) # %div_6 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%pow_3, %add_4), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_6, -0.5), kwargs = {}) # %exp_4 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_2,), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_6, %view_7), kwargs = {}) # %sqrt_2 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_5,), kwargs = {}) # %div_7 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_4, %sqrt_2), kwargs = {}) # %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%div_7,), kwargs = {}) # %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_3), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %view_5), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %view_7), kwargs = {}) # %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%pow_2, %add_2), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_4, -0.5), kwargs = {}) # %exp_3 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_1,), kwargs = {}) # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %view_7), kwargs = {}) # %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_3,), kwargs = {}) # %div_5 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_3, %sqrt_1), kwargs = {}) # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%div_5,), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 2.0), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_6, %mul_3), kwargs = {}) triton_per_fused_add_div_exp_mul_pow_sqrt_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_exp_mul_pow_sqrt_sub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 1024], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_exp_mul_pow_sqrt_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 8, 'num_reduction': 3, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_div_exp_mul_pow_sqrt_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 1024 RBLOCK: tl.constexpr = 1024 xoffset = tl.program_id(0) XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex % 64 r2 = (rindex // 256) r3 = rindex % 256 tmp0 = tl.load(in_ptr0 + (r0 + (64r2)), None, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (r3), None, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (r0 + (64r2)), None, eviction_policy='evict_last') tmp11 = tl.load(in_ptr1 + (r3), None, eviction_policy='evict_last') tmp25 = tl.load(in_ptr2 + (r0 + (64r2)), None, eviction_policy='evict_last') tmp27 = tl.load(in_ptr2 + (r3), None, eviction_policy='evict_last') tmp31 = tl.load(in_ptr3 + (r0 + (64r2)), None, eviction_policy='evict_last') tmp35 = tl.load(in_ptr3 + (r3), None, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp5 = tmp2 - tmp4 tmp6 = tmp5 * tmp5 tmp8 = tl_math.exp(tmp7) tmp9 = tmp8 * tmp1 tmp10 = tmp9 * tmp1 tmp12 = tl_math.exp(tmp11) tmp13 = tmp12 * tmp1 tmp14 = tmp13 * tmp1 tmp15 = tmp10 + tmp14 tmp16 = tmp6 / tmp15 tmp17 = -0.5 tmp18 = tmp16 * tmp17 tmp19 = tl_math.exp(tmp18) tmp20 = libdevice.sqrt(tmp15) tmp21 = tmp19 / tmp20 tmp22 = tl.broadcast_to(tmp21, [RBLOCK]) tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0)) tmp26 = tmp25 * tmp1 tmp28 = tmp27 * tmp1 tmp29 = tmp26 - tmp28 tmp30 = tmp29 * tmp29 tmp32 = tl_math.exp(tmp31) tmp33 = tmp32 * tmp1 tmp34 = tmp33 * tmp1 tmp36 = tl_math.exp(tmp35) tmp37 = tmp36 * tmp1 tmp38 = tmp37 * tmp1 tmp39 = tmp34 + tmp38 tmp40 = tmp30 / tmp39 tmp41 = tmp40 * tmp17 tmp42 = tl_math.exp(tmp41) tmp43 = libdevice.sqrt(tmp39) tmp44 = tmp42 / tmp43 tmp45 = tl.broadcast_to(tmp44, [RBLOCK]) tmp47 = triton_helpers.promote_to_tensor(tl.sum(tmp45, 0)) tmp48 = tmp2 - tmp28 tmp49 = tmp48 * tmp48 tmp50 = tmp10 + tmp38 tmp51 = tmp49 / tmp50 tmp52 = tmp51 * tmp17 tmp53 = tl_math.exp(tmp52) tmp54 = libdevice.sqrt(tmp50) tmp55 = tmp53 / tmp54 tmp56 = tl.broadcast_to(tmp55, [RBLOCK]) tmp58 = triton_helpers.promote_to_tensor(tl.sum(tmp56, 0)) tmp59 = tmp24 + tmp47 tmp60 = 2.0 tmp61 = tmp58 * tmp60 tmp62 = tmp59 - tmp61 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp62, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1, arg3_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf3 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [sub, pow_1, add, div, mul, exp_2, add_1, sqrt, div_1, vaa, sub_2, pow_3, add_4, div_4, mul_2, exp_4, add_5, sqrt_2, div_5, vbb, add_6, sub_1, pow_2, add_2, div_2, mul_1, exp_3, add_3, sqrt_1, div_3, vab, mul_3, loss], Original ATen: [aten.sub, aten.pow, aten.add, aten.div, aten.mul, aten.exp, aten.sqrt, aten.sum] stream0 = get_raw_stream(0) triton_per_fused_add_div_exp_mul_pow_sqrt_sub_sum_0.run(buf3, arg0_1, arg2_1, arg1_1, arg3_1, 1, 1024, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 del arg2_1 del arg3_1 return (buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn class Gaussian_Distance(nn.Module): def __init__(self, kern=1): super(Gaussian_Distance, self).__init__() self.kern = kern self.avgpool = nn.AvgPool2d(kernel_size=kern, stride=kern) def forward(self, mu_a, logvar_a, mu_b, logvar_b): mu_a = self.avgpool(mu_a) mu_b = self.avgpool(mu_b) var_a = self.avgpool(torch.exp(logvar_a)) / (self.kern * self.kern) var_b = self.avgpool(torch.exp(logvar_b)) / (self.kern * self.kern) mu_a1 = mu_a.view(mu_a.size(0), 1, -1) mu_a2 = mu_a.view(1, mu_a.size(0), -1) var_a1 = var_a.view(var_a.size(0), 1, -1) var_a2 = var_a.view(1, var_a.size(0), -1) mu_b1 = mu_b.view(mu_b.size(0), 1, -1) mu_b2 = mu_b.view(1, mu_b.size(0), -1) var_b1 = var_b.view(var_b.size(0), 1, -1) var_b2 = var_b.view(1, var_b.size(0), -1) vaa = torch.sum(torch.div(torch.exp(torch.mul(torch.div(torch.pow( mu_a1 - mu_a2, 2), var_a1 + var_a2), -0.5)), torch.sqrt(var_a1 + var_a2))) vab = torch.sum(torch.div(torch.exp(torch.mul(torch.div(torch.pow( mu_a1 - mu_b2, 2), var_a1 + var_b2), -0.5)), torch.sqrt(var_a1 + var_b2))) vbb = torch.sum(torch.div(torch.exp(torch.mul(torch.div(torch.pow( mu_b1 - mu_b2, 2), var_b1 + var_b2), -0.5)), torch.sqrt(var_b1 + var_b2))) loss = vaa + vbb - torch.mul(vab, 2.0) return loss def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_div_exp_mul_pow_sqrt_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 1024 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex % 64 r2 = rindex // 256 r3 = rindex % 256 tmp0 = tl.load(in_ptr0 + (r0 + 64 * r2), None, eviction_policy='evict_last' ) tmp3 = tl.load(in_ptr0 + r3, None, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (r0 + 64 * r2), None, eviction_policy='evict_last' ) tmp11 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last') tmp25 = tl.load(in_ptr2 + (r0 + 64 * r2), None, eviction_policy= 'evict_last') tmp27 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last') tmp31 = tl.load(in_ptr3 + (r0 + 64 * r2), None, eviction_policy= 'evict_last') tmp35 = tl.load(in_ptr3 + r3, None, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp5 = tmp2 - tmp4 tmp6 = tmp5 * tmp5 tmp8 = tl_math.exp(tmp7) tmp9 = tmp8 * tmp1 tmp10 = tmp9 * tmp1 tmp12 = tl_math.exp(tmp11) tmp13 = tmp12 * tmp1 tmp14 = tmp13 * tmp1 tmp15 = tmp10 + tmp14 tmp16 = tmp6 / tmp15 tmp17 = -0.5 tmp18 = tmp16 * tmp17 tmp19 = tl_math.exp(tmp18) tmp20 = libdevice.sqrt(tmp15) tmp21 = tmp19 / tmp20 tmp22 = tl.broadcast_to(tmp21, [RBLOCK]) tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0)) tmp26 = tmp25 * tmp1 tmp28 = tmp27 * tmp1 tmp29 = tmp26 - tmp28 tmp30 = tmp29 * tmp29 tmp32 = tl_math.exp(tmp31) tmp33 = tmp32 * tmp1 tmp34 = tmp33 * tmp1 tmp36 = tl_math.exp(tmp35) tmp37 = tmp36 * tmp1 tmp38 = tmp37 * tmp1 tmp39 = tmp34 + tmp38 tmp40 = tmp30 / tmp39 tmp41 = tmp40 * tmp17 tmp42 = tl_math.exp(tmp41) tmp43 = libdevice.sqrt(tmp39) tmp44 = tmp42 / tmp43 tmp45 = tl.broadcast_to(tmp44, [RBLOCK]) tmp47 = triton_helpers.promote_to_tensor(tl.sum(tmp45, 0)) tmp48 = tmp2 - tmp28 tmp49 = tmp48 * tmp48 tmp50 = tmp10 + tmp38 tmp51 = tmp49 / tmp50 tmp52 = tmp51 * tmp17 tmp53 = tl_math.exp(tmp52) tmp54 = libdevice.sqrt(tmp50) tmp55 = tmp53 / tmp54 tmp56 = tl.broadcast_to(tmp55, [RBLOCK]) tmp58 = triton_helpers.promote_to_tensor(tl.sum(tmp56, 0)) tmp59 = tmp24 + tmp47 tmp60 = 2.0 tmp61 = tmp58 * tmp60 tmp62 = tmp59 - tmp61 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp62, None) def call(args): arg0_1, arg1_1, arg2_1, arg3_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf3 = buf0 del buf0 get_raw_stream(0) triton_per_fused_add_div_exp_mul_pow_sqrt_sub_sum_0[grid(1)](buf3, arg0_1, arg2_1, arg1_1, arg3_1, 1, 1024, num_warps=8, num_stages=1) del arg0_1 del arg1_1 del arg2_1 del arg3_1 return buf3, class Gaussian_DistanceNew(nn.Module): def __init__(self, kern=1): super(Gaussian_DistanceNew, self).__init__() self.kern = kern self.avgpool = nn.AvgPool2d(kernel_size=kern, stride=kern) def forward(self, input_0, input_1, input_2, input_3): arg0_1 = input_0 arg1_1 = input_1 arg2_1 = input_2 arg3_1 = input_3 output = call([arg0_1, arg1_1, arg2_1, arg3_1]) return output[0]	FupingWu90/VarDA	Gaussian_Distance	false	8,170	[ "MIT" ]	14	cfea269a4f608128bb5b13a778619b17d7123bfa	https://github.com/FupingWu90/VarDA/tree/cfea269a4f608128bb5b13a778619b17d7123bfa
EstimationLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ez/cezdou3azfin5ilh3kwd4i2vvucikzhtsu4gqcobojprjh22un3o.py # Topologically Sorted Source Nodes: [pow_1, sub, sub_1, add, log, mul, mul_1, temp1, sub_2, pow_2, add_1, log_1, mul_2, mul_3, temp2, temp, mean], Original ATen: [aten.pow, aten.rsub, aten.add, aten.log, aten.mul, aten.neg, aten.mean] # Source node to ATen node mapping: # add => add # add_1 => add_1 # log => log # log_1 => log_1 # mean => mean # mul => mul # mul_1 => mul_1 # mul_2 => mul_2 # mul_3 => mul_3 # pow_1 => pow_1 # pow_2 => pow_2 # sub => sub # sub_1 => sub_1 # sub_2 => sub_2 # temp => add_2 # temp1 => neg # temp2 => neg_1 # Graph fragment: # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 0), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, 1e-06), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %log), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %mul), kwargs = {}) # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mul_1,), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 0), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1e-06), kwargs = {}) # %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %log_1), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %mul_2), kwargs = {}) # %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mul_3,), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %neg_1), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add_2, [0, 1]), kwargs = {}) triton_per_fused_add_log_mean_mul_neg_pow_rsub_0 = async_compile.triton('triton_per_fused_add_log_mean_mul_neg_pow_rsub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[16, 16], reduction_hint=ReductionHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_log_mean_mul_neg_pow_rsub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_log_mean_mul_neg_pow_rsub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 16 rnumel = 16 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16r1)), xmask, other=0.0) tmp1 = tl.load(in_ptr1 + (x0 + (16r1)), xmask, other=0.0) tmp2 = 1.0 tmp3 = tmp2 - tmp1 tmp4 = tmp2 - tmp0 tmp5 = 1e-06 tmp6 = tmp4 + tmp5 tmp7 = tl_math.log(tmp6) tmp8 = tmp3 * tmp7 tmp9 = tmp2 * tmp8 tmp10 = -tmp9 tmp11 = tmp0 + tmp5 tmp12 = tl_math.log(tmp11) tmp13 = tmp1 * tmp12 tmp14 = tmp2 * tmp13 tmp15 = -tmp14 tmp16 = tmp10 + tmp15 tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK]) tmp19 = tl.where(xmask, tmp17, 0) tmp20 = tl.sum(tmp19, 1)[:, None] tl.store(out_ptr0 + (x0), tmp20, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/jv/cjv34y65ewsws7xrm7m2itmkec345non5jnfp6izk4nmjaytqnqz.py # Topologically Sorted Source Nodes: [mul_4, intersection_positive, add_4, abs_1, abs_2, add_3, cardinality_positive, add_5, dice_positive, sub_3, sub_4, mul_5, intersection_negative, add_6, abs_3, sub_5, abs_4, sub_6, cardinality_negative, add_7, dice_negative], Original ATen: [aten.mul, aten.sum, aten.add, aten.abs, aten.div, aten.rsub, aten.sub] # Source node to ATen node mapping: # abs_1 => abs_1 # abs_2 => abs_2 # abs_3 => abs_3 # abs_4 => abs_4 # add_3 => add_3 # add_4 => add_4 # add_5 => add_5 # add_6 => add_6 # add_7 => add_7 # cardinality_negative => sum_5 # cardinality_positive => sum_3 # dice_negative => div_1 # dice_positive => div # intersection_negative => sum_4 # intersection_positive => sum_2 # mul_4 => mul_4 # mul_5 => mul_5 # sub_3 => sub_3 # sub_4 => sub_4 # sub_5 => sub_5 # sub_6 => sub_6 # Graph fragment: # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %arg1_1), kwargs = {}) # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_4, [1]), kwargs = {}) # %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, 1e-06), kwargs = {}) # %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {}) # %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg1_1,), kwargs = {}) # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%abs_1, %abs_2), kwargs = {}) # %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_3, [1]), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_3, 1e-06), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_4, %add_5), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg0_1), kwargs = {}) # %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg1_1), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %sub_4), kwargs = {}) # %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_5, [1]), kwargs = {}) # %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_4, 1e-06), kwargs = {}) # %abs_3 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {}) # %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (2, %abs_3), kwargs = {}) # %abs_4 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg1_1,), kwargs = {}) # %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_5, %abs_4), kwargs = {}) # %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_6, [1]), kwargs = {}) # %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_5, 1e-06), kwargs = {}) # %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_6, %add_7), kwargs = {}) triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1 = async_compile.triton('triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 16 x1 = (xindex // 16) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (64x1)), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (64x1)), xmask) tmp3 = tl.load(in_ptr0 + (16 + x0 + (64x1)), xmask) tmp4 = tl.load(in_ptr1 + (16 + x0 + (64x1)), xmask) tmp7 = tl.load(in_ptr0 + (32 + x0 + (64x1)), xmask) tmp8 = tl.load(in_ptr1 + (32 + x0 + (64x1)), xmask) tmp11 = tl.load(in_ptr0 + (48 + x0 + (64x1)), xmask) tmp12 = tl.load(in_ptr1 + (48 + x0 + (64x1)), xmask) tmp2 = tmp0 * tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tmp15 = 1e-06 tmp16 = tmp14 + tmp15 tmp17 = tl_math.abs(tmp0) tmp18 = tl_math.abs(tmp1) tmp19 = tmp17 + tmp18 tmp20 = tl_math.abs(tmp3) tmp21 = tl_math.abs(tmp4) tmp22 = tmp20 + tmp21 tmp23 = tmp19 + tmp22 tmp24 = tl_math.abs(tmp7) tmp25 = tl_math.abs(tmp8) tmp26 = tmp24 + tmp25 tmp27 = tmp23 + tmp26 tmp28 = tl_math.abs(tmp11) tmp29 = tl_math.abs(tmp12) tmp30 = tmp28 + tmp29 tmp31 = tmp27 + tmp30 tmp32 = tmp31 + tmp15 tmp33 = tmp16 / tmp32 tmp34 = 1.0 tmp35 = tmp34 - tmp0 tmp36 = tmp34 - tmp1 tmp37 = tmp35 * tmp36 tmp38 = tmp34 - tmp3 tmp39 = tmp34 - tmp4 tmp40 = tmp38 * tmp39 tmp41 = tmp37 + tmp40 tmp42 = tmp34 - tmp7 tmp43 = tmp34 - tmp8 tmp44 = tmp42 * tmp43 tmp45 = tmp41 + tmp44 tmp46 = tmp34 - tmp11 tmp47 = tmp34 - tmp12 tmp48 = tmp46 * tmp47 tmp49 = tmp45 + tmp48 tmp50 = tmp49 + tmp15 tmp51 = 2.0 tmp52 = tmp51 - tmp17 tmp53 = tmp52 - tmp18 tmp54 = tmp51 - tmp20 tmp55 = tmp54 - tmp21 tmp56 = tmp53 + tmp55 tmp57 = tmp51 - tmp24 tmp58 = tmp57 - tmp25 tmp59 = tmp56 + tmp58 tmp60 = tmp51 - tmp28 tmp61 = tmp60 - tmp29 tmp62 = tmp59 + tmp61 tmp63 = tmp62 + tmp15 tmp64 = tmp50 / tmp63 tl.store(out_ptr0 + (x2), tmp33, xmask) tl.store(out_ptr1 + (x2), tmp64, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/kz/ckzgxfstujogkk2b75muv7bj4vqqcpjzmorl3pj6ssqz2mkhtyqe.py # Topologically Sorted Source Nodes: [pow_1, sub, sub_1, add, log, mul, mul_1, temp1, sub_2, pow_2, add_1, log_1, mul_2, mul_3, temp2, temp, mean, CELoss, sub_7, sub_8, temp3, DICELoss, mul_6, add_8], Original ATen: [aten.pow, aten.rsub, aten.add, aten.log, aten.mul, aten.neg, aten.mean, aten.sum, aten.sub] # Source node to ATen node mapping: # CELoss => sum_1 # DICELoss => sum_6 # add => add # add_1 => add_1 # add_8 => add_8 # log => log # log_1 => log_1 # mean => mean # mul => mul # mul_1 => mul_1 # mul_2 => mul_2 # mul_3 => mul_3 # mul_6 => mul_6 # pow_1 => pow_1 # pow_2 => pow_2 # sub => sub # sub_1 => sub_1 # sub_2 => sub_2 # sub_7 => sub_7 # sub_8 => sub_8 # temp => add_2 # temp1 => neg # temp2 => neg_1 # temp3 => mean_1 # Graph fragment: # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 0), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, 1e-06), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %log), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %mul), kwargs = {}) # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mul_1,), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 0), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1e-06), kwargs = {}) # %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %log_1), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %mul_2), kwargs = {}) # %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mul_3,), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %neg_1), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add_2, [0, 1]), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mean,), kwargs = {}) # %sub_7 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.5, %div), kwargs = {}) # %sub_8 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_7, %div_1), kwargs = {}) # %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%sub_8, [0]), kwargs = {}) # %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mean_1,), kwargs = {}) # %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_6, 1.0), kwargs = {}) # %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %mul_6), kwargs = {}) triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2 = async_compile.triton('triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 16], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 1 rnumel = 16 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp6 = tl.load(in_ptr1 + (r0), None) tmp9 = tl.load(in_ptr2 + (r0), None) tmp11 = tl.load(in_ptr1 + (16 + r0), None) tmp13 = tl.load(in_ptr2 + (16 + r0), None) tmp16 = tl.load(in_ptr1 + (32 + r0), None) tmp18 = tl.load(in_ptr2 + (32 + r0), None) tmp21 = tl.load(in_ptr1 + (48 + r0), None) tmp23 = tl.load(in_ptr2 + (48 + r0), None) tmp1 = 16.0 tmp2 = tmp0 / tmp1 tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tmp5 = tl.sum(tmp3, 1)[:, None] tmp7 = 1.5 tmp8 = tmp7 - tmp6 tmp10 = tmp8 - tmp9 tmp12 = tmp7 - tmp11 tmp14 = tmp12 - tmp13 tmp15 = tmp10 + tmp14 tmp17 = tmp7 - tmp16 tmp19 = tmp17 - tmp18 tmp20 = tmp15 + tmp19 tmp22 = tmp7 - tmp21 tmp24 = tmp22 - tmp23 tmp25 = tmp20 + tmp24 tmp26 = 4.0 tmp27 = tmp25 / tmp26 tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK]) tmp30 = tl.sum(tmp28, 1)[:, None] tmp31 = 1.0 tmp32 = tmp30 * tmp31 tmp33 = tmp5 + tmp32 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp33, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [pow_1, sub, sub_1, add, log, mul, mul_1, temp1, sub_2, pow_2, add_1, log_1, mul_2, mul_3, temp2, temp, mean], Original ATen: [aten.pow, aten.rsub, aten.add, aten.log, aten.mul, aten.neg, aten.mean] stream0 = get_raw_stream(0) triton_per_fused_add_log_mean_mul_neg_pow_rsub_0.run(arg0_1, arg1_1, buf0, 16, 16, grid=grid(16), stream=stream0) buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mul_4, intersection_positive, add_4, abs_1, abs_2, add_3, cardinality_positive, add_5, dice_positive, sub_3, sub_4, mul_5, intersection_negative, add_6, abs_3, sub_5, abs_4, sub_6, cardinality_negative, add_7, dice_negative], Original ATen: [aten.mul, aten.sum, aten.add, aten.abs, aten.div, aten.rsub, aten.sub] triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1.run(arg0_1, arg1_1, buf2, buf3, 64, grid=grid(64), stream=stream0) del arg0_1 del arg1_1 buf1 = empty_strided_cuda((), (), torch.float32) buf5 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [pow_1, sub, sub_1, add, log, mul, mul_1, temp1, sub_2, pow_2, add_1, log_1, mul_2, mul_3, temp2, temp, mean, CELoss, sub_7, sub_8, temp3, DICELoss, mul_6, add_8], Original ATen: [aten.pow, aten.rsub, aten.add, aten.log, aten.mul, aten.neg, aten.mean, aten.sum, aten.sub] triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2.run(buf5, buf0, buf2, buf3, 1, 16, grid=grid(1), stream=stream0) del buf0 del buf2 del buf3 return (buf5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class EstimationLoss(nn.Module): def __init__(self): super(EstimationLoss, self).__init__() self.gamma = 0 self.alpha = 0 def forward(self, pred, target): temp1 = -torch.mul(pred self.gamma, torch.mul(1 - target, torch. log(1 - pred + 1e-06))) temp2 = -torch.mul((1 - pred) self.gamma, torch.mul(target, torch.log(pred + 1e-06))) temp = temp1 + temp2 CELoss = torch.sum(torch.mean(temp, (0, 1))) intersection_positive = torch.sum(pred * target, 1) cardinality_positive = torch.sum(torch.abs(pred) + torch.abs(target), 1 ) dice_positive = (intersection_positive + 1e-06) / (cardinality_positive + 1e-06) intersection_negative = torch.sum((1.0 - pred) * (1.0 - target), 1) cardinality_negative = torch.sum(2 - torch.abs(pred) - torch.abs( target), 1) dice_negative = (intersection_negative + 1e-06) / (cardinality_negative + 1e-06) temp3 = torch.mean(1.5 - dice_positive - dice_negative, 0) DICELoss = torch.sum(temp3) return CELoss + 1.0 * DICELoss def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_log_mean_mul_neg_pow_rsub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 16 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * r1), xmask, other=0.0) tmp1 = tl.load(in_ptr1 + (x0 + 16 * r1), xmask, other=0.0) tmp2 = 1.0 tmp3 = tmp2 - tmp1 tmp4 = tmp2 - tmp0 tmp5 = 1e-06 tmp6 = tmp4 + tmp5 tmp7 = tl_math.log(tmp6) tmp8 = tmp3 * tmp7 tmp9 = tmp2 * tmp8 tmp10 = -tmp9 tmp11 = tmp0 + tmp5 tmp12 = tl_math.log(tmp11) tmp13 = tmp1 * tmp12 tmp14 = tmp2 * tmp13 tmp15 = -tmp14 tmp16 = tmp10 + tmp15 tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK]) tmp19 = tl.where(xmask, tmp17, 0) tmp20 = tl.sum(tmp19, 1)[:, None] tl.store(out_ptr0 + x0, tmp20, xmask) @triton.jit def triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 16 x1 = xindex // 16 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask) tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask) tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask) tmp4 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask) tmp7 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask) tmp8 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask) tmp11 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask) tmp12 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask) tmp2 = tmp0 * tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tmp15 = 1e-06 tmp16 = tmp14 + tmp15 tmp17 = tl_math.abs(tmp0) tmp18 = tl_math.abs(tmp1) tmp19 = tmp17 + tmp18 tmp20 = tl_math.abs(tmp3) tmp21 = tl_math.abs(tmp4) tmp22 = tmp20 + tmp21 tmp23 = tmp19 + tmp22 tmp24 = tl_math.abs(tmp7) tmp25 = tl_math.abs(tmp8) tmp26 = tmp24 + tmp25 tmp27 = tmp23 + tmp26 tmp28 = tl_math.abs(tmp11) tmp29 = tl_math.abs(tmp12) tmp30 = tmp28 + tmp29 tmp31 = tmp27 + tmp30 tmp32 = tmp31 + tmp15 tmp33 = tmp16 / tmp32 tmp34 = 1.0 tmp35 = tmp34 - tmp0 tmp36 = tmp34 - tmp1 tmp37 = tmp35 * tmp36 tmp38 = tmp34 - tmp3 tmp39 = tmp34 - tmp4 tmp40 = tmp38 * tmp39 tmp41 = tmp37 + tmp40 tmp42 = tmp34 - tmp7 tmp43 = tmp34 - tmp8 tmp44 = tmp42 * tmp43 tmp45 = tmp41 + tmp44 tmp46 = tmp34 - tmp11 tmp47 = tmp34 - tmp12 tmp48 = tmp46 * tmp47 tmp49 = tmp45 + tmp48 tmp50 = tmp49 + tmp15 tmp51 = 2.0 tmp52 = tmp51 - tmp17 tmp53 = tmp52 - tmp18 tmp54 = tmp51 - tmp20 tmp55 = tmp54 - tmp21 tmp56 = tmp53 + tmp55 tmp57 = tmp51 - tmp24 tmp58 = tmp57 - tmp25 tmp59 = tmp56 + tmp58 tmp60 = tmp51 - tmp28 tmp61 = tmp60 - tmp29 tmp62 = tmp59 + tmp61 tmp63 = tmp62 + tmp15 tmp64 = tmp50 / tmp63 tl.store(out_ptr0 + x2, tmp33, xmask) tl.store(out_ptr1 + x2, tmp64, xmask) @triton.jit def triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp6 = tl.load(in_ptr1 + r0, None) tmp9 = tl.load(in_ptr2 + r0, None) tmp11 = tl.load(in_ptr1 + (16 + r0), None) tmp13 = tl.load(in_ptr2 + (16 + r0), None) tmp16 = tl.load(in_ptr1 + (32 + r0), None) tmp18 = tl.load(in_ptr2 + (32 + r0), None) tmp21 = tl.load(in_ptr1 + (48 + r0), None) tmp23 = tl.load(in_ptr2 + (48 + r0), None) tmp1 = 16.0 tmp2 = tmp0 / tmp1 tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tmp5 = tl.sum(tmp3, 1)[:, None] tmp7 = 1.5 tmp8 = tmp7 - tmp6 tmp10 = tmp8 - tmp9 tmp12 = tmp7 - tmp11 tmp14 = tmp12 - tmp13 tmp15 = tmp10 + tmp14 tmp17 = tmp7 - tmp16 tmp19 = tmp17 - tmp18 tmp20 = tmp15 + tmp19 tmp22 = tmp7 - tmp21 tmp24 = tmp22 - tmp23 tmp25 = tmp20 + tmp24 tmp26 = 4.0 tmp27 = tmp25 / tmp26 tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK]) tmp30 = tl.sum(tmp28, 1)[:, None] tmp31 = 1.0 tmp32 = tmp30 * tmp31 tmp33 = tmp5 + tmp32 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp33, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_per_fused_add_log_mean_mul_neg_pow_rsub_0[grid(16)](arg0_1, arg1_1, buf0, 16, 16, XBLOCK=1, num_warps=2, num_stages=1) buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1[grid(64)](arg0_1, arg1_1, buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1) del arg0_1 del arg1_1 buf1 = empty_strided_cuda((), (), torch.float32) buf5 = buf1 del buf1 triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2[grid(1)](buf5, buf0, buf2, buf3, 1, 16, XBLOCK=1, num_warps=2, num_stages=1) del buf0 del buf2 del buf3 return buf5, class EstimationLossNew(nn.Module): def __init__(self): super(EstimationLossNew, self).__init__() self.gamma = 0 self.alpha = 0 def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	Gorilla-Lab-SCUT/AffordanceNet	EstimationLoss	false	8,171	[ "MIT" ]	37	47c0c55a12f7e1429fd3e4a4bb781c4eec12803d	https://github.com/Gorilla-Lab-SCUT/AffordanceNet/tree/47c0c55a12f7e1429fd3e4a4bb781c4eec12803d
RRDB	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/mb/cmbojyq672q6azyet3zlhlikzyc4ptoqkybahsdk76pmj5qljz4n.py # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat => cat # Graph fragment: # %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %where], 1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 2304 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 36 x0 = xindex % 16 x2 = (xindex // 576) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16x1) + (64x2)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 36, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + (x0 + (16((-4) + x1)) + (512x2)), tmp6 & xmask, other=0.0) tmp10 = tl.load(in_ptr2 + ((-4) + x1), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp11 = tmp9 + tmp10 tmp12 = 0.0 tmp13 = tmp11 > tmp12 tmp14 = 0.2 tmp15 = tmp11 * tmp14 tmp16 = tl.where(tmp13, tmp11, tmp15) tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp6, tmp16, tmp17) tmp19 = tl.where(tmp4, tmp5, tmp18) tl.store(out_ptr0 + (x3), tmp19, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gc/cgcjnpvqduihzm57rfdaoxikl5vcmgt2sy7q7y666mevlzdqttlz.py # Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat_1 => cat_1 # Graph fragment: # %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %where, %where_1], 1), kwargs = {}) triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4352 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 68 x0 = xindex % 16 x2 = (xindex // 1088) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16x1) + (64x2)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 36, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + (16((-4) + x1)) + (512x2)), tmp9 & xmask, other=0.0) tmp11 = tl.load(in_ptr2 + ((-4) + x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0) tmp12 = tmp10 + tmp11 tmp13 = 0.0 tmp14 = tmp12 > tmp13 tmp15 = 0.2 tmp16 = tmp12 * tmp15 tmp17 = tl.where(tmp14, tmp12, tmp16) tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype) tmp19 = tl.where(tmp9, tmp17, tmp18) tmp20 = tmp0 >= tmp7 tmp21 = tl.full([1], 68, tl.int64) tmp22 = tmp0 < tmp21 tmp23 = tl.load(in_ptr3 + (x0 + (16((-36) + x1)) + (512x2)), tmp20 & xmask, other=0.0) tmp24 = tl.load(in_ptr4 + ((-36) + x1), tmp20 & xmask, eviction_policy='evict_last', other=0.0) tmp25 = tmp23 + tmp24 tmp26 = tmp25 > tmp13 tmp27 = tmp25 * tmp15 tmp28 = tl.where(tmp26, tmp25, tmp27) tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype) tmp30 = tl.where(tmp20, tmp28, tmp29) tmp31 = tl.where(tmp9, tmp19, tmp30) tmp32 = tl.where(tmp4, tmp5, tmp31) tl.store(out_ptr0 + (x3), tmp32, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/xj/cxjondky22j333xa5ah5xr5txd3rbewfdcgsofom2gjd2sy6c54x.py # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat_2 => cat_2 # Graph fragment: # %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %where, %where_1, %where_2], 1), kwargs = {}) triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 6400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 100 x0 = xindex % 16 x2 = (xindex // 1600) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16x1) + (64x2)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 36, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + (16((-4) + x1)) + (512x2)), tmp9 & xmask, other=0.0) tmp11 = tl.load(in_ptr2 + ((-4) + x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0) tmp12 = tmp10 + tmp11 tmp13 = 0.0 tmp14 = tmp12 > tmp13 tmp15 = 0.2 tmp16 = tmp12 * tmp15 tmp17 = tl.where(tmp14, tmp12, tmp16) tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype) tmp19 = tl.where(tmp9, tmp17, tmp18) tmp20 = tmp0 >= tmp7 tmp21 = tl.full([1], 68, tl.int64) tmp22 = tmp0 < tmp21 tmp23 = tmp20 & tmp22 tmp24 = tl.load(in_ptr3 + (x0 + (16((-36) + x1)) + (512x2)), tmp23 & xmask, other=0.0) tmp25 = tl.load(in_ptr4 + ((-36) + x1), tmp23 & xmask, eviction_policy='evict_last', other=0.0) tmp26 = tmp24 + tmp25 tmp27 = tmp26 > tmp13 tmp28 = tmp26 * tmp15 tmp29 = tl.where(tmp27, tmp26, tmp28) tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype) tmp31 = tl.where(tmp23, tmp29, tmp30) tmp32 = tmp0 >= tmp21 tmp33 = tl.full([1], 100, tl.int64) tmp34 = tmp0 < tmp33 tmp35 = tl.load(in_ptr5 + (x0 + (16((-68) + x1)) + (512x2)), tmp32 & xmask, other=0.0) tmp36 = tl.load(in_ptr6 + ((-68) + x1), tmp32 & xmask, eviction_policy='evict_last', other=0.0) tmp37 = tmp35 + tmp36 tmp38 = tmp37 > tmp13 tmp39 = tmp37 * tmp15 tmp40 = tl.where(tmp38, tmp37, tmp39) tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype) tmp42 = tl.where(tmp32, tmp40, tmp41) tmp43 = tl.where(tmp23, tmp31, tmp42) tmp44 = tl.where(tmp9, tmp19, tmp43) tmp45 = tl.where(tmp4, tmp5, tmp44) tl.store(out_ptr0 + (x3), tmp45, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gq/cgqy6n6iplpix6bd5zrklvka6x3imtmj6kqyplgfksznh7s57dpb.py # Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat_3 => cat_3 # Graph fragment: # %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %where, %where_1, %where_2, %where_3], 1), kwargs = {}) triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'fp32', 9: 'fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8448 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 132 x0 = xindex % 16 x2 = (xindex // 2112) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16x1) + (64x2)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 36, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + (16((-4) + x1)) + (512x2)), tmp9 & xmask, other=0.0) tmp11 = tl.load(in_ptr2 + ((-4) + x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0) tmp12 = tmp10 + tmp11 tmp13 = 0.0 tmp14 = tmp12 > tmp13 tmp15 = 0.2 tmp16 = tmp12 * tmp15 tmp17 = tl.where(tmp14, tmp12, tmp16) tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype) tmp19 = tl.where(tmp9, tmp17, tmp18) tmp20 = tmp0 >= tmp7 tmp21 = tl.full([1], 68, tl.int64) tmp22 = tmp0 < tmp21 tmp23 = tmp20 & tmp22 tmp24 = tl.load(in_ptr3 + (x0 + (16((-36) + x1)) + (512x2)), tmp23 & xmask, other=0.0) tmp25 = tl.load(in_ptr4 + ((-36) + x1), tmp23 & xmask, eviction_policy='evict_last', other=0.0) tmp26 = tmp24 + tmp25 tmp27 = tmp26 > tmp13 tmp28 = tmp26 * tmp15 tmp29 = tl.where(tmp27, tmp26, tmp28) tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype) tmp31 = tl.where(tmp23, tmp29, tmp30) tmp32 = tmp0 >= tmp21 tmp33 = tl.full([1], 100, tl.int64) tmp34 = tmp0 < tmp33 tmp35 = tmp32 & tmp34 tmp36 = tl.load(in_ptr5 + (x0 + (16((-68) + x1)) + (512x2)), tmp35 & xmask, other=0.0) tmp37 = tl.load(in_ptr6 + ((-68) + x1), tmp35 & xmask, eviction_policy='evict_last', other=0.0) tmp38 = tmp36 + tmp37 tmp39 = tmp38 > tmp13 tmp40 = tmp38 * tmp15 tmp41 = tl.where(tmp39, tmp38, tmp40) tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype) tmp43 = tl.where(tmp35, tmp41, tmp42) tmp44 = tmp0 >= tmp33 tmp45 = tl.full([1], 132, tl.int64) tmp46 = tmp0 < tmp45 tmp47 = tl.load(in_ptr7 + (x0 + (16((-100) + x1)) + (512x2)), tmp44 & xmask, other=0.0) tmp48 = tl.load(in_ptr8 + ((-100) + x1), tmp44 & xmask, eviction_policy='evict_last', other=0.0) tmp49 = tmp47 + tmp48 tmp50 = tmp49 > tmp13 tmp51 = tmp49 * tmp15 tmp52 = tl.where(tmp50, tmp49, tmp51) tmp53 = tl.full(tmp52.shape, 0.0, tmp52.dtype) tmp54 = tl.where(tmp44, tmp52, tmp53) tmp55 = tl.where(tmp35, tmp43, tmp54) tmp56 = tl.where(tmp23, tmp31, tmp55) tmp57 = tl.where(tmp9, tmp19, tmp56) tmp58 = tl.where(tmp4, tmp5, tmp57) tl.store(out_ptr0 + (x3), tmp58, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/h7/ch7sh2cdou4nxvdoxq3axaxa26zqy7k46oogec7rhlobuxvees5e.py # Topologically Sorted Source Nodes: [x5, mul, out], Original ATen: [aten.convolution, aten.mul, aten.add] # Source node to ATen node mapping: # mul => mul_4 # out => add # x5 => convolution_4 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_3, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_4, 0.2), kwargs = {}) # %add : [num_users=7] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %primals_3), kwargs = {}) triton_poi_fused_add_convolution_mul_4 = async_compile.triton('triton_poi_fused_add_convolution_mul_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_mul_4(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (x3), xmask) tmp2 = tmp0 + tmp1 tmp3 = 0.2 tmp4 = tmp2 * tmp3 tmp6 = tmp4 + tmp5 tl.store(in_out_ptr0 + (x3), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ec/ceccrz4wxoygm52iievslzcfslxqbdwkef4olj3fqqr4wo7knkek.py # Topologically Sorted Source Nodes: [x5_2, mul_2, out_2, mul_3, add_3], Original ATen: [aten.convolution, aten.mul, aten.add] # Source node to ATen node mapping: # add_3 => add_3 # mul_2 => mul_14 # mul_3 => mul_15 # out_2 => add_2 # x5_2 => convolution_14 # Graph fragment: # %convolution_14 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_11, %primals_30, %primals_31, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_14, 0.2), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_14, %add_1), kwargs = {}) # %mul_15 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 0.2), kwargs = {}) # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_15, %primals_3), kwargs = {}) triton_poi_fused_add_convolution_mul_5 = async_compile.triton('triton_poi_fused_add_convolution_mul_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_mul_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (x3), xmask) tmp8 = tl.load(in_ptr2 + (x3), xmask) tmp2 = tmp0 + tmp1 tmp3 = 0.2 tmp4 = tmp2 * tmp3 tmp6 = tmp4 + tmp5 tmp7 = tmp6 * tmp3 tmp9 = tmp7 + tmp8 tl.store(in_out_ptr0 + (x3), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/do/cdobohkqj4j4foru3eiz3y25d36l2jhmbgope5icgsecb6yfesls.py # Topologically Sorted Source Nodes: [conv2d_13, x4_2], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] # Source node to ATen node mapping: # conv2d_13 => convolution_13 # x4_2 => gt_11, mul_13, where_11 # Graph fragment: # %convolution_13 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_10, %primals_28, %primals_29, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %gt_11 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_13, 0), kwargs = {}) # %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_13, 0.2), kwargs = {}) # %where_11 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_11, %convolution_13, %mul_13), kwargs = {}) # %gt_12 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_11, 0), kwargs = {}) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 2048 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 32 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 0.2 tmp6 = tmp2 * tmp5 tmp7 = tl.where(tmp4, tmp2, tmp6) tmp8 = tmp7 > tmp3 tl.store(out_ptr0 + (x3), tmp8, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31 = args args.clear() assert_size_stride(primals_1, (32, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (32, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (32, 36, 3, 3), (324, 9, 3, 1)) assert_size_stride(primals_5, (32, ), (1, )) assert_size_stride(primals_6, (32, 68, 3, 3), (612, 9, 3, 1)) assert_size_stride(primals_7, (32, ), (1, )) assert_size_stride(primals_8, (32, 100, 3, 3), (900, 9, 3, 1)) assert_size_stride(primals_9, (32, ), (1, )) assert_size_stride(primals_10, (4, 132, 3, 3), (1188, 9, 3, 1)) assert_size_stride(primals_11, (4, ), (1, )) assert_size_stride(primals_12, (32, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_13, (32, ), (1, )) assert_size_stride(primals_14, (32, 36, 3, 3), (324, 9, 3, 1)) assert_size_stride(primals_15, (32, ), (1, )) assert_size_stride(primals_16, (32, 68, 3, 3), (612, 9, 3, 1)) assert_size_stride(primals_17, (32, ), (1, )) assert_size_stride(primals_18, (32, 100, 3, 3), (900, 9, 3, 1)) assert_size_stride(primals_19, (32, ), (1, )) assert_size_stride(primals_20, (4, 132, 3, 3), (1188, 9, 3, 1)) assert_size_stride(primals_21, (4, ), (1, )) assert_size_stride(primals_22, (32, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_23, (32, ), (1, )) assert_size_stride(primals_24, (32, 36, 3, 3), (324, 9, 3, 1)) assert_size_stride(primals_25, (32, ), (1, )) assert_size_stride(primals_26, (32, 68, 3, 3), (612, 9, 3, 1)) assert_size_stride(primals_27, (32, ), (1, )) assert_size_stride(primals_28, (32, 100, 3, 3), (900, 9, 3, 1)) assert_size_stride(primals_29, (32, ), (1, )) assert_size_stride(primals_30, (4, 132, 3, 3), (1188, 9, 3, 1)) assert_size_stride(primals_31, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 32, 4, 4), (512, 16, 4, 1)) buf1 = empty_strided_cuda((4, 36, 4, 4), (576, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(primals_3, buf0, primals_2, buf1, 2304, grid=grid(2304), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 32, 4, 4), (512, 16, 4, 1)) buf3 = empty_strided_cuda((4, 68, 4, 4), (1088, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat] triton_poi_fused_cat_1.run(primals_3, buf0, primals_2, buf2, primals_5, buf3, 4352, grid=grid(4352), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 32, 4, 4), (512, 16, 4, 1)) buf5 = empty_strided_cuda((4, 100, 4, 4), (1600, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat] triton_poi_fused_cat_2.run(primals_3, buf0, primals_2, buf2, primals_5, buf4, primals_7, buf5, 6400, grid=grid(6400), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 32, 4, 4), (512, 16, 4, 1)) buf7 = empty_strided_cuda((4, 132, 4, 4), (2112, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat] triton_poi_fused_cat_3.run(primals_3, buf0, primals_2, buf2, primals_5, buf4, primals_7, buf6, primals_9, buf7, 8448, grid=grid(8448), stream=stream0) # Topologically Sorted Source Nodes: [x5], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1)) buf9 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [x5, mul, out], Original ATen: [aten.convolution, aten.mul, aten.add] triton_poi_fused_add_convolution_mul_4.run(buf9, primals_11, primals_3, 256, grid=grid(256), stream=stream0) del primals_11 # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 32, 4, 4), (512, 16, 4, 1)) buf11 = empty_strided_cuda((4, 36, 4, 4), (576, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_4], Original ATen: [aten.cat] triton_poi_fused_cat_0.run(buf9, buf10, primals_13, buf11, 2304, grid=grid(2304), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution] buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 32, 4, 4), (512, 16, 4, 1)) buf13 = empty_strided_cuda((4, 68, 4, 4), (1088, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_5], Original ATen: [aten.cat] triton_poi_fused_cat_1.run(buf9, buf10, primals_13, buf12, primals_15, buf13, 4352, grid=grid(4352), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution] buf14 = extern_kernels.convolution(buf13, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 32, 4, 4), (512, 16, 4, 1)) buf15 = empty_strided_cuda((4, 100, 4, 4), (1600, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_6], Original ATen: [aten.cat] triton_poi_fused_cat_2.run(buf9, buf10, primals_13, buf12, primals_15, buf14, primals_17, buf15, 6400, grid=grid(6400), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution] buf16 = extern_kernels.convolution(buf15, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 32, 4, 4), (512, 16, 4, 1)) buf17 = empty_strided_cuda((4, 132, 4, 4), (2112, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_7], Original ATen: [aten.cat] triton_poi_fused_cat_3.run(buf9, buf10, primals_13, buf12, primals_15, buf14, primals_17, buf16, primals_19, buf17, 8448, grid=grid(8448), stream=stream0) # Topologically Sorted Source Nodes: [x5_1], Original ATen: [aten.convolution] buf18 = extern_kernels.convolution(buf17, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 4, 4, 4), (64, 16, 4, 1)) buf19 = buf18; del buf18 # reuse # Topologically Sorted Source Nodes: [x5_1, mul_1, out_1], Original ATen: [aten.convolution, aten.mul, aten.add] triton_poi_fused_add_convolution_mul_4.run(buf19, primals_21, buf9, 256, grid=grid(256), stream=stream0) del primals_21 # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution] buf20 = extern_kernels.convolution(buf19, primals_22, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 32, 4, 4), (512, 16, 4, 1)) buf21 = empty_strided_cuda((4, 36, 4, 4), (576, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_8], Original ATen: [aten.cat] triton_poi_fused_cat_0.run(buf19, buf20, primals_23, buf21, 2304, grid=grid(2304), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution] buf22 = extern_kernels.convolution(buf21, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf22, (4, 32, 4, 4), (512, 16, 4, 1)) buf23 = empty_strided_cuda((4, 68, 4, 4), (1088, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_9], Original ATen: [aten.cat] triton_poi_fused_cat_1.run(buf19, buf20, primals_23, buf22, primals_25, buf23, 4352, grid=grid(4352), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution] buf24 = extern_kernels.convolution(buf23, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 32, 4, 4), (512, 16, 4, 1)) buf25 = empty_strided_cuda((4, 100, 4, 4), (1600, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_10], Original ATen: [aten.cat] triton_poi_fused_cat_2.run(buf19, buf20, primals_23, buf22, primals_25, buf24, primals_27, buf25, 6400, grid=grid(6400), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution] buf26 = extern_kernels.convolution(buf25, primals_28, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf26, (4, 32, 4, 4), (512, 16, 4, 1)) buf27 = empty_strided_cuda((4, 132, 4, 4), (2112, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_11], Original ATen: [aten.cat] triton_poi_fused_cat_3.run(buf19, buf20, primals_23, buf22, primals_25, buf24, primals_27, buf26, primals_29, buf27, 8448, grid=grid(8448), stream=stream0) # Topologically Sorted Source Nodes: [x5_2], Original ATen: [aten.convolution] buf28 = extern_kernels.convolution(buf27, primals_30, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf28, (4, 4, 4, 4), (64, 16, 4, 1)) buf29 = buf28; del buf28 # reuse # Topologically Sorted Source Nodes: [x5_2, mul_2, out_2, mul_3, add_3], Original ATen: [aten.convolution, aten.mul, aten.add] triton_poi_fused_add_convolution_mul_5.run(buf29, primals_31, buf19, primals_3, 256, grid=grid(256), stream=stream0) del primals_31 buf30 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_13, x4_2], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf26, primals_29, buf30, 2048, grid=grid(2048), stream=stream0) del buf26 del primals_29 buf31 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_12, x3_2], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf24, primals_27, buf31, 2048, grid=grid(2048), stream=stream0) del buf24 del primals_27 buf32 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_11, x2_2], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf22, primals_25, buf32, 2048, grid=grid(2048), stream=stream0) del buf22 del primals_25 buf33 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_10, x1_2], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf20, primals_23, buf33, 2048, grid=grid(2048), stream=stream0) del buf20 del primals_23 buf34 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_8, x4_1], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf16, primals_19, buf34, 2048, grid=grid(2048), stream=stream0) del buf16 del primals_19 buf35 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_7, x3_1], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf14, primals_17, buf35, 2048, grid=grid(2048), stream=stream0) del buf14 del primals_17 buf36 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_6, x2_1], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf12, primals_15, buf36, 2048, grid=grid(2048), stream=stream0) del buf12 del primals_15 buf37 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_5, x1_1], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf10, primals_13, buf37, 2048, grid=grid(2048), stream=stream0) del buf10 del primals_13 buf38 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_3, x4], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf6, primals_9, buf38, 2048, grid=grid(2048), stream=stream0) del buf6 del primals_9 buf39 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_2, x3], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf4, primals_7, buf39, 2048, grid=grid(2048), stream=stream0) del buf4 del primals_7 buf40 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, x2], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf2, primals_5, buf40, 2048, grid=grid(2048), stream=stream0) del buf2 del primals_5 buf41 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d, x1], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward] triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6.run(buf0, primals_2, buf41, 2048, grid=grid(2048), stream=stream0) del buf0 del primals_2 return (buf29, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, primals_28, primals_30, buf1, buf3, buf5, buf7, buf9, buf11, buf13, buf15, buf17, buf19, buf21, buf23, buf25, buf27, buf30, buf31, buf32, buf33, buf34, buf35, buf36, buf37, buf38, buf39, buf40, buf41, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((32, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((32, 36, 3, 3), (324, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((32, 68, 3, 3), (612, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((32, 100, 3, 3), (900, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 132, 3, 3), (1188, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((32, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((32, 36, 3, 3), (324, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((32, 68, 3, 3), (612, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((32, 100, 3, 3), (900, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_19 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_20 = rand_strided((4, 132, 3, 3), (1188, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_21 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_22 = rand_strided((32, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_23 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_24 = rand_strided((32, 36, 3, 3), (324, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_25 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_26 = rand_strided((32, 68, 3, 3), (612, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_27 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_28 = rand_strided((32, 100, 3, 3), (900, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_29 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_30 = rand_strided((4, 132, 3, 3), (1188, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_31 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.utils.data from torch.utils import data as data import torch.nn as nn from torch.nn import init as init from torch.nn.modules.batchnorm import _BatchNorm from torchvision.models import vgg as vgg from torch import autograd as autograd @torch.no_grad() def default_init_weights(module_list, scale=1, bias_fill=0, kwargs): """Initialize network weights. Args: module_list (list[nn.Module] \| nn.Module): Modules to be initialized. scale (float): Scale initialized weights, especially for residual blocks. Default: 1. bias_fill (float): The value to fill bias. Default: 0 kwargs (dict): Other arguments for initialization function. """ if not isinstance(module_list, list): module_list = [module_list] for module in module_list: for m in module.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, kwargs) m.weight.data = scale if m.bias is not None: m.bias.data.fill_(bias_fill) elif isinstance(m, nn.Linear): init.kaiming_normal_(m.weight, kwargs) m.weight.data = scale if m.bias is not None: m.bias.data.fill_(bias_fill) elif isinstance(m, _BatchNorm): init.constant_(m.weight, 1) if m.bias is not None: m.bias.data.fill_(bias_fill) class ResidualDenseBlock(nn.Module): """Residual Dense Block. Used in RRDB block in ESRGAN. Args: num_feat (int): Channel number of intermediate features. num_grow_ch (int): Channels for each growth. """ def __init__(self, num_feat=64, num_grow_ch=32): super(ResidualDenseBlock, self).__init__() self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1 ) self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1 ) self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) default_init_weights([self.conv1, self.conv2, self.conv3, self. conv4, self.conv5], 0.1) def forward(self, x): x1 = self.lrelu(self.conv1(x)) x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) return x5 * 0.2 + x class RRDB(nn.Module): """Residual in Residual Dense Block. Used in RRDB-Net in ESRGAN. Args: num_feat (int): Channel number of intermediate features. num_grow_ch (int): Channels for each growth. """ def __init__(self, num_feat, num_grow_ch=32): super(RRDB, self).__init__() self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) def forward(self, x): out = self.rdb1(x) out = self.rdb2(out) out = self.rdb3(out) return out * 0.2 + x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_feat': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.utils.data from torch.utils import data as data import torch.nn as nn from torch.nn import init as init from torch.nn.modules.batchnorm import _BatchNorm from torchvision.models import vgg as vgg from torch import autograd as autograd assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 2304 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 36 x0 = xindex % 16 x2 = xindex // 576 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 36, tl.int64) tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 512 * x2), tmp6 & xmask, other=0.0) tmp10 = tl.load(in_ptr2 + (-4 + x1), tmp6 & xmask, eviction_policy= 'evict_last', other=0.0) tmp11 = tmp9 + tmp10 tmp12 = 0.0 tmp13 = tmp11 > tmp12 tmp14 = 0.2 tmp15 = tmp11 * tmp14 tmp16 = tl.where(tmp13, tmp11, tmp15) tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp6, tmp16, tmp17) tmp19 = tl.where(tmp4, tmp5, tmp18) tl.store(out_ptr0 + x3, tmp19, xmask) @triton.jit def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 4352 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 68 x0 = xindex % 16 x2 = xindex // 1088 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 36, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 512 * x2), tmp9 & xmask, other=0.0) tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy= 'evict_last', other=0.0) tmp12 = tmp10 + tmp11 tmp13 = 0.0 tmp14 = tmp12 > tmp13 tmp15 = 0.2 tmp16 = tmp12 * tmp15 tmp17 = tl.where(tmp14, tmp12, tmp16) tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype) tmp19 = tl.where(tmp9, tmp17, tmp18) tmp20 = tmp0 >= tmp7 tl.full([1], 68, tl.int64) tmp23 = tl.load(in_ptr3 + (x0 + 16 * (-36 + x1) + 512 * x2), tmp20 & xmask, other=0.0) tmp24 = tl.load(in_ptr4 + (-36 + x1), tmp20 & xmask, eviction_policy= 'evict_last', other=0.0) tmp25 = tmp23 + tmp24 tmp26 = tmp25 > tmp13 tmp27 = tmp25 * tmp15 tmp28 = tl.where(tmp26, tmp25, tmp27) tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype) tmp30 = tl.where(tmp20, tmp28, tmp29) tmp31 = tl.where(tmp9, tmp19, tmp30) tmp32 = tl.where(tmp4, tmp5, tmp31) tl.store(out_ptr0 + x3, tmp32, xmask) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 6400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 100 x0 = xindex % 16 x2 = xindex // 1600 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 36, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 512 * x2), tmp9 & xmask, other=0.0) tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy= 'evict_last', other=0.0) tmp12 = tmp10 + tmp11 tmp13 = 0.0 tmp14 = tmp12 > tmp13 tmp15 = 0.2 tmp16 = tmp12 * tmp15 tmp17 = tl.where(tmp14, tmp12, tmp16) tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype) tmp19 = tl.where(tmp9, tmp17, tmp18) tmp20 = tmp0 >= tmp7 tmp21 = tl.full([1], 68, tl.int64) tmp22 = tmp0 < tmp21 tmp23 = tmp20 & tmp22 tmp24 = tl.load(in_ptr3 + (x0 + 16 * (-36 + x1) + 512 * x2), tmp23 & xmask, other=0.0) tmp25 = tl.load(in_ptr4 + (-36 + x1), tmp23 & xmask, eviction_policy= 'evict_last', other=0.0) tmp26 = tmp24 + tmp25 tmp27 = tmp26 > tmp13 tmp28 = tmp26 * tmp15 tmp29 = tl.where(tmp27, tmp26, tmp28) tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype) tmp31 = tl.where(tmp23, tmp29, tmp30) tmp32 = tmp0 >= tmp21 tl.full([1], 100, tl.int64) tmp35 = tl.load(in_ptr5 + (x0 + 16 * (-68 + x1) + 512 * x2), tmp32 & xmask, other=0.0) tmp36 = tl.load(in_ptr6 + (-68 + x1), tmp32 & xmask, eviction_policy= 'evict_last', other=0.0) tmp37 = tmp35 + tmp36 tmp38 = tmp37 > tmp13 tmp39 = tmp37 * tmp15 tmp40 = tl.where(tmp38, tmp37, tmp39) tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype) tmp42 = tl.where(tmp32, tmp40, tmp41) tmp43 = tl.where(tmp23, tmp31, tmp42) tmp44 = tl.where(tmp9, tmp19, tmp43) tmp45 = tl.where(tmp4, tmp5, tmp44) tl.store(out_ptr0 + x3, tmp45, xmask) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 8448 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 132 x0 = xindex % 16 x2 = xindex // 2112 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 36, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 512 * x2), tmp9 & xmask, other=0.0) tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy= 'evict_last', other=0.0) tmp12 = tmp10 + tmp11 tmp13 = 0.0 tmp14 = tmp12 > tmp13 tmp15 = 0.2 tmp16 = tmp12 * tmp15 tmp17 = tl.where(tmp14, tmp12, tmp16) tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype) tmp19 = tl.where(tmp9, tmp17, tmp18) tmp20 = tmp0 >= tmp7 tmp21 = tl.full([1], 68, tl.int64) tmp22 = tmp0 < tmp21 tmp23 = tmp20 & tmp22 tmp24 = tl.load(in_ptr3 + (x0 + 16 * (-36 + x1) + 512 * x2), tmp23 & xmask, other=0.0) tmp25 = tl.load(in_ptr4 + (-36 + x1), tmp23 & xmask, eviction_policy= 'evict_last', other=0.0) tmp26 = tmp24 + tmp25 tmp27 = tmp26 > tmp13 tmp28 = tmp26 * tmp15 tmp29 = tl.where(tmp27, tmp26, tmp28) tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype) tmp31 = tl.where(tmp23, tmp29, tmp30) tmp32 = tmp0 >= tmp21 tmp33 = tl.full([1], 100, tl.int64) tmp34 = tmp0 < tmp33 tmp35 = tmp32 & tmp34 tmp36 = tl.load(in_ptr5 + (x0 + 16 * (-68 + x1) + 512 * x2), tmp35 & xmask, other=0.0) tmp37 = tl.load(in_ptr6 + (-68 + x1), tmp35 & xmask, eviction_policy= 'evict_last', other=0.0) tmp38 = tmp36 + tmp37 tmp39 = tmp38 > tmp13 tmp40 = tmp38 * tmp15 tmp41 = tl.where(tmp39, tmp38, tmp40) tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype) tmp43 = tl.where(tmp35, tmp41, tmp42) tmp44 = tmp0 >= tmp33 tl.full([1], 132, tl.int64) tmp47 = tl.load(in_ptr7 + (x0 + 16 * (-100 + x1) + 512 * x2), tmp44 & xmask, other=0.0) tmp48 = tl.load(in_ptr8 + (-100 + x1), tmp44 & xmask, eviction_policy= 'evict_last', other=0.0) tmp49 = tmp47 + tmp48 tmp50 = tmp49 > tmp13 tmp51 = tmp49 * tmp15 tmp52 = tl.where(tmp50, tmp49, tmp51) tmp53 = tl.full(tmp52.shape, 0.0, tmp52.dtype) tmp54 = tl.where(tmp44, tmp52, tmp53) tmp55 = tl.where(tmp35, tmp43, tmp54) tmp56 = tl.where(tmp23, tmp31, tmp55) tmp57 = tl.where(tmp9, tmp19, tmp56) tmp58 = tl.where(tmp4, tmp5, tmp57) tl.store(out_ptr0 + x3, tmp58, xmask) @triton.jit def triton_poi_fused_add_convolution_mul_4(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + x3, xmask) tmp2 = tmp0 + tmp1 tmp3 = 0.2 tmp4 = tmp2 * tmp3 tmp6 = tmp4 + tmp5 tl.store(in_out_ptr0 + x3, tmp6, xmask) @triton.jit def triton_poi_fused_add_convolution_mul_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + x3, xmask) tmp8 = tl.load(in_ptr2 + x3, xmask) tmp2 = tmp0 + tmp1 tmp3 = 0.2 tmp4 = tmp2 * tmp3 tmp6 = tmp4 + tmp5 tmp7 = tmp6 * tmp3 tmp9 = tmp7 + tmp8 tl.store(in_out_ptr0 + x3, tmp9, xmask) @triton.jit def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 32 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 0.2 tmp6 = tmp2 * tmp5 tmp7 = tl.where(tmp4, tmp2, tmp6) tmp8 = tmp7 > tmp3 tl.store(out_ptr0 + x3, tmp8, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31) = args args.clear() assert_size_stride(primals_1, (32, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (32,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (32, 36, 3, 3), (324, 9, 3, 1)) assert_size_stride(primals_5, (32,), (1,)) assert_size_stride(primals_6, (32, 68, 3, 3), (612, 9, 3, 1)) assert_size_stride(primals_7, (32,), (1,)) assert_size_stride(primals_8, (32, 100, 3, 3), (900, 9, 3, 1)) assert_size_stride(primals_9, (32,), (1,)) assert_size_stride(primals_10, (4, 132, 3, 3), (1188, 9, 3, 1)) assert_size_stride(primals_11, (4,), (1,)) assert_size_stride(primals_12, (32, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_13, (32,), (1,)) assert_size_stride(primals_14, (32, 36, 3, 3), (324, 9, 3, 1)) assert_size_stride(primals_15, (32,), (1,)) assert_size_stride(primals_16, (32, 68, 3, 3), (612, 9, 3, 1)) assert_size_stride(primals_17, (32,), (1,)) assert_size_stride(primals_18, (32, 100, 3, 3), (900, 9, 3, 1)) assert_size_stride(primals_19, (32,), (1,)) assert_size_stride(primals_20, (4, 132, 3, 3), (1188, 9, 3, 1)) assert_size_stride(primals_21, (4,), (1,)) assert_size_stride(primals_22, (32, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_23, (32,), (1,)) assert_size_stride(primals_24, (32, 36, 3, 3), (324, 9, 3, 1)) assert_size_stride(primals_25, (32,), (1,)) assert_size_stride(primals_26, (32, 68, 3, 3), (612, 9, 3, 1)) assert_size_stride(primals_27, (32,), (1,)) assert_size_stride(primals_28, (32, 100, 3, 3), (900, 9, 3, 1)) assert_size_stride(primals_29, (32,), (1,)) assert_size_stride(primals_30, (4, 132, 3, 3), (1188, 9, 3, 1)) assert_size_stride(primals_31, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 32, 4, 4), (512, 16, 4, 1)) buf1 = empty_strided_cuda((4, 36, 4, 4), (576, 16, 4, 1), torch.float32 ) get_raw_stream(0) triton_poi_fused_cat_0[grid(2304)](primals_3, buf0, primals_2, buf1, 2304, XBLOCK=128, num_warps=4, num_stages=1) buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 32, 4, 4), (512, 16, 4, 1)) buf3 = empty_strided_cuda((4, 68, 4, 4), (1088, 16, 4, 1), torch. float32) triton_poi_fused_cat_1[grid(4352)](primals_3, buf0, primals_2, buf2, primals_5, buf3, 4352, XBLOCK=256, num_warps=4, num_stages=1) buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 32, 4, 4), (512, 16, 4, 1)) buf5 = empty_strided_cuda((4, 100, 4, 4), (1600, 16, 4, 1), torch. float32) triton_poi_fused_cat_2[grid(6400)](primals_3, buf0, primals_2, buf2, primals_5, buf4, primals_7, buf5, 6400, XBLOCK=128, num_warps=4, num_stages=1) buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 32, 4, 4), (512, 16, 4, 1)) buf7 = empty_strided_cuda((4, 132, 4, 4), (2112, 16, 4, 1), torch. float32) triton_poi_fused_cat_3[grid(8448)](primals_3, buf0, primals_2, buf2, primals_5, buf4, primals_7, buf6, primals_9, buf7, 8448, XBLOCK =256, num_warps=4, num_stages=1) buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1)) buf9 = buf8 del buf8 triton_poi_fused_add_convolution_mul_4[grid(256)](buf9, primals_11, primals_3, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_11 buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 32, 4, 4), (512, 16, 4, 1)) buf11 = empty_strided_cuda((4, 36, 4, 4), (576, 16, 4, 1), torch. float32) triton_poi_fused_cat_0[grid(2304)](buf9, buf10, primals_13, buf11, 2304, XBLOCK=128, num_warps=4, num_stages=1) buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 32, 4, 4), (512, 16, 4, 1)) buf13 = empty_strided_cuda((4, 68, 4, 4), (1088, 16, 4, 1), torch. float32) triton_poi_fused_cat_1[grid(4352)](buf9, buf10, primals_13, buf12, primals_15, buf13, 4352, XBLOCK=256, num_warps=4, num_stages=1) buf14 = extern_kernels.convolution(buf13, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 32, 4, 4), (512, 16, 4, 1)) buf15 = empty_strided_cuda((4, 100, 4, 4), (1600, 16, 4, 1), torch. float32) triton_poi_fused_cat_2[grid(6400)](buf9, buf10, primals_13, buf12, primals_15, buf14, primals_17, buf15, 6400, XBLOCK=128, num_warps=4, num_stages=1) buf16 = extern_kernels.convolution(buf15, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 32, 4, 4), (512, 16, 4, 1)) buf17 = empty_strided_cuda((4, 132, 4, 4), (2112, 16, 4, 1), torch. float32) triton_poi_fused_cat_3[grid(8448)](buf9, buf10, primals_13, buf12, primals_15, buf14, primals_17, buf16, primals_19, buf17, 8448, XBLOCK=256, num_warps=4, num_stages=1) buf18 = extern_kernels.convolution(buf17, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 4, 4, 4), (64, 16, 4, 1)) buf19 = buf18 del buf18 triton_poi_fused_add_convolution_mul_4[grid(256)](buf19, primals_21, buf9, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_21 buf20 = extern_kernels.convolution(buf19, primals_22, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 32, 4, 4), (512, 16, 4, 1)) buf21 = empty_strided_cuda((4, 36, 4, 4), (576, 16, 4, 1), torch. float32) triton_poi_fused_cat_0[grid(2304)](buf19, buf20, primals_23, buf21, 2304, XBLOCK=128, num_warps=4, num_stages=1) buf22 = extern_kernels.convolution(buf21, primals_24, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf22, (4, 32, 4, 4), (512, 16, 4, 1)) buf23 = empty_strided_cuda((4, 68, 4, 4), (1088, 16, 4, 1), torch. float32) triton_poi_fused_cat_1[grid(4352)](buf19, buf20, primals_23, buf22, primals_25, buf23, 4352, XBLOCK=256, num_warps=4, num_stages=1) buf24 = extern_kernels.convolution(buf23, primals_26, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 32, 4, 4), (512, 16, 4, 1)) buf25 = empty_strided_cuda((4, 100, 4, 4), (1600, 16, 4, 1), torch. float32) triton_poi_fused_cat_2[grid(6400)](buf19, buf20, primals_23, buf22, primals_25, buf24, primals_27, buf25, 6400, XBLOCK=128, num_warps=4, num_stages=1) buf26 = extern_kernels.convolution(buf25, primals_28, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf26, (4, 32, 4, 4), (512, 16, 4, 1)) buf27 = empty_strided_cuda((4, 132, 4, 4), (2112, 16, 4, 1), torch. float32) triton_poi_fused_cat_3[grid(8448)](buf19, buf20, primals_23, buf22, primals_25, buf24, primals_27, buf26, primals_29, buf27, 8448, XBLOCK=256, num_warps=4, num_stages=1) buf28 = extern_kernels.convolution(buf27, primals_30, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf28, (4, 4, 4, 4), (64, 16, 4, 1)) buf29 = buf28 del buf28 triton_poi_fused_add_convolution_mul_5[grid(256)](buf29, primals_31, buf19, primals_3, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_31 buf30 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf26, primals_29, buf30, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf26 del primals_29 buf31 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf24, primals_27, buf31, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf24 del primals_27 buf32 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf22, primals_25, buf32, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf22 del primals_25 buf33 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf20, primals_23, buf33, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf20 del primals_23 buf34 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf16, primals_19, buf34, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf16 del primals_19 buf35 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf14, primals_17, buf35, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf14 del primals_17 buf36 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf12, primals_15, buf36, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf12 del primals_15 buf37 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf10, primals_13, buf37, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf10 del primals_13 buf38 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf6, primals_9, buf38, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf6 del primals_9 buf39 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf4, primals_7, buf39, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf4 del primals_7 buf40 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf2, primals_5, buf40, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf2 del primals_5 buf41 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool) triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_6[grid( 2048)](buf0, primals_2, buf41, 2048, XBLOCK=128, num_warps=4, num_stages=1) del buf0 del primals_2 return (buf29, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, primals_28, primals_30, buf1, buf3, buf5, buf7, buf9, buf11, buf13, buf15, buf17, buf19, buf21, buf23, buf25, buf27, buf30, buf31, buf32, buf33, buf34, buf35, buf36, buf37, buf38, buf39, buf40, buf41) @torch.no_grad() def default_init_weights(module_list, scale=1, bias_fill=0, kwargs): """Initialize network weights. Args: module_list (list[nn.Module] \| nn.Module): Modules to be initialized. scale (float): Scale initialized weights, especially for residual blocks. Default: 1. bias_fill (float): The value to fill bias. Default: 0 kwargs (dict): Other arguments for initialization function. """ if not isinstance(module_list, list): module_list = [module_list] for module in module_list: for m in module.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, kwargs) m.weight.data = scale if m.bias is not None: m.bias.data.fill_(bias_fill) elif isinstance(m, nn.Linear): init.kaiming_normal_(m.weight, kwargs) m.weight.data = scale if m.bias is not None: m.bias.data.fill_(bias_fill) elif isinstance(m, _BatchNorm): init.constant_(m.weight, 1) if m.bias is not None: m.bias.data.fill_(bias_fill) class ResidualDenseBlock(nn.Module): """Residual Dense Block. Used in RRDB block in ESRGAN. Args: num_feat (int): Channel number of intermediate features. num_grow_ch (int): Channels for each growth. """ def __init__(self, num_feat=64, num_grow_ch=32): super(ResidualDenseBlock, self).__init__() self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1 ) self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1 ) self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) default_init_weights([self.conv1, self.conv2, self.conv3, self. conv4, self.conv5], 0.1) def forward(self, x): x1 = self.lrelu(self.conv1(x)) x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) return x5 * 0.2 + x class RRDBNew(nn.Module): """Residual in Residual Dense Block. Used in RRDB-Net in ESRGAN. Args: num_feat (int): Channel number of intermediate features. num_grow_ch (int): Channels for each growth. """ def __init__(self, num_feat, num_grow_ch=32): super(RRDBNew, self).__init__() self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) def forward(self, input_0): primals_1 = self.rdb1.conv1.weight primals_2 = self.rdb1.conv1.bias primals_4 = self.rdb1.conv2.weight primals_5 = self.rdb1.conv2.bias primals_6 = self.rdb1.conv3.weight primals_7 = self.rdb1.conv3.bias primals_8 = self.rdb1.conv4.weight primals_9 = self.rdb1.conv4.bias primals_10 = self.rdb1.conv5.weight primals_11 = self.rdb1.conv5.bias primals_12 = self.rdb2.conv1.weight primals_13 = self.rdb2.conv1.bias primals_14 = self.rdb2.conv2.weight primals_15 = self.rdb2.conv2.bias primals_16 = self.rdb2.conv3.weight primals_17 = self.rdb2.conv3.bias primals_18 = self.rdb2.conv4.weight primals_19 = self.rdb2.conv4.bias primals_20 = self.rdb2.conv5.weight primals_21 = self.rdb2.conv5.bias primals_22 = self.rdb3.conv1.weight primals_23 = self.rdb3.conv1.bias primals_24 = self.rdb3.conv2.weight primals_25 = self.rdb3.conv2.bias primals_26 = self.rdb3.conv3.weight primals_27 = self.rdb3.conv3.bias primals_28 = self.rdb3.conv4.weight primals_29 = self.rdb3.conv4.bias primals_30 = self.rdb3.conv5.weight primals_31 = self.rdb3.conv5.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31]) return output[0]	BCV-Uniandes/RSR	RRDB	false	8,172	[ "zlib-acknowledgement" ]	14	dad60eedd3560f2655e3d1ed444153ed2616af2e	https://github.com/BCV-Uniandes/RSR/tree/dad60eedd3560f2655e3d1ed444153ed2616af2e
SimpleLSTM	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/xc/cxceqiy6okvqxrjbwoo7ygc4plr32q3u54opf7nc2ufvkbmb4dzh.py # Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat] # Source node to ATen node mapping: # combined => cat # Graph fragment: # %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %primals_1], 1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 8 x0 = xindex % 16 x2 = (xindex // 128) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16x1) + (64x2)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + (x0 + (16((-4) + x1)) + (64x2)), tmp6 & xmask, other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x3), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/2q/c2qhdukhkskgkif7niq6mlfod6k7yxtvuppk7j2vq6jn3t7oe7ti.py # Topologically Sorted Source Nodes: [i, f, o, g, mul, mul_1, c_next, tanh_1, h_next], Original ATen: [aten.sigmoid, aten.tanh, aten.mul, aten.add, aten.sigmoid_backward] # Source node to ATen node mapping: # c_next => add # f => sigmoid_1 # g => tanh # h_next => mul_2 # i => sigmoid # mul => mul # mul_1 => mul_1 # o => sigmoid_2 # tanh_1 => tanh_1 # Graph fragment: # %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {}) # %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_1,), kwargs = {}) # %sigmoid_2 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_2,), kwargs = {}) # %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_3,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %primals_5), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %tanh), kwargs = {}) # %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {}) # %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %tanh_1), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_1), kwargs = {}) # %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %sub_3), kwargs = {}) triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = (xindex // 64) x4 = xindex % 64 x1 = (xindex // 16) % 4 x3 = xindex tmp0 = tl.load(in_ptr0 + (128 + x4 + (256x2)), xmask) tmp1 = tl.load(in_ptr1 + (8 + x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (x4 + (256x2)), xmask) tmp5 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (192 + x4 + (256x2)), xmask) tmp9 = tl.load(in_ptr1 + (12 + x1), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr0 + (64 + x4 + (256x2)), xmask) tmp13 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp16 = tl.load(in_ptr2 + (x3), xmask) tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tmp6 = tmp4 + tmp5 tmp7 = tl.sigmoid(tmp6) tmp10 = tmp8 + tmp9 tmp11 = libdevice.tanh(tmp10) tmp14 = tmp12 + tmp13 tmp15 = tl.sigmoid(tmp14) tmp17 = tmp15 * tmp16 tmp18 = tmp7 * tmp11 tmp19 = tmp17 + tmp18 tmp20 = 1.0 tmp21 = tmp20 - tmp15 tmp22 = tmp15 * tmp21 tmp23 = libdevice.tanh(tmp19) tmp24 = tmp3 * tmp23 tl.store(out_ptr0 + (x3), tmp3, xmask) tl.store(out_ptr1 + (x3), tmp7, xmask) tl.store(out_ptr2 + (x3), tmp11, xmask) tl.store(out_ptr3 + (x3), tmp19, xmask) tl.store(out_ptr4 + (x3), tmp22, xmask) tl.store(out_ptr5 + (x3), tmp24, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (16, 8, 3, 3), (72, 9, 3, 1)) assert_size_stride(primals_4, (16, ), (1, )) assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(primals_2, primals_1, buf0, 512, grid=grid(512), stream=stream0) del primals_1 del primals_2 # Topologically Sorted Source Nodes: [combined_conv], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1)) buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [i, f, o, g, mul, mul_1, c_next, tanh_1, h_next], Original ATen: [aten.sigmoid, aten.tanh, aten.mul, aten.add, aten.sigmoid_backward] triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1.run(buf1, primals_4, primals_5, buf3, buf2, buf4, buf5, buf7, buf6, 256, grid=grid(256), stream=stream0) del buf1 del primals_4 return (buf6, buf5, primals_3, primals_5, buf0, buf2, buf3, buf4, buf5, buf7, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((16, 8, 3, 3), (72, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.utils.data import torch.nn as nn class SimpleLSTM(nn.Module): def __init__(self, input_dim, hidden_dim): super(SimpleLSTM, self).__init__() self.nf = input_dim self.hf = hidden_dim self.conv = nn.Conv2d(self.nf + self.hf, 4 * self.hf, 3, 1, 1, bias =True) def forward(self, input_tensor, h_cur, c_cur): if h_cur is None: tensor_size = input_tensor.size(2), input_tensor.size(3) h_cur = self._init_hidden(batch_size=input_tensor.size(0), tensor_size=tensor_size) c_cur = self._init_hidden(batch_size=input_tensor.size(0), tensor_size=tensor_size) combined = torch.cat([input_tensor, h_cur], dim=1) combined_conv = self.conv(combined) cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hf, dim=1) i = torch.sigmoid(cc_i) f = torch.sigmoid(cc_f) o = torch.sigmoid(cc_o) g = torch.tanh(cc_g) c_next = f * c_cur + i * g h_next = o * torch.tanh(c_next) return h_next, c_next def _init_hidden(self, batch_size, tensor_size): height, width = tensor_size return torch.zeros(batch_size, self.hf, height, width) def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {'input_dim': 4, 'hidden_dim': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.utils.data import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 8 x0 = xindex % 16 x2 = xindex // 128 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask, other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x3, tmp10, xmask) @triton.jit def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex // 64 x4 = xindex % 64 x1 = xindex // 16 % 4 x3 = xindex tmp0 = tl.load(in_ptr0 + (128 + x4 + 256 * x2), xmask) tmp1 = tl.load(in_ptr1 + (8 + x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (x4 + 256 * x2), xmask) tmp5 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (192 + x4 + 256 * x2), xmask) tmp9 = tl.load(in_ptr1 + (12 + x1), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr0 + (64 + x4 + 256 * x2), xmask) tmp13 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp16 = tl.load(in_ptr2 + x3, xmask) tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tmp6 = tmp4 + tmp5 tmp7 = tl.sigmoid(tmp6) tmp10 = tmp8 + tmp9 tmp11 = libdevice.tanh(tmp10) tmp14 = tmp12 + tmp13 tmp15 = tl.sigmoid(tmp14) tmp17 = tmp15 * tmp16 tmp18 = tmp7 * tmp11 tmp19 = tmp17 + tmp18 tmp20 = 1.0 tmp21 = tmp20 - tmp15 tmp22 = tmp15 * tmp21 tmp23 = libdevice.tanh(tmp19) tmp24 = tmp3 * tmp23 tl.store(out_ptr0 + x3, tmp3, xmask) tl.store(out_ptr1 + x3, tmp7, xmask) tl.store(out_ptr2 + x3, tmp11, xmask) tl.store(out_ptr3 + x3, tmp19, xmask) tl.store(out_ptr4 + x3, tmp22, xmask) tl.store(out_ptr5 + x3, tmp24, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (16, 8, 3, 3), (72, 9, 3, 1)) assert_size_stride(primals_4, (16,), (1,)) assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(512)](primals_2, primals_1, buf0, 512, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 del primals_2 buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1)) buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1[grid(256)]( buf1, primals_4, primals_5, buf3, buf2, buf4, buf5, buf7, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1) del buf1 del primals_4 return buf6, buf5, primals_3, primals_5, buf0, buf2, buf3, buf4, buf5, buf7 class SimpleLSTMNew(nn.Module): def __init__(self, input_dim, hidden_dim): super(SimpleLSTMNew, self).__init__() self.nf = input_dim self.hf = hidden_dim self.conv = nn.Conv2d(self.nf + self.hf, 4 * self.hf, 3, 1, 1, bias =True) def _init_hidden(self, batch_size, tensor_size): height, width = tensor_size return torch.zeros(batch_size, self.hf, height, width) def forward(self, input_0, input_1, input_2): primals_3 = self.conv.weight primals_4 = self.conv.bias primals_1 = input_0 primals_2 = input_1 primals_5 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0], output[1]	GuoShi28/GCP-Net	SimpleLSTM	false	8,173	[ "Apache-2.0" ]	24	cef7513fa242343055af64e612429e4384d3c1d7	https://github.com/GuoShi28/GCP-Net/tree/cef7513fa242343055af64e612429e4384d3c1d7
ShuffleConv	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/tq/ctqx5oeh223edhvuljodld2xzkmlzm4mljlxed7dn3ngoa3ettdr.py # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.constant_pad_nd] # Source node to ATen node mapping: # conv2d => constant_pad_nd # Graph fragment: # %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%view_2, [0, 1, 0, 1]), kwargs = {}) triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1296 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 9) % 9 x0 = xindex % 9 x3 = (xindex // 324) x4 = xindex tmp0 = x1 tmp1 = tl.full([1], 8, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + ((4((x1 // 2) % 4)) + (16(x0 % 2)) + (32(x1 % 2)) + (64x3) + ((x0 // 2) % 4)), tmp5 & xmask, eviction_policy='evict_last', other=0.0) tl.store(out_ptr0 + (x4), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/fr/cfr26uorpdbqjhhrw25kipixbtj7e3p2iw55hdsrscl44kvrdeyp.py # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] # Source node to ATen node mapping: # conv2d => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 64) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.constant_pad_nd] stream0 = get_raw_stream(0) triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 1296, grid=grid(1296), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1)) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] triton_poi_fused_convolution_1.run(buf2, primals_3, 1024, grid=grid(1024), stream=stream0) del primals_3 return (buf2, primals_2, buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn class ShuffleConv(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, padding_mode='zeros'): super(ShuffleConv, self).__init__() self.upscale_factor = upscale_factor self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode) def forward(self, X): X = torch.cat([X] * self.upscale_factor ** 2, dim=1) X = nn.functional.pixel_shuffle(X, self.upscale_factor) return self.conv(X) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1296 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 9 % 9 x0 = xindex % 9 x3 = xindex // 324 x4 = xindex tmp0 = x1 tmp1 = tl.full([1], 8, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + (4 * (x1 // 2 % 4) + 16 * (x0 % 2) + 32 * (x1 % 2) + 64 * x3 + x0 // 2 % 4), tmp5 & xmask, eviction_policy= 'evict_last', other=0.0) tl.store(out_ptr0 + x4, tmp6, xmask) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 64 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32) get_raw_stream(0) triton_poi_fused_constant_pad_nd_0[grid(1296)](primals_1, buf0, 1296, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1)) buf2 = buf1 del buf1 triton_poi_fused_convolution_1[grid(1024)](buf2, primals_3, 1024, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 return buf2, primals_2, buf0 class ShuffleConvNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, padding_mode='zeros'): super(ShuffleConvNew, self).__init__() self.upscale_factor = upscale_factor self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode) def forward(self, input_0): primals_1 = self.conv.weight primals_3 = self.conv.bias primals_2 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	GerbenBeintema/deepSI	ShuffleConv	false	8,174	[ "BSD-3-Clause" ]	12	580711210398064bb7f01e41d08b7a248a88b35b	https://github.com/GerbenBeintema/deepSI/tree/580711210398064bb7f01e41d08b7a248a88b35b
h_sigmoid	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/in/cinpnfhrionin5gpquwoph6guoa32baao7labi6wulqruki5b6sc.py # Topologically Sorted Source Nodes: [add, hardtanh, truediv], Original ATen: [aten.add, aten.hardtanh, aten.div] # Source node to ATen node mapping: # add => add # hardtanh => clamp_max, clamp_min # truediv => div # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 3), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0.0), kwargs = {}) # %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 6.0), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%clamp_max, 6), kwargs = {}) triton_poi_fused_add_div_hardtanh_0 = async_compile.triton('triton_poi_fused_add_div_hardtanh_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_hardtanh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_hardtanh_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 3.0 tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp5 = 6.0 tmp6 = triton_helpers.minimum(tmp4, tmp5) tmp7 = 0.16666666666666666 tmp8 = tmp6 * tmp7 tl.store(out_ptr0 + (x0), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [add, hardtanh, truediv], Original ATen: [aten.add, aten.hardtanh, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_add_div_hardtanh_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class h_sigmoid(nn.Module): def __init__(self, inplace=True): super(h_sigmoid, self).__init__() self.relu = nn.ReLU6(inplace=inplace) def forward(self, x): return self.relu(x + 3) / 6 def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_div_hardtanh_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 3.0 tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp5 = 6.0 tmp6 = triton_helpers.minimum(tmp4, tmp5) tmp7 = 0.16666666666666666 tmp8 = tmp6 * tmp7 tl.store(out_ptr0 + x0, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_div_hardtanh_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 return buf0, class h_sigmoidNew(nn.Module): def __init__(self, inplace=True): super(h_sigmoidNew, self).__init__() self.relu = nn.ReLU6(inplace=inplace) def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	GewelsJI/VPS	h_sigmoid	false	8,175	[ "Apache-2.0" ]	22	8cb7f584be3c5fc0941126860f2198cb1d88fc4e	https://github.com/GewelsJI/VPS/tree/8cb7f584be3c5fc0941126860f2198cb1d88fc4e
Upscale_Conv_block	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/gi/cgi2hszrjniw6ncyter6qipkbjwmsxrmjql3fibjwudjvgclte25.py # Topologically Sorted Source Nodes: [X, X_1, X_2], Original ATen: [aten.constant_pad_nd, aten.relu] # Source node to ATen node mapping: # X => constant_pad_nd # X_1 => relu # X_2 => constant_pad_nd_1 # Graph fragment: # %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_3, [0, 1, 0, 1]), kwargs = {}) # %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%primals_3,), kwargs = {}) # %constant_pad_nd_1 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%relu, [0, 1, 0, 1]), kwargs = {}) triton_poi_fused_constant_pad_nd_relu_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_constant_pad_nd_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 400 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 5) % 5 x0 = xindex % 5 x2 = (xindex // 25) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 4, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + (x0 + (4x1) + (16x2)), tmp5 & xmask, other=0.0) tmp7 = tl.full([1], 0, tl.int32) tmp8 = triton_helpers.maximum(tmp7, tmp6) tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype) tmp10 = tl.where(tmp5, tmp8, tmp9) tl.store(out_ptr0 + (x3), tmp6, xmask) tl.store(out_ptr1 + (x3), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/35/c35wrtw4jur3xaxq7ut2gk27upe5scdzvcwpdctdxqpgzvbnh735.py # Topologically Sorted Source Nodes: [X_4, X_5], Original ATen: [aten.relu, aten.constant_pad_nd] # Source node to ATen node mapping: # X_4 => relu_1 # X_5 => constant_pad_nd_2 # Graph fragment: # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {}) # %constant_pad_nd_2 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%relu_1, [0, 1, 0, 1]), kwargs = {}) triton_poi_fused_constant_pad_nd_relu_1 = async_compile.triton('triton_poi_fused_constant_pad_nd_relu_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_constant_pad_nd_relu_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1296 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 9) % 9 x0 = xindex % 9 x4 = (xindex // 81) x2 = (xindex // 81) % 4 x5 = xindex tmp0 = x1 tmp1 = tl.full([1], 8, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + ((4((x1 // 2) % 4)) + (16(x0 % 2)) + (32(x1 % 2)) + (64x4) + ((x0 // 2) % 4)), tmp5 & xmask, eviction_policy='evict_last', other=0.0) tmp7 = tl.load(in_ptr1 + ((2(x1 % 2)) + (4x2) + (x0 % 2)), tmp5 & xmask, eviction_policy='evict_last', other=0.0) tmp8 = tmp6 + tmp7 tmp9 = tl.full([1], 0, tl.int32) tmp10 = triton_helpers.maximum(tmp9, tmp8) tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype) tmp12 = tl.where(tmp5, tmp10, tmp11) tl.store(out_ptr0 + (x5), tmp12, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/i5/ci53bsz62habasu4hzydebxtd4pbocmefvo3vbus4kflpf7gq7ay.py # Topologically Sorted Source Nodes: [X_5, X_6], Original ATen: [aten.convolution, aten.add] # Source node to ATen node mapping: # X_5 => convolution_2 # X_6 => add # Graph fragment: # %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_2, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, %view_1), kwargs = {}) triton_poi_fused_add_convolution_2 = async_compile.triton('triton_poi_fused_add_convolution_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex x2 = (xindex // 64) % 4 x0 = xindex % 8 x1 = (xindex // 8) % 8 x5 = (xindex // 64) tmp0 = tl.load(in_out_ptr0 + (x4), xmask) tmp1 = tl.load(in_ptr0 + (x2), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + ((4(x1 // 2)) + (16(x0 % 2)) + (32(x1 % 2)) + (64x5) + (x0 // 2)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr2 + ((2(x1 % 2)) + (4x2) + (x0 % 2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tl.store(in_out_ptr0 + (x4), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/74/c74vzy264zbtl3kgxuqdurfai2hahnzoqrv7bczdbuntickhynq4.py # Topologically Sorted Source Nodes: [X_4], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # X_4 => relu_1 # Graph fragment: # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_relu_threshold_backward_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) % 8 x4 = (xindex // 64) x2 = (xindex // 64) % 4 x5 = xindex tmp0 = tl.load(in_ptr0 + ((4(x1 // 2)) + (16(x0 % 2)) + (32(x1 % 2)) + (64x4) + (x0 // 2)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + ((2(x1 % 2)) + (4x2) + (x0 % 2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x5), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (16, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (16, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (16, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_5, (16, ), (1, )) assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_7, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32) buf2 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32) # Topologically Sorted Source Nodes: [X, X_1, X_2], Original ATen: [aten.constant_pad_nd, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_constant_pad_nd_relu_0.run(primals_3, buf0, buf2, 400, grid=grid(400), stream=stream0) del primals_3 # Topologically Sorted Source Nodes: [X], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1)) # Topologically Sorted Source Nodes: [X_2], Original ATen: [aten.convolution] buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 16, 4, 4), (256, 16, 4, 1)) buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32) # Topologically Sorted Source Nodes: [X_4, X_5], Original ATen: [aten.relu, aten.constant_pad_nd] triton_poi_fused_constant_pad_nd_relu_1.run(buf3, primals_5, buf4, 1296, grid=grid(1296), stream=stream0) # Topologically Sorted Source Nodes: [X_5], Original ATen: [aten.convolution] buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf5, (4, 4, 8, 8), (256, 64, 8, 1)) buf6 = buf5; del buf5 # reuse # Topologically Sorted Source Nodes: [X_5, X_6], Original ATen: [aten.convolution, aten.add] triton_poi_fused_add_convolution_2.run(buf6, primals_7, buf1, primals_2, 1024, grid=grid(1024), stream=stream0) del buf1 del primals_2 del primals_7 buf7 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [X_4], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_3.run(buf3, primals_5, buf7, 1024, grid=grid(1024), stream=stream0) del buf3 del primals_5 return (buf6, primals_1, primals_4, primals_6, buf0, buf2, buf4, buf7, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((16, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((16, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn class ConvShuffle(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, padding_mode='zeros'): super(ConvShuffle, self).__init__() self.upscale_factor = upscale_factor self.conv = nn.Conv2d(in_channels, out_channels * upscale_factor ** 2, kernel_size, padding=padding, padding_mode=padding_mode) def forward(self, X): X = self.conv(X) return nn.functional.pixel_shuffle(X, self.upscale_factor) class Upscale_Conv_block(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, main_upscale=ConvShuffle, shortcut= ConvShuffle, padding_mode='zeros', activation=nn.functional.relu, Ch=0, Cw=0): assert isinstance(upscale_factor, int) super(Upscale_Conv_block, self).__init__() self.shortcut = shortcut(in_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode, upscale_factor= upscale_factor) self.activation = activation self.upscale = main_upscale(in_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode, upscale_factor= upscale_factor) self.conv = nn.Conv2d(out_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode) self.Ch = Ch self.Cw = Cw def forward(self, X): X_shortcut = self.shortcut(X) X = self.activation(X) X = self.upscale(X) X = self.activation(X) X = self.conv(X) H, W = X.shape[2:] H2, W2 = X_shortcut.shape[2:] if H2 > H or W2 > W: padding_height = (H2 - H) // 2 padding_width = (W2 - W) // 2 X = X + X_shortcut[:, :, padding_height:padding_height + H, padding_width:padding_width + W] else: X = X + X_shortcut return X[:, :, self.Ch:, self.Cw:] def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_constant_pad_nd_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 5 % 5 x0 = xindex % 5 x2 = xindex // 25 x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 4, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0) tmp7 = tl.full([1], 0, tl.int32) tmp8 = triton_helpers.maximum(tmp7, tmp6) tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype) tmp10 = tl.where(tmp5, tmp8, tmp9) tl.store(out_ptr0 + x3, tmp6, xmask) tl.store(out_ptr1 + x3, tmp10, xmask) @triton.jit def triton_poi_fused_constant_pad_nd_relu_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1296 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 9 % 9 x0 = xindex % 9 x4 = xindex // 81 x2 = xindex // 81 % 4 x5 = xindex tmp0 = x1 tmp1 = tl.full([1], 8, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + (4 * (x1 // 2 % 4) + 16 * (x0 % 2) + 32 * (x1 % 2) + 64 * x4 + x0 // 2 % 4), tmp5 & xmask, eviction_policy= 'evict_last', other=0.0) tmp7 = tl.load(in_ptr1 + (2 * (x1 % 2) + 4 * x2 + x0 % 2), tmp5 & xmask, eviction_policy='evict_last', other=0.0) tmp8 = tmp6 + tmp7 tmp9 = tl.full([1], 0, tl.int32) tmp10 = triton_helpers.maximum(tmp9, tmp8) tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype) tmp12 = tl.where(tmp5, tmp10, tmp11) tl.store(out_ptr0 + x5, tmp12, xmask) @triton.jit def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex x2 = xindex // 64 % 4 x0 = xindex % 8 x1 = xindex // 8 % 8 x5 = xindex // 64 tmp0 = tl.load(in_out_ptr0 + x4, xmask) tmp1 = tl.load(in_ptr0 + x2, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (4 * (x1 // 2) + 16 * (x0 % 2) + 32 * (x1 % 2) + 64 * x5 + x0 // 2), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr2 + (2 * (x1 % 2) + 4 * x2 + x0 % 2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tl.store(in_out_ptr0 + x4, tmp6, xmask) @triton.jit def triton_poi_fused_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 % 8 x4 = xindex // 64 x2 = xindex // 64 % 4 x5 = xindex tmp0 = tl.load(in_ptr0 + (4 * (x1 // 2) + 16 * (x0 % 2) + 32 * (x1 % 2) + 64 * x4 + x0 // 2), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (2 * (x1 % 2) + 4 * x2 + x0 % 2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x5, tmp6, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (16, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (16,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (16, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_5, (16,), (1,)) assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_7, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32) buf2 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32) get_raw_stream(0) triton_poi_fused_constant_pad_nd_relu_0[grid(400)](primals_3, buf0, buf2, 400, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1)) buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 16, 4, 4), (256, 16, 4, 1)) buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32) triton_poi_fused_constant_pad_nd_relu_1[grid(1296)](buf3, primals_5, buf4, 1296, XBLOCK=128, num_warps=4, num_stages=1) buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf5, (4, 4, 8, 8), (256, 64, 8, 1)) buf6 = buf5 del buf5 triton_poi_fused_add_convolution_2[grid(1024)](buf6, primals_7, buf1, primals_2, 1024, XBLOCK=128, num_warps=4, num_stages=1) del buf1 del primals_2 del primals_7 buf7 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool) triton_poi_fused_relu_threshold_backward_3[grid(1024)](buf3, primals_5, buf7, 1024, XBLOCK=128, num_warps=4, num_stages=1) del buf3 del primals_5 return buf6, primals_1, primals_4, primals_6, buf0, buf2, buf4, buf7 class ConvShuffle(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, padding_mode='zeros'): super(ConvShuffle, self).__init__() self.upscale_factor = upscale_factor self.conv = nn.Conv2d(in_channels, out_channels * upscale_factor ** 2, kernel_size, padding=padding, padding_mode=padding_mode) def forward(self, X): X = self.conv(X) return nn.functional.pixel_shuffle(X, self.upscale_factor) class Upscale_Conv_blockNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, main_upscale=ConvShuffle, shortcut= ConvShuffle, padding_mode='zeros', activation=nn.functional.relu, Ch=0, Cw=0): assert isinstance(upscale_factor, int) super(Upscale_Conv_blockNew, self).__init__() self.shortcut = shortcut(in_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode, upscale_factor= upscale_factor) self.activation = activation self.upscale = main_upscale(in_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode, upscale_factor= upscale_factor) self.conv = nn.Conv2d(out_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode) self.Ch = Ch self.Cw = Cw def forward(self, input_0): primals_1 = self.shortcut.conv.weight primals_2 = self.shortcut.conv.bias primals_4 = self.upscale.conv.weight primals_5 = self.upscale.conv.bias primals_3 = self.conv.weight primals_7 = self.conv.bias primals_6 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]	GerbenBeintema/deepSI	Upscale_Conv_block	false	8,176	[ "BSD-3-Clause" ]	12	580711210398064bb7f01e41d08b7a248a88b35b	https://github.com/GerbenBeintema/deepSI/tree/580711210398064bb7f01e41d08b7a248a88b35b
LayerNorm	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/au/caua2dicxp4puntbbqkcgq2xc4b3qsffybqzzcivwnkseuc3yabi.py # Topologically Sorted Source Nodes: [mean, std, sub, add, y, mul, y_1], Original ATen: [aten.mean, aten.std, aten.sub, aten.add, aten.div, aten.mul] # Source node to ATen node mapping: # add => add # mean => mean # mul => mul # std => var # sub => sub # y => div # y_1 => add_1 # Graph fragment: # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [1]), kwargs = {}) # %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [1]), kwargs = {correction: 1.0}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %view_1), kwargs = {}) # %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, 1e-05), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %add), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_4, %div), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %view_5), kwargs = {}) triton_per_fused_add_div_mean_mul_std_sub_0 = async_compile.triton('triton_per_fused_add_div_mean_mul_std_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[4, 64], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_std_sub_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_div_mean_mul_std_sub_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 4 rnumel = 64 RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex r3 = (rindex // 16) tmp0 = tl.load(in_ptr0 + (r1 + (64x0)), xmask, other=0.0) tmp26 = tl.load(in_ptr1 + (r3), None, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + (r3), None, eviction_policy='evict_last') tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, 0) tmp4 = tl.sum(tmp3, 1)[:, None] tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK]) tmp8 = tl.where(xmask, tmp6, 0) tmp9 = tl.sum(tmp8, 1)[:, None] tmp10 = tl.full([XBLOCK, 1], 64, tl.int32) tmp11 = tmp10.to(tl.float32) tmp12 = tmp9 / tmp11 tmp13 = tmp1 - tmp12 tmp14 = tmp13 tmp13 tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK]) tmp17 = tl.where(xmask, tmp15, 0) tmp18 = tl.sum(tmp17, 1)[:, None] tmp19 = 64.0 tmp20 = tmp4 / tmp19 tmp21 = 63.0 tmp22 = tmp18 / tmp21 tmp23 = libdevice.sqrt(tmp22) tmp24 = 1e-05 tmp25 = tmp23 + tmp24 tmp27 = tmp0 - tmp20 tmp28 = tmp27 / tmp25 tmp29 = tmp26 * tmp28 tmp31 = tmp29 + tmp30 tl.debug_barrier() tl.store(in_out_ptr0 + (x0), tmp20, xmask) tl.debug_barrier() tl.store(in_out_ptr1 + (x0), tmp25, xmask) tl.store(out_ptr0 + (r1 + (64*x0)), tmp31, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, ), (1, ), torch.float32) buf3 = empty_strided_cuda((4, ), (1, ), torch.float32) buf1 = buf0; del buf0 # reuse buf5 = reinterpret_tensor(buf3, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf3 # reuse buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mean, std, sub, add, y, mul, y_1], Original ATen: [aten.mean, aten.std, aten.sub, aten.add, aten.div, aten.mul] stream0 = get_raw_stream(0) triton_per_fused_add_div_mean_mul_std_sub_0.run(buf1, buf5, primals_1, primals_2, primals_3, buf6, 4, 64, grid=grid(4), stream=stream0) del primals_2 del primals_3 return (buf6, primals_1, reinterpret_tensor(buf1, (4, 1, 1, 1), (1, 1, 1, 1), 0), buf5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class LayerNorm(nn.Module): def __init__(self, num_features, eps=1e-05, affine=True): super(LayerNorm, self).__init__() self.num_features = num_features self.affine = affine self.eps = eps if self.affine: self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_()) self.beta = nn.Parameter(torch.zeros(num_features)) def forward(self, x): shape = [-1] + [1] * (x.dim() - 1) mean = x.view(x.size(0), -1).mean(1).view(shape) std = x.view(x.size(0), -1).std(1).view(shape) y = (x - mean) / (std + self.eps) if self.affine: shape = [1, -1] + [1] * (x.dim() - 2) y = self.gamma.view(shape) y + self.beta.view(*shape) return y def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_features': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_per_fused_add_div_mean_mul_std_sub_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 4 RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex r3 = rindex // 16 tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0) tmp26 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last') tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, 0) tmp4 = tl.sum(tmp3, 1)[:, None] tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK]) tmp8 = tl.where(xmask, tmp6, 0) tmp9 = tl.sum(tmp8, 1)[:, None] tmp10 = tl.full([XBLOCK, 1], 64, tl.int32) tmp11 = tmp10.to(tl.float32) tmp12 = tmp9 / tmp11 tmp13 = tmp1 - tmp12 tmp14 = tmp13 * tmp13 tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK]) tmp17 = tl.where(xmask, tmp15, 0) tmp18 = tl.sum(tmp17, 1)[:, None] tmp19 = 64.0 tmp20 = tmp4 / tmp19 tmp21 = 63.0 tmp22 = tmp18 / tmp21 tmp23 = libdevice.sqrt(tmp22) tmp24 = 1e-05 tmp25 = tmp23 + tmp24 tmp27 = tmp0 - tmp20 tmp28 = tmp27 / tmp25 tmp29 = tmp26 * tmp28 tmp31 = tmp29 + tmp30 tl.debug_barrier() tl.store(in_out_ptr0 + x0, tmp20, xmask) tl.debug_barrier() tl.store(in_out_ptr1 + x0, tmp25, xmask) tl.store(out_ptr0 + (r1 + 64 * x0), tmp31, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4,), (1,), torch.float32) buf3 = empty_strided_cuda((4,), (1,), torch.float32) buf1 = buf0 del buf0 buf5 = reinterpret_tensor(buf3, (4, 1, 1, 1), (1, 1, 1, 1), 0) del buf3 buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_per_fused_add_div_mean_mul_std_sub_0[grid(4)](buf1, buf5, primals_1, primals_2, primals_3, buf6, 4, 64, XBLOCK=1, num_warps=2, num_stages=1) del primals_2 del primals_3 return buf6, primals_1, reinterpret_tensor(buf1, (4, 1, 1, 1), (1, 1, 1, 1), 0), buf5 class LayerNormNew(nn.Module): def __init__(self, num_features, eps=1e-05, affine=True): super(LayerNormNew, self).__init__() self.num_features = num_features self.affine = affine self.eps = eps if self.affine: self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_()) self.beta = nn.Parameter(torch.zeros(num_features)) def forward(self, input_0): primals_2 = self.gamma primals_3 = self.beta primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	HAXRD/PIC	LayerNorm	false	8,177	[ "MIT" ]	28	658b4dd6b01e64413d5f8f0107d9167f1bd78546	https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546
Conv	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ds/cdsuicx6h7dvnwqzkmsrvyzdesymcoz5qrfjvcdj5yrkx7cxocnu.py # Topologically Sorted Source Nodes: [inputs], Original ATen: [aten.constant_pad_nd] # Source node to ATen node mapping: # inputs => constant_pad_nd # Graph fragment: # %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%permute, [2, 1], 0.0), kwargs = {}) triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 8], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 7 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = (-2) + x2 tmp1 = tl.full([1, 1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1, 1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + ((-8) + y0 + (4x2) + (16y1)), tmp5 & xmask & ymask, eviction_policy='evict_last', other=0.0) tl.store(out_ptr0 + (x2 + (7y3)), tmp6, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/bo/cbobw73zf5udllzw6ypzn2lpl3t5xgyic35frylcttxdzai224c5.py # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution] # Source node to ATen node mapping: # conv1d => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [1], [0], [1], False, [0], 1), kwargs = {}) triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 4) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 7), (28, 7, 1), torch.float32) # Topologically Sorted Source Nodes: [inputs], Original ATen: [aten.constant_pad_nd] stream0 = get_raw_stream(0) triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 16, 7, grid=grid(16, 7), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4), (16, 4, 1)) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution] triton_poi_fused_convolution_1.run(buf2, primals_3, 64, grid=grid(64), stream=stream0) del primals_3 return (reinterpret_tensor(buf2, (4, 4, 4), (16, 1, 4), 0), primals_2, buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.utils.data class Conv(nn.Module): """ Convenience class that does padding and convolution for inputs in the format [batch_size, sequence length, hidden size] """ def __init__(self, input_size, output_size, kernel_size, pad_type): """ Parameters: input_size: Input feature size output_size: Output feature size kernel_size: Kernel width pad_type: left -> pad on the left side (to mask future data), both -> pad on both sides """ super(Conv, self).__init__() padding = (kernel_size - 1, 0) if pad_type == 'left' else ( kernel_size // 2, (kernel_size - 1) // 2) self.pad = nn.ConstantPad1d(padding, 0) self.conv = nn.Conv1d(input_size, output_size, kernel_size= kernel_size, padding=0) def forward(self, inputs): inputs = self.pad(inputs.permute(0, 2, 1)) outputs = self.conv(inputs).permute(0, 2, 1) return outputs def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'input_size': 4, 'output_size': 4, 'kernel_size': 4, 'pad_type': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn import torch.utils.data assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 7 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = -2 + x2 tmp1 = tl.full([1, 1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1, 1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + (-8 + y0 + 4 * x2 + 16 * y1), tmp5 & xmask & ymask, eviction_policy='evict_last', other=0.0) tl.store(out_ptr0 + (x2 + 7 * y3), tmp6, xmask & ymask) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 4 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 7), (28, 7, 1), torch.float32) get_raw_stream(0) triton_poi_fused_constant_pad_nd_0[grid(16, 7)](primals_1, buf0, 16, 7, XBLOCK=8, YBLOCK=16, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=( 0,), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4), (16, 4, 1)) buf2 = buf1 del buf1 triton_poi_fused_convolution_1[grid(64)](buf2, primals_3, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_3 return reinterpret_tensor(buf2, (4, 4, 4), (16, 1, 4), 0), primals_2, buf0 class ConvNew(nn.Module): """ Convenience class that does padding and convolution for inputs in the format [batch_size, sequence length, hidden size] """ def __init__(self, input_size, output_size, kernel_size, pad_type): """ Parameters: input_size: Input feature size output_size: Output feature size kernel_size: Kernel width pad_type: left -> pad on the left side (to mask future data), both -> pad on both sides """ super(ConvNew, self).__init__() padding = (kernel_size - 1, 0) if pad_type == 'left' else ( kernel_size // 2, (kernel_size - 1) // 2) self.pad = nn.ConstantPad1d(padding, 0) self.conv = nn.Conv1d(input_size, output_size, kernel_size= kernel_size, padding=0) def forward(self, input_0): primals_1 = self.conv.weight primals_3 = self.conv.bias primals_2 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	HLTCHKUST/emotion-dialogue	Conv	false	8,178	[ "MIT" ]	40	0d58b339134dd9a2f386948ae474b270a77370f9	https://github.com/HLTCHKUST/emotion-dialogue/tree/0d58b339134dd9a2f386948ae474b270a77370f9
ClassicUpConv	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/lp/clp4sfajicmtcowrjqqfwhwtv7qr3gyy7355eyffjlongen5tgz2.py # Topologically Sorted Source Nodes: [X], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.floor, aten.clamp, aten.rsub, aten._unsafe_index] # Source node to ATen node mapping: # X => _unsafe_index, _unsafe_index_1, _unsafe_index_10, _unsafe_index_11, _unsafe_index_12, _unsafe_index_13, _unsafe_index_14, _unsafe_index_15, _unsafe_index_2, _unsafe_index_3, _unsafe_index_4, _unsafe_index_5, _unsafe_index_6, _unsafe_index_7, _unsafe_index_8, _unsafe_index_9, add, add_10, add_11, add_12, add_13, add_14, add_15, add_16, add_17, add_18, add_19, add_20, add_21, add_22, add_23, add_24, add_25, add_26, add_27, add_28, add_29, add_6, add_7, add_8, add_9, clamp_max, clamp_max_1, clamp_min, clamp_min_1, convert_element_type, floor, floor_1, iota, mul, mul_10, mul_11, mul_12, mul_13, mul_14, mul_15, mul_16, mul_17, mul_18, mul_19, mul_2, mul_20, mul_21, mul_22, mul_23, mul_24, mul_25, mul_26, mul_27, mul_28, mul_29, mul_3, mul_30, mul_31, mul_32, mul_33, mul_34, mul_35, mul_36, mul_37, mul_38, mul_39, mul_4, mul_40, mul_41, mul_42, mul_43, mul_44, mul_45, mul_5, mul_6, mul_7, mul_8, mul_9, sub, sub_10, sub_11, sub_12, sub_13, sub_14, sub_15, sub_16, sub_17, sub_18, sub_19, sub_2, sub_20, sub_21, sub_3, sub_6, sub_7, sub_8, sub_9 # Graph fragment: # %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota, torch.float32), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.5), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.5), kwargs = {}) # %sub : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, 0.5), kwargs = {}) # %floor : [num_users=2] = call_function[target=torch.ops.aten.floor.default](args = (%sub,), kwargs = {}) # %floor_1 : [num_users=2] = call_function[target=torch.ops.aten.floor.default](args = (%unsqueeze,), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %floor_1), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_2, 0.0), kwargs = {}) # %clamp_max : [num_users=6] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1.0), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %floor), kwargs = {}) # %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_3, 0.0), kwargs = {}) # %clamp_max_1 : [num_users=6] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 1.0), kwargs = {}) # %add_6 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%clamp_max_1, 1.0), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_6, -0.75), kwargs = {}) # %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, -3.75), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %add_6), kwargs = {}) # %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, -6.0), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_7, %add_6), kwargs = {}) # %sub_7 : [num_users=4] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_4, -3.0), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max_1, 1.25), kwargs = {}) # %sub_8 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_5, 2.25), kwargs = {}) # %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_8, %clamp_max_1), kwargs = {}) # %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, %clamp_max_1), kwargs = {}) # %add_8 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_7, 1), kwargs = {}) # %sub_9 : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %clamp_max_1), kwargs = {}) # %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_9, 1.25), kwargs = {}) # %sub_10 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_8, 2.25), kwargs = {}) # %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_10, %sub_9), kwargs = {}) # %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_9, %sub_9), kwargs = {}) # %add_9 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_10, 1), kwargs = {}) # %sub_11 : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (2.0, %clamp_max_1), kwargs = {}) # %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_11, -0.75), kwargs = {}) # %sub_12 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_11, -3.75), kwargs = {}) # %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_12, %sub_11), kwargs = {}) # %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_12, -6.0), kwargs = {}) # %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_10, %sub_11), kwargs = {}) # %sub_13 : [num_users=4] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_13, -3.0), kwargs = {}) # %add_11 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%clamp_max, 1.0), kwargs = {}) # %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_11, -0.75), kwargs = {}) # %sub_14 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_14, -3.75), kwargs = {}) # %mul_15 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_14, %add_11), kwargs = {}) # %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_15, -6.0), kwargs = {}) # %mul_16 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_12, %add_11), kwargs = {}) # %sub_15 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_16, -3.0), kwargs = {}) # %mul_17 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max, 1.25), kwargs = {}) # %sub_16 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_17, 2.25), kwargs = {}) # %mul_18 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_16, %clamp_max), kwargs = {}) # %mul_19 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_18, %clamp_max), kwargs = {}) # %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_19, 1), kwargs = {}) # %sub_17 : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %clamp_max), kwargs = {}) # %mul_20 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_17, 1.25), kwargs = {}) # %sub_18 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_20, 2.25), kwargs = {}) # %mul_21 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_18, %sub_17), kwargs = {}) # %mul_22 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_21, %sub_17), kwargs = {}) # %add_14 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_22, 1), kwargs = {}) # %sub_19 : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (2.0, %clamp_max), kwargs = {}) # %mul_23 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_19, -0.75), kwargs = {}) # %sub_20 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_23, -3.75), kwargs = {}) # %mul_24 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_20, %sub_19), kwargs = {}) # %add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_24, -6.0), kwargs = {}) # %mul_25 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_15, %sub_19), kwargs = {}) # %sub_21 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_25, -3.0), kwargs = {}) # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_2, %clamp_max_3]), kwargs = {}) # %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_2, %clamp_max_5]), kwargs = {}) # %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_2, %clamp_max_7]), kwargs = {}) # %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_2, %clamp_max_9]), kwargs = {}) # %mul_26 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index, %sub_7), kwargs = {}) # %mul_27 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_1, %add_8), kwargs = {}) # %add_16 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_26, %mul_27), kwargs = {}) # %mul_28 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_2, %add_9), kwargs = {}) # %add_17 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_16, %mul_28), kwargs = {}) # %mul_29 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_3, %sub_13), kwargs = {}) # %add_18 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_17, %mul_29), kwargs = {}) # %_unsafe_index_4 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_10, %clamp_max_3]), kwargs = {}) # %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_10, %clamp_max_5]), kwargs = {}) # %_unsafe_index_6 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_10, %clamp_max_7]), kwargs = {}) # %_unsafe_index_7 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_10, %clamp_max_9]), kwargs = {}) # %mul_30 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_4, %sub_7), kwargs = {}) # %mul_31 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_5, %add_8), kwargs = {}) # %add_19 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_30, %mul_31), kwargs = {}) # %mul_32 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_6, %add_9), kwargs = {}) # %add_20 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_19, %mul_32), kwargs = {}) # %mul_33 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_7, %sub_13), kwargs = {}) # %add_21 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_20, %mul_33), kwargs = {}) # %_unsafe_index_8 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_18, %clamp_max_3]), kwargs = {}) # %_unsafe_index_9 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_18, %clamp_max_5]), kwargs = {}) # %_unsafe_index_10 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_18, %clamp_max_7]), kwargs = {}) # %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_18, %clamp_max_9]), kwargs = {}) # %mul_34 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_8, %sub_7), kwargs = {}) # %mul_35 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_9, %add_8), kwargs = {}) # %add_22 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_34, %mul_35), kwargs = {}) # %mul_36 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_10, %add_9), kwargs = {}) # %add_23 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_22, %mul_36), kwargs = {}) # %mul_37 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_11, %sub_13), kwargs = {}) # %add_24 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_23, %mul_37), kwargs = {}) # %_unsafe_index_12 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_26, %clamp_max_3]), kwargs = {}) # %_unsafe_index_13 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_26, %clamp_max_5]), kwargs = {}) # %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_26, %clamp_max_7]), kwargs = {}) # %_unsafe_index_15 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max_26, %clamp_max_9]), kwargs = {}) # %mul_38 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_12, %sub_7), kwargs = {}) # %mul_39 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_13, %add_8), kwargs = {}) # %add_25 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_38, %mul_39), kwargs = {}) # %mul_40 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_14, %add_9), kwargs = {}) # %add_26 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_25, %mul_40), kwargs = {}) # %mul_41 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%_unsafe_index_15, %sub_13), kwargs = {}) # %add_27 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_26, %mul_41), kwargs = {}) # %mul_42 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_18, %sub_15), kwargs = {}) # %mul_43 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_21, %add_13), kwargs = {}) # %add_28 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_42, %mul_43), kwargs = {}) # %mul_44 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_24, %add_14), kwargs = {}) # %add_29 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_28, %mul_44), kwargs = {}) # %mul_45 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_27, %sub_21), kwargs = {}) triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_floor_mul_rsub_sub_0 = async_compile.triton('triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_floor_mul_rsub_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_floor_mul_rsub_sub_0', 'mutated_arg_names': ['in_out_ptr1', 'in_out_ptr2'], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_floor_mul_rsub_sub_0(in_out_ptr1, in_out_ptr2, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 8) % 8 x0 = xindex % 8 x2 = (xindex // 64) x4 = xindex tmp0 = x1 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 + tmp2 tmp4 = tmp3 * tmp2 tmp5 = tmp4 - tmp2 tmp6 = libdevice.floor(tmp5) tmp7 = tmp6.to(tl.int32) tmp8 = tl.full([1], 1, tl.int64) tmp9 = tmp7 - tmp8 tmp10 = tl.full([1], 0, tl.int64) tmp11 = triton_helpers.maximum(tmp9, tmp10) tmp12 = tl.full([1], 3, tl.int64) tmp13 = triton_helpers.minimum(tmp11, tmp12) tmp14 = x0 tmp15 = tmp14.to(tl.float32) tmp16 = tmp15 + tmp2 tmp17 = tmp16 * tmp2 tmp18 = tmp17 - tmp2 tmp19 = libdevice.floor(tmp18) tmp20 = tmp19.to(tl.int32) tmp21 = tmp20 - tmp8 tmp22 = triton_helpers.maximum(tmp21, tmp10) tmp23 = triton_helpers.minimum(tmp22, tmp12) tmp24 = tl.load(in_ptr0 + (tmp23 + (4tmp13) + (16x2)), xmask, eviction_policy='evict_last') tmp25 = tmp18 - tmp19 tmp26 = 0.0 tmp27 = triton_helpers.maximum(tmp25, tmp26) tmp28 = 1.0 tmp29 = triton_helpers.minimum(tmp27, tmp28) tmp30 = tmp29 + tmp28 tmp31 = -0.75 tmp32 = tmp30 * tmp31 tmp33 = -3.75 tmp34 = tmp32 - tmp33 tmp35 = tmp34 * tmp30 tmp36 = -6.0 tmp37 = tmp35 + tmp36 tmp38 = tmp37 * tmp30 tmp39 = -3.0 tmp40 = tmp38 - tmp39 tmp41 = tmp24 * tmp40 tmp42 = triton_helpers.maximum(tmp20, tmp10) tmp43 = triton_helpers.minimum(tmp42, tmp12) tmp44 = tl.load(in_ptr0 + (tmp43 + (4tmp13) + (16x2)), xmask, eviction_policy='evict_last') tmp45 = 1.25 tmp46 = tmp29 * tmp45 tmp47 = 2.25 tmp48 = tmp46 - tmp47 tmp49 = tmp48 * tmp29 tmp50 = tmp49 * tmp29 tmp51 = tmp50 + tmp28 tmp52 = tmp44 * tmp51 tmp53 = tmp20 + tmp8 tmp54 = triton_helpers.maximum(tmp53, tmp10) tmp55 = triton_helpers.minimum(tmp54, tmp12) tmp56 = tl.load(in_ptr0 + (tmp55 + (4tmp13) + (16x2)), xmask, eviction_policy='evict_last') tmp57 = tmp28 - tmp29 tmp58 = tmp57 * tmp45 tmp59 = tmp58 - tmp47 tmp60 = tmp59 * tmp57 tmp61 = tmp60 * tmp57 tmp62 = tmp61 + tmp28 tmp63 = tmp56 * tmp62 tmp64 = triton_helpers.maximum(tmp7, tmp10) tmp65 = triton_helpers.minimum(tmp64, tmp12) tmp66 = tl.load(in_ptr0 + (tmp23 + (4tmp65) + (16x2)), xmask, eviction_policy='evict_last') tmp67 = tmp66 * tmp40 tmp68 = tl.full([1], 2, tl.int64) tmp69 = tmp20 + tmp68 tmp70 = triton_helpers.maximum(tmp69, tmp10) tmp71 = triton_helpers.minimum(tmp70, tmp12) tmp72 = tl.load(in_ptr0 + (tmp71 + (4tmp13) + (16x2)), xmask, eviction_policy='evict_last') tmp73 = 2.0 tmp74 = tmp73 - tmp29 tmp75 = tmp74 * tmp31 tmp76 = tmp75 - tmp33 tmp77 = tmp76 * tmp74 tmp78 = tmp77 + tmp36 tmp79 = tmp78 * tmp74 tmp80 = tmp79 - tmp39 tmp81 = tmp72 * tmp80 tmp82 = tl.load(in_ptr0 + (tmp43 + (4tmp65) + (16x2)), xmask, eviction_policy='evict_last') tmp83 = tmp82 * tmp51 tmp84 = tl.load(in_ptr0 + (tmp55 + (4tmp65) + (16x2)), xmask, eviction_policy='evict_last') tmp85 = tmp84 * tmp62 tmp86 = tl.load(in_ptr0 + (tmp71 + (4tmp65) + (16x2)), xmask, eviction_policy='evict_last') tmp87 = tmp86 * tmp80 tmp88 = tmp7 + tmp8 tmp89 = triton_helpers.maximum(tmp88, tmp10) tmp90 = triton_helpers.minimum(tmp89, tmp12) tmp91 = tl.load(in_ptr0 + (tmp23 + (4tmp90) + (16x2)), xmask, eviction_policy='evict_last') tmp92 = tmp91 * tmp40 tmp93 = tl.load(in_ptr0 + (tmp43 + (4tmp90) + (16x2)), xmask, eviction_policy='evict_last') tmp94 = tmp93 * tmp51 tmp95 = tl.load(in_ptr0 + (tmp55 + (4tmp90) + (16x2)), xmask, eviction_policy='evict_last') tmp96 = tmp95 * tmp62 tmp97 = tl.load(in_ptr0 + (tmp71 + (4tmp90) + (16x2)), xmask, eviction_policy='evict_last') tmp98 = tmp97 * tmp80 tmp99 = tmp41 + tmp52 tmp100 = tmp99 + tmp63 tmp101 = tmp100 + tmp81 tmp102 = tmp5 - tmp6 tmp103 = triton_helpers.maximum(tmp102, tmp26) tmp104 = triton_helpers.minimum(tmp103, tmp28) tmp105 = tmp104 + tmp28 tmp106 = tmp105 * tmp31 tmp107 = tmp106 - tmp33 tmp108 = tmp107 * tmp105 tmp109 = tmp108 + tmp36 tmp110 = tmp109 * tmp105 tmp111 = tmp110 - tmp39 tmp112 = tmp101 * tmp111 tmp113 = tmp67 + tmp83 tmp114 = tmp113 + tmp85 tmp115 = tmp114 + tmp87 tmp116 = tmp104 * tmp45 tmp117 = tmp116 - tmp47 tmp118 = tmp117 * tmp104 tmp119 = tmp118 * tmp104 tmp120 = tmp119 + tmp28 tmp121 = tmp115 * tmp120 tmp122 = tmp112 + tmp121 tmp123 = tmp92 + tmp94 tmp124 = tmp123 + tmp96 tmp125 = tmp124 + tmp98 tmp126 = tmp28 - tmp104 tmp127 = tmp126 * tmp45 tmp128 = tmp127 - tmp47 tmp129 = tmp128 * tmp126 tmp130 = tmp129 * tmp126 tmp131 = tmp130 + tmp28 tmp132 = tmp125 * tmp131 tmp133 = tmp122 + tmp132 tmp134 = tmp7 + tmp68 tmp135 = triton_helpers.maximum(tmp134, tmp10) tmp136 = triton_helpers.minimum(tmp135, tmp12) tmp137 = tl.load(in_ptr0 + (tmp23 + (4tmp136) + (16x2)), xmask, eviction_policy='evict_last') tmp138 = tmp137 * tmp40 tmp139 = tl.load(in_ptr0 + (tmp43 + (4tmp136) + (16x2)), xmask, eviction_policy='evict_last') tmp140 = tmp139 * tmp51 tmp141 = tl.load(in_ptr0 + (tmp55 + (4tmp136) + (16x2)), xmask, eviction_policy='evict_last') tmp142 = tmp141 * tmp62 tmp143 = tl.load(in_ptr0 + (tmp71 + (4tmp136) + (16x2)), xmask, eviction_policy='evict_last') tmp144 = tmp143 * tmp80 tmp145 = tmp138 + tmp140 tmp146 = tmp145 + tmp142 tmp147 = tmp146 + tmp144 tmp148 = tmp73 - tmp104 tmp149 = tmp148 * tmp31 tmp150 = tmp149 - tmp33 tmp151 = tmp150 * tmp148 tmp152 = tmp151 + tmp36 tmp153 = tmp152 * tmp148 tmp154 = tmp153 - tmp39 tmp155 = tmp147 * tmp154 tl.store(in_out_ptr1 + (x4), tmp133, xmask) tl.store(in_out_ptr2 + (x4), tmp155, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/jd/cjdpweec4bvuhit43w2dbgtga5dqwhwzgsuxqa45ydtw4fqnki7r.py # Topologically Sorted Source Nodes: [X, conv2d], Original ATen: [aten.add, aten.constant_pad_nd] # Source node to ATen node mapping: # X => add_30 # conv2d => constant_pad_nd # Graph fragment: # %add_30 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_29, %mul_45), kwargs = {}) # %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%add_30, [0, 1, 0, 1]), kwargs = {}) triton_poi_fused_add_constant_pad_nd_1 = async_compile.triton('triton_poi_fused_add_constant_pad_nd_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_constant_pad_nd_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_constant_pad_nd_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1296 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 9) % 9 x0 = xindex % 9 x2 = (xindex // 81) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 8, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + (x0 + (8x1) + (64x2)), tmp5 & xmask, other=0.0) tmp7 = tl.load(in_ptr1 + (x0 + (8x1) + (64x2)), tmp5 & xmask, other=0.0) tmp8 = tmp6 + tmp7 tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype) tmp10 = tl.where(tmp5, tmp8, tmp9) tl.store(out_ptr0 + (x3), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yg/cygvzqx5ki2rytrku7bpeve7vub4hvfn5v3uvx6apsobmvr7xxbs.py # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] # Source node to ATen node mapping: # conv2d => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 64) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf10 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32) buf17 = buf10; del buf10 # reuse buf12 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32) buf18 = buf12; del buf12 # reuse # Topologically Sorted Source Nodes: [X], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.floor, aten.clamp, aten.rsub, aten._unsafe_index] stream0 = get_raw_stream(0) triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_floor_mul_rsub_sub_0.run(buf17, buf18, primals_1, 1024, grid=grid(1024), stream=stream0) del primals_1 buf19 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32) # Topologically Sorted Source Nodes: [X, conv2d], Original ATen: [aten.add, aten.constant_pad_nd] triton_poi_fused_add_constant_pad_nd_1.run(buf17, buf18, buf19, 1296, grid=grid(1296), stream=stream0) del buf17 del buf18 # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf20 = extern_kernels.convolution(buf19, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 4, 8, 8), (256, 64, 8, 1)) buf21 = buf20; del buf20 # reuse # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] triton_poi_fused_convolution_2.run(buf21, primals_3, 1024, grid=grid(1024), stream=stream0) del primals_3 return (buf21, primals_2, buf19, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn class ClassicUpConv(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, padding_mode='zeros'): super(ClassicUpConv, self).__init__() self.upscale_factor = upscale_factor self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode) self.up = nn.Upsample(size=None, scale_factor=upscale_factor, mode= 'bicubic', align_corners=False) def forward(self, X): X = self.up(X) return self.conv(X) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_floor_mul_rsub_sub_0( in_out_ptr1, in_out_ptr2, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 8 % 8 x0 = xindex % 8 x2 = xindex // 64 x4 = xindex tmp0 = x1 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 + tmp2 tmp4 = tmp3 * tmp2 tmp5 = tmp4 - tmp2 tmp6 = libdevice.floor(tmp5) tmp7 = tmp6.to(tl.int32) tmp8 = tl.full([1], 1, tl.int64) tmp9 = tmp7 - tmp8 tmp10 = tl.full([1], 0, tl.int64) tmp11 = triton_helpers.maximum(tmp9, tmp10) tmp12 = tl.full([1], 3, tl.int64) tmp13 = triton_helpers.minimum(tmp11, tmp12) tmp14 = x0 tmp15 = tmp14.to(tl.float32) tmp16 = tmp15 + tmp2 tmp17 = tmp16 * tmp2 tmp18 = tmp17 - tmp2 tmp19 = libdevice.floor(tmp18) tmp20 = tmp19.to(tl.int32) tmp21 = tmp20 - tmp8 tmp22 = triton_helpers.maximum(tmp21, tmp10) tmp23 = triton_helpers.minimum(tmp22, tmp12) tmp24 = tl.load(in_ptr0 + (tmp23 + 4 * tmp13 + 16 * x2), xmask, eviction_policy='evict_last') tmp25 = tmp18 - tmp19 tmp26 = 0.0 tmp27 = triton_helpers.maximum(tmp25, tmp26) tmp28 = 1.0 tmp29 = triton_helpers.minimum(tmp27, tmp28) tmp30 = tmp29 + tmp28 tmp31 = -0.75 tmp32 = tmp30 * tmp31 tmp33 = -3.75 tmp34 = tmp32 - tmp33 tmp35 = tmp34 * tmp30 tmp36 = -6.0 tmp37 = tmp35 + tmp36 tmp38 = tmp37 * tmp30 tmp39 = -3.0 tmp40 = tmp38 - tmp39 tmp41 = tmp24 * tmp40 tmp42 = triton_helpers.maximum(tmp20, tmp10) tmp43 = triton_helpers.minimum(tmp42, tmp12) tmp44 = tl.load(in_ptr0 + (tmp43 + 4 * tmp13 + 16 * x2), xmask, eviction_policy='evict_last') tmp45 = 1.25 tmp46 = tmp29 * tmp45 tmp47 = 2.25 tmp48 = tmp46 - tmp47 tmp49 = tmp48 * tmp29 tmp50 = tmp49 * tmp29 tmp51 = tmp50 + tmp28 tmp52 = tmp44 * tmp51 tmp53 = tmp20 + tmp8 tmp54 = triton_helpers.maximum(tmp53, tmp10) tmp55 = triton_helpers.minimum(tmp54, tmp12) tmp56 = tl.load(in_ptr0 + (tmp55 + 4 * tmp13 + 16 * x2), xmask, eviction_policy='evict_last') tmp57 = tmp28 - tmp29 tmp58 = tmp57 * tmp45 tmp59 = tmp58 - tmp47 tmp60 = tmp59 * tmp57 tmp61 = tmp60 * tmp57 tmp62 = tmp61 + tmp28 tmp63 = tmp56 * tmp62 tmp64 = triton_helpers.maximum(tmp7, tmp10) tmp65 = triton_helpers.minimum(tmp64, tmp12) tmp66 = tl.load(in_ptr0 + (tmp23 + 4 * tmp65 + 16 * x2), xmask, eviction_policy='evict_last') tmp67 = tmp66 * tmp40 tmp68 = tl.full([1], 2, tl.int64) tmp69 = tmp20 + tmp68 tmp70 = triton_helpers.maximum(tmp69, tmp10) tmp71 = triton_helpers.minimum(tmp70, tmp12) tmp72 = tl.load(in_ptr0 + (tmp71 + 4 * tmp13 + 16 * x2), xmask, eviction_policy='evict_last') tmp73 = 2.0 tmp74 = tmp73 - tmp29 tmp75 = tmp74 * tmp31 tmp76 = tmp75 - tmp33 tmp77 = tmp76 * tmp74 tmp78 = tmp77 + tmp36 tmp79 = tmp78 * tmp74 tmp80 = tmp79 - tmp39 tmp81 = tmp72 * tmp80 tmp82 = tl.load(in_ptr0 + (tmp43 + 4 * tmp65 + 16 * x2), xmask, eviction_policy='evict_last') tmp83 = tmp82 * tmp51 tmp84 = tl.load(in_ptr0 + (tmp55 + 4 * tmp65 + 16 * x2), xmask, eviction_policy='evict_last') tmp85 = tmp84 * tmp62 tmp86 = tl.load(in_ptr0 + (tmp71 + 4 * tmp65 + 16 * x2), xmask, eviction_policy='evict_last') tmp87 = tmp86 * tmp80 tmp88 = tmp7 + tmp8 tmp89 = triton_helpers.maximum(tmp88, tmp10) tmp90 = triton_helpers.minimum(tmp89, tmp12) tmp91 = tl.load(in_ptr0 + (tmp23 + 4 * tmp90 + 16 * x2), xmask, eviction_policy='evict_last') tmp92 = tmp91 * tmp40 tmp93 = tl.load(in_ptr0 + (tmp43 + 4 * tmp90 + 16 * x2), xmask, eviction_policy='evict_last') tmp94 = tmp93 * tmp51 tmp95 = tl.load(in_ptr0 + (tmp55 + 4 * tmp90 + 16 * x2), xmask, eviction_policy='evict_last') tmp96 = tmp95 * tmp62 tmp97 = tl.load(in_ptr0 + (tmp71 + 4 * tmp90 + 16 * x2), xmask, eviction_policy='evict_last') tmp98 = tmp97 * tmp80 tmp99 = tmp41 + tmp52 tmp100 = tmp99 + tmp63 tmp101 = tmp100 + tmp81 tmp102 = tmp5 - tmp6 tmp103 = triton_helpers.maximum(tmp102, tmp26) tmp104 = triton_helpers.minimum(tmp103, tmp28) tmp105 = tmp104 + tmp28 tmp106 = tmp105 * tmp31 tmp107 = tmp106 - tmp33 tmp108 = tmp107 * tmp105 tmp109 = tmp108 + tmp36 tmp110 = tmp109 * tmp105 tmp111 = tmp110 - tmp39 tmp112 = tmp101 * tmp111 tmp113 = tmp67 + tmp83 tmp114 = tmp113 + tmp85 tmp115 = tmp114 + tmp87 tmp116 = tmp104 * tmp45 tmp117 = tmp116 - tmp47 tmp118 = tmp117 * tmp104 tmp119 = tmp118 * tmp104 tmp120 = tmp119 + tmp28 tmp121 = tmp115 * tmp120 tmp122 = tmp112 + tmp121 tmp123 = tmp92 + tmp94 tmp124 = tmp123 + tmp96 tmp125 = tmp124 + tmp98 tmp126 = tmp28 - tmp104 tmp127 = tmp126 * tmp45 tmp128 = tmp127 - tmp47 tmp129 = tmp128 * tmp126 tmp130 = tmp129 * tmp126 tmp131 = tmp130 + tmp28 tmp132 = tmp125 * tmp131 tmp133 = tmp122 + tmp132 tmp134 = tmp7 + tmp68 tmp135 = triton_helpers.maximum(tmp134, tmp10) tmp136 = triton_helpers.minimum(tmp135, tmp12) tmp137 = tl.load(in_ptr0 + (tmp23 + 4 * tmp136 + 16 * x2), xmask, eviction_policy='evict_last') tmp138 = tmp137 * tmp40 tmp139 = tl.load(in_ptr0 + (tmp43 + 4 * tmp136 + 16 * x2), xmask, eviction_policy='evict_last') tmp140 = tmp139 * tmp51 tmp141 = tl.load(in_ptr0 + (tmp55 + 4 * tmp136 + 16 * x2), xmask, eviction_policy='evict_last') tmp142 = tmp141 * tmp62 tmp143 = tl.load(in_ptr0 + (tmp71 + 4 * tmp136 + 16 * x2), xmask, eviction_policy='evict_last') tmp144 = tmp143 * tmp80 tmp145 = tmp138 + tmp140 tmp146 = tmp145 + tmp142 tmp147 = tmp146 + tmp144 tmp148 = tmp73 - tmp104 tmp149 = tmp148 * tmp31 tmp150 = tmp149 - tmp33 tmp151 = tmp150 * tmp148 tmp152 = tmp151 + tmp36 tmp153 = tmp152 * tmp148 tmp154 = tmp153 - tmp39 tmp155 = tmp147 * tmp154 tl.store(in_out_ptr1 + x4, tmp133, xmask) tl.store(in_out_ptr2 + x4, tmp155, xmask) @triton.jit def triton_poi_fused_add_constant_pad_nd_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1296 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 9 % 9 x0 = xindex % 9 x2 = xindex // 81 x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 8, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + (x0 + 8 * x1 + 64 * x2), tmp5 & xmask, other=0.0) tmp7 = tl.load(in_ptr1 + (x0 + 8 * x1 + 64 * x2), tmp5 & xmask, other=0.0) tmp8 = tmp6 + tmp7 tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype) tmp10 = tl.where(tmp5, tmp8, tmp9) tl.store(out_ptr0 + x3, tmp10, xmask) @triton.jit def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 64 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf10 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32 ) buf17 = buf10 del buf10 buf12 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32 ) buf18 = buf12 del buf12 get_raw_stream(0) triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_floor_mul_rsub_sub_0[ grid(1024)](buf17, buf18, primals_1, 1024, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 buf19 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32 ) triton_poi_fused_add_constant_pad_nd_1[grid(1296)](buf17, buf18, buf19, 1296, XBLOCK=256, num_warps=4, num_stages=1) del buf17 del buf18 buf20 = extern_kernels.convolution(buf19, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 4, 8, 8), (256, 64, 8, 1)) buf21 = buf20 del buf20 triton_poi_fused_convolution_2[grid(1024)](buf21, primals_3, 1024, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 return buf21, primals_2, buf19 class ClassicUpConvNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, padding_mode='zeros'): super(ClassicUpConvNew, self).__init__() self.upscale_factor = upscale_factor self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding, padding_mode=padding_mode) self.up = nn.Upsample(size=None, scale_factor=upscale_factor, mode= 'bicubic', align_corners=False) def forward(self, input_0): primals_1 = self.conv.weight primals_3 = self.conv.bias primals_2 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	GerbenBeintema/deepSI	ClassicUpConv	false	8,179	[ "BSD-3-Clause" ]	12	580711210398064bb7f01e41d08b7a248a88b35b	https://github.com/GerbenBeintema/deepSI/tree/580711210398064bb7f01e41d08b7a248a88b35b
ScalarFilter	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/5r/c5ru25krebl6igmnztyu4pnvzz3ovvcrwoxifku4pn362zw6gweh.py # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max] # Source node to ATen node mapping: # max_1 => getitem # Graph fragment: # %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%max_1, 0), kwargs = {}) triton_poi_fused_max_0 = async_compile.triton('triton_poi_fused_max_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (64x2)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (16 + x0 + (64x2)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr1 + (32 + x0 + (64x2)), xmask, eviction_policy='evict_last') tmp13 = tl.load(in_ptr1 + (48 + x0 + (64x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 - tmp1 tmp3 = 0.0 tmp4 = tmp2 == tmp3 tmp6 = tmp0 - tmp5 tmp7 = tmp6 == tmp3 tmp8 = triton_helpers.maximum(tmp4, tmp7) tmp10 = tmp0 - tmp9 tmp11 = tmp10 == tmp3 tmp12 = triton_helpers.maximum(tmp8, tmp11) tmp14 = tmp0 - tmp13 tmp15 = tmp14 == tmp3 tmp16 = triton_helpers.maximum(tmp12, tmp15) tl.store(out_ptr0 + (x3), tmp16, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max] stream0 = get_raw_stream(0) triton_poi_fused_max_0.run(arg0_1, arg1_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 del arg1_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch as th import torch.nn as nn class ScalarFilter(nn.Module): def __init__(self): super(ScalarFilter, self).__init__() def forward(self, p_x, g_x): """ input should be scalar: bsz x l1, bsz x l2 return bsz x l2 """ matrix = g_x.unsqueeze(2) - p_x.unsqueeze(1) return th.max(matrix == 0, dim=2)[0] def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_max_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp9 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp13 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 - tmp1 tmp3 = 0.0 tmp4 = tmp2 == tmp3 tmp6 = tmp0 - tmp5 tmp7 = tmp6 == tmp3 tmp8 = triton_helpers.maximum(tmp4, tmp7) tmp10 = tmp0 - tmp9 tmp11 = tmp10 == tmp3 tmp12 = triton_helpers.maximum(tmp8, tmp11) tmp14 = tmp0 - tmp13 tmp15 = tmp14 == tmp3 tmp16 = triton_helpers.maximum(tmp12, tmp15) tl.store(out_ptr0 + x3, tmp16, xmask) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_max_0[grid(256)](arg0_1, arg1_1, buf0, 256, XBLOCK =256, num_warps=4, num_stages=1) del arg0_1 del arg1_1 return buf0, class ScalarFilterNew(nn.Module): def __init__(self): super(ScalarFilterNew, self).__init__() def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	HKUST-KnowComp/DualMessagePassing	ScalarFilter	false	8,180	[ "MIT" ]	12	d29d627be2a8c8f24b52e3db2c383e33a059aaa7	https://github.com/HKUST-KnowComp/DualMessagePassing/tree/d29d627be2a8c8f24b52e3db2c383e33a059aaa7
WeightedBCELoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/cv/ccvlk4yzzvsomtdpb7pmyki2zpkpvikdfjidje4om25y4dydxkgi.py # Topologically Sorted Source Nodes: [neg_1, log_sigmoid_1, mul_1, log_sigmoid, neg, sub, neg_vals, vals, mul_2, sum_1, losses, mean], Original ATen: [aten.neg, aten.log_sigmoid_forward, aten.mul, aten.rsub, aten.sub, aten.sum, aten.div, aten.mean] # Source node to ATen node mapping: # log_sigmoid => abs_1, exp, full_default, log1p, minimum, neg_1, sub # log_sigmoid_1 => abs_2, exp_1, full_default_1, log1p_1, minimum_1, neg_3, sub_2 # losses => div # mean => mean # mul_1 => mul_1 # mul_2 => mul_2 # neg => neg # neg_1 => neg_2 # neg_vals => mul # sub => sub_1 # sum_1 => sum_1 # vals => sub_3 # Graph fragment: # %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg1_1,), kwargs = {}) # %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %minimum_1 : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default_1, %arg0_1), kwargs = {}) # %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {}) # %neg_3 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_2,), kwargs = {}) # %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_3,), kwargs = {}) # %log1p_1 : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp_1,), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum_1, %log1p_1), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg_2, %sub_2), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %neg : [num_users=2] = call_function[target=torch.ops.aten.neg.default](args = (%arg0_1,), kwargs = {}) # %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default, %neg), kwargs = {}) # %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%neg,), kwargs = {}) # %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {}) # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {}) # %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %sub_1), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, %mul), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %arg2_1), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [-1]), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 4), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%div,), kwargs = {}) triton_per_fused_div_log_sigmoid_forward_mean_mul_neg_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_div_log_sigmoid_forward_mean_mul_neg_rsub_sub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 64], reduction_hint=ReductionHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_log_sigmoid_forward_mean_mul_neg_rsub_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_div_log_sigmoid_forward_mean_mul_neg_rsub_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 1 rnumel = 64 RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (4r0), None, eviction_policy='evict_last') tmp2 = tl.load(in_ptr1 + (4r0), None, eviction_policy='evict_last') tmp22 = tl.load(in_ptr2 + (4r0), None, eviction_policy='evict_last') tmp24 = tl.load(in_ptr0 + (1 + (4r0)), None, eviction_policy='evict_last') tmp26 = tl.load(in_ptr1 + (1 + (4r0)), None, eviction_policy='evict_last') tmp44 = tl.load(in_ptr2 + (1 + (4r0)), None, eviction_policy='evict_last') tmp47 = tl.load(in_ptr0 + (2 + (4r0)), None, eviction_policy='evict_last') tmp49 = tl.load(in_ptr1 + (2 + (4r0)), None, eviction_policy='evict_last') tmp67 = tl.load(in_ptr2 + (2 + (4r0)), None, eviction_policy='evict_last') tmp70 = tl.load(in_ptr0 + (3 + (4r0)), None, eviction_policy='evict_last') tmp72 = tl.load(in_ptr1 + (3 + (4r0)), None, eviction_policy='evict_last') tmp90 = tl.load(in_ptr2 + (3 + (4r0)), None, eviction_policy='evict_last') tmp1 = -tmp0 tmp3 = 0.0 tmp4 = triton_helpers.minimum(tmp3, tmp2) tmp5 = tl_math.abs(tmp2) tmp6 = -tmp5 tmp7 = tl_math.exp(tmp6) tmp8 = libdevice.log1p(tmp7) tmp9 = tmp4 - tmp8 tmp10 = tmp1 * tmp9 tmp11 = -tmp2 tmp12 = triton_helpers.minimum(tmp3, tmp11) tmp13 = tl_math.abs(tmp11) tmp14 = -tmp13 tmp15 = tl_math.exp(tmp14) tmp16 = libdevice.log1p(tmp15) tmp17 = tmp12 - tmp16 tmp18 = 1.0 tmp19 = tmp18 - tmp0 tmp20 = tmp17 * tmp19 tmp21 = tmp10 - tmp20 tmp23 = tmp21 * tmp22 tmp25 = -tmp24 tmp27 = triton_helpers.minimum(tmp3, tmp26) tmp28 = tl_math.abs(tmp26) tmp29 = -tmp28 tmp30 = tl_math.exp(tmp29) tmp31 = libdevice.log1p(tmp30) tmp32 = tmp27 - tmp31 tmp33 = tmp25 * tmp32 tmp34 = -tmp26 tmp35 = triton_helpers.minimum(tmp3, tmp34) tmp36 = tl_math.abs(tmp34) tmp37 = -tmp36 tmp38 = tl_math.exp(tmp37) tmp39 = libdevice.log1p(tmp38) tmp40 = tmp35 - tmp39 tmp41 = tmp18 - tmp24 tmp42 = tmp40 * tmp41 tmp43 = tmp33 - tmp42 tmp45 = tmp43 * tmp44 tmp46 = tmp23 + tmp45 tmp48 = -tmp47 tmp50 = triton_helpers.minimum(tmp3, tmp49) tmp51 = tl_math.abs(tmp49) tmp52 = -tmp51 tmp53 = tl_math.exp(tmp52) tmp54 = libdevice.log1p(tmp53) tmp55 = tmp50 - tmp54 tmp56 = tmp48 * tmp55 tmp57 = -tmp49 tmp58 = triton_helpers.minimum(tmp3, tmp57) tmp59 = tl_math.abs(tmp57) tmp60 = -tmp59 tmp61 = tl_math.exp(tmp60) tmp62 = libdevice.log1p(tmp61) tmp63 = tmp58 - tmp62 tmp64 = tmp18 - tmp47 tmp65 = tmp63 * tmp64 tmp66 = tmp56 - tmp65 tmp68 = tmp66 * tmp67 tmp69 = tmp46 + tmp68 tmp71 = -tmp70 tmp73 = triton_helpers.minimum(tmp3, tmp72) tmp74 = tl_math.abs(tmp72) tmp75 = -tmp74 tmp76 = tl_math.exp(tmp75) tmp77 = libdevice.log1p(tmp76) tmp78 = tmp73 - tmp77 tmp79 = tmp71 * tmp78 tmp80 = -tmp72 tmp81 = triton_helpers.minimum(tmp3, tmp80) tmp82 = tl_math.abs(tmp80) tmp83 = -tmp82 tmp84 = tl_math.exp(tmp83) tmp85 = libdevice.log1p(tmp84) tmp86 = tmp81 - tmp85 tmp87 = tmp18 - tmp70 tmp88 = tmp86 * tmp87 tmp89 = tmp79 - tmp88 tmp91 = tmp89 * tmp90 tmp92 = tmp69 + tmp91 tmp93 = 0.25 tmp94 = tmp92 * tmp93 tmp95 = tl.broadcast_to(tmp94, [XBLOCK, RBLOCK]) tmp97 = tl.sum(tmp95, 1)[:, None] tmp98 = 64.0 tmp99 = tmp97 / tmp98 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp99, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf1 = empty_strided_cuda((), (), torch.float32) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [neg_1, log_sigmoid_1, mul_1, log_sigmoid, neg, sub, neg_vals, vals, mul_2, sum_1, losses, mean], Original ATen: [aten.neg, aten.log_sigmoid_forward, aten.mul, aten.rsub, aten.sub, aten.sum, aten.div, aten.mean] stream0 = get_raw_stream(0) triton_per_fused_div_log_sigmoid_forward_mean_mul_neg_rsub_sub_sum_0.run(buf2, arg1_1, arg0_1, arg2_1, 1, 64, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 del arg2_1 return (buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch class WeightedBCELoss(torch.nn.Module): def __init__(self, neg_scale=-1, bce_sum=False): super(WeightedBCELoss, self).__init__() self.log_sigmoid = torch.nn.LogSigmoid() self.neg_scale = neg_scale self.bce_sum = bce_sum def forward(self, logits, targets, target_weights): neg_vals = self.log_sigmoid(-logits) * (1 - targets) if self.neg_scale > 0: neg_vals = self.neg_scale vals = -targets self.log_sigmoid(logits) - neg_vals if self.bce_sum: losses = torch.sum(vals * target_weights, dim=-1) else: losses = torch.sum(vals * target_weights, dim=-1) / logits.size()[1 ] return torch.mean(losses) def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_div_log_sigmoid_forward_mean_mul_neg_rsub_sub_sum_0( in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl. constexpr): RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last') tmp2 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last') tmp22 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last') tmp24 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last') tmp26 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last') tmp44 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last') tmp47 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last') tmp49 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last') tmp67 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last') tmp70 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last') tmp72 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last') tmp90 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last') tmp1 = -tmp0 tmp3 = 0.0 tmp4 = triton_helpers.minimum(tmp3, tmp2) tmp5 = tl_math.abs(tmp2) tmp6 = -tmp5 tmp7 = tl_math.exp(tmp6) tmp8 = libdevice.log1p(tmp7) tmp9 = tmp4 - tmp8 tmp10 = tmp1 * tmp9 tmp11 = -tmp2 tmp12 = triton_helpers.minimum(tmp3, tmp11) tmp13 = tl_math.abs(tmp11) tmp14 = -tmp13 tmp15 = tl_math.exp(tmp14) tmp16 = libdevice.log1p(tmp15) tmp17 = tmp12 - tmp16 tmp18 = 1.0 tmp19 = tmp18 - tmp0 tmp20 = tmp17 * tmp19 tmp21 = tmp10 - tmp20 tmp23 = tmp21 * tmp22 tmp25 = -tmp24 tmp27 = triton_helpers.minimum(tmp3, tmp26) tmp28 = tl_math.abs(tmp26) tmp29 = -tmp28 tmp30 = tl_math.exp(tmp29) tmp31 = libdevice.log1p(tmp30) tmp32 = tmp27 - tmp31 tmp33 = tmp25 * tmp32 tmp34 = -tmp26 tmp35 = triton_helpers.minimum(tmp3, tmp34) tmp36 = tl_math.abs(tmp34) tmp37 = -tmp36 tmp38 = tl_math.exp(tmp37) tmp39 = libdevice.log1p(tmp38) tmp40 = tmp35 - tmp39 tmp41 = tmp18 - tmp24 tmp42 = tmp40 * tmp41 tmp43 = tmp33 - tmp42 tmp45 = tmp43 * tmp44 tmp46 = tmp23 + tmp45 tmp48 = -tmp47 tmp50 = triton_helpers.minimum(tmp3, tmp49) tmp51 = tl_math.abs(tmp49) tmp52 = -tmp51 tmp53 = tl_math.exp(tmp52) tmp54 = libdevice.log1p(tmp53) tmp55 = tmp50 - tmp54 tmp56 = tmp48 * tmp55 tmp57 = -tmp49 tmp58 = triton_helpers.minimum(tmp3, tmp57) tmp59 = tl_math.abs(tmp57) tmp60 = -tmp59 tmp61 = tl_math.exp(tmp60) tmp62 = libdevice.log1p(tmp61) tmp63 = tmp58 - tmp62 tmp64 = tmp18 - tmp47 tmp65 = tmp63 * tmp64 tmp66 = tmp56 - tmp65 tmp68 = tmp66 * tmp67 tmp69 = tmp46 + tmp68 tmp71 = -tmp70 tmp73 = triton_helpers.minimum(tmp3, tmp72) tmp74 = tl_math.abs(tmp72) tmp75 = -tmp74 tmp76 = tl_math.exp(tmp75) tmp77 = libdevice.log1p(tmp76) tmp78 = tmp73 - tmp77 tmp79 = tmp71 * tmp78 tmp80 = -tmp72 tmp81 = triton_helpers.minimum(tmp3, tmp80) tmp82 = tl_math.abs(tmp80) tmp83 = -tmp82 tmp84 = tl_math.exp(tmp83) tmp85 = libdevice.log1p(tmp84) tmp86 = tmp81 - tmp85 tmp87 = tmp18 - tmp70 tmp88 = tmp86 * tmp87 tmp89 = tmp79 - tmp88 tmp91 = tmp89 * tmp90 tmp92 = tmp69 + tmp91 tmp93 = 0.25 tmp94 = tmp92 * tmp93 tmp95 = tl.broadcast_to(tmp94, [XBLOCK, RBLOCK]) tmp97 = tl.sum(tmp95, 1)[:, None] tmp98 = 64.0 tmp99 = tmp97 / tmp98 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp99, None) def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf1 = empty_strided_cuda((), (), torch.float32) buf2 = buf1 del buf1 get_raw_stream(0) triton_per_fused_div_log_sigmoid_forward_mean_mul_neg_rsub_sub_sum_0[ grid(1)](buf2, arg1_1, arg0_1, arg2_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1) del arg0_1 del arg1_1 del arg2_1 return buf2, class WeightedBCELossNew(torch.nn.Module): def __init__(self, neg_scale=-1, bce_sum=False): super(WeightedBCELossNew, self).__init__() self.log_sigmoid = torch.nn.LogSigmoid() self.neg_scale = neg_scale self.bce_sum = bce_sum def forward(self, input_0, input_1, input_2): arg0_1 = input_0 arg1_1 = input_1 arg2_1 = input_2 output = call([arg0_1, arg1_1, arg2_1]) return output[0]	HKUST-KnowComp/MLMET	WeightedBCELoss	false	8,181	[ "MIT" ]	10	ae1188a929a5ca6a8e087bb091853b328ea2c7e7	https://github.com/HKUST-KnowComp/MLMET/tree/ae1188a929a5ca6a8e087bb091853b328ea2c7e7
Actor	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/4g/c4guhk7x6skkidedvs2gxz2kcu6gb76l3ig5crjjvjtzvnjlhlte.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x_1 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_3, buf6, 256, grid=grid(256), stream=stream0) del primals_3 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf5, 256, grid=grid(256), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [mu], Original ATen: [aten.addmm] extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4) del primals_7 return (reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), primals_6, buf5, primals_4, buf6, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class Actor(nn.Module): def __init__(self, hidden_size, num_inputs, num_outputs): super(Actor, self).__init__() self.linear1 = nn.Linear(num_inputs, hidden_size) self.linear2 = nn.Linear(hidden_size, hidden_size) self.mu = nn.Linear(hidden_size, num_outputs) self.mu.weight.data.mul_(0.1) self.mu.bias.data.mul_(0.1) def forward(self, inputs): x = inputs x = self.linear1(x) x = F.relu(x) x = self.linear2(x) x = F.relu(x) mu = self.mu(x) return mu def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'hidden_size': 4, 'num_inputs': 4, 'num_outputs': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf0 buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, primals_3, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf2 buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3, primals_5, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), ( 4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4) del primals_7 return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor( buf3, (64, 4), (4, 1), 0), primals_6, buf5, primals_4, buf6 class ActorNew(nn.Module): def __init__(self, hidden_size, num_inputs, num_outputs): super(ActorNew, self).__init__() self.linear1 = nn.Linear(num_inputs, hidden_size) self.linear2 = nn.Linear(hidden_size, hidden_size) self.mu = nn.Linear(hidden_size, num_outputs) self.mu.weight.data.mul_(0.1) self.mu.bias.data.mul_(0.1) def forward(self, input_0): primals_2 = self.linear1.weight primals_3 = self.linear1.bias primals_4 = self.linear2.weight primals_5 = self.linear2.bias primals_6 = self.mu.weight primals_7 = self.mu.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]	HAXRD/PIC	Actor	false	8,182	[ "MIT" ]	28	658b4dd6b01e64413d5f8f0107d9167f1bd78546	https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546
Sparsemax	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/js/cjsf4uqjzylafiiybsips6b2aetis42drlwk6wumjptot26b57lm.py # Topologically Sorted Source Nodes: [x_3, sort, cumulative_sum_zs], Original ATen: [aten.sub, aten.sort, aten.cumsum] # Source node to ATen node mapping: # cumulative_sum_zs => cumsum # sort => sort # x_3 => sub # Graph fragment: # %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%permute_1, %expand), kwargs = {}) # %sort : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%sub, 1, True), kwargs = {}) # %cumsum : [num_users=1] = call_function[target=torch.ops.aten.cumsum.default](args = (%getitem_2, 1), kwargs = {}) triton_per_fused_cumsum_sort_sub_0 = async_compile.triton('triton_per_fused_cumsum_sort_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton.jit def _triton_helper_fn_add0(arg0_0, arg1_0): tmp0 = arg0_0 + arg1_0 return tmp0 @triton_heuristics.persistent_reduction( size_hints=[64, 4], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_cumsum_sort_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_cumsum_sort_sub_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 64 rnumel = 4 RBLOCK: tl.constexpr = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + (4(x0 // 4)) + (64(x0 % 4))), xmask, other=0.0) tmp1 = tl.load(in_ptr0 + ((4(x0 // 4)) + (64(x0 % 4))), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4(x0 // 4)) + (64(x0 % 4))), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4(x0 // 4)) + (64(x0 % 4))), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4(x0 // 4)) + (64(x0 % 4))), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = r1 tmp10 = tmp9.to(tl.int16) tmp11 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK]) tmp12 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK]) tmp13, tmp14, = triton_helpers.sort_with_index(tmp11, tmp12, None, 1, stable=False, descending=True) tmp15 = tmp13.to(tl.float32) tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK]) tmp17, = tl.associative_scan((tmp16,), 1, _triton_helper_fn_add0) tl.store(out_ptr0 + (r1 + (4x0)), tmp8, xmask) tl.store(out_ptr1 + (r1 + (4x0)), tmp13, xmask) tl.store(out_ptr2 + (r1 + (4x0)), tmp17, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gg/cgg54ytfiftmzzwxjkbzjvy7ejj3xfreqq4bcjkfbsl27pbo4lsj.py # Topologically Sorted Source Nodes: [mul, bound, gt, is_gt, mul_1, max_2, zs_sparse, sum_1], Original ATen: [aten.mul, aten.add, aten.gt, aten._to_copy, aten.max, aten.sum] # Source node to ATen node mapping: # bound => add_1 # gt => gt # is_gt => convert_element_type_1 # max_2 => max_2 # mul => mul_1 # mul_1 => mul_2 # sum_1 => sum_1 # zs_sparse => mul_3 # Graph fragment: # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_1, %getitem_2), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 1), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%add_1, %cumsum), kwargs = {}) # %convert_element_type_1 : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%gt, torch.float32), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_1, %expand_1), kwargs = {}) # %max_2 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul_2, 1, True), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_1, %getitem_2), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_3, [1], True), kwargs = {}) triton_poi_fused__to_copy_add_gt_max_mul_sum_1 = async_compile.triton('triton_poi_fused__to_copy_add_gt_max_mul_sum_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_gt_max_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_gt_max_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4x0), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (4x0), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (1 + (4x0)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (1 + (4x0)), xmask, eviction_policy='evict_last') tmp17 = tl.load(in_ptr0 + (2 + (4x0)), xmask, eviction_policy='evict_last') tmp21 = tl.load(in_ptr1 + (2 + (4x0)), xmask, eviction_policy='evict_last') tmp26 = tl.load(in_ptr0 + (3 + (4x0)), xmask, eviction_policy='evict_last') tmp30 = tl.load(in_ptr1 + (3 + (4x0)), xmask, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp1 * tmp0 tmp3 = tmp2 + tmp1 tmp5 = tmp3 > tmp4 tmp6 = tmp5.to(tl.float32) tmp7 = tmp6 * tmp1 tmp9 = 2.0 tmp10 = tmp9 * tmp8 tmp11 = tmp10 + tmp1 tmp13 = tmp11 > tmp12 tmp14 = tmp13.to(tl.float32) tmp15 = tmp14 * tmp9 tmp16 = triton_helpers.maximum(tmp7, tmp15) tmp18 = 3.0 tmp19 = tmp18 * tmp17 tmp20 = tmp19 + tmp1 tmp22 = tmp20 > tmp21 tmp23 = tmp22.to(tl.float32) tmp24 = tmp23 * tmp18 tmp25 = triton_helpers.maximum(tmp16, tmp24) tmp27 = 4.0 tmp28 = tmp27 * tmp26 tmp29 = tmp28 + tmp1 tmp31 = tmp29 > tmp30 tmp32 = tmp31.to(tl.float32) tmp33 = tmp32 * tmp27 tmp34 = triton_helpers.maximum(tmp25, tmp33) tmp35 = tmp6 * tmp0 tmp36 = tmp14 * tmp8 tmp37 = tmp35 + tmp36 tmp38 = tmp23 * tmp17 tmp39 = tmp37 + tmp38 tmp40 = tmp32 * tmp26 tmp41 = tmp39 + tmp40 tl.store(out_ptr0 + (x0), tmp34, xmask) tl.store(out_ptr1 + (x0), tmp41, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ad/cadcfahigrw3wizafgdfwd54uboceevqimjnto77j3lagazvzihn.py # Topologically Sorted Source Nodes: [zeros_like, sub_2, output], Original ATen: [aten.zeros_like, aten.sub, aten.maximum] # Source node to ATen node mapping: # output => maximum # sub_2 => sub_2 # zeros_like => full_default # Graph fragment: # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([64, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %expand_2), kwargs = {}) # %maximum : [num_users=2] = call_function[target=torch.ops.aten.maximum.default](args = (%full_default, %sub_2), kwargs = {}) triton_poi_fused_maximum_sub_zeros_like_2 = async_compile.triton('triton_poi_fused_maximum_sub_zeros_like_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_maximum_sub_zeros_like_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_maximum_sub_zeros_like_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last') tmp2 = 1.0 tmp3 = tmp1 - tmp2 tmp5 = tmp3 / tmp4 tmp6 = tmp0 - tmp5 tmp7 = 0.0 tmp8 = triton_helpers.maximum(tmp7, tmp6) tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [x_3, sort, cumulative_sum_zs], Original ATen: [aten.sub, aten.sort, aten.cumsum] stream0 = get_raw_stream(0) triton_per_fused_cumsum_sort_sub_0.run(arg0_1, buf0, buf1, buf3, 64, 4, grid=grid(64), stream=stream0) del arg0_1 buf4 = empty_strided_cuda((64, 1), (1, 64), torch.float32) buf5 = empty_strided_cuda((64, 1), (1, 64), torch.float32) # Topologically Sorted Source Nodes: [mul, bound, gt, is_gt, mul_1, max_2, zs_sparse, sum_1], Original ATen: [aten.mul, aten.add, aten.gt, aten._to_copy, aten.max, aten.sum] triton_poi_fused__to_copy_add_gt_max_mul_sum_1.run(buf1, buf3, buf4, buf5, 64, grid=grid(64), stream=stream0) del buf1 buf6 = buf3; del buf3 # reuse # Topologically Sorted Source Nodes: [zeros_like, sub_2, output], Original ATen: [aten.zeros_like, aten.sub, aten.maximum] triton_poi_fused_maximum_sub_zeros_like_2.run(buf0, buf5, buf4, buf6, 256, grid=grid(256), stream=stream0) del buf0 del buf4 del buf5 return (reinterpret_tensor(buf6, (4, 4, 4, 4), (4, 64, 16, 1), 0), buf6, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch as th import torch.nn as nn class Sparsemax(nn.Module): """Sparsemax function.""" def __init__(self, dim=-1): """Initialize sparsemax activation Args: dim (int, optional): The dimension over which to apply the sparsemax function. """ super(Sparsemax, self).__init__() self.dim = dim def forward(self, x): """Forward function. Args: x (th.Tensor): Input tensor. First dimension should be the batch size Returns: th.Tensor: [batch_size x number_of_logits] Output tensor """ x = x.transpose(0, self.dim) original_size = x.size() x = x.reshape(x.size(0), -1) x = x.transpose(0, 1) dim = 1 number_of_logits = x.size(dim) x = x - th.max(x, dim=dim, keepdim=True)[0].expand_as(x) zs = th.sort(x, dim=dim, descending=True)[0] rg = th.arange(start=1, end=number_of_logits + 1, step=1, device=x. device, dtype=x.dtype).view(1, -1) rg = rg.expand_as(zs) bound = 1 + rg * zs cumulative_sum_zs = th.cumsum(zs, dim) is_gt = th.gt(bound, cumulative_sum_zs).type(x.type()) k = th.max(is_gt * rg, dim, keepdim=True)[0] zs_sparse = is_gt * zs taus = (th.sum(zs_sparse, dim, keepdim=True) - 1) / k taus = taus.expand_as(x) self.output = th.max(th.zeros_like(x), x - taus) output = self.output output = output.transpose(0, 1) output = output.reshape(original_size) output = output.transpose(0, self.dim) return output def backward(self, x): """Backward function.""" dim = 1 nonzeros = th.ne(self.output, 0) x_sum = th.sum(x * nonzeros, dim=dim) / th.sum(nonzeros, dim=dim) self.grad_input = nonzeros * (x - x_sum.expand_as(x)) return self.grad_input def extra_repr(self): return 'dim={}'.format(self.dim) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch as th import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def _triton_helper_fn_add0(arg0_0, arg1_0): tmp0 = arg0_0 + arg1_0 return tmp0 @triton.jit def triton_per_fused_cumsum_sort_sub_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 64 RBLOCK: tl.constexpr = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + 4 * (x0 // 4) + 64 * (x0 % 4)), xmask, other=0.0) tmp1 = tl.load(in_ptr0 + (4 * (x0 // 4) + 64 * (x0 % 4)), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * (x0 // 4) + 64 * (x0 % 4)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * (x0 // 4) + 64 * (x0 % 4)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * (x0 // 4) + 64 * (x0 % 4)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = r1 tmp10 = tmp9.to(tl.int16) tmp11 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK]) tmp12 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK]) tmp13, _tmp14 = triton_helpers.sort_with_index(tmp11, tmp12, None, 1, stable=False, descending=True) tmp15 = tmp13.to(tl.float32) tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK]) tmp17, = tl.associative_scan((tmp16,), 1, _triton_helper_fn_add0) tl.store(out_ptr0 + (r1 + 4 * x0), tmp8, xmask) tl.store(out_ptr1 + (r1 + 4 * x0), tmp13, xmask) tl.store(out_ptr2 + (r1 + 4 * x0), tmp17, xmask) @triton.jit def triton_poi_fused__to_copy_add_gt_max_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp17 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp21 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp26 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp30 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp1 = 1.0 tmp2 = tmp1 * tmp0 tmp3 = tmp2 + tmp1 tmp5 = tmp3 > tmp4 tmp6 = tmp5.to(tl.float32) tmp7 = tmp6 * tmp1 tmp9 = 2.0 tmp10 = tmp9 * tmp8 tmp11 = tmp10 + tmp1 tmp13 = tmp11 > tmp12 tmp14 = tmp13.to(tl.float32) tmp15 = tmp14 * tmp9 tmp16 = triton_helpers.maximum(tmp7, tmp15) tmp18 = 3.0 tmp19 = tmp18 * tmp17 tmp20 = tmp19 + tmp1 tmp22 = tmp20 > tmp21 tmp23 = tmp22.to(tl.float32) tmp24 = tmp23 * tmp18 tmp25 = triton_helpers.maximum(tmp16, tmp24) tmp27 = 4.0 tmp28 = tmp27 * tmp26 tmp29 = tmp28 + tmp1 tmp31 = tmp29 > tmp30 tmp32 = tmp31.to(tl.float32) tmp33 = tmp32 * tmp27 tmp34 = triton_helpers.maximum(tmp25, tmp33) tmp35 = tmp6 * tmp0 tmp36 = tmp14 * tmp8 tmp37 = tmp35 + tmp36 tmp38 = tmp23 * tmp17 tmp39 = tmp37 + tmp38 tmp40 = tmp32 * tmp26 tmp41 = tmp39 + tmp40 tl.store(out_ptr0 + x0, tmp34, xmask) tl.store(out_ptr1 + x0, tmp41, xmask) @triton.jit def triton_poi_fused_maximum_sub_zeros_like_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last') tmp2 = 1.0 tmp3 = tmp1 - tmp2 tmp5 = tmp3 / tmp4 tmp6 = tmp0 - tmp5 tmp7 = 0.0 tmp8 = triton_helpers.maximum(tmp7, tmp6) tl.store(out_ptr0 + x2, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32) get_raw_stream(0) triton_per_fused_cumsum_sort_sub_0[grid(64)](arg0_1, buf0, buf1, buf3, 64, 4, XBLOCK=32, num_warps=2, num_stages=1) del arg0_1 buf4 = empty_strided_cuda((64, 1), (1, 64), torch.float32) buf5 = empty_strided_cuda((64, 1), (1, 64), torch.float32) triton_poi_fused__to_copy_add_gt_max_mul_sum_1[grid(64)](buf1, buf3, buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf1 buf6 = buf3 del buf3 triton_poi_fused_maximum_sub_zeros_like_2[grid(256)](buf0, buf5, buf4, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1) del buf0 del buf4 del buf5 return reinterpret_tensor(buf6, (4, 4, 4, 4), (4, 64, 16, 1), 0), buf6 class SparsemaxNew(nn.Module): """Sparsemax function.""" def __init__(self, dim=-1): """Initialize sparsemax activation Args: dim (int, optional): The dimension over which to apply the sparsemax function. """ super(SparsemaxNew, self).__init__() self.dim = dim def backward(self, x): """Backward function.""" dim = 1 nonzeros = th.ne(self.output, 0) x_sum = th.sum(x * nonzeros, dim=dim) / th.sum(nonzeros, dim=dim) self.grad_input = nonzeros * (x - x_sum.expand_as(x)) return self.grad_input def extra_repr(self): return 'dim={}'.format(self.dim) def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	HKUST-KnowComp/DualMessagePassing	Sparsemax	false	8,183	[ "MIT" ]	12	d29d627be2a8c8f24b52e3db2c383e33a059aaa7	https://github.com/HKUST-KnowComp/DualMessagePassing/tree/d29d627be2a8c8f24b52e3db2c383e33a059aaa7
Minimum	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/je/cje3xfj2swy2ojrjjdyuotp46mr442dg6fe72pkrzynlnvy3mdtu.py # Topologically Sorted Source Nodes: [min_1, min_mask, float_1, masked_x], Original ATen: [aten.min, aten.eq, aten._to_copy, aten.mul] # Source node to ATen node mapping: # float_1 => convert_element_type # masked_x => mul # min_1 => min_1 # min_mask => eq # Graph fragment: # %min_1 : [num_users=1] = call_function[target=torch.ops.aten.min.dim](args = (%arg0_1, -1, True), kwargs = {}) # %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%arg0_1, %getitem), kwargs = {}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %convert_element_type), kwargs = {}) triton_poi_fused__to_copy_eq_min_mul_0 = async_compile.triton('triton_poi_fused__to_copy_eq_min_mul_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_eq_min_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_eq_min_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.minimum(tmp1, tmp2) tmp5 = triton_helpers.minimum(tmp3, tmp4) tmp7 = triton_helpers.minimum(tmp5, tmp6) tmp8 = tmp0 == tmp7 tmp9 = tmp8.to(tl.float32) tmp10 = tmp0 * tmp9 tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [min_1, min_mask, float_1, masked_x], Original ATen: [aten.min, aten.eq, aten._to_copy, aten.mul] stream0 = get_raw_stream(0) triton_poi_fused__to_copy_eq_min_mul_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch as th import torch.nn as nn def minimum(x, dim=-1, scale_up=False, inplace=False): if inplace: x_ = x.clone() min_x = th.min(x_, dim=dim, keepdim=True)[0] min_mask = x_ == min_x x.masked_fill_(min_mask == 0, 0.0) if scale_up: x_sum = th.sum(x_, dim=dim, keepdim=True) min_sum = th.sum(x, dim=dim, keepdim=True) scale = x_sum / min_sum scale.masked_fill_(scale.isnan(), 0.0) x = scale return x else: min_x = th.min(x, dim=dim, keepdim=True)[0] min_mask = x == min_x masked_x = x min_mask.float() if scale_up: x_sum = th.sum(x, dim=dim, keepdim=True) min_sum = th.sum(masked_x, dim=dim, keepdim=True) scale = x_sum / min_sum scale.masked_fill_(scale.isnan(), 0.0) masked_x = masked_x * scale return masked_x class Minimum(nn.Module): def __init__(self, dim=-1, scale_up=False, inplace=False): super(Minimum, self).__init__() self.dim = dim self.scale_up = scale_up self.inplace = inplace def forward(self, x): return minimum(x, self.dim, self.scale_up, self.inplace) def extra_repr(self): return 'dim={}, scale_up={}{}'.format(self.dim, self.scale_up, ', inplace=True' if self.inplace else '') def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch as th import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused__to_copy_eq_min_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.minimum(tmp1, tmp2) tmp5 = triton_helpers.minimum(tmp3, tmp4) tmp7 = triton_helpers.minimum(tmp5, tmp6) tmp8 = tmp0 == tmp7 tmp9 = tmp8.to(tl.float32) tmp10 = tmp0 * tmp9 tl.store(out_ptr0 + x2, tmp10, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__to_copy_eq_min_mul_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) del arg0_1 return buf0, def minimum(x, dim=-1, scale_up=False, inplace=False): if inplace: x_ = x.clone() min_x = th.min(x_, dim=dim, keepdim=True)[0] min_mask = x_ == min_x x.masked_fill_(min_mask == 0, 0.0) if scale_up: x_sum = th.sum(x_, dim=dim, keepdim=True) min_sum = th.sum(x, dim=dim, keepdim=True) scale = x_sum / min_sum scale.masked_fill_(scale.isnan(), 0.0) x = scale return x else: min_x = th.min(x, dim=dim, keepdim=True)[0] min_mask = x == min_x masked_x = x min_mask.float() if scale_up: x_sum = th.sum(x, dim=dim, keepdim=True) min_sum = th.sum(masked_x, dim=dim, keepdim=True) scale = x_sum / min_sum scale.masked_fill_(scale.isnan(), 0.0) masked_x = masked_x * scale return masked_x class MinimumNew(nn.Module): def __init__(self, dim=-1, scale_up=False, inplace=False): super(MinimumNew, self).__init__() self.dim = dim self.scale_up = scale_up self.inplace = inplace def extra_repr(self): return 'dim={}, scale_up={}{}'.format(self.dim, self.scale_up, ', inplace=True' if self.inplace else '') def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	HKUST-KnowComp/DualMessagePassing	Minimum	false	8,184	[ "MIT" ]	12	d29d627be2a8c8f24b52e3db2c383e33a059aaa7	https://github.com/HKUST-KnowComp/DualMessagePassing/tree/d29d627be2a8c8f24b52e3db2c383e33a059aaa7
MeanPooling	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/n3/cn3o4dezei64lr7bdxvfquoiyk6yi7dx7zux4s6v5c3cwdpoxmgc.py # Topologically Sorted Source Nodes: [entity_states, sum_1], Original ATen: [aten.mul, aten.sum] # Source node to ATen node mapping: # entity_states => mul # sum_1 => sum_1 # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {}) triton_poi_fused_mul_sum_0 = async_compile.triton('triton_poi_fused_mul_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x4 = (xindex // 16) x3 = (xindex // 64) x5 = xindex % 16 x6 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x5 + (64x3)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (4 + x0 + (16x4)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (16 + x5 + (64x3)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (8 + x0 + (16x4)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (32 + x5 + (64x3)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (12 + x0 + (16x4)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (48 + x5 + (64x3)), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tl.store(out_ptr0 + (x6), tmp14, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/vg/cvgs5ehteicuve6ahigd33noqqtswbzgqqq33s4ndlhocntdqx2x.py # Topologically Sorted Source Nodes: [entity_states, sum_1, mean_pooled], Original ATen: [aten.mul, aten.sum, aten.div] # Source node to ATen node mapping: # entity_states => mul # mean_pooled => div # sum_1 => sum_1 # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %unsqueeze_2), kwargs = {}) triton_poi_fused_div_mul_sum_1 = async_compile.triton('triton_poi_fused_div_mul_sum_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_div_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex % 256 x0 = xindex % 16 x5 = (xindex // 64) x6 = xindex tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x0 + (16*x5)), xmask, eviction_policy='evict_last') tmp2 = tmp0 / tmp1 tl.store(out_ptr0 + (x6), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [entity_states, sum_1], Original ATen: [aten.mul, aten.sum] stream0 = get_raw_stream(0) triton_poi_fused_mul_sum_0.run(arg0_1, arg1_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 del arg1_1 buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [entity_states, sum_1, mean_pooled], Original ATen: [aten.mul, aten.sum, aten.div] triton_poi_fused_div_mul_sum_1.run(buf0, arg2_1, buf1, 1024, grid=grid(1024), stream=stream0) del arg2_1 del buf0 return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn class MeanPooling(nn.Module): def __init__(self): super(MeanPooling, self).__init__() def forward(self, doc_state, entity_mapping, entity_lens): entity_states = entity_mapping.unsqueeze(3) * doc_state.unsqueeze(1) mean_pooled = torch.sum(entity_states, dim=2) / entity_lens.unsqueeze(2 ) return mean_pooled def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x4 = xindex // 16 x3 = xindex // 64 x5 = xindex % 16 x6 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * x4), xmask, eviction_policy= 'evict_last') tmp1 = tl.load(in_ptr1 + (x5 + 64 * x3), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x4), xmask, eviction_policy= 'evict_last') tmp4 = tl.load(in_ptr1 + (16 + x5 + 64 * x3), xmask, eviction_policy= 'evict_last') tmp7 = tl.load(in_ptr0 + (8 + x0 + 16 * x4), xmask, eviction_policy= 'evict_last') tmp8 = tl.load(in_ptr1 + (32 + x5 + 64 * x3), xmask, eviction_policy= 'evict_last') tmp11 = tl.load(in_ptr0 + (12 + x0 + 16 * x4), xmask, eviction_policy= 'evict_last') tmp12 = tl.load(in_ptr1 + (48 + x5 + 64 * x3), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 * tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tl.store(out_ptr0 + x6, tmp14, xmask) @triton.jit def triton_poi_fused_div_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex % 256 x0 = xindex % 16 x5 = xindex // 64 x6 = xindex tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x0 + 16 * x5), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 / tmp1 tl.store(out_ptr0 + x6, tmp2, xmask) def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_mul_sum_0[grid(256)](arg0_1, arg1_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 del arg1_1 buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32) triton_poi_fused_div_mul_sum_1[grid(1024)](buf0, arg2_1, buf1, 1024, XBLOCK=128, num_warps=4, num_stages=1) del arg2_1 del buf0 return buf1, class MeanPoolingNew(nn.Module): def __init__(self): super(MeanPoolingNew, self).__init__() def forward(self, input_0, input_1, input_2): arg0_1 = input_0 arg1_1 = input_1 arg2_1 = input_2 output = call([arg0_1, arg1_1, arg2_1]) return output[0]	HLTCHKUST/MulQG	MeanPooling	false	8,185	[ "MIT" ]	19	8e257f2d6c0f03c07ea8a0bf0e8f55b0cde60605	https://github.com/HLTCHKUST/MulQG/tree/8e257f2d6c0f03c07ea8a0bf0e8f55b0cde60605
SFT_torch	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ud/cudnc3myzo6afxdzfmuiljv6eppa3hcnbvf2yxg23qalaxyuhv5h.py # Topologically Sorted Source Nodes: [norm, emb_org_norm, emb_org_norm_1], Original ATen: [aten.linalg_vector_norm, aten.clamp, aten.div] # Source node to ATen node mapping: # emb_org_norm => clamp_min # emb_org_norm_1 => div # norm => pow_1, pow_2, sum_1 # Graph fragment: # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_2, 1e-12), kwargs = {}) # %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %clamp_min), kwargs = {}) triton_poi_fused_clamp_div_linalg_vector_norm_0 = async_compile.triton('triton_poi_fused_clamp_div_linalg_vector_norm_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_div_linalg_vector_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clamp_div_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = libdevice.sqrt(tmp11) tmp13 = 1e-12 tmp14 = triton_helpers.maximum(tmp12, tmp13) tmp15 = tmp0 / tmp14 tl.store(out_ptr0 + (x2), tmp15, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/cb/ccbnuhwhepsngos3zd4ieqxozlzqhq5ankolvsfvgmdhnkk72obb.py # Topologically Sorted Source Nodes: [T], Original ATen: [aten._softmax] # Source node to ATen node mapping: # T => exp # Graph fragment: # %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 1), kwargs = {}) # %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [1], True), kwargs = {}) # %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {}) # %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 0.1), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp3 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 10.0 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + (x2), tmp17, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/wp/cwpr2jp7bpqwkbaxgttcl2ekjdtkf6lezuspoiunutzd3j2qgeyk.py # Topologically Sorted Source Nodes: [T], Original ATen: [aten._softmax] # Source node to ATen node mapping: # T => div_2, sum_2 # Graph fragment: # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {}) # %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_2), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [norm, emb_org_norm, emb_org_norm_1], Original ATen: [aten.linalg_vector_norm, aten.clamp, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_clamp_div_linalg_vector_norm_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0) buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [W], Original ATen: [aten.mm] extern_kernels.mm(buf0, reinterpret_tensor(buf0, (4, 4), (1, 4), 0), out=buf1) buf2 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [T], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf1, buf2, 16, grid=grid(16), stream=stream0) buf3 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [T], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf2, buf3, 16, grid=grid(16), stream=stream0) buf4 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [T, emb_sft], Original ATen: [aten._softmax, aten.mm] extern_kernels.mm(buf3, arg0_1, out=buf4) del arg0_1 del buf3 return (buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F from torchvision.transforms import * class SFT_torch(nn.Module): def __init__(self, sigma=0.1, args, kwargs): super(SFT_torch, self).__init__(args, **kwargs) self.sigma = sigma def forward(self, emb_org): emb_org_norm = torch.norm(emb_org, 2, 1, True).clamp(min=1e-12) emb_org_norm = torch.div(emb_org, emb_org_norm) W = torch.mm(emb_org_norm, emb_org_norm.t()) W = torch.div(W, self.sigma) T = F.softmax(W, 1) emb_sft = torch.mm(T, emb_org) return emb_sft def get_inputs(): return [torch.rand([4, 4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn from torchvision.transforms import * assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clamp_div_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = libdevice.sqrt(tmp11) tmp13 = 1e-12 tmp14 = triton_helpers.maximum(tmp12, tmp13) tmp15 = tmp0 / tmp14 tl.store(out_ptr0 + x2, tmp15, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 10.0 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + x2, tmp17, xmask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clamp_div_linalg_vector_norm_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16, num_warps=1, num_stages=1) buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(buf0, reinterpret_tensor(buf0, (4, 4), (1, 4), 0), out=buf1) buf2 = buf0 del buf0 triton_poi_fused__softmax_1[grid(16)](buf1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1) buf3 = buf1 del buf1 triton_poi_fused__softmax_2[grid(16)](buf2, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) buf4 = buf2 del buf2 extern_kernels.mm(buf3, arg0_1, out=buf4) del arg0_1 del buf3 return buf4, class SFT_torchNew(nn.Module): def __init__(self, sigma=0.1, args, kwargs): super(SFT_torchNew, self).__init__(args, **kwargs) self.sigma = sigma def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	CoinCheung/SFT-ReID	SFT_torch	false	8,186	[ "MIT" ]	22	2df67554732393df5a231b7281e12fc3435f1e8c	https://github.com/CoinCheung/SFT-ReID/tree/2df67554732393df5a231b7281e12fc3435f1e8c
ConvShuffle	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/zm/czm5xlclvigamxygpggs2l53p7xugkmd3t5brhd4sz3j6ylqkueq.py # Topologically Sorted Source Nodes: [X], Original ATen: [aten.constant_pad_nd] # Source node to ATen node mapping: # X => constant_pad_nd # Graph fragment: # %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_3, [0, 1, 0, 1]), kwargs = {}) triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 5) % 5 x0 = xindex % 5 x2 = (xindex // 25) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 4, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + (x0 + (4x1) + (16x2)), tmp5 & xmask, other=0.0) tl.store(out_ptr0 + (x3), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/qm/cqm5j7bkkf53akwmzjrwg3zgvainy2dvckvg5m6lleytgu5qjzvi.py # Topologically Sorted Source Nodes: [pixel_shuffle], Original ATen: [aten.pixel_shuffle] # Source node to ATen node mapping: # pixel_shuffle => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_pixel_shuffle_1 = async_compile.triton('triton_poi_fused_pixel_shuffle_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512, 2], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_pixel_shuffle_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_pixel_shuffle_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 512 xnumel = 2 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x5 = xindex y0 = yindex % 4 y1 = (yindex // 4) % 2 y2 = (yindex // 8) % 4 y6 = (yindex // 32) y3 = (yindex // 32) % 4 y7 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4y2) + (16x5) + (32y1) + (64y6)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x5 + (2y1) + (4y3)), xmask & ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x5 + (2*y7)), tmp2, xmask & ymask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (16, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (16, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32) # Topologically Sorted Source Nodes: [X], Original ATen: [aten.constant_pad_nd] stream0 = get_raw_stream(0) triton_poi_fused_constant_pad_nd_0.run(primals_3, buf0, 400, grid=grid(400), stream=stream0) del primals_3 # Topologically Sorted Source Nodes: [X], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1)) buf2 = empty_strided_cuda((4, 4, 4, 2, 4, 2), (256, 64, 16, 8, 2, 1), torch.float32) # Topologically Sorted Source Nodes: [pixel_shuffle], Original ATen: [aten.pixel_shuffle] triton_poi_fused_pixel_shuffle_1.run(buf1, primals_2, buf2, 512, 2, grid=grid(512, 2), stream=stream0) del buf1 del primals_2 return (reinterpret_tensor(buf2, (4, 4, 8, 8), (256, 64, 8, 1), 0), primals_1, buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((16, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn class ConvShuffle(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, padding_mode='zeros'): super(ConvShuffle, self).__init__() self.upscale_factor = upscale_factor self.conv = nn.Conv2d(in_channels, out_channels * upscale_factor ** 2, kernel_size, padding=padding, padding_mode=padding_mode) def forward(self, X): X = self.conv(X) return nn.functional.pixel_shuffle(X, self.upscale_factor) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 5 % 5 x0 = xindex % 5 x2 = xindex // 25 x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 4, tl.int64) tmp2 = tmp0 < tmp1 tmp3 = x0 tmp4 = tmp3 < tmp1 tmp5 = tmp2 & tmp4 tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0) tl.store(out_ptr0 + x3, tmp6, xmask) @triton.jit def triton_poi_fused_pixel_shuffle_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 512 xnumel = 2 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x5 = xindex y0 = yindex % 4 y1 = yindex // 4 % 2 y2 = yindex // 8 % 4 y6 = yindex // 32 y3 = yindex // 32 % 4 y7 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * y2 + 16 * x5 + 32 * y1 + 64 * y6), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x5 + 2 * y1 + 4 * y3), xmask & ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x5 + 2 * y7), tmp2, xmask & ymask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (16, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (16,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32) get_raw_stream(0) triton_poi_fused_constant_pad_nd_0[grid(400)](primals_3, buf0, 400, XBLOCK=256, num_warps=4, num_stages=1) del primals_3 buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1)) buf2 = empty_strided_cuda((4, 4, 4, 2, 4, 2), (256, 64, 16, 8, 2, 1 ), torch.float32) triton_poi_fused_pixel_shuffle_1[grid(512, 2)](buf1, primals_2, buf2, 512, 2, XBLOCK=1, YBLOCK=256, num_warps=4, num_stages=1) del buf1 del primals_2 return reinterpret_tensor(buf2, (4, 4, 8, 8), (256, 64, 8, 1), 0 ), primals_1, buf0 class ConvShuffleNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, padding= 'same', upscale_factor=2, padding_mode='zeros'): super(ConvShuffleNew, self).__init__() self.upscale_factor = upscale_factor self.conv = nn.Conv2d(in_channels, out_channels * upscale_factor ** 2, kernel_size, padding=padding, padding_mode=padding_mode) def forward(self, input_0): primals_1 = self.conv.weight primals_2 = self.conv.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	GerbenBeintema/deepSI	ConvShuffle	false	8,187	[ "BSD-3-Clause" ]	12	580711210398064bb7f01e41d08b7a248a88b35b	https://github.com/GerbenBeintema/deepSI/tree/580711210398064bb7f01e41d08b7a248a88b35b
MlpNet	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/y2/cy2ahtligm6mxckolwfrfoxrz62xr4hhzcefsobim46u2dekqbro.py # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu] # Source node to ATen node mapping: # x_2 => relu # Graph fragment: # %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_3), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {}) triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 16), (16, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (1, 4), (4, 1)) assert_size_stride(primals_7, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (4, 16), (16, 1), 0), reinterpret_tensor(primals_2, (16, 4), (1, 16), 0), out=buf0) del primals_2 buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_relu_0.run(buf1, primals_3, 16, grid=grid(16), stream=stream0) del primals_3 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.relu] triton_poi_fused_relu_0.run(buf3, primals_5, 16, grid=grid(16), stream=stream0) del primals_5 buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [V], Original ATen: [aten.addmm] extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf5) del primals_7 return (buf5, reinterpret_tensor(primals_1, (4, 16), (16, 1), 0), buf1, buf3, primals_6, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import numpy as np import torch.nn as nn import torch.nn.functional as F class MlpNet(nn.Module): """Implements a simple fully connected mlp network.""" def __init__(self, sa_dim, n_agents, hidden_size, agent_id=0, agent_shuffle='none'): super(MlpNet, self).__init__() self.linear1 = nn.Linear(sa_dim * n_agents, hidden_size) self.linear2 = nn.Linear(hidden_size, hidden_size) self.V = nn.Linear(hidden_size, 1) self.V.weight.data.mul_(0.1) self.V.bias.data.mul_(0.1) self.n_agents = n_agents self.agent_id = agent_id self.agent_shuffle = agent_shuffle def forward(self, x): bz = x.shape[0] if self.agent_shuffle == 'all': x_out = [] for k in range(bz): rand_idx = np.random.permutation(self.n_agents) x_out.append(x[k, :, rand_idx].unsqueeze(0)) x = torch.cat(x_out, 0) elif self.agent_shuffle == 'others': x_out = [] for k in range(bz): rand_idx = np.random.permutation(self.n_agents - 1) index_except = np.concatenate([np.arange(0, self.agent_id), np.arange(self.agent_id + 1, self.n_agents)]) except_shuffle = index_except[rand_idx] x_tmp = x[k, :, :] x_tmp[:, index_except] = x_tmp[:, except_shuffle] x_out.append(x_tmp.unsqueeze(0)) x = torch.cat(x_out, 0) elif self.agent_shuffle == 'none': pass else: raise NotImplementedError('Unsupported agent_shuffle opt: %s' % self.agent_shuffle) x = x.view(bz, -1) x = self.linear1(x) x = F.relu(x) x = self.linear2(x) x = F.relu(x) V = self.V(x) return V def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'sa_dim': 4, 'n_agents': 4, 'hidden_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 16), (16, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (1, 4), (4, 1)) assert_size_stride(primals_7, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (4, 16), (16, 1), 0 ), reinterpret_tensor(primals_2, (16, 4), (1, 16), 0), out=buf0) del primals_2 buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_relu_0[grid(16)](buf1, primals_3, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_3 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4 ), 0), out=buf2) buf3 = buf2 del buf2 triton_poi_fused_relu_0[grid(16)](buf3, primals_5, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_5 buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32) extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf5) del primals_7 return buf5, reinterpret_tensor(primals_1, (4, 16), (16, 1), 0 ), buf1, buf3, primals_6, primals_4 class MlpNetNew(nn.Module): """Implements a simple fully connected mlp network.""" def __init__(self, sa_dim, n_agents, hidden_size, agent_id=0, agent_shuffle='none'): super(MlpNetNew, self).__init__() self.linear1 = nn.Linear(sa_dim * n_agents, hidden_size) self.linear2 = nn.Linear(hidden_size, hidden_size) self.V = nn.Linear(hidden_size, 1) self.V.weight.data.mul_(0.1) self.V.bias.data.mul_(0.1) self.n_agents = n_agents self.agent_id = agent_id self.agent_shuffle = agent_shuffle def forward(self, input_0): primals_2 = self.linear1.weight primals_3 = self.linear1.bias primals_4 = self.linear2.weight primals_5 = self.linear2.bias primals_6 = self.V.weight primals_7 = self.V.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]	HAXRD/PIC	MlpNet	false	8,188	[ "MIT" ]	28	658b4dd6b01e64413d5f8f0107d9167f1bd78546	https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546
MeanMaxPooling	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/sm/csmgx57ri3mpgapt57s7dinvbnt7h6cz7b4lqkjxpveoae5fnye4.py # Topologically Sorted Source Nodes: [entity_states, max_1, sum_1, mean_pooled], Original ATen: [aten.mul, aten.max, aten.sum, aten.div] # Source node to ATen node mapping: # entity_states => mul # max_1 => max_1 # mean_pooled => div # sum_1 => sum_1 # Graph fragment: # %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {}) # %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul, 2), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %unsqueeze_2), kwargs = {}) triton_poi_fused_div_max_mul_sum_0 = async_compile.triton('triton_poi_fused_div_max_mul_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_max_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_div_max_mul_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = (xindex // 16) % 4 x3 = (xindex // 64) % 4 x4 = (xindex // 256) x5 = xindex % 64 x6 = xindex % 16 x7 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x0 + (4x2) + (64x3)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x5 + (256x4)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (16 + x0 + (4x2) + (64x3)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (64 + x5 + (256x4)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (32 + x0 + (4x2) + (64x3)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (128 + x5 + (256x4)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (48 + x0 + (4x2) + (64x3)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (192 + x5 + (256x4)), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr2 + (x6 + (16x7)), xmask, eviction_policy='evict_last') tmp2 = tmp0 tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tmp16 = tmp14 / tmp15 tmp17 = triton_helpers.maximum(tmp2, tmp5) tmp18 = triton_helpers.maximum(tmp17, tmp9) tmp19 = triton_helpers.maximum(tmp18, tmp13) tl.store(out_ptr0 + (x5 + (128x7)), tmp16, xmask) tl.store(out_ptr1 + (x5 + (128x7)), tmp19, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf2 = empty_strided_cuda((4, 4, 8, 4, 4), (512, 128, 16, 4, 1), torch.float32) buf0 = reinterpret_tensor(buf2, (4, 4, 4, 4, 4), (512, 128, 16, 4, 1), 64) # alias buf1 = reinterpret_tensor(buf2, (4, 4, 4, 4, 4), (512, 128, 16, 4, 1), 0) # alias # Topologically Sorted Source Nodes: [entity_states, max_1, sum_1, mean_pooled], Original ATen: [aten.mul, aten.max, aten.sum, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_div_max_mul_sum_0.run(arg0_1, arg1_1, arg2_1, buf0, buf1, 1024, grid=grid(1024), stream=stream0) del arg0_1 del arg1_1 del arg2_1 return (buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn class MeanMaxPooling(nn.Module): def __init__(self): super(MeanMaxPooling, self).__init__() def forward(self, doc_state, entity_mapping, entity_lens): """ :param doc_state: N x L x d :param entity_mapping: N x E x L :param entity_lens: N x E :return: N x E x 2d """ entity_states = entity_mapping.unsqueeze(3) * doc_state.unsqueeze(1) max_pooled = torch.max(entity_states, dim=2)[0] mean_pooled = torch.sum(entity_states, dim=2) / entity_lens.unsqueeze(2 ) output = torch.cat([max_pooled, mean_pooled], dim=2) return output def get_inputs(): return [torch.rand([4, 4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch. rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_div_max_mul_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex // 16 % 4 x3 = xindex // 64 % 4 x4 = xindex // 256 x5 = xindex % 64 x6 = xindex % 16 x7 = xindex // 64 tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 64 * x3), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x5 + 256 * x4), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr0 + (16 + x0 + 4 * x2 + 64 * x3), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (64 + x5 + 256 * x4), xmask, eviction_policy= 'evict_last') tmp7 = tl.load(in_ptr0 + (32 + x0 + 4 * x2 + 64 * x3), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (128 + x5 + 256 * x4), xmask, eviction_policy= 'evict_last') tmp11 = tl.load(in_ptr0 + (48 + x0 + 4 * x2 + 64 * x3), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (192 + x5 + 256 * x4), xmask, eviction_policy ='evict_last') tmp15 = tl.load(in_ptr2 + (x6 + 16 * x7), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 * tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tmp16 = tmp14 / tmp15 tmp17 = triton_helpers.maximum(tmp2, tmp5) tmp18 = triton_helpers.maximum(tmp17, tmp9) tmp19 = triton_helpers.maximum(tmp18, tmp13) tl.store(out_ptr0 + (x5 + 128 * x7), tmp16, xmask) tl.store(out_ptr1 + (x5 + 128 * x7), tmp19, xmask) def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf2 = empty_strided_cuda((4, 4, 8, 4, 4), (512, 128, 16, 4, 1), torch.float32) buf0 = reinterpret_tensor(buf2, (4, 4, 4, 4, 4), (512, 128, 16, 4, 1), 64) buf1 = reinterpret_tensor(buf2, (4, 4, 4, 4, 4), (512, 128, 16, 4, 1), 0) get_raw_stream(0) triton_poi_fused_div_max_mul_sum_0[grid(1024)](arg0_1, arg1_1, arg2_1, buf0, buf1, 1024, XBLOCK=128, num_warps=4, num_stages=1) del arg0_1 del arg1_1 del arg2_1 return buf2, class MeanMaxPoolingNew(nn.Module): def __init__(self): super(MeanMaxPoolingNew, self).__init__() def forward(self, input_0, input_1, input_2): arg1_1 = input_0 arg0_1 = input_1 arg2_1 = input_2 output = call([arg0_1, arg1_1, arg2_1]) return output[0]	HLTCHKUST/MulQG	MeanMaxPooling	false	8,189	[ "MIT" ]	19	8e257f2d6c0f03c07ea8a0bf0e8f55b0cde60605	https://github.com/HLTCHKUST/MulQG/tree/8e257f2d6c0f03c07ea8a0bf0e8f55b0cde60605
DownsampleA	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/pe/cpedcbglvu7cuqs4x62fnrxhuwranjzyimqahut7rqwpcgfb2okf.py # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%avg_pool2d, %mul], 1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[128], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = (xindex // 4) % 8 x0 = xindex % 2 x1 = (xindex // 2) % 2 x3 = (xindex // 32) x4 = xindex tmp0 = x2 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((2x0) + (8x1) + (16x2) + (64x3)), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = 1.0 tmp7 = tmp5 * tmp6 tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype) tmp9 = tl.where(tmp4, tmp7, tmp8) tmp10 = tmp0 >= tmp3 tmp11 = tl.full([1], 8, tl.int64) tmp12 = tmp0 < tmp11 tmp13 = tl.load(in_ptr0 + ((2x0) + (8x1) + (16((-4) + x2)) + (64x3)), tmp10 & xmask, eviction_policy='evict_last', other=0.0) tmp14 = tmp13 * tmp6 tmp15 = 0.0 tmp16 = tmp14 * tmp15 tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp10, tmp16, tmp17) tmp19 = tl.where(tmp4, tmp9, tmp18) tl.store(out_ptr0 + (x4), tmp19, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 8, 2, 2), (32, 4, 2, 1), torch.float32) # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(arg0_1, buf0, 128, grid=grid(128), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.utils.data.distributed class DownsampleA(nn.Module): def __init__(self, nIn, nOut, stride): super(DownsampleA, self).__init__() assert stride == 2 self.avg = nn.AvgPool2d(kernel_size=1, stride=stride) def forward(self, x): x = self.avg(x) return torch.cat((x, x.mul(0)), 1) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'nIn': 4, 'nOut': 4, 'stride': 2}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn import torch.utils.data.distributed assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex // 4 % 8 x0 = xindex % 2 x1 = xindex // 2 % 2 x3 = xindex // 32 x4 = xindex tmp0 = x2 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (2 * x0 + 8 * x1 + 16 * x2 + 64 * x3), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = 1.0 tmp7 = tmp5 * tmp6 tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype) tmp9 = tl.where(tmp4, tmp7, tmp8) tmp10 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp13 = tl.load(in_ptr0 + (2 * x0 + 8 * x1 + 16 * (-4 + x2) + 64 * x3), tmp10 & xmask, eviction_policy='evict_last', other=0.0) tmp14 = tmp13 * tmp6 tmp15 = 0.0 tmp16 = tmp14 * tmp15 tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp10, tmp16, tmp17) tmp19 = tl.where(tmp4, tmp9, tmp18) tl.store(out_ptr0 + x4, tmp19, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 8, 2, 2), (32, 4, 2, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(128)](arg0_1, buf0, 128, XBLOCK=128, num_warps=4, num_stages=1) del arg0_1 return buf0, class DownsampleANew(nn.Module): def __init__(self, nIn, nOut, stride): super(DownsampleANew, self).__init__() assert stride == 2 self.avg = nn.AvgPool2d(kernel_size=1, stride=stride) def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	HKBU-HPML/gtopkssgd	DownsampleA	false	8,190	[ "Apache-2.0" ]	33	6f57343f3749939b0345d36fcb2c24470942aefd	https://github.com/HKBU-HPML/gtopkssgd/tree/6f57343f3749939b0345d36fcb2c24470942aefd
ResidualBlock_noBN	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/nd/cndrb2by2g3ednlxsjtytxjhjdm7bdlrgvzzodhsxvxnpml2di7p.py # Topologically Sorted Source Nodes: [conv2d, out], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d => convolution # out => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1048576 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 4096) % 64 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ee/cee5rlyewrt7mgwaib2prtpk3sps6e32amj5myetakd7b423m57w.py # Topologically Sorted Source Nodes: [out_1, add], Original ATen: [aten.convolution, aten.add] # Source node to ATen node mapping: # add => add # out_1 => convolution_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %convolution_1), kwargs = {}) triton_poi_fused_add_convolution_1 = async_compile.triton('triton_poi_fused_add_convolution_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 1048576 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 4096) % 64 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_out_ptr0 + (x3), None) tmp2 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp4 = tmp0 + tmp3 tl.store(in_out_ptr0 + (x3), tmp4, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 64, 64, 64), (262144, 4096, 64, 1)) assert_size_stride(primals_2, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_3, (64, ), (1, )) assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_5, (64, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [conv2d, out], Original ATen: [aten.convolution, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_0.run(buf1, primals_3, 1048576, grid=grid(1048576), stream=stream0) del primals_3 # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 64, 64, 64), (262144, 4096, 64, 1)) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [out_1, add], Original ATen: [aten.convolution, aten.add] triton_poi_fused_add_convolution_1.run(buf3, primals_1, primals_5, 1048576, grid=grid(1048576), stream=stream0) del primals_5 return (buf3, primals_1, primals_2, primals_4, buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 64, 64, 64), (262144, 4096, 64, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.utils.data import torch.nn as nn import torch.nn.functional as F import torch.nn.init as init def initialize_weights(net_l, scale=1): if not isinstance(net_l, list): net_l = [net_l] for net in net_l: for m in net.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, a=0, mode='fan_in') m.weight.data = scale if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.Linear): init.kaiming_normal_(m.weight, a=0, mode='fan_in') m.weight.data = scale if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias.data, 0.0) class ResidualBlock_noBN(nn.Module): """Residual block w/o BN ---Conv-ReLU-Conv-+- \|________________\| """ def __init__(self, nf=64): super(ResidualBlock_noBN, self).__init__() self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) initialize_weights([self.conv1, self.conv2], 0.1) def forward(self, x): identity = x out = F.relu(self.conv1(x), inplace=True) out = self.conv2(out) return identity + out def get_inputs(): return [torch.rand([4, 64, 64, 64])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.utils.data import torch.nn as nn import torch.nn.init as init assert_size_stride = torch._C._dynamo.guards.assert_size_stride @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 4096 % 64 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, None) @triton.jit def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 4096 % 64 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_out_ptr0 + x3, None) tmp2 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp4 = tmp0 + tmp3 tl.store(in_out_ptr0 + x3, tmp4, None) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 64, 64, 64), (262144, 4096, 64, 1)) assert_size_stride(primals_2, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_3, (64,), (1,)) assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_5, (64,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_relu_0[grid(1048576)](buf1, primals_3, 1048576, XBLOCK=1024, num_warps=4, num_stages=1) del primals_3 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 64, 64, 64), (262144, 4096, 64, 1)) buf3 = buf2 del buf2 triton_poi_fused_add_convolution_1[grid(1048576)](buf3, primals_1, primals_5, 1048576, XBLOCK=512, num_warps=8, num_stages=1) del primals_5 return buf3, primals_1, primals_2, primals_4, buf1 def initialize_weights(net_l, scale=1): if not isinstance(net_l, list): net_l = [net_l] for net in net_l: for m in net.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, a=0, mode='fan_in') m.weight.data = scale if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.Linear): init.kaiming_normal_(m.weight, a=0, mode='fan_in') m.weight.data = scale if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias.data, 0.0) class ResidualBlock_noBNNew(nn.Module): """Residual block w/o BN ---Conv-ReLU-Conv-+- \|________________\| """ def __init__(self, nf=64): super(ResidualBlock_noBNNew, self).__init__() self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) initialize_weights([self.conv1, self.conv2], 0.1) def forward(self, input_0): primals_2 = self.conv1.weight primals_3 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	GuoShi28/GCP-Net	ResidualBlock_noBN	false	8,191	[ "Apache-2.0" ]	24	cef7513fa242343055af64e612429e4384d3c1d7	https://github.com/GuoShi28/GCP-Net/tree/cef7513fa242343055af64e612429e4384d3c1d7
ANN	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/u2/cu2beycg2t2ghizs6f4qom7bxbxmajhdaakuyq6y2korxywhp6ba.py # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten._log_softmax] # Source node to ATen node mapping: # output_1 => amax, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_3, [1], True), kwargs = {}) # %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_3, %amax), kwargs = {}) triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x0 + (64x2)), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (16 + x0 + (64x2)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (32 + x0 + (64x2)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (48 + x0 + (64x2)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tl.store(out_ptr0 + (x3), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/rz/crzznyrp25xw7vat6ii2ydzqrgg4pltpke5lcsjsywnjqglf2sli.py # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten._log_softmax] # Source node to ATen node mapping: # output_1 => exp, log, sub_1, sum_1 # Graph fragment: # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {}) triton_poi_fused__log_softmax_1 = async_compile.triton('triton_poi_fused__log_softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x0 + (64x2)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (16 + x0 + (64x2)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (32 + x0 + (64x2)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (48 + x0 + (64x2)), xmask, eviction_policy='evict_last') tmp2 = tl_math.exp(tmp1) tmp4 = tl_math.exp(tmp3) tmp5 = tmp2 + tmp4 tmp7 = tl_math.exp(tmp6) tmp8 = tmp5 + tmp7 tmp10 = tl_math.exp(tmp9) tmp11 = tmp8 + tmp10 tmp12 = tl_math.log(tmp11) tmp13 = tmp0 - tmp12 tl.store(out_ptr0 + (x3), tmp13, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [hidden], Original ATen: [aten.addmm] extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [output], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_5 buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten._log_softmax] stream0 = get_raw_stream(0) triton_poi_fused__log_softmax_0.run(buf1, buf2, 256, grid=grid(256), stream=stream0) buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1 # reuse # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten._log_softmax] triton_poi_fused__log_softmax_1.run(buf2, buf3, 256, grid=grid(256), stream=stream0) del buf2 return (buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf3, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class ANN(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(ANN, self).__init__() self.i2h = nn.Linear(input_size, hidden_size) self.h2o = nn.Linear(hidden_size, output_size) self.softmax = nn.LogSoftmax() def forward(self, inpt): hidden = self.i2h(inpt) output = self.h2o(hidden) output = self.softmax(output) return output def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'input_size': 4, 'hidden_size': 4, 'output_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tl.store(out_ptr0 + x3, tmp8, xmask) @triton.jit def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tl_math.exp(tmp1) tmp4 = tl_math.exp(tmp3) tmp5 = tmp2 + tmp4 tmp7 = tl_math.exp(tmp6) tmp8 = tmp5 + tmp7 tmp10 = tl_math.exp(tmp9) tmp11 = tmp8 + tmp10 tmp12 = tl_math.log(tmp11) tmp13 = tmp0 - tmp12 tl.store(out_ptr0 + x3, tmp13, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_5 buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__log_softmax_0[grid(256)](buf1, buf2, 256, XBLOCK= 128, num_warps=4, num_stages=1) buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf1 triton_poi_fused__log_softmax_1[grid(256)](buf2, buf3, 256, XBLOCK= 128, num_warps=4, num_stages=1) del buf2 return buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), buf0, buf3, primals_4 class ANNNew(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(ANNNew, self).__init__() self.i2h = nn.Linear(input_size, hidden_size) self.h2o = nn.Linear(hidden_size, output_size) self.softmax = nn.LogSoftmax() def forward(self, input_0): primals_1 = self.i2h.weight primals_2 = self.i2h.bias primals_4 = self.h2o.weight primals_5 = self.h2o.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	GopikrishnanSasikumar/Rita	ANN	false	8,192	[ "BSD-3-Clause" ]	17	a9537c863140fc8c212f82b51f3d556e683e5f5a	https://github.com/GopikrishnanSasikumar/Rita/tree/a9537c863140fc8c212f82b51f3d556e683e5f5a
TripletSemihardLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/5m/c5msm2swedganbgicpwv7z6bxnojfd4zpoz7zdoyyr7c54v4kppa.py # Topologically Sorted Source Nodes: [eq, type_as, eye, type_as_1, pos], Original ATen: [aten.eq, aten._to_copy, aten.eye, aten.sub] # Source node to ATen node mapping: # eq => eq # eye => eq_1, full_default, full_default_1, iota_1, where # pos => sub_1 # type_as => convert_element_type # type_as_1 => device_put # Graph fragment: # %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%expand_1, %expand_2), kwargs = {}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {}) # %iota_1 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cpu, requires_grad: False}) # %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%unsqueeze_3, %iota_1), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False}) # %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %full_default, %full_default_1), kwargs = {}) # %device_put : [num_users=1] = call_function[target=torch.ops.prims.device_put.default](args = (%where, cuda:0), kwargs = {}) # %sub_1 : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convert_element_type, %device_put), kwargs = {}) triton_poi_fused__to_copy_eq_eye_sub_0 = async_compile.triton('triton_poi_fused__to_copy_eq_eye_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_eq_eye_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_eq_eye_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 == tmp1 tmp3 = tmp2.to(tl.float32) tmp4 = x1 tmp5 = x0 tmp6 = tmp4 == tmp5 tmp7 = 1.0 tmp8 = 0.0 tmp9 = tl.where(tmp6, tmp7, tmp8) tmp10 = tmp3 - tmp9 tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/mt/cmtcrf4vhqnkl4r7fcq3i54oe5rrchhvsrm2blx7bs5cw25yxwxg.py # Topologically Sorted Source Nodes: [sub_2, M, dist_diff, lt, type_as_2, mask], Original ATen: [aten.rsub, aten.mul, aten.sub, aten.lt, aten._to_copy] # Source node to ATen node mapping: # M => mul_1 # dist_diff => sub_3 # lt => lt # mask => mul_2 # sub_2 => sub_2 # type_as_2 => convert_element_type_2 # Graph fragment: # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %expand_5), kwargs = {}) # %mul_1 : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_4, %sub_2), kwargs = {}) # %sub_3 : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand_3, %permute_2), kwargs = {}) # %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%sub_3, 0), kwargs = {}) # %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt, torch.float32), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %convert_element_type_2), kwargs = {}) triton_poi_fused__to_copy_lt_mul_rsub_sub_1 = async_compile.triton('triton_poi_fused__to_copy_lt_mul_rsub_sub_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_lt_mul_rsub_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_lt_mul_rsub_sub_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = (xindex // 16) x4 = (xindex // 4) x3 = xindex x1 = (xindex // 4) % 4 tmp0 = tl.load(in_ptr0 + (x0 + (4x2)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (5x2), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr1 + (5x0), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (x0 + (4x2)), xmask, eviction_policy='evict_last') tmp17 = tl.load(in_ptr1 + (5x1), xmask, eviction_policy='evict_last') tmp19 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last') tmp2 = 1.0 tmp3 = tmp2 - tmp1 tmp4 = tmp0 tmp3 tmp7 = tmp5 + tmp6 tmp9 = 2.0 tmp10 = tmp8 * tmp9 tmp11 = tmp7 - tmp10 tmp12 = 0.0 tmp13 = triton_helpers.maximum(tmp11, tmp12) tmp14 = 0.0001 tmp15 = triton_helpers.maximum(tmp13, tmp14) tmp16 = libdevice.sqrt(tmp15) tmp18 = tmp5 + tmp17 tmp20 = tmp19 * tmp9 tmp21 = tmp18 - tmp20 tmp22 = triton_helpers.maximum(tmp21, tmp12) tmp23 = triton_helpers.maximum(tmp22, tmp14) tmp24 = libdevice.sqrt(tmp23) tmp25 = tmp16 - tmp24 tmp26 = tmp25 < tmp12 tmp27 = tmp26.to(tl.float32) tmp28 = tmp4 * tmp27 tl.store(out_ptr0 + (x3), tmp4, xmask) tl.store(out_ptr1 + (x3), tmp25, xmask) tl.store(out_ptr2 + (x3), tmp28, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/4s/c4szmh55bascmq3yoquvdbwh2ngiqzyyqps6uo54bbb2gdafugky.py # Topologically Sorted Source Nodes: [lt_1, type_as_3, mul_3, mask_final, mask_final_1], Original ATen: [aten.lt, aten._to_copy, aten.mul, aten.sum, aten.gt] # Source node to ATen node mapping: # lt_1 => lt_1 # mask_final => sum_1 # mask_final_1 => gt # mul_3 => mul_3 # type_as_3 => convert_element_type_3 # Graph fragment: # %lt_1 : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%sub_3, 0), kwargs = {}) # %convert_element_type_3 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt_1, torch.float32), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %convert_element_type_3), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_3, [1], True), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {}) triton_poi_fused__to_copy_gt_lt_mul_sum_2 = async_compile.triton('triton_poi_fused__to_copy_gt_lt_mul_sum_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_gt_lt_mul_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_gt_lt_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16x1)), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (16x1)), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + (16x1)), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0 + (16x1)), xmask) tmp12 = tl.load(in_ptr0 + (8 + x0 + (16x1)), xmask) tmp13 = tl.load(in_ptr1 + (8 + x0 + (16x1)), xmask) tmp18 = tl.load(in_ptr0 + (12 + x0 + (16x1)), xmask) tmp19 = tl.load(in_ptr1 + (12 + x0 + (16x1)), xmask) tmp2 = 0.0 tmp3 = tmp1 < tmp2 tmp4 = tmp3.to(tl.float32) tmp5 = tmp0 * tmp4 tmp8 = tmp7 < tmp2 tmp9 = tmp8.to(tl.float32) tmp10 = tmp6 * tmp9 tmp11 = tmp5 + tmp10 tmp14 = tmp13 < tmp2 tmp15 = tmp14.to(tl.float32) tmp16 = tmp12 * tmp15 tmp17 = tmp11 + tmp16 tmp20 = tmp19 < tmp2 tmp21 = tmp20.to(tl.float32) tmp22 = tmp18 * tmp21 tmp23 = tmp17 + tmp22 tmp24 = tmp23 > tmp2 tl.store(out_ptr0 + (x2), tmp24, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [eq, type_as, eye, type_as_1, pos], Original ATen: [aten.eq, aten._to_copy, aten.eye, aten.sub] stream0 = get_raw_stream(0) triton_poi_fused__to_copy_eq_eye_sub_0.run(arg1_1, buf0, 16, grid=grid(16), stream=stream0) del arg1_1 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [prod], Original ATen: [aten.mm] extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf2) del arg0_1 buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [sub_2, M, dist_diff, lt, type_as_2, mask], Original ATen: [aten.rsub, aten.mul, aten.sub, aten.lt, aten._to_copy] triton_poi_fused__to_copy_lt_mul_rsub_sub_1.run(buf0, buf2, buf1, buf3, buf4, 64, grid=grid(64), stream=stream0) del buf2 buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [lt_1, type_as_3, mul_3, mask_final, mask_final_1], Original ATen: [aten.lt, aten._to_copy, aten.mul, aten.sum, aten.gt] triton_poi_fused__to_copy_gt_lt_mul_sum_2.run(buf1, buf3, buf5, 16, grid=grid(16), stream=stream0) return (buf0, buf1, buf3, buf4, reinterpret_tensor(buf5, (4, 4), (4, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torchvision.transforms.functional as F import torch.nn.functional as F import torch.utils.model_zoo def pdist(A, squared=False, eps=0.0001): prod = torch.mm(A, A.t()) norm = prod.diag().unsqueeze(1).expand_as(prod) res = (norm + norm.t() - 2 * prod).clamp(min=0) if squared: return res else: return res.clamp(min=eps).sqrt() def masked_maximum(data, mask, dim=1): """Computes the axis wise maximum over chosen elements. Args: data: 2-D float `Tensor` of size [n, m]. mask: 2-D Boolean `Tensor` of size [n, m]. dim: The dimension over which to compute the maximum. Returns: masked_maximums: N-D `Tensor`. The maximized dimension is of size 1 after the operation. """ axis_minimums = data.min(dim, keepdim=True)[0] masked_maximums = torch.max(torch.mul(data - axis_minimums, mask), dim= dim, keepdim=True)[0] + axis_minimums return masked_maximums def masked_minimum(data, mask, dim=1): """Computes the axis wise minimum over chosen elements. Args: data: 2-D float `Tensor` of size [n, m]. mask: 2-D Boolean `Tensor` of size [n, m]. dim: The dimension over which to compute the minimum. Returns: masked_minimums: N-D `Tensor`. The minimized dimension is of size 1 after the operation. """ axis_maximums = data.max(dim, keepdim=True)[0] masked_minimums = torch.min(torch.mul(data - axis_maximums, mask), dim= dim, keepdim=True)[0] + axis_maximums return masked_minimums class TripletSemihardLoss(torch.nn.Module): """ Computes the triplet loss with semi-hard negative mining. For every positive pair find only one hardest semihard negative example. The loss encourages the positive distances (between a pair of embeddings with the same labels) to be smaller than the minimum negative distance - margin. The negative distance is selected among the neg pairsi which are at least greater than the positive distance (called semi-hard negatives), but withing the margin radius from anchor. If no such negative exists, uses the largest negative distance instead. See: https://arxiv.org/abs/1503.03832. Args: labels: 1-D int32 `Tensor` with shape [batch_size] of multiclass integer labels. embeddings: 2-D float `Tensor` of embedding vectors. Embeddings should be l2 normalized. margin: Float, margin term in the loss definition. Returns: triplet_loss: float32 scalar. """ def __init__(self, margin=1.0, soft=False, *kwargs): self.margin = margin self.soft = soft torch.nn.Module.__init__(self) def forward(self, embeddings, labels): d = pdist(embeddings) pos = torch.eq([labels.unsqueeze(dim).expand_as(d) for dim in [0, 1]] ).type_as(d) - torch.eye(len(d)).type_as(d) T = d.unsqueeze(1).expand(((len(d),) 3)) M = pos.unsqueeze(1).expand_as(T) * (1 - pos.unsqueeze(2).expand_as(T)) dist_diff = T - T.transpose(1, 2) mask = M * (dist_diff < 0).type_as(M) mask_final = (M * (dist_diff < 0).type_as(M)).sum(dim=1, keepdim=True) mask_final = mask_final > 0 mask_final.squeeze_(dim=1) assert len(mask_final.shape) == 2 dist_diff_negatives_outside = masked_maximum(dist_diff, mask, dim=1 ).squeeze_(dim=1) dist_diff_negatives_inside = masked_minimum(dist_diff, M, dim=1 ).squeeze_(dim=1) dist_diff_semi_hard_negatives = torch.where(mask_final, dist_diff_negatives_outside, dist_diff_negatives_inside) if self.soft: loss_mat = dist_diff_semi_hard_negatives.exp().log1p() else: loss_mat = dist_diff_semi_hard_negatives + self.margin assert len(loss_mat.shape) == 2 assert len(pos.shape) == 2 return F.relu(pos * loss_mat).sum() / pos.sum() def get_inputs(): return [torch.rand([4, 4]), torch.rand([4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import torch.utils.model_zoo assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__to_copy_eq_eye_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 == tmp1 tmp3 = tmp2.to(tl.float32) tmp4 = x1 tmp5 = x0 tmp6 = tmp4 == tmp5 tmp7 = 1.0 tmp8 = 0.0 tmp9 = tl.where(tmp6, tmp7, tmp8) tmp10 = tmp3 - tmp9 tl.store(out_ptr0 + x2, tmp10, xmask) @triton.jit def triton_poi_fused__to_copy_lt_mul_rsub_sub_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex // 16 x4 = xindex // 4 x3 = xindex x1 = xindex // 4 % 4 tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last' ) tmp1 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + 5 * x2, xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr1 + 5 * x0, xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last' ) tmp17 = tl.load(in_ptr1 + 5 * x1, xmask, eviction_policy='evict_last') tmp19 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last') tmp2 = 1.0 tmp3 = tmp2 - tmp1 tmp4 = tmp0 * tmp3 tmp7 = tmp5 + tmp6 tmp9 = 2.0 tmp10 = tmp8 * tmp9 tmp11 = tmp7 - tmp10 tmp12 = 0.0 tmp13 = triton_helpers.maximum(tmp11, tmp12) tmp14 = 0.0001 tmp15 = triton_helpers.maximum(tmp13, tmp14) tmp16 = libdevice.sqrt(tmp15) tmp18 = tmp5 + tmp17 tmp20 = tmp19 * tmp9 tmp21 = tmp18 - tmp20 tmp22 = triton_helpers.maximum(tmp21, tmp12) tmp23 = triton_helpers.maximum(tmp22, tmp14) tmp24 = libdevice.sqrt(tmp23) tmp25 = tmp16 - tmp24 tmp26 = tmp25 < tmp12 tmp27 = tmp26.to(tl.float32) tmp28 = tmp4 * tmp27 tl.store(out_ptr0 + x3, tmp4, xmask) tl.store(out_ptr1 + x3, tmp25, xmask) tl.store(out_ptr2 + x3, tmp28, xmask) @triton.jit def triton_poi_fused__to_copy_gt_lt_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr1 + (x0 + 16 * x1), xmask) tmp6 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask) tmp7 = tl.load(in_ptr1 + (4 + x0 + 16 * x1), xmask) tmp12 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask) tmp13 = tl.load(in_ptr1 + (8 + x0 + 16 * x1), xmask) tmp18 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask) tmp19 = tl.load(in_ptr1 + (12 + x0 + 16 * x1), xmask) tmp2 = 0.0 tmp3 = tmp1 < tmp2 tmp4 = tmp3.to(tl.float32) tmp5 = tmp0 * tmp4 tmp8 = tmp7 < tmp2 tmp9 = tmp8.to(tl.float32) tmp10 = tmp6 * tmp9 tmp11 = tmp5 + tmp10 tmp14 = tmp13 < tmp2 tmp15 = tmp14.to(tl.float32) tmp16 = tmp12 * tmp15 tmp17 = tmp11 + tmp16 tmp20 = tmp19 < tmp2 tmp21 = tmp20.to(tl.float32) tmp22 = tmp18 * tmp21 tmp23 = tmp17 + tmp22 tmp24 = tmp23 > tmp2 tl.store(out_ptr0 + x2, tmp24, xmask) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__to_copy_eq_eye_sub_0[grid(16)](arg1_1, buf0, 16, XBLOCK=16, num_warps=1, num_stages=1) del arg1_1 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf2) del arg0_1 buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__to_copy_lt_mul_rsub_sub_1[grid(64)](buf0, buf2, buf1, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf2 buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.bool) triton_poi_fused__to_copy_gt_lt_mul_sum_2[grid(16)](buf1, buf3, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1) return buf0, buf1, buf3, buf4, reinterpret_tensor(buf5, (4, 4), (4, 1), 0) def pdist(A, squared=False, eps=0.0001): prod = torch.mm(A, A.t()) norm = prod.diag().unsqueeze(1).expand_as(prod) res = (norm + norm.t() - 2 * prod).clamp(min=0) if squared: return res else: return res.clamp(min=eps).sqrt() def masked_maximum(data, mask, dim=1): """Computes the axis wise maximum over chosen elements. Args: data: 2-D float `Tensor` of size [n, m]. mask: 2-D Boolean `Tensor` of size [n, m]. dim: The dimension over which to compute the maximum. Returns: masked_maximums: N-D `Tensor`. The maximized dimension is of size 1 after the operation. """ axis_minimums = data.min(dim, keepdim=True)[0] masked_maximums = torch.max(torch.mul(data - axis_minimums, mask), dim= dim, keepdim=True)[0] + axis_minimums return masked_maximums def masked_minimum(data, mask, dim=1): """Computes the axis wise minimum over chosen elements. Args: data: 2-D float `Tensor` of size [n, m]. mask: 2-D Boolean `Tensor` of size [n, m]. dim: The dimension over which to compute the minimum. Returns: masked_minimums: N-D `Tensor`. The minimized dimension is of size 1 after the operation. """ axis_maximums = data.max(dim, keepdim=True)[0] masked_minimums = torch.min(torch.mul(data - axis_maximums, mask), dim= dim, keepdim=True)[0] + axis_maximums return masked_minimums class TripletSemihardLossNew(torch.nn.Module): """ Computes the triplet loss with semi-hard negative mining. For every positive pair find only one hardest semihard negative example. The loss encourages the positive distances (between a pair of embeddings with the same labels) to be smaller than the minimum negative distance - margin. The negative distance is selected among the neg pairsi which are at least greater than the positive distance (called semi-hard negatives), but withing the margin radius from anchor. If no such negative exists, uses the largest negative distance instead. See: https://arxiv.org/abs/1503.03832. Args: labels: 1-D int32 `Tensor` with shape [batch_size] of multiclass integer labels. embeddings: 2-D float `Tensor` of embedding vectors. Embeddings should be l2 normalized. margin: Float, margin term in the loss definition. Returns: triplet_loss: float32 scalar. """ def __init__(self, margin=1.0, soft=False, **kwargs): self.margin = margin self.soft = soft torch.nn.Module.__init__(self) def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	CompVis/metric-learning-divide-and-conquer-improved	TripletSemihardLoss	false	8,193	[ "MIT" ]	11	33fe768a54376a090e2d7139898177b06e8903d2	https://github.com/CompVis/metric-learning-divide-and-conquer-improved/tree/33fe768a54376a090e2d7139898177b06e8903d2
FocalLossBinary	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/3r/c3rw5f7jc2cs3yfadkpmj7x6rfdl55mhdz4kpw57mcvcaihapbgq.py # Topologically Sorted Source Nodes: [binary_cross_entropy_with_logits, logpt, pt, sub, pow_1, neg_1, loss, mul_1, sub_1, mul_2, add, loss_1, loss_2], Original ATen: [aten.binary_cross_entropy_with_logits, aten.neg, aten.exp, aten.rsub, aten.pow, aten.mul, aten.add, aten.mean] # Source node to ATen node mapping: # add => add # binary_cross_entropy_with_logits => abs_1, exp, full_default, log1p, minimum, mul, neg, sub, sub_1, sub_2 # logpt => neg_1 # loss => mul_1 # loss_1 => mul_4 # loss_2 => mean # mul_1 => mul_2 # mul_2 => mul_3 # neg_1 => neg_2 # pow_1 => pow_1 # pt => exp_1 # sub => sub_3 # sub_1 => sub_4 # Graph fragment: # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %view), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %view_1), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default, %view_1), kwargs = {}) # %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%view_1,), kwargs = {}) # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {}) # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {}) # %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %sub_1), kwargs = {}) # %neg_1 : [num_users=2] = call_function[target=torch.ops.aten.neg.default](args = (%sub_2,), kwargs = {}) # %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %exp_1), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2.0), kwargs = {}) # %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%pow_1,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg_2, %neg_1), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, 0.25), kwargs = {}) # %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %view), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, 0.75), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %add), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_4,), kwargs = {}) triton_per_fused_add_binary_cross_entropy_with_logits_exp_mean_mul_neg_pow_rsub_0 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_with_logits_exp_mean_mul_neg_pow_rsub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_binary_cross_entropy_with_logits_exp_mean_mul_neg_pow_rsub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_binary_cross_entropy_with_logits_exp_mean_mul_neg_pow_rsub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp3 = tl.load(in_ptr1 + (r0), None) tmp1 = 1.0 tmp2 = tmp1 - tmp0 tmp4 = tmp2 * tmp3 tmp5 = 0.0 tmp6 = triton_helpers.minimum(tmp5, tmp3) tmp7 = tl_math.abs(tmp3) tmp8 = -tmp7 tmp9 = tl_math.exp(tmp8) tmp10 = libdevice.log1p(tmp9) tmp11 = tmp6 - tmp10 tmp12 = tmp4 - tmp11 tmp13 = -tmp12 tmp14 = tl_math.exp(tmp13) tmp15 = tmp1 - tmp14 tmp16 = tmp15 * tmp15 tmp17 = -tmp16 tmp18 = tmp17 * tmp13 tmp19 = 0.25 tmp20 = tmp0 * tmp19 tmp21 = 0.75 tmp22 = tmp2 * tmp21 tmp23 = tmp20 + tmp22 tmp24 = tmp18 * tmp23 tmp25 = tl.broadcast_to(tmp24, [RBLOCK]) tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0)) tmp28 = 256.0 tmp29 = tmp27 / tmp28 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp29, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [binary_cross_entropy_with_logits, logpt, pt, sub, pow_1, neg_1, loss, mul_1, sub_1, mul_2, add, loss_1, loss_2], Original ATen: [aten.binary_cross_entropy_with_logits, aten.neg, aten.exp, aten.rsub, aten.pow, aten.mul, aten.add, aten.mean] stream0 = get_raw_stream(0) triton_per_fused_add_binary_cross_entropy_with_logits_exp_mean_mul_neg_pow_rsub_0.run(buf1, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.jit import torch.nn.functional as F import torch.nn.functional import torch.nn from functools import partial from torch.nn.modules.loss import _Loss def reduced_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor', threshold: 'float'=0.5, gamma: 'float'=2.0, reduction='mean'): """ Compute reduced focal loss between target and output logits. Source https://github.com/BloodAxe/pytorch-toolbelt See :class:`~pytorch_toolbelt.losses` for details. Args: outputs: Tensor of arbitrary shape targets: Tensor of the same shape as input reduction (string, optional): Specifies the reduction to apply to the output: "none" \| "mean" \| "sum" \| "batchwise_mean". "none": no reduction will be applied, "mean": the sum of the output will be divided by the number of elements in the output, "sum": the output will be summed. Note: :attr:`size_average` and :attr:`reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override :attr:`reduction`. "batchwise_mean" computes mean loss per sample in batch. Default: "mean" See https://arxiv.org/abs/1903.01347 """ targets = targets.type(outputs.type()) logpt = -F.binary_cross_entropy_with_logits(outputs, targets, reduction ='none') pt = torch.exp(logpt) focal_reduction = ((1.0 - pt) / threshold).pow(gamma) focal_reduction[pt < threshold] = 1 loss = -focal_reduction * logpt if reduction == 'mean': loss = loss.mean() if reduction == 'sum': loss = loss.sum() if reduction == 'batchwise_mean': loss = loss.sum(0) return loss def sigmoid_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor', gamma: 'float'=2.0, alpha: 'float'=0.25, reduction: 'str'='mean'): """ Compute binary focal loss between target and output logits. Source https://github.com/BloodAxe/pytorch-toolbelt See :class:`~pytorch_toolbelt.losses` for details. Args: outputs: Tensor of arbitrary shape targets: Tensor of the same shape as input reduction (string, optional): Specifies the reduction to apply to the output: "none" \| "mean" \| "sum" \| "batchwise_mean". "none": no reduction will be applied, "mean": the sum of the output will be divided by the number of elements in the output, "sum": the output will be summed. See https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/loss/losses.py # noqa: E501 """ targets = targets.type(outputs.type()) logpt = -F.binary_cross_entropy_with_logits(outputs, targets, reduction ='none') pt = torch.exp(logpt) loss = -(1 - pt).pow(gamma) * logpt if alpha is not None: loss = loss * (alpha * targets + (1 - alpha) * (1 - targets)) if reduction == 'mean': loss = loss.mean() if reduction == 'sum': loss = loss.sum() if reduction == 'batchwise_mean': loss = loss.sum(0) return loss class FocalLossBinary(_Loss): def __init__(self, ignore: 'int'=None, reduced: 'bool'=False, gamma: 'float'=2.0, alpha: 'float'=0.25, threshold: 'float'=0.5, reduction: 'str'='mean'): """ Compute focal loss for binary classification problem. """ super().__init__() self.ignore = ignore if reduced: self.loss_fn = partial(reduced_focal_loss, gamma=gamma, threshold=threshold, reduction=reduction) else: self.loss_fn = partial(sigmoid_focal_loss, gamma=gamma, alpha= alpha, reduction=reduction) def forward(self, logits, targets): """ Args: logits: [bs; ...] targets: [bs; ...] """ targets = targets.view(-1) logits = logits.view(-1) if self.ignore is not None: not_ignored = targets != self.ignore logits = logits[not_ignored] targets = targets[not_ignored] loss = self.loss_fn(logits, targets) return loss def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.jit import torch.nn.functional as F import torch.nn.functional import torch.nn from functools import partial from torch.nn.modules.loss import _Loss assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_binary_cross_entropy_with_logits_exp_mean_mul_neg_pow_rsub_0( in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp3 = tl.load(in_ptr1 + r0, None) tmp1 = 1.0 tmp2 = tmp1 - tmp0 tmp4 = tmp2 * tmp3 tmp5 = 0.0 tmp6 = triton_helpers.minimum(tmp5, tmp3) tmp7 = tl_math.abs(tmp3) tmp8 = -tmp7 tmp9 = tl_math.exp(tmp8) tmp10 = libdevice.log1p(tmp9) tmp11 = tmp6 - tmp10 tmp12 = tmp4 - tmp11 tmp13 = -tmp12 tmp14 = tl_math.exp(tmp13) tmp15 = tmp1 - tmp14 tmp16 = tmp15 * tmp15 tmp17 = -tmp16 tmp18 = tmp17 * tmp13 tmp19 = 0.25 tmp20 = tmp0 * tmp19 tmp21 = 0.75 tmp22 = tmp2 * tmp21 tmp23 = tmp20 + tmp22 tmp24 = tmp18 * tmp23 tmp25 = tl.broadcast_to(tmp24, [RBLOCK]) tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0)) tmp28 = 256.0 tmp29 = tmp27 / tmp28 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp29, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0 del buf0 get_raw_stream(0) triton_per_fused_add_binary_cross_entropy_with_logits_exp_mean_mul_neg_pow_rsub_0[ grid(1)](buf1, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf1, def reduced_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor', threshold: 'float'=0.5, gamma: 'float'=2.0, reduction='mean'): """ Compute reduced focal loss between target and output logits. Source https://github.com/BloodAxe/pytorch-toolbelt See :class:`~pytorch_toolbelt.losses` for details. Args: outputs: Tensor of arbitrary shape targets: Tensor of the same shape as input reduction (string, optional): Specifies the reduction to apply to the output: "none" \| "mean" \| "sum" \| "batchwise_mean". "none": no reduction will be applied, "mean": the sum of the output will be divided by the number of elements in the output, "sum": the output will be summed. Note: :attr:`size_average` and :attr:`reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override :attr:`reduction`. "batchwise_mean" computes mean loss per sample in batch. Default: "mean" See https://arxiv.org/abs/1903.01347 """ targets = targets.type(outputs.type()) logpt = -F.binary_cross_entropy_with_logits(outputs, targets, reduction ='none') pt = torch.exp(logpt) focal_reduction = ((1.0 - pt) / threshold).pow(gamma) focal_reduction[pt < threshold] = 1 loss = -focal_reduction * logpt if reduction == 'mean': loss = loss.mean() if reduction == 'sum': loss = loss.sum() if reduction == 'batchwise_mean': loss = loss.sum(0) return loss def sigmoid_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor', gamma: 'float'=2.0, alpha: 'float'=0.25, reduction: 'str'='mean'): """ Compute binary focal loss between target and output logits. Source https://github.com/BloodAxe/pytorch-toolbelt See :class:`~pytorch_toolbelt.losses` for details. Args: outputs: Tensor of arbitrary shape targets: Tensor of the same shape as input reduction (string, optional): Specifies the reduction to apply to the output: "none" \| "mean" \| "sum" \| "batchwise_mean". "none": no reduction will be applied, "mean": the sum of the output will be divided by the number of elements in the output, "sum": the output will be summed. See https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/loss/losses.py # noqa: E501 """ targets = targets.type(outputs.type()) logpt = -F.binary_cross_entropy_with_logits(outputs, targets, reduction ='none') pt = torch.exp(logpt) loss = -(1 - pt).pow(gamma) * logpt if alpha is not None: loss = loss * (alpha * targets + (1 - alpha) * (1 - targets)) if reduction == 'mean': loss = loss.mean() if reduction == 'sum': loss = loss.sum() if reduction == 'batchwise_mean': loss = loss.sum(0) return loss class FocalLossBinaryNew(_Loss): def __init__(self, ignore: 'int'=None, reduced: 'bool'=False, gamma: 'float'=2.0, alpha: 'float'=0.25, threshold: 'float'=0.5, reduction: 'str'='mean'): """ Compute focal loss for binary classification problem. """ super().__init__() self.ignore = ignore if reduced: self.loss_fn = partial(reduced_focal_loss, gamma=gamma, threshold=threshold, reduction=reduction) else: self.loss_fn = partial(sigmoid_focal_loss, gamma=gamma, alpha= alpha, reduction=reduction) def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	Gitsamshi/nnUNet-1	FocalLossBinary	false	8,194	[ "Apache-2.0" ]	28	5341684211e6d91dab6ad76a7595a95addff23be	https://github.com/Gitsamshi/nnUNet-1/tree/5341684211e6d91dab6ad76a7595a95addff23be
Maximum	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/pu/cpuvbygco5vmf5obyee6s2p7oqyswbidykd7pi74xq4tl5wy4jtn.py # Topologically Sorted Source Nodes: [max_1, max_mask, float_1, masked_x], Original ATen: [aten.max, aten.eq, aten._to_copy, aten.mul] # Source node to ATen node mapping: # float_1 => convert_element_type # masked_x => mul # max_1 => max_1 # max_mask => eq # Graph fragment: # %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%arg0_1, -1, True), kwargs = {}) # %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%arg0_1, %getitem), kwargs = {}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %convert_element_type), kwargs = {}) triton_poi_fused__to_copy_eq_max_mul_0 = async_compile.triton('triton_poi_fused__to_copy_eq_max_mul_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_eq_max_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_eq_max_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 == tmp7 tmp9 = tmp8.to(tl.float32) tmp10 = tmp0 * tmp9 tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [max_1, max_mask, float_1, masked_x], Original ATen: [aten.max, aten.eq, aten._to_copy, aten.mul] stream0 = get_raw_stream(0) triton_poi_fused__to_copy_eq_max_mul_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch as th import torch.nn as nn def maximum(x, dim=-1, scale_up=False, inplace=False): if inplace: x_ = x.clone() max_x = th.max(x_, dim=dim, keepdim=True)[0] max_mask = x_ == max_x x.masked_fill_(max_mask == 0, 0.0) if scale_up: x_sum = th.sum(x_, dim=dim, keepdim=True) max_sum = th.sum(x, dim=dim, keepdim=True) scale = x_sum / max_sum scale.masked_fill_(scale.isnan(), 0.0) x = scale return x else: max_x = th.max(x, dim=dim, keepdim=True)[0] max_mask = x == max_x masked_x = x max_mask.float() if scale_up: x_sum = th.sum(x, dim=dim, keepdim=True) max_sum = th.sum(masked_x, dim=dim, keepdim=True) scale = x_sum / max_sum scale.masked_fill_(scale.isnan(), 0.0) masked_x = masked_x * scale return masked_x class Maximum(nn.Module): def __init__(self, dim=-1, scale_up=False, inplace=False): super(Maximum, self).__init__() self.dim = dim self.scale_up = scale_up self.inplace = inplace def forward(self, x): return maximum(x, self.dim, self.scale_up, self.inplace) def extra_repr(self): return 'dim={}, scale_up={}{}'.format(self.dim, self.scale_up, ', inplace=True' if self.inplace else '') def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch as th import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused__to_copy_eq_max_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 == tmp7 tmp9 = tmp8.to(tl.float32) tmp10 = tmp0 * tmp9 tl.store(out_ptr0 + x2, tmp10, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__to_copy_eq_max_mul_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 return buf0, def maximum(x, dim=-1, scale_up=False, inplace=False): if inplace: x_ = x.clone() max_x = th.max(x_, dim=dim, keepdim=True)[0] max_mask = x_ == max_x x.masked_fill_(max_mask == 0, 0.0) if scale_up: x_sum = th.sum(x_, dim=dim, keepdim=True) max_sum = th.sum(x, dim=dim, keepdim=True) scale = x_sum / max_sum scale.masked_fill_(scale.isnan(), 0.0) x = scale return x else: max_x = th.max(x, dim=dim, keepdim=True)[0] max_mask = x == max_x masked_x = x max_mask.float() if scale_up: x_sum = th.sum(x, dim=dim, keepdim=True) max_sum = th.sum(masked_x, dim=dim, keepdim=True) scale = x_sum / max_sum scale.masked_fill_(scale.isnan(), 0.0) masked_x = masked_x * scale return masked_x class MaximumNew(nn.Module): def __init__(self, dim=-1, scale_up=False, inplace=False): super(MaximumNew, self).__init__() self.dim = dim self.scale_up = scale_up self.inplace = inplace def extra_repr(self): return 'dim={}, scale_up={}{}'.format(self.dim, self.scale_up, ', inplace=True' if self.inplace else '') def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	HKUST-KnowComp/DualMessagePassing	Maximum	false	8,195	[ "MIT" ]	12	d29d627be2a8c8f24b52e3db2c383e33a059aaa7	https://github.com/HKUST-KnowComp/DualMessagePassing/tree/d29d627be2a8c8f24b52e3db2c383e33a059aaa7
HuberLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/zd/czdy2rdwjzwjkrxa3uneax6ph26777yzaefdhz6ayvv6pkkk54mp.py # Topologically Sorted Source Nodes: [truediv, truediv_1, loss, mul, mul_1], Original ATen: [aten.div, aten.smooth_l1_loss, aten.mul] # Source node to ATen node mapping: # loss => abs_1, div_2, lt, mean, mul, pow_1, sub, sub_1, where # mul => mul_1 # mul_1 => mul_2 # truediv => div # truediv_1 => div_1 # Graph fragment: # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, 1), kwargs = {}) # %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg1_1, 1), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_1), kwargs = {}) # %abs_1 : [num_users=3] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {}) # %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%abs_1, 1.0), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 2), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.5), kwargs = {}) # %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, 1.0), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_1, 0.5), kwargs = {}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %div_2, %sub_1), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%where,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, 1), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, 1), kwargs = {}) triton_per_fused_div_mul_smooth_l1_loss_0 = async_compile.triton('triton_per_fused_div_mul_smooth_l1_loss_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_mul_smooth_l1_loss_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_div_mul_smooth_l1_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp3 = tl.load(in_ptr1 + (r0), None) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp5 = tmp2 - tmp4 tmp6 = tl_math.abs(tmp5) tmp7 = tmp6 < tmp1 tmp8 = tmp6 * tmp6 tmp9 = 0.5 tmp10 = tmp8 * tmp9 tmp11 = tmp10 * tmp1 tmp12 = tmp6 - tmp9 tmp13 = tl.where(tmp7, tmp11, tmp12) tmp14 = tl.broadcast_to(tmp13, [RBLOCK]) tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0)) tmp17 = 256.0 tmp18 = tmp16 / tmp17 tmp19 = tmp18 * tmp1 tmp20 = tmp19 * tmp1 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp20, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [truediv, truediv_1, loss, mul, mul_1], Original ATen: [aten.div, aten.smooth_l1_loss, aten.mul] stream0 = get_raw_stream(0) triton_per_fused_div_mul_smooth_l1_loss_0.run(buf1, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn import torch.utils.data class HuberLoss(nn.Module): def __init__(self, delta=1): super().__init__() self.huber_loss_delta1 = nn.SmoothL1Loss() self.delta = delta def forward(self, x, x_hat): loss = self.huber_loss_delta1(x / self.delta, x_hat / self.delta) return loss * self.delta * self.delta def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math from torch import nn import torch.utils.data assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_div_mul_smooth_l1_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp3 = tl.load(in_ptr1 + r0, None) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp5 = tmp2 - tmp4 tmp6 = tl_math.abs(tmp5) tmp7 = tmp6 < tmp1 tmp8 = tmp6 * tmp6 tmp9 = 0.5 tmp10 = tmp8 * tmp9 tmp11 = tmp10 * tmp1 tmp12 = tmp6 - tmp9 tmp13 = tl.where(tmp7, tmp11, tmp12) tmp14 = tl.broadcast_to(tmp13, [RBLOCK]) tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0)) tmp17 = 256.0 tmp18 = tmp16 / tmp17 tmp19 = tmp18 * tmp1 tmp20 = tmp19 * tmp1 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp20, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0 del buf0 get_raw_stream(0) triton_per_fused_div_mul_smooth_l1_loss_0[grid(1)](buf1, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf1, class HuberLossNew(nn.Module): def __init__(self, delta=1): super().__init__() self.huber_loss_delta1 = nn.SmoothL1Loss() self.delta = delta def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	Haichao-Zhang/leap	HuberLoss	false	8,196	[ "MIT" ]	36	4d75961ff2ff203d4412633cbeb12889de3c79b6	https://github.com/Haichao-Zhang/leap/tree/4d75961ff2ff203d4412633cbeb12889de3c79b6
UpdateFunc	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/nk/cnkqvnf65csnchzgcwfmzvir35zlp7mwibh6ixgyd2q5lxsz4ye6.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] # Source node to ATen node mapping: # x => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%bmm, %primals_3], 2), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[128], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + ((4x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 8), (8, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [agg], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(primals_2, (4, 4, 4), (0, 4, 1), 0), primals_1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(buf0, primals_3, buf1, 128, grid=grid(128), stream=stream0) del primals_3 buf2 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [v], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (16, 8), (8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf2) del primals_4 del primals_5 return (reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf1, (16, 8), (8, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	from torch.nn import Module import torch import torch.nn as nn from torch.nn.modules.module import Module class UpdateFunc(Module): """Implements a Message function""" def __init__(self, sa_dim, n_agents, hidden_size): super(UpdateFunc, self).__init__() self.fv = nn.Linear(hidden_size + sa_dim, hidden_size) self.input_dim = hidden_size + sa_dim self.output_dim = hidden_size self.n_agents = n_agents def forward(self, input_feature, x, extended_adj): """ :param input_feature: [batch_size, n_agent 2, self.sa_dim] tensor :param x: [batch_size, n_agent, self.sa_dim] tensor :param extended_adj: [n_agent, n_agent 2] tensor :return v: [batch_size, n_agent, hidden_size] tensor """ agg = torch.matmul(extended_adj, input_feature) x = torch.cat((agg, x), dim=2) v = self.fv(x) return v def __repr__(self): return self.__class__.__name__ + ' (' + str(self.input_dim ) + ' -> ' + str(self.output_dim) + ')' def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {'sa_dim': 4, 'n_agents': 4, 'hidden_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch.nn import Module import torch.nn as nn from torch.nn.modules.module import Module assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x2, tmp10, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 8), (8, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(primals_2, (4, 4, 4), (0, 4, 1), 0), primals_1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(128)](buf0, primals_3, buf1, 128, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 buf2 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0) del buf0 extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (16, 8), ( 8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf2) del primals_4 del primals_5 return reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0 ), reinterpret_tensor(buf1, (16, 8), (8, 1), 0) class UpdateFuncNew(Module): """Implements a Message function""" def __init__(self, sa_dim, n_agents, hidden_size): super(UpdateFuncNew, self).__init__() self.fv = nn.Linear(hidden_size + sa_dim, hidden_size) self.input_dim = hidden_size + sa_dim self.output_dim = hidden_size self.n_agents = n_agents def __repr__(self): return self.__class__.__name__ + ' (' + str(self.input_dim ) + ' -> ' + str(self.output_dim) + ')' def forward(self, input_0, input_1, input_2): primals_4 = self.fv.weight primals_5 = self.fv.bias primals_1 = input_0 primals_3 = input_1 primals_2 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	HAXRD/PIC	UpdateFunc	false	8,197	[ "MIT" ]	28	658b4dd6b01e64413d5f8f0107d9167f1bd78546	https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546
ResidualAttentionBlock	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/vw/cvwzgjnwchzbgmiyxlglq6bkbb26iojjmh7lyv6svo4v2kjv7ukj.py # Topologically Sorted Source Nodes: [u, sub], Original ATen: [aten.mean, aten.sub] # Source node to ATen node mapping: # sub => sub # u => mean # Graph fragment: # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [-1], True), kwargs = {}) # %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean), kwargs = {}) triton_poi_fused_mean_sub_0 = async_compile.triton('triton_poi_fused_mean_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mean_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = 4.0 tmp9 = tmp7 / tmp8 tmp10 = tmp0 - tmp9 tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/zb/czby7i6icb2vi47qymtgio3yiuc4zwtrfgf7narxnyd4jdv4iuv4.py # Topologically Sorted Source Nodes: [pow_1, s, add, sqrt, x_1, mul, add_1], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul] # Source node to ATen node mapping: # add => add # add_1 => add_1 # mul => mul # pow_1 => pow_1 # s => mean_1 # sqrt => sqrt # x_1 => div # Graph fragment: # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {}) # %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [-1], True), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_1, 1e-05), kwargs = {}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sqrt), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %div), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_3), kwargs = {}) triton_poi_fused_add_div_mean_mul_pow_sqrt_1 = async_compile.triton('triton_poi_fused_add_div_mean_mul_pow_sqrt_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_pow_sqrt_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_mean_mul_pow_sqrt_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask) tmp2 = tl.load(in_ptr1 + (4x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr1 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp20 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp3 = tmp2 * tmp2 tmp5 = tmp4 * tmp4 tmp6 = tmp3 + tmp5 tmp8 = tmp7 * tmp7 tmp9 = tmp6 + tmp8 tmp11 = tmp10 * tmp10 tmp12 = tmp9 + tmp11 tmp13 = 4.0 tmp14 = tmp12 / tmp13 tmp15 = 1e-05 tmp16 = tmp14 + tmp15 tmp17 = libdevice.sqrt(tmp16) tmp18 = tmp1 / tmp17 tmp19 = tmp0 * tmp18 tmp21 = tmp19 + tmp20 tl.store(out_ptr0 + (x2), tmp21, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/5o/c5oauw3z3ep2ftanta4riae32t6z5u67plt43eekrznregbmlpjr.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul] # Source node to ATen node mapping: # multi_head_attention_forward => mul_1 # Graph fragment: # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Scalar](args = (%view_9, 1.0), kwargs = {}) triton_poi_fused_mul_2 = async_compile.triton('triton_poi_fused_mul_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tl.store(in_out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/pe/cpeuwb6odun6kyfch2fujopahr225z4a5acqfktisl43nfgedsn7.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul] # Source node to ATen node mapping: # multi_head_attention_forward => mul_2 # Graph fragment: # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Scalar](args = (%permute_6, 1.0), kwargs = {}) triton_poi_fused_mul_3 = async_compile.triton('triton_poi_fused_mul_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tl.store(in_out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/xj/cxj2hmquy3ddfbpokchzqnhirvhk27qcfadslgslmxc7atmgb6e6.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax] # Source node to ATen node mapping: # multi_head_attention_forward => amax, exp, sub_2 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_14, [-1], True), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_14, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {}) triton_poi_fused__safe_softmax_4 = async_compile.triton('triton_poi_fused__safe_softmax_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__safe_softmax_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__safe_softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/bd/cbd3w5eob5otb3hdndg3dzwf6z5s6drp46xybotd3giji25blkl2.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax] # Source node to ATen node mapping: # multi_head_attention_forward => any_1, div_1, eq, full_default, logical_not, logical_not_1, sum_1, where # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) # %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%view_14, -inf), kwargs = {}) # %logical_not : [num_users=1] = call_function[target=torch.ops.aten.logical_not.default](args = (%eq,), kwargs = {}) # %any_1 : [num_users=1] = call_function[target=torch.ops.aten.any.dim](args = (%logical_not, -1, True), kwargs = {}) # %logical_not_1 : [num_users=1] = call_function[target=torch.ops.aten.logical_not.default](args = (%any_1,), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1, 4, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%logical_not_1, %full_default, %div_1), kwargs = {}) triton_poi_fused__safe_softmax_5 = async_compile.triton('triton_poi_fused__safe_softmax_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__safe_softmax_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__safe_softmax_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp18 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp25 = tl.load(in_ptr1 + (x2), xmask) tmp26 = tl.load(in_ptr1 + (4x1), xmask, eviction_policy='evict_last') tmp27 = tl.load(in_ptr1 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp29 = tl.load(in_ptr1 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp31 = tl.load(in_ptr1 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp1 = float("-inf") tmp2 = tmp0 == tmp1 tmp3 = tmp2 == 0 tmp4 = tmp3.to(tl.int64) tmp5 = (tmp4 != 0) tmp7 = tmp6 == tmp1 tmp8 = tmp7 == 0 tmp9 = tmp8.to(tl.int64) tmp10 = (tmp9 != 0) tmp11 = tmp5 \| tmp10 tmp13 = tmp12 == tmp1 tmp14 = tmp13 == 0 tmp15 = tmp14.to(tl.int64) tmp16 = (tmp15 != 0) tmp17 = tmp11 \| tmp16 tmp19 = tmp18 == tmp1 tmp20 = tmp19 == 0 tmp21 = tmp20.to(tl.int64) tmp22 = (tmp21 != 0) tmp23 = tmp17 \| tmp22 tmp24 = tmp23 == 0 tmp28 = tmp26 + tmp27 tmp30 = tmp28 + tmp29 tmp32 = tmp30 + tmp31 tmp33 = tmp25 / tmp32 tmp34 = 0.0 tmp35 = tl.where(tmp24, tmp34, tmp33) tl.store(out_ptr0 + (x2), tmp35, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/mo/cmom2jnedlhton5nnyviqb5zqossp7cjhsuhxlr67v53mlgbofbg.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone] # Source node to ATen node mapping: # multi_head_attention_forward => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_6 = async_compile.triton('triton_poi_fused_clone_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 4 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x1 = xindex y0 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4x1)), xmask & ymask) tl.store(out_ptr0 + (x1 + (4y0)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/j5/cj5mdh37fus72j2oxs2ev3ah6p7dgnutllsxbgupbqbquj36dfwg.py # Topologically Sorted Source Nodes: [x_2, u_1, sub_2, pow_2, s_1], Original ATen: [aten.add, aten.mean, aten.sub, aten.pow] # Source node to ATen node mapping: # pow_2 => pow_2 # s_1 => mean_3 # sub_2 => sub_3 # u_1 => mean_2 # x_2 => add_2 # Graph fragment: # %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %squeeze), kwargs = {}) # %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add_2, [-1], True), kwargs = {}) # %sub_3 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %mean_2), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {}) # %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_2, [-1], True), kwargs = {}) triton_poi_fused_add_mean_pow_sub_7 = async_compile.triton('triton_poi_fused_add_mean_pow_sub_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_pow_sub_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mean_pow_sub_7(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4x0)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + (4x0)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + (4x0)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + (4x0)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4x0)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (3 + (4x0)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 + tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 + tmp12 tmp14 = tmp10 + tmp13 tmp15 = 4.0 tmp16 = tmp14 / tmp15 tmp17 = tmp2 - tmp16 tmp18 = tmp17 * tmp17 tmp19 = tmp5 - tmp16 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp9 - tmp16 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp13 - tmp16 tmp26 = tmp25 * tmp25 tmp27 = tmp24 + tmp26 tmp28 = tmp27 / tmp15 tl.store(out_ptr0 + (x0), tmp16, xmask) tl.store(out_ptr1 + (x0), tmp28, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/4n/c4nqaxbai62yz6uqfry35nyp6bmdgc4ngooquosooawoadghjv7e.py # Topologically Sorted Source Nodes: [x_2, u_1, sub_2, add_3, sqrt_1, x_4, mul_1, add_4], Original ATen: [aten.add, aten.mean, aten.sub, aten.sqrt, aten.div, aten.mul] # Source node to ATen node mapping: # add_3 => add_3 # add_4 => add_4 # mul_1 => mul_3 # sqrt_1 => sqrt_1 # sub_2 => sub_3 # u_1 => mean_2 # x_2 => add_2 # x_4 => div_2 # Graph fragment: # %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %squeeze), kwargs = {}) # %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add_2, [-1], True), kwargs = {}) # %sub_3 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %mean_2), kwargs = {}) # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_3, 1e-05), kwargs = {}) # %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_3,), kwargs = {}) # %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_3, %sqrt_1), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_8, %div_2), kwargs = {}) # %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_9), kwargs = {}) triton_poi_fused_add_div_mean_mul_sqrt_sub_8 = async_compile.triton('triton_poi_fused_add_div_mean_mul_sqrt_sub_8', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_sqrt_sub_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_mean_mul_sqrt_sub_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask) tmp2 = tl.load(in_ptr2 + (x2), xmask) tmp4 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 - tmp4 tmp7 = 1e-05 tmp8 = tmp6 + tmp7 tmp9 = libdevice.sqrt(tmp8) tmp10 = tmp5 / tmp9 tmp11 = tmp0 * tmp10 tmp13 = tmp11 + tmp12 tl.store(out_ptr0 + (x2), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/bo/cbokxp2vzoiwoa4wrpa36mavzo6xb3qpj6m5dblai76cdifhf373.py # Topologically Sorted Source Nodes: [mul_2, sigmoid, input_2], Original ATen: [aten.mul, aten.sigmoid] # Source node to ATen node mapping: # input_2 => mul_5 # mul_2 => mul_4 # sigmoid => sigmoid # Graph fragment: # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%addmm_4, 1.702), kwargs = {}) # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mul_4,), kwargs = {}) # %mul_5 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%addmm_4, %sigmoid), kwargs = {}) triton_poi_fused_mul_sigmoid_9 = async_compile.triton('triton_poi_fused_mul_sigmoid_9', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_sigmoid_9(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 1.702 tmp2 = tmp0 * tmp1 tmp3 = tl.sigmoid(tmp2) tmp4 = tmp0 * tmp3 tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/c5/cc5u3ctv3kvqkccxk6pl5ztteehlh44dlyclcjmb7nc6wusntsea.py # Topologically Sorted Source Nodes: [x_2, x_5], Original ATen: [aten.add] # Source node to ATen node mapping: # x_2 => add_2 # x_5 => add_5 # Graph fragment: # %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %squeeze), kwargs = {}) # %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_13), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %add_tensor), kwargs = {}) triton_poi_fused_add_10 = async_compile.triton('triton_poi_fused_add_10', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr1 + (x2), xmask) tmp3 = tl.load(in_out_ptr0 + (x2), xmask) tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tl.store(in_out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (12, 4), (4, 1)) assert_size_stride(primals_5, (12, ), (1, )) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, ), (1, )) assert_size_stride(primals_8, (4, ), (1, )) assert_size_stride(primals_9, (4, ), (1, )) assert_size_stride(primals_10, (16, 4), (4, 1)) assert_size_stride(primals_11, (16, ), (1, )) assert_size_stride(primals_12, (4, 16), (16, 1)) assert_size_stride(primals_13, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [u, sub], Original ATen: [aten.mean, aten.sub] stream0 = get_raw_stream(0) triton_poi_fused_mean_sub_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0) buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [pow_1, s, add, sqrt, x_1, mul, add_1], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul] triton_poi_fused_add_div_mean_mul_pow_sqrt_1.run(primals_2, buf0, primals_3, buf1, 16, grid=grid(16), stream=stream0) del primals_2 del primals_3 buf2 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 16), out=buf3) buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.addmm] extern_kernels.addmm(reinterpret_tensor(primals_5, (4, ), (1, ), 8), buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha=1, beta=1, out=buf4) buf5 = reinterpret_tensor(buf2, (1, 4, 4, 1), (16, 1, 4, 16), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul] triton_poi_fused_mul_2.run(buf5, primals_5, 16, grid=grid(16), stream=stream0) buf6 = reinterpret_tensor(buf3, (1, 4, 1, 4), (16, 1, 16, 4), 0); del buf3 # reuse # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul] triton_poi_fused_mul_3.run(buf6, primals_5, 16, grid=grid(16), stream=stream0) del primals_5 buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf5, (4, 4, 1), (1, 4, 0), 0), reinterpret_tensor(buf6, (4, 1, 4), (1, 0, 4), 0), out=buf7) buf8 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax] triton_poi_fused__safe_softmax_4.run(buf7, buf8, 64, grid=grid(64), stream=stream0) buf9 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax] triton_poi_fused__safe_softmax_5.run(buf7, buf8, buf9, 64, grid=grid(64), stream=stream0) buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf4, (4, 4, 1), (1, 4, 0), 0), out=buf10) buf11 = empty_strided_cuda((4, 1, 4, 1), (4, 1, 1, 4), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone] triton_poi_fused_clone_6.run(buf10, buf11, 4, 4, grid=grid(4, 4), stream=stream0) buf12 = reinterpret_tensor(buf10, (4, 4), (4, 1), 0); del buf10 # reuse # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.addmm] extern_kernels.addmm(primals_7, reinterpret_tensor(buf11, (4, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf12) del primals_7 buf13 = empty_strided_cuda((4, 1), (1, 4), torch.float32) buf14 = empty_strided_cuda((4, 1), (1, 4), torch.float32) # Topologically Sorted Source Nodes: [x_2, u_1, sub_2, pow_2, s_1], Original ATen: [aten.add, aten.mean, aten.sub, aten.pow] triton_poi_fused_add_mean_pow_sub_7.run(primals_1, buf12, buf13, buf14, 4, grid=grid(4), stream=stream0) buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [x_2, u_1, sub_2, add_3, sqrt_1, x_4, mul_1, add_4], Original ATen: [aten.add, aten.mean, aten.sub, aten.sqrt, aten.div, aten.mul] triton_poi_fused_add_div_mean_mul_sqrt_sub_8.run(primals_8, primals_1, buf12, buf13, buf14, primals_9, buf15, 16, grid=grid(16), stream=stream0) del buf13 del buf14 del primals_9 buf16 = reinterpret_tensor(buf8, (4, 16), (16, 1), 0); del buf8 # reuse # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_11, buf15, reinterpret_tensor(primals_10, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf16) del primals_11 buf17 = reinterpret_tensor(buf7, (4, 16), (16, 1), 0); del buf7 # reuse # Topologically Sorted Source Nodes: [mul_2, sigmoid, input_2], Original ATen: [aten.mul, aten.sigmoid] triton_poi_fused_mul_sigmoid_9.run(buf16, buf17, 64, grid=grid(64), stream=stream0) buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf17, reinterpret_tensor(primals_12, (16, 4), (1, 16), 0), out=buf18) buf19 = buf18; del buf18 # reuse # Topologically Sorted Source Nodes: [x_2, x_5], Original ATen: [aten.add] triton_poi_fused_add_10.run(buf19, primals_1, buf12, primals_13, 16, grid=grid(16), stream=stream0) del primals_13 return (buf19, primals_1, primals_8, buf1, buf9, reinterpret_tensor(buf11, (4, 4), (4, 1), 0), buf12, buf15, buf16, buf17, primals_12, primals_10, primals_6, reinterpret_tensor(buf4, (4, 1, 4), (1, 1, 4), 0), reinterpret_tensor(buf5, (4, 1, 4), (1, 4, 4), 0), reinterpret_tensor(buf6, (4, 4, 1), (1, 4, 16), 0), reinterpret_tensor(primals_4, (4, 4), (4, 1), 32), reinterpret_tensor(primals_4, (4, 4), (4, 1), 16), reinterpret_tensor(primals_4, (4, 4), (4, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((12, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn from collections import OrderedDict class LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-05): """Construct a layernorm module in the TF style (epsilon inside the square root). """ super(LayerNorm, self).__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.bias = nn.Parameter(torch.zeros(hidden_size)) self.variance_epsilon = eps def forward(self, x): x = x.float() u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) return self.weight * x + self.bias class QuickGELU(nn.Module): def forward(self, x: 'torch.Tensor'): return x * torch.sigmoid(1.702 * x) class ResidualAttentionBlock(nn.Module): def __init__(self, d_model: 'int', n_head: 'int', attn_mask: 'torch.Tensor'=None, drop_path=0.0): super().__init__() self.attn = nn.MultiheadAttention(d_model, n_head) self.ln_1 = LayerNorm(d_model) self.mlp = nn.Sequential(OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()), ('c_proj', nn.Linear( d_model * 4, d_model))])) self.ln_2 = LayerNorm(d_model) self.attn_mask = attn_mask self.drop_path = DropPath(drop_path ) if drop_path > 0.0 else nn.Identity() def add_drop_path(self, drop_path): self.drop_path = DropPath(drop_path ) if drop_path > 0.0 else nn.Identity() def attention(self, x: 'torch.Tensor'): self.attn_mask = self.attn_mask if self.attn_mask is not None else None return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask )[0] def forward(self, x: 'torch.Tensor'): x = x + self.drop_path(self.attention(self.ln_1(x))) x = x + self.drop_path(self.mlp(self.ln_2(x))) return x def get_inputs(): return [torch.rand([4, 4])] def get_init_inputs(): return [[], {'d_model': 4, 'n_head': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn from collections import OrderedDict assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_mean_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = 4.0 tmp9 = tmp7 / tmp8 tmp10 = tmp0 - tmp9 tl.store(out_ptr0 + x2, tmp10, xmask) @triton.jit def triton_poi_fused_add_div_mean_mul_pow_sqrt_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask) tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp20 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp3 = tmp2 * tmp2 tmp5 = tmp4 * tmp4 tmp6 = tmp3 + tmp5 tmp8 = tmp7 * tmp7 tmp9 = tmp6 + tmp8 tmp11 = tmp10 * tmp10 tmp12 = tmp9 + tmp11 tmp13 = 4.0 tmp14 = tmp12 / tmp13 tmp15 = 1e-05 tmp16 = tmp14 + tmp15 tmp17 = libdevice.sqrt(tmp16) tmp18 = tmp1 / tmp17 tmp19 = tmp0 * tmp18 tmp21 = tmp19 + tmp20 tl.store(out_ptr0 + x2, tmp21, xmask) @triton.jit def triton_poi_fused_mul_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tl.store(in_out_ptr0 + x2, tmp4, xmask) @triton.jit def triton_poi_fused_mul_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tl.store(in_out_ptr0 + x2, tmp4, xmask) @triton.jit def triton_poi_fused__safe_softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__safe_softmax_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp25 = tl.load(in_ptr1 + x2, xmask) tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last') tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp1 = float('-inf') tmp2 = tmp0 == tmp1 tmp3 = tmp2 == 0 tmp4 = tmp3.to(tl.int64) tmp5 = tmp4 != 0 tmp7 = tmp6 == tmp1 tmp8 = tmp7 == 0 tmp9 = tmp8.to(tl.int64) tmp10 = tmp9 != 0 tmp11 = tmp5 \| tmp10 tmp13 = tmp12 == tmp1 tmp14 = tmp13 == 0 tmp15 = tmp14.to(tl.int64) tmp16 = tmp15 != 0 tmp17 = tmp11 \| tmp16 tmp19 = tmp18 == tmp1 tmp20 = tmp19 == 0 tmp21 = tmp20.to(tl.int64) tmp22 = tmp21 != 0 tmp23 = tmp17 \| tmp22 tmp24 = tmp23 == 0 tmp28 = tmp26 + tmp27 tmp30 = tmp28 + tmp29 tmp32 = tmp30 + tmp31 tmp33 = tmp25 / tmp32 tmp34 = 0.0 tmp35 = tl.where(tmp24, tmp34, tmp33) tl.store(out_ptr0 + x2, tmp35, xmask) @triton.jit def triton_poi_fused_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 4 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x1 = xindex y0 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask) tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_add_mean_pow_sub_7(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 + tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 + tmp12 tmp14 = tmp10 + tmp13 tmp15 = 4.0 tmp16 = tmp14 / tmp15 tmp17 = tmp2 - tmp16 tmp18 = tmp17 * tmp17 tmp19 = tmp5 - tmp16 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp9 - tmp16 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp13 - tmp16 tmp26 = tmp25 * tmp25 tmp27 = tmp24 + tmp26 tmp28 = tmp27 / tmp15 tl.store(out_ptr0 + x0, tmp16, xmask) tl.store(out_ptr1 + x0, tmp28, xmask) @triton.jit def triton_poi_fused_add_div_mean_mul_sqrt_sub_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask) tmp2 = tl.load(in_ptr2 + x2, xmask) tmp4 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 - tmp4 tmp7 = 1e-05 tmp8 = tmp6 + tmp7 tmp9 = libdevice.sqrt(tmp8) tmp10 = tmp5 / tmp9 tmp11 = tmp0 * tmp10 tmp13 = tmp11 + tmp12 tl.store(out_ptr0 + x2, tmp13, xmask) @triton.jit def triton_poi_fused_mul_sigmoid_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 1.702 tmp2 = tmp0 * tmp1 tmp3 = tl.sigmoid(tmp2) tmp4 = tmp0 * tmp3 tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr1 + x2, xmask) tmp3 = tl.load(in_out_ptr0 + x2, xmask) tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tl.store(in_out_ptr0 + x2, tmp6, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (12, 4), (4, 1)) assert_size_stride(primals_5, (12,), (1,)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4,), (1,)) assert_size_stride(primals_8, (4,), (1,)) assert_size_stride(primals_9, (4,), (1,)) assert_size_stride(primals_10, (16, 4), (4, 1)) assert_size_stride(primals_11, (16,), (1,)) assert_size_stride(primals_12, (4, 16), (16, 1)) assert_size_stride(primals_13, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_mean_sub_0[grid(16)](primals_1, buf0, 16, XBLOCK= 16, num_warps=1, num_stages=1) buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_div_mean_mul_pow_sqrt_1[grid(16)](primals_2, buf0, primals_3, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_2 del primals_3 buf2 = buf0 del buf0 extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4 ), 0), out=buf2) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4 ), 16), out=buf3) buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 8), buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha= 1, beta=1, out=buf4) buf5 = reinterpret_tensor(buf2, (1, 4, 4, 1), (16, 1, 4, 16), 0) del buf2 triton_poi_fused_mul_2[grid(16)](buf5, primals_5, 16, XBLOCK=16, num_warps=1, num_stages=1) buf6 = reinterpret_tensor(buf3, (1, 4, 1, 4), (16, 1, 16, 4), 0) del buf3 triton_poi_fused_mul_3[grid(16)](buf6, primals_5, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_5 buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf5, (4, 4, 1), (1, 4, 0), 0 ), reinterpret_tensor(buf6, (4, 1, 4), (1, 0, 4), 0), out=buf7) buf8 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused__safe_softmax_4[grid(64)](buf7, buf8, 64, XBLOCK= 64, num_warps=1, num_stages=1) buf9 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused__safe_softmax_5[grid(64)](buf7, buf8, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1) buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf4, (4, 4, 1), (1, 4, 0), 0), out=buf10) buf11 = empty_strided_cuda((4, 1, 4, 1), (4, 1, 1, 4), torch.float32) triton_poi_fused_clone_6[grid(4, 4)](buf10, buf11, 4, 4, XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1) buf12 = reinterpret_tensor(buf10, (4, 4), (4, 1), 0) del buf10 extern_kernels.addmm(primals_7, reinterpret_tensor(buf11, (4, 4), ( 4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf12) del primals_7 buf13 = empty_strided_cuda((4, 1), (1, 4), torch.float32) buf14 = empty_strided_cuda((4, 1), (1, 4), torch.float32) triton_poi_fused_add_mean_pow_sub_7[grid(4)](primals_1, buf12, buf13, buf14, 4, XBLOCK=4, num_warps=1, num_stages=1) buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_div_mean_mul_sqrt_sub_8[grid(16)](primals_8, primals_1, buf12, buf13, buf14, primals_9, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1) del buf13 del buf14 del primals_9 buf16 = reinterpret_tensor(buf8, (4, 16), (16, 1), 0) del buf8 extern_kernels.addmm(primals_11, buf15, reinterpret_tensor( primals_10, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf16) del primals_11 buf17 = reinterpret_tensor(buf7, (4, 16), (16, 1), 0) del buf7 triton_poi_fused_mul_sigmoid_9[grid(64)](buf16, buf17, 64, XBLOCK= 64, num_warps=1, num_stages=1) buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(buf17, reinterpret_tensor(primals_12, (16, 4), (1, 16), 0), out=buf18) buf19 = buf18 del buf18 triton_poi_fused_add_10[grid(16)](buf19, primals_1, buf12, primals_13, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_13 return (buf19, primals_1, primals_8, buf1, buf9, reinterpret_tensor( buf11, (4, 4), (4, 1), 0), buf12, buf15, buf16, buf17, primals_12, primals_10, primals_6, reinterpret_tensor(buf4, (4, 1, 4), (1, 1, 4 ), 0), reinterpret_tensor(buf5, (4, 1, 4), (1, 4, 4), 0), reinterpret_tensor(buf6, (4, 4, 1), (1, 4, 16), 0), reinterpret_tensor(primals_4, (4, 4), (4, 1), 32), reinterpret_tensor(primals_4, (4, 4), (4, 1), 16), reinterpret_tensor(primals_4, (4, 4), (4, 1), 0)) class LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-05): """Construct a layernorm module in the TF style (epsilon inside the square root). """ super(LayerNorm, self).__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.bias = nn.Parameter(torch.zeros(hidden_size)) self.variance_epsilon = eps def forward(self, x): x = x.float() u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) return self.weight * x + self.bias class QuickGELU(nn.Module): def forward(self, x: 'torch.Tensor'): return x * torch.sigmoid(1.702 * x) class ResidualAttentionBlockNew(nn.Module): def __init__(self, d_model: 'int', n_head: 'int', attn_mask: 'torch.Tensor'=None, drop_path=0.0): super().__init__() self.attn = nn.MultiheadAttention(d_model, n_head) self.ln_1 = LayerNorm(d_model) self.mlp = nn.Sequential(OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()), ('c_proj', nn.Linear( d_model * 4, d_model))])) self.ln_2 = LayerNorm(d_model) self.attn_mask = attn_mask self.drop_path = DropPath(drop_path ) if drop_path > 0.0 else nn.Identity() def add_drop_path(self, drop_path): self.drop_path = DropPath(drop_path ) if drop_path > 0.0 else nn.Identity() def attention(self, x: 'torch.Tensor'): self.attn_mask = self.attn_mask if self.attn_mask is not None else None return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask )[0] def forward(self, input_0): primals_4 = self.attn.in_proj_weight primals_5 = self.attn.in_proj_bias primals_1 = self.attn.out_proj.weight primals_2 = self.attn.out_proj.bias primals_3 = self.ln_1.weight primals_7 = self.ln_1.bias primals_10 = self.mlp.c_fc.weight primals_11 = self.mlp.c_fc.bias primals_12 = self.mlp.c_proj.weight primals_8 = self.mlp.c_proj.bias primals_9 = self.ln_2.weight primals_13 = self.ln_2.bias primals_6 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13]) return output[0]	FacePerceiver/FaRL	ResidualAttentionBlock	false	8,198	[ "MIT" ]	23	38f1d32f4e63940fae524e9f501b88a947ec09cd	https://github.com/FacePerceiver/FaRL/tree/38f1d32f4e63940fae524e9f501b88a947ec09cd
maxPool23DUinit	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ez/cez2c66hdhdi53r34wosyuvhkd634k7fob676evmdhjkemg7anii.py # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # max_pool2d => getitem # Graph fragment: # %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_0 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 144 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 3) % 3 x0 = xindex % 3 x2 = (xindex // 9) x4 = xindex tmp0 = (-1) + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tmp2 & tmp4 tmp6 = (-1) + x0 tmp7 = tmp6 >= tmp1 tmp8 = tmp6 < tmp3 tmp9 = tmp7 & tmp8 tmp10 = tmp5 & tmp9 tmp11 = tl.load(in_ptr0 + ((-5) + x0 + (4x1) + (16x2)), tmp10 & xmask, other=float("-inf")) tmp12 = x0 tmp13 = tmp12 >= tmp1 tmp14 = tmp12 < tmp3 tmp15 = tmp13 & tmp14 tmp16 = tmp5 & tmp15 tmp17 = tl.load(in_ptr0 + ((-4) + x0 + (4x1) + (16x2)), tmp16 & xmask, other=float("-inf")) tmp18 = triton_helpers.maximum(tmp17, tmp11) tmp19 = 1 + x0 tmp20 = tmp19 >= tmp1 tmp21 = tmp19 < tmp3 tmp22 = tmp20 & tmp21 tmp23 = tmp5 & tmp22 tmp24 = tl.load(in_ptr0 + ((-3) + x0 + (4x1) + (16x2)), tmp23 & xmask, other=float("-inf")) tmp25 = triton_helpers.maximum(tmp24, tmp18) tmp26 = 2 + x0 tmp27 = tmp26 >= tmp1 tmp28 = tmp26 < tmp3 tmp29 = tmp27 & tmp28 tmp30 = tmp5 & tmp29 tmp31 = tl.load(in_ptr0 + ((-2) + x0 + (4x1) + (16x2)), tmp30 & xmask, other=float("-inf")) tmp32 = triton_helpers.maximum(tmp31, tmp25) tmp33 = x1 tmp34 = tmp33 >= tmp1 tmp35 = tmp33 < tmp3 tmp36 = tmp34 & tmp35 tmp37 = tmp36 & tmp9 tmp38 = tl.load(in_ptr0 + ((-1) + x0 + (4x1) + (16x2)), tmp37 & xmask, other=float("-inf")) tmp39 = triton_helpers.maximum(tmp38, tmp32) tmp40 = tmp36 & tmp15 tmp41 = tl.load(in_ptr0 + (x0 + (4x1) + (16x2)), tmp40 & xmask, other=float("-inf")) tmp42 = triton_helpers.maximum(tmp41, tmp39) tmp43 = tmp36 & tmp22 tmp44 = tl.load(in_ptr0 + (1 + x0 + (4x1) + (16x2)), tmp43 & xmask, other=float("-inf")) tmp45 = triton_helpers.maximum(tmp44, tmp42) tmp46 = tmp36 & tmp29 tmp47 = tl.load(in_ptr0 + (2 + x0 + (4x1) + (16x2)), tmp46 & xmask, other=float("-inf")) tmp48 = triton_helpers.maximum(tmp47, tmp45) tmp49 = 1 + x1 tmp50 = tmp49 >= tmp1 tmp51 = tmp49 < tmp3 tmp52 = tmp50 & tmp51 tmp53 = tmp52 & tmp9 tmp54 = tl.load(in_ptr0 + (3 + x0 + (4x1) + (16x2)), tmp53 & xmask, other=float("-inf")) tmp55 = triton_helpers.maximum(tmp54, tmp48) tmp56 = tmp52 & tmp15 tmp57 = tl.load(in_ptr0 + (4 + x0 + (4x1) + (16x2)), tmp56 & xmask, other=float("-inf")) tmp58 = triton_helpers.maximum(tmp57, tmp55) tmp59 = tmp52 & tmp22 tmp60 = tl.load(in_ptr0 + (5 + x0 + (4x1) + (16x2)), tmp59 & xmask, other=float("-inf")) tmp61 = triton_helpers.maximum(tmp60, tmp58) tmp62 = tmp52 & tmp29 tmp63 = tl.load(in_ptr0 + (6 + x0 + (4x1) + (16x2)), tmp62 & xmask, other=float("-inf")) tmp64 = triton_helpers.maximum(tmp63, tmp61) tmp65 = 2 + x1 tmp66 = tmp65 >= tmp1 tmp67 = tmp65 < tmp3 tmp68 = tmp66 & tmp67 tmp69 = tmp68 & tmp9 tmp70 = tl.load(in_ptr0 + (7 + x0 + (4x1) + (16x2)), tmp69 & xmask, other=float("-inf")) tmp71 = triton_helpers.maximum(tmp70, tmp64) tmp72 = tmp68 & tmp15 tmp73 = tl.load(in_ptr0 + (8 + x0 + (4x1) + (16x2)), tmp72 & xmask, other=float("-inf")) tmp74 = triton_helpers.maximum(tmp73, tmp71) tmp75 = tmp68 & tmp22 tmp76 = tl.load(in_ptr0 + (9 + x0 + (4x1) + (16x2)), tmp75 & xmask, other=float("-inf")) tmp77 = triton_helpers.maximum(tmp76, tmp74) tmp78 = tmp68 & tmp29 tmp79 = tl.load(in_ptr0 + (10 + x0 + (4x1) + (16x2)), tmp78 & xmask, other=float("-inf")) tmp80 = triton_helpers.maximum(tmp79, tmp77) tl.store(out_ptr0 + (x4), tmp80, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32) # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices] stream0 = get_raw_stream(0) triton_poi_fused_max_pool2d_with_indices_0.run(arg0_1, buf0, 144, grid=grid(144), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn import torch.utils.data import torch.nn.init class maxPool23DUinit(nn.Module): def __init__(self, kernel_size, stride, padding=1, dilation=1, nd=2): super(maxPool23DUinit, self).__init__() assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}' if nd == 2: self.pool1 = nn.MaxPool2d(kernel_size=kernel_size, stride= stride, padding=padding, dilation=dilation) elif nd == 3: self.pool1 = nn.MaxPool3d(kernel_size=kernel_size, stride= stride, padding=padding, dilation=dilation) else: self.pool1 = nn.MaxPool1d(kernel_size=kernel_size, stride= stride, padding=padding, dilation=dilation) def forward(self, x): return self.pool1(x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'kernel_size': 4, 'stride': 1}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch import nn import torch.utils.data import torch.nn.init assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_max_pool2d_with_indices_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 144 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 3 % 3 x0 = xindex % 3 x2 = xindex // 9 x4 = xindex tmp0 = -1 + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tmp2 & tmp4 tmp6 = -1 + x0 tmp7 = tmp6 >= tmp1 tmp8 = tmp6 < tmp3 tmp9 = tmp7 & tmp8 tmp10 = tmp5 & tmp9 tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask, other=float('-inf')) tmp12 = x0 tmp13 = tmp12 >= tmp1 tmp14 = tmp12 < tmp3 tmp15 = tmp13 & tmp14 tmp16 = tmp5 & tmp15 tmp17 = tl.load(in_ptr0 + (-4 + x0 + 4 * x1 + 16 * x2), tmp16 & xmask, other=float('-inf')) tmp18 = triton_helpers.maximum(tmp17, tmp11) tmp19 = 1 + x0 tmp20 = tmp19 >= tmp1 tmp21 = tmp19 < tmp3 tmp22 = tmp20 & tmp21 tmp23 = tmp5 & tmp22 tmp24 = tl.load(in_ptr0 + (-3 + x0 + 4 * x1 + 16 * x2), tmp23 & xmask, other=float('-inf')) tmp25 = triton_helpers.maximum(tmp24, tmp18) tmp26 = 2 + x0 tmp27 = tmp26 >= tmp1 tmp28 = tmp26 < tmp3 tmp29 = tmp27 & tmp28 tmp30 = tmp5 & tmp29 tmp31 = tl.load(in_ptr0 + (-2 + x0 + 4 * x1 + 16 * x2), tmp30 & xmask, other=float('-inf')) tmp32 = triton_helpers.maximum(tmp31, tmp25) tmp33 = x1 tmp34 = tmp33 >= tmp1 tmp35 = tmp33 < tmp3 tmp36 = tmp34 & tmp35 tmp37 = tmp36 & tmp9 tmp38 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1 + 16 * x2), tmp37 & xmask, other=float('-inf')) tmp39 = triton_helpers.maximum(tmp38, tmp32) tmp40 = tmp36 & tmp15 tmp41 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp40 & xmask, other =float('-inf')) tmp42 = triton_helpers.maximum(tmp41, tmp39) tmp43 = tmp36 & tmp22 tmp44 = tl.load(in_ptr0 + (1 + x0 + 4 * x1 + 16 * x2), tmp43 & xmask, other=float('-inf')) tmp45 = triton_helpers.maximum(tmp44, tmp42) tmp46 = tmp36 & tmp29 tmp47 = tl.load(in_ptr0 + (2 + x0 + 4 * x1 + 16 * x2), tmp46 & xmask, other=float('-inf')) tmp48 = triton_helpers.maximum(tmp47, tmp45) tmp49 = 1 + x1 tmp50 = tmp49 >= tmp1 tmp51 = tmp49 < tmp3 tmp52 = tmp50 & tmp51 tmp53 = tmp52 & tmp9 tmp54 = tl.load(in_ptr0 + (3 + x0 + 4 * x1 + 16 * x2), tmp53 & xmask, other=float('-inf')) tmp55 = triton_helpers.maximum(tmp54, tmp48) tmp56 = tmp52 & tmp15 tmp57 = tl.load(in_ptr0 + (4 + x0 + 4 * x1 + 16 * x2), tmp56 & xmask, other=float('-inf')) tmp58 = triton_helpers.maximum(tmp57, tmp55) tmp59 = tmp52 & tmp22 tmp60 = tl.load(in_ptr0 + (5 + x0 + 4 * x1 + 16 * x2), tmp59 & xmask, other=float('-inf')) tmp61 = triton_helpers.maximum(tmp60, tmp58) tmp62 = tmp52 & tmp29 tmp63 = tl.load(in_ptr0 + (6 + x0 + 4 * x1 + 16 * x2), tmp62 & xmask, other=float('-inf')) tmp64 = triton_helpers.maximum(tmp63, tmp61) tmp65 = 2 + x1 tmp66 = tmp65 >= tmp1 tmp67 = tmp65 < tmp3 tmp68 = tmp66 & tmp67 tmp69 = tmp68 & tmp9 tmp70 = tl.load(in_ptr0 + (7 + x0 + 4 * x1 + 16 * x2), tmp69 & xmask, other=float('-inf')) tmp71 = triton_helpers.maximum(tmp70, tmp64) tmp72 = tmp68 & tmp15 tmp73 = tl.load(in_ptr0 + (8 + x0 + 4 * x1 + 16 * x2), tmp72 & xmask, other=float('-inf')) tmp74 = triton_helpers.maximum(tmp73, tmp71) tmp75 = tmp68 & tmp22 tmp76 = tl.load(in_ptr0 + (9 + x0 + 4 * x1 + 16 * x2), tmp75 & xmask, other=float('-inf')) tmp77 = triton_helpers.maximum(tmp76, tmp74) tmp78 = tmp68 & tmp29 tmp79 = tl.load(in_ptr0 + (10 + x0 + 4 * x1 + 16 * x2), tmp78 & xmask, other=float('-inf')) tmp80 = triton_helpers.maximum(tmp79, tmp77) tl.store(out_ptr0 + x4, tmp80, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32) get_raw_stream(0) triton_poi_fused_max_pool2d_with_indices_0[grid(144)](arg0_1, buf0, 144, XBLOCK=128, num_warps=4, num_stages=1) del arg0_1 return buf0, class maxPool23DUinitNew(nn.Module): def __init__(self, kernel_size, stride, padding=1, dilation=1, nd=2): super(maxPool23DUinitNew, self).__init__() assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}' if nd == 2: self.pool1 = nn.MaxPool2d(kernel_size=kernel_size, stride= stride, padding=padding, dilation=dilation) elif nd == 3: self.pool1 = nn.MaxPool3d(kernel_size=kernel_size, stride= stride, padding=padding, dilation=dilation) else: self.pool1 = nn.MaxPool1d(kernel_size=kernel_size, stride= stride, padding=padding, dilation=dilation) def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	ForrestPi/Unsupervised-Defect-Segmentation	maxPool23DUinit	false	8,199	[ "MIT" ]	17	e366ac7c757bb1b45f38ebbc502dfee7ccb72398	https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398
TripletAllLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/qd/cqdfud7hfeqnmonz4fwgzv6mzxfxb3gsgns7bgjcslwhyyutfiy5.py # Topologically Sorted Source Nodes: [sub_2, M, sub_3, add_1, dist_diff, mul_2, sum_1, sum_2, truediv], Original ATen: [aten.rsub, aten.mul, aten.sub, aten.add, aten.relu, aten.sum, aten.div] # Source node to ATen node mapping: # M => mul_1 # add_1 => add_1 # dist_diff => relu # mul_2 => mul_2 # sub_2 => sub_2 # sub_3 => sub_3 # sum_1 => sum_1 # sum_2 => sum_2 # truediv => div # Graph fragment: # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %expand_5), kwargs = {}) # %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_4, %sub_2), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand_3, %permute_2), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_3, 1.0), kwargs = {}) # %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %relu), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_2,), kwargs = {}) # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_1,), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %sum_2), kwargs = {}) triton_per_fused_add_div_mul_relu_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_mul_relu_rsub_sub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 64], reduction_hint=ReductionHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mul_relu_rsub_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_div_mul_relu_rsub_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 1 rnumel = 64 RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex % 4 r2 = (rindex // 16) r1 = (rindex // 4) % 4 r5 = (rindex // 4) r3 = rindex tmp0 = tl.load(in_ptr0 + (r0), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (r2), None, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (r1), None, eviction_policy='evict_last') tmp20 = tl.load(in_ptr1 + (5r2), None, eviction_policy='evict_last') tmp21 = tl.load(in_ptr1 + (5r0), None, eviction_policy='evict_last') tmp23 = tl.load(in_ptr1 + (r0 + (4r2)), None, eviction_policy='evict_last') tmp31 = tl.load(in_ptr1 + (5r1), None, eviction_policy='evict_last') tmp33 = tl.load(in_ptr1 + (r5), None, eviction_policy='evict_last') tmp2 = tmp0 == tmp1 tmp3 = tmp2.to(tl.float32) tmp4 = r2 tmp5 = r0 tmp6 = tmp4 == tmp5 tmp7 = 1.0 tmp8 = 0.0 tmp9 = tl.where(tmp6, tmp7, tmp8) tmp10 = tmp3 - tmp9 tmp12 = tmp11 == tmp1 tmp13 = tmp12.to(tl.float32) tmp14 = r1 tmp15 = tmp4 == tmp14 tmp16 = tl.where(tmp15, tmp7, tmp8) tmp17 = tmp13 - tmp16 tmp18 = tmp7 - tmp17 tmp19 = tmp10 * tmp18 tmp22 = tmp20 + tmp21 tmp24 = 2.0 tmp25 = tmp23 * tmp24 tmp26 = tmp22 - tmp25 tmp27 = triton_helpers.maximum(tmp26, tmp8) tmp28 = 0.0001 tmp29 = triton_helpers.maximum(tmp27, tmp28) tmp30 = libdevice.sqrt(tmp29) tmp32 = tmp20 + tmp31 tmp34 = tmp33 * tmp24 tmp35 = tmp32 - tmp34 tmp36 = triton_helpers.maximum(tmp35, tmp8) tmp37 = triton_helpers.maximum(tmp36, tmp28) tmp38 = libdevice.sqrt(tmp37) tmp39 = tmp30 - tmp38 tmp40 = tmp39 + tmp7 tmp41 = tl.full([1, 1], 0, tl.int32) tmp42 = triton_helpers.maximum(tmp41, tmp40) tmp43 = tmp19 * tmp42 tmp44 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK]) tmp46 = tl.sum(tmp44, 1)[:, None] tmp47 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK]) tmp49 = tl.sum(tmp47, 1)[:, None] tmp50 = tmp46 / tmp49 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp50, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [prod], Original ATen: [aten.mm] extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0) del arg0_1 buf2 = empty_strided_cuda((), (), torch.float32) buf4 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [sub_2, M, sub_3, add_1, dist_diff, mul_2, sum_1, sum_2, truediv], Original ATen: [aten.rsub, aten.mul, aten.sub, aten.add, aten.relu, aten.sum, aten.div] stream0 = get_raw_stream(0) triton_per_fused_add_div_mul_relu_rsub_sub_sum_0.run(buf4, arg1_1, buf0, 1, 64, grid=grid(1), stream=stream0) del arg1_1 del buf0 return (buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torchvision.transforms.functional as F import torch.nn.functional as F import torch.utils.model_zoo def pdist(A, squared=False, eps=0.0001): prod = torch.mm(A, A.t()) norm = prod.diag().unsqueeze(1).expand_as(prod) res = (norm + norm.t() - 2 * prod).clamp(min=0) if squared: return res else: return res.clamp(min=eps).sqrt() class TripletAllLoss(torch.nn.Module): """ Compute all possible triplets in the batch, where loss > 0 """ def __init__(self, margin=1.0, *kwargs): None self.margin = margin torch.nn.Module.__init__(self) def forward(self, embeddings, labels): d = pdist(embeddings) pos = torch.eq([labels.unsqueeze(dim).expand_as(d) for dim in [0, 1]] ).type_as(d) - torch.eye(len(d)).type_as(d) T = d.unsqueeze(1).expand(((len(d),) 3)) M = pos.unsqueeze(1).expand_as(T) * (1 - pos.unsqueeze(2).expand_as(T)) dist_diff = F.relu(T - T.transpose(1, 2) + self.margin) return (M * dist_diff).sum() / M.sum() def get_inputs(): return [torch.rand([4, 4]), torch.rand([4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import torch.utils.model_zoo assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_per_fused_add_div_mul_relu_rsub_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr): RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex % 4 r2 = rindex // 16 r1 = rindex // 4 % 4 r5 = rindex // 4 tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + r1, None, eviction_policy='evict_last') tmp20 = tl.load(in_ptr1 + 5 * r2, None, eviction_policy='evict_last') tmp21 = tl.load(in_ptr1 + 5 * r0, None, eviction_policy='evict_last') tmp23 = tl.load(in_ptr1 + (r0 + 4 * r2), None, eviction_policy='evict_last' ) tmp31 = tl.load(in_ptr1 + 5 * r1, None, eviction_policy='evict_last') tmp33 = tl.load(in_ptr1 + r5, None, eviction_policy='evict_last') tmp2 = tmp0 == tmp1 tmp3 = tmp2.to(tl.float32) tmp4 = r2 tmp5 = r0 tmp6 = tmp4 == tmp5 tmp7 = 1.0 tmp8 = 0.0 tmp9 = tl.where(tmp6, tmp7, tmp8) tmp10 = tmp3 - tmp9 tmp12 = tmp11 == tmp1 tmp13 = tmp12.to(tl.float32) tmp14 = r1 tmp15 = tmp4 == tmp14 tmp16 = tl.where(tmp15, tmp7, tmp8) tmp17 = tmp13 - tmp16 tmp18 = tmp7 - tmp17 tmp19 = tmp10 * tmp18 tmp22 = tmp20 + tmp21 tmp24 = 2.0 tmp25 = tmp23 * tmp24 tmp26 = tmp22 - tmp25 tmp27 = triton_helpers.maximum(tmp26, tmp8) tmp28 = 0.0001 tmp29 = triton_helpers.maximum(tmp27, tmp28) tmp30 = libdevice.sqrt(tmp29) tmp32 = tmp20 + tmp31 tmp34 = tmp33 * tmp24 tmp35 = tmp32 - tmp34 tmp36 = triton_helpers.maximum(tmp35, tmp8) tmp37 = triton_helpers.maximum(tmp36, tmp28) tmp38 = libdevice.sqrt(tmp37) tmp39 = tmp30 - tmp38 tmp40 = tmp39 + tmp7 tmp41 = tl.full([1, 1], 0, tl.int32) tmp42 = triton_helpers.maximum(tmp41, tmp40) tmp43 = tmp19 * tmp42 tmp44 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK]) tmp46 = tl.sum(tmp44, 1)[:, None] tmp47 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK]) tmp49 = tl.sum(tmp47, 1)[:, None] tmp50 = tmp46 / tmp49 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp50, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0) del arg0_1 buf2 = empty_strided_cuda((), (), torch.float32) buf4 = buf2 del buf2 get_raw_stream(0) triton_per_fused_add_div_mul_relu_rsub_sub_sum_0[grid(1)](buf4, arg1_1, buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1) del arg1_1 del buf0 return buf4, def pdist(A, squared=False, eps=0.0001): prod = torch.mm(A, A.t()) norm = prod.diag().unsqueeze(1).expand_as(prod) res = (norm + norm.t() - 2 * prod).clamp(min=0) if squared: return res else: return res.clamp(min=eps).sqrt() class TripletAllLossNew(torch.nn.Module): """ Compute all possible triplets in the batch, where loss > 0 """ def __init__(self, margin=1.0, **kwargs): None self.margin = margin torch.nn.Module.__init__(self) def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	CompVis/metric-learning-divide-and-conquer-improved	TripletAllLoss	false	8,200	[ "MIT" ]	11	33fe768a54376a090e2d7139898177b06e8903d2	https://github.com/CompVis/metric-learning-divide-and-conquer-improved/tree/33fe768a54376a090e2d7139898177b06e8903d2
LRN	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/fp/cfpylbinohoan7yh5fcfp46fvbiwpo63lxt42umo3xnsuu3aeumz.py # Topologically Sorted Source Nodes: [mul, add, div_2, x], Original ATen: [aten.mul, aten.add, aten.pow, aten.div] # Source node to ATen node mapping: # add => add # div_2 => pow_2 # mul => mul # x => div # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze, 1.0), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 1.0), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 0.75), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %pow_2), kwargs = {}) triton_poi_fused_add_div_mul_pow_0 = async_compile.triton('triton_poi_fused_add_div_mul_pow_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_pow_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_mul_pow_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tmp0 * tmp0 tmp2 = 1.0 tmp3 = tmp1 * tmp2 tmp4 = tmp3 * tmp2 tmp5 = tmp4 + tmp2 tmp6 = 0.75 tmp7 = libdevice.pow(tmp5, tmp6) tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x0), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mul, add, div_2, x], Original ATen: [aten.mul, aten.add, aten.pow, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_add_div_mul_pow_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.utils.data.distributed class LRN(nn.Module): def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True ): super(LRN, self).__init__() self.ACROSS_CHANNELS = ACROSS_CHANNELS if ACROSS_CHANNELS: self.average = nn.AvgPool3d(kernel_size=(local_size, 1, 1), stride=1, padding=(int((local_size - 1.0) / 2), 0, 0)) else: self.average = nn.AvgPool2d(kernel_size=local_size, stride=1, padding=int((local_size - 1.0) / 2)) self.alpha = alpha self.beta = beta def forward(self, x): if self.ACROSS_CHANNELS: div = x.pow(2).unsqueeze(1) div = self.average(div).squeeze(1) div = div.mul(self.alpha).add(1.0).pow(self.beta) else: div = x.pow(2) div = self.average(div) div = div.mul(self.alpha).add(1.0).pow(self.beta) x = x.div(div) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn import torch.utils.data.distributed assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_div_mul_pow_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tmp0 * tmp0 tmp2 = 1.0 tmp3 = tmp1 * tmp2 tmp4 = tmp3 * tmp2 tmp5 = tmp4 + tmp2 tmp6 = 0.75 tmp7 = libdevice.pow(tmp5, tmp6) tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x0, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_div_mul_pow_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 return buf0, class LRNNew(nn.Module): def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True ): super(LRNNew, self).__init__() self.ACROSS_CHANNELS = ACROSS_CHANNELS if ACROSS_CHANNELS: self.average = nn.AvgPool3d(kernel_size=(local_size, 1, 1), stride=1, padding=(int((local_size - 1.0) / 2), 0, 0)) else: self.average = nn.AvgPool2d(kernel_size=local_size, stride=1, padding=int((local_size - 1.0) / 2)) self.alpha = alpha self.beta = beta def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	HKBU-HPML/gtopkssgd	LRN	false	8,201	[ "Apache-2.0" ]	33	6f57343f3749939b0345d36fcb2c24470942aefd	https://github.com/HKBU-HPML/gtopkssgd/tree/6f57343f3749939b0345d36fcb2c24470942aefd
NoiseZ	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/74/c74z3ko5fu2qrhbb3ec3qohksuwrx3lqrih2sf5b5fjye7zvzqwv.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.mul] # Source node to ATen node mapping: # out => mul # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_2), kwargs = {}) triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8192 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 512 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), None) tmp2 = tmp0 * tmp1 tl.store(out_ptr0 + (x2), tmp2, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 128), (128, 1)) assert_size_stride(primals_2, (4, 4, 4, 128), (2048, 512, 128, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.float32) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.mul] stream0 = get_raw_stream(0) triton_poi_fused_mul_0.run(primals_1, primals_2, buf0, 8192, grid=grid(8192), stream=stream0) del primals_1 return (buf0, primals_2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 128), (128, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 128), (2048, 512, 128, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn import torch.utils.data import torch.nn.init class NoiseZ(nn.Module): def __init__(self, batchSize): super(NoiseZ, self).__init__() self.Z = nn.Parameter(torch.randn(batchSize, 128), requires_grad=True) def forward(self, input): out = self.Z * input return out def get_inputs(): return [torch.rand([4, 4, 4, 128])] def get_init_inputs(): return [[], {'batchSize': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn import torch.utils.data import torch.nn.init assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 512 x2 = xindex tmp0 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, None) tmp2 = tmp0 * tmp1 tl.store(out_ptr0 + x2, tmp2, None) def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 128), (128, 1)) assert_size_stride(primals_2, (4, 4, 4, 128), (2048, 512, 128, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.float32) get_raw_stream(0) triton_poi_fused_mul_0[grid(8192)](primals_1, primals_2, buf0, 8192, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 return buf0, primals_2 class NoiseZNew(nn.Module): def __init__(self, batchSize): super(NoiseZNew, self).__init__() self.Z = nn.Parameter(torch.randn(batchSize, 128), requires_grad=True) def forward(self, input_0): primals_1 = self.Z primals_2 = input_0 output = call([primals_1, primals_2]) return output[0]	ForrestPi/Unsupervised-Defect-Segmentation	NoiseZ	false	8,202	[ "MIT" ]	17	e366ac7c757bb1b45f38ebbc502dfee7ccb72398	https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398
DiscriminatorLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/vm/cvmu3pcp6sssf4jlkop6zf67yn3gcikbg6whd4pg76zrtley3tne.py # Topologically Sorted Source Nodes: [sub, d_loss, mean], Original ATen: [aten.rsub, aten.add, aten.mean] # Source node to ATen node mapping: # d_loss => add # mean => mean # sub => sub # Graph fragment: # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %arg1_1), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add,), kwargs = {}) triton_per_fused_add_mean_rsub_0 = async_compile.triton('triton_per_fused_add_mean_rsub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mean_rsub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_mean_rsub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp3 = tl.load(in_ptr1 + (r0), None) tmp1 = 1.0 tmp2 = tmp1 - tmp0 tmp4 = tmp2 + tmp3 tmp5 = tl.broadcast_to(tmp4, [RBLOCK]) tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0)) tmp8 = 256.0 tmp9 = tmp7 / tmp8 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp9, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [sub, d_loss, mean], Original ATen: [aten.rsub, aten.add, aten.mean] stream0 = get_raw_stream(0) triton_per_fused_add_mean_rsub_0.run(buf1, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn import torch.utils.data import torch.nn.init class DiscriminatorLoss(nn.Module): def __init__(self): super(DiscriminatorLoss, self).__init__() def forward(self, real_out, fake_out): d_loss = 1 - real_out + fake_out return d_loss.mean() def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch import nn import torch.utils.data import torch.nn.init assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_mean_rsub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp3 = tl.load(in_ptr1 + r0, None) tmp1 = 1.0 tmp2 = tmp1 - tmp0 tmp4 = tmp2 + tmp3 tmp5 = tl.broadcast_to(tmp4, [RBLOCK]) tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0)) tmp8 = 256.0 tmp9 = tmp7 / tmp8 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp9, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0 del buf0 get_raw_stream(0) triton_per_fused_add_mean_rsub_0[grid(1)](buf1, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf1, class DiscriminatorLossNew(nn.Module): def __init__(self): super(DiscriminatorLossNew, self).__init__() def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	ForrestPi/Unsupervised-Defect-Segmentation	DiscriminatorLoss	false	8,203	[ "MIT" ]	17	e366ac7c757bb1b45f38ebbc502dfee7ccb72398	https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398
LayerNorm	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/jf/cjfodn6ytrtuoaceb5njpdwxiajd3oeiid7qxygpb2adoxjoyvvf.py # Topologically Sorted Source Nodes: [mean, std, sub, add, output, output_1], Original ATen: [aten.mean, aten.std, aten.sub, aten.add, aten.div] # Source node to ATen node mapping: # add => add # mean => mean # output => div # output_1 => add_1 # std => sqrt, var # sub => sub # Graph fragment: # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [-1], True), kwargs = {}) # %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%primals_1, [-1]), kwargs = {correction: 1.0, keepdim: True}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-06), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %add), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %primals_2), kwargs = {}) triton_poi_fused_add_div_mean_std_sub_0 = async_compile.triton('triton_poi_fused_add_div_mean_std_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_std_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_mean_std_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = 4.0 tmp9 = tmp7 / tmp8 tmp10 = tmp0 - tmp9 tmp11 = tmp1 - tmp9 tmp12 = tmp11 * tmp11 tmp13 = tmp2 - tmp9 tmp14 = tmp13 * tmp13 tmp15 = tmp12 + tmp14 tmp16 = tmp4 - tmp9 tmp17 = tmp16 * tmp16 tmp18 = tmp15 + tmp17 tmp19 = tmp6 - tmp9 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = 3.0 tmp23 = tmp21 / tmp22 tmp24 = libdevice.sqrt(tmp23) tmp25 = 1e-06 tmp26 = tmp24 + tmp25 tmp27 = tmp10 / tmp26 tmp29 = tmp27 + tmp28 tl.store(out_ptr0 + (x2), tmp29, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mean, std, sub, add, output, output_1], Original ATen: [aten.mean, aten.std, aten.sub, aten.add, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_add_div_mean_std_sub_0.run(primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0) del primals_1 del primals_2 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn import torch.utils.data class LayerNorm(nn.Module): """ Simple 1D LayerNorm. """ def __init__(self, features, center=True, scale=False, eps=1e-06): super().__init__() self.center = center self.scale = scale self.eps = eps if self.scale: self.scale_param = nn.Parameter(torch.ones(features)) else: self.scale_param = None if self.center: self.center_param = nn.Parameter(torch.zeros(features)) else: self.center_param = None def forward(self, x): mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) output = (x - mean) / (std + self.eps) if self.scale: output = output * self.scale_param if self.center: output = output + self.center_param return output def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'features': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice from torch import nn import torch.utils.data assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_div_mean_std_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = 4.0 tmp9 = tmp7 / tmp8 tmp10 = tmp0 - tmp9 tmp11 = tmp1 - tmp9 tmp12 = tmp11 * tmp11 tmp13 = tmp2 - tmp9 tmp14 = tmp13 * tmp13 tmp15 = tmp12 + tmp14 tmp16 = tmp4 - tmp9 tmp17 = tmp16 * tmp16 tmp18 = tmp15 + tmp17 tmp19 = tmp6 - tmp9 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = 3.0 tmp23 = tmp21 / tmp22 tmp24 = libdevice.sqrt(tmp23) tmp25 = 1e-06 tmp26 = tmp24 + tmp25 tmp27 = tmp10 / tmp26 tmp29 = tmp27 + tmp28 tl.store(out_ptr0 + x2, tmp29, xmask) def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_div_mean_std_sub_0[grid(256)](primals_1, primals_2, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 del primals_2 return buf0, class LayerNormNew(nn.Module): """ Simple 1D LayerNorm. """ def __init__(self, features, center=True, scale=False, eps=1e-06): super().__init__() self.center = center self.scale = scale self.eps = eps if self.scale: self.scale_param = nn.Parameter(torch.ones(features)) else: self.scale_param = None if self.center: self.center_param = nn.Parameter(torch.zeros(features)) else: self.center_param = None def forward(self, input_0): primals_2 = self.center_param primals_1 = input_0 output = call([primals_1, primals_2]) return output[0]	Haichao-Zhang/leap	LayerNorm	false	8,204	[ "MIT" ]	36	4d75961ff2ff203d4412633cbeb12889de3c79b6	https://github.com/Haichao-Zhang/leap/tree/4d75961ff2ff203d4412633cbeb12889de3c79b6
par_start_encoder	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/on/conftg7pkgnt773qoe3if3p4dzmxcls3usbuq2dpbxeu6vedrx2k.py # Topologically Sorted Source Nodes: [getitem], Original ATen: [aten.index] # Source node to ATen node mapping: # getitem => index # Graph fragment: # %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%primals_1, [%primals_2]), kwargs = {}) triton_poi_fused_index_0 = async_compile.triton('triton_poi_fused_index_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_index_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) \| ~(xmask), "index out of bounds: 0 <= tmp4 < 4") tmp6 = tl.load(in_ptr1 + (x0 + (4*tmp4)), xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [getitem], Original ATen: [aten.index] stream0 = get_raw_stream(0) triton_poi_fused_index_0.run(primals_2, primals_1, buf0, 16, grid=grid(16), stream=stream0) del primals_1 return (buf0, primals_2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64) fn = lambda: call([primals_1, primals_2]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import numpy as np from torch import nn class par_start_encoder(nn.Module): """A network which makes the initial states a parameter of the network""" def __init__(self, nx, nsamples): super(par_start_encoder, self).__init__() self.start_state = nn.parameter.Parameter(data=torch.as_tensor(np. random.normal(scale=0.1, size=(nsamples, nx)), dtype=torch.float32) ) def forward(self, ids): return self.start_state[ids] def get_inputs(): return [torch.ones([4], dtype=torch.int64)] def get_init_inputs(): return [[], {'nx': 4, 'nsamples': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import numpy as np from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_index_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tl.device_assert((0 <= tmp4) & (tmp4 < 4) \| ~xmask, 'index out of bounds: 0 <= tmp4 < 4') tmp6 = tl.load(in_ptr1 + (x0 + 4 * tmp4), xmask) tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_index_0[grid(16)](primals_2, primals_1, buf0, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_1 return buf0, primals_2 class par_start_encoderNew(nn.Module): """A network which makes the initial states a parameter of the network""" def __init__(self, nx, nsamples): super(par_start_encoderNew, self).__init__() self.start_state = nn.parameter.Parameter(data=torch.as_tensor(np. random.normal(scale=0.1, size=(nsamples, nx)), dtype=torch.float32) ) def forward(self, input_0): primals_1 = self.start_state primals_2 = input_0 output = call([primals_1, primals_2]) return output[0]	GerbenBeintema/deepSI	par_start_encoder	false	8,205	[ "BSD-3-Clause" ]	12	580711210398064bb7f01e41d08b7a248a88b35b	https://github.com/GerbenBeintema/deepSI/tree/580711210398064bb7f01e41d08b7a248a88b35b
Attn	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/xc/cxcslufnu6arlkllhqsj22g4bei6y35vjp7mzlkgg2t2qgxxhlbc.py # Topologically Sorted Source Nodes: [hidden_feat], Original ATen: [aten.tanh] # Source node to ATen node mapping: # hidden_feat => tanh # Graph fragment: # %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_1,), kwargs = {}) triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/oo/coobrm7cytaeij2eyoctjbuz6lnjrauzvpxipvlrrepxba5rhlgq.py # Topologically Sorted Source Nodes: [attn_weight], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_weight => amax, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_3, [1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_3, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/wp/cwpr2jp7bpqwkbaxgttcl2ekjdtkf6lezuspoiunutzd3j2qgeyk.py # Topologically Sorted Source Nodes: [attn_weight], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_weight => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (1, 4), (4, 1)) assert_size_stride(primals_5, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [hidden_feat], Original ATen: [aten.tanh] stream0 = get_raw_stream(0) triton_poi_fused_tanh_0.run(buf1, primals_3, 64, grid=grid(64), stream=stream0) del primals_3 buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3) del primals_5 buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) # Topologically Sorted Source Nodes: [attn_weight], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf3, buf4, 16, grid=grid(16), stream=stream0) buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_weight], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf4, buf5, 16, grid=grid(16), stream=stream0) buf6 = reinterpret_tensor(buf4, (4, 1, 4), (4, 4, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [attn_X], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf5, (4, 1, 4), (4, 0, 1), 0), primals_1, out=buf6) del buf5 return (reinterpret_tensor(buf6, (4, 4), (4, 1), 0), primals_1, buf1, buf3, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn.functional as F import torch.nn as nn import torch.autograd import torch.nn class Attn(nn.Module): """ Unit attention operation for alternating co-attention. ``https://arxiv.org/pdf/1606.00061.pdf`` .. math:: \\begin{array}{ll} H = \\tanh(W_x * X + (W_g * g)) \\\\ a = softmax(w_{hx}^T * H}) \\\\ output = sum a_i * x_i \\end{array} Args: num_hidden: Number of output hidden size input_feat_size: Feature size of input image guidance_size: Feature size of attention guidance [default: 0] dropout: Dropout rate of attention operation [default: 0.5] Inputs: - X (batch, input_seq_size, input_feat_size): Input image feature - g (batch, guidance_size): Attention guidance """ def __init__(self, num_hidden, input_feat_size, guidance_size=0, dropout=0.5): super(Attn, self).__init__() self.num_hidden = num_hidden self.input_feat_size = input_feat_size self.guidance_size = guidance_size self.dropout = nn.Dropout(dropout) if dropout > 0 else None self.W_x = nn.Linear(input_feat_size, num_hidden) if guidance_size > 0: self.W_g = nn.Linear(guidance_size, num_hidden) self.W_hx = nn.Linear(num_hidden, 1) def forward(self, X, g=None): _batch_size, input_seq_size, input_feat_size = X.size() feat = self.W_x(X) if g is not None: g_emb = self.W_g(g).view(-1, 1, self.num_hidden) feat = feat + g_emb.expand_as(feat) hidden_feat = torch.tanh(feat) if self.dropout is not None: hidden_feat = self.dropout(hidden_feat) attn_weight = F.softmax(self.W_hx(hidden_feat), dim=1) attn_X = torch.bmm(attn_weight.view(-1, 1, input_seq_size), X.view( -1, input_seq_size, input_feat_size)) return attn_X.view(-1, input_feat_size) def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'num_hidden': 4, 'input_feat_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn import torch.autograd import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + x2, tmp3, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (1, 4), (4, 1)) assert_size_stride(primals_5, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0) del buf0 get_raw_stream(0) triton_poi_fused_tanh_0[grid(64)](buf1, primals_3, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_3 buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (16, 4), ( 4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3) del primals_5 buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) triton_poi_fused__softmax_1[grid(16)](buf3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1) buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) triton_poi_fused__softmax_2[grid(16)](buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1) buf6 = reinterpret_tensor(buf4, (4, 1, 4), (4, 4, 1), 0) del buf4 extern_kernels.bmm(reinterpret_tensor(buf5, (4, 1, 4), (4, 0, 1), 0 ), primals_1, out=buf6) del buf5 return reinterpret_tensor(buf6, (4, 4), (4, 1), 0 ), primals_1, buf1, buf3, primals_4 class AttnNew(nn.Module): """ Unit attention operation for alternating co-attention. ``https://arxiv.org/pdf/1606.00061.pdf`` .. math:: \\begin{array}{ll} H = \\tanh(W_x * X + (W_g * g)) \\\\ a = softmax(w_{hx}^T * H}) \\\\ output = sum a_i * x_i \\end{array} Args: num_hidden: Number of output hidden size input_feat_size: Feature size of input image guidance_size: Feature size of attention guidance [default: 0] dropout: Dropout rate of attention operation [default: 0.5] Inputs: - X (batch, input_seq_size, input_feat_size): Input image feature - g (batch, guidance_size): Attention guidance """ def __init__(self, num_hidden, input_feat_size, guidance_size=0, dropout=0.5): super(AttnNew, self).__init__() self.num_hidden = num_hidden self.input_feat_size = input_feat_size self.guidance_size = guidance_size self.dropout = nn.Dropout(dropout) if dropout > 0 else None self.W_x = nn.Linear(input_feat_size, num_hidden) if guidance_size > 0: self.W_g = nn.Linear(guidance_size, num_hidden) self.W_hx = nn.Linear(num_hidden, 1) def forward(self, input_0): primals_2 = self.W_x.weight primals_3 = self.W_x.bias primals_4 = self.W_hx.weight primals_5 = self.W_hx.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	HCY123902/visdial-gnn	Attn	false	8,206	[ "MIT" ]	44	c38090c672cdf04a4fabe139f96d944fd82cb123	https://github.com/HCY123902/visdial-gnn/tree/c38090c672cdf04a4fabe139f96d944fd82cb123
DiceLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ea/cearhta6w7rf34dd4b546u6wdxtdglcgwwjm6uyyzg2gtbwkg6e6.py # Topologically Sorted Source Nodes: [mul, intersection, mul_1, add, sum_2, sum_3, add_1, add_2, dice, sub], Original ATen: [aten.mul, aten.sum, aten.add, aten.div, aten.rsub] # Source node to ATen node mapping: # add => add # add_1 => add_1 # add_2 => add_2 # dice => div # intersection => sum_1 # mul => mul # mul_1 => mul_1 # sub => sub # sum_2 => sum_2 # sum_3 => sum_3 # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_1), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, 2.0), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 1), kwargs = {}) # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view,), kwargs = {}) # %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view_1,), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %sum_3), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, 1), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %add_2), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div), kwargs = {}) triton_per_fused_add_div_mul_rsub_sum_0 = async_compile.triton('triton_per_fused_add_div_mul_rsub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mul_rsub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 3, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_div_mul_rsub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp2 = tl.load(in_ptr1 + (r0), None) tmp1 = tl.sigmoid(tmp0) tmp3 = tmp1 * tmp2 tmp4 = tl.broadcast_to(tmp3, [RBLOCK]) tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0)) tmp7 = tl.broadcast_to(tmp1, [RBLOCK]) tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0)) tmp10 = tl.broadcast_to(tmp2, [RBLOCK]) tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0)) tmp13 = 2.0 tmp14 = tmp6 * tmp13 tmp15 = 1.0 tmp16 = tmp14 + tmp15 tmp17 = tmp9 + tmp12 tmp18 = tmp17 + tmp15 tmp19 = tmp16 / tmp18 tmp20 = tmp15 - tmp19 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp20, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf3 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [mul, intersection, mul_1, add, sum_2, sum_3, add_1, add_2, dice, sub], Original ATen: [aten.mul, aten.sum, aten.add, aten.div, aten.rsub] stream0 = get_raw_stream(0) triton_per_fused_add_div_mul_rsub_sum_0.run(buf3, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn import torch.nn.functional as F class DiceLoss(nn.Module): def __init__(self): super(DiceLoss, self).__init__() def forward(self, inputs, targets, smooth=1): inputs = F.sigmoid(inputs) inputs = inputs.view(-1) targets = targets.view(-1) intersection = (inputs * targets).sum() dice = (2.0 * intersection + smooth) / (inputs.sum() + targets.sum( ) + smooth) return 1 - dice def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_div_mul_rsub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp2 = tl.load(in_ptr1 + r0, None) tmp1 = tl.sigmoid(tmp0) tmp3 = tmp1 * tmp2 tmp4 = tl.broadcast_to(tmp3, [RBLOCK]) tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0)) tmp7 = tl.broadcast_to(tmp1, [RBLOCK]) tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0)) tmp10 = tl.broadcast_to(tmp2, [RBLOCK]) tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0)) tmp13 = 2.0 tmp14 = tmp6 * tmp13 tmp15 = 1.0 tmp16 = tmp14 + tmp15 tmp17 = tmp9 + tmp12 tmp18 = tmp17 + tmp15 tmp19 = tmp16 / tmp18 tmp20 = tmp15 - tmp19 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp20, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf3 = buf0 del buf0 get_raw_stream(0) triton_per_fused_add_div_mul_rsub_sum_0[grid(1)](buf3, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf3, class DiceLossNew(nn.Module): def __init__(self): super(DiceLossNew, self).__init__() def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	HealthML/ContIG	DiceLoss	false	8,207	[ "Apache-2.0" ]	10	641d76e0e9a5878e456f9729f2b0a81e51764b16	https://github.com/HealthML/ContIG/tree/641d76e0e9a5878e456f9729f2b0a81e51764b16
CAM_Module	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/tw/ctwtwzqouqbkulqsxqw7mdfona6xg6hcsai7rcu7cymxazwvy5qg.py # Topologically Sorted Source Nodes: [energy_new], Original ATen: [aten.sub] # Source node to ATen node mapping: # energy_new => sub # Graph fragment: # %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand, %bmm), kwargs = {}) triton_poi_fused_sub_0 = async_compile.triton('triton_poi_fused_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (x2), xmask) tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp8 = tmp6 - tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/a5/ca56rgpdjbilaspdaau44lnrilsxekhmcnbwzhtrcgfbilkik27p.py # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attention => amax, exp, sub_1 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%sub, [-1], True), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/xl/cxls6dl5dz3ua4ilno7rjcfd6m7p4ydnd3mzfaq2cepnph6e2y7h.py # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attention => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/qh/cqhs5lw3elidh54gmkq4mqssha34oqtaev4psa5m7itjs2k3wdhm.py # Topologically Sorted Source Nodes: [mul, out_2], Original ATen: [aten.mul, aten.add] # Source node to ATen node mapping: # mul => mul # out_2 => add # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %view_3), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_1), kwargs = {}) triton_poi_fused_add_mul_3 = async_compile.triton('triton_poi_fused_add_mul_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (0)) tmp1 = tl.broadcast_to(tmp0, [XBLOCK]) tmp2 = tl.load(in_ptr1 + (x0), xmask) tmp4 = tl.load(in_ptr2 + (x0), xmask) tmp3 = tmp1 * tmp2 tmp5 = tmp3 + tmp4 tl.store(out_ptr0 + (x0), tmp5, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [energy], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(primals_1, (4, 16, 4), (64, 1, 16), 0), out=buf0) buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [energy_new], Original ATen: [aten.sub] stream0 = get_raw_stream(0) triton_poi_fused_sub_0.run(buf0, buf1, 64, grid=grid(64), stream=stream0) buf2 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf1, buf2, 64, grid=grid(64), stream=stream0) buf3 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf2, buf3, 64, grid=grid(64), stream=stream0) del buf2 buf4 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [attention, out], Original ATen: [aten._softmax, aten.bmm] extern_kernels.bmm(buf3, reinterpret_tensor(primals_1, (4, 4, 16), (64, 16, 1), 0), out=buf4) del buf3 buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mul, out_2], Original ATen: [aten.mul, aten.add] triton_poi_fused_add_mul_3.run(primals_2, buf4, primals_1, buf5, 256, grid=grid(256), stream=stream0) del primals_1 del primals_2 return (buf5, buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	from torch.nn import Module import torch from torch.nn import Parameter from torch.nn import Softmax class CAM_Module(Module): """ Channel attention module""" def __init__(self, in_dim): super(CAM_Module, self).__init__() self.chanel_in = in_dim self.gamma = Parameter(torch.zeros(1)) self.softmax = Softmax(dim=-1) def forward(self, x): """ inputs : x : input feature maps( B X C X H X W) returns : out : attention value + input feature attention: B X C X C """ m_batchsize, C, height, width = x.size() proj_query = x.view(m_batchsize, C, -1) proj_key = x.view(m_batchsize, C, -1).permute(0, 2, 1) energy = torch.bmm(proj_query, proj_key) energy_new = torch.max(energy, -1, keepdim=True)[0].expand_as(energy ) - energy attention = self.softmax(energy_new) proj_value = x.view(m_batchsize, C, -1) out = torch.bmm(attention, proj_value) out = out.view(m_batchsize, C, height, width) out = self.gamma * out + x return out def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_dim': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math from torch.nn import Module from torch.nn import Parameter from torch.nn import Softmax assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + x2, xmask) tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp8 = tmp6 - tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused_add_mul_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK]) tmp2 = tl.load(in_ptr1 + x0, xmask) tmp4 = tl.load(in_ptr2 + x0, xmask) tmp3 = tmp1 * tmp2 tmp5 = tmp3 + tmp4 tl.store(out_ptr0 + x0, tmp5, xmask) def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(primals_1, (4, 16, 4), (64, 1, 16), 0), out=buf0) buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_sub_0[grid(64)](buf0, buf1, 64, XBLOCK=64, num_warps=1, num_stages=1) buf2 = buf0 del buf0 triton_poi_fused__softmax_1[grid(64)](buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1) buf3 = buf1 del buf1 triton_poi_fused__softmax_2[grid(64)](buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf2 buf4 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32) extern_kernels.bmm(buf3, reinterpret_tensor(primals_1, (4, 4, 16), (64, 16, 1), 0), out=buf4) del buf3 buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_add_mul_3[grid(256)](primals_2, buf4, primals_1, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 del primals_2 return buf5, buf4 class CAM_ModuleNew(Module): """ Channel attention module""" def __init__(self, in_dim): super(CAM_ModuleNew, self).__init__() self.chanel_in = in_dim self.gamma = Parameter(torch.zeros(1)) self.softmax = Softmax(dim=-1) def forward(self, input_0): primals_2 = self.gamma primals_1 = input_0 output = call([primals_1, primals_2]) return output[0]	HUuxiaobin/Face-Super-Resolution-Guided-by-3D-Facial-Priors	CAM_Module	false	8,208	[ "MIT" ]	29	987e7c74d33d26cc5e9d1c0e395a06519a31792f	https://github.com/HUuxiaobin/Face-Super-Resolution-Guided-by-3D-Facial-Priors/tree/987e7c74d33d26cc5e9d1c0e395a06519a31792f
Project3D	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/kr/ckrqlzua65nhcln4midrf2pl3tvnjymm2dp3k72pz4ewuznk4gxv.py # Topologically Sorted Source Nodes: [cam_points], Original ATen: [aten.clone] # Source node to ATen node mapping: # cam_points => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_2,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 48 x1 = (xindex // 48) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (64x1)), xmask) tl.store(out_ptr0 + (x2), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ti/ctifpnitbty4pbrwraiafpaln2vpnt2xczhg7z4f23xzmp6d2kwd.py # Topologically Sorted Source Nodes: [add, pix_coords, itruediv, itruediv_1, sub, pix_coords_2], Original ATen: [aten.add, aten.div, aten.sub, aten.mul] # Source node to ATen node mapping: # add => add # itruediv => div_1 # itruediv_1 => div_2 # pix_coords => div # pix_coords_2 => mul # sub => sub # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, 1e-07), kwargs = {}) # %div : [num_users=4] = call_function[target=torch.ops.aten.div.Tensor](args = (%slice_5, %add), kwargs = {}) # %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select_1, 3), kwargs = {}) # %slice_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%select_int, %div_1, 2, 0, 9223372036854775807), kwargs = {}) # %select_scatter_default : [num_users=4] = call_function[target=torch.ops.aten.select_scatter.default](args = (%div, %slice_scatter_default, 1, 0), kwargs = {}) # %select_scatter_default_1 : [num_users=4] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default, %select_3, 1, 0), kwargs = {}) # %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select_9, 3), kwargs = {}) # %slice_scatter_default_1 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%select_int_1, %div_2, 2, 0, 9223372036854775807), kwargs = {}) # %select_scatter_default_2 : [num_users=4] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_1, %slice_scatter_default_1, 1, 1), kwargs = {}) # %select_scatter_default_3 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_2, %select_11, 1, 1), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_scatter_default_3, 0.5), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 2), kwargs = {}) triton_poi_fused_add_div_mul_sub_1 = async_compile.triton('triton_poi_fused_add_div_mul_sub_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[128], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_mul_sub_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 2 x0 = xindex % 16 x2 = (xindex // 32) x3 = xindex % 32 x4 = xindex tmp7 = tl.load(in_ptr0 + (x0 + (48x2)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (32 + x0 + (48x2)), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr0 + (16 + x0 + (48x2)), xmask, eviction_policy='evict_last') tmp22 = tl.load(in_ptr0 + (x3 + (48x2)), xmask) tmp0 = x1 tmp1 = tl.full([1], 1, tl.int32) tmp2 = tmp0 == tmp1 tmp3 = tmp1 == tmp1 tmp4 = tl.full([1], 0, tl.int32) tmp5 = tmp1 == tmp4 tmp6 = tmp4 == tmp4 tmp9 = 1e-07 tmp10 = tmp8 + tmp9 tmp11 = tmp7 / tmp10 tmp12 = 0.3333333333333333 tmp13 = tmp11 * tmp12 tmp14 = tl.where(tmp6, tmp13, tmp11) tmp16 = tmp15 / tmp10 tmp17 = tl.where(tmp5, tmp13, tmp16) tmp18 = tl.where(tmp5, tmp14, tmp17) tmp19 = tmp18 * tmp12 tmp20 = tl.where(tmp3, tmp19, tmp18) tmp21 = tmp0 == tmp4 tmp23 = tmp22 / tmp10 tmp24 = tl.where(tmp21, tmp13, tmp23) tmp25 = tl.where(tmp21, tmp14, tmp24) tmp26 = tl.where(tmp2, tmp19, tmp25) tmp27 = tl.where(tmp2, tmp20, tmp26) tmp28 = 0.5 tmp29 = tmp27 - tmp28 tmp30 = 2.0 tmp31 = tmp29 * tmp30 tl.store(out_ptr0 + (x4), tmp31, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 3, 4, 4), (48, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(arg0_1, (16, 4, 4), (16, 4, 1), 0), out=buf0) del arg0_1 del arg1_1 buf1 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cam_points], Original ATen: [aten.clone] stream0 = get_raw_stream(0) triton_poi_fused_clone_0.run(buf0, buf1, 192, grid=grid(192), stream=stream0) del buf0 buf2 = empty_strided_cuda((12, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cam_points], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf1, (12, 4, 4), (16, 4, 1), 0), reinterpret_tensor(arg2_1, (12, 4, 4), (16, 4, 1), 0), out=buf2) del arg2_1 del buf1 buf3 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [add, pix_coords, itruediv, itruediv_1, sub, pix_coords_2], Original ATen: [aten.add, aten.div, aten.sub, aten.mul] triton_poi_fused_add_div_mul_sub_1.run(buf2, buf3, 128, grid=grid(128), stream=stream0) del buf2 return (buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class Project3D(nn.Module): """Layer which projects 3D points into a camera with intrinsics K and at position T """ def __init__(self, batch_size, height, width, eps=1e-07): super(Project3D, self).__init__() self.batch_size = batch_size self.height = height self.width = width self.eps = eps def forward(self, points, K, T): P = torch.matmul(K, T)[:, :3, :] cam_points = torch.matmul(P, points) pix_coords = cam_points[:, :2, :] / (cam_points[:, 2, :].unsqueeze( 1) + self.eps) pix_coords = pix_coords.view(self.batch_size, 2, self.height, self. width) pix_coords[:, 0, :, :] /= self.width - 1 pix_coords[:, 1, :, :] /= self.height - 1 pix_coords = (pix_coords - 0.5) * 2 return pix_coords def get_inputs(): return [torch.rand([4, 3, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {'batch_size': 4, 'height': 4, 'width': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 48 x1 = xindex // 48 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask) tl.store(out_ptr0 + x2, tmp0, xmask) @triton.jit def triton_poi_fused_add_div_mul_sub_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 2 x0 = xindex % 16 x2 = xindex // 32 x3 = xindex % 32 x4 = xindex tmp7 = tl.load(in_ptr0 + (x0 + 48 * x2), xmask, eviction_policy= 'evict_last') tmp8 = tl.load(in_ptr0 + (32 + x0 + 48 * x2), xmask, eviction_policy= 'evict_last') tmp15 = tl.load(in_ptr0 + (16 + x0 + 48 * x2), xmask, eviction_policy= 'evict_last') tmp22 = tl.load(in_ptr0 + (x3 + 48 * x2), xmask) tmp0 = x1 tmp1 = tl.full([1], 1, tl.int32) tmp2 = tmp0 == tmp1 tmp3 = tmp1 == tmp1 tmp4 = tl.full([1], 0, tl.int32) tmp5 = tmp1 == tmp4 tmp6 = tmp4 == tmp4 tmp9 = 1e-07 tmp10 = tmp8 + tmp9 tmp11 = tmp7 / tmp10 tmp12 = 0.3333333333333333 tmp13 = tmp11 * tmp12 tmp14 = tl.where(tmp6, tmp13, tmp11) tmp16 = tmp15 / tmp10 tmp17 = tl.where(tmp5, tmp13, tmp16) tmp18 = tl.where(tmp5, tmp14, tmp17) tmp19 = tmp18 * tmp12 tmp20 = tl.where(tmp3, tmp19, tmp18) tmp21 = tmp0 == tmp4 tmp23 = tmp22 / tmp10 tmp24 = tl.where(tmp21, tmp13, tmp23) tmp25 = tl.where(tmp21, tmp14, tmp24) tmp26 = tl.where(tmp2, tmp19, tmp25) tmp27 = tl.where(tmp2, tmp20, tmp26) tmp28 = 0.5 tmp29 = tmp27 - tmp28 tmp30 = 2.0 tmp31 = tmp29 * tmp30 tl.store(out_ptr0 + x4, tmp31, xmask) def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 3, 4, 4), (48, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1 ), 0), reinterpret_tensor(arg0_1, (16, 4, 4), (16, 4, 1), 0), out=buf0) del arg0_1 del arg1_1 buf1 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clone_0[grid(192)](buf0, buf1, 192, XBLOCK=128, num_warps=4, num_stages=1) del buf0 buf2 = empty_strided_cuda((12, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf1, (12, 4, 4), (16, 4, 1), 0), reinterpret_tensor(arg2_1, (12, 4, 4), (16, 4, 1), 0), out=buf2 ) del arg2_1 del buf1 buf3 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32) triton_poi_fused_add_div_mul_sub_1[grid(128)](buf2, buf3, 128, XBLOCK=128, num_warps=4, num_stages=1) del buf2 return buf3, class Project3DNew(nn.Module): """Layer which projects 3D points into a camera with intrinsics K and at position T """ def __init__(self, batch_size, height, width, eps=1e-07): super(Project3DNew, self).__init__() self.batch_size = batch_size self.height = height self.width = width self.eps = eps def forward(self, input_0, input_1, input_2): arg2_1 = input_0 arg0_1 = input_1 arg1_1 = input_2 output = call([arg0_1, arg1_1, arg2_1]) return output[0]	HalleyJiang/PLNet	Project3D	false	8,209	[ "MIT" ]	16	a02bd5f343b9e4766891fd234e3a338c1eaa26ff	https://github.com/HalleyJiang/PLNet/tree/a02bd5f343b9e4766891fd234e3a338c1eaa26ff
AsymmetricLossOptimized	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/vh/cvh4dyohodzhstpjtxh3i5go5m6tlbiywu7xwqwn4pyejtg4ysp7.py # Topologically Sorted Source Nodes: [sigmoid, clamp, log, mul, sub, sub_1, add_, clamp_, clamp_1, log_1, mul_1, add__1, mul_2, sub_2, mul_3, sub_3, mul_4, mul_5, add, pow_1, imul, sum_1, neg], Original ATen: [aten.sigmoid, aten.clamp, aten.log, aten.mul, aten.rsub, aten.add, aten.sub, aten.pow, aten.sum, aten.neg] # Source node to ATen node mapping: # add => add_2 # add_ => add # add__1 => add_1 # clamp => clamp_min # clamp_ => clamp_max # clamp_1 => clamp_min_1 # imul => mul_6 # log => log # log_1 => log_1 # mul => mul # mul_1 => mul_1 # mul_2 => mul_2 # mul_3 => mul_3 # mul_4 => mul_4 # mul_5 => mul_5 # neg => neg # pow_1 => pow_1 # sigmoid => sigmoid # sub => sub # sub_1 => sub_1 # sub_2 => sub_2 # sub_3 => sub_3 # sum_1 => sum_1 # Graph fragment: # %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg1_1,), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sigmoid, 1e-08), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_min,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %log), kwargs = {}) # %sub : [num_users=4] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %sigmoid), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, 0.05), kwargs = {}) # %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add, 1), kwargs = {}) # %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%clamp_max, 1e-08), kwargs = {}) # %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_min_1,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %log_1), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {}) # %mul_2 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %arg0_1), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %mul_2), kwargs = {}) # %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max, %sub), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %mul_3), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 1), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 4), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %mul_5), kwargs = {}) # %pow_1 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%sub_3, %add_2), kwargs = {}) # %mul_6 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, %pow_1), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_6,), kwargs = {}) # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_1,), kwargs = {}) triton_per_fused_add_clamp_log_mul_neg_pow_rsub_sigmoid_sub_sum_0 = async_compile.triton('triton_per_fused_add_clamp_log_mul_neg_pow_rsub_sigmoid_sub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'i32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {8: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 9), equal_to_1=(8,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_log_mul_neg_pow_rsub_sigmoid_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_clamp_log_mul_neg_pow_rsub_sigmoid_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp3 = tl.load(in_ptr1 + (r0), None) tmp1 = 1.0 tmp2 = tmp1 - tmp0 tmp4 = tl.sigmoid(tmp3) tmp5 = tmp4 * tmp0 tmp6 = tmp1 - tmp4 tmp7 = 0.05 tmp8 = tmp6 + tmp7 tmp9 = triton_helpers.minimum(tmp8, tmp1) tmp10 = tmp9 * tmp2 tmp11 = tmp1 - tmp5 tmp12 = tmp11 - tmp10 tmp13 = tmp0 * tmp1 tmp14 = 4.0 tmp15 = tmp2 * tmp14 tmp16 = tmp13 + tmp15 tmp17 = libdevice.pow(tmp12, tmp16) tmp18 = 1e-08 tmp19 = triton_helpers.maximum(tmp4, tmp18) tmp20 = tl_math.log(tmp19) tmp21 = tmp0 * tmp20 tmp22 = triton_helpers.maximum(tmp9, tmp18) tmp23 = tl_math.log(tmp22) tmp24 = tmp2 * tmp23 tmp25 = tmp21 + tmp24 tmp26 = tmp25 * tmp17 tmp27 = tl.broadcast_to(tmp26, [RBLOCK]) tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0)) tmp30 = -tmp29 tl.store(out_ptr0 + (tl.broadcast_to(r0, [RBLOCK])), tmp2, None) tl.store(out_ptr1 + (tl.broadcast_to(r0, [RBLOCK])), tmp5, None) tl.store(out_ptr2 + (tl.broadcast_to(r0, [RBLOCK])), tmp10, None) tl.store(out_ptr3 + (tl.broadcast_to(r0, [RBLOCK])), tmp17, None) tl.store(out_ptr4 + (tl.broadcast_to(r0, [RBLOCK])), tmp26, None) tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp30, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf5 = empty_strided_cuda((), (), torch.float32) buf6 = buf5; del buf5 # reuse # Topologically Sorted Source Nodes: [sigmoid, clamp, log, mul, sub, sub_1, add_, clamp_, clamp_1, log_1, mul_1, add__1, mul_2, sub_2, mul_3, sub_3, mul_4, mul_5, add, pow_1, imul, sum_1, neg], Original ATen: [aten.sigmoid, aten.clamp, aten.log, aten.mul, aten.rsub, aten.add, aten.sub, aten.pow, aten.sum, aten.neg] stream0 = get_raw_stream(0) triton_per_fused_add_clamp_log_mul_neg_pow_rsub_sigmoid_sub_sum_0.run(buf6, arg0_1, arg1_1, buf0, buf1, buf2, buf3, buf4, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf6, buf3, buf4, buf2, buf1, buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torchvision import datasets as datasets import torch.nn as nn import torch.nn.parallel import torch.optim import torch.utils.data.distributed class AsymmetricLossOptimized(nn.Module): """ Notice - optimized version, minimizes memory allocation and gpu uploading, favors inplace operations""" def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08, disable_torch_grad_focal_loss=False): super(AsymmetricLossOptimized, self).__init__() self.gamma_neg = gamma_neg self.gamma_pos = gamma_pos self.clip = clip self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss self.eps = eps (self.targets) = (self.anti_targets) = (self.xs_pos) = (self.xs_neg ) = (self.asymmetric_w) = (self.loss) = None def forward(self, x, y): """" Parameters ---------- x: input logits y: targets (multi-label binarized vector) """ self.targets = y self.anti_targets = 1 - y self.xs_pos = torch.sigmoid(x) self.xs_neg = 1.0 - self.xs_pos if self.clip is not None and self.clip > 0: self.xs_neg.add_(self.clip).clamp_(max=1) self.loss = self.targets * torch.log(self.xs_pos.clamp(min=self.eps)) self.loss.add_(self.anti_targets * torch.log(self.xs_neg.clamp(min= self.eps))) if self.gamma_neg > 0 or self.gamma_pos > 0: if self.disable_torch_grad_focal_loss: torch.set_grad_enabled(False) self.xs_pos = self.xs_pos * self.targets self.xs_neg = self.xs_neg * self.anti_targets self.asymmetric_w = torch.pow(1 - self.xs_pos - self.xs_neg, self.gamma_pos * self.targets + self.gamma_neg * self. anti_targets) if self.disable_torch_grad_focal_loss: torch.set_grad_enabled(True) self.loss *= self.asymmetric_w return -self.loss.sum() def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torchvision import datasets as datasets import torch.nn as nn import torch.nn.parallel import torch.optim import torch.utils.data.distributed assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_clamp_log_mul_neg_pow_rsub_sigmoid_sub_sum_0( in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp3 = tl.load(in_ptr1 + r0, None) tmp1 = 1.0 tmp2 = tmp1 - tmp0 tmp4 = tl.sigmoid(tmp3) tmp5 = tmp4 * tmp0 tmp6 = tmp1 - tmp4 tmp7 = 0.05 tmp8 = tmp6 + tmp7 tmp9 = triton_helpers.minimum(tmp8, tmp1) tmp10 = tmp9 * tmp2 tmp11 = tmp1 - tmp5 tmp12 = tmp11 - tmp10 tmp13 = tmp0 * tmp1 tmp14 = 4.0 tmp15 = tmp2 * tmp14 tmp16 = tmp13 + tmp15 tmp17 = libdevice.pow(tmp12, tmp16) tmp18 = 1e-08 tmp19 = triton_helpers.maximum(tmp4, tmp18) tmp20 = tl_math.log(tmp19) tmp21 = tmp0 * tmp20 tmp22 = triton_helpers.maximum(tmp9, tmp18) tmp23 = tl_math.log(tmp22) tmp24 = tmp2 * tmp23 tmp25 = tmp21 + tmp24 tmp26 = tmp25 * tmp17 tmp27 = tl.broadcast_to(tmp26, [RBLOCK]) tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0)) tmp30 = -tmp29 tl.store(out_ptr0 + tl.broadcast_to(r0, [RBLOCK]), tmp2, None) tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp5, None) tl.store(out_ptr2 + tl.broadcast_to(r0, [RBLOCK]), tmp10, None) tl.store(out_ptr3 + tl.broadcast_to(r0, [RBLOCK]), tmp17, None) tl.store(out_ptr4 + tl.broadcast_to(r0, [RBLOCK]), tmp26, None) tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp30, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf5 = empty_strided_cuda((), (), torch.float32) buf6 = buf5 del buf5 get_raw_stream(0) triton_per_fused_add_clamp_log_mul_neg_pow_rsub_sigmoid_sub_sum_0[grid (1)](buf6, arg0_1, arg1_1, buf0, buf1, buf2, buf3, buf4, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf6, buf3, buf4, buf2, buf1, buf0 class AsymmetricLossOptimizedNew(nn.Module): """ Notice - optimized version, minimizes memory allocation and gpu uploading, favors inplace operations""" def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08, disable_torch_grad_focal_loss=False): super(AsymmetricLossOptimizedNew, self).__init__() self.gamma_neg = gamma_neg self.gamma_pos = gamma_pos self.clip = clip self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss self.eps = eps (self.targets) = (self.anti_targets) = (self.xs_pos) = (self.xs_neg ) = (self.asymmetric_w) = (self.loss) = None def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	Coler1994/robust-loss-mlml	AsymmetricLossOptimized	false	8,210	[ "MIT" ]	15	a68718eba7efa82c3eca79031eeee444f8eb5fa3	https://github.com/Coler1994/robust-loss-mlml/tree/a68718eba7efa82c3eca79031eeee444f8eb5fa3
unetConvUnit	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ux/cux7axsckwo5dxgyf2kefdy5fcl44asubo6jxnefaltmzk6rznwv.py # Topologically Sorted Source Nodes: [conv2d, out], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d => convolution # out => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/jc/cjcy2of6gdz3hcspprv77r5iujbt643dtko3a7ibjvgf4ldvsuka.py # Topologically Sorted Source Nodes: [conv2d_1, out_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # out_1 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, xmask) tl.store(out_ptr0 + (x3), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [conv2d, out], Original ATen: [aten.convolution, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 256, grid=grid(256), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1)) buf3 = buf2; del buf2 # reuse buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, out_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_1.run(buf3, primals_5, buf4, 256, grid=grid(256), stream=stream0) del primals_5 return (buf3, primals_1, primals_3, primals_4, buf1, buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import numpy as np from torch import nn import torch.nn.functional as F import torch.utils.data import torch.nn.init as init import torch.nn.init class conv23DUnit(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, bias=True, dilation=1, nd=2): super(conv23DUnit, self).__init__() assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}' if nd == 2: self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) elif nd == 3: self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) else: self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) init.xavier_uniform(self.conv.weight, gain=np.sqrt(2.0)) init.constant(self.conv.bias, 0) def forward(self, x): return self.conv(x) class unetConvUnit(nn.Module): def __init__(self, in_size, out_size, kernel_size=3, activation=F.relu, nd=2): super(unetConvUnit, self).__init__() self.conv = conv23DUnit(in_size, out_size, kernel_size=3, stride=1, padding=1, nd=nd) self.conv2 = conv23DUnit(out_size, out_size, kernel_size=3, stride= 1, padding=1, nd=nd) self.activation = activation def forward(self, x): out = self.activation(self.conv(x)) out = self.activation(self.conv2(out)) return out def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_size': 4, 'out_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import numpy as np from torch import nn import torch.nn.functional as F import torch.utils.data import torch.nn.init as init import torch.nn.init assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, xmask) tl.store(out_ptr0 + x3, tmp6, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_relu_0[grid(256)](buf1, primals_2, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1)) buf3 = buf2 del buf2 buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_1[grid(256)](buf3, primals_5, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 return buf3, primals_1, primals_3, primals_4, buf1, buf4 class conv23DUnit(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, bias=True, dilation=1, nd=2): super(conv23DUnit, self).__init__() assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}' if nd == 2: self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) elif nd == 3: self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) else: self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) init.xavier_uniform(self.conv.weight, gain=np.sqrt(2.0)) init.constant(self.conv.bias, 0) def forward(self, x): return self.conv(x) class unetConvUnitNew(nn.Module): def __init__(self, in_size, out_size, kernel_size=3, activation=F.relu, nd=2): super(unetConvUnitNew, self).__init__() self.conv = conv23DUnit(in_size, out_size, kernel_size=3, stride=1, padding=1, nd=nd) self.conv2 = conv23DUnit(out_size, out_size, kernel_size=3, stride= 1, padding=1, nd=nd) self.activation = activation def forward(self, input_0): primals_1 = self.conv.conv.weight primals_2 = self.conv.conv.bias primals_4 = self.conv2.conv.weight primals_5 = self.conv2.conv.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	ForrestPi/Unsupervised-Defect-Segmentation	unetConvUnit	false	8,211	[ "MIT" ]	17	e366ac7c757bb1b45f38ebbc502dfee7ccb72398	https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398
TAE_decoder	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/77/c773buhxkqj74y3lbtcuzrbfzcc4mm2eu7g7ocy36krblbxuszqn.py # Topologically Sorted Source Nodes: [upsampled], Original ATen: [aten._unsafe_index] # Source node to ATen node mapping: # upsampled => _unsafe_index # Graph fragment: # %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %convert_element_type_1]), kwargs = {}) triton_poi_fused__unsafe_index_0 = async_compile.triton('triton_poi_fused__unsafe_index_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 2048 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 8 x1 = (xindex // 8) x2 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tmp5 = tl.load(in_ptr0 + (tmp4 + (4x1)), None, eviction_policy='evict_last') tl.store(out_ptr0 + (x2), tmp5, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/r5/cr5n2odkcxkryyb7m3oil332r3rrnsdt5bjo7jem2kiqb64aceb5.py # Topologically Sorted Source Nodes: [out_deconv], Original ATen: [aten.clone] # Source node to ATen node mapping: # out_deconv => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%slice_3,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 2048 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 8 x3 = (xindex // 8) x1 = (xindex // 8) % 64 x4 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (9*x3)), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x4), tmp2, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 64, 4), (256, 4, 1)) assert_size_stride(primals_2, (64, 64, 10), (640, 10, 1)) assert_size_stride(primals_3, (64, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [upsampled], Original ATen: [aten._unsafe_index] stream0 = get_raw_stream(0) triton_poi_fused__unsafe_index_0.run(primals_1, buf0, 2048, grid=grid(2048), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [conv_transpose1d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(4,), dilation=(1,), transposed=True, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf1, (4, 64, 9), (576, 9, 1)) buf2 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [out_deconv], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf1, primals_3, buf2, 2048, grid=grid(2048), stream=stream0) del buf1 del primals_3 return (reinterpret_tensor(buf2, (4, 512), (512, 1), 0), primals_2, buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 64, 4), (256, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, 64, 10), (640, 10, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class TAE_decoder(nn.Module): """ Class for temporal autoencoder decoder. filter_1 : filter size of the first convolution layer filter_lstm : hidden size of the lstm. """ def __init__(self, n_hidden=64, pooling=8): super().__init__() self.pooling = pooling self.n_hidden = n_hidden self.up_layer = nn.Upsample(size=pooling) self.deconv_layer = nn.ConvTranspose1d(in_channels=self.n_hidden, out_channels=self.n_hidden, kernel_size=10, stride=1, padding= self.pooling // 2) def forward(self, features): upsampled = self.up_layer(features) out_deconv = self.deconv_layer(upsampled)[:, :, :self.pooling ].contiguous() out_deconv = out_deconv.view(out_deconv.shape[0], -1) return out_deconv def get_inputs(): return [torch.rand([4, 64, 4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 8 x1 = xindex // 8 x2 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tmp5 = tl.load(in_ptr0 + (tmp4 + 4 * x1), None, eviction_policy= 'evict_last') tl.store(out_ptr0 + x2, tmp5, None) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl .constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 8 x3 = xindex // 8 x1 = xindex // 8 % 64 x4 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 9 * x3), None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x4, tmp2, None) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 64, 4), (256, 4, 1)) assert_size_stride(primals_2, (64, 64, 10), (640, 10, 1)) assert_size_stride(primals_3, (64,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32) get_raw_stream(0) triton_poi_fused__unsafe_index_0[grid(2048)](primals_1, buf0, 2048, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(4,), dilation=(1,), transposed=True, output_padding=(0 ,), groups=1, bias=None) assert_size_stride(buf1, (4, 64, 9), (576, 9, 1)) buf2 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32) triton_poi_fused_clone_1[grid(2048)](buf1, primals_3, buf2, 2048, XBLOCK=256, num_warps=4, num_stages=1) del buf1 del primals_3 return reinterpret_tensor(buf2, (4, 512), (512, 1), 0), primals_2, buf0 class TAE_decoderNew(nn.Module): """ Class for temporal autoencoder decoder. filter_1 : filter size of the first convolution layer filter_lstm : hidden size of the lstm. """ def __init__(self, n_hidden=64, pooling=8): super().__init__() self.pooling = pooling self.n_hidden = n_hidden self.up_layer = nn.Upsample(size=pooling) self.deconv_layer = nn.ConvTranspose1d(in_channels=self.n_hidden, out_channels=self.n_hidden, kernel_size=10, stride=1, padding= self.pooling // 2) def forward(self, input_0): primals_2 = self.deconv_layer.weight primals_3 = self.deconv_layer.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	HamzaG737/Deep-temporal-clustering---Pytorch	TAE_decoder	false	8,212	[ "MIT" ]	12	5ee423d833e655e73b6ba2f1c13be5f1b83f92d2	https://github.com/HamzaG737/Deep-temporal-clustering---Pytorch/tree/5ee423d833e655e73b6ba2f1c13be5f1b83f92d2
Encoder4	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/cn/ccn3wvv7hvajmiytvd4cxr5ymsooreymqjbzit5wvlzzy3qc5dck.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4096], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 12 xnumel = 4096 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1) x2 = xindex y3 = yindex y0 = yindex % 3 y1 = (yindex // 3) tmp0 = tl.load(in_ptr0 + (x2 + (4096y3)), ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (3x2) + (12288y1)), tmp0, ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/r4/cr42tvpznzo4jto3kd2nndggpyldbuuskklxhoqx7cipetnq44st.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 192 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 3 y1 = (yindex // 3) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (3x2) + (27y1)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/5o/c5obv3n3z3nigs3ufrll6zerfbkjuaxny47v7qwuboce4vc6t226.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 4096 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = (yindex // 64) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (64x2) + (576y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/z6/cz6baxmxj4vnpfhcx7vlebdugozcmmuhwadqps4sypq4ddnvvvvh.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_3 = async_compile.triton('triton_poi_fused_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 8192 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = (yindex // 64) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (64x2) + (576y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/db/cdb2sjzbytfspypoa6pp2oxhbkctfipulwxnhjr5sqo5qqjfizvu.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_4 = async_compile.triton('triton_poi_fused_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16384 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = (yindex // 128) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (128x2) + (1152y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gd/cgdnlfwbslhdmfriewbfv2omlx6rnligyuanv24xvvvpftjpu6rp.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_5 = async_compile.triton('triton_poi_fused_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 32768 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = (yindex // 128) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (128x2) + (1152y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/z4/cz4wj3gfuvykrcxztkqxjno5cjlbaxcfmlbyfebxkzznk7viznxb.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_6 = async_compile.triton('triton_poi_fused_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 65536 xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = (yindex // 256) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (256x2) + (2304y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/i6/ci6263jjpb6aa6asa26vhonj4fac7oamux3ss2n4bs2h2mrc3dzu.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_7 = async_compile.triton('triton_poi_fused_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 131072 xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = (yindex // 256) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (256x2) + (2304y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/l5/cl5wxifxvx4r3ewdju4gczs3qw274hx6dzx7fazxtarj5kb23fzc.py # Topologically Sorted Source Nodes: [y, pad], Original ATen: [aten.convolution, aten.reflection_pad2d] # Source node to ATen node mapping: # pad => _unsafe_index, _unsafe_index_1 # y => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_8 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_8', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_8(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 52272 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 3 x1 = (xindex // 3) % 66 x2 = (xindex // 198) % 66 x3 = (xindex // 13068) x4 = xindex tmp0 = tl.load(in_ptr0 + (12285 + x0 + ((-192)(tl_math.abs((-63) + (tl_math.abs((-1) + x2))))) + ((-3)(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (12288x3)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x4), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/r5/cr5akdxfhzeksx7huovolcicmugkenf4pw2e3linlsvjxucec7dg.py # Topologically Sorted Source Nodes: [conv2d_1, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # pad_1 => _unsafe_index_2, _unsafe_index_3 # y_1 => relu # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_9 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_9', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2097152], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = (xindex // 64) % 66 x2 = (xindex // 4224) % 66 x3 = (xindex // 278784) x4 = xindex tmp0 = tl.load(in_ptr0 + (262080 + x0 + ((-4096)(tl_math.abs((-63) + (tl_math.abs((-1) + x2))))) + ((-64)(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x4), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/zu/czufcpnbtszloncrsooj3pdhgtcbxdw5nc32njjk5mlb6af5lmpo.py # Topologically Sorted Source Nodes: [conv2d_2, y_2], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_2 => convolution_2 # y_2 => relu_1 # Graph fragment: # %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_3, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {}) triton_poi_fused_convolution_relu_10 = async_compile.triton('triton_poi_fused_convolution_relu_10', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1048576 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/b4/cb4sydikuih37zfspwtcua6cx7ua73nuuxoo3acxnsao7dcphicv.py # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_3 => getitem_1 # Graph fragment: # %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_11 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_11', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i8', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_11(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 64 x1 = (xindex // 64) % 32 x2 = (xindex // 2048) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (128x1) + (8192x2)), None) tmp1 = tl.load(in_ptr0 + (64 + x0 + (128x1) + (8192x2)), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + (128x1) + (8192x2)), None) tmp12 = tl.load(in_ptr0 + (4160 + x0 + (128x1) + (8192x2)), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) tmp16 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/w3/cw35zhyq6qguviqc4jblo5rjawuah7hqq6twtvwm5ssuc4tntkev.py # Topologically Sorted Source Nodes: [y_3, pad_2], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] # Source node to ATen node mapping: # pad_2 => _unsafe_index_4, _unsafe_index_5 # y_3 => _low_memory_max_pool2d_with_offsets # Graph fragment: # %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {}) # %_unsafe_index_4 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem, [None, None, %sub_9, None]), kwargs = {}) # %_unsafe_index_5 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_4, [None, None, None, %sub_9]), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_12 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_12', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_12(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = (xindex // 64) % 34 x2 = (xindex // 2176) % 34 x3 = (xindex // 73984) x4 = xindex tmp0 = tl.load(in_ptr0 + (257920 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp1 = tl.load(in_ptr0 + (257984 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp3 = tl.load(in_ptr0 + (262016 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp5 = tl.load(in_ptr0 + (262080 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + (x4), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/4s/c4sic5ix6u77ha4rzplocmwoynopl7sxtvqlybwm4ucmyllw3kb6.py # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # pad_3 => _unsafe_index_6, _unsafe_index_7 # y_4 => relu_2 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %_unsafe_index_6 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_2, [None, None, %sub_9, None]), kwargs = {}) # %_unsafe_index_7 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_6, [None, None, None, %sub_9]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_13 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_13', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 591872 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = (xindex // 128) % 34 x2 = (xindex // 4352) % 34 x3 = (xindex // 147968) x4 = xindex tmp0 = tl.load(in_ptr0 + (130944 + x0 + ((-4096)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x4), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/cr/ccrfbxcv3sd4c3qlcwbntpe5bvgck5uz6ewlitursh3lhnfcpbb6.py # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # y_5 => relu_3 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_7, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) triton_poi_fused_convolution_relu_14 = async_compile.triton('triton_poi_fused_convolution_relu_14', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_14', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_14(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 524288 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/6y/c6y6nlrdvy2tf64rn7jpcaa6zj3lrnils7hexy4infbtjz6rq4qf.py # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_6 => getitem_3 # Graph fragment: # %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_15 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_15', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i8', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_15(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = (xindex // 128) % 16 x2 = (xindex // 2048) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (256x1) + (8192x2)), None) tmp1 = tl.load(in_ptr0 + (128 + x0 + (256x1) + (8192x2)), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + (256x1) + (8192x2)), None) tmp12 = tl.load(in_ptr0 + (4224 + x0 + (256x1) + (8192x2)), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) tmp16 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/4o/c4okredjqagwt37iu7uw26n25bztc2ccqif2gxtjhq4l6rpckjp5.py # Topologically Sorted Source Nodes: [y_6, pad_4], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] # Source node to ATen node mapping: # pad_4 => _unsafe_index_8, _unsafe_index_9 # y_6 => _low_memory_max_pool2d_with_offsets_1 # Graph fragment: # %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_3, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {}) # %_unsafe_index_8 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem_2, [None, None, %sub_17, None]), kwargs = {}) # %_unsafe_index_9 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_8, [None, None, None, %sub_17]), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_16 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_16', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_16(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 165888 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = (xindex // 128) % 18 x2 = (xindex // 2304) % 18 x3 = (xindex // 41472) x4 = xindex tmp0 = tl.load(in_ptr0 + (126720 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp1 = tl.load(in_ptr0 + (126848 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp3 = tl.load(in_ptr0 + (130816 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp5 = tl.load(in_ptr0 + (130944 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + (x4), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/qu/cqupra63gmlfwfxj4sfm4nr7bshb3cm2ncqg5zbmqhou3wd2upbd.py # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_5], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_5 => convolution_5 # pad_5 => _unsafe_index_10, _unsafe_index_11 # y_7 => relu_4 # Graph fragment: # %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {}) # %_unsafe_index_10 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_4, [None, None, %sub_17, None]), kwargs = {}) # %_unsafe_index_11 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_10, [None, None, None, %sub_17]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_17 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_17', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 331776 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = (xindex // 256) % 18 x2 = (xindex // 4608) % 18 x3 = (xindex // 82944) x4 = xindex tmp0 = tl.load(in_ptr0 + (65280 + x0 + ((-4096)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x4), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ch/cchhliynuum77ope2a2iwxgdemzz63h4ouc2inxks5dyjmycgg64.py # Topologically Sorted Source Nodes: [conv2d_8, y_10], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_8 => convolution_8 # y_10 => relu_7 # Graph fragment: # %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {}) triton_poi_fused_convolution_relu_18 = async_compile.triton('triton_poi_fused_convolution_relu_18', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_18', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_18(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/uo/cuom3lf7cw6gjvjm5cx27gszrvfpbt7yfaruq2myxdktjfnlkiil.py # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_11 => getitem_5 # Graph fragment: # %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_19 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_19', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i8', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_19(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 65536 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = (xindex // 256) % 8 x2 = (xindex // 2048) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (512x1) + (8192x2)), None) tmp1 = tl.load(in_ptr0 + (256 + x0 + (512x1) + (8192x2)), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + (512x1) + (8192x2)), None) tmp12 = tl.load(in_ptr0 + (4352 + x0 + (512x1) + (8192x2)), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) tmp16 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/aj/caji3viacisggbedkjsdnulz2fb7eaxhomlrjjfx7eectzcbmyjo.py # Topologically Sorted Source Nodes: [y_11, pad_8], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] # Source node to ATen node mapping: # pad_8 => _unsafe_index_16, _unsafe_index_17 # y_11 => _low_memory_max_pool2d_with_offsets_2 # Graph fragment: # %_low_memory_max_pool2d_with_offsets_2 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_7, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {}) # %_unsafe_index_16 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem_4, [None, None, %sub_33, None]), kwargs = {}) # %_unsafe_index_17 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_16, [None, None, None, %sub_33]), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_20 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_20', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_20(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 102400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = (xindex // 256) % 10 x2 = (xindex // 2560) % 10 x3 = (xindex // 25600) x4 = xindex tmp0 = tl.load(in_ptr0 + (60928 + x0 + ((-8192)(tl_math.abs((-7) + (tl_math.abs((-1) + x2))))) + ((-512)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp1 = tl.load(in_ptr0 + (61184 + x0 + ((-8192)(tl_math.abs((-7) + (tl_math.abs((-1) + x2))))) + ((-512)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp3 = tl.load(in_ptr0 + (65024 + x0 + ((-8192)(tl_math.abs((-7) + (tl_math.abs((-1) + x2))))) + ((-512)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp5 = tl.load(in_ptr0 + (65280 + x0 + ((-8192)(tl_math.abs((-7) + (tl_math.abs((-1) + x2))))) + ((-512)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + (x4), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/hj/chj4nxymhihtbntxxldiyxwjfvr5hpy6qdieaixjxvbxpobalp7a.py # Topologically Sorted Source Nodes: [conv2d_9, y_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_9 => convolution_9 # y_12 => relu_8 # Graph fragment: # %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_17, %primals_20, %primals_21, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_8, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_21 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_21', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048, 64], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i1', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 2048 xnumel = 64 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 512 y1 = (yindex // 512) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (512x2) + (32768y1)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (y0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1, 1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2 + (64y3)), tmp4, xmask) tl.store(out_ptr1 + (y0 + (512x2) + (32768y1)), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/oh/cohqdo4zizapzqfqac4fudfdyg2xkh2u3m73etkdh7qa4efcnoht.py # Topologically Sorted Source Nodes: [conv2d_7, y_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_7 => convolution_7 # y_9 => relu_6 # Graph fragment: # %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {}) # %le_38 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_22 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_22', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_22', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_ptr0 + (x2), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/dw/cdwu7ryvyt42h3nbwug2und2fkbvesevlz45ivntj2btxhulmslv.py # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # y_4 => relu_2 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %le_114 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_23 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_23', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_23', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_23(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 524288 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_ptr0 + (x2), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/e3/ce3oftzj6jsrfagzn27rayexhqtvmr7blwbsv7nd5tpdkzwr6wtu.py # Topologically Sorted Source Nodes: [conv2d_1, y_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # y_1 => relu # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %le_152 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_24 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_24', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_24', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_24(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1048576 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_ptr0 + (x2), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21 = args args.clear() assert_size_stride(primals_1, (3, 3, 1, 1), (3, 1, 1, 1)) assert_size_stride(primals_2, (3, ), (1, )) assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1)) assert_size_stride(primals_4, (64, 3, 3, 3), (27, 9, 3, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64, ), (1, )) assert_size_stride(primals_8, (128, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_9, (128, ), (1, )) assert_size_stride(primals_10, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_11, (128, ), (1, )) assert_size_stride(primals_12, (256, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_13, (256, ), (1, )) assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_15, (256, ), (1, )) assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_17, (256, ), (1, )) assert_size_stride(primals_18, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_19, (256, ), (1, )) assert_size_stride(primals_20, (512, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_21, (512, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] stream0 = get_raw_stream(0) triton_poi_fused_0.run(primals_3, buf0, 12, 4096, grid=grid(12, 4096), stream=stream0) del primals_3 buf1 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_1.run(primals_4, buf1, 192, 9, grid=grid(192, 9), stream=stream0) del primals_4 buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_6, buf2, 4096, 9, grid=grid(4096, 9), stream=stream0) del primals_6 buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_3.run(primals_8, buf3, 8192, 9, grid=grid(8192, 9), stream=stream0) del primals_8 buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_4.run(primals_10, buf4, 16384, 9, grid=grid(16384, 9), stream=stream0) del primals_10 buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_5.run(primals_12, buf5, 32768, 9, grid=grid(32768, 9), stream=stream0) del primals_12 buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_6.run(primals_14, buf6, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_14 buf7 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_6.run(primals_16, buf7, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_16 buf8 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_6.run(primals_18, buf8, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_18 buf9 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_7.run(primals_20, buf9, 131072, 9, grid=grid(131072, 9), stream=stream0) del primals_20 # Topologically Sorted Source Nodes: [y], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 3, 64, 64), (12288, 1, 192, 3)) buf11 = empty_strided_cuda((4, 3, 66, 66), (13068, 1, 198, 3), torch.float32) # Topologically Sorted Source Nodes: [y, pad], Original ATen: [aten.convolution, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_8.run(buf10, primals_2, buf11, 52272, grid=grid(52272), stream=stream0) del buf10 del primals_2 # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf12 = extern_kernels.convolution(buf11, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf13 = empty_strided_cuda((4, 64, 66, 66), (278784, 1, 4224, 64), torch.float32) # Topologically Sorted Source Nodes: [conv2d_1, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_9.run(buf12, primals_5, buf13, 1115136, grid=grid(1115136), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf14 = extern_kernels.convolution(buf13, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf15 = buf14; del buf14 # reuse # Topologically Sorted Source Nodes: [conv2d_2, y_2], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_10.run(buf15, primals_7, 1048576, grid=grid(1048576), stream=stream0) del primals_7 buf16 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64), torch.int8) # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_11.run(buf15, buf16, 262144, grid=grid(262144), stream=stream0) buf17 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.float32) # Topologically Sorted Source Nodes: [y_3, pad_2], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_12.run(buf15, buf17, 295936, grid=grid(295936), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf18 = extern_kernels.convolution(buf17, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf19 = empty_strided_cuda((4, 128, 34, 34), (147968, 1, 4352, 128), torch.float32) # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_13.run(buf18, primals_9, buf19, 591872, grid=grid(591872), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution] buf20 = extern_kernels.convolution(buf19, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf21 = buf20; del buf20 # reuse # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_14.run(buf21, primals_11, 524288, grid=grid(524288), stream=stream0) del primals_11 buf22 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128), torch.int8) # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_15.run(buf21, buf22, 131072, grid=grid(131072), stream=stream0) buf23 = empty_strided_cuda((4, 128, 18, 18), (41472, 1, 2304, 128), torch.float32) # Topologically Sorted Source Nodes: [y_6, pad_4], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_16.run(buf21, buf23, 165888, grid=grid(165888), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution] buf24 = extern_kernels.convolution(buf23, buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf25 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_5], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_17.run(buf24, primals_13, buf25, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution] buf26 = extern_kernels.convolution(buf25, buf6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf26, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf27 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) # Topologically Sorted Source Nodes: [conv2d_6, y_8, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_17.run(buf26, primals_15, buf27, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution] buf28 = extern_kernels.convolution(buf27, buf7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf28, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf29 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) # Topologically Sorted Source Nodes: [conv2d_7, y_9, pad_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_17.run(buf28, primals_17, buf29, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution] buf30 = extern_kernels.convolution(buf29, buf8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf31 = buf30; del buf30 # reuse # Topologically Sorted Source Nodes: [conv2d_8, y_10], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_18.run(buf31, primals_19, 262144, grid=grid(262144), stream=stream0) del primals_19 buf32 = empty_strided_cuda((4, 256, 8, 8), (16384, 1, 2048, 256), torch.int8) # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_19.run(buf31, buf32, 65536, grid=grid(65536), stream=stream0) buf33 = empty_strided_cuda((4, 256, 10, 10), (25600, 1, 2560, 256), torch.float32) # Topologically Sorted Source Nodes: [y_11, pad_8], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_20.run(buf31, buf33, 102400, grid=grid(102400), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution] buf34 = extern_kernels.convolution(buf33, buf9, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf34, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf35 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.float32) buf36 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512), torch.bool) # Topologically Sorted Source Nodes: [conv2d_9, y_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_21.run(buf34, primals_21, buf35, buf36, 2048, 64, grid=grid(2048, 64), stream=stream0) del buf34 del primals_21 buf37 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) # Topologically Sorted Source Nodes: [conv2d_7, y_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_22.run(buf28, primals_17, buf37, 262144, grid=grid(262144), stream=stream0) del buf28 del primals_17 buf38 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) # Topologically Sorted Source Nodes: [conv2d_6, y_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_22.run(buf26, primals_15, buf38, 262144, grid=grid(262144), stream=stream0) del buf26 del primals_15 buf39 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_22.run(buf24, primals_13, buf39, 262144, grid=grid(262144), stream=stream0) del buf24 del primals_13 buf40 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128), torch.bool) # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_23.run(buf18, primals_9, buf40, 524288, grid=grid(524288), stream=stream0) del buf18 del primals_9 buf41 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, y_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_24.run(buf12, primals_5, buf41, 1048576, grid=grid(1048576), stream=stream0) del buf12 del primals_5 return (buf35, primals_1, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf11, buf13, buf15, buf16, buf17, buf19, buf21, buf22, buf23, buf25, buf27, buf29, buf31, buf32, buf33, buf36, buf37, buf38, buf39, buf40, buf41, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_19 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_20 = rand_strided((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_21 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class Encoder4(nn.Module): def __init__(self, model=None, fixed=False): super(Encoder4, self).__init__() self.fixed = fixed self.conv0 = nn.Conv2d(3, 3, 1, 1, 0) self.conv11 = nn.Conv2d(3, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv21 = nn.Conv2d(64, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv31 = nn.Conv2d(128, 256, 3, 1, 0) self.conv32 = nn.Conv2d(256, 256, 3, 1, 0) self.conv33 = nn.Conv2d(256, 256, 3, 1, 0) self.conv34 = nn.Conv2d(256, 256, 3, 1, 0) self.conv41 = nn.Conv2d(256, 512, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.pool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=False) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input): y = self.conv0(input) y = self.relu(self.conv11(self.pad(y))) y = self.relu(self.conv12(self.pad(y))) y = self.pool(y) y = self.relu(self.conv21(self.pad(y))) y = self.relu(self.conv22(self.pad(y))) y = self.pool(y) y = self.relu(self.conv31(self.pad(y))) y = self.relu(self.conv32(self.pad(y))) y = self.relu(self.conv33(self.pad(y))) y = self.relu(self.conv34(self.pad(y))) y = self.pool(y) y = self.relu(self.conv41(self.pad(y))) return y def get_inputs(): return [torch.rand([4, 3, 64, 64])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 12 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, YBLOCK], True, tl.int1) x2 = xindex y3 = yindex y0 = yindex % 3 y1 = yindex // 3 tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy= 'evict_last') tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 192 xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 3 y1 = yindex // 3 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy= 'evict_last') tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = yindex // 64 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = yindex // 64 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = yindex // 128 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = yindex // 128 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = yindex // 256 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = yindex // 256 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_8(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 52272 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 3 x1 = xindex // 3 % 66 x2 = xindex // 198 % 66 x3 = xindex // 13068 x4 = xindex tmp0 = tl.load(in_ptr0 + (12285 + x0 + -192 * tl_math.abs(-63 + tl_math .abs(-1 + x2)) + -3 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 12288 * x3), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x4, tmp2, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = xindex // 64 % 66 x2 = xindex // 4224 % 66 x3 = xindex // 278784 x4 = xindex tmp0 = tl.load(in_ptr0 + (262080 + x0 + -4096 * tl_math.abs(-63 + tl_math.abs(-1 + x2)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1 )) + 262144 * x3), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x4, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_11(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 64 x1 = xindex // 64 % 32 x2 = xindex // 2048 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 8192 * x2), None) tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 8192 * x2), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + 128 * x1 + 8192 * x2), None) tmp12 = tl.load(in_ptr0 + (4160 + x0 + 128 * x1 + 8192 * x2), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_12(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = xindex // 64 % 34 x2 = xindex // 2176 % 34 x3 = xindex // 73984 x4 = xindex tmp0 = tl.load(in_ptr0 + (257920 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp1 = tl.load(in_ptr0 + (257984 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp3 = tl.load(in_ptr0 + (262016 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp5 = tl.load(in_ptr0 + (262080 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + x4, tmp6, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = xindex // 128 % 34 x2 = xindex // 4352 % 34 x3 = xindex // 147968 x4 = xindex tmp0 = tl.load(in_ptr0 + (130944 + x0 + -4096 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x4, tmp4, None) @triton.jit def triton_poi_fused_convolution_relu_14(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_15(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = xindex // 128 % 16 x2 = xindex // 2048 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 8192 * x2), None) tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 8192 * x2), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + 256 * x1 + 8192 * x2), None) tmp12 = tl.load(in_ptr0 + (4224 + x0 + 256 * x1 + 8192 * x2), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_16(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = xindex // 128 % 18 x2 = xindex // 2304 % 18 x3 = xindex // 41472 x4 = xindex tmp0 = tl.load(in_ptr0 + (126720 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp1 = tl.load(in_ptr0 + (126848 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp3 = tl.load(in_ptr0 + (130816 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp5 = tl.load(in_ptr0 + (130944 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + x4, tmp6, None) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = xindex // 256 % 18 x2 = xindex // 4608 % 18 x3 = xindex // 82944 x4 = xindex tmp0 = tl.load(in_ptr0 + (65280 + x0 + -4096 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x4, tmp4, None) @triton.jit def triton_poi_fused_convolution_relu_18(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_19(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = xindex // 256 % 8 x2 = xindex // 2048 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 512 * x1 + 8192 * x2), None) tmp1 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 8192 * x2), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + 512 * x1 + 8192 * x2), None) tmp12 = tl.load(in_ptr0 + (4352 + x0 + 512 * x1 + 8192 * x2), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_20(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = xindex // 256 % 10 x2 = xindex // 2560 % 10 x3 = xindex // 25600 x4 = xindex tmp0 = tl.load(in_ptr0 + (60928 + x0 + -8192 * tl_math.abs(-7 + tl_math .abs(-1 + x2)) + -512 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp1 = tl.load(in_ptr0 + (61184 + x0 + -8192 * tl_math.abs(-7 + tl_math .abs(-1 + x2)) + -512 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp3 = tl.load(in_ptr0 + (65024 + x0 + -8192 * tl_math.abs(-7 + tl_math .abs(-1 + x2)) + -512 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp5 = tl.load(in_ptr0 + (65280 + x0 + -8192 * tl_math.abs(-7 + tl_math .abs(-1 + x2)) + -512 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + x4, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): xnumel = 64 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 512 y1 = yindex // 512 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 512 * x2 + 32768 * y1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1, 1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2 + 64 * y3), tmp4, xmask) tl.store(out_ptr1 + (y0 + 512 * x2 + 32768 * y1), tmp6, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_ptr0 + x2, None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_23(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_ptr0 + x2, None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_24(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_ptr0 + x2, None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21) = args args.clear() assert_size_stride(primals_1, (3, 3, 1, 1), (3, 1, 1, 1)) assert_size_stride(primals_2, (3,), (1,)) assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1)) assert_size_stride(primals_4, (64, 3, 3, 3), (27, 9, 3, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64,), (1,)) assert_size_stride(primals_8, (128, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_9, (128,), (1,)) assert_size_stride(primals_10, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_11, (128,), (1,)) assert_size_stride(primals_12, (256, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_13, (256,), (1,)) assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_15, (256,), (1,)) assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_17, (256,), (1,)) assert_size_stride(primals_18, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_19, (256,), (1,)) assert_size_stride(primals_20, (512, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_21, (512,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch .float32) get_raw_stream(0) triton_poi_fused_0[grid(12, 4096)](primals_3, buf0, 12, 4096, XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1) del primals_3 buf1 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32) triton_poi_fused_1[grid(192, 9)](primals_4, buf1, 192, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_4 buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch. float32) triton_poi_fused_2[grid(4096, 9)](primals_6, buf2, 4096, 9, XBLOCK= 16, YBLOCK=64, num_warps=4, num_stages=1) del primals_6 buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch .float32) triton_poi_fused_3[grid(8192, 9)](primals_8, buf3, 8192, 9, XBLOCK= 16, YBLOCK=64, num_warps=4, num_stages=1) del primals_8 buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32) triton_poi_fused_4[grid(16384, 9)](primals_10, buf4, 16384, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_10 buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32) triton_poi_fused_5[grid(32768, 9)](primals_12, buf5, 32768, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_12 buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_6[grid(65536, 9)](primals_14, buf6, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_14 buf7 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_6[grid(65536, 9)](primals_16, buf7, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_16 buf8 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_6[grid(65536, 9)](primals_18, buf8, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_18 buf9 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_7[grid(131072, 9)](primals_20, buf9, 131072, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_20 buf10 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 3, 64, 64), (12288, 1, 192, 3)) buf11 = empty_strided_cuda((4, 3, 66, 66), (13068, 1, 198, 3), torch.float32) triton_poi_fused_convolution_reflection_pad2d_8[grid(52272)](buf10, primals_2, buf11, 52272, XBLOCK=512, num_warps=4, num_stages=1) del buf10 del primals_2 buf12 = extern_kernels.convolution(buf11, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf13 = empty_strided_cuda((4, 64, 66, 66), (278784, 1, 4224, 64), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_9[grid(1115136)]( buf12, primals_5, buf13, 1115136, XBLOCK=1024, num_warps=4, num_stages=1) buf14 = extern_kernels.convolution(buf13, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf15 = buf14 del buf14 triton_poi_fused_convolution_relu_10[grid(1048576)](buf15, primals_7, 1048576, XBLOCK=1024, num_warps=4, num_stages=1) del primals_7 buf16 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64), torch.int8) triton_poi_fused_max_pool2d_with_indices_11[grid(262144)](buf15, buf16, 262144, XBLOCK=1024, num_warps=4, num_stages=1) buf17 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.float32) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_12[grid( 295936)](buf15, buf17, 295936, XBLOCK=512, num_warps=8, num_stages=1) buf18 = extern_kernels.convolution(buf17, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf19 = empty_strided_cuda((4, 128, 34, 34), (147968, 1, 4352, 128), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_13[grid(591872)]( buf18, primals_9, buf19, 591872, XBLOCK=1024, num_warps=4, num_stages=1) buf20 = extern_kernels.convolution(buf19, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf21 = buf20 del buf20 triton_poi_fused_convolution_relu_14[grid(524288)](buf21, primals_11, 524288, XBLOCK=1024, num_warps=4, num_stages=1) del primals_11 buf22 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128), torch.int8) triton_poi_fused_max_pool2d_with_indices_15[grid(131072)](buf21, buf22, 131072, XBLOCK=1024, num_warps=4, num_stages=1) buf23 = empty_strided_cuda((4, 128, 18, 18), (41472, 1, 2304, 128), torch.float32) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_16[grid( 165888)](buf21, buf23, 165888, XBLOCK=512, num_warps=8, num_stages=1) buf24 = extern_kernels.convolution(buf23, buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf25 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_17[grid(331776)]( buf24, primals_13, buf25, 331776, XBLOCK=1024, num_warps=4, num_stages=1) buf26 = extern_kernels.convolution(buf25, buf6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf26, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf27 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_17[grid(331776)]( buf26, primals_15, buf27, 331776, XBLOCK=1024, num_warps=4, num_stages=1) buf28 = extern_kernels.convolution(buf27, buf7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf28, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf29 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_17[grid(331776)]( buf28, primals_17, buf29, 331776, XBLOCK=1024, num_warps=4, num_stages=1) buf30 = extern_kernels.convolution(buf29, buf8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf31 = buf30 del buf30 triton_poi_fused_convolution_relu_18[grid(262144)](buf31, primals_19, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del primals_19 buf32 = empty_strided_cuda((4, 256, 8, 8), (16384, 1, 2048, 256), torch.int8) triton_poi_fused_max_pool2d_with_indices_19[grid(65536)](buf31, buf32, 65536, XBLOCK=512, num_warps=4, num_stages=1) buf33 = empty_strided_cuda((4, 256, 10, 10), (25600, 1, 2560, 256), torch.float32) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_20[grid( 102400)](buf31, buf33, 102400, XBLOCK=512, num_warps=8, num_stages=1) buf34 = extern_kernels.convolution(buf33, buf9, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf34, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf35 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch .float32) buf36 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_21[grid(2048, 64) ](buf34, primals_21, buf35, buf36, 2048, 64, XBLOCK=32, YBLOCK= 32, num_warps=4, num_stages=1) del buf34 del primals_21 buf37 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_22[grid(262144)]( buf28, primals_17, buf37, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf28 del primals_17 buf38 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_22[grid(262144)]( buf26, primals_15, buf38, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf26 del primals_15 buf39 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_22[grid(262144)]( buf24, primals_13, buf39, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf24 del primals_13 buf40 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_23[grid(524288)]( buf18, primals_9, buf40, 524288, XBLOCK=1024, num_warps=4, num_stages=1) del buf18 del primals_9 buf41 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_24[grid(1048576)]( buf12, primals_5, buf41, 1048576, XBLOCK=1024, num_warps=4, num_stages=1) del buf12 del primals_5 return (buf35, primals_1, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf11, buf13, buf15, buf16, buf17, buf19, buf21, buf22, buf23, buf25, buf27, buf29, buf31, buf32, buf33, buf36, buf37, buf38, buf39, buf40, buf41) class Encoder4New(nn.Module): def __init__(self, model=None, fixed=False): super(Encoder4New, self).__init__() self.fixed = fixed self.conv0 = nn.Conv2d(3, 3, 1, 1, 0) self.conv11 = nn.Conv2d(3, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv21 = nn.Conv2d(64, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv31 = nn.Conv2d(128, 256, 3, 1, 0) self.conv32 = nn.Conv2d(256, 256, 3, 1, 0) self.conv33 = nn.Conv2d(256, 256, 3, 1, 0) self.conv34 = nn.Conv2d(256, 256, 3, 1, 0) self.conv41 = nn.Conv2d(256, 512, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.pool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=False) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input_0): primals_1 = self.conv0.weight primals_2 = self.conv0.bias primals_4 = self.conv11.weight primals_5 = self.conv11.bias primals_6 = self.conv12.weight primals_7 = self.conv12.bias primals_8 = self.conv21.weight primals_9 = self.conv21.bias primals_10 = self.conv22.weight primals_11 = self.conv22.bias primals_12 = self.conv31.weight primals_13 = self.conv31.bias primals_14 = self.conv32.weight primals_15 = self.conv32.bias primals_16 = self.conv33.weight primals_17 = self.conv33.bias primals_18 = self.conv34.weight primals_19 = self.conv34.bias primals_20 = self.conv41.weight primals_21 = self.conv41.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21]) return output[0]	EndyWon/Texture-Reformer	Encoder4	false	8,214	[ "MIT" ]	11	f84f95accb3574c7b759a7f03c0b0b4e150314b5	https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5
MultiHeadAttn	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/p3/cp3vh5ge4dyj3r4smr5dvjovuocujri2yvhft44mt2rcrwo3tz5y.py # Topologically Sorted Source Nodes: [attn_score], Original ATen: [aten.clone] # Source node to ATen node mapping: # attn_score => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_5,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) % 4 x2 = (xindex // 16) % 4 x3 = (xindex // 64) x4 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (4x2) + (16x1) + (64x3)), xmask) tl.store(out_ptr0 + (x4), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ba/cbanjdalincnmde2hitei4pjt6tx6h22a24dhbavlznbqpwpvdny.py # Topologically Sorted Source Nodes: [attn_score], Original ATen: [aten.clone] # Source node to ATen node mapping: # attn_score => clone_1 # Graph fragment: # %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_6,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 64 xnumel = 4 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = (yindex // 16) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (16x2) + (64y1)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + (4y3)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/vh/cvh44ihf3rj7wi2aoxl3uq44btcgqorq4462l2bab7tvaxlzfcgv.py # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_prob => amax, clone_2, exp, sub # Graph fragment: # %clone_2 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%view_17,), kwargs = {memory_format: torch.contiguous_format}) # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone_2, [2], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_2, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp3 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = tl_math.exp(tmp14) tl.store(out_ptr0 + (x2), tmp15, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/od/codkvimqko2gq72r6wc7j3vkhsgdlcpwz5vf5zdhs76zwhyzdtdu.py # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_prob => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {}) # %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 64 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x3 = xindex y2 = (yindex // 16) y4 = yindex % 16 y1 = (yindex // 4) % 4 y5 = yindex tmp0 = tl.load(in_ptr0 + (y4 + (16x3) + (64y2)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + ((4y1) + (16x3) + (64y2)), xmask & ymask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4y1) + (16x3) + (64y2)), xmask & ymask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4y1) + (16x3) + (64y2)), xmask & ymask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4y1) + (16x3) + (64y2)), xmask & ymask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x3 + (4y5)), tmp8, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/or/cord2wpxwqwenorb6s2qqpk4avusobzyevskmzrufeni6z234ftg.py # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] # Source node to ATen node mapping: # attn_vec => clone_4 # Graph fragment: # %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_14,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4x2) + (64y1)), xmask & ymask) tl.store(out_ptr0 + (x2 + (16y3)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yv/cyvu7b655f7w4y6fs3cr3d3vawpnn3vmcirao3tw5zgpuuobc2mb.py # Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten.add, aten.native_layer_norm] # Source node to ATen node mapping: # output => add # output_1 => var_mean # Graph fragment: # %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %view_26), kwargs = {}) # %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add, [2]), kwargs = {correction: 0, keepdim: True}) triton_poi_fused_add_native_layer_norm_5 = async_compile.triton('triton_poi_fused_add_native_layer_norm_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_native_layer_norm_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4x0)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + (4x0)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + (4x0)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + (4x0)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4x0)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (3 + (4x0)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 + tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 + tmp12 tmp14 = tmp10 + tmp13 tmp15 = 4.0 tmp16 = tmp14 / tmp15 tmp17 = tmp2 - tmp16 tmp18 = tmp17 * tmp17 tmp19 = tmp5 - tmp16 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp9 - tmp16 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp13 - tmp16 tmp26 = tmp25 * tmp25 tmp27 = tmp24 + tmp26 tmp28 = tmp27 / tmp15 tl.store(out_ptr0 + (x0), tmp16, xmask) tl.store(out_ptr1 + (x0), tmp28, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/rp/crp6yznjcr5keantuusvl77ssv2xcxe4iqpzesafqd5zf32kmhfv.py # Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten.add, aten.native_layer_norm] # Source node to ATen node mapping: # output => add # output_1 => add_1, add_2, mul_1, mul_2, rsqrt, sub_1 # Graph fragment: # %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %view_26), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {}) # %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %getitem_1), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %primals_6), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %primals_7), kwargs = {}) triton_poi_fused_add_native_layer_norm_6 = async_compile.triton('triton_poi_fused_add_native_layer_norm_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr1 + (x2), xmask) tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 - tmp3 tmp6 = 1e-05 tmp7 = tmp5 + tmp6 tmp8 = libdevice.rsqrt(tmp7) tmp9 = tmp4 * tmp8 tmp11 = tmp9 * tmp10 tmp13 = tmp11 + tmp12 tl.store(out_ptr0 + (x2), tmp13, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (16, 4), (4, 1)) assert_size_stride(primals_3, (16, 4), (4, 1)) assert_size_stride(primals_4, (16, 4), (4, 1)) assert_size_stride(primals_5, (4, 16), (16, 1)) assert_size_stride(primals_6, (4, ), (1, )) assert_size_stride(primals_7, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 16), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 16), (1, 4), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf2) del primals_4 buf3 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_score], Original ATen: [aten.clone] stream0 = get_raw_stream(0) triton_poi_fused_clone_0.run(buf0, buf3, 256, grid=grid(256), stream=stream0) buf4 = reinterpret_tensor(buf0, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [attn_score], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf1, buf4, 64, 4, grid=grid(64, 4), stream=stream0) buf5 = reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0); del buf1 # reuse # Topologically Sorted Source Nodes: [attn_score], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0), out=buf5) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 4, 1, 16), torch.float32) # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf5, buf6, 256, grid=grid(256), stream=stream0) buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf5 # reuse # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] triton_poi_fused__softmax_3.run(buf6, buf7, 64, 4, grid=grid(64, 4), stream=stream0) buf8 = reinterpret_tensor(buf6, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0); del buf6 # reuse # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] triton_poi_fused_clone_4.run(buf7, buf8, 16, 16, grid=grid(16, 16), stream=stream0) buf9 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] triton_poi_fused_clone_0.run(buf2, buf9, 256, grid=grid(256), stream=stream0) buf10 = reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1), 0), out=buf10) buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] triton_poi_fused_clone_0.run(buf10, buf11, 256, grid=grid(256), stream=stream0) del buf10 buf12 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_out], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf11, (16, 16), (16, 1), 0), reinterpret_tensor(primals_5, (16, 4), (1, 16), 0), out=buf12) buf13 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) # Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten.add, aten.native_layer_norm] triton_poi_fused_add_native_layer_norm_5.run(primals_1, buf12, buf13, buf14, 16, grid=grid(16), stream=stream0) buf15 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten.add, aten.native_layer_norm] triton_poi_fused_add_native_layer_norm_6.run(primals_1, buf12, buf13, buf14, primals_6, primals_7, buf15, 64, grid=grid(64), stream=stream0) del buf13 del buf14 del primals_7 return (buf15, primals_1, primals_6, buf7, reinterpret_tensor(buf11, (16, 16), (16, 1), 0), buf12, primals_5, reinterpret_tensor(buf8, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf9, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf4, (16, 4, 4), (16, 1, 4), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class MultiHeadAttn(nn.Module): def __init__(self, n_head, d_model, d_head, dropout, dropatt=0, pre_lnorm=False): super(MultiHeadAttn, self).__init__() self.n_head = n_head self.d_model = d_model self.d_head = d_head self.dropout = dropout self.q_net = nn.Linear(d_model, n_head * d_head, bias=False) self.k_net = nn.Linear(d_model, n_head * d_head, bias=False) self.v_net = nn.Linear(d_model, n_head * d_head, bias=False) self.drop = nn.Dropout(dropout) self.dropatt = nn.Dropout(dropatt) self.o_net = nn.Linear(n_head * d_head, d_model, bias=False) self.layer_norm = nn.LayerNorm(d_model) self.scale = 1 / d_head ** 0.5 self.pre_lnorm = pre_lnorm for m in [self.q_net, self.k_net, self.v_net, self.o_net]: nn.init.normal_(m.weight, 0.0, self.scale) def forward(self, h, attn_mask=None, mems=None): bsz, qlen = h.size(0), h.size(1) if mems is not None: c = torch.cat([mems, h], dim=1) else: c = h c.size(1) if self.pre_lnorm: h = self.layer_norm(h) c = self.layer_norm(c) head_q = self.q_net(h).view(h.size(0), h.size(1), self.n_head, self .d_head) head_k = self.k_net(c).view(c.size(0), c.size(1), self.n_head, self .d_head) head_v = self.v_net(c).view(c.size(0), c.size(1), self.n_head, self .d_head) attn_score = torch.einsum('bind,bjnd->bijn', (head_q, head_k)) attn_score.mul_(self.scale) if attn_mask is not None and attn_mask.any().item(): if attn_mask.dim() == 2: attn_score.masked_fill_((attn_mask == 0).unsqueeze(1). unsqueeze(-1), _INF) elif attn_mask.dim() == 3: attn_score.masked_fill_((attn_mask == 0).unsqueeze(-1), _INF) attn_prob = F.softmax(attn_score, dim=2) attn_prob = self.dropatt(attn_prob) attn_vec = torch.einsum('bijn,bjnd->bind', (attn_prob, head_v)) attn_vec = attn_vec.contiguous().view(bsz, qlen, self.n_head * self .d_head) attn_out = self.o_net(attn_vec) attn_out = self.drop(attn_out) output = h + attn_out if not self.pre_lnorm: output = self.layer_norm(output) return output def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'n_head': 4, 'd_model': 4, 'd_head': 4, 'dropout': 0.5}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 % 4 x2 = xindex // 16 % 4 x3 = xindex // 64 x4 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask) tl.store(out_ptr0 + x4, tmp0, xmask) @triton.jit def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 64 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = yindex // 16 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = tl_math.exp(tmp14) tl.store(out_ptr0 + x2, tmp15, xmask) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 64 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x3 = xindex y2 = yindex // 16 y4 = yindex % 16 y1 = yindex // 4 % 4 y5 = yindex tmp0 = tl.load(in_ptr0 + (y4 + 16 * x3 + 64 * y2), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (4 * y1 + 16 * x3 + 64 * y2), xmask & ymask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * y1 + 16 * x3 + 64 * y2), xmask & ymask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * y1 + 16 * x3 + 64 * y2), xmask & ymask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * y1 + 16 * x3 + 64 * y2), xmask & ymask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x3 + 4 * y5), tmp8, xmask & ymask) @triton.jit def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask) tl.store(out_ptr0 + (x2 + 16 * y3), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_add_native_layer_norm_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 + tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 + tmp12 tmp14 = tmp10 + tmp13 tmp15 = 4.0 tmp16 = tmp14 / tmp15 tmp17 = tmp2 - tmp16 tmp18 = tmp17 * tmp17 tmp19 = tmp5 - tmp16 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp9 - tmp16 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp13 - tmp16 tmp26 = tmp25 * tmp25 tmp27 = tmp24 + tmp26 tmp28 = tmp27 / tmp15 tl.store(out_ptr0 + x0, tmp16, xmask) tl.store(out_ptr1 + x0, tmp28, xmask) @triton.jit def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr1 + x2, xmask) tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 - tmp3 tmp6 = 1e-05 tmp7 = tmp5 + tmp6 tmp8 = libdevice.rsqrt(tmp7) tmp9 = tmp4 * tmp8 tmp11 = tmp9 * tmp10 tmp13 = tmp11 + tmp12 tl.store(out_ptr0 + x2, tmp13, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (16, 4), (4, 1)) assert_size_stride(primals_3, (16, 4), (4, 1)) assert_size_stride(primals_4, (16, 4), (4, 1)) assert_size_stride(primals_5, (4, 16), (16, 1)) assert_size_stride(primals_6, (4,), (1,)) assert_size_stride(primals_7, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 16), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 16), (1, 4), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf2) del primals_4 buf3 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch .float32) get_raw_stream(0) triton_poi_fused_clone_0[grid(256)](buf0, buf3, 256, XBLOCK=128, num_warps=4, num_stages=1) buf4 = reinterpret_tensor(buf0, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0) del buf0 triton_poi_fused_clone_1[grid(64, 4)](buf1, buf4, 64, 4, XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1) buf5 = reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0) del buf1 extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0), out=buf5) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 4, 1, 16), torch.float32) triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1) buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf5 triton_poi_fused__softmax_3[grid(64, 4)](buf6, buf7, 64, 4, XBLOCK= 4, YBLOCK=32, num_warps=4, num_stages=1) buf8 = reinterpret_tensor(buf6, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0) del buf6 triton_poi_fused_clone_4[grid(16, 16)](buf7, buf8, 16, 16, XBLOCK= 16, YBLOCK=16, num_warps=4, num_stages=1) buf9 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch .float32) triton_poi_fused_clone_0[grid(256)](buf2, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1) buf10 = reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0) del buf2 extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1), 0), out=buf10) buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_clone_0[grid(256)](buf10, buf11, 256, XBLOCK=128, num_warps=4, num_stages=1) del buf10 buf12 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf11, (16, 16), (16, 1), 0), reinterpret_tensor(primals_5, (16, 4), (1, 16), 0), out=buf12) buf13 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) triton_poi_fused_add_native_layer_norm_5[grid(16)](primals_1, buf12, buf13, buf14, 16, XBLOCK=16, num_warps=1, num_stages=1) buf15 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_add_native_layer_norm_6[grid(64)](primals_1, buf12, buf13, buf14, primals_6, primals_7, buf15, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf13 del buf14 del primals_7 return buf15, primals_1, primals_6, buf7, reinterpret_tensor(buf11, (16, 16), (16, 1), 0), buf12, primals_5, reinterpret_tensor(buf8, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf9, (16, 4, 4), (16, 1, 4), 0 ), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0 ), reinterpret_tensor(buf4, (16, 4, 4), (16, 1, 4), 0) class MultiHeadAttnNew(nn.Module): def __init__(self, n_head, d_model, d_head, dropout, dropatt=0, pre_lnorm=False): super(MultiHeadAttnNew, self).__init__() self.n_head = n_head self.d_model = d_model self.d_head = d_head self.dropout = dropout self.q_net = nn.Linear(d_model, n_head * d_head, bias=False) self.k_net = nn.Linear(d_model, n_head * d_head, bias=False) self.v_net = nn.Linear(d_model, n_head * d_head, bias=False) self.drop = nn.Dropout(dropout) self.dropatt = nn.Dropout(dropatt) self.o_net = nn.Linear(n_head * d_head, d_model, bias=False) self.layer_norm = nn.LayerNorm(d_model) self.scale = 1 / d_head ** 0.5 self.pre_lnorm = pre_lnorm for m in [self.q_net, self.k_net, self.v_net, self.o_net]: nn.init.normal_(m.weight, 0.0, self.scale) def forward(self, input_0): primals_2 = self.q_net.weight primals_3 = self.k_net.weight primals_4 = self.v_net.weight primals_5 = self.o_net.weight primals_6 = self.layer_norm.weight primals_7 = self.layer_norm.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]	HKUST-KnowComp/NeuralSubIsoCnt	MultiHeadAttn	false	8,215	[ "MIT" ]	28	7d1deef8e49af90122ea0ad099dec1de390927b6	https://github.com/HKUST-KnowComp/NeuralSubIsoCnt/tree/7d1deef8e49af90122ea0ad099dec1de390927b6
GatedMultiHeadAttn	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/tp/ctpiguxmcpckre2mit64cbirohbry7ukusrmf4e7xnoeurunx2lg.py # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_prob => amax, exp, sub, sum_1 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_11, [2], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_11, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {}) triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 4 x2 = (xindex // 16) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (16x2)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (4 + x0 + (16x2)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr1 + (8 + x0 + (16x2)), xmask, eviction_policy='evict_last') tmp13 = tl.load(in_ptr1 + (12 + x0 + (16x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tmp6 = tmp0 * tmp5 tmp7 = tmp6 * tmp3 tmp8 = triton_helpers.maximum(tmp4, tmp7) tmp10 = tmp0 * tmp9 tmp11 = tmp10 * tmp3 tmp12 = triton_helpers.maximum(tmp8, tmp11) tmp14 = tmp0 * tmp13 tmp15 = tmp14 * tmp3 tmp16 = triton_helpers.maximum(tmp12, tmp15) tmp17 = tmp4 - tmp16 tmp18 = tl_math.exp(tmp17) tmp19 = tmp7 - tmp16 tmp20 = tl_math.exp(tmp19) tmp21 = tmp18 + tmp20 tmp22 = tmp11 - tmp16 tmp23 = tl_math.exp(tmp22) tmp24 = tmp21 + tmp23 tmp25 = tmp15 - tmp16 tmp26 = tl_math.exp(tmp25) tmp27 = tmp24 + tmp26 tl.store(out_ptr0 + (x3), tmp16, xmask) tl.store(out_ptr1 + (x3), tmp27, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/mx/cmxglwcqshsz56cbqetuotr5dueji2qhvsgqhs3rspu5vyljlcxs.py # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] # Source node to ATen node mapping: # attn_vec => clone_1 # Graph fragment: # %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = (xindex // 4) x0 = xindex % 4 x1 = (xindex // 4) % 4 x3 = (xindex // 64) x2 = (xindex // 16) % 4 tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x1 + (4x0) + (16x3)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr3 + (x4), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tmp6 = tmp4 - tmp5 tmp7 = tl_math.exp(tmp6) tmp9 = tmp7 / tmp8 tl.store(out_ptr0 + (x0 + (4x2) + (16x1) + (64x3)), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/3a/c3aaucsolrfdjphevn4qawbbrmr3jqka2dfrruxbjgx42g3fnwu6.py # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] # Source node to ATen node mapping: # attn_vec => clone_2 # Graph fragment: # %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_8,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4x2) + (16y1)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + (4y3)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/oz/cozicgllm5gq5az3npyul2g2afvsz5ievjw5qizps6nqjsvarc22.py # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %view_19], 2), kwargs = {}) triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[128], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + ((4x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/6j/c6jx5qsbwmrvybg6qdxymggyqcxvc33mfvf2whbhr35uyx25r5or.py # Topologically Sorted Source Nodes: [gate, mul, sub, mul_1, attn_out_1], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add] # Source node to ATen node mapping: # attn_out_1 => add # gate => sigmoid # mul => mul_2 # mul_1 => mul_3 # sub => sub_1 # Graph fragment: # %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_21,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %primals_1), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %view_19), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {}) triton_poi_fused_add_mul_rsub_sigmoid_4 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_rsub_sigmoid_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_rsub_sigmoid_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp2 = tl.load(in_ptr1 + (x0), xmask) tmp6 = tl.load(in_ptr2 + (x0), xmask) tmp1 = tl.sigmoid(tmp0) tmp3 = tmp1 * tmp2 tmp4 = 1.0 tmp5 = tmp4 - tmp1 tmp7 = tmp5 * tmp6 tmp8 = tmp3 + tmp7 tl.store(out_ptr0 + (x0), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4, 8), (8, 1)) assert_size_stride(primals_9, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf0) del primals_4 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1) del primals_5 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2) del primals_6 buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] stream0 = get_raw_stream(0) triton_poi_fused__softmax_0.run(buf0, buf1, buf3, buf4, 64, grid=grid(64), stream=stream0) buf5 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf0, buf1, buf3, buf4, buf5, 256, grid=grid(256), stream=stream0) buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] triton_poi_fused_clone_2.run(buf2, buf6, 16, 4, grid=grid(16, 4), stream=stream0) buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7) buf8 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf3 # reuse # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] triton_poi_fused_clone_2.run(buf7, buf8, 16, 4, grid=grid(16, 4), stream=stream0) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0); del buf7 # reuse # Topologically Sorted Source Nodes: [attn_out], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf9) buf10 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] triton_poi_fused_cat_3.run(primals_1, buf9, buf10, 128, grid=grid(128), stream=stream0) buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.addmm] extern_kernels.addmm(primals_9, reinterpret_tensor(buf10, (16, 8), (8, 1), 0), reinterpret_tensor(primals_8, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf11) del primals_9 buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [gate, mul, sub, mul_1, attn_out_1], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add] triton_poi_fused_add_mul_rsub_sigmoid_4.run(buf11, primals_1, buf9, buf12, 64, grid=grid(64), stream=stream0) return (buf12, primals_1, buf0, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(buf8, (16, 4), (4, 1), 0), buf9, reinterpret_tensor(buf10, (16, 8), (8, 1), 0), buf11, primals_8, primals_7, reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class GatedMultiHeadAttn(nn.Module): def __init__(self, query_dim, key_dim, value_dim, hidden_dim, num_head, dropatt=0.0, act_func='softmax', add_zero_attn=False, pre_lnorm= False, post_lnorm=False): super(GatedMultiHeadAttn, self).__init__() assert hidden_dim % num_head == 0 self.query_dim = query_dim self.key_dim = key_dim self.value_dim = value_dim self.hidden_dim = hidden_dim self.num_head = num_head self.dropatt = nn.Dropout(dropatt) head_dim = hidden_dim // num_head self.q_net = nn.Linear(query_dim, hidden_dim, bias=False) self.k_net = nn.Linear(key_dim, hidden_dim, bias=False) self.v_net = nn.Linear(value_dim, hidden_dim, bias=False) self.o_net = nn.Linear(hidden_dim, query_dim, bias=False) self.g_net = nn.Linear(2 * query_dim, query_dim, bias=True) self.scale = 1 / head_dim ** 0.5 self.act_func = act_func self.add_zero_attn = add_zero_attn self.pre_lnorm = pre_lnorm self.post_lnorm = post_lnorm if pre_lnorm: self.q_layer_norm = nn.LayerNorm(query_dim) self.k_layer_norm = nn.LayerNorm(key_dim) self.v_layer_norm = nn.LayerNorm(value_dim) if post_lnorm: self.o_layer_norm = nn.LayerNorm(query_dim) for net in [self.q_net, self.k_net, self.v_net, self.o_net]: nn.init.xavier_uniform_(net.weight, 1.0) if hasattr(net, 'bias') and net.bias is not None: nn.init.constant_(net.bias, 0.0) nn.init.normal_(self.g_net.weight, 0.0, self.scale) if hasattr(self.g_net, 'bias') and self.g_net.bias is not None: nn.init.constant_(self.g_net.bias, 1.0) if self.pre_lnorm: for layer_norm in [self.q_layer_norm, self.k_layer_norm, self. v_layer_norm]: if hasattr(layer_norm, 'weight'): nn.init.normal_(layer_norm.weight, 1.0, self.scale) if hasattr(layer_norm, 'bias') and layer_norm.bias is not None: nn.init.constant_(layer_norm.bias, 0.0) if self.post_lnorm: if hasattr(self.o_layer_norm, 'weight'): nn.init.normal_(self.o_layer_norm.weight, 1.0, self.scale) if hasattr(self.o_layer_norm, 'bias' ) and self.o_layer_norm.bias is not None: nn.init.constant_(self.o_layer_norm.bias, 0.0) def forward(self, query, key, value, attn_mask=None): bsz = query.size(0) if self.add_zero_attn: key = torch.cat([key, torch.zeros((bsz, 1) + key.size()[2:], dtype=key.dtype, device=key.device)], dim=1) value = torch.cat([value, torch.zeros((bsz, 1) + value.size()[2 :], dtype=value.dtype, device=value.device)], dim=1) if attn_mask is not None: attn_mask = torch.cat([attn_mask, torch.ones((bsz, 1), dtype=attn_mask.dtype, device=attn_mask.device)], dim=1) qlen, klen, vlen = query.size(1), key.size(1), value.size(1) if self.pre_lnorm: query = self.q_layer_norm(query) key = self.k_layer_norm(key) value = self.v_layer_norm(value) head_q = self.q_net(query).view(bsz, qlen, self.num_head, self. hidden_dim // self.num_head) head_k = self.k_net(key).view(bsz, klen, self.num_head, self. hidden_dim // self.num_head) head_v = self.v_net(value).view(bsz, vlen, self.num_head, self. hidden_dim // self.num_head) attn_score = torch.einsum('bind,bjnd->bijn', (head_q, head_k)) attn_score.mul_(self.scale) if attn_mask is not None: if attn_mask.dim() == 2: attn_score.masked_fill_((attn_mask == 0).unsqueeze(1). unsqueeze(-1), _INF) elif attn_mask.dim() == 3: attn_score.masked_fill_((attn_mask == 0).unsqueeze(-1), _INF) if self.act_func is None or self.act_func == 'None': attn_prob = attn_score elif self.act_func == 'softmax': attn_prob = F.softmax(attn_score, dim=2) elif self.act_func == 'sigmoid': attn_prob = F.sigmoid(attn_score) elif self.act_func == 'tanh': attn_prob = F.tanh(attn_score) elif self.act_func == 'relu': attn_prob = F.relu(attn_score) elif self.act_func == 'leaky_relu': attn_prob = F.leaky_relu(attn_score) elif self.act_func == 'maximum': max_score = torch.max(attn_score, dim=2, keepdim=True)[0] max_mask = attn_score == max_score cnt = torch.sum(max_mask, dim=2, keepdim=True) attn_prob = max_mask.float() / cnt.float() else: raise NotImplementedError attn_prob = self.dropatt(attn_prob) attn_vec = torch.einsum('bijn,bjnd->bind', (attn_prob, head_v)) attn_vec = attn_vec.contiguous().view(bsz, qlen, self.hidden_dim) attn_out = self.o_net(attn_vec) gate = F.sigmoid(self.g_net(torch.cat([query, attn_out], dim=2))) attn_out = gate * query + (1 - gate) * attn_out if self.post_lnorm: attn_out = self.o_layer_norm(attn_out) return attn_out def get_output_dim(self): return self.query_dim def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4]) ] def get_init_inputs(): return [[], {'query_dim': 4, 'key_dim': 4, 'value_dim': 4, 'hidden_dim': 4, 'num_head': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__softmax_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 4 x2 = xindex // 16 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp5 = tl.load(in_ptr1 + (4 + x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp9 = tl.load(in_ptr1 + (8 + x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp13 = tl.load(in_ptr1 + (12 + x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 * tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tmp6 = tmp0 * tmp5 tmp7 = tmp6 * tmp3 tmp8 = triton_helpers.maximum(tmp4, tmp7) tmp10 = tmp0 * tmp9 tmp11 = tmp10 * tmp3 tmp12 = triton_helpers.maximum(tmp8, tmp11) tmp14 = tmp0 * tmp13 tmp15 = tmp14 * tmp3 tmp16 = triton_helpers.maximum(tmp12, tmp15) tmp17 = tmp4 - tmp16 tmp18 = tl_math.exp(tmp17) tmp19 = tmp7 - tmp16 tmp20 = tl_math.exp(tmp19) tmp21 = tmp18 + tmp20 tmp22 = tmp11 - tmp16 tmp23 = tl_math.exp(tmp22) tmp24 = tmp21 + tmp23 tmp25 = tmp15 - tmp16 tmp26 = tl_math.exp(tmp25) tmp27 = tmp24 + tmp26 tl.store(out_ptr0 + x3, tmp16, xmask) tl.store(out_ptr1 + x3, tmp27, xmask) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex // 4 x0 = xindex % 4 x1 = xindex // 4 % 4 x3 = xindex // 64 x2 = xindex // 16 % 4 tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x1 + 4 * x0 + 16 * x3), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tmp6 = tmp4 - tmp5 tmp7 = tl_math.exp(tmp6) tmp9 = tmp7 / tmp8 tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp9, xmask) @triton.jit def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x2, tmp10, xmask) @triton.jit def triton_poi_fused_add_mul_rsub_sigmoid_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp2 = tl.load(in_ptr1 + x0, xmask) tmp6 = tl.load(in_ptr2 + x0, xmask) tmp1 = tl.sigmoid(tmp0) tmp3 = tmp1 * tmp2 tmp4 = 1.0 tmp5 = tmp4 - tmp1 tmp7 = tmp5 * tmp6 tmp8 = tmp3 + tmp7 tl.store(out_ptr0 + x0, tmp8, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9) = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4, 8), (8, 1)) assert_size_stride(primals_9, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf0) del primals_4 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1) del primals_5 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2) del primals_6 buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32) get_raw_stream(0) triton_poi_fused__softmax_0[grid(64)](buf0, buf1, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1) buf5 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch .float32) triton_poi_fused_clone_1[grid(256)](buf0, buf1, buf3, buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1) buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0) del buf4 triton_poi_fused_clone_2[grid(16, 4)](buf2, buf6, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1) buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0) del buf2 extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7) buf8 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0) del buf3 triton_poi_fused_clone_2[grid(16, 4)](buf7, buf8, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0) del buf7 extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf9) buf10 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) triton_poi_fused_cat_3[grid(128)](primals_1, buf9, buf10, 128, XBLOCK=128, num_warps=4, num_stages=1) buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_9, reinterpret_tensor(buf10, (16, 8), (8, 1), 0), reinterpret_tensor(primals_8, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf11) del primals_9 buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_add_mul_rsub_sigmoid_4[grid(64)](buf11, primals_1, buf9, buf12, 64, XBLOCK=64, num_warps=1, num_stages=1) return buf12, primals_1, buf0, reinterpret_tensor(primals_2, (16, 4), ( 4, 1), 0), buf1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0 ), reinterpret_tensor(buf8, (16, 4), (4, 1), 0 ), buf9, reinterpret_tensor(buf10, (16, 8), (8, 1), 0 ), buf11, primals_8, primals_7, reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0) class GatedMultiHeadAttnNew(nn.Module): def __init__(self, query_dim, key_dim, value_dim, hidden_dim, num_head, dropatt=0.0, act_func='softmax', add_zero_attn=False, pre_lnorm= False, post_lnorm=False): super(GatedMultiHeadAttnNew, self).__init__() assert hidden_dim % num_head == 0 self.query_dim = query_dim self.key_dim = key_dim self.value_dim = value_dim self.hidden_dim = hidden_dim self.num_head = num_head self.dropatt = nn.Dropout(dropatt) head_dim = hidden_dim // num_head self.q_net = nn.Linear(query_dim, hidden_dim, bias=False) self.k_net = nn.Linear(key_dim, hidden_dim, bias=False) self.v_net = nn.Linear(value_dim, hidden_dim, bias=False) self.o_net = nn.Linear(hidden_dim, query_dim, bias=False) self.g_net = nn.Linear(2 * query_dim, query_dim, bias=True) self.scale = 1 / head_dim ** 0.5 self.act_func = act_func self.add_zero_attn = add_zero_attn self.pre_lnorm = pre_lnorm self.post_lnorm = post_lnorm if pre_lnorm: self.q_layer_norm = nn.LayerNorm(query_dim) self.k_layer_norm = nn.LayerNorm(key_dim) self.v_layer_norm = nn.LayerNorm(value_dim) if post_lnorm: self.o_layer_norm = nn.LayerNorm(query_dim) for net in [self.q_net, self.k_net, self.v_net, self.o_net]: nn.init.xavier_uniform_(net.weight, 1.0) if hasattr(net, 'bias') and net.bias is not None: nn.init.constant_(net.bias, 0.0) nn.init.normal_(self.g_net.weight, 0.0, self.scale) if hasattr(self.g_net, 'bias') and self.g_net.bias is not None: nn.init.constant_(self.g_net.bias, 1.0) if self.pre_lnorm: for layer_norm in [self.q_layer_norm, self.k_layer_norm, self. v_layer_norm]: if hasattr(layer_norm, 'weight'): nn.init.normal_(layer_norm.weight, 1.0, self.scale) if hasattr(layer_norm, 'bias') and layer_norm.bias is not None: nn.init.constant_(layer_norm.bias, 0.0) if self.post_lnorm: if hasattr(self.o_layer_norm, 'weight'): nn.init.normal_(self.o_layer_norm.weight, 1.0, self.scale) if hasattr(self.o_layer_norm, 'bias' ) and self.o_layer_norm.bias is not None: nn.init.constant_(self.o_layer_norm.bias, 0.0) def get_output_dim(self): return self.query_dim def forward(self, input_0, input_1, input_2): primals_4 = self.q_net.weight primals_5 = self.k_net.weight primals_6 = self.v_net.weight primals_7 = self.o_net.weight primals_8 = self.g_net.weight primals_9 = self.g_net.bias primals_1 = input_0 primals_2 = input_1 primals_3 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return output[0]	HKUST-KnowComp/BMGF-RoBERTa	GatedMultiHeadAttn	false	8,216	[ "MIT" ]	16	8e9eebd7e9fb6cc2492131fc8eaa5b5b29d999fd	https://github.com/HKUST-KnowComp/BMGF-RoBERTa/tree/8e9eebd7e9fb6cc2492131fc8eaa5b5b29d999fd
PairwiseNetwork	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/u3/cu3qzskzpxv36evsd4vsxrnefkxpkzatid4k3d67rymu37o33lp4.py # Topologically Sorted Source Nodes: [x, x_3], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x => relu # x_3 => relu_3 # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_9,), kwargs = {}) # %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {}) # %le_5 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i1', 4: 'i1', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 8 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_out_ptr1 + (x2), xmask) tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tmp8 = tmp7 + tmp1 tmp9 = triton_helpers.maximum(tmp3, tmp8) tmp10 = tmp9 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) tl.store(in_out_ptr1 + (x2), tmp9, xmask) tl.store(out_ptr1 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yo/cyoa4kdmv3rmfiej7zccwfnrrd34dknemhwizfoi2pxsdy7q7k74.py # Topologically Sorted Source Nodes: [x_1, x_4], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x_1 => relu_1 # x_4 => relu_4 # Graph fragment: # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_11,), kwargs = {}) # %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {}) # %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i1', 4: 'i1', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_out_ptr1 + (x2), xmask) tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tmp8 = tmp7 + tmp1 tmp9 = triton_helpers.maximum(tmp3, tmp8) tmp10 = tmp9 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) tl.store(in_out_ptr1 + (x2), tmp9, xmask) tl.store(out_ptr1 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ev/cev3gklz3a4t4k346obvbqkoxzxqq635evhcwqhobu7vj7aa4qsa.py # Topologically Sorted Source Nodes: [pos_out, neg_out], Original ATen: [aten.sigmoid] # Source node to ATen node mapping: # neg_out => sigmoid_1 # pos_out => sigmoid # Graph fragment: # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_7,), kwargs = {}) # %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_15,), kwargs = {}) triton_poi_fused_sigmoid_2 = async_compile.triton('triton_poi_fused_sigmoid_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_2', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_sigmoid_2(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr0 + (0)) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp5 = tl.load(in_out_ptr1 + (x0), xmask) tmp3 = tmp0 + tmp2 tmp4 = tl.sigmoid(tmp3) tmp6 = tmp5 + tmp2 tmp7 = tl.sigmoid(tmp6) tl.store(in_out_ptr0 + (x0), tmp4, xmask) tl.store(in_out_ptr1 + (x0), tmp7, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10 = args args.clear() assert_size_stride(primals_1, (8, 4), (4, 1)) assert_size_stride(primals_2, (8, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 8), (8, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, ), (1, )) assert_size_stride(primals_8, (1, 4), (4, 1)) assert_size_stride(primals_9, (1, ), (1, )) assert_size_stride(primals_10, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf0) buf8 = empty_strided_cuda((64, 8), (8, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_10, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf8) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 8), (128, 32, 8, 1), 0); del buf0 # reuse buf21 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool) buf9 = reinterpret_tensor(buf8, (4, 4, 4, 8), (128, 32, 8, 1), 0); del buf8 # reuse buf18 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [x, x_3], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, buf9, primals_2, buf21, buf18, 512, grid=grid(512), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 8), (8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf2) buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf9, (64, 8), (8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf10) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf11 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf10 # reuse buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_1, x_4], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_1.run(buf3, buf11, primals_5, buf20, buf17, 256, grid=grid(256), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4) buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf12) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4 # reuse buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf13 = reinterpret_tensor(buf12, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf12 # reuse buf16 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_2, x_5], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_1.run(buf5, buf13, primals_7, buf19, buf16, 256, grid=grid(256), stream=stream0) del primals_7 buf6 = empty_strided_cuda((64, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 1), (1, 4), 0), out=buf6) buf14 = empty_strided_cuda((64, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf13, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 1), (1, 4), 0), out=buf14) buf7 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf6 # reuse buf15 = reinterpret_tensor(buf14, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf14 # reuse # Topologically Sorted Source Nodes: [pos_out, neg_out], Original ATen: [aten.sigmoid] triton_poi_fused_sigmoid_2.run(buf7, buf15, primals_9, 64, grid=grid(64), stream=stream0) del primals_9 return (buf7, buf15, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 8), (8, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1), 0), buf7, reinterpret_tensor(primals_10, (64, 4), (4, 1), 0), reinterpret_tensor(buf9, (64, 8), (8, 1), 0), reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(buf13, (64, 4), (4, 1), 0), buf15, primals_8, buf16, primals_6, buf17, primals_4, buf18, buf19, buf20, buf21, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class PairwiseNetwork(nn.Module): def __init__(self, hidden_size): super().__init__() self.fc1 = nn.Linear(hidden_size, 2 * hidden_size) self.fc2 = nn.Linear(2 * hidden_size, hidden_size) self.fc3 = nn.Linear(hidden_size, hidden_size) self.fc4 = nn.Linear(hidden_size, 1) def forward(self, pos_features, neg_features): x = F.relu(self.fc1(pos_features)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) pos_out = torch.sigmoid(self.fc4(x)) x = F.relu(self.fc1(neg_features)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) neg_out = torch.sigmoid(self.fc4(x)) return pos_out, neg_out def predict(self, test_feat): x = F.relu(self.fc1(test_feat)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) test_out = torch.sigmoid(self.fc4(x)) return test_out def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'hidden_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.nn.functional as F assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 8 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp7 = tl.load(in_out_ptr1 + x2, xmask) tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tmp8 = tmp7 + tmp1 tmp9 = triton_helpers.maximum(tmp3, tmp8) tmp10 = tmp9 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) tl.store(in_out_ptr1 + x2, tmp9, xmask) tl.store(out_ptr1 + x2, tmp10, xmask) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp7 = tl.load(in_out_ptr1 + x2, xmask) tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tmp8 = tmp7 + tmp1 tmp9 = triton_helpers.maximum(tmp3, tmp8) tmp10 = tmp9 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) tl.store(in_out_ptr1 + x2, tmp9, xmask) tl.store(out_ptr1 + x2, tmp10, xmask) @triton.jit def triton_poi_fused_sigmoid_2(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr0 + 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp5 = tl.load(in_out_ptr1 + x0, xmask) tmp3 = tmp0 + tmp2 tmp4 = tl.sigmoid(tmp3) tmp6 = tmp5 + tmp2 tmp7 = tl.sigmoid(tmp6) tl.store(in_out_ptr0 + x0, tmp4, xmask) tl.store(in_out_ptr1 + x0, tmp7, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10) = args args.clear() assert_size_stride(primals_1, (8, 4), (4, 1)) assert_size_stride(primals_2, (8,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 8), (8, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4,), (1,)) assert_size_stride(primals_8, (1, 4), (4, 1)) assert_size_stride(primals_9, (1,), (1,)) assert_size_stride(primals_10, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf0) buf8 = empty_strided_cuda((64, 8), (8, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_10, (64, 4), (4, 1), 0 ), reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf8) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 8), (128, 32, 8, 1), 0) del buf0 buf21 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool) buf9 = reinterpret_tensor(buf8, (4, 4, 4, 8), (128, 32, 8, 1), 0) del buf8 buf18 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(512)](buf1, buf9, primals_2, buf21, buf18, 512, XBLOCK=128, num_warps=4, num_stages=1 ) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 8), (8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf2) buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf9, (64, 8), (8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf10) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf2 buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf11 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf10 buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_1[grid(256)](buf3, buf11, primals_5, buf20, buf17, 256, XBLOCK=256, num_warps=4, num_stages=1 ) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4) buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf12) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf4 buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf13 = reinterpret_tensor(buf12, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf12 buf16 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_1[grid(256)](buf5, buf13, primals_7, buf19, buf16, 256, XBLOCK=256, num_warps=4, num_stages=1 ) del primals_7 buf6 = empty_strided_cuda((64, 1), (1, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 1), (1, 4), 0), out=buf6) buf14 = empty_strided_cuda((64, 1), (1, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf13, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 1), (1, 4), 0), out=buf14) buf7 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0) del buf6 buf15 = reinterpret_tensor(buf14, (4, 4, 4, 1), (16, 4, 1, 1), 0) del buf14 triton_poi_fused_sigmoid_2[grid(64)](buf7, buf15, primals_9, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_9 return (buf7, buf15, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 8), (8, 1), 0), reinterpret_tensor( buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1), 0), buf7, reinterpret_tensor(primals_10, (64, 4), (4, 1), 0), reinterpret_tensor(buf9, (64, 8), (8, 1), 0), reinterpret_tensor( buf11, (64, 4), (4, 1), 0), reinterpret_tensor(buf13, (64, 4), (4, 1), 0), buf15, primals_8, buf16, primals_6, buf17, primals_4, buf18, buf19, buf20, buf21) class PairwiseNetworkNew(nn.Module): def __init__(self, hidden_size): super().__init__() self.fc1 = nn.Linear(hidden_size, 2 * hidden_size) self.fc2 = nn.Linear(2 * hidden_size, hidden_size) self.fc3 = nn.Linear(hidden_size, hidden_size) self.fc4 = nn.Linear(hidden_size, 1) def predict(self, test_feat): x = F.relu(self.fc1(test_feat)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) test_out = torch.sigmoid(self.fc4(x)) return test_out def forward(self, input_0, input_1): primals_1 = self.fc1.weight primals_2 = self.fc1.bias primals_4 = self.fc2.weight primals_5 = self.fc2.bias primals_6 = self.fc3.weight primals_7 = self.fc3.bias primals_8 = self.fc4.weight primals_9 = self.fc4.bias primals_3 = input_0 primals_10 = input_1 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10]) return output[0], output[1]	HardiRathod/table-linker	PairwiseNetwork	false	8,217	[ "MIT" ]	21	5d0542608cdba72b0d7d8afc58c27f27b8a59192	https://github.com/HardiRathod/table-linker/tree/5d0542608cdba72b0d7d8afc58c27f27b8a59192
SANet	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ym/cymg6qdt5jm4qekx3x5d6hyii4szjh2aapwfe5cntjchsk67j6ee.py # Topologically Sorted Source Nodes: [var, mean, sub, normalized_feat], Original ATen: [aten.var, aten.mean, aten.sub, aten.div] # Source node to ATen node mapping: # mean => mean # normalized_feat => div # sub => sub # var => var # Graph fragment: # %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [2]), kwargs = {correction: 1}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [2]), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %expand), kwargs = {}) # %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %expand_1), kwargs = {}) triton_per_fused_div_mean_sub_var_0 = async_compile.triton('triton_per_fused_div_mean_sub_var_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[16, 16], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_mean_sub_var_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_div_mean_sub_var_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 16 rnumel = 16 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + (16x0)), xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, 0) tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK]) tmp6 = tl.where(xmask, tmp4, 0) tmp7 = tl.sum(tmp6, 1)[:, None] tmp8 = tl.full([XBLOCK, 1], 16, tl.int32) tmp9 = tmp8.to(tl.float32) tmp10 = tmp7 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp11 tmp11 tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK]) tmp15 = tl.where(xmask, tmp13, 0) tmp16 = tl.sum(tmp15, 1)[:, None] tmp18 = tl.sum(tmp3, 1)[:, None] tmp19 = 16.0 tmp20 = tmp18 / tmp19 tmp21 = tmp0 - tmp20 tmp22 = 15.0 tmp23 = tmp16 / tmp22 tmp24 = 1e-05 tmp25 = tmp23 + tmp24 tmp26 = libdevice.sqrt(tmp25) tmp27 = tmp21 / tmp26 tl.store(out_ptr2 + (r1 + (16x0)), tmp27, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/7r/c7r2u57hr54idc3of6lw2ouxuoyy44tzonl7cy4k7awnnjece2kt.py # Topologically Sorted Source Nodes: [F], Original ATen: [aten.convolution] # Source node to ATen node mapping: # F => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%div, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/7l/c7lvofwajnoxsuknxf55lbahwz4fgxvepmfscd6nrkqg6j36hgpf.py # Topologically Sorted Source Nodes: [S_1], Original ATen: [aten._softmax] # Source node to ATen node mapping: # S_1 => amax, div_2, exp, sub_2, sum_1 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%bmm, [-1], True), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div_2 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_per_fused__softmax_2 = async_compile.triton('triton_per_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[64, 16], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 64 rnumel = 16 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + (16x0)), xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, float("-inf")) tmp4 = triton_helpers.max2(tmp3, 1)[:, None] tmp5 = tmp0 - tmp4 tmp6 = tl_math.exp(tmp5) tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = tl.where(xmask, tmp7, 0) tmp10 = tl.sum(tmp9, 1)[:, None] tmp11 = tmp6 / tmp10 tl.store(out_ptr2 + (r1 + (16x0)), tmp11, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/hd/chde6iejpwkot32cwlk4sopynkkm2nprqp36dremvkw6p2rbwzzb.py # Topologically Sorted Source Nodes: [O_2, O_3], Original ATen: [aten.convolution, aten.add] # Source node to ATen node mapping: # O_2 => convolution_3 # O_3 => add_2 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_11, %primals_9, %primals_10, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_3, %primals_1), kwargs = {}) triton_poi_fused_add_convolution_3 = async_compile.triton('triton_poi_fused_add_convolution_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (x3), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tl.store(in_out_ptr0 + (x3), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_5, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_6, (4, ), (1, )) assert_size_stride(primals_7, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_8, (4, ), (1, )) assert_size_stride(primals_9, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_10, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [var, mean, sub, normalized_feat], Original ATen: [aten.var, aten.mean, aten.sub, aten.div] stream0 = get_raw_stream(0) triton_per_fused_div_mean_sub_var_0.run(primals_1, buf4, 16, 16, grid=grid(16), stream=stream0) # Topologically Sorted Source Nodes: [F], Original ATen: [aten.convolution] buf5 = extern_kernels.convolution(buf4, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1)) buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [var_1, mean_1, sub_1, normalized_feat_1], Original ATen: [aten.var, aten.mean, aten.sub, aten.div] triton_per_fused_div_mean_sub_var_0.run(primals_4, buf10, 16, 16, grid=grid(16), stream=stream0) # Topologically Sorted Source Nodes: [G], Original ATen: [aten.convolution] buf11 = extern_kernels.convolution(buf10, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf11, (4, 4, 4, 4), (64, 16, 4, 1)) # Topologically Sorted Source Nodes: [H], Original ATen: [aten.convolution] buf12 = extern_kernels.convolution(primals_4, primals_7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1)) buf13 = buf5; del buf5 # reuse # Topologically Sorted Source Nodes: [F], Original ATen: [aten.convolution] triton_poi_fused_convolution_1.run(buf13, primals_3, 256, grid=grid(256), stream=stream0) del primals_3 buf14 = buf11; del buf11 # reuse # Topologically Sorted Source Nodes: [G], Original ATen: [aten.convolution] triton_poi_fused_convolution_1.run(buf14, primals_6, 256, grid=grid(256), stream=stream0) del primals_6 buf15 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [S], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf13, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf14, (4, 4, 16), (64, 16, 1), 0), out=buf15) buf18 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [S_1], Original ATen: [aten._softmax] triton_per_fused__softmax_2.run(buf15, buf18, 64, 16, grid=grid(64), stream=stream0) del buf15 buf19 = buf12; del buf12 # reuse # Topologically Sorted Source Nodes: [H], Original ATen: [aten.convolution] triton_poi_fused_convolution_1.run(buf19, primals_8, 256, grid=grid(256), stream=stream0) del primals_8 buf20 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [O], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf19, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(buf18, (4, 16, 16), (256, 1, 16), 0), out=buf20) # Topologically Sorted Source Nodes: [O_2], Original ATen: [aten.convolution] buf21 = extern_kernels.convolution(reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_9, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf21, (4, 4, 4, 4), (64, 16, 4, 1)) buf22 = buf21; del buf21 # reuse # Topologically Sorted Source Nodes: [O_2, O_3], Original ATen: [aten.convolution, aten.add] triton_poi_fused_add_convolution_3.run(buf22, primals_10, primals_1, 256, grid=grid(256), stream=stream0) del primals_1 del primals_10 return (buf22, primals_2, primals_4, primals_5, primals_7, primals_9, buf4, buf10, buf18, reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf19, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf13, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(buf14, (4, 16, 4), (64, 1, 16), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn def calc_mean_std(feat, eps=1e-05): size = feat.size() assert len(size) == 4 N, C = size[:2] feat_var = feat.view(N, C, -1).var(dim=2) + eps feat_std = feat_var.sqrt().view(N, C, 1, 1) feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1) return feat_mean, feat_std def mean_variance_norm(feat): size = feat.size() mean, std = calc_mean_std(feat) normalized_feat = (feat - mean.expand(size)) / std.expand(size) return normalized_feat class SANet(nn.Module): def __init__(self, in_planes): super(SANet, self).__init__() self.f = nn.Conv2d(in_planes, in_planes, (1, 1)) self.g = nn.Conv2d(in_planes, in_planes, (1, 1)) self.h = nn.Conv2d(in_planes, in_planes, (1, 1)) self.sm = nn.Softmax(dim=-1) self.out_conv = nn.Conv2d(in_planes, in_planes, (1, 1)) def forward(self, content, style): F = self.f(mean_variance_norm(content)) G = self.g(mean_variance_norm(style)) H = self.h(style) b, c, h, w = F.size() F = F.view(b, -1, w * h).permute(0, 2, 1) b, c, h, w = G.size() G = G.view(b, -1, w * h) S = torch.bmm(F, G) S = self.sm(S) b, c, h, w = H.size() H = H.view(b, -1, w * h) O = torch.bmm(H, S.permute(0, 2, 1)) b, c, h, w = content.size() O = O.view(b, c, h, w) O = self.out_conv(O) O += content return O def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_planes': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_per_fused_div_mean_sub_var_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 16 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, 0) tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK]) tmp6 = tl.where(xmask, tmp4, 0) tmp7 = tl.sum(tmp6, 1)[:, None] tmp8 = tl.full([XBLOCK, 1], 16, tl.int32) tmp9 = tmp8.to(tl.float32) tmp10 = tmp7 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp11 * tmp11 tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK]) tmp15 = tl.where(xmask, tmp13, 0) tmp16 = tl.sum(tmp15, 1)[:, None] tmp18 = tl.sum(tmp3, 1)[:, None] tmp19 = 16.0 tmp20 = tmp18 / tmp19 tmp21 = tmp0 - tmp20 tmp22 = 15.0 tmp23 = tmp16 / tmp22 tmp24 = 1e-05 tmp25 = tmp23 + tmp24 tmp26 = libdevice.sqrt(tmp25) tmp27 = tmp21 / tmp26 tl.store(out_ptr2 + (r1 + 16 * x0), tmp27, xmask) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) @triton.jit def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 64 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, float('-inf')) tmp4 = triton_helpers.max2(tmp3, 1)[:, None] tmp5 = tmp0 - tmp4 tmp6 = tl_math.exp(tmp5) tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = tl.where(xmask, tmp7, 0) tmp10 = tl.sum(tmp9, 1)[:, None] tmp11 = tmp6 / tmp10 tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask) @triton.jit def triton_poi_fused_add_convolution_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + x3, xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tl.store(in_out_ptr0 + x3, tmp4, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10) = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_5, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_6, (4,), (1,)) assert_size_stride(primals_7, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_8, (4,), (1,)) assert_size_stride(primals_9, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_10, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_per_fused_div_mean_sub_var_0[grid(16)](primals_1, buf4, 16, 16, XBLOCK=8, num_warps=2, num_stages=1) buf5 = extern_kernels.convolution(buf4, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1)) buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_per_fused_div_mean_sub_var_0[grid(16)](primals_4, buf10, 16, 16, XBLOCK=8, num_warps=2, num_stages=1) buf11 = extern_kernels.convolution(buf10, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf11, (4, 4, 4, 4), (64, 16, 4, 1)) buf12 = extern_kernels.convolution(primals_4, primals_7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1)) buf13 = buf5 del buf5 triton_poi_fused_convolution_1[grid(256)](buf13, primals_3, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 buf14 = buf11 del buf11 triton_poi_fused_convolution_1[grid(256)](buf14, primals_6, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_6 buf15 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf13, (4, 16, 4), (64, 1, 16 ), 0), reinterpret_tensor(buf14, (4, 4, 16), (64, 16, 1), 0), out=buf15) buf18 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32) triton_per_fused__softmax_2[grid(64)](buf15, buf18, 64, 16, XBLOCK= 32, num_warps=4, num_stages=1) del buf15 buf19 = buf12 del buf12 triton_poi_fused_convolution_1[grid(256)](buf19, primals_8, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_8 buf20 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf19, (4, 4, 16), (64, 16, 1 ), 0), reinterpret_tensor(buf18, (4, 16, 16), (256, 1, 16), 0), out=buf20) buf21 = extern_kernels.convolution(reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_9, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf21, (4, 4, 4, 4), (64, 16, 4, 1)) buf22 = buf21 del buf21 triton_poi_fused_add_convolution_3[grid(256)](buf22, primals_10, primals_1, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 del primals_10 return (buf22, primals_2, primals_4, primals_5, primals_7, primals_9, buf4, buf10, buf18, reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf19, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf13, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(buf14, (4, 16, 4), (64, 1, 16), 0)) def calc_mean_std(feat, eps=1e-05): size = feat.size() assert len(size) == 4 N, C = size[:2] feat_var = feat.view(N, C, -1).var(dim=2) + eps feat_std = feat_var.sqrt().view(N, C, 1, 1) feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1) return feat_mean, feat_std def mean_variance_norm(feat): size = feat.size() mean, std = calc_mean_std(feat) normalized_feat = (feat - mean.expand(size)) / std.expand(size) return normalized_feat class SANetNew(nn.Module): def __init__(self, in_planes): super(SANetNew, self).__init__() self.f = nn.Conv2d(in_planes, in_planes, (1, 1)) self.g = nn.Conv2d(in_planes, in_planes, (1, 1)) self.h = nn.Conv2d(in_planes, in_planes, (1, 1)) self.sm = nn.Softmax(dim=-1) self.out_conv = nn.Conv2d(in_planes, in_planes, (1, 1)) def forward(self, input_0, input_1): primals_2 = self.f.weight primals_3 = self.f.bias primals_5 = self.g.weight primals_6 = self.g.bias primals_7 = self.h.weight primals_8 = self.h.bias primals_9 = self.out_conv.weight primals_10 = self.out_conv.bias primals_1 = input_0 primals_4 = input_1 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10]) return output[0]	HalbertCH/IEContraAST	SANet	false	8,218	[ "MIT" ]	39	50ee949f5302a7e4a3cae3226610c03462093c21	https://github.com/HalbertCH/IEContraAST/tree/50ee949f5302a7e4a3cae3226610c03462093c21
residualUnit	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/dl/cdlhimrfynpsyr4bn3ioohi3xavenbjzspef6ijrorhi4nnrrg77.py # Topologically Sorted Source Nodes: [conv2d, elu], Original ATen: [aten.convolution, aten.elu] # Source node to ATen node mapping: # conv2d => convolution # elu => expm1, gt, mul, mul_2, where # Graph fragment: # %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {}) # %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {}) # %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {}) # %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {}) triton_poi_fused_convolution_elu_0 = async_compile.triton('triton_poi_fused_convolution_elu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_elu_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 1.0 tmp6 = tmp2 * tmp5 tmp7 = libdevice.expm1(tmp6) tmp8 = tmp7 * tmp5 tmp9 = tl.where(tmp4, tmp6, tmp8) tl.store(in_out_ptr0 + (x3), tmp2, xmask) tl.store(out_ptr0 + (x3), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/pq/cpqhw7bi4zzzieaos6kzlxy7mmwq5pcns7riradiuhvxvg65qpy6.py # Topologically Sorted Source Nodes: [conv2d_1, add, relu], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # add => add # conv2d_1 => convolution_1 # relu => relu # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %primals_3), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_add_convolution_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_add_convolution_relu_threshold_backward_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (x3), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.full([1], 0, tl.int32) tmp6 = triton_helpers.maximum(tmp5, tmp4) tmp7 = 0.0 tmp8 = tmp6 <= tmp7 tl.store(in_out_ptr0 + (x3), tmp6, xmask) tl.store(out_ptr0 + (x3), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1)) buf1 = buf0; del buf0 # reuse buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d, elu], Original ATen: [aten.convolution, aten.elu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_elu_0.run(buf1, primals_2, buf2, 256, grid=grid(256), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1)) buf4 = buf3; del buf3 # reuse buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, add, relu], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward] triton_poi_fused_add_convolution_relu_threshold_backward_1.run(buf4, primals_5, primals_3, buf5, 256, grid=grid(256), stream=stream0) del primals_5 return (buf4, primals_1, primals_3, primals_4, buf1, buf2, buf5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import numpy as np from torch import nn import torch.nn.functional as F import torch.utils.data import torch.nn.init as init import torch.nn.init class conv23DUnit(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, bias=True, dilation=1, nd=2): super(conv23DUnit, self).__init__() assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}' if nd == 2: self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) elif nd == 3: self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) else: self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) init.xavier_uniform(self.conv.weight, gain=np.sqrt(2.0)) init.constant(self.conv.bias, 0) def forward(self, x): return self.conv(x) class residualUnit(nn.Module): def __init__(self, in_size, out_size, kernel_size=3, stride=1, padding= 1, activation=F.relu, nd=2): super(residualUnit, self).__init__() self.conv1 = conv23DUnit(in_size, out_size, kernel_size, stride, padding, nd=nd) self.conv2 = conv23DUnit(out_size, out_size, kernel_size, stride, padding, nd=nd) def forward(self, x): return F.relu(self.conv2(F.elu(self.conv1(x))) + x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_size': 4, 'out_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import numpy as np from torch import nn import torch.nn.functional as F import torch.utils.data import torch.nn.init as init import torch.nn.init assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_convolution_elu_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 1.0 tmp6 = tmp2 * tmp5 tmp7 = libdevice.expm1(tmp6) tmp8 = tmp7 * tmp5 tmp9 = tl.where(tmp4, tmp6, tmp8) tl.store(in_out_ptr0 + x3, tmp2, xmask) tl.store(out_ptr0 + x3, tmp9, xmask) @triton.jit def triton_poi_fused_add_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + x3, xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp5 = tl.full([1], 0, tl.int32) tmp6 = triton_helpers.maximum(tmp5, tmp4) tmp7 = 0.0 tmp8 = tmp6 <= tmp7 tl.store(in_out_ptr0 + x3, tmp6, xmask) tl.store(out_ptr0 + x3, tmp8, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1)) buf1 = buf0 del buf0 buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_convolution_elu_0[grid(256)](buf1, primals_2, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1)) buf4 = buf3 del buf3 buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)]( buf4, primals_5, primals_3, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 return buf4, primals_1, primals_3, primals_4, buf1, buf2, buf5 class conv23DUnit(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, bias=True, dilation=1, nd=2): super(conv23DUnit, self).__init__() assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}' if nd == 2: self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) elif nd == 3: self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) else: self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, dilation=dilation) init.xavier_uniform(self.conv.weight, gain=np.sqrt(2.0)) init.constant(self.conv.bias, 0) def forward(self, x): return self.conv(x) class residualUnitNew(nn.Module): def __init__(self, in_size, out_size, kernel_size=3, stride=1, padding= 1, activation=F.relu, nd=2): super(residualUnitNew, self).__init__() self.conv1 = conv23DUnit(in_size, out_size, kernel_size, stride, padding, nd=nd) self.conv2 = conv23DUnit(out_size, out_size, kernel_size, stride, padding, nd=nd) def forward(self, input_0): primals_1 = self.conv1.conv.weight primals_2 = self.conv1.conv.bias primals_4 = self.conv2.conv.weight primals_5 = self.conv2.conv.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	ForrestPi/Unsupervised-Defect-Segmentation	residualUnit	false	8,219	[ "MIT" ]	17	e366ac7c757bb1b45f38ebbc502dfee7ccb72398	https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398
Encoder5	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/cn/ccn3wvv7hvajmiytvd4cxr5ymsooreymqjbzit5wvlzzy3qc5dck.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4096], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 12 xnumel = 4096 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1) x2 = xindex y3 = yindex y0 = yindex % 3 y1 = (yindex // 3) tmp0 = tl.load(in_ptr0 + (x2 + (4096y3)), ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (3x2) + (12288y1)), tmp0, ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/r4/cr42tvpznzo4jto3kd2nndggpyldbuuskklxhoqx7cipetnq44st.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 192 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 3 y1 = (yindex // 3) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (3x2) + (27y1)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/5o/c5obv3n3z3nigs3ufrll6zerfbkjuaxny47v7qwuboce4vc6t226.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 4096 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = (yindex // 64) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (64x2) + (576y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/z6/cz6baxmxj4vnpfhcx7vlebdugozcmmuhwadqps4sypq4ddnvvvvh.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_3 = async_compile.triton('triton_poi_fused_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 8192 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = (yindex // 64) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (64x2) + (576y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/db/cdb2sjzbytfspypoa6pp2oxhbkctfipulwxnhjr5sqo5qqjfizvu.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_4 = async_compile.triton('triton_poi_fused_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16384 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = (yindex // 128) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (128x2) + (1152y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gd/cgdnlfwbslhdmfriewbfv2omlx6rnligyuanv24xvvvpftjpu6rp.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_5 = async_compile.triton('triton_poi_fused_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 32768 xnumel = 9 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = (yindex // 128) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (128x2) + (1152y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/z4/cz4wj3gfuvykrcxztkqxjno5cjlbaxcfmlbyfebxkzznk7viznxb.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_6 = async_compile.triton('triton_poi_fused_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 65536 xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = (yindex // 256) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (256x2) + (2304y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/i6/ci6263jjpb6aa6asa26vhonj4fac7oamux3ss2n4bs2h2mrc3dzu.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_7 = async_compile.triton('triton_poi_fused_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 131072 xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = (yindex // 256) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (256x2) + (2304y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/dm/cdm3usnxijp2eimatmjyjzygbcn4zxnk7cbq66kqjwpny2xcv5ml.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_8 = async_compile.triton('triton_poi_fused_8', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 262144 xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 512 y1 = (yindex // 512) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (512x2) + (4608y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/2a/c2amebj2jxwmfq6ph5km4upiaatf26jx43aqgfqk4qakgww3taqu.py # Topologically Sorted Source Nodes: [y, pad], Original ATen: [aten.convolution, aten.reflection_pad2d] # Source node to ATen node mapping: # pad => _unsafe_index, _unsafe_index_1 # y => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_9 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_9', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 52272 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 3 x1 = (xindex // 3) % 66 x2 = (xindex // 198) % 66 x3 = (xindex // 13068) x4 = xindex tmp0 = tl.load(in_ptr0 + (12285 + x0 + ((-192)(tl_math.abs((-63) + (tl_math.abs((-1) + x2))))) + ((-3)(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (12288x3)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x4), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yu/cyubbusnkvrh3gc22tgbzfqvvaycnhu6j46fvxua5xvl2gj6r75i.py # Topologically Sorted Source Nodes: [conv2d_1, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # pad_1 => _unsafe_index_2, _unsafe_index_3 # y_1 => relu # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_10 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_10', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2097152], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_10(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = (xindex // 64) % 66 x2 = (xindex // 4224) % 66 x3 = (xindex // 278784) x4 = xindex tmp0 = tl.load(in_ptr0 + (262080 + x0 + ((-4096)(tl_math.abs((-63) + (tl_math.abs((-1) + x2))))) + ((-64)(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x4), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/32/c32zfxwddj5rbagitaqxrr4nwlcp6vmbkcthucxbr5tzewg74f56.py # Topologically Sorted Source Nodes: [conv2d_2, y_2], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_2 => convolution_2 # y_2 => relu_1 # Graph fragment: # %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_3, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {}) triton_poi_fused_convolution_relu_11 = async_compile.triton('triton_poi_fused_convolution_relu_11', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_11', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1048576 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/me/cmefvy4vgll6tsiimcw7jctm7q4gn4bda3ikk3o3hvigob6m42nn.py # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_3 => getitem_1 # Graph fragment: # %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_12 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_12', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i8', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_12(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 64 x1 = (xindex // 64) % 32 x2 = (xindex // 2048) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (128x1) + (8192x2)), None) tmp1 = tl.load(in_ptr0 + (64 + x0 + (128x1) + (8192x2)), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + (128x1) + (8192x2)), None) tmp12 = tl.load(in_ptr0 + (4160 + x0 + (128x1) + (8192x2)), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) tmp16 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/7k/c7kummukc5hs2yqyykfpqxn3v2tpp5a6tur4zqitp2ssopx6xhl4.py # Topologically Sorted Source Nodes: [y_3, pad_2], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] # Source node to ATen node mapping: # pad_2 => _unsafe_index_4, _unsafe_index_5 # y_3 => _low_memory_max_pool2d_with_offsets # Graph fragment: # %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {}) # %_unsafe_index_4 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem, [None, None, %sub_9, None]), kwargs = {}) # %_unsafe_index_5 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_4, [None, None, None, %sub_9]), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_13 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_13', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_13(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = (xindex // 64) % 34 x2 = (xindex // 2176) % 34 x3 = (xindex // 73984) x4 = xindex tmp0 = tl.load(in_ptr0 + (257920 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp1 = tl.load(in_ptr0 + (257984 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp3 = tl.load(in_ptr0 + (262016 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp5 = tl.load(in_ptr0 + (262080 + x0 + ((-8192)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (262144x3)), xmask) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + (x4), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/rb/crb2fpebi6bpsgndwylsmumszhz2wk6pw4xmuaht45rsxlnwwsgv.py # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # pad_3 => _unsafe_index_6, _unsafe_index_7 # y_4 => relu_2 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %_unsafe_index_6 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_2, [None, None, %sub_9, None]), kwargs = {}) # %_unsafe_index_7 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_6, [None, None, None, %sub_9]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_14 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_14', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 591872 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = (xindex // 128) % 34 x2 = (xindex // 4352) % 34 x3 = (xindex // 147968) x4 = xindex tmp0 = tl.load(in_ptr0 + (130944 + x0 + ((-4096)(tl_math.abs((-31) + (tl_math.abs((-1) + x2))))) + ((-128)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x4), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/aw/cawlhvv6vlduhfrhnyesdb52hfak2pd5v5nzmfvmj5ecurk62qm6.py # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # y_5 => relu_3 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_7, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) triton_poi_fused_convolution_relu_15 = async_compile.triton('triton_poi_fused_convolution_relu_15', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_15', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_15(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 524288 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/kr/ckrsqbuwskp3c346lztwf3w2rhcmut37yapmgiqkwixo4jn57i2n.py # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_6 => getitem_3 # Graph fragment: # %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_16 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_16', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i8', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_16(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = (xindex // 128) % 16 x2 = (xindex // 2048) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (256x1) + (8192x2)), None) tmp1 = tl.load(in_ptr0 + (128 + x0 + (256x1) + (8192x2)), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + (256x1) + (8192x2)), None) tmp12 = tl.load(in_ptr0 + (4224 + x0 + (256x1) + (8192x2)), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) tmp16 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/r3/cr3vsffaueoupcmgr4yjybyd6ldptm3kx2v5gu7sh5kvzsxhwl6r.py # Topologically Sorted Source Nodes: [y_6, pad_4], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] # Source node to ATen node mapping: # pad_4 => _unsafe_index_8, _unsafe_index_9 # y_6 => _low_memory_max_pool2d_with_offsets_1 # Graph fragment: # %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_3, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {}) # %_unsafe_index_8 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem_2, [None, None, %sub_17, None]), kwargs = {}) # %_unsafe_index_9 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_8, [None, None, None, %sub_17]), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_17 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_17', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_17(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 165888 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = (xindex // 128) % 18 x2 = (xindex // 2304) % 18 x3 = (xindex // 41472) x4 = xindex tmp0 = tl.load(in_ptr0 + (126720 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp1 = tl.load(in_ptr0 + (126848 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp3 = tl.load(in_ptr0 + (130816 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp5 = tl.load(in_ptr0 + (130944 + x0 + ((-8192)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (131072x3)), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + (x4), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/zx/czxhsgzl6mw3duolyzjvylfui6a6icql4mgjw35opc26xmuv5wy2.py # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_5], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_5 => convolution_5 # pad_5 => _unsafe_index_10, _unsafe_index_11 # y_7 => relu_4 # Graph fragment: # %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {}) # %_unsafe_index_10 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_4, [None, None, %sub_17, None]), kwargs = {}) # %_unsafe_index_11 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_10, [None, None, None, %sub_17]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_18 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_18', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 331776 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = (xindex // 256) % 18 x2 = (xindex // 4608) % 18 x3 = (xindex // 82944) x4 = xindex tmp0 = tl.load(in_ptr0 + (65280 + x0 + ((-4096)(tl_math.abs((-15) + (tl_math.abs((-1) + x2))))) + ((-256)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x4), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/f4/cf4msozczcrrdfmz3itlqb4dr2tqpgz63g5vpm2g3qzg3vdgbny7.py # Topologically Sorted Source Nodes: [conv2d_8, y_10], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_8 => convolution_8 # y_10 => relu_7 # Graph fragment: # %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {}) triton_poi_fused_convolution_relu_19 = async_compile.triton('triton_poi_fused_convolution_relu_19', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_19', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_19(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ao/caoclaf3xlhqasavmtbsc2ilvxx4vin5htcq3jaqmxsff7uaznpg.py # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_11 => getitem_5 # Graph fragment: # %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_20 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_20', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i8', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_20(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 65536 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = (xindex // 256) % 8 x2 = (xindex // 2048) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (512x1) + (8192x2)), None) tmp1 = tl.load(in_ptr0 + (256 + x0 + (512x1) + (8192x2)), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + (512x1) + (8192x2)), None) tmp12 = tl.load(in_ptr0 + (4352 + x0 + (512x1) + (8192x2)), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) tmp16 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ag/cagzzyud754acypowg7u2qmy77deimnbhdpubksmf4bbgahcddho.py # Topologically Sorted Source Nodes: [y_11, pad_8], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] # Source node to ATen node mapping: # pad_8 => _unsafe_index_16, _unsafe_index_17 # y_11 => _low_memory_max_pool2d_with_offsets_2 # Graph fragment: # %_low_memory_max_pool2d_with_offsets_2 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_7, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {}) # %_unsafe_index_16 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem_4, [None, None, %sub_33, None]), kwargs = {}) # %_unsafe_index_17 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_16, [None, None, None, %sub_33]), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_21 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_21', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_21(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 102400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = (xindex // 256) % 10 x2 = (xindex // 2560) % 10 x3 = (xindex // 25600) x4 = xindex tmp0 = tl.load(in_ptr0 + (60928 + x0 + ((-8192)(tl_math.abs((-7) + (tl_math.abs((-1) + x2))))) + ((-512)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp1 = tl.load(in_ptr0 + (61184 + x0 + ((-8192)(tl_math.abs((-7) + (tl_math.abs((-1) + x2))))) + ((-512)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp3 = tl.load(in_ptr0 + (65024 + x0 + ((-8192)(tl_math.abs((-7) + (tl_math.abs((-1) + x2))))) + ((-512)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp5 = tl.load(in_ptr0 + (65280 + x0 + ((-8192)(tl_math.abs((-7) + (tl_math.abs((-1) + x2))))) + ((-512)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (65536x3)), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + (x4), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ac/cacv4msohyvk7qpltvxxp4xtumao7huki6ruk7gvos75swiunqnx.py # Topologically Sorted Source Nodes: [conv2d_9, y_12, pad_9], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_9 => convolution_9 # pad_9 => _unsafe_index_18, _unsafe_index_19 # y_12 => relu_8 # Graph fragment: # %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_17, %primals_20, %primals_21, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {}) # %_unsafe_index_18 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_8, [None, None, %sub_33, None]), kwargs = {}) # %_unsafe_index_19 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_18, [None, None, None, %sub_33]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_22 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_22', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_22', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 204800 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 512 x1 = (xindex // 512) % 10 x2 = (xindex // 5120) % 10 x3 = (xindex // 51200) x4 = xindex tmp0 = tl.load(in_ptr0 + (32256 + x0 + ((-4096)(tl_math.abs((-7) + (tl_math.abs((-1) + x2))))) + ((-512)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (32768x3)), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x4), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/dg/cdga65rflxewai4xjavclzfuxf7apl5u3e7s4ocbm4di6btnokkl.py # Topologically Sorted Source Nodes: [conv2d_12, y_15], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_12 => convolution_12 # y_15 => relu_11 # Graph fragment: # %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_23, %primals_26, %primals_27, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_12,), kwargs = {}) triton_poi_fused_convolution_relu_23 = async_compile.triton('triton_poi_fused_convolution_relu_23', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_23', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_23(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 512 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/fe/cfe5mgoiz6aowu6nc4h5sluw2dnhxqqtoif33d3hmstkioell62g.py # Topologically Sorted Source Nodes: [y_16], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_16 => getitem_7 # Graph fragment: # %getitem_7 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_3, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_24 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_24', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i8', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_24', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_24(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32768 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 512 x1 = (xindex // 512) % 4 x2 = (xindex // 2048) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (1024x1) + (8192x2)), None) tmp1 = tl.load(in_ptr0 + (512 + x0 + (1024x1) + (8192x2)), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + (1024x1) + (8192x2)), None) tmp12 = tl.load(in_ptr0 + (4608 + x0 + (1024x1) + (8192x2)), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) tmp16 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/kx/ckxq3n35ouo5yibwj2ekl6xm7hkpivgh2qgojzn4l5ohp5v3kgph.py # Topologically Sorted Source Nodes: [y_16, pad_12], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] # Source node to ATen node mapping: # pad_12 => _unsafe_index_24, _unsafe_index_25 # y_16 => _low_memory_max_pool2d_with_offsets_3 # Graph fragment: # %_low_memory_max_pool2d_with_offsets_3 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_11, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {}) # %_unsafe_index_24 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem_6, [None, None, %sub_49, None]), kwargs = {}) # %_unsafe_index_25 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_24, [None, None, None, %sub_49]), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_25 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_25', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_25', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_25(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 73728 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 512 x1 = (xindex // 512) % 6 x2 = (xindex // 3072) % 6 x3 = (xindex // 18432) x4 = xindex tmp0 = tl.load(in_ptr0 + (27648 + x0 + ((-8192)(tl_math.abs((-3) + (tl_math.abs((-1) + x2))))) + ((-1024)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (32768x3)), None) tmp1 = tl.load(in_ptr0 + (28160 + x0 + ((-8192)(tl_math.abs((-3) + (tl_math.abs((-1) + x2))))) + ((-1024)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (32768x3)), None) tmp3 = tl.load(in_ptr0 + (31744 + x0 + ((-8192)(tl_math.abs((-3) + (tl_math.abs((-1) + x2))))) + ((-1024)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (32768x3)), None) tmp5 = tl.load(in_ptr0 + (32256 + x0 + ((-8192)(tl_math.abs((-3) + (tl_math.abs((-1) + x2))))) + ((-1024)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (32768x3)), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + (x4), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ih/cihfgd7ufuqooepj47jyqvucjd7fr5ykglali6knbe22quqgmopl.py # Topologically Sorted Source Nodes: [conv2d_13, y_17], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_13 => convolution_13 # y_17 => relu_12 # Graph fragment: # %convolution_13 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_25, %primals_28, %primals_29, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_12 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_13,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_12, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_26 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_26', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048, 16], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i1', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_26', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_26(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 2048 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 512 y1 = (yindex // 512) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (512x2) + (8192y1)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (y0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1, 1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2 + (16y3)), tmp4, xmask) tl.store(out_ptr1 + (y0 + (512x2) + (8192y1)), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/mw/cmwo64mtfpi35kk3e2sl7tumbh5td4udhw3sy6ysme6tn6pzz2be.py # Topologically Sorted Source Nodes: [conv2d_11, y_14], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_11 => convolution_11 # y_14 => relu_10 # Graph fragment: # %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_21, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {}) # %le_38 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_10, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_27 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_27', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_27', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_27(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 512 tmp0 = tl.load(in_ptr0 + (x2), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/bx/cbxn4atvl3zzuvm7thqnaes75eie2ttjvdv35t23hn3zajiae6oa.py # Topologically Sorted Source Nodes: [conv2d_7, y_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_7 => convolution_7 # y_9 => relu_6 # Graph fragment: # %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {}) # %le_114 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_28 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_28', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_28', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_28(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_ptr0 + (x2), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/qf/cqff2cotwyxmp4h3s3ltetbvbhpvawjvxlfxyes6xng7urntwaet.py # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # y_4 => relu_2 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %le_190 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_29 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_29', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_29', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_29(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 524288 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_ptr0 + (x2), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gm/cgmhg6hwnxhhz25vq5geasfdk7u5o4hpglisc7hg65yhkjzuuc7a.py # Topologically Sorted Source Nodes: [conv2d_1, y_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # y_1 => relu # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %le_228 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_30 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_30', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_30', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_30(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1048576 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_ptr0 + (x2), None) tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29 = args args.clear() assert_size_stride(primals_1, (3, 3, 1, 1), (3, 1, 1, 1)) assert_size_stride(primals_2, (3, ), (1, )) assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1)) assert_size_stride(primals_4, (64, 3, 3, 3), (27, 9, 3, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64, ), (1, )) assert_size_stride(primals_8, (128, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_9, (128, ), (1, )) assert_size_stride(primals_10, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_11, (128, ), (1, )) assert_size_stride(primals_12, (256, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_13, (256, ), (1, )) assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_15, (256, ), (1, )) assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_17, (256, ), (1, )) assert_size_stride(primals_18, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_19, (256, ), (1, )) assert_size_stride(primals_20, (512, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_21, (512, ), (1, )) assert_size_stride(primals_22, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_23, (512, ), (1, )) assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_25, (512, ), (1, )) assert_size_stride(primals_26, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_27, (512, ), (1, )) assert_size_stride(primals_28, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_29, (512, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] stream0 = get_raw_stream(0) triton_poi_fused_0.run(primals_3, buf0, 12, 4096, grid=grid(12, 4096), stream=stream0) del primals_3 buf1 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_1.run(primals_4, buf1, 192, 9, grid=grid(192, 9), stream=stream0) del primals_4 buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_6, buf2, 4096, 9, grid=grid(4096, 9), stream=stream0) del primals_6 buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_3.run(primals_8, buf3, 8192, 9, grid=grid(8192, 9), stream=stream0) del primals_8 buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_4.run(primals_10, buf4, 16384, 9, grid=grid(16384, 9), stream=stream0) del primals_10 buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_5.run(primals_12, buf5, 32768, 9, grid=grid(32768, 9), stream=stream0) del primals_12 buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_6.run(primals_14, buf6, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_14 buf7 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_6.run(primals_16, buf7, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_16 buf8 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_6.run(primals_18, buf8, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_18 buf9 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_7.run(primals_20, buf9, 131072, 9, grid=grid(131072, 9), stream=stream0) del primals_20 buf10 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_8.run(primals_22, buf10, 262144, 9, grid=grid(262144, 9), stream=stream0) del primals_22 buf11 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_8.run(primals_24, buf11, 262144, 9, grid=grid(262144, 9), stream=stream0) del primals_24 buf12 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_8.run(primals_26, buf12, 262144, 9, grid=grid(262144, 9), stream=stream0) del primals_26 buf13 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_8.run(primals_28, buf13, 262144, 9, grid=grid(262144, 9), stream=stream0) del primals_28 # Topologically Sorted Source Nodes: [y], Original ATen: [aten.convolution] buf14 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 3, 64, 64), (12288, 1, 192, 3)) buf15 = empty_strided_cuda((4, 3, 66, 66), (13068, 1, 198, 3), torch.float32) # Topologically Sorted Source Nodes: [y, pad], Original ATen: [aten.convolution, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_9.run(buf14, primals_2, buf15, 52272, grid=grid(52272), stream=stream0) del buf14 del primals_2 # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf16 = extern_kernels.convolution(buf15, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf17 = empty_strided_cuda((4, 64, 66, 66), (278784, 1, 4224, 64), torch.float32) # Topologically Sorted Source Nodes: [conv2d_1, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_10.run(buf16, primals_5, buf17, 1115136, grid=grid(1115136), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf18 = extern_kernels.convolution(buf17, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf19 = buf18; del buf18 # reuse # Topologically Sorted Source Nodes: [conv2d_2, y_2], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_11.run(buf19, primals_7, 1048576, grid=grid(1048576), stream=stream0) del primals_7 buf20 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64), torch.int8) # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_12.run(buf19, buf20, 262144, grid=grid(262144), stream=stream0) buf21 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.float32) # Topologically Sorted Source Nodes: [y_3, pad_2], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_13.run(buf19, buf21, 295936, grid=grid(295936), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf22 = extern_kernels.convolution(buf21, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf22, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf23 = empty_strided_cuda((4, 128, 34, 34), (147968, 1, 4352, 128), torch.float32) # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_14.run(buf22, primals_9, buf23, 591872, grid=grid(591872), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution] buf24 = extern_kernels.convolution(buf23, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf25 = buf24; del buf24 # reuse # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_15.run(buf25, primals_11, 524288, grid=grid(524288), stream=stream0) del primals_11 buf26 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128), torch.int8) # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_16.run(buf25, buf26, 131072, grid=grid(131072), stream=stream0) buf27 = empty_strided_cuda((4, 128, 18, 18), (41472, 1, 2304, 128), torch.float32) # Topologically Sorted Source Nodes: [y_6, pad_4], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_17.run(buf25, buf27, 165888, grid=grid(165888), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution] buf28 = extern_kernels.convolution(buf27, buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf28, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf29 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_5], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_18.run(buf28, primals_13, buf29, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution] buf30 = extern_kernels.convolution(buf29, buf6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf31 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) # Topologically Sorted Source Nodes: [conv2d_6, y_8, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_18.run(buf30, primals_15, buf31, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution] buf32 = extern_kernels.convolution(buf31, buf7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf32, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf33 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) # Topologically Sorted Source Nodes: [conv2d_7, y_9, pad_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_18.run(buf32, primals_17, buf33, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution] buf34 = extern_kernels.convolution(buf33, buf8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf34, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf35 = buf34; del buf34 # reuse # Topologically Sorted Source Nodes: [conv2d_8, y_10], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_19.run(buf35, primals_19, 262144, grid=grid(262144), stream=stream0) del primals_19 buf36 = empty_strided_cuda((4, 256, 8, 8), (16384, 1, 2048, 256), torch.int8) # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_20.run(buf35, buf36, 65536, grid=grid(65536), stream=stream0) buf37 = empty_strided_cuda((4, 256, 10, 10), (25600, 1, 2560, 256), torch.float32) # Topologically Sorted Source Nodes: [y_11, pad_8], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_21.run(buf35, buf37, 102400, grid=grid(102400), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution] buf38 = extern_kernels.convolution(buf37, buf9, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf38, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf39 = empty_strided_cuda((4, 512, 10, 10), (51200, 1, 5120, 512), torch.float32) # Topologically Sorted Source Nodes: [conv2d_9, y_12, pad_9], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_22.run(buf38, primals_21, buf39, 204800, grid=grid(204800), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution] buf40 = extern_kernels.convolution(buf39, buf10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf40, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf41 = empty_strided_cuda((4, 512, 10, 10), (51200, 1, 5120, 512), torch.float32) # Topologically Sorted Source Nodes: [conv2d_10, y_13, pad_10], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_22.run(buf40, primals_23, buf41, 204800, grid=grid(204800), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution] buf42 = extern_kernels.convolution(buf41, buf11, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf42, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf43 = empty_strided_cuda((4, 512, 10, 10), (51200, 1, 5120, 512), torch.float32) # Topologically Sorted Source Nodes: [conv2d_11, y_14, pad_11], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_22.run(buf42, primals_25, buf43, 204800, grid=grid(204800), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution] buf44 = extern_kernels.convolution(buf43, buf12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf44, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf45 = buf44; del buf44 # reuse # Topologically Sorted Source Nodes: [conv2d_12, y_15], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_23.run(buf45, primals_27, 131072, grid=grid(131072), stream=stream0) del primals_27 buf46 = empty_strided_cuda((4, 512, 4, 4), (8192, 1, 2048, 512), torch.int8) # Topologically Sorted Source Nodes: [y_16], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_24.run(buf45, buf46, 32768, grid=grid(32768), stream=stream0) buf47 = empty_strided_cuda((4, 512, 6, 6), (18432, 1, 3072, 512), torch.float32) # Topologically Sorted Source Nodes: [y_16, pad_12], Original ATen: [aten.max_pool2d_with_indices, aten.reflection_pad2d] triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_25.run(buf45, buf47, 73728, grid=grid(73728), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_13], Original ATen: [aten.convolution] buf48 = extern_kernels.convolution(buf47, buf13, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf48, (4, 512, 4, 4), (8192, 1, 2048, 512)) buf49 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.float32) buf50 = empty_strided_cuda((4, 512, 4, 4), (8192, 1, 2048, 512), torch.bool) # Topologically Sorted Source Nodes: [conv2d_13, y_17], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_26.run(buf48, primals_29, buf49, buf50, 2048, 16, grid=grid(2048, 16), stream=stream0) del buf48 del primals_29 buf51 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512), torch.bool) # Topologically Sorted Source Nodes: [conv2d_11, y_14], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_27.run(buf42, primals_25, buf51, 131072, grid=grid(131072), stream=stream0) del buf42 del primals_25 buf52 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512), torch.bool) # Topologically Sorted Source Nodes: [conv2d_10, y_13], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_27.run(buf40, primals_23, buf52, 131072, grid=grid(131072), stream=stream0) del buf40 del primals_23 buf53 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512), torch.bool) # Topologically Sorted Source Nodes: [conv2d_9, y_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_27.run(buf38, primals_21, buf53, 131072, grid=grid(131072), stream=stream0) del buf38 del primals_21 buf54 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) # Topologically Sorted Source Nodes: [conv2d_7, y_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_28.run(buf32, primals_17, buf54, 262144, grid=grid(262144), stream=stream0) del buf32 del primals_17 buf55 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) # Topologically Sorted Source Nodes: [conv2d_6, y_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_28.run(buf30, primals_15, buf55, 262144, grid=grid(262144), stream=stream0) del buf30 del primals_15 buf56 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_28.run(buf28, primals_13, buf56, 262144, grid=grid(262144), stream=stream0) del buf28 del primals_13 buf57 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128), torch.bool) # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_29.run(buf22, primals_9, buf57, 524288, grid=grid(524288), stream=stream0) del buf22 del primals_9 buf58 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, y_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_30.run(buf16, primals_5, buf58, 1048576, grid=grid(1048576), stream=stream0) del buf16 del primals_5 return (buf49, primals_1, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, buf11, buf12, buf13, buf15, buf17, buf19, buf20, buf21, buf23, buf25, buf26, buf27, buf29, buf31, buf33, buf35, buf36, buf37, buf39, buf41, buf43, buf45, buf46, buf47, buf50, buf51, buf52, buf53, buf54, buf55, buf56, buf57, buf58, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((3, 3, 1, 1), (3, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((256, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_19 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_20 = rand_strided((512, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_21 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) primals_22 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_23 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) primals_24 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_25 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) primals_26 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_27 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) primals_28 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_29 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import numpy as np import torch.nn as nn class Encoder5(nn.Module): def __init__(self, model=None, fixed=False): super(Encoder5, self).__init__() self.fixed = fixed self.conv0 = nn.Conv2d(3, 3, 1, 1, 0) self.conv0.weight = nn.Parameter(torch.from_numpy(np.array([[[[0]], [[0]], [[255]]], [[[0]], [[255]], [[0]]], [[[255]], [[0]], [[0] ]]])).float()) self.conv0.bias = nn.Parameter(torch.from_numpy(np.array([-103.939, -116.779, -123.68])).float()) self.conv11 = nn.Conv2d(3, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv21 = nn.Conv2d(64, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv31 = nn.Conv2d(128, 256, 3, 1, 0) self.conv32 = nn.Conv2d(256, 256, 3, 1, 0) self.conv33 = nn.Conv2d(256, 256, 3, 1, 0) self.conv34 = nn.Conv2d(256, 256, 3, 1, 0) self.conv41 = nn.Conv2d(256, 512, 3, 1, 0) self.conv42 = nn.Conv2d(512, 512, 3, 1, 0) self.conv43 = nn.Conv2d(512, 512, 3, 1, 0) self.conv44 = nn.Conv2d(512, 512, 3, 1, 0) self.conv51 = nn.Conv2d(512, 512, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.pool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=False) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input): y = self.conv0(input) y = self.relu(self.conv11(self.pad(y))) y = self.relu(self.conv12(self.pad(y))) y = self.pool(y) y = self.relu(self.conv21(self.pad(y))) y = self.relu(self.conv22(self.pad(y))) y = self.pool(y) y = self.relu(self.conv31(self.pad(y))) y = self.relu(self.conv32(self.pad(y))) y = self.relu(self.conv33(self.pad(y))) y = self.relu(self.conv34(self.pad(y))) y = self.pool(y) y = self.relu(self.conv41(self.pad(y))) y = self.relu(self.conv42(self.pad(y))) y = self.relu(self.conv43(self.pad(y))) y = self.relu(self.conv44(self.pad(y))) y = self.pool(y) y = self.relu(self.conv51(self.pad(y))) return y def get_inputs(): return [torch.rand([4, 3, 64, 64])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import numpy as np import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 12 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, YBLOCK], True, tl.int1) x2 = xindex y3 = yindex y0 = yindex % 3 y1 = yindex // 3 tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy= 'evict_last') tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 192 xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 3 y1 = yindex // 3 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy= 'evict_last') tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = yindex // 64 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = yindex // 64 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = yindex // 128 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 128 y1 = yindex // 128 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = yindex // 256 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = yindex // 256 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 512 y1 = yindex // 512 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 52272 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 3 x1 = xindex // 3 % 66 x2 = xindex // 198 % 66 x3 = xindex // 13068 x4 = xindex tmp0 = tl.load(in_ptr0 + (12285 + x0 + -192 * tl_math.abs(-63 + tl_math .abs(-1 + x2)) + -3 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 12288 * x3), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x4, tmp2, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_10(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = xindex // 64 % 66 x2 = xindex // 4224 % 66 x3 = xindex // 278784 x4 = xindex tmp0 = tl.load(in_ptr0 + (262080 + x0 + -4096 * tl_math.abs(-63 + tl_math.abs(-1 + x2)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1 )) + 262144 * x3), xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x4, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_12(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 64 x1 = xindex // 64 % 32 x2 = xindex // 2048 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 8192 * x2), None) tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 8192 * x2), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + 128 * x1 + 8192 * x2), None) tmp12 = tl.load(in_ptr0 + (4160 + x0 + 128 * x1 + 8192 * x2), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_13(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 295936 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = xindex // 64 % 34 x2 = xindex // 2176 % 34 x3 = xindex // 73984 x4 = xindex tmp0 = tl.load(in_ptr0 + (257920 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp1 = tl.load(in_ptr0 + (257984 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp3 = tl.load(in_ptr0 + (262016 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp5 = tl.load(in_ptr0 + (262080 + x0 + -8192 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 262144 * x3), xmask) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + x4, tmp6, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = xindex // 128 % 34 x2 = xindex // 4352 % 34 x3 = xindex // 147968 x4 = xindex tmp0 = tl.load(in_ptr0 + (130944 + x0 + -4096 * tl_math.abs(-31 + tl_math.abs(-1 + x2)) + -128 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x4, tmp4, None) @triton.jit def triton_poi_fused_convolution_relu_15(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_16(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = xindex // 128 % 16 x2 = xindex // 2048 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 8192 * x2), None) tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 8192 * x2), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + 256 * x1 + 8192 * x2), None) tmp12 = tl.load(in_ptr0 + (4224 + x0 + 256 * x1 + 8192 * x2), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_17(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 128 x1 = xindex // 128 % 18 x2 = xindex // 2304 % 18 x3 = xindex // 41472 x4 = xindex tmp0 = tl.load(in_ptr0 + (126720 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp1 = tl.load(in_ptr0 + (126848 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp3 = tl.load(in_ptr0 + (130816 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp5 = tl.load(in_ptr0 + (130944 + x0 + -8192 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 131072 * x3), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + x4, tmp6, None) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = xindex // 256 % 18 x2 = xindex // 4608 % 18 x3 = xindex // 82944 x4 = xindex tmp0 = tl.load(in_ptr0 + (65280 + x0 + -4096 * tl_math.abs(-15 + tl_math.abs(-1 + x2)) + -256 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x4, tmp4, None) @triton.jit def triton_poi_fused_convolution_relu_19(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_20(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = xindex // 256 % 8 x2 = xindex // 2048 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 512 * x1 + 8192 * x2), None) tmp1 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 8192 * x2), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + 512 * x1 + 8192 * x2), None) tmp12 = tl.load(in_ptr0 + (4352 + x0 + 512 * x1 + 8192 * x2), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_21(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 256 x1 = xindex // 256 % 10 x2 = xindex // 2560 % 10 x3 = xindex // 25600 x4 = xindex tmp0 = tl.load(in_ptr0 + (60928 + x0 + -8192 * tl_math.abs(-7 + tl_math .abs(-1 + x2)) + -512 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp1 = tl.load(in_ptr0 + (61184 + x0 + -8192 * tl_math.abs(-7 + tl_math .abs(-1 + x2)) + -512 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp3 = tl.load(in_ptr0 + (65024 + x0 + -8192 * tl_math.abs(-7 + tl_math .abs(-1 + x2)) + -512 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp5 = tl.load(in_ptr0 + (65280 + x0 + -8192 * tl_math.abs(-7 + tl_math .abs(-1 + x2)) + -512 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 65536 * x3), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + x4, tmp6, None) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 512 x1 = xindex // 512 % 10 x2 = xindex // 5120 % 10 x3 = xindex // 51200 x4 = xindex tmp0 = tl.load(in_ptr0 + (32256 + x0 + -4096 * tl_math.abs(-7 + tl_math .abs(-1 + x2)) + -512 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 32768 * x3), None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x4, tmp4, None) @triton.jit def triton_poi_fused_convolution_relu_23(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 512 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_24(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 512 x1 = xindex // 512 % 4 x2 = xindex // 2048 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 1024 * x1 + 8192 * x2), None) tmp1 = tl.load(in_ptr0 + (512 + x0 + 1024 * x1 + 8192 * x2), None) tmp7 = tl.load(in_ptr0 + (4096 + x0 + 1024 * x1 + 8192 * x2), None) tmp12 = tl.load(in_ptr0 + (4608 + x0 + 1024 * x1 + 8192 * x2), None) tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tmp8 = tmp7 > tmp6 tmp9 = tl.full([1], 2, tl.int8) tmp10 = tl.where(tmp8, tmp9, tmp5) tmp11 = triton_helpers.maximum(tmp7, tmp6) tmp13 = tmp12 > tmp11 tmp14 = tl.full([1], 3, tl.int8) tmp15 = tl.where(tmp13, tmp14, tmp10) triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_25(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 512 x1 = xindex // 512 % 6 x2 = xindex // 3072 % 6 x3 = xindex // 18432 x4 = xindex tmp0 = tl.load(in_ptr0 + (27648 + x0 + -8192 * tl_math.abs(-3 + tl_math .abs(-1 + x2)) + -1024 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 32768 * x3), None) tmp1 = tl.load(in_ptr0 + (28160 + x0 + -8192 * tl_math.abs(-3 + tl_math .abs(-1 + x2)) + -1024 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 32768 * x3), None) tmp3 = tl.load(in_ptr0 + (31744 + x0 + -8192 * tl_math.abs(-3 + tl_math .abs(-1 + x2)) + -1024 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 32768 * x3), None) tmp5 = tl.load(in_ptr0 + (32256 + x0 + -8192 * tl_math.abs(-3 + tl_math .abs(-1 + x2)) + -1024 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 32768 * x3), None) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tl.store(out_ptr0 + x4, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_26(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 512 y1 = yindex // 512 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 512 * x2 + 8192 * y1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1, 1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2 + 16 * y3), tmp4, xmask) tl.store(out_ptr1 + (y0 + 512 * x2 + 8192 * y1), tmp6, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_27(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 512 tmp0 = tl.load(in_ptr0 + x2, None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_28(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_ptr0 + x2, None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_29(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_ptr0 + x2, None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_30(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_ptr0 + x2, None) tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29) = args args.clear() assert_size_stride(primals_1, (3, 3, 1, 1), (3, 1, 1, 1)) assert_size_stride(primals_2, (3,), (1,)) assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1)) assert_size_stride(primals_4, (64, 3, 3, 3), (27, 9, 3, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64,), (1,)) assert_size_stride(primals_8, (128, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_9, (128,), (1,)) assert_size_stride(primals_10, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_11, (128,), (1,)) assert_size_stride(primals_12, (256, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_13, (256,), (1,)) assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_15, (256,), (1,)) assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_17, (256,), (1,)) assert_size_stride(primals_18, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_19, (256,), (1,)) assert_size_stride(primals_20, (512, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_21, (512,), (1,)) assert_size_stride(primals_22, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_23, (512,), (1,)) assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_25, (512,), (1,)) assert_size_stride(primals_26, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_27, (512,), (1,)) assert_size_stride(primals_28, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_29, (512,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch .float32) get_raw_stream(0) triton_poi_fused_0[grid(12, 4096)](primals_3, buf0, 12, 4096, XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1) del primals_3 buf1 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32) triton_poi_fused_1[grid(192, 9)](primals_4, buf1, 192, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_4 buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch. float32) triton_poi_fused_2[grid(4096, 9)](primals_6, buf2, 4096, 9, XBLOCK= 16, YBLOCK=64, num_warps=4, num_stages=1) del primals_6 buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch .float32) triton_poi_fused_3[grid(8192, 9)](primals_8, buf3, 8192, 9, XBLOCK= 16, YBLOCK=64, num_warps=4, num_stages=1) del primals_8 buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128), torch.float32) triton_poi_fused_4[grid(16384, 9)](primals_10, buf4, 16384, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_10 buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128), torch.float32) triton_poi_fused_5[grid(32768, 9)](primals_12, buf5, 32768, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_12 buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_6[grid(65536, 9)](primals_14, buf6, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_14 buf7 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_6[grid(65536, 9)](primals_16, buf7, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_16 buf8 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_6[grid(65536, 9)](primals_18, buf8, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_18 buf9 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_7[grid(131072, 9)](primals_20, buf9, 131072, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_20 buf10 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32) triton_poi_fused_8[grid(262144, 9)](primals_22, buf10, 262144, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_22 buf11 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32) triton_poi_fused_8[grid(262144, 9)](primals_24, buf11, 262144, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_24 buf12 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32) triton_poi_fused_8[grid(262144, 9)](primals_26, buf12, 262144, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_26 buf13 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512), torch.float32) triton_poi_fused_8[grid(262144, 9)](primals_28, buf13, 262144, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_28 buf14 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 3, 64, 64), (12288, 1, 192, 3)) buf15 = empty_strided_cuda((4, 3, 66, 66), (13068, 1, 198, 3), torch.float32) triton_poi_fused_convolution_reflection_pad2d_9[grid(52272)](buf14, primals_2, buf15, 52272, XBLOCK=256, num_warps=4, num_stages=1) del buf14 del primals_2 buf16 = extern_kernels.convolution(buf15, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf16, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf17 = empty_strided_cuda((4, 64, 66, 66), (278784, 1, 4224, 64), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_10[grid(1115136)]( buf16, primals_5, buf17, 1115136, XBLOCK=1024, num_warps=4, num_stages=1) buf18 = extern_kernels.convolution(buf17, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf18, (4, 64, 64, 64), (262144, 1, 4096, 64)) buf19 = buf18 del buf18 triton_poi_fused_convolution_relu_11[grid(1048576)](buf19, primals_7, 1048576, XBLOCK=1024, num_warps=4, num_stages=1) del primals_7 buf20 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64), torch.int8) triton_poi_fused_max_pool2d_with_indices_12[grid(262144)](buf19, buf20, 262144, XBLOCK=1024, num_warps=4, num_stages=1) buf21 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64), torch.float32) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_13[grid( 295936)](buf19, buf21, 295936, XBLOCK=512, num_warps=8, num_stages=1) buf22 = extern_kernels.convolution(buf21, buf3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf22, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf23 = empty_strided_cuda((4, 128, 34, 34), (147968, 1, 4352, 128), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_14[grid(591872)]( buf22, primals_9, buf23, 591872, XBLOCK=1024, num_warps=4, num_stages=1) buf24 = extern_kernels.convolution(buf23, buf4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 128, 32, 32), (131072, 1, 4096, 128)) buf25 = buf24 del buf24 triton_poi_fused_convolution_relu_15[grid(524288)](buf25, primals_11, 524288, XBLOCK=1024, num_warps=4, num_stages=1) del primals_11 buf26 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128), torch.int8) triton_poi_fused_max_pool2d_with_indices_16[grid(131072)](buf25, buf26, 131072, XBLOCK=1024, num_warps=4, num_stages=1) buf27 = empty_strided_cuda((4, 128, 18, 18), (41472, 1, 2304, 128), torch.float32) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_17[grid( 165888)](buf25, buf27, 165888, XBLOCK=512, num_warps=8, num_stages=1) buf28 = extern_kernels.convolution(buf27, buf5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf28, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf29 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_18[grid(331776)]( buf28, primals_13, buf29, 331776, XBLOCK=1024, num_warps=4, num_stages=1) buf30 = extern_kernels.convolution(buf29, buf6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf31 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_18[grid(331776)]( buf30, primals_15, buf31, 331776, XBLOCK=1024, num_warps=4, num_stages=1) buf32 = extern_kernels.convolution(buf31, buf7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf32, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf33 = empty_strided_cuda((4, 256, 18, 18), (82944, 1, 4608, 256), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_18[grid(331776)]( buf32, primals_17, buf33, 331776, XBLOCK=1024, num_warps=4, num_stages=1) buf34 = extern_kernels.convolution(buf33, buf8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf34, (4, 256, 16, 16), (65536, 1, 4096, 256)) buf35 = buf34 del buf34 triton_poi_fused_convolution_relu_19[grid(262144)](buf35, primals_19, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del primals_19 buf36 = empty_strided_cuda((4, 256, 8, 8), (16384, 1, 2048, 256), torch.int8) triton_poi_fused_max_pool2d_with_indices_20[grid(65536)](buf35, buf36, 65536, XBLOCK=512, num_warps=4, num_stages=1) buf37 = empty_strided_cuda((4, 256, 10, 10), (25600, 1, 2560, 256), torch.float32) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_21[grid( 102400)](buf35, buf37, 102400, XBLOCK=512, num_warps=8, num_stages=1) buf38 = extern_kernels.convolution(buf37, buf9, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf38, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf39 = empty_strided_cuda((4, 512, 10, 10), (51200, 1, 5120, 512), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_22[grid(204800)]( buf38, primals_21, buf39, 204800, XBLOCK=1024, num_warps=4, num_stages=1) buf40 = extern_kernels.convolution(buf39, buf10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf40, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf41 = empty_strided_cuda((4, 512, 10, 10), (51200, 1, 5120, 512), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_22[grid(204800)]( buf40, primals_23, buf41, 204800, XBLOCK=1024, num_warps=4, num_stages=1) buf42 = extern_kernels.convolution(buf41, buf11, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf42, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf43 = empty_strided_cuda((4, 512, 10, 10), (51200, 1, 5120, 512), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_22[grid(204800)]( buf42, primals_25, buf43, 204800, XBLOCK=1024, num_warps=4, num_stages=1) buf44 = extern_kernels.convolution(buf43, buf12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf44, (4, 512, 8, 8), (32768, 1, 4096, 512)) buf45 = buf44 del buf44 triton_poi_fused_convolution_relu_23[grid(131072)](buf45, primals_27, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del primals_27 buf46 = empty_strided_cuda((4, 512, 4, 4), (8192, 1, 2048, 512), torch.int8) triton_poi_fused_max_pool2d_with_indices_24[grid(32768)](buf45, buf46, 32768, XBLOCK=128, num_warps=4, num_stages=1) buf47 = empty_strided_cuda((4, 512, 6, 6), (18432, 1, 3072, 512), torch.float32) triton_poi_fused_max_pool2d_with_indices_reflection_pad2d_25[grid( 73728)](buf45, buf47, 73728, XBLOCK=512, num_warps=8, num_stages=1) buf48 = extern_kernels.convolution(buf47, buf13, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf48, (4, 512, 4, 4), (8192, 1, 2048, 512)) buf49 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch. float32) buf50 = empty_strided_cuda((4, 512, 4, 4), (8192, 1, 2048, 512), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_26[grid(2048, 16) ](buf48, primals_29, buf49, buf50, 2048, 16, XBLOCK=16, YBLOCK= 64, num_warps=4, num_stages=1) del buf48 del primals_29 buf51 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_27[grid(131072)]( buf42, primals_25, buf51, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del buf42 del primals_25 buf52 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_27[grid(131072)]( buf40, primals_23, buf52, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del buf40 del primals_23 buf53 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_27[grid(131072)]( buf38, primals_21, buf53, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del buf38 del primals_21 buf54 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_28[grid(262144)]( buf32, primals_17, buf54, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf32 del primals_17 buf55 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_28[grid(262144)]( buf30, primals_15, buf55, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf30 del primals_15 buf56 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_28[grid(262144)]( buf28, primals_13, buf56, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf28 del primals_13 buf57 = empty_strided_cuda((4, 128, 32, 32), (131072, 1, 4096, 128), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_29[grid(524288)]( buf22, primals_9, buf57, 524288, XBLOCK=1024, num_warps=4, num_stages=1) del buf22 del primals_9 buf58 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_30[grid(1048576)]( buf16, primals_5, buf58, 1048576, XBLOCK=1024, num_warps=4, num_stages=1) del buf16 del primals_5 return (buf49, primals_1, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf10, buf11, buf12, buf13, buf15, buf17, buf19, buf20, buf21, buf23, buf25, buf26, buf27, buf29, buf31, buf33, buf35, buf36, buf37, buf39, buf41, buf43, buf45, buf46, buf47, buf50, buf51, buf52, buf53, buf54, buf55, buf56, buf57, buf58) class Encoder5New(nn.Module): def __init__(self, model=None, fixed=False): super(Encoder5New, self).__init__() self.fixed = fixed self.conv0 = nn.Conv2d(3, 3, 1, 1, 0) self.conv0.weight = nn.Parameter(torch.from_numpy(np.array([[[[0]], [[0]], [[255]]], [[[0]], [[255]], [[0]]], [[[255]], [[0]], [[0] ]]])).float()) self.conv0.bias = nn.Parameter(torch.from_numpy(np.array([-103.939, -116.779, -123.68])).float()) self.conv11 = nn.Conv2d(3, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv21 = nn.Conv2d(64, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv31 = nn.Conv2d(128, 256, 3, 1, 0) self.conv32 = nn.Conv2d(256, 256, 3, 1, 0) self.conv33 = nn.Conv2d(256, 256, 3, 1, 0) self.conv34 = nn.Conv2d(256, 256, 3, 1, 0) self.conv41 = nn.Conv2d(256, 512, 3, 1, 0) self.conv42 = nn.Conv2d(512, 512, 3, 1, 0) self.conv43 = nn.Conv2d(512, 512, 3, 1, 0) self.conv44 = nn.Conv2d(512, 512, 3, 1, 0) self.conv51 = nn.Conv2d(512, 512, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.pool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=False) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input_0): primals_1 = self.conv0.weight primals_2 = self.conv0.bias primals_4 = self.conv11.weight primals_5 = self.conv11.bias primals_6 = self.conv12.weight primals_7 = self.conv12.bias primals_8 = self.conv21.weight primals_9 = self.conv21.bias primals_10 = self.conv22.weight primals_11 = self.conv22.bias primals_12 = self.conv31.weight primals_13 = self.conv31.bias primals_14 = self.conv32.weight primals_15 = self.conv32.bias primals_16 = self.conv33.weight primals_17 = self.conv33.bias primals_18 = self.conv34.weight primals_19 = self.conv34.bias primals_20 = self.conv41.weight primals_21 = self.conv41.bias primals_22 = self.conv42.weight primals_23 = self.conv42.bias primals_24 = self.conv43.weight primals_25 = self.conv43.bias primals_26 = self.conv44.weight primals_27 = self.conv44.bias primals_28 = self.conv51.weight primals_29 = self.conv51.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29]) return output[0]	EndyWon/Texture-Reformer	Encoder5	false	8,220	[ "MIT" ]	11	f84f95accb3574c7b759a7f03c0b0b4e150314b5	https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5
convTranspose23DUnit	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/tw/ctwf4snrycwisz3zzemsdugxzwavdryo3gjrfk4fnpwddmadthrt.py # Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution] # Source node to ATen node mapping: # conv_transpose2d => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], True, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 784 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 49) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 7, 7), (196, 49, 7, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution] stream0 = get_raw_stream(0) triton_poi_fused_convolution_0.run(buf1, primals_2, 784, grid=grid(784), stream=stream0) del primals_2 return (buf1, primals_1, primals_3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import numpy as np from torch import nn import torch.utils.data import torch.nn.init as init import torch.nn.init class convTranspose23DUnit(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, nd=2): super(convTranspose23DUnit, self).__init__() assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}' if nd == 2: self.conv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding =output_padding, groups=groups, bias=bias, dilation=dilation) elif nd == 3: self.conv = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding =output_padding, groups=groups, bias=bias, dilation=dilation) else: self.conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding =output_padding, groups=groups, bias=bias, dilation=dilation) init.xavier_uniform(self.conv.weight, gain=np.sqrt(2.0)) init.constant(self.conv.bias, 0) def forward(self, x): return self.conv(x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import numpy as np from torch import nn import torch.utils.data import torch.nn.init as init import torch.nn.init assert_size_stride = torch._C._dynamo.guards.assert_size_stride @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 784 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 49 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 7, 7), (196, 49, 7, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_0[grid(784)](buf1, primals_2, 784, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 return buf1, primals_1, primals_3 class convTranspose23DUnitNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, nd=2): super(convTranspose23DUnitNew, self).__init__() assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}' if nd == 2: self.conv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding =output_padding, groups=groups, bias=bias, dilation=dilation) elif nd == 3: self.conv = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding =output_padding, groups=groups, bias=bias, dilation=dilation) else: self.conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding =output_padding, groups=groups, bias=bias, dilation=dilation) init.xavier_uniform(self.conv.weight, gain=np.sqrt(2.0)) init.constant(self.conv.bias, 0) def forward(self, input_0): primals_1 = self.conv.weight primals_2 = self.conv.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	ForrestPi/Unsupervised-Defect-Segmentation	convTranspose23DUnit	false	8,221	[ "MIT" ]	17	e366ac7c757bb1b45f38ebbc502dfee7ccb72398	https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398
ConvBlock	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/en/cenor6dex7zpom3aeh3ta3nhvd2atidlhqjgxcs2ntulzteifzqt.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.constant_pad_nd] # Source node to ATen node mapping: # out => constant_pad_nd # Graph fragment: # %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [1, 1, 1, 1], 0.0), kwargs = {}) triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 576 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 6) % 6 x0 = xindex % 6 x2 = (xindex // 36) x4 = xindex tmp0 = (-1) + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = (-1) + x0 tmp6 = tmp5 >= tmp1 tmp7 = tmp5 < tmp3 tmp8 = tmp2 & tmp4 tmp9 = tmp8 & tmp6 tmp10 = tmp9 & tmp7 tmp11 = tl.load(in_ptr0 + ((-5) + x0 + (4x1) + (16x2)), tmp10 & xmask, other=0.0) tl.store(out_ptr0 + (x4), tmp11, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ty/ctyzpua7fjfn7hztwdpigb3gi7yf6hmfsexovkmknohzjtrj2z3h.py # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.elu] # Source node to ATen node mapping: # out_1 => convolution # out_2 => expm1, gt, mul, mul_2, where # Graph fragment: # %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {}) # %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {}) # %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {}) triton_poi_fused_convolution_elu_1 = async_compile.triton('triton_poi_fused_convolution_elu_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 1.0 tmp6 = tmp2 * tmp5 tmp7 = libdevice.expm1(tmp6) tmp8 = tmp7 * tmp5 tmp9 = tl.where(tmp4, tmp6, tmp8) tl.store(in_out_ptr0 + (x3), tmp9, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.constant_pad_nd] stream0 = get_raw_stream(0) triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.elu] triton_poi_fused_convolution_elu_1.run(buf2, primals_3, 256, grid=grid(256), stream=stream0) del primals_3 return (buf2, primals_2, buf0, buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class Conv3x3(nn.Module): """Layer to pad and convolve input """ def __init__(self, in_channels, out_channels, bias=True): super(Conv3x3, self).__init__() self.pad = nn.ZeroPad2d(1) self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3, bias=bias ) def forward(self, x): out = self.pad(x) out = self.conv(out) return out class ConvBlock(nn.Module): """Layer to perform a convolution followed by ELU """ def __init__(self, in_channels, out_channels, bias=True): super(ConvBlock, self).__init__() self.conv = Conv3x3(in_channels, out_channels, bias) self.nonlin = nn.ELU(inplace=True) def forward(self, x): out = self.conv(x) out = self.nonlin(out) return out def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 576 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 6 % 6 x0 = xindex % 6 x2 = xindex // 36 x4 = xindex tmp0 = -1 + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = -1 + x0 tmp6 = tmp5 >= tmp1 tmp7 = tmp5 < tmp3 tmp8 = tmp2 & tmp4 tmp9 = tmp8 & tmp6 tmp10 = tmp9 & tmp7 tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask, other=0.0) tl.store(out_ptr0 + x4, tmp11, xmask) @triton.jit def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 1.0 tmp6 = tmp2 * tmp5 tmp7 = libdevice.expm1(tmp6) tmp8 = tmp7 * tmp5 tmp9 = tl.where(tmp4, tmp6, tmp8) tl.store(in_out_ptr0 + x3, tmp9, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32) get_raw_stream(0) triton_poi_fused_constant_pad_nd_0[grid(576)](primals_1, buf0, 576, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1 del buf1 triton_poi_fused_convolution_elu_1[grid(256)](buf2, primals_3, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 return buf2, primals_2, buf0, buf2 class Conv3x3(nn.Module): """Layer to pad and convolve input """ def __init__(self, in_channels, out_channels, bias=True): super(Conv3x3, self).__init__() self.pad = nn.ZeroPad2d(1) self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3, bias=bias ) def forward(self, x): out = self.pad(x) out = self.conv(out) return out class ConvBlockNew(nn.Module): """Layer to perform a convolution followed by ELU """ def __init__(self, in_channels, out_channels, bias=True): super(ConvBlockNew, self).__init__() self.conv = Conv3x3(in_channels, out_channels, bias) self.nonlin = nn.ELU(inplace=True) def forward(self, input_0): primals_2 = self.conv.conv.weight primals_3 = self.conv.conv.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	HalleyJiang/UniFuse-Unidirectional-Fusion	ConvBlock	false	8,222	[ "MIT" ]	30	27a4441fe3d3031d1c9f3eb2d72a3624407d19fc	https://github.com/HalleyJiang/UniFuse-Unidirectional-Fusion/tree/27a4441fe3d3031d1c9f3eb2d72a3624407d19fc
NodeMaxpool3by3	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/r6/cr63646lmkteelgl5qsjhd5ljcimmvo4mupanu2rzy42gq5f6vzq.py # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # max_pool2d => getitem # Graph fragment: # %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_0 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) % 4 x0 = xindex % 4 x4 = xindex tmp0 = (-1) + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tmp2 & tmp4 tmp6 = (-1) + x0 tmp7 = tmp6 >= tmp1 tmp8 = tmp6 < tmp3 tmp9 = tmp7 & tmp8 tmp10 = tmp5 & tmp9 tmp11 = tl.load(in_ptr0 + ((-5) + x4), tmp10 & xmask, other=float("-inf")) tmp12 = x0 tmp13 = tmp12 >= tmp1 tmp14 = tmp12 < tmp3 tmp15 = tmp13 & tmp14 tmp16 = tmp5 & tmp15 tmp17 = tl.load(in_ptr0 + ((-4) + x4), tmp16 & xmask, other=float("-inf")) tmp18 = triton_helpers.maximum(tmp17, tmp11) tmp19 = 1 + x0 tmp20 = tmp19 >= tmp1 tmp21 = tmp19 < tmp3 tmp22 = tmp20 & tmp21 tmp23 = tmp5 & tmp22 tmp24 = tl.load(in_ptr0 + ((-3) + x4), tmp23 & xmask, other=float("-inf")) tmp25 = triton_helpers.maximum(tmp24, tmp18) tmp26 = x1 tmp27 = tmp26 >= tmp1 tmp28 = tmp26 < tmp3 tmp29 = tmp27 & tmp28 tmp30 = tmp29 & tmp9 tmp31 = tl.load(in_ptr0 + ((-1) + x4), tmp30 & xmask, other=float("-inf")) tmp32 = triton_helpers.maximum(tmp31, tmp25) tmp33 = tmp29 & tmp15 tmp34 = tl.load(in_ptr0 + (x4), tmp33 & xmask, other=float("-inf")) tmp35 = triton_helpers.maximum(tmp34, tmp32) tmp36 = tmp29 & tmp22 tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=float("-inf")) tmp38 = triton_helpers.maximum(tmp37, tmp35) tmp39 = 1 + x1 tmp40 = tmp39 >= tmp1 tmp41 = tmp39 < tmp3 tmp42 = tmp40 & tmp41 tmp43 = tmp42 & tmp9 tmp44 = tl.load(in_ptr0 + (3 + x4), tmp43 & xmask, other=float("-inf")) tmp45 = triton_helpers.maximum(tmp44, tmp38) tmp46 = tmp42 & tmp15 tmp47 = tl.load(in_ptr0 + (4 + x4), tmp46 & xmask, other=float("-inf")) tmp48 = triton_helpers.maximum(tmp47, tmp45) tmp49 = tmp42 & tmp22 tmp50 = tl.load(in_ptr0 + (5 + x4), tmp49 & xmask, other=float("-inf")) tmp51 = triton_helpers.maximum(tmp50, tmp48) tl.store(out_ptr0 + (x4), tmp51, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices] stream0 = get_raw_stream(0) triton_poi_fused_max_pool2d_with_indices_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.cuda class NodeMaxpool3by3(nn.Module): def __init__(self): super(NodeMaxpool3by3, self).__init__() self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) def init_weights(self): pass def forward(self, x): return self.maxpool(x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.cuda assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_max_pool2d_with_indices_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 % 4 x0 = xindex % 4 x4 = xindex tmp0 = -1 + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tmp2 & tmp4 tmp6 = -1 + x0 tmp7 = tmp6 >= tmp1 tmp8 = tmp6 < tmp3 tmp9 = tmp7 & tmp8 tmp10 = tmp5 & tmp9 tmp11 = tl.load(in_ptr0 + (-5 + x4), tmp10 & xmask, other=float('-inf')) tmp12 = x0 tmp13 = tmp12 >= tmp1 tmp14 = tmp12 < tmp3 tmp15 = tmp13 & tmp14 tmp16 = tmp5 & tmp15 tmp17 = tl.load(in_ptr0 + (-4 + x4), tmp16 & xmask, other=float('-inf')) tmp18 = triton_helpers.maximum(tmp17, tmp11) tmp19 = 1 + x0 tmp20 = tmp19 >= tmp1 tmp21 = tmp19 < tmp3 tmp22 = tmp20 & tmp21 tmp23 = tmp5 & tmp22 tmp24 = tl.load(in_ptr0 + (-3 + x4), tmp23 & xmask, other=float('-inf')) tmp25 = triton_helpers.maximum(tmp24, tmp18) tmp26 = x1 tmp27 = tmp26 >= tmp1 tmp28 = tmp26 < tmp3 tmp29 = tmp27 & tmp28 tmp30 = tmp29 & tmp9 tmp31 = tl.load(in_ptr0 + (-1 + x4), tmp30 & xmask, other=float('-inf')) tmp32 = triton_helpers.maximum(tmp31, tmp25) tmp33 = tmp29 & tmp15 tmp34 = tl.load(in_ptr0 + x4, tmp33 & xmask, other=float('-inf')) tmp35 = triton_helpers.maximum(tmp34, tmp32) tmp36 = tmp29 & tmp22 tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=float('-inf')) tmp38 = triton_helpers.maximum(tmp37, tmp35) tmp39 = 1 + x1 tmp40 = tmp39 >= tmp1 tmp41 = tmp39 < tmp3 tmp42 = tmp40 & tmp41 tmp43 = tmp42 & tmp9 tmp44 = tl.load(in_ptr0 + (3 + x4), tmp43 & xmask, other=float('-inf')) tmp45 = triton_helpers.maximum(tmp44, tmp38) tmp46 = tmp42 & tmp15 tmp47 = tl.load(in_ptr0 + (4 + x4), tmp46 & xmask, other=float('-inf')) tmp48 = triton_helpers.maximum(tmp47, tmp45) tmp49 = tmp42 & tmp22 tmp50 = tl.load(in_ptr0 + (5 + x4), tmp49 & xmask, other=float('-inf')) tmp51 = triton_helpers.maximum(tmp50, tmp48) tl.store(out_ptr0 + x4, tmp51, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_max_pool2d_with_indices_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) del arg0_1 return buf0, class NodeMaxpool3by3New(nn.Module): def __init__(self): super(NodeMaxpool3by3New, self).__init__() self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) def init_weights(self): pass def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	HanseulJo/COMBO_NKmodel	NodeMaxpool3by3	false	8,223	[ "BSD-2-Clause-FreeBSD" ]	38	6dcee4c39d4cf200f44677925712ce57255d1489	https://github.com/HanseulJo/COMBO_NKmodel/tree/6dcee4c39d4cf200f44677925712ce57255d1489
dnn_generator	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/4g/c4guhk7x6skkidedvs2gxz2kcu6gb76l3ig5crjjvjtzvnjlhlte.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, ), (1, )) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf9, 256, grid=grid(256), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf8, 256, grid=grid(256), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4 # reuse buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf5, primals_7, buf7, 256, grid=grid(256), stream=stream0) del primals_7 buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.addmm] extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6) del primals_9 return (reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1), 0), primals_8, buf7, primals_6, buf8, primals_4, buf9, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class dnn_generator(nn.Module): def weight_init(self): nn.init.xavier_uniform_(self.fc1.weight) nn.init.xavier_uniform_(self.fc2.weight) nn.init.xavier_uniform_(self.fc3.weight) nn.init.xavier_uniform_(self.out.weight) nn.init.xavier_uniform_(self.fc1.bias) nn.init.xavier_uniform_(self.fc2.bias) nn.init.xavier_uniform_(self.fc3.bias) nn.init.xavier_uniform_(self.out.bias) def __init__(self, G_in, G_out, w1, w2, w3): super(dnn_generator, self).__init__() self.fc1 = nn.Linear(G_in, w1) self.fc2 = nn.Linear(w1, w2) self.fc3 = nn.Linear(w2, w3) self.out = nn.Linear(w3, G_out) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = self.out(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'G_in': 4, 'G_out': 4, 'w1': 4, 'w2': 4, 'w3': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4,), (1,)) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf0 buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, primals_2, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf2 buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3, primals_5, buf8, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf4 buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf5, primals_7, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_7 buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 4), ( 4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6) del primals_9 return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor( buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1), 0 ), primals_8, buf7, primals_6, buf8, primals_4, buf9 class dnn_generatorNew(nn.Module): def weight_init(self): nn.init.xavier_uniform_(self.fc1.weight) nn.init.xavier_uniform_(self.fc2.weight) nn.init.xavier_uniform_(self.fc3.weight) nn.init.xavier_uniform_(self.out.weight) nn.init.xavier_uniform_(self.fc1.bias) nn.init.xavier_uniform_(self.fc2.bias) nn.init.xavier_uniform_(self.fc3.bias) nn.init.xavier_uniform_(self.out.bias) def __init__(self, G_in, G_out, w1, w2, w3): super(dnn_generatorNew, self).__init__() self.fc1 = nn.Linear(G_in, w1) self.fc2 = nn.Linear(w1, w2) self.fc3 = nn.Linear(w2, w3) self.out = nn.Linear(w3, G_out) def forward(self, input_0): primals_1 = self.fc1.weight primals_2 = self.fc1.bias primals_4 = self.fc2.weight primals_5 = self.fc2.bias primals_6 = self.fc3.weight primals_7 = self.fc3.bias primals_8 = self.out.weight primals_9 = self.out.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return output[0]	Harshitmalaviya/whisper-to-normal-speech-conversion	dnn_generator	false	8,224	[ "MIT" ]	23	a6d411b27a3c5cc4ad12e3968350b22d88b9b4d9	https://github.com/Harshitmalaviya/whisper-to-normal-speech-conversion/tree/a6d411b27a3c5cc4ad12e3968350b22d88b9b4d9
CircleLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/uk/cukuhlvspb6brmktd7ullkhxpd7nv43wkmlmuiylskqscjxuiyng.py # Topologically Sorted Source Nodes: [add_2, an, sub_1, mul_2, logit_n, logsumexp, neg, add, add_1, ap, neg_1, sub, mul, logit_p, logsumexp_1, add_3, loss], Original ATen: [aten.add, aten.clamp_min, aten.sub, aten.mul, aten.logsumexp, aten.neg, aten.softplus] # Source node to ATen node mapping: # add => add # add_1 => add_1 # add_2 => add_2 # add_3 => add_5 # an => clamp_min_1 # ap => clamp_min # logit_n => mul_3 # logit_p => mul_1 # logsumexp => abs_1, add_3, amax, eq, exp, full_default, log, sub_2, sum_1, where # logsumexp_1 => abs_2, add_4, amax_1, eq_1, exp_1, full_default_1, log_1, sub_3, sum_2, where_1 # loss => div, exp_2, gt, log1p, mul_4, where_2 # mul => mul # mul_2 => mul_2 # neg => neg # neg_1 => neg_1 # sub => sub # sub_1 => sub_1 # Graph fragment: # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg1_1, 4), kwargs = {}) # %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_2, 0.0), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, 4), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_min_1, %sub_1), kwargs = {}) # %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, 4), kwargs = {}) # %amax : [num_users=2] = call_function[target=torch.ops.aten.amax.default](args = (%mul_3, [0], True), kwargs = {}) # %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%amax,), kwargs = {}) # %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%abs_1, inf), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %amax), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_3, %where), kwargs = {}) # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [0]), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {}) # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, %squeeze), kwargs = {}) # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg0_1,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, 1), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, 4), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_1, 0.0), kwargs = {}) # %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%clamp_min,), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, -3), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg_1, %sub), kwargs = {}) # %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 4), kwargs = {}) # %amax_1 : [num_users=2] = call_function[target=torch.ops.aten.amax.default](args = (%mul_1, [0], True), kwargs = {}) # %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%amax_1,), kwargs = {}) # %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%abs_2, inf), kwargs = {}) # %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %full_default_1, %amax_1), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, %where_1), kwargs = {}) # %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_3,), kwargs = {}) # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [0]), kwargs = {}) # %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_2,), kwargs = {}) # %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log_1, %squeeze_1), kwargs = {}) # %add_5 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %add_4), kwargs = {}) # %mul_4 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 1.0), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mul_4, 20.0), kwargs = {}) # %exp_2 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_4,), kwargs = {}) # %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp_2,), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%log1p, 1.0), kwargs = {}) # %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add_5, %div), kwargs = {}) triton_poi_fused_add_clamp_min_logsumexp_mul_neg_softplus_sub_0 = async_compile.triton('triton_poi_fused_add_clamp_min_logsumexp_mul_neg_softplus_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_min_logsumexp_mul_neg_softplus_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_clamp_min_logsumexp_mul_neg_softplus_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp8 = tl.load(in_ptr0 + (64 + x0), xmask) tmp15 = tl.load(in_ptr0 + (128 + x0), xmask) tmp22 = tl.load(in_ptr0 + (192 + x0), xmask) tmp44 = tl.load(in_ptr1 + (x0), xmask) tmp55 = tl.load(in_ptr1 + (64 + x0), xmask) tmp65 = tl.load(in_ptr1 + (128 + x0), xmask) tmp75 = tl.load(in_ptr1 + (192 + x0), xmask) tmp1 = 4.0 tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp5 = tmp0 - tmp1 tmp6 = tmp4 * tmp5 tmp7 = tmp6 * tmp1 tmp9 = tmp8 + tmp1 tmp10 = triton_helpers.maximum(tmp9, tmp3) tmp11 = tmp8 - tmp1 tmp12 = tmp10 * tmp11 tmp13 = tmp12 * tmp1 tmp14 = triton_helpers.maximum(tmp7, tmp13) tmp16 = tmp15 + tmp1 tmp17 = triton_helpers.maximum(tmp16, tmp3) tmp18 = tmp15 - tmp1 tmp19 = tmp17 * tmp18 tmp20 = tmp19 * tmp1 tmp21 = triton_helpers.maximum(tmp14, tmp20) tmp23 = tmp22 + tmp1 tmp24 = triton_helpers.maximum(tmp23, tmp3) tmp25 = tmp22 - tmp1 tmp26 = tmp24 * tmp25 tmp27 = tmp26 * tmp1 tmp28 = triton_helpers.maximum(tmp21, tmp27) tmp29 = tl_math.abs(tmp28) tmp30 = float("inf") tmp31 = tmp29 == tmp30 tmp32 = tl.where(tmp31, tmp3, tmp28) tmp33 = tmp7 - tmp32 tmp34 = tl_math.exp(tmp33) tmp35 = tmp13 - tmp32 tmp36 = tl_math.exp(tmp35) tmp37 = tmp34 + tmp36 tmp38 = tmp20 - tmp32 tmp39 = tl_math.exp(tmp38) tmp40 = tmp37 + tmp39 tmp41 = tmp27 - tmp32 tmp42 = tl_math.exp(tmp41) tmp43 = tmp40 + tmp42 tmp45 = -tmp44 tmp46 = 1.0 tmp47 = tmp45 + tmp46 tmp48 = tmp47 + tmp1 tmp49 = triton_helpers.maximum(tmp48, tmp3) tmp50 = -tmp49 tmp51 = -3.0 tmp52 = tmp44 - tmp51 tmp53 = tmp50 * tmp52 tmp54 = tmp53 * tmp1 tmp56 = -tmp55 tmp57 = tmp56 + tmp46 tmp58 = tmp57 + tmp1 tmp59 = triton_helpers.maximum(tmp58, tmp3) tmp60 = -tmp59 tmp61 = tmp55 - tmp51 tmp62 = tmp60 * tmp61 tmp63 = tmp62 * tmp1 tmp64 = triton_helpers.maximum(tmp54, tmp63) tmp66 = -tmp65 tmp67 = tmp66 + tmp46 tmp68 = tmp67 + tmp1 tmp69 = triton_helpers.maximum(tmp68, tmp3) tmp70 = -tmp69 tmp71 = tmp65 - tmp51 tmp72 = tmp70 * tmp71 tmp73 = tmp72 * tmp1 tmp74 = triton_helpers.maximum(tmp64, tmp73) tmp76 = -tmp75 tmp77 = tmp76 + tmp46 tmp78 = tmp77 + tmp1 tmp79 = triton_helpers.maximum(tmp78, tmp3) tmp80 = -tmp79 tmp81 = tmp75 - tmp51 tmp82 = tmp80 * tmp81 tmp83 = tmp82 * tmp1 tmp84 = triton_helpers.maximum(tmp74, tmp83) tmp85 = tl_math.abs(tmp84) tmp86 = tmp85 == tmp30 tmp87 = tl.where(tmp86, tmp3, tmp84) tmp88 = tmp54 - tmp87 tmp89 = tl_math.exp(tmp88) tmp90 = tmp63 - tmp87 tmp91 = tl_math.exp(tmp90) tmp92 = tmp89 + tmp91 tmp93 = tmp73 - tmp87 tmp94 = tl_math.exp(tmp93) tmp95 = tmp92 + tmp94 tmp96 = tmp83 - tmp87 tmp97 = tl_math.exp(tmp96) tmp98 = tmp95 + tmp97 tmp99 = tl_math.log(tmp43) tmp100 = tmp99 + tmp32 tmp101 = tl_math.log(tmp98) tmp102 = tmp101 + tmp87 tmp103 = tmp100 + tmp102 tmp104 = tmp103 * tmp46 tmp105 = 20.0 tmp106 = tmp104 > tmp105 tmp107 = tl_math.exp(tmp104) tmp108 = libdevice.log1p(tmp107) tmp109 = tmp108 * tmp46 tmp110 = tl.where(tmp106, tmp103, tmp109) tl.store(in_out_ptr0 + (x0), tmp110, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf5 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [add_2, an, sub_1, mul_2, logit_n, logsumexp, neg, add, add_1, ap, neg_1, sub, mul, logit_p, logsumexp_1, add_3, loss], Original ATen: [aten.add, aten.clamp_min, aten.sub, aten.mul, aten.logsumexp, aten.neg, aten.softplus] stream0 = get_raw_stream(0) triton_poi_fused_add_clamp_min_logsumexp_mul_neg_softplus_sub_0.run(buf5, arg1_1, arg0_1, 64, grid=grid(64), stream=stream0) del arg0_1 del arg1_1 return (buf5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import Tensor import torch.nn as nn class CircleLoss(nn.Module): def __init__(self, m: 'float', gamma: 'float') ->None: super(CircleLoss, self).__init__() self.m = m self.gamma = gamma self.soft_plus = nn.Softplus() def forward(self, sp: 'Tensor', sn: 'Tensor') ->Tensor: ap = torch.clamp_min(-sp.detach() + 1 + self.m, min=0.0) an = torch.clamp_min(sn.detach() + self.m, min=0.0) delta_p = 1 - self.m delta_n = self.m logit_p = -ap * (sp - delta_p) * self.gamma logit_n = an * (sn - delta_n) * self.gamma loss = self.soft_plus(torch.logsumexp(logit_n, dim=0) + torch. logsumexp(logit_p, dim=0)) return loss def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'m': 4, 'gamma': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_clamp_min_logsumexp_mul_neg_softplus_sub_0(in_out_ptr0 , in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp8 = tl.load(in_ptr0 + (64 + x0), xmask) tmp15 = tl.load(in_ptr0 + (128 + x0), xmask) tmp22 = tl.load(in_ptr0 + (192 + x0), xmask) tmp44 = tl.load(in_ptr1 + x0, xmask) tmp55 = tl.load(in_ptr1 + (64 + x0), xmask) tmp65 = tl.load(in_ptr1 + (128 + x0), xmask) tmp75 = tl.load(in_ptr1 + (192 + x0), xmask) tmp1 = 4.0 tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp5 = tmp0 - tmp1 tmp6 = tmp4 * tmp5 tmp7 = tmp6 * tmp1 tmp9 = tmp8 + tmp1 tmp10 = triton_helpers.maximum(tmp9, tmp3) tmp11 = tmp8 - tmp1 tmp12 = tmp10 * tmp11 tmp13 = tmp12 * tmp1 tmp14 = triton_helpers.maximum(tmp7, tmp13) tmp16 = tmp15 + tmp1 tmp17 = triton_helpers.maximum(tmp16, tmp3) tmp18 = tmp15 - tmp1 tmp19 = tmp17 * tmp18 tmp20 = tmp19 * tmp1 tmp21 = triton_helpers.maximum(tmp14, tmp20) tmp23 = tmp22 + tmp1 tmp24 = triton_helpers.maximum(tmp23, tmp3) tmp25 = tmp22 - tmp1 tmp26 = tmp24 * tmp25 tmp27 = tmp26 * tmp1 tmp28 = triton_helpers.maximum(tmp21, tmp27) tmp29 = tl_math.abs(tmp28) tmp30 = float('inf') tmp31 = tmp29 == tmp30 tmp32 = tl.where(tmp31, tmp3, tmp28) tmp33 = tmp7 - tmp32 tmp34 = tl_math.exp(tmp33) tmp35 = tmp13 - tmp32 tmp36 = tl_math.exp(tmp35) tmp37 = tmp34 + tmp36 tmp38 = tmp20 - tmp32 tmp39 = tl_math.exp(tmp38) tmp40 = tmp37 + tmp39 tmp41 = tmp27 - tmp32 tmp42 = tl_math.exp(tmp41) tmp43 = tmp40 + tmp42 tmp45 = -tmp44 tmp46 = 1.0 tmp47 = tmp45 + tmp46 tmp48 = tmp47 + tmp1 tmp49 = triton_helpers.maximum(tmp48, tmp3) tmp50 = -tmp49 tmp51 = -3.0 tmp52 = tmp44 - tmp51 tmp53 = tmp50 * tmp52 tmp54 = tmp53 * tmp1 tmp56 = -tmp55 tmp57 = tmp56 + tmp46 tmp58 = tmp57 + tmp1 tmp59 = triton_helpers.maximum(tmp58, tmp3) tmp60 = -tmp59 tmp61 = tmp55 - tmp51 tmp62 = tmp60 * tmp61 tmp63 = tmp62 * tmp1 tmp64 = triton_helpers.maximum(tmp54, tmp63) tmp66 = -tmp65 tmp67 = tmp66 + tmp46 tmp68 = tmp67 + tmp1 tmp69 = triton_helpers.maximum(tmp68, tmp3) tmp70 = -tmp69 tmp71 = tmp65 - tmp51 tmp72 = tmp70 * tmp71 tmp73 = tmp72 * tmp1 tmp74 = triton_helpers.maximum(tmp64, tmp73) tmp76 = -tmp75 tmp77 = tmp76 + tmp46 tmp78 = tmp77 + tmp1 tmp79 = triton_helpers.maximum(tmp78, tmp3) tmp80 = -tmp79 tmp81 = tmp75 - tmp51 tmp82 = tmp80 * tmp81 tmp83 = tmp82 * tmp1 tmp84 = triton_helpers.maximum(tmp74, tmp83) tmp85 = tl_math.abs(tmp84) tmp86 = tmp85 == tmp30 tmp87 = tl.where(tmp86, tmp3, tmp84) tmp88 = tmp54 - tmp87 tmp89 = tl_math.exp(tmp88) tmp90 = tmp63 - tmp87 tmp91 = tl_math.exp(tmp90) tmp92 = tmp89 + tmp91 tmp93 = tmp73 - tmp87 tmp94 = tl_math.exp(tmp93) tmp95 = tmp92 + tmp94 tmp96 = tmp83 - tmp87 tmp97 = tl_math.exp(tmp96) tmp98 = tmp95 + tmp97 tmp99 = tl_math.log(tmp43) tmp100 = tmp99 + tmp32 tmp101 = tl_math.log(tmp98) tmp102 = tmp101 + tmp87 tmp103 = tmp100 + tmp102 tmp104 = tmp103 * tmp46 tmp105 = 20.0 tmp106 = tmp104 > tmp105 tmp107 = tl_math.exp(tmp104) tmp108 = libdevice.log1p(tmp107) tmp109 = tmp108 * tmp46 tmp110 = tl.where(tmp106, tmp103, tmp109) tl.store(in_out_ptr0 + x0, tmp110, xmask) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf5 = buf2 del buf2 get_raw_stream(0) triton_poi_fused_add_clamp_min_logsumexp_mul_neg_softplus_sub_0[grid (64)](buf5, arg1_1, arg0_1, 64, XBLOCK=64, num_warps=1, num_stages=1) del arg0_1 del arg1_1 return buf5, class CircleLossNew(nn.Module): def __init__(self, m: 'float', gamma: 'float') ->None: super(CircleLossNew, self).__init__() self.m = m self.gamma = gamma self.soft_plus = nn.Softplus() def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]	HaochengWan/PVT	CircleLoss	false	8,225	[ "MIT" ]	27	95818d303ee63084f044a057344b2049d1fa4492	https://github.com/HaochengWan/PVT/tree/95818d303ee63084f044a057344b2049d1fa4492
Decoder5	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/g3/cg3ccnqa6wyzxanbi6fgd6vkyhgnnoo3va4nix3ohszhmyutqgep.py # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] # Source node to ATen node mapping: # pad => _unsafe_index, _unsafe_index_1 # Graph fragment: # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 73728 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 6 x1 = (xindex // 6) % 6 x2 = (xindex // 36) x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16x2)), None, eviction_policy='evict_last') tl.store(out_ptr0 + (x3), tmp0, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ki/ckii6ld2pjui2kyh33ieyagpaxjso55jvdwg3kttj5omjlrhkys6.py # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_1 => add, add_1, convert_element_type, convert_element_type_1, iota_2, mul, mul_1 # Graph fragment: # %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {}) # %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_1 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/jb/cjbtqp44rv5g6gp6xheddrm2gqlumlvkhqfivbddyr2zt6dvb2bl.py # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d => convolution # pad_1 => _unsafe_index_3, _unsafe_index_4 # y => relu # y_1 => _unsafe_index_2 # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {}) # %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 204800 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x1 = (xindex // 10) % 10 x0 = xindex % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 512 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (4tmp4) + (16x4)), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yj/cyjjvld53umyu5izmkpdhznlumejgejh2466jzod3qgr7fvb5pdo.py # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # pad_2 => _unsafe_index_5, _unsafe_index_6 # y_2 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {}) # %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_5, [None, None, None, %sub_5]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 204800 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 10 x1 = (xindex // 10) % 10 x4 = (xindex // 100) x2 = (xindex // 100) % 512 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + ((-1)(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64x4)), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/od/codkvvxpur7vfcixyfgg6ayyzatao2cw5of6bxy53exotyuzofwb.py # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_6 => add_4, add_5, convert_element_type_4, convert_element_type_5, iota_12, mul_4, mul_5 # Graph fragment: # %iota_12 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_12, 1), kwargs = {}) # %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, 0), kwargs = {}) # %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_4, torch.float32), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.5), kwargs = {}) # %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_4 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/n4/cn4gysdmqghrvngp47oenyvvto7s3aethdrxi4sbmrcf3xjjaxe5.py # Topologically Sorted Source Nodes: [conv2d_4, y_5, y_6, pad_5], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # pad_5 => _unsafe_index_12, _unsafe_index_13 # y_5 => relu_4 # y_6 => _unsafe_index_11 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) # %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_4, [None, None, %unsqueeze_1, %convert_element_type_5]), kwargs = {}) # %_unsafe_index_12 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_11, [None, None, %sub_21, None]), kwargs = {}) # %_unsafe_index_13 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_12, [None, None, None, %sub_21]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 331776 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x1 = (xindex // 18) % 18 x0 = xindex % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 256 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (8tmp4) + (64x4)), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/qo/cqosfn35mg3vfwgwbfco5wnax7omnfdeld4mf5zgmi57rqhvu546.py # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_5 => convolution_5 # pad_6 => _unsafe_index_14, _unsafe_index_15 # y_7 => relu_5 # Graph fragment: # %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {}) # %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_5, [None, None, %sub_21, None]), kwargs = {}) # %_unsafe_index_15 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_14, [None, None, None, %sub_21]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 331776 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 18 x1 = (xindex // 18) % 18 x4 = (xindex // 324) x2 = (xindex // 324) % 256 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + ((-1)(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256x4)), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/6w/c6wyyvxovkkzfk6k64bfon5db5xfxiywvm7oionlfmjjhdvc6ugb.py # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_11 => add_8, add_9, convert_element_type_8, convert_element_type_9, iota_22, mul_8, mul_9 # Graph fragment: # %iota_22 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_22, 1), kwargs = {}) # %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, 0), kwargs = {}) # %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_8, torch.float32), kwargs = {}) # %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_8, 0.0), kwargs = {}) # %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_9, 0.5), kwargs = {}) # %convert_element_type_9 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_9, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_7 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/zx/czxyrb5nedwpsce4kihomb7jh3xlalitdbzogjhvllw7tvju45wx.py # Topologically Sorted Source Nodes: [conv2d_8, y_10, y_11, pad_9], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_8 => convolution_8 # pad_9 => _unsafe_index_21, _unsafe_index_22 # y_10 => relu_8 # y_11 => _unsafe_index_20 # Graph fragment: # %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_19, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {}) # %_unsafe_index_20 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_8, [None, None, %unsqueeze_2, %convert_element_type_9]), kwargs = {}) # %_unsafe_index_21 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_20, [None, None, %sub_37, None]), kwargs = {}) # %_unsafe_index_22 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_21, [None, None, None, %sub_37]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 591872 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x1 = (xindex // 34) % 34 x0 = xindex % 34 x4 = (xindex // 1156) x2 = (xindex // 1156) % 128 x7 = xindex tmp0 = tl.load(in_ptr0 + (31 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (31 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 16, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (16tmp4) + (256x4)), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/t5/ct5pmhupse6j54x6sdpzr6ounpgjr2tjepxqrep6mpshhz6d5nmj.py # Topologically Sorted Source Nodes: [conv2d_9, y_12, pad_10], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_9 => convolution_9 # pad_10 => _unsafe_index_23, _unsafe_index_24 # y_12 => relu_9 # Graph fragment: # %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_22, %primals_20, %primals_21, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {}) # %_unsafe_index_23 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [None, None, %sub_37, None]), kwargs = {}) # %_unsafe_index_24 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_23, [None, None, None, %sub_37]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_9 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_9', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 591872 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 34 x1 = (xindex // 34) % 34 x4 = (xindex // 1156) x2 = (xindex // 1156) % 128 x5 = xindex tmp0 = tl.load(in_ptr0 + (1023 + ((-1)(tl_math.abs((-31) + (tl_math.abs((-1) + x0))))) + ((-32)(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (1024x4)), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/or/corqi4rcleb4nedtprk7hbxxrjl3jr4bncq2rzvazmq4w47jdfgd.py # Topologically Sorted Source Nodes: [y_14], Original ATen: [aten.arange] # Source node to ATen node mapping: # y_14 => iota_28 # Graph fragment: # %iota_28 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (64,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) triton_poi_fused_arange_10 = async_compile.triton('triton_poi_fused_arange_10', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_arange_10(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + (x0), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/tu/ctuiw5by2zagl46pvth5lodtjm7rgoqdm2h4nc7rofycgb7l2f5p.py # Topologically Sorted Source Nodes: [y_14], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] # Source node to ATen node mapping: # y_14 => add_12, add_13, convert_element_type_12, convert_element_type_13, mul_12, mul_13 # Graph fragment: # %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_28, 1), kwargs = {}) # %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_12, 0), kwargs = {}) # %convert_element_type_12 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_12, torch.float32), kwargs = {}) # %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_12, 0.0), kwargs = {}) # %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_13, 0.5), kwargs = {}) # %convert_element_type_13 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_13, torch.int64), kwargs = {}) triton_poi_fused__to_copy_add_arange_mul_11 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_11', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_11(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/qo/cqo35fiyesrqzo22llfixo2ceuvfeafmio72vhbbceo6rq3nomeh.py # Topologically Sorted Source Nodes: [conv2d_10, y_13, y_14, pad_11], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_10 => convolution_10 # pad_11 => _unsafe_index_26, _unsafe_index_27 # y_13 => relu_10 # y_14 => _unsafe_index_25 # Graph fragment: # %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_24, %primals_22, %primals_23, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {}) # %_unsafe_index_25 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_10, [None, None, %unsqueeze_3, %convert_element_type_13]), kwargs = {}) # %_unsafe_index_26 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_25, [None, None, %sub_45, None]), kwargs = {}) # %_unsafe_index_27 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_26, [None, None, None, %sub_45]), kwargs = {}) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2097152], filename=__file__, triton_meta={'signature': {0: 'i64', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 66) % 66 x0 = xindex % 66 x4 = (xindex // 4356) x2 = (xindex // 4356) % 64 x7 = xindex tmp0 = tl.load(in_ptr0 + (63 + ((-1)(tl_math.abs((-63) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (63 + ((-1)(tl_math.abs((-63) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 32, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + (32tmp4) + (1024x4)), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + (x7), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ru/cruijqz2dfjd47jaznipfhrikp7vpe4l3vowrihrs46t4ur5qh3q.py # Topologically Sorted Source Nodes: [conv2d_11, y_15, pad_12], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] # Source node to ATen node mapping: # conv2d_11 => convolution_11 # pad_12 => _unsafe_index_28, _unsafe_index_29 # y_15 => relu_11 # Graph fragment: # %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_27, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {}) # %_unsafe_index_28 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_11, [None, None, %sub_45, None]), kwargs = {}) # %_unsafe_index_29 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_28, [None, None, None, %sub_45]), kwargs = {}) triton_poi_fused_convolution_reflection_pad2d_relu_13 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_13', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2097152], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 66 x1 = (xindex // 66) % 66 x4 = (xindex // 4356) x2 = (xindex // 4356) % 64 x5 = xindex tmp0 = tl.load(in_ptr0 + (4095 + ((-1)(tl_math.abs((-63) + (tl_math.abs((-1) + x0))))) + ((-64)(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (4096x4)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ko/cko7uyeqbchxeagylp3k5f3pytnz2j2e2fu3cshxrj5jkbtdduan.py # Topologically Sorted Source Nodes: [conv2d_12, y_16], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_12 => convolution_12 # y_16 => relu_12 # Graph fragment: # %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_29, %primals_26, %primals_27, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_12 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_12,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_12, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_14 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_14', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_14', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_14(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 49152 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 4096) % 3 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, None) tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gw/cgww5gopybzpq23cobl4c3okhpizmjt3qwz5psrtu4u5y2aayof4.py # Topologically Sorted Source Nodes: [conv2d_11, y_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_11 => convolution_11 # y_15 => relu_11 # Graph fragment: # %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_27, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {}) # %le_19 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_11, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_15 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_15', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1048576 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 4096) % 64 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/hu/chuaxrcezvpnqxen3zhi6dy4wtnw5xk7fgoy4m7eqxsgsej3akm7.py # Topologically Sorted Source Nodes: [conv2d_10, y_13], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_10 => convolution_10 # y_13 => relu_10 # Graph fragment: # %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_24, %primals_22, %primals_23, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {}) # %le_38 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_10, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_16', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 1024) % 64 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/u3/cu365fyiqrrwqanu2tbumkdnzceorgvl56vwfpbp6hzh6cfd52ln.py # Topologically Sorted Source Nodes: [conv2d_9, y_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_9 => convolution_9 # y_12 => relu_9 # Graph fragment: # %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_22, %primals_20, %primals_21, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {}) # %le_57 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_9, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_17 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_17', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 524288 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 1024) % 128 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ds/cdsftonakf5p3pywbrmqutzkxmu6uw7qadvzd2gvaemkwahcviq5.py # Topologically Sorted Source Nodes: [conv2d_8, y_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_8 => convolution_8 # y_10 => relu_8 # Graph fragment: # %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_19, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {}) # %le_76 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_8, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_18 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_18', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 256) % 128 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/nc/cncr74hmgexnjng5woys5mmwhc6i42nfmpbr7ps5bjpztikrhibj.py # Topologically Sorted Source Nodes: [conv2d_7, y_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_7 => convolution_7 # y_9 => relu_7 # Graph fragment: # %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_17, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {}) # %le_95 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_7, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_19 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_19', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_19(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 262144 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 256) % 256 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/k5/ck5k7e2bzuyqgp742kzidsjiq4mpdyfuxqpvwha4lv3nbv3kk6in.py # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_4 => convolution_4 # y_5 => relu_4 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {}) # %le_152 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_20 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_20', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_20(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 65536 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 256 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/nb/cnbenfnapnsjbfdmibjqcsb2xvjvoiopdhhqmngsiygd46sc2rcg.py # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_3 => convolution_3 # y_4 => relu_3 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_8, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %le_171 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_21 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_21', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 64) % 512 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ev/cevzongfbe5yon2fp6xblelcr3xptsebsdf2ugzjhpldgpbgy2ph.py # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d => convolution # y => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le_228 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_22 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_22', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_22', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32768 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 512 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27 = args args.clear() assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1)) assert_size_stride(primals_2, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_3, (512, ), (1, )) assert_size_stride(primals_4, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_5, (512, ), (1, )) assert_size_stride(primals_6, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_7, (512, ), (1, )) assert_size_stride(primals_8, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_9, (512, ), (1, )) assert_size_stride(primals_10, (256, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_11, (256, ), (1, )) assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_13, (256, ), (1, )) assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_15, (256, ), (1, )) assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_17, (256, ), (1, )) assert_size_stride(primals_18, (128, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_19, (128, ), (1, )) assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_21, (128, ), (1, )) assert_size_stride(primals_22, (64, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_23, (64, ), (1, )) assert_size_stride(primals_24, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_25, (64, ), (1, )) assert_size_stride(primals_26, (3, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_27, (3, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d] stream0 = get_raw_stream(0) triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 73728, grid=grid(73728), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 512, 4, 4), (8192, 16, 4, 1)) buf2 = empty_strided_cuda((8, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_1.run(buf2, 8, grid=grid(8), stream=stream0) buf3 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d, y, y_1, pad_1], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2.run(buf2, buf1, primals_3, buf3, 204800, grid=grid(204800), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 512, 8, 8), (32768, 64, 8, 1)) buf5 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_1, y_2, pad_2], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf4, primals_5, buf5, 204800, grid=grid(204800), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 512, 8, 8), (32768, 64, 8, 1)) buf7 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_2, y_3, pad_3], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf6, primals_7, buf7, 204800, grid=grid(204800), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 512, 8, 8), (32768, 64, 8, 1)) buf9 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_3, y_4, pad_4], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf8, primals_9, buf9, 204800, grid=grid(204800), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 256, 8, 8), (16384, 64, 8, 1)) buf11 = empty_strided_cuda((16, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_4.run(buf11, 16, grid=grid(16), stream=stream0) buf12 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_4, y_5, y_6, pad_5], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5.run(buf11, buf10, primals_11, buf12, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution] buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf13, (4, 256, 16, 16), (65536, 256, 16, 1)) buf14 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_5, y_7, pad_6], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf13, primals_13, buf14, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution] buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf15, (4, 256, 16, 16), (65536, 256, 16, 1)) buf16 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_6, y_8, pad_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf15, primals_15, buf16, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_7], Original ATen: [aten.convolution] buf17 = extern_kernels.convolution(buf16, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf17, (4, 256, 16, 16), (65536, 256, 16, 1)) buf18 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_7, y_9, pad_8], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf17, primals_17, buf18, 331776, grid=grid(331776), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution] buf19 = extern_kernels.convolution(buf18, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1)) buf20 = empty_strided_cuda((32, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_7.run(buf20, 32, grid=grid(32), stream=stream0) buf21 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_8, y_10, y_11, pad_9], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8.run(buf20, buf19, primals_19, buf21, 591872, grid=grid(591872), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_9], Original ATen: [aten.convolution] buf22 = extern_kernels.convolution(buf21, primals_20, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf22, (4, 128, 32, 32), (131072, 1024, 32, 1)) buf23 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_9, y_12, pad_10], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_9.run(buf22, primals_21, buf23, 591872, grid=grid(591872), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_10], Original ATen: [aten.convolution] buf24 = extern_kernels.convolution(buf23, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 64, 32, 32), (65536, 1024, 32, 1)) buf25 = empty_strided_cuda((64, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_14], Original ATen: [aten.arange] triton_poi_fused_arange_10.run(buf25, 64, grid=grid(64), stream=stream0) buf26 = empty_strided_cuda((64, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [y_14], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy] triton_poi_fused__to_copy_add_arange_mul_11.run(buf26, 64, grid=grid(64), stream=stream0) buf27 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_10, y_13, y_14, pad_11], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d] triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12.run(buf26, buf24, primals_23, buf27, 1115136, grid=grid(1115136), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_11], Original ATen: [aten.convolution] buf28 = extern_kernels.convolution(buf27, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf28, (4, 64, 64, 64), (262144, 4096, 64, 1)) buf29 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1), torch.float32) # Topologically Sorted Source Nodes: [conv2d_11, y_15, pad_12], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d] triton_poi_fused_convolution_reflection_pad2d_relu_13.run(buf28, primals_25, buf29, 1115136, grid=grid(1115136), stream=stream0) # Topologically Sorted Source Nodes: [conv2d_12], Original ATen: [aten.convolution] buf30 = extern_kernels.convolution(buf29, primals_26, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 3, 64, 64), (12288, 4096, 64, 1)) buf31 = buf30; del buf30 # reuse buf32 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_12, y_16], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_14.run(buf31, primals_27, buf32, 49152, grid=grid(49152), stream=stream0) del primals_27 buf33 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_11, y_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_15.run(buf28, primals_25, buf33, 1048576, grid=grid(1048576), stream=stream0) del buf28 del primals_25 buf34 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_10, y_13], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_16.run(buf24, primals_23, buf34, 262144, grid=grid(262144), stream=stream0) del buf24 del primals_23 buf35 = empty_strided_cuda((4, 128, 32, 32), (131072, 1024, 32, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_9, y_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_17.run(buf22, primals_21, buf35, 524288, grid=grid(524288), stream=stream0) del buf22 del primals_21 buf36 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_8, y_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_18.run(buf19, primals_19, buf36, 131072, grid=grid(131072), stream=stream0) del buf19 del primals_19 buf37 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_7, y_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_19.run(buf17, primals_17, buf37, 262144, grid=grid(262144), stream=stream0) del buf17 del primals_17 buf38 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_6, y_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_19.run(buf15, primals_15, buf38, 262144, grid=grid(262144), stream=stream0) del buf15 del primals_15 buf39 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_5, y_7], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_19.run(buf13, primals_13, buf39, 262144, grid=grid(262144), stream=stream0) del buf13 del primals_13 buf40 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_4, y_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_20.run(buf10, primals_11, buf40, 65536, grid=grid(65536), stream=stream0) del buf10 del primals_11 buf41 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_3, y_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_21.run(buf8, primals_9, buf41, 131072, grid=grid(131072), stream=stream0) del buf8 del primals_9 buf42 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_2, y_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_21.run(buf6, primals_7, buf42, 131072, grid=grid(131072), stream=stream0) del buf6 del primals_7 buf43 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_1, y_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_21.run(buf4, primals_5, buf43, 131072, grid=grid(131072), stream=stream0) del buf4 del primals_5 buf44 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_22.run(buf1, primals_3, buf44, 32768, grid=grid(32768), stream=stream0) del buf1 del primals_3 return (buf31, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf14, buf16, buf18, buf20, buf21, buf23, buf25, buf26, buf27, buf29, buf32, buf33, buf34, buf35, buf36, buf37, buf38, buf39, buf40, buf41, buf42, buf43, buf44, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 512, 4, 4), (8192, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_20 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_21 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_22 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_23 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_24 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_25 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_26 = rand_strided((3, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_27 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class Decoder5(nn.Module): def __init__(self, model=None, fixed=False): super(Decoder5, self).__init__() self.fixed = fixed self.conv51 = nn.Conv2d(512, 512, 3, 1, 0) self.conv44 = nn.Conv2d(512, 512, 3, 1, 0) self.conv43 = nn.Conv2d(512, 512, 3, 1, 0) self.conv42 = nn.Conv2d(512, 512, 3, 1, 0) self.conv41 = nn.Conv2d(512, 256, 3, 1, 0) self.conv34 = nn.Conv2d(256, 256, 3, 1, 0) self.conv33 = nn.Conv2d(256, 256, 3, 1, 0) self.conv32 = nn.Conv2d(256, 256, 3, 1, 0) self.conv31 = nn.Conv2d(256, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv21 = nn.Conv2d(128, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv11 = nn.Conv2d(64, 3, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input): y = self.relu(self.conv51(self.pad(input))) y = self.unpool(y) y = self.relu(self.conv44(self.pad(y))) y = self.relu(self.conv43(self.pad(y))) y = self.relu(self.conv42(self.pad(y))) y = self.relu(self.conv41(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv34(self.pad(y))) y = self.relu(self.conv33(self.pad(y))) y = self.relu(self.conv32(self.pad(y))) y = self.relu(self.conv31(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv22(self.pad(y))) y = self.relu(self.conv21(self.pad(y))) y = self.unpool(y) y = self.relu(self.conv12(self.pad(y))) y = self.relu(self.conv11(self.pad(y))) return y def get_inputs(): return [torch.rand([4, 512, 4, 4])] def get_init_inputs(): return [[], {}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 6 x1 = xindex // 6 % 6 x2 = xindex // 36 x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 + x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None, eviction_policy='evict_last') tl.store(out_ptr0 + x3, tmp0, None) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 8 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x1 = xindex // 10 % 10 x0 = xindex % 10 x4 = xindex // 100 x2 = xindex // 100 % 512 x7 = xindex tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1 ))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0 ))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 4, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 4 * tmp4 + 16 * x4), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, None) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 10 x1 = xindex // 10 % 10 x4 = xindex // 100 x2 = xindex // 100 % 512 x5 = xindex tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, None) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x1 = xindex // 18 % 18 x0 = xindex % 18 x4 = xindex // 324 x2 = xindex // 324 % 256 x7 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x1))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 8, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 8 * tmp4 + 64 * x4), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, None) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 18 x1 = xindex // 18 % 18 x4 = xindex // 324 x2 = xindex // 324 % 256 x5 = xindex tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 + x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, None) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x1 = xindex // 34 % 34 x0 = xindex % 34 x4 = xindex // 1156 x2 = xindex // 1156 % 128 x7 = xindex tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x1))), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x0))), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 16, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 16 * tmp4 + 256 * x4), None, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, None) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 34 x1 = xindex // 34 % 34 x4 = xindex // 1156 x2 = xindex // 1156 % 128 x5 = xindex tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 + x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x4), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, None) @triton.jit def triton_poi_fused_arange_10(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tl.store(out_ptr0 + x0, tmp0, xmask) @triton.jit def triton_poi_fused__to_copy_add_arange_mul_11(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 * tmp2 tmp4 = tmp3.to(tl.int32) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12(in_ptr0 , in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 66 % 66 x0 = xindex % 66 x4 = xindex // 4356 x2 = xindex // 4356 % 64 x7 = xindex tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 + x1))), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 + x0))), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 32, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr1 + (tmp8 + 32 * tmp4 + 1024 * x4), xmask, eviction_policy='evict_last') tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tl.store(out_ptr0 + x7, tmp13, xmask) @triton.jit def triton_poi_fused_convolution_reflection_pad2d_relu_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1115136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 66 x1 = xindex // 66 % 66 x4 = xindex // 4356 x2 = xindex // 4356 % 64 x5 = xindex tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-1 + x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 4096 * x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + x5, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_14(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 4096 % 3 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, None) tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 4096 % 64 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 1024 % 64 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 1024 % 128 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 256 % 128 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_19(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 256 % 256 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_20(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 256 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 64 % 512 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 512 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x3, tmp6, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27) = args args.clear() assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1)) assert_size_stride(primals_2, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_3, (512,), (1,)) assert_size_stride(primals_4, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_5, (512,), (1,)) assert_size_stride(primals_6, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_7, (512,), (1,)) assert_size_stride(primals_8, (512, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_9, (512,), (1,)) assert_size_stride(primals_10, (256, 512, 3, 3), (4608, 9, 3, 1)) assert_size_stride(primals_11, (256,), (1,)) assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_13, (256,), (1,)) assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_15, (256,), (1,)) assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_17, (256,), (1,)) assert_size_stride(primals_18, (128, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_19, (128,), (1,)) assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_21, (128,), (1,)) assert_size_stride(primals_22, (64, 128, 3, 3), (1152, 9, 3, 1)) assert_size_stride(primals_23, (64,), (1,)) assert_size_stride(primals_24, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_25, (64,), (1,)) assert_size_stride(primals_26, (3, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_27, (3,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch. float32) get_raw_stream(0) triton_poi_fused_reflection_pad2d_0[grid(73728)](primals_1, buf0, 73728, XBLOCK=1024, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 512, 4, 4), (8192, 16, 4, 1)) buf2 = empty_strided_cuda((8,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK =8, num_warps=1, num_stages=1) buf3 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid (204800)](buf2, buf1, primals_3, buf3, 204800, XBLOCK=512, num_warps=8, num_stages=1) buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 512, 8, 8), (32768, 64, 8, 1)) buf5 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(204800)](buf4 , primals_5, buf5, 204800, XBLOCK=1024, num_warps=4, num_stages=1) buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 512, 8, 8), (32768, 64, 8, 1)) buf7 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(204800)](buf6 , primals_7, buf7, 204800, XBLOCK=1024, num_warps=4, num_stages=1) buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 512, 8, 8), (32768, 64, 8, 1)) buf9 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(204800)](buf8 , primals_9, buf9, 204800, XBLOCK=1024, num_warps=4, num_stages=1) buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 256, 8, 8), (16384, 64, 8, 1)) buf11 = empty_strided_cuda((16,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_4[grid(16)](buf11, 16, XBLOCK=16, num_warps=1, num_stages=1) buf12 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5[grid (331776)](buf11, buf10, primals_11, buf12, 331776, XBLOCK=512, num_warps=8, num_stages=1) buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf13, (4, 256, 16, 16), (65536, 256, 16, 1)) buf14 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(331776)]( buf13, primals_13, buf14, 331776, XBLOCK=512, num_warps=8, num_stages=1) buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf15, (4, 256, 16, 16), (65536, 256, 16, 1)) buf16 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(331776)]( buf15, primals_15, buf16, 331776, XBLOCK=512, num_warps=8, num_stages=1) buf17 = extern_kernels.convolution(buf16, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf17, (4, 256, 16, 16), (65536, 256, 16, 1)) buf18 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(331776)]( buf17, primals_17, buf18, 331776, XBLOCK=512, num_warps=8, num_stages=1) buf19 = extern_kernels.convolution(buf18, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1)) buf20 = empty_strided_cuda((32,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_7[grid(32)](buf20, 32, XBLOCK=32, num_warps=1, num_stages=1) buf21 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8[grid (591872)](buf20, buf19, primals_19, buf21, 591872, XBLOCK=1024, num_warps=4, num_stages=1) buf22 = extern_kernels.convolution(buf21, primals_20, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf22, (4, 128, 32, 32), (131072, 1024, 32, 1)) buf23 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_9[grid(591872)]( buf22, primals_21, buf23, 591872, XBLOCK=1024, num_warps=4, num_stages=1) buf24 = extern_kernels.convolution(buf23, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf24, (4, 64, 32, 32), (65536, 1024, 32, 1)) buf25 = empty_strided_cuda((64,), (1,), torch.int64) triton_poi_fused_arange_10[grid(64)](buf25, 64, XBLOCK=64, num_warps=1, num_stages=1) buf26 = empty_strided_cuda((64,), (1,), torch.int64) triton_poi_fused__to_copy_add_arange_mul_11[grid(64)](buf26, 64, XBLOCK=64, num_warps=1, num_stages=1) buf27 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1), torch.float32) triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12[ grid(1115136)](buf26, buf24, primals_23, buf27, 1115136, XBLOCK =512, num_warps=8, num_stages=1) buf28 = extern_kernels.convolution(buf27, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf28, (4, 64, 64, 64), (262144, 4096, 64, 1)) buf29 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1), torch.float32) triton_poi_fused_convolution_reflection_pad2d_relu_13[grid(1115136)]( buf28, primals_25, buf29, 1115136, XBLOCK=1024, num_warps=4, num_stages=1) buf30 = extern_kernels.convolution(buf29, primals_26, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 3, 64, 64), (12288, 4096, 64, 1)) buf31 = buf30 del buf30 buf32 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_14[grid(49152)]( buf31, primals_27, buf32, 49152, XBLOCK=512, num_warps=4, num_stages=1) del primals_27 buf33 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_15[grid(1048576)]( buf28, primals_25, buf33, 1048576, XBLOCK=1024, num_warps=4, num_stages=1) del buf28 del primals_25 buf34 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_16[grid(262144)]( buf24, primals_23, buf34, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf24 del primals_23 buf35 = empty_strided_cuda((4, 128, 32, 32), (131072, 1024, 32, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_17[grid(524288)]( buf22, primals_21, buf35, 524288, XBLOCK=1024, num_warps=4, num_stages=1) del buf22 del primals_21 buf36 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_18[grid(131072)]( buf19, primals_19, buf36, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del buf19 del primals_19 buf37 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_19[grid(262144)]( buf17, primals_17, buf37, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf17 del primals_17 buf38 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_19[grid(262144)]( buf15, primals_15, buf38, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf15 del primals_15 buf39 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_19[grid(262144)]( buf13, primals_13, buf39, 262144, XBLOCK=1024, num_warps=4, num_stages=1) del buf13 del primals_13 buf40 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch .bool) triton_poi_fused_convolution_relu_threshold_backward_20[grid(65536)]( buf10, primals_11, buf40, 65536, XBLOCK=512, num_warps=4, num_stages=1) del buf10 del primals_11 buf41 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch .bool) triton_poi_fused_convolution_relu_threshold_backward_21[grid(131072)]( buf8, primals_9, buf41, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del buf8 del primals_9 buf42 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch .bool) triton_poi_fused_convolution_relu_threshold_backward_21[grid(131072)]( buf6, primals_7, buf42, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del buf6 del primals_7 buf43 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch .bool) triton_poi_fused_convolution_relu_threshold_backward_21[grid(131072)]( buf4, primals_5, buf43, 131072, XBLOCK=1024, num_warps=4, num_stages=1) del buf4 del primals_5 buf44 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool ) triton_poi_fused_convolution_relu_threshold_backward_22[grid(32768)]( buf1, primals_3, buf44, 32768, XBLOCK=128, num_warps=4, num_stages=1) del buf1 del primals_3 return (buf31, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf14, buf16, buf18, buf20, buf21, buf23, buf25, buf26, buf27, buf29, buf32, buf33, buf34, buf35, buf36, buf37, buf38, buf39, buf40, buf41, buf42, buf43, buf44) class Decoder5New(nn.Module): def __init__(self, model=None, fixed=False): super(Decoder5New, self).__init__() self.fixed = fixed self.conv51 = nn.Conv2d(512, 512, 3, 1, 0) self.conv44 = nn.Conv2d(512, 512, 3, 1, 0) self.conv43 = nn.Conv2d(512, 512, 3, 1, 0) self.conv42 = nn.Conv2d(512, 512, 3, 1, 0) self.conv41 = nn.Conv2d(512, 256, 3, 1, 0) self.conv34 = nn.Conv2d(256, 256, 3, 1, 0) self.conv33 = nn.Conv2d(256, 256, 3, 1, 0) self.conv32 = nn.Conv2d(256, 256, 3, 1, 0) self.conv31 = nn.Conv2d(256, 128, 3, 1, 0) self.conv22 = nn.Conv2d(128, 128, 3, 1, 0) self.conv21 = nn.Conv2d(128, 64, 3, 1, 0) self.conv12 = nn.Conv2d(64, 64, 3, 1, 0) self.conv11 = nn.Conv2d(64, 3, 3, 1, 0) self.relu = nn.ReLU(inplace=True) self.unpool = nn.UpsamplingNearest2d(scale_factor=2) self.pad = nn.ReflectionPad2d((1, 1, 1, 1)) if model: self.load_state_dict(torch.load(model, map_location=lambda storage, location: storage)) if fixed: for param in self.parameters(): param.requires_grad = False def forward(self, input_0): primals_2 = self.conv51.weight primals_3 = self.conv51.bias primals_4 = self.conv44.weight primals_5 = self.conv44.bias primals_6 = self.conv43.weight primals_7 = self.conv43.bias primals_8 = self.conv42.weight primals_9 = self.conv42.bias primals_10 = self.conv41.weight primals_11 = self.conv41.bias primals_12 = self.conv34.weight primals_13 = self.conv34.bias primals_14 = self.conv33.weight primals_15 = self.conv33.bias primals_16 = self.conv32.weight primals_17 = self.conv32.bias primals_18 = self.conv31.weight primals_19 = self.conv31.bias primals_20 = self.conv22.weight primals_21 = self.conv22.bias primals_22 = self.conv21.weight primals_23 = self.conv21.bias primals_24 = self.conv12.weight primals_25 = self.conv12.bias primals_26 = self.conv11.weight primals_27 = self.conv11.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27]) return output[0]	EndyWon/Texture-Reformer	Decoder5	false	8,226	[ "MIT" ]	11	f84f95accb3574c7b759a7f03c0b0b4e150314b5	https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5
Swish	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/om/comaldgg7rn4hqmylto753cokwvf6khpxnn5mnpvfcbxzeqqpbsc.py # Topologically Sorted Source Nodes: [sigmoid, mul_], Original ATen: [aten.sigmoid, aten.mul] # Source node to ATen node mapping: # mul_ => mul # sigmoid => sigmoid # Graph fragment: # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %sigmoid), kwargs = {}) # %copy_ : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%arg0_1, %mul), kwargs = {}) triton_poi_fused_mul_sigmoid_0 = async_compile.triton('triton_poi_fused_mul_sigmoid_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_0', 'mutated_arg_names': ['in_ptr0', 'out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_sigmoid_0(in_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tl.sigmoid(tmp0) tmp2 = tmp0 * tmp1 tl.store(out_ptr1 + (x0), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [sigmoid, mul_], Original ATen: [aten.sigmoid, aten.mul] stream0 = get_raw_stream(0) triton_poi_fused_mul_sigmoid_0.run(arg0_1, arg0_1, 256, grid=grid(256), stream=stream0) return (arg0_1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.utils.data import torch.nn as nn from math import sqrt as sqrt from itertools import product as product class Swish(nn.Module): def forward(self, x): return x.mul_(torch.sigmoid(x)) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.utils.data import torch.nn as nn from math import sqrt as sqrt from itertools import product as product assert_size_stride = torch._C._dynamo.guards.assert_size_stride @triton.jit def triton_poi_fused_mul_sigmoid_0(in_ptr0, out_ptr1, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tl.sigmoid(tmp0) tmp2 = tmp0 * tmp1 tl.store(out_ptr1 + x0, tmp2, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) get_raw_stream(0) triton_poi_fused_mul_sigmoid_0[grid(256)](arg0_1, arg0_1, 256, XBLOCK=128, num_warps=4, num_stages=1) return arg0_1, class SwishNew(nn.Module): def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	Het-Shah/Monk_Object_Detection	Swish	false	8,227	[ "Apache-2.0" ]	15	1d7a07193ea3455221caa41d07c33c81d50c6b3f	https://github.com/Het-Shah/Monk_Object_Detection/tree/1d7a07193ea3455221caa41d07c33c81d50c6b3f
BackprojectDepth	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/m4/cm4e6s5hnyo5vujmzt4q7nka57mncin5uq4eo5vri5ra7mzvldlh.py # Topologically Sorted Source Nodes: [cam_points_1], Original ATen: [aten.cat] # Source node to ATen node mapping: # cam_points_1 => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view, %arg2_1], 1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 320 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 5 x0 = xindex % 16 x2 = (xindex // 80) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16x1) + (64x2)), tmp4 & xmask, other=0.0) tmp6 = tl.load(in_ptr1 + (x0 + (16x1) + (64x2)), tmp4 & xmask, other=0.0) tmp7 = tmp5 * tmp6 tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype) tmp9 = tl.where(tmp4, tmp7, tmp8) tmp10 = tmp0 >= tmp3 tmp11 = tl.full([1], 5, tl.int64) tmp12 = tmp0 < tmp11 tmp13 = tl.load(in_ptr2 + (x0 + (16*x2)), tmp10 & xmask, eviction_policy='evict_last', other=0.0) tmp14 = tl.where(tmp4, tmp9, tmp13) tl.store(out_ptr0 + (x3), tmp14, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 1, 16), (16, 16, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 5, 16), (80, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [cam_points_1], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(arg0_1, arg1_1, arg2_1, buf0, 320, grid=grid(320), stream=stream0) del arg0_1 del arg1_1 del arg2_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 1, 16), (16, 16, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class BackprojectDepth(nn.Module): """Layer to transform a depth image into a point cloud """ def __init__(self, batch_size, height, width): super(BackprojectDepth, self).__init__() self.batch_size = batch_size self.height = height self.width = width self.ones = nn.Parameter(torch.ones(self.batch_size, 1, self.height * self.width), requires_grad=False) def forward(self, depth, norm_pix_coords): cam_points = depth * norm_pix_coords cam_points = torch.cat([cam_points.view(self.batch_size, cam_points .shape[1], -1), self.ones], 1) return cam_points def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'batch_size': 4, 'height': 4, 'width': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 320 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 5 x0 = xindex % 16 x2 = xindex // 80 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp6 = tl.load(in_ptr1 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp7 = tmp5 * tmp6 tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype) tmp9 = tl.where(tmp4, tmp7, tmp8) tmp10 = tmp0 >= tmp3 tl.full([1], 5, tl.int64) tmp13 = tl.load(in_ptr2 + (x0 + 16 * x2), tmp10 & xmask, eviction_policy='evict_last', other=0.0) tmp14 = tl.where(tmp4, tmp9, tmp13) tl.store(out_ptr0 + x3, tmp14, xmask) def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 1, 16), (16, 16, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 5, 16), (80, 16, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(320)](arg0_1, arg1_1, arg2_1, buf0, 320, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 del arg1_1 del arg2_1 return buf0, class BackprojectDepthNew(nn.Module): """Layer to transform a depth image into a point cloud """ def __init__(self, batch_size, height, width): super(BackprojectDepthNew, self).__init__() self.batch_size = batch_size self.height = height self.width = width self.ones = nn.Parameter(torch.ones(self.batch_size, 1, self.height * self.width), requires_grad=False) def forward(self, input_0, input_1): arg2_1 = self.ones arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1, arg2_1]) return output[0]	HalleyJiang/PLNet	BackprojectDepth	false	8,228	[ "MIT" ]	16	a02bd5f343b9e4766891fd234e3a338c1eaa26ff	https://github.com/HalleyJiang/PLNet/tree/a02bd5f343b9e4766891fd234e3a338c1eaa26ff
AlterCoAttn	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ep/cephjjbdwu6testt2i7mcyzbq3s4pfvk2ixd4n4wnesozrntcmpj.py # Topologically Sorted Source Nodes: [hidden_feat], Original ATen: [aten.tanh] # Source node to ATen node mapping: # hidden_feat => tanh # Graph fragment: # %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_1,), kwargs = {}) triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/4h/c4hjo2rydfss6y37bfpikr6qruu37ownn7q4wldxnigmo3nyy2dp.py # Topologically Sorted Source Nodes: [attn_weight], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_weight => amax, div, exp, sub, sum_1 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_3, [1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_3, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tmp0 - tmp0 tmp2 = tl_math.exp(tmp1) tmp3 = tmp2 / tmp2 tl.store(out_ptr0 + (x0), tmp3, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/hj/chjwuldmh3s4mh4d5y6hzziociipznxarw27tdjn3vi3p4wi4peb.py # Topologically Sorted Source Nodes: [feat_2, hidden_feat_2], Original ATen: [aten.add, aten.tanh] # Source node to ATen node mapping: # feat_2 => add # hidden_feat_2 => tanh_1 # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_8, %expand), kwargs = {}) # %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {}) triton_poi_fused_add_tanh_2 = async_compile.triton('triton_poi_fused_add_tanh_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_tanh_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 4 x2 = (xindex // 16) tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (x0 + (4x2)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp7 = libdevice.tanh(tmp6) tl.store(in_out_ptr0 + (x3), tmp7, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/us/cusyqra2bg2vbuuxs26cz3sihdy7z4jtgux6v3orbezw7iu5eot5.py # Topologically Sorted Source Nodes: [attn_weight_1], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_weight_1 => amax_1, exp_1, sub_1 # Graph fragment: # %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_11, [1], True), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_11, %amax_1), kwargs = {}) # %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {}) triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/wg/cwgoxrbhmwbtk44l4btvoekxvdbagjk6vkr5n42rshizrndijbnh.py # Topologically Sorted Source Nodes: [attn_weight_1], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_weight_1 => div_1, sum_2 # Graph fragment: # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [1], True), kwargs = {}) # %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_2), kwargs = {}) triton_poi_fused__softmax_4 = async_compile.triton('triton_poi_fused__softmax_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/2m/c2mv4hqncezx6odxrxznasmrym4v2lk6oeijrlf422aetqukoofq.py # Topologically Sorted Source Nodes: [feat_4, hidden_feat_4], Original ATen: [aten.add, aten.tanh] # Source node to ATen node mapping: # feat_4 => add_1 # hidden_feat_4 => tanh_2 # Graph fragment: # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_16, %expand_1), kwargs = {}) # %tanh_2 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {}) triton_poi_fused_add_tanh_5 = async_compile.triton('triton_poi_fused_add_tanh_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_tanh_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (x2), xmask) tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp7 = libdevice.tanh(tmp6) tl.store(in_out_ptr0 + (x2), tmp7, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (1, 4), (4, 1)) assert_size_stride(primals_5, (1, ), (1, )) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4, ), (1, )) assert_size_stride(primals_9, (4, 4), (4, 1)) assert_size_stride(primals_10, (4, ), (1, )) assert_size_stride(primals_11, (1, 4), (4, 1)) assert_size_stride(primals_12, (1, ), (1, )) assert_size_stride(primals_13, (4, 4), (4, 1)) assert_size_stride(primals_14, (4, ), (1, )) assert_size_stride(primals_15, (4, 4), (4, 1)) assert_size_stride(primals_16, (4, ), (1, )) assert_size_stride(primals_17, (1, 4), (4, 1)) assert_size_stride(primals_18, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [hidden_feat], Original ATen: [aten.tanh] stream0 = get_raw_stream(0) triton_poi_fused_tanh_0.run(buf1, primals_3, 16, grid=grid(16), stream=stream0) del primals_3 buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3) del primals_5 buf4 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32) # Topologically Sorted Source Nodes: [attn_weight], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf3, buf4, 4, grid=grid(4), stream=stream0) buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_weight, view, attn_X], Original ATen: [aten._softmax, aten.view, aten.bmm] extern_kernels.bmm(buf4, reinterpret_tensor(primals_1, (4, 1, 4), (4, 4, 1), 0), out=buf5) buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf6) del primals_7 buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf5, (4, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf7) buf8 = reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0); del buf6 # reuse # Topologically Sorted Source Nodes: [feat_2, hidden_feat_2], Original ATen: [aten.add, aten.tanh] triton_poi_fused_add_tanh_2.run(buf8, primals_8, buf7, primals_10, 64, grid=grid(64), stream=stream0) del primals_10 del primals_8 buf10 = reinterpret_tensor(buf7, (16, 1), (1, 1), 0); del buf7 # reuse # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.addmm] extern_kernels.addmm(primals_12, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf10) del primals_12 buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) # Topologically Sorted Source Nodes: [attn_weight_1], Original ATen: [aten._softmax] triton_poi_fused__softmax_3.run(buf10, buf11, 16, grid=grid(16), stream=stream0) buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_weight_1], Original ATen: [aten._softmax] triton_poi_fused__softmax_4.run(buf11, buf12, 16, grid=grid(16), stream=stream0) buf13 = reinterpret_tensor(buf11, (4, 1, 4), (4, 4, 1), 0); del buf11 # reuse # Topologically Sorted Source Nodes: [attn_X_1], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf12, (4, 1, 4), (4, 0, 1), 0), primals_6, out=buf13) buf14 = reinterpret_tensor(buf12, (4, 4), (4, 1), 0); del buf12 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(primals_1, reinterpret_tensor(primals_13, (4, 4), (1, 4), 0), out=buf14) del primals_13 buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf13, (4, 4), (4, 1), 0), reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf15) buf16 = reinterpret_tensor(buf14, (4, 1, 4), (4, 4, 1), 0); del buf14 # reuse # Topologically Sorted Source Nodes: [feat_4, hidden_feat_4], Original ATen: [aten.add, aten.tanh] triton_poi_fused_add_tanh_5.run(buf16, primals_14, buf15, primals_16, 16, grid=grid(16), stream=stream0) del primals_14 del primals_16 buf18 = reinterpret_tensor(buf4, (4, 1), (1, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [linear_7], Original ATen: [aten.addmm] extern_kernels.addmm(primals_18, reinterpret_tensor(buf16, (4, 4), (4, 1), 0), reinterpret_tensor(primals_17, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf18) del primals_18 buf19 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32) # Topologically Sorted Source Nodes: [attn_weight_2], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf18, buf19, 4, grid=grid(4), stream=stream0) buf20 = reinterpret_tensor(buf15, (4, 1, 4), (4, 4, 1), 0); del buf15 # reuse # Topologically Sorted Source Nodes: [attn_weight_2, view_8, attn_X_2], Original ATen: [aten._softmax, aten.view, aten.bmm] extern_kernels.bmm(buf19, reinterpret_tensor(primals_1, (4, 1, 4), (4, 4, 1), 0), out=buf20) del buf19 return (reinterpret_tensor(buf20, (4, 4), (4, 1), 0), reinterpret_tensor(buf13, (4, 4), (4, 1), 0), primals_1, primals_6, buf1, buf3, reinterpret_tensor(buf5, (4, 4), (4, 1), 0), buf8, buf10, reinterpret_tensor(buf13, (4, 4), (4, 1), 0), buf16, buf18, primals_17, primals_15, primals_11, primals_9, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn.functional as F import torch.nn as nn import torch.autograd import torch.nn class Attn(nn.Module): """ Unit attention operation for alternating co-attention. ``https://arxiv.org/pdf/1606.00061.pdf`` .. math:: \\begin{array}{ll} H = \\tanh(W_x * X + (W_g * g)) \\\\ a = softmax(w_{hx}^T * H}) \\\\ output = sum a_i * x_i \\end{array} Args: num_hidden: Number of output hidden size input_feat_size: Feature size of input image guidance_size: Feature size of attention guidance [default: 0] dropout: Dropout rate of attention operation [default: 0.5] Inputs: - X (batch, input_seq_size, input_feat_size): Input image feature - g (batch, guidance_size): Attention guidance """ def __init__(self, num_hidden, input_feat_size, guidance_size=0, dropout=0.5): super(Attn, self).__init__() self.num_hidden = num_hidden self.input_feat_size = input_feat_size self.guidance_size = guidance_size self.dropout = nn.Dropout(dropout) if dropout > 0 else None self.W_x = nn.Linear(input_feat_size, num_hidden) if guidance_size > 0: self.W_g = nn.Linear(guidance_size, num_hidden) self.W_hx = nn.Linear(num_hidden, 1) def forward(self, X, g=None): _batch_size, input_seq_size, input_feat_size = X.size() feat = self.W_x(X) if g is not None: g_emb = self.W_g(g).view(-1, 1, self.num_hidden) feat = feat + g_emb.expand_as(feat) hidden_feat = torch.tanh(feat) if self.dropout is not None: hidden_feat = self.dropout(hidden_feat) attn_weight = F.softmax(self.W_hx(hidden_feat), dim=1) attn_X = torch.bmm(attn_weight.view(-1, 1, input_seq_size), X.view( -1, input_seq_size, input_feat_size)) return attn_X.view(-1, input_feat_size) class AlterCoAttn(nn.Module): """ Alternatiing Co-Attention mechanism according to ``https://arxiv.org/pdf/1606.00061.pdf`` .. math:: \\begin{array}{ll} \\hat{s} = Attn(Q, 0) \\\\ \\hat{v} = Attn(\\hat{s}, V) \\\\ \\hat{q} = Attn(Q, \\hat{v}) \\end{array} """ def __init__(self, num_hidden, img_feat_size, ques_feat_size, dropout=0.5): super(AlterCoAttn, self).__init__() self.num_hidden = num_hidden self.img_feat_size = img_feat_size self.ques_feat_size = ques_feat_size self.attn1 = Attn(num_hidden, ques_feat_size, guidance_size=0, dropout=dropout) self.attn2 = Attn(num_hidden, img_feat_size, ques_feat_size, dropout=dropout) self.attn3 = Attn(num_hidden, ques_feat_size, img_feat_size, dropout=dropout) def forward(self, ques_feat, img_feat): """ : param ques_feat: [batch, ques_feat_size] : param img_feat: [batch, img_seq_size, ques_feat_size] """ ques_self_attn = self.attn1(ques_feat.unsqueeze(1), None) img_attn_feat = self.attn2(img_feat, ques_self_attn) ques_attn_feat = self.attn3(ques_feat.unsqueeze(1), img_attn_feat) return ques_attn_feat, img_attn_feat def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'num_hidden': 4, 'img_feat_size': 4, 'ques_feat_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn.functional as F import torch.nn as nn import torch.autograd import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + x2, tmp3, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tmp0 - tmp0 tmp2 = tl_math.exp(tmp1) tmp3 = tmp2 / tmp2 tl.store(out_ptr0 + x0, tmp3, xmask) @triton.jit def triton_poi_fused_add_tanh_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 4 x2 = xindex // 16 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last' ) tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp7 = libdevice.tanh(tmp6) tl.store(in_out_ptr0 + x3, tmp7, xmask) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused_add_tanh_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + x2, xmask) tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp7 = libdevice.tanh(tmp6) tl.store(in_out_ptr0 + x2, tmp7, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18 ) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (1, 4), (4, 1)) assert_size_stride(primals_5, (1,), (1,)) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4,), (1,)) assert_size_stride(primals_9, (4, 4), (4, 1)) assert_size_stride(primals_10, (4,), (1,)) assert_size_stride(primals_11, (1, 4), (4, 1)) assert_size_stride(primals_12, (1,), (1,)) assert_size_stride(primals_13, (4, 4), (4, 1)) assert_size_stride(primals_14, (4,), (1,)) assert_size_stride(primals_15, (4, 4), (4, 1)) assert_size_stride(primals_16, (4,), (1,)) assert_size_stride(primals_17, (1, 4), (4, 1)) assert_size_stride(primals_18, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0) del buf0 get_raw_stream(0) triton_poi_fused_tanh_0[grid(16)](buf1, primals_3, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_3 buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha =1, beta=1, out=buf3) del primals_5 buf4 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32) triton_poi_fused__softmax_1[grid(4)](buf3, buf4, 4, XBLOCK=4, num_warps=1, num_stages=1) buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32) extern_kernels.bmm(buf4, reinterpret_tensor(primals_1, (4, 1, 4), ( 4, 4, 1), 0), out=buf5) buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf6) del primals_7 buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf5, (4, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf7) buf8 = reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0) del buf6 triton_poi_fused_add_tanh_2[grid(64)](buf8, primals_8, buf7, primals_10, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_10 del primals_8 buf10 = reinterpret_tensor(buf7, (16, 1), (1, 1), 0) del buf7 extern_kernels.addmm(primals_12, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf10) del primals_12 buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) triton_poi_fused__softmax_3[grid(16)](buf10, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1) buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) triton_poi_fused__softmax_4[grid(16)](buf11, buf12, 16, XBLOCK=16, num_warps=1, num_stages=1) buf13 = reinterpret_tensor(buf11, (4, 1, 4), (4, 4, 1), 0) del buf11 extern_kernels.bmm(reinterpret_tensor(buf12, (4, 1, 4), (4, 0, 1), 0), primals_6, out=buf13) buf14 = reinterpret_tensor(buf12, (4, 4), (4, 1), 0) del buf12 extern_kernels.mm(primals_1, reinterpret_tensor(primals_13, (4, 4), (1, 4), 0), out=buf14) del primals_13 buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf13, (4, 4), (4, 1), 0), reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf15) buf16 = reinterpret_tensor(buf14, (4, 1, 4), (4, 4, 1), 0) del buf14 triton_poi_fused_add_tanh_5[grid(16)](buf16, primals_14, buf15, primals_16, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_14 del primals_16 buf18 = reinterpret_tensor(buf4, (4, 1), (1, 1), 0) del buf4 extern_kernels.addmm(primals_18, reinterpret_tensor(buf16, (4, 4), (4, 1), 0), reinterpret_tensor(primals_17, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf18) del primals_18 buf19 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32) triton_poi_fused__softmax_1[grid(4)](buf18, buf19, 4, XBLOCK=4, num_warps=1, num_stages=1) buf20 = reinterpret_tensor(buf15, (4, 1, 4), (4, 4, 1), 0) del buf15 extern_kernels.bmm(buf19, reinterpret_tensor(primals_1, (4, 1, 4), (4, 4, 1), 0), out=buf20) del buf19 return reinterpret_tensor(buf20, (4, 4), (4, 1), 0), reinterpret_tensor( buf13, (4, 4), (4, 1), 0 ), primals_1, primals_6, buf1, buf3, reinterpret_tensor(buf5, (4, 4 ), (4, 1), 0), buf8, buf10, reinterpret_tensor(buf13, (4, 4), (4, 1), 0 ), buf16, buf18, primals_17, primals_15, primals_11, primals_9, primals_4 class Attn(nn.Module): """ Unit attention operation for alternating co-attention. ``https://arxiv.org/pdf/1606.00061.pdf`` .. math:: \\begin{array}{ll} H = \\tanh(W_x * X + (W_g * g)) \\\\ a = softmax(w_{hx}^T * H}) \\\\ output = sum a_i * x_i \\end{array} Args: num_hidden: Number of output hidden size input_feat_size: Feature size of input image guidance_size: Feature size of attention guidance [default: 0] dropout: Dropout rate of attention operation [default: 0.5] Inputs: - X (batch, input_seq_size, input_feat_size): Input image feature - g (batch, guidance_size): Attention guidance """ def __init__(self, num_hidden, input_feat_size, guidance_size=0, dropout=0.5): super(Attn, self).__init__() self.num_hidden = num_hidden self.input_feat_size = input_feat_size self.guidance_size = guidance_size self.dropout = nn.Dropout(dropout) if dropout > 0 else None self.W_x = nn.Linear(input_feat_size, num_hidden) if guidance_size > 0: self.W_g = nn.Linear(guidance_size, num_hidden) self.W_hx = nn.Linear(num_hidden, 1) def forward(self, X, g=None): _batch_size, input_seq_size, input_feat_size = X.size() feat = self.W_x(X) if g is not None: g_emb = self.W_g(g).view(-1, 1, self.num_hidden) feat = feat + g_emb.expand_as(feat) hidden_feat = torch.tanh(feat) if self.dropout is not None: hidden_feat = self.dropout(hidden_feat) attn_weight = F.softmax(self.W_hx(hidden_feat), dim=1) attn_X = torch.bmm(attn_weight.view(-1, 1, input_seq_size), X.view( -1, input_seq_size, input_feat_size)) return attn_X.view(-1, input_feat_size) class AlterCoAttnNew(nn.Module): """ Alternatiing Co-Attention mechanism according to ``https://arxiv.org/pdf/1606.00061.pdf`` .. math:: \\begin{array}{ll} \\hat{s} = Attn(Q, 0) \\\\ \\hat{v} = Attn(\\hat{s}, V) \\\\ \\hat{q} = Attn(Q, \\hat{v}) \\end{array} """ def __init__(self, num_hidden, img_feat_size, ques_feat_size, dropout=0.5): super(AlterCoAttnNew, self).__init__() self.num_hidden = num_hidden self.img_feat_size = img_feat_size self.ques_feat_size = ques_feat_size self.attn1 = Attn(num_hidden, ques_feat_size, guidance_size=0, dropout=dropout) self.attn2 = Attn(num_hidden, img_feat_size, ques_feat_size, dropout=dropout) self.attn3 = Attn(num_hidden, ques_feat_size, img_feat_size, dropout=dropout) def forward(self, input_0, input_1): primals_1 = self.attn1.W_x.weight primals_3 = self.attn1.W_x.bias primals_4 = self.attn1.W_hx.weight primals_5 = self.attn1.W_hx.bias primals_2 = self.attn2.W_x.weight primals_8 = self.attn2.W_x.bias primals_7 = self.attn2.W_g.weight primals_10 = self.attn2.W_g.bias primals_11 = self.attn2.W_hx.weight primals_12 = self.attn2.W_hx.bias primals_9 = self.attn3.W_x.weight primals_14 = self.attn3.W_x.bias primals_13 = self.attn3.W_g.weight primals_16 = self.attn3.W_g.bias primals_17 = self.attn3.W_hx.weight primals_18 = self.attn3.W_hx.bias primals_15 = input_0 primals_6 = input_1 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18]) return output[0], output[1]	HCY123902/visdial-gnn	AlterCoAttn	false	8,229	[ "MIT" ]	44	c38090c672cdf04a4fabe139f96d944fd82cb123	https://github.com/HCY123902/visdial-gnn/tree/c38090c672cdf04a4fabe139f96d944fd82cb123
Attention	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/wz/cwzlgmghy6nxuchbiog4puo46i4tq7yhd3qu6ftkgjf3gwib6hxn.py # Topologically Sorted Source Nodes: [e], Original ATen: [aten._softmax] # Source node to ATen node mapping: # e => amax, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%bmm, [2], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/yh/cyhf6bhaqimi2pucos5fnrpvhrt4vuaetbxnooyr5pvgjt7s6fgo.py # Topologically Sorted Source Nodes: [e], Original ATen: [aten._softmax] # Source node to ATen node mapping: # e => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {}) # %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ph/cph7lssg6ok5pn3rbmbprurourgd72q6n57v7b4qu2gqs3wcxbt7.py # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%bmm_1, %primals_3], 2), kwargs = {}) triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[128], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 128 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + ((4x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/mh/cmhexksjua66qlwvqxny5kxs3ustwgbgw7qlx4t7oqrpv2ofwcxy.py # Topologically Sorted Source Nodes: [h], Original ATen: [aten.tanh] # Source node to ATen node mapping: # h => tanh # Graph fragment: # %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_3,), kwargs = {}) triton_poi_fused_tanh_3 = async_compile.triton('triton_poi_fused_tanh_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_tanh_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_6, (4, 8), (8, 1)) assert_size_stride(primals_7, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm] extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_4, (4, 4, 4), (16, 1, 4), 0), out=buf1) buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [e], Original ATen: [aten._softmax] stream0 = get_raw_stream(0) triton_poi_fused__softmax_0.run(buf1, buf2, 64, grid=grid(64), stream=stream0) buf3 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [e], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf2, buf3, 64, grid=grid(64), stream=stream0) buf4 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [c], Original ATen: [aten.bmm] extern_kernels.bmm(buf3, primals_5, out=buf4) buf5 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] triton_poi_fused_cat_2.run(buf4, primals_3, buf5, 128, grid=grid(128), stream=stream0) buf6 = reinterpret_tensor(buf4, (16, 4), (4, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf5, (16, 8), (8, 1), 0), reinterpret_tensor(primals_6, (8, 4), (1, 8), 0), out=buf6) buf7 = reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0); del buf6 # reuse # Topologically Sorted Source Nodes: [h], Original ATen: [aten.tanh] triton_poi_fused_tanh_3.run(buf7, primals_7, 64, grid=grid(64), stream=stream0) del primals_7 return (buf7, buf3, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), buf3, reinterpret_tensor(buf5, (16, 8), (8, 1), 0), buf7, primals_6, reinterpret_tensor(primals_5, (4, 4, 4), (16, 1, 4), 0), primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import math import torch import torch.nn as nn import torch.nn.functional as F def init_linear_wt(linear): nn.init.xavier_uniform_(linear.weight) if linear.bias is not None: n = linear.bias.size(0) start, end = n // 4, n // 2 linear.bias.data.fill_(0.0) linear.bias.data[start:end].fill_(1.0) class Attention(nn.Module): def __init__(self, H, method='general'): super(Attention, self).__init__() self.method = method if self.method == 'general': self.W = nn.Linear(H, H) init_linear_wt(self.W) elif self.method == 'concat': self.W = nn.Linear(H * 2, H) self.v = nn.Parameter(torch.FloatTensor(1, H)) init_linear_wt(self.W) stdv = 1.0 / math.sqrt(self.v.size(0)) self.v.data.normal_(mean=0, std=stdv) self.W_c = nn.Linear(H * 2, H) init_linear_wt(self.W_c) def forward(self, K, V, Q): e = F.softmax(self.score(K, Q), dim=2) c = torch.bmm(e, V) h = torch.tanh(self.W_c(torch.cat((c, Q), dim=2))) return h, e def score(self, K, Q): if self.method == 'dot': return torch.bmm(Q, K.transpose(1, 2)) elif self.method == 'general': return torch.bmm(self.W(Q), K.transpose(1, 2)) elif self.method == 'concat': B, L, _ = K.shape E = self.W(torch.cat((K, Q.repeat(1, L, 1)), dim=2)) return torch.bmm(self.v.repeat(B, 1, 1), E.transpose(1, 2)) def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4]) ] def get_init_inputs(): return [[], {'H': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 128 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x2, tmp10, xmask) @triton.jit def triton_poi_fused_tanh_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + x2, tmp3, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_6, (4, 8), (8, 1)) assert_size_stride(primals_7, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_4, (4, 4, 4), (16, 1, 4), 0), out=buf1) buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0) del buf0 get_raw_stream(0) triton_poi_fused__softmax_0[grid(64)](buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1) buf3 = buf1 del buf1 triton_poi_fused__softmax_1[grid(64)](buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1) buf4 = buf2 del buf2 extern_kernels.bmm(buf3, primals_5, out=buf4) buf5 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32) triton_poi_fused_cat_2[grid(128)](buf4, primals_3, buf5, 128, XBLOCK=128, num_warps=4, num_stages=1) buf6 = reinterpret_tensor(buf4, (16, 4), (4, 1), 0) del buf4 extern_kernels.mm(reinterpret_tensor(buf5, (16, 8), (8, 1), 0), reinterpret_tensor(primals_6, (8, 4), (1, 8), 0), out=buf6) buf7 = reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0) del buf6 triton_poi_fused_tanh_3[grid(64)](buf7, primals_7, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_7 return buf7, buf3, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0 ), buf3, reinterpret_tensor(buf5, (16, 8), (8, 1), 0 ), buf7, primals_6, reinterpret_tensor(primals_5, (4, 4, 4), (16, 1, 4), 0), primals_4 def init_linear_wt(linear): nn.init.xavier_uniform_(linear.weight) if linear.bias is not None: n = linear.bias.size(0) start, end = n // 4, n // 2 linear.bias.data.fill_(0.0) linear.bias.data[start:end].fill_(1.0) class AttentionNew(nn.Module): def __init__(self, H, method='general'): super(AttentionNew, self).__init__() self.method = method if self.method == 'general': self.W = nn.Linear(H, H) init_linear_wt(self.W) elif self.method == 'concat': self.W = nn.Linear(H * 2, H) self.v = nn.Parameter(torch.FloatTensor(1, H)) init_linear_wt(self.W) stdv = 1.0 / math.sqrt(self.v.size(0)) self.v.data.normal_(mean=0, std=stdv) self.W_c = nn.Linear(H * 2, H) init_linear_wt(self.W_c) def score(self, K, Q): if self.method == 'dot': return torch.bmm(Q, K.transpose(1, 2)) elif self.method == 'general': return torch.bmm(self.W(Q), K.transpose(1, 2)) elif self.method == 'concat': B, L, _ = K.shape E = self.W(torch.cat((K, Q.repeat(1, L, 1)), dim=2)) return torch.bmm(self.v.repeat(B, 1, 1), E.transpose(1, 2)) def forward(self, input_0, input_1, input_2): primals_1 = self.W.weight primals_2 = self.W.bias primals_6 = self.W_c.weight primals_7 = self.W_c.bias primals_3 = input_0 primals_4 = input_1 primals_5 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0], output[1]	HLTCHKUST/sentiment-lookahead	Attention	false	8,230	[ "MIT" ]	13	1c076b7c5c31b0f7c454720377db4e733838ebb2	https://github.com/HLTCHKUST/sentiment-lookahead/tree/1c076b7c5c31b0f7c454720377db4e733838ebb2
ResidualBlock	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ux/cux7axsckwo5dxgyf2kefdy5fcl44asubo6jxnefaltmzk6rznwv.py # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d => convolution # y => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ex/cexxco2hfwx7xqwl3a5bhwrxujc26i4lp36x5zpghd5kdnrcvqan.py # Topologically Sorted Source Nodes: [y_1, add, relu_1], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # add => add # relu_1 => relu_1 # y_1 => convolution_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %convolution_1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_add_convolution_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_add_convolution_relu_threshold_backward_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_out_ptr0 + (x3), xmask) tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp4 = tmp0 + tmp3 tmp5 = tl.full([1], 0, tl.int32) tmp6 = triton_helpers.maximum(tmp5, tmp4) tmp7 = 0.0 tmp8 = tmp6 <= tmp7 tl.store(in_out_ptr0 + (x3), tmp6, xmask) tl.store(out_ptr0 + (x3), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 256, grid=grid(256), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1)) buf3 = buf2; del buf2 # reuse buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [y_1, add, relu_1], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward] triton_poi_fused_add_convolution_relu_threshold_backward_1.run(buf3, primals_3, primals_5, buf4, 256, grid=grid(256), stream=stream0) del primals_5 return (buf3, primals_1, primals_3, primals_4, buf1, buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn.functional as F import torch.nn as nn class ResidualBlock(nn.Module): def __init__(self, channels): super(ResidualBlock, self).__init__() self.channels = channels self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1) self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1) def forward(self, x): y = F.relu(self.conv1(x)) y = self.conv2(y) return F.relu(x + y) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'channels': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, xmask) @triton.jit def triton_poi_fused_add_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_out_ptr0 + x3, xmask) tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp4 = tmp0 + tmp3 tmp5 = tl.full([1], 0, tl.int32) tmp6 = triton_helpers.maximum(tmp5, tmp4) tmp7 = 0.0 tmp8 = tmp6 <= tmp7 tl.store(in_out_ptr0 + x3, tmp6, xmask) tl.store(out_ptr0 + x3, tmp8, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_relu_0[grid(256)](buf1, primals_2, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1)) buf3 = buf2 del buf2 buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)]( buf3, primals_3, primals_5, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 return buf3, primals_1, primals_3, primals_4, buf1, buf4 class ResidualBlockNew(nn.Module): def __init__(self, channels): super(ResidualBlockNew, self).__init__() self.channels = channels self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1) self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1) def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	HuangCongQing/pytorch	ResidualBlock	false	8,231	[ "MIT" ]	12	2b2b01d74b45cbe4e467da229798609e79cec97c	https://github.com/HuangCongQing/pytorch/tree/2b2b01d74b45cbe4e467da229798609e79cec97c
ScaleNorm	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ou/couxrbkgenwv3t7y24rmva6it72zdayzahx5dh2fmzhbdfmnp4yr.py # Topologically Sorted Source Nodes: [norm, clamp, norm_1], Original ATen: [aten.linalg_vector_norm, aten.clamp, aten.div] # Source node to ATen node mapping: # clamp => clamp_min # norm => pow_1, pow_2, sum_1 # norm_1 => div # Graph fragment: # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_2, 1e-05), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %clamp_min), kwargs = {}) triton_poi_fused_clamp_div_linalg_vector_norm_0 = async_compile.triton('triton_poi_fused_clamp_div_linalg_vector_norm_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_div_linalg_vector_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clamp_div_linalg_vector_norm_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (0)) tmp1 = tl.broadcast_to(tmp0, [XBLOCK]) tmp2 = tl.load(in_ptr1 + (4x0), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + (4x0)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (2 + (4x0)), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr1 + (3 + (4x0)), xmask, eviction_policy='evict_last') tmp3 = tmp2 * tmp2 tmp5 = tmp4 * tmp4 tmp6 = tmp3 + tmp5 tmp8 = tmp7 * tmp7 tmp9 = tmp6 + tmp8 tmp11 = tmp10 * tmp10 tmp12 = tmp9 + tmp11 tmp13 = libdevice.sqrt(tmp12) tmp14 = 1e-05 tmp15 = triton_helpers.maximum(tmp13, tmp14) tmp16 = tmp1 / tmp15 tl.store(out_ptr0 + (x0), tmp16, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/x3/cx3jkiijvakkkxpn6uepwnvbch4g75433gkxng55nqbprqso4blb.py # Topologically Sorted Source Nodes: [norm, clamp, norm_1, mul], Original ATen: [aten.linalg_vector_norm, aten.clamp, aten.div, aten.mul] # Source node to ATen node mapping: # clamp => clamp_min # mul => mul # norm => pow_1, pow_2, sum_1 # norm_1 => div # Graph fragment: # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_2, 1e-05), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %clamp_min), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %div), kwargs = {}) triton_poi_fused_clamp_div_linalg_vector_norm_mul_1 = async_compile.triton('triton_poi_fused_clamp_div_linalg_vector_norm_mul_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_div_linalg_vector_norm_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clamp_div_linalg_vector_norm_mul_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tl.store(out_ptr0 + (x2), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (), ()) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32) # Topologically Sorted Source Nodes: [norm, clamp, norm_1], Original ATen: [aten.linalg_vector_norm, aten.clamp, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_clamp_div_linalg_vector_norm_0.run(primals_1, primals_2, buf0, 64, grid=grid(64), stream=stream0) del primals_1 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [norm, clamp, norm_1, mul], Original ATen: [aten.linalg_vector_norm, aten.clamp, aten.div, aten.mul] triton_poi_fused_clamp_div_linalg_vector_norm_mul_1.run(primals_2, buf0, buf1, 256, grid=grid(256), stream=stream0) del buf0 return (buf1, primals_2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((), (), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch from torch import nn from torch.nn import Parameter class ScaleNorm(nn.Module): """ScaleNorm""" def __init__(self, scale, eps=1e-05): super(ScaleNorm, self).__init__() self.scale = Parameter(torch.tensor(scale)) self.eps = eps def forward(self, x): norm = self.scale / torch.norm(x, dim=-1, keepdim=True).clamp(min= self.eps) return x * norm def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'scale': 1.0}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice from torch import nn from torch.nn import Parameter assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_clamp_div_linalg_vector_norm_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK]) tmp2 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp3 = tmp2 * tmp2 tmp5 = tmp4 * tmp4 tmp6 = tmp3 + tmp5 tmp8 = tmp7 * tmp7 tmp9 = tmp6 + tmp8 tmp11 = tmp10 * tmp10 tmp12 = tmp9 + tmp11 tmp13 = libdevice.sqrt(tmp12) tmp14 = 1e-05 tmp15 = triton_helpers.maximum(tmp13, tmp14) tmp16 = tmp1 / tmp15 tl.store(out_ptr0 + x0, tmp16, xmask) @triton.jit def triton_poi_fused_clamp_div_linalg_vector_norm_mul_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tl.store(out_ptr0 + x2, tmp2, xmask) def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (), ()) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32) get_raw_stream(0) triton_poi_fused_clamp_div_linalg_vector_norm_0[grid(64)](primals_1, primals_2, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_1 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_clamp_div_linalg_vector_norm_mul_1[grid(256)]( primals_2, buf0, buf1, 256, XBLOCK=128, num_warps=4, num_stages=1) del buf0 return buf1, primals_2 class ScaleNormNew(nn.Module): """ScaleNorm""" def __init__(self, scale, eps=1e-05): super(ScaleNormNew, self).__init__() self.scale = Parameter(torch.tensor(scale)) self.eps = eps def forward(self, input_0): primals_1 = self.scale primals_2 = input_0 output = call([primals_1, primals_2]) return output[0]	HerbertMcSnout/transformers_with_trees	ScaleNorm	false	8,232	[ "MIT" ]	18	1afa6d4ad45207c9b2762600a9c227d721fbc825	https://github.com/HerbertMcSnout/transformers_with_trees/tree/1afa6d4ad45207c9b2762600a9c227d721fbc825
JointsMSELoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/tm/ctmm53d67esf6tqnvy43ebdc6pjkestl3fvkpf7dtv3ayg2ff4wk.py # Topologically Sorted Source Nodes: [mul, mul_1, mse_loss, loss, mul_2, mul_3, mse_loss_1, loss_1, mul_4, mul_5, mse_loss_2, loss_2, mul_6, mul_7, mse_loss_3, loss_3], Original ATen: [aten.mul, aten.mse_loss, aten.add] # Source node to ATen node mapping: # loss => add # loss_1 => add_1 # loss_2 => add_2 # loss_3 => add_3 # mse_loss => mean, pow_1, sub # mse_loss_1 => mean_1, pow_2, sub_1 # mse_loss_2 => mean_2, pow_3, sub_2 # mse_loss_3 => mean_3, pow_4, sub_3 # mul => mul # mul_1 => mul_1 # mul_2 => mul_2 # mul_3 => mul_3 # mul_4 => mul_4 # mul_5 => mul_5 # mul_6 => mul_6 # mul_7 => mul_7 # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze, %select), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_1, %select_1), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 0), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_2, %select_2), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_3, %select_3), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %mul_3), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {}) # %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_2,), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mean_1), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_4, %select_4), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_5, %select_5), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_4, %mul_5), kwargs = {}) # %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {}) # %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_3,), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mean_2), kwargs = {}) # %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_6, %select_6), kwargs = {}) # %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_7, %select_7), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_6, %mul_7), kwargs = {}) # %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {}) # %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_4,), kwargs = {}) # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mean_3), kwargs = {}) triton_per_fused_add_mse_loss_mul_0 = async_compile.triton('triton_per_fused_add_mse_loss_mul_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 4], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(4,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mse_loss_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_mse_loss_mul_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 1 rnumel = 4 RBLOCK: tl.constexpr = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (4r0), None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4r0), None, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (4r0), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr0 + (1 + (4r0)), None, eviction_policy='evict_last') tmp11 = tl.load(in_ptr1 + (1 + (4r0)), None, eviction_policy='evict_last') tmp13 = tl.load(in_ptr2 + (1 + (4r0)), None, eviction_policy='evict_last') tmp20 = tl.load(in_ptr0 + (2 + (4r0)), None, eviction_policy='evict_last') tmp21 = tl.load(in_ptr1 + (2 + (4r0)), None, eviction_policy='evict_last') tmp23 = tl.load(in_ptr2 + (2 + (4r0)), None, eviction_policy='evict_last') tmp30 = tl.load(in_ptr0 + (3 + (4r0)), None, eviction_policy='evict_last') tmp31 = tl.load(in_ptr1 + (3 + (4r0)), None, eviction_policy='evict_last') tmp33 = tl.load(in_ptr2 + (3 + (4r0)), None, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp5 = tmp2 - tmp4 tmp6 = tmp5 * tmp5 tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = tl.sum(tmp7, 1)[:, None] tmp12 = tmp10 * tmp11 tmp14 = tmp13 * tmp11 tmp15 = tmp12 - tmp14 tmp16 = tmp15 * tmp15 tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK]) tmp19 = tl.sum(tmp17, 1)[:, None] tmp22 = tmp20 * tmp21 tmp24 = tmp23 * tmp21 tmp25 = tmp22 - tmp24 tmp26 = tmp25 * tmp25 tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK]) tmp29 = tl.sum(tmp27, 1)[:, None] tmp32 = tmp30 * tmp31 tmp34 = tmp33 * tmp31 tmp35 = tmp32 - tmp34 tmp36 = tmp35 * tmp35 tmp37 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK]) tmp39 = tl.sum(tmp37, 1)[:, None] tmp40 = 4.0 tmp41 = tmp9 / tmp40 tmp42 = 0.0 tmp43 = tmp41 + tmp42 tmp44 = tmp19 / tmp40 tmp45 = tmp43 + tmp44 tmp46 = tmp29 / tmp40 tmp47 = tmp45 + tmp46 tmp48 = tmp39 / tmp40 tmp49 = tmp47 + tmp48 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp49, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, 4), (4, 1)) assert_size_stride(arg2_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf4 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [mul, mul_1, mse_loss, loss, mul_2, mul_3, mse_loss_1, loss_1, mul_4, mul_5, mse_loss_2, loss_2, mul_6, mul_7, mse_loss_3, loss_3], Original ATen: [aten.mul, aten.mse_loss, aten.add] stream0 = get_raw_stream(0) triton_per_fused_add_mse_loss_mul_0.run(buf4, arg0_1, arg2_1, arg1_1, 1, 4, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 del arg2_1 return (buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.parallel import torch.optim import torch.utils.data import torch.utils.data.distributed class JointsMSELoss(nn.Module): def __init__(self, use_target_weight): super(JointsMSELoss, self).__init__() self.criterion = nn.MSELoss(reduction='mean') self.use_target_weight = use_target_weight def forward(self, output, target, target_weight): batch_size = output.size(0) num_joints = output.size(1) heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1 ) heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1) loss = 0 for idx in range(num_joints): heatmap_pred = heatmaps_pred[idx].squeeze() heatmap_gt = heatmaps_gt[idx].squeeze() if self.use_target_weight: loss += self.criterion(heatmap_pred.mul(target_weight[:, idx]), heatmap_gt.mul(target_weight[:, idx])) else: loss += self.criterion(heatmap_pred, heatmap_gt) return loss def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {'use_target_weight': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn import torch.nn.parallel import torch.optim import torch.utils.data import torch.utils.data.distributed assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_mse_loss_mul_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): RBLOCK: tl.constexpr = 4 xoffset = tl.program_id(0) * XBLOCK xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last') tmp11 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last') tmp13 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last') tmp20 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last') tmp21 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last') tmp23 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last') tmp30 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last') tmp31 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last') tmp33 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp5 = tmp2 - tmp4 tmp6 = tmp5 * tmp5 tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = tl.sum(tmp7, 1)[:, None] tmp12 = tmp10 * tmp11 tmp14 = tmp13 * tmp11 tmp15 = tmp12 - tmp14 tmp16 = tmp15 * tmp15 tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK]) tmp19 = tl.sum(tmp17, 1)[:, None] tmp22 = tmp20 * tmp21 tmp24 = tmp23 * tmp21 tmp25 = tmp22 - tmp24 tmp26 = tmp25 * tmp25 tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK]) tmp29 = tl.sum(tmp27, 1)[:, None] tmp32 = tmp30 * tmp31 tmp34 = tmp33 * tmp31 tmp35 = tmp32 - tmp34 tmp36 = tmp35 * tmp35 tmp37 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK]) tmp39 = tl.sum(tmp37, 1)[:, None] tmp40 = 4.0 tmp41 = tmp9 / tmp40 tmp42 = 0.0 tmp43 = tmp41 + tmp42 tmp44 = tmp19 / tmp40 tmp45 = tmp43 + tmp44 tmp46 = tmp29 / tmp40 tmp47 = tmp45 + tmp46 tmp48 = tmp39 / tmp40 tmp49 = tmp47 + tmp48 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp49, None) def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, 4), (4, 1)) assert_size_stride(arg2_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf4 = buf0 del buf0 get_raw_stream(0) triton_per_fused_add_mse_loss_mul_0[grid(1)](buf4, arg0_1, arg2_1, arg1_1, 1, 4, XBLOCK=1, num_warps=2, num_stages=1) del arg0_1 del arg1_1 del arg2_1 return buf4, class JointsMSELossNew(nn.Module): def __init__(self, use_target_weight): super(JointsMSELossNew, self).__init__() self.criterion = nn.MSELoss(reduction='mean') self.use_target_weight = use_target_weight def forward(self, input_0, input_1, input_2): arg0_1 = input_0 arg1_1 = input_1 arg2_1 = input_2 output = call([arg0_1, arg1_1, arg2_1]) return output[0]	HowieMa/TransFusion-Pose	JointsMSELoss	false	8,233	[ "MIT" ]	17	b66ee5bafdc12a971088f9d54233408249e067db	https://github.com/HowieMa/TransFusion-Pose/tree/b66ee5bafdc12a971088f9d54233408249e067db
WBCEDiceLoss	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/qu/cquwvkmeubcp6wymk4u4nct43ulebw6jegaozbjmetlib4e3ue5g.py # Topologically Sorted Source Nodes: [neg, mul_2, pred, sigmoid_x, log, mul_3, sub_1, sub_2, log_1, mul_4, loss, bce, mul, intersection, mul_1, add, sum_2, sum_3, add_1, add_2, truediv, dice, mul_5, loss_1], Original ATen: [aten.neg, aten.mul, aten.sigmoid, aten.clamp, aten.log, aten.rsub, aten.sub, aten.mean, aten.sum, aten.add, aten.div] # Source node to ATen node mapping: # add => add # add_1 => add_1 # add_2 => add_2 # bce => mean # dice => sub # intersection => sum_1 # log => log # log_1 => log_1 # loss => sub_3 # loss_1 => add_3 # mul => mul # mul_1 => mul_1 # mul_2 => mul_2 # mul_3 => mul_3 # mul_4 => mul_4 # mul_5 => mul_5 # neg => neg # pred => sigmoid # sigmoid_x => clamp_max, clamp_min # sub_1 => sub_1 # sub_2 => sub_2 # sum_2 => sum_2 # sum_3 => sum_3 # truediv => div # Graph fragment: # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg2_1,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %arg1_1), kwargs = {}) # %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sigmoid, 1e-08), kwargs = {}) # %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 0.99999999), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_max,), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %log), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %clamp_max), kwargs = {}) # %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sub_2,), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %log_1), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_3, %mul_4), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_3,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_1), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, 2.0), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 1e-08), kwargs = {}) # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view,), kwargs = {}) # %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view_1,), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %sum_3), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, 1e-08), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %add_2), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 0.5), kwargs = {}) # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mul_5), kwargs = {}) triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0 = async_compile.triton('triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp2 = tl.load(in_ptr1 + (r0), None) tmp4 = tl.load(in_ptr2 + (r0), None) tmp1 = -tmp0 tmp3 = tmp1 * tmp2 tmp5 = tl.sigmoid(tmp4) tmp6 = 1e-08 tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = 0.99999999 tmp9 = triton_helpers.minimum(tmp7, tmp8) tmp10 = tl_math.log(tmp9) tmp11 = tmp3 * tmp10 tmp12 = 1.0 tmp13 = tmp12 - tmp2 tmp14 = tmp12 - tmp9 tmp15 = tl_math.log(tmp14) tmp16 = tmp13 * tmp15 tmp17 = tmp11 - tmp16 tmp18 = tl.broadcast_to(tmp17, [RBLOCK]) tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0)) tmp21 = tmp5 * tmp2 tmp22 = tl.broadcast_to(tmp21, [RBLOCK]) tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0)) tmp25 = tl.broadcast_to(tmp5, [RBLOCK]) tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0)) tmp28 = tl.broadcast_to(tmp2, [RBLOCK]) tmp30 = triton_helpers.promote_to_tensor(tl.sum(tmp28, 0)) tmp31 = 256.0 tmp32 = tmp20 / tmp31 tmp33 = 2.0 tmp34 = tmp24 * tmp33 tmp35 = tmp34 + tmp6 tmp36 = tmp27 + tmp30 tmp37 = tmp36 + tmp6 tmp38 = tmp35 / tmp37 tmp39 = tmp12 - tmp38 tmp40 = 0.5 tmp41 = tmp39 * tmp40 tmp42 = tmp32 + tmp41 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp42, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf4 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [neg, mul_2, pred, sigmoid_x, log, mul_3, sub_1, sub_2, log_1, mul_4, loss, bce, mul, intersection, mul_1, add, sum_2, sum_3, add_1, add_2, truediv, dice, mul_5, loss_1], Original ATen: [aten.neg, aten.mul, aten.sigmoid, aten.clamp, aten.log, aten.rsub, aten.sub, aten.mean, aten.sum, aten.add, aten.div] stream0 = get_raw_stream(0) triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0.run(buf4, arg2_1, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 del arg2_1 return (buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1, arg2_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F def dice_loss(pred, target, smooth=1e-08): iflat = pred.view(-1) tflat = target.view(-1) intersection = (iflat * tflat).sum() return 1 - (2.0 * intersection + smooth) / (iflat.sum() + tflat.sum() + smooth) def weighted_binary_cross_entropy(sigmoid_x, targets, pos_weight, weight= None, size_average=True, reduce=True): """ Args: sigmoid_x: predicted probability of size [N,C], N sample and C Class. Eg. Must be in range of [0,1], i.e. Output from Sigmoid. targets: true value, one-hot-like vector of size [N,C] pos_weight: Weight for postive sample """ if not targets.size() == sigmoid_x.size(): raise ValueError('Target size ({}) must be the same as input size ({})' .format(targets.size(), sigmoid_x.size())) sigmoid_x = sigmoid_x.clamp(min=1e-08, max=1 - 1e-08) loss = -pos_weight * targets * sigmoid_x.log() - (1 - targets) * (1 - sigmoid_x).log() if weight is not None: loss = loss * weight if not reduce: return loss elif size_average: return loss.mean() else: return loss.sum() class WBCEDiceLoss(nn.Module): def __init__(self, alpha=0.5): super(WBCEDiceLoss, self).__init__() self.alpha = alpha def forward(self, pred, target, weight): pred = F.sigmoid(pred) dice = dice_loss(pred, target) bce = weighted_binary_cross_entropy(pred, target, weight) loss = bce + dice * self.alpha return loss def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0( in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp2 = tl.load(in_ptr1 + r0, None) tmp4 = tl.load(in_ptr2 + r0, None) tmp1 = -tmp0 tmp3 = tmp1 * tmp2 tmp5 = tl.sigmoid(tmp4) tmp6 = 1e-08 tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = 0.99999999 tmp9 = triton_helpers.minimum(tmp7, tmp8) tmp10 = tl_math.log(tmp9) tmp11 = tmp3 * tmp10 tmp12 = 1.0 tmp13 = tmp12 - tmp2 tmp14 = tmp12 - tmp9 tmp15 = tl_math.log(tmp14) tmp16 = tmp13 * tmp15 tmp17 = tmp11 - tmp16 tmp18 = tl.broadcast_to(tmp17, [RBLOCK]) tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0)) tmp21 = tmp5 * tmp2 tmp22 = tl.broadcast_to(tmp21, [RBLOCK]) tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0)) tmp25 = tl.broadcast_to(tmp5, [RBLOCK]) tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0)) tmp28 = tl.broadcast_to(tmp2, [RBLOCK]) tmp30 = triton_helpers.promote_to_tensor(tl.sum(tmp28, 0)) tmp31 = 256.0 tmp32 = tmp20 / tmp31 tmp33 = 2.0 tmp34 = tmp24 * tmp33 tmp35 = tmp34 + tmp6 tmp36 = tmp27 + tmp30 tmp37 = tmp36 + tmp6 tmp38 = tmp35 / tmp37 tmp39 = tmp12 - tmp38 tmp40 = 0.5 tmp41 = tmp39 * tmp40 tmp42 = tmp32 + tmp41 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp42, None) def call(args): arg0_1, arg1_1, arg2_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf4 = buf0 del buf0 get_raw_stream(0) triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0[ grid(1)](buf4, arg2_1, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 del arg2_1 return buf4, def dice_loss(pred, target, smooth=1e-08): iflat = pred.view(-1) tflat = target.view(-1) intersection = (iflat * tflat).sum() return 1 - (2.0 * intersection + smooth) / (iflat.sum() + tflat.sum() + smooth) def weighted_binary_cross_entropy(sigmoid_x, targets, pos_weight, weight= None, size_average=True, reduce=True): """ Args: sigmoid_x: predicted probability of size [N,C], N sample and C Class. Eg. Must be in range of [0,1], i.e. Output from Sigmoid. targets: true value, one-hot-like vector of size [N,C] pos_weight: Weight for postive sample """ if not targets.size() == sigmoid_x.size(): raise ValueError('Target size ({}) must be the same as input size ({})' .format(targets.size(), sigmoid_x.size())) sigmoid_x = sigmoid_x.clamp(min=1e-08, max=1 - 1e-08) loss = -pos_weight * targets * sigmoid_x.log() - (1 - targets) * (1 - sigmoid_x).log() if weight is not None: loss = loss * weight if not reduce: return loss elif size_average: return loss.mean() else: return loss.sum() class WBCEDiceLossNew(nn.Module): def __init__(self, alpha=0.5): super(WBCEDiceLossNew, self).__init__() self.alpha = alpha def forward(self, input_0, input_1, input_2): arg0_1 = input_0 arg1_1 = input_1 arg2_1 = input_2 output = call([arg0_1, arg1_1, arg2_1]) return output[0]	Hhhhhhhhhhao/change_detection	WBCEDiceLoss	false	8,234	[ "MIT" ]	11	13b87c02166cc98d39d8be240a07abcf12893fe3	https://github.com/Hhhhhhhhhhao/change_detection/tree/13b87c02166cc98d39d8be240a07abcf12893fe3
Norm	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/dx/cdxcy63x3y5nnihp62h2e5onvuwtaakt3haegpxiu2p5g4hszcpn.py # Topologically Sorted Source Nodes: [mean, sub, mul, std, add, truediv, norm], Original ATen: [aten.mean, aten.sub, aten.mul, aten.std, aten.add, aten.div] # Source node to ATen node mapping: # add => add # mean => mean # mul => mul # norm => add_1 # std => sqrt, var # sub => sub # truediv => div # Graph fragment: # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_2, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %mean), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %sub), kwargs = {}) # %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%primals_2, [-1]), kwargs = {correction: 1.0, keepdim: True}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-06), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %primals_3), kwargs = {}) triton_poi_fused_add_div_mean_mul_std_sub_0 = async_compile.triton('triton_poi_fused_add_div_mean_mul_std_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_std_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_mean_mul_std_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask) tmp2 = tl.load(in_ptr1 + (4x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tmp9 = 4.0 tmp10 = tmp8 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp0 * tmp11 tmp13 = tmp2 - tmp10 tmp14 = tmp13 * tmp13 tmp15 = tmp3 - tmp10 tmp16 = tmp15 * tmp15 tmp17 = tmp14 + tmp16 tmp18 = tmp5 - tmp10 tmp19 = tmp18 * tmp18 tmp20 = tmp17 + tmp19 tmp21 = tmp7 - tmp10 tmp22 = tmp21 * tmp21 tmp23 = tmp20 + tmp22 tmp24 = 3.0 tmp25 = tmp23 / tmp24 tmp26 = libdevice.sqrt(tmp25) tmp27 = 1e-06 tmp28 = tmp26 + tmp27 tmp29 = tmp12 / tmp28 tmp31 = tmp29 + tmp30 tl.store(out_ptr0 + (x2), tmp31, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, ), (1, )) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mean, sub, mul, std, add, truediv, norm], Original ATen: [aten.mean, aten.sub, aten.mul, aten.std, aten.add, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_add_div_mean_mul_std_sub_0.run(primals_1, primals_2, primals_3, buf0, 256, grid=grid(256), stream=stream0) del primals_1 del primals_3 return (buf0, primals_2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class Norm(nn.Module): def __init__(self, d_model, eps=1e-06): super().__init__() self.size = d_model self.alpha = nn.Parameter(torch.ones(self.size)) self.bias = nn.Parameter(torch.zeros(self.size)) self.eps = eps def forward(self, x): norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim =-1, keepdim=True) + self.eps) + self.bias return norm def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'d_model': 4}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_div_mean_mul_std_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask) tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tmp9 = 4.0 tmp10 = tmp8 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp0 * tmp11 tmp13 = tmp2 - tmp10 tmp14 = tmp13 * tmp13 tmp15 = tmp3 - tmp10 tmp16 = tmp15 * tmp15 tmp17 = tmp14 + tmp16 tmp18 = tmp5 - tmp10 tmp19 = tmp18 * tmp18 tmp20 = tmp17 + tmp19 tmp21 = tmp7 - tmp10 tmp22 = tmp21 * tmp21 tmp23 = tmp20 + tmp22 tmp24 = 3.0 tmp25 = tmp23 / tmp24 tmp26 = libdevice.sqrt(tmp25) tmp27 = 1e-06 tmp28 = tmp26 + tmp27 tmp29 = tmp12 / tmp28 tmp31 = tmp29 + tmp30 tl.store(out_ptr0 + x2, tmp31, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4,), (1,)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_div_mean_mul_std_sub_0[grid(256)](primals_1, primals_2, primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 del primals_3 return buf0, primals_2 class NormNew(nn.Module): def __init__(self, d_model, eps=1e-06): super().__init__() self.size = d_model self.alpha = nn.Parameter(torch.ones(self.size)) self.bias = nn.Parameter(torch.zeros(self.size)) self.eps = eps def forward(self, input_0): primals_1 = self.alpha primals_3 = self.bias primals_2 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	Hyunseung-Kim/molGCT	Norm	false	8,236	[ "Apache-2.0" ]	10	5a2604337cf0a9d3c725295ccb7c8ea4b0144636	https://github.com/Hyunseung-Kim/molGCT/tree/5a2604337cf0a9d3c725295ccb7c8ea4b0144636
InceptionA	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/p3/cp34ywtyg7mn5mzrt67i2x2phdloiq3cptdby54b66orgo6qv4p3.py # Topologically Sorted Source Nodes: [branch5x5], Original ATen: [aten.convolution] # Source node to ATen node mapping: # branch5x5 => convolution_1 # Graph fragment: # %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 16 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/6n/c6nzvdwbwjnyulzdhatd6z2nes3yf7gyte7gdr6gtih47bvfowsn.py # Topologically Sorted Source Nodes: [branch3x3_1], Original ATen: [aten.convolution] # Source node to ATen node mapping: # branch3x3_1 => convolution_4 # Graph fragment: # %convolution_4 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_3, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1536 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 24 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gl/cglaoixeujf57rkzjd25etxx44ib22vhruxaq5jczo45rjcojkaf.py # Topologically Sorted Source Nodes: [branch_pool], Original ATen: [aten.avg_pool2d] # Source node to ATen node mapping: # branch_pool => avg_pool2d # Graph fragment: # %avg_pool2d : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%primals_3, [3, 3], [1, 1], [1, 1]), kwargs = {}) triton_poi_fused_avg_pool2d_2 = async_compile.triton('triton_poi_fused_avg_pool2d_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool2d_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) % 4 x0 = xindex % 4 x4 = xindex tmp0 = (-1) + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tmp2 & tmp4 tmp6 = (-1) + x0 tmp7 = tmp6 >= tmp1 tmp8 = tmp6 < tmp3 tmp9 = tmp7 & tmp8 tmp10 = tmp5 & tmp9 tmp11 = tl.load(in_ptr0 + ((-5) + x4), tmp10 & xmask, other=0.0) tmp12 = x0 tmp13 = tmp12 >= tmp1 tmp14 = tmp12 < tmp3 tmp15 = tmp13 & tmp14 tmp16 = tmp5 & tmp15 tmp17 = tl.load(in_ptr0 + ((-4) + x4), tmp16 & xmask, other=0.0) tmp18 = tmp17 + tmp11 tmp19 = 1 + x0 tmp20 = tmp19 >= tmp1 tmp21 = tmp19 < tmp3 tmp22 = tmp20 & tmp21 tmp23 = tmp5 & tmp22 tmp24 = tl.load(in_ptr0 + ((-3) + x4), tmp23 & xmask, other=0.0) tmp25 = tmp24 + tmp18 tmp26 = x1 tmp27 = tmp26 >= tmp1 tmp28 = tmp26 < tmp3 tmp29 = tmp27 & tmp28 tmp30 = tmp29 & tmp9 tmp31 = tl.load(in_ptr0 + ((-1) + x4), tmp30 & xmask, other=0.0) tmp32 = tmp31 + tmp25 tmp33 = tmp29 & tmp15 tmp34 = tl.load(in_ptr0 + (x4), tmp33 & xmask, other=0.0) tmp35 = tmp34 + tmp32 tmp36 = tmp29 & tmp22 tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=0.0) tmp38 = tmp37 + tmp35 tmp39 = 1 + x1 tmp40 = tmp39 >= tmp1 tmp41 = tmp39 < tmp3 tmp42 = tmp40 & tmp41 tmp43 = tmp42 & tmp9 tmp44 = tl.load(in_ptr0 + (3 + x4), tmp43 & xmask, other=0.0) tmp45 = tmp44 + tmp38 tmp46 = tmp42 & tmp15 tmp47 = tl.load(in_ptr0 + (4 + x4), tmp46 & xmask, other=0.0) tmp48 = tmp47 + tmp45 tmp49 = tmp42 & tmp22 tmp50 = tl.load(in_ptr0 + (5 + x4), tmp49 & xmask, other=0.0) tmp51 = tmp50 + tmp48 tmp52 = 1 + ((-1)x0) + ((-1)x1) + (x0x1) + (((5) ((5) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (5)))((5) ((5) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (5)))) + ((-1)x0((5) * ((5) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (5)))) + ((-1)x1((5) * ((5) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (5)))) + ((5) * ((5) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (5))) + ((5) * ((5) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (5))) tmp53 = tmp51 / tmp52 tl.store(out_ptr0 + (x4), tmp53, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ul/cul7qm7n4qdebxmw4ymiv3yqij47jzi5k4dbf4g2f3j4jeiir6cs.py # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %convolution_2, %convolution_5, %convolution_6], 1), kwargs = {}) triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'fp32', 6: 'fp32', 7: 'fp32', 8: 'fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 5632 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 88 x0 = xindex % 16 x2 = (xindex // 1408) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 16, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16x1) + (256x2)), tmp4 & xmask, other=0.0) tmp6 = tl.load(in_ptr1 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp7 = tmp5 + tmp6 tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype) tmp9 = tl.where(tmp4, tmp7, tmp8) tmp10 = tmp0 >= tmp3 tmp11 = tl.full([1], 40, tl.int64) tmp12 = tmp0 < tmp11 tmp13 = tmp10 & tmp12 tmp14 = tl.load(in_ptr2 + (x0 + (16((-16) + x1)) + (384x2)), tmp13 & xmask, other=0.0) tmp15 = tl.load(in_ptr3 + ((-16) + x1), tmp13 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tmp14 + tmp15 tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp13, tmp16, tmp17) tmp19 = tmp0 >= tmp11 tmp20 = tl.full([1], 64, tl.int64) tmp21 = tmp0 < tmp20 tmp22 = tmp19 & tmp21 tmp23 = tl.load(in_ptr4 + (x0 + (16((-40) + x1)) + (384x2)), tmp22 & xmask, other=0.0) tmp24 = tl.load(in_ptr5 + ((-40) + x1), tmp22 & xmask, eviction_policy='evict_last', other=0.0) tmp25 = tmp23 + tmp24 tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype) tmp27 = tl.where(tmp22, tmp25, tmp26) tmp28 = tmp0 >= tmp20 tmp29 = tl.full([1], 88, tl.int64) tmp30 = tmp0 < tmp29 tmp31 = tl.load(in_ptr6 + (x0 + (16((-64) + x1)) + (384x2)), tmp28 & xmask, other=0.0) tmp32 = tl.load(in_ptr7 + ((-64) + x1), tmp28 & xmask, eviction_policy='evict_last', other=0.0) tmp33 = tmp31 + tmp32 tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype) tmp35 = tl.where(tmp28, tmp33, tmp34) tmp36 = tl.where(tmp22, tmp27, tmp35) tmp37 = tl.where(tmp13, tmp18, tmp36) tmp38 = tl.where(tmp4, tmp9, tmp37) tl.store(out_ptr0 + (x3), tmp38, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15 = args args.clear() assert_size_stride(primals_1, (16, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_2, (16, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (16, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_5, (16, ), (1, )) assert_size_stride(primals_6, (24, 16, 5, 5), (400, 25, 5, 1)) assert_size_stride(primals_7, (24, ), (1, )) assert_size_stride(primals_8, (16, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_9, (16, ), (1, )) assert_size_stride(primals_10, (24, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_11, (24, ), (1, )) assert_size_stride(primals_12, (24, 24, 3, 3), (216, 9, 3, 1)) assert_size_stride(primals_13, (24, ), (1, )) assert_size_stride(primals_14, (24, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_15, (24, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [branch1x1], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 16, 4, 4), (256, 16, 4, 1)) # Topologically Sorted Source Nodes: [branch5x5], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(primals_3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1)) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [branch5x5], Original ATen: [aten.convolution] stream0 = get_raw_stream(0) triton_poi_fused_convolution_0.run(buf2, primals_5, 1024, grid=grid(1024), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [branch5x5_1], Original ATen: [aten.convolution] buf3 = extern_kernels.convolution(buf2, primals_6, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 24, 4, 4), (384, 16, 4, 1)) # Topologically Sorted Source Nodes: [branch3x3], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(primals_3, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 16, 4, 4), (256, 16, 4, 1)) buf5 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [branch3x3], Original ATen: [aten.convolution] triton_poi_fused_convolution_0.run(buf5, primals_9, 1024, grid=grid(1024), stream=stream0) del primals_9 # Topologically Sorted Source Nodes: [branch3x3_1], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 24, 4, 4), (384, 16, 4, 1)) buf7 = buf6; del buf6 # reuse # Topologically Sorted Source Nodes: [branch3x3_1], Original ATen: [aten.convolution] triton_poi_fused_convolution_1.run(buf7, primals_11, 1536, grid=grid(1536), stream=stream0) del primals_11 # Topologically Sorted Source Nodes: [branch3x3_2], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 24, 4, 4), (384, 16, 4, 1)) buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [branch_pool], Original ATen: [aten.avg_pool2d] triton_poi_fused_avg_pool2d_2.run(primals_3, buf9, 256, grid=grid(256), stream=stream0) # Topologically Sorted Source Nodes: [branch_pool_1], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 24, 4, 4), (384, 16, 4, 1)) buf11 = empty_strided_cuda((4, 88, 4, 4), (1408, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] triton_poi_fused_cat_3.run(buf0, primals_2, buf3, primals_7, buf8, primals_13, buf10, primals_15, buf11, 5632, grid=grid(5632), stream=stream0) del buf0 del buf10 del buf3 del buf8 del primals_13 del primals_15 del primals_2 del primals_7 return (buf11, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, buf2, buf5, buf7, buf9, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((16, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((16, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((24, 16, 5, 5), (400, 25, 5, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((16, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((24, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((24, 24, 3, 3), (216, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((24, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn.functional as F import torch.nn as nn class InceptionA(nn.Module): def __init__(self, in_channels): super(InceptionA, self).__init__() self.branch1x1 = nn.Conv2d(in_channels, 16, kernel_size=1) self.branch5x5_1 = nn.Conv2d(in_channels, 16, kernel_size=1) self.branch5x5_2 = nn.Conv2d(16, 24, kernel_size=5, padding=2) self.branch3x3_1 = nn.Conv2d(in_channels, 16, kernel_size=1) self.branch3x3_2 = nn.Conv2d(16, 24, kernel_size=3, padding=1) self.branch3x3_3 = nn.Conv2d(24, 24, kernel_size=3, padding=1) self.branch_pool = nn.Conv2d(in_channels, 24, kernel_size=1) def forward(self, x): branch1x1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3 = self.branch3x3_1(x) branch3x3 = self.branch3x3_2(branch3x3) branch3x3 = self.branch3x3_3(branch3x3) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch5x5, branch3x3, branch_pool] return torch.cat(outputs, dim=1) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 16 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 1536 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 24 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) @triton.jit def triton_poi_fused_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 % 4 x0 = xindex % 4 x4 = xindex tmp0 = -1 + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tmp2 & tmp4 tmp6 = -1 + x0 tmp7 = tmp6 >= tmp1 tmp8 = tmp6 < tmp3 tmp9 = tmp7 & tmp8 tmp10 = tmp5 & tmp9 tmp11 = tl.load(in_ptr0 + (-5 + x4), tmp10 & xmask, other=0.0) tmp12 = x0 tmp13 = tmp12 >= tmp1 tmp14 = tmp12 < tmp3 tmp15 = tmp13 & tmp14 tmp16 = tmp5 & tmp15 tmp17 = tl.load(in_ptr0 + (-4 + x4), tmp16 & xmask, other=0.0) tmp18 = tmp17 + tmp11 tmp19 = 1 + x0 tmp20 = tmp19 >= tmp1 tmp21 = tmp19 < tmp3 tmp22 = tmp20 & tmp21 tmp23 = tmp5 & tmp22 tmp24 = tl.load(in_ptr0 + (-3 + x4), tmp23 & xmask, other=0.0) tmp25 = tmp24 + tmp18 tmp26 = x1 tmp27 = tmp26 >= tmp1 tmp28 = tmp26 < tmp3 tmp29 = tmp27 & tmp28 tmp30 = tmp29 & tmp9 tmp31 = tl.load(in_ptr0 + (-1 + x4), tmp30 & xmask, other=0.0) tmp32 = tmp31 + tmp25 tmp33 = tmp29 & tmp15 tmp34 = tl.load(in_ptr0 + x4, tmp33 & xmask, other=0.0) tmp35 = tmp34 + tmp32 tmp36 = tmp29 & tmp22 tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=0.0) tmp38 = tmp37 + tmp35 tmp39 = 1 + x1 tmp40 = tmp39 >= tmp1 tmp41 = tmp39 < tmp3 tmp42 = tmp40 & tmp41 tmp43 = tmp42 & tmp9 tmp44 = tl.load(in_ptr0 + (3 + x4), tmp43 & xmask, other=0.0) tmp45 = tmp44 + tmp38 tmp46 = tmp42 & tmp15 tmp47 = tl.load(in_ptr0 + (4 + x4), tmp46 & xmask, other=0.0) tmp48 = tmp47 + tmp45 tmp49 = tmp42 & tmp22 tmp50 = tl.load(in_ptr0 + (5 + x4), tmp49 & xmask, other=0.0) tmp51 = tmp50 + tmp48 tmp52 = 1 + -1 * x0 + -1 * x1 + x0 * x1 + (5 * (5 <= 2 + x0) + (2 + x0) * (2 + x0 < 5)) * (5 * (5 <= 2 + x1) + (2 + x1) * (2 + x1 < 5) ) + -1 * x0 * (5 * (5 <= 2 + x1) + (2 + x1) * (2 + x1 < 5) ) + -1 * x1 * (5 * (5 <= 2 + x0) + (2 + x0) * (2 + x0 < 5)) + (5 * (5 <= 2 + x0) + (2 + x0) * (2 + x0 < 5)) + (5 * (5 <= 2 + x1) + (2 + x1) * (2 + x1 < 5)) tmp53 = tmp51 / tmp52 tl.store(out_ptr0 + x4, tmp53, xmask) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 5632 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 88 x0 = xindex % 16 x2 = xindex // 1408 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 16, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 256 * x2), tmp4 & xmask, other=0.0 ) tmp6 = tl.load(in_ptr1 + x1, tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp7 = tmp5 + tmp6 tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype) tmp9 = tl.where(tmp4, tmp7, tmp8) tmp10 = tmp0 >= tmp3 tmp11 = tl.full([1], 40, tl.int64) tmp12 = tmp0 < tmp11 tmp13 = tmp10 & tmp12 tmp14 = tl.load(in_ptr2 + (x0 + 16 * (-16 + x1) + 384 * x2), tmp13 & xmask, other=0.0) tmp15 = tl.load(in_ptr3 + (-16 + x1), tmp13 & xmask, eviction_policy= 'evict_last', other=0.0) tmp16 = tmp14 + tmp15 tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp13, tmp16, tmp17) tmp19 = tmp0 >= tmp11 tmp20 = tl.full([1], 64, tl.int64) tmp21 = tmp0 < tmp20 tmp22 = tmp19 & tmp21 tmp23 = tl.load(in_ptr4 + (x0 + 16 * (-40 + x1) + 384 * x2), tmp22 & xmask, other=0.0) tmp24 = tl.load(in_ptr5 + (-40 + x1), tmp22 & xmask, eviction_policy= 'evict_last', other=0.0) tmp25 = tmp23 + tmp24 tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype) tmp27 = tl.where(tmp22, tmp25, tmp26) tmp28 = tmp0 >= tmp20 tl.full([1], 88, tl.int64) tmp31 = tl.load(in_ptr6 + (x0 + 16 * (-64 + x1) + 384 * x2), tmp28 & xmask, other=0.0) tmp32 = tl.load(in_ptr7 + (-64 + x1), tmp28 & xmask, eviction_policy= 'evict_last', other=0.0) tmp33 = tmp31 + tmp32 tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype) tmp35 = tl.where(tmp28, tmp33, tmp34) tmp36 = tl.where(tmp22, tmp27, tmp35) tmp37 = tl.where(tmp13, tmp18, tmp36) tmp38 = tl.where(tmp4, tmp9, tmp37) tl.store(out_ptr0 + x3, tmp38, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15) = args args.clear() assert_size_stride(primals_1, (16, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_2, (16,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (16, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_5, (16,), (1,)) assert_size_stride(primals_6, (24, 16, 5, 5), (400, 25, 5, 1)) assert_size_stride(primals_7, (24,), (1,)) assert_size_stride(primals_8, (16, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_9, (16,), (1,)) assert_size_stride(primals_10, (24, 16, 3, 3), (144, 9, 3, 1)) assert_size_stride(primals_11, (24,), (1,)) assert_size_stride(primals_12, (24, 24, 3, 3), (216, 9, 3, 1)) assert_size_stride(primals_13, (24,), (1,)) assert_size_stride(primals_14, (24, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_15, (24,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 16, 4, 4), (256, 16, 4, 1)) buf1 = extern_kernels.convolution(primals_3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1)) buf2 = buf1 del buf1 get_raw_stream(0) triton_poi_fused_convolution_0[grid(1024)](buf2, primals_5, 1024, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 buf3 = extern_kernels.convolution(buf2, primals_6, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 24, 4, 4), (384, 16, 4, 1)) buf4 = extern_kernels.convolution(primals_3, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 16, 4, 4), (256, 16, 4, 1)) buf5 = buf4 del buf4 triton_poi_fused_convolution_0[grid(1024)](buf5, primals_9, 1024, XBLOCK=128, num_warps=4, num_stages=1) del primals_9 buf6 = extern_kernels.convolution(buf5, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 24, 4, 4), (384, 16, 4, 1)) buf7 = buf6 del buf6 triton_poi_fused_convolution_1[grid(1536)](buf7, primals_11, 1536, XBLOCK=256, num_warps=4, num_stages=1) del primals_11 buf8 = extern_kernels.convolution(buf7, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 24, 4, 4), (384, 16, 4, 1)) buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_avg_pool2d_2[grid(256)](primals_3, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1) buf10 = extern_kernels.convolution(buf9, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 24, 4, 4), (384, 16, 4, 1)) buf11 = empty_strided_cuda((4, 88, 4, 4), (1408, 16, 4, 1), torch. float32) triton_poi_fused_cat_3[grid(5632)](buf0, primals_2, buf3, primals_7, buf8, primals_13, buf10, primals_15, buf11, 5632, XBLOCK=256, num_warps=4, num_stages=1) del buf0 del buf10 del buf3 del buf8 del primals_13 del primals_15 del primals_2 del primals_7 return (buf11, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, buf2, buf5, buf7, buf9) class InceptionANew(nn.Module): def __init__(self, in_channels): super(InceptionANew, self).__init__() self.branch1x1 = nn.Conv2d(in_channels, 16, kernel_size=1) self.branch5x5_1 = nn.Conv2d(in_channels, 16, kernel_size=1) self.branch5x5_2 = nn.Conv2d(16, 24, kernel_size=5, padding=2) self.branch3x3_1 = nn.Conv2d(in_channels, 16, kernel_size=1) self.branch3x3_2 = nn.Conv2d(16, 24, kernel_size=3, padding=1) self.branch3x3_3 = nn.Conv2d(24, 24, kernel_size=3, padding=1) self.branch_pool = nn.Conv2d(in_channels, 24, kernel_size=1) def forward(self, input_0): primals_1 = self.branch1x1.weight primals_2 = self.branch1x1.bias primals_4 = self.branch5x5_1.weight primals_5 = self.branch5x5_1.bias primals_6 = self.branch5x5_2.weight primals_7 = self.branch5x5_2.bias primals_8 = self.branch3x3_1.weight primals_9 = self.branch3x3_1.bias primals_10 = self.branch3x3_2.weight primals_11 = self.branch3x3_2.bias primals_12 = self.branch3x3_3.weight primals_13 = self.branch3x3_3.bias primals_14 = self.branch_pool.weight primals_15 = self.branch_pool.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15]) return output[0]	HuangCongQing/pytorch	InceptionA	false	8,237	[ "MIT" ]	12	2b2b01d74b45cbe4e467da229798609e79cec97c	https://github.com/HuangCongQing/pytorch/tree/2b2b01d74b45cbe4e467da229798609e79cec97c
Concat	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/xc/cxceqiy6okvqxrjbwoo7ygc4plr32q3u54opf7nc2ufvkbmb4dzh.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] # Source node to ATen node mapping: # x => cat # Graph fragment: # %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 8 x0 = xindex % 16 x2 = (xindex // 128) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16x1) + (64x2)), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + (x0 + (16((-4) + x1)) + (64x2)), tmp6 & xmask, other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x3), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/i5/ci54ctptlcvw5pu5hbgzqmuidvim6hqgm2zdhbgpoe3lwndxwjjl.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x_1 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp3 = 0.0 tmp4 = tmp2 <= tmp3 tl.store(in_out_ptr0 + (x0), tmp2, xmask) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, 8, 1, 1), (8, 1, 1, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 512, grid=grid(512), stream=stream0) del primals_1 del primals_2 # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1; del buf1 # reuse buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_1.run(buf2, buf3, 256, grid=grid(256), stream=stream0) return (buf2, primals_3, buf0, buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 8, 1, 1), (8, 1, 1, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn class Concat(nn.Module): def __init__(self, channels, *kwargs): super(Concat, self).__init__() self.conv = nn.Conv2d(channels 2, channels, 1, bias=False) self.relu = nn.ReLU(inplace=True) def forward(self, equi_feat, c2e_feat): x = torch.cat([equi_feat, c2e_feat], 1) x = self.relu(self.conv(x)) return x def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'channels': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 8 x0 = xindex % 16 x2 = xindex // 128 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask, other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x3, tmp10, xmask) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp3 = 0.0 tmp4 = tmp2 <= tmp3 tl.store(in_out_ptr0 + x0, tmp2, xmask) tl.store(out_ptr0 + x0, tmp4, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, 8, 1, 1), (8, 1, 1, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 del primals_2 buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1 del buf1 buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_1[grid(256)](buf2, buf3, 256, XBLOCK=256, num_warps=4, num_stages=1) return buf2, primals_3, buf0, buf3 class ConcatNew(nn.Module): def __init__(self, channels, *kwargs): super(ConcatNew, self).__init__() self.conv = nn.Conv2d(channels 2, channels, 1, bias=False) self.relu = nn.ReLU(inplace=True) def forward(self, input_0, input_1): primals_3 = self.conv.weight primals_1 = input_0 primals_2 = input_1 output = call([primals_1, primals_2, primals_3]) return output[0]	HalleyJiang/UniFuse-Unidirectional-Fusion	Concat	false	8,238	[ "MIT" ]	30	27a4441fe3d3031d1c9f3eb2d72a3624407d19fc	https://github.com/HalleyJiang/UniFuse-Unidirectional-Fusion/tree/27a4441fe3d3031d1c9f3eb2d72a3624407d19fc
AveragePoolingLayer	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/an/canfj5npqa6niw4bdqtirgxqusjcd4d2hdx4mgja4vdgjyphpueg.py # Topologically Sorted Source Nodes: [avg_pool2d], Original ATen: [aten.avg_pool2d] # Source node to ATen node mapping: # avg_pool2d => avg_pool2d # Graph fragment: # %avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%arg0_1, [2, 2], [2, 2]), kwargs = {}) triton_poi_fused_avg_pool2d_0 = async_compile.triton('triton_poi_fused_avg_pool2d_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 2 x1 = (xindex // 2) x2 = xindex tmp0 = tl.load(in_ptr0 + ((2x0) + (8x1)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + (2x0) + (8x1)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (4 + (2x0) + (8x1)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (5 + (2x0) + (8x1)), xmask, eviction_policy='evict_last') tmp2 = tmp1 + tmp0 tmp4 = tmp3 + tmp2 tmp6 = tmp5 + tmp4 tmp7 = 0.25 tmp8 = tmp6 * tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32) # Topologically Sorted Source Nodes: [avg_pool2d], Original ATen: [aten.avg_pool2d] stream0 = get_raw_stream(0) triton_poi_fused_avg_pool2d_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn from torch.nn import functional as F class AveragePoolingLayer(nn.Module): """Implements the average pooling layer. Basically, this layer can be used to downsample feature maps from spatial domain. """ def __init__(self, scale_factor=2): super().__init__() self.scale_factor = scale_factor def forward(self, x): ksize = [self.scale_factor, self.scale_factor] strides = [self.scale_factor, self.scale_factor] return F.avg_pool2d(x, kernel_size=ksize, stride=strides, padding=0) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 2 x1 = xindex // 2 x2 = xindex tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy= 'evict_last') tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy= 'evict_last') tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy= 'evict_last') tmp2 = tmp1 + tmp0 tmp4 = tmp3 + tmp2 tmp6 = tmp5 + tmp4 tmp7 = 0.25 tmp8 = tmp6 * tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32) get_raw_stream(0) triton_poi_fused_avg_pool2d_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1) del arg0_1 return buf0, class AveragePoolingLayerNew(nn.Module): """Implements the average pooling layer. Basically, this layer can be used to downsample feature maps from spatial domain. """ def __init__(self, scale_factor=2): super().__init__() self.scale_factor = scale_factor def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	Hsintien-Ng/idinvert_pytorch-reproduced	AveragePoolingLayer	false	8,239	[ "MIT" ]	20	cf3302510573138cf16202add06feae7c93624ea	https://github.com/Hsintien-Ng/idinvert_pytorch-reproduced/tree/cf3302510573138cf16202add06feae7c93624ea
CoAttention	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/hu/chulvfvlz677ges2h2lx3sfvsokl7dvsmtfucil7fl5dxwayp6fa.py # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%expand, %expand_1, %mul], 3), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 768 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 12 x4 = (xindex // 48) x1 = (xindex // 12) % 4 x3 = (xindex // 192) x5 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4x4) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + ((4x1) + (16x3) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tl.load(in_ptr0 + ((4x4) + ((-8) + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0) tmp15 = tl.load(in_ptr1 + ((4x1) + (16x3) + ((-8) + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tmp14 * tmp15 tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp11, tmp16, tmp17) tmp19 = tl.where(tmp9, tmp10, tmp18) tmp20 = tl.where(tmp4, tmp5, tmp19) tl.store(out_ptr0 + (x5), tmp20, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/wb/cwbezx24576egy4r3zzf4gqkmjcrdvghwgrcmqbtdy2b3fcutjxy.py # Topologically Sorted Source Nodes: [S_q, S_c], Original ATen: [aten._softmax] # Source node to ATen node mapping: # S_c => amax_1, clone, exp_1, sub_1 # S_q => amax, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%squeeze, [2], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%squeeze, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format}) # %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone, [2], True), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %amax_1), kwargs = {}) # %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex x1 = (xindex // 4) x0 = xindex % 4 x3 = (xindex // 16) tmp0 = tl.load(in_ptr0 + (x4), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr0 + (x0 + (16x3)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (4 + x0 + (16x3)), xmask, eviction_policy='evict_last') tmp13 = tl.load(in_ptr0 + (8 + x0 + (16x3)), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr0 + (12 + x0 + (16x3)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tmp12 = triton_helpers.maximum(tmp10, tmp11) tmp14 = triton_helpers.maximum(tmp12, tmp13) tmp16 = triton_helpers.maximum(tmp14, tmp15) tmp17 = tmp0 - tmp16 tmp18 = tl_math.exp(tmp17) tl.store(out_ptr0 + (x4), tmp9, xmask) tl.store(out_ptr1 + (x4), tmp18, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/xl/cxls6dl5dz3ua4ilno7rjcfd6m7p4ydnd3mzfaq2cepnph6e2y7h.py # Topologically Sorted Source Nodes: [S_q], Original ATen: [aten._softmax] # Source node to ATen node mapping: # S_q => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {}) # %div : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/n7/cn7ebx5cr2tyvov7f7cuhxlup67vhfnqkgtnhmc3qweiewosqh5p.py # Topologically Sorted Source Nodes: [S_c], Original ATen: [aten._softmax] # Source node to ATen node mapping: # S_c => div_1, sum_2 # Graph fragment: # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [2], True), kwargs = {}) # %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_2), kwargs = {}) triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4x2) + (16y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (y0 + (16y1)), ymask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (4 + y0 + (16y1)), ymask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (8 + y0 + (16y1)), ymask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (12 + y0 + (16y1)), ymask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2 + (4y3)), tmp8, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ij/cijo4x7m27zhxa3u3whhvg27oshi7i25glbck5suiytqnxvqcrj3.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat] # Source node to ATen node mapping: # out => cat_1 # Graph fragment: # %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %bmm, %mul_1, %mul_2], -1), kwargs = {}) triton_poi_fused_cat_4 = async_compile.triton('triton_poi_fused_cat_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 16 x1 = (xindex // 16) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + ((4x1) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr0 + ((4x1) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tl.load(in_ptr1 + ((4x1) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0) tmp17 = tmp15 * tmp16 tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype) tmp19 = tl.where(tmp14, tmp17, tmp18) tmp20 = tmp0 >= tmp12 tmp21 = tl.full([1], 16, tl.int64) tmp22 = tmp0 < tmp21 tmp23 = tl.load(in_ptr0 + ((4x1) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0) tmp24 = tl.load(in_ptr2 + ((4x1) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0) tmp25 = tmp23 * tmp24 tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype) tmp27 = tl.where(tmp20, tmp25, tmp26) tmp28 = tl.where(tmp14, tmp19, tmp27) tmp29 = tl.where(tmp9, tmp10, tmp28) tmp30 = tl.where(tmp4, tmp5, tmp29) tl.store(out_ptr0 + (x2), tmp30, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (1, 12), (12, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.float32) # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(primals_2, primals_1, buf0, 768, grid=grid(768), stream=stream0) buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf0, (64, 12), (12, 1), 0), reinterpret_tensor(primals_3, (12, 1), (1, 12), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32) # Topologically Sorted Source Nodes: [S_q, S_c], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf1, buf2, buf4, 64, grid=grid(64), stream=stream0) buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0); del buf1 # reuse # Topologically Sorted Source Nodes: [S_q], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf2, buf3, 64, grid=grid(64), stream=stream0) buf5 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [S_c], Original ATen: [aten._softmax] triton_poi_fused__softmax_3.run(buf4, buf5, 16, 4, grid=grid(16, 4), stream=stream0) buf6 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [A], Original ATen: [aten.bmm] extern_kernels.bmm(buf3, primals_1, out=buf6) buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm_1], Original ATen: [aten.bmm] extern_kernels.bmm(buf3, buf5, out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [B], Original ATen: [aten.bmm] extern_kernels.bmm(buf7, primals_2, out=buf8) del buf7 buf9 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat] triton_poi_fused_cat_4.run(primals_2, buf6, buf8, buf9, 256, grid=grid(256), stream=stream0) del buf6 del buf8 return (buf9, primals_2, reinterpret_tensor(buf0, (64, 12), (12, 1), 0), buf3, buf5, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((1, 12), (12, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class CoAttention(nn.Module): """ CoAttention encoder in Dynamic Coattention Networks For Question Answering (https://arxiv.org/abs/1611.01604) check the Figure 2 in paper * Args: embed_dim: the number of input embedding dimension """ def __init__(self, embed_dim): super(CoAttention, self).__init__() self.W_0 = nn.Linear(embed_dim * 3, 1, bias=False) def forward(self, context_embed, question_embed, context_mask=None, question_mask=None): C, Q = context_embed, question_embed B, C_L, Q_L, D = C.size(0), C.size(1), Q.size(1), Q.size(2) similarity_matrix_shape = torch.zeros(B, C_L, Q_L, D) C_ = C.unsqueeze(2).expand_as(similarity_matrix_shape) Q_ = Q.unsqueeze(1).expand_as(similarity_matrix_shape) C_Q = torch.mul(C_, Q_) S = self.W_0(torch.cat([C_, Q_, C_Q], 3)).squeeze(3) S_question = S if question_mask is not None: S_question = f.add_masked_value(S_question, question_mask. unsqueeze(1), value=-10000000.0) S_q = F.softmax(S_question, 2) S_context = S.transpose(1, 2) if context_mask is not None: S_context = f.add_masked_value(S_context, context_mask. unsqueeze(1), value=-10000000.0) S_c = F.softmax(S_context, 2) A = torch.bmm(S_q, Q) B = torch.bmm(S_q, S_c).bmm(C) out = torch.cat([C, A, C * A, C * B], dim=-1) return out def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'embed_dim': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 768 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 12 x4 = xindex // 48 x1 = xindex // 12 % 4 x3 = xindex // 192 x5 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x4 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (4 * x1 + 16 * x3 + (-4 + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0) tmp11 = tmp0 >= tmp7 tl.full([1], 12, tl.int64) tmp14 = tl.load(in_ptr0 + (4 * x4 + (-8 + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0) tmp15 = tl.load(in_ptr1 + (4 * x1 + 16 * x3 + (-8 + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tmp14 * tmp15 tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp11, tmp16, tmp17) tmp19 = tl.where(tmp9, tmp10, tmp18) tmp20 = tl.where(tmp4, tmp5, tmp19) tl.store(out_ptr0 + x5, tmp20, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex x1 = xindex // 4 x0 = xindex % 4 x3 = xindex // 16 tmp0 = tl.load(in_ptr0 + x4, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr0 + (x0 + 16 * x3), xmask, eviction_policy= 'evict_last') tmp11 = tl.load(in_ptr0 + (4 + x0 + 16 * x3), xmask, eviction_policy= 'evict_last') tmp13 = tl.load(in_ptr0 + (8 + x0 + 16 * x3), xmask, eviction_policy= 'evict_last') tmp15 = tl.load(in_ptr0 + (12 + x0 + 16 * x3), xmask, eviction_policy= 'evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tmp12 = triton_helpers.maximum(tmp10, tmp11) tmp14 = triton_helpers.maximum(tmp12, tmp13) tmp16 = triton_helpers.maximum(tmp14, tmp15) tmp17 = tmp0 - tmp16 tmp18 = tl_math.exp(tmp17) tl.store(out_ptr0 + x4, tmp9, xmask) tl.store(out_ptr1 + x4, tmp18, xmask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (y0 + 16 * y1), ymask, eviction_policy= 'evict_last') tmp2 = tl.load(in_ptr0 + (4 + y0 + 16 * y1), ymask, eviction_policy= 'evict_last') tmp4 = tl.load(in_ptr0 + (8 + y0 + 16 * y1), ymask, eviction_policy= 'evict_last') tmp6 = tl.load(in_ptr0 + (12 + y0 + 16 * y1), ymask, eviction_policy= 'evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2 + 4 * y3), tmp8, xmask & ymask) @triton.jit def triton_poi_fused_cat_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 16 x1 = xindex // 16 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 12, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr0 + (4 * x1 + (-8 + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tl.load(in_ptr1 + (4 * x1 + (-8 + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0) tmp17 = tmp15 * tmp16 tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype) tmp19 = tl.where(tmp14, tmp17, tmp18) tmp20 = tmp0 >= tmp12 tl.full([1], 16, tl.int64) tmp23 = tl.load(in_ptr0 + (4 * x1 + (-12 + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0) tmp24 = tl.load(in_ptr2 + (4 * x1 + (-12 + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0) tmp25 = tmp23 * tmp24 tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype) tmp27 = tl.where(tmp20, tmp25, tmp26) tmp28 = tl.where(tmp14, tmp19, tmp27) tmp29 = tl.where(tmp9, tmp10, tmp28) tmp30 = tl.where(tmp4, tmp5, tmp29) tl.store(out_ptr0 + x2, tmp30, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (1, 12), (12, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch. float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(768)](primals_2, primals_1, buf0, 768, XBLOCK=128, num_warps=4, num_stages=1) buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf0, (64, 12), (12, 1), 0), reinterpret_tensor(primals_3, (12, 1), (1, 12), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32) triton_poi_fused__softmax_1[grid(64)](buf1, buf2, buf4, 64, XBLOCK= 64, num_warps=1, num_stages=1) buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0) del buf1 triton_poi_fused__softmax_2[grid(64)](buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1) buf5 = buf2 del buf2 triton_poi_fused__softmax_3[grid(16, 4)](buf4, buf5, 16, 4, XBLOCK= 4, YBLOCK=16, num_warps=1, num_stages=1) buf6 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0) del buf4 extern_kernels.bmm(buf3, primals_1, out=buf6) buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(buf3, buf5, out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(buf7, primals_2, out=buf8) del buf7 buf9 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32) triton_poi_fused_cat_4[grid(256)](primals_2, buf6, buf8, buf9, 256, XBLOCK=256, num_warps=4, num_stages=1) del buf6 del buf8 return buf9, primals_2, reinterpret_tensor(buf0, (64, 12), (12, 1), 0 ), buf3, buf5, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0) class CoAttentionNew(nn.Module): """ CoAttention encoder in Dynamic Coattention Networks For Question Answering (https://arxiv.org/abs/1611.01604) check the Figure 2 in paper * Args: embed_dim: the number of input embedding dimension """ def __init__(self, embed_dim): super(CoAttentionNew, self).__init__() self.W_0 = nn.Linear(embed_dim * 3, 1, bias=False) def forward(self, input_0, input_1): primals_3 = self.W_0.weight primals_1 = input_0 primals_2 = input_1 output = call([primals_1, primals_2, primals_3]) return output[0]	GMDennis/claf	CoAttention	false	8,240	[ "MIT" ]	10	d1e064e593127e5d654f000f5506c5ae1caab5ce	https://github.com/GMDennis/claf/tree/d1e064e593127e5d654f000f5506c5ae1caab5ce
ClassificationModel	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/6z/c6zb2twnjioimzrk4rie3z6om2pycozdr6corfhcdm5q6f7h4oqz.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 1024 xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 4 y1 = (yindex // 4) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (4x2) + (36y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/62/c62myuf63oepurbxeoz2olrr5dwoipydpve4ekfnunp6gerrikor.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 4 y1 = (yindex // 4) tmp0 = tl.load(in_ptr0 + (x2 + (16y3)), xmask & ymask) tl.store(out_ptr0 + (y0 + (4x2) + (64y1)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/sf/csfbo2wg4qzrcculqiz2kklr2dwz3q6kpztuuavooookip4gpwcb.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 65536 xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = (yindex // 256) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (256x2) + (2304y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/66/c66wyyjpsnatq6od5cev6tenkovm6iijhpkvolh22f7cpiccrbj4.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_3 = async_compile.triton('triton_poi_fused_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 184320 xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = (yindex // 256) tmp0 = tl.load(in_ptr0 + (x2 + (9y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (256x2) + (2304y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/gc/cgctjlgnbq4wfwdwoevcpfb7vf3b2chjk7tvdxs3kk4uy3k2jnp7.py # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # out => convolution # out_1 => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_4 = async_compile.triton('triton_poi_fused_convolution_relu_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/ds/cdsqez6wcplycyifjpbnvdmqfxrlxlpxy63dlgaxyhzokilps773.py # Topologically Sorted Source Nodes: [out_8, contiguous], Original ATen: [aten.convolution, aten.clone] # Source node to ATen node mapping: # contiguous => clone # out_8 => convolution_4 # Graph fragment: # %convolution_4 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_convolution_5 = async_compile.triton('triton_poi_fused_clone_convolution_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_convolution_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_convolution_5(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 46080 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 720 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tl.store(in_out_ptr0 + (x2), tmp2, xmask) tl.store(out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256, ), (1, )) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256, ), (1, )) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256, ), (1, )) assert_size_stride(primals_10, (720, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (720, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((256, 4, 3, 3), (36, 1, 12, 4), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] stream0 = get_raw_stream(0) triton_poi_fused_0.run(primals_1, buf0, 1024, 9, grid=grid(1024, 9), stream=stream0) del primals_1 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_1.run(primals_3, buf1, 16, 16, grid=grid(16, 16), stream=stream0) del primals_3 buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_4, buf2, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_4 buf3 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_6, buf3, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_6 buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_8, buf4, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_8 buf5 = empty_strided_cuda((720, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_3.run(primals_10, buf5, 184320, 9, grid=grid(184320, 9), stream=stream0) del primals_10 # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf7 = buf6; del buf6 # reuse # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_4.run(buf7, primals_2, 16384, grid=grid(16384), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf9 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_4.run(buf9, primals_5, 16384, grid=grid(16384), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf11 = buf10; del buf10 # reuse # Topologically Sorted Source Nodes: [out_4, out_5], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_4.run(buf11, primals_7, 16384, grid=grid(16384), stream=stream0) del primals_7 # Topologically Sorted Source Nodes: [out_6], Original ATen: [aten.convolution] buf12 = extern_kernels.convolution(buf11, buf4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf13 = buf12; del buf12 # reuse # Topologically Sorted Source Nodes: [out_6, out_7], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_4.run(buf13, primals_9, 16384, grid=grid(16384), stream=stream0) del primals_9 # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution] buf14 = extern_kernels.convolution(buf13, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 720, 4, 4), (11520, 1, 2880, 720)) buf15 = buf14; del buf14 # reuse buf16 = empty_strided_cuda((4, 4, 4, 9, 80), (11520, 2880, 720, 80, 1), torch.float32) # Topologically Sorted Source Nodes: [out_8, contiguous], Original ATen: [aten.convolution, aten.clone] triton_poi_fused_clone_convolution_5.run(buf15, primals_11, buf16, 46080, grid=grid(46080), stream=stream0) del primals_11 return (reinterpret_tensor(buf16, (4, 144, 80), (11520, 80, 1), 0), buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf9, buf11, buf13, buf15, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((256, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((720, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((720, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.utils.data import torch.nn as nn from math import sqrt as sqrt from itertools import product as product class ClassificationModel(nn.Module): def __init__(self, num_features_in, num_anchors=9, num_classes=80, prior=0.01, feature_size=256): super(ClassificationModel, self).__init__() self.num_classes = num_classes self.num_anchors = num_anchors self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * num_classes, kernel_size=3, padding=1) self.output_act = nn.Sigmoid() def forward(self, x): out = self.conv1(x) out = self.act1(out) out = self.conv2(out) out = self.act2(out) out = self.conv3(out) out = self.act3(out) out = self.conv4(out) out = self.act4(out) out = self.output(out) out = self.output_act(out) out1 = out.permute(0, 2, 3, 1) batch_size, width, height, _channels = out1.shape out2 = out1.view(batch_size, width, height, self.num_anchors, self. num_classes) return out2.contiguous().view(x.shape[0], -1, self.num_classes) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_features_in': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.utils.data import torch.nn as nn from math import sqrt as sqrt from itertools import product as product assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 4 y1 = yindex // 4 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 4 * x2 + 36 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 4 y1 = yindex // 4 tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask) tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = yindex // 256 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = yindex // 256 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_clone_convolution_5(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 46080 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 720 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tl.store(in_out_ptr0 + x2, tmp2, xmask) tl.store(out_ptr0 + x2, tmp3, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256,), (1,)) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256,), (1,)) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256,), (1,)) assert_size_stride(primals_10, (720, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (720,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((256, 4, 3, 3), (36, 1, 12, 4), torch.float32 ) get_raw_stream(0) triton_poi_fused_0[grid(1024, 9)](primals_1, buf0, 1024, 9, XBLOCK= 16, YBLOCK=64, num_warps=4, num_stages=1) del primals_1 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32) triton_poi_fused_1[grid(16, 16)](primals_3, buf1, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1) del primals_3 buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_2[grid(65536, 9)](primals_4, buf2, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_4 buf3 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_2[grid(65536, 9)](primals_6, buf3, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_6 buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_2[grid(65536, 9)](primals_8, buf4, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_8 buf5 = empty_strided_cuda((720, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_3[grid(184320, 9)](primals_10, buf5, 184320, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_10 buf6 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf7 = buf6 del buf6 triton_poi_fused_convolution_relu_4[grid(16384)](buf7, primals_2, 16384, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf8 = extern_kernels.convolution(buf7, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf9 = buf8 del buf8 triton_poi_fused_convolution_relu_4[grid(16384)](buf9, primals_5, 16384, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 buf10 = extern_kernels.convolution(buf9, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf11 = buf10 del buf10 triton_poi_fused_convolution_relu_4[grid(16384)](buf11, primals_7, 16384, XBLOCK=128, num_warps=4, num_stages=1) del primals_7 buf12 = extern_kernels.convolution(buf11, buf4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf13 = buf12 del buf12 triton_poi_fused_convolution_relu_4[grid(16384)](buf13, primals_9, 16384, XBLOCK=128, num_warps=4, num_stages=1) del primals_9 buf14 = extern_kernels.convolution(buf13, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf14, (4, 720, 4, 4), (11520, 1, 2880, 720)) buf15 = buf14 del buf14 buf16 = empty_strided_cuda((4, 4, 4, 9, 80), (11520, 2880, 720, 80, 1), torch.float32) triton_poi_fused_clone_convolution_5[grid(46080)](buf15, primals_11, buf16, 46080, XBLOCK=256, num_warps=4, num_stages=1) del primals_11 return reinterpret_tensor(buf16, (4, 144, 80), (11520, 80, 1), 0 ), buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf9, buf11, buf13, buf15 class ClassificationModelNew(nn.Module): def __init__(self, num_features_in, num_anchors=9, num_classes=80, prior=0.01, feature_size=256): super(ClassificationModelNew, self).__init__() self.num_classes = num_classes self.num_anchors = num_anchors self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * num_classes, kernel_size=3, padding=1) self.output_act = nn.Sigmoid() def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_6 = self.conv3.weight primals_7 = self.conv3.bias primals_8 = self.conv4.weight primals_9 = self.conv4.bias primals_10 = self.output.weight primals_11 = self.output.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0]	Het-Shah/Monk_Object_Detection	ClassificationModel	false	8,241	[ "Apache-2.0" ]	15	1d7a07193ea3455221caa41d07c33c81d50c6b3f	https://github.com/Het-Shah/Monk_Object_Detection/tree/1d7a07193ea3455221caa41d07c33c81d50c6b3f
Temporal_Attention	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/yh/cyhi44ojre4kwa5u32igv2uvvqurj3osagpq3uhqwe4n27jwosbr.py # Topologically Sorted Source Nodes: [padded_fm_t0], Original ATen: [aten.constant_pad_nd] # Source node to ATen node mapping: # padded_fm_t0 => constant_pad_nd # Graph fragment: # %constant_pad_nd : [num_users=3] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%getitem, [0, 0, 0, 0], 0.0), kwargs = {}) triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = (xindex // 64) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (128x1)), xmask) tl.store(out_ptr0 + (x2), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/xx/cxxfvjz6ub4yvxjqyaq362yd4rdgkxqh2n7kb2kmfkmn5uk3srln.py # Topologically Sorted Source Nodes: [k_out_2], Original ATen: [aten.cat] # Source node to ATen node mapping: # k_out_2 => cat # Graph fragment: # %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%add, %add_1], 1), kwargs = {}) triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 4 x0 = xindex % 16 x2 = (xindex // 64) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 2, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16x1) + (64x2)), tmp4 & xmask, other=0.0) tmp6 = tl.load(in_ptr1 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp7 = tmp5 + tmp6 tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype) tmp9 = tl.where(tmp4, tmp7, tmp8) tmp10 = tmp0 >= tmp3 tmp11 = tl.full([1], 4, tl.int64) tmp12 = tmp0 < tmp11 tmp13 = tl.load(in_ptr0 + (32 + x0 + (16((-2) + x1)) + (64x2)), tmp10 & xmask, other=0.0) tmp14 = tl.load(in_ptr2 + ((-2) + x1), tmp10 & xmask, eviction_policy='evict_last', other=0.0) tmp15 = tmp13 + tmp14 tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype) tmp17 = tl.where(tmp10, tmp15, tmp16) tmp18 = tl.where(tmp4, tmp9, tmp17) tl.store(out_ptr0 + (x3), tmp18, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/m7/cm7kmz27xw2d6dxn76ng23te6qi4myssyw6tmikynavr6iezyoji.py # Topologically Sorted Source Nodes: [mul, inter_out], Original ATen: [aten.mul, aten.sum] # Source node to ATen node mapping: # inter_out => sum_1 # mul => mul # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_2), kwargs = {}) # %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {}) triton_poi_fused_mul_sum_2 = async_compile.triton('triton_poi_fused_mul_sum_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 16 x1 = (xindex // 16) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (64x1)), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (64x1)), xmask) tmp3 = tl.load(in_ptr0 + (16 + x0 + (64x1)), xmask) tmp4 = tl.load(in_ptr1 + (16 + x0 + (64x1)), xmask) tmp7 = tl.load(in_ptr0 + (32 + x0 + (64x1)), xmask) tmp8 = tl.load(in_ptr1 + (32 + x0 + (64x1)), xmask) tmp11 = tl.load(in_ptr0 + (48 + x0 + (64x1)), xmask) tmp12 = tl.load(in_ptr1 + (48 + x0 + (64x1)), xmask) tmp2 = tmp0 * tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tl.store(out_ptr0 + (x2), tmp14, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/zy/czylnpu55pkqbsyty6d6yczn3ra67tlxw4xeo4s53pzbhdkdgw55.py # Topologically Sorted Source Nodes: [v_out_1, einsum], Original ATen: [aten.unfold, aten.mul] # Source node to ATen node mapping: # einsum => mul_1 # v_out_1 => unfold_3 # Graph fragment: # %unfold_3 : [num_users=2] = call_function[target=torch.ops.aten.unfold.default](args = (%unfold_2, 3, 1, 1), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute, %permute_1), kwargs = {}) triton_poi_fused_mul_unfold_3 = async_compile.triton('triton_poi_fused_mul_unfold_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_unfold_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_unfold_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex x1 = xindex % 16 x3 = (xindex // 64) tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr0 + (x1 + (16x3)), xmask, eviction_policy='evict_last') tmp2 = tmp1 - tmp1 tmp3 = tl_math.exp(tmp2) tmp4 = tmp3 / tmp3 tmp5 = tmp4 tmp0 tl.store(in_out_ptr0 + (x0), tmp0, xmask) tl.store(out_ptr0 + (x0), tmp5, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args args.clear() assert_size_stride(primals_1, (4, 8, 4, 4), (128, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_5, (2, 1, 1, 1, 1), (1, 1, 1, 1, 1)) assert_size_stride(primals_6, (2, 1, 1, 1, 1), (1, 1, 1, 1, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [padded_fm_t0], Original ATen: [aten.constant_pad_nd] stream0 = get_raw_stream(0) triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0) # Topologically Sorted Source Nodes: [q_out], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 4, 4, 4), (128, 16, 4, 1), 64), primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) # Topologically Sorted Source Nodes: [k_out], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1)) # Topologically Sorted Source Nodes: [v_out], Original ATen: [aten.convolution] buf3 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1)) buf4 = empty_strided_cuda((4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [k_out_2], Original ATen: [aten.cat] triton_poi_fused_cat_1.run(buf2, primals_5, primals_6, buf4, 256, grid=grid(256), stream=stream0) del primals_5 del primals_6 buf6 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64), torch.float32) # Topologically Sorted Source Nodes: [mul, inter_out], Original ATen: [aten.mul, aten.sum] triton_poi_fused_mul_sum_2.run(buf1, buf4, buf6, 64, grid=grid(64), stream=stream0) buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 4, 1), 0); del buf3 # reuse buf7 = reinterpret_tensor(buf2, (4, 1, 4, 4, 4, 1), (64, 64, 16, 4, 1, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [v_out_1, einsum], Original ATen: [aten.unfold, aten.mul] triton_poi_fused_mul_unfold_3.run(buf5, buf6, buf7, 256, grid=grid(256), stream=stream0) del buf6 return (reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_2, primals_3, primals_4, reinterpret_tensor(primals_1, (4, 4, 4, 4), (128, 16, 4, 1), 64), buf0, buf1, buf4, buf5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 8, 4, 4), (128, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((2, 1, 1, 1, 1), (1, 1, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((2, 1, 1, 1, 1), (1, 1, 1, 1, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F import torch.nn.init as init class Temporal_Attention(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, groups=1, bias=False, refinement=False): super(Temporal_Attention, self).__init__() self.outc = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.groups = groups self.refinement = refinement None if self.refinement: None assert self.outc % self.groups == 0, 'out_channels should be divided by groups.' self.w_q = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias =bias) self.w_k = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias =bias) self.w_v = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias =bias) self.rel_h = nn.Parameter(torch.randn(self.outc // 2, 1, 1, self. kernel_size, 1), requires_grad=True) self.rel_w = nn.Parameter(torch.randn(self.outc // 2, 1, 1, 1, self .kernel_size), requires_grad=True) init.normal_(self.rel_h, 0, 1) init.normal_(self.rel_w, 0, 1) init.kaiming_normal_(self.w_q.weight, mode='fan_out', nonlinearity= 'relu') init.kaiming_normal_(self.w_k.weight, mode='fan_out', nonlinearity= 'relu') init.kaiming_normal_(self.w_v.weight, mode='fan_out', nonlinearity= 'relu') def forward(self, feature_map): fm_t0, fm_t1 = torch.split(feature_map, feature_map.size()[1] // 2, 1) assert fm_t0.size() == fm_t1.size( ), 'The size of feature maps of image t0 and t1 should be same.' batch, _, h, w = fm_t0.size() padded_fm_t0 = F.pad(fm_t0, [self.padding, self.padding, self. padding, self.padding]) q_out = self.w_q(fm_t1) k_out = self.w_k(padded_fm_t0) v_out = self.w_v(padded_fm_t0) if self.refinement: padding = self.kernel_size padded_fm_col = F.pad(fm_t0, [0, 0, padding, padding]) padded_fm_row = F.pad(fm_t0, [padding, padding, 0, 0]) k_out_col = self.w_k(padded_fm_col) k_out_row = self.w_k(padded_fm_row) v_out_col = self.w_v(padded_fm_col) v_out_row = self.w_v(padded_fm_row) k_out_col = k_out_col.unfold(2, self.kernel_size * 2 + 1, self. stride) k_out_row = k_out_row.unfold(3, self.kernel_size * 2 + 1, self. stride) v_out_col = v_out_col.unfold(2, self.kernel_size * 2 + 1, self. stride) v_out_row = v_out_row.unfold(3, self.kernel_size * 2 + 1, self. stride) q_out_base = q_out.view(batch, self.groups, self.outc // self. groups, h, w, 1).repeat(1, 1, 1, 1, 1, self.kernel_size * self. kernel_size) q_out_ref = q_out.view(batch, self.groups, self.outc // self.groups, h, w, 1).repeat(1, 1, 1, 1, 1, self.kernel_size * 2 + 1) k_out = k_out.unfold(2, self.kernel_size, self.stride).unfold(3, self.kernel_size, self.stride) k_out_h, k_out_w = k_out.split(self.outc // 2, dim=1) k_out = torch.cat((k_out_h + self.rel_h, k_out_w + self.rel_w), dim=1) k_out = k_out.contiguous().view(batch, self.groups, self.outc // self.groups, h, w, -1) v_out = v_out.unfold(2, self.kernel_size, self.stride).unfold(3, self.kernel_size, self.stride) v_out = v_out.contiguous().view(batch, self.groups, self.outc // self.groups, h, w, -1) inter_out = (q_out_base * k_out).sum(dim=2) out = F.softmax(inter_out, dim=-1) out = torch.einsum('bnhwk,bnchwk -> bnchw', out, v_out).contiguous( ).view(batch, -1, h, w) if self.refinement: k_out_row = k_out_row.contiguous().view(batch, self.groups, self.outc // self.groups, h, w, -1) k_out_col = k_out_col.contiguous().view(batch, self.groups, self.outc // self.groups, h, w, -1) v_out_row = v_out_row.contiguous().view(batch, self.groups, self.outc // self.groups, h, w, -1) v_out_col = v_out_col.contiguous().view(batch, self.groups, self.outc // self.groups, h, w, -1) out_row = F.softmax((q_out_ref * k_out_row).sum(dim=2), dim=-1) out_col = F.softmax((q_out_ref * k_out_col).sum(dim=2), dim=-1) out += torch.einsum('bnhwk,bnchwk -> bnchw', out_row, v_out_row ).contiguous().view(batch, -1, h, w) out += torch.einsum('bnhwk,bnchwk -> bnchw', out_col, v_out_col ).contiguous().view(batch, -1, h, w) return out def get_inputs(): return [torch.rand([4, 8, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn import torch.nn.init as init assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = xindex // 64 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1), xmask) tl.store(out_ptr0 + x2, tmp0, xmask) @triton.jit def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 4 x0 = xindex % 16 x2 = xindex // 64 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 2, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp6 = tl.load(in_ptr1 + x1, tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp7 = tmp5 + tmp6 tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype) tmp9 = tl.where(tmp4, tmp7, tmp8) tmp10 = tmp0 >= tmp3 tl.full([1], 4, tl.int64) tmp13 = tl.load(in_ptr0 + (32 + x0 + 16 * (-2 + x1) + 64 * x2), tmp10 & xmask, other=0.0) tmp14 = tl.load(in_ptr2 + (-2 + x1), tmp10 & xmask, eviction_policy= 'evict_last', other=0.0) tmp15 = tmp13 + tmp14 tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype) tmp17 = tl.where(tmp10, tmp15, tmp16) tmp18 = tl.where(tmp4, tmp9, tmp17) tl.store(out_ptr0 + x3, tmp18, xmask) @triton.jit def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 16 x1 = xindex // 16 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask) tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask) tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask) tmp4 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask) tmp7 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask) tmp8 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask) tmp11 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask) tmp12 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask) tmp2 = tmp0 * tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tl.store(out_ptr0 + x2, tmp14, xmask) @triton.jit def triton_poi_fused_mul_unfold_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex x1 = xindex % 16 x3 = xindex // 64 tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr0 + (x1 + 16 * x3), xmask, eviction_policy= 'evict_last') tmp2 = tmp1 - tmp1 tmp3 = tl_math.exp(tmp2) tmp4 = tmp3 / tmp3 tmp5 = tmp4 * tmp0 tl.store(in_out_ptr0 + x0, tmp0, xmask) tl.store(out_ptr0 + x0, tmp5, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args args.clear() assert_size_stride(primals_1, (4, 8, 4, 4), (128, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_5, (2, 1, 1, 1, 1), (1, 1, 1, 1, 1)) assert_size_stride(primals_6, (2, 1, 1, 1, 1), (1, 1, 1, 1, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_constant_pad_nd_0[grid(256)](primals_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 4, 4, 4), (128, 16, 4, 1), 64), primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1)) buf3 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1)) buf4 = empty_strided_cuda((4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 1, 1), torch.float32) triton_poi_fused_cat_1[grid(256)](buf2, primals_5, primals_6, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 del primals_6 buf6 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64), torch.float32) triton_poi_fused_mul_sum_2[grid(64)](buf1, buf4, buf6, 64, XBLOCK= 64, num_warps=1, num_stages=1) buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 4, 1), 0) del buf3 buf7 = reinterpret_tensor(buf2, (4, 1, 4, 4, 4, 1), (64, 64, 16, 4, 1, 1), 0) del buf2 triton_poi_fused_mul_unfold_3[grid(256)](buf5, buf6, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1) del buf6 return reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), primals_2, primals_3, primals_4, reinterpret_tensor(primals_1, ( 4, 4, 4, 4), (128, 16, 4, 1), 64), buf0, buf1, buf4, buf5 class Temporal_AttentionNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, groups=1, bias=False, refinement=False): super(Temporal_AttentionNew, self).__init__() self.outc = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.groups = groups self.refinement = refinement None if self.refinement: None assert self.outc % self.groups == 0, 'out_channels should be divided by groups.' self.w_q = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias =bias) self.w_k = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias =bias) self.w_v = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias =bias) self.rel_h = nn.Parameter(torch.randn(self.outc // 2, 1, 1, self. kernel_size, 1), requires_grad=True) self.rel_w = nn.Parameter(torch.randn(self.outc // 2, 1, 1, 1, self .kernel_size), requires_grad=True) init.normal_(self.rel_h, 0, 1) init.normal_(self.rel_w, 0, 1) init.kaiming_normal_(self.w_q.weight, mode='fan_out', nonlinearity= 'relu') init.kaiming_normal_(self.w_k.weight, mode='fan_out', nonlinearity= 'relu') init.kaiming_normal_(self.w_v.weight, mode='fan_out', nonlinearity= 'relu') def forward(self, input_0): primals_5 = self.rel_h primals_6 = self.rel_w primals_2 = self.w_q.weight primals_3 = self.w_k.weight primals_4 = self.w_v.weight primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]) return output[0]	Herrccc/DR-TANet	Temporal_Attention	false	8,242	[ "MIT" ]	12	37cc3929833d61451b2fa4a92ccd4286cfc4fd34	https://github.com/Herrccc/DR-TANet/tree/37cc3929833d61451b2fa4a92ccd4286cfc4fd34
MultiHeadAttn	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/tp/ctpiguxmcpckre2mit64cbirohbry7ukusrmf4e7xnoeurunx2lg.py # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_prob => amax, exp, sub, sum_1 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_11, [2], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_11, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {}) triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 4 x2 = (xindex // 16) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (16x2)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (4 + x0 + (16x2)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr1 + (8 + x0 + (16x2)), xmask, eviction_policy='evict_last') tmp13 = tl.load(in_ptr1 + (12 + x0 + (16x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tmp6 = tmp0 * tmp5 tmp7 = tmp6 * tmp3 tmp8 = triton_helpers.maximum(tmp4, tmp7) tmp10 = tmp0 * tmp9 tmp11 = tmp10 * tmp3 tmp12 = triton_helpers.maximum(tmp8, tmp11) tmp14 = tmp0 * tmp13 tmp15 = tmp14 * tmp3 tmp16 = triton_helpers.maximum(tmp12, tmp15) tmp17 = tmp4 - tmp16 tmp18 = tl_math.exp(tmp17) tmp19 = tmp7 - tmp16 tmp20 = tl_math.exp(tmp19) tmp21 = tmp18 + tmp20 tmp22 = tmp11 - tmp16 tmp23 = tl_math.exp(tmp22) tmp24 = tmp21 + tmp23 tmp25 = tmp15 - tmp16 tmp26 = tl_math.exp(tmp25) tmp27 = tmp24 + tmp26 tl.store(out_ptr0 + (x3), tmp16, xmask) tl.store(out_ptr1 + (x3), tmp27, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/mx/cmxglwcqshsz56cbqetuotr5dueji2qhvsgqhs3rspu5vyljlcxs.py # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] # Source node to ATen node mapping: # attn_vec => clone_1 # Graph fragment: # %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = (xindex // 4) x0 = xindex % 4 x1 = (xindex // 4) % 4 x3 = (xindex // 64) x2 = (xindex // 16) % 4 tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x1 + (4x0) + (16x3)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr3 + (x4), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tmp6 = tmp4 - tmp5 tmp7 = tl_math.exp(tmp6) tmp9 = tmp7 / tmp8 tl.store(out_ptr0 + (x0 + (4x2) + (16x1) + (64x3)), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/3a/c3aaucsolrfdjphevn4qawbbrmr3jqka2dfrruxbjgx42g3fnwu6.py # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] # Source node to ATen node mapping: # attn_vec => clone_2 # Graph fragment: # %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_8,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4x2) + (16y1)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf0) del primals_4 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1) del primals_5 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2) del primals_6 buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] stream0 = get_raw_stream(0) triton_poi_fused__softmax_0.run(buf0, buf1, buf3, buf4, 64, grid=grid(64), stream=stream0) buf5 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf0, buf1, buf3, buf4, buf5, 256, grid=grid(256), stream=stream0) buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone] triton_poi_fused_clone_2.run(buf2, buf6, 16, 4, grid=grid(16, 4), stream=stream0) buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7) buf8 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf3 # reuse # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] triton_poi_fused_clone_2.run(buf7, buf8, 16, 4, grid=grid(16, 4), stream=stream0) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0); del buf7 # reuse # Topologically Sorted Source Nodes: [attn_out], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf9) return (reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf0, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(buf8, (16, 4), (4, 1), 0), primals_7, reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.functional as F class MultiHeadAttn(nn.Module): def __init__(self, query_dim, key_dim, value_dim, hidden_dim, num_head, dropatt=0.0, act_func='softmax', add_zero_attn=False, pre_lnorm= False, post_lnorm=False): super(MultiHeadAttn, self).__init__() assert hidden_dim % num_head == 0 self.query_dim = query_dim self.key_dim = key_dim self.value_dim = value_dim self.hidden_dim = hidden_dim self.num_head = num_head self.dropatt = nn.Dropout(dropatt) head_dim = hidden_dim // num_head self.q_net = nn.Linear(query_dim, hidden_dim, bias=False) self.k_net = nn.Linear(key_dim, hidden_dim, bias=False) self.v_net = nn.Linear(value_dim, hidden_dim, bias=False) self.o_net = nn.Linear(hidden_dim, query_dim, bias=False) self.scale = 1 / head_dim ** 0.5 self.act_func = act_func self.add_zero_attn = add_zero_attn self.pre_lnorm = pre_lnorm self.post_lnorm = post_lnorm if pre_lnorm: self.q_layer_norm = nn.LayerNorm(query_dim) self.k_layer_norm = nn.LayerNorm(key_dim) self.v_layer_norm = nn.LayerNorm(value_dim) if post_lnorm: self.o_layer_norm = nn.LayerNorm(query_dim) for net in [self.q_net, self.k_net, self.v_net, self.o_net]: nn.init.xavier_uniform_(net.weight, 1.0) if hasattr(net, 'bias') and net.bias is not None: nn.init.constant_(net.bias, 0.0) if self.pre_lnorm: for layer_norm in [self.q_layer_norm, self.k_layer_norm, self. v_layer_norm]: if hasattr(layer_norm, 'weight'): nn.init.normal_(layer_norm.weight, 1.0, self.scale) if hasattr(layer_norm, 'bias') and layer_norm.bias is not None: nn.init.constant_(layer_norm.bias, 0.0) if self.post_lnorm: if hasattr(self.o_layer_norm, 'weight'): nn.init.normal_(self.o_layer_norm.weight, 1.0, self.scale) if hasattr(self.o_layer_norm, 'bias' ) and self.o_layer_norm.bias is not None: nn.init.constant_(self.o_layer_norm.bias, 0.0) def forward(self, query, key, value, attn_mask=None): bsz = query.size(0) if self.add_zero_attn: key = torch.cat([key, torch.zeros((bsz, 1) + key.size()[2:], dtype=key.dtype, device=key.device)], dim=1) value = torch.cat([value, torch.zeros((bsz, 1) + value.size()[2 :], dtype=value.dtype, device=value.device)], dim=1) if attn_mask is not None: attn_mask = torch.cat([attn_mask, torch.ones((bsz, 1), dtype=attn_mask.dtype, device=attn_mask.device)], dim=1) qlen, klen, vlen = query.size(1), key.size(1), value.size(1) if self.pre_lnorm: query = self.q_layer_norm(query) key = self.k_layer_norm(key) value = self.v_layer_norm(value) head_q = self.q_net(query).view(bsz, qlen, self.num_head, self. hidden_dim // self.num_head) head_k = self.k_net(key).view(bsz, klen, self.num_head, self. hidden_dim // self.num_head) head_v = self.v_net(value).view(bsz, vlen, self.num_head, self. hidden_dim // self.num_head) attn_score = torch.einsum('bind,bjnd->bijn', (head_q, head_k)) attn_score.mul_(self.scale) if attn_mask is not None: if attn_mask.dim() == 2: attn_score.masked_fill_((attn_mask == 0).unsqueeze(1). unsqueeze(-1), _INF) elif attn_mask.dim() == 3: attn_score.masked_fill_((attn_mask == 0).unsqueeze(-1), _INF) if self.act_func is None or self.act_func == 'None': attn_prob = attn_score elif self.act_func == 'softmax': attn_prob = F.softmax(attn_score, dim=2) elif self.act_func == 'sigmoid': attn_prob = F.sigmoid(attn_score) elif self.act_func == 'tanh': attn_prob = F.tanh(attn_score) elif self.act_func == 'relu': attn_prob = F.relu(attn_score) elif self.act_func == 'leaky_relu': attn_prob = F.leaky_relu(attn_score) elif self.act_func == 'maximum': max_score = torch.max(attn_score, dim=2, keepdim=True)[0] max_mask = attn_score == max_score cnt = torch.sum(max_mask, dim=2, keepdim=True) attn_prob = max_mask.float() / cnt.float() else: raise NotImplementedError attn_prob = self.dropatt(attn_prob) attn_vec = torch.einsum('bijn,bjnd->bind', (attn_prob, head_v)) attn_vec = attn_vec.contiguous().view(bsz, qlen, self.hidden_dim) attn_out = self.o_net(attn_vec) if self.post_lnorm: attn_out = self.o_layer_norm(attn_out) return attn_out def get_output_dim(self): return self.query_dim def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4]) ] def get_init_inputs(): return [[], {'query_dim': 4, 'key_dim': 4, 'value_dim': 4, 'hidden_dim': 4, 'num_head': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__softmax_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 4 x2 = xindex // 16 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp5 = tl.load(in_ptr1 + (4 + x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp9 = tl.load(in_ptr1 + (8 + x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp13 = tl.load(in_ptr1 + (12 + x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 * tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tmp6 = tmp0 * tmp5 tmp7 = tmp6 * tmp3 tmp8 = triton_helpers.maximum(tmp4, tmp7) tmp10 = tmp0 * tmp9 tmp11 = tmp10 * tmp3 tmp12 = triton_helpers.maximum(tmp8, tmp11) tmp14 = tmp0 * tmp13 tmp15 = tmp14 * tmp3 tmp16 = triton_helpers.maximum(tmp12, tmp15) tmp17 = tmp4 - tmp16 tmp18 = tl_math.exp(tmp17) tmp19 = tmp7 - tmp16 tmp20 = tl_math.exp(tmp19) tmp21 = tmp18 + tmp20 tmp22 = tmp11 - tmp16 tmp23 = tl_math.exp(tmp22) tmp24 = tmp21 + tmp23 tmp25 = tmp15 - tmp16 tmp26 = tl_math.exp(tmp25) tmp27 = tmp24 + tmp26 tl.store(out_ptr0 + x3, tmp16, xmask) tl.store(out_ptr1 + x3, tmp27, xmask) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex // 4 x0 = xindex % 4 x1 = xindex // 4 % 4 x3 = xindex // 64 x2 = xindex // 16 % 4 tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x1 + 4 * x0 + 16 * x3), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tmp6 = tmp4 - tmp5 tmp7 = tl_math.exp(tmp6) tmp9 = tmp7 / tmp8 tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp9, xmask) @triton.jit def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf0) del primals_4 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1) del primals_5 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2) del primals_6 buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32) get_raw_stream(0) triton_poi_fused__softmax_0[grid(64)](buf0, buf1, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1) buf5 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch .float32) triton_poi_fused_clone_1[grid(256)](buf0, buf1, buf3, buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1) buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0) del buf4 triton_poi_fused_clone_2[grid(16, 4)](buf2, buf6, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1) buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0) del buf2 extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7) buf8 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0) del buf3 triton_poi_fused_clone_2[grid(16, 4)](buf7, buf8, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0) del buf7 extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf9) return reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0 ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0 ), buf0, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0 ), buf1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0 ), reinterpret_tensor(buf8, (16, 4), (4, 1), 0 ), primals_7, reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0 ), reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0) class MultiHeadAttnNew(nn.Module): def __init__(self, query_dim, key_dim, value_dim, hidden_dim, num_head, dropatt=0.0, act_func='softmax', add_zero_attn=False, pre_lnorm= False, post_lnorm=False): super(MultiHeadAttnNew, self).__init__() assert hidden_dim % num_head == 0 self.query_dim = query_dim self.key_dim = key_dim self.value_dim = value_dim self.hidden_dim = hidden_dim self.num_head = num_head self.dropatt = nn.Dropout(dropatt) head_dim = hidden_dim // num_head self.q_net = nn.Linear(query_dim, hidden_dim, bias=False) self.k_net = nn.Linear(key_dim, hidden_dim, bias=False) self.v_net = nn.Linear(value_dim, hidden_dim, bias=False) self.o_net = nn.Linear(hidden_dim, query_dim, bias=False) self.scale = 1 / head_dim ** 0.5 self.act_func = act_func self.add_zero_attn = add_zero_attn self.pre_lnorm = pre_lnorm self.post_lnorm = post_lnorm if pre_lnorm: self.q_layer_norm = nn.LayerNorm(query_dim) self.k_layer_norm = nn.LayerNorm(key_dim) self.v_layer_norm = nn.LayerNorm(value_dim) if post_lnorm: self.o_layer_norm = nn.LayerNorm(query_dim) for net in [self.q_net, self.k_net, self.v_net, self.o_net]: nn.init.xavier_uniform_(net.weight, 1.0) if hasattr(net, 'bias') and net.bias is not None: nn.init.constant_(net.bias, 0.0) if self.pre_lnorm: for layer_norm in [self.q_layer_norm, self.k_layer_norm, self. v_layer_norm]: if hasattr(layer_norm, 'weight'): nn.init.normal_(layer_norm.weight, 1.0, self.scale) if hasattr(layer_norm, 'bias') and layer_norm.bias is not None: nn.init.constant_(layer_norm.bias, 0.0) if self.post_lnorm: if hasattr(self.o_layer_norm, 'weight'): nn.init.normal_(self.o_layer_norm.weight, 1.0, self.scale) if hasattr(self.o_layer_norm, 'bias' ) and self.o_layer_norm.bias is not None: nn.init.constant_(self.o_layer_norm.bias, 0.0) def get_output_dim(self): return self.query_dim def forward(self, input_0, input_1, input_2): primals_4 = self.q_net.weight primals_5 = self.k_net.weight primals_6 = self.v_net.weight primals_7 = self.o_net.weight primals_1 = input_0 primals_2 = input_1 primals_3 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]	HKUST-KnowComp/BMGF-RoBERTa	MultiHeadAttn	false	8,243	[ "MIT" ]	16	8e9eebd7e9fb6cc2492131fc8eaa5b5b29d999fd	https://github.com/HKUST-KnowComp/BMGF-RoBERTa/tree/8e9eebd7e9fb6cc2492131fc8eaa5b5b29d999fd
UpConvNorm	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/y2/cy2axpijxk6u2b7v6le3asl4e5eardypooir66wsqq5fhx67j6in.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution] # Source node to ATen node mapping: # out => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 64) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 8, 8), (256, 64, 8, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution] stream0 = get_raw_stream(0) triton_poi_fused_convolution_0.run(buf1, primals_2, 1024, grid=grid(1024), stream=stream0) del primals_2 return (buf1, primals_1, primals_3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn def pixel_shuffle(input, scale_factor): batch_size, channels, in_height, in_width = input.size() out_channels = int(int(channels / scale_factor) / scale_factor) out_height = int(in_height * scale_factor) out_width = int(in_width * scale_factor) if scale_factor >= 1: input_view = input.contiguous().view(batch_size, out_channels, scale_factor, scale_factor, in_height, in_width) shuffle_out = input_view.permute(0, 1, 4, 2, 5, 3).contiguous() else: block_size = int(1 / scale_factor) input_view = input.contiguous().view(batch_size, channels, out_height, block_size, out_width, block_size) shuffle_out = input_view.permute(0, 1, 3, 5, 2, 4).contiguous() return shuffle_out.view(batch_size, out_channels, out_height, out_width) class ConvNorm(nn.Module): def __init__(self, in_feat, out_feat, kernel_size, stride=1, norm=False): super(ConvNorm, self).__init__() reflection_padding = kernel_size // 2 self.reflection_pad = nn.ReflectionPad2d(reflection_padding) self.conv = nn.Conv2d(in_feat, out_feat, stride=stride, kernel_size =kernel_size, bias=True) self.norm = norm if norm == 'IN': self.norm = nn.InstanceNorm2d(out_feat, track_running_stats=True) elif norm == 'BN': self.norm = nn.BatchNorm2d(out_feat) def forward(self, x): out = self.reflection_pad(x) out = self.conv(out) if self.norm: out = self.norm(out) return out class PixelShuffle(nn.Module): def __init__(self, scale_factor): super(PixelShuffle, self).__init__() self.scale_factor = scale_factor def forward(self, x): return pixel_shuffle(x, self.scale_factor) def extra_repr(self): return 'scale_factor={}'.format(self.scale_factor) class UpConvNorm(nn.Module): def __init__(self, in_channels, out_channels, mode='transpose', norm=False ): super(UpConvNorm, self).__init__() if mode == 'transpose': self.upconv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1) elif mode == 'shuffle': self.upconv = nn.Sequential(ConvNorm(in_channels, 4 * out_channels, kernel_size=3, stride=1, norm=norm), PixelShuffle(2)) else: self.upconv = nn.Sequential(nn.Upsample(mode='nearest', scale_factor=2, align_corners=False), ConvNorm(in_channels, out_channels, kernel_size=1, stride=1, norm=norm)) def forward(self, x): out = self.upconv(x) return out def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 64 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 8, 8), (256, 64, 8, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_0[grid(1024)](buf1, primals_2, 1024, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 return buf1, primals_1, primals_3 def pixel_shuffle(input, scale_factor): batch_size, channels, in_height, in_width = input.size() out_channels = int(int(channels / scale_factor) / scale_factor) out_height = int(in_height * scale_factor) out_width = int(in_width * scale_factor) if scale_factor >= 1: input_view = input.contiguous().view(batch_size, out_channels, scale_factor, scale_factor, in_height, in_width) shuffle_out = input_view.permute(0, 1, 4, 2, 5, 3).contiguous() else: block_size = int(1 / scale_factor) input_view = input.contiguous().view(batch_size, channels, out_height, block_size, out_width, block_size) shuffle_out = input_view.permute(0, 1, 3, 5, 2, 4).contiguous() return shuffle_out.view(batch_size, out_channels, out_height, out_width) class ConvNorm(nn.Module): def __init__(self, in_feat, out_feat, kernel_size, stride=1, norm=False): super(ConvNorm, self).__init__() reflection_padding = kernel_size // 2 self.reflection_pad = nn.ReflectionPad2d(reflection_padding) self.conv = nn.Conv2d(in_feat, out_feat, stride=stride, kernel_size =kernel_size, bias=True) self.norm = norm if norm == 'IN': self.norm = nn.InstanceNorm2d(out_feat, track_running_stats=True) elif norm == 'BN': self.norm = nn.BatchNorm2d(out_feat) def forward(self, x): out = self.reflection_pad(x) out = self.conv(out) if self.norm: out = self.norm(out) return out class PixelShuffle(nn.Module): def __init__(self, scale_factor): super(PixelShuffle, self).__init__() self.scale_factor = scale_factor def forward(self, x): return pixel_shuffle(x, self.scale_factor) def extra_repr(self): return 'scale_factor={}'.format(self.scale_factor) class UpConvNormNew(nn.Module): def __init__(self, in_channels, out_channels, mode='transpose', norm=False ): super(UpConvNormNew, self).__init__() if mode == 'transpose': self.upconv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1) elif mode == 'shuffle': self.upconv = nn.Sequential(ConvNorm(in_channels, 4 * out_channels, kernel_size=3, stride=1, norm=norm), PixelShuffle(2)) else: self.upconv = nn.Sequential(nn.Upsample(mode='nearest', scale_factor=2, align_corners=False), ConvNorm(in_channels, out_channels, kernel_size=1, stride=1, norm=norm)) def forward(self, input_0): primals_1 = self.upconv.weight primals_2 = self.upconv.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]	Hubert482/cainapp	UpConvNorm	false	8,244	[ "MIT" ]	18	7a74a9b186ee358168c8f050e445fbe9f91f9c47	https://github.com/Hubert482/cainapp/tree/7a74a9b186ee358168c8f050e445fbe9f91f9c47
tofp16	# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/qs/cqsitslx774fn4tm47bknp7iambphdxldbpgnoby4usp66iq4svq.py # Topologically Sorted Source Nodes: [half], Original ATen: [aten._to_copy] # Source node to ATen node mapping: # half => convert_element_type # Graph fragment: # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%arg0_1, torch.float16), kwargs = {}) triton_poi_fused__to_copy_0 = async_compile.triton('triton_poi_fused__to_copy_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp16', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tmp0.to(tl.float32) tl.store(out_ptr0 + (x0), tmp1, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float16) # Topologically Sorted Source Nodes: [half], Original ATen: [aten._to_copy] stream0 = get_raw_stream(0) triton_poi_fused__to_copy_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.parallel class tofp16(nn.Module): def __init__(self): super(tofp16, self).__init__() def forward(self, input): return input.half() def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]	import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn import torch.nn.parallel assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused__to_copy_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tmp0.to(tl.float32) tl.store(out_ptr0 + x0, tmp1, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float16) get_raw_stream(0) triton_poi_fused__to_copy_0[grid(256)](arg0_1, buf0, 256, XBLOCK= 256, num_warps=4, num_stages=1) del arg0_1 return buf0, class tofp16New(nn.Module): def __init__(self): super(tofp16New, self).__init__() def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]	HuaijiaLin/AGSS-VOS	tofp16	false	8,245	[ "MIT" ]	11	e9272365aa45bf098316d7111238fe0ab8df8a17	https://github.com/HuaijiaLin/AGSS-VOS/tree/e9272365aa45bf098316d7111238fe0ab8df8a17
RegressionModel	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/wm/cwm6oxykcfai3vsyhlimk6kmcltzxi6lsgfegouc5gfuvxhgklqi.py # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # out => convolution # out_1 => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 256 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/pj/cpjzruf3sgbhsxuhc7g75ay5j2xm4fehk5um7wn2n4wnxuftjobi.py # Topologically Sorted Source Nodes: [contiguous, view], Original ATen: [aten.clone, aten.view] # Source node to ATen node mapping: # contiguous => clone # view => view # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format}) # %view : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%clone, [4, -1, 4]), kwargs = {}) triton_poi_fused_clone_view_1 = async_compile.triton('triton_poi_fused_clone_view_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64, 64], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_view_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_view_1(in_out_ptr0, in_ptr0, in_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 64 xnumel = 36 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = (yindex // 16) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (16x2) + (576y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.debug_barrier() tl.store(in_out_ptr0 + (x2 + (36*y3)), tmp2, xmask & ymask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256, ), (1, )) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256, ), (1, )) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256, ), (1, )) assert_size_stride(primals_10, (36, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (36, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 256, 4, 4), (4096, 16, 4, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 16384, grid=grid(16384), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 256, 4, 4), (4096, 16, 4, 1)) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 16384, grid=grid(16384), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 256, 4, 4), (4096, 16, 4, 1)) buf5 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [out_4, out_5], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf5, primals_7, 16384, grid=grid(16384), stream=stream0) del primals_7 # Topologically Sorted Source Nodes: [out_6], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 16, 4, 1)) buf7 = buf6; del buf6 # reuse # Topologically Sorted Source Nodes: [out_6, out_7], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf7, primals_9, 16384, grid=grid(16384), stream=stream0) del primals_9 # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 36, 4, 4), (576, 16, 4, 1)) buf9 = empty_strided_cuda((4, 4, 4, 36), (576, 144, 36, 1), torch.float32) buf10 = reinterpret_tensor(buf9, (4, 144, 4), (576, 4, 1), 0); del buf9 # reuse # Topologically Sorted Source Nodes: [contiguous, view], Original ATen: [aten.clone, aten.view] triton_poi_fused_clone_view_1.run(buf10, buf8, primals_11, 64, 36, grid=grid(64, 36), stream=stream0) del buf8 del primals_11 return (buf10, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf5, buf7, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((256, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((36, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((36, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.utils.data import torch.nn as nn from math import sqrt as sqrt from itertools import product as product class RegressionModel(nn.Module): def __init__(self, num_features_in, num_anchors=9, feature_size=256): super(RegressionModel, self).__init__() self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * 4, kernel_size= 3, padding=1) def forward(self, x): out = self.conv1(x) out = self.act1(out) out = self.conv2(out) out = self.act2(out) out = self.conv3(out) out = self.act3(out) out = self.conv4(out) out = self.act4(out) out = self.output(out) out = out.permute(0, 2, 3, 1) return out.contiguous().view(out.shape[0], -1, 4) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_features_in': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.utils.data import torch.nn as nn from math import sqrt as sqrt from itertools import product as product assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 256 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, None) @triton.jit def triton_poi_fused_clone_view_1(in_out_ptr0, in_ptr0, in_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 64 xnumel = 36 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = yindex // 16 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 576 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.debug_barrier() tl.store(in_out_ptr0 + (x2 + 36 * y3), tmp2, xmask & ymask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256,), (1,)) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256,), (1,)) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256,), (1,)) assert_size_stride(primals_10, (36, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (36,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 256, 4, 4), (4096, 16, 4, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_relu_0[grid(16384)](buf1, primals_2, 16384, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 256, 4, 4), (4096, 16, 4, 1)) buf3 = buf2 del buf2 triton_poi_fused_convolution_relu_0[grid(16384)](buf3, primals_5, 16384, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 256, 4, 4), (4096, 16, 4, 1)) buf5 = buf4 del buf4 triton_poi_fused_convolution_relu_0[grid(16384)](buf5, primals_7, 16384, XBLOCK=128, num_warps=4, num_stages=1) del primals_7 buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 16, 4, 1)) buf7 = buf6 del buf6 triton_poi_fused_convolution_relu_0[grid(16384)](buf7, primals_9, 16384, XBLOCK=128, num_warps=4, num_stages=1) del primals_9 buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 36, 4, 4), (576, 16, 4, 1)) buf9 = empty_strided_cuda((4, 4, 4, 36), (576, 144, 36, 1), torch. float32) buf10 = reinterpret_tensor(buf9, (4, 144, 4), (576, 4, 1), 0) del buf9 triton_poi_fused_clone_view_1[grid(64, 36)](buf10, buf8, primals_11, 64, 36, XBLOCK=64, YBLOCK=4, num_warps=4, num_stages=1) del buf8 del primals_11 return (buf10, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf5, buf7) class RegressionModelNew(nn.Module): def __init__(self, num_features_in, num_anchors=9, feature_size=256): super(RegressionModelNew, self).__init__() self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * 4, kernel_size= 3, padding=1) def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_6 = self.conv3.weight primals_7 = self.conv3.bias primals_8 = self.conv4.weight primals_9 = self.conv4.bias primals_10 = self.output.weight primals_11 = self.output.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0]	Het-Shah/Monk_Object_Detection	RegressionModel	false	8,246	[ "Apache-2.0" ]	15	1d7a07193ea3455221caa41d07c33c81d50c6b3f	https://github.com/Het-Shah/Monk_Object_Detection/tree/1d7a07193ea3455221caa41d07c33c81d50c6b3f
SpatialAttentionLayer	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/lj/cljnyjeqdxwdc6kx22wm5wsr5l74zl4oovgwpb47c3bd5f4fyhge.py # Topologically Sorted Source Nodes: [max_1, x_spatial_mean, max_2, x_width_mean], Original ATen: [aten.max, aten.mean] # Source node to ATen node mapping: # max_1 => max_1 # max_2 => max_2 # x_spatial_mean => mean # x_width_mean => mean_1 # Graph fragment: # %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%primals_1, 1), kwargs = {}) # %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [1]), kwargs = {}) # %max_2 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%getitem, 1), kwargs = {}) # %mean_1 : [num_users=4] = call_function[target=torch.ops.aten.mean.dim](args = (%mean, [1]), kwargs = {}) triton_poi_fused_max_mean_0 = async_compile.triton('triton_poi_fused_max_mean_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_mean_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_mean_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (64x1)), xmask) tmp1 = tl.load(in_ptr0 + (16 + x0 + (64x1)), xmask) tmp3 = tl.load(in_ptr0 + (32 + x0 + (64x1)), xmask) tmp5 = tl.load(in_ptr0 + (48 + x0 + (64x1)), xmask) tmp7 = tl.load(in_ptr0 + (4 + x0 + (64x1)), xmask) tmp8 = tl.load(in_ptr0 + (20 + x0 + (64x1)), xmask) tmp10 = tl.load(in_ptr0 + (36 + x0 + (64x1)), xmask) tmp12 = tl.load(in_ptr0 + (52 + x0 + (64x1)), xmask) tmp15 = tl.load(in_ptr0 + (8 + x0 + (64x1)), xmask) tmp16 = tl.load(in_ptr0 + (24 + x0 + (64x1)), xmask) tmp18 = tl.load(in_ptr0 + (40 + x0 + (64x1)), xmask) tmp20 = tl.load(in_ptr0 + (56 + x0 + (64x1)), xmask) tmp23 = tl.load(in_ptr0 + (12 + x0 + (64x1)), xmask) tmp24 = tl.load(in_ptr0 + (28 + x0 + (64x1)), xmask) tmp26 = tl.load(in_ptr0 + (44 + x0 + (64x1)), xmask) tmp28 = tl.load(in_ptr0 + (60 + x0 + (64x1)), xmask) tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp9 = triton_helpers.maximum(tmp7, tmp8) tmp11 = triton_helpers.maximum(tmp9, tmp10) tmp13 = triton_helpers.maximum(tmp11, tmp12) tmp14 = triton_helpers.maximum(tmp6, tmp13) tmp17 = triton_helpers.maximum(tmp15, tmp16) tmp19 = triton_helpers.maximum(tmp17, tmp18) tmp21 = triton_helpers.maximum(tmp19, tmp20) tmp22 = triton_helpers.maximum(tmp14, tmp21) tmp25 = triton_helpers.maximum(tmp23, tmp24) tmp27 = triton_helpers.maximum(tmp25, tmp26) tmp29 = triton_helpers.maximum(tmp27, tmp28) tmp30 = triton_helpers.maximum(tmp22, tmp29) tmp31 = tmp0 + tmp1 tmp32 = tmp31 + tmp3 tmp33 = tmp32 + tmp5 tmp34 = 4.0 tmp35 = tmp33 / tmp34 tmp36 = tmp7 + tmp8 tmp37 = tmp36 + tmp10 tmp38 = tmp37 + tmp12 tmp39 = tmp38 / tmp34 tmp40 = tmp35 + tmp39 tmp41 = tmp15 + tmp16 tmp42 = tmp41 + tmp18 tmp43 = tmp42 + tmp20 tmp44 = tmp43 / tmp34 tmp45 = tmp40 + tmp44 tmp46 = tmp23 + tmp24 tmp47 = tmp46 + tmp26 tmp48 = tmp47 + tmp28 tmp49 = tmp48 / tmp34 tmp50 = tmp45 + tmp49 tmp51 = tmp50 / tmp34 tl.store(out_ptr0 + (x2), tmp30, xmask) tl.store(out_ptr1 + (x2), tmp51, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/rh/crhhlbr6s3ojf6mmkyc3mc7xkw4kwb4q5kufx3dvze2lphpxoprc.py # Topologically Sorted Source Nodes: [max_1, x_spatial_mean, max_3, x_height_mean], Original ATen: [aten.max, aten.mean] # Source node to ATen node mapping: # max_1 => max_1 # max_3 => max_3 # x_height_mean => mean_2 # x_spatial_mean => mean # Graph fragment: # %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%primals_1, 1), kwargs = {}) # %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [1]), kwargs = {}) # %max_3 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%getitem, 2), kwargs = {}) # %mean_2 : [num_users=4] = call_function[target=torch.ops.aten.mean.dim](args = (%mean, [2]), kwargs = {}) triton_poi_fused_max_mean_1 = async_compile.triton('triton_poi_fused_max_mean_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_mean_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_mean_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + ((4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (16 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (32 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (48 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (1 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (17 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr0 + (33 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr0 + (49 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr0 + (2 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp16 = tl.load(in_ptr0 + (18 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp18 = tl.load(in_ptr0 + (34 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp20 = tl.load(in_ptr0 + (50 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp23 = tl.load(in_ptr0 + (3 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp24 = tl.load(in_ptr0 + (19 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp26 = tl.load(in_ptr0 + (35 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr0 + (51 + (4x0) + (64x1)), xmask, eviction_policy='evict_last') tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp9 = triton_helpers.maximum(tmp7, tmp8) tmp11 = triton_helpers.maximum(tmp9, tmp10) tmp13 = triton_helpers.maximum(tmp11, tmp12) tmp14 = triton_helpers.maximum(tmp6, tmp13) tmp17 = triton_helpers.maximum(tmp15, tmp16) tmp19 = triton_helpers.maximum(tmp17, tmp18) tmp21 = triton_helpers.maximum(tmp19, tmp20) tmp22 = triton_helpers.maximum(tmp14, tmp21) tmp25 = triton_helpers.maximum(tmp23, tmp24) tmp27 = triton_helpers.maximum(tmp25, tmp26) tmp29 = triton_helpers.maximum(tmp27, tmp28) tmp30 = triton_helpers.maximum(tmp22, tmp29) tmp31 = tmp0 + tmp1 tmp32 = tmp31 + tmp3 tmp33 = tmp32 + tmp5 tmp34 = 4.0 tmp35 = tmp33 / tmp34 tmp36 = tmp7 + tmp8 tmp37 = tmp36 + tmp10 tmp38 = tmp37 + tmp12 tmp39 = tmp38 / tmp34 tmp40 = tmp35 + tmp39 tmp41 = tmp15 + tmp16 tmp42 = tmp41 + tmp18 tmp43 = tmp42 + tmp20 tmp44 = tmp43 / tmp34 tmp45 = tmp40 + tmp44 tmp46 = tmp23 + tmp24 tmp47 = tmp46 + tmp26 tmp48 = tmp47 + tmp28 tmp49 = tmp48 / tmp34 tmp50 = tmp45 + tmp49 tmp51 = tmp50 / tmp34 tl.store(out_ptr0 + (x2), tmp30, xmask) tl.store(out_ptr1 + (x2), tmp51, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/fp/cfp6aijow2g5is2smjrqj7kca6itit4xrxs2tseqvxiqv3qu5hs6.py # Topologically Sorted Source Nodes: [x0_w_s], Original ATen: [aten._softmax] # Source node to ATen node mapping: # x0_w_s => amax, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mean_1, [1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean_1, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/bx/cbx4apfrr3crlh45z5s5g364pozucicvtftrh7pt56jdgf4a2jrr.py # Topologically Sorted Source Nodes: [x0_w_s, mul_1, add_1, y1_w_t], Original ATen: [aten._softmax, aten.mul, aten.add, aten.tanh] # Source node to ATen node mapping: # add_1 => add_1 # mul_1 => mul_1 # x0_w_s => div, sum_1 # y1_w_t => tanh_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {}) # %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_1, %div), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_5), kwargs = {}) # %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {}) triton_poi_fused__softmax_add_mul_tanh_3 = async_compile.triton('triton_poi_fused__softmax_add_mul_tanh_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_mul_tanh_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_add_mul_tanh_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4x1)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tmp10 = tmp9 * tmp8 tmp12 = tmp10 + tmp11 tmp13 = libdevice.tanh(tmp12) tl.store(out_ptr0 + (x2), tmp8, xmask) tl.store(out_ptr1 + (x2), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/dx/cdx4ang72zvdfbxlznrenje56srowdkcyveiz3tbwpxvxwdaqox5.py # Topologically Sorted Source Nodes: [mul_3, add_3, y0_h_t, mul_5, y2_h_t], Original ATen: [aten.mul, aten.add, aten.tanh] # Source node to ATen node mapping: # add_3 => add_3 # mul_3 => mul_3 # mul_5 => mul_5 # y0_h_t => tanh_2 # y2_h_t => add_5 # Graph fragment: # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_3, %div_1), kwargs = {}) # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_10), kwargs = {}) # %tanh_2 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_3,), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_5, %tanh_2), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %primals_13), kwargs = {}) triton_poi_fused_add_mul_tanh_4 = async_compile.triton('triton_poi_fused_add_mul_tanh_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'fp32', 4: 'fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_tanh_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_tanh_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_out_ptr0 + (x2), xmask) tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last') tmp3 = tmp1 * tmp2 tmp5 = tmp3 + tmp4 tmp6 = libdevice.tanh(tmp5) tmp7 = tmp0 * tmp6 tmp9 = tmp7 + tmp8 tl.store(in_out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/t5/ct5efyxuuwnr646jfbmmovyebswjv3rmpbb3bfkpc3u4ig2yggqy.py # Topologically Sorted Source Nodes: [add_6, z], Original ATen: [aten.add, aten.mul] # Source node to ATen node mapping: # add_6 => add_6 # z => mul_6 # Graph fragment: # %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, 1), kwargs = {}) # %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %add_6), kwargs = {}) triton_poi_fused_add_mul_5 = async_compile.triton('triton_poi_fused_add_mul_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (16x2)), xmask, eviction_policy='evict_last') tmp2 = libdevice.tanh(tmp1) tmp3 = 1.0 tmp4 = tmp2 + tmp3 tmp5 = tmp0 tmp4 tl.store(out_ptr0 + (x3), tmp5, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 1), (1, 1)) assert_size_stride(primals_3, (4, 1), (1, 1)) assert_size_stride(primals_4, (1, 4), (4, 1)) assert_size_stride(primals_5, (1, 4), (4, 1)) assert_size_stride(primals_6, (4, 1), (1, 1)) assert_size_stride(primals_7, (1, 4), (4, 1)) assert_size_stride(primals_8, (4, 1), (1, 1)) assert_size_stride(primals_9, (4, 1), (1, 1)) assert_size_stride(primals_10, (1, 4), (4, 1)) assert_size_stride(primals_11, (1, 4), (4, 1)) assert_size_stride(primals_12, (4, 1), (1, 1)) assert_size_stride(primals_13, (1, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [max_1, x_spatial_mean, max_2, x_width_mean], Original ATen: [aten.max, aten.mean] stream0 = get_raw_stream(0) triton_poi_fused_max_mean_0.run(primals_1, buf0, buf1, 16, grid=grid(16), stream=stream0) buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [max_1, x_spatial_mean, max_3, x_height_mean], Original ATen: [aten.max, aten.mean] triton_poi_fused_max_mean_1.run(primals_1, buf2, buf3, 16, grid=grid(16), stream=stream0) buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [x0_w_s], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf1, buf4, 16, grid=grid(16), stream=stream0) buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [y1_w], Original ATen: [aten.mm] extern_kernels.mm(buf0, primals_3, out=buf7) del primals_3 buf5 = buf0; del buf0 # reuse buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [x0_w_s, mul_1, add_1, y1_w_t], Original ATen: [aten._softmax, aten.mul, aten.add, aten.tanh] triton_poi_fused__softmax_add_mul_tanh_3.run(buf4, buf7, primals_5, buf5, buf8, 16, grid=grid(16), stream=stream0) del primals_5 buf6 = buf7; del buf7 # reuse # Topologically Sorted Source Nodes: [y0_w], Original ATen: [aten.mm] extern_kernels.mm(buf1, primals_2, out=buf6) del primals_2 buf9 = empty_strided_cuda((4, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [y2_w], Original ATen: [aten.mm] extern_kernels.mm(buf8, primals_6, out=buf9) buf10 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [x0_h_s], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf3, buf10, 16, grid=grid(16), stream=stream0) buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [y1_h], Original ATen: [aten.mm] extern_kernels.mm(buf2, primals_9, out=buf13) del primals_9 buf11 = buf2; del buf2 # reuse buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [x0_h_s, mul_4, add_4, y1_h_t], Original ATen: [aten._softmax, aten.mul, aten.add, aten.tanh] triton_poi_fused__softmax_add_mul_tanh_3.run(buf10, buf13, primals_11, buf11, buf14, 16, grid=grid(16), stream=stream0) del buf10 del primals_11 buf12 = buf13; del buf13 # reuse # Topologically Sorted Source Nodes: [y0_h], Original ATen: [aten.mm] extern_kernels.mm(buf3, primals_8, out=buf12) del primals_8 buf15 = empty_strided_cuda((4, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [y2_h], Original ATen: [aten.mm] extern_kernels.mm(buf14, primals_12, out=buf15) buf16 = buf11; del buf11 # reuse # Topologically Sorted Source Nodes: [mul_3, add_3, y0_h_t, mul_5, y2_h_t], Original ATen: [aten.mul, aten.add, aten.tanh] triton_poi_fused_add_mul_tanh_4.run(buf16, buf15, buf12, primals_10, primals_13, 16, grid=grid(16), stream=stream0) del primals_13 buf17 = buf5; del buf5 # reuse # Topologically Sorted Source Nodes: [mul, add, y0_w_t, mul_2, y2_w_t], Original ATen: [aten.mul, aten.add, aten.tanh] triton_poi_fused_add_mul_tanh_4.run(buf17, buf9, buf6, primals_4, primals_7, 16, grid=grid(16), stream=stream0) del primals_7 buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul_6], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf16, (4, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf17, (4, 1, 4), (4, 0, 1), 0), out=buf18) buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [add_6, z], Original ATen: [aten.add, aten.mul] triton_poi_fused_add_mul_5.run(primals_1, buf18, buf19, 256, grid=grid(256), stream=stream0) return (buf19, primals_1, primals_4, primals_10, buf1, buf3, buf6, buf8, buf9, buf12, buf14, buf15, buf18, reinterpret_tensor(buf16, (4, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf17, (4, 4, 1), (4, 1, 4), 0), reinterpret_tensor(primals_12, (1, 4), (1, 1), 0), reinterpret_tensor(primals_6, (1, 4), (1, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn as nn import torch.nn.parallel import torch.utils.data.distributed class SpatialAttentionLayer(nn.Module): def __init__(self, spatial_size): super(SpatialAttentionLayer, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.softmax = nn.Softmax(dim=1) self.width_w0 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.width_w1 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.width_w2 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.width_bias0 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.width_bias1 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.width_bias2 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.height_w0 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.height_w1 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.height_w2 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.height_bias0 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.height_bias1 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.height_bias2 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) nn.init.xavier_uniform_(self.width_w0) nn.init.xavier_uniform_(self.width_w1) nn.init.xavier_uniform_(self.width_w2) nn.init.xavier_uniform_(self.height_w0) nn.init.xavier_uniform_(self.height_w1) nn.init.xavier_uniform_(self.height_w2) def forward(self, x): b, _c, h, w = x.size() x_spatial_max = torch.max(x, 1)[0] x_spatial_mean = torch.mean(x, 1) x_width_max = torch.max(x_spatial_max, 1)[0] x_width_mean = torch.mean(x_spatial_mean, 1) x_height_max = torch.max(x_spatial_max, 2)[0] x_height_mean = torch.mean(x_spatial_mean, 2) x0_w_s = self.softmax(x_width_mean) y0_w = torch.matmul(x_width_mean, self.width_w0) y1_w = torch.matmul(x_width_max, self.width_w1) y0_w_t = torch.tanh(y0_w * x0_w_s + self.width_bias0) y1_w_t = torch.tanh(y1_w * x0_w_s + self.width_bias1) y2_w = torch.matmul(y1_w_t, self.width_w2) y2_w_t = y2_w * y0_w_t + self.width_bias2 x0_h_s = self.softmax(x_height_mean) y0_h = torch.matmul(x_height_mean, self.height_w0) y1_h = torch.matmul(x_height_max, self.height_w1) y0_h_t = torch.tanh(y0_h * x0_h_s + self.height_bias0) y1_h_t = torch.tanh(y1_h * x0_h_s + self.height_bias1) y2_h = torch.matmul(y1_h_t, self.height_w2) y2_h_t = y2_h * y0_h_t + self.height_bias2 spatial = torch.tanh(torch.matmul(y2_h_t.view(b, h, 1), y2_w_t.view (b, 1, w))).unsqueeze(1) z = x * (spatial + 1) return z def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'spatial_size': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn import torch.nn.parallel import torch.utils.data.distributed assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_max_mean_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask) tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask) tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask) tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask) tmp7 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask) tmp8 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask) tmp10 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask) tmp12 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask) tmp15 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask) tmp16 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask) tmp18 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask) tmp20 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask) tmp23 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask) tmp24 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask) tmp26 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask) tmp28 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask) tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp9 = triton_helpers.maximum(tmp7, tmp8) tmp11 = triton_helpers.maximum(tmp9, tmp10) tmp13 = triton_helpers.maximum(tmp11, tmp12) tmp14 = triton_helpers.maximum(tmp6, tmp13) tmp17 = triton_helpers.maximum(tmp15, tmp16) tmp19 = triton_helpers.maximum(tmp17, tmp18) tmp21 = triton_helpers.maximum(tmp19, tmp20) tmp22 = triton_helpers.maximum(tmp14, tmp21) tmp25 = triton_helpers.maximum(tmp23, tmp24) tmp27 = triton_helpers.maximum(tmp25, tmp26) tmp29 = triton_helpers.maximum(tmp27, tmp28) tmp30 = triton_helpers.maximum(tmp22, tmp29) tmp31 = tmp0 + tmp1 tmp32 = tmp31 + tmp3 tmp33 = tmp32 + tmp5 tmp34 = 4.0 tmp35 = tmp33 / tmp34 tmp36 = tmp7 + tmp8 tmp37 = tmp36 + tmp10 tmp38 = tmp37 + tmp12 tmp39 = tmp38 / tmp34 tmp40 = tmp35 + tmp39 tmp41 = tmp15 + tmp16 tmp42 = tmp41 + tmp18 tmp43 = tmp42 + tmp20 tmp44 = tmp43 / tmp34 tmp45 = tmp40 + tmp44 tmp46 = tmp23 + tmp24 tmp47 = tmp46 + tmp26 tmp48 = tmp47 + tmp28 tmp49 = tmp48 / tmp34 tmp50 = tmp45 + tmp49 tmp51 = tmp50 / tmp34 tl.store(out_ptr0 + x2, tmp30, xmask) tl.store(out_ptr1 + x2, tmp51, xmask) @triton.jit def triton_poi_fused_max_mean_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (4 * x0 + 64 * x1), xmask, eviction_policy= 'evict_last') tmp1 = tl.load(in_ptr0 + (16 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (32 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (48 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (1 + 4 * x0 + 64 * x1), xmask, eviction_policy ='evict_last') tmp8 = tl.load(in_ptr0 + (17 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr0 + (33 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr0 + (49 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp15 = tl.load(in_ptr0 + (2 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp16 = tl.load(in_ptr0 + (18 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp18 = tl.load(in_ptr0 + (34 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp20 = tl.load(in_ptr0 + (50 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp23 = tl.load(in_ptr0 + (3 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp24 = tl.load(in_ptr0 + (19 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp26 = tl.load(in_ptr0 + (35 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr0 + (51 + 4 * x0 + 64 * x1), xmask, eviction_policy='evict_last') tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp9 = triton_helpers.maximum(tmp7, tmp8) tmp11 = triton_helpers.maximum(tmp9, tmp10) tmp13 = triton_helpers.maximum(tmp11, tmp12) tmp14 = triton_helpers.maximum(tmp6, tmp13) tmp17 = triton_helpers.maximum(tmp15, tmp16) tmp19 = triton_helpers.maximum(tmp17, tmp18) tmp21 = triton_helpers.maximum(tmp19, tmp20) tmp22 = triton_helpers.maximum(tmp14, tmp21) tmp25 = triton_helpers.maximum(tmp23, tmp24) tmp27 = triton_helpers.maximum(tmp25, tmp26) tmp29 = triton_helpers.maximum(tmp27, tmp28) tmp30 = triton_helpers.maximum(tmp22, tmp29) tmp31 = tmp0 + tmp1 tmp32 = tmp31 + tmp3 tmp33 = tmp32 + tmp5 tmp34 = 4.0 tmp35 = tmp33 / tmp34 tmp36 = tmp7 + tmp8 tmp37 = tmp36 + tmp10 tmp38 = tmp37 + tmp12 tmp39 = tmp38 / tmp34 tmp40 = tmp35 + tmp39 tmp41 = tmp15 + tmp16 tmp42 = tmp41 + tmp18 tmp43 = tmp42 + tmp20 tmp44 = tmp43 / tmp34 tmp45 = tmp40 + tmp44 tmp46 = tmp23 + tmp24 tmp47 = tmp46 + tmp26 tmp48 = tmp47 + tmp28 tmp49 = tmp48 / tmp34 tmp50 = tmp45 + tmp49 tmp51 = tmp50 / tmp34 tl.store(out_ptr0 + x2, tmp30, xmask) tl.store(out_ptr1 + x2, tmp51, xmask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_add_mul_tanh_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tmp10 = tmp9 * tmp8 tmp12 = tmp10 + tmp11 tmp13 = libdevice.tanh(tmp12) tl.store(out_ptr0 + x2, tmp8, xmask) tl.store(out_ptr1 + x2, tmp13, xmask) @triton.jit def triton_poi_fused_add_mul_tanh_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_out_ptr0 + x2, xmask) tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last') tmp3 = tmp1 * tmp2 tmp5 = tmp3 + tmp4 tmp6 = libdevice.tanh(tmp5) tmp7 = tmp0 * tmp6 tmp9 = tmp7 + tmp8 tl.store(in_out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused_add_mul_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp2 = libdevice.tanh(tmp1) tmp3 = 1.0 tmp4 = tmp2 + tmp3 tmp5 = tmp0 * tmp4 tl.store(out_ptr0 + x3, tmp5, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13) = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 1), (1, 1)) assert_size_stride(primals_3, (4, 1), (1, 1)) assert_size_stride(primals_4, (1, 4), (4, 1)) assert_size_stride(primals_5, (1, 4), (4, 1)) assert_size_stride(primals_6, (4, 1), (1, 1)) assert_size_stride(primals_7, (1, 4), (4, 1)) assert_size_stride(primals_8, (4, 1), (1, 1)) assert_size_stride(primals_9, (4, 1), (1, 1)) assert_size_stride(primals_10, (1, 4), (4, 1)) assert_size_stride(primals_11, (1, 4), (4, 1)) assert_size_stride(primals_12, (4, 1), (1, 1)) assert_size_stride(primals_13, (1, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_max_mean_0[grid(16)](primals_1, buf0, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1) buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_max_mean_1[grid(16)](primals_1, buf2, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused__softmax_2[grid(16)](buf1, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1) buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32) extern_kernels.mm(buf0, primals_3, out=buf7) del primals_3 buf5 = buf0 del buf0 buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused__softmax_add_mul_tanh_3[grid(16)](buf4, buf7, primals_5, buf5, buf8, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_5 buf6 = buf7 del buf7 extern_kernels.mm(buf1, primals_2, out=buf6) del primals_2 buf9 = empty_strided_cuda((4, 1), (1, 1), torch.float32) extern_kernels.mm(buf8, primals_6, out=buf9) buf10 = buf4 del buf4 triton_poi_fused__softmax_2[grid(16)](buf3, buf10, 16, XBLOCK=16, num_warps=1, num_stages=1) buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32) extern_kernels.mm(buf2, primals_9, out=buf13) del primals_9 buf11 = buf2 del buf2 buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused__softmax_add_mul_tanh_3[grid(16)](buf10, buf13, primals_11, buf11, buf14, 16, XBLOCK=16, num_warps=1, num_stages=1) del buf10 del primals_11 buf12 = buf13 del buf13 extern_kernels.mm(buf3, primals_8, out=buf12) del primals_8 buf15 = empty_strided_cuda((4, 1), (1, 1), torch.float32) extern_kernels.mm(buf14, primals_12, out=buf15) buf16 = buf11 del buf11 triton_poi_fused_add_mul_tanh_4[grid(16)](buf16, buf15, buf12, primals_10, primals_13, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_13 buf17 = buf5 del buf5 triton_poi_fused_add_mul_tanh_4[grid(16)](buf17, buf9, buf6, primals_4, primals_7, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_7 buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf16, (4, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf17, (4, 1, 4), (4, 0, 1), 0), out=buf18) buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_add_mul_5[grid(256)](primals_1, buf18, buf19, 256, XBLOCK=128, num_warps=4, num_stages=1) return (buf19, primals_1, primals_4, primals_10, buf1, buf3, buf6, buf8, buf9, buf12, buf14, buf15, buf18, reinterpret_tensor(buf16, (4, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf17, (4, 4, 1), (4, 1, 4), 0), reinterpret_tensor(primals_12, (1, 4), (1, 1), 0), reinterpret_tensor(primals_6, (1, 4), (1, 1), 0)) class SpatialAttentionLayerNew(nn.Module): def __init__(self, spatial_size): super(SpatialAttentionLayerNew, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.softmax = nn.Softmax(dim=1) self.width_w0 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.width_w1 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.width_w2 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.width_bias0 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.width_bias1 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.width_bias2 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.height_w0 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.height_w1 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.height_w2 = nn.Parameter(torch.ones(spatial_size, 1), requires_grad=True) self.height_bias0 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.height_bias1 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) self.height_bias2 = nn.Parameter(torch.zeros(1, spatial_size), requires_grad=True) nn.init.xavier_uniform_(self.width_w0) nn.init.xavier_uniform_(self.width_w1) nn.init.xavier_uniform_(self.width_w2) nn.init.xavier_uniform_(self.height_w0) nn.init.xavier_uniform_(self.height_w1) nn.init.xavier_uniform_(self.height_w2) def forward(self, input_0): primals_2 = self.width_w0 primals_3 = self.width_w1 primals_6 = self.width_w2 primals_4 = self.width_bias0 primals_5 = self.width_bias1 primals_7 = self.width_bias2 primals_8 = self.height_w0 primals_9 = self.height_w1 primals_12 = self.height_w2 primals_10 = self.height_bias0 primals_11 = self.height_bias1 primals_13 = self.height_bias2 primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13]) return output[0]	HolmesShuan/Compact-Global-Descriptor	SpatialAttentionLayer	false	8,247	[ "BSD-2-Clause" ]	24	715601bd7fce76596db960f7dc480241d443fa66	https://github.com/HolmesShuan/Compact-Global-Descriptor/tree/715601bd7fce76596db960f7dc480241d443fa66
FeedForward	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/ru/cruyugusy2rmmtp2z473yldzppsafb7s5mztfebe7bbngn4j47pf.py # Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu] # Source node to ATen node mapping: # gelu => add, erf, mul, mul_1, mul_2 # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.5), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.7071067811865476), kwargs = {}) # %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_1,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {}) triton_poi_fused_gelu_0 = async_compile.triton('triton_poi_fused_gelu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gelu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_gelu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 131072 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), None) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = 0.7071067811865476 tmp4 = tmp0 * tmp3 tmp5 = libdevice.erf(tmp4) tmp6 = 1.0 tmp7 = tmp5 + tmp6 tmp8 = tmp2 * tmp7 tl.store(out_ptr0 + (x0), tmp8, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (2048, 4), (4, 1)) assert_size_stride(primals_2, (2048, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 2048), (2048, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 2048), (2048, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm] extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2048), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4, 4, 2048), (32768, 8192, 2048, 1), torch.float32) # Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu] stream0 = get_raw_stream(0) triton_poi_fused_gelu_0.run(buf0, buf1, 131072, grid=grid(131072), stream=stream0) buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 2048), (2048, 1), 0), reinterpret_tensor(primals_4, (2048, 4), (1, 2048), 0), alpha=1, beta=1, out=buf2) del primals_5 return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, reinterpret_tensor(buf1, (64, 2048), (2048, 1), 0), primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((2048, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((2048, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 2048), (2048, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import torch import torch.nn.functional as F import torch.nn as nn class FeedForward(nn.Module): def __init__(self, d_model, d_ff=2048, dropout=0.1): super().__init__() self.linear_1 = nn.Linear(d_model, d_ff) self.dropout = nn.Dropout(dropout) self.linear_2 = nn.Linear(d_ff, d_model) def forward(self, x): x = self.dropout(F.gelu(self.linear_1(x))) x = self.linear_2(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'d_model': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_gelu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex tmp0 = tl.load(in_ptr0 + x0, None) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = 0.7071067811865476 tmp4 = tmp0 * tmp3 tmp5 = libdevice.erf(tmp4) tmp6 = 1.0 tmp7 = tmp5 + tmp6 tmp8 = tmp2 * tmp7 tl.store(out_ptr0 + x0, tmp8, None) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (2048, 4), (4, 1)) assert_size_stride(primals_2, (2048,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 2048), (2048, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 2048), (2048, 1), torch.float32) extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2048), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4, 4, 2048), (32768, 8192, 2048, 1), torch.float32) get_raw_stream(0) triton_poi_fused_gelu_0[grid(131072)](buf0, buf1, 131072, XBLOCK= 512, num_warps=8, num_stages=1) buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 2048), (2048, 1), 0), reinterpret_tensor(primals_4, (2048, 4), (1, 2048), 0), alpha=1, beta=1, out=buf2) del primals_5 return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), buf0, reinterpret_tensor(buf1, (64, 2048), (2048, 1), 0), primals_4 class FeedForwardNew(nn.Module): def __init__(self, d_model, d_ff=2048, dropout=0.1): super().__init__() self.linear_1 = nn.Linear(d_model, d_ff) self.dropout = nn.Dropout(dropout) self.linear_2 = nn.Linear(d_ff, d_model) def forward(self, input_0): primals_1 = self.linear_1.weight primals_2 = self.linear_1.bias primals_4 = self.linear_2.weight primals_5 = self.linear_2.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]	Hyunseung-Kim/molGCT	FeedForward	false	8,248	[ "Apache-2.0" ]	10	5a2604337cf0a9d3c725295ccb7c8ea4b0144636	https://github.com/Hyunseung-Kim/molGCT/tree/5a2604337cf0a9d3c725295ccb7c8ea4b0144636
MultiHeadAttention	# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_1/inductor_cache/hr/chrdug5cz3rgcks5nljq462hayvavrlindo52voz4mx5h2krmzgs.py # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone] # Source node to ATen node mapping: # matmul => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 16], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4x2) + (64y1)), xmask & ymask) tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + (16y3)), tmp2, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/5p/c5p45t43m4zlptg6m6jkckrmaa6wrg2luryc5jvv6wgqntdjctsa.py # Topologically Sorted Source Nodes: [scores_1], Original ATen: [aten._softmax] # Source node to ATen node mapping: # scores_1 => div_1, exp, sum_1 # Graph fragment: # %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_11, 1), kwargs = {}) # %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {}) # %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {}) # %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 1.0), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div_1 : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_per_fused__softmax_1 = async_compile.triton('triton_per_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[256, 16], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 256 rnumel = 16 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + (16x0)), xmask, other=0.0) tmp1 = 1.0 tmp2 = tmp0 tmp1 tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tmp5 = tl.where(xmask, tmp3, float("-inf")) tmp6 = triton_helpers.max2(tmp5, 1)[:, None] tmp7 = tmp2 - tmp6 tmp8 = tmp7 * tmp1 tmp9 = tl_math.exp(tmp8) tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK]) tmp12 = tl.where(xmask, tmp10, 0) tmp13 = tl.sum(tmp12, 1)[:, None] tmp14 = tmp9 / tmp13 tl.store(out_ptr2 + (r1 + (16x0)), tmp14, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/m4/cm4ixw3qpq6tpjhcuj6mj7tod3mxzn6d77vic7qixwiwag4sik2i.py # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] # Source node to ATen node mapping: # contiguous => clone_4 # Graph fragment: # %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 64 xnumel = 4 yoffset = tl.program_id(1) YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = (yindex // 16) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (16x2) + (64y1)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + (4y3)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_1/inductor_cache/k6/ck6ywu7aqqcxhvbatdyztfeekon32hdicttqyompfaqpvkvq3ol5.py # Topologically Sorted Source Nodes: [contiguous_1], Original ATen: [aten.clone] # Source node to ATen node mapping: # contiguous_1 => clone_5 # Graph fragment: # %clone_5 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_9,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: 'fp32', 1: 'fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex % 16 x1 = (xindex // 16) % 4 x2 = (xindex // 64) % 16 x3 = (xindex // 1024) x4 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16x2) + (256x1) + (1024*x3)), None) tl.store(out_ptr0 + (x4), tmp0, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4, ), (1, )) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4, ), (1, )) assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1) del primals_5 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2) del primals_7 buf3 = empty_strided_cuda((4, 4, 16, 1), (64, 16, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone] stream0 = get_raw_stream(0) triton_poi_fused_clone_0.run(buf1, primals_6, buf3, 16, 16, grid=grid(16, 16), stream=stream0) del primals_6 buf4 = reinterpret_tensor(buf1, (4, 4, 1, 16), (64, 16, 16, 1), 0); del buf1 # reuse # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone] triton_poi_fused_clone_0.run(buf0, primals_3, buf4, 16, 16, grid=grid(16, 16), stream=stream0) del primals_3 buf5 = empty_strided_cuda((16, 16, 16), (256, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 16), (16, 0, 1), 0), out=buf5) buf8 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [scores_1], Original ATen: [aten._softmax] triton_per_fused__softmax_1.run(buf5, buf8, 256, 16, grid=grid(256), stream=stream0) buf9 = reinterpret_tensor(buf0, (4, 4, 16, 1), (64, 16, 1, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [output], Original ATen: [aten.clone] triton_poi_fused_clone_0.run(buf2, primals_8, buf9, 16, 16, grid=grid(16, 16), stream=stream0) del primals_8 buf10 = reinterpret_tensor(buf2, (16, 16, 1), (16, 1, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf8, (16, 16, 16), (256, 16, 1), 0), reinterpret_tensor(buf9, (16, 16, 1), (16, 1, 0), 0), out=buf10) buf11 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] triton_poi_fused_clone_2.run(buf10, buf11, 64, 4, grid=grid(64, 4), stream=stream0) buf12 = reinterpret_tensor(buf10, (64, 4), (4, 1), 0); del buf10 # reuse # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf12) del primals_11 buf13 = reinterpret_tensor(buf5, (4, 16, 4, 16), (1024, 64, 16, 1), 0); del buf5 # reuse # Topologically Sorted Source Nodes: [contiguous_1], Original ATen: [aten.clone] triton_poi_fused_clone_3.run(buf8, buf13, 4096, grid=grid(4096), stream=stream0) return (reinterpret_tensor(buf12, (4, 16, 4), (64, 4, 1), 0), reinterpret_tensor(buf13, (4, 16, 64), (1024, 64, 1), 0), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), buf8, reinterpret_tensor(buf11, (64, 4), (4, 1), 0), primals_10, reinterpret_tensor(buf9, (16, 1, 16), (16, 1, 1), 0), reinterpret_tensor(buf3, (16, 1, 16), (16, 1, 1), 0), reinterpret_tensor(buf4, (16, 16, 1), (16, 1, 16), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)	import math import torch import torch.nn.functional as F import torch.nn as nn def attention(q, k, v, d_k, mask=None, dropout=None): scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: mask = mask.unsqueeze(1) scores = scores.masked_fill(mask == 0, -1000000000.0) scores = F.softmax(scores, dim=-1) scores_attn = scores if dropout is not None: scores = dropout(scores) output = torch.matmul(scores, v) return output, scores_attn class MultiHeadAttention(nn.Module): def __init__(self, heads, d_model, dropout=0.1): super().__init__() self.d_model = d_model self.d_k = d_model // heads self.h = heads self.q_linear = nn.Linear(d_model, d_model) self.v_linear = nn.Linear(d_model, d_model) self.k_linear = nn.Linear(d_model, d_model) self.dropout = nn.Dropout(dropout) self.out = nn.Linear(d_model, d_model) def forward(self, q, k, v, mask=None): bs = q.size(0) k = self.k_linear(k).view(bs, -1, self.h, self.d_k) q = self.q_linear(q).view(bs, -1, self.h, self.d_k) v = self.v_linear(v).view(bs, -1, self.h, self.d_k) k = k.transpose(1, 2) q = q.transpose(1, 2) v = v.transpose(1, 2) scores, scores_attn = attention(q, k, v, self.d_k, mask, self.dropout) concat = scores.transpose(1, 2).contiguous().view(bs, -1, self.d_model) output = self.out(concat) concat_attn = scores_attn.transpose(1, 2).contiguous().view(bs, -1, scores_attn.size(-1) * self.h) return output, concat_attn def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {'heads': 4, 'd_model': 4}]	import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import math import torch.nn.functional as F import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask) tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask & ymask) @triton.jit def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 256 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tmp5 = tl.where(xmask, tmp3, float('-inf')) tmp6 = triton_helpers.max2(tmp5, 1)[:, None] tmp7 = tmp2 - tmp6 tmp8 = tmp7 * tmp1 tmp9 = tl_math.exp(tmp8) tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK]) tmp12 = tl.where(xmask, tmp10, 0) tmp13 = tl.sum(tmp12, 1)[:, None] tmp14 = tmp9 / tmp13 tl.store(out_ptr2 + (r1 + 16 * x0), tmp14, xmask) @triton.jit def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 64 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = yindex // 16 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_clone_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex % 16 x1 = xindex // 16 % 4 x2 = xindex // 64 % 16 x3 = xindex // 1024 x4 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2 + 256 * x1 + 1024 * x3), None) tl.store(out_ptr0 + x4, tmp0, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4,), (1,)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4,), (1,)) assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1) del primals_5 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2) del primals_7 buf3 = empty_strided_cuda((4, 4, 16, 1), (64, 16, 1, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clone_0[grid(16, 16)](buf1, primals_6, buf3, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1) del primals_6 buf4 = reinterpret_tensor(buf1, (4, 4, 1, 16), (64, 16, 16, 1), 0) del buf1 triton_poi_fused_clone_0[grid(16, 16)](buf0, primals_3, buf4, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1) del primals_3 buf5 = empty_strided_cuda((16, 16, 16), (256, 16, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 16), (16, 0, 1), 0), out=buf5) buf8 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch .float32) triton_per_fused__softmax_1[grid(256)](buf5, buf8, 256, 16, XBLOCK= 32, num_warps=4, num_stages=1) buf9 = reinterpret_tensor(buf0, (4, 4, 16, 1), (64, 16, 1, 1), 0) del buf0 triton_poi_fused_clone_0[grid(16, 16)](buf2, primals_8, buf9, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1) del primals_8 buf10 = reinterpret_tensor(buf2, (16, 16, 1), (16, 1, 1), 0) del buf2 extern_kernels.bmm(reinterpret_tensor(buf8, (16, 16, 16), (256, 16, 1), 0), reinterpret_tensor(buf9, (16, 16, 1), (16, 1, 0), 0), out=buf10) buf11 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32) triton_poi_fused_clone_2[grid(64, 4)](buf10, buf11, 64, 4, XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1) buf12 = reinterpret_tensor(buf10, (64, 4), (4, 1), 0) del buf10 extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf12) del primals_11 buf13 = reinterpret_tensor(buf5, (4, 16, 4, 16), (1024, 64, 16, 1), 0) del buf5 triton_poi_fused_clone_3[grid(4096)](buf8, buf13, 4096, XBLOCK=256, num_warps=4, num_stages=1) return reinterpret_tensor(buf12, (4, 16, 4), (64, 4, 1), 0 ), reinterpret_tensor(buf13, (4, 16, 64), (1024, 64, 1), 0 ), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0 ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0 ), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0 ), buf8, reinterpret_tensor(buf11, (64, 4), (4, 1), 0 ), primals_10, reinterpret_tensor(buf9, (16, 1, 16), (16, 1, 1), 0 ), reinterpret_tensor(buf3, (16, 1, 16), (16, 1, 1), 0 ), reinterpret_tensor(buf4, (16, 16, 1), (16, 1, 16), 0) def attention(q, k, v, d_k, mask=None, dropout=None): scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: mask = mask.unsqueeze(1) scores = scores.masked_fill(mask == 0, -1000000000.0) scores = F.softmax(scores, dim=-1) scores_attn = scores if dropout is not None: scores = dropout(scores) output = torch.matmul(scores, v) return output, scores_attn class MultiHeadAttentionNew(nn.Module): def __init__(self, heads, d_model, dropout=0.1): super().__init__() self.d_model = d_model self.d_k = d_model // heads self.h = heads self.q_linear = nn.Linear(d_model, d_model) self.v_linear = nn.Linear(d_model, d_model) self.k_linear = nn.Linear(d_model, d_model) self.dropout = nn.Dropout(dropout) self.out = nn.Linear(d_model, d_model) def forward(self, input_0, input_1, input_2): primals_2 = self.q_linear.weight primals_3 = self.q_linear.bias primals_5 = self.v_linear.weight primals_6 = self.v_linear.bias primals_7 = self.k_linear.weight primals_8 = self.k_linear.bias primals_10 = self.out.weight primals_11 = self.out.bias primals_1 = input_0 primals_4 = input_1 primals_9 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0], output[1]	Hyunseung-Kim/molGCT	MultiHeadAttention	false	8,249	[ "Apache-2.0" ]	10	5a2604337cf0a9d3c725295ccb7c8ea4b0144636	https://github.com/Hyunseung-Kim/molGCT/tree/5a2604337cf0a9d3c725295ccb7c8ea4b0144636

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