entry_point
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| original_triton_python_code
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| optimised_triton_code
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| module_name
stringlengths 1
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SRNet
|
import torch
import torch.nn as nn
import torch.optim
class eca_layer(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel, k_size=3):
super(eca_layer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1
) // 2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
_b, _c, _h, _w = x.size()
y = self.avg_pool(x)
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2
).unsqueeze(-1)
y = self.sigmoid(y)
return x * y.expand_as(x)
class SRNet(nn.Module):
def __init__(self):
super(SRNet, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.pad = nn.ReflectionPad2d(1)
self.Conv1 = nn.Conv2d(3, 64, 3, 1, 1, bias=True)
self.Conv1_ = nn.Conv2d(3, 64, 5, 1, 1, bias=True)
self.Conv2 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
self.Conv2_ = nn.Conv2d(128, 64, 5, 1, 1, bias=True)
self.Conv3 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
self.Conv4 = nn.Conv2d(64, 3, 3, 1, 1, bias=True)
self.Conv5 = nn.Conv2d(3, 64, 3, 1, 1, bias=True)
self.Conv5_ = nn.Conv2d(3, 64, 5, 1, 1, bias=True)
self.Conv6 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
self.Conv6_ = nn.Conv2d(128, 64, 5, 1, 1, bias=True)
self.Conv7 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
self.Conv8 = nn.Conv2d(64, 3, 3, 1, 1, bias=True)
self.eca = eca_layer(3, 3)
def forward(self, LR_img):
x = self.relu(self.Conv1(LR_img))
x_ = self.relu(self.Conv1_(self.pad(LR_img)))
x = torch.cat((x, x_), dim=1)
x1 = self.relu(self.Conv2(x))
x1_ = self.relu(self.Conv2_(self.pad(x)))
x2 = torch.cat((x1, x1_), dim=1)
x2 = self.relu(self.Conv3(x2))
x2 = self.Conv4(x2)
x2 = self.eca(x2)
x2 = x2 + LR_img
x3 = self.relu(self.Conv5(x2))
x3_ = self.relu(self.Conv5_(self.pad(x2)))
x4 = torch.cat((x3, x3_), dim=1)
x5 = self.relu(self.Conv6(x4))
x5_ = self.relu(self.Conv6_(self.pad(x4)))
x6 = torch.cat((x5, x5_), dim=1)
x6 = self.relu(self.Conv7(x6))
x6 = self.Conv8(x6)
x6 = self.eca(x6)
x6 = x6 + x2
return x6
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
import torch.optim
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_reflection_pad2d_0(in_ptr0, 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 % 66
x1 = xindex // 66 % 66
x2 = xindex // 4356
x3 = 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 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 4096 % 128
x0 = xindex % 4096
x2 = xindex // 524288
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 64, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 262144 * x2), tmp4, other=0.0)
tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp7 = tmp5 + tmp6
tmp8 = tl.full([1], 0, tl.int32)
tmp9 = triton_helpers.maximum(tmp8, tmp7)
tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
tmp11 = tl.where(tmp4, tmp9, tmp10)
tmp12 = tmp0 >= tmp3
tl.full([1], 128, tl.int64)
tmp15 = tl.load(in_ptr2 + (x0 + 4096 * (-64 + x1) + 262144 * x2), tmp12,
other=0.0)
tmp16 = tl.load(in_ptr3 + (-64 + x1), tmp12, eviction_policy=
'evict_last', other=0.0)
tmp17 = tmp15 + tmp16
tmp18 = triton_helpers.maximum(tmp8, tmp17)
tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
tmp20 = tl.where(tmp12, tmp18, tmp19)
tmp21 = tl.where(tmp4, tmp11, tmp20)
tl.store(out_ptr0 + x3, tmp21, None)
@triton.jit
def triton_poi_fused_reflection_pad2d_2(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 % 66
x1 = xindex // 66 % 66
x2 = xindex // 4356
x3 = 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 * x2),
None, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, None)
@triton.jit
def triton_poi_fused_convolution_relu_3(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_red_fused_convolution_mean_4(in_out_ptr0, in_out_ptr1, in_ptr0,
xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
xnumel = 12
rnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rbase = tl.arange(0, RBLOCK)[None, :]
x3 = xindex
x0 = xindex % 3
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
_tmp4 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
tmp0 = tl.load(in_out_ptr0 + (r2 + 4096 * x3), rmask & xmask,
eviction_policy='evict_first', other=0.0)
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = _tmp4 + tmp3
_tmp4 = tl.where(rmask & xmask, tmp5, _tmp4)
tl.store(in_out_ptr0 + (r2 + 4096 * x3), tmp2, rmask & xmask)
tmp4 = tl.sum(_tmp4, 1)[:, None]
tmp6 = 4096.0
tmp7 = tmp4 / tmp6
tl.debug_barrier()
tl.store(in_out_ptr1 + x3, tmp7, xmask)
@triton.jit
def triton_poi_fused_add_mul_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)
x2 = xindex
x1 = xindex // 4096
tmp0 = tl.load(in_ptr0 + x2, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr2 + x2, None)
tmp2 = tl.sigmoid(tmp1)
tmp3 = tmp0 * tmp2
tmp5 = tmp3 + tmp4
tl.store(out_ptr0 + x2, tmp5, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_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 // 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)
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) = args
args.clear()
assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (64, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_7, (64,), (1,))
assert_size_stride(primals_8, (64, 128, 5, 5), (3200, 25, 5, 1))
assert_size_stride(primals_9, (64,), (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, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_13, (3,), (1,))
assert_size_stride(primals_14, (1, 1, 3), (3, 3, 1))
assert_size_stride(primals_15, (64, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_16, (64,), (1,))
assert_size_stride(primals_17, (64, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_18, (64,), (1,))
assert_size_stride(primals_19, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_20, (64,), (1,))
assert_size_stride(primals_21, (64, 128, 5, 5), (3200, 25, 5, 1))
assert_size_stride(primals_22, (64,), (1,))
assert_size_stride(primals_23, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_24, (64,), (1,))
assert_size_stride(primals_25, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_26, (3,), (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, 64, 64, 64), (262144, 4096, 64, 1))
buf1 = empty_strided_cuda((4, 3, 66, 66), (13068, 4356, 66, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(52272)](primals_3, buf1,
52272, XBLOCK=256, 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, 64, 64, 64), (262144, 4096, 64, 1))
buf3 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
torch.float32)
triton_poi_fused_cat_1[grid(2097152)](buf0, primals_2, buf2,
primals_5, buf3, 2097152, XBLOCK=1024, 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, 64, 64, 64), (262144, 4096, 64, 1))
buf5 = empty_strided_cuda((4, 128, 66, 66), (557568, 4356, 66, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_2[grid(2230272)](buf3, buf5,
2230272, XBLOCK=512, num_warps=8, 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, 64, 64, 64), (262144, 4096, 64, 1))
buf7 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
torch.float32)
triton_poi_fused_cat_1[grid(2097152)](buf4, primals_7, buf6,
primals_9, buf7, 2097152, XBLOCK=1024, 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, 64, 64, 64), (262144, 4096, 64, 1))
buf9 = buf8
del buf8
triton_poi_fused_convolution_relu_3[grid(1048576)](buf9, primals_11,
1048576, XBLOCK=1024, 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, 3, 64, 64), (12288, 4096, 64, 1))
buf11 = buf10
del buf10
buf12 = empty_strided_cuda((4, 3, 1, 1), (3, 1, 12, 12), torch.float32)
buf13 = buf12
del buf12
triton_red_fused_convolution_mean_4[grid(12)](buf11, buf13,
primals_13, 12, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
num_stages=1)
del primals_13
buf14 = extern_kernels.convolution(reinterpret_tensor(buf13, (4, 1,
3), (3, 0, 1), 0), primals_14, stride=(1,), padding=(1,),
dilation=(1,), transposed=False, output_padding=(0,), groups=1,
bias=None)
assert_size_stride(buf14, (4, 1, 3), (3, 3, 1))
buf15 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
torch.float32)
triton_poi_fused_add_mul_5[grid(49152)](buf11, buf14, primals_3,
buf15, 49152, XBLOCK=512, num_warps=4, num_stages=1)
buf16 = extern_kernels.convolution(buf15, primals_15, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf16, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf17 = empty_strided_cuda((4, 3, 66, 66), (13068, 4356, 66, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_0[grid(52272)](buf15, buf17,
52272, XBLOCK=256, num_warps=4, num_stages=1)
buf18 = extern_kernels.convolution(buf17, primals_17, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf18, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf19 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
torch.float32)
triton_poi_fused_cat_1[grid(2097152)](buf16, primals_16, buf18,
primals_18, buf19, 2097152, XBLOCK=1024, num_warps=4, num_stages=1)
buf20 = extern_kernels.convolution(buf19, primals_19, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf21 = empty_strided_cuda((4, 128, 66, 66), (557568, 4356, 66, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_2[grid(2230272)](buf19, buf21,
2230272, XBLOCK=512, num_warps=8, num_stages=1)
buf22 = extern_kernels.convolution(buf21, primals_21, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf22, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf23 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
torch.float32)
triton_poi_fused_cat_1[grid(2097152)](buf20, primals_20, buf22,
primals_22, buf23, 2097152, XBLOCK=1024, num_warps=4, num_stages=1)
buf24 = extern_kernels.convolution(buf23, primals_23, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf24, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf25 = buf24
del buf24
triton_poi_fused_convolution_relu_3[grid(1048576)](buf25,
primals_24, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_24
buf26 = extern_kernels.convolution(buf25, primals_25, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf26, (4, 3, 64, 64), (12288, 4096, 64, 1))
buf27 = buf26
del buf26
buf28 = empty_strided_cuda((4, 3, 1, 1), (3, 1, 12, 12), torch.float32)
buf29 = buf28
del buf28
triton_red_fused_convolution_mean_4[grid(12)](buf27, buf29,
primals_26, 12, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
num_stages=1)
del primals_26
buf30 = extern_kernels.convolution(reinterpret_tensor(buf29, (4, 1,
3), (3, 0, 1), 0), primals_14, stride=(1,), padding=(1,),
dilation=(1,), transposed=False, output_padding=(0,), groups=1,
bias=None)
assert_size_stride(buf30, (4, 1, 3), (3, 3, 1))
buf31 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
torch.float32)
triton_poi_fused_add_mul_5[grid(49152)](buf27, buf30, buf15, buf31,
49152, XBLOCK=512, num_warps=4, num_stages=1)
buf32 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
buf22, primals_22, buf32, 1048576, XBLOCK=512, num_warps=8,
num_stages=1)
del buf22
del primals_22
buf33 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
buf20, primals_20, buf33, 1048576, XBLOCK=512, num_warps=8,
num_stages=1)
del buf20
del primals_20
buf34 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
buf18, primals_18, buf34, 1048576, XBLOCK=512, num_warps=8,
num_stages=1)
del buf18
del primals_18
buf35 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
buf16, primals_16, buf35, 1048576, XBLOCK=512, num_warps=8,
num_stages=1)
del buf16
del primals_16
buf36 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
buf6, primals_9, buf36, 1048576, XBLOCK=512, num_warps=8,
num_stages=1)
del buf6
del primals_9
buf37 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
buf4, primals_7, buf37, 1048576, XBLOCK=512, num_warps=8,
num_stages=1)
del buf4
del primals_7
buf38 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
buf2, primals_5, buf38, 1048576, XBLOCK=512, num_warps=8,
num_stages=1)
del buf2
del primals_5
buf39 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
buf0, primals_2, buf39, 1048576, XBLOCK=512, num_warps=8,
num_stages=1)
del buf0
del primals_2
return (buf31, primals_1, primals_3, primals_4, primals_6, primals_8,
primals_10, primals_12, primals_14, primals_15, primals_17,
primals_19, primals_21, primals_23, primals_25, buf1, buf3, buf5,
buf7, buf9, buf11, reinterpret_tensor(buf13, (4, 1, 3), (3, 1, 1),
0), buf14, buf15, buf17, buf19, buf21, buf23, buf25, buf27,
reinterpret_tensor(buf29, (4, 1, 3), (3, 1, 1), 0), buf30, buf32,
buf33, buf34, buf35, buf36, buf37, buf38, buf39)
class eca_layer(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel, k_size=3):
super(eca_layer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1
) // 2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
_b, _c, _h, _w = x.size()
y = self.avg_pool(x)
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2
).unsqueeze(-1)
y = self.sigmoid(y)
return x * y.expand_as(x)
class SRNetNew(nn.Module):
def __init__(self):
super(SRNetNew, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.pad = nn.ReflectionPad2d(1)
self.Conv1 = nn.Conv2d(3, 64, 3, 1, 1, bias=True)
self.Conv1_ = nn.Conv2d(3, 64, 5, 1, 1, bias=True)
self.Conv2 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
self.Conv2_ = nn.Conv2d(128, 64, 5, 1, 1, bias=True)
self.Conv3 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
self.Conv4 = nn.Conv2d(64, 3, 3, 1, 1, bias=True)
self.Conv5 = nn.Conv2d(3, 64, 3, 1, 1, bias=True)
self.Conv5_ = nn.Conv2d(3, 64, 5, 1, 1, bias=True)
self.Conv6 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
self.Conv6_ = nn.Conv2d(128, 64, 5, 1, 1, bias=True)
self.Conv7 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
self.Conv8 = nn.Conv2d(64, 3, 3, 1, 1, bias=True)
self.eca = eca_layer(3, 3)
def forward(self, input_0):
primals_1 = self.Conv1.weight
primals_2 = self.Conv1.bias
primals_4 = self.Conv1_.weight
primals_5 = self.Conv1_.bias
primals_6 = self.Conv2.weight
primals_7 = self.Conv2.bias
primals_8 = self.Conv2_.weight
primals_9 = self.Conv2_.bias
primals_10 = self.Conv3.weight
primals_11 = self.Conv3.bias
primals_12 = self.Conv4.weight
primals_13 = self.Conv4.bias
primals_15 = self.Conv5.weight
primals_16 = self.Conv5.bias
primals_17 = self.Conv5_.weight
primals_18 = self.Conv5_.bias
primals_19 = self.Conv6.weight
primals_20 = self.Conv6.bias
primals_21 = self.Conv6_.weight
primals_22 = self.Conv6_.bias
primals_23 = self.Conv7.weight
primals_24 = self.Conv7.bias
primals_25 = self.Conv8.weight
primals_26 = self.Conv8.bias
primals_14 = self.eca.conv.weight
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])
return output[0]
|
purbayankar/PyTorch-Zero-Shot-Super-Resolution
|
SRNet
| false
| 12,968
|
[
"MIT"
] | 0
|
434fe5e84e166eef1f8c03880fc83c7e8749c49c
|
https://github.com/purbayankar/PyTorch-Zero-Shot-Super-Resolution/tree/434fe5e84e166eef1f8c03880fc83c7e8749c49c
|
EncoderLayer
|
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model):
super(MultiHeadAttention, self).__init__()
assert d_model % heads == 0
self.d_k = d_model // heads
self.heads = heads
self.dropout = nn.Dropout(0.1)
self.query = nn.Linear(d_model, d_model)
self.key = nn.Linear(d_model, d_model)
self.value = nn.Linear(d_model, d_model)
self.concat = nn.Linear(d_model, d_model)
def forward(self, query, key, value, mask):
"""
query, key, value of shape: (batch_size, max_len, 512)
mask of shape: (batch_size, 1, 1, max_words)
"""
query = self.query(query)
key = self.key(key)
value = self.value(value)
query = query.view(query.shape[0], -1, self.heads, self.d_k).permute(
0, 2, 1, 3)
key = key.view(key.shape[0], -1, self.heads, self.d_k).permute(0, 2,
1, 3)
value = value.view(value.shape[0], -1, self.heads, self.d_k).permute(
0, 2, 1, 3)
scores = torch.matmul(query, key.permute(0, 1, 3, 2)) / math.sqrt(query
.size(-1))
scores = scores.masked_fill(mask == 0, -1000000000.0)
weights = F.softmax(scores, dim=-1)
weights = self.dropout(weights)
context = torch.matmul(weights, value)
context = context.permute(0, 2, 1, 3).contiguous().view(context.
shape[0], -1, self.heads * self.d_k)
interacted = self.concat(context)
return interacted
class FeedForward(nn.Module):
def __init__(self, d_model, middle_dim=2048):
super(FeedForward, self).__init__()
self.fc1 = nn.Linear(d_model, middle_dim)
self.fc2 = nn.Linear(middle_dim, d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, x):
out = F.relu(self.fc1(x))
out = self.fc2(self.dropout(out))
return out
class EncoderLayer(nn.Module):
def __init__(self, d_model, heads):
super(EncoderLayer, self).__init__()
self.layernorm = nn.LayerNorm(d_model)
self.self_multihead = MultiHeadAttention(heads, d_model)
self.feed_forward = FeedForward(d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, embeddings, mask):
interacted = self.dropout(self.self_multihead(embeddings,
embeddings, embeddings, mask))
interacted = self.layernorm(interacted + embeddings)
feed_forward_out = self.dropout(self.feed_forward(interacted))
encoded = self.layernorm(feed_forward_out + interacted)
return encoded
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'heads': 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.utils.data
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_clone_0(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_eq_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 == tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_div_masked_fill_2(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
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp7 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp12 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp16 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp17 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = -1000000000.0
tmp5 = tl.where(tmp0, tmp4, tmp3)
tmp8 = tmp7 * tmp2
tmp9 = tl.where(tmp6, tmp4, tmp8)
tmp10 = triton_helpers.maximum(tmp5, tmp9)
tmp13 = tmp12 * tmp2
tmp14 = tl.where(tmp11, tmp4, tmp13)
tmp15 = triton_helpers.maximum(tmp10, tmp14)
tmp18 = tmp17 * tmp2
tmp19 = tl.where(tmp16, 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 + x2, tmp20, xmask)
tl.store(out_ptr1 + x2, tmp31, xmask)
@triton.jit
def triton_poi_fused__softmax_div_masked_fill_3(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 % 64
x4 = xindex
x5 = xindex // 4
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp1 = tl.load(in_out_ptr0 + x4, xmask)
tmp6 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = -1000000000.0
tmp5 = tl.where(tmp0, tmp4, tmp3)
tmp7 = tmp5 - tmp6
tmp8 = tl_math.exp(tmp7)
tmp10 = tmp8 / tmp9
tl.store(in_out_ptr0 + x4, tmp10, xmask)
@triton.jit
def triton_poi_fused_clone_4(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_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)
@triton.jit
def triton_poi_fused_relu_threshold_backward_7(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)
x2 = xindex
x0 = xindex % 2048
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
@triton.jit
def triton_poi_fused_add_8(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
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)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_9(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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_10(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
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
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, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16) = 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, 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), (16, 4, 1))
assert_size_stride(primals_9, (4, 4), (4, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (4,), (1,))
assert_size_stride(primals_13, (2048, 4), (4, 1))
assert_size_stride(primals_14, (2048,), (1,))
assert_size_stride(primals_15, (4, 2048), (2048, 1))
assert_size_stride(primals_16, (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_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
del primals_4
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, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(16, 4)](buf0, primals_2, buf3, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_2
buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf0
triton_poi_fused_clone_0[grid(16, 4)](buf1, primals_5, buf4, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_eq_1[grid(64)](primals_8, buf6, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_8
buf7 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf1
buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused__softmax_div_masked_fill_2[grid(64)](buf6, buf5,
buf7, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused__softmax_div_masked_fill_3[grid(256)](buf9, buf6,
buf7, buf8, 256, XBLOCK=128, num_warps=4, num_stages=1)
buf10 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf8
triton_poi_fused_clone_0[grid(16, 4)](buf2, primals_7, buf10, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_7
buf11 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
del buf2
extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
buf12 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf7
triton_poi_fused_clone_4[grid(16, 4)](buf11, buf12, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf13 = reinterpret_tensor(buf11, (16, 4), (4, 1), 0)
del buf11
extern_kernels.addmm(primals_10, reinterpret_tensor(buf12, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf13)
del primals_10
buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf15 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_add_native_layer_norm_5[grid(16)](buf13, primals_3,
buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_6[grid(64)](buf13, primals_3,
buf14, buf15, primals_11, primals_12, buf16, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf17 = empty_strided_cuda((16, 2048), (2048, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf16, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_13, (4, 2048), (1, 4), 0), out=buf17)
buf18 = reinterpret_tensor(buf17, (4, 4, 2048), (8192, 2048, 1), 0)
del buf17
buf24 = empty_strided_cuda((4, 4, 2048), (8192, 2048, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_7[grid(32768)](buf18,
primals_14, buf24, 32768, XBLOCK=256, num_warps=4, num_stages=1)
del primals_14
buf19 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf18, (16, 2048), (2048, 1),
0), reinterpret_tensor(primals_15, (2048, 4), (1, 2048), 0),
out=buf19)
buf20 = reinterpret_tensor(buf19, (4, 4, 4), (16, 4, 1), 0)
del buf19
triton_poi_fused_add_8[grid(64)](buf20, primals_16, buf16, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_16
buf21 = buf15
del buf15
buf22 = buf14
del buf14
triton_poi_fused_native_layer_norm_9[grid(16)](buf20, buf21, buf22,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf23 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_10[grid(64)](buf20, buf21, buf22,
primals_11, primals_12, buf23, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del buf21
del buf22
del primals_12
return buf23, primals_3, primals_11, buf6, buf9, reinterpret_tensor(buf12,
(16, 4), (4, 1), 0), buf13, reinterpret_tensor(buf16, (16, 4), (4,
1), 0), reinterpret_tensor(buf18, (16, 2048), (2048, 1), 0
), buf20, primals_15, buf24, primals_13, primals_9, reinterpret_tensor(
buf10, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf3, (16, 1,
4), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model):
super(MultiHeadAttention, self).__init__()
assert d_model % heads == 0
self.d_k = d_model // heads
self.heads = heads
self.dropout = nn.Dropout(0.1)
self.query = nn.Linear(d_model, d_model)
self.key = nn.Linear(d_model, d_model)
self.value = nn.Linear(d_model, d_model)
self.concat = nn.Linear(d_model, d_model)
def forward(self, query, key, value, mask):
"""
query, key, value of shape: (batch_size, max_len, 512)
mask of shape: (batch_size, 1, 1, max_words)
"""
query = self.query(query)
key = self.key(key)
value = self.value(value)
query = query.view(query.shape[0], -1, self.heads, self.d_k).permute(
0, 2, 1, 3)
key = key.view(key.shape[0], -1, self.heads, self.d_k).permute(0, 2,
1, 3)
value = value.view(value.shape[0], -1, self.heads, self.d_k).permute(
0, 2, 1, 3)
scores = torch.matmul(query, key.permute(0, 1, 3, 2)) / math.sqrt(query
.size(-1))
scores = scores.masked_fill(mask == 0, -1000000000.0)
weights = F.softmax(scores, dim=-1)
weights = self.dropout(weights)
context = torch.matmul(weights, value)
context = context.permute(0, 2, 1, 3).contiguous().view(context.
shape[0], -1, self.heads * self.d_k)
interacted = self.concat(context)
return interacted
class FeedForward(nn.Module):
def __init__(self, d_model, middle_dim=2048):
super(FeedForward, self).__init__()
self.fc1 = nn.Linear(d_model, middle_dim)
self.fc2 = nn.Linear(middle_dim, d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, x):
out = F.relu(self.fc1(x))
out = self.fc2(self.dropout(out))
return out
class EncoderLayerNew(nn.Module):
def __init__(self, d_model, heads):
super(EncoderLayerNew, self).__init__()
self.layernorm = nn.LayerNorm(d_model)
self.self_multihead = MultiHeadAttention(heads, d_model)
self.feed_forward = FeedForward(d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, input_0, input_1):
primals_2 = self.layernorm.weight
primals_5 = self.layernorm.bias
primals_1 = self.self_multihead.query.weight
primals_7 = self.self_multihead.query.bias
primals_4 = self.self_multihead.key.weight
primals_10 = self.self_multihead.key.bias
primals_6 = self.self_multihead.value.weight
primals_11 = self.self_multihead.value.bias
primals_9 = self.self_multihead.concat.weight
primals_12 = self.self_multihead.concat.bias
primals_13 = self.feed_forward.fc1.weight
primals_14 = self.feed_forward.fc1.bias
primals_15 = self.feed_forward.fc2.weight
primals_16 = self.feed_forward.fc2.bias
primals_3 = input_0
primals_8 = 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])
return output[0]
|
sd2001/seqModeling
|
EncoderLayer
| false
| 12,969
|
[
"MIT"
] | 0
|
393f680de711ea8477e5450633b492298d253368
|
https://github.com/sd2001/seqModeling/tree/393f680de711ea8477e5450633b492298d253368
|
WassersteinDiscriminatorLoss
|
import torch
import torch.nn as nn
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` the mean value of the
Tensor is returned. If ``sum`` the elements of the Tensor will be summed. If none of the
above then the Tensor is returning without any change.
Returns:
As per the above ``reduction`` convention.
"""
if reduction == 'mean':
return torch.mean(x)
elif reduction == 'sum':
return torch.sum(x)
else:
return x
def wasserstein_discriminator_loss(fx, fgz, reduction='mean'):
return reduce(fgz - fx, reduction)
class DiscriminatorLoss(nn.Module):
"""Base class for all discriminator losses.
.. note:: All Losses meant to be minimized for optimizing the Discriminator must subclass this.
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
"""
def __init__(self, reduction='mean', override_train_ops=None):
super(DiscriminatorLoss, self).__init__()
self.reduction = reduction
self.override_train_ops = override_train_ops
self.arg_map = {}
def set_arg_map(self, value):
"""Updates the ``arg_map`` for passing a different value to the ``train_ops``.
Args:
value (dict): A mapping of the ``argument name`` in the method signature and the
variable name in the ``Trainer`` it corresponds to.
.. note::
If the ``train_ops`` signature is
``train_ops(self, gen, disc, optimizer_discriminator, device, batch_size, labels=None)``
then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``.
In this case we make the following function call
``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``.
"""
self.arg_map.update(value)
def train_ops(self, generator, discriminator, optimizer_discriminator,
real_inputs, device, labels=None):
"""Defines the standard ``train_ops`` used by most losses. Losses which have a different
training procedure can either ``subclass`` it **(recommended approach)** or make use of
``override_train_ops`` argument.
The ``standard optimization algorithm`` for the ``discriminator`` defined in this train_ops
is as follows:
1. :math:`fake = generator(noise)`
2. :math:`value_1 = discriminator(fake)`
3. :math:`value_2 = discriminator(real)`
4. :math:`loss = loss\\_function(value_1, value_2)`
5. Backpropagate by computing :math:`\\nabla loss`
6. Run a step of the optimizer for discriminator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_discriminator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``discriminator``.
real_inputs (torch.Tensor): The real data to be fed to the ``discriminator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(self, generator, discriminator,
optimizer_discriminator, real_inputs, device, labels)
else:
if labels is None and (generator.label_type == 'required' or
discriminator.label_type == 'required'):
raise Exception('GAN model requires labels for training')
batch_size = real_inputs.size(0)
noise = torch.randn(batch_size, generator.encoding_dims, device
=device)
if generator.label_type == 'generated':
label_gen = torch.randint(0, generator.num_classes, (
batch_size,), device=device)
optimizer_discriminator.zero_grad()
if discriminator.label_type == 'none':
dx = discriminator(real_inputs)
elif discriminator.label_type == 'required':
dx = discriminator(real_inputs, labels)
else:
dx = discriminator(real_inputs, label_gen)
if generator.label_type == 'none':
fake = generator(noise)
elif generator.label_type == 'required':
fake = generator(noise, labels)
else:
fake = generator(noise, label_gen)
if discriminator.label_type == 'none':
dgz = discriminator(fake.detach())
elif generator.label_type == 'generated':
dgz = discriminator(fake.detach(), label_gen)
else:
dgz = discriminator(fake.detach(), labels)
loss = self.forward(dx, dgz)
loss.backward()
optimizer_discriminator.step()
return loss.item()
class WassersteinDiscriminatorLoss(DiscriminatorLoss):
"""Wasserstein GAN generator loss from
`"Wasserstein GAN by Arjovsky et. al." <https://arxiv.org/abs/1701.07875>`_ paper
The loss can be described as:
.. math:: L(D) = f(G(z)) - f(x)
where
- :math:`G` : Generator
- :math:`f` : Critic/Discriminator
- :math:`x` : A sample from the data distribution
- :math:`z` : A sample from the noise prior
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the mean of the output.
If ``sum`` the elements of the output will be summed.
clip (tuple, optional): Tuple that specifies the maximum and minimum parameter
clamping to be applied, as per the original version of the Wasserstein loss
without Gradient Penalty.
override_train_ops (function, optional): A function is passed to this argument,
if the default ``train_ops`` is not to be used.
"""
def __init__(self, reduction='mean', clip=None, override_train_ops=None):
super(WassersteinDiscriminatorLoss, self).__init__(reduction,
override_train_ops)
if (isinstance(clip, tuple) or isinstance(clip, list)) and len(clip
) > 1:
self.clip = clip
else:
self.clip = None
def forward(self, fx, fgz):
"""Computes the loss for the given input.
Args:
fx (torch.Tensor) : Output of the Discriminator with real data. It must have the
dimensions (N, \\*) where \\* means any number of additional
dimensions.
fgz (torch.Tensor) : Output of the Discriminator with generated data. It must have the
dimensions (N, \\*) where \\* means any number of additional
dimensions.
Returns:
scalar if reduction is applied else Tensor with dimensions (N, \\*).
"""
return wasserstein_discriminator_loss(fx, fgz, self.reduction)
def train_ops(self, generator, discriminator, optimizer_discriminator,
real_inputs, device, labels=None):
"""Defines the standard ``train_ops`` used by wasserstein discriminator loss.
The ``standard optimization algorithm`` for the ``discriminator`` defined in this train_ops
is as follows:
1. Clamp the discriminator parameters to satisfy :math:`lipschitz\\ condition`
2. :math:`fake = generator(noise)`
3. :math:`value_1 = discriminator(fake)`
4. :math:`value_2 = discriminator(real)`
5. :math:`loss = loss\\_function(value_1, value_2)`
6. Backpropagate by computing :math:`\\nabla loss`
7. Run a step of the optimizer for discriminator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_discriminator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``discriminator``.
real_inputs (torch.Tensor): The real data to be fed to the ``discriminator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(generator, discriminator,
optimizer_discriminator, real_inputs, device, labels)
else:
if self.clip is not None:
for p in discriminator.parameters():
p.data.clamp_(self.clip[0], self.clip[1])
return super(WassersteinDiscriminatorLoss, self).train_ops(
generator, discriminator, optimizer_discriminator,
real_inputs, device, labels)
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_per_fused_mean_sub_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)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
tmp6 = 256.0
tmp7 = tmp5 / tmp6
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp7, 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_mean_sub_0[grid(1)](buf1, arg1_1, arg0_1, 1, 256,
num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` the mean value of the
Tensor is returned. If ``sum`` the elements of the Tensor will be summed. If none of the
above then the Tensor is returning without any change.
Returns:
As per the above ``reduction`` convention.
"""
if reduction == 'mean':
return torch.mean(x)
elif reduction == 'sum':
return torch.sum(x)
else:
return x
def wasserstein_discriminator_loss(fx, fgz, reduction='mean'):
return reduce(fgz - fx, reduction)
class DiscriminatorLoss(nn.Module):
"""Base class for all discriminator losses.
.. note:: All Losses meant to be minimized for optimizing the Discriminator must subclass this.
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
"""
def __init__(self, reduction='mean', override_train_ops=None):
super(DiscriminatorLoss, self).__init__()
self.reduction = reduction
self.override_train_ops = override_train_ops
self.arg_map = {}
def set_arg_map(self, value):
"""Updates the ``arg_map`` for passing a different value to the ``train_ops``.
Args:
value (dict): A mapping of the ``argument name`` in the method signature and the
variable name in the ``Trainer`` it corresponds to.
.. note::
If the ``train_ops`` signature is
``train_ops(self, gen, disc, optimizer_discriminator, device, batch_size, labels=None)``
then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``.
In this case we make the following function call
``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``.
"""
self.arg_map.update(value)
def train_ops(self, generator, discriminator, optimizer_discriminator,
real_inputs, device, labels=None):
"""Defines the standard ``train_ops`` used by most losses. Losses which have a different
training procedure can either ``subclass`` it **(recommended approach)** or make use of
``override_train_ops`` argument.
The ``standard optimization algorithm`` for the ``discriminator`` defined in this train_ops
is as follows:
1. :math:`fake = generator(noise)`
2. :math:`value_1 = discriminator(fake)`
3. :math:`value_2 = discriminator(real)`
4. :math:`loss = loss\\_function(value_1, value_2)`
5. Backpropagate by computing :math:`\\nabla loss`
6. Run a step of the optimizer for discriminator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_discriminator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``discriminator``.
real_inputs (torch.Tensor): The real data to be fed to the ``discriminator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(self, generator, discriminator,
optimizer_discriminator, real_inputs, device, labels)
else:
if labels is None and (generator.label_type == 'required' or
discriminator.label_type == 'required'):
raise Exception('GAN model requires labels for training')
batch_size = real_inputs.size(0)
noise = torch.randn(batch_size, generator.encoding_dims, device
=device)
if generator.label_type == 'generated':
label_gen = torch.randint(0, generator.num_classes, (
batch_size,), device=device)
optimizer_discriminator.zero_grad()
if discriminator.label_type == 'none':
dx = discriminator(real_inputs)
elif discriminator.label_type == 'required':
dx = discriminator(real_inputs, labels)
else:
dx = discriminator(real_inputs, label_gen)
if generator.label_type == 'none':
fake = generator(noise)
elif generator.label_type == 'required':
fake = generator(noise, labels)
else:
fake = generator(noise, label_gen)
if discriminator.label_type == 'none':
dgz = discriminator(fake.detach())
elif generator.label_type == 'generated':
dgz = discriminator(fake.detach(), label_gen)
else:
dgz = discriminator(fake.detach(), labels)
loss = self.forward(dx, dgz)
loss.backward()
optimizer_discriminator.step()
return loss.item()
class WassersteinDiscriminatorLossNew(DiscriminatorLoss):
"""Wasserstein GAN generator loss from
`"Wasserstein GAN by Arjovsky et. al." <https://arxiv.org/abs/1701.07875>`_ paper
The loss can be described as:
.. math:: L(D) = f(G(z)) - f(x)
where
- :math:`G` : Generator
- :math:`f` : Critic/Discriminator
- :math:`x` : A sample from the data distribution
- :math:`z` : A sample from the noise prior
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the mean of the output.
If ``sum`` the elements of the output will be summed.
clip (tuple, optional): Tuple that specifies the maximum and minimum parameter
clamping to be applied, as per the original version of the Wasserstein loss
without Gradient Penalty.
override_train_ops (function, optional): A function is passed to this argument,
if the default ``train_ops`` is not to be used.
"""
def __init__(self, reduction='mean', clip=None, override_train_ops=None):
super(WassersteinDiscriminatorLossNew, self).__init__(reduction,
override_train_ops)
if (isinstance(clip, tuple) or isinstance(clip, list)) and len(clip
) > 1:
self.clip = clip
else:
self.clip = None
def train_ops(self, generator, discriminator, optimizer_discriminator,
real_inputs, device, labels=None):
"""Defines the standard ``train_ops`` used by wasserstein discriminator loss.
The ``standard optimization algorithm`` for the ``discriminator`` defined in this train_ops
is as follows:
1. Clamp the discriminator parameters to satisfy :math:`lipschitz\\ condition`
2. :math:`fake = generator(noise)`
3. :math:`value_1 = discriminator(fake)`
4. :math:`value_2 = discriminator(real)`
5. :math:`loss = loss\\_function(value_1, value_2)`
6. Backpropagate by computing :math:`\\nabla loss`
7. Run a step of the optimizer for discriminator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_discriminator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``discriminator``.
real_inputs (torch.Tensor): The real data to be fed to the ``discriminator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(generator, discriminator,
optimizer_discriminator, real_inputs, device, labels)
else:
if self.clip is not None:
for p in discriminator.parameters():
p.data.clamp_(self.clip[0], self.clip[1])
return super(WassersteinDiscriminatorLossNew, self).train_ops(
generator, discriminator, optimizer_discriminator,
real_inputs, device, labels)
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
shi-weili/torchgan
|
WassersteinDiscriminatorLoss
| false
| 12,970
|
[
"MIT"
] | 0
|
28ffd4026b8c0db2217b667d30a222d6758bfc41
|
https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41
|
_BoundaryRefineModule
|
import torch
from torch import nn
class _BoundaryRefineModule(nn.Module):
def __init__(self, dim):
super(_BoundaryRefineModule, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
def forward(self, x):
residual = self.conv1(x)
residual = self.relu(residual)
residual = self.conv2(residual)
out = x + residual
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'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 import nn
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):
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_1(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_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
tl.store(in_out_ptr0 + x3, tmp4, 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=256, 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
triton_poi_fused_add_convolution_1[grid(256)](buf3, primals_3,
primals_5, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
return buf3, primals_1, primals_3, primals_4, buf1
class _BoundaryRefineModuleNew(nn.Module):
def __init__(self, dim):
super(_BoundaryRefineModuleNew, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(dim, dim, 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]
|
sharanry/pytorch-semantic-segmentation
|
_BoundaryRefineModule
| false
| 12,971
|
[
"MIT"
] | 0
|
47d637e3d5fcc1e2569203306c2fa5dca6f0e68a
|
https://github.com/sharanry/pytorch-semantic-segmentation/tree/47d637e3d5fcc1e2569203306c2fa5dca6f0e68a
|
MinimaxDiscriminatorLoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def minimax_discriminator_loss(dx, dgz, label_smoothing=0.0, reduction='mean'):
target_ones = torch.ones_like(dgz) * (1.0 - label_smoothing)
target_zeros = torch.zeros_like(dx)
loss = F.binary_cross_entropy_with_logits(dx, target_ones, reduction=
reduction)
loss += F.binary_cross_entropy_with_logits(dgz, target_zeros, reduction
=reduction)
return loss
class DiscriminatorLoss(nn.Module):
"""Base class for all discriminator losses.
.. note:: All Losses meant to be minimized for optimizing the Discriminator must subclass this.
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
"""
def __init__(self, reduction='mean', override_train_ops=None):
super(DiscriminatorLoss, self).__init__()
self.reduction = reduction
self.override_train_ops = override_train_ops
self.arg_map = {}
def set_arg_map(self, value):
"""Updates the ``arg_map`` for passing a different value to the ``train_ops``.
Args:
value (dict): A mapping of the ``argument name`` in the method signature and the
variable name in the ``Trainer`` it corresponds to.
.. note::
If the ``train_ops`` signature is
``train_ops(self, gen, disc, optimizer_discriminator, device, batch_size, labels=None)``
then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``.
In this case we make the following function call
``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``.
"""
self.arg_map.update(value)
def train_ops(self, generator, discriminator, optimizer_discriminator,
real_inputs, device, labels=None):
"""Defines the standard ``train_ops`` used by most losses. Losses which have a different
training procedure can either ``subclass`` it **(recommended approach)** or make use of
``override_train_ops`` argument.
The ``standard optimization algorithm`` for the ``discriminator`` defined in this train_ops
is as follows:
1. :math:`fake = generator(noise)`
2. :math:`value_1 = discriminator(fake)`
3. :math:`value_2 = discriminator(real)`
4. :math:`loss = loss\\_function(value_1, value_2)`
5. Backpropagate by computing :math:`\\nabla loss`
6. Run a step of the optimizer for discriminator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_discriminator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``discriminator``.
real_inputs (torch.Tensor): The real data to be fed to the ``discriminator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(self, generator, discriminator,
optimizer_discriminator, real_inputs, device, labels)
else:
if labels is None and (generator.label_type == 'required' or
discriminator.label_type == 'required'):
raise Exception('GAN model requires labels for training')
batch_size = real_inputs.size(0)
noise = torch.randn(batch_size, generator.encoding_dims, device
=device)
if generator.label_type == 'generated':
label_gen = torch.randint(0, generator.num_classes, (
batch_size,), device=device)
optimizer_discriminator.zero_grad()
if discriminator.label_type == 'none':
dx = discriminator(real_inputs)
elif discriminator.label_type == 'required':
dx = discriminator(real_inputs, labels)
else:
dx = discriminator(real_inputs, label_gen)
if generator.label_type == 'none':
fake = generator(noise)
elif generator.label_type == 'required':
fake = generator(noise, labels)
else:
fake = generator(noise, label_gen)
if discriminator.label_type == 'none':
dgz = discriminator(fake.detach())
elif generator.label_type == 'generated':
dgz = discriminator(fake.detach(), label_gen)
else:
dgz = discriminator(fake.detach(), labels)
loss = self.forward(dx, dgz)
loss.backward()
optimizer_discriminator.step()
return loss.item()
class MinimaxDiscriminatorLoss(DiscriminatorLoss):
"""Minimax game discriminator loss from the original GAN paper `"Generative Adversarial Networks
by Goodfellow et. al." <https://arxiv.org/abs/1406.2661>`_
The loss can be described as:
.. math:: L(D) = -[log(D(x)) + log(1 - D(G(z)))]
where
- :math:`G` : Generator
- :math:`D` : Discriminator
- :math:`x` : A sample from the data distribution
- :math:`z` : A sample from the noise prior
Args:
label_smoothing (float, optional): The factor by which the labels (1 in this case) needs
to be smoothened. For example, label_smoothing = 0.2 changes the value of the real
labels to 0.8.
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the mean of the output.
If ``sum`` the elements of the output will be summed.
override_train_ops (function, optional): A function is passed to this argument,
if the default ``train_ops`` is not to be used.
"""
def __init__(self, label_smoothing=0.0, reduction='mean',
override_train_ops=None):
super(MinimaxDiscriminatorLoss, self).__init__(reduction,
override_train_ops)
self.label_smoothing = label_smoothing
def forward(self, dx, dgz):
"""Computes the loss for the given input.
Args:
dx (torch.Tensor) : Output of the Discriminator with real data. It must have the
dimensions (N, \\*) where \\* means any number of additional
dimensions.
dgz (torch.Tensor) : Output of the Discriminator with generated data. It must have the
dimensions (N, \\*) where \\* means any number of additional
dimensions.
Returns:
scalar if reduction is applied else Tensor with dimensions (N, \\*).
"""
return minimax_discriminator_loss(dx, dgz, label_smoothing=self.
label_smoothing, reduction=self.reduction)
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.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
@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_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)
tmp13 = tl.load(in_ptr1 + r0, None)
tmp1 = 0.0
tmp2 = tmp1 * tmp0
tmp3 = triton_helpers.minimum(tmp1, tmp0)
tmp4 = tl_math.abs(tmp0)
tmp5 = -tmp4
tmp6 = tl_math.exp(tmp5)
tmp7 = libdevice.log1p(tmp6)
tmp8 = tmp3 - tmp7
tmp9 = tmp2 - tmp8
tmp10 = tl.broadcast_to(tmp9, [RBLOCK])
tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0))
tmp14 = triton_helpers.minimum(tmp1, tmp13)
tmp15 = tl_math.abs(tmp13)
tmp16 = -tmp15
tmp17 = tl_math.exp(tmp16)
tmp18 = libdevice.log1p(tmp17)
tmp19 = tmp14 - tmp18
tmp20 = tmp13 - tmp19
tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
tmp24 = 256.0
tmp25 = tmp12 / tmp24
tmp26 = tmp23 / tmp24
tmp27 = tmp25 + tmp26
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp27, 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)
buf2 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_binary_cross_entropy_with_logits_0[grid(1)](buf2,
arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf2,
def minimax_discriminator_loss(dx, dgz, label_smoothing=0.0, reduction='mean'):
target_ones = torch.ones_like(dgz) * (1.0 - label_smoothing)
target_zeros = torch.zeros_like(dx)
loss = F.binary_cross_entropy_with_logits(dx, target_ones, reduction=
reduction)
loss += F.binary_cross_entropy_with_logits(dgz, target_zeros, reduction
=reduction)
return loss
class DiscriminatorLoss(nn.Module):
"""Base class for all discriminator losses.
.. note:: All Losses meant to be minimized for optimizing the Discriminator must subclass this.
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
"""
def __init__(self, reduction='mean', override_train_ops=None):
super(DiscriminatorLoss, self).__init__()
self.reduction = reduction
self.override_train_ops = override_train_ops
self.arg_map = {}
def set_arg_map(self, value):
"""Updates the ``arg_map`` for passing a different value to the ``train_ops``.
Args:
value (dict): A mapping of the ``argument name`` in the method signature and the
variable name in the ``Trainer`` it corresponds to.
.. note::
If the ``train_ops`` signature is
``train_ops(self, gen, disc, optimizer_discriminator, device, batch_size, labels=None)``
then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``.
In this case we make the following function call
``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``.
"""
self.arg_map.update(value)
def train_ops(self, generator, discriminator, optimizer_discriminator,
real_inputs, device, labels=None):
"""Defines the standard ``train_ops`` used by most losses. Losses which have a different
training procedure can either ``subclass`` it **(recommended approach)** or make use of
``override_train_ops`` argument.
The ``standard optimization algorithm`` for the ``discriminator`` defined in this train_ops
is as follows:
1. :math:`fake = generator(noise)`
2. :math:`value_1 = discriminator(fake)`
3. :math:`value_2 = discriminator(real)`
4. :math:`loss = loss\\_function(value_1, value_2)`
5. Backpropagate by computing :math:`\\nabla loss`
6. Run a step of the optimizer for discriminator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_discriminator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``discriminator``.
real_inputs (torch.Tensor): The real data to be fed to the ``discriminator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(self, generator, discriminator,
optimizer_discriminator, real_inputs, device, labels)
else:
if labels is None and (generator.label_type == 'required' or
discriminator.label_type == 'required'):
raise Exception('GAN model requires labels for training')
batch_size = real_inputs.size(0)
noise = torch.randn(batch_size, generator.encoding_dims, device
=device)
if generator.label_type == 'generated':
label_gen = torch.randint(0, generator.num_classes, (
batch_size,), device=device)
optimizer_discriminator.zero_grad()
if discriminator.label_type == 'none':
dx = discriminator(real_inputs)
elif discriminator.label_type == 'required':
dx = discriminator(real_inputs, labels)
else:
dx = discriminator(real_inputs, label_gen)
if generator.label_type == 'none':
fake = generator(noise)
elif generator.label_type == 'required':
fake = generator(noise, labels)
else:
fake = generator(noise, label_gen)
if discriminator.label_type == 'none':
dgz = discriminator(fake.detach())
elif generator.label_type == 'generated':
dgz = discriminator(fake.detach(), label_gen)
else:
dgz = discriminator(fake.detach(), labels)
loss = self.forward(dx, dgz)
loss.backward()
optimizer_discriminator.step()
return loss.item()
class MinimaxDiscriminatorLossNew(DiscriminatorLoss):
"""Minimax game discriminator loss from the original GAN paper `"Generative Adversarial Networks
by Goodfellow et. al." <https://arxiv.org/abs/1406.2661>`_
The loss can be described as:
.. math:: L(D) = -[log(D(x)) + log(1 - D(G(z)))]
where
- :math:`G` : Generator
- :math:`D` : Discriminator
- :math:`x` : A sample from the data distribution
- :math:`z` : A sample from the noise prior
Args:
label_smoothing (float, optional): The factor by which the labels (1 in this case) needs
to be smoothened. For example, label_smoothing = 0.2 changes the value of the real
labels to 0.8.
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the mean of the output.
If ``sum`` the elements of the output will be summed.
override_train_ops (function, optional): A function is passed to this argument,
if the default ``train_ops`` is not to be used.
"""
def __init__(self, label_smoothing=0.0, reduction='mean',
override_train_ops=None):
super(MinimaxDiscriminatorLossNew, self).__init__(reduction,
override_train_ops)
self.label_smoothing = label_smoothing
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
shi-weili/torchgan
|
MinimaxDiscriminatorLoss
| false
| 12,972
|
[
"MIT"
] | 0
|
28ffd4026b8c0db2217b667d30a222d6758bfc41
|
https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41
|
WassersteinGeneratorLoss
|
import torch
import torch.nn as nn
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` the mean value of the
Tensor is returned. If ``sum`` the elements of the Tensor will be summed. If none of the
above then the Tensor is returning without any change.
Returns:
As per the above ``reduction`` convention.
"""
if reduction == 'mean':
return torch.mean(x)
elif reduction == 'sum':
return torch.sum(x)
else:
return x
def wasserstein_generator_loss(fgz, reduction='mean'):
return reduce(-1.0 * fgz, reduction)
class GeneratorLoss(nn.Module):
"""Base class for all generator losses.
.. note:: All Losses meant to be minimized for optimizing the Generator must subclass this.
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
"""
def __init__(self, reduction='mean', override_train_ops=None):
super(GeneratorLoss, self).__init__()
self.reduction = reduction
self.override_train_ops = override_train_ops
self.arg_map = {}
def set_arg_map(self, value):
"""Updates the ``arg_map`` for passing a different value to the ``train_ops``.
Args:
value (dict): A mapping of the ``argument name`` in the method signature and the
variable name in the ``Trainer`` it corresponds to.
.. note::
If the ``train_ops`` signature is
``train_ops(self, gen, disc, optimizer_generator, device, batch_size, labels=None)``
then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``.
In this case we make the following function call
``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``.
"""
self.arg_map.update(value)
def train_ops(self, generator, discriminator, optimizer_generator,
device, batch_size, labels=None):
"""Defines the standard ``train_ops`` used by most losses. Losses which have a different
training procedure can either ``subclass`` it **(recommended approach)** or make use of
``override_train_ops`` argument.
The ``standard optimization algorithm`` for the ``generator`` defined in this train_ops
is as follows:
1. :math:`fake = generator(noise)`
2. :math:`value = discriminator(fake)`
3. :math:`loss = loss\\_function(value)`
4. Backpropagate by computing :math:`\\nabla loss`
5. Run a step of the optimizer for generator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_generator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``generator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(generator, discriminator,
optimizer_generator, device, batch_size, labels)
else:
if labels is None and generator.label_type == 'required':
raise Exception('GAN model requires labels for training')
noise = torch.randn(batch_size, generator.encoding_dims, device
=device)
optimizer_generator.zero_grad()
if generator.label_type == 'generated':
label_gen = torch.randint(0, generator.num_classes, (
batch_size,), device=device)
if generator.label_type == 'none':
fake = generator(noise)
elif generator.label_type == 'required':
fake = generator(noise, labels)
elif generator.label_type == 'generated':
fake = generator(noise, label_gen)
if discriminator.label_type == 'none':
dgz = discriminator(fake)
elif generator.label_type == 'generated':
dgz = discriminator(fake, label_gen)
else:
dgz = discriminator(fake, labels)
loss = self.forward(dgz)
loss.backward()
optimizer_generator.step()
return loss.item()
class WassersteinGeneratorLoss(GeneratorLoss):
"""Wasserstein GAN generator loss from
`"Wasserstein GAN by Arjovsky et. al." <https://arxiv.org/abs/1701.07875>`_ paper
The loss can be described as:
.. math:: L(G) = -f(G(z))
where
- :math:`G` : Generator
- :math:`f` : Critic/Discriminator
- :math:`z` : A sample from the noise prior
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the mean of the output.
If ``sum`` the elements of the output will be summed.
override_train_ops (function, optional): A function is passed to this argument,
if the default ``train_ops`` is not to be used.
"""
def forward(self, fgz):
"""Computes the loss for the given input.
Args:
dgz (torch.Tensor) : Output of the Discriminator with generated data. It must have the
dimensions (N, \\*) where \\* means any number of additional
dimensions.
Returns:
scalar if reduction is applied else Tensor with dimensions (N, \\*).
"""
return wasserstein_generator_loss(fgz, self.reduction)
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_per_fused_mean_mul_0(in_out_ptr0, in_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 = -1.0
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
tmp6 = 256.0
tmp7 = tmp5 / tmp6
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp7, 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)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_mean_mul_0[grid(1)](buf1, arg0_1, 1, 256,
num_warps=2, num_stages=1)
del arg0_1
return buf1,
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` the mean value of the
Tensor is returned. If ``sum`` the elements of the Tensor will be summed. If none of the
above then the Tensor is returning without any change.
Returns:
As per the above ``reduction`` convention.
"""
if reduction == 'mean':
return torch.mean(x)
elif reduction == 'sum':
return torch.sum(x)
else:
return x
def wasserstein_generator_loss(fgz, reduction='mean'):
return reduce(-1.0 * fgz, reduction)
class GeneratorLoss(nn.Module):
"""Base class for all generator losses.
.. note:: All Losses meant to be minimized for optimizing the Generator must subclass this.
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
"""
def __init__(self, reduction='mean', override_train_ops=None):
super(GeneratorLoss, self).__init__()
self.reduction = reduction
self.override_train_ops = override_train_ops
self.arg_map = {}
def set_arg_map(self, value):
"""Updates the ``arg_map`` for passing a different value to the ``train_ops``.
Args:
value (dict): A mapping of the ``argument name`` in the method signature and the
variable name in the ``Trainer`` it corresponds to.
.. note::
If the ``train_ops`` signature is
``train_ops(self, gen, disc, optimizer_generator, device, batch_size, labels=None)``
then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``.
In this case we make the following function call
``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``.
"""
self.arg_map.update(value)
def train_ops(self, generator, discriminator, optimizer_generator,
device, batch_size, labels=None):
"""Defines the standard ``train_ops`` used by most losses. Losses which have a different
training procedure can either ``subclass`` it **(recommended approach)** or make use of
``override_train_ops`` argument.
The ``standard optimization algorithm`` for the ``generator`` defined in this train_ops
is as follows:
1. :math:`fake = generator(noise)`
2. :math:`value = discriminator(fake)`
3. :math:`loss = loss\\_function(value)`
4. Backpropagate by computing :math:`\\nabla loss`
5. Run a step of the optimizer for generator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_generator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``generator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(generator, discriminator,
optimizer_generator, device, batch_size, labels)
else:
if labels is None and generator.label_type == 'required':
raise Exception('GAN model requires labels for training')
noise = torch.randn(batch_size, generator.encoding_dims, device
=device)
optimizer_generator.zero_grad()
if generator.label_type == 'generated':
label_gen = torch.randint(0, generator.num_classes, (
batch_size,), device=device)
if generator.label_type == 'none':
fake = generator(noise)
elif generator.label_type == 'required':
fake = generator(noise, labels)
elif generator.label_type == 'generated':
fake = generator(noise, label_gen)
if discriminator.label_type == 'none':
dgz = discriminator(fake)
elif generator.label_type == 'generated':
dgz = discriminator(fake, label_gen)
else:
dgz = discriminator(fake, labels)
loss = self.forward(dgz)
loss.backward()
optimizer_generator.step()
return loss.item()
class WassersteinGeneratorLossNew(GeneratorLoss):
"""Wasserstein GAN generator loss from
`"Wasserstein GAN by Arjovsky et. al." <https://arxiv.org/abs/1701.07875>`_ paper
The loss can be described as:
.. math:: L(G) = -f(G(z))
where
- :math:`G` : Generator
- :math:`f` : Critic/Discriminator
- :math:`z` : A sample from the noise prior
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the mean of the output.
If ``sum`` the elements of the output will be summed.
override_train_ops (function, optional): A function is passed to this argument,
if the default ``train_ops`` is not to be used.
"""
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
shi-weili/torchgan
|
WassersteinGeneratorLoss
| false
| 12,973
|
[
"MIT"
] | 0
|
28ffd4026b8c0db2217b667d30a222d6758bfc41
|
https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41
|
MinibatchDiscrimination1d
|
import torch
import torch.nn as nn
class MinibatchDiscrimination1d(nn.Module):
"""1D Minibatch Discrimination Module as proposed in the paper `"Improved Techniques for
Training GANs by Salimans et. al." <https://arxiv.org/abs/1805.08318>`_
Allows the Discriminator to easily detect mode collapse by augmenting the activations to the succeeding
layer with side information that allows it to determine the 'closeness' of the minibatch examples
with each other
.. math :: M_i = T * f(x_{i})
.. math :: c_b(x_{i}, x_{j}) = \\exp(-||M_{i, b} - M_{j, b}||_1) \\in \\mathbb{R}.
.. math :: o(x_{i})_b &= \\sum_{j=1}^{n} c_b(x_{i},x_{j}) \\in \\mathbb{R} \\\\
.. math :: o(x_{i}) &= \\Big[ o(x_{i})_1, o(x_{i})_2, \\dots, o(x_{i})_B \\Big] \\in \\mathbb{R}^B \\\\
.. math :: o(X) \\in \\mathbb{R}^{n \\times B}
This is followed by concatenating :math:`o(x_{i})` and :math:`f(x_{i})`
where
- :math:`f(x_{i}) \\in \\mathbb{R}^A` : Activations from an intermediate layer
- :math:`f(x_{i}) \\in \\mathbb{R}^A` : Parameter Tensor for generating minibatch discrimination matrix
Args:
in_features (int): Features input corresponding to dimension :math:`A`
out_features (int): Number of output features that are to be concatenated corresponding to dimension :math:`B`
intermediate_features (int): Intermediate number of features corresponding to dimension :math:`C`
Returns:
A Tensor of size :math:`(N, in_features + out_features)` where :math:`N` is the batch size
"""
def __init__(self, in_features, out_features, intermediate_features=16):
super(MinibatchDiscrimination1d, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.intermediate_features = intermediate_features
self.T = nn.Parameter(torch.Tensor(in_features, out_features,
intermediate_features))
nn.init.normal_(self.T)
def forward(self, x):
"""Computes the output of the Minibatch Discrimination Layer
Args:
x (torch.Tensor): A Torch Tensor of dimensions :math: `(N, infeatures)`
Returns:
3D Torch Tensor of size :math: `(N,infeatures + outfeatures)` after applying Minibatch Discrimination
"""
M = torch.mm(x, self.T.view(self.in_features, -1))
M = M.view(-1, self.out_features, self.intermediate_features
).unsqueeze(0)
M_t = M.permute(1, 0, 2, 3)
out = torch.sum(torch.exp(-torch.abs(M - M_t).sum(3)), dim=0) - 1
return torch.cat([x, out], 1)
def get_inputs():
return [torch.rand([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.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_per_fused_abs_exp_neg_sub_sum_0(in_out_ptr0, in_ptr0, 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)
r3 = rindex
x5 = xindex % 16
x0 = xindex % 4
x2 = xindex // 16
x4 = xindex
tmp0 = tl.load(in_ptr0 + (r3 + 16 * x5), xmask, eviction_policy=
'evict_last', other=0.0)
tmp1 = tl.load(in_ptr0 + (r3 + 16 * x0 + 64 * x2), xmask,
eviction_policy='evict_last', other=0.0)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = -tmp7
tmp9 = tl_math.exp(tmp8)
tl.debug_barrier()
tl.store(in_out_ptr0 + x4, tmp9, xmask)
@triton.jit
def triton_poi_fused_cat_1(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.load(in_ptr1 + (16 + 4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = tl.load(in_ptr1 + (32 + 4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp13 = tmp11 + tmp12
tmp14 = tl.load(in_ptr1 + (48 + 4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp15 = tmp13 + tmp14
tmp16 = 1.0
tmp17 = tmp15 - tmp16
tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
tmp19 = tl.where(tmp6, tmp17, tmp18)
tmp20 = tl.where(tmp4, tmp5, tmp19)
tl.store(out_ptr0 + x2, tmp20, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 16), (64, 16, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 64),
(64, 1), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
buf2 = buf1
del buf1
get_raw_stream(0)
triton_per_fused_abs_exp_neg_sub_sum_0[grid(64)](buf2, buf0, 64, 16,
XBLOCK=8, num_warps=2, num_stages=1)
buf3 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
triton_poi_fused_cat_1[grid(32)](primals_2, buf2, buf3, 32, XBLOCK=
32, num_warps=1, num_stages=1)
return buf3, buf0, buf2, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)
class MinibatchDiscrimination1dNew(nn.Module):
"""1D Minibatch Discrimination Module as proposed in the paper `"Improved Techniques for
Training GANs by Salimans et. al." <https://arxiv.org/abs/1805.08318>`_
Allows the Discriminator to easily detect mode collapse by augmenting the activations to the succeeding
layer with side information that allows it to determine the 'closeness' of the minibatch examples
with each other
.. math :: M_i = T * f(x_{i})
.. math :: c_b(x_{i}, x_{j}) = \\exp(-||M_{i, b} - M_{j, b}||_1) \\in \\mathbb{R}.
.. math :: o(x_{i})_b &= \\sum_{j=1}^{n} c_b(x_{i},x_{j}) \\in \\mathbb{R} \\\\
.. math :: o(x_{i}) &= \\Big[ o(x_{i})_1, o(x_{i})_2, \\dots, o(x_{i})_B \\Big] \\in \\mathbb{R}^B \\\\
.. math :: o(X) \\in \\mathbb{R}^{n \\times B}
This is followed by concatenating :math:`o(x_{i})` and :math:`f(x_{i})`
where
- :math:`f(x_{i}) \\in \\mathbb{R}^A` : Activations from an intermediate layer
- :math:`f(x_{i}) \\in \\mathbb{R}^A` : Parameter Tensor for generating minibatch discrimination matrix
Args:
in_features (int): Features input corresponding to dimension :math:`A`
out_features (int): Number of output features that are to be concatenated corresponding to dimension :math:`B`
intermediate_features (int): Intermediate number of features corresponding to dimension :math:`C`
Returns:
A Tensor of size :math:`(N, in_features + out_features)` where :math:`N` is the batch size
"""
def __init__(self, in_features, out_features, intermediate_features=16):
super(MinibatchDiscrimination1dNew, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.intermediate_features = intermediate_features
self.T = nn.Parameter(torch.Tensor(in_features, out_features,
intermediate_features))
nn.init.normal_(self.T)
def forward(self, input_0):
primals_1 = self.T
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
shi-weili/torchgan
|
MinibatchDiscrimination1d
| false
| 12,974
|
[
"MIT"
] | 0
|
28ffd4026b8c0db2217b667d30a222d6758bfc41
|
https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41
|
Swish
|
import torch
import torch.nn as nn
import torch.distributed
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
self.beta = nn.Parameter(torch.tensor(1.0))
def forward(self, x):
return x * torch.sigmoid(self.beta * 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.nn as nn
import torch.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_mul_sigmoid_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
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp2 * tmp0
tmp4 = tl.sigmoid(tmp3)
tmp5 = tmp0 * tmp4
tl.store(out_ptr0 + x0, tmp5, 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, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_sigmoid_0[grid(256)](primals_2, primals_1,
buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
return buf0, primals_1, primals_2
class SwishNew(nn.Module):
def __init__(self):
super(SwishNew, self).__init__()
self.beta = nn.Parameter(torch.tensor(1.0))
def forward(self, input_0):
primals_1 = self.beta
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
shnhrtkyk/PointFlow
|
Swish
| false
| 12,975
|
[
"MIT"
] | 0
|
26b8fac79bf3e71533f5c8b12f90cf5f9a385a99
|
https://github.com/shnhrtkyk/PointFlow/tree/26b8fac79bf3e71533f5c8b12f90cf5f9a385a99
|
PerceptronTanh
|
import torch
import torch.nn as nn
from typing import Any
import torch.nn.functional as F
class PerceptronTanh(nn.Module):
"""Implements a 1-layer perceptron with Tanh activaton."""
def _forward_unimplemented(self, *input: Any) ->None:
pass
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(PerceptronTanh, self).__init__()
self._layer1 = nn.Linear(input_dimension, hidden_dimension)
self._layer2 = nn.Linear(hidden_dimension, output_dimension, bias=False
)
def forward(self, inp):
return F.tanh(self._layer2(F.relu(self._layer1(inp))))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_dimension': 4, 'hidden_dimension': 4,
'output_dimension': 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 torch.nn as nn
from typing import Any
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)
@triton.jit
def triton_poi_fused_tanh_1(in_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 = libdevice.tanh(tmp0)
tl.store(in_out_ptr0 + x0, tmp1, 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,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (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_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
buf4 = 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, buf4, 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
triton_poi_fused_tanh_1[grid(256)](buf3, 256, XBLOCK=128, num_warps
=4, num_stages=1)
return buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, primals_4, buf4
class PerceptronTanhNew(nn.Module):
"""Implements a 1-layer perceptron with Tanh activaton."""
def _forward_unimplemented(self, *input: Any) ->None:
pass
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(PerceptronTanhNew, self).__init__()
self._layer1 = nn.Linear(input_dimension, hidden_dimension)
self._layer2 = nn.Linear(hidden_dimension, output_dimension, bias=False
)
def forward(self, input_0):
primals_1 = self._layer1.weight
primals_2 = self._layer1.bias
primals_4 = self._layer2.weight
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
shi27feng/PDP-Solver
|
PerceptronTanh
| false
| 12,976
|
[
"MIT"
] | 0
|
bf6e392f72f8a3572e0987313230943d94d53c95
|
https://github.com/shi27feng/PDP-Solver/tree/bf6e392f72f8a3572e0987313230943d94d53c95
|
Perceptron
|
import torch
import torch.nn as nn
from typing import Any
import torch.nn.functional as fn
class Perceptron(nn.Module):
"""Implements a 1-layer perceptron."""
def _forward_unimplemented(self, *input: Any) ->None:
pass
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(Perceptron, self).__init__()
self._layer1 = nn.Linear(input_dimension, hidden_dimension)
self._layer2 = nn.Linear(hidden_dimension, output_dimension, bias=False
)
def forward(self, inp):
return fn.sigmoid(self._layer2(fn.relu(self._layer1(inp))))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_dimension': 4, 'hidden_dimension': 4,
'output_dimension': 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
from typing import Any
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)
@triton.jit
def triton_poi_fused_sigmoid_1(in_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.sigmoid(tmp0)
tl.store(in_out_ptr0 + x0, tmp1, 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,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (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_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
buf4 = 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, buf4, 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
triton_poi_fused_sigmoid_1[grid(256)](buf3, 256, XBLOCK=128,
num_warps=4, num_stages=1)
return buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, primals_4, buf4
class PerceptronNew(nn.Module):
"""Implements a 1-layer perceptron."""
def _forward_unimplemented(self, *input: Any) ->None:
pass
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(PerceptronNew, self).__init__()
self._layer1 = nn.Linear(input_dimension, hidden_dimension)
self._layer2 = nn.Linear(hidden_dimension, output_dimension, bias=False
)
def forward(self, input_0):
primals_1 = self._layer1.weight
primals_2 = self._layer1.bias
primals_4 = self._layer2.weight
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
shi27feng/PDP-Solver
|
Perceptron
| false
| 12,977
|
[
"MIT"
] | 0
|
bf6e392f72f8a3572e0987313230943d94d53c95
|
https://github.com/shi27feng/PDP-Solver/tree/bf6e392f72f8a3572e0987313230943d94d53c95
|
MinimaxGeneratorLoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def minimax_generator_loss(dgz, nonsaturating=True, reduction='mean'):
if nonsaturating:
target = torch.ones_like(dgz)
return F.binary_cross_entropy_with_logits(dgz, target, reduction=
reduction)
else:
target = torch.zeros_like(dgz)
return -1.0 * F.binary_cross_entropy_with_logits(dgz, target,
reduction=reduction)
class GeneratorLoss(nn.Module):
"""Base class for all generator losses.
.. note:: All Losses meant to be minimized for optimizing the Generator must subclass this.
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
"""
def __init__(self, reduction='mean', override_train_ops=None):
super(GeneratorLoss, self).__init__()
self.reduction = reduction
self.override_train_ops = override_train_ops
self.arg_map = {}
def set_arg_map(self, value):
"""Updates the ``arg_map`` for passing a different value to the ``train_ops``.
Args:
value (dict): A mapping of the ``argument name`` in the method signature and the
variable name in the ``Trainer`` it corresponds to.
.. note::
If the ``train_ops`` signature is
``train_ops(self, gen, disc, optimizer_generator, device, batch_size, labels=None)``
then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``.
In this case we make the following function call
``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``.
"""
self.arg_map.update(value)
def train_ops(self, generator, discriminator, optimizer_generator,
device, batch_size, labels=None):
"""Defines the standard ``train_ops`` used by most losses. Losses which have a different
training procedure can either ``subclass`` it **(recommended approach)** or make use of
``override_train_ops`` argument.
The ``standard optimization algorithm`` for the ``generator`` defined in this train_ops
is as follows:
1. :math:`fake = generator(noise)`
2. :math:`value = discriminator(fake)`
3. :math:`loss = loss\\_function(value)`
4. Backpropagate by computing :math:`\\nabla loss`
5. Run a step of the optimizer for generator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_generator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``generator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(generator, discriminator,
optimizer_generator, device, batch_size, labels)
else:
if labels is None and generator.label_type == 'required':
raise Exception('GAN model requires labels for training')
noise = torch.randn(batch_size, generator.encoding_dims, device
=device)
optimizer_generator.zero_grad()
if generator.label_type == 'generated':
label_gen = torch.randint(0, generator.num_classes, (
batch_size,), device=device)
if generator.label_type == 'none':
fake = generator(noise)
elif generator.label_type == 'required':
fake = generator(noise, labels)
elif generator.label_type == 'generated':
fake = generator(noise, label_gen)
if discriminator.label_type == 'none':
dgz = discriminator(fake)
elif generator.label_type == 'generated':
dgz = discriminator(fake, label_gen)
else:
dgz = discriminator(fake, labels)
loss = self.forward(dgz)
loss.backward()
optimizer_generator.step()
return loss.item()
class MinimaxGeneratorLoss(GeneratorLoss):
"""Minimax game generator loss from the original GAN paper `"Generative Adversarial Networks
by Goodfellow et. al." <https://arxiv.org/abs/1406.2661>`_
The loss can be described as:
.. math:: L(G) = log(1 - D(G(z)))
The nonsaturating heuristic is also supported:
.. math:: L(G) = -log(D(G(z)))
where
- :math:`G` : Generator
- :math:`D` : Discriminator
- :math:`z` : A sample from the noise prior
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
nonsaturating(bool, optional): Specifies whether to use the nonsaturating heuristic
loss for the generator.
"""
def __init__(self, reduction='mean', nonsaturating=True,
override_train_ops=None):
super(MinimaxGeneratorLoss, self).__init__(reduction,
override_train_ops)
self.nonsaturating = nonsaturating
def forward(self, dgz):
"""Computes the loss for the given input.
Args:
dgz (torch.Tensor) : Output of the Discriminator with generated data. It must have the
dimensions (N, \\*) where \\* means any number of additional
dimensions.
Returns:
scalar if reduction is applied else Tensor with dimensions (N, \\*).
"""
return minimax_generator_loss(dgz, self.nonsaturating, self.reduction)
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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
@triton.jit
def triton_per_fused_binary_cross_entropy_with_logits_0(in_out_ptr0,
in_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 = 0.0
tmp2 = tmp1 * tmp0
tmp3 = triton_helpers.minimum(tmp1, tmp0)
tmp4 = tl_math.abs(tmp0)
tmp5 = -tmp4
tmp6 = tl_math.exp(tmp5)
tmp7 = libdevice.log1p(tmp6)
tmp8 = tmp3 - tmp7
tmp9 = tmp2 - tmp8
tmp10 = tl.broadcast_to(tmp9, [RBLOCK])
tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0))
tmp13 = 256.0
tmp14 = tmp12 / tmp13
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp14, 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)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_binary_cross_entropy_with_logits_0[grid(1)](buf1,
arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
return buf1,
def minimax_generator_loss(dgz, nonsaturating=True, reduction='mean'):
if nonsaturating:
target = torch.ones_like(dgz)
return F.binary_cross_entropy_with_logits(dgz, target, reduction=
reduction)
else:
target = torch.zeros_like(dgz)
return -1.0 * F.binary_cross_entropy_with_logits(dgz, target,
reduction=reduction)
class GeneratorLoss(nn.Module):
"""Base class for all generator losses.
.. note:: All Losses meant to be minimized for optimizing the Generator must subclass this.
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
"""
def __init__(self, reduction='mean', override_train_ops=None):
super(GeneratorLoss, self).__init__()
self.reduction = reduction
self.override_train_ops = override_train_ops
self.arg_map = {}
def set_arg_map(self, value):
"""Updates the ``arg_map`` for passing a different value to the ``train_ops``.
Args:
value (dict): A mapping of the ``argument name`` in the method signature and the
variable name in the ``Trainer`` it corresponds to.
.. note::
If the ``train_ops`` signature is
``train_ops(self, gen, disc, optimizer_generator, device, batch_size, labels=None)``
then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``.
In this case we make the following function call
``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``.
"""
self.arg_map.update(value)
def train_ops(self, generator, discriminator, optimizer_generator,
device, batch_size, labels=None):
"""Defines the standard ``train_ops`` used by most losses. Losses which have a different
training procedure can either ``subclass`` it **(recommended approach)** or make use of
``override_train_ops`` argument.
The ``standard optimization algorithm`` for the ``generator`` defined in this train_ops
is as follows:
1. :math:`fake = generator(noise)`
2. :math:`value = discriminator(fake)`
3. :math:`loss = loss\\_function(value)`
4. Backpropagate by computing :math:`\\nabla loss`
5. Run a step of the optimizer for generator
Args:
generator (torchgan.models.Generator): The model to be optimized.
discriminator (torchgan.models.Discriminator): The discriminator which judges the
performance of the generator.
optimizer_generator (torch.optim.Optimizer): Optimizer which updates the ``parameters``
of the ``generator``.
device (torch.device): Device on which the ``generator`` and ``discriminator`` is present.
batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``.
labels (torch.Tensor, optional): Labels for the data.
Returns:
Scalar value of the loss.
"""
if self.override_train_ops is not None:
return self.override_train_ops(generator, discriminator,
optimizer_generator, device, batch_size, labels)
else:
if labels is None and generator.label_type == 'required':
raise Exception('GAN model requires labels for training')
noise = torch.randn(batch_size, generator.encoding_dims, device
=device)
optimizer_generator.zero_grad()
if generator.label_type == 'generated':
label_gen = torch.randint(0, generator.num_classes, (
batch_size,), device=device)
if generator.label_type == 'none':
fake = generator(noise)
elif generator.label_type == 'required':
fake = generator(noise, labels)
elif generator.label_type == 'generated':
fake = generator(noise, label_gen)
if discriminator.label_type == 'none':
dgz = discriminator(fake)
elif generator.label_type == 'generated':
dgz = discriminator(fake, label_gen)
else:
dgz = discriminator(fake, labels)
loss = self.forward(dgz)
loss.backward()
optimizer_generator.step()
return loss.item()
class MinimaxGeneratorLossNew(GeneratorLoss):
"""Minimax game generator loss from the original GAN paper `"Generative Adversarial Networks
by Goodfellow et. al." <https://arxiv.org/abs/1406.2661>`_
The loss can be described as:
.. math:: L(G) = log(1 - D(G(z)))
The nonsaturating heuristic is also supported:
.. math:: L(G) = -log(D(G(z)))
where
- :math:`G` : Generator
- :math:`D` : Discriminator
- :math:`z` : A sample from the noise prior
Args:
reduction (str, optional): Specifies the reduction to apply to the output.
If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size.
If ``sum`` the elements of the output are summed.
override_train_ops (function, optional): Function to be used in place of the default ``train_ops``
nonsaturating(bool, optional): Specifies whether to use the nonsaturating heuristic
loss for the generator.
"""
def __init__(self, reduction='mean', nonsaturating=True,
override_train_ops=None):
super(MinimaxGeneratorLossNew, self).__init__(reduction,
override_train_ops)
self.nonsaturating = nonsaturating
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
shi-weili/torchgan
|
MinimaxGeneratorLoss
| false
| 12,978
|
[
"MIT"
] | 0
|
28ffd4026b8c0db2217b667d30a222d6758bfc41
|
https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41
|
SpatialCrossMapLRN
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class SpatialCrossMapLRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCrossMapLRN, 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
self.k = k
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(self.k).pow(self.beta)
else:
div = x.pow(2)
div = self.average(div)
div = div.mul(self.alpha).add(self.k).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.nn.parallel
import torch.optim
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_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 SpatialCrossMapLRNNew(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCrossMapLRNNew, 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
self.k = k
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
shubham1206agra/pretrained-models.pytorch
|
SpatialCrossMapLRN
| false
| 12,979
|
[
"BSD-3-Clause"
] | 0
|
a2940f79dd65656eabe5a0cd6d5d014ef1fc2523
|
https://github.com/shubham1206agra/pretrained-models.pytorch/tree/a2940f79dd65656eabe5a0cd6d5d014ef1fc2523
|
GraphConv
|
import torch
from torch import nn
import torch.nn
import torch.autograd
def sparse_bmm(sparse_matrix, dense_matrix_batch):
"""
Perform torch.bmm on an unbatched sparse matrix and a batched dense matrix.
Args:
sparse_matrix (torch.sparse.FloatTensor): Shape = (m, n)
dense_matrix_batch (torch.FloatTensor): Shape = (b, n, p)
Returns:
(torch.FloatTensor):
Result of the batched matrix multiplication. Shape = (b, n, p)
"""
m = sparse_matrix.shape[0]
b, n, p = dense_matrix_batch.shape
dense_matrix = dense_matrix_batch.transpose(0, 1).reshape(n, b * p)
result = torch.sparse.mm(sparse_matrix, dense_matrix)
return result.reshape(m, b, p).transpose(0, 1)
class GraphConv(nn.Module):
"""A simple graph convolution layer, similar to the one defined in Kipf et al.
https://arxiv.org/abs/1609.02907
This operation with self_layer=False is equivalent to :math:`(A H W)` where:
- :math:`H` is the node features with shape (batch_size, num_nodes, input_dim)
- :math:`W` is a weight matrix of shape (input_dim, output_dim)
- :math:`A` is the adjacency matrix of shape (num_nodes, num_nodes).
It can include self-loop.
With normalize_adj=True, it is equivalent to :math:`(D^{-1} A H W)`, where:
- :math:`D` is a diagonal matrix with :math:`D_{ii}` = the sum of the i-th row of A.
In other words, :math:`D` is the incoming degree of each node.
With self_layer=True, it is equivalent to the above plus :math:`(H W_{\\text{self}})`, where:
- :math:`W_{\\text{self}}` is a separate weight matrix to filter each node's self features.
Note that when self_layer is True, A should not include self-loop.
If you use this code, please cite the original paper in addition to Kaolin.
.. code-block::
@article{kipf2016semi,
title={Semi-Supervised Classification with Graph Convolutional Networks},
author={Kipf, Thomas N and Welling, Max},
journal={arXiv preprint arXiv:1609.02907},
year={2016}
}
Args:
input_dim (int): The number of features in each input node.
output_dim (int): The number of features in each output node.
bias (bool): Whether to add bias after the node-wise linear layer.
Example:
>>> node_feat = torch.rand(1, 3, 5)
>>> i = torch.LongTensor(
... [[0, 1, 1, 2, 2, 0], [1, 0, 2, 1, 0, 2]])
>>> v = torch.FloatTensor([1, 1, 1, 1, 1, 1])
>>> adj = torch.sparse.FloatTensor(i, v, torch.Size([3, 3]))
>>> model = GraphConv(5, 10)
>>> output = model(node_feat, adj)
>>> # pre-normalize adj
>>> adj = normalize_adj(adj)
>>> output = model(node_feat, adj, normalize_adj=False)
"""
def __init__(self, input_dim, output_dim, self_layer=True, bias=True):
super(GraphConv, self).__init__()
self.self_layer = self_layer
self.linear = nn.Linear(input_dim, output_dim, bias=bias)
if self_layer:
self.linear_self = nn.Linear(input_dim, output_dim, bias=bias)
else:
self.linear_self = None
self.initialize()
def initialize(self):
nn.init.xavier_uniform_(self.linear.weight.data)
if self.linear.bias is not None:
self.linear.bias.data.uniform_(-1.0, 1.0)
if self.self_layer:
nn.init.xavier_uniform_(self.linear_self.weight.data)
if self.linear_self.bias is not None:
self.linear_self.bias.data.uniform_(-1.0, 1.0)
def forward(self, node_feat, adj, normalize_adj=True):
"""
Args:
node_feat (torch.FloatTensor):
Shape = (batch_size, num_nodes, input_dim)
The input features of each node.
adj (torch.sparse.FloatTensor or torch.FloatTensor):
Shape = (num_nodes, num_nodes)
The adjacency matrix. adj[i, j] is non-zero if there's an
incoming edge from j to i. Should not include self-loop if
self_layer is True.
normalize_adj (bool):
Set this to true to apply normalization to adjacency; that is,
each output feature will be divided by the number of incoming
neighbors. If normalization is not desired, or if the adjacency
matrix is pre-normalized, set this to False to improve
performance.
Returns:
(torch.FloatTensor):
The output features of each node.
Shape = (batch_size, num_nodes, output_dim)
"""
if adj.type().endswith('sparse.FloatTensor'):
if normalize_adj:
norm = torch.sparse.mm(adj, torch.ones((adj.shape[0], 1),
device=node_feat.device))
result = sparse_bmm(adj, self.linear(node_feat)) / norm
else:
result = sparse_bmm(adj, self.linear(node_feat))
elif normalize_adj:
norm = torch.matmul(adj, torch.ones((adj.shape[0], 1), device=
node_feat.device))
result = torch.matmul(adj, self.linear(node_feat)) / norm
else:
result = torch.matmul(adj, self.linear(node_feat))
if self.self_layer:
result += self.linear_self(node_feat)
return result
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_dim': 4, 'output_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 import nn
import torch.nn
import torch.autograd
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_ones_0(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 = 1.0
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_div_1(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
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x1, 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
tl.store(in_out_ptr0 + x2, tmp6, 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, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 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))
assert_size_stride(primals_6, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_ones_0[grid(4)](buf0, 4, XBLOCK=4, num_warps=1,
num_stages=1)
buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
buf0, out=buf1)
del buf0
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_4, reinterpret_tensor(primals_2, (64,
4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf2)
del primals_3
del primals_4
buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
1), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0),
out=buf3)
buf4 = buf2
del buf2
extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf4)
del primals_5
buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf3
triton_poi_fused_add_div_1[grid(256)](buf5, buf1, buf4, primals_6,
256, XBLOCK=256, num_warps=4, num_stages=1)
del buf4
del primals_6
return buf5, buf1, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
), reinterpret_tensor(primals_1, (16, 4, 4), (16, 1, 4), 0)
def sparse_bmm(sparse_matrix, dense_matrix_batch):
"""
Perform torch.bmm on an unbatched sparse matrix and a batched dense matrix.
Args:
sparse_matrix (torch.sparse.FloatTensor): Shape = (m, n)
dense_matrix_batch (torch.FloatTensor): Shape = (b, n, p)
Returns:
(torch.FloatTensor):
Result of the batched matrix multiplication. Shape = (b, n, p)
"""
m = sparse_matrix.shape[0]
b, n, p = dense_matrix_batch.shape
dense_matrix = dense_matrix_batch.transpose(0, 1).reshape(n, b * p)
result = torch.sparse.mm(sparse_matrix, dense_matrix)
return result.reshape(m, b, p).transpose(0, 1)
class GraphConvNew(nn.Module):
"""A simple graph convolution layer, similar to the one defined in Kipf et al.
https://arxiv.org/abs/1609.02907
This operation with self_layer=False is equivalent to :math:`(A H W)` where:
- :math:`H` is the node features with shape (batch_size, num_nodes, input_dim)
- :math:`W` is a weight matrix of shape (input_dim, output_dim)
- :math:`A` is the adjacency matrix of shape (num_nodes, num_nodes).
It can include self-loop.
With normalize_adj=True, it is equivalent to :math:`(D^{-1} A H W)`, where:
- :math:`D` is a diagonal matrix with :math:`D_{ii}` = the sum of the i-th row of A.
In other words, :math:`D` is the incoming degree of each node.
With self_layer=True, it is equivalent to the above plus :math:`(H W_{\\text{self}})`, where:
- :math:`W_{\\text{self}}` is a separate weight matrix to filter each node's self features.
Note that when self_layer is True, A should not include self-loop.
If you use this code, please cite the original paper in addition to Kaolin.
.. code-block::
@article{kipf2016semi,
title={Semi-Supervised Classification with Graph Convolutional Networks},
author={Kipf, Thomas N and Welling, Max},
journal={arXiv preprint arXiv:1609.02907},
year={2016}
}
Args:
input_dim (int): The number of features in each input node.
output_dim (int): The number of features in each output node.
bias (bool): Whether to add bias after the node-wise linear layer.
Example:
>>> node_feat = torch.rand(1, 3, 5)
>>> i = torch.LongTensor(
... [[0, 1, 1, 2, 2, 0], [1, 0, 2, 1, 0, 2]])
>>> v = torch.FloatTensor([1, 1, 1, 1, 1, 1])
>>> adj = torch.sparse.FloatTensor(i, v, torch.Size([3, 3]))
>>> model = GraphConv(5, 10)
>>> output = model(node_feat, adj)
>>> # pre-normalize adj
>>> adj = normalize_adj(adj)
>>> output = model(node_feat, adj, normalize_adj=False)
"""
def __init__(self, input_dim, output_dim, self_layer=True, bias=True):
super(GraphConvNew, self).__init__()
self.self_layer = self_layer
self.linear = nn.Linear(input_dim, output_dim, bias=bias)
if self_layer:
self.linear_self = nn.Linear(input_dim, output_dim, bias=bias)
else:
self.linear_self = None
self.initialize()
def initialize(self):
nn.init.xavier_uniform_(self.linear.weight.data)
if self.linear.bias is not None:
self.linear.bias.data.uniform_(-1.0, 1.0)
if self.self_layer:
nn.init.xavier_uniform_(self.linear_self.weight.data)
if self.linear_self.bias is not None:
self.linear_self.bias.data.uniform_(-1.0, 1.0)
def forward(self, input_0, input_1):
primals_3 = self.linear.weight
primals_4 = self.linear.bias
primals_5 = self.linear_self.weight
primals_6 = self.linear_self.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
shumash/kaolin
|
GraphConv
| false
| 12,980
|
[
"ECL-2.0",
"Apache-2.0"
] | 0
|
2158b5ec7a28d57d7df7e606adbb0c693a0145f0
|
https://github.com/shumash/kaolin/tree/2158b5ec7a28d57d7df7e606adbb0c693a0145f0
|
Qnet
|
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
class Qnet(nn.Module):
def __init__(self):
super(Qnet, self).__init__()
self.fc1 = nn.Linear(4, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, 2)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def sample_action(self, obs, epsilon):
out = self.forward(obs)
coin = random.random()
if coin < epsilon:
return random.randint(0, 1)
else:
return out.argmax().item()
def get_inputs():
return [torch.rand([4, 4, 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
import random
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):
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
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, (128, 4), (4, 1))
assert_size_stride(primals_2, (128,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (128, 128), (128, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (2, 128), (128, 1))
assert_size_stride(primals_7, (2,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 128), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
del buf0
buf6 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf1,
primals_2, buf6, 8192, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 128), (128, 1), 0),
reinterpret_tensor(primals_4, (128, 128), (1, 128), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
del buf2
buf5 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf3,
primals_5, buf5, 8192, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 128),
(128, 1), 0), reinterpret_tensor(primals_6, (128, 2), (1, 128),
0), alpha=1, beta=1, out=buf4)
del primals_7
return reinterpret_tensor(buf4, (4, 4, 4, 2), (32, 8, 2, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 128), (128, 1), 0
), reinterpret_tensor(buf3, (64, 128), (128, 1), 0
), primals_6, buf5, primals_4, buf6
class QnetNew(nn.Module):
def __init__(self):
super(QnetNew, self).__init__()
self.fc1 = nn.Linear(4, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, 2)
def sample_action(self, obs, epsilon):
out = self.forward(obs)
coin = random.random()
if coin < epsilon:
return random.randint(0, 1)
else:
return out.argmax().item()
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_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
shwetasrsh/minimalRL
|
Qnet
| false
| 12,981
|
[
"MIT"
] | 0
|
e6fef1730238dd268b1a43fd9fca0b0c40d97837
|
https://github.com/shwetasrsh/minimalRL/tree/e6fef1730238dd268b1a43fd9fca0b0c40d97837
|
ScaleHead
|
import torch
import torch.nn as nn
class ScaleHead(nn.Module):
def __init__(self):
super().__init__()
self.flatten = torch.flatten
self.dot = torch.dot
def forward(self, mag, height):
curr_mag = self.flatten(mag, start_dim=1)
curr_height = self.flatten(height, start_dim=1)
batch_size = curr_mag.shape[0]
length = curr_mag.shape[1]
denom = torch.squeeze(torch.bmm(curr_height.view(batch_size, 1,
length), curr_height.view(batch_size, length, 1))) + 0.01
pinv = curr_height / denom.view(batch_size, 1)
scale = torch.squeeze(torch.bmm(pinv.view(batch_size, 1, length),
curr_mag.view(batch_size, length, 1)))
return scale
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 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
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_div_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 // 64
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = 0.01
tmp3 = tmp1 + tmp2
tmp4 = tmp0 / tmp3
tl.store(out_ptr0 + x2, tmp4, 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, 1, 1), (1, 1, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 64), (64, 64,
1), 0), reinterpret_tensor(arg1_1, (4, 64, 1), (64, 1, 1), 0),
out=buf0)
buf1 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_div_0[grid(256)](arg1_1, buf0, buf1, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del arg1_1
buf2 = buf0
del buf0
extern_kernels.bmm(reinterpret_tensor(buf1, (4, 1, 64), (64, 0, 1),
0), reinterpret_tensor(arg0_1, (4, 64, 1), (64, 1, 1), 0), out=buf2
)
del arg0_1
del buf1
return reinterpret_tensor(buf2, (4,), (1,), 0),
class ScaleHeadNew(nn.Module):
def __init__(self):
super().__init__()
self.flatten = torch.flatten
self.dot = torch.dot
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
shvedfun/geo_pos_baseline
|
ScaleHead
| false
| 12,982
|
[
"Apache-2.0"
] | 0
|
024716bfdaefd23baccfb5a0d2686015385d7b9c
|
https://github.com/shvedfun/geo_pos_baseline/tree/024716bfdaefd23baccfb5a0d2686015385d7b9c
|
RNNCell
|
import torch
import torch.nn as nn
class RNNCell(nn.Module):
def __init__(self, embed_dim, hidden_size, vocab_dim):
super().__init__()
self.hidden_size = hidden_size
self.input2hidden = nn.Linear(embed_dim + hidden_size, hidden_size)
def forward(self, inputs, hidden):
combined = torch.cat((inputs, hidden), 2)
hidden = torch.relu(self.input2hidden(combined))
return hidden
def init_hidden(self, batch_size):
return torch.zeros(1, batch_size, self.hidden_size)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'embed_dim': 4, 'hidden_size': 4, 'vocab_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
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 = 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_relu_threshold_backward_1(in_out_ptr0, 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')
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), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 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, 4, 8), (32, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf0, 128,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 8), (8, 1), 0),
reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1)
del primals_3
buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
del buf1
buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_1[grid(64)](buf2,
primals_4, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
del primals_4
return buf2, reinterpret_tensor(buf0, (16, 8), (8, 1), 0), buf3
class RNNCellNew(nn.Module):
def __init__(self, embed_dim, hidden_size, vocab_dim):
super().__init__()
self.hidden_size = hidden_size
self.input2hidden = nn.Linear(embed_dim + hidden_size, hidden_size)
def init_hidden(self, batch_size):
return torch.zeros(1, batch_size, self.hidden_size)
def forward(self, input_0, input_1):
primals_3 = self.input2hidden.weight
primals_4 = self.input2hidden.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
saidulislam/RNN-Sequential-Data-Processing
|
RNNCell
| false
| 12,983
|
[
"Apache-2.0"
] | 0
|
2e043f37f9a67177a3dc19cbfe67d187c9cbb5f9
|
https://github.com/saidulislam/RNN-Sequential-Data-Processing/tree/2e043f37f9a67177a3dc19cbfe67d187c9cbb5f9
|
EnsembleFC
|
import torch
import torch.nn as nn
class EnsembleFC(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int',
ensemble_size: 'int', weight_decay: 'float'=0.0, bias: 'bool'=True
) ->None:
super(EnsembleFC, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.ensemble_size = ensemble_size
self.weight = nn.Parameter(torch.Tensor(ensemble_size, in_features,
out_features))
self.weight_decay = weight_decay
if bias:
self.bias = nn.Parameter(torch.Tensor(ensemble_size, out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self) ->None:
pass
def forward(self, input: 'torch.Tensor') ->torch.Tensor:
w_times_x = torch.bmm(input, self.weight)
return torch.add(w_times_x, self.bias[:, None, :])
def extra_repr(self) ->str:
return 'in_features={}, out_features={}, bias={}'.format(self.
in_features, self.out_features, self.bias is not None)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4, 'ensemble_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 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_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
x3 = xindex
x0 = xindex % 4
x2 = xindex // 16
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 4 * x2), 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, 4), (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(primals_2, primals_1, out=buf0)
del primals_1
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_add_0[grid(64)](buf1, primals_3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_3
return buf1, reinterpret_tensor(primals_2, (4, 4, 4), (16, 1, 4), 0)
class EnsembleFCNew(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int',
ensemble_size: 'int', weight_decay: 'float'=0.0, bias: 'bool'=True
) ->None:
super(EnsembleFCNew, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.ensemble_size = ensemble_size
self.weight = nn.Parameter(torch.Tensor(ensemble_size, in_features,
out_features))
self.weight_decay = weight_decay
if bias:
self.bias = nn.Parameter(torch.Tensor(ensemble_size, out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self) ->None:
pass
def extra_repr(self) ->str:
return 'in_features={}, out_features={}, bias={}'.format(self.
in_features, self.out_features, self.bias is not None)
def forward(self, input_0):
primals_1 = self.weight
primals_3 = self.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
si0wang/transfer_dmc
|
EnsembleFC
| false
| 12,984
|
[
"MIT"
] | 0
|
6bda773244e0b709b3c13add2597f5f1cd01bfd7
|
https://github.com/si0wang/transfer_dmc/tree/6bda773244e0b709b3c13add2597f5f1cd01bfd7
|
DynamicsModel
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init_(m):
if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) or isinstance(m,
nn.ConvTranspose2d):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
x = x * F.sigmoid(x)
return x
class DynamicsModel(nn.Module):
def __init__(self, feature_size, hidden_size=256, use_decay=False):
super(DynamicsModel, self).__init__()
self.hidden_size = hidden_size
self.nn1 = nn.Linear(feature_size + feature_size, feature_size)
self.use_decay = use_decay
self.apply(weights_init_)
self.swish = Swish()
def forward(self, state_latent, action_latent):
x = torch.cat([state_latent, action_latent], 1)
nn1_output = self.nn1(x)
return nn1_output
def get_decay_loss(self):
decay_loss = 0.0
for m in self.children():
if type(m) == nn.Linear:
decay_loss += m.weight_decay * torch.sum(torch.square(m.weight)
) / 2.0
return decay_loss
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'feature_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 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_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)
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.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
(8, 4), (1, 8), 0), alpha=1, beta=1, out=buf1)
del primals_3
del primals_4
return buf1, buf0
def weights_init_(m):
if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) or isinstance(m,
nn.ConvTranspose2d):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
x = x * F.sigmoid(x)
return x
class DynamicsModelNew(nn.Module):
def __init__(self, feature_size, hidden_size=256, use_decay=False):
super(DynamicsModelNew, self).__init__()
self.hidden_size = hidden_size
self.nn1 = nn.Linear(feature_size + feature_size, feature_size)
self.use_decay = use_decay
self.apply(weights_init_)
self.swish = Swish()
def get_decay_loss(self):
decay_loss = 0.0
for m in self.children():
if type(m) == nn.Linear:
decay_loss += m.weight_decay * torch.sum(torch.square(m.weight)
) / 2.0
return decay_loss
def forward(self, input_0, input_1):
primals_3 = self.nn1.weight
primals_4 = self.nn1.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
si0wang/transfer_dmc
|
DynamicsModel
| false
| 12,985
|
[
"MIT"
] | 0
|
6bda773244e0b709b3c13add2597f5f1cd01bfd7
|
https://github.com/si0wang/transfer_dmc/tree/6bda773244e0b709b3c13add2597f5f1cd01bfd7
|
MnistMlp
|
import torch
from torch import nn as nn
from torch.nn import functional as F
class MnistMlp(nn.Module):
def __init__(self, width, dropout_p):
super().__init__()
self.fc1 = nn.Linear(784, width)
self.fc2 = nn.Linear(width, 10)
self.dropout = nn.Dropout(dropout_p)
def forward(self, x):
x = torch.reshape(x, (-1, 784))
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = F.log_softmax(self.fc2(x), dim=1)
return x
def get_inputs():
return [torch.rand([4, 784])]
def get_init_inputs():
return [[], {'width': 4, 'dropout_p': 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 math as tl_math
from torch import 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)
@triton.jit
def triton_per_fused__log_softmax_1(in_ptr0, out_ptr2, xnumel, rnumel,
XBLOCK: tl.constexpr):
xnumel = 4
rnumel = 10
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, :]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 10 * x0), rmask & xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(rmask & 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(rmask & xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp11 = tl_math.log(tmp10)
tmp12 = tmp5 - tmp11
tl.store(out_ptr2 + (r1 + 10 * x0), tmp12, rmask & xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 784), (784, 1))
assert_size_stride(primals_2, (4, 784), (784, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (10, 4), (4, 1))
assert_size_stride(primals_5, (10,), (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, (784, 4),
(1, 784), 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, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
(4, 10), (1, 4), 0), alpha=1, beta=1, out=buf2)
del primals_5
buf5 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
triton_per_fused__log_softmax_1[grid(4)](buf2, buf5, 4, 10, XBLOCK=
1, num_warps=2, num_stages=1)
del buf2
return buf5, primals_1, buf1, buf5, primals_4
class MnistMlpNew(nn.Module):
def __init__(self, width, dropout_p):
super().__init__()
self.fc1 = nn.Linear(784, width)
self.fc2 = nn.Linear(width, 10)
self.dropout = nn.Dropout(dropout_p)
def forward(self, input_0):
primals_1 = self.fc1.weight
primals_3 = self.fc1.bias
primals_4 = self.fc2.weight
primals_5 = self.fc2.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
shyam196/exptune
|
MnistMlp
| false
| 12,986
|
[
"MIT"
] | 0
|
be9bb23355ecd1a464dbc93dc35050b7f9d40227
|
https://github.com/shyam196/exptune/tree/be9bb23355ecd1a464dbc93dc35050b7f9d40227
|
EnsembleFC
|
import torch
import torch.nn as nn
class EnsembleFC(nn.Module):
__constants__ = ['in_features', 'out_features']
in_features: 'int'
out_features: 'int'
ensemble_size: 'int'
weight: 'torch.Tensor'
def __init__(self, in_features: 'int', out_features: 'int',
ensemble_size: 'int', weight_decay: 'float'=0.0) ->None:
super(EnsembleFC, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.ensemble_size = ensemble_size
self.weight = nn.Parameter(torch.Tensor(ensemble_size, in_features,
out_features))
self.weight_decay = weight_decay
self.bias = nn.Parameter(torch.Tensor(ensemble_size, 1, out_features))
def forward(self, input: 'torch.Tensor') ->torch.Tensor:
assert input.shape[0] == self.ensemble_size and len(input.shape) == 3
return torch.bmm(input, self.weight) + self.bias
def extra_repr(self) ->str:
return 'in_features={}, out_features={}'.format(self.in_features,
self.out_features)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4, 'ensemble_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 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_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
x3 = xindex
x0 = xindex % 4
x2 = xindex // 16
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 4 * x2), 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, 4), (4, 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(primals_1, primals_2, out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_add_0[grid(64)](buf1, primals_3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_3
return buf1, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0)
class EnsembleFCNew(nn.Module):
__constants__ = ['in_features', 'out_features']
in_features: 'int'
out_features: 'int'
ensemble_size: 'int'
weight: 'torch.Tensor'
def __init__(self, in_features: 'int', out_features: 'int',
ensemble_size: 'int', weight_decay: 'float'=0.0) ->None:
super(EnsembleFCNew, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.ensemble_size = ensemble_size
self.weight = nn.Parameter(torch.Tensor(ensemble_size, in_features,
out_features))
self.weight_decay = weight_decay
self.bias = nn.Parameter(torch.Tensor(ensemble_size, 1, out_features))
def extra_repr(self) ->str:
return 'in_features={}, out_features={}'.format(self.in_features,
self.out_features)
def forward(self, input_0):
primals_1 = self.weight
primals_3 = self.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
simonat2011/DI-engine
|
EnsembleFC
| false
| 12,987
|
[
"Apache-2.0"
] | 0
|
3a91c4297d58b3beff40b48bd37eb0b399c724a7
|
https://github.com/simonat2011/DI-engine/tree/3a91c4297d58b3beff40b48bd37eb0b399c724a7
|
EnsembleModel
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init_(m):
if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) or isinstance(m,
nn.ConvTranspose2d):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
x = x * F.sigmoid(x)
return x
class EnsembleFC(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int',
ensemble_size: 'int', weight_decay: 'float'=0.0, bias: 'bool'=True
) ->None:
super(EnsembleFC, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.ensemble_size = ensemble_size
self.weight = nn.Parameter(torch.Tensor(ensemble_size, in_features,
out_features))
self.weight_decay = weight_decay
if bias:
self.bias = nn.Parameter(torch.Tensor(ensemble_size, out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self) ->None:
pass
def forward(self, input: 'torch.Tensor') ->torch.Tensor:
w_times_x = torch.bmm(input, self.weight)
return torch.add(w_times_x, self.bias[:, None, :])
def extra_repr(self) ->str:
return 'in_features={}, out_features={}, bias={}'.format(self.
in_features, self.out_features, self.bias is not None)
class EnsembleModel(nn.Module):
def __init__(self, feature_size, ensemble_size, use_decay=False):
super(EnsembleModel, self).__init__()
self.nn1 = EnsembleFC(feature_size + feature_size, feature_size,
ensemble_size, weight_decay=2.5e-05)
self.use_decay = use_decay
self.apply(weights_init_)
self.swish = Swish()
def forward(self, state_latent, action_latent):
x = torch.cat([state_latent, action_latent], 2)
nn1_output = self.nn1(x)
return nn1_output
def get_decay_loss(self):
decay_loss = 0.0
for m in self.children():
if isinstance(m, EnsembleFC):
decay_loss += m.weight_decay * torch.sum(torch.square(m.weight)
) / 2.0
return decay_loss
def loss(self, mean, labels):
"""
mean, logvar: Ensemble_size x N x dim
labels: N x dim
"""
assert len(mean.shape) == len(labels.shape) == 3
mse_loss = torch.mean(torch.pow(mean - labels, 2), dim=(1, 2))
total_loss = torch.sum(mse_loss)
return total_loss, mse_loss
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'feature_size': 4, 'ensemble_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 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_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)
@triton.jit
def triton_poi_fused_add_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
x0 = xindex % 4
x2 = xindex // 16
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 4 * x2), 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, primals_4 = 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, 4), (32, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf0, 128,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(buf0, primals_3, out=buf1)
del primals_3
buf2 = buf1
del buf1
triton_poi_fused_add_1[grid(64)](buf2, primals_4, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_4
return buf2, reinterpret_tensor(buf0, (4, 8, 4), (32, 1, 8), 0)
def weights_init_(m):
if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) or isinstance(m,
nn.ConvTranspose2d):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
x = x * F.sigmoid(x)
return x
class EnsembleFC(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int',
ensemble_size: 'int', weight_decay: 'float'=0.0, bias: 'bool'=True
) ->None:
super(EnsembleFC, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.ensemble_size = ensemble_size
self.weight = nn.Parameter(torch.Tensor(ensemble_size, in_features,
out_features))
self.weight_decay = weight_decay
if bias:
self.bias = nn.Parameter(torch.Tensor(ensemble_size, out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self) ->None:
pass
def forward(self, input: 'torch.Tensor') ->torch.Tensor:
w_times_x = torch.bmm(input, self.weight)
return torch.add(w_times_x, self.bias[:, None, :])
def extra_repr(self) ->str:
return 'in_features={}, out_features={}, bias={}'.format(self.
in_features, self.out_features, self.bias is not None)
class EnsembleModelNew(nn.Module):
def __init__(self, feature_size, ensemble_size, use_decay=False):
super(EnsembleModelNew, self).__init__()
self.nn1 = EnsembleFC(feature_size + feature_size, feature_size,
ensemble_size, weight_decay=2.5e-05)
self.use_decay = use_decay
self.apply(weights_init_)
self.swish = Swish()
def get_decay_loss(self):
decay_loss = 0.0
for m in self.children():
if isinstance(m, EnsembleFC):
decay_loss += m.weight_decay * torch.sum(torch.square(m.weight)
) / 2.0
return decay_loss
def loss(self, mean, labels):
"""
mean, logvar: Ensemble_size x N x dim
labels: N x dim
"""
assert len(mean.shape) == len(labels.shape) == 3
mse_loss = torch.mean(torch.pow(mean - labels, 2), dim=(1, 2))
total_loss = torch.sum(mse_loss)
return total_loss, mse_loss
def forward(self, input_0, input_1):
primals_3 = self.nn1.weight
primals_4 = self.nn1.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
si0wang/transfer_dmc
|
EnsembleModel
| false
| 12,988
|
[
"MIT"
] | 0
|
6bda773244e0b709b3c13add2597f5f1cd01bfd7
|
https://github.com/si0wang/transfer_dmc/tree/6bda773244e0b709b3c13add2597f5f1cd01bfd7
|
Quantization
|
import torch
import torch.utils.data
import torch.nn as nn
class Quant(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
input = torch.clamp(input, 0, 1)
output = (input * 255.0).round() / 255.0
return output
@staticmethod
def backward(ctx, grad_output):
return grad_output
class Quantization(nn.Module):
def __init__(self):
super(Quantization, self).__init__()
def forward(self, input):
return Quant.apply(input)
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
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_clamp_div_mul_round_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 = 0.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp3 = 1.0
tmp4 = triton_helpers.minimum(tmp2, tmp3)
tmp5 = 255.0
tmp6 = tmp4 * tmp5
tmp7 = libdevice.nearbyint(tmp6)
tmp8 = 0.00392156862745098
tmp9 = tmp7 * tmp8
tl.store(out_ptr0 + x0, tmp9, 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_clamp_div_mul_round_0[grid(256)](arg0_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class Quant(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
input = torch.clamp(input, 0, 1)
output = (input * 255.0).round() / 255.0
return output
@staticmethod
def backward(ctx, grad_output):
return grad_output
class QuantizationNew(nn.Module):
def __init__(self):
super(QuantizationNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
skipper17/Invertible-Image-Rescaling
|
Quantization
| false
| 12,989
|
[
"Apache-2.0"
] | 0
|
4755f21faa5f7c4599dfb971a875ecee86bc35a1
|
https://github.com/skipper17/Invertible-Image-Rescaling/tree/4755f21faa5f7c4599dfb971a875ecee86bc35a1
|
FocalLoss
|
import torch
from torch import nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, gamma=2):
super().__init__()
self.gamma = gamma
def forward(self, logit, target, epoch=0):
target = target.float()
max_val = (-logit).clamp(min=0)
loss = logit - logit * target + max_val + ((-max_val).exp() + (-
logit - max_val).exp()).log()
invprobs = F.logsigmoid(-logit * (target * 2.0 - 1.0))
loss = (invprobs * self.gamma).exp() * loss
if len(loss.size()) == 2:
loss = loss.sum(dim=1)
return 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._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_clamp_exp_log_log_sigmoid_forward_mean_mul_neg_sub_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 = -tmp0
tmp3 = 2.0
tmp4 = tmp2 * tmp3
tmp5 = 1.0
tmp6 = tmp4 - tmp5
tmp7 = tmp1 * tmp6
tmp8 = 0.0
tmp9 = triton_helpers.minimum(tmp8, tmp7)
tmp10 = tl_math.abs(tmp7)
tmp11 = -tmp10
tmp12 = tl_math.exp(tmp11)
tmp13 = libdevice.log1p(tmp12)
tmp14 = tmp9 - tmp13
tmp15 = tmp14 * tmp3
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp0 * tmp2
tmp18 = tmp0 - tmp17
tmp19 = triton_helpers.maximum(tmp1, tmp8)
tmp20 = tmp18 + tmp19
tmp21 = -tmp19
tmp22 = tl_math.exp(tmp21)
tmp23 = tmp1 - tmp19
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = tl_math.log(tmp25)
tmp27 = tmp20 + tmp26
tmp28 = tmp16 * tmp27
tmp29 = tl.broadcast_to(tmp28, [RBLOCK])
tmp31 = triton_helpers.promote_to_tensor(tl.sum(tmp29, 0))
tmp32 = 256.0
tmp33 = tmp31 / tmp32
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([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((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_clamp_exp_log_log_sigmoid_forward_mean_mul_neg_sub_0[
grid(1)](buf1, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class FocalLossNew(nn.Module):
def __init__(self, gamma=2):
super().__init__()
self.gamma = gamma
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
sin1012/kaggle_baidu_autonomous_driving
|
FocalLoss
| false
| 12,990
|
[
"Apache-2.0"
] | 0
|
afa0da4fc06a05548306b885c6c804881104b403
|
https://github.com/sin1012/kaggle_baidu_autonomous_driving/tree/afa0da4fc06a05548306b885c6c804881104b403
|
PretrainedUNet
|
import torch
import torchvision
class Block(torch.nn.Module):
def __init__(self, in_channels, mid_channel, out_channels, batch_norm=False
):
super().__init__()
self.conv1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=
mid_channel, kernel_size=3, padding=1)
self.conv2 = torch.nn.Conv2d(in_channels=mid_channel, out_channels=
out_channels, kernel_size=3, padding=1)
self.batch_norm = batch_norm
if batch_norm:
self.bn1 = torch.nn.BatchNorm2d(mid_channel)
self.bn2 = torch.nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv1(x)
if self.batch_norm:
x = self.bn1(x)
x = torch.nn.ReLU(inplace=True)(x)
x = self.conv2(x)
if self.batch_norm:
x = self.bn2(x)
out = torch.nn.ReLU(inplace=True)(x)
return out
class PretrainedUNet(torch.nn.Module):
def up(self, x, size):
return torch.nn.functional.interpolate(x, size=size, mode=self.
upscale_mode)
def down(self, x):
return torch.nn.MaxPool2d(kernel_size=2)(x)
def __init__(self, in_channels, out_channels, batch_norm=False,
upscale_mode='nearest'):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.batch_norm = batch_norm
self.upscale_mode = upscale_mode
self.init_conv = torch.nn.Conv2d(in_channels, 3, 1)
endcoder = torchvision.models.vgg11(pretrained=True).features
self.conv1 = endcoder[0]
self.conv2 = endcoder[3]
self.conv3 = endcoder[6]
self.conv3s = endcoder[8]
self.conv4 = endcoder[11]
self.conv4s = endcoder[13]
self.conv5 = endcoder[16]
self.conv5s = endcoder[18]
self.center = Block(512, 512, 256, batch_norm)
self.dec5 = Block(512 + 256, 512, 256, batch_norm)
self.dec4 = Block(512 + 256, 512, 128, batch_norm)
self.dec3 = Block(256 + 128, 256, 64, batch_norm)
self.dec2 = Block(128 + 64, 128, 32, batch_norm)
self.dec1 = Block(64 + 32, 64, 32, batch_norm)
self.out = torch.nn.Conv2d(in_channels=32, out_channels=
out_channels, kernel_size=1)
def forward(self, x):
init_conv = torch.nn.ReLU(inplace=True)(self.init_conv(x))
enc1 = torch.nn.ReLU(inplace=True)(self.conv1(init_conv))
enc2 = torch.nn.ReLU(inplace=True)(self.conv2(self.down(enc1)))
enc3 = torch.nn.ReLU(inplace=True)(self.conv3(self.down(enc2)))
enc3 = torch.nn.ReLU(inplace=True)(self.conv3s(enc3))
enc4 = torch.nn.ReLU(inplace=True)(self.conv4(self.down(enc3)))
enc4 = torch.nn.ReLU(inplace=True)(self.conv4s(enc4))
enc5 = torch.nn.ReLU(inplace=True)(self.conv5(self.down(enc4)))
enc5 = torch.nn.ReLU(inplace=True)(self.conv5s(enc5))
center = self.center(self.down(enc5))
dec5 = self.dec5(torch.cat([self.up(center, enc5.size()[-2:]), enc5
], 1))
dec4 = self.dec4(torch.cat([self.up(dec5, enc4.size()[-2:]), enc4], 1))
dec3 = self.dec3(torch.cat([self.up(dec4, enc3.size()[-2:]), enc3], 1))
dec2 = self.dec2(torch.cat([self.up(dec3, enc2.size()[-2:]), enc2], 1))
dec1 = self.dec1(torch.cat([self.up(dec2, enc1.size()[-2:]), enc1], 1))
out = self.out(dec1)
return out
def get_inputs():
return [torch.rand([4, 4, 64, 64])]
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 import triton_helpers
import torchvision
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):
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)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_1(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_max_pool2d_with_indices_2(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 32
x1 = xindex // 32
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x2, tmp6, None)
tl.store(out_ptr1 + x2, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_3(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 // 1024 % 128
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_max_pool2d_with_indices_4(in_ptr0, out_ptr0, out_ptr1,
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
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x1), None, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x1), None, eviction_policy
='evict_last')
tmp5 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * x1), None, eviction_policy
='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x2, tmp6, None)
tl.store(out_ptr1 + x2, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_5(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 // 256 % 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_max_pool2d_with_indices_6(in_ptr0, out_ptr0, out_ptr1,
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 = tl.load(in_ptr0 + (2 * x0 + 32 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 32 * x1), None, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (16 + 2 * x0 + 32 * x1), None, eviction_policy
='evict_last')
tmp5 = tl.load(in_ptr0 + (17 + 2 * x0 + 32 * x1), None, eviction_policy
='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x2, tmp6, None)
tl.store(out_ptr1 + x2, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_7(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 // 64 % 512
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_max_pool2d_with_indices_8(in_ptr0, out_ptr0, out_ptr1,
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
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 16 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 16 * x1), None, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (8 + 2 * x0 + 16 * x1), None, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (9 + 2 * x0 + 16 * x1), None, eviction_policy=
'evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x2, tmp6, None)
tl.store(out_ptr1 + x2, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_9(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 % 512
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_max_pool2d_with_indices_10(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 2
x1 = xindex // 2
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), None, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), None, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), None, eviction_policy=
'evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x2, tmp6, None)
tl.store(out_ptr1 + x2, tmp16, None)
@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)
x3 = xindex
x1 = xindex // 4 % 512
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__to_copy_add_arange_mul_12(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 = 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_cat_13(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 16 % 768
x1 = xindex // 4 % 4
x0 = xindex % 4
x3 = xindex // 12288
x4 = xindex % 16
x5 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 256, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp6 = tl.full([XBLOCK], 2, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tmp10 = tl.load(in_ptr0 + x0, tmp4, eviction_policy='evict_last', other=0.0
)
tmp11 = tmp10 + tmp6
tmp12 = tmp10 < 0
tmp13 = tl.where(tmp12, tmp11, tmp10)
tmp14 = tl.load(in_ptr1 + (tmp13 + 2 * tmp9 + 4 * x2 + 1024 * x3), tmp4,
eviction_policy='evict_last', other=0.0)
tmp15 = tl.load(in_ptr2 + x2, tmp4, eviction_policy='evict_last', other=0.0
)
tmp16 = tmp14 + tmp15
tmp17 = tl.full([1], 0, tl.int32)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
tmp20 = tl.where(tmp4, tmp18, tmp19)
tmp21 = tmp0 >= tmp3
tl.full([1], 768, tl.int64)
tmp24 = tl.load(in_ptr3 + (x4 + 16 * (-256 + x2) + 8192 * x3), tmp21,
other=0.0)
tmp25 = tl.where(tmp4, tmp20, tmp24)
tl.store(out_ptr0 + x5, tmp25, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_14(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_cat_15(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 64 % 768
x1 = xindex // 8 % 8
x0 = xindex % 8
x3 = xindex // 49152
x4 = xindex % 64
x5 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 256, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp6 = tl.full([XBLOCK], 4, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tmp10 = tl.load(in_ptr0 + x0, tmp4, eviction_policy='evict_last', other=0.0
)
tmp11 = tmp10 + tmp6
tmp12 = tmp10 < 0
tmp13 = tl.where(tmp12, tmp11, tmp10)
tmp14 = tl.load(in_ptr1 + (tmp13 + 4 * tmp9 + 16 * x2 + 4096 * x3),
tmp4, eviction_policy='evict_last', other=0.0)
tmp15 = tl.load(in_ptr2 + x2, tmp4, eviction_policy='evict_last', other=0.0
)
tmp16 = tmp14 + tmp15
tmp17 = tl.full([1], 0, tl.int32)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
tmp20 = tl.where(tmp4, tmp18, tmp19)
tmp21 = tmp0 >= tmp3
tl.full([1], 768, tl.int64)
tmp24 = tl.load(in_ptr3 + (x4 + 64 * (-256 + x2) + 32768 * x3), tmp21,
other=0.0)
tmp25 = tl.where(tmp4, tmp20, tmp24)
tl.store(out_ptr0 + x5, tmp25, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_16(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_cat_17(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 256 % 384
x1 = xindex // 16 % 16
x0 = xindex % 16
x3 = xindex // 98304
x4 = xindex % 256
x5 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 128, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp6 = tl.full([XBLOCK], 8, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tmp10 = tl.load(in_ptr0 + x0, tmp4, eviction_policy='evict_last', other=0.0
)
tmp11 = tmp10 + tmp6
tmp12 = tmp10 < 0
tmp13 = tl.where(tmp12, tmp11, tmp10)
tmp14 = tl.load(in_ptr1 + (tmp13 + 8 * tmp9 + 64 * x2 + 8192 * x3),
tmp4, eviction_policy='evict_last', other=0.0)
tmp15 = tl.load(in_ptr2 + x2, tmp4, eviction_policy='evict_last', other=0.0
)
tmp16 = tmp14 + tmp15
tmp17 = tl.full([1], 0, tl.int32)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
tmp20 = tl.where(tmp4, tmp18, tmp19)
tmp21 = tmp0 >= tmp3
tl.full([1], 384, tl.int64)
tmp24 = tl.load(in_ptr3 + (x4 + 256 * (-128 + x2) + 65536 * x3), tmp21,
other=0.0)
tmp25 = tl.where(tmp4, tmp20, tmp24)
tl.store(out_ptr0 + x5, tmp25, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_18(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_cat_19(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 1024 % 192
x1 = xindex // 32 % 32
x0 = xindex % 32
x3 = xindex // 196608
x4 = xindex % 1024
x5 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 64, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp6 = tl.full([XBLOCK], 16, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tmp10 = tl.load(in_ptr0 + x0, tmp4, eviction_policy='evict_last', other=0.0
)
tmp11 = tmp10 + tmp6
tmp12 = tmp10 < 0
tmp13 = tl.where(tmp12, tmp11, tmp10)
tmp14 = tl.load(in_ptr1 + (tmp13 + 16 * tmp9 + 256 * x2 + 16384 * x3),
tmp4, eviction_policy='evict_last', other=0.0)
tmp15 = tl.load(in_ptr2 + x2, tmp4, eviction_policy='evict_last', other=0.0
)
tmp16 = tmp14 + tmp15
tmp17 = tl.full([1], 0, tl.int32)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
tmp20 = tl.where(tmp4, tmp18, tmp19)
tmp21 = tmp0 >= tmp3
tl.full([1], 192, tl.int64)
tmp24 = tl.load(in_ptr3 + (x4 + 1024 * (-64 + x2) + 131072 * x3), tmp21,
other=0.0)
tmp25 = tl.where(tmp4, tmp20, tmp24)
tl.store(out_ptr0 + x5, tmp25, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_20(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_cat_21(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 4096 % 96
x1 = xindex // 64 % 64
x0 = xindex % 64
x3 = xindex // 393216
x4 = xindex % 4096
x5 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 32, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp6 = tl.full([XBLOCK], 32, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tmp10 = tl.load(in_ptr0 + x0, tmp4, eviction_policy='evict_last', other=0.0
)
tmp11 = tmp10 + tmp6
tmp12 = tmp10 < 0
tmp13 = tl.where(tmp12, tmp11, tmp10)
tmp14 = tl.load(in_ptr1 + (tmp13 + 32 * tmp9 + 1024 * x2 + 32768 * x3),
tmp4, eviction_policy='evict_last', other=0.0)
tmp15 = tl.load(in_ptr2 + x2, tmp4, eviction_policy='evict_last', other=0.0
)
tmp16 = tmp14 + tmp15
tmp17 = tl.full([1], 0, tl.int32)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
tmp20 = tl.where(tmp4, tmp18, tmp19)
tmp21 = tmp0 >= tmp3
tl.full([1], 96, tl.int64)
tmp24 = tl.load(in_ptr3 + (x4 + 4096 * (-32 + x2) + 262144 * x3), tmp21,
other=0.0)
tmp25 = tl.where(tmp4, tmp20, tmp24)
tl.store(out_ptr0 + x5, tmp25, None)
@triton.jit
def triton_poi_fused_convolution_relu_22(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 % 32
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_convolution_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)
x3 = xindex
x1 = xindex // 4096 % 4
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, 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 // 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_25(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_26(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_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)
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)
@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)
x3 = xindex
x1 = xindex // 4 % 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, primals_20, primals_21, primals_22,
primals_23, primals_24, primals_25, primals_26, primals_27,
primals_28, primals_29, primals_30, primals_31, primals_32,
primals_33, primals_34, primals_35, primals_36, primals_37,
primals_38, primals_39, primals_40, primals_41, primals_42,
primals_43, primals_44, primals_45) = args
args.clear()
assert_size_stride(primals_1, (3, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (3,), (1,))
assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 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, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (128,), (1,))
assert_size_stride(primals_8, (256, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_9, (256,), (1,))
assert_size_stride(primals_10, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (512, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_13, (512,), (1,))
assert_size_stride(primals_14, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_15, (512,), (1,))
assert_size_stride(primals_16, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_17, (512,), (1,))
assert_size_stride(primals_18, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_19, (512,), (1,))
assert_size_stride(primals_20, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_21, (512,), (1,))
assert_size_stride(primals_22, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_23, (256,), (1,))
assert_size_stride(primals_24, (512, 768, 3, 3), (6912, 9, 3, 1))
assert_size_stride(primals_25, (512,), (1,))
assert_size_stride(primals_26, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_27, (256,), (1,))
assert_size_stride(primals_28, (512, 768, 3, 3), (6912, 9, 3, 1))
assert_size_stride(primals_29, (512,), (1,))
assert_size_stride(primals_30, (128, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_31, (128,), (1,))
assert_size_stride(primals_32, (256, 384, 3, 3), (3456, 9, 3, 1))
assert_size_stride(primals_33, (256,), (1,))
assert_size_stride(primals_34, (64, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_35, (64,), (1,))
assert_size_stride(primals_36, (128, 192, 3, 3), (1728, 9, 3, 1))
assert_size_stride(primals_37, (128,), (1,))
assert_size_stride(primals_38, (32, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_39, (32,), (1,))
assert_size_stride(primals_40, (64, 96, 3, 3), (864, 9, 3, 1))
assert_size_stride(primals_41, (64,), (1,))
assert_size_stride(primals_42, (32, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_43, (32,), (1,))
assert_size_stride(primals_44, (4, 32, 1, 1), (32, 1, 1, 1))
assert_size_stride(primals_45, (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=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 3, 64, 64), (12288, 4096, 64, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(49152)](buf1, primals_2,
49152, XBLOCK=256, 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, 64, 64, 64), (262144, 4096, 64, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_relu_1[grid(1048576)](buf3, primals_5,
1048576, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.float32)
buf5 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_2[grid(262144)](buf3, buf4,
buf5, 262144, XBLOCK=512, num_warps=8, num_stages=1)
buf6 = 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(buf6, (4, 128, 32, 32), (131072, 1024, 32, 1))
buf7 = buf6
del buf6
triton_poi_fused_convolution_relu_3[grid(524288)](buf7, primals_7,
524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_7
buf8 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.float32)
buf9 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_4[grid(131072)](buf7, buf8,
buf9, 131072, XBLOCK=512, num_warps=8, num_stages=1)
buf10 = extern_kernels.convolution(buf8, primals_8, 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, 16, 16), (65536, 256, 16, 1))
buf11 = buf10
del buf10
triton_poi_fused_convolution_relu_5[grid(262144)](buf11, primals_9,
262144, XBLOCK=512, num_warps=8, num_stages=1)
del primals_9
buf12 = extern_kernels.convolution(buf11, primals_10, 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, 16, 16), (65536, 256, 16, 1))
buf13 = buf12
del buf12
triton_poi_fused_convolution_relu_5[grid(262144)](buf13, primals_11,
262144, XBLOCK=512, num_warps=8, num_stages=1)
del primals_11
buf14 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.float32)
buf15 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.int8)
triton_poi_fused_max_pool2d_with_indices_6[grid(65536)](buf13,
buf14, buf15, 65536, XBLOCK=256, num_warps=4, num_stages=1)
buf16 = extern_kernels.convolution(buf14, primals_12, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf16, (4, 512, 8, 8), (32768, 64, 8, 1))
buf17 = buf16
del buf16
triton_poi_fused_convolution_relu_7[grid(131072)](buf17, primals_13,
131072, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_13
buf18 = extern_kernels.convolution(buf17, primals_14, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf18, (4, 512, 8, 8), (32768, 64, 8, 1))
buf19 = buf18
del buf18
triton_poi_fused_convolution_relu_7[grid(131072)](buf19, primals_15,
131072, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_15
buf20 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.
float32)
buf21 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.int8
)
triton_poi_fused_max_pool2d_with_indices_8[grid(32768)](buf19,
buf20, buf21, 32768, XBLOCK=128, num_warps=4, num_stages=1)
buf22 = extern_kernels.convolution(buf20, primals_16, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf22, (4, 512, 4, 4), (8192, 16, 4, 1))
buf23 = buf22
del buf22
triton_poi_fused_convolution_relu_9[grid(32768)](buf23, primals_17,
32768, XBLOCK=256, num_warps=4, num_stages=1)
del primals_17
buf24 = extern_kernels.convolution(buf23, primals_18, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf24, (4, 512, 4, 4), (8192, 16, 4, 1))
buf25 = buf24
del buf24
triton_poi_fused_convolution_relu_9[grid(32768)](buf25, primals_19,
32768, XBLOCK=256, num_warps=4, num_stages=1)
del primals_19
buf26 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.
float32)
buf27 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.int8)
triton_poi_fused_max_pool2d_with_indices_10[grid(8192)](buf25,
buf26, buf27, 8192, XBLOCK=128, num_warps=4, num_stages=1)
buf28 = extern_kernels.convolution(buf26, primals_20, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf28, (4, 512, 2, 2), (2048, 4, 2, 1))
buf29 = buf28
del buf28
triton_poi_fused_convolution_relu_11[grid(8192)](buf29, primals_21,
8192, XBLOCK=256, num_warps=4, num_stages=1)
del primals_21
buf30 = extern_kernels.convolution(buf29, primals_22, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf30, (4, 256, 2, 2), (1024, 4, 2, 1))
buf31 = empty_strided_cuda((4,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_12[grid(4)](buf31, 4,
XBLOCK=4, num_warps=1, num_stages=1)
buf32 = empty_strided_cuda((4, 768, 4, 4), (12288, 16, 4, 1), torch
.float32)
triton_poi_fused_cat_13[grid(49152)](buf31, buf30, primals_23,
buf25, buf32, 49152, XBLOCK=512, num_warps=4, num_stages=1)
buf33 = extern_kernels.convolution(buf32, primals_24, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf33, (4, 512, 4, 4), (8192, 16, 4, 1))
buf34 = buf33
del buf33
triton_poi_fused_convolution_relu_9[grid(32768)](buf34, primals_25,
32768, XBLOCK=256, num_warps=4, num_stages=1)
del primals_25
buf35 = extern_kernels.convolution(buf34, primals_26, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf35, (4, 256, 4, 4), (4096, 16, 4, 1))
buf36 = empty_strided_cuda((8,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_14[grid(8)](buf36, 8,
XBLOCK=8, num_warps=1, num_stages=1)
buf37 = empty_strided_cuda((4, 768, 8, 8), (49152, 64, 8, 1), torch
.float32)
triton_poi_fused_cat_15[grid(196608)](buf36, buf35, primals_27,
buf19, buf37, 196608, XBLOCK=1024, num_warps=4, num_stages=1)
buf38 = extern_kernels.convolution(buf37, primals_28, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf38, (4, 512, 8, 8), (32768, 64, 8, 1))
buf39 = buf38
del buf38
triton_poi_fused_convolution_relu_7[grid(131072)](buf39, primals_29,
131072, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_29
buf40 = extern_kernels.convolution(buf39, primals_30, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf40, (4, 128, 8, 8), (8192, 64, 8, 1))
buf41 = empty_strided_cuda((16,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_16[grid(16)](buf41, 16,
XBLOCK=16, num_warps=1, num_stages=1)
buf42 = empty_strided_cuda((4, 384, 16, 16), (98304, 256, 16, 1),
torch.float32)
triton_poi_fused_cat_17[grid(393216)](buf41, buf40, primals_31,
buf13, buf42, 393216, XBLOCK=512, num_warps=8, num_stages=1)
buf43 = extern_kernels.convolution(buf42, primals_32, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf43, (4, 256, 16, 16), (65536, 256, 16, 1))
buf44 = buf43
del buf43
triton_poi_fused_convolution_relu_5[grid(262144)](buf44, primals_33,
262144, XBLOCK=512, num_warps=8, num_stages=1)
del primals_33
buf45 = extern_kernels.convolution(buf44, primals_34, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf45, (4, 64, 16, 16), (16384, 256, 16, 1))
buf46 = empty_strided_cuda((32,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_18[grid(32)](buf46, 32,
XBLOCK=32, num_warps=1, num_stages=1)
buf47 = empty_strided_cuda((4, 192, 32, 32), (196608, 1024, 32, 1),
torch.float32)
triton_poi_fused_cat_19[grid(786432)](buf46, buf45, primals_35,
buf7, buf47, 786432, XBLOCK=1024, num_warps=4, num_stages=1)
buf48 = extern_kernels.convolution(buf47, primals_36, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf48, (4, 128, 32, 32), (131072, 1024, 32, 1))
buf49 = buf48
del buf48
triton_poi_fused_convolution_relu_3[grid(524288)](buf49, primals_37,
524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_37
buf50 = extern_kernels.convolution(buf49, primals_38, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf50, (4, 32, 32, 32), (32768, 1024, 32, 1))
buf51 = empty_strided_cuda((64,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_20[grid(64)](buf51, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf52 = empty_strided_cuda((4, 96, 64, 64), (393216, 4096, 64, 1),
torch.float32)
triton_poi_fused_cat_21[grid(1572864)](buf51, buf50, primals_39,
buf3, buf52, 1572864, XBLOCK=1024, num_warps=4, num_stages=1)
buf53 = extern_kernels.convolution(buf52, primals_40, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf53, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf54 = buf53
del buf53
triton_poi_fused_convolution_relu_1[grid(1048576)](buf54,
primals_41, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_41
buf55 = extern_kernels.convolution(buf54, primals_42, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf55, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf56 = buf55
del buf55
triton_poi_fused_convolution_relu_22[grid(524288)](buf56,
primals_43, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_43
buf57 = extern_kernels.convolution(buf56, primals_44, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf57, (4, 4, 64, 64), (16384, 4096, 64, 1))
buf58 = buf57
del buf57
triton_poi_fused_convolution_23[grid(65536)](buf58, primals_45,
65536, XBLOCK=512, num_warps=4, num_stages=1)
del primals_45
buf59 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_24[grid(131072)](
buf50, primals_39, buf59, 131072, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf50
del primals_39
buf60 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_25[grid(65536)](
buf45, primals_35, buf60, 65536, XBLOCK=256, num_warps=4,
num_stages=1)
del buf45
del primals_35
buf61 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool
)
triton_poi_fused_convolution_relu_threshold_backward_26[grid(32768)](
buf40, primals_31, buf61, 32768, XBLOCK=128, num_warps=4,
num_stages=1)
del buf40
del primals_31
buf62 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool
)
triton_poi_fused_convolution_relu_threshold_backward_27[grid(16384)](
buf35, primals_27, buf62, 16384, XBLOCK=128, num_warps=4,
num_stages=1)
del buf35
del primals_27
buf63 = empty_strided_cuda((4, 256, 2, 2), (1024, 4, 2, 1), torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_28[grid(4096)](
buf30, primals_23, buf63, 4096, XBLOCK=128, num_warps=4,
num_stages=1)
del buf30
del primals_23
return (buf58, 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, primals_32, primals_34, primals_36, primals_38,
primals_40, primals_42, primals_44, buf1, buf3, buf4, buf5, buf7,
buf8, buf9, buf11, buf13, buf14, buf15, buf17, buf19, buf20, buf21,
buf23, buf25, buf26, buf27, buf29, buf31, buf32, buf34, buf36,
buf37, buf39, buf41, buf42, buf44, buf46, buf47, buf49, buf51,
buf52, buf54, buf56, buf59, buf60, buf61, buf62, buf63)
class Block(torch.nn.Module):
def __init__(self, in_channels, mid_channel, out_channels, batch_norm=False
):
super().__init__()
self.conv1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=
mid_channel, kernel_size=3, padding=1)
self.conv2 = torch.nn.Conv2d(in_channels=mid_channel, out_channels=
out_channels, kernel_size=3, padding=1)
self.batch_norm = batch_norm
if batch_norm:
self.bn1 = torch.nn.BatchNorm2d(mid_channel)
self.bn2 = torch.nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv1(x)
if self.batch_norm:
x = self.bn1(x)
x = torch.nn.ReLU(inplace=True)(x)
x = self.conv2(x)
if self.batch_norm:
x = self.bn2(x)
out = torch.nn.ReLU(inplace=True)(x)
return out
class PretrainedUNetNew(torch.nn.Module):
def up(self, x, size):
return torch.nn.functional.interpolate(x, size=size, mode=self.
upscale_mode)
def down(self, x):
return torch.nn.MaxPool2d(kernel_size=2)(x)
def __init__(self, in_channels, out_channels, batch_norm=False,
upscale_mode='nearest'):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.batch_norm = batch_norm
self.upscale_mode = upscale_mode
self.init_conv = torch.nn.Conv2d(in_channels, 3, 1)
endcoder = torchvision.models.vgg11(pretrained=True).features
self.conv1 = endcoder[0]
self.conv2 = endcoder[3]
self.conv3 = endcoder[6]
self.conv3s = endcoder[8]
self.conv4 = endcoder[11]
self.conv4s = endcoder[13]
self.conv5 = endcoder[16]
self.conv5s = endcoder[18]
self.center = Block(512, 512, 256, batch_norm)
self.dec5 = Block(512 + 256, 512, 256, batch_norm)
self.dec4 = Block(512 + 256, 512, 128, batch_norm)
self.dec3 = Block(256 + 128, 256, 64, batch_norm)
self.dec2 = Block(128 + 64, 128, 32, batch_norm)
self.dec1 = Block(64 + 32, 64, 32, batch_norm)
self.out = torch.nn.Conv2d(in_channels=32, out_channels=
out_channels, kernel_size=1)
def forward(self, input_0):
primals_1 = self.init_conv.weight
primals_2 = self.init_conv.bias
primals_4 = self.conv1.weight
primals_5 = self.conv1.bias
primals_6 = self.conv2.weight
primals_7 = self.conv2.bias
primals_8 = self.conv3.weight
primals_9 = self.conv3.bias
primals_10 = self.conv3s.weight
primals_11 = self.conv3s.bias
primals_12 = self.conv4.weight
primals_13 = self.conv4.bias
primals_14 = self.conv4s.weight
primals_15 = self.conv4s.bias
primals_16 = self.conv5.weight
primals_17 = self.conv5.bias
primals_18 = self.conv5s.weight
primals_19 = self.conv5s.bias
primals_20 = self.center.conv1.weight
primals_21 = self.center.conv1.bias
primals_22 = self.center.conv2.weight
primals_23 = self.center.conv2.bias
primals_24 = self.dec5.conv1.weight
primals_25 = self.dec5.conv1.bias
primals_26 = self.dec5.conv2.weight
primals_27 = self.dec5.conv2.bias
primals_28 = self.dec4.conv1.weight
primals_29 = self.dec4.conv1.bias
primals_30 = self.dec4.conv2.weight
primals_31 = self.dec4.conv2.bias
primals_32 = self.dec3.conv1.weight
primals_33 = self.dec3.conv1.bias
primals_34 = self.dec3.conv2.weight
primals_35 = self.dec3.conv2.bias
primals_36 = self.dec2.conv1.weight
primals_37 = self.dec2.conv1.bias
primals_38 = self.dec2.conv2.weight
primals_39 = self.dec2.conv2.bias
primals_40 = self.dec1.conv1.weight
primals_41 = self.dec1.conv1.bias
primals_42 = self.dec1.conv2.weight
primals_43 = self.dec1.conv2.bias
primals_44 = self.out.weight
primals_45 = 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,
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, primals_32, primals_33, primals_34,
primals_35, primals_36, primals_37, primals_38, primals_39,
primals_40, primals_41, primals_42, primals_43, primals_44,
primals_45])
return output[0]
|
amrane99/lung-segmentation
|
PretrainedUNet
| false
| 12,991
|
[
"MIT"
] | 0
|
ab29db75ac78918da5cbf66b830acaf36cf7b44a
|
https://github.com/amrane99/lung-segmentation/tree/ab29db75ac78918da5cbf66b830acaf36cf7b44a
|
BertSelfAttention
|
from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transform(self, x, linear_layer):
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
norm = math.sqrt(key.shape[-1])
scores = torch.matmul(query, key.transpose(-1, -2))
bs = scores.shape[0]
s_len = scores.shape[-1]
scores = scores / norm
scores_n = scores + attention_mask
attention = nn.Softmax(dim=-1)(scores_n)
v_prime = torch.matmul(attention, value)
v_prime = v_prime.permute(0, 2, 1, 3)
v_prime = v_prime.contiguous().view(bs, s_len, self.all_head_size)
return v_prime
def forward(self, hidden_states, attention_mask):
"""
hidden_states: [bs, seq_len, hidden_state]
attention_mask: [bs, 1, 1, seq_len]
output: [bs, seq_len, hidden_state]
"""
key_layer = self.transform(hidden_states, self.key)
value_layer = self.transform(hidden_states, self.value)
query_layer = self.transform(hidden_states, self.query)
attn_value = self.attention(key_layer, query_layer, value_layer,
attention_mask)
return attn_value
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(num_attention_heads=4, hidden_size=
4, attention_probs_dropout_prob=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 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_0(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_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
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), 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 * x2), 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 * x2), 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 = triton_helpers.maximum(tmp2, tmp5)
tmp9 = tmp7 + tmp8
tmp10 = triton_helpers.maximum(tmp6, tmp9)
tmp13 = tmp11 + tmp12
tmp14 = triton_helpers.maximum(tmp10, tmp13)
tmp15 = tmp2 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp5 - tmp14
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp20 = tmp9 - tmp14
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tmp13 - tmp14
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = float('-inf')
tmp27 = tmp2 == tmp26
tmp28 = tmp27 == 0
tmp29 = tmp28.to(tl.int64)
tmp30 = tmp29 != 0
tmp31 = tmp5 == tmp26
tmp32 = tmp31 == 0
tmp33 = tmp32.to(tl.int64)
tmp34 = tmp33 != 0
tmp35 = tmp30 | tmp34
tmp36 = tmp9 == tmp26
tmp37 = tmp36 == 0
tmp38 = tmp37.to(tl.int64)
tmp39 = tmp38 != 0
tmp40 = tmp35 | tmp39
tmp41 = tmp13 == tmp26
tmp42 = tmp41 == 0
tmp43 = tmp42.to(tl.int64)
tmp44 = tmp43 != 0
tmp45 = tmp40 | tmp44
tl.store(out_ptr0 + x2, tmp14, xmask)
tl.store(out_ptr1 + x2, tmp25, xmask)
tl.store(out_ptr2 + x2, tmp45, xmask)
@triton.jit
def triton_poi_fused_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex // 4
x4 = xindex
x5 = xindex % 64
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_out_ptr0 + x4, xmask)
tmp3 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
tmp1 = tmp0 == 0
tmp4 = tmp2 + tmp3
tmp6 = tmp4 - tmp5
tmp7 = tl_math.exp(tmp6)
tmp9 = tmp7 / tmp8
tmp10 = 0.0
tmp11 = tl.where(tmp1, tmp10, tmp9)
tl.store(in_out_ptr0 + x4, tmp11, xmask)
@triton.jit
def triton_poi_fused_3(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, 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 = 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, primals_8) = 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, 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), (16, 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)
del primals_2
buf1 = 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=buf1)
del primals_4
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (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, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(16, 4)](buf2, primals_7, buf3, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_7
buf4 = reinterpret_tensor(buf2, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf2
triton_poi_fused_0[grid(16, 4)](buf0, primals_3, buf4, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_3
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
buf6 = reinterpret_tensor(buf0, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf0
buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.bool)
triton_poi_fused_1[grid(64)](buf5, primals_8, buf6, buf7, buf8, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused_2[grid(256)](buf9, buf8, primals_8, buf6, buf7,
256, XBLOCK=128, num_warps=4, num_stages=1)
del buf8
del primals_8
buf10 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf7
triton_poi_fused_3[grid(16, 4)](buf1, primals_5, buf10, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_5
buf11 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
buf12 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf6
triton_poi_fused_clone_4[grid(16, 4)](buf11, buf12, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
del buf11
return reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
), buf9, reinterpret_tensor(buf10, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)
class BertSelfAttentionNew(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transform(self, x, linear_layer):
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
norm = math.sqrt(key.shape[-1])
scores = torch.matmul(query, key.transpose(-1, -2))
bs = scores.shape[0]
s_len = scores.shape[-1]
scores = scores / norm
scores_n = scores + attention_mask
attention = nn.Softmax(dim=-1)(scores_n)
v_prime = torch.matmul(attention, value)
v_prime = v_prime.permute(0, 2, 1, 3)
v_prime = v_prime.contiguous().view(bs, s_len, self.all_head_size)
return v_prime
def forward(self, input_0, input_1):
primals_2 = self.query.weight
primals_3 = self.query.bias
primals_4 = self.key.weight
primals_5 = self.key.bias
primals_6 = self.value.weight
primals_7 = self.value.bias
primals_1 = input_0
primals_8 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
shrishabh/cs769-assignments
|
BertSelfAttention
| false
| 12,992
|
[
"MIT"
] | 0
|
babce1def0d65728bf1d4e4a725d8939f1d5f9a7
|
https://github.com/shrishabh/cs769-assignments/tree/babce1def0d65728bf1d4e4a725d8939f1d5f9a7
|
diceloss
|
import torch
class diceloss(torch.nn.Module):
def init(self):
super(diceloss, self).init()
def forward(self, pred, target):
smooth = 1.0
iflat = pred.contiguous().view(-1)
tflat = target.contiguous().view(-1)
intersection = (iflat * tflat).sum()
A_sum = torch.sum(iflat * iflat)
B_sum = torch.sum(tflat * tflat)
return 1 - (2.0 * intersection + smooth) / (A_sum + B_sum + smooth)
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
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)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
tmp6 = tmp0 * tmp0
tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tmp10 = tmp1 * tmp1
tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
tmp14 = 2.0
tmp15 = tmp5 * tmp14
tmp16 = 1.0
tmp17 = tmp15 + tmp16
tmp18 = tmp9 + tmp13
tmp19 = tmp18 + tmp16
tmp20 = tmp17 / tmp19
tmp21 = tmp16 - tmp20
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp21, 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(torch.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]
|
soffiafdz/nma-dl-modality-mongoose
|
diceloss
| false
| 12,993
|
[
"MIT"
] | 0
|
41ac1f2e0e818538bafedae93e5c68f8857411bd
|
https://github.com/soffiafdz/nma-dl-modality-mongoose/tree/41ac1f2e0e818538bafedae93e5c68f8857411bd
|
ConvReluPool
|
import torch
from torch.nn import Conv2d
from torch import nn
from torch.nn import functional as F
def Pool(k, stride=1, pad=0):
return torch.nn.MaxPool2d(k, stride=stride, padding=pad)
class ConvReluPool(nn.Module):
def __init__(self, fIn, fOut, k, stride=1, pool=2):
super().__init__()
self.conv = Conv2d(fIn, fOut, k, stride)
self.pool = Pool(k)
def forward(self, x):
x = self.conv(x)
x = F.relu(x)
x = self.pool(x)
return x
def get_inputs():
return [torch.rand([4, 4, 64, 64])]
def get_init_inputs():
return [[], {'fIn': 4, 'fOut': 4, 'k': 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.nn import Conv2d
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_convolution_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 59536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 3721 % 4
x0 = xindex % 3721
x4 = xindex // 3721
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, 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 + (x0 + 3744 * x4), tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 53824
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 58
x1 = xindex // 58 % 58
x2 = xindex // 3364
x3 = xindex
x4 = xindex % 3364
tmp0 = tl.load(in_ptr0 + (x0 + 61 * x1 + 3744 * x2), xmask)
tmp1 = tl.load(in_ptr0 + (1 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp3 = tl.load(in_ptr0 + (2 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp5 = tl.load(in_ptr0 + (3 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp7 = tl.load(in_ptr0 + (61 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp9 = tl.load(in_ptr0 + (62 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp11 = tl.load(in_ptr0 + (63 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp13 = tl.load(in_ptr0 + (64 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp15 = tl.load(in_ptr0 + (122 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp17 = tl.load(in_ptr0 + (123 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp19 = tl.load(in_ptr0 + (124 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp21 = tl.load(in_ptr0 + (125 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp23 = tl.load(in_ptr0 + (183 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp25 = tl.load(in_ptr0 + (184 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp27 = tl.load(in_ptr0 + (185 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp29 = tl.load(in_ptr0 + (186 + x0 + 61 * x1 + 3744 * x2), xmask)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp8 = triton_helpers.maximum(tmp7, tmp6)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tmp14 = triton_helpers.maximum(tmp13, tmp12)
tmp16 = triton_helpers.maximum(tmp15, tmp14)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp20 = triton_helpers.maximum(tmp19, tmp18)
tmp22 = triton_helpers.maximum(tmp21, tmp20)
tmp24 = triton_helpers.maximum(tmp23, tmp22)
tmp26 = triton_helpers.maximum(tmp25, tmp24)
tmp28 = triton_helpers.maximum(tmp27, tmp26)
tmp30 = triton_helpers.maximum(tmp29, tmp28)
tmp31 = tmp1 > tmp0
tmp32 = tl.full([1], 1, tl.int8)
tmp33 = tl.full([1], 0, tl.int8)
tmp34 = tl.where(tmp31, tmp32, tmp33)
tmp35 = tmp3 > tmp2
tmp36 = tl.full([1], 2, tl.int8)
tmp37 = tl.where(tmp35, tmp36, tmp34)
tmp38 = tmp5 > tmp4
tmp39 = tl.full([1], 3, tl.int8)
tmp40 = tl.where(tmp38, tmp39, tmp37)
tmp41 = tmp7 > tmp6
tmp42 = tl.full([1], 4, tl.int8)
tmp43 = tl.where(tmp41, tmp42, tmp40)
tmp44 = tmp9 > tmp8
tmp45 = tl.full([1], 5, tl.int8)
tmp46 = tl.where(tmp44, tmp45, tmp43)
tmp47 = tmp11 > tmp10
tmp48 = tl.full([1], 6, tl.int8)
tmp49 = tl.where(tmp47, tmp48, tmp46)
tmp50 = tmp13 > tmp12
tmp51 = tl.full([1], 7, tl.int8)
tmp52 = tl.where(tmp50, tmp51, tmp49)
tmp53 = tmp15 > tmp14
tmp54 = tl.full([1], 8, tl.int8)
tmp55 = tl.where(tmp53, tmp54, tmp52)
tmp56 = tmp17 > tmp16
tmp57 = tl.full([1], 9, tl.int8)
tmp58 = tl.where(tmp56, tmp57, tmp55)
tmp59 = tmp19 > tmp18
tmp60 = tl.full([1], 10, tl.int8)
tmp61 = tl.where(tmp59, tmp60, tmp58)
tmp62 = tmp21 > tmp20
tmp63 = tl.full([1], 11, tl.int8)
tmp64 = tl.where(tmp62, tmp63, tmp61)
tmp65 = tmp23 > tmp22
tmp66 = tl.full([1], 12, tl.int8)
tmp67 = tl.where(tmp65, tmp66, tmp64)
tmp68 = tmp25 > tmp24
tmp69 = tl.full([1], 13, tl.int8)
tmp70 = tl.where(tmp68, tmp69, tmp67)
tmp71 = tmp27 > tmp26
tmp72 = tl.full([1], 14, tl.int8)
tmp73 = tl.where(tmp71, tmp72, tmp70)
tmp74 = tmp29 > tmp28
tmp75 = tl.full([1], 15, tl.int8)
tmp76 = tl.where(tmp74, tmp75, tmp73)
tl.store(out_ptr0 + x3, tmp30, xmask)
tl.store(out_ptr1 + (x4 + 3456 * x2), tmp76, 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, 64, 64), (16384, 4096, 64, 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, 4, 61, 61), (14884, 3721, 61, 1))
buf1 = empty_strided_cuda((4, 4, 61, 61), (14976, 3744, 61, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(59536)](buf0, primals_2,
buf1, 59536, XBLOCK=256, num_warps=4, num_stages=1)
del buf0
del primals_2
buf2 = empty_strided_cuda((4, 4, 58, 58), (13456, 3364, 58, 1),
torch.float32)
buf3 = empty_strided_cuda((4, 4, 58, 58), (13824, 3456, 58, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(53824)](buf1, buf2,
buf3, 53824, XBLOCK=256, num_warps=4, num_stages=1)
return buf2, primals_1, primals_3, buf1, buf3
def Pool(k, stride=1, pad=0):
return torch.nn.MaxPool2d(k, stride=stride, padding=pad)
class ConvReluPoolNew(nn.Module):
def __init__(self, fIn, fOut, k, stride=1, pool=2):
super().__init__()
self.conv = Conv2d(fIn, fOut, k, stride)
self.pool = Pool(k)
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]
|
smearle/neural-mmo
|
ConvReluPool
| false
| 12,994
|
[
"MIT"
] | 0
|
7f1e98857cb32bdb59a273eb71ec43bbd9793b34
|
https://github.com/smearle/neural-mmo/tree/7f1e98857cb32bdb59a273eb71ec43bbd9793b34
|
mix_Linear
|
import torch
from torch import nn
def Binarize(tensor):
"""
Binarize function: binarize input tensors
Input:
tensor: the input tensor.
Output:
binarized: the binarized tensor.
"""
binarized = torch.where(tensor > 0, torch.ones_like(tensor, dtype=torch
.float32, device='cuda'), torch.full(tensor.shape, -1, dtype=torch.
float32, device='cuda'))
return binarized
def quantization(input, bits):
"""
Combination of quantization and de-quantization function
Input:
input: the original full-precision tensor.
bits: number of quantized bits.
Output:
dequantized: the de-quantized tensor.
"""
quantized_max = 2 ** (bits - 1) - 1
quantized_min = -2 ** (bits - 1)
pmax = input.max()
pmin = input.min()
scale_int = quantized_max - quantized_min
scale_fp = pmax - pmin
quantized = torch.round((input - pmin) * (scale_int / scale_fp)
) + quantized_min
dequantized = (quantized - quantized_min) * (scale_fp / scale_int) + pmin
return dequantized
class mix_Linear(nn.Module):
"""
class mix_Linear: provide implementations of 32-bit and 1-bit layers
Input:
input (Tensor)
bit_1_quantize (1-bit quantization flag)
layer_number (the layer number)
bit_32_quantize (full-precision flag)
Output:
out (Tensor)
"""
def __init__(self, in_features, out_features, bias=True):
super(mix_Linear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Parameter(torch.Tensor(out_features, in_features))
self.quantized_weight = nn.Parameter(torch.Tensor(out_features,
in_features))
if bias:
self.bias = nn.Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
def forward(self, input, bit_1_quantize=False, layer_number=0,
bit_32_quantize=False):
if layer_number > 2:
if input.dtype == torch.half:
input.data = Binarize(input.data).half()
else:
input.data = Binarize(input.data)
if bit_32_quantize is True:
if input.data.dtype == torch.half:
out = nn.functional.linear(input, self.weight.half(), self.bias
)
else:
out = nn.functional.linear(input, self.weight, self.bias)
elif bit_1_quantize is True:
input.data = Binarize(input.data)
with torch.no_grad():
if input.data.dtype == torch.float:
self.quantized_weight.data = Binarize(self.weight)
else:
input.data = input.data.half()
self.quantized_weight.data = Binarize(self.weight)
self.quantized_weight.data = (self.quantized_weight.
data.half())
out = nn.functional.linear(input, self.quantized_weight, self.bias)
else:
if not hasattr(self.weight, 'org'):
self.weight.org = self.weight.data.clone()
self.weight.data = quantization(self.weight.org, 8)
if input.dtype == torch.half:
self.weight.data = self.weight.data
out = nn.functional.linear(input, self.weight, self.bias)
return out
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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_add_div_max_min_mul_reciprocal_round_sub_0(in_ptr0,
out_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)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
tmp5 = triton_helpers.min2(tmp1, 1)[:, None]
tmp6 = tmp0 - tmp5
tmp7 = tmp3 - tmp5
tmp8 = tl.full([1, 1], 1, tl.int32)
tmp9 = tmp8 / tmp7
tmp10 = 255.0
tmp11 = tmp9 * tmp10
tmp12 = tmp6 * tmp11
tmp13 = libdevice.nearbyint(tmp12)
tmp14 = -128.0
tmp15 = tmp13 + tmp14
tmp16 = tmp15 - tmp14
tmp17 = 0.00392156862745098
tmp18 = tmp7 * tmp17
tmp19 = tmp16 * tmp18
tmp20 = tmp19 + tmp5
tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp20, None)
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, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused_add_div_max_min_mul_reciprocal_round_sub_0[grid(1)](
primals_2, buf2, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
del primals_2
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_4, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(buf2, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_4
buf4 = torch.ops.aten.set_.source_Tensor(primals_1, buf2)
assert_size_stride(buf4, (4, 4), (4, 1))
del primals_1
return reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)
def Binarize(tensor):
"""
Binarize function: binarize input tensors
Input:
tensor: the input tensor.
Output:
binarized: the binarized tensor.
"""
binarized = torch.where(tensor > 0, torch.ones_like(tensor, dtype=torch
.float32, device='cuda'), torch.full(tensor.shape, -1, dtype=torch.
float32, device='cuda'))
return binarized
def quantization(input, bits):
"""
Combination of quantization and de-quantization function
Input:
input: the original full-precision tensor.
bits: number of quantized bits.
Output:
dequantized: the de-quantized tensor.
"""
quantized_max = 2 ** (bits - 1) - 1
quantized_min = -2 ** (bits - 1)
pmax = input.max()
pmin = input.min()
scale_int = quantized_max - quantized_min
scale_fp = pmax - pmin
quantized = torch.round((input - pmin) * (scale_int / scale_fp)
) + quantized_min
dequantized = (quantized - quantized_min) * (scale_fp / scale_int) + pmin
return dequantized
class mix_LinearNew(nn.Module):
"""
class mix_Linear: provide implementations of 32-bit and 1-bit layers
Input:
input (Tensor)
bit_1_quantize (1-bit quantization flag)
layer_number (the layer number)
bit_32_quantize (full-precision flag)
Output:
out (Tensor)
"""
def __init__(self, in_features, out_features, bias=True):
super(mix_LinearNew, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Parameter(torch.Tensor(out_features, in_features))
self.quantized_weight = nn.Parameter(torch.Tensor(out_features,
in_features))
if bias:
self.bias = nn.Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
def forward(self, input_0):
primals_1 = self.weight
primals_2 = self.quantized_weight
primals_4 = self.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
snudatalab/SensiMix
|
mix_Linear
| false
| 12,995
|
[
"Apache-2.0"
] | 0
|
e5d790f48a96806e9ae01449bb4a66e8f09c4d3a
|
https://github.com/snudatalab/SensiMix/tree/e5d790f48a96806e9ae01449bb4a66e8f09c4d3a
|
Actor
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 128)
self.l2 = nn.Linear(128, 128)
self.l3 = nn.Linear(128, action_dim)
self.max_action = max_action
def forward(self, state):
a = F.relu(self.l1(state))
a = F.relu(self.l2(a))
return self.max_action * torch.tanh(self.l3(a))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'state_dim': 4, 'action_dim': 4, 'max_action': 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 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):
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
@triton.jit
def triton_poi_fused_mul_tanh_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = libdevice.tanh(tmp0)
tmp2 = 4.0
tmp3 = tmp1 * tmp2
tl.store(out_ptr0 + x0, 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, (128, 4), (4, 1))
assert_size_stride(primals_2, (128,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (128, 128), (128, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (4, 128), (128, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 128), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
del buf0
buf7 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf1,
primals_2, buf7, 8192, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 128), (128, 1), 0),
reinterpret_tensor(primals_4, (128, 128), (1, 128), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
del buf2
buf6 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf3,
primals_5, buf6, 8192, XBLOCK=256, 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, 128),
(128, 1), 0), reinterpret_tensor(primals_6, (128, 4), (1, 128),
0), alpha=1, beta=1, out=buf4)
del primals_7
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_mul_tanh_1[grid(256)](buf4, buf5, 256, XBLOCK=256,
num_warps=4, num_stages=1)
return buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 128), (128, 1), 0
), reinterpret_tensor(buf3, (64, 128), (128, 1), 0
), buf4, primals_6, buf6, primals_4, buf7
class ActorNew(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(ActorNew, self).__init__()
self.l1 = nn.Linear(state_dim, 128)
self.l2 = nn.Linear(128, 128)
self.l3 = nn.Linear(128, action_dim)
self.max_action = max_action
def forward(self, input_0):
primals_1 = self.l1.weight
primals_2 = self.l1.bias
primals_4 = self.l2.weight
primals_5 = self.l2.bias
primals_6 = self.l3.weight
primals_7 = self.l3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
sridas123/TD3
|
Actor
| false
| 12,996
|
[
"MIT"
] | 0
|
2556c952ef7623c8201fdfdd9102e23d98101f5c
|
https://github.com/sridas123/TD3/tree/2556c952ef7623c8201fdfdd9102e23d98101f5c
|
BackwardsNet
|
import torch
from torch import nn
class BackwardsNet(nn.Module):
def __init__(self, h, ydim):
super().__init__()
self.loss = torch.nn.CrossEntropyLoss()
self.fc1 = torch.nn.Linear(2 * h, h)
self.fc2 = torch.nn.Linear(h, ydim)
def forward(self, phiPrev, phi, atn):
x = torch.cat((phiPrev, phi), 1)
x = torch.nn.functional.relu(self.fc1(x))
x = self.fc2(x)
loss = self.loss(x, atn)
return loss
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'h': 4, 'ydim': 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
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_1(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)
@triton.jit
def triton_poi_fused__log_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
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_per_fused__log_softmax_div_mul_neg_sum_3(in_out_ptr0, in_ptr0,
in_ptr1, 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)
r2 = rindex
r1 = rindex // 4
tmp0 = tl.load(in_ptr0 + r2, None)
tmp1 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr1 + r2, None)
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
tmp15 = tmp13 * tmp14
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.sum(tmp16, 1)[:, None]
tmp19 = -tmp18
tmp20 = 0.25
tmp21 = tmp19 * tmp20
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp21, None)
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, 4), (4, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
assert_size_stride(primals_4, (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))
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
triton_poi_fused_relu_1[grid(16)](buf2, primals_4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_6, buf2, reinterpret_tensor(primals_5,
(4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
del primals_6
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__log_softmax_2[grid(16)](buf3, buf4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((), (), torch.float32)
buf6 = buf5
del buf5
triton_per_fused__log_softmax_div_mul_neg_sum_3[grid(1)](buf6, buf4,
primals_7, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
del buf4
return buf6, primals_7, buf0, buf2, buf3, primals_5
class BackwardsNetNew(nn.Module):
def __init__(self, h, ydim):
super().__init__()
self.loss = torch.nn.CrossEntropyLoss()
self.fc1 = torch.nn.Linear(2 * h, h)
self.fc2 = torch.nn.Linear(h, ydim)
def forward(self, input_0, input_1, input_2):
primals_3 = self.fc1.weight
primals_4 = self.fc1.bias
primals_1 = self.fc2.weight
primals_6 = self.fc2.bias
primals_2 = input_0
primals_5 = input_1
primals_7 = input_2
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
smearle/neural-mmo
|
BackwardsNet
| false
| 12,997
|
[
"MIT"
] | 0
|
7f1e98857cb32bdb59a273eb71ec43bbd9793b34
|
https://github.com/smearle/neural-mmo/tree/7f1e98857cb32bdb59a273eb71ec43bbd9793b34
|
DQN
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class DQN(nn.Module):
"""A simple deep Q network implementation.
Computes Q values for each (action, object) tuple given an input state vector
"""
def __init__(self, state_dim, action_dim, object_dim, hidden_size=100):
super(DQN, self).__init__()
self.state_encoder = nn.Linear(state_dim, hidden_size)
self.state2action = nn.Linear(hidden_size, action_dim)
self.state2object = nn.Linear(hidden_size, object_dim)
def forward(self, x):
state = F.relu(self.state_encoder(x))
return self.state2action(state), self.state2object(state)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'state_dim': 4, 'action_dim': 4, 'object_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
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 = 6400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x0 = xindex % 100
x2 = xindex % 1600
x3 = xindex // 1600
tmp0 = tl.load(in_out_ptr0 + x4, 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 + x4, tmp4, xmask)
tl.store(out_ptr0 + (x2 + 1664 * x3), 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, (100, 4), (4, 1))
assert_size_stride(primals_2, (100,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 100), (100, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 100), (100, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 100), (100, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 100), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 100), (1600, 400, 100, 1), 0)
del buf0
buf4 = empty_strided_cuda((4, 4, 4, 100), (1664, 400, 100, 1),
torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(6400)](buf1,
primals_2, buf4, 6400, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 100),
(100, 1), 0), reinterpret_tensor(primals_4, (100, 4), (1, 100),
0), alpha=1, beta=1, out=buf2)
del primals_5
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 100),
(100, 1), 0), reinterpret_tensor(primals_6, (100, 4), (1, 100),
0), alpha=1, beta=1, out=buf3)
del primals_7
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 100), (100, 1), 0
), primals_6, primals_4, buf4
class DQNNew(nn.Module):
"""A simple deep Q network implementation.
Computes Q values for each (action, object) tuple given an input state vector
"""
def __init__(self, state_dim, action_dim, object_dim, hidden_size=100):
super(DQNNew, self).__init__()
self.state_encoder = nn.Linear(state_dim, hidden_size)
self.state2action = nn.Linear(hidden_size, action_dim)
self.state2object = nn.Linear(hidden_size, object_dim)
def forward(self, input_0):
primals_1 = self.state_encoder.weight
primals_2 = self.state_encoder.bias
primals_4 = self.state2action.weight
primals_5 = self.state2action.bias
primals_6 = self.state2object.weight
primals_7 = self.state2object.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0], output[1]
|
stepinski/machinelearning
|
DQN
| false
| 12,998
|
[
"MIT"
] | 0
|
1f84883a25616da4cd76bb4655267efd3421e561
|
https://github.com/stepinski/machinelearning/tree/1f84883a25616da4cd76bb4655267efd3421e561
|
SelfAttentionPooling
|
import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentionPooling, self).__init__()
self.W = nn.Linear(input_dim, 1)
self.softmax = nn.functional.softmax
def forward(self, batch_rep, att_mask):
"""
input:
batch_rep : size (N, T, H), N: batch size, T: sequence length, H: Hidden dimension
attention_weight:
att_w : size (N, T, 1)
return:
utter_rep: size (N, H)
"""
batch_rep.shape[1]
att_logits = self.W(batch_rep).squeeze(-1)
att_logits = att_mask + att_logits
att_w = self.softmax(att_logits, dim=-1).unsqueeze(-1)
utter_rep = torch.sum(batch_rep * att_w, dim=1)
return utter_rep
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_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__softmax_add_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
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), 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 * x2), 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 * x2), 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 = triton_helpers.maximum(tmp2, tmp5)
tmp9 = tmp7 + tmp8
tmp10 = triton_helpers.maximum(tmp6, tmp9)
tmp13 = tmp11 + tmp12
tmp14 = triton_helpers.maximum(tmp10, tmp13)
tmp15 = tmp2 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp5 - tmp14
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp20 = tmp9 - tmp14
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tmp13 - tmp14
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tl.store(out_ptr0 + x2, tmp14, xmask)
tl.store(out_ptr1 + x2, tmp25, xmask)
@triton.jit
def triton_poi_fused_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
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 % 64
x3 = xindex // 64
x5 = xindex // 4 % 16
x2 = xindex // 16 % 4
x7 = xindex
tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr3 + (x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr4 + (x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp10 = tl.load(in_ptr0 + (64 + x4), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr1 + (16 + x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp12 = tl.load(in_ptr2 + (16 + x5), xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr3 + (4 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp17 = tl.load(in_ptr4 + (4 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp21 = tl.load(in_ptr0 + (128 + x4), xmask, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr1 + (32 + x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp23 = tl.load(in_ptr2 + (32 + x5), xmask, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr3 + (8 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp28 = tl.load(in_ptr4 + (8 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp32 = tl.load(in_ptr0 + (192 + x4), xmask, eviction_policy='evict_last')
tmp33 = tl.load(in_ptr1 + (48 + x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp34 = tl.load(in_ptr2 + (48 + x5), xmask, eviction_policy='evict_last')
tmp36 = tl.load(in_ptr3 + (12 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp39 = tl.load(in_ptr4 + (12 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 - tmp4
tmp6 = tl_math.exp(tmp5)
tmp8 = tmp6 / tmp7
tmp9 = tmp0 * tmp8
tmp13 = tmp11 + tmp12
tmp15 = tmp13 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp18 = tmp16 / tmp17
tmp19 = tmp10 * tmp18
tmp20 = tmp9 + tmp19
tmp24 = tmp22 + tmp23
tmp26 = tmp24 - tmp25
tmp27 = tl_math.exp(tmp26)
tmp29 = tmp27 / tmp28
tmp30 = tmp21 * tmp29
tmp31 = tmp20 + tmp30
tmp35 = tmp33 + tmp34
tmp37 = tmp35 - tmp36
tmp38 = tl_math.exp(tmp37)
tmp40 = tmp38 / tmp39
tmp41 = tmp32 * tmp40
tmp42 = tmp31 + tmp41
tl.store(out_ptr0 + x7, tmp42, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1, 4), (4, 1))
assert_size_stride(primals_3, (1,), (1,))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64,
4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0
), alpha=1, beta=1, out=buf1)
del primals_2
del primals_3
buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_add_0[grid(64)](primals_4, buf1, buf2,
buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_mul_sum_1[grid(256)](primals_1, primals_4, buf1,
buf2, buf3, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf2
del buf3
return buf4, primals_1, primals_4, buf1
class SelfAttentionPoolingNew(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentionPoolingNew, self).__init__()
self.W = nn.Linear(input_dim, 1)
self.softmax = nn.functional.softmax
def forward(self, input_0, input_1):
primals_2 = self.W.weight
primals_3 = self.W.bias
primals_1 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
sumanthd17/s3prl
|
SelfAttentionPooling
| false
| 12,999
|
[
"MIT"
] | 0
|
bb74c705295d121c4308ceb6b6a2c8d1814d6f4c
|
https://github.com/sumanthd17/s3prl/tree/bb74c705295d121c4308ceb6b6a2c8d1814d6f4c
|
BertSelfAttention
|
from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transform(self, x, linear_layer):
"""
x : bs * seq_len * hidden_size(word_embedding)
fc : hidden_size * all_head_size
x <- bs * seq_len * all_head_size(hidden_size)
x <- bs * seq_len * (num_heads * head_size)
x <- bs * num_heads * seq_len * head_size
"""
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
""" key, query, value: [bs, num_heads, seq_len, head_size] """
score = query @ key.transpose(2, 3)
score = score / math.sqrt(self.attention_head_size)
""" score: [bs, num_heads, seq_len, seq_len] """
score = score + attention_mask
score = F.softmax(score, dim=3)
score = self.dropout(score)
score = score @ value
""" score: [bs, num_heads, seq_len, head_size] """
bs, num_attention_heads, seq_len, attention_head_size = score.shape
score = score.view(bs, seq_len, num_attention_heads *
attention_head_size)
return score
def forward(self, hidden_states, attention_mask):
"""
hidden_states: [bs, seq_len, hidden_state]
attention_mask: [bs, 1, 1, seq_len]
output: [bs, seq_len, hidden_state]
"""
key_layer = self.transform(hidden_states, self.key)
value_layer = self.transform(hidden_states, self.value)
query_layer = self.transform(hidden_states, self.query)
attn_value = self.attention(key_layer, query_layer, value_layer,
attention_mask)
return attn_value
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(num_attention_heads=4, hidden_size=
4, attention_probs_dropout_prob=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 math as tl_math
import math
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_0(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_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
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), 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 * x2), 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 * x2), 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 = triton_helpers.maximum(tmp2, tmp5)
tmp9 = tmp7 + tmp8
tmp10 = triton_helpers.maximum(tmp6, tmp9)
tmp13 = tmp11 + tmp12
tmp14 = triton_helpers.maximum(tmp10, tmp13)
tmp15 = tmp2 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp5 - tmp14
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp20 = tmp9 - tmp14
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tmp13 - tmp14
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = float('-inf')
tmp27 = tmp2 == tmp26
tmp28 = tmp27 == 0
tmp29 = tmp28.to(tl.int64)
tmp30 = tmp29 != 0
tmp31 = tmp5 == tmp26
tmp32 = tmp31 == 0
tmp33 = tmp32.to(tl.int64)
tmp34 = tmp33 != 0
tmp35 = tmp30 | tmp34
tmp36 = tmp9 == tmp26
tmp37 = tmp36 == 0
tmp38 = tmp37.to(tl.int64)
tmp39 = tmp38 != 0
tmp40 = tmp35 | tmp39
tmp41 = tmp13 == tmp26
tmp42 = tmp41 == 0
tmp43 = tmp42.to(tl.int64)
tmp44 = tmp43 != 0
tmp45 = tmp40 | tmp44
tl.store(out_ptr0 + x2, tmp14, xmask)
tl.store(out_ptr1 + x2, tmp25, xmask)
tl.store(out_ptr2 + x2, tmp45, xmask)
@triton.jit
def triton_poi_fused_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex // 4
x4 = xindex
x5 = xindex % 64
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_out_ptr0 + x4, xmask)
tmp3 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
tmp1 = tmp0 == 0
tmp4 = tmp2 + tmp3
tmp6 = tmp4 - tmp5
tmp7 = tl_math.exp(tmp6)
tmp9 = tmp7 / tmp8
tmp10 = 0.0
tmp11 = tl.where(tmp1, tmp10, tmp9)
tl.store(in_out_ptr0 + x4, tmp11, xmask)
@triton.jit
def triton_poi_fused_3(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
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), (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,))
assert_size_stride(primals_8, (4, 4, 4), (16, 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)
del primals_2
buf1 = 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=buf1)
del primals_4
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (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, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(16, 4)](buf2, primals_7, buf3, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_7
buf4 = reinterpret_tensor(buf2, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf2
triton_poi_fused_0[grid(16, 4)](buf0, primals_3, buf4, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_3
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
buf6 = reinterpret_tensor(buf0, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf0
buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.bool)
triton_poi_fused_1[grid(64)](buf5, primals_8, buf6, buf7, buf8, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused_2[grid(256)](buf9, buf8, primals_8, buf6, buf7,
256, XBLOCK=128, num_warps=4, num_stages=1)
del buf6
del buf8
del primals_8
buf10 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf7
triton_poi_fused_3[grid(16, 4)](buf1, primals_5, buf10, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_5
buf11 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
return reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
), buf9, reinterpret_tensor(buf10, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)
class BertSelfAttentionNew(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transform(self, x, linear_layer):
"""
x : bs * seq_len * hidden_size(word_embedding)
fc : hidden_size * all_head_size
x <- bs * seq_len * all_head_size(hidden_size)
x <- bs * seq_len * (num_heads * head_size)
x <- bs * num_heads * seq_len * head_size
"""
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
""" key, query, value: [bs, num_heads, seq_len, head_size] """
score = query @ key.transpose(2, 3)
score = score / math.sqrt(self.attention_head_size)
""" score: [bs, num_heads, seq_len, seq_len] """
score = score + attention_mask
score = F.softmax(score, dim=3)
score = self.dropout(score)
score = score @ value
""" score: [bs, num_heads, seq_len, head_size] """
bs, num_attention_heads, seq_len, attention_head_size = score.shape
score = score.view(bs, seq_len, num_attention_heads *
attention_head_size)
return score
def forward(self, input_0, input_1):
primals_2 = self.query.weight
primals_3 = self.query.bias
primals_4 = self.key.weight
primals_5 = self.key.bias
primals_6 = self.value.weight
primals_7 = self.value.bias
primals_1 = input_0
primals_8 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
sleepope/cs769-assignments
|
BertSelfAttention
| false
| 13,000
|
[
"MIT"
] | 0
|
36c7a75d39507b7fe7b2b1bf1de6b8033b110da5
|
https://github.com/sleepope/cs769-assignments/tree/36c7a75d39507b7fe7b2b1bf1de6b8033b110da5
|
Critic
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 128)
self.l2 = nn.Linear(128, 128)
self.l3 = nn.Linear(128, 1)
def forward(self, state, action):
sa = torch.cat([state, action], 1)
q1 = F.relu(self.l1(sa))
q1 = F.relu(self.l2(q1))
q1 = self.l3(q1)
return q1
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'state_dim': 4, 'action_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
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_1(in_out_ptr0, in_ptr0, 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 % 128
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, 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, (128, 8), (8, 1))
assert_size_stride(primals_4, (128,), (1,))
assert_size_stride(primals_5, (128, 128), (128, 1))
assert_size_stride(primals_6, (128,), (1,))
assert_size_stride(primals_7, (1, 128), (128, 1))
assert_size_stride(primals_8, (1,), (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, 128), (128, 1), torch.float32)
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 128), (1,
8), 0), out=buf1)
del primals_3
buf2 = buf1
del buf1
triton_poi_fused_relu_1[grid(512)](buf2, primals_4, 512, XBLOCK=256,
num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (128, 128), (
1, 128), 0), out=buf3)
buf4 = buf3
del buf3
triton_poi_fused_relu_1[grid(512)](buf4, primals_6, 512, XBLOCK=256,
num_warps=4, num_stages=1)
del primals_6
buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
(128, 1), (1, 128), 0), alpha=1, beta=1, out=buf6)
del primals_8
return buf6, buf0, buf2, buf4, primals_7, primals_5
class CriticNew(nn.Module):
def __init__(self, state_dim, action_dim):
super(CriticNew, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 128)
self.l2 = nn.Linear(128, 128)
self.l3 = nn.Linear(128, 1)
def forward(self, input_0, input_1):
primals_3 = self.l1.weight
primals_4 = self.l1.bias
primals_5 = self.l2.weight
primals_6 = self.l2.bias
primals_7 = self.l3.weight
primals_8 = self.l3.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
sridas123/TD3
|
Critic
| false
| 13,001
|
[
"MIT"
] | 0
|
2556c952ef7623c8201fdfdd9102e23d98101f5c
|
https://github.com/sridas123/TD3/tree/2556c952ef7623c8201fdfdd9102e23d98101f5c
|
BertLayer
|
from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transform(self, x, linear_layer):
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
norm = math.sqrt(key.shape[-1])
scores = torch.matmul(query, key.transpose(-1, -2))
bs = scores.shape[0]
s_len = scores.shape[-1]
scores = scores / norm
scores_n = scores + attention_mask
attention = nn.Softmax(dim=-1)(scores_n)
v_prime = torch.matmul(attention, value)
v_prime = v_prime.permute(0, 2, 1, 3)
v_prime = v_prime.contiguous().view(bs, s_len, self.all_head_size)
return v_prime
def forward(self, hidden_states, attention_mask):
"""
hidden_states: [bs, seq_len, hidden_state]
attention_mask: [bs, 1, 1, seq_len]
output: [bs, seq_len, hidden_state]
"""
key_layer = self.transform(hidden_states, self.key)
value_layer = self.transform(hidden_states, self.value)
query_layer = self.transform(hidden_states, self.query)
attn_value = self.attention(key_layer, query_layer, value_layer,
attention_mask)
return attn_value
class BertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.self_attention = BertSelfAttention(config)
self.attention_dense = nn.Linear(config.hidden_size, config.hidden_size
)
self.attention_layer_norm = nn.LayerNorm(config.hidden_size, eps=
config.layer_norm_eps)
self.attention_dropout = nn.Dropout(config.hidden_dropout_prob)
self.interm_dense = nn.Linear(config.hidden_size, config.
intermediate_size)
self.interm_af = F.gelu
self.out_dense = nn.Linear(config.intermediate_size, config.hidden_size
)
self.out_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_eps)
self.out_dropout = nn.Dropout(config.hidden_dropout_prob)
def add_norm(self, input, output, dense_layer, dropout, ln_layer):
"""
input: the input
output: the input that requires the sublayer to transform
dense_layer, dropput: the sublayer
ln_layer: layer norm that takes input+sublayer(output)
"""
out = dense_layer(output)
out = dropout(out)
out = out + input
out = ln_layer(out)
return out
def forward(self, hidden_states, attention_mask):
"""
hidden_states: either from the embedding layer (first bert layer) or from the previous bert layer
as shown in the left of Figure 1 of https://arxiv.org/pdf/1706.03762.pdf
each block consists of
1. a multi-head attention layer (BertSelfAttention)
2. a add-norm that takes the output of BertSelfAttention and the input of BertSelfAttention
3. a feed forward layer
4. a add-norm that takes the output of feed forward layer and the input of feed forward layer
"""
attn_value = self.self_attention(hidden_states, attention_mask)
out_attn = self.add_norm(hidden_states, attn_value, self.
attention_dense, self.attention_dropout, self.attention_layer_norm)
out = self.interm_dense(out_attn)
out = self.interm_af(out)
out = self.add_norm(out_attn, out, self.out_dense, self.out_dropout,
self.out_layer_norm)
return out
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(num_attention_heads=4, hidden_size=
4, attention_probs_dropout_prob=0.5, layer_norm_eps=1,
hidden_dropout_prob=0.5, intermediate_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 math
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_0(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_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
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), 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 * x2), 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 * x2), 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 = triton_helpers.maximum(tmp2, tmp5)
tmp9 = tmp7 + tmp8
tmp10 = triton_helpers.maximum(tmp6, tmp9)
tmp13 = tmp11 + tmp12
tmp14 = triton_helpers.maximum(tmp10, tmp13)
tmp15 = tmp2 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp5 - tmp14
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp20 = tmp9 - tmp14
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tmp13 - tmp14
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = float('-inf')
tmp27 = tmp2 == tmp26
tmp28 = tmp27 == 0
tmp29 = tmp28.to(tl.int64)
tmp30 = tmp29 != 0
tmp31 = tmp5 == tmp26
tmp32 = tmp31 == 0
tmp33 = tmp32.to(tl.int64)
tmp34 = tmp33 != 0
tmp35 = tmp30 | tmp34
tmp36 = tmp9 == tmp26
tmp37 = tmp36 == 0
tmp38 = tmp37.to(tl.int64)
tmp39 = tmp38 != 0
tmp40 = tmp35 | tmp39
tmp41 = tmp13 == tmp26
tmp42 = tmp41 == 0
tmp43 = tmp42.to(tl.int64)
tmp44 = tmp43 != 0
tmp45 = tmp40 | tmp44
tl.store(out_ptr0 + x2, tmp14, xmask)
tl.store(out_ptr1 + x2, tmp25, xmask)
tl.store(out_ptr2 + x2, tmp45, xmask)
@triton.jit
def triton_poi_fused_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex // 4
x4 = xindex
x5 = xindex % 64
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_out_ptr0 + x4, xmask)
tmp3 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
tmp1 = tmp0 == 0
tmp4 = tmp2 + tmp3
tmp6 = tmp4 - tmp5
tmp7 = tl_math.exp(tmp6)
tmp9 = tmp7 / tmp8
tmp10 = 0.0
tmp11 = tl.where(tmp1, tmp10, tmp9)
tl.store(in_out_ptr0 + x4, tmp11, xmask)
@triton.jit
def triton_poi_fused_3(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, 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 = 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_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 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + x2, tmp13, xmask)
@triton.jit
def triton_poi_fused_gelu_7(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 = 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)
@triton.jit
def triton_poi_fused_add_8(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
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)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_9(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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1.0
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_10(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
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
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, 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), (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,))
assert_size_stride(primals_8, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_9, (4, 4), (4, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (4,), (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, (4,), (1,))
assert_size_stride(primals_18, (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)
del primals_2
buf1 = 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=buf1)
del primals_4
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (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, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(16, 4)](buf2, primals_7, buf3, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_7
buf4 = reinterpret_tensor(buf2, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf2
triton_poi_fused_0[grid(16, 4)](buf0, primals_3, buf4, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_3
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
buf6 = reinterpret_tensor(buf0, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf0
buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.bool)
triton_poi_fused_1[grid(64)](buf5, primals_8, buf6, buf7, buf8, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused_2[grid(256)](buf9, buf8, primals_8, buf6, buf7,
256, XBLOCK=128, num_warps=4, num_stages=1)
del buf8
del primals_8
buf10 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf7
triton_poi_fused_3[grid(16, 4)](buf1, primals_5, buf10, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_5
buf11 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
buf12 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf6
triton_poi_fused_clone_4[grid(16, 4)](buf11, buf12, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf13 = reinterpret_tensor(buf11, (16, 4), (4, 1), 0)
del buf11
extern_kernels.addmm(primals_10, reinterpret_tensor(buf12, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf13)
del primals_10
buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf15 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_add_native_layer_norm_5[grid(16)](buf13, primals_1,
buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_6[grid(64)](buf13, primals_1,
buf14, buf15, primals_11, primals_12, buf16, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_12
buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_14, reinterpret_tensor(buf16, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_13, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf17)
del primals_14
buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_gelu_7[grid(64)](buf17, buf18, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf19 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf18, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf19)
buf20 = reinterpret_tensor(buf19, (4, 4, 4), (16, 4, 1), 0)
del buf19
triton_poi_fused_add_8[grid(64)](buf20, primals_16, buf16, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_16
buf21 = buf15
del buf15
buf22 = buf14
del buf14
triton_poi_fused_native_layer_norm_9[grid(16)](buf20, buf21, buf22,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf23 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_10[grid(64)](buf20, buf21, buf22,
primals_17, primals_18, buf23, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del buf21
del buf22
del primals_18
return buf23, primals_1, primals_11, primals_17, buf9, reinterpret_tensor(
buf10, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf3, (16, 1,
4), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0
), reinterpret_tensor(buf12, (16, 4), (4, 1), 0
), buf13, reinterpret_tensor(buf16, (16, 4), (4, 1), 0
), buf17, reinterpret_tensor(buf18, (16, 4), (4, 1), 0
), buf20, primals_15, primals_13, primals_9
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transform(self, x, linear_layer):
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
norm = math.sqrt(key.shape[-1])
scores = torch.matmul(query, key.transpose(-1, -2))
bs = scores.shape[0]
s_len = scores.shape[-1]
scores = scores / norm
scores_n = scores + attention_mask
attention = nn.Softmax(dim=-1)(scores_n)
v_prime = torch.matmul(attention, value)
v_prime = v_prime.permute(0, 2, 1, 3)
v_prime = v_prime.contiguous().view(bs, s_len, self.all_head_size)
return v_prime
def forward(self, hidden_states, attention_mask):
"""
hidden_states: [bs, seq_len, hidden_state]
attention_mask: [bs, 1, 1, seq_len]
output: [bs, seq_len, hidden_state]
"""
key_layer = self.transform(hidden_states, self.key)
value_layer = self.transform(hidden_states, self.value)
query_layer = self.transform(hidden_states, self.query)
attn_value = self.attention(key_layer, query_layer, value_layer,
attention_mask)
return attn_value
class BertLayerNew(nn.Module):
def __init__(self, config):
super().__init__()
self.self_attention = BertSelfAttention(config)
self.attention_dense = nn.Linear(config.hidden_size, config.hidden_size
)
self.attention_layer_norm = nn.LayerNorm(config.hidden_size, eps=
config.layer_norm_eps)
self.attention_dropout = nn.Dropout(config.hidden_dropout_prob)
self.interm_dense = nn.Linear(config.hidden_size, config.
intermediate_size)
self.interm_af = F.gelu
self.out_dense = nn.Linear(config.intermediate_size, config.hidden_size
)
self.out_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_eps)
self.out_dropout = nn.Dropout(config.hidden_dropout_prob)
def add_norm(self, input, output, dense_layer, dropout, ln_layer):
"""
input: the input
output: the input that requires the sublayer to transform
dense_layer, dropput: the sublayer
ln_layer: layer norm that takes input+sublayer(output)
"""
out = dense_layer(output)
out = dropout(out)
out = out + input
out = ln_layer(out)
return out
def forward(self, input_0, input_1):
primals_2 = self.self_attention.query.weight
primals_3 = self.self_attention.query.bias
primals_4 = self.self_attention.key.weight
primals_5 = self.self_attention.key.bias
primals_6 = self.self_attention.value.weight
primals_7 = self.self_attention.value.bias
primals_9 = self.attention_dense.weight
primals_10 = self.attention_dense.bias
primals_11 = self.attention_layer_norm.weight
primals_12 = self.attention_layer_norm.bias
primals_13 = self.interm_dense.weight
primals_14 = self.interm_dense.bias
primals_15 = self.out_dense.weight
primals_16 = self.out_dense.bias
primals_17 = self.out_layer_norm.weight
primals_18 = self.out_layer_norm.bias
primals_1 = input_0
primals_8 = 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]
|
shrishabh/cs769-assignments
|
BertLayer
| false
| 13,002
|
[
"MIT"
] | 0
|
babce1def0d65728bf1d4e4a725d8939f1d5f9a7
|
https://github.com/shrishabh/cs769-assignments/tree/babce1def0d65728bf1d4e4a725d8939f1d5f9a7
|
DecoderLayer
|
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model):
super(MultiHeadAttention, self).__init__()
assert d_model % heads == 0
self.d_k = d_model // heads
self.heads = heads
self.dropout = nn.Dropout(0.1)
self.query = nn.Linear(d_model, d_model)
self.key = nn.Linear(d_model, d_model)
self.value = nn.Linear(d_model, d_model)
self.concat = nn.Linear(d_model, d_model)
def forward(self, query, key, value, mask):
"""
query, key, value of shape: (batch_size, max_len, 512)
mask of shape: (batch_size, 1, 1, max_words)
"""
query = self.query(query)
key = self.key(key)
value = self.value(value)
query = query.view(query.shape[0], -1, self.heads, self.d_k).permute(
0, 2, 1, 3)
key = key.view(key.shape[0], -1, self.heads, self.d_k).permute(0, 2,
1, 3)
value = value.view(value.shape[0], -1, self.heads, self.d_k).permute(
0, 2, 1, 3)
scores = torch.matmul(query, key.permute(0, 1, 3, 2)) / math.sqrt(query
.size(-1))
scores = scores.masked_fill(mask == 0, -1000000000.0)
weights = F.softmax(scores, dim=-1)
weights = self.dropout(weights)
context = torch.matmul(weights, value)
context = context.permute(0, 2, 1, 3).contiguous().view(context.
shape[0], -1, self.heads * self.d_k)
interacted = self.concat(context)
return interacted
class FeedForward(nn.Module):
def __init__(self, d_model, middle_dim=2048):
super(FeedForward, self).__init__()
self.fc1 = nn.Linear(d_model, middle_dim)
self.fc2 = nn.Linear(middle_dim, d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, x):
out = F.relu(self.fc1(x))
out = self.fc2(self.dropout(out))
return out
class DecoderLayer(nn.Module):
def __init__(self, d_model, heads):
super(DecoderLayer, self).__init__()
self.layernorm = nn.LayerNorm(d_model)
self.self_multihead = MultiHeadAttention(heads, d_model)
self.src_multihead = MultiHeadAttention(heads, d_model)
self.feed_forward = FeedForward(d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, embeddings, encoded, src_mask, target_mask):
query = self.dropout(self.self_multihead(embeddings, embeddings,
embeddings, target_mask))
query = self.layernorm(query + embeddings)
interacted = self.dropout(self.src_multihead(query, encoded,
encoded, src_mask))
interacted = self.layernorm(interacted + query)
feed_forward_out = self.dropout(self.feed_forward(interacted))
decoded = self.layernorm(feed_forward_out + interacted)
return decoded
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4,
4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'heads': 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.utils.data
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_clone_0(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_eq_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 == tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_div_masked_fill_2(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
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp7 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp12 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp16 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp17 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = -1000000000.0
tmp5 = tl.where(tmp0, tmp4, tmp3)
tmp8 = tmp7 * tmp2
tmp9 = tl.where(tmp6, tmp4, tmp8)
tmp10 = triton_helpers.maximum(tmp5, tmp9)
tmp13 = tmp12 * tmp2
tmp14 = tl.where(tmp11, tmp4, tmp13)
tmp15 = triton_helpers.maximum(tmp10, tmp14)
tmp18 = tmp17 * tmp2
tmp19 = tl.where(tmp16, 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 + x2, tmp20, xmask)
tl.store(out_ptr1 + x2, tmp31, xmask)
@triton.jit
def triton_poi_fused__softmax_div_masked_fill_3(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 % 64
x4 = xindex
x5 = xindex // 4
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp1 = tl.load(in_out_ptr0 + x4, xmask)
tmp6 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = -1000000000.0
tmp5 = tl.where(tmp0, tmp4, tmp3)
tmp7 = tmp5 - tmp6
tmp8 = tl_math.exp(tmp7)
tmp10 = tmp8 / tmp9
tl.store(in_out_ptr0 + x4, tmp10, xmask)
@triton.jit
def triton_poi_fused_clone_4(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_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)
@triton.jit
def triton_poi_fused_add_7(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
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)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_8(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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_9(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
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_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)
x2 = xindex
x0 = xindex % 2048
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
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) = 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, 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), (16, 4, 1))
assert_size_stride(primals_9, (4, 4), (4, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (4,), (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, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_18, (4, 4), (4, 1))
assert_size_stride(primals_19, (4,), (1,))
assert_size_stride(primals_20, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_21, (4, 4), (4, 1))
assert_size_stride(primals_22, (4,), (1,))
assert_size_stride(primals_23, (2048, 4), (4, 1))
assert_size_stride(primals_24, (2048,), (1,))
assert_size_stride(primals_25, (4, 2048), (2048, 1))
assert_size_stride(primals_26, (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_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
del primals_4
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, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(16, 4)](buf0, primals_2, buf3, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_2
buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf0
triton_poi_fused_clone_0[grid(16, 4)](buf1, primals_5, buf4, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_eq_1[grid(64)](primals_8, buf6, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_8
buf7 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf1
buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused__softmax_div_masked_fill_2[grid(64)](buf6, buf5,
buf7, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused__softmax_div_masked_fill_3[grid(256)](buf9, buf6,
buf7, buf8, 256, XBLOCK=128, num_warps=4, num_stages=1)
buf10 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf8
triton_poi_fused_clone_0[grid(16, 4)](buf2, primals_7, buf10, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_7
buf11 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
del buf2
extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
buf12 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf7
triton_poi_fused_clone_4[grid(16, 4)](buf11, buf12, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf13 = reinterpret_tensor(buf11, (16, 4), (4, 1), 0)
del buf11
extern_kernels.addmm(primals_10, reinterpret_tensor(buf12, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf13)
del primals_10
buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf15 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_add_native_layer_norm_5[grid(16)](buf13, primals_3,
buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_6[grid(64)](buf13, primals_3,
buf14, buf15, primals_11, primals_12, buf16, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf16, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_13, (4, 4), (1, 4), 0), out=buf17)
buf18 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_17, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf18)
del primals_15
buf19 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_17, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_18, (4, 4), (1, 4), 0), out=buf19)
del primals_18
buf20 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_0[grid(16, 4)](buf17, primals_14, buf20, 16,
4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_14
buf21 = reinterpret_tensor(buf17, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf17
triton_poi_fused_clone_0[grid(16, 4)](buf18, primals_16, buf21, 16,
4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_16
buf22 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf20, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf21, (16, 1, 4), (4, 0, 1), 0), out=buf22)
buf23 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_eq_1[grid(64)](primals_20, buf23, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_20
buf24 = reinterpret_tensor(buf18, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf18
buf25 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused__softmax_div_masked_fill_2[grid(64)](buf23, buf22,
buf24, buf25, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf26 = reinterpret_tensor(buf22, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf22
triton_poi_fused__softmax_div_masked_fill_3[grid(256)](buf26, buf23,
buf24, buf25, 256, XBLOCK=128, num_warps=4, num_stages=1)
buf27 = reinterpret_tensor(buf25, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf25
triton_poi_fused_clone_0[grid(16, 4)](buf19, primals_19, buf27, 16,
4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_19
buf28 = reinterpret_tensor(buf19, (16, 4, 1), (4, 1, 1), 0)
del buf19
extern_kernels.bmm(reinterpret_tensor(buf26, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf27, (16, 4, 1), (4, 1, 0), 0), out=buf28)
buf29 = reinterpret_tensor(buf24, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf24
triton_poi_fused_clone_4[grid(16, 4)](buf28, buf29, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf30 = reinterpret_tensor(buf28, (16, 4), (4, 1), 0)
del buf28
extern_kernels.mm(reinterpret_tensor(buf29, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_21, (4, 4), (1, 4), 0), out=buf30)
buf31 = reinterpret_tensor(buf30, (4, 4, 4), (16, 4, 1), 0)
del buf30
triton_poi_fused_add_7[grid(64)](buf31, primals_22, buf16, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_22
buf32 = buf15
del buf15
buf33 = buf14
del buf14
triton_poi_fused_native_layer_norm_8[grid(16)](buf31, buf32, buf33,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf34 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_9[grid(64)](buf31, buf32, buf33,
primals_11, primals_12, buf34, 64, XBLOCK=64, num_warps=1,
num_stages=1)
buf35 = empty_strided_cuda((16, 2048), (2048, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf34, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_23, (4, 2048), (1, 4), 0), out=buf35)
buf36 = reinterpret_tensor(buf35, (4, 4, 2048), (8192, 2048, 1), 0)
del buf35
buf42 = empty_strided_cuda((4, 4, 2048), (8192, 2048, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_10[grid(32768)](buf36,
primals_24, buf42, 32768, XBLOCK=128, num_warps=4, num_stages=1)
del primals_24
buf37 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf36, (16, 2048), (2048, 1),
0), reinterpret_tensor(primals_25, (2048, 4), (1, 2048), 0),
out=buf37)
buf38 = reinterpret_tensor(buf37, (4, 4, 4), (16, 4, 1), 0)
del buf37
triton_poi_fused_add_7[grid(64)](buf38, primals_26, buf34, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_26
buf39 = buf33
del buf33
buf40 = buf32
del buf32
triton_poi_fused_native_layer_norm_8[grid(16)](buf38, buf39, buf40,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf41 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_9[grid(64)](buf38, buf39, buf40,
primals_11, primals_12, buf41, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del buf39
del buf40
del primals_12
return buf41, primals_3, primals_11, buf6, buf9, reinterpret_tensor(buf12,
(16, 4), (4, 1), 0), buf13, reinterpret_tensor(buf16, (16, 4), (4,
1), 0), reinterpret_tensor(primals_17, (16, 4), (4, 1), 0
), buf23, buf26, reinterpret_tensor(buf29, (16, 4), (4, 1), 0
), buf31, reinterpret_tensor(buf34, (16, 4), (4, 1), 0
), reinterpret_tensor(buf36, (16, 2048), (2048, 1), 0
), buf38, primals_25, buf42, primals_23, primals_21, reinterpret_tensor(
buf27, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf20, (16, 1,
4), (4, 1, 1), 0), reinterpret_tensor(buf21, (16, 4, 1), (4, 1, 4), 0
), primals_13, primals_9, reinterpret_tensor(buf10, (16, 1, 4), (4,
1, 1), 0), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model):
super(MultiHeadAttention, self).__init__()
assert d_model % heads == 0
self.d_k = d_model // heads
self.heads = heads
self.dropout = nn.Dropout(0.1)
self.query = nn.Linear(d_model, d_model)
self.key = nn.Linear(d_model, d_model)
self.value = nn.Linear(d_model, d_model)
self.concat = nn.Linear(d_model, d_model)
def forward(self, query, key, value, mask):
"""
query, key, value of shape: (batch_size, max_len, 512)
mask of shape: (batch_size, 1, 1, max_words)
"""
query = self.query(query)
key = self.key(key)
value = self.value(value)
query = query.view(query.shape[0], -1, self.heads, self.d_k).permute(
0, 2, 1, 3)
key = key.view(key.shape[0], -1, self.heads, self.d_k).permute(0, 2,
1, 3)
value = value.view(value.shape[0], -1, self.heads, self.d_k).permute(
0, 2, 1, 3)
scores = torch.matmul(query, key.permute(0, 1, 3, 2)) / math.sqrt(query
.size(-1))
scores = scores.masked_fill(mask == 0, -1000000000.0)
weights = F.softmax(scores, dim=-1)
weights = self.dropout(weights)
context = torch.matmul(weights, value)
context = context.permute(0, 2, 1, 3).contiguous().view(context.
shape[0], -1, self.heads * self.d_k)
interacted = self.concat(context)
return interacted
class FeedForward(nn.Module):
def __init__(self, d_model, middle_dim=2048):
super(FeedForward, self).__init__()
self.fc1 = nn.Linear(d_model, middle_dim)
self.fc2 = nn.Linear(middle_dim, d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, x):
out = F.relu(self.fc1(x))
out = self.fc2(self.dropout(out))
return out
class DecoderLayerNew(nn.Module):
def __init__(self, d_model, heads):
super(DecoderLayerNew, self).__init__()
self.layernorm = nn.LayerNorm(d_model)
self.self_multihead = MultiHeadAttention(heads, d_model)
self.src_multihead = MultiHeadAttention(heads, d_model)
self.feed_forward = FeedForward(d_model)
self.dropout = nn.Dropout(0.1)
def forward(self, input_0, input_1, input_2, input_3):
primals_2 = self.layernorm.weight
primals_5 = self.layernorm.bias
primals_1 = self.self_multihead.query.weight
primals_7 = self.self_multihead.query.bias
primals_4 = self.self_multihead.key.weight
primals_10 = self.self_multihead.key.bias
primals_6 = self.self_multihead.value.weight
primals_11 = self.self_multihead.value.bias
primals_9 = self.self_multihead.concat.weight
primals_12 = self.self_multihead.concat.bias
primals_13 = self.src_multihead.query.weight
primals_14 = self.src_multihead.query.bias
primals_15 = self.src_multihead.key.weight
primals_16 = self.src_multihead.key.bias
primals_18 = self.src_multihead.value.weight
primals_19 = self.src_multihead.value.bias
primals_21 = self.src_multihead.concat.weight
primals_22 = self.src_multihead.concat.bias
primals_23 = self.feed_forward.fc1.weight
primals_24 = self.feed_forward.fc1.bias
primals_25 = self.feed_forward.fc2.weight
primals_26 = self.feed_forward.fc2.bias
primals_3 = input_0
primals_8 = input_1
primals_17 = input_2
primals_20 = input_3
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])
return output[0]
|
sd2001/seqModeling
|
DecoderLayer
| false
| 13,003
|
[
"MIT"
] | 0
|
393f680de711ea8477e5450633b492298d253368
|
https://github.com/sd2001/seqModeling/tree/393f680de711ea8477e5450633b492298d253368
|
BertLayer
|
from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transform(self, x, linear_layer):
"""
x : bs * seq_len * hidden_size(word_embedding)
fc : hidden_size * all_head_size
x <- bs * seq_len * all_head_size(hidden_size)
x <- bs * seq_len * (num_heads * head_size)
x <- bs * num_heads * seq_len * head_size
"""
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
""" key, query, value: [bs, num_heads, seq_len, head_size] """
score = query @ key.transpose(2, 3)
score = score / math.sqrt(self.attention_head_size)
""" score: [bs, num_heads, seq_len, seq_len] """
score = score + attention_mask
score = F.softmax(score, dim=3)
score = self.dropout(score)
score = score @ value
""" score: [bs, num_heads, seq_len, head_size] """
bs, num_attention_heads, seq_len, attention_head_size = score.shape
score = score.view(bs, seq_len, num_attention_heads *
attention_head_size)
return score
def forward(self, hidden_states, attention_mask):
"""
hidden_states: [bs, seq_len, hidden_state]
attention_mask: [bs, 1, 1, seq_len]
output: [bs, seq_len, hidden_state]
"""
key_layer = self.transform(hidden_states, self.key)
value_layer = self.transform(hidden_states, self.value)
query_layer = self.transform(hidden_states, self.query)
attn_value = self.attention(key_layer, query_layer, value_layer,
attention_mask)
return attn_value
class BertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.self_attention = BertSelfAttention(config)
self.attention_dense = nn.Linear(config.hidden_size, config.hidden_size
)
self.attention_layer_norm = nn.LayerNorm(config.hidden_size, eps=
config.layer_norm_eps)
self.attention_dropout = nn.Dropout(config.hidden_dropout_prob)
self.interm_dense = nn.Linear(config.hidden_size, config.
intermediate_size)
self.interm_af = F.gelu
self.out_dense = nn.Linear(config.intermediate_size, config.hidden_size
)
self.out_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_eps)
self.out_dropout = nn.Dropout(config.hidden_dropout_prob)
def add_norm(self, input, output, dense_layer, dropout, ln_layer):
"""
input: the input
output: the input that requires the sublayer to transform
dense_layer, dropput: the sublayer
ln_layer: layer norm that takes input+sublayer(output)
"""
output = dense_layer(output)
output = dropout(output)
add_norm_output = ln_layer(input + output)
return add_norm_output
def forward(self, hidden_states, attention_mask):
"""
hidden_states: either from the embedding layer (first bert layer) or from the previous bert layer
as shown in the left of Figure 1 of https://arxiv.org/pdf/1706.03762.pdf
each block consists of
1. a multi-head attention layer (BertSelfAttention)
2. a add-norm that takes the output of BertSelfAttention and the input of BertSelfAttention
3. a feed forward layer
4. a add-norm that takes the output of feed forward layer and the input of feed forward layer
"""
attention_output = self.self_attention(hidden_states, attention_mask)
add_norm_output = self.add_norm(hidden_states, attention_output,
self.attention_dense, self.attention_dropout, self.
attention_layer_norm)
interm_output = self.interm_dense(add_norm_output)
interm_output = self.interm_af(interm_output)
output = self.add_norm(add_norm_output, interm_output, self.
out_dense, self.out_dropout, self.out_layer_norm)
return output
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(num_attention_heads=4, hidden_size=
4, attention_probs_dropout_prob=0.5, layer_norm_eps=1,
hidden_dropout_prob=0.5, intermediate_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 math
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_0(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_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
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), 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 * x2), 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 * x2), 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 = triton_helpers.maximum(tmp2, tmp5)
tmp9 = tmp7 + tmp8
tmp10 = triton_helpers.maximum(tmp6, tmp9)
tmp13 = tmp11 + tmp12
tmp14 = triton_helpers.maximum(tmp10, tmp13)
tmp15 = tmp2 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp5 - tmp14
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp20 = tmp9 - tmp14
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tmp13 - tmp14
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = float('-inf')
tmp27 = tmp2 == tmp26
tmp28 = tmp27 == 0
tmp29 = tmp28.to(tl.int64)
tmp30 = tmp29 != 0
tmp31 = tmp5 == tmp26
tmp32 = tmp31 == 0
tmp33 = tmp32.to(tl.int64)
tmp34 = tmp33 != 0
tmp35 = tmp30 | tmp34
tmp36 = tmp9 == tmp26
tmp37 = tmp36 == 0
tmp38 = tmp37.to(tl.int64)
tmp39 = tmp38 != 0
tmp40 = tmp35 | tmp39
tmp41 = tmp13 == tmp26
tmp42 = tmp41 == 0
tmp43 = tmp42.to(tl.int64)
tmp44 = tmp43 != 0
tmp45 = tmp40 | tmp44
tl.store(out_ptr0 + x2, tmp14, xmask)
tl.store(out_ptr1 + x2, tmp25, xmask)
tl.store(out_ptr2 + x2, tmp45, xmask)
@triton.jit
def triton_poi_fused_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex // 4
x4 = xindex
x5 = xindex % 64
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_out_ptr0 + x4, xmask)
tmp3 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
tmp1 = tmp0 == 0
tmp4 = tmp2 + tmp3
tmp6 = tmp4 - tmp5
tmp7 = tl_math.exp(tmp6)
tmp9 = tmp7 / tmp8
tmp10 = 0.0
tmp11 = tl.where(tmp1, tmp10, tmp9)
tl.store(in_out_ptr0 + x4, tmp11, xmask)
@triton.jit
def triton_poi_fused_3(in_ptr0, in_ptr1, 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_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_4(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_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
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 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + x2, tmp13, xmask)
@triton.jit
def triton_poi_fused_gelu_6(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 = 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)
@triton.jit
def triton_poi_fused_add_7(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
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
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_8(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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1.0
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_9(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
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
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, 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), (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,))
assert_size_stride(primals_8, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_9, (4, 4), (4, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (4,), (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, (4,), (1,))
assert_size_stride(primals_18, (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)
del primals_2
buf1 = 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=buf1)
del primals_4
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (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, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(16, 4)](buf2, primals_7, buf3, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_7
buf4 = reinterpret_tensor(buf2, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf2
triton_poi_fused_0[grid(16, 4)](buf0, primals_3, buf4, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_3
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
buf6 = reinterpret_tensor(buf0, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf0
buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.bool)
triton_poi_fused_1[grid(64)](buf5, primals_8, buf6, buf7, buf8, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused_2[grid(256)](buf9, buf8, primals_8, buf6, buf7,
256, XBLOCK=128, num_warps=4, num_stages=1)
del buf8
del primals_8
buf10 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf7
triton_poi_fused_3[grid(16, 4)](buf1, primals_5, buf10, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_5
buf11 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
buf12 = reinterpret_tensor(buf6, (16, 4), (4, 1), 0)
del buf6
extern_kernels.addmm(primals_10, reinterpret_tensor(buf11, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf12)
del primals_10
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_4[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_5[grid(64)](primals_1, buf12,
buf13, buf14, primals_11, primals_12, buf15, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_12
buf16 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_14, reinterpret_tensor(buf15, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_13, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf16)
del primals_14
buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_gelu_6[grid(64)](buf16, buf17, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf18 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf17, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf18)
buf19 = reinterpret_tensor(buf18, (4, 4, 4), (16, 4, 1), 0)
del buf18
triton_poi_fused_add_7[grid(64)](buf19, buf15, primals_16, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_16
buf20 = buf14
del buf14
buf21 = buf13
del buf13
triton_poi_fused_native_layer_norm_8[grid(16)](buf19, buf20, buf21,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf22 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_9[grid(64)](buf19, buf20, buf21,
primals_17, primals_18, buf22, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del buf20
del buf21
del primals_18
return buf22, primals_1, primals_11, primals_17, buf9, reinterpret_tensor(
buf10, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf3, (16, 1,
4), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0
), reinterpret_tensor(buf11, (16, 4), (4, 1), 0
), buf12, reinterpret_tensor(buf15, (16, 4), (4, 1), 0
), buf16, reinterpret_tensor(buf17, (16, 4), (4, 1), 0
), buf19, primals_15, primals_13, primals_9
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transform(self, x, linear_layer):
"""
x : bs * seq_len * hidden_size(word_embedding)
fc : hidden_size * all_head_size
x <- bs * seq_len * all_head_size(hidden_size)
x <- bs * seq_len * (num_heads * head_size)
x <- bs * num_heads * seq_len * head_size
"""
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
""" key, query, value: [bs, num_heads, seq_len, head_size] """
score = query @ key.transpose(2, 3)
score = score / math.sqrt(self.attention_head_size)
""" score: [bs, num_heads, seq_len, seq_len] """
score = score + attention_mask
score = F.softmax(score, dim=3)
score = self.dropout(score)
score = score @ value
""" score: [bs, num_heads, seq_len, head_size] """
bs, num_attention_heads, seq_len, attention_head_size = score.shape
score = score.view(bs, seq_len, num_attention_heads *
attention_head_size)
return score
def forward(self, hidden_states, attention_mask):
"""
hidden_states: [bs, seq_len, hidden_state]
attention_mask: [bs, 1, 1, seq_len]
output: [bs, seq_len, hidden_state]
"""
key_layer = self.transform(hidden_states, self.key)
value_layer = self.transform(hidden_states, self.value)
query_layer = self.transform(hidden_states, self.query)
attn_value = self.attention(key_layer, query_layer, value_layer,
attention_mask)
return attn_value
class BertLayerNew(nn.Module):
def __init__(self, config):
super().__init__()
self.self_attention = BertSelfAttention(config)
self.attention_dense = nn.Linear(config.hidden_size, config.hidden_size
)
self.attention_layer_norm = nn.LayerNorm(config.hidden_size, eps=
config.layer_norm_eps)
self.attention_dropout = nn.Dropout(config.hidden_dropout_prob)
self.interm_dense = nn.Linear(config.hidden_size, config.
intermediate_size)
self.interm_af = F.gelu
self.out_dense = nn.Linear(config.intermediate_size, config.hidden_size
)
self.out_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_eps)
self.out_dropout = nn.Dropout(config.hidden_dropout_prob)
def add_norm(self, input, output, dense_layer, dropout, ln_layer):
"""
input: the input
output: the input that requires the sublayer to transform
dense_layer, dropput: the sublayer
ln_layer: layer norm that takes input+sublayer(output)
"""
output = dense_layer(output)
output = dropout(output)
add_norm_output = ln_layer(input + output)
return add_norm_output
def forward(self, input_0, input_1):
primals_2 = self.self_attention.query.weight
primals_3 = self.self_attention.query.bias
primals_4 = self.self_attention.key.weight
primals_5 = self.self_attention.key.bias
primals_6 = self.self_attention.value.weight
primals_7 = self.self_attention.value.bias
primals_9 = self.attention_dense.weight
primals_10 = self.attention_dense.bias
primals_11 = self.attention_layer_norm.weight
primals_12 = self.attention_layer_norm.bias
primals_13 = self.interm_dense.weight
primals_14 = self.interm_dense.bias
primals_15 = self.out_dense.weight
primals_16 = self.out_dense.bias
primals_17 = self.out_layer_norm.weight
primals_18 = self.out_layer_norm.bias
primals_1 = input_0
primals_8 = 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]
|
sleepope/cs769-assignments
|
BertLayer
| false
| 13,004
|
[
"MIT"
] | 0
|
36c7a75d39507b7fe7b2b1bf1de6b8033b110da5
|
https://github.com/sleepope/cs769-assignments/tree/36c7a75d39507b7fe7b2b1bf1de6b8033b110da5
|
FuseUnit
|
import torch
import torch.nn as nn
class FuseUnit(nn.Module):
def __init__(self, channels):
super(FuseUnit, self).__init__()
self.proj1 = nn.Conv2d(2 * channels, channels, (1, 1))
self.proj2 = nn.Conv2d(channels, channels, (1, 1))
self.proj3 = nn.Conv2d(channels, channels, (1, 1))
self.fuse1x = nn.Conv2d(channels, 1, (1, 1), stride=1)
self.fuse3x = nn.Conv2d(channels, 1, (3, 3), stride=1)
self.fuse5x = nn.Conv2d(channels, 1, (5, 5), stride=1)
self.pad3x = nn.ReflectionPad2d((1, 1, 1, 1))
self.pad5x = nn.ReflectionPad2d((2, 2, 2, 2))
self.sigmoid = nn.Sigmoid()
def forward(self, F1, F2):
Fcat = self.proj1(torch.cat((F1, F2), dim=1))
F1 = self.proj2(F1)
F2 = self.proj3(F2)
fusion1 = self.sigmoid(self.fuse1x(Fcat))
fusion3 = self.sigmoid(self.fuse3x(self.pad3x(Fcat)))
fusion5 = self.sigmoid(self.fuse5x(self.pad5x(Fcat)))
fusion = (fusion1 + fusion3 + fusion5) / 3
return torch.clamp(fusion, min=0, max=1.0) * F1 + torch.clamp(1 -
fusion, min=0, max=1.0) * F2
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
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_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_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_poi_fused_reflection_pad2d_2(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
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_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
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(in_out_ptr0 + x0, tmp3, xmask)
@triton.jit
def triton_poi_fused_reflection_pad2d_4(in_ptr0, 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
x2 = xindex // 64
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-2 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-2 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_clamp_convolution_div_mul_rsub_sigmoid_5(in_out_ptr0,
in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_out_ptr1 + x3, xmask)
tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr2 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr3 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr4 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp7 = tl.sigmoid(tmp6)
tmp9 = tl.sigmoid(tmp8)
tmp10 = tmp7 + tmp9
tmp12 = tl.sigmoid(tmp11)
tmp13 = tmp10 + tmp12
tmp14 = 0.3333333333333333
tmp15 = tmp13 * tmp14
tmp16 = 0.0
tmp17 = triton_helpers.maximum(tmp15, tmp16)
tmp18 = 1.0
tmp19 = triton_helpers.minimum(tmp17, tmp18)
tmp20 = tmp19 * tmp2
tmp21 = tmp18 - tmp15
tmp22 = triton_helpers.maximum(tmp21, tmp16)
tmp23 = triton_helpers.minimum(tmp22, tmp18)
tmp24 = tmp23 * tmp5
tmp25 = tmp20 + tmp24
tl.store(in_out_ptr0 + x3, tmp2, xmask)
tl.store(in_out_ptr1 + x3, tmp5, xmask)
tl.store(out_ptr0 + x3, tmp25, 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) = 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))
assert_size_stride(primals_4, (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, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_10, (1,), (1,))
assert_size_stride(primals_11, (1, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_12, (1,), (1,))
assert_size_stride(primals_13, (1, 4, 5, 5), (100, 25, 5, 1))
assert_size_stride(primals_14, (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)
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
triton_poi_fused_convolution_1[grid(256)](buf2, primals_4, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_4
buf3 = extern_kernels.convolution(primals_1, primals_5, 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))
buf9 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
triton_poi_fused_reflection_pad2d_2[grid(576)](buf2, buf9, 576,
XBLOCK=128, num_warps=4, num_stages=1)
buf10 = extern_kernels.convolution(buf9, primals_11, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 1, 4, 4), (16, 16, 4, 1))
buf11 = buf10
del buf10
triton_poi_fused_convolution_3[grid(64)](buf11, primals_12, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_12
buf12 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32
)
triton_poi_fused_reflection_pad2d_4[grid(1024)](buf2, buf12, 1024,
XBLOCK=256, num_warps=4, num_stages=1)
buf13 = extern_kernels.convolution(buf12, primals_13, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf13, (4, 1, 4, 4), (16, 16, 4, 1))
buf14 = buf13
del buf13
triton_poi_fused_convolution_3[grid(64)](buf14, primals_14, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_14
buf5 = extern_kernels.convolution(primals_2, primals_7, 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))
buf7 = extern_kernels.convolution(buf2, primals_9, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 1, 4, 4), (16, 16, 4, 1))
buf8 = buf7
del buf7
triton_poi_fused_convolution_3[grid(64)](buf8, primals_10, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_10
buf4 = buf3
del buf3
buf6 = buf5
del buf5
buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_clamp_convolution_div_mul_rsub_sigmoid_5[grid(256)
](buf4, buf6, primals_6, primals_8, buf8, buf11, buf14, buf15,
256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_6
del primals_8
return (buf15, primals_1, primals_2, primals_3, primals_5, primals_7,
primals_9, primals_11, primals_13, buf0, buf2, buf4, buf6, buf8,
buf9, buf11, buf12, buf14)
class FuseUnitNew(nn.Module):
def __init__(self, channels):
super(FuseUnitNew, self).__init__()
self.proj1 = nn.Conv2d(2 * channels, channels, (1, 1))
self.proj2 = nn.Conv2d(channels, channels, (1, 1))
self.proj3 = nn.Conv2d(channels, channels, (1, 1))
self.fuse1x = nn.Conv2d(channels, 1, (1, 1), stride=1)
self.fuse3x = nn.Conv2d(channels, 1, (3, 3), stride=1)
self.fuse5x = nn.Conv2d(channels, 1, (5, 5), stride=1)
self.pad3x = nn.ReflectionPad2d((1, 1, 1, 1))
self.pad5x = nn.ReflectionPad2d((2, 2, 2, 2))
self.sigmoid = nn.Sigmoid()
def forward(self, input_0, input_1):
primals_3 = self.proj1.weight
primals_4 = self.proj1.bias
primals_5 = self.proj2.weight
primals_6 = self.proj2.bias
primals_7 = self.proj3.weight
primals_8 = self.proj3.bias
primals_9 = self.fuse1x.weight
primals_10 = self.fuse1x.bias
primals_11 = self.fuse3x.weight
primals_12 = self.fuse3x.bias
primals_13 = self.fuse5x.weight
primals_14 = self.fuse5x.bias
primals_1 = input_0
primals_2 = 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])
return output[0]
|
sugi-san/PAMA
|
FuseUnit
| false
| 13,005
|
[
"MIT"
] | 0
|
95141ebf0d3b61828a0e545f989f96b8ef569f34
|
https://github.com/sugi-san/PAMA/tree/95141ebf0d3b61828a0e545f989f96b8ef569f34
|
ConvNet
|
import torch
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=
5, padding=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size
=3, padding=1)
self.conv3 = nn.Conv2d(in_channels=32, out_channels=16, kernel_size
=3, padding=1)
self.relu = nn.ReLU()
self.pooling = nn.MaxPool2d(kernel_size=2)
self.linear = nn.Linear(in_features=1296, out_features=3)
def forward(self, x):
x = self.pooling(self.relu(self.conv1(x)))
x = self.pooling(self.relu(self.conv2(x)))
x = self.pooling(self.relu(self.conv2(x)))
x = self.pooling(self.relu(self.conv3(x)))
x = self.linear(x.view(-1, 1296))
return x
def get_inputs():
return [torch.rand([4, 3, 144, 144])]
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.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_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 // 20736 % 32
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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 72
x1 = xindex // 72
x4 = xindex
x3 = xindex // 5184
x5 = xindex % 5184
tmp0 = tl.load(in_ptr0 + (2 * x0 + 288 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 288 * x1), None, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (144 + 2 * x0 + 288 * x1), None,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (145 + 2 * x0 + 288 * x1), None,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x4, tmp6, None)
tl.store(out_ptr1 + (x5 + 5248 * x3), tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_2(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 // 5184 % 32
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_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 36
x3 = xindex // 36
x2 = xindex // 1296
x4 = xindex % 1296
tmp0 = tl.load(in_ptr0 + (2 * x0 + 144 * x3), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 144 * x3), None, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (72 + 2 * x0 + 144 * x3), None,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (73 + 2 * x0 + 144 * x3), None,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + (x4 + 1312 * x2), tmp6, None)
tl.store(out_ptr1 + (x4 + 1408 * x2), tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_4(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 // 1296 % 32
x0 = xindex % 1296
x4 = xindex // 1296
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)
tl.store(out_ptr0 + (x0 + 1312 * x4), tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1,
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
x2 = xindex // 324
x3 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 72 * x1 + 1312 * x2), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 72 * x1 + 1312 * x2), xmask,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (36 + 2 * x0 + 72 * x1 + 1312 * x2), xmask,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (37 + 2 * x0 + 72 * x1 + 1312 * x2), xmask,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 20736
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 324 % 16
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_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 5184
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 9
x3 = xindex // 9
x2 = xindex // 1296
x4 = xindex % 1296
tmp0 = tl.load(in_ptr0 + (2 * x0 + 36 * x3), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 36 * x3), xmask, eviction_policy
='evict_last')
tmp7 = tl.load(in_ptr0 + (18 + 2 * x0 + 36 * x3), xmask,
eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (19 + 2 * x0 + 36 * x3), xmask,
eviction_policy='evict_last')
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 + (x4 + 1408 * x2), tmp15, xmask)
tl.store(out_ptr1 + (x4 + 1312 * x2), tmp16, 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, (32, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_2, (32,), (1,))
assert_size_stride(primals_3, (4, 3, 144, 144), (62208, 20736, 144, 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, (3, 1296), (1296, 1))
assert_size_stride(primals_9, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 32, 144, 144), (663552, 20736, 144, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(2654208)](buf1, primals_2,
2654208, XBLOCK=512, num_warps=8, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 32, 72, 72), (165888, 5184, 72, 1),
torch.float32)
buf3 = empty_strided_cuda((4, 32, 72, 72), (167936, 5248, 72, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(663552)](buf1, buf2,
buf3, 663552, XBLOCK=512, num_warps=8, num_stages=1)
buf4 = 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(buf4, (4, 32, 72, 72), (165888, 5184, 72, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_relu_2[grid(663552)](buf5, primals_5,
663552, XBLOCK=1024, num_warps=4, num_stages=1)
buf6 = empty_strided_cuda((4, 32, 36, 36), (41984, 1312, 36, 1),
torch.float32)
buf7 = empty_strided_cuda((4, 32, 36, 36), (45056, 1408, 36, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_3[grid(165888)](buf5, buf6,
buf7, 165888, XBLOCK=512, num_warps=8, num_stages=1)
buf8 = extern_kernels.convolution(buf6, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 32, 36, 36), (41472, 1296, 36, 1))
buf9 = empty_strided_cuda((4, 32, 36, 36), (41984, 1312, 36, 1),
torch.float32)
triton_poi_fused_convolution_relu_4[grid(165888)](buf8, primals_5,
buf9, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
del buf8
del primals_5
buf10 = empty_strided_cuda((4, 32, 18, 18), (10368, 324, 18, 1),
torch.float32)
buf11 = empty_strided_cuda((4, 32, 18, 18), (10368, 324, 18, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_5[grid(41472)](buf9, buf10,
buf11, 41472, XBLOCK=512, num_warps=4, num_stages=1)
buf12 = extern_kernels.convolution(buf10, primals_6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 16, 18, 18), (5184, 324, 18, 1))
buf13 = buf12
del buf12
triton_poi_fused_convolution_relu_6[grid(20736)](buf13, primals_7,
20736, XBLOCK=256, num_warps=4, num_stages=1)
del primals_7
buf14 = empty_strided_cuda((4, 16, 9, 9), (1408, 81, 9, 1), torch.int8)
buf15 = empty_strided_cuda((4, 16, 9, 9), (1312, 81, 9, 1), torch.
float32)
triton_poi_fused_max_pool2d_with_indices_7[grid(5184)](buf13, buf14,
buf15, 5184, XBLOCK=256, num_warps=4, num_stages=1)
buf16 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
extern_kernels.addmm(primals_9, reinterpret_tensor(buf15, (4, 1296),
(1312, 1), 0), reinterpret_tensor(primals_8, (1296, 3), (1,
1296), 0), alpha=1, beta=1, out=buf16)
del primals_9
return (buf16, primals_1, primals_3, primals_4, primals_6, buf1, buf2,
buf3, buf5, buf6, buf7, buf9, buf10, buf11, buf13, buf14,
reinterpret_tensor(buf15, (4, 1296), (1312, 1), 0), primals_8)
class ConvNetNew(nn.Module):
def __init__(self):
super(ConvNetNew, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=
5, padding=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size
=3, padding=1)
self.conv3 = nn.Conv2d(in_channels=32, out_channels=16, kernel_size
=3, padding=1)
self.relu = nn.ReLU()
self.pooling = nn.MaxPool2d(kernel_size=2)
self.linear = nn.Linear(in_features=1296, out_features=3)
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.linear.weight
primals_9 = self.linear.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]
|
slowy07/dffml
|
ConvNet
| false
| 13,006
|
[
"MIT"
] | 0
|
bbf491064470f1170be75b6bec572b6e576940b9
|
https://github.com/slowy07/dffml/tree/bbf491064470f1170be75b6bec572b6e576940b9
|
SAP
|
import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentionPooling, self).__init__()
self.W = nn.Linear(input_dim, 1)
self.softmax = nn.functional.softmax
def forward(self, batch_rep, att_mask):
"""
input:
batch_rep : size (N, T, H), N: batch size, T: sequence length, H: Hidden dimension
attention_weight:
att_w : size (N, T, 1)
return:
utter_rep: size (N, H)
"""
batch_rep.shape[1]
att_logits = self.W(batch_rep).squeeze(-1)
att_logits = att_mask + att_logits
att_w = self.softmax(att_logits, dim=-1).unsqueeze(-1)
utter_rep = torch.sum(batch_rep * att_w, dim=1)
return utter_rep
class SAP(nn.Module):
""" Self Attention Pooling module incoporate attention mask"""
def __init__(self, out_dim):
super(SAP, self).__init__()
self.act_fn = nn.Tanh()
self.sap_layer = SelfAttentionPooling(out_dim)
def forward(self, feature, att_mask):
"""
Arguments
feature - [BxTxD] Acoustic feature with shape
att_mask - [BxTx1] Attention Mask logits
"""
feature = self.act_fn(feature)
sap_vec = self.sap_layer(feature, att_mask)
return sap_vec
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'out_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 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_tanh_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 = libdevice.tanh(tmp0)
tl.store(out_ptr0 + x0, tmp1, xmask)
@triton.jit
def triton_poi_fused__softmax_add_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
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), 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 * x2), 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 * x2), 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 = triton_helpers.maximum(tmp2, tmp5)
tmp9 = tmp7 + tmp8
tmp10 = triton_helpers.maximum(tmp6, tmp9)
tmp13 = tmp11 + tmp12
tmp14 = triton_helpers.maximum(tmp10, tmp13)
tmp15 = tmp2 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp5 - tmp14
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp20 = tmp9 - tmp14
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tmp13 - tmp14
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tl.store(out_ptr0 + x2, tmp14, xmask)
tl.store(out_ptr1 + x2, tmp25, xmask)
@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
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 % 64
x3 = xindex // 64
x5 = xindex // 4 % 16
x2 = xindex // 16 % 4
x7 = xindex
tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr3 + (x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr4 + (x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp10 = tl.load(in_ptr0 + (64 + x4), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr1 + (16 + x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp12 = tl.load(in_ptr2 + (16 + x5), xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr3 + (4 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp17 = tl.load(in_ptr4 + (4 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp21 = tl.load(in_ptr0 + (128 + x4), xmask, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr1 + (32 + x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp23 = tl.load(in_ptr2 + (32 + x5), xmask, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr3 + (8 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp28 = tl.load(in_ptr4 + (8 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp32 = tl.load(in_ptr0 + (192 + x4), xmask, eviction_policy='evict_last')
tmp33 = tl.load(in_ptr1 + (48 + x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp34 = tl.load(in_ptr2 + (48 + x5), xmask, eviction_policy='evict_last')
tmp36 = tl.load(in_ptr3 + (12 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp39 = tl.load(in_ptr4 + (12 + x2 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 - tmp4
tmp6 = tl_math.exp(tmp5)
tmp8 = tmp6 / tmp7
tmp9 = tmp0 * tmp8
tmp13 = tmp11 + tmp12
tmp15 = tmp13 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp18 = tmp16 / tmp17
tmp19 = tmp10 * tmp18
tmp20 = tmp9 + tmp19
tmp24 = tmp22 + tmp23
tmp26 = tmp24 - tmp25
tmp27 = tl_math.exp(tmp26)
tmp29 = tmp27 / tmp28
tmp30 = tmp21 * tmp29
tmp31 = tmp20 + tmp30
tmp35 = tmp33 + tmp34
tmp37 = tmp35 - tmp36
tmp38 = tl_math.exp(tmp37)
tmp40 = tmp38 / tmp39
tmp41 = tmp32 * tmp40
tmp42 = tmp31 + tmp41
tl.store(out_ptr0 + x7, tmp42, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1, 4), (4, 1))
assert_size_stride(primals_3, (1,), (1,))
assert_size_stride(primals_4, (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_tanh_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_1
buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0),
alpha=1, beta=1, out=buf2)
del primals_2
del primals_3
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused__softmax_add_1[grid(64)](primals_4, buf2, buf3,
buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_mul_sum_2[grid(256)](buf0, primals_4, buf2, buf3,
buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf3
del buf4
return buf5, primals_4, buf0, buf2
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentionPooling, self).__init__()
self.W = nn.Linear(input_dim, 1)
self.softmax = nn.functional.softmax
def forward(self, batch_rep, att_mask):
"""
input:
batch_rep : size (N, T, H), N: batch size, T: sequence length, H: Hidden dimension
attention_weight:
att_w : size (N, T, 1)
return:
utter_rep: size (N, H)
"""
batch_rep.shape[1]
att_logits = self.W(batch_rep).squeeze(-1)
att_logits = att_mask + att_logits
att_w = self.softmax(att_logits, dim=-1).unsqueeze(-1)
utter_rep = torch.sum(batch_rep * att_w, dim=1)
return utter_rep
class SAPNew(nn.Module):
""" Self Attention Pooling module incoporate attention mask"""
def __init__(self, out_dim):
super(SAPNew, self).__init__()
self.act_fn = nn.Tanh()
self.sap_layer = SelfAttentionPooling(out_dim)
def forward(self, input_0, input_1):
primals_2 = self.sap_layer.W.weight
primals_3 = self.sap_layer.W.bias
primals_1 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
sumanthd17/s3prl
|
SAP
| false
| 13,007
|
[
"MIT"
] | 0
|
bb74c705295d121c4308ceb6b6a2c8d1814d6f4c
|
https://github.com/sumanthd17/s3prl/tree/bb74c705295d121c4308ceb6b6a2c8d1814d6f4c
|
MultiHeadAttention
|
import torch
import torch.nn as nn
from torch import matmul
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout, inplace=False)
self.softmax = nn.Softmax(dim=-1)
def forward(self, q, k, v, mask=None):
attn = matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 0, -1000000000.0)
attn = self.dropout(self.softmax(attn))
output = matmul(attn, v)
return output, attn
class MultiHeadAttention(nn.Module):
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.d_v = d_v
self.d_k = d_k
self.n_head = n_head
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
self.dropout = nn.Dropout(dropout, inplace=True)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
def forward(self, q, k, v, mask=None):
sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
residual = q
q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None:
mask = mask.unsqueeze(1)
q, attn = self.attention(q, k, v, mask)
q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
q = self.dropout(self.fc(q))
q += residual
q = self.layer_norm(q)
return q, attn
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4])
]
def get_init_inputs():
return [[], {'n_head': 4, 'd_model': 4, 'd_k': 4, 'd_v': 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 torch import matmul
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, 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)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x4, tmp2, 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)
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_3(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 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_clone_4(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_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_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-06
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, 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, (16, 4), (4, 1))
assert_size_stride(primals_5, (16, 4), (4, 1))
assert_size_stride(primals_6, (16, 4), (4, 1))
assert_size_stride(primals_7, (4, 16), (16, 1))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (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_4, (4, 16), (1, 4), 0), out=buf0)
del primals_4
buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_5, (4, 16), (1, 4), 0), out=buf1)
del primals_5
buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf2)
del primals_6
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_div_0[grid(256)](buf0, buf3, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf4 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 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, 16, 4, 1), 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(256)](buf6, buf7, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf8 = buf6
del buf6
triton_poi_fused_clone_4[grid(256)](buf2, buf8, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf9 = reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0)
del buf2
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), out=buf9)
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_4[grid(256)](buf9, buf10, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf9
buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf10, (16, 16), (16, 1), 0),
reinterpret_tensor(primals_7, (16, 4), (1, 16), 0), out=buf11)
buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf13 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_native_layer_norm_5[grid(16)](buf11, primals_1,
buf12, buf13, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_6[grid(64)](buf11, primals_1,
buf12, buf13, primals_8, primals_9, buf14, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf12
del buf13
del primals_9
return buf14, buf7, primals_1, primals_8, reinterpret_tensor(primals_2,
(16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
), buf7, reinterpret_tensor(buf10, (16, 16), (16, 1), 0
), buf11, primals_7, reinterpret_tensor(buf8, (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 ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout, inplace=False)
self.softmax = nn.Softmax(dim=-1)
def forward(self, q, k, v, mask=None):
attn = matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 0, -1000000000.0)
attn = self.dropout(self.softmax(attn))
output = matmul(attn, v)
return output, attn
class MultiHeadAttentionNew(nn.Module):
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.d_v = d_v
self.d_k = d_k
self.n_head = n_head
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
self.dropout = nn.Dropout(dropout, inplace=True)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
def forward(self, input_0, input_1, input_2):
primals_4 = self.w_qs.weight
primals_5 = self.w_ks.weight
primals_6 = self.w_vs.weight
primals_7 = self.fc.weight
primals_8 = self.layer_norm.weight
primals_9 = self.layer_norm.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], output[1]
|
superMC5657/BiLSTMTransformer
|
MultiHeadAttention
| false
| 13,008
|
[
"MIT"
] | 0
|
43aa7bb4d8831a898c79ea89fcb1d3f5e09d564a
|
https://github.com/superMC5657/BiLSTMTransformer/tree/43aa7bb4d8831a898c79ea89fcb1d3f5e09d564a
|
AttentionUnit
|
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 AttentionUnit(nn.Module):
def __init__(self, channels):
super(AttentionUnit, self).__init__()
self.relu6 = nn.ReLU6()
self.f = nn.Conv2d(channels, channels // 2, (1, 1))
self.g = nn.Conv2d(channels, channels // 2, (1, 1))
self.h = nn.Conv2d(channels, channels // 2, (1, 1))
self.out_conv = nn.Conv2d(channels // 2, channels, (1, 1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, Fc, Fs):
B, C, H, W = Fc.shape
f_Fc = self.relu6(self.f(mean_variance_norm(Fc)))
g_Fs = self.relu6(self.g(mean_variance_norm(Fs)))
h_Fs = self.relu6(self.h(Fs))
f_Fc = f_Fc.view(f_Fc.shape[0], f_Fc.shape[1], -1).permute(0, 2, 1)
g_Fs = g_Fs.view(g_Fs.shape[0], g_Fs.shape[1], -1)
Attention = self.softmax(torch.bmm(f_Fc, g_Fs))
h_Fs = h_Fs.view(h_Fs.shape[0], h_Fs.shape[1], -1)
Fcs = torch.bmm(h_Fs, Attention.permute(0, 2, 1))
Fcs = Fcs.view(B, C // 2, H, W)
Fcs = self.relu6(self.out_conv(Fcs))
return Fcs
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
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_hardtanh_hardtanh_backward_1(in_ptr0,
in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 2
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 6.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = tmp2 <= tmp3
tmp8 = tmp2 >= tmp5
tmp9 = tmp7 | tmp8
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp9, 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_convolution_hardtanh_hardtanh_backward_3(in_ptr0,
in_ptr1, 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
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 6.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = tmp2 <= tmp3
tmp8 = tmp2 >= tmp5
tmp9 = tmp7 | tmp8
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp9, 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, (2, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (2,), (1,))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (2, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_6, (2,), (1,))
assert_size_stride(primals_7, (2, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_8, (2,), (1,))
assert_size_stride(primals_9, (4, 2, 1, 1), (2, 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=1, num_warps=2, num_stages=1)
del primals_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, 2, 4, 4), (32, 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=1, 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, 2, 4, 4), (32, 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, 2, 4, 4), (32, 16, 4, 1))
buf13 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
buf26 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_hardtanh_hardtanh_backward_1[grid(128)](
buf5, primals_3, buf13, buf26, 128, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_3
buf14 = buf5
del buf5
buf25 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_hardtanh_hardtanh_backward_1[grid(128)](
buf11, primals_6, buf14, buf25, 128, 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, 2), (32, 1, 16
), 0), reinterpret_tensor(buf14, (4, 2, 16), (32, 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 = buf11
del buf11
buf24 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_hardtanh_hardtanh_backward_1[grid(128)](
buf12, primals_8, buf19, buf24, 128, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_8
buf20 = reinterpret_tensor(buf12, (4, 2, 16), (32, 16, 1), 0)
del buf12
extern_kernels.bmm(reinterpret_tensor(buf19, (4, 2, 16), (32, 16, 1
), 0), reinterpret_tensor(buf18, (4, 16, 16), (256, 1, 16), 0),
out=buf20)
buf21 = extern_kernels.convolution(reinterpret_tensor(buf20, (4, 2,
4, 4), (32, 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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf23 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_hardtanh_hardtanh_backward_3[grid(256)](
buf21, primals_10, buf22, buf23, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del buf21
del primals_10
return (buf22, primals_2, primals_4, primals_5, primals_7, primals_9,
buf4, buf10, buf18, reinterpret_tensor(buf20, (4, 2, 4, 4), (32, 16,
4, 1), 0), buf23, reinterpret_tensor(buf19, (4, 16, 2), (32, 1, 16),
0), reinterpret_tensor(buf13, (4, 2, 16), (32, 16, 1), 0),
reinterpret_tensor(buf14, (4, 16, 2), (32, 1, 16), 0), buf24, buf25,
buf26)
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 AttentionUnitNew(nn.Module):
def __init__(self, channels):
super(AttentionUnitNew, self).__init__()
self.relu6 = nn.ReLU6()
self.f = nn.Conv2d(channels, channels // 2, (1, 1))
self.g = nn.Conv2d(channels, channels // 2, (1, 1))
self.h = nn.Conv2d(channels, channels // 2, (1, 1))
self.out_conv = nn.Conv2d(channels // 2, channels, (1, 1))
self.softmax = nn.Softmax(dim=-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]
|
sugi-san/PAMA
|
AttentionUnit
| false
| 13,009
|
[
"MIT"
] | 0
|
95141ebf0d3b61828a0e545f989f96b8ef569f34
|
https://github.com/sugi-san/PAMA/tree/95141ebf0d3b61828a0e545f989f96b8ef569f34
|
Critic
|
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
in_size = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(in_size)
return -lim, lim
class Critic(nn.Module):
def __init__(self, state_size, action_size, seed=0, fc1_size=128,
fc2_size=128):
super(Critic, self).__init__()
self.seed = torch.manual_seed(seed)
self.state_size = state_size
self.action_size = action_size
self.fc1 = nn.Linear(state_size, fc1_size)
self.fc2 = nn.Linear(fc1_size + action_size, fc2_size)
self.fc3 = nn.Linear(fc2_size, 1)
self.bn1 = nn.BatchNorm1d(fc1_size)
self.bn2 = nn.BatchNorm1d(fc2_size)
self.reset_params()
def reset_params(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(-0.003, 0.003)
def forward(self, state, action):
x1 = F.relu(self.fc1(state))
x1 = self.bn1(x1)
x = torch.cat((x1, action), dim=1)
x = F.relu(self.fc2(x))
return self.fc3(x)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'state_size': 4, 'action_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
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, in_ptr2, in_ptr3, in_ptr4,
in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 528
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 132
x1 = xindex // 132
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 128, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (128 * x1 + x0), tmp4 & xmask, eviction_policy
='evict_last', other=0.0)
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
other=0.0)
tmp9 = tmp7 - tmp8
tmp10 = tl.load(in_ptr2 + x0, tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp11 = 1e-05
tmp12 = tmp10 + tmp11
tmp13 = libdevice.sqrt(tmp12)
tmp14 = tl.full([1], 1, tl.int32)
tmp15 = tmp14 / tmp13
tmp16 = 1.0
tmp17 = tmp15 * tmp16
tmp18 = tmp9 * tmp17
tmp19 = tl.load(in_ptr3 + x0, tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp20 = tmp18 * tmp19
tmp21 = tl.load(in_ptr4 + x0, tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp22 = tmp20 + tmp21
tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
tmp24 = tl.where(tmp4, tmp22, tmp23)
tmp25 = tmp0 >= tmp3
tl.full([1], 132, tl.int64)
tmp28 = tl.load(in_ptr5 + (4 * x1 + (-128 + x0)), tmp25 & xmask,
eviction_policy='evict_last', other=0.0)
tmp29 = tl.where(tmp4, tmp24, tmp28)
tl.store(out_ptr0 + x2, tmp29, xmask)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, 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 % 128
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, primals_8, primals_9, primals_10, primals_11, primals_12
) = args
args.clear()
assert_size_stride(primals_1, (128, 4), (4, 1))
assert_size_stride(primals_2, (128,), (1,))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (128,), (1,))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (128,), (1,))
assert_size_stride(primals_7, (128,), (1,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (128, 132), (132, 1))
assert_size_stride(primals_10, (128,), (1,))
assert_size_stride(primals_11, (1, 128), (128, 1))
assert_size_stride(primals_12, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
primals_1, (4, 128), (1, 4), 0), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 132), (132, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(528)](buf0, primals_4, primals_5,
primals_6, primals_7, primals_8, buf1, 528, XBLOCK=256,
num_warps=4, num_stages=1)
del primals_7
del primals_8
buf2 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
extern_kernels.mm(buf1, reinterpret_tensor(primals_9, (132, 128), (
1, 132), 0), out=buf2)
buf3 = buf2
del buf2
triton_poi_fused_relu_1[grid(512)](buf3, primals_10, 512, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_10
buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_12, buf3, reinterpret_tensor(
primals_11, (128, 1), (1, 128), 0), alpha=1, beta=1, out=buf5)
del primals_12
return (buf5, primals_3, primals_4, primals_5, primals_6, buf0, buf1,
buf3, primals_11, primals_9)
def hidden_init(layer):
in_size = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(in_size)
return -lim, lim
class CriticNew(nn.Module):
def __init__(self, state_size, action_size, seed=0, fc1_size=128,
fc2_size=128):
super(CriticNew, self).__init__()
self.seed = torch.manual_seed(seed)
self.state_size = state_size
self.action_size = action_size
self.fc1 = nn.Linear(state_size, fc1_size)
self.fc2 = nn.Linear(fc1_size + action_size, fc2_size)
self.fc3 = nn.Linear(fc2_size, 1)
self.bn1 = nn.BatchNorm1d(fc1_size)
self.bn2 = nn.BatchNorm1d(fc2_size)
self.reset_params()
def reset_params(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(-0.003, 0.003)
def forward(self, input_0, input_1):
primals_1 = self.fc1.weight
primals_2 = self.fc1.bias
primals_9 = self.fc2.weight
primals_4 = self.fc2.bias
primals_11 = self.fc3.weight
primals_12 = self.fc3.bias
primals_5 = self.bn1.weight
primals_6 = self.bn1.bias
primals_7 = self.bn2.weight
primals_10 = self.bn2.bias
primals_3 = input_0
primals_8 = 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])
return output[0]
|
swastiknath/rl_ud_2
|
Critic
| false
| 13,010
|
[
"MIT"
] | 0
|
666e538f967252fa609c6b31cb5d66f9415eae82
|
https://github.com/swastiknath/rl_ud_2/tree/666e538f967252fa609c6b31cb5d66f9415eae82
|
LinearEmbedding
|
import math
import torch
import torch.utils.data
import torch.nn as nn
class LinearEmbedding(nn.Module):
def __init__(self, inp_size, d_model):
super(LinearEmbedding, self).__init__()
self.lut = nn.Linear(inp_size, d_model)
self.d_model = d_model
def forward(self, x):
return self.lut(x) * math.sqrt(self.d_model)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'inp_size': 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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_mul_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
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 = 2.0
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + x2, tmp4, 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((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
get_raw_stream(0)
triton_poi_fused_mul_0[grid(256)](buf1, primals_2, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_2
return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)
class LinearEmbeddingNew(nn.Module):
def __init__(self, inp_size, d_model):
super(LinearEmbeddingNew, self).__init__()
self.lut = nn.Linear(inp_size, d_model)
self.d_model = d_model
def forward(self, input_0):
primals_1 = self.lut.weight
primals_2 = self.lut.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
swift88-clone/Trajectory-Transformer
|
LinearEmbedding
| false
| 13,011
|
[
"MIT"
] | 0
|
62983b645ec88d8972bc2c2af1b7b4a299d3feb0
|
https://github.com/swift88-clone/Trajectory-Transformer/tree/62983b645ec88d8972bc2c2af1b7b4a299d3feb0
|
FFN
|
import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of input
:param out_channels: dimension of output
:param kernel_size: size of kernel
:param stride: size of stride
:param padding: size of padding
:param dilation: dilation rate
:param bias: boolean. if True, bias is included.
:param w_init: str. weight inits with xavier initialization.
"""
super(Conv, self).__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=
kernel_size, stride=stride, padding=padding, dilation=dilation,
bias=bias)
nn.init.xavier_uniform_(self.conv.weight, gain=nn.init.
calculate_gain(w_init))
def forward(self, x):
x = self.conv(x)
return x
class FFN(nn.Module):
"""
Positionwise Feed-Forward Network
"""
def __init__(self, num_hidden):
"""
:param num_hidden: dimension of hidden
"""
super(FFN, self).__init__()
self.w_1 = Conv(num_hidden, num_hidden * 4, kernel_size=3, padding=
1, w_init='relu')
self.w_2 = Conv(num_hidden * 4, num_hidden, kernel_size=3, padding=1)
self.dropout = nn.Dropout(p=0.1)
self.layer_norm = nn.LayerNorm(num_hidden)
def forward(self, input_):
x = input_
x = self.w_2(torch.relu(self.w_1(x)))
x = x + input_
x = self.dropout(x)
x = self.layer_norm(x)
return x
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'num_hidden': 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 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_relu_threshold_backward_0(in_out_ptr0, 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_out_ptr0 + x2, 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 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_convolution_1(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
x1 = xindex // 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_2(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_native_layer_norm_3(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
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, 1))
assert_size_stride(primals_2, (16, 4, 3), (12, 3, 1))
assert_size_stride(primals_3, (16,), (1,))
assert_size_stride(primals_4, (4, 16, 3), (48, 3, 1))
assert_size_stride(primals_5, (4,), (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 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
4, 4), (16, 4, 1), 0), primals_2, stride=(1,), padding=(1,),
dilation=(1,), transposed=False, output_padding=(0,), groups=1,
bias=None)
assert_size_stride(buf0, (1, 16, 4), (64, 4, 1))
buf1 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0)
del buf0
buf7 = empty_strided_cuda((16, 4), (4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(64)](buf1,
primals_3, buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
del primals_3
buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 16,
4), (0, 4, 1), 0), primals_4, stride=(1,), padding=(1,),
dilation=(1,), transposed=False, output_padding=(0,), groups=1,
bias=None)
assert_size_stride(buf2, (1, 4, 4), (16, 4, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_1[grid(16)](buf3, primals_5, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_poi_fused_add_native_layer_norm_2[grid(4)](buf3, primals_1,
buf4, buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_3[grid(16)](buf3, primals_1,
buf4, buf5, primals_6, primals_7, buf6, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf4
del buf5
del primals_7
return (buf6, primals_1, primals_2, primals_4, primals_6,
reinterpret_tensor(buf1, (1, 16, 4), (64, 4, 1), 0), buf3, buf7)
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of input
:param out_channels: dimension of output
:param kernel_size: size of kernel
:param stride: size of stride
:param padding: size of padding
:param dilation: dilation rate
:param bias: boolean. if True, bias is included.
:param w_init: str. weight inits with xavier initialization.
"""
super(Conv, self).__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=
kernel_size, stride=stride, padding=padding, dilation=dilation,
bias=bias)
nn.init.xavier_uniform_(self.conv.weight, gain=nn.init.
calculate_gain(w_init))
def forward(self, x):
x = self.conv(x)
return x
class FFNNew(nn.Module):
"""
Positionwise Feed-Forward Network
"""
def __init__(self, num_hidden):
"""
:param num_hidden: dimension of hidden
"""
super(FFNNew, self).__init__()
self.w_1 = Conv(num_hidden, num_hidden * 4, kernel_size=3, padding=
1, w_init='relu')
self.w_2 = Conv(num_hidden * 4, num_hidden, kernel_size=3, padding=1)
self.dropout = nn.Dropout(p=0.1)
self.layer_norm = nn.LayerNorm(num_hidden)
def forward(self, input_0):
primals_2 = self.w_1.conv.weight
primals_3 = self.w_1.conv.bias
primals_4 = self.w_2.conv.weight
primals_5 = self.w_2.conv.bias
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]
|
supikiti/FastSpeech
|
FFN
| false
| 13,012
|
[
"MIT"
] | 0
|
775a9429c273450aefc2d346e5fc66c3f1e36832
|
https://github.com/supikiti/FastSpeech/tree/775a9429c273450aefc2d346e5fc66c3f1e36832
|
HubertFeatureProjection
|
from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class HubertFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.
layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_dropout)
def forward(self, hidden_states):
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.projection(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(conv_dim=[4, 4], layer_norm_eps=1,
hidden_size=4, feat_proj_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.triton_helpers import libdevice
from torch import nn
import torch.utils.checkpoint
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_native_layer_norm_0(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
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1.0
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, 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_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + 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,), (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((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(64)](primals_3, buf0,
buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(256)](primals_3, buf0,
buf1, primals_1, primals_2, buf2, 256, XBLOCK=256, num_warps=4,
num_stages=1)
del buf0
del buf1
del primals_1
del primals_2
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_5
return reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0
), primals_3, reinterpret_tensor(buf2, (64, 4), (4, 1), 0), primals_4
class HubertFeatureProjectionNew(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.
layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_dropout)
def forward(self, input_0):
primals_1 = self.layer_norm.weight
primals_2 = self.layer_norm.bias
primals_4 = self.projection.weight
primals_5 = self.projection.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Clemens123/transformers
|
HubertFeatureProjection
| false
| 13,013
|
[
"Apache-2.0"
] | 0
|
22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26
|
https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26
|
MaskNorm
|
import torch
from torch import nn
class MaskNorm(nn.Module):
def __init__(self, norm_nc):
super(MaskNorm, self).__init__()
self.norm_layer = nn.InstanceNorm2d(norm_nc, affine=False)
def normalize_region(self, region, mask):
_b, _c, h, w = region.size()
num_pixels = mask.sum((2, 3), keepdim=True)
num_pixels[num_pixels == 0] = 1
mu = region.sum((2, 3), keepdim=True) / num_pixels
normalized_region = self.norm_layer(region + (1 - mask) * mu)
return normalized_region * torch.sqrt(num_pixels / (h * w))
def forward(self, x, mask):
mask = mask.detach()
normalized_foreground = self.normalize_region(x * mask, mask)
normalized_background = self.normalize_region(x * (1 - mask), 1 - mask)
return normalized_foreground + normalized_background
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'norm_nc': 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
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__native_batch_norm_legit_add_div_index_put_lift_fresh_mul_rsub_sqrt_sum_0(
in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, out_ptr0, out_ptr3,
out_ptr6, 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)
tmp16 = tl.load(in_ptr1 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 1.0
tmp6 = tmp5 - tmp0
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.where(xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp11 = 0.0
tmp12 = tmp4 == tmp11
tmp13 = tl.where(tmp12, tmp5, tmp4)
tmp14 = tmp10 == tmp11
tmp15 = tl.where(tmp14, tmp5, tmp10)
tmp17 = tmp16 * tmp0
tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
tmp20 = tl.where(xmask, tmp18, 0)
tmp21 = tl.sum(tmp20, 1)[:, None]
tmp22 = tmp21 / tmp13
tmp23 = tmp6 * tmp22
tmp24 = tmp17 + tmp23
tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
tl.where(xmask, tmp25, 0)
tmp28 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
tmp30 = tl.where(xmask, tmp28, 0)
tmp31 = tl.sum(tmp30, 1)[:, None]
tmp32 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp33 = tmp32.to(tl.float32)
tmp34 = tmp31 / tmp33
tmp35 = tmp25 - tmp34
tmp36 = tmp35 * tmp35
tmp37 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
tmp39 = tl.where(xmask, tmp37, 0)
tmp40 = tl.sum(tmp39, 1)[:, None]
tmp41 = tmp16 * tmp6
tmp42 = tl.broadcast_to(tmp41, [XBLOCK, RBLOCK])
tmp44 = tl.where(xmask, tmp42, 0)
tmp45 = tl.sum(tmp44, 1)[:, None]
tmp46 = tmp5 - tmp6
tmp47 = tmp45 / tmp15
tmp48 = tmp46 * tmp47
tmp49 = tmp41 + tmp48
tmp50 = tl.broadcast_to(tmp49, [XBLOCK, RBLOCK])
tl.where(xmask, tmp50, 0)
tmp53 = tl.broadcast_to(tmp50, [XBLOCK, RBLOCK])
tmp55 = tl.where(xmask, tmp53, 0)
tmp56 = tl.sum(tmp55, 1)[:, None]
tmp57 = tmp56 / tmp33
tmp58 = tmp50 - tmp57
tmp59 = tmp58 * tmp58
tmp60 = tl.broadcast_to(tmp59, [XBLOCK, RBLOCK])
tmp62 = tl.where(xmask, tmp60, 0)
tmp63 = tl.sum(tmp62, 1)[:, None]
tmp64 = tmp24 - tmp34
tmp65 = 16.0
tmp66 = tmp40 / tmp65
tmp67 = 1e-05
tmp68 = tmp66 + tmp67
tmp69 = libdevice.rsqrt(tmp68)
tmp70 = tmp64 * tmp69
tmp71 = 0.0625
tmp72 = tmp13 * tmp71
tmp73 = libdevice.sqrt(tmp72)
tmp74 = tmp70 * tmp73
tmp75 = tmp49 - tmp57
tmp76 = tmp63 / tmp65
tmp77 = tmp76 + tmp67
tmp78 = libdevice.rsqrt(tmp77)
tmp79 = tmp75 * tmp78
tmp80 = tmp15 * tmp71
tmp81 = libdevice.sqrt(tmp80)
tmp82 = tmp79 * tmp81
tmp83 = tmp74 + tmp82
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp13, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x0, tmp15, xmask)
tl.store(out_ptr6 + (r1 + 16 * x0), tmp83, xmask)
tl.store(out_ptr0 + x0, tmp21, xmask)
tl.store(out_ptr3 + x0, tmp45, 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)
buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf2 = buf1
del buf1
buf8 = buf7
del buf7
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused__native_batch_norm_legit_add_div_index_put_lift_fresh_mul_rsub_sqrt_sum_0[
grid(16)](buf2, buf8, arg0_1, arg1_1, buf0, buf6, buf12, 16, 16,
XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
del buf0
del buf2
del buf6
del buf8
return buf12,
class MaskNormNew(nn.Module):
def __init__(self, norm_nc):
super(MaskNormNew, self).__init__()
self.norm_layer = nn.InstanceNorm2d(norm_nc, affine=False)
def normalize_region(self, region, mask):
_b, _c, h, w = region.size()
num_pixels = mask.sum((2, 3), keepdim=True)
num_pixels[num_pixels == 0] = 1
mu = region.sum((2, 3), keepdim=True) / num_pixels
normalized_region = self.norm_layer(region + (1 - mask) * mu)
return normalized_region * torch.sqrt(num_pixels / (h * w))
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
swpang/xray-align-AR
|
MaskNorm
| false
| 13,014
|
[
"MIT"
] | 0
|
43cb0173ada9d1d71a6a923d605cb6fdae4d27aa
|
https://github.com/swpang/xray-align-AR/tree/43cb0173ada9d1d71a6a923d605cb6fdae4d27aa
|
FeatureCorrelation
|
import torch
from torch import nn
class FeatureCorrelation(nn.Module):
def __init__(self):
super(FeatureCorrelation, self).__init__()
def forward(self, featureA, featureB):
b, c, h, w = featureA.size()
featureA = featureA.permute(0, 3, 2, 1).reshape(b, w * h, c)
featureB = featureB.reshape(b, c, h * w)
corr = torch.bmm(featureA, featureB).reshape(b, w * h, h, w)
return corr
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 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 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_0(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 % 4
x3 = xindex // 4
y0 = yindex % 4
y1 = yindex // 4
x5 = xindex
y4 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3 + 16 * x2 + 64 * y1), xmask & ymask)
tl.store(out_ptr0 + (x5 + 16 * y4), tmp0, xmask & ymask)
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)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(16, 16)](arg0_1, buf0, 16, 16, XBLOCK
=16, YBLOCK=16, num_warps=4, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf0, (4, 16, 4), (64, 4, 1),
0), reinterpret_tensor(arg1_1, (4, 4, 16), (64, 16, 1), 0), out
=buf1)
del arg1_1
del buf0
return reinterpret_tensor(buf1, (4, 16, 4, 4), (256, 16, 4, 1), 0),
class FeatureCorrelationNew(nn.Module):
def __init__(self):
super(FeatureCorrelationNew, 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]
|
swpang/xray-align-AR
|
FeatureCorrelation
| false
| 13,015
|
[
"MIT"
] | 0
|
43cb0173ada9d1d71a6a923d605cb6fdae4d27aa
|
https://github.com/swpang/xray-align-AR/tree/43cb0173ada9d1d71a6a923d605cb6fdae4d27aa
|
Discriminator
|
import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, num_inputs, hidden_size):
super(Discriminator, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.linear3 = nn.Linear(hidden_size, 1)
self.linear3.weight.data.mul_(0.1)
self.linear3.bias.data.mul_(0.0)
def forward(self, x):
x = torch.tanh(self.linear1(x))
x = torch.tanh(self.linear2(x))
prob = torch.sigmoid(self.linear3(x))
return prob
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_inputs': 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_tanh_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
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_sigmoid_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
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.sigmoid(tmp3)
tl.store(in_out_ptr0 + x0, 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, 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, (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((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
get_raw_stream(0)
triton_poi_fused_tanh_0[grid(256)](buf1, primals_2, 256, XBLOCK=256,
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
triton_poi_fused_tanh_0[grid(256)](buf3, primals_5, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 1), (1, 4), 0), out=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf4
triton_poi_fused_sigmoid_1[grid(64)](buf5, primals_7, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_7
return buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf1, buf3, buf5, primals_6, primals_4
class DiscriminatorNew(nn.Module):
def __init__(self, num_inputs, hidden_size):
super(DiscriminatorNew, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.linear3 = nn.Linear(hidden_size, 1)
self.linear3.weight.data.mul_(0.1)
self.linear3.bias.data.mul_(0.0)
def forward(self, input_0):
primals_1 = self.linear1.weight
primals_2 = self.linear1.bias
primals_4 = self.linear2.weight
primals_5 = self.linear2.bias
primals_6 = self.linear3.weight
primals_7 = self.linear3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
syuntoku14/flow
|
Discriminator
| false
| 13,016
|
[
"MIT"
] | 0
|
3a1157cde31d0b7d6a3cc2f91eef0ec9ea53575e
|
https://github.com/syuntoku14/flow/tree/3a1157cde31d0b7d6a3cc2f91eef0ec9ea53575e
|
Generator
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Generator(nn.Module):
def __init__(self, input_size, hidden_size, out_size):
super(Generator, self).__init__()
self.map1 = nn.Linear(input_size, hidden_size)
self.map2 = nn.Linear(hidden_size, hidden_size)
self.map3 = nn.Linear(hidden_size, out_size)
def forward(self, x):
x = F.elu(self.map1(x))
x = torch.sigmoid(self.map2(x))
return self.map3(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'hidden_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.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_elu_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 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tl.store(out_ptr0 + x0, tmp7, xmask)
@triton.jit
def triton_poi_fused_sigmoid_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
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.sigmoid(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, 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,))
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((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_elu_0[grid(256)](buf0, buf1, 256, XBLOCK=128,
num_warps=4, num_stages=1)
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
triton_poi_fused_sigmoid_1[grid(256)](buf3, primals_5, 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_3, (64, 4), (4, 1), 0
), buf0, reinterpret_tensor(buf1, (64, 4), (4, 1), 0
), buf3, primals_6, primals_4
class GeneratorNew(nn.Module):
def __init__(self, input_size, hidden_size, out_size):
super(GeneratorNew, self).__init__()
self.map1 = nn.Linear(input_size, hidden_size)
self.map2 = nn.Linear(hidden_size, hidden_size)
self.map3 = nn.Linear(hidden_size, out_size)
def forward(self, input_0):
primals_1 = self.map1.weight
primals_2 = self.map1.bias
primals_4 = self.map2.weight
primals_5 = self.map2.bias
primals_6 = self.map3.weight
primals_7 = self.map3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
tan-huaiyu/Network_science-and-Evolutionary_dynamics
|
Generator
| false
| 13,017
|
[
"Apache-2.0"
] | 0
|
4bdaaed18c6f230213fd69a31144db8e97eb0c7b
|
https://github.com/tan-huaiyu/Network_science-and-Evolutionary_dynamics/tree/4bdaaed18c6f230213fd69a31144db8e97eb0c7b
|
DepthwiseSeparableConv
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class DepthwiseSeparableConv(nn.Module):
def __init__(self, in_ch, out_ch, k, bias=True):
super().__init__()
self.depthwise_conv = nn.Conv1d(in_channels=in_ch, out_channels=
in_ch, kernel_size=k, groups=in_ch, padding=k // 2, bias=False)
self.pointwise_conv = nn.Conv1d(in_channels=in_ch, out_channels=
out_ch, kernel_size=1, padding=0, bias=bias)
def forward(self, x):
return F.relu(self.pointwise_conv(self.depthwise_conv(x)))
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_ch': 4, 'out_ch': 4, 'k': 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 = 20
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 5
tmp0 = tl.load(in_out_ptr0 + x2, 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 + 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, 1, 4), (4, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_4, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(reinterpret_tensor(primals_2, (1,
4, 4), (16, 4, 1), 0), primals_1, stride=(1,), padding=(2,),
dilation=(1,), transposed=False, output_padding=(0,), groups=4,
bias=None)
assert_size_stride(buf0, (1, 4, 5), (20, 5, 1))
buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf1, (1, 4, 5), (20, 5, 1))
buf2 = reinterpret_tensor(buf1, (4, 5), (5, 1), 0)
del buf1
buf3 = empty_strided_cuda((4, 5), (5, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(20)](buf2,
primals_4, buf3, 20, XBLOCK=32, num_warps=1, num_stages=1)
del primals_4
return buf2, primals_1, primals_3, reinterpret_tensor(primals_2, (1, 4,
4), (16, 4, 1), 0), buf0, buf3
class DepthwiseSeparableConvNew(nn.Module):
def __init__(self, in_ch, out_ch, k, bias=True):
super().__init__()
self.depthwise_conv = nn.Conv1d(in_channels=in_ch, out_channels=
in_ch, kernel_size=k, groups=in_ch, padding=k // 2, bias=False)
self.pointwise_conv = nn.Conv1d(in_channels=in_ch, out_channels=
out_ch, kernel_size=1, padding=0, bias=bias)
def forward(self, input_0):
primals_1 = self.depthwise_conv.weight
primals_3 = self.pointwise_conv.weight
primals_4 = self.pointwise_conv.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
raghavjajodia/squad
|
DepthwiseSeparableConv
| false
| 13,018
|
[
"MIT"
] | 0
|
4eb6ccdfaa904aa97215c8bc65cd77b54ff54601
|
https://github.com/raghavjajodia/squad/tree/4eb6ccdfaa904aa97215c8bc65cd77b54ff54601
|
Matcher
|
import math
import torch
import torch.nn as nn
class Matcher(nn.Module):
"""
Matching between a pair of nodes to conduct link prediction.
Use multi-head attention as matching model.
"""
def __init__(self, n_hid):
super(Matcher, self).__init__()
self.left_linear = nn.Linear(n_hid, n_hid)
self.right_linear = nn.Linear(n_hid, n_hid)
self.sqrt_hd = math.sqrt(n_hid)
self.cache = None
def forward(self, x, y, infer=False, pair=False):
ty = self.right_linear(y)
if infer:
"""
During testing, we will consider millions or even billions of nodes as candidates (x).
It's not possible to calculate them again for different query (y)
Since the model is fixed, we propose to cache them, and dirrectly use the results.
"""
if self.cache is not None:
tx = self.cache
else:
tx = self.left_linear(x)
self.cache = tx
else:
tx = self.left_linear(x)
if pair:
res = (tx * ty).sum(dim=-1)
else:
res = torch.matmul(tx, ty.transpose(0, 1))
return res / self.sqrt_hd
def __repr__(self):
return '{}(n_hid={})'.format(self.__class__.__name__, self.n_hid)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'n_hid': 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 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, 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 // 16 % 4
x3 = xindex // 64
x4 = xindex % 16
x0 = xindex % 4
x5 = xindex
tmp0 = tl.load(in_ptr0 + (x4 + 16 * x3 + 64 * x2), xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + x5, tmp2, xmask)
@triton.jit
def triton_poi_fused_div_1(in_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 = 0.5
tmp2 = tmp0 * tmp1
tl.store(in_out_ptr0 + x0, tmp2, 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, 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, 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)
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 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (64,
4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf1)
del primals_4
del primals_5
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(256)](buf0, primals_2, buf2, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf3 = reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0)
del buf0
extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), out=buf3)
buf4 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf3
triton_poi_fused_div_1[grid(256)](buf4, 256, XBLOCK=256, num_warps=
4, num_stages=1)
return buf4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0)
class MatcherNew(nn.Module):
"""
Matching between a pair of nodes to conduct link prediction.
Use multi-head attention as matching model.
"""
def __init__(self, n_hid):
super(MatcherNew, self).__init__()
self.left_linear = nn.Linear(n_hid, n_hid)
self.right_linear = nn.Linear(n_hid, n_hid)
self.sqrt_hd = math.sqrt(n_hid)
self.cache = None
def __repr__(self):
return '{}(n_hid={})'.format(self.__class__.__name__, self.n_hid)
def forward(self, input_0, input_1):
primals_1 = self.left_linear.weight
primals_2 = self.left_linear.bias
primals_4 = self.right_linear.weight
primals_5 = self.right_linear.bias
primals_3 = input_0
primals_6 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
syyunn/pyHGT-1
|
Matcher
| false
| 13,019
|
[
"MIT"
] | 0
|
ad0918a48777add1495b80f35b5f2b7a44b74625
|
https://github.com/syyunn/pyHGT-1/tree/ad0918a48777add1495b80f35b5f2b7a44b74625
|
FusionLayer
|
import torch
from torch import nn
from torch.nn import init
class FusionLayer(nn.Module):
def __init__(self, nums=6):
super(FusionLayer, self).__init__()
self.weights = nn.Parameter(torch.randn(nums))
self.nums = nums
self._reset_parameters()
def _reset_parameters(self):
init.constant_(self.weights, 1 / self.nums)
def forward(self, x):
for i in range(self.nums):
out = self.weights[i] * x[i] if i == 0 else out + self.weights[i
] * x[i]
return out
def get_inputs():
return [torch.rand([6, 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
from torch.nn import 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_add_mul_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 + 0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
tmp2 = tl.load(in_ptr1 + x0, xmask)
tmp4 = tl.load(in_ptr0 + 1)
tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
tmp6 = tl.load(in_ptr1 + (64 + x0), xmask)
tmp9 = tl.load(in_ptr0 + 2)
tmp10 = tl.broadcast_to(tmp9, [XBLOCK])
tmp11 = tl.load(in_ptr1 + (128 + x0), xmask)
tmp14 = tl.load(in_ptr0 + 3)
tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
tmp16 = tl.load(in_ptr1 + (192 + x0), xmask)
tmp19 = tl.load(in_ptr0 + 4)
tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
tmp21 = tl.load(in_ptr1 + (256 + x0), xmask)
tmp24 = tl.load(in_ptr0 + 5)
tmp25 = tl.broadcast_to(tmp24, [XBLOCK])
tmp26 = tl.load(in_ptr1 + (320 + x0), xmask)
tmp3 = tmp1 * tmp2
tmp7 = tmp5 * tmp6
tmp8 = tmp3 + tmp7
tmp12 = tmp10 * tmp11
tmp13 = tmp8 + tmp12
tmp17 = tmp15 * tmp16
tmp18 = tmp13 + tmp17
tmp22 = tmp20 * tmp21
tmp23 = tmp18 + tmp22
tmp27 = tmp25 * tmp26
tmp28 = tmp23 + tmp27
tl.store(in_out_ptr0 + x0, tmp28, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (6,), (1,))
assert_size_stride(primals_2, (6, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_add_mul_0[grid(64)](buf1, primals_1, primals_2, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_1
return buf1, reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 64
), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 128
), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 192
), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 256
), reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 320)
class FusionLayerNew(nn.Module):
def __init__(self, nums=6):
super(FusionLayerNew, self).__init__()
self.weights = nn.Parameter(torch.randn(nums))
self.nums = nums
self._reset_parameters()
def _reset_parameters(self):
init.constant_(self.weights, 1 / self.nums)
def forward(self, input_0):
primals_1 = self.weights
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
tansyl/6883-SOD
|
FusionLayer
| false
| 13,020
|
[
"MIT"
] | 0
|
3a32c45be1c6c449fc7de145fe01746e3eeb16df
|
https://github.com/tansyl/6883-SOD/tree/3a32c45be1c6c449fc7de145fe01746e3eeb16df
|
GRUCell
|
import torch
import numpy as np
import torch.nn.functional as F
import torch.utils.data
import torch.nn as nn
class GRUCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=True):
super(GRUCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
self.fc_ir = nn.Linear(input_size, hidden_size, bias=bias)
self.fc_hr = nn.Linear(hidden_size, hidden_size, bias=bias)
self.fc_iz = nn.Linear(input_size, hidden_size, bias=bias)
self.fc_hz = nn.Linear(hidden_size, hidden_size, bias=bias)
self.fc_in = nn.Linear(input_size, hidden_size, bias=bias)
self.fc_hn = nn.Linear(hidden_size, hidden_size, bias=bias)
self.init_parameters()
def init_parameters(self):
std = 1.0 / np.sqrt(self.hidden_size)
for w in self.parameters():
w.data.uniform_(-std, std)
def forward(self, x, h):
x = x.view(-1, x.shape[1])
i_r = self.fc_ir(x)
h_r = self.fc_hr(h)
i_z = self.fc_iz(x)
h_z = self.fc_hz(h)
i_n = self.fc_in(x)
h_n = self.fc_hn(h)
resetgate = F.sigmoid(i_r + h_r)
inputgate = F.sigmoid(i_z + h_z)
newgate = F.tanh(i_n + resetgate * h_n)
hy = newgate + inputgate * (h - newgate)
return hy
def get_inputs():
return [torch.rand([4, 4, 64, 4]), torch.rand([4, 4, 1024, 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 numpy as np
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_mul_sigmoid_sub_tanh_0(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, in_ptr6, 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 % 4096
x2 = xindex
tmp0 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + x2, None)
tmp5 = tl.load(in_ptr3 + x2, None)
tmp9 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr5 + x2, None)
tmp13 = tl.load(in_ptr6 + x2, None)
tmp3 = tmp1 + tmp2
tmp4 = tl.sigmoid(tmp3)
tmp6 = tmp4 * tmp5
tmp7 = tmp0 + tmp6
tmp8 = libdevice.tanh(tmp7)
tmp11 = tmp9 + tmp10
tmp12 = tl.sigmoid(tmp11)
tmp14 = tmp13 - tmp8
tmp15 = tmp12 * tmp14
tmp16 = tmp8 + tmp15
tl.store(out_ptr0 + x2, tmp16, 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) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 64, 4), (1024, 256, 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, 1024, 4), (16384, 4096, 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, (4, 4), (4, 1))
assert_size_stride(primals_12, (4,), (1,))
assert_size_stride(primals_13, (4, 4), (4, 1))
assert_size_stride(primals_14, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((1024, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (1024,
4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf0)
del primals_2
del primals_3
buf1 = empty_strided_cuda((16384, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (
16384, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1,
4), 0), alpha=1, beta=1, out=buf1)
del primals_4
del primals_5
buf2 = empty_strided_cuda((1024, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_8, reinterpret_tensor(primals_1, (1024,
4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf2)
del primals_7
del primals_8
buf3 = empty_strided_cuda((16384, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_10, reinterpret_tensor(primals_6, (
16384, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1,
4), 0), alpha=1, beta=1, out=buf3)
del primals_10
del primals_9
buf4 = empty_strided_cuda((1024, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_12, reinterpret_tensor(primals_1, (
1024, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1,
4), 0), alpha=1, beta=1, out=buf4)
del primals_11
del primals_12
buf5 = empty_strided_cuda((16384, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_14, reinterpret_tensor(primals_6, (
16384, 4), (4, 1), 0), reinterpret_tensor(primals_13, (4, 4), (
1, 4), 0), alpha=1, beta=1, out=buf5)
del primals_13
del primals_14
buf6 = empty_strided_cuda((4, 4, 1024, 4), (16384, 4096, 4, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mul_sigmoid_sub_tanh_0[grid(65536)](buf4, buf0,
buf1, buf5, buf2, buf3, primals_6, buf6, 65536, XBLOCK=512,
num_warps=4, num_stages=1)
return buf6, primals_6, reinterpret_tensor(primals_1, (1024, 4), (4, 1), 0
), buf0, buf1, buf2, buf3, buf4, buf5
class GRUCellNew(nn.Module):
def __init__(self, input_size, hidden_size, bias=True):
super(GRUCellNew, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
self.fc_ir = nn.Linear(input_size, hidden_size, bias=bias)
self.fc_hr = nn.Linear(hidden_size, hidden_size, bias=bias)
self.fc_iz = nn.Linear(input_size, hidden_size, bias=bias)
self.fc_hz = nn.Linear(hidden_size, hidden_size, bias=bias)
self.fc_in = nn.Linear(input_size, hidden_size, bias=bias)
self.fc_hn = nn.Linear(hidden_size, hidden_size, bias=bias)
self.init_parameters()
def init_parameters(self):
std = 1.0 / np.sqrt(self.hidden_size)
for w in self.parameters():
w.data.uniform_(-std, std)
def forward(self, input_0, input_1):
primals_2 = self.fc_ir.weight
primals_3 = self.fc_ir.bias
primals_4 = self.fc_hr.weight
primals_5 = self.fc_hr.bias
primals_7 = self.fc_iz.weight
primals_8 = self.fc_iz.bias
primals_9 = self.fc_hz.weight
primals_10 = self.fc_hz.bias
primals_11 = self.fc_in.weight
primals_12 = self.fc_in.bias
primals_13 = self.fc_hn.weight
primals_14 = self.fc_hn.bias
primals_1 = 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])
return output[0]
|
systemshift/PyGrid
|
GRUCell
| false
| 13,021
|
[
"Apache-2.0"
] | 0
|
d0ee3df8731a7576d6689fa8b4f5d3fe05ac11ff
|
https://github.com/systemshift/PyGrid/tree/d0ee3df8731a7576d6689fa8b4f5d3fe05ac11ff
|
Debayer2x2
|
import torch
import torch.nn
import torch.nn.functional
class Debayer2x2(torch.nn.Module):
"""Demosaicing of Bayer images using 2x2 convolutions.
Requires BG-Bayer color filter array layout. That is,
the image[1,1]='B', image[1,2]='G'. This corresponds
to OpenCV naming conventions.
"""
def __init__(self):
super(Debayer2x2, self).__init__()
self.kernels = torch.nn.Parameter(torch.tensor([[1, 0], [0, 0], [0,
0.5], [0.5, 0], [0, 0], [0, 1]]).view(3, 1, 2, 2),
requires_grad=False)
def forward(self, x):
"""Debayer image.
Parameters
----------
x : Bx1xHxW tensor
Images to debayer
Returns
-------
rgb : Bx3xHxW tensor
Color images in RGB channel order.
"""
x = torch.nn.functional.conv2d(x, self.kernels, stride=2)
x = torch.nn.functional.interpolate(x, scale_factor=2, mode=
'bilinear', align_corners=False)
return x
def get_inputs():
return [torch.rand([4, 1, 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.nn
import torch.nn.functional
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_mul_sub_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)
x1 = xindex // 64 % 64
x0 = xindex % 64
x2 = xindex // 4096
x4 = xindex
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = tmp3 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.full([1], 1, tl.int64)
tmp10 = tmp8 + tmp9
tmp11 = tl.full([1], 31, tl.int64)
tmp12 = triton_helpers.minimum(tmp10, tmp11)
tmp13 = x0
tmp14 = tmp13.to(tl.float32)
tmp15 = tmp14 + tmp2
tmp16 = tmp15 * tmp2
tmp17 = tmp16 - tmp2
tmp18 = triton_helpers.maximum(tmp17, tmp6)
tmp19 = tmp18.to(tl.int32)
tmp20 = tmp19 + tmp9
tmp21 = triton_helpers.minimum(tmp20, tmp11)
tmp22 = tl.load(in_ptr0 + (tmp21 + 32 * tmp12 + 1024 * x2), None,
eviction_policy='evict_last')
tmp23 = tl.load(in_ptr0 + (tmp19 + 32 * tmp12 + 1024 * x2), None,
eviction_policy='evict_last')
tmp24 = tmp22 - tmp23
tmp25 = tmp19.to(tl.float32)
tmp26 = tmp18 - tmp25
tmp27 = triton_helpers.maximum(tmp26, tmp6)
tmp28 = 1.0
tmp29 = triton_helpers.minimum(tmp27, tmp28)
tmp30 = tmp24 * tmp29
tmp31 = tmp23 + tmp30
tmp32 = tl.load(in_ptr0 + (tmp19 + 32 * tmp8 + 1024 * x2), None,
eviction_policy='evict_last')
tmp33 = tl.load(in_ptr0 + (tmp21 + 32 * tmp8 + 1024 * x2), None,
eviction_policy='evict_last')
tmp34 = tmp33 - tmp32
tmp35 = tmp34 * tmp29
tmp36 = tmp32 + tmp35
tmp37 = tmp31 - tmp36
tmp38 = tmp8.to(tl.float32)
tmp39 = tmp7 - tmp38
tmp40 = triton_helpers.maximum(tmp39, tmp6)
tmp41 = triton_helpers.minimum(tmp40, tmp28)
tmp42 = tmp37 * tmp41
tmp43 = tmp36 + tmp42
tl.store(in_out_ptr0 + x4, tmp43, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (3, 1, 2, 2), (4, 4, 2, 1))
assert_size_stride(arg1_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(arg1_1, arg0_1, stride=(2, 2),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 3, 32, 32), (3072, 1024, 32, 1))
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
torch.float32)
buf2 = buf1
del buf1
buf3 = buf2
del buf2
get_raw_stream(0)
triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
(49152)](buf3, buf0, 49152, XBLOCK=256, num_warps=4, num_stages=1)
del buf0
return buf3,
class Debayer2x2New(torch.nn.Module):
"""Demosaicing of Bayer images using 2x2 convolutions.
Requires BG-Bayer color filter array layout. That is,
the image[1,1]='B', image[1,2]='G'. This corresponds
to OpenCV naming conventions.
"""
def __init__(self):
super(Debayer2x2New, self).__init__()
self.kernels = torch.nn.Parameter(torch.tensor([[1, 0], [0, 0], [0,
0.5], [0.5, 0], [0, 0], [0, 1]]).view(3, 1, 2, 2),
requires_grad=False)
def forward(self, input_0):
arg0_1 = self.kernels
arg1_1 = input_0
output = call([arg0_1, arg1_1])
return output[0]
|
tasptz/pytorch-debayer
|
Debayer2x2
| false
| 13,022
|
[
"MIT"
] | 0
|
ec35f34a57c045eb2319f4ef87f371d95f7394c3
|
https://github.com/tasptz/pytorch-debayer/tree/ec35f34a57c045eb2319f4ef87f371d95f7394c3
|
PowerLaw_Compressed_Loss
|
import torch
import torch.nn as nn
import torch.utils.data
class PowerLaw_Compressed_Loss(nn.Module):
def __init__(self, power=0.3, complex_loss_ratio=0.113):
super(PowerLaw_Compressed_Loss, self).__init__()
self.power = power
self.complex_loss_ratio = complex_loss_ratio
self.criterion = nn.MSELoss()
self.epsilon = 1e-16
def forward(self, prediction, target, seq_len=None, spec_phase=None):
prediction = prediction + self.epsilon
target = target + self.epsilon
prediction = torch.pow(prediction, self.power)
target = torch.pow(target, self.power)
spec_loss = self.criterion(torch.abs(target), torch.abs(prediction))
complex_loss = self.criterion(target, prediction)
loss = spec_loss + complex_loss * self.complex_loss_ratio
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.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
@triton.jit
def triton_per_fused_abs_add_mse_loss_mul_pow_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)
tmp6 = tl.load(in_ptr1 + r0, None)
tmp1 = 1e-16
tmp2 = tmp0 + tmp1
tmp3 = 0.3
tmp4 = libdevice.pow(tmp2, tmp3)
tmp5 = tl_math.abs(tmp4)
tmp7 = tmp6 + tmp1
tmp8 = libdevice.pow(tmp7, tmp3)
tmp9 = tl_math.abs(tmp8)
tmp10 = tmp5 - tmp9
tmp11 = tmp10 * tmp10
tmp12 = tl.broadcast_to(tmp11, [RBLOCK])
tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp12, 0))
tmp15 = tmp4 - tmp8
tmp16 = tmp15 * tmp15
tmp17 = tl.broadcast_to(tmp16, [RBLOCK])
tmp19 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
tmp20 = 256.0
tmp21 = tmp14 / tmp20
tmp22 = tmp19 / tmp20
tmp23 = 0.113
tmp24 = tmp22 * tmp23
tmp25 = tmp21 + tmp24
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp25, 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)
buf2 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_abs_add_mse_loss_mul_pow_0[grid(1)](buf2, arg1_1,
arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf2,
class PowerLaw_Compressed_LossNew(nn.Module):
def __init__(self, power=0.3, complex_loss_ratio=0.113):
super(PowerLaw_Compressed_LossNew, self).__init__()
self.power = power
self.complex_loss_ratio = complex_loss_ratio
self.criterion = nn.MSELoss()
self.epsilon = 1e-16
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
taylorjdlee/VoiceSplit
|
PowerLaw_Compressed_Loss
| false
| 13,023
|
[
"Apache-2.0"
] | 0
|
bd914c42ae065bdda95d81a0ce0c173c29bb040f
|
https://github.com/taylorjdlee/VoiceSplit/tree/bd914c42ae065bdda95d81a0ce0c173c29bb040f
|
Discriminator
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Discriminator(nn.Module):
def __init__(self, input_size, hidden_size, out_size):
super(Discriminator, self).__init__()
self.map1 = nn.Linear(input_size, hidden_size)
self.map2 = nn.Linear(hidden_size, hidden_size)
self.map3 = nn.Linear(hidden_size, out_size)
def forward(self, x):
x = F.relu(self.map1(x))
x = F.elu(self.map2(x))
return torch.sigmoid(self.map3(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'hidden_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 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)
@triton.jit
def triton_poi_fused_elu_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tl.store(out_ptr0 + x0, tmp7, xmask)
@triton.jit
def triton_poi_fused_sigmoid_2(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
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.sigmoid(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, 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,))
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
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_2, buf6, 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_5, reinterpret_tensor(buf1, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf2)
del primals_5
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_elu_1[grid(256)](buf2, buf3, 256, XBLOCK=256,
num_warps=4, num_stages=1)
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
triton_poi_fused_sigmoid_2[grid(256)](buf5, primals_7, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_7
return buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0
), buf2, reinterpret_tensor(buf3, (64, 4), (4, 1), 0
), buf5, primals_6, primals_4, buf6
class DiscriminatorNew(nn.Module):
def __init__(self, input_size, hidden_size, out_size):
super(DiscriminatorNew, self).__init__()
self.map1 = nn.Linear(input_size, hidden_size)
self.map2 = nn.Linear(hidden_size, hidden_size)
self.map3 = nn.Linear(hidden_size, out_size)
def forward(self, input_0):
primals_1 = self.map1.weight
primals_2 = self.map1.bias
primals_4 = self.map2.weight
primals_5 = self.map2.bias
primals_6 = self.map3.weight
primals_7 = self.map3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
tan-huaiyu/Network_science-and-Evolutionary_dynamics
|
Discriminator
| false
| 13,024
|
[
"Apache-2.0"
] | 0
|
4bdaaed18c6f230213fd69a31144db8e97eb0c7b
|
https://github.com/tan-huaiyu/Network_science-and-Evolutionary_dynamics/tree/4bdaaed18c6f230213fd69a31144db8e97eb0c7b
|
Conv2
|
import math
import torch
import torch.nn as nn
class Conv2(nn.Module):
""" 1D conv with (kernel, stride)=(4, 2).
Input:
x: (N, 2L+2, in_channels) numeric tensor
global_cond: (N, global_cond_channels) numeric tensor
Output:
y: (N, L, out_channels) numeric tensor
"""
def __init__(self, in_channels, out_channels, global_cond_channels):
super().__init__()
ksz = 4
self.out_channels = out_channels
if 0 < global_cond_channels:
self.w_cond = nn.Linear(global_cond_channels, 2 * out_channels,
bias=False)
self.conv_wide = nn.Conv1d(in_channels, 2 * out_channels, ksz, stride=2
)
wsize = 2.967 / math.sqrt(ksz * in_channels)
self.conv_wide.weight.data.uniform_(-wsize, wsize)
self.conv_wide.bias.data.zero_()
def forward(self, x, global_cond):
x1 = self.conv_wide(x.transpose(1, 2)).transpose(1, 2)
if global_cond is not None:
x2 = self.w_cond(global_cond).unsqueeze(1).expand(-1, x1.size(1
), -1)
else:
x2 = torch.zeros_like(x1)
a, b = (x1 + x2).split(self.out_channels, dim=2)
return torch.sigmoid(a) * torch.tanh(b)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4,
'global_cond_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 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_convolution_0(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_mul_sigmoid_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
out_ptr1, out_ptr2, 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 + 8 * x1), xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x0 + 8 * x1), xmask)
tmp6 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr2 + (4 + x0 + 8 * x1), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp5 = tl.sigmoid(tmp4)
tmp8 = tmp6 + tmp7
tmp10 = tmp8 + tmp9
tmp11 = libdevice.tanh(tmp10)
tmp12 = tmp5 * tmp11
tl.store(out_ptr0 + x2, tmp5, xmask)
tl.store(out_ptr1 + x2, tmp11, xmask)
tl.store(out_ptr2 + x2, tmp12, 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, (8, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (8,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (8, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(2,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 8, 1), (8, 1, 1))
del buf0
buf2 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
extern_kernels.mm(primals_4, reinterpret_tensor(primals_5, (4, 8),
(1, 4), 0), out=buf2)
del primals_5
buf3 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
buf4 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
buf5 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
triton_poi_fused_mul_sigmoid_tanh_1[grid(16)](buf1, primals_3, buf2,
buf3, buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
del buf1
del buf2
del primals_3
return buf5, primals_2, primals_4, reinterpret_tensor(primals_1, (4, 4,
4), (16, 1, 4), 0), buf3, buf4
class Conv2New(nn.Module):
""" 1D conv with (kernel, stride)=(4, 2).
Input:
x: (N, 2L+2, in_channels) numeric tensor
global_cond: (N, global_cond_channels) numeric tensor
Output:
y: (N, L, out_channels) numeric tensor
"""
def __init__(self, in_channels, out_channels, global_cond_channels):
super().__init__()
ksz = 4
self.out_channels = out_channels
if 0 < global_cond_channels:
self.w_cond = nn.Linear(global_cond_channels, 2 * out_channels,
bias=False)
self.conv_wide = nn.Conv1d(in_channels, 2 * out_channels, ksz, stride=2
)
wsize = 2.967 / math.sqrt(ksz * in_channels)
self.conv_wide.weight.data.uniform_(-wsize, wsize)
self.conv_wide.bias.data.zero_()
def forward(self, input_0, input_1):
primals_5 = self.w_cond.weight
primals_2 = self.conv_wide.weight
primals_3 = self.conv_wide.bias
primals_1 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
tarepan/vqvaevc
|
Conv2
| false
| 13,025
|
[
"MIT"
] | 0
|
dabbb9bae5ccb9d5dcb110caf3f0a59f68006a97
|
https://github.com/tarepan/vqvaevc/tree/dabbb9bae5ccb9d5dcb110caf3f0a59f68006a97
|
Debayer3x3
|
import torch
import torch.nn
import torch.nn.functional
class Debayer3x3(torch.nn.Module):
"""Demosaicing of Bayer images using 3x3 convolutions.
Requires BG-Bayer color filter array layout. That is,
the image[1,1]='B', image[1,2]='G'. This corresponds
to OpenCV naming conventions.
Compared to Debayer2x2 this method does not use upsampling.
Instead, we identify five 3x3 interpolation kernels that
are sufficient to reconstruct every color channel at every
pixel location.
We convolve the image with these 5 kernels using stride=1
and a one pixel replication padding. Finally, we gather
the correct channel values for each pixel location. Todo so,
we recognize that the Bayer pattern repeats horizontally and
vertically every 2 pixels. Therefore, we define the correct
index lookups for a 2x2 grid cell and then repeat to image
dimensions.
Note, in every 2x2 grid cell we have red, blue and two greens
(G1,G2). The lookups for the two greens differ.
"""
def __init__(self):
super(Debayer3x3, self).__init__()
self.kernels = torch.nn.Parameter(torch.tensor([[0, 0, 0], [0, 1, 0
], [0, 0, 0], [0, 0.25, 0], [0.25, 0, 0.25], [0, 0.25, 0], [
0.25, 0, 0.25], [0, 0, 0], [0.25, 0, 0.25], [0, 0, 0], [0.5, 0,
0.5], [0, 0, 0], [0, 0.5, 0], [0, 0, 0], [0, 0.5, 0]]).view(5,
1, 3, 3), requires_grad=False)
self.index = torch.nn.Parameter(torch.tensor([[0, 3], [4, 2], [1, 0
], [0, 1], [2, 4], [3, 0]]).view(1, 3, 2, 2), requires_grad=False)
def forward(self, x):
"""Debayer image.
Parameters
----------
x : Bx1xHxW tensor
Images to debayer
Returns
-------
rgb : Bx3xHxW tensor
Color images in RGB channel order.
"""
B, _C, H, W = x.shape
x = torch.nn.functional.pad(x, (1, 1, 1, 1), mode='replicate')
c = torch.nn.functional.conv2d(x, self.kernels, stride=1)
rgb = torch.gather(c, 1, self.index.repeat(B, 1, H // 2, W // 2))
return rgb
def get_inputs():
return [torch.rand([4, 1, 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
import torch.nn
import torch.nn.functional
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_replication_pad2d_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
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -1 + x1) + (-1 + x1) *
(-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) *
(0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 3)) + 16 * x2 + (
3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 * (0 >=
-1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) + (-1 +
x0) * (-1 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_gather_repeat_1(in_ptr0, in_ptr1, 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 % 4
x1 = xindex // 4 % 4
x2 = xindex // 16 % 3
x3 = xindex // 48
x4 = xindex % 16
x5 = xindex
tmp0 = tl.load(in_ptr0 + (2 * (x1 % 2) + 4 * x2 + x0 % 2), xmask,
eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 5, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tl.device_assert((0 <= tmp4) & (tmp4 < 5) | ~xmask,
'index out of bounds: 0 <= tmp4 < 5')
tmp6 = tl.load(in_ptr1 + (x4 + 16 * tmp4 + 80 * x3), xmask)
tl.store(out_ptr0 + x5, tmp6, xmask)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 1, 4, 4), (16, 16, 4, 1))
assert_size_stride(arg1_1, (5, 1, 3, 3), (9, 9, 3, 1))
assert_size_stride(arg2_1, (1, 3, 2, 2), (12, 4, 2, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 6, 6), (36, 36, 6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_replication_pad2d_0[grid(144)](arg0_1, buf0, 144,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 5, 4, 4), (80, 16, 4, 1))
del arg1_1
del buf0
buf2 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
triton_poi_fused_gather_repeat_1[grid(192)](arg2_1, buf1, buf2, 192,
XBLOCK=128, num_warps=4, num_stages=1)
del arg2_1
del buf1
return buf2,
class Debayer3x3New(torch.nn.Module):
"""Demosaicing of Bayer images using 3x3 convolutions.
Requires BG-Bayer color filter array layout. That is,
the image[1,1]='B', image[1,2]='G'. This corresponds
to OpenCV naming conventions.
Compared to Debayer2x2 this method does not use upsampling.
Instead, we identify five 3x3 interpolation kernels that
are sufficient to reconstruct every color channel at every
pixel location.
We convolve the image with these 5 kernels using stride=1
and a one pixel replication padding. Finally, we gather
the correct channel values for each pixel location. Todo so,
we recognize that the Bayer pattern repeats horizontally and
vertically every 2 pixels. Therefore, we define the correct
index lookups for a 2x2 grid cell and then repeat to image
dimensions.
Note, in every 2x2 grid cell we have red, blue and two greens
(G1,G2). The lookups for the two greens differ.
"""
def __init__(self):
super(Debayer3x3New, self).__init__()
self.kernels = torch.nn.Parameter(torch.tensor([[0, 0, 0], [0, 1, 0
], [0, 0, 0], [0, 0.25, 0], [0.25, 0, 0.25], [0, 0.25, 0], [
0.25, 0, 0.25], [0, 0, 0], [0.25, 0, 0.25], [0, 0, 0], [0.5, 0,
0.5], [0, 0, 0], [0, 0.5, 0], [0, 0, 0], [0, 0.5, 0]]).view(5,
1, 3, 3), requires_grad=False)
self.index = torch.nn.Parameter(torch.tensor([[0, 3], [4, 2], [1, 0
], [0, 1], [2, 4], [3, 0]]).view(1, 3, 2, 2), requires_grad=False)
def forward(self, input_0):
arg1_1 = self.kernels
arg2_1 = self.index
arg0_1 = input_0
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
tasptz/pytorch-debayer
|
Debayer3x3
| false
| 13,026
|
[
"MIT"
] | 0
|
ec35f34a57c045eb2319f4ef87f371d95f7394c3
|
https://github.com/tasptz/pytorch-debayer/tree/ec35f34a57c045eb2319f4ef87f371d95f7394c3
|
nSGC
|
import math
import torch
import torch.nn.functional as F
import torch.utils.dlpack
import torch.nn as nn
class nSGC(nn.Module):
def __init__(self, nfeat, nclass):
super(nSGC, self).__init__()
self.W1 = nn.Linear(nfeat, nclass * 2)
self.W2 = nn.Linear(nclass * 2, nclass)
self.init()
def init(self):
stdv = 1.0 / math.sqrt(self.W1.weight.size(1))
self.W1.weight.data.uniform_(-stdv, stdv)
def forward(self, x):
x = self.W1(x)
x = F.relu(x)
x = self.W2(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'nfeat': 4, 'nclass': 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 math
import torch.utils.dlpack
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 = 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')
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, (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,))
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)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 8), (128, 32, 8, 1), 0)
del buf0
buf3 = 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,
primals_2, buf3, 512, 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_5, reinterpret_tensor(buf1, (64, 8), (
8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 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
), reinterpret_tensor(buf1, (64, 8), (8, 1), 0), primals_4, buf3
class nSGCNew(nn.Module):
def __init__(self, nfeat, nclass):
super(nSGCNew, self).__init__()
self.W1 = nn.Linear(nfeat, nclass * 2)
self.W2 = nn.Linear(nclass * 2, nclass)
self.init()
def init(self):
stdv = 1.0 / math.sqrt(self.W1.weight.size(1))
self.W1.weight.data.uniform_(-stdv, stdv)
def forward(self, input_0):
primals_1 = self.W1.weight
primals_2 = self.W1.bias
primals_4 = self.W2.weight
primals_5 = self.W2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
tealminivan/FinalProject
|
nSGC
| false
| 13,027
|
[
"MIT"
] | 0
|
ef6e0cda619b7e00f112ffadd56d259a5cc8a85b
|
https://github.com/tealminivan/FinalProject/tree/ef6e0cda619b7e00f112ffadd56d259a5cc8a85b
|
Joiner
|
import torch
from torch import nn
import torch.nn.functional as F
class Joiner(nn.Module):
def __init__(self, input_dim: 'int', output_dim: 'int'):
super().__init__()
self.output_linear = nn.Linear(input_dim, output_dim)
def forward(self, encoder_out: 'torch.Tensor', decoder_out: 'torch.Tensor'
) ->torch.Tensor:
"""
Args:
encoder_out:
Output from the encoder. Its shape is (N, T, C).
decoder_out:
Output from the decoder. Its shape is (N, U, C).
Returns:
Return a tensor of shape (N, T, U, C).
"""
assert encoder_out.ndim == decoder_out.ndim == 3
assert encoder_out.size(0) == decoder_out.size(0)
assert encoder_out.size(2) == decoder_out.size(2)
encoder_out = encoder_out.unsqueeze(2)
decoder_out = decoder_out.unsqueeze(1)
logit = encoder_out + decoder_out
logit = F.relu(logit)
output = self.output_linear(logit)
return output
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'input_dim': 4, 'output_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 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_add_relu_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 + 4 * x4), xmask, eviction_policy='evict_last'
)
tmp1 = tl.load(in_ptr1 + (x5 + 16 * x3), 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 + x6, tmp4, 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, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (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_relu_0[grid(256)](primals_1, primals_2, buf0,
256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf1)
del primals_3
del primals_4
return reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(buf0, (64, 4), (4, 1), 0)
class JoinerNew(nn.Module):
def __init__(self, input_dim: 'int', output_dim: 'int'):
super().__init__()
self.output_linear = nn.Linear(input_dim, output_dim)
def forward(self, input_0, input_1):
primals_3 = self.output_linear.weight
primals_4 = self.output_linear.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
thangdepzai/icefall
|
Joiner
| false
| 13,028
|
[
"Apache-2.0"
] | 0
|
8c7995d493c4309c3d09bdabfa1ab12b4eec2657
|
https://github.com/thangdepzai/icefall/tree/8c7995d493c4309c3d09bdabfa1ab12b4eec2657
|
NNTest
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class NNTest(nn.Module):
def __init__(self, input_size, output_size):
super(NNTest, self).__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, 100)
self.fc3 = nn.Linear(100, 50)
self.fc4 = nn.Linear(50, output_size)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = self.fc4(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_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
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 = 3200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 50
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_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 6400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x0 = xindex % 100
x2 = xindex % 1600
x3 = xindex // 1600
tmp0 = tl.load(in_out_ptr0 + x4, 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 + x4, tmp4, xmask)
tl.store(out_ptr0 + (x2 + 1664 * x3), 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, (50, 4), (4, 1))
assert_size_stride(primals_2, (50,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (100, 50), (50, 1))
assert_size_stride(primals_5, (100,), (1,))
assert_size_stride(primals_6, (50, 100), (100, 1))
assert_size_stride(primals_7, (50,), (1,))
assert_size_stride(primals_8, (4, 50), (50, 1))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 50), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 50), (800, 200, 50, 1), 0)
del buf0
buf9 = empty_strided_cuda((4, 4, 4, 50), (800, 200, 50, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(3200)](buf1,
primals_2, buf9, 3200, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 100), (100, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 50), (50, 1), 0),
reinterpret_tensor(primals_4, (50, 100), (1, 50), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 100), (1600, 400, 100, 1), 0)
del buf2
buf8 = empty_strided_cuda((4, 4, 4, 100), (1664, 400, 100, 1),
torch.bool)
triton_poi_fused_relu_threshold_backward_1[grid(6400)](buf3,
primals_5, buf8, 6400, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf3, (64, 100), (100, 1), 0),
reinterpret_tensor(primals_6, (100, 50), (1, 100), 0), out=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 50), (800, 200, 50, 1), 0)
del buf4
buf7 = empty_strided_cuda((4, 4, 4, 50), (800, 200, 50, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(3200)](buf5,
primals_7, buf7, 3200, XBLOCK=256, 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, 50),
(50, 1), 0), reinterpret_tensor(primals_8, (50, 4), (1, 50), 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, 50), (50, 1), 0), reinterpret_tensor(
buf3, (64, 100), (100, 1), 0), reinterpret_tensor(buf5, (64, 50), (
50, 1), 0), primals_8, buf7, primals_6, buf8, primals_4, buf9
class NNTestNew(nn.Module):
def __init__(self, input_size, output_size):
super(NNTestNew, self).__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, 100)
self.fc3 = nn.Linear(100, 50)
self.fc4 = nn.Linear(50, output_size)
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.fc4.weight
primals_9 = self.fc4.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]
|
tassotirap/data-science
|
NNTest
| false
| 13,029
|
[
"Apache-2.0"
] | 0
|
644bc351740cda90c0d8c907132d9da9630266c9
|
https://github.com/tassotirap/data-science/tree/644bc351740cda90c0d8c907132d9da9630266c9
|
ConvElement
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvElement(nn.Module):
"""
Residual Core element used inside the NN. Control the number of filters
and batch normalization.
"""
def __init__(self, input_size, num_filters, use_leaky=True, stride=1,
leaky_p=0.2):
super(ConvElement, self).__init__()
self.use_lr = use_leaky
self.leaky_p = leaky_p
self.conv1 = nn.Conv3d(input_size, num_filters, kernel_size=3,
padding=1, stride=stride)
def forward(self, x):
"""
include residual model
"""
x_1 = self.conv1(x)
return F.leaky_relu(x_1, self.leaky_p) if self.use_lr else F.relu(x_1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'num_filters': 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_leaky_relu_0(in_ptr0, in_ptr1, 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
x1 = xindex // 64
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, 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)
tl.store(out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr1 + x2, tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3, 3), (108, 27, 9, 3, 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(reinterpret_tensor(primals_3, (1,
4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1,
1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
output_padding=(0, 0, 0), groups=1, bias=None)
assert_size_stride(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_leaky_relu_0[grid(256)](buf0, primals_2, buf1,
buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf0
del primals_2
return buf2, primals_1, reinterpret_tensor(primals_3, (1, 4, 4, 4, 4),
(256, 64, 16, 4, 1), 0), buf1
class ConvElementNew(nn.Module):
"""
Residual Core element used inside the NN. Control the number of filters
and batch normalization.
"""
def __init__(self, input_size, num_filters, use_leaky=True, stride=1,
leaky_p=0.2):
super(ConvElementNew, self).__init__()
self.use_lr = use_leaky
self.leaky_p = leaky_p
self.conv1 = nn.Conv3d(input_size, num_filters, kernel_size=3,
padding=1, stride=stride)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
tensormedical/PARIETAL
|
ConvElement
| false
| 13,030
|
[
"Apache-2.0"
] | 0
|
25bf1cf7828b24d60ccff42efbd0537989aaf160
|
https://github.com/tensormedical/PARIETAL/tree/25bf1cf7828b24d60ccff42efbd0537989aaf160
|
Hill
|
import torch
import torch.nn as nn
class Hill(nn.Module):
def forward(self, p):
n = 2
return 1 / (1 + p ** n)
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_add_mul_pow_reciprocal_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 = tl.full([1], 1, tl.int32)
tmp5 = tmp4 / tmp3
tmp6 = tmp5 * tmp2
tl.store(out_ptr0 + x0, tmp6, 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_mul_pow_reciprocal_0[grid(256)](arg0_1, buf0,
256, XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class HillNew(nn.Module):
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
tianyu-lu/latent_ode
|
Hill
| false
| 13,031
|
[
"MIT"
] | 0
|
1a9e9415eda1837ed78e50009752b90eda3ca0db
|
https://github.com/tianyu-lu/latent_ode/tree/1a9e9415eda1837ed78e50009752b90eda3ca0db
|
Net
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 12, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(12, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def get_inputs():
return [torch.rand([4, 3, 32, 32])]
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.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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 37632
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 784 % 12
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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 9408
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 14
x3 = xindex // 14
x2 = xindex // 2352
x4 = xindex % 2352
tmp0 = tl.load(in_ptr0 + (2 * x0 + 56 * x3), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 56 * x3), xmask, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (28 + 2 * x0 + 56 * x3), xmask,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (29 + 2 * x0 + 56 * x3), xmask,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + (x4 + 2368 * x2), tmp6, xmask)
tl.store(out_ptr1 + (x4 + 2432 * x2), tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 6400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 100 % 16
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_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 5
x1 = xindex // 5
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 20 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 20 * x1), xmask, eviction_policy
='evict_last')
tmp7 = tl.load(in_ptr0 + (10 + 2 * x0 + 20 * x1), xmask,
eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (11 + 2 * x0 + 20 * x1), xmask,
eviction_policy='evict_last')
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 + x2, tmp15, xmask)
tl.store(out_ptr1 + x2, tmp16, xmask)
@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 480
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 120
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)
@triton.jit
def triton_poi_fused_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 336
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 84
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, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (12, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_2, (12,), (1,))
assert_size_stride(primals_3, (4, 3, 32, 32), (3072, 1024, 32, 1))
assert_size_stride(primals_4, (16, 12, 5, 5), (300, 25, 5, 1))
assert_size_stride(primals_5, (16,), (1,))
assert_size_stride(primals_6, (120, 400), (400, 1))
assert_size_stride(primals_7, (120,), (1,))
assert_size_stride(primals_8, (84, 120), (120, 1))
assert_size_stride(primals_9, (84,), (1,))
assert_size_stride(primals_10, (10, 84), (84, 1))
assert_size_stride(primals_11, (10,), (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, 12, 28, 28), (9408, 784, 28, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(37632)](buf1, primals_2,
37632, XBLOCK=512, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 12, 14, 14), (2368, 196, 14, 1),
torch.float32)
buf3 = empty_strided_cuda((4, 12, 14, 14), (2432, 196, 14, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(9408)](buf1, buf2,
buf3, 9408, XBLOCK=128, num_warps=4, num_stages=1)
buf4 = extern_kernels.convolution(buf2, 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, 16, 10, 10), (1600, 100, 10, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_relu_2[grid(6400)](buf5, primals_5,
6400, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf6 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.int8)
buf7 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.float32
)
triton_poi_fused_max_pool2d_with_indices_3[grid(1600)](buf5, buf6,
buf7, 1600, XBLOCK=256, num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((4, 120), (120, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf7, (4, 400), (400, 1), 0),
reinterpret_tensor(primals_6, (400, 120), (1, 400), 0), out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_relu_4[grid(480)](buf9, primals_7, 480, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_7
buf10 = empty_strided_cuda((4, 84), (84, 1), torch.float32)
extern_kernels.mm(buf9, reinterpret_tensor(primals_8, (120, 84), (1,
120), 0), out=buf10)
buf11 = buf10
del buf10
triton_poi_fused_relu_5[grid(336)](buf11, primals_9, 336, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_9
buf12 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_11, buf11, reinterpret_tensor(
primals_10, (84, 10), (1, 84), 0), alpha=1, beta=1, out=buf12)
del primals_11
return (buf12, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5,
buf6, reinterpret_tensor(buf7, (4, 400), (400, 1), 0), buf9, buf11,
primals_10, primals_8, primals_6)
class NetNew(nn.Module):
def __init__(self):
super(NetNew, self).__init__()
self.conv1 = nn.Conv2d(3, 12, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(12, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
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.fc1.weight
primals_7 = self.fc1.bias
primals_8 = self.fc2.weight
primals_9 = self.fc2.bias
primals_10 = self.fc3.weight
primals_11 = self.fc3.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]
|
tassotirap/data-science
|
Net
| false
| 13,032
|
[
"Apache-2.0"
] | 0
|
644bc351740cda90c0d8c907132d9da9630266c9
|
https://github.com/tassotirap/data-science/tree/644bc351740cda90c0d8c907132d9da9630266c9
|
MaxPoolStride1
|
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
import torch._utils
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, x):
padding = int(self.pad / 2)
padded_x = F.pad(x, (padding, padding, padding, padding), mode=
'constant', value=0)
pooled_x = nn.MaxPool2d(self.kernel_size, 1)(padded_x)
return pooled_x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'kernel_size': 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
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
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 = -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)
tmp12 = x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp8 & tmp13
tmp16 = tmp15 & tmp14
tmp17 = tl.load(in_ptr0 + (-4 + x0 + 4 * x1 + 16 * x2), tmp16 & xmask,
other=0.0)
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = 1 + x0
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp8 & tmp20
tmp23 = tmp22 & tmp21
tmp24 = tl.load(in_ptr0 + (-3 + x0 + 4 * x1 + 16 * x2), tmp23 & xmask,
other=0.0)
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = 2 + x0
tmp27 = tmp26 >= tmp1
tmp28 = tmp26 < tmp3
tmp29 = tmp8 & tmp27
tmp30 = tmp29 & tmp28
tmp31 = tl.load(in_ptr0 + (-2 + x0 + 4 * x1 + 16 * x2), tmp30 & xmask,
other=0.0)
tmp32 = triton_helpers.maximum(tmp31, tmp25)
tmp33 = x1
tmp34 = tmp33 >= tmp1
tmp35 = tmp33 < tmp3
tmp36 = tmp34 & tmp35
tmp37 = tmp36 & tmp6
tmp38 = tmp37 & tmp7
tmp39 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1 + 16 * x2), tmp38 & xmask,
other=0.0)
tmp40 = triton_helpers.maximum(tmp39, tmp32)
tmp41 = tmp36 & tmp13
tmp42 = tmp41 & tmp14
tmp43 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp42 & xmask, other=0.0
)
tmp44 = triton_helpers.maximum(tmp43, tmp40)
tmp45 = tmp36 & tmp20
tmp46 = tmp45 & tmp21
tmp47 = tl.load(in_ptr0 + (1 + x0 + 4 * x1 + 16 * x2), tmp46 & xmask,
other=0.0)
tmp48 = triton_helpers.maximum(tmp47, tmp44)
tmp49 = tmp36 & tmp27
tmp50 = tmp49 & tmp28
tmp51 = tl.load(in_ptr0 + (2 + x0 + 4 * x1 + 16 * x2), tmp50 & xmask,
other=0.0)
tmp52 = triton_helpers.maximum(tmp51, tmp48)
tmp53 = 1 + x1
tmp54 = tmp53 >= tmp1
tmp55 = tmp53 < tmp3
tmp56 = tmp54 & tmp55
tmp57 = tmp56 & tmp6
tmp58 = tmp57 & tmp7
tmp59 = tl.load(in_ptr0 + (3 + x0 + 4 * x1 + 16 * x2), tmp58 & xmask,
other=0.0)
tmp60 = triton_helpers.maximum(tmp59, tmp52)
tmp61 = tmp56 & tmp13
tmp62 = tmp61 & tmp14
tmp63 = tl.load(in_ptr0 + (4 + x0 + 4 * x1 + 16 * x2), tmp62 & xmask,
other=0.0)
tmp64 = triton_helpers.maximum(tmp63, tmp60)
tmp65 = tmp56 & tmp20
tmp66 = tmp65 & tmp21
tmp67 = tl.load(in_ptr0 + (5 + x0 + 4 * x1 + 16 * x2), tmp66 & xmask,
other=0.0)
tmp68 = triton_helpers.maximum(tmp67, tmp64)
tmp69 = tmp56 & tmp27
tmp70 = tmp69 & tmp28
tmp71 = tl.load(in_ptr0 + (6 + x0 + 4 * x1 + 16 * x2), tmp70 & xmask,
other=0.0)
tmp72 = triton_helpers.maximum(tmp71, tmp68)
tmp73 = 2 + x1
tmp74 = tmp73 >= tmp1
tmp75 = tmp73 < tmp3
tmp76 = tmp74 & tmp75
tmp77 = tmp76 & tmp6
tmp78 = tmp77 & tmp7
tmp79 = tl.load(in_ptr0 + (7 + x0 + 4 * x1 + 16 * x2), tmp78 & xmask,
other=0.0)
tmp80 = triton_helpers.maximum(tmp79, tmp72)
tmp81 = tmp76 & tmp13
tmp82 = tmp81 & tmp14
tmp83 = tl.load(in_ptr0 + (8 + x0 + 4 * x1 + 16 * x2), tmp82 & xmask,
other=0.0)
tmp84 = triton_helpers.maximum(tmp83, tmp80)
tmp85 = tmp76 & tmp20
tmp86 = tmp85 & tmp21
tmp87 = tl.load(in_ptr0 + (9 + x0 + 4 * x1 + 16 * x2), tmp86 & xmask,
other=0.0)
tmp88 = triton_helpers.maximum(tmp87, tmp84)
tmp89 = tmp76 & tmp27
tmp90 = tmp89 & tmp28
tmp91 = tl.load(in_ptr0 + (10 + x0 + 4 * x1 + 16 * x2), tmp90 & xmask,
other=0.0)
tmp92 = triton_helpers.maximum(tmp91, tmp88)
tl.store(out_ptr0 + x4, tmp92, 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 MaxPoolStride1New(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1New, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
tiahflorens/AlphaPose
|
MaxPoolStride1
| false
| 13,033
|
[
"Apache-2.0"
] | 0
|
84b844eff543eaa619d994ea0b15cb6caf69950d
|
https://github.com/tiahflorens/AlphaPose/tree/84b844eff543eaa619d994ea0b15cb6caf69950d
|
ConcatBlock
|
import torch
import torch.nn as nn
import torch.nn.functional
class ConcatBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConcatBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns, kernel_size=1,
padding=0)
self.conv2 = nn.Conv2d(self.in_chns, self.out_chns, kernel_size=1,
padding=0)
self.ac1 = nn.LeakyReLU()
self.ac2 = nn.LeakyReLU()
def forward(self, x):
x = self.conv1(x)
x = self.ac1(x)
x = self.conv2(x)
x = self.ac2(x)
return x
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
import torch.nn.functional
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_leaky_relu_0(in_ptr0, in_ptr1, 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
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 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, 1, 1), (4, 1, 1, 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=(0, 0), 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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(256)](buf0,
primals_2, buf1, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf3 = extern_kernels.convolution(buf2, 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), (64, 16, 4, 1), torch.bool)
buf5 = buf0
del buf0
triton_poi_fused_convolution_leaky_relu_0[grid(256)](buf3,
primals_5, buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf3
del primals_5
return buf5, primals_1, primals_3, primals_4, buf1, buf2, buf4
class ConcatBlockNew(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConcatBlockNew, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns, kernel_size=1,
padding=0)
self.conv2 = nn.Conv2d(self.in_chns, self.out_chns, kernel_size=1,
padding=0)
self.ac1 = nn.LeakyReLU()
self.ac2 = nn.LeakyReLU()
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]
|
timothytancy/SSL4MIS
|
ConcatBlock
| false
| 13,034
|
[
"MIT"
] | 0
|
7879ad3483223e31a2785f5112eac1d4fa36b66e
|
https://github.com/timothytancy/SSL4MIS/tree/7879ad3483223e31a2785f5112eac1d4fa36b66e
|
RingLoss
|
import torch
import warnings
import torch.nn as nn
from torchvision.transforms import *
class RingLoss(nn.Module):
"""Ring loss.
Reference:
Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
"""
def __init__(self):
super(RingLoss, self).__init__()
warnings.warn('This method is deprecated')
self.radius = nn.Parameter(torch.ones(1, dtype=torch.float))
def forward(self, x):
loss = ((x.norm(p=2, dim=1) - self.radius) ** 2).mean()
return loss
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 warnings
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
@triton.jit
def triton_per_fused_linalg_vector_norm_mean_mul_pow_sub_0(in_out_ptr0,
in_ptr0, in_ptr1, out_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 % 16
r1 = rindex // 16
r2 = rindex
tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
tmp5 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
tmp8 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
tmp12 = tl.load(in_ptr1 + 0)
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp1 = tmp0 * tmp0
tmp3 = tmp2 * tmp2
tmp4 = tmp1 + tmp3
tmp6 = tmp5 * tmp5
tmp7 = tmp4 + tmp6
tmp9 = tmp8 * tmp8
tmp10 = tmp7 + tmp9
tmp11 = libdevice.sqrt(tmp10)
tmp14 = tmp11 - tmp13
tmp15 = 2.0
tmp16 = tmp14 * tmp15
tmp17 = tmp14 * tmp14
tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
tmp20 = tl.sum(tmp18, 1)[:, None]
tmp21 = 64.0
tmp22 = tmp20 / tmp21
tl.store(out_ptr1 + tl.broadcast_to(r2, [XBLOCK, RBLOCK]), tmp16, None)
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp22, None)
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)
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
buf1 = empty_strided_cuda((), (), torch.float32)
buf3 = buf1
del buf1
get_raw_stream(0)
triton_per_fused_linalg_vector_norm_mean_mul_pow_sub_0[grid(1)](buf3,
primals_1, primals_2, buf2, 1, 64, XBLOCK=1, num_warps=2,
num_stages=1)
del primals_1
del primals_2
return buf3, buf2
class RingLossNew(nn.Module):
"""Ring loss.
Reference:
Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
"""
def __init__(self):
super(RingLossNew, self).__init__()
warnings.warn('This method is deprecated')
self.radius = nn.Parameter(torch.ones(1, dtype=torch.float))
def forward(self, input_0):
primals_2 = self.radius
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
theodorhusefest/ABD-Net
|
RingLoss
| false
| 13,035
|
[
"MIT"
] | 0
|
4ad71205954726b88d081ca079c28378f74e3007
|
https://github.com/theodorhusefest/ABD-Net/tree/4ad71205954726b88d081ca079c28378f74e3007
|
OutPutBlock
|
import torch
import torch.nn as nn
import torch.nn.functional
class OutPutBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutPutBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns // 2, kernel_size
=1, padding=0)
self.conv2 = nn.Conv2d(self.in_chns // 2, self.out_chns,
kernel_size=1, padding=0)
self.drop1 = nn.Dropout2d(0.3)
self.drop2 = nn.Dropout2d(0.3)
self.ac1 = nn.LeakyReLU()
def forward(self, x):
x = self.drop1(x)
x = self.conv1(x)
x = self.ac1(x)
x = self.drop2(x)
x = self.conv2(x)
return x
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
import torch.nn.functional
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 2
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, 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)
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, (2, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (2,), (1,))
assert_size_stride(primals_4, (4, 2, 1, 1), (2, 1, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 2, 4, 4), (32, 16, 4, 1))
buf1 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(128)](buf0,
primals_3, buf1, buf2, 128, XBLOCK=128, num_warps=4, num_stages=1)
del buf0
del primals_3
buf3 = extern_kernels.convolution(buf2, 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 = buf3
del buf3
triton_poi_fused_convolution_1[grid(256)](buf4, primals_5, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
return buf4, primals_1, primals_2, primals_4, buf1, buf2
class OutPutBlockNew(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutPutBlockNew, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns // 2, kernel_size
=1, padding=0)
self.conv2 = nn.Conv2d(self.in_chns // 2, self.out_chns,
kernel_size=1, padding=0)
self.drop1 = nn.Dropout2d(0.3)
self.drop2 = nn.Dropout2d(0.3)
self.ac1 = nn.LeakyReLU()
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]
|
timothytancy/SSL4MIS
|
OutPutBlock
| false
| 13,036
|
[
"MIT"
] | 0
|
7879ad3483223e31a2785f5112eac1d4fa36b66e
|
https://github.com/timothytancy/SSL4MIS/tree/7879ad3483223e31a2785f5112eac1d4fa36b66e
|
ConvolutionModule
|
import torch
from torch import Tensor
from torch import nn
class Swish(torch.nn.Module):
"""Construct an Swish object."""
def forward(self, x: 'Tensor') ->Tensor:
"""Return Swich activation function."""
return x * torch.sigmoid(x)
class ConvolutionModule(nn.Module):
"""ConvolutionModule in Conformer model.
Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py
Args:
channels (int): The number of channels of conv layers.
kernel_size (int): Kernerl size of conv layers.
bias (bool): Whether to use bias in conv layers (default=True).
"""
def __init__(self, channels: 'int', kernel_size: 'int', bias: 'bool'=True
) ->None:
"""Construct an ConvolutionModule object."""
super(ConvolutionModule, self).__init__()
assert (kernel_size - 1) % 2 == 0
self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels,
kernel_size=1, stride=1, padding=0, bias=bias)
self.depthwise_conv = nn.Conv1d(channels, channels, kernel_size,
stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias
=bias)
self.norm = nn.LayerNorm(channels)
self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1,
stride=1, padding=0, bias=bias)
self.activation = Swish()
def forward(self, x: 'Tensor') ->Tensor:
"""Compute convolution module.
Args:
x: Input tensor (#time, batch, channels).
Returns:
Tensor: Output tensor (#time, batch, channels).
"""
x = x.permute(1, 2, 0)
x = self.pointwise_conv1(x)
x = nn.functional.glu(x, dim=1)
x = self.depthwise_conv(x)
x = x.permute(0, 2, 1)
x = self.norm(x)
x = x.permute(0, 2, 1)
x = self.activation(x)
x = self.pointwise_conv2(x)
return x.permute(2, 0, 1)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'channels': 4, 'kernel_size': 1}]
|
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 torch import Tensor
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_convolution_0(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
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 8
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_glu_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
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 32 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (16 + x0 + 32 * x1), xmask)
tmp2 = tl.sigmoid(tmp1)
tmp3 = tmp0 * tmp2
tl.store(out_ptr0 + x2, tmp3, xmask)
@triton.jit
def triton_poi_fused_convolution_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
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)
@triton.jit
def triton_poi_fused_native_layer_norm_4(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 + 16 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x2, tmp8, xmask)
tl.store(out_ptr1 + x2, tmp23, xmask)
@triton.jit
def triton_poi_fused_convolution_mul_native_layer_norm_sigmoid_5(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr1, out_ptr2, 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
y3 = yindex
y1 = yindex // 4
y0 = yindex % 4
tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr1 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr2 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr3 + y0, ymask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.sigmoid(tmp8)
tmp10 = tmp8 * tmp9
tl.store(out_ptr1 + (y0 + 4 * x2 + 16 * y1), tmp10, xmask & ymask)
tl.store(out_ptr2 + (x2 + 4 * y3), tmp10, xmask & ymask)
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, (8, 4, 1), (4, 1, 1))
assert_size_stride(primals_3, (8,), (1,))
assert_size_stride(primals_4, (4, 1, 1), (1, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=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, 8, 4), (32, 4, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(128)](buf2, primals_3, 128,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
buf3 = buf0
del buf0
triton_poi_fused_glu_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=4, bias=None)
assert_size_stride(buf4, (4, 4, 4), (16, 4, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_3[grid(64)](buf5, primals_5, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_5
buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf7 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_native_layer_norm_4[grid(16)](buf5, buf6, buf7, 16,
XBLOCK=16, num_warps=1, num_stages=1)
buf9 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_convolution_mul_native_layer_norm_sigmoid_5[grid(
16, 4)](buf5, buf6, buf7, primals_6, primals_7, buf9, buf10, 16,
4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del buf6
del buf7
buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf11, (4, 4, 4), (16, 4, 1))
del buf10
buf12 = buf11
del buf11
triton_poi_fused_convolution_3[grid(64)](buf12, primals_9, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_9
return (reinterpret_tensor(buf12, (4, 4, 4), (1, 16, 4), 0), primals_2,
primals_4, primals_6, primals_7, primals_8, reinterpret_tensor(
primals_1, (4, 4, 4), (4, 1, 16), 0), buf2, buf3, buf5, buf9)
class Swish(torch.nn.Module):
"""Construct an Swish object."""
def forward(self, x: 'Tensor') ->Tensor:
"""Return Swich activation function."""
return x * torch.sigmoid(x)
class ConvolutionModuleNew(nn.Module):
"""ConvolutionModule in Conformer model.
Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py
Args:
channels (int): The number of channels of conv layers.
kernel_size (int): Kernerl size of conv layers.
bias (bool): Whether to use bias in conv layers (default=True).
"""
def __init__(self, channels: 'int', kernel_size: 'int', bias: 'bool'=True
) ->None:
"""Construct an ConvolutionModule object."""
super(ConvolutionModuleNew, self).__init__()
assert (kernel_size - 1) % 2 == 0
self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels,
kernel_size=1, stride=1, padding=0, bias=bias)
self.depthwise_conv = nn.Conv1d(channels, channels, kernel_size,
stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias
=bias)
self.norm = nn.LayerNorm(channels)
self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1,
stride=1, padding=0, bias=bias)
self.activation = Swish()
def forward(self, input_0):
primals_2 = self.pointwise_conv1.weight
primals_3 = self.pointwise_conv1.bias
primals_4 = self.depthwise_conv.weight
primals_5 = self.depthwise_conv.bias
primals_6 = self.norm.weight
primals_7 = self.norm.bias
primals_8 = self.pointwise_conv2.weight
primals_9 = self.pointwise_conv2.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])
return output[0]
|
thangdepzai/icefall
|
ConvolutionModule
| false
| 13,037
|
[
"Apache-2.0"
] | 0
|
8c7995d493c4309c3d09bdabfa1ab12b4eec2657
|
https://github.com/thangdepzai/icefall/tree/8c7995d493c4309c3d09bdabfa1ab12b4eec2657
|
PAMA
|
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 AttentionUnit(nn.Module):
def __init__(self, channels):
super(AttentionUnit, self).__init__()
self.relu6 = nn.ReLU6()
self.f = nn.Conv2d(channels, channels // 2, (1, 1))
self.g = nn.Conv2d(channels, channels // 2, (1, 1))
self.h = nn.Conv2d(channels, channels // 2, (1, 1))
self.out_conv = nn.Conv2d(channels // 2, channels, (1, 1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, Fc, Fs):
B, C, H, W = Fc.shape
f_Fc = self.relu6(self.f(mean_variance_norm(Fc)))
g_Fs = self.relu6(self.g(mean_variance_norm(Fs)))
h_Fs = self.relu6(self.h(Fs))
f_Fc = f_Fc.view(f_Fc.shape[0], f_Fc.shape[1], -1).permute(0, 2, 1)
g_Fs = g_Fs.view(g_Fs.shape[0], g_Fs.shape[1], -1)
Attention = self.softmax(torch.bmm(f_Fc, g_Fs))
h_Fs = h_Fs.view(h_Fs.shape[0], h_Fs.shape[1], -1)
Fcs = torch.bmm(h_Fs, Attention.permute(0, 2, 1))
Fcs = Fcs.view(B, C // 2, H, W)
Fcs = self.relu6(self.out_conv(Fcs))
return Fcs
class FuseUnit(nn.Module):
def __init__(self, channels):
super(FuseUnit, self).__init__()
self.proj1 = nn.Conv2d(2 * channels, channels, (1, 1))
self.proj2 = nn.Conv2d(channels, channels, (1, 1))
self.proj3 = nn.Conv2d(channels, channels, (1, 1))
self.fuse1x = nn.Conv2d(channels, 1, (1, 1), stride=1)
self.fuse3x = nn.Conv2d(channels, 1, (3, 3), stride=1)
self.fuse5x = nn.Conv2d(channels, 1, (5, 5), stride=1)
self.pad3x = nn.ReflectionPad2d((1, 1, 1, 1))
self.pad5x = nn.ReflectionPad2d((2, 2, 2, 2))
self.sigmoid = nn.Sigmoid()
def forward(self, F1, F2):
Fcat = self.proj1(torch.cat((F1, F2), dim=1))
F1 = self.proj2(F1)
F2 = self.proj3(F2)
fusion1 = self.sigmoid(self.fuse1x(Fcat))
fusion3 = self.sigmoid(self.fuse3x(self.pad3x(Fcat)))
fusion5 = self.sigmoid(self.fuse5x(self.pad5x(Fcat)))
fusion = (fusion1 + fusion3 + fusion5) / 3
return torch.clamp(fusion, min=0, max=1.0) * F1 + torch.clamp(1 -
fusion, min=0, max=1.0) * F2
class PAMA(nn.Module):
def __init__(self, channels):
super(PAMA, self).__init__()
self.conv_in = nn.Conv2d(channels, channels, (3, 3), stride=1)
self.attn = AttentionUnit(channels)
self.fuse = FuseUnit(channels)
self.conv_out = nn.Conv2d(channels, channels, (3, 3), stride=1)
self.pad = nn.ReflectionPad2d((1, 1, 1, 1))
self.relu6 = nn.ReLU6()
def forward(self, Fc, Fs):
Fc = self.relu6(self.conv_in(self.pad(Fc)))
Fs = self.relu6(self.conv_in(self.pad(Fs)))
Fcs = self.attn(Fc, Fs)
Fcs = self.relu6(self.conv_out(self.pad(Fcs)))
Fcs = self.fuse(Fc, Fcs)
return Fcs
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
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_reflection_pad2d_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
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_per_fused_add_convolution_div_hardtanh_hardtanh_backward_mean_sqrt_sub_var_1(
in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
out_ptr2, out_ptr3, out_ptr6, out_ptr7, 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)
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (r2 + 16 * x3), xmask, other=0.0)
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 6.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = tmp2 <= tmp3
tmp8 = tmp2 >= tmp5
tmp9 = tmp7 | tmp8
tmp11 = tmp10 + tmp1
tmp12 = triton_helpers.maximum(tmp11, tmp3)
tmp13 = triton_helpers.minimum(tmp12, tmp5)
tmp14 = tmp11 <= tmp3
tmp15 = tmp11 >= tmp5
tmp16 = tmp14 | tmp15
tmp17 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
tmp19 = tl.where(xmask, tmp17, 0)
tmp20 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
tmp22 = tl.where(xmask, tmp20, 0)
tmp23 = tl.sum(tmp22, 1)[:, None]
tmp24 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp25 = tmp24.to(tl.float32)
tmp26 = tmp23 / tmp25
tmp27 = tmp17 - tmp26
tmp28 = tmp27 * tmp27
tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
tmp31 = tl.where(xmask, tmp29, 0)
tmp32 = tl.sum(tmp31, 1)[:, None]
tmp34 = tl.sum(tmp19, 1)[:, None]
tmp35 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp37 = tl.where(xmask, tmp35, 0)
tmp38 = tl.broadcast_to(tmp35, [XBLOCK, RBLOCK])
tmp40 = tl.where(xmask, tmp38, 0)
tmp41 = tl.sum(tmp40, 1)[:, None]
tmp42 = tmp41 / tmp25
tmp43 = tmp35 - tmp42
tmp44 = tmp43 * tmp43
tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
tmp47 = tl.where(xmask, tmp45, 0)
tmp48 = tl.sum(tmp47, 1)[:, None]
tmp50 = tl.sum(tmp37, 1)[:, None]
tmp51 = 15.0
tmp52 = tmp32 / tmp51
tmp53 = 1e-05
tmp54 = tmp52 + tmp53
tmp55 = libdevice.sqrt(tmp54)
tmp56 = tmp48 / tmp51
tmp57 = tmp56 + tmp53
tmp58 = libdevice.sqrt(tmp57)
tmp59 = 16.0
tmp60 = tmp50 / tmp59
tmp61 = tmp6 - tmp60
tmp62 = tmp61 / tmp58
tmp63 = tmp34 / tmp59
tmp64 = tmp13 - tmp63
tmp65 = tmp64 / tmp55
tl.store(out_ptr0 + (r2 + 16 * x3), tmp6, xmask)
tl.store(out_ptr1 + (r2 + 16 * x3), tmp9, xmask)
tl.store(out_ptr2 + (r2 + 16 * x3), tmp13, xmask)
tl.store(out_ptr3 + (r2 + 16 * x3), tmp16, xmask)
tl.debug_barrier()
tl.store(in_out_ptr0 + x3, tmp55, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x3, tmp58, xmask)
tl.store(out_ptr6 + (r2 + 16 * x3), tmp62, xmask)
tl.store(out_ptr7 + (r2 + 16 * x3), tmp65, xmask)
@triton.jit
def triton_poi_fused_convolution_hardtanh_hardtanh_backward_2(in_ptr0,
in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 2
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 6.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = tmp2 <= tmp3
tmp8 = tmp2 >= tmp5
tmp9 = tmp7 | tmp8
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp9, xmask)
@triton.jit
def triton_per_fused__softmax_3(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_convolution_hardtanh_reflection_pad2d_4(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x4 = xindex // 36
x2 = xindex // 36 % 4
x5 = 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 * x4),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 6.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tl.store(out_ptr0 + x5, tmp6, xmask)
@triton.jit
def triton_poi_fused_convolution_hardtanh_hardtanh_backward_5(in_ptr0,
in_ptr1, 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
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 6.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = tmp2 <= tmp3
tmp8 = tmp2 >= tmp5
tmp9 = tmp7 | tmp8
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp9, xmask)
@triton.jit
def triton_poi_fused_cat_6(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_convolution_7(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_poi_fused_convolution_8(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
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(in_out_ptr0 + x0, tmp3, xmask)
@triton.jit
def triton_poi_fused_reflection_pad2d_9(in_ptr0, 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
x2 = xindex // 64
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-2 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-2 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_clamp_convolution_div_mul_rsub_sigmoid_10(in_out_ptr0,
in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_out_ptr1 + x3, xmask)
tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr2 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr3 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr4 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp7 = tl.sigmoid(tmp6)
tmp9 = tl.sigmoid(tmp8)
tmp10 = tmp7 + tmp9
tmp12 = tl.sigmoid(tmp11)
tmp13 = tmp10 + tmp12
tmp14 = 0.3333333333333333
tmp15 = tmp13 * tmp14
tmp16 = 0.0
tmp17 = triton_helpers.maximum(tmp15, tmp16)
tmp18 = 1.0
tmp19 = triton_helpers.minimum(tmp17, tmp18)
tmp20 = tmp19 * tmp2
tmp21 = tmp18 - tmp15
tmp22 = triton_helpers.maximum(tmp21, tmp16)
tmp23 = triton_helpers.minimum(tmp22, tmp18)
tmp24 = tmp23 * tmp5
tmp25 = tmp20 + tmp24
tl.store(in_out_ptr0 + x3, tmp2, xmask)
tl.store(in_out_ptr1 + x3, tmp5, xmask)
tl.store(out_ptr0 + x3, tmp25, xmask)
@triton.jit
def triton_poi_fused_convolution_hardtanh_backward_11(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_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tmp5 = 6.0
tmp6 = tmp2 >= tmp5
tmp7 = tmp4 | tmp6
tl.store(out_ptr0 + x3, 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, primals_19, primals_20, primals_21, primals_22,
primals_23, primals_24, primals_25, primals_26) = 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,))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (2, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_6, (2,), (1,))
assert_size_stride(primals_7, (2, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_8, (2,), (1,))
assert_size_stride(primals_9, (2, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_10, (2,), (1,))
assert_size_stride(primals_11, (4, 2, 1, 1), (2, 1, 1, 1))
assert_size_stride(primals_12, (4,), (1,))
assert_size_stride(primals_13, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_14, (4,), (1,))
assert_size_stride(primals_15, (4, 8, 1, 1), (8, 1, 1, 1))
assert_size_stride(primals_16, (4,), (1,))
assert_size_stride(primals_17, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_18, (4,), (1,))
assert_size_stride(primals_19, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_20, (4,), (1,))
assert_size_stride(primals_21, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_22, (1,), (1,))
assert_size_stride(primals_23, (1, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_24, (1,), (1,))
assert_size_stride(primals_25, (1, 4, 5, 5), (100, 25, 5, 1))
assert_size_stride(primals_26, (1,), (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_reflection_pad2d_0[grid(576)](primals_1, buf0, 576,
XBLOCK=256, 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))
buf3 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
triton_poi_fused_reflection_pad2d_0[grid(576)](primals_4, buf3, 576,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_4
buf4 = extern_kernels.convolution(buf3, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf54 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf55 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf9 = buf7
del buf7
buf16 = buf14
del buf14
buf18 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_per_fused_add_convolution_div_hardtanh_hardtanh_backward_mean_sqrt_sub_var_1[
grid(16)](buf9, buf16, buf4, primals_3, buf1, buf5, buf54, buf2,
buf55, buf18, buf11, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
del buf1
del primals_3
buf12 = extern_kernels.convolution(buf11, primals_5, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 2, 4, 4), (32, 16, 4, 1))
buf19 = extern_kernels.convolution(buf18, primals_7, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf19, (4, 2, 4, 4), (32, 16, 4, 1))
buf20 = extern_kernels.convolution(buf5, primals_9, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 2, 4, 4), (32, 16, 4, 1))
buf21 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
buf53 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_hardtanh_hardtanh_backward_2[grid(128)](
buf12, primals_6, buf21, buf53, 128, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_6
buf22 = buf12
del buf12
buf52 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_hardtanh_hardtanh_backward_2[grid(128)](
buf19, primals_8, buf22, buf52, 128, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_8
buf23 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf21, (4, 16, 2), (32, 1, 16
), 0), reinterpret_tensor(buf22, (4, 2, 16), (32, 16, 1), 0),
out=buf23)
buf26 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
triton_per_fused__softmax_3[grid(64)](buf23, buf26, 64, 16, XBLOCK=
1, num_warps=2, num_stages=1)
buf27 = buf19
del buf19
buf51 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_hardtanh_hardtanh_backward_2[grid(128)](
buf20, primals_10, buf27, buf51, 128, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_10
buf28 = reinterpret_tensor(buf20, (4, 2, 16), (32, 16, 1), 0)
del buf20
extern_kernels.bmm(reinterpret_tensor(buf27, (4, 2, 16), (32, 16, 1
), 0), reinterpret_tensor(buf26, (4, 16, 16), (256, 1, 16), 0),
out=buf28)
buf29 = extern_kernels.convolution(reinterpret_tensor(buf28, (4, 2,
4, 4), (32, 16, 4, 1), 0), primals_11, stride=(1, 1), padding=(
0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
groups=1, bias=None)
assert_size_stride(buf29, (4, 4, 4, 4), (64, 16, 4, 1))
buf30 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32
)
triton_poi_fused_convolution_hardtanh_reflection_pad2d_4[grid(576)](
buf29, primals_12, buf30, 576, XBLOCK=128, num_warps=4,
num_stages=1)
buf31 = extern_kernels.convolution(buf30, primals_13, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf31, (4, 4, 4, 4), (64, 16, 4, 1))
buf32 = buf4
del buf4
buf49 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_hardtanh_hardtanh_backward_5[grid(256)](
buf31, primals_14, buf32, buf49, 256, XBLOCK=256, num_warps=4,
num_stages=1)
del primals_14
buf33 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32
)
triton_poi_fused_cat_6[grid(512)](buf2, buf32, buf33, 512, XBLOCK=
256, num_warps=4, num_stages=1)
buf34 = extern_kernels.convolution(buf33, primals_15, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf34, (4, 4, 4, 4), (64, 16, 4, 1))
buf35 = buf34
del buf34
triton_poi_fused_convolution_7[grid(256)](buf35, primals_16, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_16
buf36 = extern_kernels.convolution(buf2, primals_17, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf36, (4, 4, 4, 4), (64, 16, 4, 1))
buf38 = extern_kernels.convolution(buf32, primals_19, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf38, (4, 4, 4, 4), (64, 16, 4, 1))
buf40 = extern_kernels.convolution(buf35, primals_21, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf40, (4, 1, 4, 4), (16, 16, 4, 1))
buf41 = buf40
del buf40
triton_poi_fused_convolution_8[grid(64)](buf41, primals_22, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_22
buf42 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32
)
triton_poi_fused_reflection_pad2d_0[grid(576)](buf35, buf42, 576,
XBLOCK=256, num_warps=4, num_stages=1)
buf43 = extern_kernels.convolution(buf42, primals_23, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf43, (4, 1, 4, 4), (16, 16, 4, 1))
buf44 = buf43
del buf43
triton_poi_fused_convolution_8[grid(64)](buf44, primals_24, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_24
buf45 = reinterpret_tensor(buf23, (4, 4, 8, 8), (256, 64, 8, 1), 0)
del buf23
triton_poi_fused_reflection_pad2d_9[grid(1024)](buf35, buf45, 1024,
XBLOCK=128, num_warps=4, num_stages=1)
buf46 = extern_kernels.convolution(buf45, primals_25, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf46, (4, 1, 4, 4), (16, 16, 4, 1))
buf47 = buf46
del buf46
triton_poi_fused_convolution_8[grid(64)](buf47, primals_26, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_26
buf37 = buf36
del buf36
buf39 = buf38
del buf38
buf48 = buf31
del buf31
triton_poi_fused_add_clamp_convolution_div_mul_rsub_sigmoid_10[grid
(256)](buf37, buf39, primals_18, primals_20, buf41, buf44,
buf47, buf48, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_18
del primals_20
buf50 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_hardtanh_backward_11[grid(256)](buf29,
primals_12, buf50, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf29
del primals_12
return (buf48, primals_2, primals_5, primals_7, primals_9, primals_11,
primals_13, primals_15, primals_17, primals_19, primals_21,
primals_23, primals_25, buf0, buf2, buf3, buf5, buf9, buf11, buf16,
buf18, buf26, reinterpret_tensor(buf28, (4, 2, 4, 4), (32, 16, 4, 1
), 0), buf30, buf32, buf33, buf35, buf37, buf39, buf41, buf42,
buf44, buf45, buf47, buf49, buf50, reinterpret_tensor(buf27, (4, 16,
2), (32, 1, 16), 0), reinterpret_tensor(buf21, (4, 2, 16), (32, 16,
1), 0), reinterpret_tensor(buf22, (4, 16, 2), (32, 1, 16), 0),
buf51, buf52, buf53, buf54, buf55)
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 AttentionUnit(nn.Module):
def __init__(self, channels):
super(AttentionUnit, self).__init__()
self.relu6 = nn.ReLU6()
self.f = nn.Conv2d(channels, channels // 2, (1, 1))
self.g = nn.Conv2d(channels, channels // 2, (1, 1))
self.h = nn.Conv2d(channels, channels // 2, (1, 1))
self.out_conv = nn.Conv2d(channels // 2, channels, (1, 1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, Fc, Fs):
B, C, H, W = Fc.shape
f_Fc = self.relu6(self.f(mean_variance_norm(Fc)))
g_Fs = self.relu6(self.g(mean_variance_norm(Fs)))
h_Fs = self.relu6(self.h(Fs))
f_Fc = f_Fc.view(f_Fc.shape[0], f_Fc.shape[1], -1).permute(0, 2, 1)
g_Fs = g_Fs.view(g_Fs.shape[0], g_Fs.shape[1], -1)
Attention = self.softmax(torch.bmm(f_Fc, g_Fs))
h_Fs = h_Fs.view(h_Fs.shape[0], h_Fs.shape[1], -1)
Fcs = torch.bmm(h_Fs, Attention.permute(0, 2, 1))
Fcs = Fcs.view(B, C // 2, H, W)
Fcs = self.relu6(self.out_conv(Fcs))
return Fcs
class FuseUnit(nn.Module):
def __init__(self, channels):
super(FuseUnit, self).__init__()
self.proj1 = nn.Conv2d(2 * channels, channels, (1, 1))
self.proj2 = nn.Conv2d(channels, channels, (1, 1))
self.proj3 = nn.Conv2d(channels, channels, (1, 1))
self.fuse1x = nn.Conv2d(channels, 1, (1, 1), stride=1)
self.fuse3x = nn.Conv2d(channels, 1, (3, 3), stride=1)
self.fuse5x = nn.Conv2d(channels, 1, (5, 5), stride=1)
self.pad3x = nn.ReflectionPad2d((1, 1, 1, 1))
self.pad5x = nn.ReflectionPad2d((2, 2, 2, 2))
self.sigmoid = nn.Sigmoid()
def forward(self, F1, F2):
Fcat = self.proj1(torch.cat((F1, F2), dim=1))
F1 = self.proj2(F1)
F2 = self.proj3(F2)
fusion1 = self.sigmoid(self.fuse1x(Fcat))
fusion3 = self.sigmoid(self.fuse3x(self.pad3x(Fcat)))
fusion5 = self.sigmoid(self.fuse5x(self.pad5x(Fcat)))
fusion = (fusion1 + fusion3 + fusion5) / 3
return torch.clamp(fusion, min=0, max=1.0) * F1 + torch.clamp(1 -
fusion, min=0, max=1.0) * F2
class PAMANew(nn.Module):
def __init__(self, channels):
super(PAMANew, self).__init__()
self.conv_in = nn.Conv2d(channels, channels, (3, 3), stride=1)
self.attn = AttentionUnit(channels)
self.fuse = FuseUnit(channels)
self.conv_out = nn.Conv2d(channels, channels, (3, 3), stride=1)
self.pad = nn.ReflectionPad2d((1, 1, 1, 1))
self.relu6 = nn.ReLU6()
def forward(self, input_0, input_1):
primals_2 = self.conv_in.weight
primals_3 = self.conv_in.bias
primals_5 = self.attn.f.weight
primals_6 = self.attn.f.bias
primals_7 = self.attn.g.weight
primals_8 = self.attn.g.bias
primals_9 = self.attn.h.weight
primals_10 = self.attn.h.bias
primals_11 = self.attn.out_conv.weight
primals_12 = self.attn.out_conv.bias
primals_15 = self.fuse.proj1.weight
primals_14 = self.fuse.proj1.bias
primals_17 = self.fuse.proj2.weight
primals_16 = self.fuse.proj2.bias
primals_19 = self.fuse.proj3.weight
primals_18 = self.fuse.proj3.bias
primals_21 = self.fuse.fuse1x.weight
primals_22 = self.fuse.fuse1x.bias
primals_23 = self.fuse.fuse3x.weight
primals_24 = self.fuse.fuse3x.bias
primals_25 = self.fuse.fuse5x.weight
primals_26 = self.fuse.fuse5x.bias
primals_13 = self.conv_out.weight
primals_20 = self.conv_out.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, 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])
return output[0]
|
sugi-san/PAMA
|
PAMA
| false
| 13,038
|
[
"MIT"
] | 0
|
95141ebf0d3b61828a0e545f989f96b8ef569f34
|
https://github.com/sugi-san/PAMA/tree/95141ebf0d3b61828a0e545f989f96b8ef569f34
|
Actor
|
import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc_units=128,
fc_units2=64):
super(Actor, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_size, fc_units)
self.fc2 = nn.Linear(fc_units, fc_units2)
self.fc3 = nn.Linear(fc_units2, action_size)
self.reset_parameters()
def reset_parameters(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(-0.003, 0.003)
def forward(self, state):
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
return F.tanh(self.fc3(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'state_size': 4, 'action_size': 4, 'seed': 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
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):
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(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)
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
@triton.jit
def triton_poi_fused_tanh_2(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
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, (128, 4), (4, 1))
assert_size_stride(primals_2, (128,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (64, 128), (128, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (4, 64), (64, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 128), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
del buf0
buf7 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf1,
primals_2, buf7, 8192, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 128), (128, 1), 0),
reinterpret_tensor(primals_4, (128, 64), (1, 128), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
del buf2
buf6 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
)
triton_poi_fused_relu_threshold_backward_1[grid(4096)](buf3,
primals_5, buf6, 4096, 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, 64), (64, 1), 0),
reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), out=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf4
triton_poi_fused_tanh_2[grid(256)](buf5, primals_7, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_7
return buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 128), (128, 1), 0
), reinterpret_tensor(buf3, (64, 64), (64, 1), 0
), buf5, primals_6, buf6, primals_4, buf7
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class ActorNew(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc_units=128,
fc_units2=64):
super(ActorNew, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_size, fc_units)
self.fc2 = nn.Linear(fc_units, fc_units2)
self.fc3 = nn.Linear(fc_units2, action_size)
self.reset_parameters()
def reset_parameters(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(-0.003, 0.003)
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_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
tjkemp/tennis-example
|
Actor
| false
| 13,039
|
[
"MIT"
] | 0
|
3cb0c52a93c65f88872cf44e3782bf87d9d8cef3
|
https://github.com/tjkemp/tennis-example/tree/3cb0c52a93c65f88872cf44e3782bf87d9d8cef3
|
CausalAttentionSortNet
|
import torch
from torch.nn import functional as F
from torch import nn
def bucket(buckets, t, dim=1):
shape = list(t.shape)
shape[dim:dim + 1] = [buckets, -1]
return t.reshape(*shape)
def differentiable_topk(x, k, temperature=1.0):
*_, n, dim = x.shape
topk_tensors = []
for i in range(k):
is_last = i == k - 1
values, indices = (x / temperature).softmax(dim=-1).topk(1, dim=-1)
topks = torch.zeros_like(x).scatter_(-1, indices, values)
topk_tensors.append(topks)
if not is_last:
x.scatter_(-1, indices, float('-inf'))
topks = torch.cat(topk_tensors, dim=-1)
return topks.reshape(*_, k * n, dim)
def max_neg_value(tensor):
return -torch.finfo(tensor.dtype).max
def cumavg(t, dim):
r = torch.arange(1, t.shape[dim] + 1, device=t.device, dtype=t.dtype)
expand_slice = [None] * len(t.shape)
expand_slice[dim] = slice(None, None)
return t.cumsum(dim=dim) / r[tuple(expand_slice)]
def mask_reordering_matrix(R, topk, temperature):
buckets = R.shape[1]
mask_value = max_neg_value(R)
mask = torch.zeros(R.shape, device=R.device).bool()
i, j = torch.triu_indices(buckets, buckets)
mask[:, i, j + topk] = True
R.masked_fill_(mask, mask_value)
return differentiable_topk(R, topk, temperature)
class CausalAttentionSortNet(nn.Module):
def __init__(self, heads, bucket_size, dim, temperature):
super().__init__()
self.heads = heads
self.bucket_size = bucket_size
self.dim = dim
self.temperature = temperature
def forward(self, q, k, topk=1):
bh, *_, h, dim = *q.shape, self.heads, self.dim
bh // h
buckets = q.shape[1] // self.bucket_size
kv_buckets = k.shape[1] // self.bucket_size
q_r = bucket(buckets, cumavg(q, dim=1))
k_r = bucket(kv_buckets, cumavg(k, dim=1))
sq = q_r[:, :, 0]
sk = k_r.sum(dim=2)
sk = F.pad(sk, (0, 0, topk, 0))
R = torch.einsum('bie,bje->bij', sq, sk) * dim ** -0.5
return mask_reordering_matrix(R, topk, self.temperature)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'heads': 4, 'bucket_size': 4, 'dim': 4, 'temperature': 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
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_triu_indices_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 2
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp0.to(tl.float64)
tmp6 = tl.full([1], 2.0, tl.float64)
tmp7 = tmp5 * tmp6
tmp8 = tl.full([1], 2.25, tl.float64)
tmp9 = tmp8 - tmp7
tmp10 = libdevice.sqrt(tmp9)
tmp11 = tl.full([1], 1.5, tl.float64)
tmp12 = tmp11 - tmp10
tmp13 = libdevice.floor(tmp12)
tmp14 = tmp13.to(tl.int64)
tmp15 = tmp14 + tmp1
tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp17 = tl.where(tmp4, tmp15, tmp16)
tmp18 = tmp0 >= tmp3
tl.full([1], 2, tl.int64)
tmp21 = -1 + x0
tmp22 = tmp21.to(tl.float64)
tmp23 = tmp22 * tmp6
tmp24 = tmp8 - tmp23
tmp25 = libdevice.sqrt(tmp24)
tmp26 = tmp11 - tmp25
tmp27 = libdevice.floor(tmp26)
tmp28 = tl.full([1], 1.0, tl.float64)
tmp29 = tmp28 - tmp27
tmp30 = tmp29 * tmp27
tmp31 = tl.full([1], 0.5, tl.float64)
tmp32 = tmp30 * tmp31
tmp33 = tmp22 - tmp32
tmp34 = libdevice.floor(tmp33)
tmp35 = tmp34.to(tl.int64)
tmp36 = tmp35 + tmp1
tmp37 = tl.full(tmp36.shape, 0.0, tmp36.dtype)
tmp38 = tl.where(tmp18, tmp36, tmp37)
tmp39 = tl.where(tmp4, tmp17, tmp38)
tl.store(out_ptr0 + x0, tmp39, xmask)
@triton.jit
def triton_poi_fused__to_copy_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 = tl.full([1], False, tl.int1)
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused__to_copy_index_put_lift_fresh_2(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 + 0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
tmp7 = tl.load(in_ptr0 + 1)
tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
tmp2 = tl.full([XBLOCK], 1, tl.int32)
tmp3 = tmp1 + tmp2
tmp4 = tmp1 < 0
tmp5 = tl.where(tmp4, tmp3, tmp1)
tl.device_assert((0 <= tmp5) & (tmp5 < 1),
'index out of bounds: 0 <= tmp5 < 1')
tmp9 = tl.full([1], 1, tl.int64)
tmp10 = tmp8 + tmp9
tmp11 = tl.full([XBLOCK], 2, tl.int32)
tmp12 = tmp10 + tmp11
tmp13 = tmp10 < 0
tmp14 = tl.where(tmp13, tmp12, tmp10)
tl.device_assert((0 <= tmp14) & (tmp14 < 2),
'index out of bounds: 0 <= tmp14 < 2')
tmp16 = tl.full([1], True, tl.int1)
tl.store(out_ptr0 + (tmp14 + 2 * x0), tmp16, xmask)
@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_3(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl
.constexpr):
xnumel = 16
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)
r2 = rindex
x0 = xindex % 4
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + (x0 + 4 * r2 + 16 * x1), xmask, other=0.0)
tmp1 = tmp0.to(tl.float32)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp3, = tl.associative_scan((tmp2,), 1, _triton_helper_fn_add0)
tl.store(out_ptr0 + (x0 + 4 * r2 + 16 * x1), tmp3, xmask)
@triton.jit
def triton_poi_fused_constant_pad_nd_sum_4(in_ptr0, 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 % 2
x0 = xindex % 4
x2 = xindex // 8
x3 = xindex
tmp0 = -1 + x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp2 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp4 = 1.0
tmp5 = tmp3 / tmp4
tmp6 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), tmp2 & xmask,
eviction_policy='evict_last', other=0.0)
tmp7 = 2.0
tmp8 = tmp6 / tmp7
tmp9 = tmp5 + tmp8
tmp10 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), tmp2 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = 3.0
tmp12 = tmp10 / tmp11
tmp13 = tmp9 + tmp12
tmp14 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), tmp2 & xmask,
eviction_policy='evict_last', other=0.0)
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp13 + tmp16
tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
tmp19 = tl.where(tmp2, tmp17, tmp18)
tl.store(out_ptr0 + x3, tmp19, xmask)
@triton.jit
def triton_poi_fused_div_5(in_out_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 = 1 + x1
tmp2 = tmp1.to(tl.float32)
tmp3 = tmp0 / tmp2
tl.store(in_out_ptr0 + x3, tmp3, xmask)
@triton.jit
def triton_poi_fused__softmax_masked_fill_mul_6(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 8
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 2
tmp0 = tl.load(in_ptr0 + x2, xmask).to(tl.int1)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp8 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp9 = tl.load(in_ptr1 + 2 * x1, xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp14 = tl.load(in_ptr1 + (1 + 2 * x1), xmask, eviction_policy='evict_last'
)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = -3.4028234663852886e+38
tmp5 = tl.where(tmp0, tmp4, tmp3)
tmp6 = 1.0
tmp7 = tmp5 * tmp6
tmp10 = tmp9 * tmp2
tmp11 = tl.where(tmp8, tmp4, tmp10)
tmp12 = tmp11 * tmp6
tmp15 = tmp14 * tmp2
tmp16 = tl.where(tmp13, tmp4, tmp15)
tmp17 = tmp16 * tmp6
tmp18 = triton_helpers.maximum(tmp12, tmp17)
tmp19 = tmp7 - tmp18
tmp20 = 0.25
tmp21 = tmp19 * tmp20
tmp22 = tl_math.exp(tmp21)
tl.store(out_ptr0 + x2, tmp22, xmask)
@triton.jit
def triton_poi_fused__softmax_7(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 8
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 2
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 / tmp3
tl.store(out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_scatter_zeros_like_8(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 = 0.0
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused_scatter_zeros_like_9(in_ptr0, in_ptr1, 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)
tmp2 = tl.load(in_ptr1 + x0, xmask)
tl.device_assert((0 <= tmp0) & (tmp0 < 2) | ~xmask,
'index out of bounds: 0 <= tmp0 < 2')
tl.store(out_ptr0 + (tmp0 + 2 * x0), tmp2, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((2,), (1,), torch.int64)
get_raw_stream(0)
triton_poi_fused_triu_indices_0[grid(2)](buf0, 2, XBLOCK=2,
num_warps=1, num_stages=1)
buf1 = empty_strided_cuda((4, 1, 2), (2, 2, 1), torch.bool)
triton_poi_fused__to_copy_1[grid(8)](buf1, 8, XBLOCK=8, num_warps=1,
num_stages=1)
triton_poi_fused__to_copy_index_put_lift_fresh_2[grid(4)](buf0,
buf1, 4, XBLOCK=4, num_warps=1, num_stages=1)
del buf0
buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_per_fused_cumsum_3[grid(16)](arg0_1, buf3, 16, 4, XBLOCK=1,
num_warps=2, num_stages=1)
del arg0_1
buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_per_fused_cumsum_3[grid(16)](arg1_1, buf4, 16, 4, XBLOCK=1,
num_warps=2, num_stages=1)
del arg1_1
buf5 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
triton_poi_fused_constant_pad_nd_sum_4[grid(32)](buf4, buf5, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del buf4
buf6 = buf3
del buf3
triton_poi_fused_div_5[grid(64)](buf6, 64, XBLOCK=64, num_warps=1,
num_stages=1)
buf7 = empty_strided_cuda((4, 1, 2), (2, 2, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf6, (4, 1, 4), (16, 0, 1),
0), reinterpret_tensor(buf5, (4, 4, 2), (8, 1, 4), 0), out=buf7)
del buf5
del buf6
buf8 = empty_strided_cuda((4, 1, 2), (2, 8, 1), torch.float32)
triton_poi_fused__softmax_masked_fill_mul_6[grid(8)](buf1, buf7,
buf8, 8, XBLOCK=8, num_warps=1, num_stages=1)
del buf1
buf9 = reinterpret_tensor(buf7, (4, 1, 2), (2, 8, 1), 0)
del buf7
triton_poi_fused__softmax_7[grid(8)](buf8, buf9, 8, XBLOCK=8,
num_warps=1, num_stages=1)
del buf8
buf10 = torch.ops.aten.topk.default(buf9, 1)
buf11 = buf10[0]
buf12 = buf10[1]
del buf10
buf13 = reinterpret_tensor(buf9, (4, 1, 2), (2, 2, 1), 0)
del buf9
triton_poi_fused_scatter_zeros_like_8[grid(8)](buf13, 8, XBLOCK=8,
num_warps=1, num_stages=1)
triton_poi_fused_scatter_zeros_like_9[grid(4)](buf12, buf11, buf13,
4, XBLOCK=4, num_warps=1, num_stages=1)
del buf11
del buf12
return buf13,
def bucket(buckets, t, dim=1):
shape = list(t.shape)
shape[dim:dim + 1] = [buckets, -1]
return t.reshape(*shape)
def differentiable_topk(x, k, temperature=1.0):
*_, n, dim = x.shape
topk_tensors = []
for i in range(k):
is_last = i == k - 1
values, indices = (x / temperature).softmax(dim=-1).topk(1, dim=-1)
topks = torch.zeros_like(x).scatter_(-1, indices, values)
topk_tensors.append(topks)
if not is_last:
x.scatter_(-1, indices, float('-inf'))
topks = torch.cat(topk_tensors, dim=-1)
return topks.reshape(*_, k * n, dim)
def max_neg_value(tensor):
return -torch.finfo(tensor.dtype).max
def cumavg(t, dim):
r = torch.arange(1, t.shape[dim] + 1, device=t.device, dtype=t.dtype)
expand_slice = [None] * len(t.shape)
expand_slice[dim] = slice(None, None)
return t.cumsum(dim=dim) / r[tuple(expand_slice)]
def mask_reordering_matrix(R, topk, temperature):
buckets = R.shape[1]
mask_value = max_neg_value(R)
mask = torch.zeros(R.shape, device=R.device).bool()
i, j = torch.triu_indices(buckets, buckets)
mask[:, i, j + topk] = True
R.masked_fill_(mask, mask_value)
return differentiable_topk(R, topk, temperature)
class CausalAttentionSortNetNew(nn.Module):
def __init__(self, heads, bucket_size, dim, temperature):
super().__init__()
self.heads = heads
self.bucket_size = bucket_size
self.dim = dim
self.temperature = temperature
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
tatp22/sinkhorn-transformer
|
CausalAttentionSortNet
| false
| 13,040
|
[
"MIT"
] | 0
|
3eaa76e99efeee75cf8298defaaef51621c55ff4
|
https://github.com/tatp22/sinkhorn-transformer/tree/3eaa76e99efeee75cf8298defaaef51621c55ff4
|
Critic
|
import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, fcs1_units=64,
fc2_units=32, fc3_units=32):
super(Critic, self).__init__()
self.seed = torch.manual_seed(seed)
self.fcs1 = nn.Linear(state_size, fcs1_units)
self.fc2 = nn.Linear(fcs1_units + action_size, fc2_units)
self.fc3 = nn.Linear(fc2_units, fc3_units)
self.fc4 = nn.Linear(fc3_units, 1)
self.reset_parameters()
def reset_parameters(self):
self.fcs1.weight.data.uniform_(*hidden_init(self.fcs1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(*hidden_init(self.fc3))
self.fc4.weight.data.uniform_(-0.003, 0.003)
def forward(self, state, action):
xs = F.relu(self.fcs1(state))
x = torch.cat((xs, action), dim=1)
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
return self.fc4(x)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'state_size': 4, 'action_size': 4, 'seed': 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
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, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 272
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 68
x1 = xindex // 68
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 64, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (64 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
other=0.0)
tmp7 = tmp5 + tmp6
tmp8 = tl.full([1], 0, tl.int32)
tmp9 = triton_helpers.maximum(tmp8, tmp7)
tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
tmp11 = tl.where(tmp4, tmp9, tmp10)
tmp12 = tmp0 >= tmp3
tl.full([1], 68, tl.int64)
tmp15 = tl.load(in_ptr2 + (4 * x1 + (-64 + x0)), tmp12 & xmask,
eviction_policy='evict_last', other=0.0)
tmp16 = tl.where(tmp4, tmp11, tmp15)
tl.store(out_ptr0 + x2, tmp16, xmask)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 32
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)
@triton.jit
def triton_poi_fused_relu_threshold_backward_2(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
x0 = xindex % 64
tmp0 = tl.load(in_ptr0 + x2, 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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
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, primals_10) = args
args.clear()
assert_size_stride(primals_1, (64, 4), (4, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (32, 68), (68, 1))
assert_size_stride(primals_6, (32,), (1,))
assert_size_stride(primals_7, (32, 32), (32, 1))
assert_size_stride(primals_8, (32,), (1,))
assert_size_stride(primals_9, (1, 32), (32, 1))
assert_size_stride(primals_10, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 64),
(1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 68), (68, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(272)](buf0, primals_2, primals_4, buf1,
272, XBLOCK=128, num_warps=4, num_stages=1)
del primals_4
buf2 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
extern_kernels.mm(buf1, reinterpret_tensor(primals_5, (68, 32), (1,
68), 0), out=buf2)
buf3 = buf2
del buf2
triton_poi_fused_relu_1[grid(128)](buf3, primals_6, 128, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_6
buf4 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
extern_kernels.mm(buf3, reinterpret_tensor(primals_7, (32, 32), (1,
32), 0), out=buf4)
buf5 = buf4
del buf4
triton_poi_fused_relu_1[grid(128)](buf5, primals_8, 128, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_8
buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_10, buf5, reinterpret_tensor(primals_9,
(32, 1), (1, 32), 0), alpha=1, beta=1, out=buf7)
del primals_10
buf8 = empty_strided_cuda((4, 64), (64, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_2[grid(256)](buf0,
primals_2, buf8, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf0
del primals_2
return (buf7, primals_3, buf1, buf3, buf5, primals_9, primals_7,
primals_5, buf8)
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class CriticNew(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, fcs1_units=64,
fc2_units=32, fc3_units=32):
super(CriticNew, self).__init__()
self.seed = torch.manual_seed(seed)
self.fcs1 = nn.Linear(state_size, fcs1_units)
self.fc2 = nn.Linear(fcs1_units + action_size, fc2_units)
self.fc3 = nn.Linear(fc2_units, fc3_units)
self.fc4 = nn.Linear(fc3_units, 1)
self.reset_parameters()
def reset_parameters(self):
self.fcs1.weight.data.uniform_(*hidden_init(self.fcs1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(*hidden_init(self.fc3))
self.fc4.weight.data.uniform_(-0.003, 0.003)
def forward(self, input_0, input_1):
primals_1 = self.fcs1.weight
primals_2 = self.fcs1.bias
primals_5 = self.fc2.weight
primals_6 = self.fc2.bias
primals_7 = self.fc3.weight
primals_8 = self.fc3.bias
primals_9 = self.fc4.weight
primals_10 = self.fc4.bias
primals_3 = 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]
|
tjkemp/tennis-example
|
Critic
| false
| 13,041
|
[
"MIT"
] | 0
|
3cb0c52a93c65f88872cf44e3782bf87d9d8cef3
|
https://github.com/tjkemp/tennis-example/tree/3cb0c52a93c65f88872cf44e3782bf87d9d8cef3
|
DeepModel
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class DeepModel(nn.Module):
def __init__(self, in_size, out_size):
super().__init__()
self.linear1 = nn.Linear(in_size, 1024)
self.linear2 = nn.Linear(1024, 512)
self.linear3 = nn.Linear(512, 256)
self.linear4 = nn.Linear(256, 128)
self.linear5 = nn.Linear(128, 64)
self.linear6 = nn.Linear(64, 32)
self.linear7 = nn.Linear(32, out_size)
def forward(self, xb):
xb = xb.view(xb.size(0), -1)
out = self.linear1(xb)
out = F.relu(out)
out = self.linear2(out)
out = F.relu(out)
out = self.linear3(out)
out = F.relu(out)
out = self.linear4(out)
out = F.relu(out)
out = self.linear5(out)
out = F.relu(out)
out = self.linear6(out)
out = F.relu(out)
out = self.linear7(out)
return out
def get_inputs():
return [torch.rand([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 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
):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 1024
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_relu_1(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_relu_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
x2 = xindex
x0 = xindex % 256
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)
@triton.jit
def triton_poi_fused_relu_3(in_out_ptr0, in_ptr0, 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 % 128
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)
@triton.jit
def triton_poi_fused_relu_4(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
x2 = xindex
x0 = xindex % 64
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)
@triton.jit
def triton_poi_fused_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 32
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, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (1024, 4), (4, 1))
assert_size_stride(primals_3, (1024,), (1,))
assert_size_stride(primals_4, (512, 1024), (1024, 1))
assert_size_stride(primals_5, (512,), (1,))
assert_size_stride(primals_6, (256, 512), (512, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (128, 256), (256, 1))
assert_size_stride(primals_9, (128,), (1,))
assert_size_stride(primals_10, (64, 128), (128, 1))
assert_size_stride(primals_11, (64,), (1,))
assert_size_stride(primals_12, (32, 64), (64, 1))
assert_size_stride(primals_13, (32,), (1,))
assert_size_stride(primals_14, (4, 32), (32, 1))
assert_size_stride(primals_15, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 1024
), (1, 4), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(4096)](buf1, primals_3, 4096, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (1024, 512),
(1, 1024), 0), out=buf2)
buf3 = buf2
del buf2
triton_poi_fused_relu_1[grid(2048)](buf3, primals_5, 2048, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (512, 256), (
1, 512), 0), out=buf4)
buf5 = buf4
del buf4
triton_poi_fused_relu_2[grid(1024)](buf5, primals_7, 1024, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_7
buf6 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (256, 128), (
1, 256), 0), out=buf6)
buf7 = buf6
del buf6
triton_poi_fused_relu_3[grid(512)](buf7, primals_9, 512, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_9
buf8 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
extern_kernels.mm(buf7, reinterpret_tensor(primals_10, (128, 64), (
1, 128), 0), out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_relu_4[grid(256)](buf9, primals_11, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_11
buf10 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
extern_kernels.mm(buf9, reinterpret_tensor(primals_12, (64, 32), (1,
64), 0), out=buf10)
buf11 = buf10
del buf10
triton_poi_fused_relu_5[grid(128)](buf11, primals_13, 128, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_13
buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_15, buf11, reinterpret_tensor(
primals_14, (32, 4), (1, 32), 0), alpha=1, beta=1, out=buf12)
del primals_15
return (buf12, primals_1, buf1, buf3, buf5, buf7, buf9, buf11,
primals_14, primals_12, primals_10, primals_8, primals_6, primals_4)
class DeepModelNew(nn.Module):
def __init__(self, in_size, out_size):
super().__init__()
self.linear1 = nn.Linear(in_size, 1024)
self.linear2 = nn.Linear(1024, 512)
self.linear3 = nn.Linear(512, 256)
self.linear4 = nn.Linear(256, 128)
self.linear5 = nn.Linear(128, 64)
self.linear6 = nn.Linear(64, 32)
self.linear7 = nn.Linear(32, out_size)
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.linear3.weight
primals_7 = self.linear3.bias
primals_8 = self.linear4.weight
primals_9 = self.linear4.bias
primals_10 = self.linear5.weight
primals_11 = self.linear5.bias
primals_12 = self.linear6.weight
primals_13 = self.linear6.bias
primals_14 = self.linear7.weight
primals_15 = self.linear7.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])
return output[0]
|
tianyi-ge/eecs598-a1
|
DeepModel
| false
| 13,042
|
[
"MIT"
] | 0
|
540140c5c2a59931ee051a0064932a1e81f84806
|
https://github.com/tianyi-ge/eecs598-a1/tree/540140c5c2a59931ee051a0064932a1e81f84806
|
GaussianNoiseSampler
|
import torch
import torch as th
import torch.nn as nn
class GaussianNoiseSampler(nn.Module):
def __init__(self, scale=0.01, inplace=False):
super(GaussianNoiseSampler, self).__init__()
if scale < 0:
raise ValueError(
'noise scale has to be greather than 0, but got {}'.format(
scale))
self.scale = scale
self.inplace = inplace
def forward(self, inputs):
if self.scale:
inputs.add_(th.randn_like(inputs) * self.scale)
return inputs
def extra_repr(self):
inplace_str = ', inplace' if self.inplace else ''
return 'scale={}{}'.format(self.scale, inplace_str)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch import device
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_add_mul_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_out_ptr0 + x0, xmask)
tmp2 = 0.01
tmp3 = tmp1 * tmp2
tmp4 = tmp0 + tmp3
tl.store(out_ptr0 + x0, tmp4, 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 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.
float32, device=device(type='cuda', index=0), pin_memory=False)
buf1 = buf0
del buf0
buf2 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused_add_mul_0[grid(256)](buf2, arg0_1, arg0_1, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del buf2
return arg0_1,
class GaussianNoiseSamplerNew(nn.Module):
def __init__(self, scale=0.01, inplace=False):
super(GaussianNoiseSamplerNew, self).__init__()
if scale < 0:
raise ValueError(
'noise scale has to be greather than 0, but got {}'.format(
scale))
self.scale = scale
self.inplace = inplace
def extra_repr(self):
inplace_str = ', inplace' if self.inplace else ''
return 'scale={}{}'.format(self.scale, inplace_str)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
tritas/mixdat
|
GaussianNoiseSampler
| false
| 13,043
|
[
"BSD-3-Clause"
] | 0
|
38fb10df76df55cc1eddba5375c7699c23771fb3
|
https://github.com/tritas/mixdat/tree/38fb10df76df55cc1eddba5375c7699c23771fb3
|
Projection
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class TimeDistributed(nn.Module):
def __init__(self, layer, activation='relu'):
super().__init__()
self.layer = layer
self.activation = self.select_activation(activation)
def forward(self, x):
x_reshaped = x.contiguous().view(-1, x.size(-1))
y = self.layer(x_reshaped)
y = self.activation(y)
y = y.contiguous().view(x.size(0), -1, y.size(-1))
return y
def select_activation(self, activation):
if activation == 'relu':
return nn.ReLU()
elif activation == 'sigmoid':
return nn.Sigmoid()
elif activation == 'tanh':
return nn.Tanh()
raise KeyError
class Projection(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_fnn = TimeDistributed(nn.Linear(config['cnn_features'], 3))
self.softmax = nn.LogSoftmax(dim=-1)
def forward(self, x):
x = torch.transpose(x, 1, 0)
x = self.seq_fnn(x)
x = torch.transpose(x, 1, 0)
x = self.softmax(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(cnn_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
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_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 % 16
x1 = xindex // 16 % 4
x2 = xindex // 64
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2 + 64 * x1), xmask)
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused__log_softmax_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 + 3 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp6 = tl.load(in_ptr0 + (1 + 3 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + 1)
tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
tmp12 = tl.load(in_ptr0 + (2 + 3 * x0), xmask, eviction_policy='evict_last'
)
tmp13 = tl.load(in_ptr1 + 2)
tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = triton_helpers.maximum(tmp4, tmp3)
tmp9 = tmp6 + tmp8
tmp10 = triton_helpers.maximum(tmp4, tmp9)
tmp11 = triton_helpers.maximum(tmp5, tmp10)
tmp15 = tmp12 + tmp14
tmp16 = triton_helpers.maximum(tmp4, tmp15)
tmp17 = triton_helpers.maximum(tmp11, tmp16)
tmp18 = tmp5 - tmp17
tmp19 = tl_math.exp(tmp18)
tmp20 = tmp10 - tmp17
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tmp16 - tmp17
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = tl_math.log(tmp25)
tl.store(out_ptr0 + x0, tmp17, xmask)
tl.store(out_ptr1 + x0, tmp26, xmask)
@triton.jit
def triton_poi_fused__log_softmax_relu_threshold_backward_2(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr
):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x0 = xindex % 3
x3 = xindex // 3
x1 = xindex // 3 % 16
x2 = xindex // 48
tmp0 = tl.load(in_ptr0 + x4, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = tmp4 - tmp5
tmp8 = tmp6 - tmp7
tmp9 = 0.0
tmp10 = tmp4 <= tmp9
tl.store(out_ptr0 + (x0 + 3 * x2 + 12 * x1), tmp8, xmask)
tl.store(out_ptr1 + x4, tmp10, 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, (3, 4), (4, 1))
assert_size_stride(primals_3, (3,), (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_clone_0[grid(256)](primals_1, buf0, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 3), (1, 4), 0), out=buf1)
del primals_2
buf2 = empty_strided_cuda((16, 4, 1), (1, 16, 64), torch.float32)
buf3 = empty_strided_cuda((16, 4, 1), (1, 16, 64), torch.float32)
triton_poi_fused__log_softmax_1[grid(64)](buf1, primals_3, buf2,
buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((16, 4, 3), (12, 3, 1), torch.float32)
buf5 = empty_strided_cuda((64, 3), (3, 1), torch.bool)
triton_poi_fused__log_softmax_relu_threshold_backward_2[grid(192)](buf1
, primals_3, buf2, buf3, buf4, buf5, 192, XBLOCK=256, num_warps
=4, num_stages=1)
del buf1
del buf2
del buf3
del primals_3
return buf4, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), buf4, buf5
class TimeDistributed(nn.Module):
def __init__(self, layer, activation='relu'):
super().__init__()
self.layer = layer
self.activation = self.select_activation(activation)
def forward(self, x):
x_reshaped = x.contiguous().view(-1, x.size(-1))
y = self.layer(x_reshaped)
y = self.activation(y)
y = y.contiguous().view(x.size(0), -1, y.size(-1))
return y
def select_activation(self, activation):
if activation == 'relu':
return nn.ReLU()
elif activation == 'sigmoid':
return nn.Sigmoid()
elif activation == 'tanh':
return nn.Tanh()
raise KeyError
class ProjectionNew(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_fnn = TimeDistributed(nn.Linear(config['cnn_features'], 3))
self.softmax = nn.LogSoftmax(dim=-1)
def forward(self, input_0):
primals_2 = self.seq_fnn.layer.weight
primals_3 = self.seq_fnn.layer.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
tndls9304/chatspace
|
Projection
| false
| 13,044
|
[
"Apache-2.0"
] | 0
|
42cb4bd9bd3b553706d9ac227150329103d681aa
|
https://github.com/tndls9304/chatspace/tree/42cb4bd9bd3b553706d9ac227150329103d681aa
|
Model
|
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, output_class_num, **kwargs):
super(Model, self).__init__()
self.linear = nn.Linear(input_dim, output_class_num)
def forward(self, features):
pooled = features.mean(dim=1)
predicted = self.linear(pooled)
return predicted
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_dim': 4, 'output_class_num': 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_mean_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 % 16
x1 = xindex // 16
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)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tl.store(out_ptr0 + x2, tmp8, 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, 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), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mean_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (16, 4), (
4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf1)
del primals_2
del primals_3
return reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(buf0, (16, 4), (4, 1), 0)
class ModelNew(nn.Module):
def __init__(self, input_dim, output_class_num, **kwargs):
super(ModelNew, self).__init__()
self.linear = nn.Linear(input_dim, output_class_num)
def forward(self, input_0):
primals_2 = self.linear.weight
primals_3 = self.linear.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
triper1022/s3prl
|
Model
| false
| 13,045
|
[
"MIT"
] | 0
|
d48e9e1d062d6cb14b66048eb56193fb50c60c24
|
https://github.com/triper1022/s3prl/tree/d48e9e1d062d6cb14b66048eb56193fb50c60c24
|
resnet_block
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class resnet_block(nn.Module):
def __init__(self, dim_in, dim_out):
super(resnet_block, self).__init__()
self.dim_in = dim_in
self.dim_out = dim_out
if self.dim_in == self.dim_out:
self.conv_1 = nn.Conv2d(self.dim_in, self.dim_out, 3, stride=1,
padding=1, bias=False)
self.conv_2 = nn.Conv2d(self.dim_out, self.dim_out, 3, stride=1,
padding=1, bias=False)
nn.init.xavier_uniform_(self.conv_1.weight)
nn.init.xavier_uniform_(self.conv_2.weight)
else:
self.conv_1 = nn.Conv2d(self.dim_in, self.dim_out, 3, stride=2,
padding=1, bias=False)
self.conv_2 = nn.Conv2d(self.dim_out, self.dim_out, 3, stride=1,
padding=1, bias=False)
self.conv_s = nn.Conv2d(self.dim_in, self.dim_out, 1, stride=2,
padding=0, bias=False)
nn.init.xavier_uniform_(self.conv_1.weight)
nn.init.xavier_uniform_(self.conv_2.weight)
nn.init.xavier_uniform_(self.conv_s.weight)
def forward(self, input, is_training=False):
if self.dim_in == self.dim_out:
output = self.conv_1(input)
output = F.leaky_relu(output, negative_slope=0.01, inplace=True)
output = self.conv_2(output)
output = output + input
output = F.leaky_relu(output, negative_slope=0.01, inplace=True)
else:
output = self.conv_1(input)
output = F.leaky_relu(output, negative_slope=0.01, inplace=True)
output = self.conv_2(output)
input_ = self.conv_s(input)
output = output + input_
output = F.leaky_relu(output, negative_slope=0.01, inplace=True)
return output
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'dim_in': 4, 'dim_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
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_leaky_relu_0(in_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 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tl.store(in_out_ptr0 + x0, tmp5, xmask)
@triton.jit
def triton_poi_fused_add_leaky_relu_leaky_relu_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
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask)
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tmp8 = tmp7 > tmp3
tl.store(in_out_ptr0 + x0, tmp7, xmask)
tl.store(out_ptr0 + x0, tmp8, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_2, 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_leaky_relu_0[grid(256)](buf1, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf2 = extern_kernels.convolution(buf1, primals_3, 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_leaky_relu_leaky_relu_backward_1[grid(256)](buf3,
primals_2, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
return buf3, primals_1, primals_2, primals_3, buf1, buf4
class resnet_blockNew(nn.Module):
def __init__(self, dim_in, dim_out):
super(resnet_blockNew, self).__init__()
self.dim_in = dim_in
self.dim_out = dim_out
if self.dim_in == self.dim_out:
self.conv_1 = nn.Conv2d(self.dim_in, self.dim_out, 3, stride=1,
padding=1, bias=False)
self.conv_2 = nn.Conv2d(self.dim_out, self.dim_out, 3, stride=1,
padding=1, bias=False)
nn.init.xavier_uniform_(self.conv_1.weight)
nn.init.xavier_uniform_(self.conv_2.weight)
else:
self.conv_1 = nn.Conv2d(self.dim_in, self.dim_out, 3, stride=2,
padding=1, bias=False)
self.conv_2 = nn.Conv2d(self.dim_out, self.dim_out, 3, stride=1,
padding=1, bias=False)
self.conv_s = nn.Conv2d(self.dim_in, self.dim_out, 1, stride=2,
padding=0, bias=False)
nn.init.xavier_uniform_(self.conv_1.weight)
nn.init.xavier_uniform_(self.conv_2.weight)
nn.init.xavier_uniform_(self.conv_s.weight)
def forward(self, input_0):
primals_1 = self.conv_1.weight
primals_3 = self.conv_2.weight
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
trisct/BSP-NET-pytorch
|
resnet_block
| false
| 13,046
|
[
"MIT"
] | 0
|
31f148aa3d7321bac854bc3de6c88f676236b7e4
|
https://github.com/trisct/BSP-NET-pytorch/tree/31f148aa3d7321bac854bc3de6c88f676236b7e4
|
MLP
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.fc1 = nn.Linear(in_features=28 * 28, out_features=500)
self.fc2 = nn.Linear(in_features=500, out_features=200)
self.fc3 = nn.Linear(in_features=200, out_features=100)
self.out = nn.Linear(in_features=100, out_features=10)
def forward(self, t):
t = t.view(-1, 28 * 28)
t = self.fc1(t)
t = F.relu(t)
t = self.fc2(t)
t = F.relu(t)
t = self.fc3(t)
t = F.relu(t)
t = self.out(t)
return t
def get_inputs():
return [torch.rand([4, 784])]
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.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 = 2000
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 500
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)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 800
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 200
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)
@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 100
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, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (4, 784), (784, 1))
assert_size_stride(primals_2, (500, 784), (784, 1))
assert_size_stride(primals_3, (500,), (1,))
assert_size_stride(primals_4, (200, 500), (500, 1))
assert_size_stride(primals_5, (200,), (1,))
assert_size_stride(primals_6, (100, 200), (200, 1))
assert_size_stride(primals_7, (100,), (1,))
assert_size_stride(primals_8, (10, 100), (100, 1))
assert_size_stride(primals_9, (10,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 500), (500, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784,
500), (1, 784), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(2000)](buf1, primals_3, 2000, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((4, 200), (200, 1), torch.float32)
extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (500, 200), (
1, 500), 0), out=buf2)
buf3 = buf2
del buf2
triton_poi_fused_relu_1[grid(800)](buf3, primals_5, 800, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((4, 100), (100, 1), torch.float32)
extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (200, 100), (
1, 200), 0), out=buf4)
buf5 = buf4
del buf4
triton_poi_fused_relu_2[grid(400)](buf5, primals_7, 400, XBLOCK=256,
num_warps=4, num_stages=1)
del primals_7
buf6 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_9, buf5, reinterpret_tensor(primals_8,
(100, 10), (1, 100), 0), alpha=1, beta=1, out=buf6)
del primals_9
return buf6, primals_1, buf1, buf3, buf5, primals_8, primals_6, primals_4
class MLPNew(nn.Module):
def __init__(self):
super(MLPNew, self).__init__()
self.fc1 = nn.Linear(in_features=28 * 28, out_features=500)
self.fc2 = nn.Linear(in_features=500, out_features=200)
self.fc3 = nn.Linear(in_features=200, out_features=100)
self.out = nn.Linear(in_features=100, out_features=10)
def forward(self, input_0):
primals_2 = self.fc1.weight
primals_3 = 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_1 = 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]
|
trGiang99/ml-glossary-vn
|
MLP
| false
| 13,047
|
[
"MIT"
] | 0
|
1160300cee6ccb02712c790b76bbc11c06c2ca55
|
https://github.com/trGiang99/ml-glossary-vn/tree/1160300cee6ccb02712c790b76bbc11c06c2ca55
|
generator
|
import torch
import torch.nn as nn
class generator(nn.Module):
def __init__(self, p_dim, c_dim):
super(generator, self).__init__()
self.p_dim = p_dim
self.c_dim = c_dim
convex_layer_weights = torch.zeros((self.p_dim, self.c_dim))
self.convex_layer_weights = nn.Parameter(convex_layer_weights)
nn.init.normal_(self.convex_layer_weights, mean=0.0, std=0.02)
def forward(self, points, plane_m, is_training=False):
h1 = torch.matmul(points, plane_m)
h1 = torch.clamp(h1, min=0)
h2 = torch.matmul(h1, (self.convex_layer_weights > 0.01).float())
h3 = torch.min(h2, dim=2, keepdim=True)[0]
return h2, h3
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'p_dim': 4, 'c_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
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_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 = 0.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tl.store(in_out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_gt_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.01
tmp2 = tmp0 > tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_min_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
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
tmp2 = triton_helpers.minimum(tmp0, tmp1)
tmp4 = triton_helpers.minimum(tmp2, tmp3)
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tl.store(out_ptr0 + x2, tmp6, 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, 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 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_clamp_0[grid(256)](buf1, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_gt_1[grid(16)](arg2_1, buf2, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del arg2_1
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.fallback_mixed_mm(reinterpret_tensor(buf1, (64, 4),
(4, 1), 0), buf2, out=buf3)
del buf1
del buf2
buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
triton_poi_fused_min_2[grid(64)](buf3, buf4, 64, XBLOCK=64,
num_warps=1, num_stages=1)
return reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf4
class generatorNew(nn.Module):
def __init__(self, p_dim, c_dim):
super(generatorNew, self).__init__()
self.p_dim = p_dim
self.c_dim = c_dim
convex_layer_weights = torch.zeros((self.p_dim, self.c_dim))
self.convex_layer_weights = nn.Parameter(convex_layer_weights)
nn.init.normal_(self.convex_layer_weights, mean=0.0, std=0.02)
def forward(self, input_0, input_1):
arg2_1 = self.convex_layer_weights
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1, arg2_1])
return output[0], output[1]
|
trisct/BSP-NET-pytorch
|
generator
| false
| 13,048
|
[
"MIT"
] | 0
|
31f148aa3d7321bac854bc3de6c88f676236b7e4
|
https://github.com/trisct/BSP-NET-pytorch/tree/31f148aa3d7321bac854bc3de6c88f676236b7e4
|
FocalLoss
|
import torch
from torch import nn
class FocalLoss(nn.Module):
def __init__(self, gamma=0, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = torch.nn.CrossEntropyLoss(reduction='none')
def forward(self, input, target):
logp = self.ce(input, target)
p = torch.exp(-logp)
loss = (1 - p) ** self.gamma * logp
return 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._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
@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_per_fused__log_softmax_exp_mean_mul_neg_pow_rsub_sum_1(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 % 16
r1 = rindex // 16
tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
tmp5 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
tmp8 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
tmp13 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
tmp16 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
tmp20 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
tmp24 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
tmp1 = tl_math.exp(tmp0)
tmp3 = tl_math.exp(tmp2)
tmp4 = tmp1 + tmp3
tmp6 = tl_math.exp(tmp5)
tmp7 = tmp4 + tmp6
tmp9 = tl_math.exp(tmp8)
tmp10 = tmp7 + tmp9
tmp11 = tl_math.log(tmp10)
tmp12 = tmp0 - tmp11
tmp14 = tmp12 * tmp13
tmp15 = tmp2 - tmp11
tmp17 = tmp15 * tmp16
tmp18 = tmp14 + tmp17
tmp19 = tmp5 - tmp11
tmp21 = tmp19 * tmp20
tmp22 = tmp18 + tmp21
tmp23 = tmp8 - tmp11
tmp25 = tmp23 * tmp24
tmp26 = tmp22 + tmp25
tmp27 = -tmp26
tmp28 = -tmp27
tmp29 = tl_math.exp(tmp28)
tmp30 = 1.0
tmp30 - tmp29
tmp32 = tmp30 * tmp27
tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
tmp35 = tl.sum(tmp33, 1)[:, None]
tmp36 = 64.0
tmp37 = tmp35 / tmp36
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp37, 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)
get_raw_stream(0)
triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg1_1
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2
del buf2
triton_per_fused__log_softmax_exp_mean_mul_neg_pow_rsub_sum_1[grid(1)](
buf3, buf0, arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del buf0
return buf3,
class FocalLossNew(nn.Module):
def __init__(self, gamma=0, eps=1e-07):
super(FocalLossNew, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = torch.nn.CrossEntropyLoss(reduction='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]
|
tropicbird/kaggle-landmark-recognition-2020-1st-place
|
FocalLoss
| false
| 13,049
|
[
"MIT"
] | 0
|
79a9d1b05c326a77b4859d4d41d30e52e6be710e
|
https://github.com/tropicbird/kaggle-landmark-recognition-2020-1st-place/tree/79a9d1b05c326a77b4859d4d41d30e52e6be710e
|
Conv2dStaticSamePadding
|
import math
import torch
from torch import nn
from torch.nn import functional as F
class Conv2dStaticSamePadding(nn.Module):
"""
created by Zylo117
The real keras/tensorflow conv2d with same padding
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
bias=False, groups=1, dilation=1, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
stride=stride, bias=bias, groups=groups)
self.stride = self.conv.stride
self.kernel_size = self.conv.kernel_size
self.dilation = self.conv.dilation
if isinstance(self.stride, int):
self.stride = [self.stride] * 2
elif len(self.stride) == 1:
self.stride = [self.stride[0]] * 2
if isinstance(self.kernel_size, int):
self.kernel_size = [self.kernel_size] * 2
elif len(self.kernel_size) == 1:
self.kernel_size = [self.kernel_size[0]] * 2
def forward(self, x):
h, w = x.shape[-2:]
extra_h = (math.ceil(w / self.stride[1]) - 1) * self.stride[1
] - w + self.kernel_size[1]
extra_v = (math.ceil(h / self.stride[0]) - 1) * self.stride[0
] - h + self.kernel_size[0]
left = extra_h // 2
right = extra_h - left
top = extra_v // 2
bottom = extra_v - top
x = F.pad(x, [left, right, top, bottom])
x = self.conv(x)
return 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 = 784
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 7 % 7
x0 = xindex % 7
x2 = xindex // 49
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)
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, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 7, 7), (196, 49, 7, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_constant_pad_nd_0[grid(784)](primals_1, buf0, 784,
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))
return buf1, primals_2, buf0
class Conv2dStaticSamePaddingNew(nn.Module):
"""
created by Zylo117
The real keras/tensorflow conv2d with same padding
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
bias=False, groups=1, dilation=1, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
stride=stride, bias=bias, groups=groups)
self.stride = self.conv.stride
self.kernel_size = self.conv.kernel_size
self.dilation = self.conv.dilation
if isinstance(self.stride, int):
self.stride = [self.stride] * 2
elif len(self.stride) == 1:
self.stride = [self.stride[0]] * 2
if isinstance(self.kernel_size, int):
self.kernel_size = [self.kernel_size] * 2
elif len(self.kernel_size) == 1:
self.kernel_size = [self.kernel_size[0]] * 2
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
tujikuangmo/FishNet
|
Conv2dStaticSamePadding
| false
| 13,050
|
[
"MIT"
] | 0
|
1c2f7112639416bd12a02585a9e04e1d05960520
|
https://github.com/tujikuangmo/FishNet/tree/1c2f7112639416bd12a02585a9e04e1d05960520
|
GAT
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__()
self.dropout = dropout
self.in_features = in_features
self.out_features = out_features
self.alpha = alpha
self.concat = concat
self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
self.a = nn.Parameter(torch.zeros(size=(2 * out_features, 1)))
nn.init.xavier_uniform_(self.a.data, gain=1.414)
self.leakyrelu = nn.LeakyReLU(self.alpha)
def forward(self, input, adj):
h = torch.mm(input, self.W)
"""u = torch.mm(input, self.U)
v = torch.mm(input, self.V)
h = torch.mm(torch.sigmoid(u) * torch.sigmoid(v), self.P)"""
N = h.size()[0]
a_input = torch.cat([h.repeat(1, N).view(N * N, -1), h.repeat(N, 1)
], dim=1).view(N, -1, 2 * self.out_features)
e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2))
zero_vec = -9000000000000000.0 * torch.ones_like(e)
attention = torch.where(adj > 0, e, zero_vec)
attention = F.softmax(attention, dim=1)
attention = F.dropout(attention, self.dropout, training=self.training)
h_prime = torch.matmul(attention, h)
if self.concat:
return F.elu(h_prime)
else:
return h_prime
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class GAT(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
"""Dense version of GAT."""
super(GAT, self).__init__()
self.dropout = dropout
self.encoder = nn.Parameter(torch.zeros(size=(nfeat, nhid)))
nn.init.xavier_uniform_(self.encoder.data, gain=1.414)
self.attentions = [GraphAttentionLayer(nhid, nhid, dropout=dropout,
alpha=alpha, concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=
dropout, alpha=alpha, concat=False)
def forward(self, x_org, adj):
x = torch.mm(x_org, self.encoder)
x = F.dropout(x, self.dropout, training=self.training)
x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
x = F.dropout(x, self.dropout, training=self.training)
x = F.elu(self.out_att(x, adj))
return F.log_softmax(x, dim=1)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'nfeat': 4, 'nhid': 4, 'nclass': 4, 'dropout': 0.5,
'alpha': 4, 'nheads': 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.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_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
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 * ((4 * x1 + x0) // 16 % 4) + (4 * x1 + x0) %
16 % 4), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr0 + (4 * (x1 % 4) + (-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_leaky_relu_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_2(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0,
out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, out_ptr6, out_ptr7,
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').to(tl
.int1)
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp9 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp15 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp16 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp22 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp23 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp40 = tl.load(in_ptr3 + 4 * x0, xmask, eviction_policy='evict_last').to(
tl.int1)
tmp41 = tl.load(in_ptr4 + 4 * x0, xmask, eviction_policy='evict_last')
tmp45 = tl.load(in_ptr3 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp46 = tl.load(in_ptr4 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp51 = tl.load(in_ptr3 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp52 = tl.load(in_ptr4 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp57 = tl.load(in_ptr3 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp58 = tl.load(in_ptr4 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp74 = tl.load(in_ptr5 + 4 * x0, xmask, eviction_policy='evict_last').to(
tl.int1)
tmp75 = tl.load(in_ptr6 + 4 * x0, xmask, eviction_policy='evict_last')
tmp79 = tl.load(in_ptr5 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp80 = tl.load(in_ptr6 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp85 = tl.load(in_ptr5 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp86 = tl.load(in_ptr6 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp91 = tl.load(in_ptr5 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp92 = tl.load(in_ptr6 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp108 = tl.load(in_ptr7 + 4 * x0, xmask, eviction_policy='evict_last').to(
tl.int1)
tmp109 = tl.load(in_ptr8 + 4 * x0, xmask, eviction_policy='evict_last')
tmp113 = tl.load(in_ptr7 + (1 + 4 * x0), xmask, eviction_policy=
'evict_last').to(tl.int1)
tmp114 = tl.load(in_ptr8 + (1 + 4 * x0), xmask, eviction_policy=
'evict_last')
tmp119 = tl.load(in_ptr7 + (2 + 4 * x0), xmask, eviction_policy=
'evict_last').to(tl.int1)
tmp120 = tl.load(in_ptr8 + (2 + 4 * x0), xmask, eviction_policy=
'evict_last')
tmp125 = tl.load(in_ptr7 + (3 + 4 * x0), xmask, eviction_policy=
'evict_last').to(tl.int1)
tmp126 = tl.load(in_ptr8 + (3 + 4 * x0), xmask, eviction_policy=
'evict_last')
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp11 = tmp10 * tmp3
tmp12 = tl.where(tmp9, tmp10, tmp11)
tmp13 = tl.where(tmp8, tmp12, tmp6)
tmp14 = triton_helpers.maximum(tmp7, tmp13)
tmp18 = tmp17 * tmp3
tmp19 = tl.where(tmp16, tmp17, tmp18)
tmp20 = tl.where(tmp15, tmp19, tmp6)
tmp21 = triton_helpers.maximum(tmp14, tmp20)
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp23, tmp24, tmp25)
tmp27 = tl.where(tmp22, tmp26, tmp6)
tmp28 = triton_helpers.maximum(tmp21, tmp27)
tmp29 = tmp7 - tmp28
tmp30 = tl_math.exp(tmp29)
tmp31 = tmp13 - tmp28
tmp32 = tl_math.exp(tmp31)
tmp33 = tmp30 + tmp32
tmp34 = tmp20 - tmp28
tmp35 = tl_math.exp(tmp34)
tmp36 = tmp33 + tmp35
tmp37 = tmp27 - tmp28
tmp38 = tl_math.exp(tmp37)
tmp39 = tmp36 + tmp38
tmp42 = tmp41 * tmp3
tmp43 = tl.where(tmp40, tmp41, tmp42)
tmp44 = tl.where(tmp0, tmp43, tmp6)
tmp47 = tmp46 * tmp3
tmp48 = tl.where(tmp45, tmp46, tmp47)
tmp49 = tl.where(tmp8, tmp48, tmp6)
tmp50 = triton_helpers.maximum(tmp44, tmp49)
tmp53 = tmp52 * tmp3
tmp54 = tl.where(tmp51, tmp52, tmp53)
tmp55 = tl.where(tmp15, tmp54, tmp6)
tmp56 = triton_helpers.maximum(tmp50, tmp55)
tmp59 = tmp58 * tmp3
tmp60 = tl.where(tmp57, tmp58, tmp59)
tmp61 = tl.where(tmp22, tmp60, tmp6)
tmp62 = triton_helpers.maximum(tmp56, tmp61)
tmp63 = tmp44 - tmp62
tmp64 = tl_math.exp(tmp63)
tmp65 = tmp49 - tmp62
tmp66 = tl_math.exp(tmp65)
tmp67 = tmp64 + tmp66
tmp68 = tmp55 - tmp62
tmp69 = tl_math.exp(tmp68)
tmp70 = tmp67 + tmp69
tmp71 = tmp61 - tmp62
tmp72 = tl_math.exp(tmp71)
tmp73 = tmp70 + tmp72
tmp76 = tmp75 * tmp3
tmp77 = tl.where(tmp74, tmp75, tmp76)
tmp78 = tl.where(tmp0, tmp77, tmp6)
tmp81 = tmp80 * tmp3
tmp82 = tl.where(tmp79, tmp80, tmp81)
tmp83 = tl.where(tmp8, tmp82, tmp6)
tmp84 = triton_helpers.maximum(tmp78, tmp83)
tmp87 = tmp86 * tmp3
tmp88 = tl.where(tmp85, tmp86, tmp87)
tmp89 = tl.where(tmp15, tmp88, tmp6)
tmp90 = triton_helpers.maximum(tmp84, tmp89)
tmp93 = tmp92 * tmp3
tmp94 = tl.where(tmp91, tmp92, tmp93)
tmp95 = tl.where(tmp22, tmp94, tmp6)
tmp96 = triton_helpers.maximum(tmp90, tmp95)
tmp97 = tmp78 - tmp96
tmp98 = tl_math.exp(tmp97)
tmp99 = tmp83 - tmp96
tmp100 = tl_math.exp(tmp99)
tmp101 = tmp98 + tmp100
tmp102 = tmp89 - tmp96
tmp103 = tl_math.exp(tmp102)
tmp104 = tmp101 + tmp103
tmp105 = tmp95 - tmp96
tmp106 = tl_math.exp(tmp105)
tmp107 = tmp104 + tmp106
tmp110 = tmp109 * tmp3
tmp111 = tl.where(tmp108, tmp109, tmp110)
tmp112 = tl.where(tmp0, tmp111, tmp6)
tmp115 = tmp114 * tmp3
tmp116 = tl.where(tmp113, tmp114, tmp115)
tmp117 = tl.where(tmp8, tmp116, tmp6)
tmp118 = triton_helpers.maximum(tmp112, tmp117)
tmp121 = tmp120 * tmp3
tmp122 = tl.where(tmp119, tmp120, tmp121)
tmp123 = tl.where(tmp15, tmp122, tmp6)
tmp124 = triton_helpers.maximum(tmp118, tmp123)
tmp127 = tmp126 * tmp3
tmp128 = tl.where(tmp125, tmp126, tmp127)
tmp129 = tl.where(tmp22, tmp128, tmp6)
tmp130 = triton_helpers.maximum(tmp124, tmp129)
tmp131 = tmp112 - tmp130
tmp132 = tl_math.exp(tmp131)
tmp133 = tmp117 - tmp130
tmp134 = tl_math.exp(tmp133)
tmp135 = tmp132 + tmp134
tmp136 = tmp123 - tmp130
tmp137 = tl_math.exp(tmp136)
tmp138 = tmp135 + tmp137
tmp139 = tmp129 - tmp130
tmp140 = tl_math.exp(tmp139)
tmp141 = tmp138 + tmp140
tl.store(out_ptr0 + x0, tmp28, xmask)
tl.store(out_ptr1 + x0, tmp39, xmask)
tl.store(out_ptr2 + x0, tmp62, xmask)
tl.store(out_ptr3 + x0, tmp73, xmask)
tl.store(out_ptr4 + x0, tmp96, xmask)
tl.store(out_ptr5 + x0, tmp107, xmask)
tl.store(out_ptr6 + x0, tmp130, xmask)
tl.store(out_ptr7 + x0, tmp141, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_3(in_out_ptr0,
in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10,
in_ptr11, in_ptr12, 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).to(tl.int1)
tmp1 = tl.load(in_ptr1 + x2, xmask).to(tl.int1)
tmp2 = tl.load(in_out_ptr0 + x2, xmask)
tmp8 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr4 + x2, xmask).to(tl.int1)
tmp14 = tl.load(in_out_ptr1 + x2, xmask)
tmp18 = tl.load(in_ptr5 + x1, xmask, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr6 + x1, xmask, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr7 + x2, xmask).to(tl.int1)
tmp24 = tl.load(in_out_ptr2 + x2, xmask)
tmp28 = tl.load(in_ptr8 + x1, xmask, eviction_policy='evict_last')
tmp31 = tl.load(in_ptr9 + x1, xmask, eviction_policy='evict_last')
tmp33 = tl.load(in_ptr10 + x2, xmask).to(tl.int1)
tmp34 = tl.load(in_out_ptr3 + x2, xmask)
tmp38 = tl.load(in_ptr11 + x1, xmask, eviction_policy='evict_last')
tmp41 = tl.load(in_ptr12 + x1, xmask, eviction_policy='evict_last')
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp9 = tmp7 - tmp8
tmp10 = tl_math.exp(tmp9)
tmp12 = tmp10 / tmp11
tmp15 = tmp14 * tmp3
tmp16 = tl.where(tmp13, tmp14, tmp15)
tmp17 = tl.where(tmp0, tmp16, tmp6)
tmp19 = tmp17 - tmp18
tmp20 = tl_math.exp(tmp19)
tmp22 = tmp20 / tmp21
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp23, tmp24, tmp25)
tmp27 = tl.where(tmp0, tmp26, tmp6)
tmp29 = tmp27 - tmp28
tmp30 = tl_math.exp(tmp29)
tmp32 = tmp30 / tmp31
tmp35 = tmp34 * tmp3
tmp36 = tl.where(tmp33, tmp34, tmp35)
tmp37 = tl.where(tmp0, tmp36, tmp6)
tmp39 = tmp37 - tmp38
tmp40 = tl_math.exp(tmp39)
tmp42 = tmp40 / tmp41
tl.store(in_out_ptr0 + x2, tmp12, xmask)
tl.store(in_out_ptr1 + x2, tmp22, xmask)
tl.store(in_out_ptr2 + x2, tmp32, xmask)
tl.store(in_out_ptr3 + x2, tmp42, xmask)
@triton.jit
def triton_poi_fused_cat_4(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 % 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 = 0.0
tmp7 = tmp5 > tmp6
tmp8 = 1.0
tmp9 = tmp5 * tmp8
tmp10 = libdevice.expm1(tmp9)
tmp11 = tmp10 * tmp8
tmp12 = tl.where(tmp7, tmp9, tmp11)
tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
tmp14 = tl.where(tmp4, tmp12, tmp13)
tmp15 = tmp0 >= tmp3
tmp16 = tl.full([1], 8, tl.int64)
tmp17 = tmp0 < tmp16
tmp18 = tmp15 & tmp17
tmp19 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp18 & xmask,
eviction_policy='evict_last', other=0.0)
tmp20 = tmp19 > tmp6
tmp21 = tmp19 * tmp8
tmp22 = libdevice.expm1(tmp21)
tmp23 = tmp22 * tmp8
tmp24 = tl.where(tmp20, tmp21, tmp23)
tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
tmp26 = tl.where(tmp18, tmp24, tmp25)
tmp27 = tmp0 >= tmp16
tmp28 = tl.full([1], 12, tl.int64)
tmp29 = tmp0 < tmp28
tmp30 = tmp27 & tmp29
tmp31 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp30 & xmask,
eviction_policy='evict_last', other=0.0)
tmp32 = tmp31 > tmp6
tmp33 = tmp31 * tmp8
tmp34 = libdevice.expm1(tmp33)
tmp35 = tmp34 * tmp8
tmp36 = tl.where(tmp32, tmp33, tmp35)
tmp37 = tl.full(tmp36.shape, 0.0, tmp36.dtype)
tmp38 = tl.where(tmp30, tmp36, tmp37)
tmp39 = tmp0 >= tmp28
tl.full([1], 16, tl.int64)
tmp42 = tl.load(in_ptr3 + (4 * x1 + (-12 + x0)), tmp39 & xmask,
eviction_policy='evict_last', other=0.0)
tmp43 = tmp42 > tmp6
tmp44 = tmp42 * tmp8
tmp45 = libdevice.expm1(tmp44)
tmp46 = tmp45 * tmp8
tmp47 = tl.where(tmp43, tmp44, tmp46)
tmp48 = tl.full(tmp47.shape, 0.0, tmp47.dtype)
tmp49 = tl.where(tmp39, tmp47, tmp48)
tmp50 = tl.where(tmp30, tmp38, tmp49)
tmp51 = tl.where(tmp18, tmp26, tmp50)
tmp52 = tl.where(tmp4, tmp14, tmp51)
tl.store(out_ptr0 + x2, tmp52, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_5(in_ptr0, in_ptr1,
in_ptr2, 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').to(tl
.int1)
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp9 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp15 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp16 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp22 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp23 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp11 = tmp10 * tmp3
tmp12 = tl.where(tmp9, tmp10, tmp11)
tmp13 = tl.where(tmp8, tmp12, tmp6)
tmp14 = triton_helpers.maximum(tmp7, tmp13)
tmp18 = tmp17 * tmp3
tmp19 = tl.where(tmp16, tmp17, tmp18)
tmp20 = tl.where(tmp15, tmp19, tmp6)
tmp21 = triton_helpers.maximum(tmp14, tmp20)
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp23, tmp24, tmp25)
tmp27 = tl.where(tmp22, tmp26, tmp6)
tmp28 = triton_helpers.maximum(tmp21, tmp27)
tmp29 = tmp7 - tmp28
tmp30 = tl_math.exp(tmp29)
tmp31 = tmp13 - tmp28
tmp32 = tl_math.exp(tmp31)
tmp33 = tmp30 + tmp32
tmp34 = tmp20 - tmp28
tmp35 = tl_math.exp(tmp34)
tmp36 = tmp33 + tmp35
tmp37 = tmp27 - tmp28
tmp38 = tl_math.exp(tmp37)
tmp39 = tmp36 + tmp38
tl.store(out_ptr0 + x0, tmp28, xmask)
tl.store(out_ptr1 + x0, tmp39, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_6(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
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask).to(tl.int1)
tmp1 = tl.load(in_ptr1 + x2, xmask).to(tl.int1)
tmp2 = tl.load(in_out_ptr0 + x2, xmask)
tmp8 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp9 = tmp7 - tmp8
tmp10 = tl_math.exp(tmp9)
tmp12 = tmp10 / tmp11
tl.store(in_out_ptr0 + x2, tmp12, xmask)
@triton.jit
def triton_poi_fused__log_softmax_elu_7(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)
tmp8 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp21 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp28 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tmp9 = tmp8 > tmp1
tmp10 = tmp8 * tmp3
tmp11 = libdevice.expm1(tmp10)
tmp12 = tmp11 * tmp3
tmp13 = tl.where(tmp9, tmp10, tmp12)
tmp15 = tmp14 > tmp1
tmp16 = tmp14 * tmp3
tmp17 = libdevice.expm1(tmp16)
tmp18 = tmp17 * tmp3
tmp19 = tl.where(tmp15, tmp16, tmp18)
tmp20 = triton_helpers.maximum(tmp13, tmp19)
tmp22 = tmp21 > tmp1
tmp23 = tmp21 * tmp3
tmp24 = libdevice.expm1(tmp23)
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp22, tmp23, tmp25)
tmp27 = triton_helpers.maximum(tmp20, tmp26)
tmp29 = tmp28 > tmp1
tmp30 = tmp28 * tmp3
tmp31 = libdevice.expm1(tmp30)
tmp32 = tmp31 * tmp3
tmp33 = tl.where(tmp29, tmp30, tmp32)
tmp34 = triton_helpers.maximum(tmp27, tmp33)
tmp35 = tmp7 - tmp34
tl.store(out_ptr0 + x2, tmp35, xmask)
@triton.jit
def triton_poi_fused__log_softmax_8(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 = 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 + x2, tmp13, 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, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (8, 1), (1, 1))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (8, 1), (1, 1))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (8, 1), (1, 1))
assert_size_stride(primals_10, (4, 4), (4, 1))
assert_size_stride(primals_11, (8, 1), (1, 1))
assert_size_stride(primals_12, (16, 4), (4, 1))
assert_size_stride(primals_13, (8, 1), (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_2, primals_1, out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf0, primals_3, out=buf1)
buf2 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(128)](buf1, buf2, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf2, primals_4, out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf3, buf4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](primals_5, buf5, 16, XBLOCK
=16, num_warps=1, num_stages=1)
del primals_5
buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf0, primals_6, out=buf10)
buf11 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
triton_poi_fused_cat_0[grid(128)](buf10, buf11, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf12 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf11, primals_7, out=buf12)
buf13 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf12, buf13, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf0, primals_8, out=buf18)
buf19 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
triton_poi_fused_cat_0[grid(128)](buf18, buf19, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf20 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf19, primals_9, out=buf20)
buf21 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf20, buf21, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf26 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf0, primals_10, out=buf26)
buf27 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
triton_poi_fused_cat_0[grid(128)](buf26, buf27, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf28 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf27, primals_11, out=buf28)
buf29 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf28, buf29, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf6 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf7 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf14 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf15 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf22 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf23 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf30 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf31 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_poi_fused__softmax_leaky_relu_mul_where_2[grid(4)](buf5,
buf4, buf3, buf13, buf12, buf21, buf20, buf29, buf28, buf6,
buf7, buf14, buf15, buf22, buf23, buf30, buf31, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf8 = reinterpret_tensor(buf3, (4, 4), (4, 1), 0)
del buf3
buf16 = reinterpret_tensor(buf12, (4, 4), (4, 1), 0)
del buf12
buf24 = reinterpret_tensor(buf20, (4, 4), (4, 1), 0)
del buf20
buf32 = reinterpret_tensor(buf28, (4, 4), (4, 1), 0)
del buf28
triton_poi_fused__softmax_leaky_relu_mul_where_3[grid(16)](buf8,
buf16, buf24, buf32, buf5, buf4, buf6, buf7, buf13, buf14,
buf15, buf21, buf22, buf23, buf29, buf30, buf31, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf14
del buf15
del buf22
del buf23
del buf30
del buf31
buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf8, buf1, out=buf9)
buf17 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf16, buf10, out=buf17)
buf25 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf24, buf18, out=buf25)
buf33 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf32, buf26, out=buf33)
buf34 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
triton_poi_fused_cat_4[grid(64)](buf9, buf17, buf25, buf33, buf34,
64, XBLOCK=64, num_warps=1, num_stages=1)
buf35 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf34, primals_12, out=buf35)
buf36 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
triton_poi_fused_cat_0[grid(128)](buf35, buf36, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf37 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf36, primals_13, out=buf37)
buf38 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf37, buf38, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf39 = buf7
del buf7
buf40 = buf6
del buf6
triton_poi_fused__softmax_leaky_relu_mul_where_5[grid(4)](buf5,
buf38, buf37, buf39, buf40, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf41 = reinterpret_tensor(buf37, (4, 4), (4, 1), 0)
del buf37
triton_poi_fused__softmax_leaky_relu_mul_where_6[grid(16)](buf41,
buf5, buf38, buf39, buf40, 16, XBLOCK=16, num_warps=1, num_stages=1
)
del buf39
del buf40
buf42 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf41, buf35, out=buf42)
buf43 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__log_softmax_elu_7[grid(16)](buf42, buf43, 16,
XBLOCK=16, num_warps=1, num_stages=1)
buf44 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__log_softmax_8[grid(16)](buf43, buf44, 16, XBLOCK=
16, num_warps=1, num_stages=1)
del buf43
return (buf44, buf4, buf5, buf8, buf9, buf13, buf16, buf17, buf21,
buf24, buf25, buf29, buf32, buf33, buf38, buf41, buf42, buf44,
reinterpret_tensor(buf35, (4, 4), (1, 4), 0), reinterpret_tensor(
buf36, (8, 16), (1, 8), 0), reinterpret_tensor(primals_13, (1, 8),
(1, 1), 0), reinterpret_tensor(buf34, (16, 4), (1, 16), 0),
reinterpret_tensor(primals_12, (4, 16), (1, 4), 0),
reinterpret_tensor(buf26, (4, 4), (1, 4), 0), reinterpret_tensor(
buf27, (8, 16), (1, 8), 0), reinterpret_tensor(primals_11, (1, 8),
(1, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0),
reinterpret_tensor(primals_10, (4, 4), (1, 4), 0),
reinterpret_tensor(buf18, (4, 4), (1, 4), 0), reinterpret_tensor(
buf19, (8, 16), (1, 8), 0), reinterpret_tensor(primals_9, (1, 8), (
1, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
reinterpret_tensor(buf10, (4, 4), (1, 4), 0), reinterpret_tensor(
buf11, (8, 16), (1, 8), 0), reinterpret_tensor(primals_7, (1, 8), (
1, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
reinterpret_tensor(buf1, (4, 4), (1, 4), 0), reinterpret_tensor(
buf2, (8, 16), (1, 8), 0), reinterpret_tensor(primals_4, (1, 8), (1,
1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0))
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__()
self.dropout = dropout
self.in_features = in_features
self.out_features = out_features
self.alpha = alpha
self.concat = concat
self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
self.a = nn.Parameter(torch.zeros(size=(2 * out_features, 1)))
nn.init.xavier_uniform_(self.a.data, gain=1.414)
self.leakyrelu = nn.LeakyReLU(self.alpha)
def forward(self, input, adj):
h = torch.mm(input, self.W)
"""u = torch.mm(input, self.U)
v = torch.mm(input, self.V)
h = torch.mm(torch.sigmoid(u) * torch.sigmoid(v), self.P)"""
N = h.size()[0]
a_input = torch.cat([h.repeat(1, N).view(N * N, -1), h.repeat(N, 1)
], dim=1).view(N, -1, 2 * self.out_features)
e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2))
zero_vec = -9000000000000000.0 * torch.ones_like(e)
attention = torch.where(adj > 0, e, zero_vec)
attention = F.softmax(attention, dim=1)
attention = F.dropout(attention, self.dropout, training=self.training)
h_prime = torch.matmul(attention, h)
if self.concat:
return F.elu(h_prime)
else:
return h_prime
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class GATNew(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
"""Dense version of GAT."""
super(GATNew, self).__init__()
self.dropout = dropout
self.encoder = nn.Parameter(torch.zeros(size=(nfeat, nhid)))
nn.init.xavier_uniform_(self.encoder.data, gain=1.414)
self.attentions = [GraphAttentionLayer(nhid, nhid, dropout=dropout,
alpha=alpha, concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=
dropout, alpha=alpha, concat=False)
def forward(self, input_0, input_1):
primals_1 = self.encoder
primals_2 = self.attention_0.W
primals_4 = self.attention_0.a
primals_3 = self.attention_1.W
primals_7 = self.attention_1.a
primals_5 = self.attention_2.W
primals_9 = self.attention_2.a
primals_6 = self.attention_3.W
primals_11 = self.attention_3.a
primals_12 = self.out_att.W
primals_13 = self.out_att.a
primals_8 = 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, primals_11, primals_12, primals_13])
return output[0]
|
thilinicooray/pyGAT
|
GAT
| false
| 13,051
|
[
"MIT"
] | 0
|
0c8fd0fdae20e42a41116cc9691e1223fd9d0a93
|
https://github.com/thilinicooray/pyGAT/tree/0c8fd0fdae20e42a41116cc9691e1223fd9d0a93
|
BinaryFocalLoss
|
import torch
class BinaryFocalLoss(torch.nn.Module):
""" from https://github.com/qubvel/segmentation_models"""
def __init__(self, gamma=2.0, alpha=0.25, eps=1e-07):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.eps = eps
def forward(self, pr, gt):
pr = torch.clamp(pr, self.eps, 1 - self.eps)
loss_1 = -gt * (self.alpha * torch.pow(1 - pr, self.gamma)
) * torch.log(pr)
loss_0 = -(1 - gt) * ((1 - self.alpha) * torch.pow(pr, self.gamma)
) * torch.log(1 - pr)
loss = loss_0 + loss_1
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 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_poi_fused_add_clamp_log_mul_neg_pow_rsub_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
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp4 = tl.load(in_ptr1 + x0, xmask)
tmp1 = 1.0
tmp2 = tmp1 - tmp0
tmp3 = -tmp2
tmp5 = 1e-07
tmp6 = triton_helpers.maximum(tmp4, tmp5)
tmp7 = 0.9999999
tmp8 = triton_helpers.minimum(tmp6, tmp7)
tmp9 = tmp8 * tmp8
tmp10 = 0.75
tmp11 = tmp9 * tmp10
tmp12 = tmp3 * tmp11
tmp13 = tmp1 - tmp8
tmp14 = tl_math.log(tmp13)
tmp15 = tmp12 * tmp14
tmp16 = -tmp0
tmp17 = tmp13 * tmp13
tmp18 = 0.25
tmp19 = tmp17 * tmp18
tmp20 = tmp16 * tmp19
tmp21 = tl_math.log(tmp8)
tmp22 = tmp20 * tmp21
tmp23 = tmp15 + tmp22
tl.store(out_ptr0 + x0, tmp23, 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.float32)
get_raw_stream(0)
triton_poi_fused_add_clamp_log_mul_neg_pow_rsub_0[grid(256)](arg1_1,
arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class BinaryFocalLossNew(torch.nn.Module):
""" from https://github.com/qubvel/segmentation_models"""
def __init__(self, gamma=2.0, alpha=0.25, eps=1e-07):
super().__init__()
self.gamma = gamma
self.alpha = alpha
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]
|
uncharted-distil/d3m-primitives
|
BinaryFocalLoss
| false
| 13,054
|
[
"Apache-2.0"
] | 0
|
e8d37dbe302c0f2bae4e7f7fa241a46faebc9b79
|
https://github.com/uncharted-distil/d3m-primitives/tree/e8d37dbe302c0f2bae4e7f7fa241a46faebc9b79
|
GeM
|
import torch
from torch import nn
from torch.nn import functional as F
from torchvision.transforms import *
class GeM(nn.Module):
def __init__(self, dim=2048, p=3, eps=1e-06):
super(GeM, self).__init__()
self.p = nn.Parameter(torch.ones(dim) * p, requires_grad=True)
self.eps = eps
self.dim = dim
def forward(self, x):
return self.gem(x, p=self.p, eps=self.eps)
def gem(self, x, p=3, eps=1e-06):
x = torch.transpose(x, 1, -1)
x = x.clamp(min=eps).pow(p)
x = torch.transpose(x, 1, -1)
x = F.avg_pool2d(x, (x.size(-2), x.size(-1)))
x = x.view(x.size(0), x.size(1))
x = x.pow(1.0 / p)
return x
def __repr__(self):
return self.__class__.__name__ + '(' + 'p=' + '{:.4f}'.format(self.
p.data.tolist()[0]) + ', ' + 'eps=' + str(self.eps
) + ',' + 'dim=' + str(self.dim) + ')'
def get_inputs():
return [torch.rand([4, 2048, 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
from torch.nn import functional as F
from torchvision.transforms import *
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_pow_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 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]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 8192 * y1), ymask,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp1 = 1e-06
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp4 = libdevice.pow(tmp2, tmp3)
tl.store(out_ptr0 + (x2 + 2048 * y3), tmp4, ymask)
@triton.jit
def triton_poi_fused_clamp_eq_ge_log_logical_and_mul_pow_transpose_where_zeros_1(
in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK:
tl.constexpr, XBLOCK: tl.constexpr):
xnumel = 4
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 % 2048
y1 = yindex // 2048
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 2048 * x2 + 8192 * y1), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2 + 4 * y3), xmask, eviction_policy='evict_last'
)
tmp6 = tl.load(in_ptr2 + y0, None, eviction_policy='evict_last')
tmp2 = 1e-06
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp4 = 0.0
tmp5 = tmp3 == tmp4
tmp7 = tmp6 >= tmp4
tmp8 = tmp5 & tmp7
tmp9 = tl_math.log(tmp3)
tmp10 = tmp0 * tmp9
tmp11 = tl.where(tmp8, tmp4, tmp10)
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask)
tl.store(out_ptr1 + (x2 + 4 * y3), tmp11, xmask)
@triton.jit
def triton_poi_fused_mul_pow_reciprocal_2(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)
x1 = xindex // 2048
x0 = xindex % 2048
x2 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp2 = tl.full([1], 1, tl.int32)
tmp3 = tmp2 / tmp1
tmp4 = 1.0
tmp5 = tmp3 * tmp4
tmp6 = libdevice.pow(tmp0, tmp5)
tl.store(out_ptr0 + x2, tmp6, None)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (2048,), (1,))
assert_size_stride(primals_2, (4, 2048, 4), (8192, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 2048), (8192, 2048, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clamp_pow_0[grid(16, 2048)](primals_2, primals_1,
buf0, 16, 2048, XBLOCK=256, YBLOCK=1, num_warps=4, num_stages=1)
buf1 = empty_strided_cuda((4, 2048, 4), (8192, 4, 1), torch.float32)
buf5 = empty_strided_cuda((4, 4, 2048), (8192, 1, 4), torch.float32)
triton_poi_fused_clamp_eq_ge_log_logical_and_mul_pow_transpose_where_zeros_1[
grid(8192, 4)](buf0, primals_2, primals_1, buf1, buf5, 8192, 4,
XBLOCK=1, YBLOCK=256, num_warps=4, num_stages=1)
del buf0
del primals_2
buf2 = torch.ops.aten.avg_pool2d.default(buf1, [2048, 4], [2048, 4],
[0, 0], False, True, None)
buf3 = buf2
del buf2
buf4 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
triton_poi_fused_mul_pow_reciprocal_2[grid(8192)](buf3, primals_1,
buf4, 8192, XBLOCK=128, num_warps=4, num_stages=1)
return buf4, primals_1, buf1, buf3, buf4, buf5
class GeMNew(nn.Module):
def __init__(self, dim=2048, p=3, eps=1e-06):
super(GeMNew, self).__init__()
self.p = nn.Parameter(torch.ones(dim) * p, requires_grad=True)
self.eps = eps
self.dim = dim
def gem(self, x, p=3, eps=1e-06):
x = torch.transpose(x, 1, -1)
x = x.clamp(min=eps).pow(p)
x = torch.transpose(x, 1, -1)
x = F.avg_pool2d(x, (x.size(-2), x.size(-1)))
x = x.view(x.size(0), x.size(1))
x = x.pow(1.0 / p)
return x
def __repr__(self):
return self.__class__.__name__ + '(' + 'p=' + '{:.4f}'.format(self.
p.data.tolist()[0]) + ', ' + 'eps=' + str(self.eps
) + ',' + 'dim=' + str(self.dim) + ')'
def forward(self, input_0):
primals_1 = self.p
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
uestcwcw/University1652-Baseline
|
GeM
| false
| 13,056
|
[
"MIT"
] | 0
|
fda1e4773fc911cbb43a9b96901d436298dc1284
|
https://github.com/uestcwcw/University1652-Baseline/tree/fda1e4773fc911cbb43a9b96901d436298dc1284
|
CrossEntropy
|
import torch
from torch import nn
import torch.nn.functional as F
def cross_entropy(y, target, mask=None):
if target.ndim == 1:
loss = F.cross_entropy(y, target, reduction='none')
else:
loss = -(target * F.log_softmax(y, 1)).sum(1)
if mask is not None:
loss = mask * loss
return loss.mean()
class CrossEntropy(nn.Module):
def forward(self, y, target, mask=None, *args, **kwargs):
return cross_entropy(y, target.detach(), mask)
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.nn.functional as F
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__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_per_fused__log_softmax_mean_mul_neg_sum_1(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 % 16
r1 = rindex // 16
tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
tmp3 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
tmp6 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
tmp9 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
tmp15 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
tmp19 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
tmp23 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
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 = tmp1 - tmp12
tmp14 = tmp0 * tmp13
tmp16 = tmp3 - tmp12
tmp17 = tmp15 * tmp16
tmp18 = tmp14 + tmp17
tmp20 = tmp6 - tmp12
tmp21 = tmp19 * tmp20
tmp22 = tmp18 + tmp21
tmp24 = tmp9 - tmp12
tmp25 = tmp23 * tmp24
tmp26 = tmp22 + tmp25
tmp27 = -tmp26
tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
tmp30 = tl.sum(tmp28, 1)[:, None]
tmp31 = 64.0
tmp32 = tmp30 / tmp31
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp32, 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)
get_raw_stream(0)
triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1
del buf1
triton_per_fused__log_softmax_mean_mul_neg_sum_1[grid(1)](buf2,
arg0_1, buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del buf0
return buf2,
def cross_entropy(y, target, mask=None):
if target.ndim == 1:
loss = F.cross_entropy(y, target, reduction='none')
else:
loss = -(target * F.log_softmax(y, 1)).sum(1)
if mask is not None:
loss = mask * loss
return loss.mean()
class CrossEntropyNew(nn.Module):
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
uncharted-distil/d3m-primitives
|
CrossEntropy
| false
| 13,057
|
[
"Apache-2.0"
] | 0
|
e8d37dbe302c0f2bae4e7f7fa241a46faebc9b79
|
https://github.com/uncharted-distil/d3m-primitives/tree/e8d37dbe302c0f2bae4e7f7fa241a46faebc9b79
|
CircleLoss
|
import torch
from torch import Tensor
from torch import nn
from torchvision.transforms import *
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
from torch import 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
@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]
|
uestcwcw/University1652-Baseline
|
CircleLoss
| false
| 13,058
|
[
"MIT"
] | 0
|
fda1e4773fc911cbb43a9b96901d436298dc1284
|
https://github.com/uestcwcw/University1652-Baseline/tree/fda1e4773fc911cbb43a9b96901d436298dc1284
|
Image2Patch
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Image2Patch(nn.Module):
"""Some Information about Image2Patch"""
def __init__(self, channels, image_size, patch_size):
super(Image2Patch, self).__init__()
if type(patch_size) == int:
patch_size = [patch_size, patch_size]
self.patch_size = patch_size
if type(image_size) == int:
image_size = [image_size, image_size]
self.image_size = image_size
self.channels = channels
self.num_patch = [image_size[0] // patch_size[0], image_size[1] //
patch_size[1]]
def forward(self, x):
x = F.unfold(x, kernel_size=self.patch_size, stride=self.patch_size)
x = x.swapaxes(1, 2)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'channels': 4, 'image_size': 4, 'patch_size': 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_im2col_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
tmp0 = tl.load(in_ptr0 + x3, xmask)
tl.store(out_ptr0 + x3, tmp0, 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, 1, 4, 1), (64, 16, 4, 4, 1, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_im2col_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
return reinterpret_tensor(buf0, (4, 1, 64), (64, 4, 1), 0),
class Image2PatchNew(nn.Module):
"""Some Information about Image2Patch"""
def __init__(self, channels, image_size, patch_size):
super(Image2PatchNew, self).__init__()
if type(patch_size) == int:
patch_size = [patch_size, patch_size]
self.patch_size = patch_size
if type(image_size) == int:
image_size = [image_size, image_size]
self.image_size = image_size
self.channels = channels
self.num_patch = [image_size[0] // patch_size[0], image_size[1] //
patch_size[1]]
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
uthree/ReMixer
|
Image2Patch
| false
| 13,059
|
[
"MIT"
] | 0
|
587e1b6a01850df649eccf043689f84a7dd5e2dc
|
https://github.com/uthree/ReMixer/tree/587e1b6a01850df649eccf043689f84a7dd5e2dc
|
CEFL
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class CEFL(nn.Module):
def __init__(self, gamma=1):
super(CEFL, self).__init__()
self.gamma = gamma
def get_prob(self, input, target):
prob = F.softmax(input, dim=-1)
prob = prob[range(target.shape[0]), target]
return prob
def get_attention(self, input, target):
prob = self.get_prob(input, target)
prob = 1 - prob
prob = prob ** self.gamma
return prob
def get_celoss(self, input, target):
ce_loss = F.log_softmax(input, dim=1)
ce_loss = -ce_loss[range(target.shape[0]), target]
return ce_loss
def forward(self, input, target):
attn = self.get_attention(input, target)
ce_loss = self.get_celoss(input, target)
prob = self.get_prob(input, target)
loss = (1 - prob) * ce_loss + prob * attn * ce_loss
return loss.mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]
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
import torch.nn.functional as F
import torch.onnx
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__log_softmax__softmax_0(in_ptr0, out_ptr0, out_ptr1,
out_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x5 = xindex
x1 = xindex // 4
x2 = xindex % 16
x4 = xindex // 64
tmp0 = tl.load(in_ptr0 + x5, 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 + (x2 + 64 * x4), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr0 + (16 + x2 + 64 * x4), xmask, eviction_policy=
'evict_last')
tmp13 = tl.load(in_ptr0 + (32 + x2 + 64 * x4), xmask, eviction_policy=
'evict_last')
tmp15 = tl.load(in_ptr0 + (48 + x2 + 64 * x4), 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
tl.store(out_ptr0 + x5, tmp9, xmask)
tl.store(out_ptr1 + x5, tmp9, xmask)
tl.store(out_ptr2 + x5, tmp17, xmask)
@triton.jit
def triton_per_fused__log_softmax__softmax_add_index_mean_mul_neg_rsub_1(
in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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)
r2 = rindex // 16
r3 = rindex % 16
r1 = rindex // 4 % 4
tmp11 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
tmp0 = r2
tmp1 = tl.full([1, 1], 2, tl.int64)
tmp2 = tmp0 < tmp1
tmp3 = tl.full([1, 1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.full([1, 1], 0, tl.int64)
tmp6 = tl.where(tmp4, tmp5, tmp3)
tmp7 = tl.full([1, 1], 3, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.where(tmp8, tmp1, tmp7)
tmp10 = tl.where(tmp2, tmp6, tmp9)
tmp12 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp13 = tmp11 + tmp12
tmp14 = tmp11 < 0
tmp15 = tl.where(tmp14, tmp13, tmp11)
tl.device_assert((0 <= tmp15) & (tmp15 < 4),
'index out of bounds: 0 <= tmp15 < 4')
tmp17 = tl.load(in_ptr1 + (r3 + 16 * tmp15 + 64 * tmp10), None)
tmp18 = tl.load(in_ptr1 + (4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp19 = tl.load(in_ptr1 + (1 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp20 = tmp18 + tmp19
tmp21 = tl.load(in_ptr1 + (2 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp22 = tmp20 + tmp21
tmp23 = tl.load(in_ptr1 + (3 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp24 = tmp22 + tmp23
tmp25 = tmp17 / tmp24
tmp26 = tl.load(in_ptr2 + (r3 + 16 * tmp15 + 64 * tmp10), None)
tmp27 = tl.load(in_ptr2 + (r3 + 64 * tmp10), None)
tmp28 = tl_math.exp(tmp27)
tmp29 = tl.load(in_ptr2 + (16 + r3 + 64 * tmp10), None)
tmp30 = tl_math.exp(tmp29)
tmp31 = tmp28 + tmp30
tmp32 = tl.load(in_ptr2 + (32 + r3 + 64 * tmp10), None)
tmp33 = tl_math.exp(tmp32)
tmp34 = tmp31 + tmp33
tmp35 = tl.load(in_ptr2 + (48 + r3 + 64 * tmp10), None)
tmp36 = tl_math.exp(tmp35)
tmp37 = tmp34 + tmp36
tmp38 = tl_math.log(tmp37)
tmp39 = tmp26 - tmp38
tmp40 = -tmp39
tmp41 = 1.0
tmp42 = tmp41 - tmp25
tmp43 = tmp42 * tmp40
tmp44 = tl.load(in_ptr3 + (r3 + 16 * tmp15 + 64 * tmp10), None)
tmp45 = tl.load(in_ptr3 + (4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp46 = tl.load(in_ptr3 + (1 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp47 = tmp45 + tmp46
tmp48 = tl.load(in_ptr3 + (2 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp49 = tmp47 + tmp48
tmp50 = tl.load(in_ptr3 + (3 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp51 = tmp49 + tmp50
tmp52 = tmp44 / tmp51
tmp53 = tmp41 - tmp52
tmp54 = tmp25 * tmp53
tmp55 = tmp54 * tmp40
tmp56 = tmp43 + tmp55
tmp57 = tl.broadcast_to(tmp56, [XBLOCK, RBLOCK])
tmp59 = tl.sum(tmp57, 1)[:, None]
tmp60 = 64.0
tmp61 = tmp59 / tmp60
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp61, 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,), (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)
buf4 = 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)
get_raw_stream(0)
triton_poi_fused__log_softmax__softmax_0[grid(256)](arg0_1, buf0,
buf4, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
buf5 = empty_strided_cuda((), (), torch.float32)
buf6 = buf5
del buf5
triton_per_fused__log_softmax__softmax_add_index_mean_mul_neg_rsub_1[
grid(1)](buf6, arg1_1, buf0, buf2, buf4, 1, 64, XBLOCK=1,
num_warps=2, num_stages=1)
del arg1_1
del buf0
del buf2
del buf4
return buf6,
class CEFLNew(nn.Module):
def __init__(self, gamma=1):
super(CEFLNew, self).__init__()
self.gamma = gamma
def get_prob(self, input, target):
prob = F.softmax(input, dim=-1)
prob = prob[range(target.shape[0]), target]
return prob
def get_attention(self, input, target):
prob = self.get_prob(input, target)
prob = 1 - prob
prob = prob ** self.gamma
return prob
def get_celoss(self, input, target):
ce_loss = F.log_softmax(input, dim=1)
ce_loss = -ce_loss[range(target.shape[0]), target]
return ce_loss
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
umairjavaid/staff-employee-classification
|
CEFL
| false
| 13,060
|
[
"MIT"
] | 0
|
fc5fe32acfbde2b188094df90d888eeb0f4f4acd
|
https://github.com/umairjavaid/staff-employee-classification/tree/fc5fe32acfbde2b188094df90d888eeb0f4f4acd
|
FocalLoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class FocalLoss(nn.Module):
def __init__(self, alpha=1, gamma=0):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
def get_attention(self, input, target):
prob = F.softmax(input, dim=-1)
prob = prob[range(target.shape[0]), target]
prob = 1 - prob
prob = prob ** self.gamma
return prob
def get_celoss(self, input, target):
ce_loss = F.log_softmax(input, dim=1)
ce_loss = -ce_loss[range(target.shape[0]), target]
return ce_loss
def forward(self, input, target):
attn = self.get_attention(input, target)
ce_loss = self.get_celoss(input, target)
loss = self.alpha * ce_loss * attn
return loss.mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]
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
import torch.nn.functional as F
import torch.onnx
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__log_softmax__softmax_0(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
x5 = xindex
x0 = xindex % 16
x2 = xindex // 64
x4 = xindex // 4
tmp0 = tl.load(in_ptr0 + x5, 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')
tmp9 = tl.load(in_ptr0 + 4 * x4, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr0 + (1 + 4 * x4), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr0 + (2 + 4 * x4), xmask, eviction_policy='evict_last'
)
tmp14 = tl.load(in_ptr0 + (3 + 4 * x4), 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
tmp11 = triton_helpers.maximum(tmp9, tmp10)
tmp13 = triton_helpers.maximum(tmp11, tmp12)
tmp15 = triton_helpers.maximum(tmp13, tmp14)
tmp16 = tmp0 - tmp15
tmp17 = tl_math.exp(tmp16)
tl.store(out_ptr0 + x5, tmp8, xmask)
tl.store(out_ptr1 + x5, tmp17, xmask)
@triton.jit
def triton_per_fused__log_softmax__softmax_index_mean_mul_neg_pow_rsub_1(
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)
r2 = rindex // 16
r4 = rindex % 16
r1 = rindex // 4 % 4
tmp11 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
tmp0 = r2
tmp1 = tl.full([1, 1], 2, tl.int64)
tmp2 = tmp0 < tmp1
tmp3 = tl.full([1, 1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.full([1, 1], 0, tl.int64)
tmp6 = tl.where(tmp4, tmp5, tmp3)
tmp7 = tl.full([1, 1], 3, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.where(tmp8, tmp1, tmp7)
tmp10 = tl.where(tmp2, tmp6, tmp9)
tmp12 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp13 = tmp11 + tmp12
tmp14 = tmp11 < 0
tmp15 = tl.where(tmp14, tmp13, tmp11)
tl.device_assert((0 <= tmp15) & (tmp15 < 4),
'index out of bounds: 0 <= tmp15 < 4')
tmp17 = tl.load(in_ptr1 + (r4 + 16 * tmp15 + 64 * tmp10), None)
tmp18 = tl.load(in_ptr1 + (r4 + 64 * tmp10), None)
tmp19 = tl_math.exp(tmp18)
tmp20 = tl.load(in_ptr1 + (16 + r4 + 64 * tmp10), None)
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tl.load(in_ptr1 + (32 + r4 + 64 * tmp10), None)
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = tl.load(in_ptr1 + (48 + r4 + 64 * tmp10), None)
tmp27 = tl_math.exp(tmp26)
tmp28 = tmp25 + tmp27
tmp29 = tl_math.log(tmp28)
tmp30 = tmp17 - tmp29
tmp31 = -tmp30
tmp32 = 1.0
tmp33 = tmp31 * tmp32
tmp34 = tl.load(in_ptr2 + (r4 + 16 * tmp15 + 64 * tmp10), None)
tmp35 = tl.load(in_ptr2 + (4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp36 = tl.load(in_ptr2 + (1 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp37 = tmp35 + tmp36
tmp38 = tl.load(in_ptr2 + (2 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp39 = tmp37 + tmp38
tmp40 = tl.load(in_ptr2 + (3 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
eviction_policy='evict_last')
tmp41 = tmp39 + tmp40
tmp42 = tmp34 / tmp41
tmp32 - tmp42
tmp44 = tmp33 * tmp32
tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
tmp47 = tl.sum(tmp45, 1)[:, None]
tmp48 = 64.0
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 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 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, 4), (64, 16, 4, 1), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__log_softmax__softmax_0[grid(256)](arg0_1, buf0,
buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
buf3 = empty_strided_cuda((), (), torch.float32)
buf4 = buf3
del buf3
triton_per_fused__log_softmax__softmax_index_mean_mul_neg_pow_rsub_1[
grid(1)](buf4, arg1_1, buf0, buf1, 1, 64, XBLOCK=1, num_warps=2,
num_stages=1)
del arg1_1
del buf0
del buf1
return buf4,
class FocalLossNew(nn.Module):
def __init__(self, alpha=1, gamma=0):
super(FocalLossNew, self).__init__()
self.gamma = gamma
self.alpha = alpha
def get_attention(self, input, target):
prob = F.softmax(input, dim=-1)
prob = prob[range(target.shape[0]), target]
prob = 1 - prob
prob = prob ** self.gamma
return prob
def get_celoss(self, input, target):
ce_loss = F.log_softmax(input, dim=1)
ce_loss = -ce_loss[range(target.shape[0]), target]
return ce_loss
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
umairjavaid/staff-employee-classification
|
FocalLoss
| false
| 13,061
|
[
"MIT"
] | 0
|
fc5fe32acfbde2b188094df90d888eeb0f4f4acd
|
https://github.com/umairjavaid/staff-employee-classification/tree/fc5fe32acfbde2b188094df90d888eeb0f4f4acd
|
encoder
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class encoder(nn.Module):
def __init__(self, ef_dim):
super(encoder, self).__init__()
self.ef_dim = ef_dim
self.conv_1 = nn.Conv3d(1, self.ef_dim, 4, stride=2, padding=1,
bias=True)
self.conv_2 = nn.Conv3d(self.ef_dim, self.ef_dim * 2, 4, stride=2,
padding=1, bias=True)
self.conv_3 = nn.Conv3d(self.ef_dim * 2, self.ef_dim * 4, 4, stride
=2, padding=1, bias=True)
self.conv_4 = nn.Conv3d(self.ef_dim * 4, self.ef_dim * 8, 4, stride
=2, padding=1, bias=True)
self.conv_5 = nn.Conv3d(self.ef_dim * 8, self.ef_dim * 8, 4, stride
=1, padding=0, bias=True)
nn.init.xavier_uniform_(self.conv_1.weight)
nn.init.constant_(self.conv_1.bias, 0)
nn.init.xavier_uniform_(self.conv_2.weight)
nn.init.constant_(self.conv_2.bias, 0)
nn.init.xavier_uniform_(self.conv_3.weight)
nn.init.constant_(self.conv_3.bias, 0)
nn.init.xavier_uniform_(self.conv_4.weight)
nn.init.constant_(self.conv_4.bias, 0)
nn.init.xavier_uniform_(self.conv_5.weight)
nn.init.constant_(self.conv_5.bias, 0)
def forward(self, inputs, is_training=False):
d_1 = self.conv_1(inputs)
d_1 = F.leaky_relu(d_1, negative_slope=0.01, inplace=True)
d_2 = self.conv_2(d_1)
d_2 = F.leaky_relu(d_2, negative_slope=0.01, inplace=True)
d_3 = self.conv_3(d_2)
d_3 = F.leaky_relu(d_3, negative_slope=0.01, inplace=True)
d_4 = self.conv_4(d_3)
d_4 = F.leaky_relu(d_4, negative_slope=0.01, inplace=True)
d_5 = self.conv_5(d_4)
d_5 = d_5.view(-1, self.ef_dim * 8)
d_5 = torch.sigmoid(d_5)
return d_5
def get_inputs():
return [torch.rand([4, 1, 64, 64, 64])]
def get_init_inputs():
return [[], {'ef_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_leaky_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 // 32768 % 4
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(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 % 8
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_2(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 // 512 % 16
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(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 // 64 % 32
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_sigmoid_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 32
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, 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, (4, 1, 4, 4, 4), (64, 64, 16, 4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 1, 64, 64, 64), (262144, 262144, 4096,
64, 1))
assert_size_stride(primals_4, (8, 4, 4, 4, 4), (256, 64, 16, 4, 1))
assert_size_stride(primals_5, (8,), (1,))
assert_size_stride(primals_6, (16, 8, 4, 4, 4), (512, 64, 16, 4, 1))
assert_size_stride(primals_7, (16,), (1,))
assert_size_stride(primals_8, (32, 16, 4, 4, 4), (1024, 64, 16, 4, 1))
assert_size_stride(primals_9, (32,), (1,))
assert_size_stride(primals_10, (32, 32, 4, 4, 4), (2048, 64, 16, 4, 1))
assert_size_stride(primals_11, (32,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2,
2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
output_padding=(0, 0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 32, 32, 32), (131072, 32768, 1024,
32, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf1,
primals_2, 524288, XBLOCK=512, num_warps=8, num_stages=1)
del primals_2
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2, 2),
padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
output_padding=(0, 0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 8, 16, 16, 16), (32768, 4096, 256, 16, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_leaky_relu_1[grid(131072)](buf3,
primals_5, 131072, XBLOCK=512, num_warps=8, num_stages=1)
del primals_5
buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2, 2),
padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
output_padding=(0, 0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 16, 8, 8, 8), (8192, 512, 64, 8, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_leaky_relu_2[grid(32768)](buf5,
primals_7, 32768, XBLOCK=256, num_warps=4, num_stages=1)
del primals_7
buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2, 2),
padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
output_padding=(0, 0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 32, 4, 4, 4), (2048, 64, 16, 4, 1))
buf7 = buf6
del buf6
triton_poi_fused_convolution_leaky_relu_3[grid(8192)](buf7,
primals_9, 8192, XBLOCK=256, num_warps=4, num_stages=1)
del primals_9
buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1, 1
), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
output_padding=(0, 0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 32, 1, 1, 1), (32, 1, 1, 1, 1))
buf9 = reinterpret_tensor(buf8, (4, 32), (32, 1), 0)
del buf8
triton_poi_fused_sigmoid_4[grid(128)](buf9, primals_11, 128, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_11
return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8,
primals_10, buf1, buf3, buf5, buf7, buf9)
class encoderNew(nn.Module):
def __init__(self, ef_dim):
super(encoderNew, self).__init__()
self.ef_dim = ef_dim
self.conv_1 = nn.Conv3d(1, self.ef_dim, 4, stride=2, padding=1,
bias=True)
self.conv_2 = nn.Conv3d(self.ef_dim, self.ef_dim * 2, 4, stride=2,
padding=1, bias=True)
self.conv_3 = nn.Conv3d(self.ef_dim * 2, self.ef_dim * 4, 4, stride
=2, padding=1, bias=True)
self.conv_4 = nn.Conv3d(self.ef_dim * 4, self.ef_dim * 8, 4, stride
=2, padding=1, bias=True)
self.conv_5 = nn.Conv3d(self.ef_dim * 8, self.ef_dim * 8, 4, stride
=1, padding=0, bias=True)
nn.init.xavier_uniform_(self.conv_1.weight)
nn.init.constant_(self.conv_1.bias, 0)
nn.init.xavier_uniform_(self.conv_2.weight)
nn.init.constant_(self.conv_2.bias, 0)
nn.init.xavier_uniform_(self.conv_3.weight)
nn.init.constant_(self.conv_3.bias, 0)
nn.init.xavier_uniform_(self.conv_4.weight)
nn.init.constant_(self.conv_4.bias, 0)
nn.init.xavier_uniform_(self.conv_5.weight)
nn.init.constant_(self.conv_5.bias, 0)
def forward(self, input_0):
primals_1 = self.conv_1.weight
primals_2 = self.conv_1.bias
primals_4 = self.conv_2.weight
primals_5 = self.conv_2.bias
primals_6 = self.conv_3.weight
primals_7 = self.conv_3.bias
primals_8 = self.conv_4.weight
primals_9 = self.conv_4.bias
primals_10 = self.conv_5.weight
primals_11 = self.conv_5.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]
|
trisct/BSP-NET-pytorch
|
encoder
| false
| 13,062
|
[
"MIT"
] | 0
|
31f148aa3d7321bac854bc3de6c88f676236b7e4
|
https://github.com/trisct/BSP-NET-pytorch/tree/31f148aa3d7321bac854bc3de6c88f676236b7e4
|
CustomInverse
|
import torch
class CustomTorchOp(torch.autograd.Function):
@staticmethod
def symbolic(g, input):
return g.op('torchcustom::Add10', input)
@staticmethod
def forward(ctx, x):
return x + 10
class CustomInverse(torch.nn.Module):
def forward(self, x, y):
ress = CustomTorchOp.apply(torch.inverse(x))
return ress, torch.all(y)
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
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_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 = 10.0
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_per_fused_all_1(in_out_ptr0, in_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 = tmp0 != 0
tmp2 = tmp1 == 0
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(triton_helpers.any(tmp3, 0))
tmp6 = tmp5 == 0
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp6, 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 = torch.ops.aten.linalg_inv_ex.default(arg0_1)
del arg0_1
buf1 = buf0[0]
del buf0
buf3 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused_add_0[grid(256)](buf3, 256, XBLOCK=256, num_warps=
4, num_stages=1)
buf4 = empty_strided_cuda((), (), torch.bool)
buf5 = buf4
del buf4
triton_per_fused_all_1[grid(1)](buf5, arg1_1, 1, 256, num_warps=2,
num_stages=1)
del arg1_1
return buf3, buf5
class CustomTorchOp(torch.autograd.Function):
@staticmethod
def symbolic(g, input):
return g.op('torchcustom::Add10', input)
@staticmethod
def forward(ctx, x):
return x + 10
class CustomInverseNew(torch.nn.Module):
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0], output[1]
|
shaahji/onnxruntime-extensions
|
CustomInverse
| false
| 13,063
|
[
"MIT"
] | 0
|
c30df08aee69db761b97185be9f87160a4efa6bc
|
https://github.com/shaahji/onnxruntime-extensions/tree/c30df08aee69db761b97185be9f87160a4efa6bc
|
MixerMLP
|
import torch
import torch.nn as nn
class MixerMLP(nn.Module):
"""Some Information about MixerMLP"""
def __init__(self, dim, activation='gelu'):
super(MixerMLP, self).__init__()
if activation == 'gelu':
self.activation = nn.GELU()
elif activation == 'relu':
self.activation = nn.ReLU()
elif activation == 'leakyrelu':
self.activation = nn.LeakyReLU(0.2)
self.fc1 = nn.Linear(dim, dim)
self.fc2 = nn.Linear(dim, dim)
self.ln = nn.LayerNorm(dim)
def forward(self, x):
x = x.swapaxes(1, 2)
x = self.ln(x)
x = self.activation(self.fc1(x))
x = self.fc2(x)
x = x.swapaxes(1, 2)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'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.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_native_layer_norm_0(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
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, 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
x5 = xindex // 4
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16 % 4
x3 = xindex // 64
tmp0 = tl.load(in_ptr0 + x4, xmask)
tmp1 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp8, xmask)
@triton.jit
def triton_poi_fused_gelu_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
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):
(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,), (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((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(64)](primals_1, buf0,
buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(256)](primals_1, buf0,
buf1, primals_2, primals_3, buf2, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del buf0
del buf1
del primals_2
del primals_3
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_5
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_gelu_2[grid(256)](buf3, buf4, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf5)
del primals_7
return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 4, 16, 1), 0
), primals_1, reinterpret_tensor(buf2, (64, 4), (4, 1), 0
), buf3, reinterpret_tensor(buf4, (64, 4), (4, 1), 0
), primals_6, primals_4
class MixerMLPNew(nn.Module):
"""Some Information about MixerMLP"""
def __init__(self, dim, activation='gelu'):
super(MixerMLPNew, self).__init__()
if activation == 'gelu':
self.activation = nn.GELU()
elif activation == 'relu':
self.activation = nn.ReLU()
elif activation == 'leakyrelu':
self.activation = nn.LeakyReLU(0.2)
self.fc1 = nn.Linear(dim, dim)
self.fc2 = nn.Linear(dim, dim)
self.ln = nn.LayerNorm(dim)
def forward(self, input_0):
primals_4 = self.fc1.weight
primals_2 = self.fc1.bias
primals_6 = self.fc2.weight
primals_3 = self.fc2.bias
primals_5 = self.ln.weight
primals_7 = self.ln.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
uthree/ReMixer
|
MixerMLP
| false
| 13,064
|
[
"MIT"
] | 0
|
587e1b6a01850df649eccf043689f84a7dd5e2dc
|
https://github.com/uthree/ReMixer/tree/587e1b6a01850df649eccf043689f84a7dd5e2dc
|
SpatialShift2d
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class SpatialShift2d(nn.Module):
def __init__(self, channels, padding_mode='replicate'):
super(SpatialShift2d, self).__init__()
qc = channels // 4
self.num_shift_left = qc
self.num_shift_right = qc
self.num_shift_up = qc
self.num_shift_down = channels - qc * 3
self.padding_mode = padding_mode
def forward(self, x):
_l, _r, _u, _d = (self.num_shift_left, self.num_shift_right, self.
num_shift_up, self.num_shift_down)
x = F.pad(x, (1, 1, 1, 1), self.padding_mode)
l, r, u, d = torch.split(x, [_l, _r, _u, _d], dim=1)
l = l[:, :, 1:-1, 0:-2]
r = r[:, :, 1:-1, 2:]
u = u[:, :, 0:-2, 1:-1]
d = d[:, :, 2:, 1:-1]
x = torch.cat([l, r, u, d], dim=1)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'channels': 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, 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 // 16 % 4
x0 = xindex % 4
x1 = xindex // 4 % 4
x3 = xindex // 64
x4 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * (3 * (3 <= x1) + x1 * (x1 < 3)) + 16 * x2 +
64 * x3 + (3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)
) + (0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -
1 + x0) + (-1 + x0) * (-1 + x0 > 0) < 3))), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 2, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (16 + 4 * (3 * (3 <= x1) + x1 * (x1 < 3)) +
16 * (-1 + x2) + 64 * x3 + (3 * (3 <= 1 + x0) + (1 + x0) * (1 + x0 <
3))), tmp9 & xmask, other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 3, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (32 + 4 * (3 * (3 <= 0 * (0 >= -1 + x1) + (-1 +
x1) * (-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 >
0)) * (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 3)) + 16 *
(-2 + x2) + 64 * x3 + (3 * (3 <= x0) + x0 * (x0 < 3))), tmp14 &
xmask, other=0.0)
tmp16 = tmp0 >= tmp12
tl.full([1], 4, tl.int64)
tmp19 = tl.load(in_ptr0 + (48 + 4 * (3 * (3 <= 1 + x1) + (1 + x1) * (1 +
x1 < 3)) + 16 * (-3 + x2) + 64 * x3 + (3 * (3 <= x0) + x0 * (x0 < 3
))), tmp16 & xmask, other=0.0)
tmp20 = tl.where(tmp14, tmp15, tmp19)
tmp21 = tl.where(tmp9, tmp10, tmp20)
tmp22 = tl.where(tmp4, tmp5, tmp21)
tl.store(out_ptr0 + x4, tmp22, 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_cat_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class SpatialShift2dNew(nn.Module):
def __init__(self, channels, padding_mode='replicate'):
super(SpatialShift2dNew, self).__init__()
qc = channels // 4
self.num_shift_left = qc
self.num_shift_right = qc
self.num_shift_up = qc
self.num_shift_down = channels - qc * 3
self.padding_mode = padding_mode
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
uthree/ReMixer
|
SpatialShift2d
| false
| 13,065
|
[
"MIT"
] | 0
|
587e1b6a01850df649eccf043689f84a7dd5e2dc
|
https://github.com/uthree/ReMixer/tree/587e1b6a01850df649eccf043689f84a7dd5e2dc
|
ElementWiseMLP
|
import torch
import torch.nn as nn
class ElementWiseMLP(nn.Module):
"""Some Information about ElementWiseMLP"""
def __init__(self, dim, activation='gelu'):
super(ElementWiseMLP, self).__init__()
if activation == 'gelu':
self.activation = nn.GELU()
elif activation == 'relu':
self.activation = nn.ReLU()
elif activation == 'leakyrelu':
self.activation = nn.LeakyReLU(0.2)
self.fc1 = nn.Linear(dim, dim)
self.fc2 = nn.Linear(dim, dim)
self.ln = nn.LayerNorm(dim)
def forward(self, x):
x = self.ln(x)
x = self.activation(self.fc1(x))
x = self.fc2(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'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.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_native_layer_norm_0(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
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, 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_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_gelu_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
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):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (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,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(64)](primals_3, buf0,
buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(256)](primals_3, buf0,
buf1, primals_1, primals_2, buf2, 256, XBLOCK=256, num_warps=4,
num_stages=1)
del buf0
del buf1
del primals_1
del primals_2
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_5
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_gelu_2[grid(256)](buf3, buf4, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf5)
del primals_7
return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0
), primals_3, reinterpret_tensor(buf2, (64, 4), (4, 1), 0
), buf3, reinterpret_tensor(buf4, (64, 4), (4, 1), 0
), primals_6, primals_4
class ElementWiseMLPNew(nn.Module):
"""Some Information about ElementWiseMLP"""
def __init__(self, dim, activation='gelu'):
super(ElementWiseMLPNew, self).__init__()
if activation == 'gelu':
self.activation = nn.GELU()
elif activation == 'relu':
self.activation = nn.ReLU()
elif activation == 'leakyrelu':
self.activation = nn.LeakyReLU(0.2)
self.fc1 = nn.Linear(dim, dim)
self.fc2 = nn.Linear(dim, dim)
self.ln = nn.LayerNorm(dim)
def forward(self, input_0):
primals_4 = self.fc1.weight
primals_1 = self.fc1.bias
primals_6 = self.fc2.weight
primals_2 = self.fc2.bias
primals_5 = self.ln.weight
primals_7 = self.ln.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
uthree/ReMixer
|
ElementWiseMLP
| false
| 13,066
|
[
"MIT"
] | 0
|
587e1b6a01850df649eccf043689f84a7dd5e2dc
|
https://github.com/uthree/ReMixer/tree/587e1b6a01850df649eccf043689f84a7dd5e2dc
|
DQN_Linear
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN_Linear(nn.Module):
def __init__(self, input_size, output_size):
super(DQN_Linear, self).__init__()
self.l1 = nn.Linear(input_size, 32)
self.l2 = nn.Linear(32, 64)
self.head = nn.Linear(64, output_size)
def forward(self, x):
x = F.relu(self.l1(x))
x = F.relu(self.l2(x))
return self.head(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_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
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):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 32
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(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)
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
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, (32, 4), (4, 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, (64, 32), (32, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (4, 64), (64, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 32), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 32), (512, 128, 32, 1), 0)
del buf0
buf6 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(2048)](buf1,
primals_2, buf6, 2048, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 32), (32, 1), 0),
reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
del buf2
buf5 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
)
triton_poi_fused_relu_threshold_backward_1[grid(4096)](buf3,
primals_5, buf5, 4096, 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, 64),
(64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 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_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 32), (32, 1), 0), reinterpret_tensor(
buf3, (64, 64), (64, 1), 0), primals_6, buf5, primals_4, buf6
class DQN_LinearNew(nn.Module):
def __init__(self, input_size, output_size):
super(DQN_LinearNew, self).__init__()
self.l1 = nn.Linear(input_size, 32)
self.l2 = nn.Linear(32, 64)
self.head = nn.Linear(64, output_size)
def forward(self, input_0):
primals_1 = self.l1.weight
primals_2 = self.l1.bias
primals_4 = self.l2.weight
primals_5 = self.l2.bias
primals_6 = self.head.weight
primals_7 = self.head.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
vashineyu/dqn_cartpole
|
DQN_Linear
| false
| 13,067
|
[
"MIT"
] | 0
|
7d3d2c26e29d40fce7710dbd56c59045514f2e84
|
https://github.com/vashineyu/dqn_cartpole/tree/7d3d2c26e29d40fce7710dbd56c59045514f2e84
|
EnvModel
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class EnvModel(nn.Module):
def __init__(self, phi_dim, action_dim):
super(EnvModel, self).__init__()
self.hidden_dim = 300
self.fc_r1 = layer_init(nn.Linear(phi_dim + action_dim, self.
hidden_dim))
self.fc_r2 = layer_init(nn.Linear(self.hidden_dim, 1))
self.fc_t1 = layer_init(nn.Linear(phi_dim, phi_dim))
self.fc_t2 = layer_init(nn.Linear(phi_dim + action_dim, phi_dim))
def forward(self, phi_s, action):
phi = torch.cat([phi_s, action], dim=-1)
r = self.fc_r2(F.tanh(self.fc_r1(phi)))
phi_s_prime = phi_s + F.tanh(self.fc_t1(phi_s))
phi_sa_prime = torch.cat([phi_s_prime, action], dim=-1)
phi_s_prime = phi_s_prime + F.tanh(self.fc_t2(phi_sa_prime))
return phi_s_prime, r
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'phi_dim': 4, 'action_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.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, in_ptr2, 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
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)
tmp11 = tl.load(in_ptr2 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp12 = libdevice.tanh(tmp11)
tmp13 = tmp5 + tmp12
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp4, tmp13, tmp14)
tmp16 = tl.where(tmp4, tmp15, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
tl.store(out_ptr1 + x2, tmp16, xmask)
@triton.jit
def triton_poi_fused_tanh_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 19200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 300
x2 = xindex // 1200
x4 = xindex % 1200
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(out_ptr0 + (x4 + 1216 * x2), tmp3, xmask)
@triton.jit
def triton_poi_fused_addmm_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 19200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 300
x1 = xindex // 300
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 300 * (x1 % 4) + 1216 * (x1 // 4)), xmask)
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_tanh_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 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp4 = tl.load(in_ptr2 + x0, xmask)
tmp2 = libdevice.tanh(tmp1)
tmp3 = tmp0 + tmp2
tmp5 = libdevice.tanh(tmp4)
tmp6 = tmp3 + tmp5
tl.store(out_ptr0 + x0, 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) = 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, (300, 8), (8, 1))
assert_size_stride(primals_4, (300,), (1,))
assert_size_stride(primals_5, (1, 300), (300, 1))
assert_size_stride(primals_6, (1,), (1,))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4, 8), (8, 1))
assert_size_stride(primals_10, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_8, reinterpret_tensor(primals_1, (64,
4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf6)
del primals_7
del primals_8
buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
buf7 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf6, buf0,
buf7, 512, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
reinterpret_tensor(primals_3, (8, 300), (1, 8), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1),
torch.float32)
triton_poi_fused_tanh_1[grid(19200)](buf1, primals_4, buf2, 19200,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_4
buf3 = buf1
del buf1
triton_poi_fused_addmm_2[grid(19200)](buf2, buf3, 19200, XBLOCK=256,
num_warps=4, num_stages=1)
buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_6, buf3, reinterpret_tensor(primals_5,
(300, 1), (1, 300), 0), alpha=1, beta=1, out=buf5)
del buf3
del primals_6
buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_10, reinterpret_tensor(buf7, (64, 8),
(8, 1), 0), reinterpret_tensor(primals_9, (8, 4), (1, 8), 0),
alpha=1, beta=1, out=buf8)
del primals_10
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_tanh_3[grid(256)](primals_1, buf6, buf8, buf9,
256, XBLOCK=256, num_warps=4, num_stages=1)
return buf9, reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0
), reinterpret_tensor(buf0, (64, 8), (8, 1), 0
), buf2, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
), buf6, reinterpret_tensor(buf7, (64, 8), (8, 1), 0
), buf8, primals_9, primals_5
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class EnvModelNew(nn.Module):
def __init__(self, phi_dim, action_dim):
super(EnvModelNew, self).__init__()
self.hidden_dim = 300
self.fc_r1 = layer_init(nn.Linear(phi_dim + action_dim, self.
hidden_dim))
self.fc_r2 = layer_init(nn.Linear(self.hidden_dim, 1))
self.fc_t1 = layer_init(nn.Linear(phi_dim, phi_dim))
self.fc_t2 = layer_init(nn.Linear(phi_dim + action_dim, phi_dim))
def forward(self, input_0, input_1):
primals_3 = self.fc_r1.weight
primals_4 = self.fc_r1.bias
primals_5 = self.fc_r2.weight
primals_6 = self.fc_r2.bias
primals_7 = self.fc_t1.weight
primals_8 = self.fc_t1.bias
primals_9 = self.fc_t2.weight
primals_10 = self.fc_t2.bias
primals_1 = input_0
primals_2 = 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]
|
spacegoing/oc_hrl_pytorch
|
EnvModel
| false
| 13,068
|
[
"MIT"
] | 0
|
3e6c3b32b41d7dad40a9ee35f436f8cbcde8633b
|
https://github.com/spacegoing/oc_hrl_pytorch/tree/3e6c3b32b41d7dad40a9ee35f436f8cbcde8633b
|
Scale
|
import math
import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, d_model):
super(Scale, self).__init__()
self.d_model = d_model
def forward(self, x):
return x * math.sqrt(self.d_model)
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
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_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 2.0
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + 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)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class ScaleNew(nn.Module):
def __init__(self, d_model):
super(ScaleNew, self).__init__()
self.d_model = d_model
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
voidism/End-to-end-ASR-Pytorch
|
Scale
| false
| 13,069
|
[
"MIT"
] | 0
|
509c389fa6ab98c30e227c6f4c8f7474adbc1bb2
|
https://github.com/voidism/End-to-end-ASR-Pytorch/tree/509c389fa6ab98c30e227c6f4c8f7474adbc1bb2
|
GeneratorBlock
|
import torch
import torch.nn as nn
def leaky_relu(p=0.2):
return nn.LeakyReLU(p)
class GeneratorBlock(nn.Module):
def __init__(self, input_channels, latent_channels, output_channels,
upsample=True):
super(GeneratorBlock, self).__init__()
if upsample:
self.upsample = nn.Upsample(scale_factor=2)
else:
self.upsample = nn.Identity()
self.conv1 = nn.Conv2d(input_channels, latent_channels, 3, 1, 1,
padding_mode='replicate')
self.conv1.bias.data = torch.zeros(*self.conv1.bias.data.shape)
self.act1 = leaky_relu()
self.conv2 = nn.Conv2d(latent_channels, output_channels, 3, 1, 1,
padding_mode='replicate')
self.conv2.bias.data = torch.zeros(*self.conv2.bias.data.shape)
self.act2 = leaky_relu()
self.to_rgb = nn.Conv2d(output_channels, 3, 1, 1, 0)
def forward(self, x):
x = self.upsample(x)
x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.act2(x)
rgb = self.to_rgb(x)
return x, rgb
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_channels': 4, 'latent_channels': 4,
'output_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__unsafe_index_replication_pad2d_0(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 10 % 10
x0 = xindex % 10
x2 = xindex // 100
x5 = xindex
tmp0 = 7 * (7 <= 0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) + (0 *
(0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) * (0 * (0 >= -1 + x1) +
(-1 + x1) * (-1 + x1 > 0) < 7)
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tmp5 = 7 * (7 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 *
(0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) +
(-1 + x0) * (-1 + x0 > 0) < 7)
tmp6 = tmp5.to(tl.float32)
tmp7 = tmp6 * tmp2
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
eviction_policy='evict_last')
tl.store(out_ptr0 + x5, tmp9, xmask)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, 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
x3 = xindex
x1 = xindex // 64 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, 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)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
@triton.jit
def triton_poi_fused_replication_pad2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 10
x1 = xindex // 10 % 10
x2 = xindex // 100
x3 = xindex
tmp0 = tl.load(in_ptr0 + (8 * (7 * (7 <= 0 * (0 >= -1 + x1) + (-1 + x1) *
(-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) *
(0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 7)) + 64 * x2 + (
7 * (7 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 * (0 >=
-1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) + (-1 +
x0) * (-1 + x0 > 0) < 7))), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 64 % 3
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, 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, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (3, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_7, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 10, 10), (400, 100, 10, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused__unsafe_index_replication_pad2d_0[grid(1600)](
primals_1, buf0, 1600, XBLOCK=256, 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, 8, 8), (256, 64, 8, 1))
buf2 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
buf3 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
triton_poi_fused_convolution_leaky_relu_1[grid(1024)](buf1,
primals_3, buf2, buf3, 1024, XBLOCK=256, num_warps=4, num_stages=1)
del primals_3
buf4 = empty_strided_cuda((4, 4, 10, 10), (400, 100, 10, 1), torch.
float32)
triton_poi_fused_replication_pad2d_2[grid(1600)](buf3, buf4, 1600,
XBLOCK=128, num_warps=4, num_stages=1)
buf5 = extern_kernels.convolution(buf4, primals_4, 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, 8, 8), (256, 64, 8, 1))
buf6 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
buf7 = buf1
del buf1
triton_poi_fused_convolution_leaky_relu_1[grid(1024)](buf5,
primals_5, buf6, buf7, 1024, XBLOCK=256, num_warps=4, num_stages=1)
del buf5
del primals_5
buf8 = extern_kernels.convolution(buf7, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 3, 8, 8), (192, 64, 8, 1))
buf9 = buf8
del buf8
triton_poi_fused_convolution_3[grid(768)](buf9, primals_7, 768,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_7
return (buf7, buf9, primals_2, primals_4, primals_6, buf0, buf2, buf3,
buf4, buf6, buf7)
def leaky_relu(p=0.2):
return nn.LeakyReLU(p)
class GeneratorBlockNew(nn.Module):
def __init__(self, input_channels, latent_channels, output_channels,
upsample=True):
super(GeneratorBlockNew, self).__init__()
if upsample:
self.upsample = nn.Upsample(scale_factor=2)
else:
self.upsample = nn.Identity()
self.conv1 = nn.Conv2d(input_channels, latent_channels, 3, 1, 1,
padding_mode='replicate')
self.conv1.bias.data = torch.zeros(*self.conv1.bias.data.shape)
self.act1 = leaky_relu()
self.conv2 = nn.Conv2d(latent_channels, output_channels, 3, 1, 1,
padding_mode='replicate')
self.conv2.bias.data = torch.zeros(*self.conv2.bias.data.shape)
self.act2 = leaky_relu()
self.to_rgb = nn.Conv2d(output_channels, 3, 1, 1, 0)
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_6 = self.to_rgb.weight
primals_7 = self.to_rgb.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0], output[1]
|
uthree/pg-gan
|
GeneratorBlock
| false
| 13,070
|
[
"MIT"
] | 0
|
7a72a9f3487a66ddc6c8c51a774e3d8128369b2a
|
https://github.com/uthree/pg-gan/tree/7a72a9f3487a66ddc6c8c51a774e3d8128369b2a
|
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