entry_point stringlengths 1 65 | original_triton_code stringlengths 4.5k 619k | python_code stringlengths 208 60.9k | triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 | pytorch_code stringlengths 200 4.05k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
CrossNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from sklearn.metrics import *
class CrossNet(nn.Module):
"""The Cross Network part of Deep&Cross Network model,
which leans both low and high degree cross feature.
Input shape
- 2D tensor with shape: ``(batch_size, units)``.
Output shape
- 2D tens... | 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
from sklearn.metrics import *
assert_size_stride = torch._... | Fanxingye/DeepRS | CrossNet | false | 14,035 | [
"Apache-2.0"
] | 1,770 | 06b98cf2cb2781656805eafc577fbd088f37d17d | https://github.com/Fanxingye/DeepRS/tree/06b98cf2cb2781656805eafc577fbd088f37d17d | import torch
import torch.nn as nn
from sklearn.metrics import *
class Model(nn.Module):
"""The Cross Network part of Deep&Cross Network model,
which leans both low and high degree cross feature.
Input shape
- 2D tensor with shape: ``(batch_size, units)``.
Output shape
- 2D tensor ... |
Encoder3 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Encoder3(nn.Module):
def __init__(self, model=None, fixed=False):
super(Encoder3, self).__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv11 = nn.Conv2d(3, 64, 3, 1, 0)
self.conv12 = nn.Conv2d(64, 64, 3, 1... | 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.... | MingSun-Tse/Collaborative-Distillation | Encoder3 | false | 14,036 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, model=None, fixed=False):
super().__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv11 = nn.Conv2d(3, 64, 3, 1, 0)
self.conv12 = nn.Conv2d(64, 64, 3, 1, 0)
self... |
TensorMax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
def tensor_max(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.max(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.max(input, dim=d, keepdim=keepdim)[0]
retur... | 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 = torc... | Minyus/pipelinex | TensorMax | false | 14,037 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 | import torch
def tensor_max(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.max(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.max(input, dim=d, keepdim=keepdim)[0]
retur... |
TensorLog | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class TensorLog(torch.nn.Module):
def forward(self, input):
return torch.log(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.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Minyus/pipelinex | TensorLog | false | 14,038 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 | import torch
class Model(torch.nn.Module):
def forward(self, input):
return torch.log(input)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
TensorNearestPad | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class TensorNearestPad(torch.nn.Module):
def __init__(self, lower=1, upper=1):
super().__init__()
assert isinstance(lower, int) and lower >= 0
assert isinstance(upper, int) and upper >= 0
self.lower = lower
self.upper = upper
def forward(self, input):
... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Minyus/pipelinex | TensorNearestPad | false | 14,039 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 | import torch
class Model(torch.nn.Module):
def __init__(self, lower=1, upper=1):
super().__init__()
assert isinstance(lower, int) and lower >= 0
assert isinstance(upper, int) and upper >= 0
self.lower = lower
self.upper = upper
def forward(self, input):
return... |
Encoder2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Encoder2(nn.Module):
def __init__(self, model=None, fixed=False):
super(Encoder2, self).__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv11 = nn.Conv2d(3, 64, 3, 1, 0, dilation=1)
self.conv12 = nn.Conv2d(... | 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.... | MingSun-Tse/Collaborative-Distillation | Encoder2 | false | 14,040 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, model=None, fixed=False):
super().__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv11 = nn.Conv2d(3, 64, 3, 1, 0, dilation=1)
self.conv12 = nn.Conv2d(64, 64, 3, 1, 0, ... |
SmallDecoder3_16x | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class SmallDecoder3_16x(nn.Module):
def __init__(self, model=None, fixed=False):
super(SmallDecoder3_16x, self).__init__()
self.fixed = fixed
self.conv31 = nn.Conv2d(64, 32, 3, 1, 0)
self.conv22 = nn.Conv2d(32, 32, 3, 1, 0)
self.conv21 = ... | 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.... | MingSun-Tse/Collaborative-Distillation | SmallDecoder3_16x | false | 14,041 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, model=None, fixed=False):
super().__init__()
self.fixed = fixed
self.conv31 = nn.Conv2d(64, 32, 3, 1, 0)
self.conv22 = nn.Conv2d(32, 32, 3, 1, 0)
self.conv21 = nn.Conv2d(32, 16, 3, 1, 0)
... |
TensorRange | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
def tensor_max(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.max(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.max(input, dim=d, keepdim=keepdim)[0]
retur... | 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 = torc... | Minyus/pipelinex | TensorRange | false | 14,042 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 | import torch
def tensor_max(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.max(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.max(input, dim=d, keepdim=keepdim)[0]
retur... |
TensorSum | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class StatModule(torch.nn.Module):
def __init__(self, dim, keepdim=False):
if isinstance(dim, list):
dim = tuple(dim)
if isinstance(dim, int):
dim = dim,
assert isinstance(dim, tuple)
self.dim = dim
self.keepdim = keepdim
super(... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Minyus/pipelinex | TensorSum | false | 14,043 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 | import torch
class StatModule(torch.nn.Module):
def __init__(self, dim, keepdim=False):
if isinstance(dim, list):
dim = tuple(dim)
if isinstance(dim, int):
dim = dim,
assert isinstance(dim, tuple)
self.dim = dim
self.keepdim = keepdim
super(... |
SmallDecoder4_16x | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class SmallDecoder4_16x(nn.Module):
def __init__(self, model=None, fixed=False):
super(SmallDecoder4_16x, self).__init__()
self.fixed = fixed
self.conv41 = nn.Conv2d(128, 64, 3, 1, 0)
self.conv34 = nn.Conv2d(64, 64, 3, 1, 0)
self.conv33 =... | 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.... | MingSun-Tse/Collaborative-Distillation | SmallDecoder4_16x | false | 14,044 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, model=None, fixed=False):
super().__init__()
self.fixed = fixed
self.conv41 = nn.Conv2d(128, 64, 3, 1, 0)
self.conv34 = nn.Conv2d(64, 64, 3, 1, 0)
self.conv33 = nn.Conv2d(64, 64, 3, 1, 0)
... |
TensorMin | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
def tensor_min(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.min(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.min(input, dim=d, keepdim=keepdim)[0]
retur... | 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 = torc... | Minyus/pipelinex | TensorMin | false | 14,045 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 | import torch
def tensor_min(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.min(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.min(input, dim=d, keepdim=keepdim)[0]
retur... |
SmoothL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
import torch.multiprocessing
class SmoothL1Loss(nn.Module):
"""Smooth L1 Loss"""
def __init__(self, beta=0.11):
super().__init__()
self.beta = beta
def forward(self, pred, target):
x = (pred - target).a... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.cuda
import torch.distributed
import t... | Mo5mami/retinanet-examples | SmoothL1Loss | false | 14,046 | [
"BSD-3-Clause"
] | 848 | f7ad4ff6a99fe3e66f8a9c8e8a6e03b870f84700 | https://github.com/Mo5mami/retinanet-examples/tree/f7ad4ff6a99fe3e66f8a9c8e8a6e03b870f84700 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
import torch.multiprocessing
class Model(nn.Module):
"""Smooth L1 Loss"""
def __init__(self, beta=0.11):
super().__init__()
self.beta = beta
def forward(self, pred, target):
x = (pred - target).abs()
... |
InteractingLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
class InteractingLayer(nn.Module):
"""A Layer used in AutoInt that model the correlations between different feature fields by multi-head self-attention mechanism.
Input shape
- A 3D tensor with shape... | 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.... | Fanxingye/DeepRS | InteractingLayer | false | 14,047 | [
"Apache-2.0"
] | 1,770 | 06b98cf2cb2781656805eafc577fbd088f37d17d | https://github.com/Fanxingye/DeepRS/tree/06b98cf2cb2781656805eafc577fbd088f37d17d | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
class Model(nn.Module):
"""A Layer used in AutoInt that model the correlations between different feature fields by multi-head self-attention mechanism.
Input shape
- A 3D tensor with shape: ``(batch_... |
AUGRUCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
class AUGRUCell(nn.Module):
""" Effect of GRU with attentional update gate (AUGRU)
Reference:
- Deep Interest Evolution Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1809.03672, ... | 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 ... | Fanxingye/DeepRS | AUGRUCell | false | 14,048 | [
"Apache-2.0"
] | 1,770 | 06b98cf2cb2781656805eafc577fbd088f37d17d | https://github.com/Fanxingye/DeepRS/tree/06b98cf2cb2781656805eafc577fbd088f37d17d | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
class Model(nn.Module):
""" Effect of GRU with attentional update gate (AUGRU)
Reference:
- Deep Interest Evolution Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1809.03672, 2018... |
InvGridSamplerNumerator | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
def ravel_multi_index(indices, shape):
indices_ravel = indices[0]
for i in range(1, len(indices)):
indices_ravel = indices_ravel * shape[i] + indices[i]
return indices_ravel
... | 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 numpy as np
imp... | Minsoo2022/Pose-Transfer | InvGridSamplerNumerator | false | 14,049 | [
"MIT"
] | 692 | 10a60bb33d51a06e1200f5726f2367b5be4a6b79 | https://github.com/Minsoo2022/Pose-Transfer/tree/10a60bb33d51a06e1200f5726f2367b5be4a6b79 | import torch
import numpy as np
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
def ravel_multi_index(indices, shape):
indices_ravel = indices[0]
for i in range(1, len(indices)):
indices_ravel = indices_ravel * shape[i] + indices[i]
return indices_ravel
... |
_final_conv_block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(Spectr... | 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.... | Minsoo2022/Pose-Transfer | _final_conv_block | false | 14,050 | [
"MIT"
] | 692 | 10a60bb33d51a06e1200f5726f2367b5be4a6b79 | https://github.com/Minsoo2022/Pose-Transfer/tree/10a60bb33d51a06e1200f5726f2367b5be4a6b79 | import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super().__in... |
encoderVH | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class encoderVH(nn.Module):
def __init__(self):
super(encoderVH, self).__init__()
self.conv1 = nn.Conv3d(in_channels=1, out_channels=64, kernel_size=
4, stride=2, padding=1, bias=True)
self.gn1 = nn.GroupNorm(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.... | Miles629/TransparentShapeRealData | encoderVH | false | 14,051 | [
"MIT"
] | 91 | b81098a2d1882f5fd33fba6167d7258dbe02d6d2 | https://github.com/Miles629/TransparentShapeRealData/tree/b81098a2d1882f5fd33fba6167d7258dbe02d6d2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv3d(in_channels=1, out_channels=64, kernel_size=
4, stride=2, padding=1, bias=True)
self.gn1 = nn.GroupNorm(4, 64)
self.... |
TensorProba | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class TensorProba(torch.nn.Module):
def __init__(self, dim=1):
self.dim = dim
super().__init__()
def forward(self, input):
total = torch.sum(input, dim=self.dim, keepdim=True)
return input / total
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ge... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Minyus/pipelinex | TensorProba | false | 14,052 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 | import torch
class Model(torch.nn.Module):
def __init__(self, dim=1):
self.dim = dim
super().__init__()
def forward(self, input):
total = torch.sum(input, dim=self.dim, keepdim=True)
return input / total
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init... |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda
import torch.distributed
import torch.multiprocessing
class FocalLoss(nn.Module):
"""Focal Loss - https://arxiv.org/abs/1708.02002"""
def __init__(self, alpha=0.25, gamma=2):
super().__init__()
self.alpha = a... | 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 torc... | Mo5mami/retinanet-examples | FocalLoss | false | 14,053 | [
"BSD-3-Clause"
] | 848 | f7ad4ff6a99fe3e66f8a9c8e8a6e03b870f84700 | https://github.com/Mo5mami/retinanet-examples/tree/f7ad4ff6a99fe3e66f8a9c8e8a6e03b870f84700 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda
import torch.distributed
import torch.multiprocessing
class Model(nn.Module):
"""Focal Loss - https://arxiv.org/abs/1708.02002"""
def __init__(self, alpha=0.25, gamma=2):
super().__init__()
self.alpha = alpha... |
DenseCrossEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class DenseCrossEntropy(nn.Module):
def __init__(self):
super(DenseCrossEntropy, self).__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(log... | 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
... | Mo5mami/wtfml | DenseCrossEntropy | false | 14,054 | [
"MIT"
] | 283 | afddec88d9c3a94e30ab2897525daf3f5cf8b774 | https://github.com/Mo5mami/wtfml/tree/afddec88d9c3a94e30ab2897525daf3f5cf8b774 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(logits, dim=-1)
loss = -labels... |
TensorMean | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class StatModule(torch.nn.Module):
def __init__(self, dim, keepdim=False):
if isinstance(dim, list):
dim = tuple(dim)
if isinstance(dim, int):
dim = dim,
assert isinstance(dim, tuple)
self.dim = dim
self.keepdim = keepdim
super(... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Minyus/pipelinex | TensorMean | false | 14,055 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 | import torch
class StatModule(torch.nn.Module):
def __init__(self, dim, keepdim=False):
if isinstance(dim, list):
dim = tuple(dim)
if isinstance(dim, int):
dim = dim,
assert isinstance(dim, tuple)
self.dim = dim
self.keepdim = keepdim
super(... |
BCE_LOSS | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import math
import torch
from torch.nn.modules.loss import _Loss
import torch.optim
import torch._utils
import torch.nn
class BCE_LOSS(_Loss):
def __init__(self, loss_weight=1.0, bias=False):
super().__init__()
self.bce_loss = torch.nn.BCEWithLogitsLoss()
self.loss_weight = loss_weight
... | 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.... | ModelTC/EOD | BCE_LOSS | false | 14,056 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import math
import torch
from torch.nn.modules.loss import _Loss
import torch.optim
import torch._utils
import torch.nn
class Model(_Loss):
def __init__(self, loss_weight=1.0, bias=False):
super().__init__()
self.bce_loss = torch.nn.BCEWithLogitsLoss()
self.loss_weight = loss_weight
... |
SeparableConv2d_same | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
def fixed_padding(inputs, kernel_size, rate):
kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
padded_inputs = F.pad(i... | 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... | Mnsy-Syl/new_20201103 | SeparableConv2d_same | false | 14,057 | [
"MIT"
] | 46 | 9ee39f1c69a4cba896b30f007560fcbe8ac89c02 | https://github.com/Mnsy-Syl/new_20201103/tree/9ee39f1c69a4cba896b30f007560fcbe8ac89c02 | import torch
import torch.nn as nn
import torch.nn.functional as F
def fixed_padding(inputs, kernel_size, rate):
kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
padded_inputs = F.pad(i... |
Space2Depth | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.optim
import torch._utils
import torch.nn
class Space2Depth(nn.Module):
def __init__(self, block_size):
super(Space2Depth, self).__init__()
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // s... | 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.optim
import torch._utils
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | ModelTC/EOD | Space2Depth | false | 14,058 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
import torch.nn as nn
import torch.optim
import torch._utils
import torch.nn
class Model(nn.Module):
def __init__(self, block_size):
super().__init__()
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, self.bs, W // s... |
CrossEntropyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch.nn.modules.loss import _Loss
import torch.optim
import torch._utils
import torch.nn
class CrossEntropyLoss(_Loss):
def __init__(self, loss_weight=1.0):
super().__init__()
self.ce_loss = torch.nn.CrossEntropyLoss()
self.loss_weight = loss_weight
def forward(sel... | 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.nn.modules.... | ModelTC/EOD | CrossEntropyLoss | false | 14,059 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
from torch.nn.modules.loss import _Loss
import torch.optim
import torch._utils
import torch.nn
class Model(_Loss):
def __init__(self, loss_weight=1.0):
super().__init__()
self.ce_loss = torch.nn.CrossEntropyLoss()
self.loss_weight = loss_weight
def forward(self, input, l... |
GELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class GELU(nn.Module):
@staticmethod
def forward(x):
erf = F.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3)))
return 0.5 * x * (1 + erf)
de... | 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.optim
import torch._utils
import torch.nn
as... | ModelTC/EOD | GELU | false | 14,060 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class Model(nn.Module):
@staticmethod
def forward(x):
erf = F.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3)))
return 0.5 * x * (1 + erf)
d... |
FeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
def activation(act_type='swish'):
if act_type == 'swish':
act = swish()
return act
else:
act = nn.ReLU(inplace=True)
return act
class s... | 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
... | ModelTC/EOD | FeedForward | false | 14,061 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
def activation(act_type='swish'):
if act_type == 'swish':
act = swish()
return act
else:
act = nn.ReLU(inplace=True)
return act
class s... |
SoftTargetCrossEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
import torch.optim
import torch._utils
import torch.nn
class SoftTargetCrossEntropy(_Loss):
def __init__(self, loss_weight=1.0):
super(SoftTargetCrossEntropy, self).__init__()
self.loss_weight = loss_weight
... | 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.nn.modules.... | ModelTC/EOD | SoftTargetCrossEntropy | false | 14,062 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
import torch.optim
import torch._utils
import torch.nn
class Model(_Loss):
def __init__(self, loss_weight=1.0):
super().__init__()
self.loss_weight = loss_weight
def forward(self, input, label) ->torch.Tenso... |
MulScalarNegative | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class MulScalarNegative(nn.Module):
def __init__(self):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.quant = QuantStub()
self.dequant = D... | 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.quantization import QuantStub
from torch.quantization import DeQuantStub
assert_size_stride = torch._C._dyna... | Mookel/tvm | MulScalarNegative | false | 14,063 | [
"Zlib",
"Unlicense",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0"
] | 90 | ae58f2c387de9944d241a083ce9a0dd4c9ae613d | https://github.com/Mookel/tvm/tree/ae58f2c387de9944d241a083ce9a0dd4c9ae613d | import torch
from torch import nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Model(nn.Module):
def __init__(self):
super().__init__()
self.float_op = nn.quantized.FloatFunctional()
self.quant = QuantStub()
self.dequant = DeQuantStub()... |
SpatialGather_Module | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method t... | 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.... | ModelTC/EOD | SpatialGather_Module | false | 14,064 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class Model(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the... |
Decoder4 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Decoder4(nn.Module):
def __init__(self, model=None, fixed=False):
super(Decoder4, self).__init__()
self.fixed = fixed
self.conv41 = nn.Conv2d(512, 256, 3, 1, 0)
self.conv34 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv33 = nn.Conv2d(256,... | 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.... | MingSun-Tse/Collaborative-Distillation | Decoder4 | false | 14,065 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, model=None, fixed=False):
super().__init__()
self.fixed = fixed
self.conv41 = nn.Conv2d(512, 256, 3, 1, 0)
self.conv34 = nn.Conv2d(256, 256, 3, 1, 0)
self.conv33 = nn.Conv2d(256, 256, 3, 1, 0)
... |
IOU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
def _iou(pred, target, size_average=True):
b = pred.shape[0]
IoU = 0.0
for i in range(0, b):
Iand1 = torch.sum(target[i, :, :, :] * pred[i, :, :, :])
Ior1 = torch.sum(target[i, :, :, :]) + torch.sum(pred[i, :, :, :]
) - Iand1
IoU1 = Iand1 / Ior1
IoU... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Morales97/BASNet | IOU | false | 14,066 | [
"MIT"
] | 977 | 4c2074f769ec0a3f61b2de60b56666ebe67da858 | https://github.com/Morales97/BASNet/tree/4c2074f769ec0a3f61b2de60b56666ebe67da858 | import torch
def _iou(pred, target, size_average=True):
b = pred.shape[0]
IoU = 0.0
for i in range(0, b):
Iand1 = torch.sum(target[i, :, :, :] * pred[i, :, :, :])
Ior1 = torch.sum(target[i, :, :, :]) + torch.sum(pred[i, :, :, :]
) - Iand1
IoU1 = Iand1 / Ior1
IoU... |
PSNR | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch as th
class PSNR(th.nn.Module):
def __init__(self):
super(PSNR, self).__init__()
self.mse = th.nn.MSELoss()
def forward(self, out, ref):
mse = self.mse(out, ref)
return -10 * th.log10(mse)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), tor... | 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 as th
assert_si... | MohamadHMousavi/demosaicnet | PSNR | false | 14,067 | [
"MIT"
] | 140 | 43f013c79395ee5bccaa0f3525cc61007808845b | https://github.com/MohamadHMousavi/demosaicnet/tree/43f013c79395ee5bccaa0f3525cc61007808845b | import torch
import torch as th
class Model(th.nn.Module):
def __init__(self):
super().__init__()
self.mse = th.nn.MSELoss()
def forward(self, out, ref):
mse = self.mse(out, ref)
return -10 * th.log10(mse)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([... |
GMoF | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class GMoF(nn.Module):
def __init__(self, rho=1):
super(GMoF, self).__init__()
self.rho = rho
def extra_repr(self):
return 'rho = {}'.format(self.rho)
def forward(self, residual):
squared_res = residual ** 2
dist = torch.div(squ... | 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_st... | MoyGcc/hpcwild | GMoF | false | 14,068 | [
"MIT"
] | 47 | 8ed35c3f188284af2a4dd0d68b09fbceb105c2ba | https://github.com/MoyGcc/hpcwild/tree/8ed35c3f188284af2a4dd0d68b09fbceb105c2ba | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, rho=1):
super().__init__()
self.rho = rho
def extra_repr(self):
return 'rho = {}'.format(self.rho)
def forward(self, residual):
squared_res = residual ** 2
dist = torch.div(squared_res,... |
Generator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Generator(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model, vocab):
super(Generator, self).__init__()
self.proj = nn.Linear(d_model, vocab)
def forward(self, x):
... | 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.... | MolecularAI/deep-molecular-optimization | Generator | false | 14,069 | [
"Apache-2.0"
] | 52 | 815fecabd210662db1a89c4a2ab13d5e0ff9c037 | https://github.com/MolecularAI/deep-molecular-optimization/tree/815fecabd210662db1a89c4a2ab13d5e0ff9c037 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model, vocab):
super().__init__()
self.proj = nn.Linear(d_model, vocab)
def forward(self, x):
return F.log_softm... |
Elu | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.fx
class Elu(torch.nn.Module):
def __init__(self):
super(Elu, self).__init__()
self.elu = torch.nn.ELU()
def forward(self, x):
return self.elu(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.fx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | NVIDIA/Torch-TensorRT | Elu | false | 14,070 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 | import torch
import torch.fx
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.elu = torch.nn.ELU()
def forward(self, x):
return self.elu(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
SPPblock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class SPPblock(nn.Module):
def __init__(self, in_channels):
super(SPPblock, self).__init__()
self.pool1 = nn.MaxPool2d(kernel_size=[2, 2], stride=2)
self.pool2 = nn.MaxPool2d(kernel_size=[3, 3], str... | 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
import t... | Minerva-J/Pytorch-Segmentation-multi-models | SPPblock | false | 14,071 | [
"Apache-2.0"
] | 84 | 0845b54d4fbc8d38c70f158054b7ab1be2b3ceb9 | https://github.com/Minerva-J/Pytorch-Segmentation-multi-models/tree/0845b54d4fbc8d38c70f158054b7ab1be2b3ceb9 | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Model(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.pool1 = nn.MaxPool2d(kernel_size=[2, 2], stride=2)
self.pool2 = nn.MaxPool2d(kernel_size=[3, 3], stride=3)
se... |
SmallDecoder5_16x | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class SmallDecoder5_16x(nn.Module):
def __init__(self, model=None, fixed=False):
super(SmallDecoder5_16x, self).__init__()
self.fixed = fixed
self.conv51 = nn.Conv2d(128, 128, 3, 1, 0)
self.conv44 = nn.Conv2d(128, 128, 3, 1, 0)
self.conv4... | 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.... | MingSun-Tse/Collaborative-Distillation | SmallDecoder5_16x | false | 14,072 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, model=None, fixed=False):
super().__init__()
self.fixed = fixed
self.conv51 = nn.Conv2d(128, 128, 3, 1, 0)
self.conv44 = nn.Conv2d(128, 128, 3, 1, 0)
self.conv43 = nn.Conv2d(128, 128, 3, 1, 0)
... |
CELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class CELoss(nn.Module):
"""
Distilling the Knowledge in a Neural Network, NIPS2014.
https://arxiv.org/pdf/1503.02531.pdf
"""
def __init__(self, T=1, loss_weight=1.0):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ModelTC/EOD | CELoss | false | 14,073 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class Model(nn.Module):
"""
Distilling the Knowledge in a Neural Network, NIPS2014.
https://arxiv.org/pdf/1503.02531.pdf
"""
def __init__(self, T=1, loss_weight=1.0):
... |
BiaffineScorer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class BiaffineScorer(nn.Module):
def __init__(self, input1_size, input2_size, output_size):
super().__init__()
self.W_bilin = nn.Bilinear(input1_size + 1, input2_size + 1,
output_size)
self.W_bilin.weight.data.zero_()
self.W_bilin.bia... | 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_st... | NLPInBLCU/BiaffineDependencyParsing | BiaffineScorer | false | 14,074 | [
"MIT"
] | 67 | 40b133648c747957dacd59916add0403371fe680 | https://github.com/NLPInBLCU/BiaffineDependencyParsing/tree/40b133648c747957dacd59916add0403371fe680 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input1_size, input2_size, output_size):
super().__init__()
self.W_bilin = nn.Bilinear(input1_size + 1, input2_size + 1,
output_size)
self.W_bilin.weight.data.zero_()
self.W_bilin.bias.data.ze... |
DeepHeadModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt as sqrt
class DeepHeadModule(nn.Module):
def __init__(self, input_channels, output_channels):
super(DeepHeadModule, self).__init__()
self._input_channels = input_channels
self._output_channels = outpu... | 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 ma... | NTech-Lab/deepfake-detection-challenge | DeepHeadModule | false | 14,075 | [
"Apache-2.0"
] | 98 | 52095ce4a49f298faf075a5eb28391722b9e4103 | https://github.com/NTech-Lab/deepfake-detection-challenge/tree/52095ce4a49f298faf075a5eb28391722b9e4103 | import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt as sqrt
class Model(nn.Module):
def __init__(self, input_channels, output_channels):
super().__init__()
self._input_channels = input_channels
self._output_channels = output_channels
self._mid_... |
ConvGelu | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.fx
class ConvGelu(torch.nn.Module):
def __init__(self):
super(ConvGelu, self).__init__()
self.conv = nn.Conv2d(3, 32, 3, 1)
self.gelu = nn.GELU()
def forward(self, x):
x = self.conv(x)
x = self.gelu(x)
return x
... | 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 ... | NVIDIA/Torch-TensorRT | ConvGelu | false | 14,076 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 | import torch
import torch.nn as nn
import torch.fx
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 32, 3, 1)
self.gelu = nn.GELU()
def forward(self, x):
x = self.conv(x)
x = self.gelu(x)
return x
def get_inputs()... |
decoderVH | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class decoderVH(nn.Module):
def __init__(self):
super(decoderVH, self).__init__()
self.dconv0 = nn.Conv2d(in_channels=256, out_channels=128,
kernel_size=3, stride=1, padding=1, bias=True)
self.dgn0 = nn.GroupNo... | 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.... | Miles629/TransparentShapeRealData | decoderVH | false | 14,077 | [
"MIT"
] | 91 | b81098a2d1882f5fd33fba6167d7258dbe02d6d2 | https://github.com/Miles629/TransparentShapeRealData/tree/b81098a2d1882f5fd33fba6167d7258dbe02d6d2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.dconv0 = nn.Conv2d(in_channels=256, out_channels=128,
kernel_size=3, stride=1, padding=1, bias=True)
self.dgn0 = nn.GroupNorm(8, 128)
... |
Pool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.fx
class Pool(nn.Module):
def __init__(self):
super(Pool, self).__init__()
def forward(self, x):
return F.adaptive_avg_pool2d(x, (5, 5))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_in... | 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.fx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | NVIDIA/Torch-TensorRT | Pool | false | 14,078 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.fx
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return F.adaptive_avg_pool2d(x, (5, 5))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... |
LoopFallbackNoEval | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.fx
class LoopFallbackNoEval(nn.Module):
def __init__(self):
super(LoopFallbackNoEval, self).__init__()
def forward(self, x):
for _ in range(x.shape[1]):
x = x + torch.ones_like(x)
return x
def get_inputs():
return [tor... | 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.fx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | NVIDIA/Torch-TensorRT | LoopFallbackNoEval | false | 14,079 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 | import torch
import torch.nn as nn
import torch.fx
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
for _ in range(x.shape[1]):
x = x + torch.ones_like(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init... |
KDLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class KDLoss(nn.Module):
"""
Distilling the Knowledge in a Neural Network, NIPS2014.
https://arxiv.org/pdf/1503.02531.pdf
"""
def __init__(self, T=1, loss_weight=1.0):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ModelTC/EOD | KDLoss | false | 14,080 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class Model(nn.Module):
"""
Distilling the Knowledge in a Neural Network, NIPS2014.
https://arxiv.org/pdf/1503.02531.pdf
"""
def __init__(self, T=1, loss_weight=1.0):
... |
SeqExpandConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from math import sqrt as sqrt
class SeqExpandConv(nn.Module):
def __init__(self, in_channels, out_channels, seq_length):
super(SeqExpandConv, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1,
1), padding=(1, 0, 0), ... | 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
from math import sqrt as sqrt
assert_size_stride = torch._... | NTech-Lab/deepfake-detection-challenge | SeqExpandConv | false | 14,081 | [
"Apache-2.0"
] | 98 | 52095ce4a49f298faf075a5eb28391722b9e4103 | https://github.com/NTech-Lab/deepfake-detection-challenge/tree/52095ce4a49f298faf075a5eb28391722b9e4103 | import torch
import torch.nn as nn
from math import sqrt as sqrt
class Model(nn.Module):
def __init__(self, in_channels, out_channels, seq_length):
super().__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1,
1), padding=(1, 0, 0), bias=False)
self.se... |
KLLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class KLLoss(nn.Module):
"""
KL Divergence loss
"""
def __init__(self, norm='softmax', loss_weight=1.0):
super(KLLoss, self).__init__()
self.loss_weight = loss_wei... | 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 torc... | ModelTC/EOD | KLLoss | false | 14,082 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch._utils
import torch.nn
class Model(nn.Module):
"""
KL Divergence loss
"""
def __init__(self, norm='softmax', loss_weight=1.0):
super().__init__()
self.loss_weight = loss_weight
s... |
Norm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.fx
class Norm(torch.nn.Module):
def __init__(self):
super(Norm, self).__init__()
def forward(self, x):
return torch.norm(x, 2, None, False)
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.fx
assert_size_... | NVIDIA/Torch-TensorRT | Norm | false | 14,083 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 | import torch
import torch.fx
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.norm(x, 2, None, False)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
ModuleFallbackSub | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.fx
class ModuleFallbackSub(nn.Module):
def __init__(self):
super(ModuleFallbackSub, self).__init__()
self.conv = nn.Conv2d(1, 3, 3)
self.relu = nn.ReLU()
def forward(self, x):
return self.relu(self.conv(x))
def get_inputs():
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | NVIDIA/Torch-TensorRT | ModuleFallbackSub | false | 14,084 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 | import torch
import torch.nn as nn
import torch.fx
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(1, 3, 3)
self.relu = nn.ReLU()
def forward(self, x):
return self.relu(self.conv(x))
def get_inputs():
return [torch.rand([4, 1, 64, 64... |
NoiseLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class NoiseLayer(nn.Module):
"""adds noise. noise is per pixel (constant over channels) with per-channel weight"""
def __init__(self, channels):
super().__init__()
self.weight = nn.Parameter(torch.zeros(channels))
self.noise = None
def forward(s... | 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
empty_strided_cuda = torch._C... | NeuralBending/StyleCLIP | NoiseLayer | false | 14,085 | [
"MIT"
] | 91 | 190d3a0d48823ccdbdd15c7f8af6e08703a6dbd8 | https://github.com/NeuralBending/StyleCLIP/tree/190d3a0d48823ccdbdd15c7f8af6e08703a6dbd8 | import torch
import torch.nn as nn
class Model(nn.Module):
"""adds noise. noise is per pixel (constant over channels) with per-channel weight"""
def __init__(self, channels):
super().__init__()
self.weight = nn.Parameter(torch.zeros(channels))
self.noise = None
def forward(self, ... |
TestModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.fx
class TestModel(nn.Module):
def __init__(self):
super().__init__()
self.a = torch.nn.Module()
self.b = torch.nn.Module()
self.a.weights = torch.nn.Parameter(torch.randn(1, 2))
self.b.weights = torch.nn.Parameter(torch.rand... | 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.fx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | NVIDIA/Torch-TensorRT | TestModel | false | 14,086 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 | import torch
import torch.nn as nn
import torch.fx
class Model(nn.Module):
def __init__(self):
super().__init__()
self.a = torch.nn.Module()
self.b = torch.nn.Module()
self.a.weights = torch.nn.Parameter(torch.randn(1, 2))
self.b.weights = torch.nn.Parameter(torch.randn(1)... |
FEM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt as sqrt
class FEM(nn.Module):
def __init__(self, channel_size):
super(FEM, self).__init__()
self.cs = channel_size
self.cpm1 = nn.Conv2d(self.cs, 256, kernel_size=3, dilation=1,
stride=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 import triton_helpers
import torch.nn as nn
from ma... | NTech-Lab/deepfake-detection-challenge | FEM | false | 14,087 | [
"Apache-2.0"
] | 98 | 52095ce4a49f298faf075a5eb28391722b9e4103 | https://github.com/NTech-Lab/deepfake-detection-challenge/tree/52095ce4a49f298faf075a5eb28391722b9e4103 | import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt as sqrt
class Model(nn.Module):
def __init__(self, channel_size):
super().__init__()
self.cs = channel_size
self.cpm1 = nn.Conv2d(self.cs, 256, kernel_size=3, dilation=1,
stride=1, padding... |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import torch.optim
import torch._utils
import torch.nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path ... | 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.... | ModelTC/EOD | Encoder | false | 14,089 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import torch.optim
import torch._utils
import torch.nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path ... |
UpSampleLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
class Conv1dKeepLength(torch_nn.Conv1d):
""" Wrapper for causal convolution
Input tensor: (batchsize=1, length, dim_in)
Output tensor: (batchsize=1, length, dim_out)
https://github.com/pytorch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
assert_size_stride = torch._C._dynamo.guards... | Ninushkat/Impact-Synth-Hardware | UpSampleLayer | false | 14,090 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | import torch
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
class Conv1dKeepLength(torch_nn.Conv1d):
""" Wrapper for causal convolution
Input tensor: (batchsize=1, length, dim_in)
Output tensor: (batchsize=1, length, dim_out)
https://github.com/pytorch... |
TimeVarFIRFilter | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
class TimeVarFIRFilter(torch_nn.Module):
""" TimeVarFIRFilter
Given sequences of filter coefficients and a signal, do filtering
Filter coefs: (batchsize=1, signal_length, filter_order = K)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as torch_nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | Ninushkat/Impact-Synth-Hardware | TimeVarFIRFilter | false | 14,091 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | import torch
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
class Model(torch_nn.Module):
""" TimeVarFIRFilter
Given sequences of filter coefficients and a signal, do filtering
Filter coefs: (batchsize=1, signal_length, filter_order = K)
Signal: ... |
ModuleFallbackMain | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.fx
class ModuleFallbackSub(nn.Module):
def __init__(self):
super(ModuleFallbackSub, self).__init__()
self.conv = nn.Conv2d(1, 3, 3)
self.relu = nn.ReLU()
def forward(self, x):
return self.relu(self.conv(x))
class ModuleFallbac... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | NVIDIA/Torch-TensorRT | ModuleFallbackMain | false | 14,092 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 | import torch
import torch.nn as nn
import torch.fx
class ModuleFallbackSub(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(1, 3, 3)
self.relu = nn.ReLU()
def forward(self, x):
return self.relu(self.conv(x))
class Model(nn.Module):
def __init__(... |
Decoder5 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Decoder5(nn.Module):
def __init__(self, model=None, fixed=False):
super(Decoder5, self).__init__()
self.fixed = fixed
self.conv51 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv44 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv43 = nn.Conv2d(512,... | 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.... | MingSun-Tse/Collaborative-Distillation | Decoder5 | false | 14,093 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, model=None, fixed=False):
super().__init__()
self.fixed = fixed
self.conv51 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv44 = nn.Conv2d(512, 512, 3, 1, 0)
self.conv43 = nn.Conv2d(512, 512, 3, 1, 0)
... |
MyLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
super().__init_... | 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_s... | NeuralBending/StyleCLIP | MyLinear | false | 14,094 | [
"MIT"
] | 91 | 190d3a0d48823ccdbdd15c7f8af6e08703a6dbd8 | https://github.com/NeuralBending/StyleCLIP/tree/190d3a0d48823ccdbdd15c7f8af6e08703a6dbd8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
super().__init__()... |
encoderDepth | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class encoderDepth(nn.Module):
def __init__(self):
super(encoderDepth, self).__init__()
self.conv1 = nn.Conv2d(in_channels=13, out_channels=64, kernel_size
=4, stride=2, padding=1, bias=True)
self.gn1 = nn.Grou... | 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.... | Miles629/TransparentShapeRealData | encoderDepth | false | 14,095 | [
"MIT"
] | 91 | b81098a2d1882f5fd33fba6167d7258dbe02d6d2 | https://github.com/Miles629/TransparentShapeRealData/tree/b81098a2d1882f5fd33fba6167d7258dbe02d6d2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=13, out_channels=64, kernel_size
=4, stride=2, padding=1, bias=True)
self.gn1 = nn.GroupNorm(4, 64)
self... |
StyleMod | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
super().__init_... | 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... | NeuralBending/StyleCLIP | StyleMod | false | 14,096 | [
"MIT"
] | 91 | 190d3a0d48823ccdbdd15c7f8af6e08703a6dbd8 | https://github.com/NeuralBending/StyleCLIP/tree/190d3a0d48823ccdbdd15c7f8af6e08703a6dbd8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
super().__init_... |
MovingAverage | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
class Conv1dKeepLength(torch_nn.Conv1d):
""" Wrapper for causal convolution
Input tensor: (batchsize=1, length, dim_in)
Output tensor: (batchsize=1, length, dim_out)
https://github.com/pytorch... | 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 torch_nn
import torch.nn.functional a... | Ninushkat/Impact-Synth-Hardware | MovingAverage | false | 14,097 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | import torch
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
class Conv1dKeepLength(torch_nn.Conv1d):
""" Wrapper for causal convolution
Input tensor: (batchsize=1, length, dim_in)
Output tensor: (batchsize=1, length, dim_out)
https://github.com/pytorch... |
FlowEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class FlowEntropy(nn.Module):
"""
Computes entropy from matching cost
"""
def __init__(self):
super(FlowEntropy, self).__init__()
def forward(self, x):
"""
Performs forward pass.
... | 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
... | NeelayS/ezflow | FlowEntropy | false | 14,098 | [
"MIT"
] | 94 | b93a48c4adf5021f7eacbfc43220c7efa5ae55cd | https://github.com/NeelayS/ezflow/tree/b93a48c4adf5021f7eacbfc43220c7efa5ae55cd | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Computes entropy from matching cost
"""
def __init__(self):
super().__init__()
def forward(self, x):
"""
Performs forward pass.
Parameters
-... |
Depth_Pointwise_Conv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class Depth_Pointwise_Conv1d(nn.Module):
def __init__(self, in_ch, out_ch, k):
super().__init__()
if k == 1:
self.depth_conv = nn.Identity()
else:
self.depth_conv = nn.Conv1d(in_channels=in_ch, out_channels=
in_ch, ... | 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_st... | Nitin-Mane/External-Attention-pytorch | Depth_Pointwise_Conv1d | false | 14,099 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_ch, out_ch, k):
super().__init__()
if k == 1:
self.depth_conv = nn.Identity()
else:
self.depth_conv = nn.Conv1d(in_channels=in_ch, out_channels=
in_ch, kernel_size=k, gr... |
FocalLossSigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class FocalLossSigmoid(nn.Module):
"""
sigmoid version focal loss
"""
def __init__(self, alpha=0.25, gamma=2, size_average=False):
super(FocalLossSigmoid, self).__init__()
self.al... | 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
... | No43problem/SSD_Pytorch | FocalLossSigmoid | false | 14,100 | [
"MIT"
] | 163 | ddc548824bffbc83b540a68b176ee0261b133ee0 | https://github.com/No43problem/SSD_Pytorch/tree/ddc548824bffbc83b540a68b176ee0261b133ee0 | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class Model(nn.Module):
"""
sigmoid version focal loss
"""
def __init__(self, alpha=0.25, gamma=2, size_average=False):
super().__init__()
self.alpha = alpha
self.gamma = ... |
FlowHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class FlowHead(nn.Module):
"""
Applies two 2D convolutions over an input feature map
to generate a flow tensor of shape N x 2 x H x W.
Parameters
----------
input_dim : int, default: 128
Number of input dimensions.
hidden_dim : int, default: 256
... | 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_... | NeelayS/ezflow | FlowHead | false | 14,101 | [
"MIT"
] | 94 | b93a48c4adf5021f7eacbfc43220c7efa5ae55cd | https://github.com/NeelayS/ezflow/tree/b93a48c4adf5021f7eacbfc43220c7efa5ae55cd | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Applies two 2D convolutions over an input feature map
to generate a flow tensor of shape N x 2 x H x W.
Parameters
----------
input_dim : int, default: 128
Number of input dimensions.
hidden_dim : int, default: 256
... |
DC_layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
def Maxout(x1, x2, x3, x4):
mask_1 = torch.ge(x1, x2)
mask_1 = mask_1.float()
x = mask_1 * x1 + (1 - mask_1) * x2
mask_2 = torch.ge(x, x3)
mask_2 = mask_2.float()
x = mask_2 * x + (1 - mask_2) * x3
mask_3 = torch.ge(x, x4)
mask_3 = mask_3.float()
... | 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_s... | Min-Sheng/Local-Crowd-Counting | DC_layer | false | 14,103 | [
"MIT"
] | 75 | 388343d3ec2d08747d537437e4c880fd0047df83 | https://github.com/Min-Sheng/Local-Crowd-Counting/tree/388343d3ec2d08747d537437e4c880fd0047df83 | import torch
import torch.nn as nn
def Maxout(x1, x2, x3, x4):
mask_1 = torch.ge(x1, x2)
mask_1 = mask_1.float()
x = mask_1 * x1 + (1 - mask_1) * x2
mask_2 = torch.ge(x, x3)
mask_2 = mask_2.float()
x = mask_2 * x + (1 - mask_2) * x3
mask_3 = torch.ge(x, x4)
mask_3 = mask_3.float()
... |
ChannelAttentionModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | 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.... | Nitin-Mane/External-Attention-pytorch | ChannelAttentionModule | false | 14,104 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... |
ExternalAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import init
class ExternalAttention(nn.Module):
def __init__(self, d_model, S=64):
super().__init__()
self.mk = nn.Linear(d_model, S, bias=False)
self.mv = nn.Linear(S, d_model, bias=False)
self.softmax = nn.Softmax(dim=1)
se... | 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.... | Nitin-Mane/External-Attention-pytorch | ExternalAttention | false | 14,105 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
from torch import nn
from torch.nn import init
class Model(nn.Module):
def __init__(self, d_model, S=64):
super().__init__()
self.mk = nn.Linear(d_model, S, bias=False)
self.mv = nn.Linear(S, d_model, bias=False)
self.softmax = nn.Softmax(dim=1)
self.init_weig... |
GAT | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
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(GraphAttentionLay... | 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.... | EagleW/PaperRobot-Incremental-Draft-Generation-of-Scientific-Ideas | GAT | false | 14,106 | [
"MIT"
] | 453 | a338abf3974ba9ce916ae846835063a42b9e6689 | https://github.com/EagleW/PaperRobot-Incremental-Draft-Generation-of-Scientific-Ideas/tree/a338abf3974ba9ce916ae846835063a42b9e6689 | import torch
import numpy as np
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().__init__()
... |
DoubleAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import init
class DoubleAttention(nn.Module):
def __init__(self, in_channels, c_m, c_n, reconstruct=True):
super().__init__()
self.in_channels = in_channels
self.reconstruct = reconstruct
self.c_m... | 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.... | Nitin-Mane/External-Attention-pytorch | DoubleAttention | false | 14,107 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import init
class Model(nn.Module):
def __init__(self, in_channels, c_m, c_n, reconstruct=True):
super().__init__()
self.in_channels = in_channels
self.reconstruct = reconstruct
self.c_m = c_m
... |
SineGen | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
import torch.utils.data
import torch.nn as torch_nn
class SineGen(torch_nn.Module):
""" Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
... | 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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.utils... | Ninushkat/Impact-Synth-Hardware | SineGen | false | 14,108 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | import torch
import numpy as np
import torch.utils.data
import torch.nn as torch_nn
class Model(torch_nn.Module):
""" Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
s... |
SpatialGroupEnhance | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import init
class SpatialGroupEnhance(nn.Module):
def __init__(self, groups):
super().__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.bias ... | 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
from torch.nn import init
assert_size_stride = torch._C._d... | Nitin-Mane/External-Attention-pytorch | SpatialGroupEnhance | false | 14,109 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
from torch import nn
from torch.nn import init
class Model(nn.Module):
def __init__(self, groups):
super().__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.bias = nn.Parameter... |
SimplifiedScaledDotProductAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | 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.... | Nitin-Mane/External-Attention-pytorch | SimplifiedScaledDotProductAttention | false | 14,110 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
import numpy as np
from torch import nn
from torch.nn import init
class Model(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries... |
Conv1dKeepLength | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
class Conv1dKeepLength(torch_nn.Conv1d):
""" Wrapper for causal convolution
Input tensor: (batchsize=1, length, dim_in)
Output tensor: (batchsize=1, length, dim_out)
https://github.com/pytorch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | Ninushkat/Impact-Synth-Hardware | Conv1dKeepLength | false | 14,111 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | import torch
import torch.utils.data
import torch.nn as torch_nn
import torch.nn.functional as torch_nn_func
class Model(torch_nn.Conv1d):
""" Wrapper for causal convolution
Input tensor: (batchsize=1, length, dim_in)
Output tensor: (batchsize=1, length, dim_out)
https://github.com/pytorch/pytorch/is... |
ECAAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import init
class ECAAttention(nn.Module):
def __init__(self, kernel_size=3):
super().__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(
kernel_size - 1) // 2)
sel... | 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
from torch.nn import init
assert_size_stride = torch._C._dy... | Nitin-Mane/External-Attention-pytorch | ECAAttention | false | 14,112 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
from torch import nn
from torch.nn import init
class Model(nn.Module):
def __init__(self, kernel_size=3):
super().__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(
kernel_size - 1) // 2)
self.sigmo... |
MlpBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class MlpBlock(nn.Module):
def __init__(self, input_dim, mlp_dim=512):
super().__init__()
self.fc1 = nn.Linear(input_dim, mlp_dim)
self.gelu = nn.GELU()
self.fc2 = nn.Linear(mlp_dim, input_dim)
def forward(self, x):
return self.fc2(se... | 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 n... | Nitin-Mane/External-Attention-pytorch | MlpBlock | false | 14,113 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, input_dim, mlp_dim=512):
super().__init__()
self.fc1 = nn.Linear(input_dim, mlp_dim)
self.gelu = nn.GELU()
self.fc2 = nn.Linear(mlp_dim, input_dim)
def forward(self, x):
return self.fc2(self.... |
OutlookAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
from torch.nn import functional as F
class OutlookAttention(nn.Module):
def __init__(self, dim, num_heads=1, kernel_size=3, padding=1, stride=1,
qkv_bias=False, attn_drop=0.1):
super().__init__()
self.dim = dim
self.num_heads = num_hea... | 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.... | Nitin-Mane/External-Attention-pytorch | OutlookAttention | false | 14,114 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import math
import torch
from torch import nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, dim, num_heads=1, kernel_size=3, padding=1, stride=1,
qkv_bias=False, attn_drop=0.1):
super().__init__()
self.dim = dim
self.num_heads = num_heads
... |
Encoder5 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
class Encoder5(nn.Module):
def __init__(self, model=None, fixed=False):
super(Encoder5, self).__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv0.weight = nn.Parameter(torch.from_numpy(np.array([[[[0]... | 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.... | MingSun-Tse/Collaborative-Distillation | Encoder5 | false | 14,115 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 | import torch
import numpy as np
import torch.nn as nn
class Model(nn.Module):
def __init__(self, model=None, fixed=False):
super().__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv0.weight = nn.Parameter(torch.from_numpy(np.array([[[[0]],
[[... |
SincFilter | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
import torch.utils.data
import torch.nn as torch_nn
class SincFilter(torch_nn.Module):
""" SincFilter
Given the cut-off-frequency, produce the low-pass and high-pass
windowed-sinc-filters.
If input cut-off-frequency is (batchsize=1, signal_length, 1),
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.utils.data
import torch.nn as torch_nn
as... | Ninushkat/Impact-Synth-Hardware | SincFilter | false | 14,116 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | import torch
import numpy as np
import torch.utils.data
import torch.nn as torch_nn
class Model(torch_nn.Module):
""" SincFilter
Given the cut-off-frequency, produce the low-pass and high-pass
windowed-sinc-filters.
If input cut-off-frequency is (batchsize=1, signal_length, 1),
out... |
ScaledDotProductAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | 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.... | Nitin-Mane/External-Attention-pytorch | ScaledDotProductAttention | false | 14,117 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
import numpy as np
from torch import nn
from torch.nn import init
class Model(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality ... |
GetMask | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class GetMask(torch.nn.Module):
"""
inputs: x: any size
outputs:mask: same size as input x
"""
def __init__(self, pad_idx=0):
super(GetMask, self).__init__()
self.pad_idx = pad_idx
def forward(self, x):
mask = torch.ne(x, self.pad_idx).floa... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NoteXYX/ACL2017 | GetMask | false | 14,118 | [
"Apache-2.0"
] | 119 | 436f59f2aa0044a9d57c95a2a58b2158cb99738d | https://github.com/NoteXYX/ACL2017/tree/436f59f2aa0044a9d57c95a2a58b2158cb99738d | import torch
class Model(torch.nn.Module):
"""
inputs: x: any size
outputs:mask: same size as input x
"""
def __init__(self, pad_idx=0):
super().__init__()
self.pad_idx = pad_idx
def forward(self, x):
mask = torch.ne(x, self.pad_idx).float()
ret... |
SpatialAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=
kernel_size // 2)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
max_result,... | 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_s... | Nitin-Mane/External-Attention-pytorch | SpatialAttention | false | 14,119 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=
kernel_size // 2)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
max_result, _ = torch.... |
UFOAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import init
def XNorm(x, gamma):
norm_tensor = torch.norm(x, 2, -1, True)
return x * gamma / norm_tensor
class UFOAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
... | 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 n... | Nitin-Mane/External-Attention-pytorch | UFOAttention | false | 14,120 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
from torch import nn
from torch.nn import init
def XNorm(x, gamma):
norm_tensor = torch.norm(x, 2, -1, True)
return x * gamma / norm_tensor
class Model(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:p... |
NoopLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from torch.nn import Module
import functools
import torch
import torch.utils.data
import torch.nn as nn
from torchvision.models import *
import torch.nn.init
class NoopLoss(Module):
"""Just returns the mean of the `output`."""
def forward(self, output, *args):
return output.mean()
class PrePostInit... | 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.nn import Module
import functools
import torch.utils.data
import torch.nn as n... | JiahuaWU/fastai | NoopLoss | false | 14,121 | [
"Apache-2.0"
] | 59 | 13a2df812d875abf0558004283392ab40d9bdea1 | https://github.com/JiahuaWU/fastai/tree/13a2df812d875abf0558004283392ab40d9bdea1 | from torch.nn import Module
import functools
import torch
import torch.utils.data
import torch.nn as nn
from torchvision.models import *
import torch.nn.init
class Model(Module):
"""Just returns the mean of the `output`."""
def forward(self, output, *args):
return output.mean()
class PrePostInitMet... |
ShuffleBlock | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.size(... | 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_st... | P2333/Bag-of-Tricks-for-AT | ShuffleBlock | false | 14,122 | [
"Apache-2.0"
] | 192 | 314683adcfe9ea7c7bfbff50007da510b21f56e1 | https://github.com/P2333/Bag-of-Tricks-for-AT/tree/314683adcfe9ea7c7bfbff50007da510b21f56e1 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, groups=2):
super().__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.size()
g = self.groups... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class Attention(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1,
score_function='dot_product', dropout=0):
""" Attention Mechanism
:param embed_dim:
... | 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.... | NouamaneTazi/conv-emotion | Attention | false | 14,123 | [
"MIT"
] | 488 | 0c9dcb9cc5234a7ca8cf6af81aabe28ef3814d0e | https://github.com/NouamaneTazi/conv-emotion/tree/0c9dcb9cc5234a7ca8cf6af81aabe28ef3814d0e | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class Model(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1,
score_function='dot_product', dropout=0):
""" Attention Mechanism
:param embed_dim:
:par... |
SimpleAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class SimpleAttention(nn.Module):
def __init__(self, input_dim):
super(SimpleAttention, self).__init__()
self.input_dim = input_dim
self.scalar = nn.Linear(self.input_dim, 1, bias=False)
def forward... | 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.... | NouamaneTazi/conv-emotion | SimpleAttention | false | 14,124 | [
"MIT"
] | 488 | 0c9dcb9cc5234a7ca8cf6af81aabe28ef3814d0e | https://github.com/NouamaneTazi/conv-emotion/tree/0c9dcb9cc5234a7ca8cf6af81aabe28ef3814d0e | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.input_dim = input_dim
self.scalar = nn.Linear(self.input_dim, 1, bias=False)
def forward(self, M, x=None):
"""
... |
MaskedMSELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.optim
class MaskedMSELoss(nn.Module):
def __init__(self):
super(MaskedMSELoss, self).__init__()
self.loss = nn.MSELoss(reduction='sum')
def forward(self, pred, target, mask):
"""
pred -> batch*seq_len
target -> batch*seq... | 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.optim
assert_size_stride = torch._C._dynamo.guards.ass... | NouamaneTazi/conv-emotion | MaskedMSELoss | false | 14,125 | [
"MIT"
] | 488 | 0c9dcb9cc5234a7ca8cf6af81aabe28ef3814d0e | https://github.com/NouamaneTazi/conv-emotion/tree/0c9dcb9cc5234a7ca8cf6af81aabe28ef3814d0e | import torch
import torch.nn as nn
import torch.optim
class Model(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.MSELoss(reduction='sum')
def forward(self, pred, target, mask):
"""
pred -> batch*seq_len
target -> batch*seq_len
mask -> batch*... |
GraphAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
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... | 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.... | Nmegha2601/activitygraph_transformer | GraphAttentionLayer | false | 14,126 | [
"MIT"
] | 63 | 4e21a4ea12527df470b7586d149fa4168a41307c | https://github.com/Nmegha2601/activitygraph_transformer/tree/4e21a4ea12527df470b7586d149fa4168a41307c | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(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().__init__()
... |
MaskedSoftmax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch.nn import functional as F
import torch.utils.data
import torch.nn as nn
class MaskedSoftmax(nn.Module):
def __init__(self, dim):
super(MaskedSoftmax, self).__init__()
self.dim = dim
def forward(self, logit, mask=None):
if mask is None:
dist = F.sof... | 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.utils.dat... | Nullius-2020/TAKG-Paddle | MaskedSoftmax | false | 14,127 | [
"MIT"
] | 130 | 7ebb5c4cdd1d2c68b1ca4a518b73c5e815fc5812 | https://github.com/Nullius-2020/TAKG-Paddle/tree/7ebb5c4cdd1d2c68b1ca4a518b73c5e815fc5812 | import torch
from torch.nn import functional as F
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, logit, mask=None):
if mask is None:
dist = F.softmax(logit - torch.max(logi... |
DAModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | 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.... | Nitin-Mane/External-Attention-pytorch | DAModule | false | 14,128 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
def masked_softmax(x, m=None, axis=-1):
"""
Softmax with mask (optional)
"""
x = torch.clamp(x, min=-15.0, max=15.0)
if m is not None:
m = m.float()
x = x * m
e_x = torch.exp(x - torch.max(x, dim=axis, keepdim=True)[0])
if m is not None:
... | 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.... | NoteXYX/ACL2017 | Attention | false | 14,129 | [
"Apache-2.0"
] | 119 | 436f59f2aa0044a9d57c95a2a58b2158cb99738d | https://github.com/NoteXYX/ACL2017/tree/436f59f2aa0044a9d57c95a2a58b2158cb99738d | import torch
import torch.nn as nn
def masked_softmax(x, m=None, axis=-1):
"""
Softmax with mask (optional)
"""
x = torch.clamp(x, min=-15.0, max=15.0)
if m is not None:
m = m.float()
x = x * m
e_x = torch.exp(x - torch.max(x, dim=axis, keepdim=True)[0])
if m is not None:
... |
FastRCNNPredictor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class FastRCNNPredictor(nn.Module):
def __init__(self, in_channels, mid_channels, num_classes):
super().__init__()
self.fc1 = nn.Linear(in_channels, mid_channels)
self.fc2 = nn.Linear(mid_channels, mid_channels)
sel... | 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_s... | Okery/PyTorch-Simple-MaskRCNN | FastRCNNPredictor | false | 14,130 | [
"MIT"
] | 147 | 5e57a353f211c7130bfcf1d55cacd80057d81423 | https://github.com/Okery/PyTorch-Simple-MaskRCNN/tree/5e57a353f211c7130bfcf1d55cacd80057d81423 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, in_channels, mid_channels, num_classes):
super().__init__()
self.fc1 = nn.Linear(in_channels, mid_channels)
self.fc2 = nn.Linear(mid_channels, mid_channels)
self.cls_score ... |
StandardNLL | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class StandardNLL(torch.nn.modules.loss._Loss):
"""
Shape:
log_prob: batch x time x class
y_true: batch x time
mask: batch x time
output: batch
"""
def forward(self, log_prob, y_true, mask):
mask = mask.float()
log_P = torch... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NoteXYX/ACL2017 | StandardNLL | false | 14,131 | [
"Apache-2.0"
] | 119 | 436f59f2aa0044a9d57c95a2a58b2158cb99738d | https://github.com/NoteXYX/ACL2017/tree/436f59f2aa0044a9d57c95a2a58b2158cb99738d | import torch
class Model(torch.nn.modules.loss._Loss):
"""
Shape:
log_prob: batch x time x class
y_true: batch x time
mask: batch x time
output: batch
"""
def forward(self, log_prob, y_true, mask):
mask = mask.float()
log_P = torch.gathe... |
GraphEncoderDecoderAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphEncoderDecoderAttentionLayer(nn.Module):
"""
Graph-to-Graph message passing, adapted from https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_src_features, in_tgt_features, out_fe... | 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.... | Nmegha2601/activitygraph_transformer | GraphEncoderDecoderAttentionLayer | false | 14,132 | [
"MIT"
] | 63 | 4e21a4ea12527df470b7586d149fa4168a41307c | https://github.com/Nmegha2601/activitygraph_transformer/tree/4e21a4ea12527df470b7586d149fa4168a41307c | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Graph-to-Graph message passing, adapted from https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_src_features, in_tgt_features, out_features,
dropout, alp... |
h_tanh | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class h_tanh(nn.Module):
def __init__(self, inplace=True, h_max=1):
super(h_tanh, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
self.h_max = ... | 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.nn.parallel
import torch.optim
import torch.utils.data... | PINTO0309/micronet | h_tanh | false | 14,133 | [
"MIT"
] | 221 | 97ff01d0ea9a42f0a3f0a93ac67660df26411f28 | https://github.com/PINTO0309/micronet/tree/97ff01d0ea9a42f0a3f0a93ac67660df26411f28 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self, inplace=True, h_max=1):
super().__init__()
self.relu = nn.ReLU6(inplace=inplace)
self.h_max = h_max
de... |
AllReduceLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import Tensor
import torch.distributed as dist
import torch.nn as nn
from torch.nn import Linear
class ParallelModule(nn.Module):
"""Parents of all parallel layer classes"""
def __init__(self):
super().__init__()
self.mp_group = None
def allreduce(self, outputs):
... | 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.distributed as dist
import torch.nn as nn
from torch.nn import Line... | Oaklight/parallelformers | AllReduceLinear | false | 14,134 | [
"Apache-2.0"
] | 454 | 57fc36f81734c29aaf814e092ce13681d3c28ede | https://github.com/Oaklight/parallelformers/tree/57fc36f81734c29aaf814e092ce13681d3c28ede | import torch
from torch import Tensor
import torch.distributed as dist
import torch.nn as nn
from torch.nn import Linear
class ParallelModule(nn.Module):
"""Parents of all parallel layer classes"""
def __init__(self):
super().__init__()
self.mp_group = None
def allreduce(self, outputs):
... |
DenseSAGEConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn.functional as F
import torch.utils.data
from torch.nn import Parameter
def uniform(size, tensor):
stdv = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-stdv, stdv)
class DenseSAGEConv(torch.nn.Module):
def __init__(self, in_channels, ... | 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.... | NunoEdgarGFlowHub/pytorch_geometric | DenseSAGEConv | false | 14,135 | [
"MIT"
] | 62 | 4a03a7e6484c38805a24a2e7362ef32b7e279036 | https://github.com/NunoEdgarGFlowHub/pytorch_geometric/tree/4a03a7e6484c38805a24a2e7362ef32b7e279036 | import math
import torch
import torch.nn.functional as F
import torch.utils.data
from torch.nn import Parameter
def uniform(size, tensor):
stdv = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-stdv, stdv)
class Model(torch.nn.Module):
def __init__(self, in_channels, out_chan... |
RPNHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class RPNHead(nn.Module):
def __init__(self, in_channels, num_anchors):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
self.cls_logits = nn.Conv2d(in_channels, num_anchors, 1)
self.bbox_... | 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_s... | Okery/PyTorch-Simple-MaskRCNN | RPNHead | false | 14,136 | [
"MIT"
] | 147 | 5e57a353f211c7130bfcf1d55cacd80057d81423 | https://github.com/Okery/PyTorch-Simple-MaskRCNN/tree/5e57a353f211c7130bfcf1d55cacd80057d81423 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, in_channels, num_anchors):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
self.cls_logits = nn.Conv2d(in_channels, num_anchors, 1)
self.bbox_pr... |
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