entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_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 |
|---|---|---|---|---|---|---|---|---|---|---|
GCT | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | yoxu515/CFBI | GCT | false | 16,775 | [
"BSD-3-Clause"
] | 312 | 0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 | https://github.com/yoxu515/CFBI/tree/0bab1e3c9fc3e3ba0629f716d60221e8f8d9d586 |
ConvEncoder | import torch
from torch import nn
class ConvEncoder(nn.Module):
""" Simple convolutional encoder network.
It consists of 5 convolutional layers, each downsampling the input by a
factor of 2, and a final fully-connected layer projecting the output to
c_dim dimenions.
Args:
c_dim (int): ou... | 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... | crysoberil/ObjectReconstruction_ONetBased | ConvEncoder | false | 12,242 | [
"MIT"
] | 0 | 7c15ea8a64ee3647c86b57b16f0c85bd51ccdd47 | https://github.com/crysoberil/ObjectReconstruction_ONetBased/tree/7c15ea8a64ee3647c86b57b16f0c85bd51ccdd47 |
LayerNormalization | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | VarnithChordia/Multlingual_Punctuation_restoration | LayerNormalization | false | 18,037 | [
"MIT"
] | 8 | 17c026e8935b9fecae01d446a756926c7733fcd1 | https://github.com/VarnithChordia/Multlingual_Punctuation_restoration/tree/17c026e8935b9fecae01d446a756926c7733fcd1 |
OcclusionAwareSimilarity | import torch
import torch.nn as nn
class OcclusionAwareSimilarity(nn.Module):
def __init__(self, threshold):
super(OcclusionAwareSimilarity, self).__init__()
self.threshold = threshold
def forward(self, similarity_matrix):
indicator_zero = similarity_matrix <= self.threshold
... | 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
@triton.jit
def triton_poi_fused_index_put_lift_fres... | nv-nguyen/template-pose | OcclusionAwareSimilarity | false | 4,095 | [
"MIT"
] | 0 | ce1ffead1887b54efc8031e8e2442ba884e512ec | https://github.com/nv-nguyen/template-pose/tree/ce1ffead1887b54efc8031e8e2442ba884e512ec |
Classifier3 | import torch
import torch.nn
import torch.utils.data
import torch.nn.functional as F
import torch.nn.parallel
class Classifier3(torch.nn.Module):
def __init__(self):
super(Classifier3, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels=3, out_channels=64,
kernel_size=3, stride=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | yuping1624/1082NCTU-Deep-Learning | Classifier3 | false | 4,739 | [
"MIT"
] | 0 | dc83e1c8709e9610a996f02091fe626f07b3c10f | https://github.com/yuping1624/1082NCTU-Deep-Learning/tree/dc83e1c8709e9610a996f02091fe626f07b3c10f |
residualUnit | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init as init
import torch.nn.init
class conv23DUnit(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, groups=1, bias=True, dilation=1, nd=... | 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.... | ForrestPi/Unsupervised-Defect-Segmentation | residualUnit | false | 8,219 | [
"MIT"
] | 17 | e366ac7c757bb1b45f38ebbc502dfee7ccb72398 | https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398 |
AFMLayer | # 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._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 | AFMLayer | false | 13,822 | [
"Apache-2.0"
] | 1,770 | 06b98cf2cb2781656805eafc577fbd088f37d17d | https://github.com/Fanxingye/DeepRS/tree/06b98cf2cb2781656805eafc577fbd088f37d17d |
MlpNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | HAXRD/PIC | MlpNet | false | 8,188 | [
"MIT"
] | 28 | 658b4dd6b01e64413d5f8f0107d9167f1bd78546 | https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546 |
CGRU_cell | # 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._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 ... | zhujiagang/realtime-refined-random | CGRU_cell | false | 11,041 | [
"MIT"
] | 0 | 3aa8169049ab8be8b1ea5a78bbe9b89ac6c15593 | https://github.com/zhujiagang/realtime-refined-random/tree/3aa8169049ab8be8b1ea5a78bbe9b89ac6c15593 |
SimpleAvgPool1dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleAvgPool1dModule(torch.nn.Module):
def __init__(self, kernel_size, stride=None, padding=0):
super(SimpleAvgPool1dModule, self).__init__()
self.kernel_size = kernel_size
self.padding ... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleAvgPool1dModule | false | 3,317 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
FCNet | # 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._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 ... | KaihuaTang/VQA2.0-Recent-Approachs-2018.pytorch | FCNet | false | 13,937 | [
"MIT"
] | 298 | 52e1ba5a7f3b88c617115ccc755e2e7868e8de2b | https://github.com/KaihuaTang/VQA2.0-Recent-Approachs-2018.pytorch/tree/52e1ba5a7f3b88c617115ccc755e2e7868e8de2b |
RewardCriterion | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.autograd import *
def to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, 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
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | daqingliu/CAVP | RewardCriterion | false | 15,116 | [
"MIT"
] | 49 | d383affde78dbc75e369095c27954dcdd79478d0 | https://github.com/daqingliu/CAVP/tree/d383affde78dbc75e369095c27954dcdd79478d0 |
RBFExpansion | # 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 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.nn as nn
assert_size_stride = torch._C._d... | Erfaan-Rostami/dgl-lifesci | RBFExpansion | false | 5,135 | [
"Apache-2.0"
] | 1 | 08fc317f634fbaee4a8d074c332e871357845e4f | https://github.com/Erfaan-Rostami/dgl-lifesci/tree/08fc317f634fbaee4a8d074c332e871357845e4f |
NetVLAD | # 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._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.... | Shubodh/NetVLAD-pytorch | NetVLAD | false | 9,510 | [
"MIT"
] | 0 | ea45bac16dbb3e3bec4172df58715bf3526ee502 | https://github.com/Shubodh/NetVLAD-pytorch/tree/ea45bac16dbb3e3bec4172df58715bf3526ee502 |
TracedModule | import torch
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class TracedModule(torch.nn.Module):
def forward(self, x):
x = x.type(torch.float32)
return torch.floor(torch.sqrt(x) ... | 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.quantization
import torch.onnx
import torch.nn.parallel
import tor... | MartinRenaudin/tutorials | TracedModule | false | 2,755 | [
"BSD-3-Clause"
] | 0 | 035d6827d77c52fed2a927f105e39fd73516f093 | https://github.com/MartinRenaudin/tutorials/tree/035d6827d77c52fed2a927f105e39fd73516f093 |
SimpleDropoutOptimizer | import torch
import torch.nn as nn
class SimpleDropoutOptimizer(nn.Module):
def __init__(self, p):
super().__init__()
if p is not None:
self.dropout = nn.Dropout(p=p)
else:
self.dropout = None
def forward(self, x):
if self.dropout is not None:
... | 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... | Danish-VSL/deep-person-reid | SimpleDropoutOptimizer | false | 13,554 | [
"MIT"
] | 244 | 2e3a4b6706b84c77203f9905683b917ab0871b93 | https://github.com/Danish-VSL/deep-person-reid/tree/2e3a4b6706b84c77203f9905683b917ab0871b93 |
SqueezeNet | # 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._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 copy
import torch.nn a... | matherm/ummon3 | SqueezeNet | false | 7,339 | [
"BSD-3-Clause"
] | 1 | 08476d21ce17cc95180525d48202a1690dfc8a08 | https://github.com/matherm/ummon3/tree/08476d21ce17cc95180525d48202a1690dfc8a08 |
GeM | import torch
import torch.nn.functional as F
def gem(x, p=3, eps=1e-06):
return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(
1.0 / p)
class GeM(torch.nn.Module):
"""
Implementation of GeM pooling.
https://paperswithcode.com/method/generalized-mean-pooling
NOTE:
... | 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.nn.functional a... | rskmoi/landmark-retrieval-2020-with-pytorch | GeM | false | 7,577 | [
"MIT"
] | 1 | 41917b1f588b5ad396cb1095867a0f042c611675 | https://github.com/rskmoi/landmark-retrieval-2020-with-pytorch/tree/41917b1f588b5ad396cb1095867a0f042c611675 |
EncoderLayer | # 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._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.... | bhubanendra-mishra/dense-video-cap | EncoderLayer | false | 14,955 | [
"BSD-3-Clause"
] | 174 | 43914e17769701b9cf98eda203ae4c465b315fab | https://github.com/bhubanendra-mishra/dense-video-cap/tree/43914e17769701b9cf98eda203ae4c465b315fab |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | Aleph0Inc/HDSA-Dialog | PositionwiseFeedForward | false | 13,257 | [
"MIT"
] | 146 | 88e2604adb5dc38ae32205410b15b2ac39116ecd | https://github.com/Aleph0Inc/HDSA-Dialog/tree/88e2604adb5dc38ae32205410b15b2ac39116ecd |
ChebConv | import math
import torch
def cheb_conv(laplacian, inputs, weight):
"""Chebyshev convolution.
Args:
laplacian (:obj:`torch.sparse.Tensor`): The laplacian corresponding to the current sampling of the sphere.
inputs (:obj:`torch.Tensor`): The current input data being forwarded.
weight (:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | phil-hawkins/deepsphere-pytorch | ChebConv | false | 16,261 | [
"MIT"
] | 99 | f23c531445b3ddf234c7e98cdadb010163051e6d | https://github.com/phil-hawkins/deepsphere-pytorch/tree/f23c531445b3ddf234c7e98cdadb010163051e6d |
Hswish | # 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 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 torch.quantization import QuantStub
from torch.quantization im... | akosik-anyvision/incubator-tvm | Hswish | false | 18,240 | [
"Apache-2.0"
] | 9 | e1b11712ac09c32614483d24a4c7e0245ee4cb4b | https://github.com/akosik-anyvision/incubator-tvm/tree/e1b11712ac09c32614483d24a4c7e0245ee4cb4b |
Residual_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
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.... | MatusBako/MakeFacesGreatAgain | Residual_Block | false | 845 | [
"MIT"
] | 0 | e4941a8460db79dec566ed02d4b23eafb416a6db | https://github.com/MatusBako/MakeFacesGreatAgain/tree/e4941a8460db79dec566ed02d4b23eafb416a6db |
GraphDiffusedAttentionLayer | # 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._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.... | Yindong-Zhang/myGAT | GraphDiffusedAttentionLayer | false | 18,161 | [
"MIT"
] | 6 | f69132f21785d3a6bf1ec014890adeb124c89e8d | https://github.com/Yindong-Zhang/myGAT/tree/f69132f21785d3a6bf1ec014890adeb124c89e8d |
DenseModelV2 | # 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._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_... | chawins/adv-exp | DenseModelV2 | false | 6,434 | [
"MIT"
] | 1 | 5423e135c5599e4ec2bf90372916d8d05c89f285 | https://github.com/chawins/adv-exp/tree/5423e135c5599e4ec2bf90372916d8d05c89f285 |
GeM | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | Liuhongzhi2018/Person_ReID | GeM | false | 2,552 | [
"MIT"
] | 0 | 51c576ed5b4ed960801669d6d59c0a77405b369d | https://github.com/Liuhongzhi2018/Person_ReID/tree/51c576ed5b4ed960801669d6d59c0a77405b369d |
ODEfunc | # 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._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.... | MaricelaM/torchdiffeq | ODEfunc | false | 14,003 | [
"MIT"
] | 4,088 | 4e070fb687167e53082a91f32e102af7f4521058 | https://github.com/MaricelaM/torchdiffeq/tree/4e070fb687167e53082a91f32e102af7f4521058 |
CNNLayerNorm | import torch
import torch.nn as nn
class CNNLayerNorm(nn.Module):
"""Layer normalization built for cnns input"""
def __init__(self, n_feats):
super(CNNLayerNorm, self).__init__()
self.layer_norm = nn.LayerNorm(n_feats)
def forward(self, x):
x = x.transpose(2, 3).contiguous()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | BlackyYen/Speech_Recognition-PyTorch | CNNLayerNorm | false | 7,788 | [
"MIT"
] | 16 | 0a986f467c540c2be88f65064ebf5ce0f6bcf70a | https://github.com/BlackyYen/Speech_Recognition-PyTorch/tree/0a986f467c540c2be88f65064ebf5ce0f6bcf70a |
Coral | import torch
import torch.nn as nn
import torch.nn.init
class Coral(nn.Module):
def __init__(self):
super(Coral, self).__init__()
def forward(self, a, b):
"""
Arguments:
a: a float tensor with shape [n, d].
b: a float tensor with shape [m, d].
Returns:... | 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.init
assert_size_stride = torch._C._dynamo... | TropComplique/associative-domain-adaptation | Coral | false | 18,010 | [
"MIT"
] | 8 | a2ec0a9e678af20624f79e40c8042c969a69e8f3 | https://github.com/TropComplique/associative-domain-adaptation/tree/a2ec0a9e678af20624f79e40c8042c969a69e8f3 |
ResnetBlockConv2d | # 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | DveloperY0115/texture_fields | ResnetBlockConv2d | false | 13,621 | [
"MIT"
] | 78 | 28c277696e0a658ffff3496892810d5a0ef03f65 | https://github.com/DveloperY0115/texture_fields/tree/28c277696e0a658ffff3496892810d5a0ef03f65 |
Conv1DHighwayLayer | # 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._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_... | avinashsai/Highway-Networks | Conv1DHighwayLayer | false | 3,148 | [
"MIT"
] | 0 | fe30629e47b919776f981eaa2bea7d21e648a17f | https://github.com/avinashsai/Highway-Networks/tree/fe30629e47b919776f981eaa2bea7d21e648a17f |
MaskedMSELoss | import torch
import torch.nn as nn
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_len
mask -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Anshul044/Project-NN | MaskedMSELoss | false | 33 | [
"MIT"
] | 0 | ef080846715a95b735f0381e4f60742e40791630 | https://github.com/Anshul044/Project-NN/tree/ef080846715a95b735f0381e4f60742e40791630 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim, max_action):
super(Actor, self).__init__()
self.linear1 = nn.Linear(state_dim, hidden_dim)
self.linear2 = nn.Linear(hidden_dim, hidden_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.... | abcdcamey/RL-learning | Actor | false | 6,051 | [
"MIT"
] | 1 | 84e3be15a22bc05fec063b4c3dd56c4836c5981a | https://github.com/abcdcamey/RL-learning/tree/84e3be15a22bc05fec063b4c3dd56c4836c5981a |
Conv1DBlock | import torch
import torch.nn.functional as F
import torch.nn as nn
class ConvNorm(nn.Module):
""" 1D Convolution """
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if p... | 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... | ishine/FastPitchFormant | Conv1DBlock | false | 15,617 | [
"MIT"
] | 54 | dd86032953be04fb526b658b19ecdc5600ff25a5 | https://github.com/ishine/FastPitchFormant/tree/dd86032953be04fb526b658b19ecdc5600ff25a5 |
BatchMeanKLDivWithLogSoftmax | # 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 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... | cadurosar/graph_kd_dense_cifar100 | BatchMeanKLDivWithLogSoftmax | false | 1,629 | [
"MIT"
] | 0 | 84054ab4f8f61c9db3460993661ba7bf1d951b36 | https://github.com/cadurosar/graph_kd_dense_cifar100/tree/84054ab4f8f61c9db3460993661ba7bf1d951b36 |
BinaryExpSquare | # 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 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 abc
import inspect
import warnings
import torch.nn as nn
import to... | Johnsonms/NNI_master | BinaryExpSquare | false | 11,584 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
CNNLayerNorm | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | loopdigga96/numbers_recognition | CNNLayerNorm | false | 7,116 | [
"Apache-2.0"
] | 1 | dd1110d3fd18b5ca20278a010c550aeaad495e19 | https://github.com/loopdigga96/numbers_recognition/tree/dd1110d3fd18b5ca20278a010c550aeaad495e19 |
PyTorchMlp | import torch
import torch.nn as nn
class PyTorchMlp(nn.Module):
def __init__(self, n_inputs=4, n_actions=2):
nn.Module.__init__(self)
self.fc1 = nn.Linear(n_inputs, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, n_actions)
self.activ_fn = nn.ReLU()
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 import triton_helpers
from torch._inductor.runtime.... | jasonjabbour/motion_imitation | PyTorchMlp | false | 3,710 | [
"Apache-2.0"
] | 0 | a28e7cd9dca2fbdd6823f19db4f66b496dd29144 | https://github.com/jasonjabbour/motion_imitation/tree/a28e7cd9dca2fbdd6823f19db4f66b496dd29144 |
GrayscaleLoss | import torch
import torch.nn as nn
class GrayscaleLayer(nn.Module):
def __init__(self):
super(GrayscaleLayer, self).__init__()
def forward(self, x):
return torch.mean(x, 1, keepdim=True)
class GrayscaleLoss(nn.Module):
def __init__(self):
super(GrayscaleLoss, self).__init__()
... | 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... | GuYuanjie/DeepFusionPrior | GrayscaleLoss | false | 5,231 | [
"MIT"
] | 1 | a7126e073ed8c49b6a9a662492b64aaeee56cc01 | https://github.com/GuYuanjie/DeepFusionPrior/tree/a7126e073ed8c49b6a9a662492b64aaeee56cc01 |
SuperPointNet | # 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._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.... | LeikvollE/pytorch-superpoint | SuperPointNet | false | 11,678 | [
"MIT"
] | 0 | 52144a760e0cc46259e57397a5a55f0585fe6d0b | https://github.com/LeikvollE/pytorch-superpoint/tree/52144a760e0cc46259e57397a5a55f0585fe6d0b |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.utils.data
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool2... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | BigFishMaster/tnt | GlobalAvgPool2d | false | 17,141 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
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 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... | Exdenta/torchsat | FocalLoss | false | 13,653 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
HuberLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _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
... | Thibaud-Ardoin/d4rl_evaluations | HuberLoss | false | 14,480 | [
"Apache-2.0"
] | 123 | 135b23d3aecc234aacaeaaa019fbc7101d9b87ec | https://github.com/Thibaud-Ardoin/d4rl_evaluations/tree/135b23d3aecc234aacaeaaa019fbc7101d9b87ec |
LocationLayer | # 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._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... | BenAAndrew/tacotron2-model | LocationLayer | false | 16,975 | [
"BSD-3-Clause"
] | 4 | cd2aaf605f94e97225319fbf876e4213ae517b40 | https://github.com/BenAAndrew/tacotron2-model/tree/cd2aaf605f94e97225319fbf876e4213ae517b40 |
GroupNorm | # 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 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.nn import Module
from torch import nn
import torch.utils.data
import... | Hadryan/nn | GroupNorm | false | 9,386 | [
"MIT"
] | 0 | b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d | https://github.com/Hadryan/nn/tree/b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d |
Critic | # 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._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_... | fengzhengyong-github/Deep-reinforcement-learning-with-pytorch | Critic | false | 6,685 | [
"MIT"
] | 1 | 3c56b601d14b0b0c8cde4b6bc6df5c1e8f298c7b | https://github.com/fengzhengyong-github/Deep-reinforcement-learning-with-pytorch/tree/3c56b601d14b0b0c8cde4b6bc6df5c1e8f298c7b |
BridgeFeatLoss | import torch
from torch import nn
from torch.optim.lr_scheduler import *
class BridgeFeatLoss(nn.Module):
def __init__(self):
super(BridgeFeatLoss, self).__init__()
def forward(self, feats_s, feats_t, feats_mixed, lam):
dist_mixed2s = ((feats_mixed - feats_s) ** 2).sum(1, keepdim=True)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from to... | Luxios22/IDM | BridgeFeatLoss | false | 2,602 | [
"MIT"
] | 0 | 8d51103b7c252e6304e2a361976e16ed4b523944 | https://github.com/Luxios22/IDM/tree/8d51103b7c252e6304e2a361976e16ed4b523944 |
AttentionBlock | # 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._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.... | Royeqiu/Nemo_ASR | AttentionBlock | false | 17,861 | [
"Apache-2.0"
] | 10 | 12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e | https://github.com/Royeqiu/Nemo_ASR/tree/12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e |
CustomizedLayer | import torch
import torch.nn as nn
import torch.utils.data
class CustomizedLayer(nn.Module):
def __init__(self, in_dim):
super().__init__()
self.in_dim = in_dim
self.scale = nn.Parameter(torch.Tensor(self.in_dim))
self.bias = nn.Parameter(torch.Tensor(self.in_dim))
def forwar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | Serjio42/Torch-Pruning | CustomizedLayer | false | 5,808 | [
"MIT"
] | 1 | 8a096df38ddd95a2db39eca5f87b8a26c8d134ef | https://github.com/Serjio42/Torch-Pruning/tree/8a096df38ddd95a2db39eca5f87b8a26c8d134ef |
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
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.... | Yucao42/DeepLearning2019 | Attention | false | 12,010 | [
"MIT"
] | 0 | 90421a85686655e969bc473c60dfafc3558b6f33 | https://github.com/Yucao42/DeepLearning2019/tree/90421a85686655e969bc473c60dfafc3558b6f33 |
BasicModel3 | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | aravipati12/captum | BasicModel3 | false | 10,092 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
Denoise_NormalizeLayer | import torch
import torch.nn as nn
class Denoise_NormalizeLayer(nn.Module):
def __init__(self):
super(Denoise_NormalizeLayer, self).__init__()
def forward(self, inputs: 'torch.tensor'):
permute_RGBtoBGR = [2, 1, 0]
inputs = inputs[:, permute_RGBtoBGR, :, :]
out = inputs / 0.5... | 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... | Equationliu/GA-Attack | Denoise_NormalizeLayer | false | 17,264 | [
"MIT"
] | 8 | b0280674a211f6451774ec6b1d4cee2fc19a4de6 | https://github.com/Equationliu/GA-Attack/tree/b0280674a211f6451774ec6b1d4cee2fc19a4de6 |
BCEDiceLoss | # 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 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 ... | PerceptionComputingLab/PARSE2022 | BCEDiceLoss | false | 9,430 | [
"Apache-2.0"
] | 0 | a34886ed9d06b424bc93953f1b2f79540ad9ebf6 | https://github.com/PerceptionComputingLab/PARSE2022/tree/a34886ed9d06b424bc93953f1b2f79540ad9ebf6 |
Attention | from torch.nn import Module
import torch
from torch.nn.modules import Module
from torch.nn.functional import softmax
from torch.nn import Linear
def neginf(dtype):
"""
Return a representable finite
number near -inf for a dtype.
"""
if dtype is torch.float16:
return -65504
else:
... | 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.... | Mrpatekful/supervised-translation | Attention | false | 5,627 | [
"MIT"
] | 1 | d03db6a0fc25900fd42b8057a12adad0b8d025f8 | https://github.com/Mrpatekful/supervised-translation/tree/d03db6a0fc25900fd42b8057a12adad0b8d025f8 |
HamidaEtAl | import torch
import torch.utils
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class HamidaEtAl(nn.Module):
"""
3-D Deep Learning Approach for Remote Sensing Image Classification
Amina Ben Hamida, Alexandre Benoit, Patrick Lambert, Chokri Ben Amar
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils
import tor... | giorgosouz/HSI-classification-using-state-of-the-art-models | HamidaEtAl | false | 12,751 | [
"MIT"
] | 0 | a925972ffe02c2cd1e5dde2b163e1faa854a4966 | https://github.com/giorgosouz/HSI-classification-using-state-of-the-art-models/tree/a925972ffe02c2cd1e5dde2b163e1faa854a4966 |
EnsembleDense | # 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | vermouth1992/rlutils | EnsembleDense | false | 4,487 | [
"Apache-2.0"
] | 0 | a326373b9e39dbf147c6c4261b82a688d4dc3e78 | https://github.com/vermouth1992/rlutils/tree/a326373b9e39dbf147c6c4261b82a688d4dc3e78 |
FirstBlock | import torch
import numpy as np
import torch.nn as nn
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of 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
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | Twizwei/idinvert_pytorch | FirstBlock | false | 1,160 | [
"MIT"
] | 0 | 11f1126aab517fbe32b488d92f6fdea339463d04 | https://github.com/Twizwei/idinvert_pytorch/tree/11f1126aab517fbe32b488d92f6fdea339463d04 |
CharbonnierLoss | import torch
import torch.nn as nn
import torch.nn.parallel
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=0.001):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
loss = torch.mean(torch.sq... | 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.nn as nn
import... | Blatts01/VckImageRestoration | CharbonnierLoss | false | 2,026 | [
"MIT"
] | 0 | ae4e2221d9d4e236a08722cb92ac5cc88947e311 | https://github.com/Blatts01/VckImageRestoration/tree/ae4e2221d9d4e236a08722cb92ac5cc88947e311 |
QREmbeddingBag | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
import torch.nn as nn
from torch.nn.parameter import Paramet... | SplitInfinity/dlrm | QREmbeddingBag | false | 5,845 | [
"MIT"
] | 1 | 726dc9059be94b249d41e9b5a399c991fe687edb | https://github.com/SplitInfinity/dlrm/tree/726dc9059be94b249d41e9b5a399c991fe687edb |
TransitionUp | import torch
import torch.nn
import torch.nn.functional as F
import torch.nn as nn
class TransitionUp(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
def forward(self, x, skip, concat=True):
out = F.interpolate(x, size=(skip.size(2), skip.size(3)), mode=
... | 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | FUTUREEEEEE/FCHarDNet | TransitionUp | false | 9,077 | [
"MIT"
] | 0 | fc4b854b5cfa01a449bcfaece6bb3c32d84d9e2b | https://github.com/FUTUREEEEEE/FCHarDNet/tree/fc4b854b5cfa01a449bcfaece6bb3c32d84d9e2b |
FeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff=512, dropout=0.5):
super().__init__()
self.linear_1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.linear_2 = nn.Linear(d_ff, d_mod... | 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_... | MadanMl/PyTorch-Transformer-for-RUL-Prediction | FeedForward | false | 8,497 | [
"Apache-2.0"
] | 25 | 5bf0a4739abdecbbc88118ea413393997bdc1e24 | https://github.com/MadanMl/PyTorch-Transformer-for-RUL-Prediction/tree/5bf0a4739abdecbbc88118ea413393997bdc1e24 |
Auto_Encoder_Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | yutian-zhao/MICCAI19-MedVQA | Auto_Encoder_Model | false | 11,087 | [
"MIT"
] | 0 | 7df92c529ed87d67281efb2f568fc6c57cebfef1 | https://github.com/yutian-zhao/MICCAI19-MedVQA/tree/7df92c529ed87d67281efb2f568fc6c57cebfef1 |
SpatialGate2d | import torch
import torch.nn as nn
class SpatialGate2d(nn.Module):
def __init__(self, in_channels):
super(SpatialGate2d, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 1, kernel_size=1, stride=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
cal = self.conv1(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | bantiitnab/kaggle-TGS-salt-identification | SpatialGate2d | false | 1,521 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
LossyYCbCr | # 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 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.parallel
import torch.utils.data
from torch import nn
import torch.fft
assert_size_stride = torch._C._dynamo.guards.assert_s... | KazutakaYamanouchi/bachelor-study | LossyYCbCr | false | 2,623 | [
"Apache-2.0"
] | 0 | a5b8392459e7649cb8a35d09e65bd269d13b5297 | https://github.com/KazutakaYamanouchi/bachelor-study/tree/a5b8392459e7649cb8a35d09e65bd269d13b5297 |
FactorizationMachine | import torch
import torch.utils.data
class FactorizationMachine(torch.nn.Module):
def __init__(self, reduce_sum=True):
super().__init__()
self.reduce_sum = reduce_sum
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | Fanxingye/Autotabular | FactorizationMachine | false | 5,145 | [
"Apache-2.0"
] | 1 | d630c78290a52f8c73885afb16884e18135c34f6 | https://github.com/Fanxingye/Autotabular/tree/d630c78290a52f8c73885afb16884e18135c34f6 |
DDPGConvBody | # 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._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 ... | Marianoetchart/DeepRL | DDPGConvBody | false | 2,651 | [
"Apache-2.0"
] | 0 | 40d4825694c0890440859166de56701fc1f61d5b | https://github.com/Marianoetchart/DeepRL/tree/40d4825694c0890440859166de56701fc1f61d5b |
DecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.2):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | 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.... | alipay/Pyraformer | DecoderLayer | false | 18,307 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 |
SoftBinaryCrossEntropyLoss | import torch
class SoftBinaryCrossEntropyLoss(torch.nn.Module):
def __init__(self, tau=1.0):
super().__init__()
self.tau = tau
self.bce_logit = torch.nn.BCEWithLogitsLoss()
def forward(self, pred, true):
logits = pred / self.tau
l = self.bce_logit(logits, true)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size... | MargauxMasson/semanticGAN_code | SoftBinaryCrossEntropyLoss | false | 2,621 | [
"BSD-2-Clause",
"MIT"
] | 0 | a5b7fbbc505f8ae08c8aab8e199aa6406fffdb07 | https://github.com/MargauxMasson/semanticGAN_code/tree/a5b7fbbc505f8ae08c8aab8e199aa6406fffdb07 |
GGCL_F | # 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._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.... | marblet/DeepRobust | GGCL_F | false | 10,559 | [
"MIT"
] | 0 | 126c05818e38062c2423cd40dc8937ccc43c738b | https://github.com/marblet/DeepRobust/tree/126c05818e38062c2423cd40dc8937ccc43c738b |
SimpleLinearModule | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleLinearModule(torch.nn.Module):
def __init__(self):
super(SimpleLinearModule, self).__init__()
def forward(self, input, weight, bias=None):
return F.linear(input + input, weight, bias)
... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | YaronBenAtar/glow | SimpleLinearModule | false | 14,660 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
RFDBsmall | import torch
import torch.nn as nn
import torch.nn.functional as F
def activation(act_type, inplace=True, neg_slope=0.05, n_prelu=1):
act_type = act_type.lower()
if act_type == 'relu':
layer = nn.ReLU(inplace)
elif act_type == 'lrelu':
layer = nn.LeakyReLU(neg_slope, False)
elif act_ty... | 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 ... | BigKingXXL/RFDN | RFDBsmall | false | 8,925 | [
"MIT"
] | 0 | 35efe7db2558ca063206f3b5ab8341ba9c5e2dc8 | https://github.com/BigKingXXL/RFDN/tree/35efe7db2558ca063206f3b5ab8341ba9c5e2dc8 |
Adversarial_Loss | import torch
import torch.nn as nn
from numpy import *
class Adversarial_Loss(nn.Module):
def __init__(self, lambda_adv):
super(Adversarial_Loss, self).__init__()
self.lambda_adv = lambda_adv
pass
def forward(self, input_p, input_h):
dis_p = input_p * torch.log(input_p)
... | 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
... | ducviet00/HMER | Adversarial_Loss | false | 6,616 | [
"MIT"
] | 1 | 0fa322ed35412737a24ec3955c9a3d96d1989bd4 | https://github.com/ducviet00/HMER/tree/0fa322ed35412737a24ec3955c9a3d96d1989bd4 |
SimpleModel | import torch
import torch.cuda
class SimpleModel(torch.nn.Module):
def __init__(self, hidden_dim, empty_grad=False):
super(SimpleModel, self).__init__()
self.linear = torch.nn.Linear(hidden_dim, hidden_dim)
if empty_grad:
self.layers2 = torch.nn.ModuleList([torch.nn.Linear(hid... | 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.... | mbeacom/DeepSpeed | SimpleModel | false | 12,817 | [
"MIT"
] | 0 | 012d91df67a9ddd66df847c7608481af027cace9 | https://github.com/mbeacom/DeepSpeed/tree/012d91df67a9ddd66df847c7608481af027cace9 |
GatedConv | import torch
from torch import nn
import torch.nn.init as init
class GatedConv(nn.Module):
"""GatedConv."""
def __init__(self, input_size, width=3, dropout=0.2, nopad=False):
"""init."""
super(GatedConv, self).__init__()
self.conv = nn.Conv2d(in_channels=input_size, out_channels=2 *
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.init as init
assert_size_stride = torch._C.... | pppku/SVS_system | GatedConv | false | 16,277 | [
"Apache-2.0"
] | 78 | 95ef1076c51bfc0b74349b8058a9c918ff24c500 | https://github.com/pppku/SVS_system/tree/95ef1076c51bfc0b74349b8058a9c918ff24c500 |
Shifted_softplus | import torch
import torch.nn as nn
import torch.nn.parallel
class Shifted_softplus(nn.Module):
"""
Performs a Shifter softplus loss, which modifies with a value of log(2)
"""
def __init__(self):
super(Shifted_softplus, self).__init__()
self.act = nn.Softplus()
self.shift = nn.Para... | 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, math as tl_math
import torch.nn as nn
import torch.nn.parallel
assert_size_str... | JoseAntonioSiguenza/deepchem | Shifted_softplus | false | 9,212 | [
"MIT"
] | 0 | 05fe1b186ec154e18de9aa1b110e9258dc484e21 | https://github.com/JoseAntonioSiguenza/deepchem/tree/05fe1b186ec154e18de9aa1b110e9258dc484e21 |
MaskedMSE | import torch
import torch.nn as nn
class MaskedMSE(nn.Module):
def __init__(self):
super(MaskedMSE, self).__init__()
self.criterion = nn.MSELoss()
def forward(self, input, target, mask):
self.loss = self.criterion(input, target * mask)
return self.loss
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ngerstle/soccerontable | MaskedMSE | false | 16,165 | [
"BSD-2-Clause"
] | 465 | 25426ff0f8fe0ce008b99c5c0fdbb35091d8d92c | https://github.com/ngerstle/soccerontable/tree/25426ff0f8fe0ce008b99c5c0fdbb35091d8d92c |
ScaledDotProductAttention | import torch
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, temperature, dropout=0.1):
super(ScaledDotProductAttention, self).__init__()
self.temperature = temperature
self.dropout = nn.Dropout(p=dropout)
def forward(self, q, k, v, mask=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.... | connoisseures/vedastr | ScaledDotProductAttention | false | 10,029 | [
"Apache-2.0"
] | 0 | 5dc64f3f6f810f615414aec3508e5dfba1239216 | https://github.com/connoisseures/vedastr/tree/5dc64f3f6f810f615414aec3508e5dfba1239216 |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
import torch.multiprocessing
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature):
super().__init__()
self.temperature = temperature
self.softmax = nn.Softmax(dim=2)
... | 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.... | AppleHolic/FastSpeech2 | ScaledDotProductAttention | false | 16,946 | [
"MIT"
] | 8 | 8f6969edd0c86c05b1dd70a0b7841bd86505455e | https://github.com/AppleHolic/FastSpeech2/tree/8f6969edd0c86c05b1dd70a0b7841bd86505455e |
Downsample | import torch
import torch.nn as nn
import torch.hub
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels, in_channels,
kernel_size=3, st... | 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.hub
assert_size_stride = torch._C._dynamo.gua... | Rushi314/taming-transformers | Downsample | false | 11,820 | [
"MIT"
] | 0 | 4c0309823f57be3ca2266c1244e3efce13aaee98 | https://github.com/Rushi314/taming-transformers/tree/4c0309823f57be3ca2266c1244e3efce13aaee98 |
Noise_injector | import torch
import torch.nn as nn
def truncated_normal_(tensor, mean=0, std=1):
size = tensor.shape
tmp = tensor.new_empty(size + (4,)).normal_()
valid = (tmp < 2) & (tmp > -2)
ind = valid.max(-1, keepdim=True)[1]
tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
tensor.data.mul_(std).add_(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dkgupta90/CARMSS | Noise_injector | false | 6,646 | [
"Apache-2.0"
] | 1 | 1f397caa39b9f504951285eff150857f7d86a7c3 | https://github.com/dkgupta90/CARMSS/tree/1f397caa39b9f504951285eff150857f7d86a7c3 |
ResUnit | # 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._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 ... | MRSAIL-Mini-Robotics-Software-AI-Lab/GANVAS-models | ResUnit | false | 17,668 | [
"MIT"
] | 5 | 9bc1530d5998da3908929152da2a3120832ca104 | https://github.com/MRSAIL-Mini-Robotics-Software-AI-Lab/GANVAS-models/tree/9bc1530d5998da3908929152da2a3120832ca104 |
CNormalized_Linear | # 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | TheSignPainter/CausalDiscoveryToolbox | CNormalized_Linear | false | 14,481 | [
"MIT"
] | 528 | 33eae18184905e505be978b08003b9477bf38e0c | https://github.com/TheSignPainter/CausalDiscoveryToolbox/tree/33eae18184905e505be978b08003b9477bf38e0c |
NormedLinear | # 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._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... | ENOT-AutoDL/mmdetection-enot | NormedLinear | false | 5,111 | [
"Apache-2.0"
] | 1 | f541749554436e3327bac00eee89b84f66c03551 | https://github.com/ENOT-AutoDL/mmdetection-enot/tree/f541749554436e3327bac00eee89b84f66c03551 |
LayerNormalization | # 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 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.autograd import *
assert_size_stride = torch._C... | learnerhouse/ner-bert | LayerNormalization | false | 15,873 | [
"MIT"
] | 391 | 606328a27a7313b6c22b78590e06618ad77402cd | https://github.com/learnerhouse/ner-bert/tree/606328a27a7313b6c22b78590e06618ad77402cd |
StateActionEmbedding | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from abc import ABC
from abc import abstractmethod
from abc import abstractproperty
from torch import nn
from... | Sebastian-Griesbach/Improving-Policy-Conditioned-Value-Functions | StateActionEmbedding | false | 1,052 | [
"MIT"
] | 0 | ec4125c5e056753e507df0406fcd60b6b6c3dc25 | https://github.com/Sebastian-Griesbach/Improving-Policy-Conditioned-Value-Functions/tree/ec4125c5e056753e507df0406fcd60b6b6c3dc25 |
ConvLayer | import torch
import torch.nn.functional as F
from typing import *
import torch.utils.data
import torch.nn as nn
import torch.onnx.operators
import torch.optim
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConvLayer, self).__init__()
self.in_channels = in_channel... | 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 typing import *
i... | code-backdoor/code-backdoor | ConvLayer | false | 15,055 | [
"MIT"
] | 71 | 1eeb3d79aa8a54c8f08e8d0156b569de5edd974e | https://github.com/code-backdoor/code-backdoor/tree/1eeb3d79aa8a54c8f08e8d0156b569de5edd974e |
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 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... | JoohyungLee0106/rectal_MR_volume_classification | FocalLoss | false | 665 | [
"MIT"
] | 0 | d2a7d13dae9fe7255b983cbc210567dd452a936f | https://github.com/JoohyungLee0106/rectal_MR_volume_classification/tree/d2a7d13dae9fe7255b983cbc210567dd452a936f |
GatedConv1d | # 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._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... | Raiselimit/TorchBlocks | GatedConv1d | false | 5,744 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
WeightedAverage | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
def find_local_patch(x, patch_size):
N, _C, H, W = x.shape
x_unfold = F.unfold(x, kernel_size=(patch_size, patch_size), padding=(
patch_size // 2, patch_size // 2), stride=(1, 1))
return x_unfold.view(N, x_... | 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
... | qiyuqianxai/debvc | WeightedAverage | false | 10,803 | [
"MIT"
] | 0 | 1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 | https://github.com/qiyuqianxai/debvc/tree/1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 |
PosNACLayer | # 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._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 collections
import torch.utils.data
assert_size_stride = torch._C._dynamo... | hoedt/stable-nalu | PosNACLayer | false | 3,606 | [
"MIT"
] | 0 | 64b3d240db8bff4da857d955f213ef3c7e38e035 | https://github.com/hoedt/stable-nalu/tree/64b3d240db8bff4da857d955f213ef3c7e38e035 |
Normalize | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | slyviacassell/Multi-taks-UNITE | Normalize | false | 4,358 | [
"MIT"
] | 0 | a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 | https://github.com/slyviacassell/Multi-taks-UNITE/tree/a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.parallel
impo... | EGO4D/episodic-memory | ResidualBlock | false | 8,070 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
HSwish | import torch
import torch.nn as nn
import torch.quantization
class HSigmoid(nn.Module):
"""Hard Sigmoid."""
def __init__(self, inplace: 'bool'=True) ->None:
"""Initialize."""
super(HSigmoid, self).__init__()
self.relu6 = nn.ReLU6(inplace=inplace)
def forward(self, x: 'torch.Tenso... | 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.quantization
assert_size_stride = torch._C._dynamo.gua... | dhlee347/model_compression | HSwish | false | 6,563 | [
"MIT"
] | 1 | 274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 | https://github.com/dhlee347/model_compression/tree/274b85ff56d81f0b7cf6907cbc1bd10e16cdb956 |
Discriminator | # 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._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.... | carsault/chord_sequence_prediction | Discriminator | false | 1,645 | [
"MIT"
] | 0 | 6eb539a963ca6350bcf0c88b8d8756775ad7c488 | https://github.com/carsault/chord_sequence_prediction/tree/6eb539a963ca6350bcf0c88b8d8756775ad7c488 |
ResBlock | # 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._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.... | JiwanChung/tapm | ResBlock | false | 8,388 | [
"MIT"
] | 14 | ec42b139d1c012daccc55f85e67744488d526476 | https://github.com/JiwanChung/tapm/tree/ec42b139d1c012daccc55f85e67744488d526476 |
CausalConv2d | # 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._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... | MioChiu/vqvae2 | CausalConv2d | false | 2,672 | [
"MIT"
] | 0 | e57cc7546d3bd02c61387367936f7cd76b75eaae | https://github.com/MioChiu/vqvae2/tree/e57cc7546d3bd02c61387367936f7cd76b75eaae |
Task | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.utils.data.distributed
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.dat... | lipovsek/bagua | Task | false | 12,718 | [
"MIT"
] | 0 | d8b03333ab6cf3745279311b9da76e99d5c2c00a | https://github.com/lipovsek/bagua/tree/d8b03333ab6cf3745279311b9da76e99d5c2c00a |
BboxHead | # 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._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 itertools import product as product
assert_size_strid... | FacePerceiver/facer | BboxHead | false | 8,131 | [
"MIT"
] | 12 | cbb01dc457f3713050e89af7b2c9c0d98663842c | https://github.com/FacePerceiver/facer/tree/cbb01dc457f3713050e89af7b2c9c0d98663842c |
Attention | import math
import torch
import torch.nn.functional as F
import torch.fx
import torch.utils.data
def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (mar... | 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.... | HWSelf/pytorch_geometric | Attention | false | 516 | [
"MIT"
] | 0 | c1214de674079b5e39e57c045d0f844b60caf590 | https://github.com/HWSelf/pytorch_geometric/tree/c1214de674079b5e39e57c045d0f844b60caf590 |
MySigmoidFocalLoss | # 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 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... | yuruiqi/FCOS | MySigmoidFocalLoss | false | 13,156 | [
"BSD-2-Clause"
] | 0 | f03f984a03f4e23a0c1c8b470e401d4319e56c3f | https://github.com/yuruiqi/FCOS/tree/f03f984a03f4e23a0c1c8b470e401d4319e56c3f |
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