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 |
|---|---|---|---|---|---|---|---|---|---|---|
injective_pad | import torch
import torch.nn as nn
class injective_pad(nn.Module):
def __init__(self, pad_size):
super(injective_pad, self).__init__()
self.pad_size = pad_size
self.pad = nn.ZeroPad2d((0, 0, 0, pad_size))
def forward(self, x):
x = x.permute(0, 2, 1, 3)
x = self.pad(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Arnakii/invertinggradients | injective_pad | false | 8,878 | [
"MIT"
] | 0 | c4f66fc9c73f0a18e9ddf01650c0e82fe3998013 | https://github.com/Arnakii/invertinggradients/tree/c4f66fc9c73f0a18e9ddf01650c0e82fe3998013 |
Swish | import torch
import torch.nn as nn
class Swish(nn.Module):
def forward(self, x):
return x.mul_(torch.sigmoid(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_mul_sigmoid_0(in_pt... | absallh/A_yolov3 | Swish | false | 18,206 | [
"Apache-2.0"
] | 6 | 550ec41de42b8efe638e887c51a568189947e049 | https://github.com/absallh/A_yolov3/tree/550ec41de42b8efe638e887c51a568189947e049 |
DiceLoss | import torch
import torch.nn as nn
import torch.optim
class DiceLoss(nn.Module):
def __init__(self, smooth=1.0):
super(DiceLoss, self).__init__()
self.smooth = smooth
def _dice_coeff(self, pred, target):
"""
Args:
pred: [N, 1] within [0, 1]
target: [N,... | 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... | Bobholamovic/SimpleCV | DiceLoss | false | 7,798 | [
"MIT"
] | 44 | f4edacf088d0155725a469e227de847820bdfa53 | https://github.com/Bobholamovic/SimpleCV/tree/f4edacf088d0155725a469e227de847820bdfa53 |
FeedForward_NN | import torch
import torch.nn as nn
class FeedForward_NN(nn.Module):
def __init__(self, input_size, hidden_layer, output_size):
super(FeedForward_NN, self).__init__()
self.layer1 = nn.Linear(input_size, hidden_layer)
self.relu = nn.ReLU()
self.layer2 = nn.Linear(hidden_layer, 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
assert_... | AqibJavaid899/PyTorch_Models | FeedForward_NN | false | 11,204 | [
"MIT"
] | 0 | cf81f6ef5d81aed76dca3f1a15be1a308b5d450f | https://github.com/AqibJavaid899/PyTorch_Models/tree/cf81f6ef5d81aed76dca3f1a15be1a308b5d450f |
CircleLoss | import torch
from typing import *
from torch import nn
import torch.nn.functional as F
from torch import functional as F
from torch.nn import functional as F
class CircleLoss(nn.Module):
"""CircleLoss from
`"Circle Loss: A Unified Perspective of Pair Similarity Optimization"
<https://arxiv.org/pdf/2002.10857>`_... | 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.... | JacobARose/image-utils | CircleLoss | false | 594 | [
"MIT"
] | 0 | aa0e005c0b4df5198d188b074f4e21f8d8f97962 | https://github.com/JacobARose/image-utils/tree/aa0e005c0b4df5198d188b074f4e21f8d8f97962 |
US | # 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 as nn
from torch.nn import init as init
from torch.utils im... | aesrgan/A-ESRGAN | US | false | 14,751 | [
"BSD-3-Clause"
] | 58 | e1a71deb4a47e332cad6b3d6bbbbb21a56bdd9c6 | https://github.com/aesrgan/A-ESRGAN/tree/e1a71deb4a47e332cad6b3d6bbbbb21a56bdd9c6 |
CausalConv1d | # 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | kschamplin/astro-classifier-neo | CausalConv1d | false | 3,861 | [
"MIT"
] | 0 | 44fcb8ba41ef549c16360df7fd470f56c42da9b3 | https://github.com/kschamplin/astro-classifier-neo/tree/44fcb8ba41ef549c16360df7fd470f56c42da9b3 |
RegressionModel | import torch
import torch.nn as nn
class RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=5, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReL... | 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_... | glhr/swig | RegressionModel | false | 3,557 | [
"MIT"
] | 0 | d6465862ae9adaab6594f79ec8eed211b5d7e4d8 | https://github.com/glhr/swig/tree/d6465862ae9adaab6594f79ec8eed211b5d7e4d8 |
RSoftmax | # 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
... | Bin-ze/Food_detection | RSoftmax | false | 16,991 | [
"Apache-2.0"
] | 4 | 1c1a067f12644f2b0289e49aec4637d580722f70 | https://github.com/Bin-ze/Food_detection/tree/1c1a067f12644f2b0289e49aec4637d580722f70 |
Splitter | # 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 nump... | balla2081/SpliiterPytorch | Splitter | false | 3,174 | [
"MIT"
] | 0 | 366742166470dc730fe761bae081779d737e1315 | https://github.com/balla2081/SpliiterPytorch/tree/366742166470dc730fe761bae081779d737e1315 |
ATLoss | # 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... | DA-southampton/KD_Lib | ATLoss | false | 5,057 | [
"MIT"
] | 1 | bd4a9b93b9674607ecf467d280d5cab1c516bdc6 | https://github.com/DA-southampton/KD_Lib/tree/bd4a9b93b9674607ecf467d280d5cab1c516bdc6 |
mlp_5layer | # 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_... | Mahoumaru/auto_LiRPA | mlp_5layer | false | 11,681 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
HDRLoss | import torch
import torch.nn as nn
class HDRLoss(nn.Module):
"""High dynamic range loss."""
def __init__(self, eps=0.01):
"""Initializes loss with numerical stability epsilon."""
super(HDRLoss, self).__init__()
self._eps = eps
def forward(self, denoised, target):
"""Compu... | 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... | CirilBohak/noise2noise-pytorch | HDRLoss | false | 8,908 | [
"MIT"
] | 0 | e517366248a62ce0b7e3710199b02b27261aa639 | https://github.com/CirilBohak/noise2noise-pytorch/tree/e517366248a62ce0b7e3710199b02b27261aa639 |
PixelNorm | import torch
import torch.nn as nn
import torch.utils.model_zoo
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=
True) + 1e-08)
def get_inputs():
return [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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._... | Aitical/ADspeech2face | PixelNorm | false | 4,790 | [
"MIT"
] | 1 | 2e811ff8cc7333729f4b77d1b1067296253e8e38 | https://github.com/Aitical/ADspeech2face/tree/2e811ff8cc7333729f4b77d1b1067296253e8e38 |
SpaceToDepth | import torch
import torch.optim
import torch.nn as nn
import torch.utils.data
class SpaceToDepth(nn.Module):
def __init__(self, block_size):
super(SpaceToDepth, self).__init__()
self.block_size = block_size
self.block_size_sq = block_size * block_size
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
import torch.optim
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | Merical/pytorch-superpoint | SpaceToDepth | false | 2,644 | [
"MIT"
] | 0 | b1f6e587b0f68a8a647773e4128b4f504edb4d58 | https://github.com/Merical/pytorch-superpoint/tree/b1f6e587b0f68a8a647773e4128b4f504edb4d58 |
ChamferLoss | # 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._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... | DeVriesMatt/pointMLP-pytorch | ChamferLoss | false | 557 | [
"Apache-2.0"
] | 0 | e9c09a2038551e83b072353f3fd7e3294463e892 | https://github.com/DeVriesMatt/pointMLP-pytorch/tree/e9c09a2038551e83b072353f3fd7e3294463e892 |
RingLoss | import torch
import torch.nn as nn
class RingLoss(nn.Module):
"""Ring loss.
Reference:
Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
"""
def __init__(self, weight_ring=1.0):
super(RingLoss, self).__init__()
self.radius = nn.Parameter(to... | 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_... | linkserendipity/AlignedReID | RingLoss | false | 3,912 | [
"MIT"
] | 0 | 142a9ebdc200ef4da001f91c1f592e4ff02b2f77 | https://github.com/linkserendipity/AlignedReID/tree/142a9ebdc200ef4da001f91c1f592e4ff02b2f77 |
NonLinearProbe3 | # 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... | PAL-ML/atari-representation-learning | NonLinearProbe3 | false | 2,802 | [
"MIT"
] | 0 | 11977da174d9ef74c0b2333322b9f0b28e15239e | https://github.com/PAL-ML/atari-representation-learning/tree/11977da174d9ef74c0b2333322b9f0b28e15239e |
Concat | # 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
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
assert_size_stride =... | Rohan-Chaudhury/aimet | Concat | false | 17,995 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a |
Swish | import torch
def swish(tensor: 'torch.Tensor', beta: 'float'=1.0) ->torch.Tensor:
"""
Applies Swish function element-wise.
See :class:`torchlayers.activations.Swish` for more details.
Arguments:
tensor :
Tensor activated element-wise
beta :
Multiplier used for... | 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... | klaudiapalasz/torchlayers | Swish | false | 15,843 | [
"MIT"
] | 573 | e6edd8797875325b7c0539d75a12f0d51f494127 | https://github.com/klaudiapalasz/torchlayers/tree/e6edd8797875325b7c0539d75a12f0d51f494127 |
LR | import torch
class LR(torch.nn.Module):
def __init__(self, input_size, output_size):
super(LR, self).__init__()
self.lr = torch.ones(input_size)
self.lr = torch.nn.Parameter(self.lr)
def forward(self, grad):
return self.lr * grad
def get_inputs():
return [torch.rand([4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | OliverWang-Au/learn2learn | LR | false | 5,677 | [
"MIT"
] | 1 | df3c3291b4681440a80a69a7815090a4bd3cd661 | https://github.com/OliverWang-Au/learn2learn/tree/df3c3291b4681440a80a69a7815090a4bd3cd661 |
SPPLayer | import torch
class SPPLayer(torch.nn.Module):
def __init__(self, level):
super(SPPLayer, self).__init__()
self.level = level
def forward(self, x):
_n, _c, _h, _w = x.size()
a = 6 + (self.level - 1) * -2
zero_pad = torch.nn.ZeroPad2d((a, a, a, a))
x = zero_pad(... | 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... | IrisDinge/YoloV3_DOTA | SPPLayer | false | 5,342 | [
"MIT"
] | 1 | cdfe6375a2323e9ee162e50a46478d8a66529e6c | https://github.com/IrisDinge/YoloV3_DOTA/tree/cdfe6375a2323e9ee162e50a46478d8a66529e6c |
ResMLPLayer | import torch
from torch import nn
from torch.nn import functional as F
class AffineTransform(nn.Module):
def __init__(self, num_features):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1, 1, num_features))
self.beta = nn.Parameter(torch.zeros(1, 1, num_features))
def forwar... | 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... | jaketae/res-mlp | ResMLPLayer | false | 12,597 | [
"MIT"
] | 0 | 6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd | https://github.com/jaketae/res-mlp/tree/6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd |
Normalization | # 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | liuzeyuMr/ELEGANT_cvte | Normalization | false | 3,926 | [
"MIT"
] | 0 | eb8039310023f91e25e37ff8d907844afd50e0a5 | https://github.com/liuzeyuMr/ELEGANT_cvte/tree/eb8039310023f91e25e37ff8d907844afd50e0a5 |
CosineFastRCNNOutputLayers | # 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.... | tonysy/cvpods | CosineFastRCNNOutputLayers | false | 16,611 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
CustomKLDivLoss | # 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
assert_size... | PiaCuk/KD_Lib | CustomKLDivLoss | false | 14,233 | [
"MIT"
] | 360 | 153299d484e4c6b33793749709dbb0f33419f190 | https://github.com/PiaCuk/KD_Lib/tree/153299d484e4c6b33793749709dbb0f33419f190 |
ResNetV2 | import torch
import torch.nn.functional as F
from collections import OrderedDict
import torch.nn as nn
def conv1x1(cin, cout, stride=1, bias=False):
return StdConv2d(cin, cout, kernel_size=1, stride=stride, padding=0,
bias=bias)
def conv3x3(cin, cout, stride=1, groups=1, bias=False):
return StdConv2... | 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.... | MetaMain/ViTRobust | ResNetV2 | false | 17,999 | [
"BSD-3-Clause"
] | 6 | 5bca523f430933469d9f82022e334839388cee7a | https://github.com/MetaMain/ViTRobust/tree/5bca523f430933469d9f82022e334839388cee7a |
DiaynDiscrimNet | import torch
import torch.nn as nn
from torch.nn.init import kaiming_uniform_
import torch.utils.data
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class DiaynDiscrimNet(nn.Module):
def __init__(self, f_spa... | 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 to... | KtechB/machina | DiaynDiscrimNet | false | 2,466 | [
"MIT"
] | 0 | 24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab | https://github.com/KtechB/machina/tree/24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab |
Accuracy | from torch.nn import Module
import torch
from torch import Tensor
class Accuracy(Module):
"""
Class for calculating the accuracy for a given prediction and the labels
for comparison.
Expects the inputs to be from a range of 0 to 1 and sets a crossing threshold at 0.5
the labels are similarly round... | 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
from torch import Tensor
assert_size_stride = torch._C._dynam... | neuralmagic/sparseml | Accuracy | false | 16,153 | [
"Apache-2.0"
] | 922 | 3fe5f4d75796eba43508401de4070cb494370683 | https://github.com/neuralmagic/sparseml/tree/3fe5f4d75796eba43508401de4070cb494370683 |
AttentionUnit | # 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.... | lohzhunyewcs/aster.pytorch | AttentionUnit | false | 10,484 | [
"MIT"
] | 0 | 9441d386135a73b1baa3ec8c505f5eba99c26905 | https://github.com/lohzhunyewcs/aster.pytorch/tree/9441d386135a73b1baa3ec8c505f5eba99c26905 |
CNN | # 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 import nn
assert_s... | jubueche/cleverhans | CNN | false | 10,322 | [
"MIT"
] | 0 | 2e45b75ccc7b04ffec27fd9e6079f00451586266 | https://github.com/jubueche/cleverhans/tree/2e45b75ccc7b04ffec27fd9e6079f00451586266 |
DistmultCenterSet | import torch
import torch.nn as nn
import torch.nn.functional as F
class DistmultCenterSet(nn.Module):
def __init__(self, dim, aggr=torch.max, nonlinear=True):
super(DistmultCenterSet, self).__init__()
self.dim = dim
self.layers = nn.Parameter(torch.zeros(self.dim * 4 + 4, self.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
import torch.nn as nn
assert_... | google-research/smore | DistmultCenterSet | false | 15,463 | [
"Apache-2.0"
] | 78 | e4ba95a7466ef7d018987bce7688b77bf2ea7e4f | https://github.com/google-research/smore/tree/e4ba95a7466ef7d018987bce7688b77bf2ea7e4f |
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.... | NikV-JS/DualVPRUtil | NetVLAD | false | 8,759 | [
"MIT"
] | 31 | 6533e21641faa9156db6e8d95bb5c51cc4b7d377 | https://github.com/NikV-JS/DualVPRUtil/tree/6533e21641faa9156db6e8d95bb5c51cc4b7d377 |
GAB | # 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 ... | Bhaskers-Blu-Org2/seismic-deeplearning | GAB | false | 158 | [
"MIT"
] | 0 | 15d45fb8c9cef463fd01fae2e087ba62c98cb799 | https://github.com/Bhaskers-Blu-Org2/seismic-deeplearning/tree/15d45fb8c9cef463fd01fae2e087ba62c98cb799 |
ProbabilityBilinear | # 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ashutosh1919/neuro-symbolic-sudoku-solver | ProbabilityBilinear | false | 14,907 | [
"Apache-2.0"
] | 52 | ecb4274ff66d3b6a86f64584e0a767bf785f107f | https://github.com/ashutosh1919/neuro-symbolic-sudoku-solver/tree/ecb4274ff66d3b6a86f64584e0a767bf785f107f |
SMOEScaleMap | import torch
import torch.nn as nn
import torch.autograd
class SMOEScaleMap(nn.Module):
"""
Compute SMOE Scale on a 4D tensor. This acts as a standard PyTorch layer.
SMOE Scale is computed independantly for each batch item at each location x,y
Input should be:
... | 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.autograd
assert_size_stride = torch._C._dyna... | LLNL/fastcam | SMOEScaleMap | false | 8,423 | [
"BSD-3-Clause"
] | 25 | 99cefe37528014247319468cf05f54fef259d3bf | https://github.com/LLNL/fastcam/tree/99cefe37528014247319468cf05f54fef259d3bf |
gaussian_layer | import math
import torch
import torch.nn as nn
class gaussian_downsample(nn.Module):
"""
Downsampling module with Gaussian filtering
"""
def __init__(self, kernel_size, sigma, stride, pad=False):
super(gaussian_downsample, self).__init__()
self.gauss = nn.Conv2d(3, 3, kernel_size, 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
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | Xmaster6y/wgenpatex | gaussian_layer | false | 18,120 | [
"MIT"
] | 8 | 08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 | https://github.com/Xmaster6y/wgenpatex/tree/08079dc131cc2e9c74ee4f9e16cf9b58667f2b07 |
ConvLayer | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, norm
='instance'):
super(ConvLayer, self).__init__()
padding_size = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(padding_size)
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.triton_helpers import libdevice, math as tl_math
im... | suryawanshishantanu6/Multi-Style-Transfer | ConvLayer | false | 10,855 | [
"MIT"
] | 0 | c5c211847de676596580a8a9afda940ac76abbb1 | https://github.com/suryawanshishantanu6/Multi-Style-Transfer/tree/c5c211847de676596580a8a9afda940ac76abbb1 |
SimpleCumSumModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCumSumModule(torch.nn.Module):
def __init__(self, dim):
super(SimpleCumSumModule, self).__init__()
self.dim = dim
def forward(self, tensor):
return torch.cumsum(tensor, self.dim)
def get_inputs():
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
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 | SimpleCumSumModule | false | 3,319 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
DQN | # 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 random
import torch.nn... | Gromy1211/torch-light | DQN | false | 11,453 | [
"MIT"
] | 0 | c7d7a9bc5ab1eab03d800a27d9325859516f01e6 | https://github.com/Gromy1211/torch-light/tree/c7d7a9bc5ab1eab03d800a27d9325859516f01e6 |
BasicModel_MaxPool_ReLU | # 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 | BasicModel_MaxPool_ReLU | false | 10,090 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
NasMaxPoolBlock | # 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | HyperGAN/imgclsmob | NasMaxPoolBlock | false | 17,684 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
ConcatBlock | import torch
import torch.nn as nn
class ConcatBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConcatBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns, kernel_size=1,
... | 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... | tea321000/SSL4MIS | ConcatBlock | false | 16,537 | [
"MIT"
] | 854 | 8d1b0be08cf089943481a47877b36eb6405fffb2 | https://github.com/tea321000/SSL4MIS/tree/8d1b0be08cf089943481a47877b36eb6405fffb2 |
InnerProductProbe | # 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... | daemon/vizbert | InnerProductProbe | false | 1,766 | [
"MIT"
] | 0 | e40b7d1529f8857050313f8d87ff03b1b7226c9e | https://github.com/daemon/vizbert/tree/e40b7d1529f8857050313f8d87ff03b1b7226c9e |
GAT | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
def __init__(self, in_feature, out_feature, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__()
self.dropout = dropout
self.in_feature = in_feature
self.o... | 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.... | Alienge/Graph-Network | GAT | false | 16,924 | [
"MIT"
] | 3 | 559cccb6af4e6ca50c44fd51cac8df5713f255bf | https://github.com/Alienge/Graph-Network/tree/559cccb6af4e6ca50c44fd51cac8df5713f255bf |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _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.... | adriaciurana/udacity-project-3 | Actor | false | 9,677 | [
"MIT"
] | 0 | 806f78e35a6699eeb0a3272e326d0edc199d16be | https://github.com/adriaciurana/udacity-project-3/tree/806f78e35a6699eeb0a3272e326d0edc199d16be |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.conv1 = nn.Conv2d(2, 4, kernel_size=6, stride=2, bias=False)
self.conv2 = nn.Conv2d(4, 16, kernel_size=6, stride=4)
self.size = 9 * 9... | 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_... | aklein1995/Pong_REINFORCE | Policy | false | 18,252 | [
"MIT"
] | 4 | 3b57777fd3ab6e98c8a1191989bd65140e19fc6e | https://github.com/aklein1995/Pong_REINFORCE/tree/3b57777fd3ab6e98c8a1191989bd65140e19fc6e |
SimplePowModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimplePowModule | false | 3,352 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
BertAttention | # 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.... | shubham-gupta-iitr/mmmlX | BertAttention | false | 4,339 | [
"Apache-2.0"
] | 0 | 3485e6191e0e45bf1c8168e4e928a36ab9264d22 | https://github.com/shubham-gupta-iitr/mmmlX/tree/3485e6191e0e45bf1c8168e4e928a36ab9264d22 |
DNN | # 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.... | AmberLJC/Fluid | DNN | false | 8,769 | [
"Apache-2.0"
] | 12 | 85dee374eb2a1c96fecea83d5484ad83d1739e95 | https://github.com/AmberLJC/Fluid/tree/85dee374eb2a1c96fecea83d5484ad83d1739e95 |
DPDALayear | # 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.... | nju-websoft/Jeeves | DPDALayear | false | 12,838 | [
"Apache-2.0"
] | 0 | 6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 | https://github.com/nju-websoft/Jeeves/tree/6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 |
AsymmetricLossOptimized | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _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... | LanXiangExcavator/challenge2021_submission_4 | AsymmetricLossOptimized | false | 762 | [
"BSD-2-Clause"
] | 0 | ca0d4d4dd219119f7dc46464c92062ecdb7f9c49 | https://github.com/LanXiangExcavator/challenge2021_submission_4/tree/ca0d4d4dd219119f7dc46464c92062ecdb7f9c49 |
BahdanauAttention | # 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.... | manhtrantienhn/Sentiment-with-pretrain-model | BahdanauAttention | false | 7,161 | [
"MIT"
] | 1 | bbbbaa94cf481afcfe704cbcb27b602308f43de5 | https://github.com/manhtrantienhn/Sentiment-with-pretrain-model/tree/bbbbaa94cf481afcfe704cbcb27b602308f43de5 |
SpatialAttn | import torch
from torch import nn
class SpatialAttn(nn.Module):
"""Spatial Attention Layer"""
def __init__(self):
super(SpatialAttn, self).__init__()
def forward(self, x):
x = x.mean(1, keepdim=True)
h = x.size(2)
w = x.size(3)
x = x.view(x.size(0), -1)
z ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | johnzhang1999/Spatial-Attention | SpatialAttn | false | 15,730 | [
"MIT"
] | 228 | 9e8e90ba624e52dcccba47c7289bb305765f5da6 | https://github.com/johnzhang1999/Spatial-Attention/tree/9e8e90ba624e52dcccba47c7289bb305765f5da6 |
MaskL1Loss | import torch
from torch import nn
class MaskL1Loss(nn.Module):
def __init__(self, eps=1e-06):
super(MaskL1Loss, self).__init__()
self.eps = eps
def forward(self, pred: 'torch.Tensor', gt, mask):
loss = (torch.abs(pred - gt) * mask).sum() / (mask.sum() + self.eps)
return loss
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | LDOUBLEV/DBNet.pytorch | MaskL1Loss | false | 9,423 | [
"Apache-2.0"
] | 0 | 206f4a1e5cc3686284476f029a26fc69f610e898 | https://github.com/LDOUBLEV/DBNet.pytorch/tree/206f4a1e5cc3686284476f029a26fc69f610e898 |
MaskedBCE | import torch
import torch.nn as nn
class MaskedBCE(nn.Module):
def __init__(self):
super(MaskedBCE, self).__init__()
self.criterion = nn.BCELoss()
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ngerstle/soccerontable | MaskedBCE | false | 16,166 | [
"BSD-2-Clause"
] | 465 | 25426ff0f8fe0ce008b99c5c0fdbb35091d8d92c | https://github.com/ngerstle/soccerontable/tree/25426ff0f8fe0ce008b99c5c0fdbb35091d8d92c |
PyConv4 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1,
dilation=1, groups=1):
"""standard convolution with padding"""
return nn.Conv2d(in_planes, out_plan... | 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.parallel
import torch.optim
import torch.u... | lkf59553/pyconv | PyConv4 | false | 15,946 | [
"MIT"
] | 295 | d8b39cf43014b8fd277dcefc9eb7f8880511e977 | https://github.com/lkf59553/pyconv/tree/d8b39cf43014b8fd277dcefc9eb7f8880511e977 |
GlobalAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _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.... | ChenRocks/Distill-BERT-Textgen-ONMT | GlobalAttention | false | 17,113 | [
"MIT"
] | 7 | d83dd1a95af7513cbfae4a2768f6effc2f3a589f | https://github.com/ChenRocks/Distill-BERT-Textgen-ONMT/tree/d83dd1a95af7513cbfae4a2768f6effc2f3a589f |
elu_modified | import torch
import torch.nn as nn
import torch.utils.data
class elu_modified(nn.Module):
def __init__(self, alpha=1.0, shift=5.0, epsilon=1e-07):
super(elu_modified, self).__init__()
self.alpha = alpha
self.shift = shift
self.epsilon = epsilon
self.elu = nn.ELU(alpha=alph... | 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... | aasensio/umal_pytorch | elu_modified | false | 9,634 | [
"MIT"
] | 0 | 17bf1fee006c26dc277eb31f22aee022246c0367 | https://github.com/aasensio/umal_pytorch/tree/17bf1fee006c26dc277eb31f22aee022246c0367 |
NeuralNet | # 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.... | mohiitgupta/named-entity-recognition-nlp-purdue | NeuralNet | false | 7,267 | [
"MIT"
] | 1 | 68232bbd5d17f3e3989e5df37175cdc670896608 | https://github.com/mohiitgupta/named-entity-recognition-nlp-purdue/tree/68232bbd5d17f3e3989e5df37175cdc670896608 |
LogCoshWithIgnore | # 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... | drivendataorg/DrivenData-2021-Geopose-Solution | LogCoshWithIgnore | false | 6,603 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
PoolFormerBlock | import torch
from torch import Tensor
from torch import nn
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Copied from timm
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original na... | 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.... | sithu31296/image_classification | PoolFormerBlock | false | 16,476 | [
"MIT"
] | 57 | 6b8cbce96100225621cee3166a73e852ba216cc3 | https://github.com/sithu31296/image_classification/tree/6b8cbce96100225621cee3166a73e852ba216cc3 |
nnNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
class nnNorm(nn.Module):
def __init__(self, dim=-1):
super().__init__()
self.dim = dim
def forward(self, x):
return F.normalize(x, dim=self.dim)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | learning-group-structure/paper | nnNorm | false | 3,882 | [
"MIT"
] | 0 | 96abf7e25cb7e95f45d6eb025257c0ba9e22fc55 | https://github.com/learning-group-structure/paper/tree/96abf7e25cb7e95f45d6eb025257c0ba9e22fc55 |
BinModel | # 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_... | amperie/user-models | BinModel | false | 3,100 | [
"Apache-2.0"
] | 0 | 5236c50d0f20a7bac81acc5d1936a3502de2f5f3 | https://github.com/amperie/user-models/tree/5236c50d0f20a7bac81acc5d1936a3502de2f5f3 |
TripletLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class TripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, margin):
super(TripletLoss, self).__init__()
self.margin = margin
... | 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... | AytacKahveci/siamese-triplet | TripletLoss | false | 11,229 | [
"BSD-3-Clause"
] | 0 | 09860e36d934bb1773a4d49dbad183a5152cb0b0 | https://github.com/AytacKahveci/siamese-triplet/tree/09860e36d934bb1773a4d49dbad183a5152cb0b0 |
BiencoderLoss | # 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._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.... | Eric-Yang-yz/dense_passage_retrieval | BiencoderLoss | false | 394 | [
"MIT"
] | 0 | 5d6e210d5ff3304b4891c94edd2bd2aee5b34655 | https://github.com/Eric-Yang-yz/dense_passage_retrieval/tree/5d6e210d5ff3304b4891c94edd2bd2aee5b34655 |
IOULoss | import torch
import torch.nn as nn
class IOULoss(nn.Module):
def __init__(self, eps: 'float'=1e-06):
super(IOULoss, self).__init__()
self.eps = eps
def forward(self, predict, target):
assert predict.shape[0] == target.shape[0
], 'Predict and target must be same shape'
... | 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... | ZiyunClaudeWang/e3d | IOULoss | false | 18,207 | [
"MIT"
] | 9 | 2efd01167350c29423babb6233907fa54156268f | https://github.com/ZiyunClaudeWang/e3d/tree/2efd01167350c29423babb6233907fa54156268f |
AveragePoolingLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _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... | Hsintien-Ng/idinvert_pytorch-reproduced | AveragePoolingLayer | false | 8,239 | [
"MIT"
] | 20 | cf3302510573138cf16202add06feae7c93624ea | https://github.com/Hsintien-Ng/idinvert_pytorch-reproduced/tree/cf3302510573138cf16202add06feae7c93624ea |
FirstBlock | # 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 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 |
MeanReweightLayer | # 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 as nn
import torch.nn.parallel
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_... | SanderKlomp/channel-attention | MeanReweightLayer | false | 9,500 | [
"MIT"
] | 0 | 9dfdb28f3ad4de13b4c076d1423f21c05c907bd7 | https://github.com/SanderKlomp/channel-attention/tree/9dfdb28f3ad4de13b4c076d1423f21c05c907bd7 |
MaxPool | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
class MaxPool(nn.Module):
"""1-d max-pooling module."""
def __init__(self, stride=None, padding=0, dilation=1):
super(MaxPool, self).__init__()
self.stride = stride
self.padding = 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
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards... | LindaCY/fastNLP | MaxPool | false | 17,612 | [
"Apache-2.0"
] | 4 | 3fa95b6cfc31211453bc21792e3eef87948858da | https://github.com/LindaCY/fastNLP/tree/3fa95b6cfc31211453bc21792e3eef87948858da |
GramMatrix | import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
a, b, c, d = input.size()
features = input.view(a * b, c * d)
G = torch.mm(features, features.t())
return G.div(a * b * c * d)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ... | 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... | asapypy/mokumokuTorch | GramMatrix | false | 1,477 | [
"MIT"
] | 0 | ec59877b407a3f0ac1a9627ea5609698f2979278 | https://github.com/asapypy/mokumokuTorch/tree/ec59877b407a3f0ac1a9627ea5609698f2979278 |
AttMseLoss | # 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... | lonePatient/TorchBlocks | AttMseLoss | false | 15,947 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
ProjectionHead | import torch
from torch import nn as nn
class ProjectionHead(nn.Module):
def __init__(self, embedding_dim, projection_dim, dropout):
super().__init__()
self.projection = nn.Linear(embedding_dim, projection_dim)
self.gelu = nn.GELU()
self.fc = nn.Linear(projection_dim, projection_d... | 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... | jianzhnie/MultimodalTransformer | ProjectionHead | false | 6,963 | [
"Apache-2.0"
] | 1 | 6cd4ca8034a53da361149745aecead68fbe304a0 | https://github.com/jianzhnie/MultimodalTransformer/tree/6cd4ca8034a53da361149745aecead68fbe304a0 |
ConvCIFAR | # 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.... | mnguyen0226/soo_non_convex_ml | ConvCIFAR | false | 10,556 | [
"MIT"
] | 0 | 2ffbedbe5eb536e017c643f725cc08551a5b1e9f | https://github.com/mnguyen0226/soo_non_convex_ml/tree/2ffbedbe5eb536e017c643f725cc08551a5b1e9f |
MultiHeadAttentionBlock | import math
import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out... | 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.... | EGO4D/episodic-memory | MultiHeadAttentionBlock | false | 8,121 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
MultiHeadAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model... | 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.... | Blickwinkel1107/NJUNMT-pytorch | MultiHeadAttn | false | 17,038 | [
"MIT"
] | 9 | 82f684fe768b137ca0649b7b79a1820077917385 | https://github.com/Blickwinkel1107/NJUNMT-pytorch/tree/82f684fe768b137ca0649b7b79a1820077917385 |
upconv | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.utils.data.distributed
class upconv(nn.Module):
def __init__(self, in_channels, out_channels, ratio=2):
super(upconv, self).__init__()
self.elu = nn.ELU()
self.conv = nn.Conv2d(in_channels=in_channels, 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.triton_helpers import libdevice
import torch.nn as ... | syKevinPeng/TransDepth | upconv | false | 16,517 | [
"MIT"
] | 118 | 2282039da7bc0812e19a27b2d73a25bdef97d739 | https://github.com/syKevinPeng/TransDepth/tree/2282039da7bc0812e19a27b2d73a25bdef97d739 |
SAModule_Head | # 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.... | Yuuchuin/C3_V2 | SAModule_Head | false | 6,026 | [
"MIT"
] | 1 | 92a5edbc2c2b3452c5f57e74f928591192293e81 | https://github.com/Yuuchuin/C3_V2/tree/92a5edbc2c2b3452c5f57e74f928591192293e81 |
Hswish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hswish(nn.Module):
def __init__(self, inplace=True):
super(Hswish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
re... | 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... | IgorDavidyuk/pytorch-mobilenet-v3 | Hswish | false | 2,360 | [
"Apache-2.0"
] | 0 | 48678f80d9390b530cb97966db492cf01d1c4a43 | https://github.com/IgorDavidyuk/pytorch-mobilenet-v3/tree/48678f80d9390b530cb97966db492cf01d1c4a43 |
QNetwork | # 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_... | kiranprasad/multiagent-particle-envs | QNetwork | false | 3,843 | [
"MIT"
] | 0 | e28e3ff6606e80f11ee16bb2c42f21c442ad29a8 | https://github.com/kiranprasad/multiagent-particle-envs/tree/e28e3ff6606e80f11ee16bb2c42f21c442ad29a8 |
DotProductLoss | # 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... | ehsanik/dogTorch | DotProductLoss | false | 15,282 | [
"MIT"
] | 74 | 3a898862f6283e6603833991eeb62427216f2af7 | https://github.com/ehsanik/dogTorch/tree/3a898862f6283e6603833991eeb62427216f2af7 |
DirectEncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
def __init__(self, d_model, eps=1e-05):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(d_model))
self.b_2 = nn.Parameter(torch.zeros(d_model))
self.eps = eps
d... | 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.... | wukevin/RoseTTAFold | DirectEncoderLayer | false | 4,593 | [
"MIT"
] | 0 | e3c15dbf4bc1e4f8726e26c63aca1625188da803 | https://github.com/wukevin/RoseTTAFold/tree/e3c15dbf4bc1e4f8726e26c63aca1625188da803 |
Conv2d_GN_ReLUx2 | # 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.... | Guangyun-Xu/uois | Conv2d_GN_ReLUx2 | false | 13,744 | [
"MIT"
] | 106 | 00069af841dd3ea9a86e6e3a89c3b7222240e6e5 | https://github.com/Guangyun-Xu/uois/tree/00069af841dd3ea9a86e6e3a89c3b7222240e6e5 |
CombinedTargetMSELoss | # 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... | ZephyrII/mmpose_charger | CombinedTargetMSELoss | false | 12,024 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd |
HidingRes | # 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.... | Hwihuni/Deep-Model-Watermarking | HidingRes | false | 682 | [
"MIT"
] | 0 | 73ea2286ace0aac3d55f6056da38ea2bc38ed00d | https://github.com/Hwihuni/Deep-Model-Watermarking/tree/73ea2286ace0aac3d55f6056da38ea2bc38ed00d |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
"""Sørensen–Dice coefficient loss to calculate
the mean loss over a batch of data.This loss mainly
calculates the similarity between two samples.
To know more about this loss check this link:
https://en.wikipedia.org/wiki/S%C3%B8rensen%... | 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... | NajusAnaxi/UNet-based-for-Brain-Tumor-Segmentation | DiceLoss | false | 11,734 | [
"MIT"
] | 0 | 24ca4432873f145ad33810f40c851ac10bf030fa | https://github.com/NajusAnaxi/UNet-based-for-Brain-Tumor-Segmentation/tree/24ca4432873f145ad33810f40c851ac10bf030fa |
Mutan | import torch
from torch import nn
from torch.nn import functional as F
class Mutan(nn.Module):
def __init__(self, input_dims, output_dim, mm_dim=1600, rank=15, shared
=False, normalize=False, dropout_input=0.0, dropout_pre_lin=0.0,
dropout_output=0.0):
super(Mutan, self).__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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | AndresPMD/GCN_classification | Mutan | false | 7,931 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
AdaptiveConcatPool2d | # 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.utils.data
import torch.nn as nn
import torch.backends.cudnn
assert_size_str... | CalebEverett/fastai-dl2 | AdaptiveConcatPool2d | false | 17,161 | [
"Apache-2.0"
] | 4 | 64d23592eddca6ca1f3647e73c319e97c8eb392b | https://github.com/CalebEverett/fastai-dl2/tree/64d23592eddca6ca1f3647e73c319e97c8eb392b |
TotalVariations | # 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
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dy... | wizofe/urus-mri-recon | TotalVariations | false | 4,540 | [
"MIT"
] | 0 | eab8e48dca31d2b936ce69ccc251ec5a4a10facc | https://github.com/wizofe/urus-mri-recon/tree/eab8e48dca31d2b936ce69ccc251ec5a4a10facc |
Patch2Image | import torch
from torch import nn
class Patch2Image(nn.Module):
""" take in patch and copy n_up times to form the full image"""
def __init__(self, patch_sz, n_up):
super(Patch2Image, self).__init__()
self.patch_sz = patch_sz
self.n_up = n_up
def forward(self, x):
assert 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | vitskvara/shape-guided-anomaly-detection | Patch2Image | false | 4,498 | [
"MIT"
] | 0 | 6685b2e0b97968a6d0f478d2920486da107b277f | https://github.com/vitskvara/shape-guided-anomaly-detection/tree/6685b2e0b97968a6d0f478d2920486da107b277f |
ChannelPool | # 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
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.... | HolmesShuan/AIM2020-Real-Super-Resolution | ChannelPool | false | 8,251 | [
"BSD-2-Clause"
] | 19 | 0ea4d7db0f4f7ed488cc162b90bb08fc02082106 | https://github.com/HolmesShuan/AIM2020-Real-Super-Resolution/tree/0ea4d7db0f4f7ed488cc162b90bb08fc02082106 |
CrossAttention | # 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.... | JosephChenHub/DPANet | CrossAttention | false | 8,415 | [
"MIT"
] | 19 | 68cf40a405d8c8c6506884079cd0a206d6d58e63 | https://github.com/JosephChenHub/DPANet/tree/68cf40a405d8c8c6506884079cd0a206d6d58e63 |
AttentionUnit | import torch
import torch.nn as nn
def calc_mean_std(feat, eps=1e-05):
size = feat.size()
assert len(size) == 4
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return 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.... | sugi-san/PAMA | AttentionUnit | false | 13,009 | [
"MIT"
] | 0 | 95141ebf0d3b61828a0e545f989f96b8ef569f34 | https://github.com/sugi-san/PAMA/tree/95141ebf0d3b61828a0e545f989f96b8ef569f34 |
TemporalFusion | # 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... | juyongjiang/Simple-SR | TemporalFusion | false | 7,019 | [
"MIT"
] | 1 | 76820511abc04fbe6e4a79d23c67aee97406d563 | https://github.com/juyongjiang/Simple-SR/tree/76820511abc04fbe6e4a79d23c67aee97406d563 |
GLU | import torch
from torch import nn
class GLU(nn.Module):
"""
The Gated Linear Unit GLU(a,b) = mult(a,sigmoid(b)) is common in NLP
architectures like the Gated CNN. Here sigmoid(b) corresponds to a gate
that controls what information from a is passed to the following layer.
Args:
... | 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... | KalleBylin/tft_webapp | GLU | false | 9,232 | [
"Apache-2.0"
] | 0 | 008f109e77f8bada417655dab482f340adb8cb6b | https://github.com/KalleBylin/tft_webapp/tree/008f109e77f8bada417655dab482f340adb8cb6b |
NoiseInjection | import torch
import torch.nn as nn
import torch.nn.parallel
class NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(0.01 * torch.randn(1, channel, 1, 1))
def forward(self, feat, noise=None):
if noise is None:
noise = tor... | 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
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | rakshithShetty/SemanticAdversary | NoiseInjection | false | 7,526 | [
"MIT"
] | 1 | e6d50f00af6f7d847cba4210613afea4be773254 | https://github.com/rakshithShetty/SemanticAdversary/tree/e6d50f00af6f7d847cba4210613afea4be773254 |
ActorDeep | # 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.... | KuangenZhang/StructuredRL | ActorDeep | false | 5,465 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a |
BasicModel | # 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... | ngduduong/captum | BasicModel | false | 4,064 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
InteractiveKLLoss | # 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... | HarshCasper/nni | InteractiveKLLoss | false | 5,264 | [
"MIT"
] | 1 | 291bbbba9f296382015a77b2c88eb5db5b44bf94 | https://github.com/HarshCasper/nni/tree/291bbbba9f296382015a77b2c88eb5db5b44bf94 |
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