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 |
|---|---|---|---|---|---|---|---|---|---|---|
LogitCosineDistance | import torch
import torch.utils.data.dataloader
import torch.nn
def dot_product(a: 'torch.Tensor', b: 'torch.Tensor', normalize=False):
"""
Computes dot product for pairs of vectors.
:param normalize: Vectors are normalized (leads to cosine similarity)
:return: Matrix with res[i][j] = dot_product(a[i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | adriensas/flair | LogitCosineDistance | false | 9,745 | [
"MIT"
] | 0 | f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 | https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 |
ChannelNorm | import torch
import torch.nn as nn
import torch._utils
import torch.optim
class ChannelNorm(nn.Module):
def __init__(self):
super(ChannelNorm, self).__init__()
def forward(self, featmap):
n, c, _h, _w = featmap.shape
featmap = featmap.reshape((n, c, -1))
featmap = featmap.sof... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | yubin1219/Semantic-Seg | ChannelNorm | false | 4,633 | [
"BSD-2-Clause"
] | 0 | c40bd43d3d7e44bc995b8d041736580dec084251 | https://github.com/yubin1219/Semantic-Seg/tree/c40bd43d3d7e44bc995b8d041736580dec084251 |
ScModel | import torch
import torch as t
import torch.nn as nn
from torch.nn.parameter import Parameter
class ScModel(nn.Module):
""" Model for singel cell data """
def __init__(self, n_genes: 'int', n_celltypes: 'int', device: 't.device'
) ->None:
super().__init__()
self.K = n_celltypes
... | 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... | almaan/STereoSCope | ScModel | false | 1,417 | [
"MIT"
] | 0 | 8f6a2021b6cb73aecda14f6bbbd25e26bfc9301a | https://github.com/almaan/STereoSCope/tree/8f6a2021b6cb73aecda14f6bbbd25e26bfc9301a |
FakeRKHSConvNet | import math
import torch
import numpy as np
from torch import nn
class MaybeBatchNorm2d(nn.Module):
def __init__(self, n_ftr, affine, use_bn):
super().__init__()
self.bn = nn.BatchNorm2d(n_ftr, affine=affine)
self.use_bn = use_bn
def forward(self, x):
if self.use_bn:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Benjamin-Etheredge/lightning-bolts | FakeRKHSConvNet | false | 163 | [
"Apache-2.0"
] | 0 | 1971d6a924729940b98793aa7751bdf769350aca | https://github.com/Benjamin-Etheredge/lightning-bolts/tree/1971d6a924729940b98793aa7751bdf769350aca |
GELU | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
import torch.hub
class GELU(nn.Module):
"""
Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
"""
def forward(self, 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.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | EddieMG/LateTemporalModeling3DCNN | GELU | false | 2,265 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
Router | # 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 warnings
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guar... | yulinfeng000/AdaptiveNeuralTrees | Router | false | 13,153 | [
"MIT"
] | 0 | bbcb381b9cb0c91ae1af33ce43b43f352055041c | https://github.com/yulinfeng000/AdaptiveNeuralTrees/tree/bbcb381b9cb0c91ae1af33ce43b43f352055041c |
ConConv | import torch
import torch.nn as nn
class ConConv(nn.Module):
def __init__(self, inplanes_x1, inplanes_x2, planes):
super(ConConv, self).__init__()
self.conv = nn.Conv2d(inplanes_x1 + inplanes_x2, planes,
kernel_size=1, bias=True)
def forward(self, x1, x2):
x1 = torch.cat(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | karoly-hars/DE_hybrid_CNN | ConConv | false | 12,659 | [
"BSD-3-Clause"
] | 0 | d74ba4291d6db335151d5262ab96e8e3806a7587 | https://github.com/karoly-hars/DE_hybrid_CNN/tree/d74ba4291d6db335151d5262ab96e8e3806a7587 |
LevelVariabilityLoss | # 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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | venkatkorapaty/esrnn | LevelVariabilityLoss | false | 11,011 | [
"MIT"
] | 0 | 411d3191e7e12f29e521e06bc18f9b9b0fdf0f0c | https://github.com/venkatkorapaty/esrnn/tree/411d3191e7e12f29e521e06bc18f9b9b0fdf0f0c |
MSBlock | # 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_... | Ding1119/BDCN-Fiber_Detect | MSBlock | false | 9,017 | [
"MIT"
] | 0 | 7f3db5210a1a87d02c7ef8e79038ba00a8e5ef62 | https://github.com/Ding1119/BDCN-Fiber_Detect/tree/7f3db5210a1a87d02c7ef8e79038ba00a8e5ef62 |
AngleLoss | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from typing import *
class AngleLoss(nn.Module):
def __init__(self):
super(AngleLoss, self).__init__()
def forward(self, angle, an... | 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
i... | DeVriesMatt/pointMLP-pytorch | AngleLoss | false | 365 | [
"Apache-2.0"
] | 0 | e9c09a2038551e83b072353f3fd7e3294463e892 | https://github.com/DeVriesMatt/pointMLP-pytorch/tree/e9c09a2038551e83b072353f3fd7e3294463e892 |
_DenseBlockInput | # 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... | KyleDavisSA/pde-surrogate | _DenseBlockInput | false | 13,972 | [
"MIT"
] | 62 | 41ad2c9eb73c323e389174080f4b3df6cbd3c900 | https://github.com/KyleDavisSA/pde-surrogate/tree/41ad2c9eb73c323e389174080f4b3df6cbd3c900 |
GCN | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.3):
super(GraphConvolution, self).__init__()
self.in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Yottaxx/T-LSTM | GCN | false | 18,158 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 |
Conv1dSamePadding | import torch
import torch.nn.functional as F
import torch.nn as nn
class Conv1dSamePadding(nn.Conv1d):
"""
1D convolutional layer with "same" padding (no downsampling),
that is also compatible with strides > 1
"""
def __init__(self, *args, **kwargs):
super(Conv1dSamePadding, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Wadaboa/titanet | Conv1dSamePadding | false | 18,073 | [
"MIT"
] | 4 | b07e3074e79ea8c1129fb0adb8315e06bb4943ea | https://github.com/Wadaboa/titanet/tree/b07e3074e79ea8c1129fb0adb8315e06bb4943ea |
LinearNormalGamma | # 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 math as tl_math
from torch im... | wanzysky/evidential-deep-learning | LinearNormalGamma | false | 4,524 | [
"Apache-2.0"
] | 0 | 71ebd59ab3a4b66c38d919e8aa9ad3711a416796 | https://github.com/wanzysky/evidential-deep-learning/tree/71ebd59ab3a4b66c38d919e8aa9ad3711a416796 |
MyLinear | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
super().__init_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | NeuralBending/StyleCLIP | MyLinear | false | 14,094 | [
"MIT"
] | 91 | 190d3a0d48823ccdbdd15c7f8af6e08703a6dbd8 | https://github.com/NeuralBending/StyleCLIP/tree/190d3a0d48823ccdbdd15c7f8af6e08703a6dbd8 |
CSAM | import torch
import torch.nn as nn
class CSAM(nn.Module):
"""
Compact Spatial Attention Module
"""
def __init__(self, channels):
super(CSAM, self).__init__()
mid_channels = 4
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ZitongYu/pidinet | CSAM | false | 18,251 | [
"MIT"
] | 5 | 15cdf9fb056549934877675bf7571b427f86db55 | https://github.com/ZitongYu/pidinet/tree/15cdf9fb056549934877675bf7571b427f86db55 |
LastBlock | 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... | Hsintien-Ng/idinvert_pytorch-reproduced | LastBlock | false | 8,257 | [
"MIT"
] | 20 | cf3302510573138cf16202add06feae7c93624ea | https://github.com/Hsintien-Ng/idinvert_pytorch-reproduced/tree/cf3302510573138cf16202add06feae7c93624ea |
ClassHead | # 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 itertools import product as product
import torch.nn as nn
assert_size_strid... | BossunWang/Pytorch_Retinaface | ClassHead | false | 8,983 | [
"MIT"
] | 0 | 01ec6cfbcced1e8cc8802084e4e566ccaf2513a8 | https://github.com/BossunWang/Pytorch_Retinaface/tree/01ec6cfbcced1e8cc8802084e4e566ccaf2513a8 |
SineGen | import torch
import numpy as np
import torch.utils.data
import torch.nn as torch_nn
class SineGen(torch_nn.Module):
""" Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.utils... | Ninushkat/Impact-Synth-Hardware | SineGen | false | 14,108 | [
"MIT"
] | 55 | 37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 | https://github.com/Ninushkat/Impact-Synth-Hardware/tree/37a2ecfec51b052b39d1ad0d4676f09d5f00e3c2 |
FCLayer | import torch
import torch.nn as nn
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.0,
use_activation=True):
super(FCLayer, self).__init__()
self.use_activation = use_activation
self.dropout = nn.Dropout(dropout_rate)
self.linear = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | JaeheeRyu/R-BERT | FCLayer | false | 13,857 | [
"Apache-2.0"
] | 246 | 0f9048a1612a77a0a920e6fe2349430c7f608d77 | https://github.com/JaeheeRyu/R-BERT/tree/0f9048a1612a77a0a920e6fe2349430c7f608d77 |
ORPooling | import torch
import torch.nn as nn
class ORPooling(nn.Module):
def __init__(self, orientations):
super(ORPooling, self).__init__()
self.orientations = orientations
def forward(self, x):
B, C, H, W = x.shape
assert C % self.orientations == 0
x = x.view(B, -1, self.orie... | 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... | filick/torchcv | ORPooling | false | 3,508 | [
"MIT"
] | 0 | 6e3f6780f00037e086c0ee48bf2b93a177a3b4bc | https://github.com/filick/torchcv/tree/6e3f6780f00037e086c0ee48bf2b93a177a3b4bc |
AdaptiveConcatPool2d | import torch
from typing import Type
from typing import Optional
import torch.nn as nn
class AdaptiveConcatPool2d(nn.Module):
"""Layer that concats `AdaptiveAvgPool2d` and `AdaptiveMaxPool2d`."""
def __init__(self, size: 'Optional[int]'=None):
"""Output will be 2*size or 2 if size is 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
from torch._inductor.runtime import triton_helpers
from typing import Optional
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | Erlemar/kekas | AdaptiveConcatPool2d | false | 2,205 | [
"MIT"
] | 0 | 6fd8413f15390bf079bdb57a38a7094a5c53ab0f | https://github.com/Erlemar/kekas/tree/6fd8413f15390bf079bdb57a38a7094a5c53ab0f |
ResizeTransform | import torch
import torch.nn as nn
import torch.nn.functional as nnf
class ResizeTransform(nn.Module):
"""
Resize a transform, which involves resizing the vector field *and* rescaling it.
"""
def __init__(self, vel_resize, ndims):
super().__init__()
self.factor = 1.0 / vel_resize
... | 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... | mariakesa/ZebraFishRegistrationPipeline | ResizeTransform | false | 7,160 | [
"MIT"
] | 1 | 4955044eb69dc04c579f59ccb24e02e4451aebcc | https://github.com/mariakesa/ZebraFishRegistrationPipeline/tree/4955044eb69dc04c579f59ccb24e02e4451aebcc |
Mnist_CNN | import torch
from torch import nn
import torch.nn.functional as F
class Mnist_CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1)
self.conv3 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | LbsIrving/PyTorch | Mnist_CNN | false | 786 | [
"MIT"
] | 0 | 314dbe9efc9e0116a7342d4ae3ab168c1c3afa32 | https://github.com/LbsIrving/PyTorch/tree/314dbe9efc9e0116a7342d4ae3ab168c1c3afa32 |
NoiseInjection | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import 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 |
Biaffine | # 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... | KoichiYasuoka/SuPar | Biaffine | false | 11,629 | [
"MIT"
] | 0 | 9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 | https://github.com/KoichiYasuoka/SuPar/tree/9bcf10fb946cae75b6a311d4cd19bec5bb1a9487 |
CDiceLoss | # 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._C
import... | shuaizzZ/mmsegmentation | CDiceLoss | false | 4,324 | [
"Apache-2.0"
] | 0 | a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c | https://github.com/shuaizzZ/mmsegmentation/tree/a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c |
resBlock | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class resBlock(nn.Module):
def __init__(self, channelDepth, windowSize=3):
super(resBlock, self).__init__()
padding = math.floor(windowSize / 2)
self.conv1 = nn.Conv2d(channelDepth, channelDepth, windowSize, 1,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SeokjaeLIM/DSLR-release | resBlock | false | 8,744 | [
"Apache-2.0"
] | 14 | 861429482faf50ee3d6570948af8c48df1fc7f43 | https://github.com/SeokjaeLIM/DSLR-release/tree/861429482faf50ee3d6570948af8c48df1fc7f43 |
NavigatorUnit | import torch
import torch.nn as nn
def conv1x1(in_channels, out_channels, stride=1, groups=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
stride : int or tuple/list... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | iofthetiger/pkuad | NavigatorUnit | false | 6,991 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
PMA | import math
import torch
import torch.nn.functional as F
from torch import nn
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
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.... | ydiller/NoMoreNMS | PMA | false | 4,613 | [
"Apache-2.0"
] | 0 | 1c1557357e5312c287f0971c840060deb1bcd039 | https://github.com/ydiller/NoMoreNMS/tree/1c1557357e5312c287f0971c840060deb1bcd039 |
Linear | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
def keep_variance_fn(x):
return x + 0.001
class Linear(nn.Module):
def __init__(self, in_features, out_features, bias=True,
keep_variance_fn=None):
super(Linear, 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
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_strid... | DoggyLiu0116/MamboNet | Linear | false | 5,087 | [
"MIT"
] | 1 | 3b708091422491f660c4bd5eb12b06ce3b8a5f79 | https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79 |
ClassificationSubNet | import torch
import numpy as np
import torch.nn as nn
class ClassificationSubNet(nn.Module):
def __init__(self, in_channels, num_classes, num_anchors=9):
super().__init__()
self.num_classes = num_classes
self.conv2d_1 = nn.Conv2d(in_channels, 256, 3, padding=1)
nn.init.normal_(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | geez0219/ARC | ClassificationSubNet | false | 6,753 | [
"Apache-2.0"
] | 1 | f2176f0d442d4a2d6028f0770b1efc1a9ae982b8 | https://github.com/geez0219/ARC/tree/f2176f0d442d4a2d6028f0770b1efc1a9ae982b8 |
LossMSE | # 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
from torch.nn import Module
... | LucaZampieri/DL | LossMSE | false | 779 | [
"MIT"
] | 0 | e53ade2638ccc3ca368e15c8454845856776e719 | https://github.com/LucaZampieri/DL/tree/e53ade2638ccc3ca368e15c8454845856776e719 |
HR2O_NL | # 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.... | StephenStorm/ACAR | HR2O_NL | false | 1,161 | [
"Apache-2.0"
] | 0 | 21ef3eca7330bd62eccb645018c8e48d9fc52153 | https://github.com/StephenStorm/ACAR/tree/21ef3eca7330bd62eccb645018c8e48d9fc52153 |
FencepostModule | import torch
import torch.nn as nn
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
class FencepostModule(nn.Module):
def __init__(self, input_dim, repr_dim, n_labels, disentangle=False,
label_bias=True, span_bias=False, activation='tanh'):
super(FencepostModule, self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | FilippoC/-deep-syntactic-dependency-parsing-release | FencepostModule | false | 17,281 | [
"MIT"
] | 4 | 30e2571ea930c2fd81559f5a2a971e3738cc6d39 | https://github.com/FilippoC/-deep-syntactic-dependency-parsing-release/tree/30e2571ea930c2fd81559f5a2a971e3738cc6d39 |
InputConv | # 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_... | henningpohl/body-based-ar | InputConv | false | 6,800 | [
"MIT"
] | 1 | dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb | https://github.com/henningpohl/body-based-ar/tree/dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb |
PairwiseNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class PairwiseNetwork(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.fc1 = nn.Linear(hidden_size, 2 * hidden_size)
self.fc2 = nn.Linear(2 * hidden_size, hidden_size)
self.fc3 = nn.Linear(hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | HardiRathod/table-linker | PairwiseNetwork | false | 8,217 | [
"MIT"
] | 21 | 5d0542608cdba72b0d7d8afc58c27f27b8a59192 | https://github.com/HardiRathod/table-linker/tree/5d0542608cdba72b0d7d8afc58c27f27b8a59192 |
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.triton_helpers import libdevice
import torch.nn as ... | Aminah92/saint | PositionwiseFeedForward | false | 16,886 | [
"MIT"
] | 7 | e18f5d5d093dce458c7d427eed4a375021c05bb9 | https://github.com/Aminah92/saint/tree/e18f5d5d093dce458c7d427eed4a375021c05bb9 |
DQN | import torch
import torch.nn as nn
class DQN(nn.Module):
def __init__(self, size, upscale_factor, layer_size, channels):
super(DQN, self).__init__()
self.relu = nn.ReLU()
self.fc1 = nn.Linear(in_features=size ** 2, out_features=layer_size)
self.fc2 = nn.Linear(in_features=layer_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Thibaud-Ardoin/Dial-a-Ride | DQN | false | 5,875 | [
"MIT"
] | 1 | 7d9b3cd904d3194dccad31fec2533e2cf58cad0c | https://github.com/Thibaud-Ardoin/Dial-a-Ride/tree/7d9b3cd904d3194dccad31fec2533e2cf58cad0c |
upconv | # 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.... | QiuhongAnnaWei/IBRNet | upconv | false | 14,263 | [
"Apache-2.0"
] | 254 | 6c8b68e6d95eae04535ff0906387ec7899f5d5ce | https://github.com/QiuhongAnnaWei/IBRNet/tree/6c8b68e6d95eae04535ff0906387ec7899f5d5ce |
BaseFactorizationMachine | # 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... | Ahren09/RecBole | BaseFactorizationMachine | false | 1,923 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
SwiGLU | import torch
import torch.nn as nn
class PositionWiseFeedForward(nn.Module):
"""
title: Position-wise Feed-Forward Network (FFN)
summary: Documented reusable implementation of the position wise feedforward network.
# Position-wise Feed-Forward Network (FFN)
This is a [PyTorch](https://pytorch.org... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Actis92/pytorch_tabular | SwiGLU | false | 4,798 | [
"MIT"
] | 1 | 78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe | https://github.com/Actis92/pytorch_tabular/tree/78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe |
BertLMHead | # 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.nn impor... | yulonglin/bert | BertLMHead | false | 13,167 | [
"MIT"
] | 0 | 7f992e88f109e4267b0e84f8398cab0561a67f4f | https://github.com/yulonglin/bert/tree/7f992e88f109e4267b0e84f8398cab0561a67f4f |
LSTMCell | # 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.nn impor... | mcx/annotated_deep_learning_paper_implementations | LSTMCell | false | 7,204 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
ReduceMinModule | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | MichaelZhero/nncase | ReduceMinModule | false | 11,932 | [
"Apache-2.0"
] | 0 | 0fae6ce90d7adff386e1a286cd2b42422f4b850a | https://github.com/MichaelZhero/nncase/tree/0fae6ce90d7adff386e1a286cd2b42422f4b850a |
Return | import torch
import numpy as np
class Return(torch.nn.Module):
def __init__(self, discount_factor):
super().__init__()
assert 0 <= discount_factor < 1
self.coefficient = 1 / (1 - discount_factor)
self.min_reward = np.float32(-1)
self.max_reward = np.float32(1)
self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | Eyalcohenx/tonic | Return | false | 13,667 | [
"MIT"
] | 350 | afc15c6fa23fed4f696f68f0acf961964b0172dc | https://github.com/Eyalcohenx/tonic/tree/afc15c6fa23fed4f696f68f0acf961964b0172dc |
JaccardLoss | # 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.utils.data
import torch.nn as nn
from abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_stri... | matinraayai/pytorch_connectomics | JaccardLoss | false | 3,985 | [
"MIT"
] | 0 | b11a2f7e71a8d1442fb05f7a6edfaaaa7b0d9205 | https://github.com/matinraayai/pytorch_connectomics/tree/b11a2f7e71a8d1442fb05f7a6edfaaaa7b0d9205 |
Accuracy | import torch
import torch.nn.functional as F
import torch.nn as nn
class Accuracy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, prediction, target, mask=None, token_dim=-1,
sequence_dim=-2):
prediction = F.softmax(prediction, token_dim).argmax(sequence_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | karadeli98/BBM406-Project | Accuracy | false | 10,297 | [
"MIT"
] | 0 | 6de0fa2cbebb93dec272dc7c54a25024880ed1e7 | https://github.com/karadeli98/BBM406-Project/tree/6de0fa2cbebb93dec272dc7c54a25024880ed1e7 |
PAM_Module | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _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.... | bfjei2825401/siamban | PAM_Module | false | 10,404 | [
"Apache-2.0"
] | 0 | c41d58742b146dfc8960053453227c6e9fec1bac | https://github.com/bfjei2825401/siamban/tree/c41d58742b146dfc8960053453227c6e9fec1bac |
LR | # 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
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 |
MLP | # 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
assert_size_stride ... | somu15/hamiltonian-nn | MLP | false | 10,828 | [
"Apache-2.0"
] | 0 | 0c62e92cd50d4bda4b1d0345a4676a6c003aee5e | https://github.com/somu15/hamiltonian-nn/tree/0c62e92cd50d4bda4b1d0345a4676a6c003aee5e |
Down2d | import torch
import torch.utils.data
import torch.nn as nn
class Down2d(nn.Module):
"""docstring for Down2d."""
def __init__(self, in_channel, out_channel, kernel, stride, padding):
super(Down2d, self).__init__()
self.c1 = nn.Conv2d(in_channel, out_channel, kernel_size=kernel,
str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | ChanganVR/hifigan-denoiser | Down2d | false | 13,464 | [
"Apache-2.0"
] | 100 | 9bd77c53556e1372b4bbff8dce8b120297cc4e5c | https://github.com/ChanganVR/hifigan-denoiser/tree/9bd77c53556e1372b4bbff8dce8b120297cc4e5c |
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
import torch.nn as ... | FMsunyh/mmdetection | NormedLinear | false | 13,665 | [
"Apache-2.0"
] | 240 | d3683eb06d1041aa3d55f35ad81d8c37718a4c2d | https://github.com/FMsunyh/mmdetection/tree/d3683eb06d1041aa3d55f35ad81d8c37718a4c2d |
DiscrepancyLossWithLogits | import torch
import torch.utils.data
import torch
from torchvision.transforms import functional as F
from torch import nn
from torch.nn import functional as F
class AbstractConsistencyLoss(nn.Module):
def __init__(self, reduction='mean'):
super().__init__()
self.reduction = reduction
def 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | lizhenbang56/END-TO-END-TEMPORAL-FEATURE-AGGREGATION-FOR-SIAMESE-TRACKERS | DiscrepancyLossWithLogits | false | 12,748 | [
"MIT"
] | 0 | 132b2e28b7f66c6ba0719774e9abd9b6515dd7e2 | https://github.com/lizhenbang56/END-TO-END-TEMPORAL-FEATURE-AGGREGATION-FOR-SIAMESE-TRACKERS/tree/132b2e28b7f66c6ba0719774e9abd9b6515dd7e2 |
CELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | nthuy190991/geoseg | CELoss | false | 7,357 | [
"MIT"
] | 1 | b679af5dc558720df36dddc7abfd4e6ecb46d7de | https://github.com/nthuy190991/geoseg/tree/b679af5dc558720df36dddc7abfd4e6ecb46d7de |
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... | Brikwerk/learn2learn | LR | false | 13,418 | [
"MIT"
] | 1,774 | 7997c13c26ec627d13ce77ba98427260df78ada8 | https://github.com/Brikwerk/learn2learn/tree/7997c13c26ec627d13ce77ba98427260df78ada8 |
MixedPad | import torch
def mixed_pad(input, pad, mode='constant', value=0, reversed_axes=False):
"""Mixed mode padding.
:type input: tensor[B,C,D1,D2,...,DD]
:type pad: int or tuple of ints with 2*D length
:type mode: str or tuple
:type value: float or tuple
Dimension numbering: reverse... | 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... | dvolgyes/highresnet | MixedPad | false | 6,617 | [
"MIT"
] | 1 | 12b8831ed52e2dc45d2e14cc6f2954c583c97a46 | https://github.com/dvolgyes/highresnet/tree/12b8831ed52e2dc45d2e14cc6f2954c583c97a46 |
L2Norm | # 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
import torch.nn.init as init
from itertools import produc... | AndOneDay/PytorchSSD | L2Norm | false | 8,876 | [
"MIT"
] | 0 | a9f2cde8d149e14cab3feb0084b5be3c1e6c97c6 | https://github.com/AndOneDay/PytorchSSD/tree/a9f2cde8d149e14cab3feb0084b5be3c1e6c97c6 |
TracedModule | # 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.quantization
import torch.onnx
import torch.nn.parallel
import tor... | Ismail-Mustapha/tutorials | TracedModule | false | 13,856 | [
"BSD-3-Clause"
] | 6,424 | 0ccfbf0047db855e93e2aadb43c89c92e89f52b8 | https://github.com/Ismail-Mustapha/tutorials/tree/0ccfbf0047db855e93e2aadb43c89c92e89f52b8 |
Get_gradient_nopadding | import torch
import torch.nn as nn
import torch.nn.functional as F
class Get_gradient_nopadding(nn.Module):
def __init__(self):
super(Get_gradient_nopadding, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
kernel_h = tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | mansum6/ESRGAN | Get_gradient_nopadding | false | 3,978 | [
"Apache-2.0"
] | 0 | 8a6b2ce20600840490ee0525cb105617b8e85c73 | https://github.com/mansum6/ESRGAN/tree/8a6b2ce20600840490ee0525cb105617b8e85c73 |
Dec | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.norm... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | naraysa/gzsl-od | Dec | false | 16,134 | [
"MIT"
] | 50 | be771e12e17a4c02386c70697c4b26e3170a7557 | https://github.com/naraysa/gzsl-od/tree/be771e12e17a4c02386c70697c4b26e3170a7557 |
SCse | import torch
import torch.nn as nn
class SpatialAttention2d(nn.Module):
def __init__(self, channel):
super(SpatialAttention2d, self).__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
z = self.squeeze(x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | khodwe56/kaggle-birdsong-recognition | SCse | false | 12,676 | [
"MIT"
] | 0 | 95a902c37355619cf02558968f000038e487db47 | https://github.com/khodwe56/kaggle-birdsong-recognition/tree/95a902c37355619cf02558968f000038e487db47 |
NextSentencePrediction | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class NextSentencePrediction(nn.Module):
"""
2-class classification model : is_next, is_random_next
Args:
hidden_size (int): BERT model output size
"""
def __init__(self, hidden_size):
super(NextSen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SivilTaram/dialogue-utterance-rewriter-pytorch | NextSentencePrediction | false | 2,915 | [
"MIT"
] | 0 | 92c2254958b7a1ee9199836f7f2236575270983f | https://github.com/SivilTaram/dialogue-utterance-rewriter-pytorch/tree/92c2254958b7a1ee9199836f7f2236575270983f |
ResizeGatedConv2d | # 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
import torch.utils.data
assert_size_stride = torch._C._dyna... | RobertYCXu/vae_vampprior | ResizeGatedConv2d | false | 9,622 | [
"MIT"
] | 0 | edcec4f5f7af673172c5b5b9aa2a22f993564fab | https://github.com/RobertYCXu/vae_vampprior/tree/edcec4f5f7af673172c5b5b9aa2a22f993564fab |
DiceLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryDiceLoss(nn.Module):
"""Dice loss of binary class
Args:
smooth: A float number to smooth loss, and avoid NaN error, default: 1
p: Denominator value: \\sum{x^p} + \\sum{y^p}, default: 2
predict: A tensor of 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ljrprocc/Motif-Removal | DiceLoss | false | 3,941 | [
"MIT"
] | 0 | 8979ca91398212248a2be61345c99bdec53ae37e | https://github.com/ljrprocc/Motif-Removal/tree/8979ca91398212248a2be61345c99bdec53ae37e |
SoftAttention | import torch
import torch.utils.data
import torch.nn as nn
class SoftAttention(torch.nn.Module):
"""
v = tanh(hW + b)
w = softmax(v*u)
out = sum wh
see eqs 5-7 in https://www.sciencedirect.com/science/article/abs/pii/S0924271619300115
"""
def __init__(self, hidden_dim):
super(Sof... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Doomski99/MarcCoru2019CropType | SoftAttention | false | 11,380 | [
"MIT"
] | 0 | 17db294ef51bdd39fd884e0052141d8092b98b86 | https://github.com/Doomski99/MarcCoru2019CropType/tree/17db294ef51bdd39fd884e0052141d8092b98b86 |
CustomLSTMCell | import torch
import torch.nn as nn
class CustomLSTMCell(nn.Module):
def __init__(self, input_size, hidden_size):
super().__init__()
self.lstm = nn.LSTMCell(input_size, hidden_size)
def forward(self, x):
output = self.lstm(x)
return output[0]
def get_inputs():
return [to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | vr100/rl-trading | CustomLSTMCell | false | 10,983 | [
"MIT"
] | 0 | 0e3383e383bdfd46c40df65f3c709ba88169153c | https://github.com/vr100/rl-trading/tree/0e3383e383bdfd46c40df65f3c709ba88169153c |
AttentionLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttentionLayer(nn.Module):
def __init__(self, hidden_dim_en, hidden_dim_de, projected_size):
super(AttentionLayer, self).__init__()
self.linear1 = nn.Linear(hidden_dim_en, projected_size)
self.linear2 = 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.... | lost-person/AREL | AttentionLayer | false | 7,143 | [
"MIT"
] | 1 | cee8bc542a2226f41fcbf65ed805fd585512689d | https://github.com/lost-person/AREL/tree/cee8bc542a2226f41fcbf65ed805fd585512689d |
RDivFloat | import torch
class RDivFloat(torch.nn.Module):
def __init__(self):
super(RDivFloat, self).__init__()
def forward(self, x):
return 100.0 / 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | PogChamper/torch2trt | RDivFloat | false | 14,232 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
AugCNN | # 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
import torch.nn.functional as F
assert_size_stride = torch... | agarwl/auto-drac | AugCNN | false | 3,034 | [
"MIT"
] | 0 | d86c480b51929e6e4ec0ae1adba84d9f78e91705 | https://github.com/agarwl/auto-drac/tree/d86c480b51929e6e4ec0ae1adba84d9f78e91705 |
conv2 | # 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... | CarlosPena00/pytorch-unet | conv2 | false | 213 | [
"MIT"
] | 0 | 8365bace23e4b04b9c5b75cd6720807ea8cac5ab | https://github.com/CarlosPena00/pytorch-unet/tree/8365bace23e4b04b9c5b75cd6720807ea8cac5ab |
NormalLoss | import torch
import torch.nn as nn
class NormalLoss(nn.Module):
def __init__(self):
super(NormalLoss, self).__init__()
def forward(self, grad_fake, grad_real):
prod = (grad_fake[:, :, None, :] @ grad_real[:, :, :, None]).squeeze(-1
).squeeze(-1)
fake_norm = torch.sqrt(tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Khoronus/MonoDepth-FPN-PyTorch | NormalLoss | false | 719 | [
"MIT"
] | 0 | 6e41e297723d1490c537e04afff905c61d6f0ff8 | https://github.com/Khoronus/MonoDepth-FPN-PyTorch/tree/6e41e297723d1490c537e04afff905c61d6f0ff8 |
ZeroPad1d | # 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
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
import torch.di... | DCMMC/chineseocr | ZeroPad1d | false | 9,202 | [
"MIT"
] | 0 | 0b8772615239ea7f212b1ab5bc75183e7e9f16b0 | https://github.com/DCMMC/chineseocr/tree/0b8772615239ea7f212b1ab5bc75183e7e9f16b0 |
ArcMarginProduct | import math
import torch
import torchvision.transforms.functional as F
from torch import nn
from torch.nn import functional as F
class ArcMarginProduct(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = nn.Parameter(torch.FloatTensor(out_features, in_featu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aaron276h/kaggle-rcic-1st | ArcMarginProduct | false | 12,031 | [
"MIT"
] | 0 | d35e97847df3c29f548e60bc936d3fec7a0a4c08 | https://github.com/aaron276h/kaggle-rcic-1st/tree/d35e97847df3c29f548e60bc936d3fec7a0a4c08 |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self, out_dim=64):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | cloughurd/SimCLR | Encoder | false | 3,303 | [
"MIT"
] | 0 | 79029b6cb422aa16c939bcc550ca4acd495c2651 | https://github.com/cloughurd/SimCLR/tree/79029b6cb422aa16c939bcc550ca4acd495c2651 |
RankingLoss | # 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 abc import abstractmethod
import torch.utils.data.dataloader
import torch.nn as nn
i... | FranziskaKuhls/flair | RankingLoss | false | 2,257 | [
"MIT"
] | 0 | 2bd9e72c961651c7c020076cb8fd80cbbb36da7c | https://github.com/FranziskaKuhls/flair/tree/2bd9e72c961651c7c020076cb8fd80cbbb36da7c |
PatchApplier | import torch
import torch.nn as nn
class PatchApplier(nn.Module):
"""PatchApplier: applies adversarial patches to images.
Module providing the functionality necessary to apply a patch to all detections in all images in the batch.
"""
def __init__(self):
super(PatchApplier, 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... | Zhang-Jack/adversarial_yolo2 | PatchApplier | false | 18,183 | [
"MIT"
] | 8 | 91c2a4793047f656482cebf0309984db823e8030 | https://github.com/Zhang-Jack/adversarial_yolo2/tree/91c2a4793047f656482cebf0309984db823e8030 |
PSNR | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
from... | YDDDDG/3D2Unet | PSNR | false | 6,006 | [
"MIT"
] | 1 | daca056958fb2ae319dc18a350e04b3cefe0d99f | https://github.com/YDDDDG/3D2Unet/tree/daca056958fb2ae319dc18a350e04b3cefe0d99f |
A2CActorCont | # 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.... | iffiX/machin | A2CActorCont | false | 15,596 | [
"MIT"
] | 287 | 7fa986b1bafdefff117d6ff73d14644a5488de9d | https://github.com/iffiX/machin/tree/7fa986b1bafdefff117d6ff73d14644a5488de9d |
PolicyNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size=256):
super(PolicyNetwork, self).__init__()
self.num_actions = num_actions
self.linear1 = nn.Linear(num_inputs, hidden_size)
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.... | linklab/link_rl_book_codes | PolicyNetwork | false | 10,420 | [
"MIT"
] | 0 | b272b46d5ecd2802f34648440ff53641c68cbbf0 | https://github.com/linklab/link_rl_book_codes/tree/b272b46d5ecd2802f34648440ff53641c68cbbf0 |
ArcMarginProduct_subcenter | # 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.... | Tanmengxuan/Google-Landmark-Recognition-2020-3rd-Place-Solution | ArcMarginProduct_subcenter | false | 1,135 | [
"Apache-2.0"
] | 0 | 8e2d9056d5c88c6415827086809e73522b336fbb | https://github.com/Tanmengxuan/Google-Landmark-Recognition-2020-3rd-Place-Solution/tree/8e2d9056d5c88c6415827086809e73522b336fbb |
FusedDownsample | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class FusedDownsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | nazarblch/style-based-gan-pytorch | FusedDownsample | false | 4,055 | [
"MIT"
] | 0 | 5ed7fa114904501d77b414921cd9f439773ba24c | https://github.com/nazarblch/style-based-gan-pytorch/tree/5ed7fa114904501d77b414921cd9f439773ba24c |
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
from torch._inductor.runtime.... | bluebibi/flask_rest | MLPNet | false | 12,181 | [
"MIT"
] | 0 | 9b1ee876060bca5d97459bb894c73530f66c4c15 | https://github.com/bluebibi/flask_rest/tree/9b1ee876060bca5d97459bb894c73530f66c4c15 |
DetLoss | # 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 ... | dotchen/LAV | DetLoss | false | 15,206 | [
"Apache-2.0"
] | 122 | dc9b4cfca39abd50c7438e8749d49f6ac0fe5e4e | https://github.com/dotchen/LAV/tree/dc9b4cfca39abd50c7438e8749d49f6ac0fe5e4e |
MLPAutoencoder | import torch
def choose_nonlinearity(name):
nl = None
if name == 'tanh':
nl = torch.tanh
elif name == 'relu':
nl = torch.relu
elif name == 'sigmoid':
nl = torch.sigmoid
elif name == 'softplus':
nl = torch.nn.functional.softplus
elif name == 'selu':
nl = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
assert_size_stride ... | UlyssesZh/selfsup_hnn | MLPAutoencoder | false | 9,638 | [
"MIT"
] | 0 | fedd261be81b38ec179cc71ea75d91964985a9e8 | https://github.com/UlyssesZh/selfsup_hnn/tree/fedd261be81b38ec179cc71ea75d91964985a9e8 |
PositionalEncoding | # 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | cmiras/BSL-segmentation | PositionalEncoding | false | 3,335 | [
"MIT"
] | 0 | 35a66d6c758dcf4734adb0ebc5a40ea7238d7a1d | https://github.com/cmiras/BSL-segmentation/tree/35a66d6c758dcf4734adb0ebc5a40ea7238d7a1d |
Discrete | # 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
... | WillDudley/client | Discrete | false | 11,972 | [
"MIT"
] | 0 | 957f93c43eb8e5b0f51fabf3b47c362bce25389e | https://github.com/WillDudley/client/tree/957f93c43eb8e5b0f51fabf3b47c362bce25389e |
OneLayerFCBodyWithAction | import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class OneLayerFCBodyWithAction(nn.Module):
def __init__(self, state_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | RaviTej310/mrpvf | OneLayerFCBodyWithAction | false | 11,883 | [
"MIT"
] | 0 | f026b4704f26b85161de26ada5d6390ab549fbbd | https://github.com/RaviTej310/mrpvf/tree/f026b4704f26b85161de26ada5d6390ab549fbbd |
SkipConnection | import torch
from torch import nn
class SkipConnection(nn.Module):
"""
Skip-connection over the sequence of layers in the constructor.
The module passes input data sequentially through these layers
and then adds original data to the result.
"""
def __init__(self, *args):
super().__ini... | 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... | HugoSenetaire/vaeac | SkipConnection | false | 13,807 | [
"MIT"
] | 70 | 451d34dd4986c52f2f37c508f03ee3db9e7408d3 | https://github.com/HugoSenetaire/vaeac/tree/451d34dd4986c52f2f37c508f03ee3db9e7408d3 |
SelfAttentive | # 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.... | chenyangh/SemEval2019-Task3 | SelfAttentive | false | 15,024 | [
"MIT"
] | 50 | c6204797b4b6cc08cb4d2d88108405f959d63ee9 | https://github.com/chenyangh/SemEval2019-Task3/tree/c6204797b4b6cc08cb4d2d88108405f959d63ee9 |
complex_relu_layer | # 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... | RemyLau/SimpleMagNet | complex_relu_layer | false | 2,742 | [
"MIT"
] | 0 | ee3cc5fc9a7793d2e2cf5a4b635fb690bb5b988e | https://github.com/RemyLau/SimpleMagNet/tree/ee3cc5fc9a7793d2e2cf5a4b635fb690bb5b988e |
CharbonnierLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _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
from t... | ljzycmd/SimDeblur | CharbonnierLoss | false | 15,929 | [
"MIT"
] | 190 | dd2f60c41176b75c4eaf80d740f547c206aa8227 | https://github.com/ljzycmd/SimDeblur/tree/dd2f60c41176b75c4eaf80d740f547c206aa8227 |
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.... | QiuhongAnnaWei/IBRNet | PositionwiseFeedForward | false | 14,261 | [
"Apache-2.0"
] | 254 | 6c8b68e6d95eae04535ff0906387ec7899f5d5ce | https://github.com/QiuhongAnnaWei/IBRNet/tree/6c8b68e6d95eae04535ff0906387ec7899f5d5ce |
UpSample | # 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... | techthiyanes/annotated_deep_learning_paper_implementations | UpSample | false | 16,581 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
BertOutput | # 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 ... | shubham-gupta-iitr/mmmlX | BertOutput | false | 4,333 | [
"Apache-2.0"
] | 0 | 3485e6191e0e45bf1c8168e4e928a36ab9264d22 | https://github.com/shubham-gupta-iitr/mmmlX/tree/3485e6191e0e45bf1c8168e4e928a36ab9264d22 |
TCN_SLID | import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x, pad_right=True):
return x[:, :, :-self.chomp_size].contiguous() if pad... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ddcas/singing-language-identification | TCN_SLID | false | 1,843 | [
"MIT"
] | 0 | d104419b196d56d4de37cff47c32e88e28c58690 | https://github.com/ddcas/singing-language-identification/tree/d104419b196d56d4de37cff47c32e88e28c58690 |
ln_mod | # 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
from torch.nn.parameter import Parameter
assert_size_stri... | lienghongky/image-gpt2 | ln_mod | false | 12,707 | [
"MIT"
] | 0 | ef9f3c61d4a09cbb75114dd067d0014948e82d7b | https://github.com/lienghongky/image-gpt2/tree/ef9f3c61d4a09cbb75114dd067d0014948e82d7b |
MDN | # 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.... | adelaunay3/DL-Seq2Seq | MDN | false | 1,378 | [
"MIT"
] | 0 | 7b83e2501b26780ff2dec140ac15a6664699dc16 | https://github.com/adelaunay3/DL-Seq2Seq/tree/7b83e2501b26780ff2dec140ac15a6664699dc16 |
DepthwiseSeparableConv | # 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_... | minjoong507/TVRetrieval | DepthwiseSeparableConv | false | 10,746 | [
"MIT"
] | 0 | 919e1766ab8aa1ef267bd3b80d4f87b06cde09a9 | https://github.com/minjoong507/TVRetrieval/tree/919e1766ab8aa1ef267bd3b80d4f87b06cde09a9 |
Net2 | # 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.testing._internal.common_utils import *
assert_size_stri... | LexcaliburR/notebook | Net2 | false | 7,611 | [
"MIT"
] | 1 | 84a8f3801dff20d07caa0ed2584e722656fb5726 | https://github.com/LexcaliburR/notebook/tree/84a8f3801dff20d07caa0ed2584e722656fb5726 |
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