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 |
|---|---|---|---|---|---|---|---|---|---|---|
ShakeResNeXt | import math
import torch
from torch import nn
from numpy import int64 as int64
import torch.nn.functional as F
from torch.autograd import Variable
class ShakeShake(torch.autograd.Function):
@staticmethod
def forward(ctx, x1, x2, training=True):
if training:
alpha = torch.FloatTensor(x1.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 math
from torch import... | aierh/autoML | ShakeResNeXt | false | 14,761 | [
"MIT"
] | 185 | 8e31966edf6de2c223d5eeb6cd4b4dbd6ddbbf77 | https://github.com/aierh/autoML/tree/8e31966edf6de2c223d5eeb6cd4b4dbd6ddbbf77 |
Conv2d | # 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.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pa... | DoggyLiu0116/MamboNet | Conv2d | false | 5,089 | [
"MIT"
] | 1 | 3b708091422491f660c4bd5eb12b06ce3b8a5f79 | https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79 |
BasicDeconv | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicDeconv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
use_bn=False):
super(BasicDeconv, self).__init__()
self.use_bn = use_bn
self.tconv = nn.ConvTranspose2d(in_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | DecAngel/C-3-Framework | BasicDeconv | false | 363 | [
"MIT"
] | 0 | 00b792b4481a0ec9d7e30e290c66e7020235e79b | https://github.com/DecAngel/C-3-Framework/tree/00b792b4481a0ec9d7e30e290c66e7020235e79b |
KLDiscCriterion | # 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
... | PaperCodeSubmission/ICML2020-697 | KLDiscCriterion | false | 8,657 | [
"MIT"
] | 12 | 00f7732c236b9c6234e76a47dfebe5de314d5c01 | https://github.com/PaperCodeSubmission/ICML2020-697/tree/00f7732c236b9c6234e76a47dfebe5de314d5c01 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, smooth=0, eps=1e-07):
super(DiceLoss, self).__init__()
self.smooth = smooth
self.eps = eps
def forward(self, output, target):
return 1 - (2 * torch.sum(output * target) + self.smooth) / (torch.
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | neptune-ml/data-science-bowl-2018 | DiceLoss | false | 16,154 | [
"MIT"
] | 92 | 2f76f2fc9836e53ada16d1e084afa0108b119011 | https://github.com/neptune-ml/data-science-bowl-2018/tree/2f76f2fc9836e53ada16d1e084afa0108b119011 |
FeatureAttentionLayer | # 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.... | lawson-source/mtad-gat-pytorch | FeatureAttentionLayer | false | 15,883 | [
"MIT"
] | 93 | 9e671ea99dedd82ac55f53e53af1d1b56c13ebff | https://github.com/lawson-source/mtad-gat-pytorch/tree/9e671ea99dedd82ac55f53e53af1d1b56c13ebff |
SoftDiceLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class SoftDiceLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(SoftDiceLoss, self).__init__()
def forward(self, logits, targets):
num = targets.size(0)
probs = F.sigmoid(logits)
m1 = ... | 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... | cmarasinou/carvana-challenge | SoftDiceLoss | false | 15,047 | [
"MIT"
] | 93 | 4e1c43f306cfbef1df267acfce59bdcf19504850 | https://github.com/cmarasinou/carvana-challenge/tree/4e1c43f306cfbef1df267acfce59bdcf19504850 |
UpConv_Blocks | import torch
import torch.nn as nn
class UpConv_Blocks(nn.Module):
def __init__(self, input_dim, output_dim, filter=4, padding=1,
first_block=False, last_block=False, batch_norm=False, non_lin=
'relu', dropout=0, skip_connection=False):
super(UpConv_Blocks, self).__init__()
self.B... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | HRHLALALA/GoalGAN | UpConv_Blocks | false | 9,103 | [
"MIT"
] | 0 | 01443f2a578333a0d5ab3a449bc7da69f5023190 | https://github.com/HRHLALALA/GoalGAN/tree/01443f2a578333a0d5ab3a449bc7da69f5023190 |
Attention | # 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.... | FGDBTKD/decaNLP | Attention | false | 13,675 | [
"BSD-3-Clause"
] | 2,361 | ff2d7e18cc226197bb8fe5fe796c4b8bc0395e86 | https://github.com/FGDBTKD/decaNLP/tree/ff2d7e18cc226197bb8fe5fe796c4b8bc0395e86 |
GeM | # 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.functional a... | LightnessOfBeing/kaggle-bengali-classification | GeM | false | 17,625 | [
"MIT"
] | 5 | 342bc2a9bf57f9f03fa25f5271cb178ab8f7b4ff | https://github.com/LightnessOfBeing/kaggle-bengali-classification/tree/342bc2a9bf57f9f03fa25f5271cb178ab8f7b4ff |
LogitKLDivLoss | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class LogitKLDivLoss(nn.Module):
"""Kullback–Leibler divergence loss. Inputs predicted and ground truth logits.
Args:
T (float): Softmax temperature.
"... | 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 ... | dqawami/openvino_training_extensions | LogitKLDivLoss | false | 15,209 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad |
OuterProductLayer | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | MIracleyin/RecBole-notebook | OuterProductLayer | false | 9,582 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
ConvBlock | # 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_... | QinbinLi/FedKT | ConvBlock | false | 8,664 | [
"MIT"
] | 14 | 0bb9a89ea266c057990a4a326b586ed3d2fb2df8 | https://github.com/QinbinLi/FedKT/tree/0bb9a89ea266c057990a4a326b586ed3d2fb2df8 |
Qnet | # 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... | DeepHaeJoong/reinforcement-learning | Qnet | false | 9,031 | [
"MIT"
] | 0 | 63e3053e3209809e67e97d51adaf5f85ce3799ba | https://github.com/DeepHaeJoong/reinforcement-learning/tree/63e3053e3209809e67e97d51adaf5f85ce3799ba |
PositionMSELoss | import torch
import torch.nn as nn
class PositionMSELoss(nn.Module):
mse = nn.MSELoss()
def forward(self, pred, target, mask):
pred = torch.mul(pred, mask.unsqueeze(2))
return self.mse(pred, target)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), 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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | vegetablejuiceftw/soft-pointer-networks | PositionMSELoss | false | 11,073 | [
"MIT"
] | 0 | 9705d9688b6b69db3948172771df4c367165c948 | https://github.com/vegetablejuiceftw/soft-pointer-networks/tree/9705d9688b6b69db3948172771df4c367165c948 |
TopicEmbeddingAttention | import torch
from torch.nn import functional as F
import torch.multiprocessing
from torch import nn
import torch.utils.data
class TopicEmbeddingAttention(nn.Module):
"""
query： encoder的隐藏状态 key value：主题嵌入向量
计算每个时间步t 对于加权topic embedding向量
"""
def __init__(self, encoder_hidden_size, topic_num, topi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | WuDiDaBinGe/TAKG | TopicEmbeddingAttention | false | 1,243 | [
"MIT"
] | 0 | 83e608e677a4ee74722d18cb5ef430f4f6c6ad31 | https://github.com/WuDiDaBinGe/TAKG/tree/83e608e677a4ee74722d18cb5ef430f4f6c6ad31 |
WassersteinDiscriminatorLoss | # 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... | shi-weili/torchgan | WassersteinDiscriminatorLoss | false | 12,970 | [
"MIT"
] | 0 | 28ffd4026b8c0db2217b667d30a222d6758bfc41 | https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41 |
RewardModelNetwork | import torch
import torch.nn as nn
class RewardModelNetwork(nn.Module):
def __init__(self, input_size: 'int', hidden_size: 'int', output_size:
'int') ->None:
super(RewardModelNetwork, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, 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 ... | PaParaZz1/DI-engine | RewardModelNetwork | false | 11,865 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
TransformerEncoderLayer | # 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.... | FGDBTKD/decaNLP | TransformerEncoderLayer | false | 13,727 | [
"BSD-3-Clause"
] | 2,361 | ff2d7e18cc226197bb8fe5fe796c4b8bc0395e86 | https://github.com/FGDBTKD/decaNLP/tree/ff2d7e18cc226197bb8fe5fe796c4b8bc0395e86 |
NavigatorUnit | # 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.utils.data
impor... | HyperGAN/imgclsmob | NavigatorUnit | false | 17,792 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
Linker | import torch
import torch.nn as nn
import torch.nn.functional as F
class Linker(nn.Module):
def __init__(self, inplanes, outplanes, kernel_size, strides):
super(Linker, self).__init__()
self.avgpool = nn.AvgPool2d(kernel_size, strides)
self.pad = (0, 0, 0, 0) + ((outplanes - inplanes) // ... | 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... | daniel03c1/audio_augment | Linker | false | 1,770 | [
"MIT"
] | 0 | ee73bb0844e22c57c9cbeb129560da4a3853f77d | https://github.com/daniel03c1/audio_augment/tree/ee73bb0844e22c57c9cbeb129560da4a3853f77d |
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... | Hsintien-Ng/idinvert_pytorch-reproduced | FirstBlock | false | 8,252 | [
"MIT"
] | 20 | cf3302510573138cf16202add06feae7c93624ea | https://github.com/Hsintien-Ng/idinvert_pytorch-reproduced/tree/cf3302510573138cf16202add06feae7c93624ea |
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
from math import sqrt as sqrt
from itertools import product as product
import t... | zhujiagang/realtime-neg | L2Norm | false | 13,174 | [
"MIT"
] | 0 | 7e228edc5f2d93d0eee7f3880f0b8473d8c71d27 | https://github.com/zhujiagang/realtime-neg/tree/7e228edc5f2d93d0eee7f3880f0b8473d8c71d27 |
BatchLinear | import torch
import torch.nn as nn
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited from `MetaModule` are fully compa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | KIMGEONUNG/multi-memory-siren | BatchLinear | false | 707 | [
"MIT"
] | 0 | b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b | https://github.com/KIMGEONUNG/multi-memory-siren/tree/b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b |
SineLayer | import torch
import numpy as np
import torch.nn as nn
class SineLayer(nn.Module):
def __init__(self, in_features, out_features, bias=True, is_first=False,
omega_0=30):
super().__init__()
self.omega_0 = omega_0
self.is_first = is_first
self.in_features = in_features
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | Juju-botu/diffeqml-research | SineLayer | false | 13,952 | [
"Apache-2.0"
] | 49 | aa796c87447e5299ec4f25a07fc4d032afb1f63e | https://github.com/Juju-botu/diffeqml-research/tree/aa796c87447e5299ec4f25a07fc4d032afb1f63e |
_Residual_Block_SR | import torch
import torch.nn.functional
import torch.nn as nn
class _Residual_Block_SR(nn.Module):
"""
residual block in feature module
"""
def __init__(self, num_ft):
super(_Residual_Block_SR, self).__init__()
self.conv1 = nn.Conv2d(in_channels=num_ft, out_channels=num_ft,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional
import torch.nn as nn
assert_size_stride = torch._C._... | HelenGuohx/cv-ferattn-code | _Residual_Block_SR | false | 5,291 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
ConvSwishOutplace | # 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.cuda
import torch.backends.cudnn
import torch.... | XiaobingSuper/intel-extension-for-pytorch | ConvSwishOutplace | false | 9,720 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
FFN | import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of input
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | stefantaubert/FastSpeech | FFN | false | 10,852 | [
"MIT"
] | 0 | 4ef8ce2ff8f6a69f9b52ef9bd5b37f8e2783c17e | https://github.com/stefantaubert/FastSpeech/tree/4ef8ce2ff8f6a69f9b52ef9bd5b37f8e2783c17e |
SelfAttention | import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, num_q_channels: 'int', num_kv_channels: 'int',
num_heads: 'int', dropout: 'float'):
super().__init__()
self.attention = nn.MultiheadAttention(embed_dim=num_q_channels,
num_heads=num_head... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | felixyu7/perceiver-io-1 | SelfAttention | false | 3,540 | [
"Apache-2.0"
] | 0 | 895f09e75e5a4b5e90dfef5d3a86ea26c2f48f4e | https://github.com/felixyu7/perceiver-io-1/tree/895f09e75e5a4b5e90dfef5d3a86ea26c2f48f4e |
HuberLossWithIgnore | # 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.functi... | drivendataorg/DrivenData-2021-Geopose-Solution | HuberLossWithIgnore | false | 6,606 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
StackTime | import torch
import torch.nn as nn
import torch.utils.data
import torch.jit
import torch.optim
import torch.utils.collect_env
import torch.nn.parallel
import torch.utils.data.distributed
class StackTime(nn.Module):
def __init__(self, factor):
super().__init__()
self.factor = int(factor)
def ... | 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.jit
import torch.optim
import torch.utils.collect_env
import torch.nn.parallel
im... | sharathts/training | StackTime | false | 12,965 | [
"Apache-2.0"
] | 0 | f294d135a6b1ac12a19ea68c1f0e42e8acc39401 | https://github.com/sharathts/training/tree/f294d135a6b1ac12a19ea68c1f0e42e8acc39401 |
FastRCNNPredictor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | CancerDataScience/NuCLS | FastRCNNPredictor | false | 17,390 | [
"MIT"
] | 7 | c172b55b18d4ea78c3f51a8fd28ee6c2595c8360 | https://github.com/CancerDataScience/NuCLS/tree/c172b55b18d4ea78c3f51a8fd28ee6c2595c8360 |
GetGradient | import torch
import torch.nn as nn
import torch.nn.functional as F
class GetGradient(nn.Module):
""" generate the gradient map
"""
def __init__(self):
super(GetGradient, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | TencentARC/FAIG | GetGradient | false | 14,487 | [
"Apache-2.0"
] | 74 | 14f856a87e3696953304029532e2f84997d12278 | https://github.com/TencentARC/FAIG/tree/14f856a87e3696953304029532e2f84997d12278 |
LowRankPositionwiseFeedForward | # 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.... | bahducoup/factorized_training | LowRankPositionwiseFeedForward | false | 12,155 | [
"MIT"
] | 0 | 0af38f16338a9bcfcc11091b1a6b75befd67f234 | https://github.com/bahducoup/factorized_training/tree/0af38f16338a9bcfcc11091b1a6b75befd67f234 |
bilinear_classifier | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class Sparse_dropout(nn.Module):
def __init__(self, p):
super(Sparse_dropout, self).__init__()
self.dropout_rate = p
def forward(self, input, noise_shape):
if not self.training:
return i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
assert_... | db-bionlp/CLNER | bilinear_classifier | false | 15,161 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
LastLevelMaxPool | import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class LastLevelMaxPool(nn.Module):
def forward(self, x):
return [F.max_pool2d(x, 1, 2, 0)]
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
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | AmanKishore/maskrcnn-benchmark | LastLevelMaxPool | false | 9,333 | [
"MIT"
] | 0 | c95a00feaeba6fb4f9c3cd9a60bf1fdab98e696d | https://github.com/AmanKishore/maskrcnn-benchmark/tree/c95a00feaeba6fb4f9c3cd9a60bf1fdab98e696d |
ConvLayer | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
from itertools import product as product
class NormLayer(nn.Module):
"""Normalization Layers.
Args:
channels: input channels, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | Cospel/facexlib | ConvLayer | false | 9,486 | [
"MIT"
] | 0 | 2471ddb44b1d61306c6d7fcf56846b9e4aeea4aa | https://github.com/Cospel/facexlib/tree/2471ddb44b1d61306c6d7fcf56846b9e4aeea4aa |
Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | m-decoster/DeepHand-PyTorch | Model | false | 7,484 | [
"MIT"
] | 1 | ece77e04ec261a540b011fd00584bfc6d7337dc5 | https://github.com/m-decoster/DeepHand-PyTorch/tree/ece77e04ec261a540b011fd00584bfc6d7337dc5 |
MaskedMSE | import torch
import torch.nn as nn
class MaskedMSE(nn.Module):
mse = nn.MSELoss()
def forward(self, pred, target, mask):
pred = torch.mul(pred, mask)
return self.mse(pred, target)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | vegetablejuiceftw/soft-pointer-networks | MaskedMSE | false | 11,077 | [
"MIT"
] | 0 | 9705d9688b6b69db3948172771df4c367165c948 | https://github.com/vegetablejuiceftw/soft-pointer-networks/tree/9705d9688b6b69db3948172771df4c367165c948 |
SequentialPolarizedSelfAttention | # 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.... | LiChengChen666/DetectDee | SequentialPolarizedSelfAttention | false | 9,851 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
PatchedSelfAttentionLayer | # 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.... | eldadp100/The-Mesh-Transformer | PatchedSelfAttentionLayer | false | 6,657 | [
"MIT"
] | 1 | b3ab18f774251feff1093040dfdcf7b836a43505 | https://github.com/eldadp100/The-Mesh-Transformer/tree/b3ab18f774251feff1093040dfdcf7b836a43505 |
gram_mse_loss | import torch
import torch.nn as nn
class gram_matrix(nn.Module):
def forward(self, input):
b, c, w, h = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
class gram_mse_loss(nn.Module):
def forward(self, input, targe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ipjessica/neural-style-transfer | gram_mse_loss | false | 12,532 | [
"MIT"
] | 0 | ae0fc5e1e69d5d52997e5cab69e880085e04723b | https://github.com/ipjessica/neural-style-transfer/tree/ae0fc5e1e69d5d52997e5cab69e880085e04723b |
MyLoss | # 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
assert... | abrar-fahim/ann-benchmarks | MyLoss | false | 12,048 | [
"MIT"
] | 0 | e5493ddda333bf6a930415566d4f1c697b439aca | https://github.com/abrar-fahim/ann-benchmarks/tree/e5493ddda333bf6a930415566d4f1c697b439aca |
AlexNet | import torch
import torch.nn as nn
class AlexNet(nn.Module):
"""
Our first more "succesful" network, slightly modified AlexNet
that accepts images in with 1 channel (i.e. grayscale).
Attributes
----------
c: torch.nn.modules.conv.Conv2d
Applies a 2D convolution over an input signal co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | jamessoni/x-ray-ML-classification | AlexNet | false | 3,854 | [
"MIT"
] | 0 | 6934f37631d367cdbe813fa6a2cbdc673c64c503 | https://github.com/jamessoni/x-ray-ML-classification/tree/6934f37631d367cdbe813fa6a2cbdc673c64c503 |
SkipModule | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | qway/nerfmeshes | SkipModule | false | 16,307 | [
"MIT"
] | 113 | d983dcbbcfec1337c9f2040969213c6d1ea0c39e | https://github.com/qway/nerfmeshes/tree/d983dcbbcfec1337c9f2040969213c6d1ea0c39e |
ExpNormalBasis | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._d... | ClintvanHoesel/MXMNet_adapted | ExpNormalBasis | false | 304 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
offset_pos | import torch
import torch.nn as nn
class offset_pos(nn.Module):
def __init__(self):
super(offset_pos, self).__init__()
self.smoothl1 = nn.SmoothL1Loss(reduction='none')
def forward(self, offset_pred, offset_label):
l1_loss = offset_label[:, 2, :, :].unsqueeze(dim=1) * self.smoothl1(
... | 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
... | FrancesC0de/Pedestron | offset_pos | false | 9,100 | [
"Apache-2.0"
] | 0 | 9ef6a408f97f8c8af98096b7945df18c9d3656ca | https://github.com/FrancesC0de/Pedestron/tree/9ef6a408f97f8c8af98096b7945df18c9d3656ca |
LayerNormConv2d | import torch
import torch.nn as nn
import torch.nn.functional
class LayerNormConv2d(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional
assert_size_stride = torch._C.... | ChenFengYe/relightable-nr | LayerNormConv2d | false | 13,462 | [
"MIT"
] | 105 | 239a97406f4df01cf5786dcdde58e464395a682d | https://github.com/ChenFengYe/relightable-nr/tree/239a97406f4df01cf5786dcdde58e464395a682d |
ResBlock | import torch
import torch.nn as nn
from torch.nn import functional as F
class ResBlock(nn.Module):
"""Residual block with upsampling/downsampling.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
"""
def __init__(self, in_channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | PrimeshShamilka/GFPGAN | ResBlock | false | 9,424 | [
"BSD-3-Clause"
] | 0 | 3ba48b932d41a4faa906e5cd39794b60845db708 | https://github.com/PrimeshShamilka/GFPGAN/tree/3ba48b932d41a4faa906e5cd39794b60845db708 |
tofp16 | import torch
import torch.nn as nn
from typing import *
class tofp16(nn.Module):
"""
Utility module that implements::
def forward(self, input):
return input.half()
"""
def __init__(self):
super(tofp16, self).__init__()
def forward(self, input):
return input.h... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | DineshChauhan/fastai_docs | tofp16 | false | 11,339 | [
"Apache-2.0"
] | 0 | cf4d88073fb6f3ef7331b5360618b8dd95eb9345 | https://github.com/DineshChauhan/fastai_docs/tree/cf4d88073fb6f3ef7331b5360618b8dd95eb9345 |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | akanametov/CycleGAN | BasicBlock | false | 6,134 | [
"MIT"
] | 1 | a61e76134cfdda43306e326e3dbba38d8cb21163 | https://github.com/akanametov/CycleGAN/tree/a61e76134cfdda43306e326e3dbba38d8cb21163 |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | Atharva-Phatak/torchflare | FocalLoss | false | 13,327 | [
"Apache-2.0"
] | 86 | 945f4bee73a855edd8cb19cd646731155499a27f | https://github.com/Atharva-Phatak/torchflare/tree/945f4bee73a855edd8cb19cd646731155499a27f |
WassersteinDiscriminatorLoss | import torch
import torch.nn as nn
import torch.autograd
import torch.utils.data
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` t... | 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.autograd
import torch.utils.data
assert_size_stride = ... | kayuksel/torchgan | WassersteinDiscriminatorLoss | false | 10,554 | [
"MIT"
] | 0 | 739d97cef4c49fb80155de84e609471efafab107 | https://github.com/kayuksel/torchgan/tree/739d97cef4c49fb80155de84e609471efafab107 |
MultiheadAttention | # 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.... | VIRC-lab-csust/AGMNet | MultiheadAttention | false | 1,173 | [
"Apache-2.0"
] | 0 | ead95466da343cf9436774138c642d2ca12da4e4 | https://github.com/VIRC-lab-csust/AGMNet/tree/ead95466da343cf9436774138c642d2ca12da4e4 |
PixelNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | jiangwenj02/mmgeneration | PixelNorm | false | 12,611 | [
"Apache-2.0"
] | 0 | da9ad377ae19260467fc332ddb88f505c38a915a | https://github.com/jiangwenj02/mmgeneration/tree/da9ad377ae19260467fc332ddb88f505c38a915a |
Dueling_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | wotmd5731/pseudo_random_gen | Dueling_DQN | false | 4,545 | [
"MIT"
] | 0 | f79810cd5ac79afe0a73dee73aa21bd8c01aeb9b | https://github.com/wotmd5731/pseudo_random_gen/tree/f79810cd5ac79afe0a73dee73aa21bd8c01aeb9b |
BasePolicy | # 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... | pohanchi/DL_final_project | BasePolicy | false | 7,480 | [
"Apache-2.0"
] | 1 | 8ade422f61a2e8bd4256523ebda56e19b189fe91 | https://github.com/pohanchi/DL_final_project/tree/8ade422f61a2e8bd4256523ebda56e19b189fe91 |
Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""conv. autoencoder"""
def __init__(self):
"""constructor"""
super().__init__()
self.conv1 = nn.Conv2d(3, 32, 5, padding=2)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | positivevaib/semi-supervised-imagenet-classification | Model | false | 4,144 | [
"MIT"
] | 0 | 4fb6427f5a72951c1b866a1ddbc2599811bb5770 | https://github.com/positivevaib/semi-supervised-imagenet-classification/tree/4fb6427f5a72951c1b866a1ddbc2599811bb5770 |
SpectralEigenConv | # 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... | EdisonLeeeee/Graphgallery | SpectralEigenConv | false | 5,117 | [
"MIT"
] | 1 | 8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 | https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 |
MetaPathAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class MetaPathAttention(nn.Module):
def __init__(self, att_size, latent_dim, metapath_type_num):
super(MetaPathAttention, self).__init__()
self.att_size = att_size
self.latent_dim = latent_dim
self.metapath_type_nu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Hui-Li/MCRec_PyTorch | MetaPathAttention | false | 17,401 | [
"MIT"
] | 9 | da4da77d2cade40c0a1961481c8e47ac396d12ee | https://github.com/Hui-Li/MCRec_PyTorch/tree/da4da77d2cade40c0a1961481c8e47ac396d12ee |
HSwish | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
import torch.optim
import torch.nn.parallel
import torch.utils.data.distributed
class HSwish(nn.Module):
""" Applies the Hard-Swish function element-wise.
`"Searching for MobileN... | 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
import torch.nn as nn
import torch.nn.functional
imp... | PhelaPoscam/SRGAN-PyTorch | HSwish | false | 957 | [
"Apache-2.0"
] | 0 | c1c68707dbddd1130b2ea71023df748080bcbd52 | https://github.com/PhelaPoscam/SRGAN-PyTorch/tree/c1c68707dbddd1130b2ea71023df748080bcbd52 |
MFM2_1 | import torch
class MFM2_1(torch.nn.Module):
"""Max-Feature-Map (MFM) 2/1 operation. """
def forward(self, input):
input = input.reshape((input.shape[0], 2, -1, *input.shape[2:]))
output = input.max(dim=1)[0]
return output
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def... | 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... | x6rulin/TP-GAN | MFM2_1 | false | 4,565 | [
"MIT"
] | 0 | 1716cf06aaff8a6a2cee2548ec662dcdd68c0449 | https://github.com/x6rulin/TP-GAN/tree/1716cf06aaff8a6a2cee2548ec662dcdd68c0449 |
ResidualConvUnit | import torch
import torch.nn as nn
import torch.utils.data
class ResidualConvUnit(nn.Module):
"""Residual convolution module."""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Zacchaeus14/lang-seg | ResidualConvUnit | false | 9,763 | [
"MIT"
] | 0 | ad1196a4d33830f3219dbe2260a69364a745f094 | https://github.com/Zacchaeus14/lang-seg/tree/ad1196a4d33830f3219dbe2260a69364a745f094 |
C3D | import torch
from torch import nn
def get_10x_lr_params(model):
"""
This generator returns all the parameters for the fc layer of the net.
"""
b = [model.linear]
for j in range(len(b)):
for k in b[j].parameters():
if k.requires_grad:
yield k
def get_1x_lr_para... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | datamllab/autovideo | C3D | false | 15,325 | [
"MIT"
] | 233 | 34a702fe9d3114e7128dcff12cb43369e4932919 | https://github.com/datamllab/autovideo/tree/34a702fe9d3114e7128dcff12cb43369e4932919 |
NpairLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def cross_entropy(logits, target, size_average=True):
if size_average:
return torch.mean(torch.sum(-target * F.log_softmax(logits, -1), -1))
else:
return torch.sum(torch.sum(-target * F.log_softmax(logits, -1), -1))
class Npa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Chilydream/SyncNet | NpairLoss | false | 5,006 | [
"MIT"
] | 1 | 8555fe13364a5ecf32fbc0eb72a733c35e256da2 | https://github.com/Chilydream/SyncNet/tree/8555fe13364a5ecf32fbc0eb72a733c35e256da2 |
PatchEmbedding | import torch
import torch.nn as nn
class PatchEmbedding(nn.Module):
"""
small patches embedding
image(B, C, H, W) -> projection(B, emb_dims, H/P, W/P) -> flatten & transpose(B, {(H/P) * (W/P)}, embed_dims)
"""
def __init__(self, image_size=224, patch_size=16, in_channels=3,
embed_dims=768... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Junhojuno/vision-transformer | PatchEmbedding | false | 5,424 | [
"MIT"
] | 1 | 38f8a17967e91e98f767c8e5754081ee8bcd72b4 | https://github.com/Junhojuno/vision-transformer/tree/38f8a17967e91e98f767c8e5754081ee8bcd72b4 |
act_PT | # 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.utils.model_zoo
assert_size_stride = torch._... | Cheeun/FDSR | act_PT | false | 4,978 | [
"MIT"
] | 1 | 28b1c3c102334c5336038d0a0f6e1fceb393659a | https://github.com/Cheeun/FDSR/tree/28b1c3c102334c5336038d0a0f6e1fceb393659a |
SigmoidFocalClassificationLoss | # 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... | CSL-KU/OpenPCDet | SigmoidFocalClassificationLoss | false | 2,086 | [
"Apache-2.0"
] | 0 | 2c5fca0da1521add4b40e6cdfe75d02d4285b83f | https://github.com/CSL-KU/OpenPCDet/tree/2c5fca0da1521add4b40e6cdfe75d02d4285b83f |
ResidualDenseBlock | # 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.utils.data
from torch.utils import data as data
import torch.nn as ... | achrefjarray/ESRGANplus-master | ResidualDenseBlock | false | 1,389 | [
"Apache-2.0"
] | 0 | ba470ec5c565a6dc8b48575b1e185ef6b796aec6 | https://github.com/achrefjarray/ESRGANplus-master/tree/ba470ec5c565a6dc8b48575b1e185ef6b796aec6 |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ChristianLin0420/DeepRL | Critic | false | 2,110 | [
"MIT"
] | 0 | 143a9bfebd264229d9d26fcdc070065225774e04 | https://github.com/ChristianLin0420/DeepRL/tree/143a9bfebd264229d9d26fcdc070065225774e04 |
ChanNorm | import torch
from torch import nn
class ChanNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
std = torch.var(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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | destefani/lightweight-gan | ChanNorm | false | 15,171 | [
"MIT"
] | 1,187 | 5ba61c21c8c9c8d4574a4a3ddd4759f86debf9bf | https://github.com/destefani/lightweight-gan/tree/5ba61c21c8c9c8d4574a4a3ddd4759f86debf9bf |
ScaledDotProductAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Eddie-Hwang/Co-Eye_Motion_Generation | ScaledDotProductAttention | false | 5,094 | [
"MIT"
] | 1 | 8e244680115fb63bc26018cb6b53bcfbd04e9683 | https://github.com/Eddie-Hwang/Co-Eye_Motion_Generation/tree/8e244680115fb63bc26018cb6b53bcfbd04e9683 |
BCEWithLogitsLossWeighted | import torch
import torch.nn as nn
class WeightedLoss(nn.Module):
def __init__(self):
super(WeightedLoss, self).__init__()
self.weighted = False
def generate_weight_mask(self, mask, to_ignore=None):
""" Generates a weight mask where pixel weights are inversely proportional 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | FANG-Xiaolin/uois | BCEWithLogitsLossWeighted | false | 2,225 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
TokenEmbedding | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class TokenEmbedding(nn.Module):
def __init__(self, vocab_size: 'int', emb_... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | LeeSHa00/PyTorch-tutorials-kr | TokenEmbedding | false | 11,846 | [
"BSD-3-Clause"
] | 0 | 6a25b48b1a6cc96ea4edebeede2e419ef73b96fc | https://github.com/LeeSHa00/PyTorch-tutorials-kr/tree/6a25b48b1a6cc96ea4edebeede2e419ef73b96fc |
Swish | import torch
from torch import nn
class Swish(nn.Module):
def forward(self, x):
return torch.sigmoid(x) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | jseppanen/sacking | Swish | false | 3,773 | [
"Apache-2.0"
] | 0 | ff16d9a0cbec2661bc84be33ee4b3987be22228e | https://github.com/jseppanen/sacking/tree/ff16d9a0cbec2661bc84be33ee4b3987be22228e |
mySConv | import torch
import torch.nn as nn
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn import InstanceNorm2d
class mySConv(nn.Module):
def __init__(self, num_filter=128, stride=1, in_channels=128):
super(mySConv, self).__init__()
self.conv = Conv2d(out_channels=num_filter, kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | junhocho/ShapeMatchingGAN | mySConv | false | 15,779 | [
"MIT"
] | 117 | b90e9c2490bfdf62c5da9b1eb6b0cdf0618cf570 | https://github.com/junhocho/ShapeMatchingGAN/tree/b90e9c2490bfdf62c5da9b1eb6b0cdf0618cf570 |
EmbeddingLayer | import torch
import torch.nn as nn
class EmbeddingLayer(nn.Module):
def __init__(self, in_channel, out_channel, img_size, patch_size):
super(EmbeddingLayer, self).__init__()
self.embedding1 = nn.Conv2d(in_channel, out_channel, kernel_size=
patch_size, stride=patch_size, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Phoenix1153/ViT_OOD_generalization | EmbeddingLayer | false | 14,176 | [
"MIT"
] | 51 | 7c5b542e5f5279032c9cd20667cc9e09a86b653d | https://github.com/Phoenix1153/ViT_OOD_generalization/tree/7c5b542e5f5279032c9cd20667cc9e09a86b653d |
ConvRelu | # 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
import t... | ImmortalTurtle/robot-surgery-segmentation | ConvRelu | false | 9,280 | [
"MIT"
] | 0 | dd86cec33d800c1104e9f89296ef8b1d38e968e2 | https://github.com/ImmortalTurtle/robot-surgery-segmentation/tree/dd86cec33d800c1104e9f89296ef8b1d38e968e2 |
NullaryPrimitivesPredefined_v2 | # 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | NullaryPrimitivesPredefined_v2 | false | 17,151 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
Generator | import math
import torch
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, hidden_size, variance_epsilon=1e-12):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(hidden_size))
self.beta = nn.Parameter(torch.zeros(hidden_size))
self.variance... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jh-SYSU/MolRep | Generator | false | 13,884 | [
"MIT"
] | 57 | b2c802d18d41d7db26c19c6dd644098f945e48a1 | https://github.com/Jh-SYSU/MolRep/tree/b2c802d18d41d7db26c19c6dd644098f945e48a1 |
GELU | import torch
import torch.nn as nn
class GELU(nn.Module):
def forward(self, x):
return torch.sigmoid(1.702 * x) * 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
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | endaaman/augmix | GELU | false | 6,645 | [
"Apache-2.0"
] | 1 | 11c86a126c7b261ca178a715763763ca22b20b81 | https://github.com/endaaman/augmix/tree/11c86a126c7b261ca178a715763763ca22b20b81 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | TraceOnBrainOff/pytorch-dc-tts | LayerNorm | false | 18,006 | [
"MIT"
] | 4 | 993a0fbace561729b04df2179b41a0a7ea502e93 | https://github.com/TraceOnBrainOff/pytorch-dc-tts/tree/993a0fbace561729b04df2179b41a0a7ea502e93 |
BertSelfOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.onnx
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, 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
from torch import n... | Alwaysproblem/examples-1 | BertSelfOutput | false | 1,364 | [
"MIT"
] | 0 | 9754fa63ed1931489a21ac1f5b299f945e369a5c | https://github.com/Alwaysproblem/examples-1/tree/9754fa63ed1931489a21ac1f5b299f945e369a5c |
ResNetBlockGroupNorm | # 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.... | wp03052/wolf | ResNetBlockGroupNorm | false | 13,193 | [
"Apache-2.0"
] | 0 | 49a582cafb829a2642db360c7d94c21439247ec7 | https://github.com/wp03052/wolf/tree/49a582cafb829a2642db360c7d94c21439247ec7 |
CrossLayer | import torch
import torch.nn as nn
import torch.optim
class CrossLayer(nn.Module):
def __init__(self, d, dropout):
super().__init__()
self.linear = nn.Linear(d, d)
self.dropout = nn.Dropout(dropout)
def forward(self, x0, x):
return self.dropout(x0 * self.linear(x)) + x
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
import torch.optim
assert_size_stride = torch._C._dynamo.g... | ppmdatix/rtdl | CrossLayer | false | 16,273 | [
"Apache-2.0"
] | 298 | a01ecd9ae6b673f4e82e51f804ffd7031c7350a0 | https://github.com/ppmdatix/rtdl/tree/a01ecd9ae6b673f4e82e51f804ffd7031c7350a0 |
UpsamplingBlock | # 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.utils.data
impor... | andrewjong/Guided-pix2pix | UpsamplingBlock | false | 6,212 | [
"BSD-3-Clause"
] | 1 | 0c6a7b5fde50ad7ea4fb20a6136fc6cb6c4e5542 | https://github.com/andrewjong/Guided-pix2pix/tree/0c6a7b5fde50ad7ea4fb20a6136fc6cb6c4e5542 |
HighwayLayer | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.utils.checkpoint
import torch.utils.tensorboard
def my_xavier_init(m, gain=1):
"""Xavier initialization: weights initialization that tries to make variance of outputs
of a layer equal to variance of its ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ali-senguel/fairo | HighwayLayer | false | 15,249 | [
"MIT"
] | 669 | 1ec5d8ecbdfc782de63a92aad9bf8534110ce762 | https://github.com/ali-senguel/fairo/tree/1ec5d8ecbdfc782de63a92aad9bf8534110ce762 |
adaILN | # 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
from torch.nn.parameter import Parameter
assert_size_stri... | SubZero12556/Cats2dogs_ONNX | adaILN | false | 14,462 | [
"MIT"
] | 2,519 | 52a6a60d519e23b02f0847f0fa9f9ead89ca5f4e | https://github.com/SubZero12556/Cats2dogs_ONNX/tree/52a6a60d519e23b02f0847f0fa9f9ead89ca5f4e |
BertMixedLayer | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn
import torch.nn as nn
class BertAttention(nn.Module):
"""BERT attention layer.
Based on: BERT (pytorch-transformer)
https://github.com/huggingface/transformers
"""
def __init__(self, config) ->None:
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jianrong-Lu/MONAI | BertMixedLayer | false | 7,464 | [
"Apache-2.0"
] | 1 | c319ca8ff31aa980a045f1b913fb2eb22aadb080 | https://github.com/Jianrong-Lu/MONAI/tree/c319ca8ff31aa980a045f1b913fb2eb22aadb080 |
BertLayer | # 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.... | SamarthMM/cs769-assignments | BertLayer | false | 4,736 | [
"MIT"
] | 0 | bac2ad57c50043608276df8e0f21181ef62696c7 | https://github.com/SamarthMM/cs769-assignments/tree/bac2ad57c50043608276df8e0f21181ef62696c7 |
AvgConsensus | # 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... | ZJCV/X3D | AvgConsensus | false | 18,179 | [
"Apache-2.0"
] | 10 | 1635fe4ade5ac5e0bd8f272262cec73c7a12f0fb | https://github.com/ZJCV/X3D/tree/1635fe4ade5ac5e0bd8f272262cec73c7a12f0fb |
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
from torch._inductor.runtime.... | Sai-56/Multi-Agent-DQN-Routing | DQN | false | 5,797 | [
"MIT"
] | 1 | c8e7038bd0dfb69b3bdbdeb60ff9b98bb081e95e | https://github.com/Sai-56/Multi-Agent-DQN-Routing/tree/c8e7038bd0dfb69b3bdbdeb60ff9b98bb081e95e |
Attn | # 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.... | gusalsdmlwlq/DAMD | Attn | false | 12,474 | [
"Apache-2.0"
] | 0 | e98feaf5d9f251132e655bbc5fdb2c080cbed90e | https://github.com/gusalsdmlwlq/DAMD/tree/e98feaf5d9f251132e655bbc5fdb2c080cbed90e |
BilinearUpsample | import torch
from typing import Union
from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class BilinearUpsample(nn.Module):
"""
Overview:
Upsamples the input to the given member varible scale_factor using mode biliner
Interface:
forward
... | 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 Union
from typing import List
import torch.nn as nn
import torch.utils... | Hcnaeg/DI-engine | BilinearUpsample | false | 2,371 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
SoftDetectionModule | # 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
... | liuyuzhenn/d2-net | SoftDetectionModule | false | 15,925 | [
"BSD-3-Clause-Clear"
] | 603 | bc3394934c87cba232144756b1fece4c8ed3aba1 | https://github.com/liuyuzhenn/d2-net/tree/bc3394934c87cba232144756b1fece4c8ed3aba1 |
FocalLoss | import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class FocalLoss(nn.Module):
def __init__(self, weight=None, gamma=1.0, num_classes=80):
super(FocalLoss, self).__init__()
assert gamma >= 0
self.gamma = gamma
self.weight = weight
sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | Feobi1999/unbiased-teacher | FocalLoss | false | 2,256 | [
"MIT"
] | 0 | 9baacec16833bdff0dc089057e50903a92c700cb | https://github.com/Feobi1999/unbiased-teacher/tree/9baacec16833bdff0dc089057e50903a92c700cb |
L1CompositionLoss | # 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 functools
impor... | hejm37/mmediting | L1CompositionLoss | false | 12,485 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
DEC_Conv | import torch
import torch.nn as nn
import torch.nn.functional as F
class crop(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
N, C, H, W = x.shape
x = x[0:N, 0:C, 0:H - 1, 0:W]
return x
class Conv(nn.Module):
def __init__(self, in_channels, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | amonod/udvd | DEC_Conv | false | 1,440 | [
"MIT"
] | 0 | a1ccb777d205255ac68c40efb93dd3996f562c45 | https://github.com/amonod/udvd/tree/a1ccb777d205255ac68c40efb93dd3996f562c45 |
TVLoss | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | MKFMIKU/Enhancing-Loss.pytorch | TVLoss | false | 17,634 | [
"MIT"
] | 6 | 1e8b7cbdc53f6ef912955c19193e0a538e38dc7e | https://github.com/MKFMIKU/Enhancing-Loss.pytorch/tree/1e8b7cbdc53f6ef912955c19193e0a538e38dc7e |
GINPreTransition | import torch
import typing
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, input_dim, hidden_sizes: 'typing.Iterable[int]',
out_dim, activation_function=nn.Sigmoid(), activation_out=None):
super(MLP, self).__init__()
i_h_sizes = [input_dim] + hidden_sizes
self.mlp =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 typing
impor... | mtiezzi/gnn_site | GINPreTransition | false | 7,299 | [
"BSD-3-Clause"
] | 1 | 79a13603db876ac24e66a152104faa8b76e1d8e7 | https://github.com/mtiezzi/gnn_site/tree/79a13603db876ac24e66a152104faa8b76e1d8e7 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.