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 |
|---|---|---|---|---|---|---|---|---|---|---|
LandmarkHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | ai18435136351/facenet-retinaface-pytorch | LandmarkHead | false | 14,784 | [
"MIT"
] | 48 | f228969e46d7402170b708798a210de552879d16 | https://github.com/ai18435136351/facenet-retinaface-pytorch/tree/f228969e46d7402170b708798a210de552879d16 |
ReLU | import torch
import numpy as np
import torch.nn as nn
from numbers import Number
def keep_variance_fn(x):
return x + 0.001
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | THAKAORI/SalsaNext | ReLU | false | 11,919 | [
"MIT"
] | 0 | 855cd7e9ebb83ee62538ba4753a011ada7bbfb6c | https://github.com/THAKAORI/SalsaNext/tree/855cd7e9ebb83ee62538ba4753a011ada7bbfb6c |
BasicModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | YNNEKUW/captum | BasicModel | false | 11,979 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
MyEntropy | import torch
import torch.nn as nn
class MyEntropy(nn.Module):
def __init__(self):
super(MyEntropy, self).__init__()
def forward(self, predictions, target):
b_size = predictions.size(0)
lsm_func = nn.LogSoftmax(dim=1)
logsoftmax = lsm_func(predictions)
loss = -logsoft... | 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
... | atimashov/object_detection | MyEntropy | false | 3,135 | [
"MIT"
] | 0 | 922cd88f429156fa4668c7d718b2665e4ab875fd | https://github.com/atimashov/object_detection/tree/922cd88f429156fa4668c7d718b2665e4ab875fd |
BCEWithLogitsLoss | # 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... | albanie/collaborative-experts | BCEWithLogitsLoss | false | 14,780 | [
"Apache-2.0"
] | 237 | b41defc4fb8de451809014c970ccbe518621909f | https://github.com/albanie/collaborative-experts/tree/b41defc4fb8de451809014c970ccbe518621909f |
RemoveChannelMeanStd | import torch
class RemoveChannelMeanStd(torch.nn.Module):
def forward(self, x):
x2 = x.view(x.size(0), x.size(1), -1)
mean = x2.mean(dim=2).view(x.size(0), x.size(1), 1, 1)
std = x2.std(dim=2).view(x.size(0), x.size(1), 1, 1)
return (x - mean) / std
def get_inputs():
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | cadurosar/graph_kd_dense_cifar100 | RemoveChannelMeanStd | false | 1,624 | [
"MIT"
] | 0 | 84054ab4f8f61c9db3460993661ba7bf1d951b36 | https://github.com/cadurosar/graph_kd_dense_cifar100/tree/84054ab4f8f61c9db3460993661ba7bf1d951b36 |
TokenEmbedding | import torch
import torch.nn as nn
class TokenEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= '1.5.0' else 2
self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
kernel_size=3, pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | TheaperDeng/Informer2020 | TokenEmbedding | false | 14,475 | [
"Apache-2.0"
] | 2,296 | 90e080593e9c345f5f9676359bb3d1618e9aa735 | https://github.com/TheaperDeng/Informer2020/tree/90e080593e9c345f5f9676359bb3d1618e9aa735 |
AttentionBlock | import math
import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
def convert_pad_shape(pad_shape):
"""
Used to get arguments for F.pad
"""
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Royeqiu/Nemo_ASR | AttentionBlock | false | 17,861 | [
"Apache-2.0"
] | 10 | 12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e | https://github.com/Royeqiu/Nemo_ASR/tree/12b91b06dc5e4d0aa29d43bc7e701a93ee5eec4e |
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 ... | ayyyq/T-LSTM | GCN | false | 6,299 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
Cat | # 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... | yifanpu001/PytorchToCaffe | Cat | false | 4,702 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, eps: 'float'=1e-09):
super(DiceLoss, self).__init__()
self.smooth = 1.0
self.eps = eps
def forward(self, y_pred, y_true):
num = y_true.size(0)
probability = torch.sigmoid(y_pred)
... | 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... | Yukei7/Multimodal-Segmentation-Network | DiceLoss | false | 1,277 | [
"MIT"
] | 0 | 0a38aa8bbd2eb87e28209c810438248c0464a240 | https://github.com/Yukei7/Multimodal-Segmentation-Network/tree/0a38aa8bbd2eb87e28209c810438248c0464a240 |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=
5, padding=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size
=3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | agriyakhetarpal/dffml | ConvNet | false | 14,771 | [
"MIT"
] | 171 | f76f2ce94c3972634053377b00e7c16530f7f0a4 | https://github.com/agriyakhetarpal/dffml/tree/f76f2ce94c3972634053377b00e7c16530f7f0a4 |
RegLoss | # 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
... | Ssong24/CenterNet_Custom | RegLoss | false | 9,544 | [
"MIT"
] | 0 | 526ec70f8dfabf9fb9179c9be28ce50fb2a7961c | https://github.com/Ssong24/CenterNet_Custom/tree/526ec70f8dfabf9fb9179c9be28ce50fb2a7961c |
Biaffine | import torch
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
self.weight = nn.Parameter(torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | icewing1996/biaffine-parser | Biaffine | false | 6,851 | [
"MIT"
] | 1 | f5a4ece7ba9a087d81b76dd6a8ea6aa7d90c6c82 | https://github.com/icewing1996/biaffine-parser/tree/f5a4ece7ba9a087d81b76dd6a8ea6aa7d90c6c82 |
kernelPredictor | # 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_... | qbhan/pathembed | kernelPredictor | false | 7,515 | [
"MIT"
] | 1 | c21823529840593bf606e10696f5879e5adb51b2 | https://github.com/qbhan/pathembed/tree/c21823529840593bf606e10696f5879e5adb51b2 |
FactorizedReduce | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
def get_norm_layer(norm, C):
if norm in [None, '', 'none']:
norm_layer = nn.Identity()
elif ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CrispyHarder/ppuda | FactorizedReduce | false | 704 | [
"MIT"
] | 0 | 15950ba297188163eaadd8ab69268ee7f6ffcf2a | https://github.com/CrispyHarder/ppuda/tree/15950ba297188163eaadd8ab69268ee7f6ffcf2a |
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_... | YoniSchirris/SimCLR | Encoder | false | 1,300 | [
"MIT"
] | 0 | a99b7f7d0fdbc5a9747abf70a8b216b328608796 | https://github.com/YoniSchirris/SimCLR/tree/a99b7f7d0fdbc5a9747abf70a8b216b328608796 |
SmoothPinballLoss | # 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, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.gua... | Javicadserres/wind-production-forecast | SmoothPinballLoss | false | 630 | [
"MIT"
] | 0 | 903fbf53d2ea34dc1a63e89cee252e76d6c25876 | https://github.com/Javicadserres/wind-production-forecast/tree/903fbf53d2ea34dc1a63e89cee252e76d6c25876 |
disparityregression | from _paritybench_helpers import _mock_config
import torch
import numpy as np
from torch import nn
import torch.utils.data
from torch.autograd import Variable
import torch.nn.parallel
import torch.utils.data.distributed
class disparityregression(nn.Module):
def __init__(self, maxdisp, cfg):
super(dispari... | 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
from torch import nn
import torch.utils.data
from torch.autograd import Variable
import torch.nn.parallel
import torch.ut... | Sarah20187/X-StereoLab | disparityregression | false | 14,453 | [
"MIT"
] | 192 | 9ae8c1413307e7df91b14a7f31e8a95f9e5754f9 | https://github.com/Sarah20187/X-StereoLab/tree/9ae8c1413307e7df91b14a7f31e8a95f9e5754f9 |
Attention | import math
import torch
import torch.nn.functional as F
import torch.utils.data
def restricted_softmax(src, dim=-1, margin=0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).exp())
return out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | cshjin/pytorch_geometric | Attention | false | 1,753 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f |
Transition | import torch
import torch.nn as nn
import torch.nn.parallel
class Transition(nn.Module):
def __init__(self, in_features, out_features, act_layer=nn.GELU):
super(Transition, self).__init__()
self.act = act_layer()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | druzhkov-paul/T2T-ViT | Transition | false | 12,324 | [
"BSD-3-Clause-Clear"
] | 0 | 819c3ddc4cb6f464d4a9866d8713c7ace42ebf6c | https://github.com/druzhkov-paul/T2T-ViT/tree/819c3ddc4cb6f464d4a9866d8713c7ace42ebf6c |
DenseAtt | import torch
import torch.nn as nn
import torch.optim
import torch.nn.modules.loss
class DenseAtt(nn.Module):
def __init__(self, in_features, dropout):
super(DenseAtt, self).__init__()
self.dropout = dropout
self.linear = nn.Linear(2 * in_features, 1, bias=True)
self.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
import torch.nn as nn
import torch.optim
import torch.nn.modules.loss
assert_siz... | RingBDStack/ACE-HGNN | DenseAtt | false | 17,846 | [
"MIT"
] | 5 | afc610dd838951dcd6c3910795b472566f0c23ca | https://github.com/RingBDStack/ACE-HGNN/tree/afc610dd838951dcd6c3910795b472566f0c23ca |
MatrixTree | # 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
import torch.cuda
import torch.distributed
assert_s... | SivilTaram/dialogue-utterance-rewriter-pytorch | MatrixTree | false | 2,956 | [
"MIT"
] | 0 | 92c2254958b7a1ee9199836f7f2236575270983f | https://github.com/SivilTaram/dialogue-utterance-rewriter-pytorch/tree/92c2254958b7a1ee9199836f7f2236575270983f |
G_Large | # 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_... | RQuispeC/pytorch-ACSCP | G_Large | false | 8,766 | [
"MIT"
] | 25 | c83f08632012c2245250ff9c5140814461db575c | https://github.com/RQuispeC/pytorch-ACSCP/tree/c83f08632012c2245250ff9c5140814461db575c |
TransformerEncoderPostNormLayer | # 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.... | JDBumgardner/stone_ground_hearth_battles | TransformerEncoderPostNormLayer | false | 8,309 | [
"Apache-2.0"
] | 20 | 9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f | https://github.com/JDBumgardner/stone_ground_hearth_battles/tree/9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f |
AutoEncoder | import torch
from torch import nn
class Encoder(nn.Module):
def __init__(self, latent_channel_dim):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=
(3, 3), stride=(1, 1), padding=(1, 1))
self.conv2 = nn.Conv2d(in_channels=16,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | quickgrid/CodeLab | AutoEncoder | false | 10,718 | [
"MIT"
] | 0 | 710ebf107b7938f09c055e806c1fed5574d91308 | https://github.com/quickgrid/CodeLab/tree/710ebf107b7938f09c055e806c1fed5574d91308 |
WeightedBCELoss | import torch
class WeightedBCELoss(torch.nn.Module):
def __init__(self, neg_scale=-1, bce_sum=False):
super(WeightedBCELoss, self).__init__()
self.log_sigmoid = torch.nn.LogSigmoid()
self.neg_scale = neg_scale
self.bce_sum = bce_sum
def forward(self, logits, targets, target_w... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size... | HKUST-KnowComp/MLMET | WeightedBCELoss | false | 8,181 | [
"MIT"
] | 10 | ae1188a929a5ca6a8e087bb091853b328ea2c7e7 | https://github.com/HKUST-KnowComp/MLMET/tree/ae1188a929a5ca6a8e087bb091853b328ea2c7e7 |
DentReLU | import torch
import torch.nn as nn
class DentReLUFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, input, p):
ctx.save_for_backward(input)
ctx.p = p
output = input.clone()
mask1 = p <= input
mask2 = input <= 0
output[mask1 & mask2] = 0
r... | 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... | bfeng/pytorch-cifar | DentReLU | false | 6,329 | [
"MIT"
] | 1 | 6de257bb4b489429785502d487044c55bec62aae | https://github.com/bfeng/pytorch-cifar/tree/6de257bb4b489429785502d487044c55bec62aae |
SEModule | # 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 torchvision import datas... | jasonnoy/COMP5329 | SEModule | false | 10,324 | [
"MIT"
] | 0 | fc17c80b1ac41d788cc0a92d3a033dbe2f9b8b81 | https://github.com/jasonnoy/COMP5329/tree/fc17c80b1ac41d788cc0a92d3a033dbe2f9b8b81 |
Policy | import copy
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def square(a):
return torch.pow(a, 2.0)
class Policy(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(Policy, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | tpbarron/pytorch-ppo | Policy | false | 16,616 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
Normalize | import torch
import torch.nn as nn
import torch.optim
import torch.nn.parallel
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
import torch.nn.parallel
assert_size_s... | abeSanchez/FeatureDecoupling | Normalize | false | 9,655 | [
"MIT"
] | 0 | 2a5ace5d057714b0b8657c75f1cff41e779b0ba4 | https://github.com/abeSanchez/FeatureDecoupling/tree/2a5ace5d057714b0b8657c75f1cff41e779b0ba4 |
TwoHiddenLayerFc | import torch
import torch.nn as nn
import torch.nn.functional as F
class TwoHiddenLayerFc(nn.Module):
def __init__(self, input_shape, out_dim):
super(TwoHiddenLayerFc, self).__init__()
self.fc1 = nn.Linear(input_shape, 200)
self.fc2 = nn.Linear(200, 200)
self.fc3 = nn.Linear(200, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | bokunwang/fedavgpy | TwoHiddenLayerFc | false | 9,799 | [
"MIT"
] | 0 | 22f2fae287f15025e953ab595aa6fd6faedf83d2 | https://github.com/bokunwang/fedavgpy/tree/22f2fae287f15025e953ab595aa6fd6faedf83d2 |
Scale | # 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... | CuongNguyen218/ObjectDetection-OneStageDet | Scale | false | 359 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
ImgPatches | # 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.utils.data
assert_size_stride = torch._C._dyn... | zoosecretbase/TransGAN | ImgPatches | false | 13,184 | [
"MIT"
] | 0 | f2546aec5b80bdddb2c8621a6e011532df3e2d73 | https://github.com/zoosecretbase/TransGAN/tree/f2546aec5b80bdddb2c8621a6e011532df3e2d73 |
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class MultiHeadAttention(nn.Module):
"""
input:
query [N, T_q, query_dim]
key [N, T_k, key_dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Ella77/tacotron2_multispeaker_pytorch | MultiHeadAttention | false | 5,133 | [
"BSD-3-Clause"
] | 1 | 859eab0a8e3bd7545e623ce47fe1563702d38442 | https://github.com/Ella77/tacotron2_multispeaker_pytorch/tree/859eab0a8e3bd7545e623ce47fe1563702d38442 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SHIVOH/DeepReinforcementLearning-DDPG-for-RoboticsControl | Actor | false | 11,901 | [
"MIT"
] | 0 | f3e811a3ae3eb603173c2475bbfe1de91074ecdc | https://github.com/SHIVOH/DeepReinforcementLearning-DDPG-for-RoboticsControl/tree/f3e811a3ae3eb603173c2475bbfe1de91074ecdc |
ZeroConv2d | import torch
from torch import nn
from torch.nn import functional as F
class ZeroConv2d(nn.Module):
def __init__(self, in_channel, out_channel, padding=1):
super().__init__()
self.conv = nn.Conv2d(in_channel, out_channel, 3, padding=0)
self.conv.weight.data.zero_()
self.conv.bias.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | hologerry/glow-pytorch-1 | ZeroConv2d | false | 3,616 | [
"MIT"
] | 0 | 9d3f95f4ff7f0a1361796a9b2554e3c229aad9b7 | https://github.com/hologerry/glow-pytorch-1/tree/9d3f95f4ff7f0a1361796a9b2554e3c229aad9b7 |
Dropout2d | import torch
import torch.backends
import torch.nn.functional as F
from torch.nn.modules.dropout import _DropoutNd
class Dropout2d(_DropoutNd):
"""Randomly zero out entire channels (a channel is a 2D feature map,
e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
batched input is a 2D tensor... | 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.backends
from torch.nn.modules.dropout import _DropoutNd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | ThierryJudge/baal | Dropout2d | false | 11,971 | [
"Apache-2.0"
] | 0 | 8c1b1e2a47e5dd6c6b75d57b8c2152a00ba6b323 | https://github.com/ThierryJudge/baal/tree/8c1b1e2a47e5dd6c6b75d57b8c2152a00ba6b323 |
ConvNet | # 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_... | SGeetansh/dffml | ConvNet | false | 5,810 | [
"MIT"
] | 1 | 04647bdcadef2f7e7b59cdd8ac1e89f17ef1095b | https://github.com/SGeetansh/dffml/tree/04647bdcadef2f7e7b59cdd8ac1e89f17ef1095b |
NormalizeScale | import torch
import torch.nn as nn
import torch.nn.functional as F
class NormalizeScale(nn.Module):
def __init__(self, dim, init_norm=20):
super(NormalizeScale, self).__init__()
self.init_norm = init_norm
self.weight = nn.Parameter(torch.ones(1, dim) * init_norm)
def forward(self, bo... | 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... | sibeiyang/sgmn | NormalizeScale | false | 16,436 | [
"MIT"
] | 130 | 00731b4f2202246d40a36d2a6727c599e6e649aa | https://github.com/sibeiyang/sgmn/tree/00731b4f2202246d40a36d2a6727c599e6e649aa |
HingeDiscriminatorLossCutMix | # 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... | ChristophReich1996/Multi-StyleGAN | HingeDiscriminatorLossCutMix | false | 17,112 | [
"MIT"
] | 7 | 988f2dfea85b3205126b40c61edfb28107eb3173 | https://github.com/ChristophReich1996/Multi-StyleGAN/tree/988f2dfea85b3205126b40c61edfb28107eb3173 |
MultiAccuracy | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | namiyousef/ml-utils | MultiAccuracy | false | 12,813 | [
"MIT"
] | 0 | b67611e9e112f8bbc004a083ce4c9fcd8c1949fa | https://github.com/namiyousef/ml-utils/tree/b67611e9e112f8bbc004a083ce4c9fcd8c1949fa |
GDL | import torch
import numpy as np
from torch import nn
import torch.jit
import torch.nn.functional
def sum_tensor(inp, axes, keepdim=False):
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=Tr... | 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
from torch import nn
import torch.jit
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size... | CamilaGL/nnUNet | GDL | false | 207 | [
"Apache-2.0"
] | 0 | 471ab73a6e4f67fc72d476183b5344be4cccf7ca | https://github.com/CamilaGL/nnUNet/tree/471ab73a6e4f67fc72d476183b5344be4cccf7ca |
GaussMembFunc | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | GradyKurpasi/anfis-pytorch | GaussMembFunc | false | 9,089 | [
"MIT"
] | 0 | 4cce596193a8bc65e632405ca66d116c771033d7 | https://github.com/GradyKurpasi/anfis-pytorch/tree/4cce596193a8bc65e632405ca66d116c771033d7 |
ShortWave | import torch
import torch.nn as nn
import torch.nn.functional as F
class CausalConv1d(nn.Conv1d):
def __init__(self, input_size, hidden_size, kernel_size, stride=1,
dilation=1, groups=1, bias=True, sigmoid=None, tanh=None):
self.left_padding = (kernel_size - 1) * dilation
super(CausalConv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | jpeg729/pytorch-bits | ShortWave | false | 15,736 | [
"MIT"
] | 73 | 5d107094042c27472dfb7dee77506b603f5d3e45 | https://github.com/jpeg729/pytorch-bits/tree/5d107094042c27472dfb7dee77506b603f5d3e45 |
ComplexConv | # 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.utils.data
assert_size_stride = torch._C._dyn... | ishine/multiASR | ComplexConv | false | 3,683 | [
"Apache-2.0"
] | 0 | 991ea2b12ea8ea4a4beeeba42c156e632c389062 | https://github.com/ishine/multiASR/tree/991ea2b12ea8ea4a4beeeba42c156e632c389062 |
NextSentencePrediction | 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 NextSentencePrediction(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, hidden):
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EddieMG/LateTemporalModeling3DCNN | NextSentencePrediction | false | 2,284 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
FakeReLUM | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | GeoffNN/robustness | FakeReLUM | false | 479 | [
"MIT"
] | 0 | 2cefabb5b0ceab62a77e0572f209144d7124cc9f | https://github.com/GeoffNN/robustness/tree/2cefabb5b0ceab62a77e0572f209144d7124cc9f |
GMoF | import torch
import torch.nn as nn
class GMoF(nn.Module):
def __init__(self, rho=1):
super(GMoF, self).__init__()
self.rho = rho
def extra_repr(self):
return 'rho = {}'.format(self.rho)
def forward(self, residual):
squared_res = residual ** 2
dist = torch.div(squ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | MoyGcc/hpcwild | GMoF | false | 14,068 | [
"MIT"
] | 47 | 8ed35c3f188284af2a4dd0d68b09fbceb105c2ba | https://github.com/MoyGcc/hpcwild/tree/8ed35c3f188284af2a4dd0d68b09fbceb105c2ba |
CE_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import 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... | Little0o0/FedML | CE_Loss | false | 5,552 | [
"Apache-2.0"
] | 1 | 720015c90fcfec88d465a81b1e8fb45676dce9fb | https://github.com/Little0o0/FedML/tree/720015c90fcfec88d465a81b1e8fb45676dce9fb |
BboxHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | ai18435136351/facenet-retinaface-pytorch | BboxHead | false | 14,765 | [
"MIT"
] | 48 | f228969e46d7402170b708798a210de552879d16 | https://github.com/ai18435136351/facenet-retinaface-pytorch/tree/f228969e46d7402170b708798a210de552879d16 |
Cosine | # 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.triton_helpers import libdevice
from torch.optim.lr... | johnson7788/mt-dnn | Cosine | false | 3,897 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
PairwiseBCELoss | # 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 abc im... | OatsProduction/flair | PairwiseBCELoss | false | 11,761 | [
"MIT"
] | 0 | 1cf2c9a9ae487e279dce9f6b92c41fa32c4563cf | https://github.com/OatsProduction/flair/tree/1cf2c9a9ae487e279dce9f6b92c41fa32c4563cf |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, n_channels, with_conv=True):
super(Upsample, self).__init__()
self.with_conv = with_conv
self.n_channels = n_channels
self.conv = nn.Conv2d(self.n_channels, self.n_channels, 3, stride=1,
p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | FengNiMa/pytorch_diffusion_model_celebahq | Upsample | false | 8,107 | [
"MIT"
] | 17 | b81e57453066e05d71feb8451bbff766df401386 | https://github.com/FengNiMa/pytorch_diffusion_model_celebahq/tree/b81e57453066e05d71feb8451bbff766df401386 |
HighwayLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.onnx.operators
class HighwayLayer(nn.Module):
def __init__(self, input_dim, transform_activation=F.relu,
gate_activation=F.softmax, gate_bias=-2):
super().__init__()
self.highway_transform_activation = transfo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gardenia22/translate | HighwayLayer | false | 6,725 | [
"BSD-3-Clause"
] | 1 | 0be57c8f55b52fc9d39197efa02e05d1c1cda024 | https://github.com/gardenia22/translate/tree/0be57c8f55b52fc9d39197efa02e05d1c1cda024 |
cnn_4layer_LeakyRelu | import torch
import torch.nn as nn
import torch.nn.functional as F
class cnn_4layer_LeakyRelu(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=256, alpha=0.1):
super(cnn_4layer_LeakyRelu, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 4, stride=2, padding=1)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Mahoumaru/auto_LiRPA | cnn_4layer_LeakyRelu | false | 11,686 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
BCEDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | oskarnatan/RGBDVS-fusion | BCEDiceLoss | false | 7,425 | [
"MIT"
] | 1 | 5e560f54442d387a86e3a469107cf65859693987 | https://github.com/oskarnatan/RGBDVS-fusion/tree/5e560f54442d387a86e3a469107cf65859693987 |
AffinityLoss | # 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... | RMeli/gnina-torch | AffinityLoss | false | 17,827 | [
"MIT"
] | 5 | eb57e2a62628d39f2a66e7fa1748e80705366761 | https://github.com/RMeli/gnina-torch/tree/eb57e2a62628d39f2a66e7fa1748e80705366761 |
ReLUDeepLiftModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ngduduong/captum | ReLUDeepLiftModel | false | 4,076 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
GCN | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Linear(nn.Module):
def __init__(self, in_features, out_features, dropout, bias=False):
super(Linear, self).__init__()
self.dropout = dropout
self.in_features = in_features
self.out_features = out_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DongHande/PT_propagation_then_training | GCN | false | 7,996 | [
"MIT"
] | 21 | 3f346ff161d2a0b807e3c0269ad26a7266305cc3 | https://github.com/DongHande/PT_propagation_then_training/tree/3f346ff161d2a0b807e3c0269ad26a7266305cc3 |
SpatialAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | HT-hlf/mmdetection_miner-2.22.0 | SpatialAttention | false | 2,311 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
FEM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from ma... | juanmed/FaceDetection-DSFD | FEM | false | 10,328 | [
"Apache-2.0"
] | 0 | 23650ca492444f9f052ca9b8db8b068a9be5bc68 | https://github.com/juanmed/FaceDetection-DSFD/tree/23650ca492444f9f052ca9b8db8b068a9be5bc68 |
AttentionModule | # 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 ... | cloudcjf/SG_PR | AttentionModule | false | 15,043 | [
"MIT"
] | 105 | 1339d00811ea3c4c18963efa24bf6fc778e15794 | https://github.com/cloudcjf/SG_PR/tree/1339d00811ea3c4c18963efa24bf6fc778e15794 |
TranspConv3DBlock | import torch
import torch.nn as nn
class TranspConv3DBlock(nn.Module):
def __init__(self, in_planes, out_planes):
super().__init__()
self.block = nn.ConvTranspose3d(in_planes, out_planes, kernel_size=
2, stride=2, padding=0, output_padding=0)
def forward(self, x):
return ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Siyuan89/self-attention-cv | TranspConv3DBlock | false | 14,427 | [
"MIT"
] | 759 | b39cde2fb68e05351bf3bc8048f4af13bbab256a | https://github.com/Siyuan89/self-attention-cv/tree/b39cde2fb68e05351bf3bc8048f4af13bbab256a |
Oracle | # 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
... | yanxurui/portfolio | Oracle | false | 4,605 | [
"MIT"
] | 0 | 032cf47ccac1c5815fd4827bf0d5f3cf43cec990 | https://github.com/yanxurui/portfolio/tree/032cf47ccac1c5815fd4827bf0d5f3cf43cec990 |
ChanNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Netruk44/stylegan2-deepspeed | ChanNorm | false | 5,651 | [
"MIT"
] | 1 | d6efe64a2f8cdfa9477d2229652c5e1a2348d52d | https://github.com/Netruk44/stylegan2-deepspeed/tree/d6efe64a2f8cdfa9477d2229652c5e1a2348d52d |
MaskedTemporalPooling | import torch
from typing import Optional
import torch.utils.data
import torch.nn
class MaskedTemporalPooling(torch.nn.Module):
"""
Applies temporal pooling operations on masked inputs. For each pooling operation
all masked values are ignored.
"""
def __init__(self, method: 'str'):
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn
assert_size_stride = torch._C._dynamo.guards.asse... | zijian-hu/pytorchvideo | MaskedTemporalPooling | false | 4,703 | [
"Apache-2.0"
] | 0 | 51589b100437af2285c56ce2ccc7ccecb7f9b18b | https://github.com/zijian-hu/pytorchvideo/tree/51589b100437af2285c56ce2ccc7ccecb7f9b18b |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | Gregory-Eales/mban | ResBlock | false | 5,233 | [
"Apache-2.0"
] | 1 | d8b35db51c7e601b1db777d9a80343600374250b | https://github.com/Gregory-Eales/mban/tree/d8b35db51c7e601b1db777d9a80343600374250b |
SimpleClampModel | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | briancoutinho/glow | SimpleClampModel | false | 12,556 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
MultiheadAttention | import torch
import torch.nn as nn
class MultiheadAttention(nn.Module):
"""A warpper for torch.nn.MultiheadAttention.
This module implements MultiheadAttention with residual connection,
and positional encoding used in DETR is also passed as input.
Args:
embed_dims (int): The embedding dimens... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CityU-AIM-Group/HTD | MultiheadAttention | false | 17,123 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
GELayerv1 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data.distributed
class GELayerv1(nn.Module):
def __init__(self):
super(GELayerv1, self).__init__()
self.avg_pool = nn.AvgPool2d(kernel_size=(15, 15), stride=8)
self.sigmod = nn.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
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distributed
assert... | SSusantAchary/OctaveConv_pytorch | GELayerv1 | false | 14,340 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 5)
self.conv2 = nn.Conv2d(32, 64, 5)
self.conv3 = nn.Conv2d(64, 128, 5)
x = torch.randn(50, 50).view(-1, 1, 50, 50)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Nijaoui-Wassim/Omrika | Net | false | 6,052 | [
"Apache-2.0"
] | 1 | 526d466d10e8461f4b23b42308d3e77607ea9812 | https://github.com/Nijaoui-Wassim/Omrika/tree/526d466d10e8461f4b23b42308d3e77607ea9812 |
SelfAttentionLayer | import torch
from torch import nn
from torch.nn import functional as F
class SelfAttentionLayer(nn.Module):
def __init__(self, dim, da, alpha=0.2, dropout=0.5):
super(SelfAttentionLayer, self).__init__()
self.dim = dim
self.da = da
self.alpha = alpha
self.dropout = dropout... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | yuka1369/KBRD | SelfAttentionLayer | false | 13,147 | [
"MIT"
] | 0 | fc0f723c448299f00eef6daabff675640a930c26 | https://github.com/yuka1369/KBRD/tree/fc0f723c448299f00eef6daabff675640a930c26 |
Generator | import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import *
class Generator(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model, vocab):
super(Generator, self).__init__()
self.proj = nn.Linear(d_model, vocab)
d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GeorgeKostenkov/ImageCaptioning.pytorch | Generator | false | 11,443 | [
"MIT"
] | 0 | 8f17433fdaba2f89774e45ad5a3a88b880932ee6 | https://github.com/GeorgeKostenkov/ImageCaptioning.pytorch/tree/8f17433fdaba2f89774e45ad5a3a88b880932ee6 |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
if target.ndim == 3:
target = target.reshape(-1, target.shape[2])
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | helloMickey/self-critical.pytorch | LanguageModelCriterion | false | 10,177 | [
"MIT"
] | 0 | 3a26111012099e13daeb688136fea45186127935 | https://github.com/helloMickey/self-critical.pytorch/tree/3a26111012099e13daeb688136fea45186127935 |
MY_NGM_FFNN | import random
import torch
import torch.nn as nn
from collections import defaultdict
import torch.nn.functional as F
import torch.optim as optim
class MY_NGM_FFNN(nn.Module):
def __init__(self, alpha, input_dim, hidden1_dim, hidden2_dim,
output_dim, device=torch.device('cpu')):
super(MY_NGM_FFNN,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | calpoly-bioinf/knowledge_driven_modeling | MY_NGM_FFNN | false | 1,644 | [
"MIT"
] | 0 | dbe55d5bb07f7c5a1834a21fde8833f295e3ac96 | https://github.com/calpoly-bioinf/knowledge_driven_modeling/tree/dbe55d5bb07f7c5a1834a21fde8833f295e3ac96 |
SimpleMulModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleMulModule | false | 14,668 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
gen_ba_cf | import torch
from torch import nn
import torch.nn.functional as F
class gen_ba_cf(nn.Module):
def __init__(self):
super().__init__()
self.d1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3,
stride=1, padding=1)
self.d2 = nn.Conv2d(in_channels=8, out_channels=16, kerne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | layel2/layyer-lib | gen_ba_cf | false | 3,903 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
RingLoss | import torch
import warnings
import torch.nn as nn
from torchvision.transforms import *
class RingLoss(nn.Module):
"""Ring loss.
Reference:
Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
"""
def __init__(self):
super(RingLoss, 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
from torch._inductor.runtime.triton_helpers import libdevice
import warnings
import torch.nn as nn
from torchvision.transforms import *
asse... | DRACOyu/deep-person-reid | RingLoss | false | 5,195 | [
"MIT"
] | 1 | 8ca8be28c204dbc37cff76e77691f29045773aa2 | https://github.com/DRACOyu/deep-person-reid/tree/8ca8be28c204dbc37cff76e77691f29045773aa2 |
ScoreCap | import torch
from torch import nn
import torch.nn
import torch.optim
class ScoreCap(nn.Module):
def __init__(self, cap: 'float'):
super().__init__()
self.cap = cap
def forward(self, input):
return torch.clip(input, max=self.cap)
def get_inputs():
return [torch.rand([4, 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
from torch import nn
import torch.nn
import torch.optim
assert_size_stride = torch._C._dy... | mcx/ReAgent | ScoreCap | false | 4,113 | [
"BSD-3-Clause"
] | 0 | 57b58a8b3a6b74bb87a197b73a6cd108ddad895e | https://github.com/mcx/ReAgent/tree/57b58a8b3a6b74bb87a197b73a6cd108ddad895e |
SelfAttentionLayer | # 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.... | yuka1369/KBRD | SelfAttentionLayer | false | 13,147 | [
"MIT"
] | 0 | fc0f723c448299f00eef6daabff675640a930c26 | https://github.com/yuka1369/KBRD/tree/fc0f723c448299f00eef6daabff675640a930c26 |
CosineBasisLinear | import torch
import numpy as np
from torch import nn
def cosine_basis_functions(x, n_basis_functions=64):
"""Cosine basis functions used to embed quantile thresholds.
Args:
x (torch.Tensor): Input.
n_basis_functions (int): Number of cosine basis functions.
Returns:
ndarray: Embed... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | KtechB/pfrl | CosineBasisLinear | false | 2,471 | [
"MIT"
] | 0 | 9be4726d327b7ce32d9008c40119c98c93febad5 | https://github.com/KtechB/pfrl/tree/9be4726d327b7ce32d9008c40119c98c93febad5 |
cha_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import 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... | Weepingchestnut/OVSR | cha_loss | false | 1,203 | [
"Apache-2.0"
] | 0 | 11554a3b1072d50a8c88cf59b4b986df1fda73f9 | https://github.com/Weepingchestnut/OVSR/tree/11554a3b1072d50a8c88cf59b4b986df1fda73f9 |
GE2ELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class GE2ELoss(nn.Module):
def __init__(self, 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.... | greenstar1151/pytorch-benchmark | GE2ELoss | false | 10,464 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b |
InnerProductModel | import torch
class InnerProductModel(torch.nn.Module):
@staticmethod
def is_valid_model_type(model_type):
raise NotImplementedError
@staticmethod
def get_model_from_type(model_type):
raise NotImplementedError
@property
def loss_criterion(self):
return torch.nn.MSELos... | 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
reinterpret... | SheepiesLab/plato | InnerProductModel | false | 12,079 | [
"Apache-2.0"
] | 0 | 9f5bbfa4b6952d1b3af24be409982d303d54a169 | https://github.com/SheepiesLab/plato/tree/9f5bbfa4b6952d1b3af24be409982d303d54a169 |
CausalAttentionSortNet | import torch
from torch.nn import functional as F
from torch import nn
def bucket(buckets, t, dim=1):
shape = list(t.shape)
shape[dim:dim + 1] = [buckets, -1]
return t.reshape(*shape)
def differentiable_topk(x, k, temperature=1.0):
*_, n, dim = x.shape
topk_tensors = []
for i in range(k):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lucidrains/sinkhorn-transformer | CausalAttentionSortNet | false | 15,994 | [
"MIT"
] | 216 | 531bdbe46dfc2abd20183dbcede669bc9df567c6 | https://github.com/lucidrains/sinkhorn-transformer/tree/531bdbe46dfc2abd20183dbcede669bc9df567c6 |
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.... | Shawn-Guo-CN/EGG | TransformerEncoderLayer | false | 2,892 | [
"MIT"
] | 0 | 0a5b258108e2cd1c873d7f67e8c92551bb3d809c | https://github.com/Shawn-Guo-CN/EGG/tree/0a5b258108e2cd1c873d7f67e8c92551bb3d809c |
ArcFaceLoss | # 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 math... | i-pan/kaggle-melanoma | ArcFaceLoss | false | 15,586 | [
"MIT"
] | 68 | caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9 | https://github.com/i-pan/kaggle-melanoma/tree/caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9 |
SE | import torch
import torch.nn as nn
import torch.nn.functional as F
class SE(nn.Module):
"""Squeeze-and-Excitation block."""
def __init__(self, in_planes, se_planes):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | BXuan694/basemodel-pytorch | SE | false | 4,892 | [
"MIT"
] | 1 | a36c96904580be902e323db17eebbe2ea1f54176 | https://github.com/BXuan694/basemodel-pytorch/tree/a36c96904580be902e323db17eebbe2ea1f54176 |
PretrainedUNet | import torch
import torchvision
class Block(torch.nn.Module):
def __init__(self, in_channels, mid_channel, out_channels, batch_norm=False
):
super().__init__()
self.conv1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=
mid_channel, kernel_size=3, padding=1)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torchvision
assert_siz... | amrane99/lung-segmentation | PretrainedUNet | false | 12,991 | [
"MIT"
] | 0 | ab29db75ac78918da5cbf66b830acaf36cf7b44a | https://github.com/amrane99/lung-segmentation/tree/ab29db75ac78918da5cbf66b830acaf36cf7b44a |
RMSE | import torch
import torch.nn as nn
from torch.optim import *
class RMSE(nn.Module):
def __init__(self):
super().__init__()
def forward(self, outputs, target, *args):
val_pixels = (target > 0.001).float()
err = (target * val_pixels - outputs * val_pixels) ** 2
loss = torch.sum... | 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.optim import *
assert_size_stride = torch._C._... | kakaxi314/GuideNet | RMSE | false | 15,778 | [
"MIT"
] | 142 | 9f53b4086d707e94d48a47bbac7dd87aaba9fdea | https://github.com/kakaxi314/GuideNet/tree/9f53b4086d707e94d48a47bbac7dd87aaba9fdea |
MyUpsample2 | # 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.nn.parallel
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.... | Dou-Yiming/YouRefIt_ERU | MyUpsample2 | false | 7,989 | [
"MIT"
] | 13 | 2a8e849380ed2d253c467b1af744a514bc171372 | https://github.com/Dou-Yiming/YouRefIt_ERU/tree/2a8e849380ed2d253c467b1af744a514bc171372 |
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 ... | Barisimre/TD3-Generative | Critic | false | 4,895 | [
"MIT"
] | 1 | 434419b020b88010f09f194c40feac1d420b2086 | https://github.com/Barisimre/TD3-Generative/tree/434419b020b88010f09f194c40feac1d420b2086 |
SurfaceClassifier | # 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... | KORguy/PIFu_Part | SurfaceClassifier | false | 9,299 | [
"MIT"
] | 0 | bd199d439a94f8bc8b4036898b0f1ec01e56ab9e | https://github.com/KORguy/PIFu_Part/tree/bd199d439a94f8bc8b4036898b0f1ec01e56ab9e |
GlobalAvgPool | # 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... | bjuncek/video_feature_extractor | GlobalAvgPool | false | 12,168 | [
"Apache-2.0"
] | 0 | cac06b450d1164beb3f3710d5018c19091bce348 | https://github.com/bjuncek/video_feature_extractor/tree/cac06b450d1164beb3f3710d5018c19091bce348 |
BatchNorm2D_noparam | # 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... | ericlearning/General-I2I | BatchNorm2D_noparam | false | 6,652 | [
"MIT"
] | 1 | ba7c5d6a582bdf2e7b53c0e20c31e9097b1883a9 | https://github.com/ericlearning/General-I2I/tree/ba7c5d6a582bdf2e7b53c0e20c31e9097b1883a9 |
BertLayer | from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT"s gelu is slightly different (and gives slightly different ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IsmaelElsharkawi/new_pororo_repo | BertLayer | false | 8,820 | [
"MIT"
] | 19 | 4617083b420615b8a3eb0f44d02e4e91a8f407f7 | https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7 |
VideoAttText | import torch
from torch import nn
import torch.utils.checkpoint
from collections import OrderedDict
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(LayerNorm, self).__init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jiazheng-xing/Swin_Multimodal | VideoAttText | false | 10,341 | [
"MIT"
] | 0 | 7bc41977fe7d8d4f0091852c63a6a32a0fada0fb | https://github.com/jiazheng-xing/Swin_Multimodal/tree/7bc41977fe7d8d4f0091852c63a6a32a0fada0fb |
DDPGConvBody | 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 DDPGConvBody(nn.Module):
def __init__(self, in_channels=4):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Fieps1/p3-tennis | DDPGConvBody | false | 512 | [
"MIT"
] | 0 | 29f3dab5810d7cd7f84120416a615956d266c256 | https://github.com/Fieps1/p3-tennis/tree/29f3dab5810d7cd7f84120416a615956d266c256 |
GAE | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GAE(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(GAE, self).__init__()
self.num_inputs = num_inputs
self.num_outputs = num_outputs
self.fc = nn.Linear(num_inputs, 128)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | g6ling/Pytorch-Cartpole | GAE | false | 15,392 | [
"MIT"
] | 116 | ecb7b622cfefe825ac95388cceb6752413d90a2a | https://github.com/g6ling/Pytorch-Cartpole/tree/ecb7b622cfefe825ac95388cceb6752413d90a2a |
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