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 |
|---|---|---|---|---|---|---|---|---|---|---|
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | bcahlit/MP-DQN | Actor | false | 1,526 | [
"MIT"
] | 0 | d80d34680e20192134f39e5b7c43abbc6bff3ba1 | https://github.com/bcahlit/MP-DQN/tree/d80d34680e20192134f39e5b7c43abbc6bff3ba1 |
UpBlock | # 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_... | Whatsetsthisend/mmocr | UpBlock | false | 11,966 | [
"Apache-2.0"
] | 0 | 6444b3226a10162378b5ed3109991cc618e89fa4 | https://github.com/Whatsetsthisend/mmocr/tree/6444b3226a10162378b5ed3109991cc618e89fa4 |
ContrastiveLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class ContrastiveLoss(nn.Module):
def __init__(self, margin=1.5):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, weight):
pairdist = F.pairwise_distance(output1, outpu... | 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... | kvswim/kv_jhu_cv | ContrastiveLoss | false | 3,864 | [
"MIT"
] | 0 | 2ddf7a9d497aef116a7c043157b8631cea45000d | https://github.com/kvswim/kv_jhu_cv/tree/2ddf7a9d497aef116a7c043157b8631cea45000d |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, gamma=1, weight=None, balance=0.75):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.weight = weight
self.balance = balance
return
def forward(s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | quqixun/ECG-MLC | FocalLoss | false | 10,731 | [
"MIT"
] | 0 | 582d68200b79e3b2ac322c1ed17630727e283605 | https://github.com/quqixun/ECG-MLC/tree/582d68200b79e3b2ac322c1ed17630727e283605 |
PatchEmbed | import torch
import torch.nn as nn
class PatchEmbed(nn.Module):
def __init__(self, img_size, patch_size, in_c=3, embed_dim=512):
super(PatchEmbed, self).__init__()
self.img_size = img_size
self.patch_size = patch_size
self.n_patches = (img_size // patch_size) ** 2
self.pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | AgamChopra/WGAN-GP | PatchEmbed | false | 1,926 | [
"MIT"
] | 0 | cbe15f4d2ef2ebaef477524103cbda0741098186 | https://github.com/AgamChopra/WGAN-GP/tree/cbe15f4d2ef2ebaef477524103cbda0741098186 |
GHMR | # 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... | ChHanXiao/mmdetection | GHMR | false | 9,156 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 |
series_decomp_multi | import math
import torch
import torch.nn as nn
class moving_avg(nn.Module):
"""
Moving average block to highlight the trend of time series
"""
def __init__(self, kernel_size, stride):
super(moving_avg, self).__init__()
self.kernel_size = kernel_size
self.avg = nn.AvgPool1d(ker... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MAZiqing/FEDformer | series_decomp_multi | false | 17,652 | [
"MIT"
] | 7 | 7914d39df829494a8172afb9676982c3789d491d | https://github.com/MAZiqing/FEDformer/tree/7914d39df829494a8172afb9676982c3789d491d |
NormalizationLayer | import torch
import torch.nn as nn
class NormalizationLayer(nn.Module):
def __init__(self):
super(NormalizationLayer, self).__init__()
def forward(self, x, epsilon=1e-08):
return x * ((x ** 2).mean(dim=1, keepdim=True) + epsilon).rsqrt()
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AnetaKaczynska/video-GAN | NormalizationLayer | false | 30 | [
"BSD-3-Clause"
] | 0 | e30e54c18265c658a65b1b26b57b4f499b58bfc6 | https://github.com/AnetaKaczynska/video-GAN/tree/e30e54c18265c658a65b1b26b57b4f499b58bfc6 |
LinearWithConstraint | import torch
import torch.nn as nn
class LinearWithConstraint(nn.Linear):
def __init__(self, *args, max_norm=1, **kwargs):
self.max_norm = max_norm
super(LinearWithConstraint, self).__init__(*args, **kwargs)
def forward(self, x):
self.weight.data = torch.renorm(self.weight.data, p=2,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | jiuney/XAI606-EEGNet | LinearWithConstraint | false | 6,967 | [
"MIT"
] | 1 | 45ff28630ed1b09d0853f2cfb148a5dd2693e5ab | https://github.com/jiuney/XAI606-EEGNet/tree/45ff28630ed1b09d0853f2cfb148a5dd2693e5ab |
IBNbResInitBlock | import torch
import torch.nn as nn
import torch.utils.data
def ibnb_conv7x7_block(in_channels, out_channels, stride=1, padding=3, bias
=False, activate=True):
"""
7x7 version of the IBN(b)-ResNet specific convolution block.
Parameters:
----------
in_channels : int
Number of input chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | earhian/imgclsmob | IBNbResInitBlock | false | 6,629 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
decoder6 | # 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.... | Holmes-Alan/RefVAE | decoder6 | false | 8,304 | [
"MIT"
] | 13 | 836b8f1168f1b0f923b609a48e202ace7806f79c | https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c |
LinearBlock | import torch
from scipy.stats import truncnorm
def truncated_normal_(tensor, mean=0.0, std=1.0):
values = truncnorm.rvs(-2, 2, size=tensor.shape)
values = mean + std * values
tensor.copy_(torch.from_numpy(values))
return tensor
def fc_init_(module):
if hasattr(module, 'weight') and module.weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | aylagulcu/TripletMAML | LinearBlock | false | 9,904 | [
"MIT"
] | 0 | 98cb4a23847ec24937963292cd6f162bcbf724ba | https://github.com/aylagulcu/TripletMAML/tree/98cb4a23847ec24937963292cd6f162bcbf724ba |
Hflip | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-1])
class Hflip(nn.Module):
"""Horizontally flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | 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... | ChristophReich1996/kornia | Hflip | false | 277 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
L1Loss | # 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
... | atoaiari/mmpose | L1Loss | false | 6,280 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
NormalLoss | # 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
import torch.nn as ... | Khoronus/MonoDepth-FPN-PyTorch | NormalLoss | false | 719 | [
"MIT"
] | 0 | 6e41e297723d1490c537e04afff905c61d6f0ff8 | https://github.com/Khoronus/MonoDepth-FPN-PyTorch/tree/6e41e297723d1490c537e04afff905c61d6f0ff8 |
TorchSub | # 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... | PogChamper/torch2trt | TorchSub | false | 14,225 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
Encoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Scaled_Dot_Product_Attention(nn.Module):
"""Scaled Dot-Product Attention """
def __init__(self):
super(Scaled_Dot_Product_Attention, self).__init__()
def forward(self, Q, K, V, scale=None):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | NTDXYG/Text-Classify-based-pytorch | Encoder | false | 8,641 | [
"Apache-2.0"
] | 20 | b12a264a0ea64b2f8b46fafd5383ef0a8025ef2f | https://github.com/NTDXYG/Text-Classify-based-pytorch/tree/b12a264a0ea64b2f8b46fafd5383ef0a8025ef2f |
SelfAttention | # 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.... | evelynmitchell/rasp | SelfAttention | false | 6,665 | [
"MIT"
] | 1 | 9b33bbf911e6c4ff018c9883c39eb698c0abe803 | https://github.com/evelynmitchell/rasp/tree/9b33bbf911e6c4ff018c9883c39eb698c0abe803 |
NegativeScaledDotProduct | # 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
import torch.utils.data.dataloader
import torch.nn
assert_size_stride = torch._C... | adriensas/flair | NegativeScaledDotProduct | false | 9,742 | [
"MIT"
] | 0 | f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 | https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 |
HanoiEnvEncoder | import torch
import torch.nn.functional as F
import torch.nn as nn
class HanoiEnvEncoder(nn.Module):
"""
Implement an encoder (f_enc) specific to the List environment. It encodes observations e_t into
vectors s_t of size D = encoding_dim.
"""
def __init__(self, observation_dim, encoding_dim):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | geektoni/AlphaNPI | HanoiEnvEncoder | false | 3,535 | [
"MIT"
] | 0 | ab48cb9cfb74f3960e264da4f3eb2d6917bfb9c9 | https://github.com/geektoni/AlphaNPI/tree/ab48cb9cfb74f3960e264da4f3eb2d6917bfb9c9 |
FCLayer | import torch
import torch.nn as nn
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.0,
use_activation=True):
super().__init__()
self.use_activation = use_activation
self.dropout = nn.Dropout(dropout_rate)
self.linear = nn.Linear(input_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.triton_helpers import libdevice
import torch.nn as ... | alexandre-do/r-bert | FCLayer | false | 12,071 | [
"Apache-2.0"
] | 0 | 4e35bcbb0fe0602e708e18010e2394ebbfb074c4 | https://github.com/alexandre-do/r-bert/tree/4e35bcbb0fe0602e708e18010e2394ebbfb074c4 |
ScalingFactor | # 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 logging
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_c... | Open-Catalyst-Project/baselines | ScalingFactor | false | 17,799 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
MaxPool | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
class MaxPool(nn.Module):
def __init__(self, kernel_size, stride=1, padding=1, zero_pad=False):
super(MaxPool, self).__init__()
self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_pad else None
self.pool = nn.Max... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.optim
import torch.utils.data
assert_size_stride = tor... | LongKt7/Face_Recognize_Pytorch | MaxPool | false | 5,559 | [
"MIT"
] | 1 | baa02e633d379abe1001c8b8acb942617177329c | https://github.com/LongKt7/Face_Recognize_Pytorch/tree/baa02e633d379abe1001c8b8acb942617177329c |
IoULoss | # 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... | Latterlig96/DCUnet | IoULoss | false | 8,489 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""Construct a layernorm module (See citation for details)."""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(fe... | 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_... | Aminah92/saint | LayerNorm | false | 16,890 | [
"MIT"
] | 7 | e18f5d5d093dce458c7d427eed4a375021c05bb9 | https://github.com/Aminah92/saint/tree/e18f5d5d093dce458c7d427eed4a375021c05bb9 |
MyGroupNorm | import torch
from torch import nn
class AffineChannelwise(nn.Module):
def __init__(self, num_channels):
super().__init__()
self.num_channels = num_channels
self.register_parameter('weight', nn.Parameter(torch.ones(
num_channels)))
self.register_parameter('bias', nn.Par... | 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... | dniku/dl-norms | MyGroupNorm | false | 6,583 | [
"MIT"
] | 1 | 0f1eef942bd318ac988ec7dfa9caea300d17e82a | https://github.com/dniku/dl-norms/tree/0f1eef942bd318ac988ec7dfa9caea300d17e82a |
MetaBilinear | # 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 re
import warnings
import torch.nn as nn
from collections import OrderedDict
assert_size_stride = torch._C._dynamo.guards.assert_size... | Steffen-Wolf/pytorch-meta | MetaBilinear | false | 9,554 | [
"MIT"
] | 0 | d2dfb902cfa49574eac898045c8e9cf64ce29f96 | https://github.com/Steffen-Wolf/pytorch-meta/tree/d2dfb902cfa49574eac898045c8e9cf64ce29f96 |
ClassHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | FacePerceiver/facer | ClassHead | false | 8,143 | [
"MIT"
] | 12 | cbb01dc457f3713050e89af7b2c9c0d98663842c | https://github.com/FacePerceiver/facer/tree/cbb01dc457f3713050e89af7b2c9c0d98663842c |
ModMSELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | HeosSacer/saliency_web_mapper | ModMSELoss | false | 11,485 | [
"MIT"
] | 0 | a2fd744b821086dc1a0af0498361207f7bcddee6 | https://github.com/HeosSacer/saliency_web_mapper/tree/a2fd744b821086dc1a0af0498361207f7bcddee6 |
NormedLinear | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch
import torch.nn.functional as F
from torch.nn import Parameter
class NormedLinear(nn.Module):
def __init__(self, in_features, out_features):
super(NormedLinear, self).__init__()
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | caisarl76/LDAM-DRW | NormedLinear | false | 9,871 | [
"MIT"
] | 0 | f3d7e98ec40bfbf2c9a806387764a54c5a31d22d | https://github.com/caisarl76/LDAM-DRW/tree/f3d7e98ec40bfbf2c9a806387764a54c5a31d22d |
Conv2dWS | # 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... | cooked-sashimi/Yet-Another-YOLOv4-Pytorch | Conv2dWS | false | 15,083 | [
"MIT"
] | 133 | c884ef8849987a75b0e17eba1b739c22d3782e90 | https://github.com/cooked-sashimi/Yet-Another-YOLOv4-Pytorch/tree/c884ef8849987a75b0e17eba1b739c22d3782e90 |
C3 | # 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 co... | xxchenxx/otdd | C3 | false | 13,132 | [
"MIT"
] | 0 | e63d1d170fed36957052b7bb0a0af1553b980381 | https://github.com/xxchenxx/otdd/tree/e63d1d170fed36957052b7bb0a0af1553b980381 |
pHAbsModel | import torch
import numpy as np
from torch import nn
class pHAbsLayer(nn.Module):
"""Custom pHAbs Layer: Amax/(1+e^(pKa-pH)/phi)"""
def __init__(self):
super().__init__()
weights = np.random.normal([1, 7.6, 0.5], [0.2, 0.5, 0.1])
weights = torch.from_numpy(weights)
self.weight... | 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
from torch import nn
assert_size_stride = torch._C._dy... | rokapre/Nonlinear_Regression | pHAbsModel | false | 12,944 | [
"MIT"
] | 0 | d705f6a010fc0bf000531c967ffcf8ed79a5f92e | https://github.com/rokapre/Nonlinear_Regression/tree/d705f6a010fc0bf000531c967ffcf8ed79a5f92e |
LogSumExpPool | import torch
from torch import nn
class LogSumExpPool(nn.Module):
def __init__(self, gamma):
super(LogSumExpPool, self).__init__()
self.gamma = gamma
def forward(self, feat_map):
"""
Numerically stable implementation of the operation
Arguments:
feat_map(Te... | 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... | DavidChenL/Chexpert | LogSumExpPool | false | 13,569 | [
"Apache-2.0"
] | 202 | 0300057d3a51301cff35a65f79729436678b4a79 | https://github.com/DavidChenL/Chexpert/tree/0300057d3a51301cff35a65f79729436678b4a79 |
LossPredLoss | import torch
import torch.nn as nn
class LossPredLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred_loss, target_loss):
pred_loss = (pred_loss - pred_loss.flip(0))[:len(pred_loss) // 2]
target_loss = (target_loss - target_loss.flip(0))[:len(target_loss) //... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | KMU-AELAB/Active_Learning | LossPredLoss | false | 2,449 | [
"MIT"
] | 0 | bc569c16b5f12b58989a8f3db59b7eb4e35cce1b | https://github.com/KMU-AELAB/Active_Learning/tree/bc569c16b5f12b58989a8f3db59b7eb4e35cce1b |
AdaptiveSin | import torch
from torch.nn.parameter import Parameter
class AdaptiveSin(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._d... | ndem0/PINA | AdaptiveSin | false | 10,720 | [
"MIT"
] | 0 | 1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 | https://github.com/ndem0/PINA/tree/1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 |
LogisticLoss | # 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
assert_size_stride = t... | brainsqueeze/Kaggle-competitions | LogisticLoss | false | 3,244 | [
"MIT"
] | 0 | e734ca71303619fd2c9a6f10aaf98b2c0a800758 | https://github.com/brainsqueeze/Kaggle-competitions/tree/e734ca71303619fd2c9a6f10aaf98b2c0a800758 |
Net | # 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.... | AhmetTavli/Olivetti-CNN | Net | false | 11,237 | [
"MIT"
] | 0 | 174747382f17e02c0e5f964d08a449429ac6fbd8 | https://github.com/AhmetTavli/Olivetti-CNN/tree/174747382f17e02c0e5f964d08a449429ac6fbd8 |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | luiz-rocha94/navigation | QNetwork | false | 10,414 | [
"MIT"
] | 0 | fd5e00d8b9051e82dfe15793e53f8d1f86e8ecbe | https://github.com/luiz-rocha94/navigation/tree/fd5e00d8b9051e82dfe15793e53f8d1f86e8ecbe |
DenseGraphConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch.nn import Parameter
import torch.utils.data
assert_size_s... | CFF-Dream/pytorch_geometric | DenseGraphConv | false | 2,031 | [
"MIT"
] | 0 | 7c19ad74957409ee9e07314ce81524b3113b9c84 | https://github.com/CFF-Dream/pytorch_geometric/tree/7c19ad74957409ee9e07314ce81524b3113b9c84 |
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
import torch.nn as nn
import torch.optim
import torch.utils.data
from torch.nn.init import *
assert_size_stride = torch._C._dynamo.guards.as... | EVA4-RS-Group/Phase2 | TVLoss | false | 383 | [
"Apache-2.0"
] | 0 | 7c551e3894979cc425dd51baeddbfa5a51b7878d | https://github.com/EVA4-RS-Group/Phase2/tree/7c551e3894979cc425dd51baeddbfa5a51b7878d |
RGAN_D | # 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.... | COMP6248-Reproducability-Challenge/Reproducible-Or-Not-Reproducible-That-Is-The-Question | RGAN_D | false | 4,954 | [
"MIT"
] | 1 | 7e2e632189a3669397f67efa99c8de4924967968 | https://github.com/COMP6248-Reproducability-Challenge/Reproducible-Or-Not-Reproducible-That-Is-The-Question/tree/7e2e632189a3669397f67efa99c8de4924967968 |
PureUpsampling | # 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... | vlbthambawita/polyp-inpainting | PureUpsampling | false | 4,502 | [
"MIT"
] | 0 | f1d754f8ffb3f6d991206b2a661933ff32de0d7a | https://github.com/vlbthambawita/polyp-inpainting/tree/f1d754f8ffb3f6d991206b2a661933ff32de0d7a |
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.... | zwc662/SequentialAttack | Actor | false | 16,844 | [
"MIT"
] | 116 | 677b19c51ea76d794939ee126fccd75ffa0e6fe6 | https://github.com/zwc662/SequentialAttack/tree/677b19c51ea76d794939ee126fccd75ffa0e6fe6 |
CNN_small | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.utils.data
class CNN_small(nn.Module):
def __init__(self, num_classes=10):
super(CNN_small, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | JiarunLiu/Co-correcting | CNN_small | false | 8,361 | [
"Apache-2.0"
] | 19 | 4e3ca4951de5d73ca812bbbcfe666273082ff2fd | https://github.com/JiarunLiu/Co-correcting/tree/4e3ca4951de5d73ca812bbbcfe666273082ff2fd |
TVLoss | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
from torch.nn.init import *
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[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
import torch.nn as nn
import torch.optim
import torch.utils.data
from torch.nn.init import *
assert_size_stride = torch._C._dynamo.guards.as... | EVA4-RS-Group/Phase2 | TVLoss | false | 383 | [
"Apache-2.0"
] | 0 | 7c551e3894979cc425dd51baeddbfa5a51b7878d | https://github.com/EVA4-RS-Group/Phase2/tree/7c551e3894979cc425dd51baeddbfa5a51b7878d |
WeightNet | import torch
import torch.nn as nn
class WeightNet(nn.Module):
"""WeightNet in Temporal interlace module.
The WeightNet consists of two parts: one convolution layer
and a sigmoid function. Following the convolution layer, the sigmoid
function and rescale module can scale our output to the range (0, 2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION | WeightNet | false | 5,933 | [
"MIT"
] | 1 | 6f4d1c7e6883d6b0664fcd04265f437247afab54 | https://github.com/VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION/tree/6f4d1c7e6883d6b0664fcd04265f437247afab54 |
Classifier | # 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... | VinAIResearch/mDSDI | Classifier | false | 18,035 | [
"Apache-2.0"
] | 9 | 8ec49085d8389ab490ec633c3ae4bf66be085366 | https://github.com/VinAIResearch/mDSDI/tree/8ec49085d8389ab490ec633c3ae4bf66be085366 |
stack_pool | import torch
import torch.nn as nn
class stack_pool(nn.Module):
def __init__(self):
super(stack_pool, self).__init__()
self.pool2 = nn.MaxPool2d(2, stride=2)
self.pool2s1 = nn.MaxPool2d(2, stride=1)
self.pool3s1 = nn.MaxPool2d(3, stride=1, padding=1)
self.padding = nn.Repl... | 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... | siyuhuang/crowdcount-stackedpool | stack_pool | false | 16,477 | [
"MIT"
] | 93 | bbba3d9e91a5a89642b4bd3638ae8e68801ea7bf | https://github.com/siyuhuang/crowdcount-stackedpool/tree/bbba3d9e91a5a89642b4bd3638ae8e68801ea7bf |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, in_channels, output):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=20,
kernel_size=3, stride=1, padding=1)
self.pool1 = nn.Ma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Sheriff-A/CNN | CNN | false | 9,478 | [
"MIT"
] | 0 | 59fc187e7cdf92379f52c4f942424d3a5042bf3e | https://github.com/Sheriff-A/CNN/tree/59fc187e7cdf92379f52c4f942424d3a5042bf3e |
ConvD | # 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.... | ieee820/BraTS2018-tumor-segmentation | ConvD | false | 15,602 | [
"MIT"
] | 157 | 22e1a22909a0c21503b5ef5fc6860a1e1131e851 | https://github.com/ieee820/BraTS2018-tumor-segmentation/tree/22e1a22909a0c21503b5ef5fc6860a1e1131e851 |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_size, hidden_size, dropout_rate, out_size):
super(Net, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | PatWalters/yamc | Net | false | 17,785 | [
"MIT"
] | 7 | 8fcde09305d6600fdea6211d0941977bb2cff65b | https://github.com/PatWalters/yamc/tree/8fcde09305d6600fdea6211d0941977bb2cff65b |
SigmoidDeepLiftModel | import torch
import torch.nn as nn
class SigmoidDeepLiftModel(nn.Module):
"""
Model architecture from:
https://medium.com/coinmonks/create-a-neural-network-in
-pytorch-and-make-your-life-simpler-ec5367895199
"""
def __init__(self, num_in, num_hidden, num_out) ->None:
super().__ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | LMdeLiangMi/captum | SigmoidDeepLiftModel | false | 5,480 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
Decoder | # 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.... | J-zin/Semantic-Hashing-Models | Decoder | false | 5,365 | [
"MIT"
] | 1 | 2e4a2348bc8399a9739016e1a1a5e25a77babbbd | https://github.com/J-zin/Semantic-Hashing-Models/tree/2e4a2348bc8399a9739016e1a1a5e25a77babbbd |
ClassificationModel | # 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 ... | DerekGloudemans/3D-detector-trials | ClassificationModel | false | 2,186 | [
"MIT"
] | 0 | 480274567eaa84c5c883260ef62f150c7a23ffd3 | https://github.com/DerekGloudemans/3D-detector-trials/tree/480274567eaa84c5c883260ef62f150c7a23ffd3 |
ResnetBlock | import torch
class ResnetBlock(torch.nn.Module):
def __init__(self, num_filter, kernel_size=3, stride=1, padding=1, bias
=True):
super(ResnetBlock, self).__init__()
self.conv1 = torch.nn.Conv2d(num_filter, num_filter, kernel_size,
stride, padding, bias=bias)
self.conv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Holmes-Alan/RefVAE | ResnetBlock | false | 8,264 | [
"MIT"
] | 13 | 836b8f1168f1b0f923b609a48e202ace7806f79c | https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c |
ConvLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | Chandan-h-509/ignite | ConvLayer | false | 8,972 | [
"BSD-3-Clause"
] | 0 | f8c39828cb1dac49b6ef358cdf77865bf2430106 | https://github.com/Chandan-h-509/ignite/tree/f8c39828cb1dac49b6ef358cdf77865bf2430106 |
mean_norm | import torch
class mean_norm(torch.nn.Module):
def __init__(self):
super(mean_norm, self).__init__()
def forward(self, x):
col_mean = x.mean(dim=0)
x = x - col_mean
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | ngohienduong/Deep_GCN_Benchmarking | mean_norm | false | 16,171 | [
"MIT"
] | 70 | 3ee57a265bbfd62d8e6f3ee6e3e9062dd5a44633 | https://github.com/ngohienduong/Deep_GCN_Benchmarking/tree/3ee57a265bbfd62d8e6f3ee6e3e9062dd5a44633 |
Net | import torch
import torch.nn as tnn
class Net(tnn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = tnn.Conv2d(3, 6, 5)
self.pool = tnn.MaxPool2d(2, 2)
self.conv2 = tnn.Conv2d(6, 16, 5)
self.fc1 = tnn.Linear(16 * 5 * 5, 120)
self.fc2 = tnn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 tnn
assert... | Exusial/jittor | Net | false | 13,674 | [
"Apache-2.0"
] | 2,571 | eca21d5bba5098bce4f492fa44908677b6e76588 | https://github.com/Exusial/jittor/tree/eca21d5bba5098bce4f492fa44908677b6e76588 |
Whitening2d | import torch
import torch.nn as nn
from torch.cuda.amp import custom_fwd
from torch.nn.functional import conv2d
class Whitening2d(nn.Module):
def __init__(self, output_dim: 'int', eps: 'float'=0.0):
"""Layer that computes hard whitening for W-MSE using the Cholesky decomposition.
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | TranNhiem/solo-learn | Whitening2d | false | 1,151 | [
"MIT"
] | 0 | 7539732b68d153087d09a26a23e1edfdc49bc086 | https://github.com/TranNhiem/solo-learn/tree/7539732b68d153087d09a26a23e1edfdc49bc086 |
FeaturePyramidNetwork | # 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... | kiyohiro8/SemanticReasoningNetworks | FeaturePyramidNetwork | false | 12,697 | [
"MIT"
] | 0 | 9dc20706a2234511789a7a2fa07cc3b77c64bf81 | https://github.com/kiyohiro8/SemanticReasoningNetworks/tree/9dc20706a2234511789a7a2fa07cc3b77c64bf81 |
Block | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of
residual blocks).
Args:
drop_prob (float): Drop rate for paths of model. Dropout rate has
to be between 0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CuttlefishXuan/mmsegmentation-1 | Block | false | 13,573 | [
"Apache-2.0"
] | 789 | 13771312da1a66d5cd642df6aa370affd3f5ceac | https://github.com/CuttlefishXuan/mmsegmentation-1/tree/13771312da1a66d5cd642df6aa370affd3f5ceac |
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.triton_helpers import libdevice
import torch.nn as ... | amy12xx/lets-do-irl | Actor | false | 14,840 | [
"MIT"
] | 408 | fd469e9fb7426e41b07c83ce4b87962ac3543b1e | https://github.com/amy12xx/lets-do-irl/tree/fd469e9fb7426e41b07c83ce4b87962ac3543b1e |
FCN8_VGG16 | import torch
import numpy as np
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
def conv3x3(in_planes, out_planes, stride=1, padding=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=(3, 3), stride=(
stride, stride), padding=(padding, padding))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | rdbadra/LCFCN | FCN8_VGG16 | false | 4,752 | [
"Apache-2.0"
] | 0 | 85ba21abb5de443d36d414fb7f732a3672d82c67 | https://github.com/rdbadra/LCFCN/tree/85ba21abb5de443d36d414fb7f732a3672d82c67 |
L2Norm | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | RiyaoDong/HGSL | L2Norm | false | 2,778 | [
"Apache-2.0"
] | 0 | 19fa984b3bfde0e3b7acbce87dd40177cd64f9b0 | https://github.com/RiyaoDong/HGSL/tree/19fa984b3bfde0e3b7acbce87dd40177cd64f9b0 |
MLP_g | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.norm... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | tasfia/BMCoGAN | MLP_g | false | 13,113 | [
"MIT"
] | 0 | 0d400c2c71dbfb69af422afc487f65afb98de8af | https://github.com/tasfia/BMCoGAN/tree/0d400c2c71dbfb69af422afc487f65afb98de8af |
ChainCRF | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
def logsumexp(x, dim=None):
"""
Args:
x: A pytorch tensor (any dimension will do)
dim: int or None, over which to perform the summation. `None`, the
default, performs over all axes.
Returns: The result... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_strid... | shabnam-b/crosslingual-nlp | ChainCRF | false | 16,398 | [
"MIT"
] | 64 | ccd91baaea23004eab9c4d871910945ca3e61ab7 | https://github.com/shabnam-b/crosslingual-nlp/tree/ccd91baaea23004eab9c4d871910945ca3e61ab7 |
LogSumPenalty | from torch.nn import Module
import torch
class LogSumPenalty(Module):
def __init__(self, epsilon=1):
super(LogSumPenalty, self).__init__()
self.epsilon = epsilon
def forward(self, input):
return torch.sum(torch.log(torch.abs(input) + self.epsilon))
def eta_hat(self, 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 math as tl_math
from torch.nn import M... | dlej/adaptive-dropout | LogSumPenalty | false | 10,067 | [
"MIT"
] | 0 | 0540b2d06f1f97eb5861c6917eec6c086d33dfa8 | https://github.com/dlej/adaptive-dropout/tree/0540b2d06f1f97eb5861c6917eec6c086d33dfa8 |
Brightness | # 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... | Hayoung93/UDA | Brightness | false | 980 | [
"Apache-2.0"
] | 0 | a587b01c76141d64e7cead55b62e0f3ed75890bf | https://github.com/Hayoung93/UDA/tree/a587b01c76141d64e7cead55b62e0f3ed75890bf |
MegatronFastGelu | # 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
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.... | RyanUnderhill/onnxruntime | MegatronFastGelu | false | 11,824 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
GeneralizedMeanPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | hfyer/NAIC2020_ReID_R1 | GeneralizedMeanPooling | false | 6,806 | [
"Apache-2.0"
] | 1 | 240f0c9f65e482e6b0090f01d9f9e3373a337033 | https://github.com/hfyer/NAIC2020_ReID_R1/tree/240f0c9f65e482e6b0090f01d9f9e3373a337033 |
Adversarial_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
import torch.nn as nn
... | ducviet00/HMER | Adversarial_Loss | false | 6,616 | [
"MIT"
] | 1 | 0fa322ed35412737a24ec3955c9a3d96d1989bd4 | https://github.com/ducviet00/HMER/tree/0fa322ed35412737a24ec3955c9a3d96d1989bd4 |
Readout | from torch.nn import Module
import torch
import torch.utils.data
def aggregate(x, dim, aggr='add', mask=None, keepdim=False):
"""
Args:
x: (..., A, ..., F), Features to be aggregated.
mask: (..., A, ...)
Returns:
(..., , ..., F), if keepdim == False
(..., 1, ..., F), if... | 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.nn import Module
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | LichenYang-Jeffrey/GAT-for-COVID-19 | Readout | false | 5,520 | [
"MIT"
] | 1 | 91cc6048f14856f3ef9dfebf2db45e2a36975159 | https://github.com/LichenYang-Jeffrey/GAT-for-COVID-19/tree/91cc6048f14856f3ef9dfebf2db45e2a36975159 |
PixelNormLayer | import torch
import torch.utils.data
import torch
from torch import nn
class PixelNormLayer(nn.Module):
"""Implements pixel-wise feature vector normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, x):
return x / 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.triton_helpers import libdevice
import torch.utils.data
import torch
from torch import nn
assert_size_stride = ... | IVRL/BIGPrior | PixelNormLayer | false | 578 | [
"MIT"
] | 0 | 6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 | https://github.com/IVRL/BIGPrior/tree/6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 |
Conv1dBlock | # 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... | DK-Jang/human_motion_manifold | Conv1dBlock | false | 7,918 | [
"MIT"
] | 23 | dd3b603b892d66685204909c8818f3e1621ab7dc | https://github.com/DK-Jang/human_motion_manifold/tree/dd3b603b892d66685204909c8818f3e1621ab7dc |
MultiLayerPerceptron | import torch
import torch.utils.data
import torch.optim
class MultiLayerPerceptron(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (int): the size of each layer
num_cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ShantanuNair/NeMo | MultiLayerPerceptron | false | 17,901 | [
"Apache-2.0"
] | 10 | d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b | https://github.com/ShantanuNair/NeMo/tree/d01b7bbc3fdb1bbf14789f71b8f368cf0aa8f86b |
ExampleBackbone | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class ExampleBackbone(nn.Module):
def __init__(self):
super(ExampleBackbone, self).__init__()
self.conv = nn.Conv2d(3, 3, 3)
def init_weights(self, pretrained=None):
pass
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
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_str... | CVIU-CSU/M2MRF-Lesion-Segmentation | ExampleBackbone | false | 17,054 | [
"Apache-2.0"
] | 10 | 13af87927f4cdeca70e35d570edd1aec43b387b6 | https://github.com/CVIU-CSU/M2MRF-Lesion-Segmentation/tree/13af87927f4cdeca70e35d570edd1aec43b387b6 |
TwoLayerFCBodyWithAction | 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 TwoLayerFCBodyWithAction(nn.Module):
def __init__(self, state_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | RaviTej310/mrpvf | TwoLayerFCBodyWithAction | false | 11,835 | [
"MIT"
] | 0 | f026b4704f26b85161de26ada5d6390ab549fbbd | https://github.com/RaviTej310/mrpvf/tree/f026b4704f26b85161de26ada5d6390ab549fbbd |
AUGRUCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Ahren09/RecBole | AUGRUCell | false | 1,936 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
GeLU | # 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_... | Blind-Aid/sentiment-discovery | GeLU | false | 13,397 | [
"BSD-3-Clause"
] | 1,093 | 081c7c855e00864b52e97cac0b0e097cc86d9731 | https://github.com/Blind-Aid/sentiment-discovery/tree/081c7c855e00864b52e97cac0b0e097cc86d9731 |
GRUGatingUnit | import torch
import torch.nn as nn
import torch.utils.data
class GRUGatingUnit(torch.nn.Module):
"""
Overview:
GRU Gating Unit used in GTrXL.
"""
def __init__(self, input_dim: 'int', bg: 'float'=2.0):
"""
Arguments:
- input_dim: (:obj:`int`): 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.triton_helpers import libdevice
import torch.nn as ... | L-Net-1992/DI-engine | GRUGatingUnit | false | 5,491 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f |
ProteinResNetPooler | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class ProteinResNetPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.attention_weights = nn.Linear(config.hidden_size, 1)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
fr... | IC-hub/ProteinLM | ProteinResNetPooler | false | 15,190 | [
"Apache-2.0"
] | 59 | 58fbf1f674569cf814becf32f71dd0d8f0c592fa | https://github.com/IC-hub/ProteinLM/tree/58fbf1f674569cf814becf32f71dd0d8f0c592fa |
Projection | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class TimeDistributed(nn.Module):
def __init__(self, layer, activation='relu'):
super().__init__()
self.layer = layer
self.activation = self.select_activation(activation)
def forward(self, x):
x_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | tndls9304/chatspace | Projection | false | 13,044 | [
"Apache-2.0"
] | 0 | 42cb4bd9bd3b553706d9ac227150329103d681aa | https://github.com/tndls9304/chatspace/tree/42cb4bd9bd3b553706d9ac227150329103d681aa |
MultiHeadAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AbubakrHassan/attention-is-all-you-need-pytorch | MultiHeadAttention | false | 11,200 | [
"MIT"
] | 0 | 2bf9a477dea6271b082556069f3665ffed2745cd | https://github.com/AbubakrHassan/attention-is-all-you-need-pytorch/tree/2bf9a477dea6271b082556069f3665ffed2745cd |
InnerProductDecoder | # 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.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | JinheonBaek/pytorch_geometric | InnerProductDecoder | false | 17,501 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 |
DiffLoss | # 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
import torch.utils.... | Prathyusha-Akundi/Adversarial-Continual-Learning | DiffLoss | false | 14,239 | [
"MIT"
] | 237 | edf4bbd2c4c61f1cc20818793702ef8c6cf4e0df | https://github.com/Prathyusha-Akundi/Adversarial-Continual-Learning/tree/edf4bbd2c4c61f1cc20818793702ef8c6cf4e0df |
CoordConvSinAct | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | justinjohn0306/CIPS-3D | CoordConvSinAct | false | 7,004 | [
"MIT"
] | 1 | 69a910a7841846419a6b5e03182c8cf061a82584 | https://github.com/justinjohn0306/CIPS-3D/tree/69a910a7841846419a6b5e03182c8cf061a82584 |
PixelwiseLossL1 | import torch
from torch import nn
class PixelwiseLossL1(nn.Module):
"""
L1 loss function
Args:
alpha (default: int=1): Coefficient by which loss will be multiplied
"""
def __init__(self, alpha=1):
super().__init__()
self.alpha = alpha
self.criterion = nn.L1Loss()
... | 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... | akanametov/pathgan | PixelwiseLossL1 | false | 18,304 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
CQAttention | # 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.... | dcy2018/QANA | CQAttention | false | 3,517 | [
"MIT"
] | 0 | 69d1e4ff408a56317479e22ecc854c91fc0f420f | https://github.com/dcy2018/QANA/tree/69d1e4ff408a56317479e22ecc854c91fc0f420f |
ConvMeanPool | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | kolchinski/humanception-score | ConvMeanPool | false | 12,682 | [
"MIT"
] | 0 | da8880eec3be39574718409cfe8ca303f41c64e6 | https://github.com/kolchinski/humanception-score/tree/da8880eec3be39574718409cfe8ca303f41c64e6 |
BERTMultSelfOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BERTLayerNorm(nn.Module):
def __init__(self, config, multi_params=None, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BERTLayerNorm... | 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_... | DAQuestionAnswering/Bert-n-Pals | BERTMultSelfOutput | false | 6,464 | [
"MIT"
] | 1 | d5a288b9ac62259e70c249635108ba3906e19f00 | https://github.com/DAQuestionAnswering/Bert-n-Pals/tree/d5a288b9ac62259e70c249635108ba3906e19f00 |
ListNetLoss | # 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
... | Pepijnnn/MasterThesis | ListNetLoss | false | 938 | [
"MIT"
] | 0 | 7ec831f5e55f5f181e0196fa78284e2846ce2e26 | https://github.com/Pepijnnn/MasterThesis/tree/7ec831f5e55f5f181e0196fa78284e2846ce2e26 |
pixel_attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SCUT-AILab/AFA | pixel_attention | false | 17,889 | [
"BSD-3-Clause"
] | 7 | acfb42236ce0114d63f22a821fc5954c8c149f45 | https://github.com/SCUT-AILab/AFA/tree/acfb42236ce0114d63f22a821fc5954c8c149f45 |
TransformerFFN | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | katsura-jp/generate-syosetu-title | TransformerFFN | false | 7,014 | [
"MIT"
] | 1 | f1db8f87d6ebd58117df1e7c0b76a4fe92cae810 | https://github.com/katsura-jp/generate-syosetu-title/tree/f1db8f87d6ebd58117df1e7c0b76a4fe92cae810 |
BasicLinearReLULinear | # 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_... | dkrako/captum | BasicLinearReLULinear | false | 10,031 | [
"BSD-3-Clause"
] | 0 | b5297bacbaec4e37f353a27de5e728bc2cbc1694 | https://github.com/dkrako/captum/tree/b5297bacbaec4e37f353a27de5e728bc2cbc1694 |
MSECompositionLoss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | 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 functools
import torch.nn as nn
from torch.nn import functional as F
assert_size_s... | Juggernaut93/mmediting | MSECompositionLoss | false | 13,909 | [
"Apache-2.0"
] | 1,884 | 8ef46ace29756dd2df1d92f2f73a33646e33e007 | https://github.com/Juggernaut93/mmediting/tree/8ef46ace29756dd2df1d92f2f73a33646e33e007 |
MultiplicationComposition | import torch
from torch import nn
from abc import abstractmethod
import torch.utils.data
class Composition(nn.Module):
"""A base class for compositions."""
@abstractmethod
def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
"""
Compose two batches vectors.
..... | 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
from abc import abstractmethod
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | DimitrisAlivas/StarQE | MultiplicationComposition | false | 7,965 | [
"MIT"
] | 11 | c17676e5f1e3f19c0c4c117a50abe2ce22ffef28 | https://github.com/DimitrisAlivas/StarQE/tree/c17676e5f1e3f19c0c4c117a50abe2ce22ffef28 |
SoftEntropy | # 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
f... | ChienHsuan/MMT | SoftEntropy | false | 13,478 | [
"MIT"
] | 425 | fe4a559b8af3ec93242b24acb4c8e962a00a1248 | https://github.com/ChienHsuan/MMT/tree/fe4a559b8af3ec93242b24acb4c8e962a00a1248 |
CentralizedCritic | # 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_... | AYUSHKABIRVERMA/Multi-agent-reinforcement-learning | CentralizedCritic | false | 13,278 | [
"MIT"
] | 62 | cd7c13d723cd74dc278939d81d5dd1b0906cee7c | https://github.com/AYUSHKABIRVERMA/Multi-agent-reinforcement-learning/tree/cd7c13d723cd74dc278939d81d5dd1b0906cee7c |
PoolingF | import torch
import torch.utils.data
import torch
import torch.nn as nn
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)
out ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | a11isonliu/contrastive-unpaired-translation | PoolingF | false | 9,846 | [
"BSD-3-Clause"
] | 0 | 67651ed9877cae121d9398f46094ce8dbc678802 | https://github.com/a11isonliu/contrastive-unpaired-translation/tree/67651ed9877cae121d9398f46094ce8dbc678802 |
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