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 |
|---|---|---|---|---|---|---|---|---|---|---|
Biaffine | import torch
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, in_features, out_features=1, bias=(True, True)):
super(Biaffine, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.bias = bias
self.linear_input_size = in_f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | LindgeW/BiaffineNER | Biaffine | false | 8,471 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
GaussianKernel | import torch
from typing import Optional
import torch.nn as nn
import torch.utils.data
class GaussianKernel(nn.Module):
"""Gaussian Kernel Matrix
Gaussian Kernel k is defined by
.. math::
k(x_1, x_2) = \\exp \\left( - \\dfrac{\\| x_1 - x_2 \\|^2}{2\\sigma^2} \\right)
where :math:`x_1, x_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Opt... | Neronjust2017/TransferBed | GaussianKernel | false | 5,672 | [
"MIT"
] | 1 | eaa703a4bc10eaf6216fe1394cd272f6e75489e2 | https://github.com/Neronjust2017/TransferBed/tree/eaa703a4bc10eaf6216fe1394cd272f6e75489e2 |
MeanMaxPooling | import torch
from torch import nn
class MeanMaxPooling(nn.Module):
def __init__(self):
super(MeanMaxPooling, self).__init__()
def forward(self, doc_state, entity_mapping, entity_lens):
"""
:param doc_state: N x L x d
:param entity_mapping: N x E x L
:param entity_le... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | HLTCHKUST/MulQG | MeanMaxPooling | false | 8,189 | [
"MIT"
] | 19 | 8e257f2d6c0f03c07ea8a0bf0e8f55b0cde60605 | https://github.com/HLTCHKUST/MulQG/tree/8e257f2d6c0f03c07ea8a0bf0e8f55b0cde60605 |
VariationalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class VariationalLoss(nn.Module):
""" Variational loss to enforce continuity of images
"""
def forward(self, input):
""" forward pass """
self.loss = F.mse_loss(input[:, :, 1:, :], input[:, :, :-1, :]
) + F.mse... | 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... | jaredaevans/UltrafastNST | VariationalLoss | false | 6,926 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
ScaledDotProductAttention | import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim
class Bottle(nn.Module):
""" Perform the reshape routine before and after an operation """
def forward(self, input):
if len(input.size()) <= 2:
return super(Bottle, self).forward(input)
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 import triton_helpers
from torch._inductor.runtime.... | howardchenhd/Transformer-pytorch | ScaledDotProductAttention | false | 6,819 | [
"MIT"
] | 1 | ae71ed5767272feb7e717be6d5bfce46f80ec57a | https://github.com/howardchenhd/Transformer-pytorch/tree/ae71ed5767272feb7e717be6d5bfce46f80ec57a |
SE_Connect | # 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
import torch.... | qlindazm/asv-subtools | SE_Connect | false | 4,228 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 |
Charbonnier | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | davefiorino/EDSR-PyTorch | Charbonnier | false | 1,797 | [
"MIT"
] | 0 | 97ad32a09a71816a36c45d92cdb2ea7ab42ba685 | https://github.com/davefiorino/EDSR-PyTorch/tree/97ad32a09a71816a36c45d92cdb2ea7ab42ba685 |
Repeat_Explore_Mechanism | # 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.... | dreaming-qin/RecBole | Repeat_Explore_Mechanism | false | 12,318 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
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.... | verages/PaddleOCR2Pytorch | TransformerEncoderLayer | false | 4,690 | [
"Apache-2.0"
] | 0 | 201f0d5d6007f49620c49af7d222c3b220eb3e70 | https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70 |
GumbelSoftmaxLayer | # 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
from torch.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch... | Slowika/GameBias-EmeCom2020 | GumbelSoftmaxLayer | false | 17,957 | [
"MIT"
] | 5 | 5b94c47559f8202bca99c26fc1bcb078dd0509a6 | https://github.com/Slowika/GameBias-EmeCom2020/tree/5b94c47559f8202bca99c26fc1bcb078dd0509a6 |
HuberLoss | import torch
from torch import nn
from torch import torch
class HuberLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, Yp, Yt):
num = Yt.size(0)
Yp = Yp.view(num, -1)
Yt = Yt.view(num, -1)
loss = nn.functional.smooth_l1_loss(Yp, Yt, beta=0.5)
... | 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... | oskarnatan/RGBDVS-fusion | HuberLoss | false | 7,424 | [
"MIT"
] | 1 | 5e560f54442d387a86e3a469107cf65859693987 | https://github.com/oskarnatan/RGBDVS-fusion/tree/5e560f54442d387a86e3a469107cf65859693987 |
EncoderLayer | # 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.... | rcasero/Transformer | EncoderLayer | false | 4,195 | [
"Apache-2.0"
] | 0 | 82f51e04f80634d56b134e0ac87f67d6ba8c736a | https://github.com/rcasero/Transformer/tree/82f51e04f80634d56b134e0ac87f67d6ba8c736a |
ScaledDotProductAttention | # 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.... | GavinGuan95/Generative-VQA | ScaledDotProductAttention | false | 5,225 | [
"MIT"
] | 1 | 0912e3a2426809ef4d4eb40bae667b31c2269161 | https://github.com/GavinGuan95/Generative-VQA/tree/0912e3a2426809ef4d4eb40bae667b31c2269161 |
Scale | # 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... | FranardoHuang/ROAR | Scale | false | 5,168 | [
"Apache-2.0"
] | 1 | 859e22389907dd0e61c83980ae5ff6dae51341d3 | https://github.com/FranardoHuang/ROAR/tree/859e22389907dd0e61c83980ae5ff6dae51341d3 |
CrossEntropyLossOneHot | # 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.nn.modules.... | NikolayZakharevich/music-processing | CrossEntropyLossOneHot | false | 895 | [
"MIT"
] | 0 | 516a3bca585f211d232cac7ede6cc417fb8878fe | https://github.com/NikolayZakharevich/music-processing/tree/516a3bca585f211d232cac7ede6cc417fb8878fe |
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.... | chencsgit/luoxi_models | Attention | false | 15,025 | [
"Apache-2.0"
] | 58 | ea9e69dfb81b29f41ed92c75faacf81114c69a2f | https://github.com/chencsgit/luoxi_models/tree/ea9e69dfb81b29f41ed92c75faacf81114c69a2f |
MaxpoolMatchLay | import torch
import torch.nn as nn
from torch.nn.functional import cosine_similarity
def multi_perspective_expand_for_2D(in_tensor, decompose_params):
"""
Return: [batch_size, decompse_dim, dim]
"""
in_tensor = in_tensor.unsqueeze(1)
decompose_params = decompose_params.unsqueeze(0)
return torc... | 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... | NeilWangziyu/torch_light | MaxpoolMatchLay | false | 5,697 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
CoAttention | # 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.... | DongjunLee/claf | CoAttention | false | 13,605 | [
"MIT"
] | 225 | ef548dda27c9aac8ce4db09774c8a1459d25bde1 | https://github.com/DongjunLee/claf/tree/ef548dda27c9aac8ce4db09774c8a1459d25bde1 |
ConvSig | # 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
import torch.nn.functional as F
from torch.nn import Conv2d
from tor... | shlomi-amitai/monorec | ConvSig | false | 10,908 | [
"MIT"
] | 0 | 74571c6cd8d06ae4fb15cbee5a41147c54c78556 | https://github.com/shlomi-amitai/monorec/tree/74571c6cd8d06ae4fb15cbee5a41147c54c78556 |
MeanEmbedding | # 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.multiprocessing
import torch.utils.data
import torch.nn.modules.loss
assert_size_stride = torch._C._dynam... | cminusQAQ/graph4nlp | MeanEmbedding | false | 15,046 | [
"Apache-2.0"
] | 1,269 | d980e897131f1b9d3766750c06316d94749904fa | https://github.com/cminusQAQ/graph4nlp/tree/d980e897131f1b9d3766750c06316d94749904fa |
LogCosh | import torch
from torch import nn
def _assert_no_grad(tensor):
assert not tensor.requires_grad
class LogCosh(nn.Module):
def __init__(self, bias=1e-12):
super().__init__()
self.bias = bias
def forward(self, output, target):
_assert_no_grad(target)
return torch.mean(torc... | 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 ... | dattientran/attorch | LogCosh | false | 12,389 | [
"MIT"
] | 0 | 469b225846c6d8a7d833ebac19d040c7a407a0ff | https://github.com/dattientran/attorch/tree/469b225846c6d8a7d833ebac19d040c7a407a0ff |
AttentionSelf | # 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.... | haophancs/TREQS | AttentionSelf | false | 15,493 | [
"MIT"
] | 149 | 49e354ce2a08cf963ec139d99936020e0f80ced8 | https://github.com/haophancs/TREQS/tree/49e354ce2a08cf963ec139d99936020e0f80ced8 |
SeE_Block | # 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_... | Mhaiyang/TCSVT2021_DCENet | SeE_Block | false | 17,710 | [
"BSD-3-Clause"
] | 4 | aae8c7643402c15847836c0ce4934b743e11fd8a | https://github.com/Mhaiyang/TCSVT2021_DCENet/tree/aae8c7643402c15847836c0ce4934b743e11fd8a |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn as nn
import torch.utils.data.distributed
assert_size_strid... | MatthewMasters/grover | PositionwiseFeedForward | false | 825 | [
"MIT"
] | 0 | 737a340754bc4c63134ef84019a0a84023fd69a3 | https://github.com/MatthewMasters/grover/tree/737a340754bc4c63134ef84019a0a84023fd69a3 |
MeanPoolConv | import torch
from torch import nn
class IWConv2d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
stride=1, bias=True):
super(IWConv2d, self).__init__()
self.he_init = he_init
self.padding = int((kernel_size - 1) / 2)
self.conv = nn.Conv2d(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | MIC-DKFZ/mood | MeanPoolConv | false | 8,499 | [
"Apache-2.0"
] | 42 | a01303adb4256653b133e2f7cd4741d366b681f7 | https://github.com/MIC-DKFZ/mood/tree/a01303adb4256653b133e2f7cd4741d366b681f7 |
AMBinaryLoss | # 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
... | daniil-lyakhov/deep-object-reid | AMBinaryLoss | false | 1,777 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The 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 import triton_helpers
from torch._inductor.runtime.... | DerryHub/the-TaobaoLive-Commodity-Identify-Competition | BertSelfAttention | false | 18,365 | [
"MIT"
] | 4 | 7e5e5c4fbddd9949fe01810d58bd7994889c007c | https://github.com/DerryHub/the-TaobaoLive-Commodity-Identify-Competition/tree/7e5e5c4fbddd9949fe01810d58bd7994889c007c |
make_style | import torch
import torch.nn as nn
import torch.nn.functional as F
class make_style(nn.Module):
def __init__(self):
super().__init__()
self.flatten = nn.Flatten()
def forward(self, x0):
style = F.avg_pool2d(x0, kernel_size=(x0.shape[-2], x0.shape[-1]))
style = self.flatten(st... | 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_... | YinuoJin/cellpose | make_style | false | 12,017 | [
"BSD-3-Clause"
] | 0 | eb8df70f295ac8465633f468d487aee1dd13a181 | https://github.com/YinuoJin/cellpose/tree/eb8df70f295ac8465633f468d487aee1dd13a181 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ZfSangkuan/ASER | Attention | false | 14,715 | [
"MIT"
] | 256 | c34d6f2432b181bae9f4ee4fa70ce270dbc1dee7 | https://github.com/ZfSangkuan/ASER/tree/c34d6f2432b181bae9f4ee4fa70ce270dbc1dee7 |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | roee89871324/Evolutionary_Selective_Imitation | MLP | false | 12,940 | [
"MIT"
] | 0 | 84b31fce6dcd6d79686244b9b53cde584a713723 | https://github.com/roee89871324/Evolutionary_Selective_Imitation/tree/84b31fce6dcd6d79686244b9b53cde584a713723 |
SoftDiceLoss | # 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.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | Will3577/Medical-Transformer | SoftDiceLoss | false | 1,212 | [
"MIT"
] | 0 | e72bfe68fcd55268f57bc7c27b4cbce8029d1b81 | https://github.com/Will3577/Medical-Transformer/tree/e72bfe68fcd55268f57bc7c27b4cbce8029d1b81 |
GCN | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', stride=1, dilation=1, groups=1):
super(Conv2D, self).__init__()
assert type(kernel_size) in [int,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
as... | Eurus-Holmes/CHABCNet | GCN | false | 8,125 | [
"BSD-2-Clause"
] | 11 | 8d3985c7680981e58751d043880b5b5a818cc1d3 | https://github.com/Eurus-Holmes/CHABCNet/tree/8d3985c7680981e58751d043880b5b5a818cc1d3 |
Patch2Image | # 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... | vitskvara/shape-guided-anomaly-detection | Patch2Image | false | 4,498 | [
"MIT"
] | 0 | 6685b2e0b97968a6d0f478d2920486da107b277f | https://github.com/vitskvara/shape-guided-anomaly-detection/tree/6685b2e0b97968a6d0f478d2920486da107b277f |
ScaleExp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | TencentYoutuResearch/ActionDetection-AFSD | ScaleExp | false | 14,464 | [
"BSD-3-Clause"
] | 112 | ed86a0df91e58baa7d78c796ed29cff82b1f3fa6 | https://github.com/TencentYoutuResearch/ActionDetection-AFSD/tree/ed86a0df91e58baa7d78c796ed29cff82b1f3fa6 |
AdaptiveSoftplus | # 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, math as tl_math
from torch.nn.parameter import Parameter
assert_size_stride = ... | ndem0/PINA | AdaptiveSoftplus | false | 10,730 | [
"MIT"
] | 0 | 1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 | https://github.com/ndem0/PINA/tree/1812ddb8d96a9c8aeb80ce35002dbd115e7d7931 |
PositionwiseFeedForward | import math
import torch
import torch.nn as nn
from itertools import chain as chain
import torch.utils.data
import torch.hub
import torch.nn.parallel
import torch.optim
class GELU(nn.Module):
"""
Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
"""
def forward(self, x)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | byeongjokim/LateTemporalModeling3DCNN_for_sign | PositionwiseFeedForward | false | 1,626 | [
"MIT"
] | 0 | e3a802fcf91dc3930aea782464ee34d9b747d3ab | https://github.com/byeongjokim/LateTemporalModeling3DCNN_for_sign/tree/e3a802fcf91dc3930aea782464ee34d9b747d3ab |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JuliusSchwartz/FlowMO | MultiHeadAttention | false | 13,967 | [
"MIT"
] | 53 | e221d989914f906501e1ad19cd3629d88eac1785 | https://github.com/JuliusSchwartz/FlowMO/tree/e221d989914f906501e1ad19cd3629d88eac1785 |
UFOAttention | import torch
from torch import nn
from torch.nn import init
def XNorm(x, gamma):
norm_tensor = torch.norm(x, 2, -1, True)
return x * gamma / norm_tensor
class UFOAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
... | 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.triton_helpers import libdevice
from torch import n... | Nitin-Mane/External-Attention-pytorch | UFOAttention | false | 14,120 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
Mish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Mish(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x * torch.tanh(F.softplus(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | Archaic-Atom/JackFramework | Mish | false | 7,709 | [
"MIT"
] | 13 | e847d0bafe335ee33caf174676d12ad3c28011a6 | https://github.com/Archaic-Atom/JackFramework/tree/e847d0bafe335ee33caf174676d12ad3c28011a6 |
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.... | RISC-NYUAD/SiamTPNTracker | Attention | false | 8,719 | [
"MIT"
] | 12 | cbff7373941cb30d4a970cac1ee29706d422c212 | https://github.com/RISC-NYUAD/SiamTPNTracker/tree/cbff7373941cb30d4a970cac1ee29706d422c212 |
ClassicalFC1 | # 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.... | mit-han-lab/pytorch-quantum | ClassicalFC1 | false | 16,094 | [
"MIT"
] | 98 | 05cf000d689307f6b1fe02d12744ad455685935b | https://github.com/mit-han-lab/pytorch-quantum/tree/05cf000d689307f6b1fe02d12744ad455685935b |
LayerNormalization | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
def __init__(self, d_hid, eps=0.001):
super(LayerNormalization, self).__init__()
self.gamma = nn.Parameter(torch.ones(d_hid), requires_grad=True)
self.beta = nn.Parameter(torch.zeros(d_hid), requires_grad=True)
... | 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_... | lz-chen/ner-bert | LayerNormalization | false | 7,147 | [
"MIT"
] | 1 | 86e73c1e7124a4fb6ee65d42b72333573841fe5b | https://github.com/lz-chen/ner-bert/tree/86e73c1e7124a4fb6ee65d42b72333573841fe5b |
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.... | karush17/gym-pybullet-drones | Actor | false | 10,316 | [
"MIT"
] | 0 | 7a7acd4f51dcb1cbea8eb9ef0cfcfc7dcf1c90ba | https://github.com/karush17/gym-pybullet-drones/tree/7a7acd4f51dcb1cbea8eb9ef0cfcfc7dcf1c90ba |
_MLP_C | # 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... | colorfulbrain/brain2020 | _MLP_C | false | 15,075 | [
"MIT"
] | 91 | 1dde5d34fd2ba1f38bcc38f2c973d167c8c3a168 | https://github.com/colorfulbrain/brain2020/tree/1dde5d34fd2ba1f38bcc38f2c973d167c8c3a168 |
Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Daniangio/pheno_phases | Norm | false | 11,323 | [
"MIT"
] | 0 | c7229f4ec56fea42988768b02e8deb8615f683fa | https://github.com/Daniangio/pheno_phases/tree/c7229f4ec56fea42988768b02e8deb8615f683fa |
CLNLayer | import torch
import torch.nn.functional as F
from torch import nn
class CLN(nn.Module):
def __init__(self, in_dim, use_style_fc=False, style_dim=None,
which_linear=nn.Linear, spectral_norm=False, eps=1e-05, **kwargs):
super(CLN, self).__init__()
self.in_dim = in_dim
self.use_style... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.fun... | xh-liu-tech/CIPS-3D | CLNLayer | false | 11,109 | [
"MIT"
] | 0 | 8910dfcf19bb86aab2287d652ae4e3666806b511 | https://github.com/xh-liu-tech/CIPS-3D/tree/8910dfcf19bb86aab2287d652ae4e3666806b511 |
GenerateMask | # 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.... | Crazy-Jack/BigGAN-PyTorch | GenerateMask | false | 380 | [
"MIT"
] | 0 | 1a5644e9c87cc399580c96cfeb180052076888da | https://github.com/Crazy-Jack/BigGAN-PyTorch/tree/1a5644e9c87cc399580c96cfeb180052076888da |
SelfAttnLayer | # 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.... | mensudza/C-Tran | SelfAttnLayer | false | 7,237 | [
"MIT"
] | 1 | 4895ccb0e675ae2dcd2b619a9e47f30707062668 | https://github.com/mensudza/C-Tran/tree/4895ccb0e675ae2dcd2b619a9e47f30707062668 |
D_GCN | # 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 math
from torch import... | mpourhoma/PWWB-London | D_GCN | false | 12,820 | [
"MIT"
] | 0 | cfe7a6e3d92ff6b1f18bb5d5bc6a86334e9509d8 | https://github.com/mpourhoma/PWWB-London/tree/cfe7a6e3d92ff6b1f18bb5d5bc6a86334e9509d8 |
alpha | import torch
import torch.utils.data
import torch.nn as nn
class alpha(nn.Module):
def __init__(self, alpha_val=0):
super(alpha, self).__init__()
self.alpha = nn.Parameter(torch.Tensor([alpha_val]))
self.alpha.requires_grad = True
def forward(self, x):
out = torch.mul(self.al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | LayerFolding/Layer-Folding | alpha | false | 17,563 | [
"BSD-3-Clause"
] | 7 | 9c010edc17b1a4a68b36a67cf00c94840d76b735 | https://github.com/LayerFolding/Layer-Folding/tree/9c010edc17b1a4a68b36a67cf00c94840d76b735 |
SimpleSliceModel | import torch
import torch.onnx
import torch.nn
class SimpleSliceModel(torch.nn.Module):
def __init__(self):
super(SimpleSliceModel, self).__init__()
def forward(self, tensor):
other = (tensor + tensor)[1:]
return other[0][1:]
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
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | mlupon/glow | SimpleSliceModel | false | 4,020 | [
"Apache-2.0"
] | 0 | aedaa7b98617f1a2db651608e7f7c916a7d2c766 | https://github.com/mlupon/glow/tree/aedaa7b98617f1a2db651608e7f7c916a7d2c766 |
DecoderSlot | import torch
from torch import nn
class DecoderSlot(nn.Module):
def __init__(self):
super().__init__()
self.conv_1 = nn.ConvTranspose2d(in_channels=66, out_channels=64,
kernel_size=5, stride=(2, 2))
self.conv_2 = nn.ConvTranspose2d(in_channels=64, out_channels=64,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | CatarauCorina/representation_learning | DecoderSlot | false | 8,954 | [
"Apache-2.0"
] | 0 | bb467761b03e5d8ac20c2f705f3bfdb84a7c3842 | https://github.com/CatarauCorina/representation_learning/tree/bb467761b03e5d8ac20c2f705f3bfdb84a7c3842 |
PermEqui2_mean | import torch
from torch import nn
class PermEqui2_mean(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.Gamma = nn.Linear(in_dim, out_dim)
self.Lambda = nn.Linear(in_dim, out_dim, bias=False)
self.weight = self.Gamma.weight
self.bias = self.Gamma.bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | dvirsamuel/CrowdDet | PermEqui2_mean | false | 1,869 | [
"MIT"
] | 0 | db729bf71c0ca72229e5d446019769e095fdaa79 | https://github.com/dvirsamuel/CrowdDet/tree/db729bf71c0ca72229e5d446019769e095fdaa79 |
TransformerNet | import torch
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EdenBD/MultiModalStory-demo | TransformerNet | false | 13,699 | [
"Apache-2.0"
] | 154 | 5e95e2aca766ca7c850e8db4973b8d51dfdba7f8 | https://github.com/EdenBD/MultiModalStory-demo/tree/5e95e2aca766ca7c850e8db4973b8d51dfdba7f8 |
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.nn as ... | byamao1/MMSA | DiffLoss | false | 14,991 | [
"MIT"
] | 198 | 1a894d042144c9ac75b3465d38871ce8c2987251 | https://github.com/byamao1/MMSA/tree/1a894d042144c9ac75b3465d38871ce8c2987251 |
FieldAllTypeBilinear | import math
import torch
import torch.nn as nn
import torch.utils.data
from typing import Dict
from typing import Tuple
from abc import ABC
from abc import abstractmethod
class BaseLayer(nn.Module, ABC):
"""
Base Layer for the torecsys module
"""
def __init__(self, **kwargs):
"""
Init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.utils.data
from typing import Dic... | p768lwy3/torecsys | FieldAllTypeBilinear | false | 16,227 | [
"MIT"
] | 92 | 2251366268b4fbe6f8c3ab1628fa72a0db043dcd | https://github.com/p768lwy3/torecsys/tree/2251366268b4fbe6f8c3ab1628fa72a0db043dcd |
GATMutiHeadAttLayer | import torch
import torch.nn as nn
from torch.nn import functional as F
class GATMutiHeadAttLayer(nn.Module):
def __init__(self, in_features, out_features, heads, dropout=0.4, alpha
=0.2, concat=True):
super(GATMutiHeadAttLayer, self).__init__()
self.dropout = dropout
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
from torch._inductor.runtime.... | gitubee/pyGAT | GATMutiHeadAttLayer | false | 10,157 | [
"MIT"
] | 0 | bc4cc2b6565b7f2ad99daf88013207f64991c273 | https://github.com/gitubee/pyGAT/tree/bc4cc2b6565b7f2ad99daf88013207f64991c273 |
Space2Depth | import torch
import torch.nn as nn
import torch.optim
import torch._utils
import torch.nn
class Space2Depth(nn.Module):
def __init__(self, block_size):
super(Space2Depth, self).__init__()
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // 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
import torch.nn as nn
import torch.optim
import torch._utils
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | ModelTC/EOD | Space2Depth | false | 14,058 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
ChannelAttention | import torch
import torch.nn as nn
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=4):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, in_planes // ratio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | lee-zq/VesselSeg-pytorch | ChannelAttention | false | 15,897 | [
"Apache-2.0"
] | 83 | b4f6571fc1fb1fbdaad60ff9282a54a1f1c455fa | https://github.com/lee-zq/VesselSeg-pytorch/tree/b4f6571fc1fb1fbdaad60ff9282a54a1f1c455fa |
PytorchRegression | # 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_... | freescania/advdsi_at2 | PytorchRegression | false | 10,151 | [
"MIT"
] | 0 | 13fa0b8beaeccc28975aea40ee5a1db3dd3e33be | https://github.com/freescania/advdsi_at2/tree/13fa0b8beaeccc28975aea40ee5a1db3dd3e33be |
ANNClassifier | # 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.... | Sam-limyr/End-to-end-ASR-Pytorch | ANNClassifier | false | 1,035 | [
"MIT"
] | 0 | 623a50792f48218228549ea17b8ea5e8bb1b342f | https://github.com/Sam-limyr/End-to-end-ASR-Pytorch/tree/623a50792f48218228549ea17b8ea5e8bb1b342f |
CustomGruCell | import torch
import numpy as np
from torch import nn
class CustomGruCell(nn.Module):
"""
A forward only GRU cell.
Input should be: (sequence length x batch size x input_size).
The output is the output of the final forward call.
It's not clear if it would be possible to use the output from each cel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy as np
... | juharris/PySyft | CustomGruCell | false | 3,787 | [
"Apache-2.0"
] | 0 | dbb70f24cc55a7dca032fb06f1a8662cb15092a9 | https://github.com/juharris/PySyft/tree/dbb70f24cc55a7dca032fb06f1a8662cb15092a9 |
ConvertPointsToHomogeneous | # 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... | aravinho/frankmocap | ConvertPointsToHomogeneous | false | 3,121 | [
"BSD-3-Clause"
] | 0 | 6a150a9cb96e9b32a60d8047eaa84d0c37e471f5 | https://github.com/aravinho/frankmocap/tree/6a150a9cb96e9b32a60d8047eaa84d0c37e471f5 |
PReLU | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.utils.data
import torch.cuda
from torch.nn import Parameter
import torch.optim
class PReLU(nn.Module):
def __init__(self):
super(PReLU, self).__init__()
self.alpha = Parameter(torch.tensor(0.25))
def for... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.utils.data
im... | Flamexmt/LMA | PReLU | false | 13,684 | [
"MIT"
] | 321 | f6fdec2d17a2d7a7733dd5a5745312bad392cdf3 | https://github.com/Flamexmt/LMA/tree/f6fdec2d17a2d7a7733dd5a5745312bad392cdf3 |
ComboLoss | # 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... | MPWARE-TEAM/HPA2021 | ComboLoss | false | 17,638 | [
"Apache-2.0"
] | 7 | 06c45c5465d9b586f35cba3da5129ea28a1cd85b | https://github.com/MPWARE-TEAM/HPA2021/tree/06c45c5465d9b586f35cba3da5129ea28a1cd85b |
Attention | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class Attention(nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | FGDBTKD/decaNLP | Attention | false | 13,675 | [
"BSD-3-Clause"
] | 2,361 | ff2d7e18cc226197bb8fe5fe796c4b8bc0395e86 | https://github.com/FGDBTKD/decaNLP/tree/ff2d7e18cc226197bb8fe5fe796c4b8bc0395e86 |
GlobalAveragePool2d | import torch
import torch.nn as nn
class GlobalAveragePool2d(nn.Module):
def __init__(self):
super(GlobalAveragePool2d, self).__init__()
def forward(self, x: 'torch.Tensor'):
assert len(x.size()) >= 2
x_size = x.size()
out = x.view(*x_size[:-2], -1)
out = out.mean(dim... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | slyviacassell/Multi-taks-UNITE | GlobalAveragePool2d | false | 4,359 | [
"MIT"
] | 0 | a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 | https://github.com/slyviacassell/Multi-taks-UNITE/tree/a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 |
Cnv2d_separable | import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from time import time as time
class Cnv2d_separable(nn.Module):
def __init__(self, n_input_ch, n_output_ch, kernel_size, stride,
padding, bias=False, red_portion=0.5):
super(Cnv2d_separable, self).__in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 time
import torch.nn as nn
import torch.nn.parallel
import torch.utils.da... | aosokin/biogans | Cnv2d_separable | false | 14,892 | [
"Apache-2.0"
] | 105 | cb72bb0457be335fad6c27a16bb1761b937a6d06 | https://github.com/aosokin/biogans/tree/cb72bb0457be335fad6c27a16bb1761b937a6d06 |
QuantPointwiseConv | # 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... | TeaPoly/wenet | QuantPointwiseConv | false | 2,886 | [
"Apache-2.0"
] | 0 | 5681887e338e4c8b2c75ffc283140e11a9d56a6d | https://github.com/TeaPoly/wenet/tree/5681887e338e4c8b2c75ffc283140e11a9d56a6d |
ASPP | import torch
from torch import nn
import torch.nn.functional as F
import torch.cuda
class ASPP(nn.Module):
"""
Atrous spatial pyramid pooling used in object detection and segmentation.
"""
def __init__(self, in_channel=512, depth=256):
super().__init__()
self.mean = nn.AdaptiveAvgPool... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | LoveEachDay/towhee | ASPP | false | 11,683 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 |
TLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from torch.nn.parameter import Parameter
from torch.optim.lr_schedul... | Challyfilio/NAIC2021 | TLU | false | 235 | [
"MIT"
] | 0 | 11b38a920dcc902f9b798dc43ae360062862e6e4 | https://github.com/Challyfilio/NAIC2021/tree/11b38a920dcc902f9b798dc43ae360062862e6e4 |
DiceLoss | # 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... | sebasmos/Spacenet7TRDP | DiceLoss | false | 12,955 | [
"Apache-2.0"
] | 0 | 03b5819321108017f8f8c2d359264c8e18d9e38a | https://github.com/sebasmos/Spacenet7TRDP/tree/03b5819321108017f8f8c2d359264c8e18d9e38a |
Mish | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | Aditya239233/MDP | Mish | false | 16,889 | [
"MIT"
] | 4 | 87491e1d67e547c11f4bdd5d784d120473429eae | https://github.com/Aditya239233/MDP/tree/87491e1d67e547c11f4bdd5d784d120473429eae |
GatedLinearUnit | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class GatedLinearUnit(nn.Module):
def __init__(self, input_size, output_size, dropout=0):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.w4 = nn.Linear(input_size, outp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 torchvision import models as models
import torch.onnx
... | dqawami/openvino_training_extensions | GatedLinearUnit | false | 15,227 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | CaptainJa/demo-torch-gpt2 | MLP | false | 3,730 | [
"MIT"
] | 0 | 83d6074e8b321101e08c0aa5749c8eb988a5faa8 | https://github.com/CaptainJa/demo-torch-gpt2/tree/83d6074e8b321101e08c0aa5749c8eb988a5faa8 |
RobertaSelfAttention | # 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.... | BlackNoodle/TUCORE-GCN | RobertaSelfAttention | false | 8,790 | [
"MIT"
] | 27 | 16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 | https://github.com/BlackNoodle/TUCORE-GCN/tree/16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 |
SelfAttn | # 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.... | jzhanson/alfred | SelfAttn | false | 3,792 | [
"MIT"
] | 0 | d5b540e7c9b53d3f70cc2907503935fecff00018 | https://github.com/jzhanson/alfred/tree/d5b540e7c9b53d3f70cc2907503935fecff00018 |
TonemappedMSE | import torch
def _tonemap(im):
"""Helper Reinhards tonemapper.
Args:
im(torch.Tensor): image to tonemap.
Returns:
(torch.Tensor) tonemaped image.
"""
im = torch.clamp(im, min=0)
return im / (1 + im)
class TonemappedMSE(torch.nn.Module):
"""Mean-squared error on tonemaped ... | 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... | qbhan/pathembed | TonemappedMSE | false | 7,505 | [
"MIT"
] | 1 | c21823529840593bf606e10696f5879e5adb51b2 | https://github.com/qbhan/pathembed/tree/c21823529840593bf606e10696f5879e5adb51b2 |
UNETWithoutConcat | # 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... | quenting44/semantic_segmentation | UNETWithoutConcat | false | 10,831 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a |
CNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | ZJU-DistributedAI/RDFL-GAN | CNN | false | 9,675 | [
"Apache-2.0"
] | 0 | e5f10b071d25db7931749515b1b8a3c477a91257 | https://github.com/ZJU-DistributedAI/RDFL-GAN/tree/e5f10b071d25db7931749515b1b8a3c477a91257 |
SimpleConv | # 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_... | junoon53/pcam_challenge | SimpleConv | false | 7,151 | [
"MIT"
] | 1 | 283c98b2d2e211424cdcb56d8230a7a29dc5af46 | https://github.com/junoon53/pcam_challenge/tree/283c98b2d2e211424cdcb56d8230a7a29dc5af46 |
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
assert_... | HzcIrving/DLRL_PlayGround | Critic | false | 8,259 | [
"MIT"
] | 27 | 0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b | https://github.com/HzcIrving/DLRL_PlayGround/tree/0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b |
FixedScaleController | # 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... | niloofar17/MetaDialog | FixedScaleController | false | 16,186 | [
"Apache-2.0"
] | 204 | d75b84a02807d53d9596e72c2f698e5a4f180369 | https://github.com/niloofar17/MetaDialog/tree/d75b84a02807d53d9596e72c2f698e5a4f180369 |
RegressionModel | import torch
import torch.nn as nn
class RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=21, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=(
3, 3), padding=1)
self.act1 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | HenryOsborne/Rotation | RegressionModel | false | 9,115 | [
"Apache-2.0"
] | 0 | 417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 | https://github.com/HenryOsborne/Rotation/tree/417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 |
TestConvTranspose2d | import torch
import torch.nn as nn
class TestConvTranspose2d(nn.Module):
"""Module for Dense conversion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
super(TestConvTranspose2d, self).__init__()
self.conv2d = nn.ConvTranspose2d(inp, out, padding=1, stride=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... | AliaksandrSiarohin/pytorch2keras | TestConvTranspose2d | false | 8,903 | [
"MIT"
] | 0 | 9c8ee213cff43ade152b1de78fa76fd05ec8b40a | https://github.com/AliaksandrSiarohin/pytorch2keras/tree/9c8ee213cff43ade152b1de78fa76fd05ec8b40a |
Distance | import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list = 'axyz'
... | 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... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | Distance | false | 17,125 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
CO_Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class CO_Attention(nn.Module):
def __init__(self, in_dim, co_attention_dim):
super(CO_Attention, self).__init__()
self.leak_relu = nn.LeakyReLU()
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(in_dim, 64, kernel_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 import triton_helpers
import torch.nn as nn
assert_... | huanglf714/COMatchNet | CO_Attention | false | 6,858 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
LogVarLayer | # 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
... | High-East/BCI-ToolBox | LogVarLayer | false | 17,377 | [
"MIT"
] | 10 | 57015ae5fd008e8636889b9afba49c64c3a35ff3 | https://github.com/High-East/BCI-ToolBox/tree/57015ae5fd008e8636889b9afba49c64c3a35ff3 |
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.... | benellis3/pymarl2 | SelfAttention | false | 14,950 | [
"Apache-2.0"
] | 401 | 0875995a0e0b9692ea64484478b369c7f6c0cf44 | https://github.com/benellis3/pymarl2/tree/0875995a0e0b9692ea64484478b369c7f6c0cf44 |
SimpleConvNet | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
import torch.onnx
class SimpleConvNet(nn.Module):
def __init__(self, hidden=1000):
super(SimpleConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | CorentinChauvin/style-transfer-KD | SimpleConvNet | false | 5,085 | [
"MIT"
] | 1 | 87bcb2963dbb8d09faf94c74a744f358cafe5427 | https://github.com/CorentinChauvin/style-transfer-KD/tree/87bcb2963dbb8d09faf94c74a744f358cafe5427 |
Quantize | # 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
from torch.nn import functional as F
assert_size_stride = t... | jkulhanek/viewformer | Quantize | false | 15,728 | [
"MIT"
] | 87 | 9ad2c5a2f7abe4b7ff490ced0132bf3d2f07e29c | https://github.com/jkulhanek/viewformer/tree/9ad2c5a2f7abe4b7ff490ced0132bf3d2f07e29c |
ResidualAttentionBlock | import torch
from collections import OrderedDict
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
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.... | Jinsu-L/KELIP | ResidualAttentionBlock | false | 5,426 | [
"Apache-2.0"
] | 1 | d3261cbb9ba3c3ad474dd560a5add8b69ed78477 | https://github.com/Jinsu-L/KELIP/tree/d3261cbb9ba3c3ad474dd560a5add8b69ed78477 |
MiniBatchStddevLayer | # 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
import torch.distributed as dist
import torch.autograd as... | arkel23/mmgeneration | MiniBatchStddevLayer | false | 9,947 | [
"Apache-2.0"
] | 0 | 41a30e2972f2037f6aac60ed761bed3fe47bfe4d | https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d |
Gaussian | import torch
from torch import nn
class Gaussian(nn.Module):
def __init__(self, hidden_size, output_size):
"""
Gaussian Likelihood Supports Continuous Data
Args:
input_size (int): hidden h_{i,t} column size
output_size (int): embedding size
"""
super(Gaussi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ashfarhangi/COVID-19_Impact | Gaussian | false | 9,755 | [
"Apache-2.0"
] | 0 | 7ce46616278cac95e31b3e853bb28ea7b8e58b7e | https://github.com/ashfarhangi/COVID-19_Impact/tree/7ce46616278cac95e31b3e853bb28ea7b8e58b7e |
QAConvSDSLayer | import torch
import torch.nn as nn
class QAConvSDSLayer(nn.Module):
"""Conv SDS layer for qa output"""
def __init__(self, input_size: 'int', hidden_dim: 'int'):
"""
Args:
input_size (int): max sequence lengths
hidden_dim (int): backbones's hidden dimension
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Amber-Chaeeunk/Open-Domain-Question-Answering | QAConvSDSLayer | false | 16,910 | [
"MIT"
] | 5 | 725e369a4409c54bf11bcfb9db53865d8fc1f935 | https://github.com/Amber-Chaeeunk/Open-Domain-Question-Answering/tree/725e369a4409c54bf11bcfb9db53865d8fc1f935 |
ToRGB | from torch.autograd import Function
import math
import torch
from torch import nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
def make_kernel(k):
k = torch.tensor(k, dtype=torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
from torch import nn
import torc... | ArashVahabpour/encoder4editing | ToRGB | false | 1,986 | [
"MIT"
] | 0 | 819b90ecd7397fbe2ab7cb30eb451dab0f3149fd | https://github.com/ArashVahabpour/encoder4editing/tree/819b90ecd7397fbe2ab7cb30eb451dab0f3149fd |
LandmarkHead | import torch
from torch import nn
import torch.nn
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 40, kernel_size=
(1, 1), stride=1, padding=0)
def forward(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 import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guard... | ZongqingHou/Pytorch_Retinaface | LandmarkHead | false | 3,097 | [
"MIT"
] | 0 | 6284b7158a0d9d3d4a2cc267a393c21863a1b938 | https://github.com/ZongqingHou/Pytorch_Retinaface/tree/6284b7158a0d9d3d4a2cc267a393c21863a1b938 |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pa... | SaumilShah66/dqn_uav | Conv2d | false | 9,583 | [
"MIT"
] | 0 | 2bf780369e964b870624aebcff16c0714cad03c1 | https://github.com/SaumilShah66/dqn_uav/tree/2bf780369e964b870624aebcff16c0714cad03c1 |
Attn | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | marvinzh/ConvLab | Attn | false | 12,758 | [
"MIT"
] | 0 | 45ac46b805e064f783b3a1a409b0902ac81da661 | https://github.com/marvinzh/ConvLab/tree/45ac46b805e064f783b3a1a409b0902ac81da661 |
SimSiamLoss | # 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... | NeurAI-Lab/DoGo | SimSiamLoss | false | 17,739 | [
"MIT"
] | 3 | e3038204f15a40a2d5caca20bb171c87a40d95ba | https://github.com/NeurAI-Lab/DoGo/tree/e3038204f15a40a2d5caca20bb171c87a40d95ba |
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