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 |
|---|---|---|---|---|---|---|---|---|---|---|
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
「A Structured Self-Attentive Sentence Embedding」 Paper
https://arxiv.org/abs/1703.03130
.. math::
\\begin{array}{ll}
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | qute012/Korean-Speech-Recognition | Attention | false | 7,517 | [
"Apache-2.0"
] | 1 | 0e037fd03df1ad6bf1084ee748781cdf4d428940 | https://github.com/qute012/Korean-Speech-Recognition/tree/0e037fd03df1ad6bf1084ee748781cdf4d428940 |
Normalization | import numbers
import torch
import torch.nn as nn
class Normalization(nn.Module):
"""A normalization layer."""
def __init__(self, eps: 'numbers.Real'=1e-15):
"""Creates a new instance of ``Normalization``.
Args:
eps (numbers.Real, optional): A tiny number to be added to t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numbers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guar... | phohenecker/pytorch-transformer | Normalization | false | 16,246 | [
"BSD-2-Clause"
] | 50 | 211406d82ac04a7b473bcdebda77cc3c2e9af0cf | https://github.com/phohenecker/pytorch-transformer/tree/211406d82ac04a7b473bcdebda77cc3c2e9af0cf |
NaiveGroupNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch.nn import Parameter
from torch.nn import... | UrwLee/AdelaiDet | NaiveGroupNorm | false | 9,633 | [
"BSD-2-Clause"
] | 0 | 4cd88a80355d21261e94400767f44701ebc4b402 | https://github.com/UrwLee/AdelaiDet/tree/4cd88a80355d21261e94400767f44701ebc4b402 |
AdaptiveAvgPool3dOutSize1 | # 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 typing import Tuple
import torch.nn as nn
from abc import abstractmethod
import torch.utils.data
import torch.nn
assert_size_stride = t... | zijian-hu/pytorchvideo | AdaptiveAvgPool3dOutSize1 | false | 4,701 | [
"Apache-2.0"
] | 0 | 51589b100437af2285c56ce2ccc7ccecb7f9b18b | https://github.com/zijian-hu/pytorchvideo/tree/51589b100437af2285c56ce2ccc7ccecb7f9b18b |
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.... | YufengJin/deep-reinforcement-learning | Actor | false | 2,988 | [
"MIT"
] | 0 | 141cf00f169b46aa492c9e7520429bfdaab0117d | https://github.com/YufengJin/deep-reinforcement-learning/tree/141cf00f169b46aa492c9e7520429bfdaab0117d |
BertPSIHead | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class BertPSIHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
self.decoder = nn.Linear(conf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Sologa/awesome-align | BertPSIHead | false | 14,424 | [
"BSD-3-Clause"
] | 173 | 62eaae7eac9bac06c10627fac6cc942c07a50e64 | https://github.com/Sologa/awesome-align/tree/62eaae7eac9bac06c10627fac6cc942c07a50e64 |
binary_last_fc | # 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.autograd... | RuiLin0212/BATMANN | binary_last_fc | false | 17,870 | [
"MIT"
] | 6 | 5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 | https://github.com/RuiLin0212/BATMANN/tree/5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 |
NormedConv2d | import torch
import torch.nn as nn
class NormedConv2d(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Bin-ze/Food_detection | NormedConv2d | false | 17,010 | [
"Apache-2.0"
] | 4 | 1c1a067f12644f2b0289e49aec4637d580722f70 | https://github.com/Bin-ze/Food_detection/tree/1c1a067f12644f2b0289e49aec4637d580722f70 |
StyleAdaptiveLayerNorm | # 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 ... | ishine/StyleSpeech-1 | StyleAdaptiveLayerNorm | false | 15,625 | [
"MIT"
] | 106 | f939cf9cb981db7b738fa9c9c9a7fea2dfdd0766 | https://github.com/ishine/StyleSpeech-1/tree/f939cf9cb981db7b738fa9c9c9a7fea2dfdd0766 |
ResidualAttention | # 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... | LiChengChen666/DetectDee | ResidualAttention | false | 9,836 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
Baseblock | # 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 to... | FENGShuanglang/PyTorch_Feat_Vision | Baseblock | false | 11,426 | [
"MIT"
] | 0 | c45dd001c3354e430e9772ddca6f4ba779656761 | https://github.com/FENGShuanglang/PyTorch_Feat_Vision/tree/c45dd001c3354e430e9772ddca6f4ba779656761 |
ExU | # 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.... | mrahman93/nam | ExU | false | 4,037 | [
"MIT"
] | 0 | 1a2f286a87ffa024040e3330088b4a375700c1c6 | https://github.com/mrahman93/nam/tree/1a2f286a87ffa024040e3330088b4a375700c1c6 |
Position_wise_Feed_Forward | # 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.... | Ch4ndelier/Transformer_Zero_Velocity_classification | Position_wise_Feed_Forward | false | 17,090 | [
"MIT"
] | 6 | 857efb66189c503e983c11bd7dde16ad19c51ada | https://github.com/Ch4ndelier/Transformer_Zero_Velocity_classification/tree/857efb66189c503e983c11bd7dde16ad19c51ada |
SmoothJaccardLoss | # 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... | BloodAxe/segmentation-networks-benchmark | SmoothJaccardLoss | false | 7,870 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
Dice | import torch
import torch.nn as nn
import torch.nn.functional as F
class DiceLoss(nn.Module):
def __init__(self, dims=(1, 2, 3)) ->None:
super(DiceLoss, self).__init__()
self.eps: 'float' = 1e-06
self.dims = dims
def forward(self, input: 'torch.Tensor', target: 'torch.Tensor',
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | CarlosPena00/pytorch-unet | Dice | false | 208 | [
"MIT"
] | 0 | 8365bace23e4b04b9c5b75cd6720807ea8cac5ab | https://github.com/CarlosPena00/pytorch-unet/tree/8365bace23e4b04b9c5b75cd6720807ea8cac5ab |
CenteredLayer | import torch
from torch import nn
class CenteredLayer(nn.Module):
def __init__(self):
super().__init__()
def forward(self, X):
return X - X.mean()
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 import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | JunoCheon/D2L | CenteredLayer | false | 2,610 | [
"MIT"
] | 0 | 9464709862e55151aec28fc637c5942738bdd72b | https://github.com/JunoCheon/D2L/tree/9464709862e55151aec28fc637c5942738bdd72b |
QNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_uniform_
from torch.nn.init import uniform_
import torch.utils.data
def mini_weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(uniform_(m.weight.data, -0.003, 0.003))
m.bias.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | AswinRetnakumar/Machina | QNet | false | 13,331 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
GumbelSigmoid | # 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... | daniil-lyakhov/deep-object-reid | GumbelSigmoid | false | 1,781 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 |
ConvBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.nn
assert_size_stride ... | shimon-c/Machine-Learning-Collection | ConvBlock | false | 16,413 | [
"MIT"
] | 3,094 | ac5dcd03a40a08a8af7e1a67ade37f28cf88db43 | https://github.com/shimon-c/Machine-Learning-Collection/tree/ac5dcd03a40a08a8af7e1a67ade37f28cf88db43 |
AsymmetricLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torchv... | MinliangLin/ASL | AsymmetricLoss | false | 2,921 | [
"MIT"
] | 0 | beda0989a8e30ac51a7ce9f9e247a12bbe84ec96 | https://github.com/MinliangLin/ASL/tree/beda0989a8e30ac51a7ce9f9e247a12bbe84ec96 |
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.... | rish-16/pytorch-graphdl | Attention | false | 7,570 | [
"MIT"
] | 1 | 631da8cbf24e67fab2122c507e1935d4acf26e41 | https://github.com/rish-16/pytorch-graphdl/tree/631da8cbf24e67fab2122c507e1935d4acf26e41 |
LogitCosineDistance | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | adriensas/flair | LogitCosineDistance | false | 9,745 | [
"MIT"
] | 0 | f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 | https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 |
RenormSoftmax | # 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 numpy as np
imp... | simonverret/deep_continuation | RenormSoftmax | false | 4,338 | [
"MIT"
] | 0 | 986bfba7f6806dc4869a023ff1fc1d0d18324b25 | https://github.com/simonverret/deep_continuation/tree/986bfba7f6806dc4869a023ff1fc1d0d18324b25 |
Pooling | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride of the convolution
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | HarshCasper/nni | Pooling | false | 5,274 | [
"MIT"
] | 1 | 291bbbba9f296382015a77b2c88eb5db5b44bf94 | https://github.com/HarshCasper/nni/tree/291bbbba9f296382015a77b2c88eb5db5b44bf94 |
ExampleBackbone | import torch
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
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
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch impo... | Atten4Vis/DemystifyLocalViT | ExampleBackbone | false | 13,342 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
Squash | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Squash(Module):
'\n ## Squash\n\n This is **squashing** function from paper, given by equation $(1)$.\n\n $$\\mathbf{v}_j = \x0crac{{\\lVert \\mathbf{s}_j \rVert}^2}{1 + {\\lVert \\math... | 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.nn import Module
import torch.utils.data
import torch.nn.functional
... | techthiyanes/annotated_deep_learning_paper_implementations | Squash | false | 16,570 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
Expand | # 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... | HarryPham0123/FPT_data_centric_competition | Expand | false | 5,304 | [
"Apache-2.0"
] | 1 | 3fa1e0ac48fdae2649b639229d9a74f75e461878 | https://github.com/HarryPham0123/FPT_data_centric_competition/tree/3fa1e0ac48fdae2649b639229d9a74f75e461878 |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Manojbhat09/Sane-annotation-shape-complete | Critic | false | 17,701 | [
"Apache-2.0"
] | 9 | 03b298b2c0a187be979ff31ad2a39238b72a6d78 | https://github.com/Manojbhat09/Sane-annotation-shape-complete/tree/03b298b2c0a187be979ff31ad2a39238b72a6d78 |
DiceScore | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
class DiceScore(nn.Module):
def __init__(self, threshold=0.5):
super(DiceScore, self).__init__()
self.threshold = threshold
def forward(self, logits, labels):
probs ... | 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.backends.cudnn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | ArmenGhambaryan/kaggle_carvana_segmentation | DiceScore | false | 13,292 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
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.... | surya00060/tvm | Net | false | 10,814 | [
"Zlib",
"Unlicense",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0"
] | 0 | fd4601514aee1ecf080b74578849c60438f55b0c | https://github.com/surya00060/tvm/tree/fd4601514aee1ecf080b74578849c60438f55b0c |
OverfitNet | # 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 ... | Lornatang/Deep-learning-with-python3 | OverfitNet | false | 17,605 | [
"Apache-2.0"
] | 4 | 11794d871e68f8f80486a07bf5137325b4ee1526 | https://github.com/Lornatang/Deep-learning-with-python3/tree/11794d871e68f8f80486a07bf5137325b4ee1526 |
Div | import torch
class Div(torch.nn.Module):
def __init__(self):
super(Div, self).__init__()
def forward(self, x, y):
return x / y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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... | Akababa/torch2trt | Div | false | 18,416 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, 5)
self.gap = nn.AdaptiveAvgPool2d(1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ai-antena/cifar10 | CNN | false | 9,681 | [
"MIT"
] | 0 | a3c72693cffae4a5150f1ca5f19472098163ed1a | https://github.com/ai-antena/cifar10/tree/a3c72693cffae4a5150f1ca5f19472098163ed1a |
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
import ... | Yuibooo/BEAR | QNetwork | false | 18,159 | [
"MIT"
] | 4 | d8cf22e3bf0017db0702a6b8b8eb00f22e760991 | https://github.com/Yuibooo/BEAR/tree/d8cf22e3bf0017db0702a6b8b8eb00f22e760991 |
Fp32LayerNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
import torch.distributed
class Fp32LayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
impor... | DCMMC/chineseocr | Fp32LayerNorm | false | 9,241 | [
"MIT"
] | 0 | 0b8772615239ea7f212b1ab5bc75183e7e9f16b0 | https://github.com/DCMMC/chineseocr/tree/0b8772615239ea7f212b1ab5bc75183e7e9f16b0 |
Linear | import torch
from torch import Tensor
from warnings import warn
from torch.nn import functional as F
from torch.nn import Linear as normal_linear
import torch.utils.data
from torchvision import transforms as transforms
class Linear(normal_linear):
def __init__(self, *args, **kwargs):
super(Linear, self).... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from warnings import warn
from torch.nn import Linear as normal_linear
import to... | wang93/pytorch-cifar10 | Linear | false | 4,513 | [
"Apache-2.0"
] | 0 | 07a54dd575aad9b011114352d08fdd9f61e360a1 | https://github.com/wang93/pytorch-cifar10/tree/07a54dd575aad9b011114352d08fdd9f61e360a1 |
AxialPositionalEmbedding | import torch
from torch import nn
class AxialPositionalEmbedding(nn.Module):
def __init__(self, dim, shape, emb_dim_index=1):
super().__init__()
total_dimensions = len(shape) + 2
ax_dim_indexes = [i for i in range(1, total_dimensions) if i !=
emb_dim_index]
self.num_ax... | 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... | lukeleeai/metnet | AxialPositionalEmbedding | false | 12,734 | [
"MIT"
] | 0 | 1dc0bf11780f413f3d55207866e0fa921b8aa60d | https://github.com/lukeleeai/metnet/tree/1dc0bf11780f413f3d55207866e0fa921b8aa60d |
Conv1DSame | # 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 as nn
assert_size_stride = torch._C._dynamo.guards.a... | alexchartrand/IoT | Conv1DSame | false | 1,406 | [
"MIT"
] | 0 | 2cc0d40b7f8305b9f82fc83ad4ed55c83efa1bfd | https://github.com/alexchartrand/IoT/tree/2cc0d40b7f8305b9f82fc83ad4ed55c83efa1bfd |
GaussianSample | import torch
import torch.nn as nn
class Stochastic(nn.Module):
"""
Base stochastic layer that uses the
reparametrization trick [Kingma 2013]
to draw a sample from a distribution
parametrised by mu and log_var.
"""
def reparametrize(self, mu, logvar):
epsilon = torch.randn(mu.size... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ChengF-Lab/scIVA | GaussianSample | false | 8,884 | [
"MIT"
] | 0 | f70a927531dd16236dff30decbe77f0552ad4f2d | https://github.com/ChengF-Lab/scIVA/tree/f70a927531dd16236dff30decbe77f0552ad4f2d |
MixerBlock | # 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.fun... | GimmeSpoon/mlp-singer | MixerBlock | false | 5,240 | [
"MIT"
] | 1 | 36d10a23c46fa7400994ccd063de79ff089efd5e | https://github.com/GimmeSpoon/mlp-singer/tree/36d10a23c46fa7400994ccd063de79ff089efd5e |
Hidden2Discrete | # 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.... | msft-shahins/ConvLab-2 | Hidden2Discrete | false | 12,810 | [
"Apache-2.0"
] | 0 | ad74c0e9e021916f9330af11e046ed72914b7740 | https://github.com/msft-shahins/ConvLab-2/tree/ad74c0e9e021916f9330af11e046ed72914b7740 |
Classifier | import torch
import torch.utils.data
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zaynmi/semantic-equivalent-da-for-vqa | Classifier | false | 16,805 | [
"MIT"
] | 298 | f121fb3e8fee8af5f1935a7526f19e0d884bd95b | https://github.com/zaynmi/semantic-equivalent-da-for-vqa/tree/f121fb3e8fee8af5f1935a7526f19e0d884bd95b |
group | # 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_... | BradyFU/DVG | group | false | 13,421 | [
"MIT"
] | 102 | 53fd50cdc51d783b33394726b8f8a2b2216f157b | https://github.com/BradyFU/DVG/tree/53fd50cdc51d783b33394726b8f8a2b2216f157b |
PaddedMaxPool2d | # 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... | CuongNguyen218/ObjectDetection-OneStageDet | PaddedMaxPool2d | false | 328 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
ThreeNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class ThreeNet(nn.Module):
def __init__(self, n_features, e1=2048, e2=1024, e3=640, e4=512, e5=216,
p=0.4):
super(ThreeNet, self).__init__()
self.a1 = nn.Linear(n_features, e1)
self.a2 = nn.Linear(e1, e2)
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
import torch.nn as ... | SkBlaz/KBNR | ThreeNet | false | 5,840 | [
"MIT"
] | 1 | 4c37fe3fdfa7719572affd617e2dab43a54ba1d5 | https://github.com/SkBlaz/KBNR/tree/4c37fe3fdfa7719572affd617e2dab43a54ba1d5 |
Encoder | import torch
from torch import nn
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, latent_dim):
super(Encoder, self).__init__()
self.FC_input = nn.Linear(input_dim, hidden_dim)
self.FC_mean = nn.Linear(hidden_dim, latent_dim)
self.FC_var = nn.Linear(hidden_dim, ... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | georgezefko/dtu_mlops | Encoder | false | 10,091 | [
"Apache-2.0"
] | 0 | 3b715bcb934d0c2827d89395823b7d4768faac97 | https://github.com/georgezefko/dtu_mlops/tree/3b715bcb934d0c2827d89395823b7d4768faac97 |
BertTextPooler | # 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... | eaidova/lxmert | BertTextPooler | false | 1,866 | [
"MIT"
] | 0 | c74616907125242112c6ee5c516b54c250168e8b | https://github.com/eaidova/lxmert/tree/c74616907125242112c6ee5c516b54c250168e8b |
weightedFeatureFusion | import torch
import torch.nn as nn
class weightedFeatureFusion(nn.Module):
def __init__(self, layers, weight=False):
super(weightedFeatureFusion, self).__init__()
self.layers = layers
self.weight = weight
self.n = len(layers) + 1
if weight:
self.w = torch.nn.Pa... | 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... | Nigel233/Different-Backbones-for-YOLO-v3 | weightedFeatureFusion | false | 9,360 | [
"MIT"
] | 0 | 030e7860e966b079afc9b53a320a41f3eb7950be | https://github.com/Nigel233/Different-Backbones-for-YOLO-v3/tree/030e7860e966b079afc9b53a320a41f3eb7950be |
Reshape | # 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... | NGoetz/NF | Reshape | false | 5,625 | [
"MIT"
] | 1 | 935886db48f4675db1a2c42f7c264b12d5014ed8 | https://github.com/NGoetz/NF/tree/935886db48f4675db1a2c42f7c264b12d5014ed8 |
RegressionNN | import torch
import numpy as np
import torch.nn as nn
class RegressionNN(nn.Module):
def __init__(self, feature_number):
super(RegressionNN, self).__init__()
self.feature_number = feature_number
self.fc1 = nn.Linear(self.feature_number, 12)
self.fc2 = nn.Linear(12, 8)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | BuildFL/BuildFL | RegressionNN | false | 17,018 | [
"MIT"
] | 6 | 2b9fb786c9655b52d54b53e3efaf25e033a5b532 | https://github.com/BuildFL/BuildFL/tree/2b9fb786c9655b52d54b53e3efaf25e033a5b532 |
ResizeConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | RobertYCXu/vae_vampprior | ResizeConv2d | false | 9,571 | [
"MIT"
] | 0 | edcec4f5f7af673172c5b5b9aa2a22f993564fab | https://github.com/RobertYCXu/vae_vampprior/tree/edcec4f5f7af673172c5b5b9aa2a22f993564fab |
TransformerFFNLayer | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Linear
import torch.utils.data
import torch.optim
import torch.distributions
def _get_full_incremental_state_key(module_instance, key):
module_name = module_instance.__class__.__name__
if not hasattr(module_instance, '_inst... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Rexiome/NATSpeech | TransformerFFNLayer | false | 14,307 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ashim95/parser | MLP | false | 6,238 | [
"MIT"
] | 1 | 61e9cd6bf16dcf1aa2b9d51b3a6c04ed048b3199 | https://github.com/ashim95/parser/tree/61e9cd6bf16dcf1aa2b9d51b3a6c04ed048b3199 |
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
import torch.nn as nn
assert_... | bongsang/face-landmark | Net | false | 6,350 | [
"MIT"
] | 1 | bc7644480be1ddf8d35c2875d251bc84c00ccaa7 | https://github.com/bongsang/face-landmark/tree/bc7644480be1ddf8d35c2875d251bc84c00ccaa7 |
CondConv2D | import functools
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
class _routing(nn.Module):
def __init__(self, in_channels, num_experts, dropout_rate):
super(_rout... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 functools
from torch import nn
import torch.nn.functional as F
from torch... | yifanpu001/CondConv-pytorch | CondConv2D | false | 13,152 | [
"MIT"
] | 0 | d5198f1c53de97304f8a23f4ca287cf5b4d33561 | https://github.com/yifanpu001/CondConv-pytorch/tree/d5198f1c53de97304f8a23f4ca287cf5b4d33561 |
lp_KL_divergence | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | loveorchids/local_patch_retrieval | lp_KL_divergence | false | 3,938 | [
"Apache-2.0"
] | 0 | 52b2e8fdac965d56ef9f89a8c4de96d0b41d3981 | https://github.com/loveorchids/local_patch_retrieval/tree/52b2e8fdac965d56ef9f89a8c4de96d0b41d3981 |
MaxPoolPad | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torchvision.transforms import *
assert_size_stride = torch._C.... | DRACOyu/deep-person-reid | MaxPoolPad | false | 5,204 | [
"MIT"
] | 1 | 8ca8be28c204dbc37cff76e77691f29045773aa2 | https://github.com/DRACOyu/deep-person-reid/tree/8ca8be28c204dbc37cff76e77691f29045773aa2 |
AttMseLoss | # 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... | Raiselimit/TorchBlocks | AttMseLoss | false | 5,733 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
PosEnc | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchv... | XuelianCheng/ppuda | PosEnc | false | 6,010 | [
"MIT"
] | 1 | d5b89928e430e2d5b976f84b1ea66b4b901e6cda | https://github.com/XuelianCheng/ppuda/tree/d5b89928e430e2d5b976f84b1ea66b4b901e6cda |
Net_BP | import torch
import torch.nn.functional as F
class Net_BP(torch.nn.Module):
def __init__(self, n_features, n_hidden=50, n_output=1):
super(Net_BP, self).__init__()
self.hidden = torch.nn.Linear(n_features, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | Tappai/PV_prediction | Net_BP | false | 5,874 | [
"Apache-2.0"
] | 1 | 2ff1e1af183a28f07ebc2ec2979488eb8e246813 | https://github.com/Tappai/PV_prediction/tree/2ff1e1af183a28f07ebc2ec2979488eb8e246813 |
SilogLoss | # 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... | aliasghar53/packnet-sfm | SilogLoss | false | 9,776 | [
"MIT"
] | 0 | d07dcbf026194b618a2bd9fc05b599563611f9a3 | https://github.com/aliasghar53/packnet-sfm/tree/d07dcbf026194b618a2bd9fc05b599563611f9a3 |
MetaAconC | # 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... | PoCInnovation/Koic | MetaAconC | false | 8,663 | [
"MIT"
] | 13 | eca53b53b7242c1e83213ef9408366ca0a346358 | https://github.com/PoCInnovation/Koic/tree/eca53b53b7242c1e83213ef9408366ca0a346358 |
SoftmaxWithTemperature | # 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
... | bingrao/Bug-Transformer | SoftmaxWithTemperature | false | 3,252 | [
"MIT"
] | 0 | 9e39dc553c281f6372b7a8cfc8205aa186645899 | https://github.com/bingrao/Bug-Transformer/tree/9e39dc553c281f6372b7a8cfc8205aa186645899 |
RandomCrop | # 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 import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch... | nudro/counterfactual_generative_networks | RandomCrop | false | 10,771 | [
"MIT"
] | 0 | 0d000903ad9da4eab0f4d397395a769c9c7bff5d | https://github.com/nudro/counterfactual_generative_networks/tree/0d000903ad9da4eab0f4d397395a769c9c7bff5d |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
if target.ndim == 3:
target = target.reshape(-1, target.shape[2])
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Romero027/ImageCaptioning.pytorch | LanguageModelCriterion | false | 2,776 | [
"MIT"
] | 0 | 069c95f5d343fb126afa8b10ec18e472f30b7b35 | https://github.com/Romero027/ImageCaptioning.pytorch/tree/069c95f5d343fb126afa8b10ec18e472f30b7b35 |
SpatialEmbedding | # 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.... | ashwinpn/Computer-Vision | SpatialEmbedding | false | 6,270 | [
"MIT"
] | 1 | 9dc3abfe416385171b76e2bad6872e10f36a12b4 | https://github.com/ashwinpn/Computer-Vision/tree/9dc3abfe416385171b76e2bad6872e10f36a12b4 |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def scaled_dot_product_attention(query, keys, values, mask=None):
d_k = keys.shape[-1]
dot_score = query @ keys.transpose(-2, -1) / math.sqrt(d_k)
if mask is not None:
dot_score = dot_score.masked_fill(mask == 0, -10000... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | NathanYanJing/TransformerReplication | EncoderLayer | false | 11,749 | [
"MIT"
] | 0 | b20f987dcc507724971f843c2d214c9c76bd8e34 | https://github.com/NathanYanJing/TransformerReplication/tree/b20f987dcc507724971f843c2d214c9c76bd8e34 |
Perplexity | import torch
from torch import nn as nn
from torch.nn.modules.loss import CrossEntropyLoss
class Perplexity(CrossEntropyLoss):
__constants__ = ['weight', 'ignore_index', 'reduction']
def __init__(self, weight=None, size_average=None, ignore_index=-100,
reduce=None):
super(Perplexity, self).__... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn a... | cesarali/Tyche | Perplexity | false | 1,651 | [
"MIT"
] | 0 | d892df9e0b982f538ae38221ff5848f6d726a4fb | https://github.com/cesarali/Tyche/tree/d892df9e0b982f538ae38221ff5848f6d726a4fb |
CBDNet | import torch
import torch.nn as nn
class CBDNet(nn.Module):
def __init__(self):
super(CBDNet, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.E01 = nn.Conv2d(3, 32, kernel_size=[3, 3], stride=(1, 1),
padding=(1, 1))
self.E02 = nn.Conv2d(32, 32, kernel_size=[3, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | delldu/ImageClean | CBDNet | false | 1,852 | [
"MIT"
] | 0 | ffa5b180d36afb3840c6b36c08a767c520068498 | https://github.com/delldu/ImageClean/tree/ffa5b180d36afb3840c6b36c08a767c520068498 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.init
import torch.optim.lr_scheduler
import torch.nn
import tor... | codedecde/BiMPM | LayerNorm | false | 9,958 | [
"Apache-2.0"
] | 0 | 818602fcf7a018632707b8fbfe33200036795731 | https://github.com/codedecde/BiMPM/tree/818602fcf7a018632707b8fbfe33200036795731 |
MinimaxDiscriminatorLoss | # 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... | shi-weili/torchgan | MinimaxDiscriminatorLoss | false | 12,972 | [
"MIT"
] | 0 | 28ffd4026b8c0db2217b667d30a222d6758bfc41 | https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41 |
ParallelPolarizedSelfAttention | import torch
from torch import nn
class ParallelPolarizedSelfAttention(nn.Module):
def __init__(self, channel=512):
super().__init__()
self.ch_wv = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
self.ch_wq = nn.Conv2d(channel, 1, kernel_size=(1, 1))
self.softmax_channel = 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.... | LiChengChen666/DetectDee | ParallelPolarizedSelfAttention | false | 9,841 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
FillUpLuminance | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ashwinpn/Computer-Vision | FillUpLuminance | false | 6,254 | [
"MIT"
] | 1 | 9dc3abfe416385171b76e2bad6872e10f36a12b4 | https://github.com/ashwinpn/Computer-Vision/tree/9dc3abfe416385171b76e2bad6872e10f36a12b4 |
MaskedLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | vegetablejuiceftw/soft-pointer-networks | MaskedLoss | false | 11,075 | [
"MIT"
] | 0 | 9705d9688b6b69db3948172771df4c367165c948 | https://github.com/vegetablejuiceftw/soft-pointer-networks/tree/9705d9688b6b69db3948172771df4c367165c948 |
RMSPE | # 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... | Phimos/SIGSPATIAL-2021-GISCUP-3rd-Solution | RMSPE | false | 8,648 | [
"MIT"
] | 11 | 79fcf9941c28cdb2eb38a3654e1514a1d998a41c | https://github.com/Phimos/SIGSPATIAL-2021-GISCUP-3rd-Solution/tree/79fcf9941c28cdb2eb38a3654e1514a1d998a41c |
MinibatchDiscrimination1d | import torch
import torch.nn as nn
class MinibatchDiscrimination1d(nn.Module):
"""1D Minibatch Discrimination Module as proposed in the paper `"Improved Techniques for
Training GANs by Salimans et. al." <https://arxiv.org/abs/1805.08318>`_
Allows the Discriminator to easily detect mode collapse by augmen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | torchgan/torchgan | MinibatchDiscrimination1d | false | 16,612 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
RMulFloat | import torch
class RMulFloat(torch.nn.Module):
def __init__(self):
super(RMulFloat, self).__init__()
def forward(self, x):
return 10.0 * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Akababa/torch2trt | RMulFloat | false | 18,402 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
DistilledLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Graeme22/VisionTransformer-Pytorch | DistilledLoss | false | 17,311 | [
"Apache-2.0"
] | 5 | 4e8abecf27e92dffd8d00f3d9b5ad4a21079cd0e | https://github.com/Graeme22/VisionTransformer-Pytorch/tree/4e8abecf27e92dffd8d00f3d9b5ad4a21079cd0e |
Wang | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
imp... | WladimirSidorenko/DASA | Wang | false | 18,074 | [
"MIT"
] | 7 | 618d9060a5fd6f567628c8dec5e26943c8c49ad4 | https://github.com/WladimirSidorenko/DASA/tree/618d9060a5fd6f567628c8dec5e26943c8c49ad4 |
SingleHeadAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class SingleHeadAttention(nn.Module):
def __init__(self, cfg):
super(SingleHeadAttention, self).__init__()
self.input_dim = cfg.embedding_dim
self.embedding_dim = cfg.embedding_dim
self.va... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JustinLiam/DAN | SingleHeadAttention | false | 7,625 | [
"MIT"
] | 1 | eb29cddad6c93e591854b115ef524643b1cd471c | https://github.com/JustinLiam/DAN/tree/eb29cddad6c93e591854b115ef524643b1cd471c |
TanhTransform | import torch
import torch.nn as nn
def arctanh(x, eps=1e-06):
"""
Calculates the inverse hyperbolic tangent.
"""
x *= 1.0 - eps
return torch.log((1 + x) / (1 - x)) * 0.5
class TanhTransform(nn.Module):
"""
Computes the tanh transform used to
remove box constraints from C&W paper
... | 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_... | knalbant/oppel | TanhTransform | false | 7,048 | [
"MIT"
] | 1 | 03f840565ef64587ddb7a8b4145d8df7fb0279a3 | https://github.com/knalbant/oppel/tree/03f840565ef64587ddb7a8b4145d8df7fb0279a3 |
WDV52Linear | import math
import torch
import torch.nn.functional as F
import torch.utils.data
from torch.nn import Parameter
import torch.onnx.operators
from torch.nn.parameter import Parameter
from torch.nn import init
import torch.optim
import torch.optim.lr_scheduler
class WDV52Linear(torch.nn.Module):
"""Applies a linear ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Lollipop321/weight-distillation | WDV52Linear | false | 5,568 | [
"BSD-3-Clause"
] | 1 | cfc76ec58e3e88094dde1825287b2968f9718431 | https://github.com/Lollipop321/weight-distillation/tree/cfc76ec58e3e88094dde1825287b2968f9718431 |
ClipLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | skat00sh/Handcrafted-DP | ClipLayer | false | 16,479 | [
"MIT"
] | 48 | d1f8bc004adc240d5c424a10bdcc30fc266c8218 | https://github.com/skat00sh/Handcrafted-DP/tree/d1f8bc004adc240d5c424a10bdcc30fc266c8218 |
MedianPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn.modules.utils import _pair
from torch... | PJ-Steeman/2020_Masterproef | MedianPool2d | false | 5,704 | [
"MIT"
] | 1 | 5bd77b4039a897d328fafe9a0b70dc8e593e2899 | https://github.com/PJ-Steeman/2020_Masterproef/tree/5bd77b4039a897d328fafe9a0b70dc8e593e2899 |
PositionwiseFeedForward | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from itertools import chain as chain
import torch.hub
class GELU(nn.Module):
"""
Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
"""
def forward(self, x)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EddieMG/LateTemporalModeling3DCNN | PositionwiseFeedForward | false | 2,285 | [
"MIT"
] | 0 | 94c87dc1d31d09bc310d0e735a2e55453976cb0d | https://github.com/EddieMG/LateTemporalModeling3DCNN/tree/94c87dc1d31d09bc310d0e735a2e55453976cb0d |
NsKlCriterion | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
def stable_kl(logit, target, epsilon=1e-06, reduce=True):
logit = logit.view(-1, logit.size(-1)).float()
target = target.view(-1, target.size(-1)).float()
bs = logit.size(0)
p = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | chunhuililili/mt_dnn | NsKlCriterion | false | 10,206 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(FocalLoss, self).__init__()
def forward(self, inputs, targets, alpha=0.8, gamma=2, smooth=1):
inputs = inputs.view(-1)
targets = ta... | 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... | NakedKoala/sed_time_freq_segmentation | FocalLoss | false | 2,660 | [
"MIT"
] | 0 | 5379e9cdddfba34b6ce4a243580671d32afdac9a | https://github.com/NakedKoala/sed_time_freq_segmentation/tree/5379e9cdddfba34b6ce4a243580671d32afdac9a |
Shared | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class Shared(torch.nn.Module):
def __init__(self, args):
super(Shared, self).__init__()
ncha, self.size, _ = args.inputsize
self.taskcla = args.taskcla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | Prathyusha-Akundi/Adversarial-Continual-Learning | Shared | false | 16,266 | [
"MIT"
] | 237 | edf4bbd2c4c61f1cc20818793702ef8c6cf4e0df | https://github.com/Prathyusha-Akundi/Adversarial-Continual-Learning/tree/edf4bbd2c4c61f1cc20818793702ef8c6cf4e0df |
SpatialAttention | import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv1d(2, 1, kernel_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
assert_... | Neronjust2017/challenge2020_test4 | SpatialAttention | false | 9,472 | [
"BSD-2-Clause"
] | 0 | 6494107a459b563aa51f8ea75c580c17557b13af | https://github.com/Neronjust2017/challenge2020_test4/tree/6494107a459b563aa51f8ea75c580c17557b13af |
AutoEncoderMlp | # 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 abc
from torch import ... | IanWangg/OSRPG | AutoEncoderMlp | false | 2,500 | [
"MIT"
] | 0 | 2817cfa5049a1bf52110fb30c4cf532d7b8e9b5b | https://github.com/IanWangg/OSRPG/tree/2817cfa5049a1bf52110fb30c4cf532d7b8e9b5b |
ResidualConnection | import torch
import torch.nn as nn
class ResidualConnection(nn.Module):
def __init__(self, *layers):
super(ResidualConnection, self).__init__()
self.layers = nn.Sequential(*layers)
def forward(self, input):
return (input + self.layers(input)) / 2.0
def get_inputs():
return [tor... | 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... | maxkvant/LinearizedNNs | ResidualConnection | false | 7,177 | [
"Apache-2.0"
] | 1 | eb0198be70ca55e7463b97a5023d2f6ffe0f8ba6 | https://github.com/maxkvant/LinearizedNNs/tree/eb0198be70ca55e7463b97a5023d2f6ffe0f8ba6 |
Decoder | import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, latent_size, out_size):
super().__init__()
self.linear1 = nn.Linear(latent_size, int(out_size / 4))
self.linear2 = nn.Linear(int(out_size / 4), int(out_size / 2))
self.linear3 = nn.Linear(int(out_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_... | finloop/usad | Decoder | false | 15,350 | [
"BSD-3-Clause"
] | 65 | 5e1bf326af5f1325fa4676a2de978cae6db0481c | https://github.com/finloop/usad/tree/5e1bf326af5f1325fa4676a2de978cae6db0481c |
FeatExemplarAvgBlock | # 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.nn as nn
import torch.optim
import torch.nn.parallel
assert_size_st... | nikran1/Few_shot | FeatExemplarAvgBlock | false | 16,169 | [
"MIT"
] | 497 | 5298c98e208411e44ee7767e6f4d457006d373cb | https://github.com/nikran1/Few_shot/tree/5298c98e208411e44ee7767e6f4d457006d373cb |
BetaVAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | EdwardYGLi/Mnist_b_vae | BetaVAE | false | 11,421 | [
"MIT"
] | 0 | 5c568798bcaa5ec8154aaee8eff2906cf651e958 | https://github.com/EdwardYGLi/Mnist_b_vae/tree/5c568798bcaa5ec8154aaee8eff2906cf651e958 |
UNET | # 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_... | TerenceChen95/Retina-Unet-Pytorch | UNET | false | 18,067 | [
"MIT"
] | 5 | fad5a9a0bcab5d81a0f1bb2537b9a2ead87828ca | https://github.com/TerenceChen95/Retina-Unet-Pytorch/tree/fad5a9a0bcab5d81a0f1bb2537b9a2ead87828ca |
DuelingMLP | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable
def identity(x: 'torch.Tensor') ->torch.Tensor:
"""Return input without any change."""
return x
def init_layer_uniform(layer: 'nn.Linear', init_w: 'float'=0.003) ->nn.Linear:
"""Init uniform parameters on the ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | MrSyee/rl_algorithms | DuelingMLP | false | 5,619 | [
"MIT"
] | 1 | 5b5276982032f8a8a614b9466849b7b3ef245b3e | https://github.com/MrSyee/rl_algorithms/tree/5b5276982032f8a8a614b9466849b7b3ef245b3e |
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_... | SeanNobel/d4rl-pybullet | Critic | false | 14,379 | [
"MIT"
] | 130 | 9f2f56c63bb7a80ebcbc4217cd7689e446aafd41 | https://github.com/SeanNobel/d4rl-pybullet/tree/9f2f56c63bb7a80ebcbc4217cd7689e446aafd41 |
Message_Passing_Unit_v1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Message_Passing_Unit_v1(nn.Module):
def __init__(self, fea_size, filter_size=128):
super(Message_Passing_Unit_v1, self).__init__()
self.w = nn.Linear(fea_size * 2, filter_size, bias=True)
self.fea_size = fea_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_... | EricssonResearch/scott-eu | Message_Passing_Unit_v1 | false | 8,092 | [
"Apache-2.0"
] | 19 | aad7fd2f767a3c5e7d89223a593fd979ad596db3 | https://github.com/EricssonResearch/scott-eu/tree/aad7fd2f767a3c5e7d89223a593fd979ad596db3 |
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
import math
import torch.nn a... | Groenbech96/Learning-to-See-in-the-Dark | Net | false | 5,319 | [
"MIT"
] | 1 | a068c8642a651e4af195cd71e253694d88dfe3c5 | https://github.com/Groenbech96/Learning-to-See-in-the-Dark/tree/a068c8642a651e4af195cd71e253694d88dfe3c5 |
ConvUnit | import torch
import torch.nn as nn
class ConvUnit(nn.Module):
def __init__(self, in_channels):
super(ConvUnit, self).__init__()
self.conv0 = nn.Conv2d(in_channels=in_channels, out_channels=32,
kernel_size=9, stride=2, bias=True)
def forward(self, x):
return self.conv0(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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | juingzhou/Base-on-PyTorch-implementation-CapsuleNet | ConvUnit | false | 10,319 | [
"MIT"
] | 0 | 6b030bf93b258d9d6496379bcbe4b94542366817 | https://github.com/juingzhou/Base-on-PyTorch-implementation-CapsuleNet/tree/6b030bf93b258d9d6496379bcbe4b94542366817 |
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