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 |
|---|---|---|---|---|---|---|---|---|---|---|
GroupNorm32 | import torch
import torch.nn as nn
import torch.nn.functional as F
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-05):
super().__init__(num_groups=num_groups, num_channels=num_channels,
eps=eps)
self.swish = swish
def forward(self, x):... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | johnpaulbin/glide-text2im | GroupNorm32 | false | 12,625 | [
"MIT"
] | 0 | 4897050c4c540316dfb1ec7e6ff95698bcb20487 | https://github.com/johnpaulbin/glide-text2im/tree/4897050c4c540316dfb1ec7e6ff95698bcb20487 |
Classifier | import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, latent_size, output_size):
super().__init__()
self.fc1 = nn.Linear(latent_size, 100)
self.relu1 = nn.LeakyReLU(0.2)
self.fc2 = nn.Linear(100, 50)
self.relu2 = nn.LeakyReLU(0.2)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | BruceWen120/neurips-reproducibility-challenge-2019 | Classifier | false | 8,955 | [
"Apache-2.0"
] | 0 | b0635aefe83e3f895ce0991913824e861bb7d02d | https://github.com/BruceWen120/neurips-reproducibility-challenge-2019/tree/b0635aefe83e3f895ce0991913824e861bb7d02d |
BridgeFeatLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from to... | Luxios22/IDM | BridgeFeatLoss | false | 2,602 | [
"MIT"
] | 0 | 8d51103b7c252e6304e2a361976e16ed4b523944 | https://github.com/Luxios22/IDM/tree/8d51103b7c252e6304e2a361976e16ed4b523944 |
ConvertPointsToHomogeneous | import torch
import torch.nn as nn
def convert_points_to_homogeneous(points):
"""Function that converts points from Euclidean to homogeneous space.
See :class:`~torchgeometry.ConvertPointsToHomogeneous` for details.
Examples::
>>> input = torch.rand(2, 4, 3) # BxNx3
>>> output = tgm.co... | 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... | Paultool/frankmocap | ConvertPointsToHomogeneous | false | 14,151 | [
"BSD-3-Clause"
] | 1,612 | b8bb7b587c0841b9292edb147729de581c66054c | https://github.com/Paultool/frankmocap/tree/b8bb7b587c0841b9292edb147729de581c66054c |
TorchGloVeLoss | import torch
import torch.nn as nn
import torch.utils.data
class TorchGloVeLoss(nn.Module):
def __init__(self):
super().__init__()
self.reduction = 'sum'
def forward(self, diffs, weights):
return torch.sum(0.5 * torch.mul(weights, diffs ** 2))
def get_inputs():
return [torch.ra... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | atticusg/cs224u | TorchGloVeLoss | false | 1,491 | [
"Apache-2.0"
] | 0 | 66e0f2714e246dcb8836f706ae9ff5613c51ed34 | https://github.com/atticusg/cs224u/tree/66e0f2714e246dcb8836f706ae9ff5613c51ed34 |
Router | # 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.... | ppvalluri09/annotated_deep_learning_paper_implementations | Router | false | 11,117 | [
"MIT"
] | 0 | 387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 | https://github.com/ppvalluri09/annotated_deep_learning_paper_implementations/tree/387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 |
ScaledDotProductAttention | # 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.... | ygnn123/training_extensions | ScaledDotProductAttention | false | 4,687 | [
"Apache-2.0"
] | 0 | c3aeba9359b0d4e0ef9c054de777d3ec081a9892 | https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892 |
MLPSoftQNetwork | # 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_... | SAMMiCA/DL_based_E2E_Driving | MLPSoftQNetwork | false | 17,886 | [
"MIT"
] | 4 | 01f7d74a0db7ed745cf27b9a1ebab0246015ecbd | https://github.com/SAMMiCA/DL_based_E2E_Driving/tree/01f7d74a0db7ed745cf27b9a1ebab0246015ecbd |
AdaptiveCatAvgMaxPool2d | # 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 torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.functi... | Alibaba-MIIL/ZS_SDL | AdaptiveCatAvgMaxPool2d | false | 8,041 | [
"MIT"
] | 20 | 769fe4f57d2d458a7c4b5468a6395c9b296b1dad | https://github.com/Alibaba-MIIL/ZS_SDL/tree/769fe4f57d2d458a7c4b5468a6395c9b296b1dad |
Spatial_Attention_layer | import torch
import torch.utils.data
import torch.nn.functional as F
import torch.nn as nn
class Spatial_Attention_layer(nn.Module):
"""
compute spatial attention scores
"""
def __init__(self, DEVICE, in_channels, num_of_vertices, num_of_timesteps):
super(Spatial_Attention_layer, self).__init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kevin-xuan/Traffic-Benchmark | Spatial_Attention_layer | false | 15,836 | [
"MIT"
] | 120 | b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 | https://github.com/kevin-xuan/Traffic-Benchmark/tree/b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 |
ConvBlockFixup | import torch
from torch import nn
class ConvBlockFixup(nn.Module):
def __init__(self, filter_width, input_filters, nb_filters, dilation):
super(ConvBlockFixup, self).__init__()
self.filter_width = filter_width
self.input_filters = input_filters
self.nb_filters = nb_filters
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | OmarNajdi/dl-for-har | ConvBlockFixup | false | 934 | [
"MIT"
] | 0 | 5e1b7c29caf2b41fcba106cd901c45d8f2d18429 | https://github.com/OmarNajdi/dl-for-har/tree/5e1b7c29caf2b41fcba106cd901c45d8f2d18429 |
SqueezeExcite | # 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 ... | a1004123217/pytorch-mobile | SqueezeExcite | false | 1,349 | [
"MIT"
] | 0 | 97974af3259a2073efbc334d57841efbd3eaadfb | https://github.com/a1004123217/pytorch-mobile/tree/97974af3259a2073efbc334d57841efbd3eaadfb |
PixelSort | import torch
from torch import nn
class PixelSort(nn.Module):
"""The inverse operation of PixelShuffle
Reduces the spatial resolution, increasing the number of channels.
Currently, scale 0.5 is supported only.
Later, torch.nn.functional.pixel_sort may be implemented.
Reference:
http://pyto... | 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... | pshn111/803-Project | PixelSort | false | 12,903 | [
"MIT"
] | 0 | 19430f25d91b31e4b9a7f1d864e2aa2851dcddf0 | https://github.com/pshn111/803-Project/tree/19430f25d91b31e4b9a7f1d864e2aa2851dcddf0 |
FocalLoss | import torch
import torch.nn as nn
from torch.nn.functional import binary_cross_entropy
class FocalLoss(nn.Module):
"""
Focal Loss for Dense Object Detection [https://arxiv.org/abs/1708.02002]
Digest the paper as below:
α, balances the importance of positive/negative examples
γ, focusing 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 libdevice, math as tl_math
import torc... | Luoxd1996/Rank2nuclearSegmentation | FocalLoss | false | 17,623 | [
"MIT"
] | 5 | bd85ac13eec7ce18c286efd521a27486483da904 | https://github.com/Luoxd1996/Rank2nuclearSegmentation/tree/bd85ac13eec7ce18c286efd521a27486483da904 |
Autoencoder | # 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 ... | LongerVision/oidn | Autoencoder | false | 5,629 | [
"Apache-2.0"
] | 1 | 2f9e59f8b747b217f78c5c274f4f2bff347a03a7 | https://github.com/LongerVision/oidn/tree/2f9e59f8b747b217f78c5c274f4f2bff347a03a7 |
Decoder | # 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... | bicv/POLO | Decoder | false | 6,333 | [
"MIT"
] | 1 | b8d4f9014796a4eb24c178d8be611a0b3b4c44df | https://github.com/bicv/POLO/tree/b8d4f9014796a4eb24c178d8be611a0b3b4c44df |
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.... | MIMUW-RL/spp-rl | Actor | false | 17,647 | [
"MIT"
] | 7 | 86b96cdd220cc4eae86f7cfd26924c69b498dcc6 | https://github.com/MIMUW-RL/spp-rl/tree/86b96cdd220cc4eae86f7cfd26924c69b498dcc6 |
ChannelWiseLayerNorm | # 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_... | intflow/FullSubNet | ChannelWiseLayerNorm | false | 12,537 | [
"MIT"
] | 0 | 193091acac4c747730db5ace33fd1b8870e7c735 | https://github.com/intflow/FullSubNet/tree/193091acac4c747730db5ace33fd1b8870e7c735 |
EuclideanDistance | # 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.utils.data.dataloader
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | adriensas/flair | EuclideanDistance | false | 9,756 | [
"MIT"
] | 0 | f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 | https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 |
LossyRGB | import torch
import torch.nn.parallel
import torch.utils.data
from torch import nn
import torch.fft
class LossyRGB(nn.Module):
def forward(self, ycbcr: 'torch.Tensor'):
return torch.cat([ycbcr[:, 0:1] + 1.402 * ycbcr[:, 2:3], ycbcr[:, 0
:1] - 0.34413 * ycbcr[:, 1:2] - 0.71414 * ycbcr[:, 2:3],... | 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.parallel
import torch.utils.data
from torch import nn
import torch.fft
assert_size_stride = torch._C._dynamo.guards.assert_s... | KazutakaYamanouchi/bachelor-study | LossyRGB | false | 2,625 | [
"Apache-2.0"
] | 0 | a5b8392459e7649cb8a35d09e65bd269d13b5297 | https://github.com/KazutakaYamanouchi/bachelor-study/tree/a5b8392459e7649cb8a35d09e65bd269d13b5297 |
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.... | Nitin-Mane/External-Attention-pytorch | ScaledDotProductAttention | false | 14,117 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
PositionAttentionModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LiChengChen666/DetectDee | PositionAttentionModule | false | 9,875 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
PairwiseNetwork | # 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 ... | HardiRathod/table-linker | PairwiseNetwork | false | 8,217 | [
"MIT"
] | 21 | 5d0542608cdba72b0d7d8afc58c27f27b8a59192 | https://github.com/HardiRathod/table-linker/tree/5d0542608cdba72b0d7d8afc58c27f27b8a59192 |
LinearCaps | import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class LinearCaps(nn.Module):
def __init__(self, in_features, num_C, num_D, bias=False, eps=0.0001):
super(LinearCaps, self).__init__()
self.in_features = in_features
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 math
from to... | WdBlink/AugMix-3DOCUNet-Brats2019 | LinearCaps | false | 5,964 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
FCDiscriminator_low | import torch
from torch import nn
class FCDiscriminator_low(nn.Module):
def __init__(self, inplanes, planes=64):
super(FCDiscriminator_low, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=2,
padding=1)
self.conv2 = nn.Conv2d(planes, planes * 2, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | seabearlmx/PA-DAN | FCDiscriminator_low | false | 4,298 | [
"MIT"
] | 0 | bdd1200396d102e68acdd265db9d22ddb83b6404 | https://github.com/seabearlmx/PA-DAN/tree/bdd1200396d102e68acdd265db9d22ddb83b6404 |
EqualizedWeight | # 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 math
import numpy as np
from torch import nn
import torch.utils.data
from typing import List
import torch.nn.functional
import torch.... | techthiyanes/annotated_deep_learning_paper_implementations | EqualizedWeight | false | 16,550 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
PatchEmbed | import torch
from torch import nn
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches = img_size // patch_size * (img_size // patch_size)
self.img_size = img_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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | EscVM/EscVM_YT | PatchEmbed | false | 8,068 | [
"Apache-2.0"
] | 19 | 0ff1c47d7604d2452d7c9c2edd9b2db66781670a | https://github.com/EscVM/EscVM_YT/tree/0ff1c47d7604d2452d7c9c2edd9b2db66781670a |
Quantize | import torch
from torch import nn
from torch.nn import functional as F
class Quantize(nn.Module):
"""
Discretization bottleneck part of the VQ-VAE.
Inputs:
- n_e : number of embeddings
- e_dim : dimension of embedding
- beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
""... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch 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 |
SeeInDark | import torch
import torch.nn as nn
class SeeInDark(nn.Module):
def __init__(self, num_classes=10):
super(SeeInDark, self).__init__()
self.conv1_1 = nn.Conv2d(4, 32, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1)
self.pool1 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | hyeokjae-choi/pytorch-Learning-to-See-in-the-Dark | SeeInDark | false | 10,220 | [
"MIT"
] | 0 | b32bf991072decb3aea348d8cd59acbf34d5da2c | https://github.com/hyeokjae-choi/pytorch-Learning-to-See-in-the-Dark/tree/b32bf991072decb3aea348d8cd59acbf34d5da2c |
SeparableConvBlock | # 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
import torch.utils.data
from itertoo... | WFDetector/WFDetection | SeparableConvBlock | false | 2,957 | [
"Apache-2.0"
] | 0 | b16d35b3a3a5de62de9e0bac83eccd21b6358b53 | https://github.com/WFDetector/WFDetection/tree/b16d35b3a3a5de62de9e0bac83eccd21b6358b53 |
Upsample | # 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... | DRL-CASIA/Perception | Upsample | false | 7,935 | [
"MIT"
] | 39 | a0e7d3957267ce92a82b03ab3eca96916d22c4f2 | https://github.com/DRL-CASIA/Perception/tree/a0e7d3957267ce92a82b03ab3eca96916d22c4f2 |
LayerNormalization | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
super(LayerNormalization, self).__init__()
self.eps = eps
self.a2 = nn.Parameter(torch.ones(1, hidden_size), requires_grad=True)
self.b2 = nn.Parameter(torch.zeros(1... | 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... | NLP-Discourse-SoochowU/rst_dp2019Bottom2Up | LayerNormalization | false | 5,633 | [
"MIT"
] | 1 | ac1624127c9c8a3301685193ac8239357e01f6ca | https://github.com/NLP-Discourse-SoochowU/rst_dp2019Bottom2Up/tree/ac1624127c9c8a3301685193ac8239357e01f6ca |
RBF | # 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... | maet3608/torchy | RBF | false | 3,966 | [
"Apache-2.0"
] | 0 | 8c73732a1d4631bd97bfafdc18e52a22ff5410f7 | https://github.com/maet3608/torchy/tree/8c73732a1d4631bd97bfafdc18e52a22ff5410f7 |
ChannelNorm2D | import torch
import torch.nn as nn
class ChannelNorm2D(nn.Module):
"""
Similar to default Torch instanceNorm2D but calculates
moments over channel dimension instead of spatial dims.
Expects input_dim in format (B,C,H,W)
"""
def __init__(self, input_channels, momentum=0.1, eps=0.001, affine=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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ahmedfgad/high-fidelity-generative-compression | ChannelNorm2D | false | 6,118 | [
"Apache-2.0"
] | 1 | f3c6aa3472e3c629cbc35eefb0957119c913054a | https://github.com/ahmedfgad/high-fidelity-generative-compression/tree/f3c6aa3472e3c629cbc35eefb0957119c913054a |
Linear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from torch.nn import Parameter
import torch... | douglasrizzo/pytorch_geometric | Linear | false | 12,294 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.linear1 = nn.Linear(28 * 28, 32)
self.linear2 = nn.Linear(32, 10)
def forward(self, inputs):
x = inputs.view(-1, 28 * 28)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | emirojaseng/pytorch-meta-optimizer | Model | false | 15,311 | [
"MIT"
] | 298 | 3641981c990150ceb6c55d25a05ba76388f9ec69 | https://github.com/emirojaseng/pytorch-meta-optimizer/tree/3641981c990150ceb6c55d25a05ba76388f9ec69 |
KernelConv | # 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... | xenbaloch/efficientderain | KernelConv | false | 16,788 | [
"MIT"
] | 109 | d5646815fd14a5a03c859102ecd2f298db7e53be | https://github.com/xenbaloch/efficientderain/tree/d5646815fd14a5a03c859102ecd2f298db7e53be |
LayerNormConv2d | import torch
from torch import nn
import torch.nn
class LayerNormConv2d(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn... | 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
import torch.nn
assert_size_stride = torch._C._dynamo.guar... | Jack12xl/scene-representation-networks | LayerNormConv2d | false | 599 | [
"MIT"
] | 0 | 2691b23c956cf188a1fe4c84a888b19871cac8f4 | https://github.com/Jack12xl/scene-representation-networks/tree/2691b23c956cf188a1fe4c84a888b19871cac8f4 |
CircleLoss | import torch
from torch import Tensor
from torch import nn
from torchvision.transforms import *
class CircleLoss(nn.Module):
def __init__(self, m: 'float', gamma: 'float') ->None:
super(CircleLoss, self).__init__()
self.m = m
self.gamma = gamma
self.soft_plus = nn.Softplus()
... | 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 ... | TxuanYu/Person_reID_baseline_pytorch | CircleLoss | false | 14,523 | [
"MIT"
] | 3,358 | 10574b17cc8fd1fc8ade88f134679e281fdb01cc | https://github.com/TxuanYu/Person_reID_baseline_pytorch/tree/10574b17cc8fd1fc8ade88f134679e281fdb01cc |
MaxPool3x3 | import torch
import torch.nn as nn
import torch.utils.data
class MaxPool3x3(nn.Module):
"""3x3 max pool with no subsampling."""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1):
super(MaxPool3x3, self).__init__()
self.maxpool = nn.MaxPool2d(kernel_size... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | mc-nya/unnas | MaxPool3x3 | false | 7,189 | [
"MIT"
] | 1 | f778bb743144cf56ce2a48ccca20e9f3a97a7b84 | https://github.com/mc-nya/unnas/tree/f778bb743144cf56ce2a48ccca20e9f3a97a7b84 |
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.... | muqiaoy/dl_signal | MultiheadAttention | false | 16,135 | [
"MIT"
] | 54 | 3a30d14982016644bfc96a7d1ca0109b441f17fd | https://github.com/muqiaoy/dl_signal/tree/3a30d14982016644bfc96a7d1ca0109b441f17fd |
InstanceNormLayer | import torch
import torch.nn as nn
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, x):
if len(x.shape) != 4:
raise ValueError(
... | 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_... | huaji0353/higan | InstanceNormLayer | false | 12,511 | [
"MIT"
] | 0 | a082dc2be8651725d38b8d48d7e1c7261740013d | https://github.com/huaji0353/higan/tree/a082dc2be8651725d38b8d48d7e1c7261740013d |
SinkhornKnopp | import torch
class SinkhornKnopp(torch.nn.Module):
def __init__(self, num_iters=3, epsilon=0.05):
super().__init__()
self.num_iters = num_iters
self.epsilon = epsilon
@torch.no_grad()
def forward(self, logits):
Q = torch.exp(logits / self.epsilon).t()
B = Q.shape[... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | DonkeyShot21/UNO | SinkhornKnopp | false | 13,639 | [
"MIT"
] | 87 | 7613d3f8c58e5f16ee0d68fdd803ef442d819af4 | https://github.com/DonkeyShot21/UNO/tree/7613d3f8c58e5f16ee0d68fdd803ef442d819af4 |
Pooler | import torch
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn.functional as F
from torch.nn.modules.linear import Linear
import torch.nn.init as init
from torch.nn.parameter import Parameter
from torch.nn import Parameter
class Pooler(nn.Module):
"""Poole... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ExtremeViscent/ColossalAI-Examples | Pooler | false | 2,658 | [
"Apache-2.0"
] | 0 | 98ced2435d8d814f06740ab10d3e277ca91a83c7 | https://github.com/ExtremeViscent/ColossalAI-Examples/tree/98ced2435d8d814f06740ab10d3e277ca91a83c7 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, action_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChristianLin0420/DeepRL | Actor | false | 2,105 | [
"MIT"
] | 0 | 143a9bfebd264229d9d26fcdc070065225774e04 | https://github.com/ChristianLin0420/DeepRL/tree/143a9bfebd264229d9d26fcdc070065225774e04 |
CoSirenModule | # 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 math
a... | qway/nerfmeshes | CoSirenModule | false | 16,303 | [
"MIT"
] | 113 | d983dcbbcfec1337c9f2040969213c6d1ea0c39e | https://github.com/qway/nerfmeshes/tree/d983dcbbcfec1337c9f2040969213c6d1ea0c39e |
ResidualConvUnit | # 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_... | Northshoot/3d-photo-inpainting | ResidualConvUnit | false | 11,768 | [
"MIT"
] | 0 | 49dd36ce4a277929831f09d978721b3fdb87eb25 | https://github.com/Northshoot/3d-photo-inpainting/tree/49dd36ce4a277929831f09d978721b3fdb87eb25 |
NormKLLoss | import torch
import torch.utils.checkpoint
import torch as th
from torch.nn.modules.loss import _Loss
import torch.jit
class NormKLLoss(_Loss):
def __init__(self, unit_average=False):
super(NormKLLoss, self).__init__()
self.unit_average = unit_average
def forward(self, recog_mu, recog_logvar... | 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.utils.checkpoint
from torch.nn.modules.loss import _Loss
imp... | RoderickGu/Pretraining_GPT | NormKLLoss | false | 17,853 | [
"Apache-2.0"
] | 4 | 0a3ecd38116dc271e273f57490b9b45b660bf401 | https://github.com/RoderickGu/Pretraining_GPT/tree/0a3ecd38116dc271e273f57490b9b45b660bf401 |
TimeIntervalTransformerLayer | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class TimeIntervalMultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It also needs position and interaction (time interval) key/value input.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Yingting-dev/ReChorus | TimeIntervalTransformerLayer | false | 2,997 | [
"MIT"
] | 0 | a16bc1e42f3e90e889133d7476c52ada44db573b | https://github.com/Yingting-dev/ReChorus/tree/a16bc1e42f3e90e889133d7476c52ada44db573b |
NoiseNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class NoiseNet(nn.Module):
def __init__(self, channels=3, kernel_size=5):
super(NoiseNet, self).__init__()
self.kernel_size = kernel_size
self.channels = channels
to_pad = int((self.kernel_size - 1) / 2)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GuYuanjie/DeepFusionPrior | NoiseNet | false | 5,242 | [
"MIT"
] | 1 | a7126e073ed8c49b6a9a662492b64aaeee56cc01 | https://github.com/GuYuanjie/DeepFusionPrior/tree/a7126e073ed8c49b6a9a662492b64aaeee56cc01 |
IOUloss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Chris-hughes10/YOLOX | IOUloss | false | 5,003 | [
"Apache-2.0"
] | 1 | 981df30285839469a23cb925ed0a0f3714e46514 | https://github.com/Chris-hughes10/YOLOX/tree/981df30285839469a23cb925ed0a0f3714e46514 |
MaxSpatialPoolP4 | import torch
class MaxSpatialPoolP4(torch.nn.Module):
def __init__(self, kernel_size, stride=None, padding=0):
super().__init__()
self.inner = torch.nn.MaxPool2d(kernel_size, stride, padding)
def forward(self, x):
y = x.view(x.size(0), -1, x.size(3), x.size(4))
y = self.inner... | 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... | claudio-unipv/groupcnn | MaxSpatialPoolP4 | false | 12,222 | [
"MIT"
] | 0 | 2b1514f5a0fb9a78c6f646e1c075e5c3d5af9c0c | https://github.com/claudio-unipv/groupcnn/tree/2b1514f5a0fb9a78c6f646e1c075e5c3d5af9c0c |
ContrastivePairwiseEmbeddingLoss | # 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.... | pokidyshev/catalyst | ContrastivePairwiseEmbeddingLoss | false | 16,264 | [
"Apache-2.0"
] | 46 | bfe2cc2af7b02bd954fb0b4a0cae8b350f56789a | https://github.com/pokidyshev/catalyst/tree/bfe2cc2af7b02bd954fb0b4a0cae8b350f56789a |
MyActivation | # 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... | CaichaoGitHub/model_optimization_demo | MyActivation | false | 11,279 | [
"Apache-2.0"
] | 0 | b3bca3ad4a1b972fe069049f9efd7365a22733c6 | https://github.com/CaichaoGitHub/model_optimization_demo/tree/b3bca3ad4a1b972fe069049f9efd7365a22733c6 |
InstanceNormLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Hsintien-Ng/idinvert_pytorch-reproduced | InstanceNormLayer | false | 8,253 | [
"MIT"
] | 20 | cf3302510573138cf16202add06feae7c93624ea | https://github.com/Hsintien-Ng/idinvert_pytorch-reproduced/tree/cf3302510573138cf16202add06feae7c93624ea |
Spatial_Attention_layer | # 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.... | abcdefg-dev-dd/asxdcvfg | Spatial_Attention_layer | false | 6,056 | [
"Apache-2.0"
] | 1 | 83421d4a133810968d6e04b256a9312895452941 | https://github.com/abcdefg-dev-dd/asxdcvfg/tree/83421d4a133810968d6e04b256a9312895452941 |
MuSigmaEncoder | import torch
from torch import nn
class MuSigmaEncoder(nn.Module):
"""
Maps a representation r to mu and sigma which will define the normal
distribution from which we sample the latent variable z.
Parameters
----------
r_dim : int
Dimension of output representation r.
z_dim : 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._inductor.runtime import triton_helpers
from torch import nn
assert_s... | benjaminalt/neural-processes | MuSigmaEncoder | false | 14,946 | [
"MIT"
] | 170 | 03d4f921fe0598c77787eecc53cbed23e326a5f5 | https://github.com/benjaminalt/neural-processes/tree/03d4f921fe0598c77787eecc53cbed23e326a5f5 |
BertPredictionHeadTransform | # 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 ... | RoshanTanisha/TVCaption | BertPredictionHeadTransform | false | 1,903 | [
"MIT"
] | 0 | 8b14a340134ec69ed87426ee1f0e93e53f6456e5 | https://github.com/RoshanTanisha/TVCaption/tree/8b14a340134ec69ed87426ee1f0e93e53f6456e5 |
CosNorm_Classifier | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
class CosNorm_Classifier(nn.Module):
def __init__(self, in_dims, out_dims, scale=16, margin=0.5, init_std=0.001
):
super(CosNorm_Classifier, self).__init__()
self.in_dims = in_dims
self.out_dims... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | MathematicalModels/OpenLongTailRecognition-OLTR | CosNorm_Classifier | false | 14,029 | [
"BSD-3-Clause"
] | 765 | bd2a3d8adc271d1ffd6d6787353ae77f3d7fdfeb | https://github.com/MathematicalModels/OpenLongTailRecognition-OLTR/tree/bd2a3d8adc271d1ffd6d6787353ae77f3d7fdfeb |
ArcFace | from torch.nn import Module
import math
import torch
from torch.nn import Parameter
import torch.nn.functional as F
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class ArcFace(Module):
"""Implementation for "ArcFace: Additive Angular Margin Loss for Deep Face Rec... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | wangxianliang/FaceX-Zoo | ArcFace | false | 13,092 | [
"Apache-2.0"
] | 0 | b0555c88a0350fa7b59c317f3a171f551fef4e6e | https://github.com/wangxianliang/FaceX-Zoo/tree/b0555c88a0350fa7b59c317f3a171f551fef4e6e |
ExpNormalSmearing | import math
import torch
from torch import nn
class CosineCutoff(nn.Module):
def __init__(self, cutoff_lower=0.0, cutoff_upper=5.0):
super(CosineCutoff, self).__init__()
self.cutoff_lower = cutoff_lower
self.cutoff_upper = cutoff_upper
def forward(self, distances):
if self.cu... | 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | lsnty5190/torchmd-net | ExpNormalSmearing | false | 15,964 | [
"MIT"
] | 51 | 0bedf43801f0c7d38900d8e1db778fe69f3a4d01 | https://github.com/lsnty5190/torchmd-net/tree/0bedf43801f0c7d38900d8e1db778fe69f3a4d01 |
Similarity | import torch
import torch.nn as nn
import torch.nn.functional as F
class Similarity(nn.Module):
def __init__(self, cuda, mem_dim, hidden_dim, num_classes):
super(Similarity, self).__init__()
self.cudaFlag = cuda
self.mem_dim = mem_dim
self.hidden_dim = hidden_dim
self.num_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zhu-y11/multilingual_treelstm | Similarity | false | 13,180 | [
"MIT"
] | 0 | 39c211f3c03db733f776aa8fe73cd615aaa47465 | https://github.com/zhu-y11/multilingual_treelstm/tree/39c211f3c03db733f776aa8fe73cd615aaa47465 |
L2Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | ephrem-git/inference | L2Norm | false | 12,345 | [
"Apache-2.0"
] | 0 | bfbda5fc419364c3f71b5b1640f6c00e7675b212 | https://github.com/ephrem-git/inference/tree/bfbda5fc419364c3f71b5b1640f6c00e7675b212 |
SimpleSumModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleSumModule(torch.nn.Module):
def __init__(self, dtype=None):
super(SimpleSumModule, self).__init__()
self.dtype = dtype
def forward(self, a):
b = a + a
return torch.sum(b, dtype=self.dtype)
def get_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | andreas-hommel/glow | SimpleSumModule | false | 3,354 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
NoiseNet | # 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.... | GuYuanjie/Deep-Retinex-fusion | NoiseNet | false | 17,351 | [
"MIT"
] | 5 | ffa2a1689fd512c8820fd87cbf665c09bcb142b4 | https://github.com/GuYuanjie/Deep-Retinex-fusion/tree/ffa2a1689fd512c8820fd87cbf665c09bcb142b4 |
AFMLayer | # 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.... | dulvqingyunLT/DeepCTR-Torch | AFMLayer | false | 10,424 | [
"Apache-2.0"
] | 0 | f40cf08f3469aa471f9ca69e44c5de51180341cc | https://github.com/dulvqingyunLT/DeepCTR-Torch/tree/f40cf08f3469aa471f9ca69e44c5de51180341cc |
TensorCumsum | import torch
class TensorCumsum(torch.nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, input):
return torch.cumsum(input, dim=self.dim)
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... | Minyus/pipelinex | TensorCumsum | false | 14,031 | [
"Apache-2.0"
] | 188 | f35c524ec9c50751ee27d9a42d98317e16f1c544 | https://github.com/Minyus/pipelinex/tree/f35c524ec9c50751ee27d9a42d98317e16f1c544 |
TargetContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AngusGLChen/qg | TargetContextGate | false | 4,871 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 |
ScaledDotProductAttention | # 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.... | Sha-Lab/CASTLE | ScaledDotProductAttention | false | 8,752 | [
"MIT"
] | 13 | 212cb7aaad1bfae7041c90143220286bde24db33 | https://github.com/Sha-Lab/CASTLE/tree/212cb7aaad1bfae7041c90143220286bde24db33 |
CMDS_Loss | import torch
from torch import nn
from sklearn.preprocessing import scale as scale
def Covariance(m, bias=False, rowvar=True, inplace=False):
""" Estimate a covariance matrix given data(tensor).
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, `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
from torch import n... | CyprienGille/Supervised-Autoencoder | CMDS_Loss | false | 2,455 | [
"MIT"
] | 0 | fc8a3002d5b06319750601be586c7ca160f2189e | https://github.com/CyprienGille/Supervised-Autoencoder/tree/fc8a3002d5b06319750601be586c7ca160f2189e |
outconv | import torch
from torch import nn
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | BloodAxe/segmentation-networks-benchmark | outconv | false | 7,862 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
BertLayerNormNoVar | import torch
import torch.nn as nn
class BertLayerNormNoVar(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNormNoVar, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsil... | 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... | jiachens/auto_LiRPA | BertLayerNormNoVar | false | 10,286 | [
"BSD-3-Clause"
] | 0 | cc1ff18e8fbc938953b20ae6a030a25761cb0b78 | https://github.com/jiachens/auto_LiRPA/tree/cc1ff18e8fbc938953b20ae6a030a25761cb0b78 |
SpatialMeanAndStd | import torch
import torch.nn.functional
import torch.nn as nn
import torch.nn.init
import torch.onnx
class SpatialMeanAndStd(nn.Module):
def __init__(self, shape, eps=0.0001, half_size=1.0):
super(SpatialMeanAndStd, self).__init__()
p = torch.empty((2, shape[0], shape[1]), dtype=torch.float32)
... | 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.functional
import torch.nn as nn
import torch.nn.init
import to... | opentrack/neuralnet-tracker-traincode | SpatialMeanAndStd | false | 4,110 | [
"ISC",
"CC0-1.0",
"Unlicense"
] | 0 | 688ada0f46cb407d1809b50c11a136a239290123 | https://github.com/opentrack/neuralnet-tracker-traincode/tree/688ada0f46cb407d1809b50c11a136a239290123 |
ToRGB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from torch.nn import functional as F
assert_siz... | ishine/GANsNRoses | ToRGB | false | 15,634 | [
"MIT"
] | 969 | 414e9e77c3df47d4ecf7941b5dcfdffec67403ee | https://github.com/ishine/GANsNRoses/tree/414e9e77c3df47d4ecf7941b5dcfdffec67403ee |
DeepQNetwork | import torch
import torch as T
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class DeepQNetwork(nn.Module):
def __init__(self, ALPHA):
super(DeepQNetwork, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 8, stride=4, padding=1)
self.conv2 = nn.Conv2d(32, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 as T
import torc... | SuperSaiyan-God/Reinforcement-Learning | DeepQNetwork | false | 4,435 | [
"MIT"
] | 0 | b43a2997e28ec3bf437c37d060637f6deecf89c6 | https://github.com/SuperSaiyan-God/Reinforcement-Learning/tree/b43a2997e28ec3bf437c37d060637f6deecf89c6 |
SEModule | import torch
import torch.nn as nn
import torch.nn.parallel
class FastAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
return x.view(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | aangelopoulos/rcps | SEModule | false | 14,733 | [
"MIT"
] | 52 | b400457f7cc7261d1ed610cdf7aa2230de657c57 | https://github.com/aangelopoulos/rcps/tree/b400457f7cc7261d1ed610cdf7aa2230de657c57 |
PositionalEmbedding | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Yuran-Zhao/THUMT | PositionalEmbedding | false | 14,697 | [
"BSD-3-Clause"
] | 422 | 10f0433c1f2fe3f992d26ccb6f4f8dec457ce695 | https://github.com/Yuran-Zhao/THUMT/tree/10f0433c1f2fe3f992d26ccb6f4f8dec457ce695 |
Attention | import math
import torch
from torch import nn
class Attention(nn.Module):
"""A generic attention module for a decoder in seq2seq"""
def __init__(self, dim, use_tanh=False, C=10):
super(Attention, self).__init__()
self.use_tanh = use_tanh
self.project_query = nn.Linear(dim, dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | rdjdejong/attention-learn-to-route | Attention | false | 16,312 | [
"MIT"
] | 540 | 3b6bbdad677a36df53eabad98b48f436be298ac8 | https://github.com/rdjdejong/attention-learn-to-route/tree/3b6bbdad677a36df53eabad98b48f436be298ac8 |
TensorClamp | import torch
class TensorClamp(torch.nn.Module):
def forward(self, x):
return x.clamp(-0.1, 0.1)
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | bunderhi/torch2trt | TensorClamp | false | 1,603 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
LargeMarginCosLoss | import torch
from torch import nn
def cosine_sim(x1, x2, dim=1, eps=1e-08):
ip = torch.mm(x1, x2.t())
w1 = torch.norm(x1, 2, dim)
w2 = torch.norm(x2, 2, dim)
return ip / torch.ger(w1, w2).clamp(min=eps)
class LargeMarginCosLoss(nn.Module):
"""
CosFace: Large Margin Cosine Loss for Deep Face ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | alexalex222/classification_loss | LargeMarginCosLoss | false | 6,168 | [
"MIT"
] | 1 | a61617e0c0d5ecf6e0ff388305dd9f3eaa5cbf94 | https://github.com/alexalex222/classification_loss/tree/a61617e0c0d5ecf6e0ff388305dd9f3eaa5cbf94 |
my_BinaryCross | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class my_BinaryCross(nn.Module):
def __init__(self, args):
super(my_BinaryCross, self).__init__()
self.args = args
def forward(self, output, target, beat):
modif_beat = 1.0 / torch.exp(beat) * 10
... | 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
... | carsault/chord_extraction_prediction_lib | my_BinaryCross | false | 3,399 | [
"MIT"
] | 0 | 6de09eef9f2852b56b04874d2e42eb504c96d33f | https://github.com/carsault/chord_extraction_prediction_lib/tree/6de09eef9f2852b56b04874d2e42eb504c96d33f |
Perceptron | # 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... | dbradf/nlp-pytorch | Perceptron | false | 3,400 | [
"Apache-2.0"
] | 0 | 957e3c5a1edf1f2ae9a8e281729395bed886bc87 | https://github.com/dbradf/nlp-pytorch/tree/957e3c5a1edf1f2ae9a8e281729395bed886bc87 |
ATLoss | # 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
... | seanswyi/R-BERT | ATLoss | false | 4,294 | [
"Apache-2.0"
] | 0 | 4a4aeab3a9314307ce4458bd2b943d94aaf4a706 | https://github.com/seanswyi/R-BERT/tree/4a4aeab3a9314307ce4458bd2b943d94aaf4a706 |
EqualLinear | # 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.autograd import Function
import math
import torch.utils.data
from tor... | WoojunePark/BasicSR | EqualLinear | false | 18,113 | [
"Apache-2.0"
] | 9 | e0910b022b924bb913045fc412a5470dc2242cf0 | https://github.com/WoojunePark/BasicSR/tree/e0910b022b924bb913045fc412a5470dc2242cf0 |
StateInitZero | # 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
from torchvision import models as models
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards... | krodyush/training_extensions | StateInitZero | false | 10,982 | [
"Apache-2.0"
] | 0 | 542f4004dfbc6fc62a622065367ba4f85a703dd3 | https://github.com/krodyush/training_extensions/tree/542f4004dfbc6fc62a622065367ba4f85a703dd3 |
CosLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class CosLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, state_S, state_T, mask=None):
"""
This is the loss used in DistilBERT
:param state_S: Tensor of shape (batch_size, length, h... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | lonePatient/TorchBlocks | CosLoss | false | 15,936 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
TLU | import torch
from torch import nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
class TLU(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super(TLU, self).__init__()
self.num_features = num_features
... | 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 import Parameter
from torch.nn.parameter import Parame... | gmt710/fast-reid | TLU | false | 12,444 | [
"Apache-2.0"
] | 0 | 44a609280013eb6928f67c418c7212d67e40fb5d | https://github.com/gmt710/fast-reid/tree/44a609280013eb6928f67c418c7212d67e40fb5d |
FeaturePyramidNetwork | import torch
import torch.nn as nn
class FeaturePyramidNetwork(nn.Module):
def __init__(self, C3_feature, C4_feature, C5_feature, feature_size=256):
super(FeaturePyramidNetwork, self).__init__()
self.P5_1 = nn.Conv2d(C5_feature, feature_size, kernel_size=1,
stride=1, padding=0)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | kiyohiro8/SemanticReasoningNetworks | FeaturePyramidNetwork | false | 12,697 | [
"MIT"
] | 0 | 9dc20706a2234511789a7a2fa07cc3b77c64bf81 | https://github.com/kiyohiro8/SemanticReasoningNetworks/tree/9dc20706a2234511789a7a2fa07cc3b77c64bf81 |
TransposedConv3d | # 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 ... | Cogito2012/OpenTAL | TransposedConv3d | false | 7,898 | [
"BSD-3-Clause"
] | 16 | a7ab938a52b3fb82163eb1ba5403888359eb7e6a | https://github.com/Cogito2012/OpenTAL/tree/a7ab938a52b3fb82163eb1ba5403888359eb7e6a |
MultiheadSimilarity | import torch
import torch.utils.data
from torch import nn
class MultiheadSimilarity(nn.Module):
def __init__(self, d_model, num_head, seq_len):
super().__init__()
self.num_head = num_head
self.seq_len = seq_len
self.d_head = d_model // num_head
self.q_in_proj = nn.Linear(d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | bearcatt/SimpleBaseline | MultiheadSimilarity | false | 3,179 | [
"MIT"
] | 0 | 9ae38f289688c0e671efb50985d3b8fe2da47d69 | https://github.com/bearcatt/SimpleBaseline/tree/9ae38f289688c0e671efb50985d3b8fe2da47d69 |
ResNetV2 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from collections import OrderedDict
def conv1x1(cin, cout, stride=1, bias=False):
return StdConv2d(cin, cout, kern... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HelenR6/imagenet-r | ResNetV2 | false | 14,853 | [
"MIT"
] | 155 | 0bf04f2bf5d60d1098fc9a78f4e8c042e434eb69 | https://github.com/HelenR6/imagenet-r/tree/0bf04f2bf5d60d1098fc9a78f4e8c042e434eb69 |
ALL_CNN_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
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | EIDOSlab/Delving-in-the-loss-landscape-to-embed-robust-watermarks-into-neural-networks | ALL_CNN_C | false | 9,059 | [
"MIT"
] | 0 | 020ea57d48c192cec03c69e66938480cf898b8f2 | https://github.com/EIDOSlab/Delving-in-the-loss-landscape-to-embed-robust-watermarks-into-neural-networks/tree/020ea57d48c192cec03c69e66938480cf898b8f2 |
SelectiveMarginLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | Dogacel/mmfashion | SelectiveMarginLoss | false | 11,411 | [
"Apache-2.0"
] | 0 | e49613245c8501042edd7aeeaa8fb93e5ea13238 | https://github.com/Dogacel/mmfashion/tree/e49613245c8501042edd7aeeaa8fb93e5ea13238 |
MLP | import torch
class MLP(torch.nn.Module):
def __init__(self, ni, no, nhidden, nlayers):
super().__init__()
self.nlayers = nlayers
for i in range(nlayers):
if i == 0:
setattr(self, 'linear%d' % i, torch.nn.Linear(ni, nhidden,
bias=False))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | ElementAI/beyond-trivial-explanations | MLP | false | 17,260 | [
"Apache-2.0"
] | 3 | c517d7bdbab68b6a26f74cee4d15e948b3b47238 | https://github.com/ElementAI/beyond-trivial-explanations/tree/c517d7bdbab68b6a26f74cee4d15e948b3b47238 |
BasicModel2 | # 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... | Europium248/captum | BasicModel2 | false | 416 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
FocalLossBinary | import torch
import torch.jit
import torch.nn.functional as F
import torch.nn.functional
import torch.nn
from functools import partial
from torch.nn.modules.loss import _Loss
def reduced_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor',
threshold: 'float'=0.5, gamma: 'float'=2.0, reduction='mean'):
... | 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... | Gitsamshi/nnUNet-1 | FocalLossBinary | false | 8,194 | [
"Apache-2.0"
] | 28 | 5341684211e6d91dab6ad76a7595a95addff23be | https://github.com/Gitsamshi/nnUNet-1/tree/5341684211e6d91dab6ad76a7595a95addff23be |
FlowHead | import torch
import torch.nn as nn
class FlowHead(nn.Module):
"""
Applies two 2D convolutions over an input feature map
to generate a flow tensor of shape N x 2 x H x W.
Parameters
----------
input_dim : int, default: 128
Number of input dimensions.
hidden_dim : int, default: 256
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | NeelayS/ezflow | FlowHead | false | 14,101 | [
"MIT"
] | 94 | b93a48c4adf5021f7eacbfc43220c7efa5ae55cd | https://github.com/NeelayS/ezflow/tree/b93a48c4adf5021f7eacbfc43220c7efa5ae55cd |
Baz | import torch
import torch.onnx
import torch.nn
class Baz(torch.nn.Module):
def __init__(self, x):
super(Baz, self).__init__()
self.x = x
def forward(self, a, b):
return a + b * self.x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_i... | 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... | geoffberry/glow | Baz | false | 12,417 | [
"Apache-2.0"
] | 0 | 24b2827c830eb58af56a0704e899968026832e9c | https://github.com/geoffberry/glow/tree/24b2827c830eb58af56a0704e899968026832e9c |
TripletLoss | # 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.... | knifefield/uda-reid-contest | TripletLoss | false | 7,058 | [
"MIT"
] | 1 | 8b642cb4c5e63bb1dbfb07d0ac6dacdc26729e91 | https://github.com/knifefield/uda-reid-contest/tree/8b642cb4c5e63bb1dbfb07d0ac6dacdc26729e91 |
EALSTM | import torch
from typing import Tuple
import torch.nn as nn
class EALSTM(nn.Module):
"""Implementation of the Entity-Aware-LSTM (EA-LSTM)
TODO: Include paper ref and latex equations
Parameters
----------
input_size_dyn : int
Number of dynamic features, which are those, passed to the LSTM... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | danielsuo/toy_flood | EALSTM | false | 15,128 | [
"MIT"
] | 49 | 471d3c4091d86d4a00fbf910937d4e60fdaf79a1 | https://github.com/danielsuo/toy_flood/tree/471d3c4091d86d4a00fbf910937d4e60fdaf79a1 |
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