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 |
|---|---|---|---|---|---|---|---|---|---|---|
MySoftmax | # 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
... | acrosson/dl | MySoftmax | false | 1,369 | [
"MIT"
] | 0 | 94e68533b38f53f09e9bef460ba96fa389fc8eb4 | https://github.com/acrosson/dl/tree/94e68533b38f53f09e9bef460ba96fa389fc8eb4 |
ConvBlockINE | # 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.... | BaekduChoi/Halftoning_v2 | ConvBlockINE | false | 2,048 | [
"BSD-3-Clause"
] | 0 | fdb7040e1a4044f23ef9c92757bbb90c23685afe | https://github.com/BaekduChoi/Halftoning_v2/tree/fdb7040e1a4044f23ef9c92757bbb90c23685afe |
UpsampleConvLayer | # 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... | hehichens/NeuralStyle | UpsampleConvLayer | false | 3,583 | [
"Apache-2.0"
] | 0 | cf28a1eefd8713f85e94f50935562a663a53e8b5 | https://github.com/hehichens/NeuralStyle/tree/cf28a1eefd8713f85e94f50935562a663a53e8b5 |
GeneralizedMeanPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | tenghehan/reid_without_id | GeneralizedMeanPooling | false | 10,868 | [
"MIT"
] | 0 | d1d0ff273b1ef19fc6da8cbbf210527779b37455 | https://github.com/tenghehan/reid_without_id/tree/d1d0ff273b1ef19fc6da8cbbf210527779b37455 |
ActNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | MarcSerraPeralta/rec-flows | ActNorm | false | 796 | [
"MIT"
] | 0 | d05c3eca944f2228cffa575698ee5b010e83f167 | https://github.com/MarcSerraPeralta/rec-flows/tree/d05c3eca944f2228cffa575698ee5b010e83f167 |
ConvLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | FabienRoger/Apnea-Detector-Interpretation | ConvLayer | false | 11,427 | [
"MIT"
] | 0 | 96b95ea5e037d328386256feda53496d28609e81 | https://github.com/FabienRoger/Apnea-Detector-Interpretation/tree/96b95ea5e037d328386256feda53496d28609e81 |
FCNet | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class FCNet(nn.Module):
def __init__(self):
super(FCNet, self).__init__()
self.fc1 = nn.Linear(3 * 28 * 28, 128)
self.fc2 = nn.Linear(128, 5)
def forward(self, x):
x = x.vie... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.u... | rilu0361/mytorch | FCNet | false | 10,681 | [
"MIT"
] | 0 | 9f00b830b3ce8fdf942cd19704dedfe6ffd359a5 | https://github.com/rilu0361/mytorch/tree/9f00b830b3ce8fdf942cd19704dedfe6ffd359a5 |
MNISTDecoder | # 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_... | SeldonIO/alibi | MNISTDecoder | false | 14,409 | [
"ECL-2.0",
"Apache-2.0"
] | 1,570 | a94b6e3cf6f47aaca560f6d4841e91a62439fa3b | https://github.com/SeldonIO/alibi/tree/a94b6e3cf6f47aaca560f6d4841e91a62439fa3b |
global_avg_pool2d | import torch
import torch.nn as nn
class global_avg_pool2d(nn.Module):
def forward(self, x):
_, _, h, w = x.shape
return nn.AvgPool2d(kernel_size=(h, w))(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | DevBruce/torch-implementation | global_avg_pool2d | false | 11,338 | [
"MIT"
] | 0 | 73bb481e67c8dee7dfe8081c1049b1f4b62ce159 | https://github.com/DevBruce/torch-implementation/tree/73bb481e67c8dee7dfe8081c1049b1f4b62ce159 |
TransformerNet | # 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.... | EdenBD/MultiModalStory-demo | TransformerNet | false | 13,699 | [
"Apache-2.0"
] | 154 | 5e95e2aca766ca7c850e8db4973b8d51dfdba7f8 | https://github.com/EdenBD/MultiModalStory-demo/tree/5e95e2aca766ca7c850e8db4973b8d51dfdba7f8 |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | jibin-liu/deep-reinforcement-learning | QNetwork | false | 3,741 | [
"MIT"
] | 0 | 2a91a66a931e891d08cd1af95da973a522381b52 | https://github.com/jibin-liu/deep-reinforcement-learning/tree/2a91a66a931e891d08cd1af95da973a522381b52 |
ParseL1loss | import torch
from torch import nn
import torch.nn.functional as F
class ParseL1loss(nn.Module):
def __init__(self):
super(ParseL1loss, self).__init__()
def forward(self, output, target, mask):
mask = (mask == 1).float()
loss = F.l1_loss(output * mask, target * mask, size_average=Fals... | 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... | litsunshine/NonCuboidRoom | ParseL1loss | false | 15,916 | [
"MIT"
] | 54 | c782222b951c622d80cae5f3217424dc2cbe6ef5 | https://github.com/litsunshine/NonCuboidRoom/tree/c782222b951c622d80cae5f3217424dc2cbe6ef5 |
ZeroConv1d | # 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.... | gorinars/VQ-VAE-Speech | ZeroConv1d | false | 6,754 | [
"MIT"
] | 1 | 60398f03eb129195bce402a423ace8cca8995f3c | https://github.com/gorinars/VQ-VAE-Speech/tree/60398f03eb129195bce402a423ace8cca8995f3c |
OffsetNet | # 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_... | Viditagarwal7479/Video-Swin-Transformer | OffsetNet | false | 18,087 | [
"Apache-2.0"
] | 9 | 37910ef3141c7b2eef76544f9ec8bdf26ec94c7d | https://github.com/Viditagarwal7479/Video-Swin-Transformer/tree/37910ef3141c7b2eef76544f9ec8bdf26ec94c7d |
RegWeightedL1Loss | # 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
... | XiangLiK/cv_course | RegWeightedL1Loss | false | 18,115 | [
"MIT"
] | 8 | da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | https://github.com/XiangLiK/cv_course/tree/da7c2318fd4128bbdab96db26ddbb2524f37d0a0 |
VectorAttentionAggregator | import torch
import numpy as np
import torch.utils.data
from torch import nn
import torch
from torch.nn import functional as F
class VectorAttentionAggregator(nn.Module):
def __init__(self, input_dim):
super(VectorAttentionAggregator, self).__init__()
self.vector = nn.Parameter(torch.zeros(input_... | 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.utils.dat... | Arnaud15/CS236_Deep_Generative_Processes | VectorAttentionAggregator | false | 16,944 | [
"MIT"
] | 6 | 179c995c4f596c19441c5e844f2ed07d954324e3 | https://github.com/Arnaud15/CS236_Deep_Generative_Processes/tree/179c995c4f596c19441c5e844f2ed07d954324e3 |
ReGLU | import torch
import torch.nn as nn
class PositionWiseFeedForward(nn.Module):
"""
title: Position-wise Feed-Forward Network (FFN)
summary: Documented reusable implementation of the position wise feedforward network.
# Position-wise Feed-Forward Network (FFN)
This is a [PyTorch](https://pytorch.org... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | robburdon/pytorch_tabular | ReGLU | false | 16,324 | [
"MIT"
] | 560 | 9bf75f22c6e1b3033ad699713e77c283d55f3555 | https://github.com/robburdon/pytorch_tabular/tree/9bf75f22c6e1b3033ad699713e77c283d55f3555 |
TokenLabelSoftTargetCrossEntropy | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class TokenLabelSoftTargetCrossEntropy(nn.Module):
"""
Token labeling dense loss with soft target, see more from token labeling
input: x is output of model, target is ground truth
return: loss
"""
def ... | 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
... | QLSong/cv-classify | TokenLabelSoftTargetCrossEntropy | false | 2,733 | [
"Apache-2.0"
] | 0 | 02f53d03868f299a08b5c97a266b50a7fdcd3f2b | https://github.com/QLSong/cv-classify/tree/02f53d03868f299a08b5c97a266b50a7fdcd3f2b |
Gate | import torch
import torch.nn as nn
import torch.nn.functional as F
class Gate(nn.Module):
"""Gate Unit
g = sigmoid(Wx)
x = g * x
"""
def __init__(self, input_size, dropout_rate=0.0):
super(Gate, self).__init__()
self.linear = nn.Linear(input_size, input_size, bias=False)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | lonePatient/TorchBlocks | Gate | false | 15,950 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
ValueNetwork | # 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 ... | Yunaik/drl_env | ValueNetwork | false | 1,291 | [
"MIT"
] | 0 | d284e79847c59daa6ccb222f30fc7e2a86375546 | https://github.com/Yunaik/drl_env/tree/d284e79847c59daa6ccb222f30fc7e2a86375546 |
GraphConv | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class GraphConv(nn.Module):
def __init__(self, input_dim, output_dim, add_self=False,
normalize_embedding=False, dropout=0.0, bias=True):
super(GraphConv, self).__init__()
self.add_self = add_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
import torch.utils.data
assert_size_stride = torch._C._dyn... | hujilin1229/diffpool | GraphConv | false | 12,514 | [
"MIT"
] | 0 | 5b9bd73d794b63f5ea6d48e60cba090aa6e3ce72 | https://github.com/hujilin1229/diffpool/tree/5b9bd73d794b63f5ea6d48e60cba090aa6e3ce72 |
CNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HeegerGao/CRIL | CNN | false | 17,398 | [
"MIT"
] | 9 | c4095bca7cf5c8e376b0014447b1422c1b5b6cec | https://github.com/HeegerGao/CRIL/tree/c4095bca7cf5c8e376b0014447b1422c1b5b6cec |
Net2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | yxtj/henn | Net2 | false | 11,060 | [
"MIT"
] | 0 | 5093f3e637ba0bb3e48c4f890b3b469c3617f2c5 | https://github.com/yxtj/henn/tree/5093f3e637ba0bb3e48c4f890b3b469c3617f2c5 |
PreNet | # 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... | KonstantinPakulev/OSM-one-shot-multispeaker | PreNet | false | 2,456 | [
"MIT"
] | 0 | 5cee1b6cb7dc7a3b4b24171340855a42824925f7 | https://github.com/KonstantinPakulev/OSM-one-shot-multispeaker/tree/5cee1b6cb7dc7a3b4b24171340855a42824925f7 |
focal_BCELoss | # 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
... | DRL-CASIA/Perception | focal_BCELoss | false | 7,925 | [
"MIT"
] | 39 | a0e7d3957267ce92a82b03ab3eca96916d22c4f2 | https://github.com/DRL-CASIA/Perception/tree/a0e7d3957267ce92a82b03ab3eca96916d22c4f2 |
HardMGUCellNUA | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def truncated_normal(t, mean=0.0, std=0.01):
torch.nn.init.normal_(t, mean=mea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | pan185/UnarySim | HardMGUCellNUA | false | 7,457 | [
"MIT"
] | 1 | c03386efdbb8151f3c33f34b44d1d6a6fc960434 | https://github.com/pan185/UnarySim/tree/c03386efdbb8151f3c33f34b44d1d6a6fc960434 |
FlatCat | # 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... | YirongMao/torch2trt | FlatCat | false | 2,976 | [
"MIT"
] | 0 | 7635051998a9cd6b9483da1569814031c04a1b52 | https://github.com/YirongMao/torch2trt/tree/7635051998a9cd6b9483da1569814031c04a1b52 |
AngleSimpleLinear | # 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.... | ygnn123/training_extensions | AngleSimpleLinear | false | 4,681 | [
"Apache-2.0"
] | 0 | c3aeba9359b0d4e0ef9c054de777d3ec081a9892 | https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892 |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.2):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | alipay/Pyraformer | EncoderLayer | false | 18,314 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 |
IdentityMessage | import torch
import torch.utils.data
class IdentityMessage(torch.nn.Module):
def __init__(self, raw_msg_dim: 'int', memory_dim: 'int', time_dim: 'int'):
super(IdentityMessage, self).__init__()
self.out_channels = raw_msg_dim + 2 * memory_dim + time_dim
def forward(self, z_src, z_dst, raw_msg... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | MINATILO/pytroch-geometric | IdentityMessage | false | 9,392 | [
"MIT"
] | 0 | 706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 | https://github.com/MINATILO/pytroch-geometric/tree/706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 |
DeepHeadModule | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from math import sqrt as sqrt
from itertools import product as product
import torchvision.transforms.functional as F
from torch.nn import functional ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | RedHenLab/RedHenAnonymizer | DeepHeadModule | false | 5,759 | [
"MIT"
] | 1 | 3560f1ac5cd5b9c6c7ed8bf322b807d57aedc06a | https://github.com/RedHenLab/RedHenAnonymizer/tree/3560f1ac5cd5b9c6c7ed8bf322b807d57aedc06a |
LSTMPredictor | # 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_... | Yu-Hao-88/NN_stock_prediction | LSTMPredictor | false | 3,030 | [
"MIT"
] | 0 | bb84a3f4450d95af317d60d83dcd53ad4f3d350d | https://github.com/Yu-Hao-88/NN_stock_prediction/tree/bb84a3f4450d95af317d60d83dcd53ad4f3d350d |
ModulatedConv2d | import math
import torch
from torch import nn
from torch.nn import functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
pad_x1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Liamkuo/SAIR | ModulatedConv2d | false | 17,637 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 |
CNN | import torch
import numpy as np
import torch.nn as nn
class CNN(nn.Module):
def __init__(self, state_dim):
super(CNN, self).__init__()
self.state_dim = state_dim
self.image_size = 64
self.in_channels = 1
self.kernel_size = 3
self.padding = 0
self.stride = 2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | MaryZolfaghar/WCSLS | CNN | false | 824 | [
"Apache-2.0"
] | 0 | fcb3bfd11c19bb90690ec772f91bbd107832d636 | https://github.com/MaryZolfaghar/WCSLS/tree/fcb3bfd11c19bb90690ec772f91bbd107832d636 |
ResidualConvUnit | import torch
import torch.nn as nn
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(features, features, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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 |
AdaptiveConcatPool2d | import torch
import torch.nn as nn
import torch.nn.init
class AdaptiveConcatPool2d(nn.Module):
def __init__(self, sz=None):
super().__init__()
sz = sz or (1, 1)
self.ap = nn.AdaptiveAvgPool2d(sz)
self.mp = nn.AdaptiveMaxPool2d(sz)
def forward(self, x):
return torch.ca... | 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.init
assert_size_stride = torch._C._dynamo.guards.a... | MichoelSnow/data_science | AdaptiveConcatPool2d | false | 9,865 | [
"MIT"
] | 0 | 7f6c054624268308ec4126a601c9fa8bc5de157c | https://github.com/MichoelSnow/data_science/tree/7f6c054624268308ec4126a601c9fa8bc5de157c |
LocationLayer | import torch
import torch.nn as nn
import torch.utils.data
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | CODEJIN/TacoSinger | LocationLayer | false | 4,938 | [
"MIT"
] | 1 | af58a8f4e8b20e8817990f28a3ba22168c853655 | https://github.com/CODEJIN/TacoSinger/tree/af58a8f4e8b20e8817990f28a3ba22168c853655 |
BatchHardTripletLoss | # 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.... | chenyanghungry/person-reid-lib | BatchHardTripletLoss | false | 15,023 | [
"MIT"
] | 81 | 783e66c9bfedf582e2cf935b9f5be960b543ac3c | https://github.com/chenyanghungry/person-reid-lib/tree/783e66c9bfedf582e2cf935b9f5be960b543ac3c |
tfAvgPool3D | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | zinzinhust96/MoViNet-pytorch | tfAvgPool3D | false | 11,081 | [
"MIT"
] | 0 | f16528a76516427a192524c512c7a7cd8e1ce2f0 | https://github.com/zinzinhust96/MoViNet-pytorch/tree/f16528a76516427a192524c512c7a7cd8e1ce2f0 |
L2loss | # 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... | ivadomed-profile-analysis-project/ivadomed | L2loss | false | 15,650 | [
"MIT"
] | 87 | 3b53e2cb2b210511943da439401e2471fd387876 | https://github.com/ivadomed-profile-analysis-project/ivadomed/tree/3b53e2cb2b210511943da439401e2471fd387876 |
ClusterAssignment | import torch
import torch.nn as nn
from torch.nn import Parameter
from typing import Optional
class ClusterAssignment(nn.Module):
def __init__(self, cluster_number: 'int', embedding_dimension: 'int',
alpha: 'float'=1.0, cluster_centers: 'Optional[torch.Tensor]'=None
) ->None:
"""
... | 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.nn import Parameter
from typing import Optional
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | giorgosVardakas/pt-dec | ClusterAssignment | false | 15,435 | [
"MIT"
] | 200 | c29b9634eb74c828efd9d2b87c613cdb0ddd1dd5 | https://github.com/giorgosVardakas/pt-dec/tree/c29b9634eb74c828efd9d2b87c613cdb0ddd1dd5 |
LinearModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Piki1989/spacetimeformer | LinearModel | false | 14,206 | [
"MIT"
] | 209 | 7e0caf17dd03e5d25e2766c4f7132805779bcc40 | https://github.com/Piki1989/spacetimeformer/tree/7e0caf17dd03e5d25e2766c4f7132805779bcc40 |
EuclideanDistance | import torch
from torch import Tensor
import torch.utils.data.dataloader
from torch import nn
import torch.nn
def arccosh(x):
"""Compute the arcosh, numerically stable."""
x = torch.clamp(x, min=1 + EPSILON)
a = torch.log(x)
b = torch.log1p(torch.sqrt(x * x - 1) / x)
return a + b
def mdot(x, y):... | 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 |
depthwise_clipseg_conv | import torch
import torch.nn as nn
import torch.utils.data
class depthwise_clipseg_conv(nn.Module):
def __init__(self):
super(depthwise_clipseg_conv, self).__init__()
self.depthwise = nn.Conv2d(1, 1, kernel_size=3, padding=1)
def depthwise_clipseg(self, x, channels):
x = torch.cat([s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | whiteking64/lang-seg | depthwise_clipseg_conv | false | 16,707 | [
"MIT"
] | 202 | 9d063b126f1b64e38ddb20cc75fc74435bfdcbd3 | https://github.com/whiteking64/lang-seg/tree/9d063b126f1b64e38ddb20cc75fc74435bfdcbd3 |
LinearCombine | # 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... | charliemorning/mlws | LinearCombine | false | 1,675 | [
"MIT"
] | 0 | 8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 | https://github.com/charliemorning/mlws/tree/8e9bad59ca9f5e774cc1ae7fe454ff3b8a8e1784 |
PreNormTransformerDecoderLayer | import torch
from torch import nn
class PreNormTransformerDecoderLayer(nn.TransformerDecoderLayer):
"""
A variant of :class:`torch.nn.TransformerDecoderLayer` where layer
normalization is included inside the residual branch, and performed before
self-attention and feedforward layers.
Refer docume... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | GeorgeBatch/arch-pre-training | PreNormTransformerDecoderLayer | false | 517 | [
"MIT"
] | 0 | 7ed75868689e9283d61d11360fdbf4e77d4ebd2e | https://github.com/GeorgeBatch/arch-pre-training/tree/7ed75868689e9283d61d11360fdbf4e77d4ebd2e |
SimpleClampModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | opti-mix/glow | SimpleClampModel | false | 7,392 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
FusedUpsample | import torch
from torch import nn
from torch.nn import functional as F
from math import sqrt
class FusedUpsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
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 import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | nazarblch/style-based-gan-pytorch | FusedUpsample | false | 4,051 | [
"MIT"
] | 0 | 5ed7fa114904501d77b414921cd9f439773ba24c | https://github.com/nazarblch/style-based-gan-pytorch/tree/5ed7fa114904501d77b414921cd9f439773ba24c |
KernelTensorRingWithCategoryAndState | # 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 Parameter
assert_size_stri... | AndresOtero/TensorDecompositionMachineLearning | KernelTensorRingWithCategoryAndState | false | 16,913 | [
"MIT"
] | 3 | 455f16b405ec9d031999b0ebf9c5a68d3c20b233 | https://github.com/AndresOtero/TensorDecompositionMachineLearning/tree/455f16b405ec9d031999b0ebf9c5a68d3c20b233 |
Conv2d | import torch
import torch.nn as nn
from torch.distributions import transforms as transform
class Flow(transform.Transform, nn.Module):
"""
Main class for a single flow.
"""
def __init__(self, amortized='none'):
""" Initialize as both transform and module """
transform.Transform.__init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.distributions import transforms as transform
as... | dendisuhubdy/flow_synthesizer | Conv2d | false | 15,159 | [
"MIT"
] | 93 | 1561e8ce2520258acb3d228beebbb626a8abc04f | https://github.com/dendisuhubdy/flow_synthesizer/tree/1561e8ce2520258acb3d228beebbb626a8abc04f |
Upsample | import torch
import torch.nn as nn
import torch.hub
class Upsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels, in_channels,
kernel_size=3, stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.hub
assert_size_stride = torch._C._dynamo.gua... | Frikallo/YAKbot | Upsample | false | 5,174 | [
"MIT"
] | 1 | bc798fe4ead1f6a3e4828960ea77e2a8f07b5fdc | https://github.com/Frikallo/YAKbot/tree/bc798fe4ead1f6a3e4828960ea77e2a8f07b5fdc |
GCN | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | seanli3/pygcn | GCN | false | 10,753 | [
"MIT"
] | 0 | 134a17f1dbd7737f8a8b7efca5f20f882ee97144 | https://github.com/seanli3/pygcn/tree/134a17f1dbd7737f8a8b7efca5f20f882ee97144 |
idct_8x8 | # 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 itertools
import numpy as np
import torch.nn as nn
assert_size_stride = t... | foxtrotmike/DiffJPEG | idct_8x8 | false | 12,397 | [
"MIT"
] | 0 | 7dbc44b1e921f20a213a7206a8578d6a1c8131b4 | https://github.com/foxtrotmike/DiffJPEG/tree/7dbc44b1e921f20a213a7206a8578d6a1c8131b4 |
BiDAFAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
mas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JNXSTJ/squad | BiDAFAttention | false | 11,536 | [
"MIT"
] | 0 | ed875a90b212e1fe2f05144edb5595cedb5dd42b | https://github.com/JNXSTJ/squad/tree/ed875a90b212e1fe2f05144edb5595cedb5dd42b |
ChamferLoss | import torch
import torch.nn as nn
class ChamferLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
"""
:param x: (bs, np, 3)
:param y: (bs, np, 3)
:return: loss
"""
x = x.unsqueeze(1)
y = y.unsqueeze(2)
dis... | 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... | phuochieu212/PointGLR | ChamferLoss | false | 16,252 | [
"MIT"
] | 104 | 37017b1af31486aa9d516a3762725a650dca9ad1 | https://github.com/phuochieu212/PointGLR/tree/37017b1af31486aa9d516a3762725a650dca9ad1 |
BasicModel6_MultiTensor | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel6_MultiTensor(nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, input1, input2):
input = input1 + input2
return 1 - F.relu(1 - input)[:, 1]
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | YNNEKUW/captum | BasicModel6_MultiTensor | false | 11,991 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
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... | DaehanKim/attention-learn-to-route | Attention | false | 11,335 | [
"MIT"
] | 0 | 9ce4fa9a3a136768f92adf3d1e7d62620442f1b7 | https://github.com/DaehanKim/attention-learn-to-route/tree/9ce4fa9a3a136768f92adf3d1e7d62620442f1b7 |
loss_Textures | # 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.utils.data
import torch.nn as nn
import torch.nn
assert_size_stride = torch.... | IceClear/MW-GAN | loss_Textures | false | 8,291 | [
"MIT"
] | 36 | acb962468c984681c4a21f7b5c14588ca8f58c00 | https://github.com/IceClear/MW-GAN/tree/acb962468c984681c4a21f7b5c14588ca8f58c00 |
RecallLoss | # 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._C
import... | shuaizzZ/mmsegmentation | RecallLoss | false | 4,326 | [
"Apache-2.0"
] | 0 | a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c | https://github.com/shuaizzZ/mmsegmentation/tree/a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c |
EnsembleModel | import math
import torch
import numpy as np
import torch.nn.functional as F
def truncated_standardized_normal(shape, a=-2.0, b=2.0):
a = torch.Tensor([a])
b = torch.Tensor([b])
U = torch.distributions.uniform.Uniform(0, 1)
u = U.sample(shape)
Fa = 0.5 * (1 + torch.erf(a / math.sqrt(2)))
Fb = 0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | numahha/wmopo | EnsembleModel | false | 7,391 | [
"MIT"
] | 1 | 1557dab2e8168c1f2e53ffbc435b4000680f1d28 | https://github.com/numahha/wmopo/tree/1557dab2e8168c1f2e53ffbc435b4000680f1d28 |
BasicModel_ConvNet_One_Conv | import torch
from torch import Tensor
from typing import Optional
import torch.nn as nn
from typing import no_type_check
class BasicModel_ConvNet_One_Conv(nn.Module):
def __init__(self, inplace: 'bool'=False) ->None:
super().__init__()
self.conv1 = nn.Conv2d(1, 2, 3, 1)
self.relu1 = nn.Re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | YNNEKUW/captum | BasicModel_ConvNet_One_Conv | false | 12,001 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
JointL2Loss | import torch
import torch.nn as nn
import torch.utils.data
class JointL2Loss(nn.Module):
def __init__(self):
super(JointL2Loss, self).__init__()
def forward(self, joint_pred, joint_gt):
batch_size, joint_num, _ = joint_gt.shape
joint_pred = joint_pred.view(batch_size * joint_num, -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
import... | dejianwei/HigherA2J | JointL2Loss | false | 6,541 | [
"MIT"
] | 1 | 655d993d4b835ec58396887a85b68ef506b5df9e | https://github.com/dejianwei/HigherA2J/tree/655d993d4b835ec58396887a85b68ef506b5df9e |
LSTM | # 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 ... | danielsuo/toy_flood | LSTM | false | 15,134 | [
"MIT"
] | 49 | 471d3c4091d86d4a00fbf910937d4e60fdaf79a1 | https://github.com/danielsuo/toy_flood/tree/471d3c4091d86d4a00fbf910937d4e60fdaf79a1 |
DeepNN_v4 | import torch
import torch.nn as nn
class DeepNN_v4(nn.Module):
def __init__(self, X_dim, i_dropout_rate, h_dropout_rate):
super().__init__()
self.v4_layer1 = nn.Linear(X_dim, 128, bias=True)
self.v4_layer2 = nn.Linear(128, 128, bias=True)
self.v4_layer3 = nn.Linear(128, 64, bias=T... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SBIlab/NetBio | DeepNN_v4 | false | 11,838 | [
"MIT"
] | 0 | 7abd24b8989cea381147d912f76a72676750b9d2 | https://github.com/SBIlab/NetBio/tree/7abd24b8989cea381147d912f76a72676750b9d2 |
ConvLSTMCell | import torch
from torch.autograd import Variable
import torch.nn as nn
class ConvLSTMCell(nn.Module):
def __init__(self, input_channels, hidden_channels, kernel_size, bias=True
):
super(ConvLSTMCell, self).__init__()
assert hidden_channels % 2 == 0
self.input_channels = input_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd... | Starboy-at-earth/DMRA | ConvLSTMCell | false | 14,442 | [
"MIT"
] | 84 | 596cc6106ab5f1f03deb60a7f4bb0c2ad1029a83 | https://github.com/Starboy-at-earth/DMRA/tree/596cc6106ab5f1f03deb60a7f4bb0c2ad1029a83 |
AdaptiveInstanceNorm_H | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
class AdaptiveInstanceNorm_H(nn.Module):
def __init__(self, in_channel, map_size):
super().__init__()
self.norm = nn.LayerNorm([map_size, map_size])
self.weight = nn.Parameter(1000.0 + torch.rand... | 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.utils.data
import torch.utils.data.distribute... | MiaoyunZhao/GANTransferLimitedData | AdaptiveInstanceNorm_H | false | 8,576 | [
"MIT"
] | 41 | 5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 | https://github.com/MiaoyunZhao/GANTransferLimitedData/tree/5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 |
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.... | liyunrui/DeepCTR-Torch | AFMLayer | false | 12,737 | [
"Apache-2.0"
] | 0 | 392fd6d39d9ca0ac854022136cdb4d5c68e3a592 | https://github.com/liyunrui/DeepCTR-Torch/tree/392fd6d39d9ca0ac854022136cdb4d5c68e3a592 |
SimpleBmmModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleBmmModule(torch.nn.Module):
def forward(self, a, b):
return (a + a).bmm(b)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | YaronBenAtar/glow | SimpleBmmModule | false | 14,652 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-05):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forwa... | 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_... | ishine/tfm-tts | LayerNorm | false | 3,679 | [
"MIT"
] | 0 | a964736467851ddec8f8e8933b9550cbe7d7d7eb | https://github.com/ishine/tfm-tts/tree/a964736467851ddec8f8e8933b9550cbe7d7d7eb |
ExpActivation | # 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
assert_size_stride = torch._C._dynamo.guards.assert... | mahkons/orthogonal | ExpActivation | false | 3,955 | [
"MIT"
] | 0 | 19a69134ca9a01ef564eab624b8c1526291770aa | https://github.com/mahkons/orthogonal/tree/19a69134ca9a01ef564eab624b8c1526291770aa |
InstanceLayerNorm2d | import torch
import torch.nn as nn
class InstanceLayerNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.9,
using_moving_average=True, using_bn=False):
super(InstanceLayerNorm2d, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_mov... | 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... | belphegor2211/khoa_luan | InstanceLayerNorm2d | false | 9,983 | [
"MIT"
] | 0 | c9c163ebf3aff3005639ce7e4020e510295d1c75 | https://github.com/belphegor2211/khoa_luan/tree/c9c163ebf3aff3005639ce7e4020e510295d1c75 |
DiceBCELoss | # 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... | ProfessorHuang/2D-UNet-Pytorch | DiceBCELoss | false | 8,653 | [
"MIT"
] | 11 | b3941e8dc0ac3e76b6eedb656f943f1bd66fa799 | https://github.com/ProfessorHuang/2D-UNet-Pytorch/tree/b3941e8dc0ac3e76b6eedb656f943f1bd66fa799 |
Auto_Encoder_Model | # 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 ... | NNDEV1/QandMedicAid | Auto_Encoder_Model | false | 5,649 | [
"MIT"
] | 1 | f229f7dcf192fd79715eba07a2e5121a13c7a571 | https://github.com/NNDEV1/QandMedicAid/tree/f229f7dcf192fd79715eba07a2e5121a13c7a571 |
MeanStd | # 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... | Andribi/pytorch_GAN_zoo | MeanStd | false | 4,848 | [
"BSD-3-Clause"
] | 1 | b37c7268cbd4ec7dc61ba65a3ccf11af71247597 | https://github.com/Andribi/pytorch_GAN_zoo/tree/b37c7268cbd4ec7dc61ba65a3ccf11af71247597 |
MSEGradLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | alsgkals2/SRResCGAN | MSEGradLoss | false | 14,816 | [
"MIT"
] | 81 | a71201a93e1819045f9c7711743812546d3a1f31 | https://github.com/alsgkals2/SRResCGAN/tree/a71201a93e1819045f9c7711743812546d3a1f31 |
BertIntermediate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | RyanWangZf/SurvTRACE | BertIntermediate | false | 18,110 | [
"MIT"
] | 8 | d55299a28629d233f49ad1feaea7ed00835f0dd0 | https://github.com/RyanWangZf/SurvTRACE/tree/d55299a28629d233f49ad1feaea7ed00835f0dd0 |
new_class | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | AluminiumOxide/pytorch_base_-tutorial | new_class | false | 1,931 | [
"Apache-2.0"
] | 0 | a6d3bea6070c7c774dcd7c55d94b0a1441548c8b | https://github.com/AluminiumOxide/pytorch_base_-tutorial/tree/a6d3bea6070c7c774dcd7c55d94b0a1441548c8b |
AUGRUCell | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
class AUGRUCell(nn.Module):
""" Effect of GRU with attentional update gate (AUGRU)
Reference:
- Deep Interest Evolution Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1809.03672, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Sunmyunghan/Final_Project | AUGRUCell | false | 1,200 | [
"MIT"
] | 0 | 28cde293dc6d07521b2e1c5613b20444aea91d21 | https://github.com/Sunmyunghan/Final_Project/tree/28cde293dc6d07521b2e1c5613b20444aea91d21 |
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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ChristianPoepselBE/fsdl-text-recognizer-2021-labs | ConvBlock | false | 2,103 | [
"MIT"
] | 0 | 161a9fa605f3fb955b1339e076248d317c881c47 | https://github.com/ChristianPoepselBE/fsdl-text-recognizer-2021-labs/tree/161a9fa605f3fb955b1339e076248d317c881c47 |
GDL | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | dkswxd/Swin-Transformer-Semantic-Segmentation | GDL | false | 1,858 | [
"Apache-2.0"
] | 0 | 6af19736e5492a01d8952d4ee86a6d59b21c2ae1 | https://github.com/dkswxd/Swin-Transformer-Semantic-Segmentation/tree/6af19736e5492a01d8952d4ee86a6d59b21c2ae1 |
HalfMSELoss | import torch
from torch.nn.modules.loss import MSELoss
class HalfMSELoss(MSELoss):
def __init__(self, reduction='mean'):
super().__init__(reduction=reduction)
def forward(self, input, target):
return super().forward(input, target) / 2
def get_inputs():
return [torch.rand([4, 4, 4, 4]),... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn.modules.loss import MSELoss
assert_size_stride = torch._C._dynamo.guards.as... | ThayaFluss/candle | HalfMSELoss | false | 1,132 | [
"MIT"
] | 0 | 4a12fde60ffbbf0cb688617fee81aded94c0b613 | https://github.com/ThayaFluss/candle/tree/4a12fde60ffbbf0cb688617fee81aded94c0b613 |
ActivationQuantizer | from torch.autograd import Function
import torch
import torch.nn as nn
class Round(Function):
@staticmethod
def forward(self, input):
output = torch.round(input)
return output
@staticmethod
def backward(self, grad_output):
grad_input = grad_output.clone()
return grad_... | 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.autograd import F... | RiccardoRuggiero/micronet | ActivationQuantizer | false | 5,766 | [
"MIT"
] | 1 | bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 | https://github.com/RiccardoRuggiero/micronet/tree/bfdac2a50a5f0f8484a253b356c06a166bf7e6a0 |
Bar | # 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.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | hl475/glow | Bar | false | 3,591 | [
"Apache-2.0"
] | 0 | f24d960e3cc80db95ac0bc17b1900dbf60ca044a | https://github.com/hl475/glow/tree/f24d960e3cc80db95ac0bc17b1900dbf60ca044a |
_Residual_Block | import torch
import torch.nn as nn
class _Residual_Block(nn.Module):
def __init__(self, num_chans=64):
super(_Residual_Block, self).__init__()
bias = True
self.conv1 = nn.Conv2d(num_chans, num_chans, kernel_size=3, stride=
1, padding=1, bias=bias)
self.relu2 = nn.PReLU... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | albangossard/Course-inverse-problems-and-unrolled-networks | _Residual_Block | false | 1,443 | [
"MIT"
] | 0 | 0d4161c905149817e3abff9e70c101f36fac4270 | https://github.com/albangossard/Course-inverse-problems-and-unrolled-networks/tree/0d4161c905149817e3abff9e70c101f36fac4270 |
compressedSigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch._utils
assert_size_stride = torch._C._... | henbucuoshanghai/crowed-count- | compressedSigmoid | false | 15,500 | [
"MIT"
] | 81 | 3353c0a8011b6b83e6e0392258a88706378b443b | https://github.com/henbucuoshanghai/crowed-count-/tree/3353c0a8011b6b83e6e0392258a88706378b443b |
adaILN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class adaILN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(adaILN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.rho.data.fill_(0.9)
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stri... | denny3388/Conditional-UGATIT | adaILN | false | 1,833 | [
"MIT"
] | 0 | 86ad35f05aaa105a814dec031d37370f44b71d5b | https://github.com/denny3388/Conditional-UGATIT/tree/86ad35f05aaa105a814dec031d37370f44b71d5b |
ScaledDotProductAttention | import torch
import numpy as np
from torch import nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | quanha72/mesh-memory-transformer | ScaledDotProductAttention | false | 12,919 | [
"BSD-3-Clause"
] | 0 | 0eeae459efdb8e85926ce8595536409fdbfc4f99 | https://github.com/quanha72/mesh-memory-transformer/tree/0eeae459efdb8e85926ce8595536409fdbfc4f99 |
CombinedTargetMSELoss | # 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... | ALISCIFP/mmpose | CombinedTargetMSELoss | false | 2,075 | [
"Apache-2.0"
] | 0 | 2433e3dbcc44baa2253e2a7c748ba0216937933e | https://github.com/ALISCIFP/mmpose/tree/2433e3dbcc44baa2253e2a7c748ba0216937933e |
ResizeTransform | # 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... | McHz1s/voxelmorph | ResizeTransform | false | 14,004 | [
"Apache-2.0"
] | 1,532 | 0ca00ccf85be5c2d0ae73a166b64460e02c01d33 | https://github.com/McHz1s/voxelmorph/tree/0ca00ccf85be5c2d0ae73a166b64460e02c01d33 |
TorchDiceLoss | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | dannyjeck-matroid/solaris | TorchDiceLoss | false | 1,782 | [
"Apache-2.0"
] | 0 | 463d220c1fe14f811cbbbf528a7353022538006e | https://github.com/dannyjeck-matroid/solaris/tree/463d220c1fe14f811cbbbf528a7353022538006e |
ResidualBlock | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=
in_channels, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | abhay97ps/visual-control-ppo-procgen | ResidualBlock | false | 1,353 | [
"MIT"
] | 0 | 765fe1ddb289d384abddc4df8eb865379c8da76a | https://github.com/abhay97ps/visual-control-ppo-procgen/tree/765fe1ddb289d384abddc4df8eb865379c8da76a |
BCEFocalLoss | # 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 ... | Stochastic-Adventure/ClinicalTransformerRelationExtraction | BCEFocalLoss | false | 14,440 | [
"MIT"
] | 78 | eef956bbfbd64b008014ef7cac5f818087816725 | https://github.com/Stochastic-Adventure/ClinicalTransformerRelationExtraction/tree/eef956bbfbd64b008014ef7cac5f818087816725 |
UNet | import torch
import torch.nn as nn
class double_conv(nn.Module):
def __init__(self, input_channels, output_channels):
super(double_conv, self).__init__()
self.conv1 = nn.Conv2d(input_channels, output_channels, kernel_size
=3, padding='same')
self.conv2 = nn.Conv2d(output_chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | mhakyash/UNet-MNIST-denoising | UNet | false | 10,664 | [
"MIT"
] | 0 | 0e3c20cbb3f34af575e33209425ae4d7cb0bcd82 | https://github.com/mhakyash/UNet-MNIST-denoising/tree/0e3c20cbb3f34af575e33209425ae4d7cb0bcd82 |
LearnedUtility | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | pabloguarda/NeuralTransportationNetworks | LearnedUtility | false | 7,438 | [
"MIT"
] | 1 | 0461c26128b09488aff237b760068b43d131f8a9 | https://github.com/pabloguarda/NeuralTransportationNetworks/tree/0461c26128b09488aff237b760068b43d131f8a9 |
TransformerEncoderLayer | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class LayerNorm(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.... | abhinavrangarajan/genienlp | TransformerEncoderLayer | false | 1,371 | [
"BSD-3-Clause"
] | 0 | ba121274b3365739ce9e5a8facc9a2904149b9c7 | https://github.com/abhinavrangarajan/genienlp/tree/ba121274b3365739ce9e5a8facc9a2904149b9c7 |
ResidualBlock_noBN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaimin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | jtang98/FFDNet-speckle | ResidualBlock_noBN | false | 7,000 | [
"MIT"
] | 1 | 084fa2782f0197d0e01ee8c595a16414aaf4ab8d | https://github.com/jtang98/FFDNet-speckle/tree/084fa2782f0197d0e01ee8c595a16414aaf4ab8d |
partCE | # 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
... | INK-USC/shifted-label-distribution | partCE | false | 8,260 | [
"Apache-2.0"
] | 37 | 3cf2b7ced3b2e18234db405f6014f049c4830d71 | https://github.com/INK-USC/shifted-label-distribution/tree/3cf2b7ced3b2e18234db405f6014f049c4830d71 |
SharpenedCosineSimilarity_ConvImpl | import torch
import torch.nn as nn
import torch.nn.functional as F
class SharpenedCosineSimilarity_ConvImpl(nn.Module):
def __init__(self, in_channels=1, out_channels=1, kernel_size=1, stride
=1, padding=0, eps=1e-12):
super(SharpenedCosineSimilarity_ConvImpl, self).__init__()
self.in_cha... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | enzokro/sharpened_cosine_similarity_torch | SharpenedCosineSimilarity_ConvImpl | false | 12,352 | [
"MIT"
] | 0 | 150c84f5cf81721baf097abdc0d4ac772fb39fc4 | https://github.com/enzokro/sharpened_cosine_similarity_torch/tree/150c84f5cf81721baf097abdc0d4ac772fb39fc4 |
DAGNNConv | import torch
import torch.nn as nn
class DAGNNConv(nn.Module):
def __init__(self, in_features, out_features=1, K=10, bias=False):
super().__init__()
assert out_features == 1, "'out_features' must be 1"
self.in_features = in_features
self.out_features = out_features
self.li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | EdisonLeeeee/Graphgallery | DAGNNConv | false | 5,115 | [
"MIT"
] | 1 | 8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 | https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 |
MaxPool3x3 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | mc-nya/unnas | MaxPool3x3 | false | 7,189 | [
"MIT"
] | 1 | f778bb743144cf56ce2a48ccca20e9f3a97a7b84 | https://github.com/mc-nya/unnas/tree/f778bb743144cf56ce2a48ccca20e9f3a97a7b84 |
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