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 |
|---|---|---|---|---|---|---|---|---|---|---|
BinaryFocalLossWithLogits | # 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... | ChristophReich1996/kornia | BinaryFocalLossWithLogits | false | 282 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
BasicModel_MaxPool_ReLU | # 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... | LMdeLiangMi/captum | BasicModel_MaxPool_ReLU | false | 5,477 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
GaussianNoise | # 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... | eezkni/UEGAN | GaussianNoise | false | 15,285 | [
"MIT"
] | 73 | a6616ac559819d487cae0f301d98cf2922a11a09 | https://github.com/eezkni/UEGAN/tree/a6616ac559819d487cae0f301d98cf2922a11a09 |
BowEncoder | import torch
import torch.nn as nn
from torch.nn import functional as F
class BowEncoder(nn.Module):
"""
static information extractor
"""
def __init__(self, num_words, bow_mid_hid, dropout):
super().__init__()
self.fc1 = nn.Linear(num_words, bow_mid_hid)
self.fc_trans = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Maxpa1n/case2vec | BowEncoder | false | 17,699 | [
"Apache-2.0"
] | 8 | 1e8f7a9ccbd5ef01409c7f03110b708bce467161 | https://github.com/Maxpa1n/case2vec/tree/1e8f7a9ccbd5ef01409c7f03110b708bce467161 |
ActorCritic | # 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, math as tl_math
im... | Tzenthin/pytorch-ppo-sac-HalfCheetah-v2 | ActorCritic | false | 5,927 | [
"MIT"
] | 1 | 282a4104ec577056a141909e29dc97ed425a566c | https://github.com/Tzenthin/pytorch-ppo-sac-HalfCheetah-v2/tree/282a4104ec577056a141909e29dc97ed425a566c |
AsymmetricLossMultiLabel | # 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... | DifferentSC/pytorch-image-models | AsymmetricLossMultiLabel | false | 11,344 | [
"Apache-2.0"
] | 0 | ccfb5751abc70d80add4f197464190c4a2637c6c | https://github.com/DifferentSC/pytorch-image-models/tree/ccfb5751abc70d80add4f197464190c4a2637c6c |
ViTClassifierPipe | # 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 ... | drunkcoding/huggingface-utils | ViTClassifierPipe | false | 6,611 | [
"MIT"
] | 1 | 4baad306857c357d94607076c6ab0cb5d6350cbe | https://github.com/drunkcoding/huggingface-utils/tree/4baad306857c357d94607076c6ab0cb5d6350cbe |
LINEAR_LOGSOFTMAX | # 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.... | Drkun/Lifelong-Zero-Shot-Learning | LINEAR_LOGSOFTMAX | false | 17,231 | [
"Apache-2.0"
] | 9 | 5cea07c25e14aed1c544c83863f4733a8213ddb0 | https://github.com/Drkun/Lifelong-Zero-Shot-Learning/tree/5cea07c25e14aed1c544c83863f4733a8213ddb0 |
ResidualBlock | # 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 ... | CFengFeng/face-nn | ResidualBlock | false | 4,932 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
MNISTConvNet | # 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.... | xinranzhu/GPTune-1 | MNISTConvNet | false | 13,118 | [
"BSD-3-Clause-LBNL"
] | 0 | 1e502295e790ab68990f657492243fd4fb3dfc0a | https://github.com/xinranzhu/GPTune-1/tree/1e502295e790ab68990f657492243fd4fb3dfc0a |
DenseCrossEntropy | # 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
... | Husky95/Google-Landmark-Recognition-2020-3rd-Place-Solution | DenseCrossEntropy | false | 9,152 | [
"Apache-2.0"
] | 0 | 48806b9e09beabf74e8f96575855dcfa13a4f996 | https://github.com/Husky95/Google-Landmark-Recognition-2020-3rd-Place-Solution/tree/48806b9e09beabf74e8f96575855dcfa13a4f996 |
SelfAttention2d | # 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.... | DeepTitan/v-diffusion-pytorch | SelfAttention2d | false | 9,045 | [
"MIT"
] | 0 | 857b6f2a4519973f9a8dc0b6c93f0134cebc3a8d | https://github.com/DeepTitan/v-diffusion-pytorch/tree/857b6f2a4519973f9a8dc0b6c93f0134cebc3a8d |
DummyModelWithSharedSubmodule | # 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 ... | Donfa1con/distiller | DummyModelWithSharedSubmodule | false | 11,521 | [
"Apache-2.0"
] | 0 | 645ee41bfebc463523b228ff087e41619607d8b2 | https://github.com/Donfa1con/distiller/tree/645ee41bfebc463523b228ff087e41619607d8b2 |
SimpleTextClassifier | # 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.... | goodmike31/pytorch_active_learning | SimpleTextClassifier | false | 15,442 | [
"MIT"
] | 629 | 1224efad1f8022efa933cd36e30f78ed06eaaea7 | https://github.com/goodmike31/pytorch_active_learning/tree/1224efad1f8022efa933cd36e30f78ed06eaaea7 |
SinReLU | import torch
import torch.nn as nn
class SinReLU(nn.Module):
def forward(self, x):
return torch.sin(x) + torch.relu(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | fmhoward/pysurvival | SinReLU | false | 12,380 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
SpatialShift2d | # 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... | uthree/ReMixer | SpatialShift2d | false | 13,065 | [
"MIT"
] | 0 | 587e1b6a01850df649eccf043689f84a7dd5e2dc | https://github.com/uthree/ReMixer/tree/587e1b6a01850df649eccf043689f84a7dd5e2dc |
LinearWeightNorm | import torch
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class LinearWeightNorm(torch.nn.Module):
def __init__(self, in_features, out_features, bias=True, weight_scale=
None, weight_init_stdv=0.1):
super(LinearWeightNorm, self).__init__()
self.in_features = 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.triton_helpers import libdevice
from torch.nn.param... | Akash-Sharma-1/Training-Triplet-Networks-with-GAN | LinearWeightNorm | false | 18,435 | [
"MIT"
] | 2 | b4b1e683162908f243c4f1ba4ec9cda2314fbadc | https://github.com/Akash-Sharma-1/Training-Triplet-Networks-with-GAN/tree/b4b1e683162908f243c4f1ba4ec9cda2314fbadc |
SimpleSpatialEmbedding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | qway/nerfmeshes | SimpleSpatialEmbedding | false | 16,306 | [
"MIT"
] | 113 | d983dcbbcfec1337c9f2040969213c6d1ea0c39e | https://github.com/qway/nerfmeshes/tree/d983dcbbcfec1337c9f2040969213c6d1ea0c39e |
FSELU | import torch
import torch.nn as nn
class FSELU(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FSELU, self).__init__()
def forward(self, x):
from torch.nn import functional as F
return F.selu(x)
def get_inputs():
return [torch.rand([4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dawnclaude/onnx2keras | FSELU | false | 15,140 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
ToLongTensor | import torch
from torch import Tensor
from typing import List
import torch.nn as nn
class ToLongTensor(nn.Module):
"""Convert a list of integers to long tensor
"""
def __init__(self):
super(ToLongTensor, self).__init__()
def forward(self, tokens: 'List[List[int]]') ->Tensor:
return 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | nateanl/text | ToLongTensor | false | 16,131 | [
"BSD-3-Clause"
] | 3,172 | b26e9350ad387a84aefe131443bbbf1c51a8a618 | https://github.com/nateanl/text/tree/b26e9350ad387a84aefe131443bbbf1c51a8a618 |
NormalizeColorSpace | import torch
from torch import nn
from typing import *
class NormalizeColorSpace(nn.Module):
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
x = x.clamp(0.0, 255.0)
return x / 255.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.as... | IntelLabs/OSCAR | NormalizeColorSpace | false | 8,287 | [
"BSD-3-Clause"
] | 13 | 25d1dea35727379117e11b7238b5a0d1ed19acad | https://github.com/IntelLabs/OSCAR/tree/25d1dea35727379117e11b7238b5a0d1ed19acad |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
self.sigmoid = nn.Sigmoid()
def forward(self, output, target):
prediction = self.sigmoid(output)
return 1 - 2 * torch.sum(prediction * target) / (torch.sum(
... | 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... | apyskir/steppy-toolkit | DiceLoss | false | 3,118 | [
"MIT"
] | 0 | 3190054954aeab043ced1c079d87bdd3582bb232 | https://github.com/apyskir/steppy-toolkit/tree/3190054954aeab043ced1c079d87bdd3582bb232 |
TransformerLayer | # 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.... | bpiyush/CLIP_prefix_caption-video | TransformerLayer | false | 12,204 | [
"MIT"
] | 0 | 3f6a4b8c841189e20b82fd4de127681424311599 | https://github.com/bpiyush/CLIP_prefix_caption-video/tree/3f6a4b8c841189e20b82fd4de127681424311599 |
MaxPoolStride1 | # 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
import torch.utils.data.distributed
import ... | AutoRaider/AlphaPose | MaxPoolStride1 | false | 8,921 | [
"Apache-2.0"
] | 0 | bf74882728901b033d45512b402c32277bf9246b | https://github.com/AutoRaider/AlphaPose/tree/bf74882728901b033d45512b402c32277bf9246b |
MarginRankingLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.opti... | hilman-dayo/active_learning | MarginRankingLoss | false | 15,529 | [
"Apache-2.0"
] | 54 | cc5b0388be25946e794d59d95e4d9c8c56e24207 | https://github.com/hilman-dayo/active_learning/tree/cc5b0388be25946e794d59d95e4d9c8c56e24207 |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | VarunBabbar/Image_Compressor | Decoder | false | 1,192 | [
"MIT"
] | 0 | 254d8d411f7cd16f3ce242275532c9fca537269c | https://github.com/VarunBabbar/Image_Compressor/tree/254d8d411f7cd16f3ce242275532c9fca537269c |
Lenet | import torch
from torch import nn
from torch.nn import Sequential
from torch.nn import Conv2d
import torch.nn
import torch.optim
class Lenet(nn.Module):
def __init__(self):
super(Lenet, self).__init__()
layer1 = Sequential()
layer1.add_module('conv1', Conv2d(3, 32, 3, 1, padding=1))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import Sequential
from torch.nn import Conv2d... | partnernetsoftware/openlab | Lenet | false | 7,442 | [
"MIT"
] | 1 | faa4e58486a7bc4140ad3d56545bfb736cb86696 | https://github.com/partnernetsoftware/openlab/tree/faa4e58486a7bc4140ad3d56545bfb736cb86696 |
TensorRange | import torch
def tensor_max(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.max(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.max(input, dim=d, keepdim=keepdim)[0]
retur... | 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... | Minyus/kedex | TensorRange | false | 9,688 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
AttentionModule | import torch
import torch.nn as nn
class AttentionModule(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv0 = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
self.conv_spatial = nn.Conv2d(dim, dim, 7, stride=1, padding=9,
groups=dim, dilation=3)
self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | LSH9832/MyPythonModules | AttentionModule | false | 752 | [
"MIT"
] | 0 | 442566a0fbd6ebe2bc20b6914686a1e2663d10c0 | https://github.com/LSH9832/MyPythonModules/tree/442566a0fbd6ebe2bc20b6914686a1e2663d10c0 |
StackedAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class StackedAttention(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(StackedAttention, self).__init__()
self.Wv = nn.Conv2d(input_dim, hidden_dim, kernel_size=(1, 1),
padding=(0, 0))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SpyrosMouselinos/DeltaFormers | StackedAttention | false | 5,865 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
BiaffineScorer | import torch
import torch.nn as nn
class BiaffineScorer(nn.Module):
def __init__(self, input1_size, input2_size, output_size):
super().__init__()
self.W_bilin = nn.Bilinear(input1_size + 1, input2_size + 1,
output_size)
self.W_bilin.weight.data.zero_()
self.W_bilin.bia... | 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... | CopticScriptorium/stanza | BiaffineScorer | false | 11,366 | [
"Apache-2.0"
] | 0 | a16b152fce3d2cc325b7d67e03952bd00c878fe3 | https://github.com/CopticScriptorium/stanza/tree/a16b152fce3d2cc325b7d67e03952bd00c878fe3 |
LearnedPositionalEncoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | potpov/TransBTS | LearnedPositionalEncoding | false | 16,267 | [
"Apache-2.0"
] | 163 | 658de5f1dde17d25db54fb07adf49370cc32d7c3 | https://github.com/potpov/TransBTS/tree/658de5f1dde17d25db54fb07adf49370cc32d7c3 |
ContrastiveEmbeddingLoss | # 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
from t... | Dokholyan/catalyst | ContrastiveEmbeddingLoss | false | 376 | [
"Apache-2.0"
] | 0 | de8e681676d76741fdb722d4cd77274ba616915d | https://github.com/Dokholyan/catalyst/tree/de8e681676d76741fdb722d4cd77274ba616915d |
ConvertPointsToHomogeneous | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | hassony2/frankmocap | ConvertPointsToHomogeneous | false | 6,785 | [
"BSD-3-Clause"
] | 1 | 50aae41d9b41d2f344ae1709bbf1b25974209fa9 | https://github.com/hassony2/frankmocap/tree/50aae41d9b41d2f344ae1709bbf1b25974209fa9 |
LblLoss | import torch
from torch import nn
from torchvision.models import *
class LblLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred_batch, true_batch):
wgt = torch.ones_like(pred_batch)
wgt[true_batch > 0] = 100
dis = (pred_batch - true_batch) ** 2
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from torchvision.models import *
assert_size_stride = torch._C._dyna... | amoshyc/human-pose-estimation | LblLoss | false | 9,729 | [
"Apache-2.0"
] | 0 | 8fd2962caee43b979f44637441d88d80f2ea951e | https://github.com/amoshyc/human-pose-estimation/tree/8fd2962caee43b979f44637441d88d80f2ea951e |
Softmax2d | import torch
from torch import nn
class Softmax2d(nn.Module):
def __init__(self):
super().__init__()
self.Softmax2d = nn.Softmax2d()
def forward(self, x):
x = self.Softmax2d(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
ret... | 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... | Yusoi/mmdetection | Softmax2d | false | 9,739 | [
"Apache-2.0"
] | 0 | cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a | https://github.com/Yusoi/mmdetection/tree/cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a |
RelLoss | import torch
import numpy as np
import torch.nn as nn
import torch.nn.init
def _mask_input(input, mask=None):
if mask is not None:
input = input * mask
count = torch.sum(mask).data[0]
else:
count = np.prod(input.size(), dtype=np.float32).item()
return input, count
class RelLoss(n... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.nn as nn
import torch.nn.init
assert_size... | simonmeister/pytorch-mono-depth | RelLoss | false | 16,446 | [
"MIT"
] | 56 | 713c70e2fdae6d9d6e0322febadfedcaee9470d3 | https://github.com/simonmeister/pytorch-mono-depth/tree/713c70e2fdae6d9d6e0322febadfedcaee9470d3 |
MeanPoolConv | import torch
import torch.nn as nn
class MeanPoolConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
padding=kernel_size // 2, bias=biases)
def forward(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | samsartor/score_sde | MeanPoolConv | false | 7,599 | [
"Apache-2.0"
] | 1 | d25c8d092a68d643c796d771c55f80075aa041d1 | https://github.com/samsartor/score_sde/tree/d25c8d092a68d643c796d771c55f80075aa041d1 |
CMlp | import torch
import torch.nn as nn
class CMlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | gaopengcuhk/deit | CMlp | false | 3,518 | [
"Apache-2.0"
] | 0 | de7db8f3a12c35e5e554b385030c574b7c78aaa6 | https://github.com/gaopengcuhk/deit/tree/de7db8f3a12c35e5e554b385030c574b7c78aaa6 |
Mish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Mish(nn.Module):
def __init__(self):
super(Mish, self).__init__()
def forward(self, x):
return x * torch.tanh(F.softplus(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | BDeMo/yolov4-pytorch | Mish | false | 2,001 | [
"MIT"
] | 0 | 2434afc88d0890bdb19c5655bb7c577d22bf18d3 | https://github.com/BDeMo/yolov4-pytorch/tree/2434afc88d0890bdb19c5655bb7c577d22bf18d3 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | IW276/IW276SS21P16 | Net | false | 9,338 | [
"MIT"
] | 0 | b798a2747c2b25a5e33fd8bcda91d9c52b9c01fc | https://github.com/IW276/IW276SS21P16/tree/b798a2747c2b25a5e33fd8bcda91d9c52b9c01fc |
Sine | import torch
import torch.nn as nn
class Sine(nn.Module):
def __init(self):
super().__init__()
def forward(self, input):
return torch.sin(30 * input)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | KIMGEONUNG/multi-memory-siren | Sine | false | 689 | [
"MIT"
] | 0 | b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b | https://github.com/KIMGEONUNG/multi-memory-siren/tree/b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b |
Conv2dBlock | import torch
import torch.nn.functional as F
from torch import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mome... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.functional as... | belphegor2211/KLTN_GANwriting | Conv2dBlock | false | 3,205 | [
"MIT"
] | 0 | 67d4d5c286ec45ef704b49c5abf9774d38bf65eb | https://github.com/belphegor2211/KLTN_GANwriting/tree/67d4d5c286ec45ef704b49c5abf9774d38bf65eb |
ChannelPool | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelPool(nn.MaxPool1d):
def forward(self, x):
n, c, w, h = x.size()
x = x.view(n, c, w * h).permute(0, 2, 1)
x = x.contiguous()
pooled = F.max_pool1d(x, c, 1)
_, _, c = pooled.size()
pooled... | 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... | Sapio-S/Neural-SLAM | ChannelPool | false | 14,370 | [
"MIT"
] | 171 | 3a1e429fc54fe5682833bfe541512c8d62c2e2f7 | https://github.com/Sapio-S/Neural-SLAM/tree/3a1e429fc54fe5682833bfe541512c8d62c2e2f7 |
HSwish | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class HSwish(nn.Module):
"""
H-Swish activation function from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
Parameters:
----------
inplace : bool
Whether to use inplace version of 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 import triton_helpers
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | HyperGAN/imgclsmob | HSwish | false | 17,669 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
FullAttention | import math
import torch
import torch.nn as nn
import torch.utils.collect_env
class FullAttention(nn.Module):
"""Implement the scaled dot product attention with softmax.
Arguments
---------
softmax_temp: The temperature to use for the softmax attention.
(default: 1/sqrt(d_key... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HaotianUpenn/scatterbrain | FullAttention | false | 13,754 | [
"Apache-2.0"
] | 49 | c026128d7362ae627641d11d4e5627bc1f400eb1 | https://github.com/HaotianUpenn/scatterbrain/tree/c026128d7362ae627641d11d4e5627bc1f400eb1 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | bsgiovanini/transformer | LayerNorm | false | 1,582 | [
"Apache-2.0"
] | 0 | e128fa862f1b3d17d7b92df169a2bbee3f08366f | https://github.com/bsgiovanini/transformer/tree/e128fa862f1b3d17d7b92df169a2bbee3f08366f |
PerceptronTanh | import torch
import torch.nn as nn
import torch.nn.functional as F
class PerceptronTanh(nn.Module):
"""Implements a 1-layer perceptron with Tanh activaton."""
def __init__(self, input_dimension, hidden_dimension, output_dimension):
super(PerceptronTanh, self).__init__()
self._layer1 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | negotiatorvivian/PDP-SP | PerceptronTanh | false | 4,060 | [
"MIT"
] | 0 | 0fa4c1145c2b881c1fde4ed8d9f0845b7967f857 | https://github.com/negotiatorvivian/PDP-SP/tree/0fa4c1145c2b881c1fde4ed8d9f0845b7967f857 |
RobertaLMHead | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | BlackNoodle/TUCORE-GCN | RobertaLMHead | false | 8,784 | [
"MIT"
] | 27 | 16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 | https://github.com/BlackNoodle/TUCORE-GCN/tree/16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 |
ReduceProd | import torch
import torch.onnx
import torch.nn as nn
class ReduceProd(nn.Module):
def forward(self, x):
return torch.prod(x, -1, keepdim=True)
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.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | mil-tokyo/webdnn | ReduceProd | false | 16,090 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Halo1236/Dive-into-DL-PyTorch | Net | false | 531 | [
"Apache-2.0"
] | 0 | 586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 | https://github.com/Halo1236/Dive-into-DL-PyTorch/tree/586b4e9ca77b2121ce5f5bec8b0a893b33f1b574 |
EmbeddingModel | # 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... | jina-ai/finetuner | EmbeddingModel | false | 15,682 | [
"Apache-2.0"
] | 270 | 6b8701c6ca372310364e6791c1c2761700dfc150 | https://github.com/jina-ai/finetuner/tree/6b8701c6ca372310364e6791c1c2761700dfc150 |
ValueNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class ValueNetwork(nn.Module):
def __init__(self, state_dim, hidden_dim, init_w=0.003):
super(ValueNetwork, self).__init__()
self.linear1 = nn.Linear(state_dim, hidden_dim)
self.linear2 = nn.Linear(hidden_dim, hidden_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JieRen98/Popular-RL-Algorithms | ValueNetwork | false | 13,908 | [
"Apache-2.0"
] | 273 | 7f2bb74a51cf9cbde92a6ccfa42e97dc129dd145 | https://github.com/JieRen98/Popular-RL-Algorithms/tree/7f2bb74a51cf9cbde92a6ccfa42e97dc129dd145 |
Policy | # 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... | francescotorregrossa/deep-reinforcement-learning-nanodegree | Policy | false | 6,706 | [
"MIT"
] | 1 | 396648570aa53c9e727a8de69175e4a139d4ded5 | https://github.com/francescotorregrossa/deep-reinforcement-learning-nanodegree/tree/396648570aa53c9e727a8de69175e4a139d4ded5 |
Conv2dDynamicSamePadding | # 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... | AustinCai/gmaxup-augmentation | Conv2dDynamicSamePadding | false | 79 | [
"MIT"
] | 0 | a64ca0a76eb333e5ce6b217c301d27ca04d73bce | https://github.com/AustinCai/gmaxup-augmentation/tree/a64ca0a76eb333e5ce6b217c301d27ca04d73bce |
RecurrentNeuralNetwork | import torch
from torch import Tensor
from torch.functional import Tensor
from torch import nn
from typing import Tuple
from typing import Any
class RecurrentNeuralNetwork(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.hidden_size = hidden_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JThissen/machine_learning | RecurrentNeuralNetwork | false | 598 | [
"MIT"
] | 0 | 82e2b003fb25111dc2d9ac1c1b2fd637e9f4fdbc | https://github.com/JThissen/machine_learning/tree/82e2b003fb25111dc2d9ac1c1b2fd637e9f4fdbc |
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
import math
import ... | Chriskuei/FedMatch | BERTIntermediate | false | 18,351 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 |
Attention_Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GhadeerElmkaiel/Trans2Seg | Attention_Decoder | false | 491 | [
"Apache-2.0"
] | 0 | 6717db602205cbed494ae1913ac7cbbca8e83463 | https://github.com/GhadeerElmkaiel/Trans2Seg/tree/6717db602205cbed494ae1913ac7cbbca8e83463 |
Pooling | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride of the convolution
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | savan77/nni | Pooling | false | 4,271 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
Encoder | import torch
import torch.nn as nn
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.Conv1 = nn.Conv2d(1, 16, 3, 1, 1)
self.Relu = nn.ReLU(inplace=True)
self.layers = nn.ModuleDict({'DenseConv1': nn.Conv2d(16, 16, 3, 1,
1), 'DenseConv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | VarunBabbar/Image_Compressor | Encoder | false | 1,187 | [
"MIT"
] | 0 | 254d8d411f7cd16f3ce242275532c9fca537269c | https://github.com/VarunBabbar/Image_Compressor/tree/254d8d411f7cd16f3ce242275532c9fca537269c |
NegativePowerNormSimilarity | import torch
from torch import nn
from abc import abstractmethod
from typing import Union
import torch.utils.data
class Similarity(nn.Module):
"""Base class for similarity functions."""
@abstractmethod
def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
"""
Compute pai... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from abc import abstractmethod
from typing import Union
import torch.utils.data
assert_size_stride = torch._C._dynamo.g... | DimitrisAlivas/StarQE | NegativePowerNormSimilarity | false | 7,978 | [
"MIT"
] | 11 | c17676e5f1e3f19c0c4c117a50abe2ce22ffef28 | https://github.com/DimitrisAlivas/StarQE/tree/c17676e5f1e3f19c0c4c117a50abe2ce22ffef28 |
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.... | Pluriscient/learn-to-learn | Model | false | 11,794 | [
"MIT"
] | 0 | 4aa0143522eb90f6439b83ed424d12b434cb344b | https://github.com/Pluriscient/learn-to-learn/tree/4aa0143522eb90f6439b83ed424d12b434cb344b |
NetworkDQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class NetworkDQN(nn.Module):
def __init__(self, fs, input_dim, fc1, fc2, n_actions):
super(NetworkDQN, self).__init__()
self.conv1 = nn.Conv2d(fs, 64, 8, 4)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(64... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | doganjr/MarioDQN | NetworkDQN | false | 6,618 | [
"MIT"
] | 1 | 62daa390f8ee0b732275e71675a2b9eae85c43a4 | https://github.com/doganjr/MarioDQN/tree/62daa390f8ee0b732275e71675a2b9eae85c43a4 |
AtteMatchLay | # 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
assert... | NeilWangziyu/torch_light | AtteMatchLay | false | 5,650 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
GlobalMaxPooling | import torch
import torch.nn as nn
class GlobalMaxPooling(nn.Module):
def __init__(self, dim=0):
super(self.__class__, self).__init__()
self.dim = dim
def forward(self, x):
return x.max(dim=self.dim)[0]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs()... | 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... | kbrodt/clog-loss | GlobalMaxPooling | false | 7,016 | [
"MIT"
] | 1 | 0831b3a01b079609a71490bb921633110927206c | https://github.com/kbrodt/clog-loss/tree/0831b3a01b079609a71490bb921633110927206c |
AvgPoolPad | import torch
import torch.nn as nn
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_pad=False)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ArronHZG/ABD-Net | AvgPoolPad | false | 9,578 | [
"MIT"
] | 0 | 4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 | https://github.com/ArronHZG/ABD-Net/tree/4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 |
SigmoidFocalLossStar | # 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... | botkop/lark | SigmoidFocalLossStar | false | 1,575 | [
"Apache-2.0"
] | 0 | edb2defdb514213fc121418578b0d9006a55f3a0 | https://github.com/botkop/lark/tree/edb2defdb514213fc121418578b0d9006a55f3a0 |
LossEnergy | # 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... | MikeEntwistle/deepqmc | LossEnergy | false | 17,715 | [
"MIT"
] | 4 | b5c20bf1768f04227becd5079c6b40aefc97d26c | https://github.com/MikeEntwistle/deepqmc/tree/b5c20bf1768f04227becd5079c6b40aefc97d26c |
SpatialEmbedding | import torch
import torch.nn
class SpatialEmbedding(torch.nn.Module):
def __init__(self, in_features, out_features, weight_multiplier=1.0):
super(SpatialEmbedding, self).__init__()
self.b = torch.zeros((in_features, out_features))
self.b.normal_(0, weight_multiplier)
self.b = torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | ashwinpn/Computer-Vision | SpatialEmbedding | false | 6,270 | [
"MIT"
] | 1 | 9dc3abfe416385171b76e2bad6872e10f36a12b4 | https://github.com/ashwinpn/Computer-Vision/tree/9dc3abfe416385171b76e2bad6872e10f36a12b4 |
NormLayer | import torch
import torch.nn as nn
class NormLayer(nn.Module):
def __init__(self, mean, std, n=None, eps=1e-08) ->None:
super().__init__()
self.mean = mean
self.std = std
self.eps = eps
def forward(self, x):
return (x - self.mean) / (self.std + self.eps)
def get_inp... | 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... | aravipati12/captum | NormLayer | false | 10,105 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
MinimaxDiscriminatorLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | torchgan/torchgan | MinimaxDiscriminatorLoss | false | 16,609 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
RelativeL1 | # 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
... | grofit/traiNNer | RelativeL1 | false | 15,474 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
MultiHeadAttentionWithMetrics | import torch
import numpy as np
import torch.nn as nn
class MultiHeadAttentionWithMetrics(nn.Module):
def __init__(self, ctx, heads_count, d_model, dropout_prob=0.1, mode=
'self-attention'):
super(MultiHeadAttentionWithMetrics, self).__init__()
assert d_model % heads_count == 0
as... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bingrao/deeplearning | MultiHeadAttentionWithMetrics | false | 9,806 | [
"MIT"
] | 0 | 8488478a4355a7f56d49c5126f529c21d5a95798 | https://github.com/bingrao/deeplearning/tree/8488478a4355a7f56d49c5126f529c21d5a95798 |
Classifier | import torch
import torch.distributed
import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, hidden_size):
super(Classifier, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = 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.distributed
import torch
import torch.nn as nn
assert_size_stride =... | Omkar-Ranadive/Fine-Tuning-BERT | Classifier | false | 5,696 | [
"Apache-2.0"
] | 1 | b046092ec4007a4a59e1a478576cca7557c18d76 | https://github.com/Omkar-Ranadive/Fine-Tuning-BERT/tree/b046092ec4007a4a59e1a478576cca7557c18d76 |
GlobalConvBlock | import torch
from torch import nn
from math import sqrt
class GlobalConvBlock(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size):
super(GlobalConvBlock, self).__init__()
pad0 = int((kernel_size[0] - 1) / 2)
pad1 = int((kernel_size[1] - 1) / 2)
self.conv_l1 = nn.Conv2d(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 import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | odgiv/SegAN | GlobalConvBlock | false | 4,104 | [
"MIT"
] | 0 | d7a91fbc10139dc81c61737326649a3a758cdf94 | https://github.com/odgiv/SegAN/tree/d7a91fbc10139dc81c61737326649a3a758cdf94 |
AdapterModule | # 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
assert_size_stride = torch._C... | adamviola/piazza-qa | AdapterModule | false | 6,081 | [
"MIT"
] | 1 | 1fd65cfeb7bae753fc74d7ab837ab408f7c06507 | https://github.com/adamviola/piazza-qa/tree/1fd65cfeb7bae753fc74d7ab837ab408f7c06507 |
MaskedMSELoss | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards... | DashaSerdyuk/tacotron2 | MaskedMSELoss | false | 5,061 | [
"BSD-3-Clause"
] | 1 | 1a88669670750f8b0e1aff76abc8b1b15300e1dc | https://github.com/DashaSerdyuk/tacotron2/tree/1a88669670750f8b0e1aff76abc8b1b15300e1dc |
LinearEncoder | import torch
import torch.nn as nn
from torch.autograd import *
import torch.nn.init as init
class LayerNormalization(nn.Module):
""" Layer normalization module """
def __init__(self, d_hid, eps=0.001):
super(LayerNormalization, self).__init__()
self.eps = eps
self.a_2 = nn.Parameter(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RussellMcGrady/Multi-head-attention-based-MetaR | LinearEncoder | false | 2,787 | [
"Apache-2.0"
] | 0 | 4e47546da35bd57ff7ab16d0fed19be31c063563 | https://github.com/RussellMcGrady/Multi-head-attention-based-MetaR/tree/4e47546da35bd57ff7ab16d0fed19be31c063563 |
LayerNormalization | # 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
assert... | NLP-Discourse-SoochowU/rst_dp2019Bottom2Up | LayerNormalization | false | 5,633 | [
"MIT"
] | 1 | ac1624127c9c8a3301685193ac8239357e01f6ca | https://github.com/NLP-Discourse-SoochowU/rst_dp2019Bottom2Up/tree/ac1624127c9c8a3301685193ac8239357e01f6ca |
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.... | YacobBY/vedastr | MultiHeadAttention | false | 1,274 | [
"Apache-2.0"
] | 0 | 2353780489b58d2398b9af49d238ef0df3f45f2a | https://github.com/YacobBY/vedastr/tree/2353780489b58d2398b9af49d238ef0df3f45f2a |
KL_Loss | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils
class KL_Loss(nn.Module):
def __init__(self, temperature=1):
super(KL_Loss, self).__init__()
self.T = temperature
def forward(self, output_batch, teacher_outputs):
output_batch = F.log_softmax(output... | 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 ... | NeutrinoLiu/FedML | KL_Loss | false | 2,669 | [
"Apache-2.0"
] | 0 | 1670b2a3f0b2d63c374a9a4a19449090c694bc78 | https://github.com/NeutrinoLiu/FedML/tree/1670b2a3f0b2d63c374a9a4a19449090c694bc78 |
FcnBinaryClassifier | # 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_... | breid1313/nlp_hw3_text_fcn_pytorch | FcnBinaryClassifier | false | 3,254 | [
"Apache-2.0"
] | 0 | a4234e90d37e94a3043d9715c90bac7543f4b0ae | https://github.com/breid1313/nlp_hw3_text_fcn_pytorch/tree/a4234e90d37e94a3043d9715c90bac7543f4b0ae |
ResNetV2 | # 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.... | MetaMain/ViTRobust | ResNetV2 | false | 17,999 | [
"BSD-3-Clause"
] | 6 | 5bca523f430933469d9f82022e334839388cee7a | https://github.com/MetaMain/ViTRobust/tree/5bca523f430933469d9f82022e334839388cee7a |
FocalTverskyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalTverskyLoss(nn.Module):
"""
A variant on the Tversky loss that also includes the gamma modifier from Focal Loss.
"""
def __init__(self, weight=None, size_average=True):
super(FocalTverskyLoss, self).__init__()
... | 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... | Exdenta/torchsat | FocalTverskyLoss | false | 13,656 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
VAE | import torch
import torch.nn.functional as F
from torch import nn
class VAE(nn.Module):
"""A classic VAE.
Params
------
input_dim : int
The size of the (flattened) image vector
latent_dim : int
The size of the latent memory
"""
def __init__(self, input_dim=784, laten... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | CoAxLab/newremagine | VAE | false | 8,940 | [
"MIT"
] | 0 | 5ae1c579121c93271ebf5dcef45bd66e8daea3a7 | https://github.com/CoAxLab/newremagine/tree/5ae1c579121c93271ebf5dcef45bd66e8daea3a7 |
WaveletConv | import torch
import torch.nn as nn
class WaveletConv(nn.Module):
def __init__(self, in_features, out_features, num_nodes, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EdisonLeeeee/Graphgallery | WaveletConv | false | 5,112 | [
"MIT"
] | 1 | 8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 | https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 |
SimpleModel | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
def forward(self, x):
return x * 2
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
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data... | Jovonni/jukebox | SimpleModel | false | 669 | [
"MIT"
] | 0 | 965a6f78aae67506a6e4fcdb205e2c39132e12e0 | https://github.com/Jovonni/jukebox/tree/965a6f78aae67506a6e4fcdb205e2c39132e12e0 |
OutlookAttention | # 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.... | rushirajsherlocked/External-Attention-pytorch | OutlookAttention | false | 4,230 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
TorchSub | import torch
class TorchSub(torch.nn.Module):
def __init__(self):
super(TorchSub, self).__init__()
def forward(self, x, y):
return torch.sub(x, y)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | PogChamper/torch2trt | TorchSub | false | 14,225 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
LinearZeros | # 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.... | GauriJagatap/glow-pytorch | LinearZeros | false | 2,331 | [
"MIT"
] | 0 | e379f524b7cc0b57a9bc2849f4115f97bda5a1de | https://github.com/GauriJagatap/glow-pytorch/tree/e379f524b7cc0b57a9bc2849f4115f97bda5a1de |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | momo-the-monster/vqgan-clip-app | TVLoss | false | 16,102 | [
"MIT"
] | 63 | 56cfc0a53928d6d8f90ed8c79439afb4430bc118 | https://github.com/momo-the-monster/vqgan-clip-app/tree/56cfc0a53928d6d8f90ed8c79439afb4430bc118 |
StyledConv | # 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
from to... | Jerry2001/StyleCLIP | StyledConv | false | 657 | [
"MIT"
] | 0 | 806216b4ce7b4c001ff05d7bd707b28d20ea6191 | https://github.com/Jerry2001/StyleCLIP/tree/806216b4ce7b4c001ff05d7bd707b28d20ea6191 |
Attention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1,
score_function='dot_product', dropout=0):
""" Attention Mechanism
:param embed_dim:
:param hidden_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.... | XuMayi/PyABSA | Attention | false | 1,265 | [
"MIT"
] | 0 | 3d71c0cdaea7ea1eff600d9091c3c63f61c111e5 | https://github.com/XuMayi/PyABSA/tree/3d71c0cdaea7ea1eff600d9091c3c63f61c111e5 |
CentralV_Critic | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class CentralV_Critic(nn.Module):
def __init__(self, input_shape, args):
super(CentralV_Critic, self).__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, 128)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | OkYongChoi/smac | CentralV_Critic | false | 18,373 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 |
Drift | import torch
import torch.nn as nn
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
module = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Teemo341/BDNN | Drift | false | 2,888 | [
"Apache-2.0"
] | 0 | d53d4634a7a43d038faa049d7dfd10b3578ae267 | https://github.com/Teemo341/BDNN/tree/d53d4634a7a43d038faa049d7dfd10b3578ae267 |
Mask | # 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.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | savan77/nni | Mask | false | 4,270 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
LearnedKernel | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class LearnedKernel(nn.Module):
def __init__(self, args: 'Namespace'):
super(LearnedKernel, self).__init__()
self.A = nn.Linear(args.ffn_hidden_size, args.ffn_hidden_size)
def forward(self, encodings: 'torch.Ten... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | wengong-jin/chemprop | LearnedKernel | false | 16,706 | [
"MIT"
] | 77 | 3ad3577367d8a53f28aade0be41b56b1f25b6125 | https://github.com/wengong-jin/chemprop/tree/3ad3577367d8a53f28aade0be41b56b1f25b6125 |
CNN3dModel | # 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
assert_size_stride = torch._C... | MilesCranmer/Sapsan | CNN3dModel | false | 8,568 | [
"BSD-3-Clause"
] | 11 | 4d21954baf196ede2d4dafc765aed98a0cfca21b | https://github.com/MilesCranmer/Sapsan/tree/4d21954baf196ede2d4dafc765aed98a0cfca21b |
HingeLoss | # 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... | DarshanPatel11/X-Transformer | HingeLoss | false | 13,558 | [
"BSD-3-Clause"
] | 120 | ee4436a5514b85692c3fb6a594f2e4ac3e8f7c6b | https://github.com/DarshanPatel11/X-Transformer/tree/ee4436a5514b85692c3fb6a594f2e4ac3e8f7c6b |
ResidualAttentionBlock | import torch
from collections import OrderedDict
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Artanic30/RentalPrediction | ResidualAttentionBlock | false | 2,002 | [
"MIT"
] | 0 | 5804ab9b453d2a40bce2bb304c31efc98a803ed8 | https://github.com/Artanic30/RentalPrediction/tree/5804ab9b453d2a40bce2bb304c31efc98a803ed8 |
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