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 |
|---|---|---|---|---|---|---|---|---|---|---|
BWCEWLoss | import torch
from torch import Tensor
from typing import Optional
from torch import nn
class BWCEWLoss(nn.Module):
""" Binary weighted cross entropy loss. """
def __init__(self, positive_class_weight: 'Optional[Tensor]'=None,
robust_lambda: 'int'=0, confidence_penalty: 'int'=0, **kwargs):
sup... | 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 ... | carlogrisetti/ludwig | BWCEWLoss | false | 1,630 | [
"Apache-2.0"
] | 0 | 5c0887f14867e1577e0ddc3806c5cf7a781fb665 | https://github.com/carlogrisetti/ludwig/tree/5c0887f14867e1577e0ddc3806c5cf7a781fb665 |
SpatialCrossMapLRN | import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class SpatialCrossMapLRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCrossMapLRN, self).__init__()
self.ACROSS_CHANNELS = A... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.u... | OrKatz7/kaggle-hubmap | SpatialCrossMapLRN | false | 9,882 | [
"MIT"
] | 0 | 5cf8c5aebe956c256fa7f3db432639e28f29c6a3 | https://github.com/OrKatz7/kaggle-hubmap/tree/5cf8c5aebe956c256fa7f3db432639e28f29c6a3 |
RoutingBase | import torch
from torch.nn import functional as F
import torch.nn as nn
def cal_normal(v, dim=-1, keepdim=False):
"""
:return:
"""
normal = torch.sum(v ** 2, dim=dim, keepdim=keepdim) ** 0.5
return normal
def squash(sr, dim=1):
"""
:param dim:
:param sr:(bs, dim)
: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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | jiangzhiwei2018/Pytorch_CapsNet | RoutingBase | false | 6,948 | [
"Apache-2.0"
] | 1 | b8931d65d5a99a4ff18fd209c16d3ff7d094d1ad | https://github.com/jiangzhiwei2018/Pytorch_CapsNet/tree/b8931d65d5a99a4ff18fd209c16d3ff7d094d1ad |
NextSentencePrediction | # 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.... | mortonjt/BERT-pytorch | NextSentencePrediction | false | 16,113 | [
"Apache-2.0"
] | 5,013 | d10dc4f9d5a6f2ca74380f62039526eb7277c671 | https://github.com/mortonjt/BERT-pytorch/tree/d10dc4f9d5a6f2ca74380f62039526eb7277c671 |
SingleLayer | # 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.... | cadurosar/graph_kd_dense_cifar100 | SingleLayer | false | 1,633 | [
"MIT"
] | 0 | 84054ab4f8f61c9db3460993661ba7bf1d951b36 | https://github.com/cadurosar/graph_kd_dense_cifar100/tree/84054ab4f8f61c9db3460993661ba7bf1d951b36 |
CNNLayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | BlackyYen/Speech_Recognition-PyTorch | CNNLayerNorm | false | 7,788 | [
"MIT"
] | 16 | 0a986f467c540c2be88f65064ebf5ce0f6bcf70a | https://github.com/BlackyYen/Speech_Recognition-PyTorch/tree/0a986f467c540c2be88f65064ebf5ce0f6bcf70a |
CrossEntropyLoss | # 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
... | Randl/Ranger_Mish_reimplementation | CrossEntropyLoss | false | 17,838 | [
"MIT"
] | 7 | 36f580ce8a02fae1929e101c9bd6987ccd2a5843 | https://github.com/Randl/Ranger_Mish_reimplementation/tree/36f580ce8a02fae1929e101c9bd6987ccd2a5843 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
assert_size_str... | jayden-chua/image-mask | DiceLoss | false | 3,697 | [
"MIT"
] | 0 | ce2c6a32bf13df582e7b57e506d58518258be292 | https://github.com/jayden-chua/image-mask/tree/ce2c6a32bf13df582e7b57e506d58518258be292 |
InteractiveKLLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
class InteractiveKLLoss(nn.Module):
def __init__(self, temperature):
super().__init__()
self.temperature = temperature
self.kl_loss = nn.KLDivLoss()
... | 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... | HarshCasper/nni | InteractiveKLLoss | false | 5,264 | [
"MIT"
] | 1 | 291bbbba9f296382015a77b2c88eb5db5b44bf94 | https://github.com/HarshCasper/nni/tree/291bbbba9f296382015a77b2c88eb5db5b44bf94 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | CVIU-CSU/M2MRF-Lesion-Segmentation | DiceLoss | false | 17,081 | [
"Apache-2.0"
] | 10 | 13af87927f4cdeca70e35d570edd1aec43b387b6 | https://github.com/CVIU-CSU/M2MRF-Lesion-Segmentation/tree/13af87927f4cdeca70e35d570edd1aec43b387b6 |
AvgReducePool1d | import torch
from torch import nn
class AvgReducePool1d(nn.Module):
"""A subclass of :torch_nn:`Module`.
Avg Pool layer for 1D inputs. The same as :torch_nn:`AvgPool1d` except that
the pooling dimension is entirely reduced (i.e., `pool_size=input_length`).
"""
def forward(self, input: 'torch.Tens... | 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... | ZhitingHu/texar-pytorch | AvgReducePool1d | false | 2,990 | [
"Apache-2.0"
] | 0 | 72ea115013ced8a5a2b004eacf6271184d3572a8 | https://github.com/ZhitingHu/texar-pytorch/tree/72ea115013ced8a5a2b004eacf6271184d3572a8 |
ChannelSELayer3D | import torch
import torch.nn as nn
class ChannelSELayer3D(nn.Module):
"""
3D extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
*Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
"""
def __init__(self, num_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | YilinLiu97/AmygNet-Pytorch | ChannelSELayer3D | false | 18,141 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 |
TorchFCNModel | import torch
class TorchFCNModel(torch.nn.Module):
def __init__(self, inputD, outputD, hiddenC=2, hiddenD=36):
super(TorchFCNModel, self).__init__()
self.device = torch.device('cuda:0' if torch.cuda.is_available() else
'cpu')
self.inputD, self.outputD = inputD, outputD
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | muratcancicek/pointer_head | TorchFCNModel | false | 12,809 | [
"MIT"
] | 0 | b2a357f0183d5ced82b6dc7f6f12e0391bdc7380 | https://github.com/muratcancicek/pointer_head/tree/b2a357f0183d5ced82b6dc7f6f12e0391bdc7380 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ricklentz/deep-reinforcement-learning | Actor | false | 4,192 | [
"MIT"
] | 0 | 4a034a955c64a630e0fd72f4380d81e2c25a4c68 | https://github.com/ricklentz/deep-reinforcement-learning/tree/4a034a955c64a630e0fd72f4380d81e2c25a4c68 |
HandTypeLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class HandTypeLoss(nn.Module):
def __init__(self):
super(HandTypeLoss, self).__init__()
def forward(self, hand_type_out, hand_type_gt, hand_type_valid):
loss = F.binary_cross_entropy(hand_type_out, han... | 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... | DuinoDu/InterHand2.6M.pl | HandTypeLoss | false | 5,093 | [
"MIT"
] | 1 | 2d216960cf95b066a197a9b49795840b1ecfd0c1 | https://github.com/DuinoDu/InterHand2.6M.pl/tree/2d216960cf95b066a197a9b49795840b1ecfd0c1 |
GlobalAvgPool2DBaseline | # 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | Bobholamovic/SimpleCV | GlobalAvgPool2DBaseline | false | 7,800 | [
"MIT"
] | 44 | f4edacf088d0155725a469e227de847820bdfa53 | https://github.com/Bobholamovic/SimpleCV/tree/f4edacf088d0155725a469e227de847820bdfa53 |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | import torch
import torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency(torch
.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency
, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | chethanpk/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 12,202 | [
"MIT"
] | 0 | c2435d24ecbeededf1dc50187ab3bd11ad4a6994 | https://github.com/chethanpk/onnxruntime/tree/c2435d24ecbeededf1dc50187ab3bd11ad4a6994 |
PostSynthesisProcessing | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | CSID-DGU/-2020-1-OSSP1-ninetynine-2 | PostSynthesisProcessing | false | 4,939 | [
"MIT"
] | 1 | b1824254882eeea0ee44e4e60896b72c51ef1d2c | https://github.com/CSID-DGU/-2020-1-OSSP1-ninetynine-2/tree/b1824254882eeea0ee44e4e60896b72c51ef1d2c |
SummaryNet_large | # 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_... | Wrede/BNN-LFI | SummaryNet_large | false | 2,971 | [
"MIT"
] | 0 | 8c5094f01c1eef286bdd84613c7259d534d2eb7e | https://github.com/Wrede/BNN-LFI/tree/8c5094f01c1eef286bdd84613c7259d534d2eb7e |
AdaIN | import torch
import torch.nn as nn
from math import sqrt
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | NethraGunti/Woven-Artificial-Profile-WARP-Face-Video-Synthesis-from-Profile-and-Audio | AdaIN | false | 883 | [
"MIT"
] | 0 | 231d8daa8dddfd5eda8a092eb99c5d0e59d8b3f7 | https://github.com/NethraGunti/Woven-Artificial-Profile-WARP-Face-Video-Synthesis-from-Profile-and-Audio/tree/231d8daa8dddfd5eda8a092eb99c5d0e59d8b3f7 |
QREmbeddingBag | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
import torch.nn as nn
from torch.nn.parameter import Paramet... | MrDoghead/dlrm | QREmbeddingBag | false | 865 | [
"MIT"
] | 0 | 9b0d8ea992daa515104c7967f30110684283ebb1 | https://github.com/MrDoghead/dlrm/tree/9b0d8ea992daa515104c7967f30110684283ebb1 |
DiceLoss | import functools
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AlexanderDokuchaev/mmsegmentation | DiceLoss | false | 11,190 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
RelLoss | # 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 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 |
Sub | # 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... | PogChamper/torch2trt | Sub | false | 14,221 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
Discriminator | import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
class Discriminator(nn.Module):
def __init__(self, img_shape, hidden_dim=1024):
super().__init__()
in_dim = int(np.prod(img_shape))
self.fc1 = nn.Linear(in_dim, hidden_dim)
self.fc2 = 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
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | Luab/pytorch-lightning-bolts | Discriminator | false | 11,722 | [
"Apache-2.0"
] | 0 | b8ac85154465956b06fd1005b21b071af5493f11 | https://github.com/Luab/pytorch-lightning-bolts/tree/b8ac85154465956b06fd1005b21b071af5493f11 |
Pooling | # 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 | Pooling | false | 4,271 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
SqueezeExcitation | import torch
from torch import Tensor
from torch import nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import *
from torch.optim import *
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel numbe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 Tensor
from... | Challyfilio/NAIC2021 | SqueezeExcitation | false | 247 | [
"MIT"
] | 0 | 11b38a920dcc902f9b798dc43ae360062862e6e4 | https://github.com/Challyfilio/NAIC2021/tree/11b38a920dcc902f9b798dc43ae360062862e6e4 |
SFU | import torch
import torch.nn as nn
import torch.nn.functional as F
class SFU(nn.Module):
"""Semantic Fusion Unit
The ouput vector is expected to not only retrieve correlative information from fusion vectors,
but also retain partly unchange as the input vector
"""
def __init__(self, input_size, fusion_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.triton_helpers import libdevice
import torch.nn as ... | hansd410/mnemonic | SFU | false | 3,577 | [
"BSD-3-Clause"
] | 0 | 409508d08da7f5d5940ffb56fd9715e6ef1e68a3 | https://github.com/hansd410/mnemonic/tree/409508d08da7f5d5940ffb56fd9715e6ef1e68a3 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | tjkemp/ubik-agent | Actor | false | 4,429 | [
"MIT"
] | 0 | 34e4dd0d6319b8f5c5dba0cd9e087490720b723b | https://github.com/tjkemp/ubik-agent/tree/34e4dd0d6319b8f5c5dba0cd9e087490720b723b |
GlobalMaxPool1d | # 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... | rlmwang/torch-tools | GlobalMaxPool1d | false | 10,801 | [
"MIT"
] | 0 | 822132534d73414f26045bad38a0a345661b057f | https://github.com/rlmwang/torch-tools/tree/822132534d73414f26045bad38a0a345661b057f |
ClassWisePool | import torch
from torch import nn
class ClassWisePool(nn.Module):
def __init__(self, num_maps):
super(ClassWisePool, self).__init__()
self.num_maps = num_maps
def forward(self, input):
batch_size, num_channels, s = input.size()
num_outputs = int(num_channels / self.num_maps)
... | 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... | Ecocytus/Roberta-ZeroShot-Label | ClassWisePool | false | 5,097 | [
"MIT"
] | 1 | 8a6d74187a0e2fd5b1b75549cfb724f54269c5a5 | https://github.com/Ecocytus/Roberta-ZeroShot-Label/tree/8a6d74187a0e2fd5b1b75549cfb724f54269c5a5 |
Synthesis_prior_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
import math
import torch.nn as nn
import torch.utils.data
assert_size_stride = t... | wemozj/Image-Compression-based-GMM-and-Attention-Module | Synthesis_prior_net | false | 4,549 | [
"Apache-2.0"
] | 0 | 93f804dbcea8ffc1621456f3d104d0342c75373b | https://github.com/wemozj/Image-Compression-based-GMM-and-Attention-Module/tree/93f804dbcea8ffc1621456f3d104d0342c75373b |
BoundaryDecoderAttention | import torch
def masked_softmax(x, m=None, axis=-1):
"""
Softmax with mask (optional)
"""
x = torch.clamp(x, min=-15.0, max=15.0)
if m is not None:
m = m.float()
x = x * m
e_x = torch.exp(x - torch.max(x, dim=axis, keepdim=True)[0])
if m is not None:
e_x = e_x * m
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | watchernyu/MatchLSTM-Analyze-Adversarial-Training | BoundaryDecoderAttention | false | 16,709 | [
"MIT"
] | 50 | 00bd33d3dd22d5291dc2c1ec5feef5eb93b59b3a | https://github.com/watchernyu/MatchLSTM-Analyze-Adversarial-Training/tree/00bd33d3dd22d5291dc2c1ec5feef5eb93b59b3a |
LatentAtten | # 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.... | AdityaLab/EpiFNP | LatentAtten | false | 4,799 | [
"MIT"
] | 1 | 476c7a40ee70fffb77b76c60c42a58adf82c62f6 | https://github.com/AdityaLab/EpiFNP/tree/476c7a40ee70fffb77b76c60c42a58adf82c62f6 |
Grayscale | # 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.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | TropComplique/WESPE | Grayscale | false | 18,018 | [
"MIT"
] | 5 | 84738f1ed802a3f6a4a0549677d8137997fac617 | https://github.com/TropComplique/WESPE/tree/84738f1ed802a3f6a4a0549677d8137997fac617 |
FM | import torch
import torch.nn as nn
from sklearn.metrics import *
import torch.onnx
import torch as torch
class FM(nn.Module):
"""Factorization Machine models pairwise (order-2) feature interactions
without linear term and bias.
Input shape
- 3D tensor with shape: ``(batch_size,field_size,embedd... | 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 sklearn.metrics import *
import torch.onnx
import torch as torch
assert_size_stride = torch._C._dynamo.guards.ass... | dulvqingyunLT/DeepCTR-Torch | FM | false | 10,317 | [
"Apache-2.0"
] | 0 | f40cf08f3469aa471f9ca69e44c5de51180341cc | https://github.com/dulvqingyunLT/DeepCTR-Torch/tree/f40cf08f3469aa471f9ca69e44c5de51180341cc |
StdConv2d | # 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... | marekb-sci/kaggle_cassava | StdConv2d | false | 12,750 | [
"Apache-2.0"
] | 0 | 158d1e398e713381c889e071329b96b9c0ba98d2 | https://github.com/marekb-sci/kaggle_cassava/tree/158d1e398e713381c889e071329b96b9c0ba98d2 |
SimpleMinModule | # 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.... | andreas-hommel/glow | SimpleMinModule | false | 3,342 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
CoFusion | # 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.... | cordob/DexiNed | CoFusion | false | 10,023 | [
"MIT"
] | 0 | 9e084652f8051155c98277c02eecefa927bfe04c | https://github.com/cordob/DexiNed/tree/9e084652f8051155c98277c02eecefa927bfe04c |
TwoLayerNet | import torch
class TwoLayerNet(torch.nn.Module):
"""
This class is copied from PyTorch's documentation and is meant to be the
simplest, non-trivial custom NN we can use for testing provenance.
See [here](https://pytorch.org/tutorials/beginner/examples_nn/two_layer_net_module.html#sphx-glr-beginner-exa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | benfogelson/provenance | TwoLayerNet | false | 3,204 | [
"MIT"
] | 0 | e61095e767e8786943ea76bef9b5dd6dd9575041 | https://github.com/benfogelson/provenance/tree/e61095e767e8786943ea76bef9b5dd6dd9575041 |
FeatureAssembler | import torch
from typing import Optional
import torch.nn as nn
import torch.nn
import torch.optim
class FeatureAssembler(nn.Module):
def __init__(self, T: 'int', embed_static: 'Optional[FeatureEmbedder]'=
None, embed_dynamic: 'Optional[FeatureEmbedder]'=None) ->None:
super().__init__()
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Optional
import torch.nn as nn
import torch.nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_siz... | RSNirwan/gluon-ts | FeatureAssembler | false | 5,737 | [
"Apache-2.0"
] | 1 | ae4cfdef539e49f93a87034aa2f2bec194c4b7d8 | https://github.com/RSNirwan/gluon-ts/tree/ae4cfdef539e49f93a87034aa2f2bec194c4b7d8 |
BoxFilter | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | allen0125/RobustVideoMatting | BoxFilter | false | 1,416 | [
"Apache-2.0"
] | 0 | c0f17ca45a9de7586c570753064187200dec487a | https://github.com/allen0125/RobustVideoMatting/tree/c0f17ca45a9de7586c570753064187200dec487a |
AvgPool2d | from torch.nn import Module
import torch
import torch as th
class AvgPool2d(Module):
"""
This class is the beginning of an exact python port of the torch.nn.AvgPool2d
module. Because PySyft cannot hook into layers which are implemented in C++,
our special functionalities (such as encrypted computation... | 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | NiWaRe/PySyft | AvgPool2d | false | 11,746 | [
"Apache-2.0"
] | 0 | b5abe66ea949d60be14a08d2e4e32e9587c7bf5c | https://github.com/NiWaRe/PySyft/tree/b5abe66ea949d60be14a08d2e4e32e9587c7bf5c |
ContextGate | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select 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
import torch.nn as nn
import torch.cuda
import torch.distributed
assert_size_str... | PiescesHusky/OpenNMT-py | ContextGate | false | 11,780 | [
"MIT"
] | 0 | 7276cf94f989c50b3169742f64e64142897d1ec0 | https://github.com/PiescesHusky/OpenNMT-py/tree/7276cf94f989c50b3169742f64e64142897d1ec0 |
Gather | import torch
import torch.nn as nn
class Gather(torch.nn.Module):
"""
gather
"""
@staticmethod
def modify_commandline_options(parser, is_train):
return parser
def __init__(self, F, K, use_mask=False):
super().__init__()
self.K = K
self.F = F
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jhp038/fashion_project | Gather | false | 3,718 | [
"MIT"
] | 0 | 719533dc60155801f567e6a9183d7a5036ee1166 | https://github.com/jhp038/fashion_project/tree/719533dc60155801f567e6a9183d7a5036ee1166 |
Actor | import torch
import torch.nn.functional as F
import torch.nn as nn
class Actor(torch.nn.Module):
def __init__(self, numObs, numActions):
super(Actor, self).__init__()
self.actor_input = nn.Linear(numObs, 32)
self.actor_fc1 = nn.Linear(32, 32)
self.actor_output = nn.Linear(32, numA... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | mpgussert/fundamentalRL | Actor | false | 7,276 | [
"MIT"
] | 1 | 4f45436226e0823c21cac316dec8bbf1df697467 | https://github.com/mpgussert/fundamentalRL/tree/4f45436226e0823c21cac316dec8bbf1df697467 |
GATLayer | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
class GATLayer(Module):
def __init__(self, input_channel, output_channel, use_bias=True):
super(GATLayer, self).__init__()
self.use_bias = u... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | chengsilin/Graph_model | GATLayer | false | 1,680 | [
"MIT"
] | 0 | 0d9714a8b02196fabf5b0ecd0680b7269a22c53b | https://github.com/chengsilin/Graph_model/tree/0d9714a8b02196fabf5b0ecd0680b7269a22c53b |
Mse | from torch.nn import Module
import torch
class Mse(Module):
def forward(self, inp, targ):
return (inp.squeeze() - targ).pow(2).mean()
def bwd(self, out, inp, targ):
inp.g = 2 * (inp.squeeze() - targ).unsqueeze(-1) / targ.shape[0]
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | Akramz/Impractical-DL | Mse | false | 11,153 | [
"MIT"
] | 0 | ff909e369fb765c0857800925e39c433057ae8ac | https://github.com/Akramz/Impractical-DL/tree/ff909e369fb765c0857800925e39c433057ae8ac |
CanineAttention | from _paritybench_helpers import _mock_config
import math
import torch
from typing import List
from typing import Tuple
from torch import nn
from typing import Set
import torch.utils.checkpoint
def find_pruneable_heads_and_indices(heads: 'List[int]', n_heads: 'int',
head_size: 'int', already_pruned_heads: 'Set[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.... | Clemens123/transformers | CanineAttention | false | 13,225 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
GeLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.... | AsmitaBhat30/lxmert | GeLU | false | 4,865 | [
"MIT"
] | 1 | 90292dc36a25c04c4f76fe9119e3141d5dc05874 | https://github.com/AsmitaBhat30/lxmert/tree/90292dc36a25c04c4f76fe9119e3141d5dc05874 |
Reorg | import torch
import torch.nn as nn
class Reorg(nn.Module):
def __init__(self, stride=2):
super(Reorg, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(1)
H = x.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | CoDaS-Lab/Contextual-Adversarial-Patches | Reorg | false | 2,102 | [
"MIT"
] | 0 | ffbd897174fc381ba7c3ba1e6f827b84ccb30fd4 | https://github.com/CoDaS-Lab/Contextual-Adversarial-Patches/tree/ffbd897174fc381ba7c3ba1e6f827b84ccb30fd4 |
GELU | import torch
import torch.nn as nn
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, x):
return torch.sigmoid(1.702 * x) * 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... | ChurchChen/SparsityRegularization | GELU | false | 8,924 | [
"Apache-2.0"
] | 0 | 5c2e050ffe511cf4307a0bcd98360d28b7db8fef | https://github.com/ChurchChen/SparsityRegularization/tree/5c2e050ffe511cf4307a0bcd98360d28b7db8fef |
SingleHead | import torch
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
class SingleHead(nn.Module):
"""
Single head used in CenterNet Head.
"""
def __init__(self, in_channel, out_channel, bias_fill=False, bias_value=0):
super(SingleHe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | WFDetector/WFDetection | SingleHead | false | 2,954 | [
"Apache-2.0"
] | 0 | b16d35b3a3a5de62de9e0bac83eccd21b6358b53 | https://github.com/WFDetector/WFDetection/tree/b16d35b3a3a5de62de9e0bac83eccd21b6358b53 |
FiLM | # 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... | CPJKU/audio_conditioned_unet | FiLM | false | 7,821 | [
"MIT"
] | 20 | 68f20f5280079e99be260f9fe9933c0064eb2d7f | https://github.com/CPJKU/audio_conditioned_unet/tree/68f20f5280079e99be260f9fe9933c0064eb2d7f |
TactileWeightModel | import torch
import torch.utils.data
import torch.nn as nn
from typing import Optional
import torch.linalg
class TactileWeightModel(nn.Module):
def __init__(self, device: 'torch.device', dim: 'int'=3, wt_init:
'Optional[torch.Tensor]'=None):
super().__init__()
wt_init_ = torch.rand(1, dim... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
from typing import Optional
import torch.linalg
assert_size_stride = torch._C._dynamo.guards.a... | jeffin07/theseus | TactileWeightModel | false | 15,676 | [
"MIT"
] | 236 | 3498bbddf9cca740c2703d0c1aa3a78a7264cb15 | https://github.com/jeffin07/theseus/tree/3498bbddf9cca740c2703d0c1aa3a78a7264cb15 |
MemoryReader | # 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.... | hzxie/RMNet | MemoryReader | false | 15,570 | [
"MIT"
] | 66 | 32a16f9c9473463a41dd6e95f72b06dd830fc1eb | https://github.com/hzxie/RMNet/tree/32a16f9c9473463a41dd6e95f72b06dd830fc1eb |
ConvGRUCell | import torch
from torch import nn as nn
import torch.nn.functional as F
def one_param(m):
"""First parameter in `m`"""
return next(m.parameters())
class ConvGRUCell(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size=(3, 3), bias=True,
activation=F.tanh, batchnorm=False):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | ValterFallenius/metnet | ConvGRUCell | false | 9,667 | [
"MIT"
] | 0 | 7cde48a7b5fc0b69a8ce9083f934949362620fd5 | https://github.com/ValterFallenius/metnet/tree/7cde48a7b5fc0b69a8ce9083f934949362620fd5 |
ToLongTensor | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Hirni-Meshram3/text | ToLongTensor | false | 5,333 | [
"BSD-3-Clause"
] | 1 | 84e6c7bd99c7fb3c229ff289aa722149e3136094 | https://github.com/Hirni-Meshram3/text/tree/84e6c7bd99c7fb3c229ff289aa722149e3136094 |
TextureLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | qwopqwop200/Fast-Invertible-Rescaling-Net | TextureLoss | false | 7,522 | [
"MIT"
] | 1 | 871733f2eee7929d6b37c4d1d6a27347b39b67a9 | https://github.com/qwopqwop200/Fast-Invertible-Rescaling-Net/tree/871733f2eee7929d6b37c4d1d6a27347b39b67a9 |
ModulatedConv2d | from torch.autograd import Function
import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
def make_kernel(k):
k = torch.tensor(k, dtype=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | DeepVoodooFX/pixel2style2pixel | ModulatedConv2d | false | 11,351 | [
"Apache-2.0",
"BSD-2-Clause",
"MIT"
] | 0 | 0254c32400d55f7e400ead15b02ad6a992ba1e21 | https://github.com/DeepVoodooFX/pixel2style2pixel/tree/0254c32400d55f7e400ead15b02ad6a992ba1e21 |
CumulativeMagSpectralNorm | # 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... | intflow/FullSubNet | CumulativeMagSpectralNorm | false | 12,538 | [
"MIT"
] | 0 | 193091acac4c747730db5ace33fd1b8870e7c735 | https://github.com/intflow/FullSubNet/tree/193091acac4c747730db5ace33fd1b8870e7c735 |
mySBlock | import torch
import torch.nn as nn
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn import InstanceNorm2d
class mySConv(nn.Module):
def __init__(self, num_filter=128, stride=1, in_channels=128):
super(mySConv, self).__init__()
self.conv = Conv2d(out_channels=num_filter, kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | junhocho/ShapeMatchingGAN | mySBlock | false | 15,782 | [
"MIT"
] | 117 | b90e9c2490bfdf62c5da9b1eb6b0cdf0618cf570 | https://github.com/junhocho/ShapeMatchingGAN/tree/b90e9c2490bfdf62c5da9b1eb6b0cdf0618cf570 |
EqualConv2d | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
class EqualConv2d(nn.Module):
"""Equalized Linear as StyleGAN2.
Args:
in_channels (int): Channel number of the input.
out_channels (int): Channel number of the output.
kernel_size (int): Size of the co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | ArdWang/GFPGAN | EqualConv2d | false | 11,246 | [
"BSD-3-Clause"
] | 0 | f984ec32754190fad0b9b7a60d372aac84e57173 | https://github.com/ArdWang/GFPGAN/tree/f984ec32754190fad0b9b7a60d372aac84e57173 |
DiceLoss | import torch
from torch import nn
from torch.nn import functional as F
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, output, target):
prediction = F.sigmoid(output)
intersection = torch.sum(prediction * target)
union = torch... | 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... | BloodAxe/segmentation-networks-benchmark | DiceLoss | false | 7,872 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | tjkemp/ubik-agent | Critic | false | 4,434 | [
"MIT"
] | 0 | 34e4dd0d6319b8f5c5dba0cd9e087490720b723b | https://github.com/tjkemp/ubik-agent/tree/34e4dd0d6319b8f5c5dba0cd9e087490720b723b |
Conv2d | import math
import torch
import torch.nn.functional
import torch.backends.cudnn
class Conv2d(torch.nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=False):
super().__init__(in_channels, out_channels, kernel_size, stride, 0,
di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional
import torch.backends.cudnn
assert_size_stride = torc... | xolbynz/EfficientNetV2-PyTorch- | Conv2d | false | 13,117 | [
"Apache-2.0"
] | 0 | 4b5039755adbd0e5f8ee0611e3d6b5be8c13ecd2 | https://github.com/xolbynz/EfficientNetV2-PyTorch-/tree/4b5039755adbd0e5f8ee0611e3d6b5be8c13ecd2 |
TimeIntervalTransformerLayer | # 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.... | nmrenyi/ReChorus | TimeIntervalTransformerLayer | false | 16,208 | [
"MIT"
] | 314 | 9ab632579d0464b0aaf365539f87b04866920b66 | https://github.com/nmrenyi/ReChorus/tree/9ab632579d0464b0aaf365539f87b04866920b66 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Axrid/cv_template | TVLoss | false | 13,335 | [
"MIT"
] | 69 | 5c344692a1fcfb08b75d7104bcc78307b5640ecf | https://github.com/Axrid/cv_template/tree/5c344692a1fcfb08b75d7104bcc78307b5640ecf |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, focusing_param=2, balance_param=0.25):
super(FocalLoss, self).__init__()
self.focusing_param = focusing_param
self.balance_param = balance_param
def forward(self, 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 math as tl_math
import torch.nn as nn
... | zwx8981/DBCNN-Pytorch | FocalLoss | false | 16,829 | [
"MIT"
] | 150 | 16c3156054a30a3eabb45dffcf538f42452a14f3 | https://github.com/zwx8981/DBCNN-Pytorch/tree/16c3156054a30a3eabb45dffcf538f42452a14f3 |
ColorJitterLayer | # 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.... | Jinoh-Cho/Visual-Genome-Image-Inpainting | ColorJitterLayer | false | 9,226 | [
"MIT"
] | 0 | f8c43bf2e4a9139d4c35903d0c323b9d8eb54859 | https://github.com/Jinoh-Cho/Visual-Genome-Image-Inpainting/tree/f8c43bf2e4a9139d4c35903d0c323b9d8eb54859 |
Conv3x3 | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = nn.ZeroPad2d(1)
self.conv = nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | ArminMasoumian/GCNDepth | Conv3x3 | false | 7,730 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
Whitening2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | xwyzsn/solo-learn | Whitening2d | false | 16,750 | [
"MIT"
] | 693 | 16d021d8053439a3de205337ab2a11d191500b09 | https://github.com/xwyzsn/solo-learn/tree/16d021d8053439a3de205337ab2a11d191500b09 |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | greenstar1151/pytorch-benchmark | PositionwiseFeedForward | false | 10,453 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b |
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.... | and-smith/Vac-Scholar-Curb-GAN | MultiHeadAttention | false | 6,213 | [
"MIT"
] | 1 | 142bd70fdf0f1cbc4a1c20c5e58fa5b6a9dbe742 | https://github.com/and-smith/Vac-Scholar-Curb-GAN/tree/142bd70fdf0f1cbc4a1c20c5e58fa5b6a9dbe742 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Net(nn.Module):
def __init__(self, num_rej=0):
super(Net, self).__init__()
self.num_rej = num_rej + 1
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | NlGG/Rejection | Net | false | 929 | [
"MIT"
] | 0 | 5f7cc64b71dacc2eb794b3f7c48390457e363cc5 | https://github.com/NlGG/Rejection/tree/5f7cc64b71dacc2eb794b3f7c48390457e363cc5 |
ATRCell | # 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 ... | Avmb/lm-robustness | ATRCell | false | 84 | [
"BSD-3-Clause"
] | 0 | b5417d9aac01bff0d2a56b506eabed899fd718d4 | https://github.com/Avmb/lm-robustness/tree/b5417d9aac01bff0d2a56b506eabed899fd718d4 |
GeM | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter
def gem(x, p=3, eps=1e-06):
return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(
1.0 / p)
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06):
super(G... | 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... | Chizuchizu/riadd | GeM | false | 4,995 | [
"MIT"
] | 1 | c3f55aebc0f582d9fa55dc517b1489963cf0506f | https://github.com/Chizuchizu/riadd/tree/c3f55aebc0f582d9fa55dc517b1489963cf0506f |
GatSymAttention | # 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.functional as F
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_siz... | AlexMinhao/NAS_GNN | GatSymAttention | false | 19 | [
"Apache-2.0"
] | 0 | 89183988a96e1d6baed910ab3843c13282f8b077 | https://github.com/AlexMinhao/NAS_GNN/tree/89183988a96e1d6baed910ab3843c13282f8b077 |
ConvReluPool | # 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_... | NeuralMMO/baselines | ConvReluPool | false | 17,752 | [
"MIT"
] | 7 | 407004cfd0c0959b871a982adf49e4fe667df8de | https://github.com/NeuralMMO/baselines/tree/407004cfd0c0959b871a982adf49e4fe667df8de |
GATgate_lp | # 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... | arwhirang/affinity_prediction_BGNN | GATgate_lp | false | 9,747 | [
"MIT"
] | 0 | b8a2a5de16a61a46dadd53856d758e7f63f9ca91 | https://github.com/arwhirang/affinity_prediction_BGNN/tree/b8a2a5de16a61a46dadd53856d758e7f63f9ca91 |
Block | import math
import torch
import torch.nn as nn
def gelu(x):
""" Original Implementation of the gelu activation function in Google Bert repo when initialy created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SpringWave1/AutoGAN | Block | false | 9,648 | [
"MIT"
] | 0 | 209bd01b02f15847bd342d4019f87aef5440bda8 | https://github.com/SpringWave1/AutoGAN/tree/209bd01b02f15847bd342d4019f87aef5440bda8 |
CAM_Module | # 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.... | HUuxiaobin/Face-Super-Resolution-Guided-by-3D-Facial-Priors | CAM_Module | false | 8,208 | [
"MIT"
] | 29 | 987e7c74d33d26cc5e9d1c0e395a06519a31792f | https://github.com/HUuxiaobin/Face-Super-Resolution-Guided-by-3D-Facial-Priors/tree/987e7c74d33d26cc5e9d1c0e395a06519a31792f |
Dice_Loss | # 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... | JoaoCarv/holistic_seg | Dice_Loss | false | 656 | [
"MIT"
] | 0 | ea4787e7e9a36dc5caf198d2be1bd1e71c06d440 | https://github.com/JoaoCarv/holistic_seg/tree/ea4787e7e9a36dc5caf198d2be1bd1e71c06d440 |
PointwiseFeedForward | # 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_... | fhamborg/NewsMTSC | PointwiseFeedForward | false | 15,348 | [
"MIT"
] | 46 | 5a8f88d7fbb921090e984cc378b02d75524c1025 | https://github.com/fhamborg/NewsMTSC/tree/5a8f88d7fbb921090e984cc378b02d75524c1025 |
ExponentialLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.... | HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping | ExponentialLoss | false | 17,367 | [
"MIT"
] | 4 | 1e2dee8d6d1f97722eba91618462537faf9efba7 | https://github.com/HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping/tree/1e2dee8d6d1f97722eba91618462537faf9efba7 |
SpatialDepthWiseSharedConvolution | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class SpatialDepthWiseSharedConvolution(Module):
"""
## Spatial Depth Wise Shared Convolution
We share the same kernel across all channels.
"""
def __init__(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import torch.utils.data
import ... | techthiyanes/annotated_deep_learning_paper_implementations | SpatialDepthWiseSharedConvolution | false | 16,565 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
WeightedBCEFocalLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class WeightedBCEFocalLoss(nn.Module):
"""Weighted binary focal loss with logits.
"""
def __init__(self, gamma=2.0, alpha=0.25, eps=0.0):
super().__init__()
self.eps = eps
... | 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... | devaansh100/pytorch_connectomics | WeightedBCEFocalLoss | false | 6,557 | [
"MIT"
] | 1 | b1e4b16b0480546ea806d14876208080815ed964 | https://github.com/devaansh100/pytorch_connectomics/tree/b1e4b16b0480546ea806d14876208080815ed964 |
NetVLAD | import torch
import torch.nn.functional as func
import torch.nn as nn
class NetVLAD(nn.Module):
"""
NetVLAD layer implementation
Credits: https://github.com/lyakaap/NetVLAD-pytorch
"""
def __init__(self, num_clusters=64, dim=128, alpha=100.0,
normalize_input=True):
"""
Arg... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | liuyuzhenn/LISRD | NetVLAD | false | 15,961 | [
"MIT"
] | 225 | bfd890b81defebea971db0b744be617ed58f5ffa | https://github.com/liuyuzhenn/LISRD/tree/bfd890b81defebea971db0b744be617ed58f5ffa |
GaussianActorNet | import torch
import numpy as np
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class BasicNet:
def __init__(self, optimizer_fn, gpu, LSTM=False):
self.gpu = gpu and torch.cuda.is_available()
self.LSTM = LSTM
if self.gpu:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | G-Flor/deeprl | GaussianActorNet | false | 5,184 | [
"Apache-2.0"
] | 1 | aeae2c5d585e5853dc638968b1f090eb60abd351 | https://github.com/G-Flor/deeprl/tree/aeae2c5d585e5853dc638968b1f090eb60abd351 |
CustomGruCell | import torch
import numpy as np
import torch.nn as nn
class CustomGruCell(nn.Module):
"""
A forward only GRU cell.
Input should be: (sequence length x batch size x input_size).
The output is the output of the final forward call.
It's not clear if it would be possible to use the output from each ce... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | NiWaRe/PySyft | CustomGruCell | false | 11,750 | [
"Apache-2.0"
] | 0 | b5abe66ea949d60be14a08d2e4e32e9587c7bf5c | https://github.com/NiWaRe/PySyft/tree/b5abe66ea949d60be14a08d2e4e32e9587c7bf5c |
PixelNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | HXWAndCL/mmgeneration | PixelNorm | false | 5,243 | [
"Apache-2.0"
] | 1 | 9afb1d740bf56a4ecde5064d5bb2a4e2d777638b | https://github.com/HXWAndCL/mmgeneration/tree/9afb1d740bf56a4ecde5064d5bb2a4e2d777638b |
Attention | import math
import torch
import torch.nn as nn
class Attention(nn.Module):
"""
Using two types of attention mechanism: "Dot" and "Bahdanau"
"""
def __init__(self, hidden_size, use_tanh=False, C=10, name='Bahdanau',
use_cuda=True):
super(Attention, self).__init__()
self.use... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Lance0226/CIS700_Convex_Hull_RL | Attention | false | 2,502 | [
"MIT"
] | 0 | 3c87e063209d535d75fde719bf17f20dd5e68635 | https://github.com/Lance0226/CIS700_Convex_Hull_RL/tree/3c87e063209d535d75fde719bf17f20dd5e68635 |
ActorDiscrete | import torch
import torch as t
import torch.nn as nn
class ActorDiscrete(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, action_dim)
def forward(self, state):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ikamensh/machin | ActorDiscrete | false | 6,859 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
WaveNetLayer | # 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
assert_size_stride ... | brentspell/hifi-gan-bwe | WaveNetLayer | false | 1,590 | [
"MIT"
] | 0 | 63579ac8055c63fc0e5a20ae90e2a86575fc8e12 | https://github.com/brentspell/hifi-gan-bwe/tree/63579ac8055c63fc0e5a20ae90e2a86575fc8e12 |
MinusRbfHSIC | import torch
import torch.nn as nn
import torch.utils.data
class HSIC(nn.Module):
"""Base class for the finite sample estimator of Hilbert-Schmidt Independence Criterion (HSIC)
..math:: HSIC (X, Y) := || C_{x, y} ||^2_{HS}, where HSIC (X, Y) = 0 iif X and Y are independent.
Empirically, we use the finite... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | SanghyukChun/rebias | MinusRbfHSIC | false | 14,382 | [
"MIT"
] | 129 | 6a4f6abdd68e080a08737d93a3c4b43e0f0ce055 | https://github.com/SanghyukChun/rebias/tree/6a4f6abdd68e080a08737d93a3c4b43e0f0ce055 |
AvgPool2d | # 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... | collector-m/LiDAR-MOS | AvgPool2d | false | 15,063 | [
"MIT"
] | 268 | 7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 | https://github.com/collector-m/LiDAR-MOS/tree/7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 |
MyLoss | # 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards... | Dora-The-Kid/culture_network | MyLoss | false | 2,159 | [
"Apache-2.0"
] | 0 | bc2bac86e821faa797eeb2670d179395724f7922 | https://github.com/Dora-The-Kid/culture_network/tree/bc2bac86e821faa797eeb2670d179395724f7922 |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self, n_channels, scale=1.0):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.scale = scale
self.eps = 1e-10
self.weight = nn.Parameter(torch.Tensor(self.n_channels))
self.wei... | 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_... | bluan2019/face-alignment | L2Norm | false | 9,889 | [
"BSD-3-Clause"
] | 0 | 9e256b18a02c7bd924a88c1203fb875853263336 | https://github.com/bluan2019/face-alignment/tree/9e256b18a02c7bd924a88c1203fb875853263336 |
MaxPool2d | # 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... | yifanpu001/PytorchToCaffe | MaxPool2d | false | 4,717 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
ComplexConv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils
assert_size_stride = torch._C._dynamo.gu... | muqiaoy/dl_signal | ComplexConv1d | false | 16,119 | [
"MIT"
] | 54 | 3a30d14982016644bfc96a7d1ca0109b441f17fd | https://github.com/muqiaoy/dl_signal/tree/3a30d14982016644bfc96a7d1ca0109b441f17fd |
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