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 |
|---|---|---|---|---|---|---|---|---|---|---|
ModifiedSmoothL1Loss | import torch
import torch.nn as nn
import torch.utils.data
class ModifiedSmoothL1Loss(nn.Module):
def __init__(self, L1_regression_alpha: 'float'):
super(ModifiedSmoothL1Loss, self).__init__()
self.alpha = L1_regression_alpha
def forward(self, normed_targets: 'torch.Tensor', pos_reg: '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.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch.... | AIpakchoi/visualDet3D | ModifiedSmoothL1Loss | false | 4,777 | [
"Apache-2.0"
] | 1 | 920f6f8ea44eac4c1896b7d157c015e039ac39f9 | https://github.com/AIpakchoi/visualDet3D/tree/920f6f8ea44eac4c1896b7d157c015e039ac39f9 |
LearnedPositionalEmbeddings | # 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.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert_size_stride... | techthiyanes/annotated_deep_learning_paper_implementations | LearnedPositionalEmbeddings | false | 16,592 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
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
from torch._inductor.runtime.... | Holmes-Alan/Photo2Sketch | ResidualBlock | false | 564 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
AconC | import torch
import torch.nn as nn
class AconC(nn.Module):
""" ACON activation (activate or not).
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"""
def __i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Lalihoo/yolov5-detect | AconC | false | 9,617 | [
"MIT"
] | 0 | 265c3137ea3586d913541501a1562488fbe59e9e | https://github.com/Lalihoo/yolov5-detect/tree/265c3137ea3586d913541501a1562488fbe59e9e |
BasicConvTestModel | # 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
from torchvision import models as models
import torch.nn.pa... | JinYAnGHe/openvino_training_extensions | BasicConvTestModel | false | 2,709 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee |
_UpsampleLinear | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | cestcedric/TSSR-GAN | _UpsampleLinear | false | 1,657 | [
"BSD-2-Clause",
"MIT"
] | 0 | d6e1b50409e0f0591660552993e6d5b70d41e766 | https://github.com/cestcedric/TSSR-GAN/tree/d6e1b50409e0f0591660552993e6d5b70d41e766 |
CoralLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | CHNxindong/corn-ordinal-neuralnet | CoralLayer | false | 4,933 | [
"MIT"
] | 1 | 7f8a45614cb6488e9c019c5e9d3a5aee0d714e94 | https://github.com/CHNxindong/corn-ordinal-neuralnet/tree/7f8a45614cb6488e9c019c5e9d3a5aee0d714e94 |
Flatten | import torch
from torch import nn
class Flatten(nn.Module):
def __init__(self):
super(Flatten, self).__init__()
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [batch_size, c, h, w].
Returns:
a float tensor with shape [batch_size, c*h... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | CTPLab/IID_representation_learning | Flatten | false | 4,944 | [
"MIT"
] | 1 | b9dc13536963f9af332b039f7cc772e2f1090c62 | https://github.com/CTPLab/IID_representation_learning/tree/b9dc13536963f9af332b039f7cc772e2f1090c62 |
AvgReadout | # 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... | BUPT-GAMMA/OpenHGNN | AvgReadout | false | 13,369 | [
"Apache-2.0"
] | 235 | 5f218dad4ed1415aa6d842bc20785c61e74e5405 | https://github.com/BUPT-GAMMA/OpenHGNN/tree/5f218dad4ed1415aa6d842bc20785c61e74e5405 |
BCEBlurWithLogitsLoss | import torch
import torch.nn as nn
class BCEBlurWithLogitsLoss(nn.Module):
def __init__(self, alpha=0.05):
super().__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
loss = self.loss_fcn(pred, true)
pred... | 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... | GoalballAnalysis/GUI | BCEBlurWithLogitsLoss | false | 2,309 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
ResBlock | # 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_... | githubhjx/Deep-Learning- | ResBlock | false | 12,443 | [
"Apache-2.0"
] | 0 | 5a22fb5696d930ed334aa1cbf2b213956b1c7026 | https://github.com/githubhjx/Deep-Learning-/tree/5a22fb5696d930ed334aa1cbf2b213956b1c7026 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, _input_size: 'int', _output_size: 'int',
_hidden_layers: 'int', _hidden_size: 'int'):
super(Net, self).__init__()
self.input = nn.Linear(_input_size, _hidden_size)
self.hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | hantonelli/AprendizajePorRefuerzos | Net | false | 12,486 | [
"MIT"
] | 0 | eeffa4aa36fa5c14739206e4c4bd0a1bd76f6af1 | https://github.com/hantonelli/AprendizajePorRefuerzos/tree/eeffa4aa36fa5c14739206e4c4bd0a1bd76f6af1 |
SoftDiceLoss | import torch
from torch.nn.modules.loss import _Loss
class SoftDiceLoss(_Loss):
def __init__(self, size_average=None, reduce=None, reduction='mean'):
super(SoftDiceLoss, self).__init__(size_average, reduce, reduction)
def forward(self, y_pred, y_gt):
numerator = torch.sum(y_pred * y_gt)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | purbayankar/pytorch-UNet | SoftDiceLoss | false | 16,288 | [
"MIT"
] | 91 | 63183199b1cf4e23a37869d30fc335e484c0c0fe | https://github.com/purbayankar/pytorch-UNet/tree/63183199b1cf4e23a37869d30fc335e484c0c0fe |
TestNet | # 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... | TuZehai/pytorch_stoi | TestNet | false | 14,515 | [
"MIT"
] | 45 | ae58e3ef4d608fc367e522150f48c58f122716fd | https://github.com/TuZehai/pytorch_stoi/tree/ae58e3ef4d608fc367e522150f48c58f122716fd |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | EpiSci/SoCRATES | Attention | false | 17,250 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d |
SelfGating | # 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... | Tiamat-Tech/just-ask | SelfGating | false | 14,492 | [
"Apache-2.0"
] | 59 | 80725161e12ad0682b4c2091f61a5889a335ba21 | https://github.com/Tiamat-Tech/just-ask/tree/80725161e12ad0682b4c2091f61a5889a335ba21 |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | ExamDay/NeuralGREWT | MLP | false | 6,474 | [
"MIT"
] | 1 | 2256eb8c88f410bf5a229911f299b216153c96ba | https://github.com/ExamDay/NeuralGREWT/tree/2256eb8c88f410bf5a229911f299b216153c96ba |
Encoder | import torch
import torch.nn as nn
class Encoder(nn.Module):
def __init__(self, in_size, latent_size):
super().__init__()
self.linear1 = nn.Linear(in_size, int(in_size / 2))
self.linear2 = nn.Linear(int(in_size / 2), int(in_size / 4))
self.linear3 = nn.Linear(int(in_size / 4), lat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | hcgcarry/usad | Encoder | false | 3,594 | [
"BSD-3-Clause"
] | 0 | 4e99a6acd43ef109be4d89b80e96978b9ad61c2f | https://github.com/hcgcarry/usad/tree/4e99a6acd43ef109be4d89b80e96978b9ad61c2f |
topk_PAM_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.... | yougoforward/OCNet1931 | topk_PAM_Module | false | 11,034 | [
"MIT"
] | 0 | e679e9f248aff2f06e1d983e4e30230e5fc5174f | https://github.com/yougoforward/OCNet1931/tree/e679e9f248aff2f06e1d983e4e30230e5fc5174f |
MovingAverage | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Gandor26/cacovid | MovingAverage | false | 2,279 | [
"MIT"
] | 0 | 2b966579dba1eac6e33f439515de6de9e802d08a | https://github.com/Gandor26/cacovid/tree/2b966579dba1eac6e33f439515de6de9e802d08a |
ReconstructLoss | import torch
import torch.nn as nn
class ReconstructLoss(nn.Module):
def __init__(self):
super(ReconstructLoss, self).__init__()
self.criterion = nn.L1Loss()
def forward(self, x, y):
loss = self.criterion(x, y)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | DevKiHyun/SRNTT.pytorch | ReconstructLoss | false | 5,064 | [
"MIT"
] | 1 | d7540921983cf42ea2a7eef544862a95318e6a35 | https://github.com/DevKiHyun/SRNTT.pytorch/tree/d7540921983cf42ea2a7eef544862a95318e6a35 |
Hardswish | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hardswish(nn.Module):
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Aditya239233/MDP | Hardswish | false | 16,893 | [
"MIT"
] | 4 | 87491e1d67e547c11f4bdd5d784d120473429eae | https://github.com/Aditya239233/MDP/tree/87491e1d67e547c11f4bdd5d784d120473429eae |
BasicModel_ConvNet | # 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.... | aravipati12/captum | BasicModel_ConvNet | false | 10,123 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
Upsample | # 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... | GastonMazzei/escher-project-website | Upsample | false | 17,296 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
SplitAndConcat | # 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.quantization.quantize_fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | petoor/d2go | SplitAndConcat | false | 10,663 | [
"Apache-2.0"
] | 0 | d0a20d048738f447945d7c948a8d3019a110d2e8 | https://github.com/petoor/d2go/tree/d0a20d048738f447945d7c948a8d3019a110d2e8 |
HLoss | # 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.utils.dat... | DanIulian/minigrid_rl | HLoss | false | 341 | [
"MIT"
] | 0 | d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa | https://github.com/DanIulian/minigrid_rl/tree/d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa |
Attention | import torch
from torch import nn
class Attention(nn.Module):
"""
Attention Network.
"""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
"""
:param encoder_dim: feature size of encoded images
:param decoder_dim: size of decoder's RNN
:param attention_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.... | AlexMeinke/serverless-hosting-of-image-captioning | Attention | false | 11,174 | [
"MIT"
] | 0 | 2b539561ac600e6a502ac4ecb25948a50e26cc54 | https://github.com/AlexMeinke/serverless-hosting-of-image-captioning/tree/2b539561ac600e6a502ac4ecb25948a50e26cc54 |
Mlp | import math
import torch
import torch.utils.data
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... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | denisleonov/pytorch-CycleGAN-and-pix2pix | Mlp | false | 12,266 | [
"BSD-3-Clause"
] | 0 | d1a5f0c5911f70ed896f826619b4067ce737a83d | https://github.com/denisleonov/pytorch-CycleGAN-and-pix2pix/tree/d1a5f0c5911f70ed896f826619b4067ce737a83d |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
def focal_loss(input_values, gamma):
"""Computes the focal loss"""
p = torch.exp(-input_values)
loss = (1 - p) ** gamma * input_values
return loss.mean()
class Focal... | 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
... | Jianf-Wang/RSG | FocalLoss | false | 13,901 | [
"MIT"
] | 108 | 3c5074511455428d81af89e1621493dcdb5db6ce | https://github.com/Jianf-Wang/RSG/tree/3c5074511455428d81af89e1621493dcdb5db6ce |
AdaptiveConcatPool2d | import torch
import torch.nn as nn
class AdaptiveConcatPool2d(nn.Module):
"""
Pools with AdaptiveMaxPool2d AND AdaptiveAvgPool2d and concatenates both
results.
Args:
target_size: the target output size (single integer or
double-integer tuple)
"""
def __init__(self, target... | 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... | Vermeille/Torchelie | AdaptiveConcatPool2d | false | 14,543 | [
"MIT"
] | 117 | 43957d83238372ae6436aac90127865c2040b76c | https://github.com/Vermeille/Torchelie/tree/43957d83238372ae6436aac90127865c2040b76c |
InfoNCE_loss_vectorized | import torch
import torch.nn as nn
class InfoNCE_loss_vectorized(nn.Module):
"""
SimCLR loss: https://github.com/google-research/simclr // https://github.com/sthalles/SimCLR
"""
def __init__(self, temperature):
super(InfoNCE_loss_vectorized, self).__init__()
self.temperature = tem... | 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... | LIIR-KULeuven/CLDR_CLNER_models | InfoNCE_loss_vectorized | false | 8,429 | [
"MIT"
] | 12 | 5fe47a988b88a36d0ccf4484aff5ab70c59f39d6 | https://github.com/LIIR-KULeuven/CLDR_CLNER_models/tree/5fe47a988b88a36d0ccf4484aff5ab70c59f39d6 |
Align | # 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.functional as F
assert_size_stride = torch._C._dynamo.guards.as... | TMUITLab/EAFR | Align | false | 1,108 | [
"MIT"
] | 0 | dadb6485d48711ccb8aa2f03760aeb437645f1ff | https://github.com/TMUITLab/EAFR/tree/dadb6485d48711ccb8aa2f03760aeb437645f1ff |
Autoencoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | SpaceMeerkat/CAE | Autoencoder | false | 5,868 | [
"MIT"
] | 1 | 8c5e2fbe751810a87ca155d0e3d53797f52fd9ea | https://github.com/SpaceMeerkat/CAE/tree/8c5e2fbe751810a87ca155d0e3d53797f52fd9ea |
InversePose | # 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... | Wizaron/torchgeometry | InversePose | false | 5,987 | [
"Apache-2.0"
] | 1 | 59a8d25dd811ded6a139d5c0c2442b06f43dc775 | https://github.com/Wizaron/torchgeometry/tree/59a8d25dd811ded6a139d5c0c2442b06f43dc775 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | HumberMe/mmclassification | FocalLoss | false | 555 | [
"Apache-2.0"
] | 0 | 68f1542068d3af4db932c97e6a728181432fff0c | https://github.com/HumberMe/mmclassification/tree/68f1542068d3af4db932c97e6a728181432fff0c |
MaxMinGroup | # 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... | Schwartz-Zha/My-invertible-resnet | MaxMinGroup | false | 1,028 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
ReduceMaxModule | import torch
class ReduceMaxModule(torch.nn.Module):
def __init__(self):
super(ReduceMaxModule, self).__init__()
def forward(self, x):
return torch.max(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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | mirecta/nncase | ReduceMaxModule | false | 4,175 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
Attention_layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | OpenNLPhub/SynSetMineOnBert | Attention_layer | false | 5,691 | [
"MIT"
] | 1 | 58853363557a2397fd8b04c8e68061f9df159d6a | https://github.com/OpenNLPhub/SynSetMineOnBert/tree/58853363557a2397fd8b04c8e68061f9df159d6a |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
"""Basic residual block class"""
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv1d(in_planes, planes, kernel_size=3, strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Maosef/easy-to-hard | BasicBlock | false | 8,519 | [
"MIT"
] | 44 | 711ec0965229444a6c51b1b06a4e2cad3e32d02e | https://github.com/Maosef/easy-to-hard/tree/711ec0965229444a6c51b1b06a4e2cad3e32d02e |
GeneralRelu | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.a... | Cedric-Perauer/DL_from_Foundations | GeneralRelu | false | 11,278 | [
"Apache-2.0"
] | 0 | c53722216a088cc9f67a2e1bf955d043023e6a85 | https://github.com/Cedric-Perauer/DL_from_Foundations/tree/c53722216a088cc9f67a2e1bf955d043023e6a85 |
BoundedIoULoss | import torch
import torch.nn as nn
import torch.utils.data
class BoundedIoULoss(nn.Module):
def __init__(self, beta=0.2, eps=0.001):
super(BoundedIoULoss, self).__init__()
self.beta = beta
self.eps = eps
def forward(self, pred, target, weight=None):
pred_ctr_2x = pred[:, :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._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | shunya-toyokawa/qanet_human_parts_segmentatiom | BoundedIoULoss | false | 16,434 | [
"MIT"
] | 72 | 5527b247acd65534b455c26e3692a14b31669602 | https://github.com/shunya-toyokawa/qanet_human_parts_segmentatiom/tree/5527b247acd65534b455c26e3692a14b31669602 |
ConvBlock | import torch
import torch.nn as nn
import torch.utils.data
class WSConv2d(nn.Module):
"""
Weight scaled Conv2d (Equalized Learning Rate)
Note that input is multiplied rather than changing weights
this will have the same result.
Inspired by:
https://github.com/nvnbny/progressive_growing_of_gan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ZonePG/Machine-Learning-Collection | ConvBlock | false | 14,735 | [
"MIT"
] | 3,094 | 85f1e761fab85b61d4dbd44285d6483b75ba649c | https://github.com/ZonePG/Machine-Learning-Collection/tree/85f1e761fab85b61d4dbd44285d6483b75ba649c |
ThreeLayerNet | # 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... | PanosAntoniadis/pattern_recognition-ntua | ThreeLayerNet | false | 17,790 | [
"MIT"
] | 6 | 6dca44de77f0ca94221980fc789446a2e10410a4 | https://github.com/PanosAntoniadis/pattern_recognition-ntua/tree/6dca44de77f0ca94221980fc789446a2e10410a4 |
BertMixedLayer | # 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.... | Project-MONAI/MONAI | BertMixedLayer | false | 16,232 | [
"Apache-2.0"
] | 2,971 | 2bab12c67c3cc1d54a4847628ce1e879064be11c | https://github.com/Project-MONAI/MONAI/tree/2bab12c67c3cc1d54a4847628ce1e879064be11c |
ShiftBias | import torch
from torch import nn
class ShiftBias(nn.Module):
def __init__(self, bias):
super(ShiftBias, self).__init__()
self.bias = bias
def forward(self, x):
return x + self.bias
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'b... | 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... | shaun95/StarGANv2-VC | ShiftBias | false | 16,392 | [
"MIT"
] | 116 | ed20538971a03d699351a349a3631767333baeb7 | https://github.com/shaun95/StarGANv2-VC/tree/ed20538971a03d699351a349a3631767333baeb7 |
CenterLoss | # 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... | Peiqi00/WS-DAN.PyTorch | CenterLoss | false | 5,709 | [
"MIT"
] | 1 | eb1307ad6d3a81ee3c18ff9ef1fb0838dd68223a | https://github.com/Peiqi00/WS-DAN.PyTorch/tree/eb1307ad6d3a81ee3c18ff9ef1fb0838dd68223a |
TokenMixer | import torch
import torch.nn.functional as F
from torch import nn
class FeedForward(nn.Module):
def __init__(self, num_features, expansion_factor, dropout):
super().__init__()
num_hidden = expansion_factor * num_features
self.fc1 = nn.Linear(num_features, num_hidden)
self.fc2 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.fun... | GimmeSpoon/mlp-singer | TokenMixer | false | 5,218 | [
"MIT"
] | 1 | 36d10a23c46fa7400994ccd063de79ff089efd5e | https://github.com/GimmeSpoon/mlp-singer/tree/36d10a23c46fa7400994ccd063de79ff089efd5e |
GEGLU | import torch
import torch.nn.functional as F
import torch.nn as nn
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | transat/latent-diffusion | GEGLU | false | 10,926 | [
"MIT"
] | 0 | 1ea0d5bb3fb0fe3f7e8c42cbae91423780977f83 | https://github.com/transat/latent-diffusion/tree/1ea0d5bb3fb0fe3f7e8c42cbae91423780977f83 |
CORblock_Z | # 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.utils.model_zoo
... | AnnaTruzzi/cornet_analysis | CORblock_Z | false | 1,965 | [
"MIT"
] | 0 | 0a2fd0c5a6b09a80d3c8a47441b08fd6129f7a2d | https://github.com/AnnaTruzzi/cornet_analysis/tree/0a2fd0c5a6b09a80d3c8a47441b08fd6129f7a2d |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, smooth=1.0, eps=1e-07):
super(DiceLoss, self).__init__()
self.smooth = smooth
self.eps = eps
def forward(self, output, target):
output = torch.sigmoid(output)
if torch.sum(target) == 0:
... | 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... | lyakaap/pytorch-template | DiceLoss | false | 15,971 | [
"MIT"
] | 140 | eff9f0a4dd50fa49c3b949065247598d5eabc91e | https://github.com/lyakaap/pytorch-template/tree/eff9f0a4dd50fa49c3b949065247598d5eabc91e |
NetVLAD | # 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.... | fede-vaccaro/cnnimageretrieval-pytorch | NetVLAD | false | 12,656 | [
"MIT"
] | 0 | 56bf4ee865e9769801819943f75fff207f0c2f00 | https://github.com/fede-vaccaro/cnnimageretrieval-pytorch/tree/56bf4ee865e9769801819943f75fff207f0c2f00 |
GELU | import math
import torch
import torch.nn as nn
def gelu(x):
""" Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT).
Also see https://arxiv.org/abs/1606.08415
"""
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
to... | 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.... | Aunsiels/qagnn | GELU | false | 11,307 | [
"MIT"
] | 0 | d89a3dd650ac4b8b8aae34e0cce7cfc698892d80 | https://github.com/Aunsiels/qagnn/tree/d89a3dd650ac4b8b8aae34e0cce7cfc698892d80 |
ConvBlock | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super(Block, self).__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Repre... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | deshwalmahesh/CURL---cpu-gpu | ConvBlock | false | 3,404 | [
"BSD-3-Clause"
] | 0 | f4e87275b6cce556b9e04a188cf7ae13d810d82a | https://github.com/deshwalmahesh/CURL---cpu-gpu/tree/f4e87275b6cce556b9e04a188cf7ae13d810d82a |
NormalizedDistance | import torch
import torch.nn as nn
import torch.jit
import torch.nn
def normalized_distance(data, distance):
data = data.view(data.size(0), -1)
reference = data[:, None]
comparison = data[None, :]
result = distance(reference, comparison)
result = result / result.sum(dim=1, keepdim=True).detach()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.jit
import torch.nn
assert_size_stride = tor... | ankmathur96/torchsupport | NormalizedDistance | false | 3,181 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
PPMConcat | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class PPMConcat(nn.ModuleList):
"""Pyramid Pooling Module that only concat the features of each layer.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
"""
def __init__(sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | CarnoZhao/mmsegmentation | PPMConcat | false | 7,851 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
GaussianNoiseSampler | import torch
import torch as th
import torch.nn as nn
class GaussianNoiseSampler(nn.Module):
def __init__(self, scale=0.01, inplace=False):
super(GaussianNoiseSampler, self).__init__()
if scale < 0:
raise ValueError(
'noise scale has to be greather than 0, but got {}'.... | 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_... | tritas/mixdat | GaussianNoiseSampler | false | 13,043 | [
"BSD-3-Clause"
] | 0 | 38fb10df76df55cc1eddba5375c7699c23771fb3 | https://github.com/tritas/mixdat/tree/38fb10df76df55cc1eddba5375c7699c23771fb3 |
SiLU | import torch
import torch.nn as nn
class SiLU(nn.Module):
@staticmethod
def forward(x):
return x * torch.sigmoid(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... | Aditya239233/MDP | SiLU | false | 16,909 | [
"MIT"
] | 4 | 87491e1d67e547c11f4bdd5d784d120473429eae | https://github.com/Aditya239233/MDP/tree/87491e1d67e547c11f4bdd5d784d120473429eae |
GNNExplainerProbe | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | S-Eggers/GraphMask | GNNExplainerProbe | false | 8,735 | [
"MIT"
] | 28 | 9e431a541279801ec46a5b38ed57b2033f795240 | https://github.com/S-Eggers/GraphMask/tree/9e431a541279801ec46a5b38ed57b2033f795240 |
BothContextGate | 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | PiescesHusky/OpenNMT-py | BothContextGate | false | 11,779 | [
"MIT"
] | 0 | 7276cf94f989c50b3169742f64e64142897d1ec0 | https://github.com/PiescesHusky/OpenNMT-py/tree/7276cf94f989c50b3169742f64e64142897d1ec0 |
SphereEmbedded | import torch
from torch import nn
def _extra_repr(**kwargs):
if 'n' in kwargs:
ret = 'n={}'.format(kwargs['n'])
elif 'dim' in kwargs:
ret = 'dim={}'.format(kwargs['dim'])
else:
ret = ''
if 'k' in kwargs:
ret += ', k={}'.format(kwargs['k'])
if 'rank' in kwargs:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | vishalbelsare/geotorch | SphereEmbedded | false | 16,683 | [
"MIT"
] | 422 | ba38d406c245d609fee4b4dac3f6427bf6d73a8e | https://github.com/vishalbelsare/geotorch/tree/ba38d406c245d609fee4b4dac3f6427bf6d73a8e |
AttentionPool | # 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.... | AjianIronSide/Datadriven-GPVAD | AttentionPool | false | 8,898 | [
"MIT"
] | 0 | 8590b5f794beb9640b8fe70ac1f5add5944425b3 | https://github.com/AjianIronSide/Datadriven-GPVAD/tree/8590b5f794beb9640b8fe70ac1f5add5944425b3 |
FocusLiteNNMinMax | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
class FocusLiteNNMinMax(nn.Module):
def __init__(self, num_channel=1):
super(FocusLiteNNMinMax, self).__init__()
self.num_channel = num_channel
self.conv = nn.Conv2d(3, self.num_channel, 7, 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 math
import torch.nn as nn
import torch.utils
assert_size_stride = torch.... | adynmiles/DARTS-FQA | FocusLiteNNMinMax | false | 6,091 | [
"MIT"
] | 1 | a088a0efeb1160d0cdbf2b2a3e30f132c16eb53f | https://github.com/adynmiles/DARTS-FQA/tree/a088a0efeb1160d0cdbf2b2a3e30f132c16eb53f |
SimpleLogModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | YaronBenAtar/glow | SimpleLogModule | false | 14,666 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
VariationalLoss | # 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... | jaredaevans/UltrafastNST | VariationalLoss | false | 6,926 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
RelativeAttention | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, heads, n_state):
super().__init__()
assert n_state % heads == 0
self.heads = heads
self.n_state = n_state
self.depth = self.n_state // self.heads
def split_heads(self, x: 'torch.Tensor',... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Aalanli/MusicGeneration | RelativeAttention | false | 53 | [
"MIT"
] | 0 | 7d268322d692013d8ac6e70be31741cea519fa28 | https://github.com/Aalanli/MusicGeneration/tree/7d268322d692013d8ac6e70be31741cea519fa28 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_size, action_size, action_parameter_size,
hidden_layers=None, init_std=0.01, init_type='normal', activation=
'leaky_relu', squashing_function=False):
super(Actor, self).__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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | jordiriu/MP-DQN | Actor | false | 15,729 | [
"MIT"
] | 75 | eec13eb9b4e2c0099649e0639f2a8b93d7d0d5be | https://github.com/jordiriu/MP-DQN/tree/eec13eb9b4e2c0099649e0639f2a8b93d7d0d5be |
MLP | import torch
class MLP(torch.nn.Module):
def __init__(self, insize, outsize=128, nonlinear=torch.nn.ReLU,
activation=torch.nn.ReLU, hidden_layer_size=1, node_size=256):
super(MLP, self).__init__()
self.net = torch.nn.Sequential()
self.net.add_module('fc_1', torch.nn.Linear(insize,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | bengisug/multiagent-particle-envs | MLP | false | 1,538 | [
"MIT"
] | 0 | c87280f18fbaf885932fe6da4d600ab474fd83fe | https://github.com/bengisug/multiagent-particle-envs/tree/c87280f18fbaf885932fe6da4d600ab474fd83fe |
TorchNotEqual | import torch
class TorchNotEqual(torch.nn.Module):
def __init__(self):
super(TorchNotEqual, self).__init__()
def forward(self, x, y):
return torch.ne(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... | NVIDIA-AI-IOT-private/torch2trt | TorchNotEqual | false | 10,545 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
Neg | import torch
import torch.onnx
import torch.nn as nn
class Neg(nn.Module):
def forward(self, x):
return torch.neg(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.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 | Neg | false | 16,073 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
LocalSubGraphLayer | import torch
from torch import nn
import torch.nn.functional as F
class LocalMLP(nn.Module):
def __init__(self, dim_in: 'int', use_norm: 'bool'=True):
"""a Local 1 layer MLP
:param dim_in: feat in size
:type dim_in: int
:param use_norm: if to apply layer norm, defaults to True
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cdicle-motional/l5kit | LocalSubGraphLayer | false | 6,414 | [
"Apache-2.0"
] | 1 | 4dc4ee5391479bb71f0b373f39c316f9eef5a961 | https://github.com/cdicle-motional/l5kit/tree/4dc4ee5391479bb71f0b373f39c316f9eef5a961 |
MLP | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self, num_class=10):
super(MLP, self).__init__()
self.fc1 = nn.Linear(32 * 32 * 3, 512)
self.fc2 = nn.Linear(512, 512)
self.fc3 = nn.Linear(512, num_class)
self.dropout = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | mattkelleher/Nasty-Teacher | MLP | false | 16,034 | [
"MIT"
] | 59 | 7cca6e41aca10dcceeb215fa15107baae91e0140 | https://github.com/mattkelleher/Nasty-Teacher/tree/7cca6e41aca10dcceeb215fa15107baae91e0140 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 5)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, 5)
self.pool2 = nn.MaxPool2d(2, 2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | piyushpathak03/Facial-key-point-detection | Net | false | 7,609 | [
"Apache-2.0"
] | 1 | 863eeeac50c46befb17ecf7610cd341ea0e65291 | https://github.com/piyushpathak03/Facial-key-point-detection/tree/863eeeac50c46befb17ecf7610cd341ea0e65291 |
GEGLU | import torch
import torch.nn as nn
class PositionWiseFeedForward(nn.Module):
"""
title: Position-wise Feed-Forward Network (FFN)
summary: Documented reusable implementation of the position wise feedforward network.
# Position-wise Feed-Forward Network (FFN)
This is a [PyTorch](https://pytorch.org... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | edchengmoore/pytorch_tabular | GEGLU | false | 3,455 | [
"MIT"
] | 0 | 25f87089fbed95b46f2a1a8a96fba1f581aa8af1 | https://github.com/edchengmoore/pytorch_tabular/tree/25f87089fbed95b46f2a1a8a96fba1f581aa8af1 |
GlobalAvgPool2d | # 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.... | Rming/Self-Correction-Human-Parsing | GlobalAvgPool2d | false | 977 | [
"MIT"
] | 0 | c2b711c0a11f3980a8bf4c7a2acf85d80732620a | https://github.com/Rming/Self-Correction-Human-Parsing/tree/c2b711c0a11f3980a8bf4c7a2acf85d80732620a |
FocalLoss | # 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
... | EricZsy/BalancedKnowledgeDistillation | FocalLoss | false | 8,049 | [
"MIT"
] | 22 | 88a2de840a3fc6eb2ee881c729f293b8e78714aa | https://github.com/EricZsy/BalancedKnowledgeDistillation/tree/88a2de840a3fc6eb2ee881c729f293b8e78714aa |
PolicyNetwork | # 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.... | bluebibi/rl_book_codes | PolicyNetwork | false | 3,228 | [
"MIT"
] | 0 | ef7fc9993eb66618e4b4e80e59cc2879a8db3522 | https://github.com/bluebibi/rl_book_codes/tree/ef7fc9993eb66618e4b4e80e59cc2879a8db3522 |
NPIArg | # 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.... | nienjiuntai/pytorch-npi | NPIArg | false | 4,088 | [
"MIT"
] | 0 | 16b413c152dfb7f1506a85997adc10ddc2d9af35 | https://github.com/nienjiuntai/pytorch-npi/tree/16b413c152dfb7f1506a85997adc10ddc2d9af35 |
SequenceBias | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Parameter
class SequenceBias(nn.Module):
"""
Adds one bias element to the end of the sequence.
so if the input has a shape ``(L, N, E)``, where
``L`` i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from torch.nn.parameter import Pa... | adriansarstedt/opacus | SequenceBias | false | 12,056 | [
"Apache-2.0"
] | 0 | a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 | https://github.com/adriansarstedt/opacus/tree/a6c89e3d6a3a4e3e4b82bc8c68d53265a9a7cba1 |
CrossEntropyLoss | import torch
import torch.utils.cpp_extension
class CrossEntropyLoss(torch.nn.Module):
def __init__(self):
super(CrossEntropyLoss, self).__init__()
self.ce_loss = torch.nn.CrossEntropyLoss()
def forward(self, cls_output, label, **_):
return self.ce_loss(cls_output, label).mean()
de... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.cpp... | ParthaEth/PyTorch-StudioGAN | CrossEntropyLoss | false | 1,887 | [
"MIT"
] | 0 | 16dd84415e4b7f4667cb1b1e0ef3fc04edf6b5a9 | https://github.com/ParthaEth/PyTorch-StudioGAN/tree/16dd84415e4b7f4667cb1b1e0ef3fc04edf6b5a9 |
VariableSoftmax | # 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 import nn
f... | llayer/pytorch_inferno | VariableSoftmax | false | 3,936 | [
"Apache-2.0"
] | 0 | 922eba5e04e447126506512eb82adcd9ed1dab25 | https://github.com/llayer/pytorch_inferno/tree/922eba5e04e447126506512eb82adcd9ed1dab25 |
GCN | # 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.nn.functional as F
import torch.nn.parallel
as... | Tanveer81/BoxVOS | GCN | false | 17,984 | [
"BSD-2-Clause"
] | 4 | c30aa319f18f3fbee2a25e0ed25cb006a4598300 | https://github.com/Tanveer81/BoxVOS/tree/c30aa319f18f3fbee2a25e0ed25cb006a4598300 |
SimpleMLP | import torch
import torch.optim
import torch.jit
import torch.nn as nn
class SimpleMLP(nn.Module):
def __init__(self, num_in_features, num_out_features, neurons_per_layer):
super(SimpleMLP, self).__init__()
self.act = nn.ELU()
self.l_in = nn.Linear(in_features=num_in_features, out_feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.optim
... | zhaofeng-shu33/deep_euler_tests | SimpleMLP | false | 13,176 | [
"MIT"
] | 0 | a3d0961af679d490b0c58873ee0726234122bc7a | https://github.com/zhaofeng-shu33/deep_euler_tests/tree/a3d0961af679d490b0c58873ee0726234122bc7a |
Accuracy | import torch
import torch.nn as nn
class Accuracy(nn.Module):
"""
This class implements the accuracy computation. No gradients supported.
"""
def __init__(self, threshold: 'float'=0.5) ->None:
"""
Constructor method
:param threshold: (float) Threshold to be applied
"""... | 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... | ChristophReich1996/Cell-DETR | Accuracy | false | 13,479 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
MultiHeadedAttention | # 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.... | carankt/FastSpeech2-1 | MultiHeadedAttention | false | 6,393 | [
"Apache-2.0"
] | 1 | 42c06e4fbdf741a0719154d1cb4617b7d3f15a5c | https://github.com/carankt/FastSpeech2-1/tree/42c06e4fbdf741a0719154d1cb4617b7d3f15a5c |
ToRGB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
import torch.nn as nn
import tor... | HappyBelief/ContraD | ToRGB | false | 13,779 | [
"MIT"
] | 168 | abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f | https://github.com/HappyBelief/ContraD/tree/abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f |
SoftmaxAffineLayer | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | qlindazm/asv-subtools | SoftmaxAffineLayer | false | 4,235 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 |
CVAE | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self, sample_size, condition_size, hidden_size):
super().__init__()
self.fc1 = nn.Linear(sample_size + condition_size, hidden_size)
self.fc2 = nn.Dropout(p=0.5)
self.fc3 = nn.... | 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... | ekrell/learn-planning-space | CVAE | false | 3,473 | [
"MIT"
] | 0 | 730e448bffa4996b2b1ef3a5b00500dc172962ec | https://github.com/ekrell/learn-planning-space/tree/730e448bffa4996b2b1ef3a5b00500dc172962ec |
Qnet | # 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 random
import torch.nn... | co24428/minimalRL | Qnet | false | 1,730 | [
"MIT"
] | 0 | c758e733438fce1d8421551e75f2117ec3f82028 | https://github.com/co24428/minimalRL/tree/c758e733438fce1d8421551e75f2117ec3f82028 |
power | # 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... | pierreglaser/MMD-gradient-flow | power | false | 10,689 | [
"BSD-3-Clause"
] | 0 | 43591137e1d04bed5153887a364fae72621b01ae | https://github.com/pierreglaser/MMD-gradient-flow/tree/43591137e1d04bed5153887a364fae72621b01ae |
Upsampler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch.... | Haabibi/RBPN-PyTorch | Upsampler | false | 5,257 | [
"MIT"
] | 1 | 0b04420b384fcc8f78a7b9afeca179fa6c0332c2 | https://github.com/Haabibi/RBPN-PyTorch/tree/0b04420b384fcc8f78a7b9afeca179fa6c0332c2 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | doem97/PSENet | DiceLoss | false | 15,192 | [
"Apache-2.0"
] | 1,213 | 4d95395658662f2223805c36dcd573d9e190ce26 | https://github.com/doem97/PSENet/tree/4d95395658662f2223805c36dcd573d9e190ce26 |
Baseline | # 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... | Karin-S/USYD-ELEC5307 | Baseline | false | 9,225 | [
"Apache-2.0"
] | 0 | 83cb40adf0c15ee703a880fc7aba5c69b82a5434 | https://github.com/Karin-S/USYD-ELEC5307/tree/83cb40adf0c15ee703a880fc7aba5c69b82a5434 |
SEModule | # 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... | DocYard-ai/UCR | SEModule | false | 8,007 | [
"Apache-2.0"
] | 10 | 7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 | https://github.com/DocYard-ai/UCR/tree/7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 |
Affine | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Nitin-Mane/External-Attention-pytorch | Affine | false | 14,152 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
SpatialAttention | # 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_... | Clayrisee/BanchelorsProject-FAS | SpatialAttention | false | 290 | [
"MIT"
] | 0 | 3da199fb2e7be04eed7f28374ef753383511dbee | https://github.com/Clayrisee/BanchelorsProject-FAS/tree/3da199fb2e7be04eed7f28374ef753383511dbee |
BCEDiceProbLoss | import torch
from torch import nn as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed... | 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 ... | ciubecca/3dunet-cavity | BCEDiceProbLoss | false | 1,722 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb |
AutoregressiveShift | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Nintorac/survae_experiments | AutoregressiveShift | false | 902 | [
"MIT"
] | 0 | d68cc25e2604aab08b53617c1f3ffe4716f166c4 | https://github.com/Nintorac/survae_experiments/tree/d68cc25e2604aab08b53617c1f3ffe4716f166c4 |
ScoreNetwork | from torch.nn import Module
import torch
from torch.nn import Tanh
from torch.nn import Linear
class ScoreNetwork(Module):
"""
An optimized single hidden layer neural network for attention scores.
The optimization idea behind this network is that projection of keys can
performed only once without conc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 impor... | stungkit/Copycat-abstractive-opinion-summarizer | ScoreNetwork | false | 16,502 | [
"MIT"
] | 51 | 04fe5393a7bb6883516766b762f6a0c530e95375 | https://github.com/stungkit/Copycat-abstractive-opinion-summarizer/tree/04fe5393a7bb6883516766b762f6a0c530e95375 |
DSCNet | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dSamePad(nn.Module):
"""
Implement Tensorflow's 'SAME' padding mode in Conv2d.
When an odd number, say `m`, of pixels are need to pad, Tensorflow will pad one more column at right or one more
row at bottom. But P... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | ShulingTang/DSC-Net | DSCNet | false | 9,506 | [
"MIT"
] | 0 | 2da1e0c654b045057c654cbcbb8a8c23fb832c9d | https://github.com/ShulingTang/DSC-Net/tree/2da1e0c654b045057c654cbcbb8a8c23fb832c9d |
BertLayerNorm | import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_si... | 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... | Adelashl6/mask_transformers | BertLayerNorm | false | 4,787 | [
"MIT"
] | 1 | 2a2e4d1b40ae3ed546cb850d041af246806b63e7 | https://github.com/Adelashl6/mask_transformers/tree/2a2e4d1b40ae3ed546cb850d041af246806b63e7 |
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