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 |
|---|---|---|---|---|---|---|---|---|---|---|
BCEBlurWithLogitsLoss | import torch
import torch.nn as nn
class BCEBlurWithLogitsLoss(nn.Module):
def __init__(self, alpha=0.05):
super(BCEBlurWithLogitsLoss, self).__init__()
self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
def forward(self, pred, true):
loss = self.loss_f... | 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... | Aditya239233/MDP | BCEBlurWithLogitsLoss | false | 16,902 | [
"MIT"
] | 4 | 87491e1d67e547c11f4bdd5d784d120473429eae | https://github.com/Aditya239233/MDP/tree/87491e1d67e547c11f4bdd5d784d120473429eae |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Xlinford/TDNet | ScaledDotProductAttention | false | 2,968 | [
"MIT"
] | 0 | e7cb59c40b8751b6dab9691d26ad224fd61c24d1 | https://github.com/Xlinford/TDNet/tree/e7cb59c40b8751b6dab9691d26ad224fd61c24d1 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IndexFziQ/ASER | Attention | false | 2,381 | [
"MIT"
] | 0 | 67dd1a2a25cec175c15675cc1f8a63ca065b447e | https://github.com/IndexFziQ/ASER/tree/67dd1a2a25cec175c15675cc1f8a63ca065b447e |
Div | # 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... | Akababa/torch2trt | Div | false | 18,416 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
Layer4NN | import torch
import torch.nn
import torch.cuda
class Layer4NN(torch.nn.Module):
def __init__(self, inputSize, numClasses, channels=3):
super(Layer4NN, self).__init__()
self.cnn_layer1 = torch.nn.Conv2d(channels, 32, kernel_size=3,
stride=1, padding=1)
self.cnn_layer2 = torch.n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import torch.... | naruarjun/SADAM-reproducibility | Layer4NN | false | 12,821 | [
"MIT"
] | 0 | 1654804268ae984f49abc3ab2495c350dc09a3e2 | https://github.com/naruarjun/SADAM-reproducibility/tree/1654804268ae984f49abc3ab2495c350dc09a3e2 |
LinearAttentionBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearAttentionBlock(nn.Module):
def __init__(self, in_features):
super(LinearAttentionBlock, self).__init__()
self.op = nn.Conv2d(in_channels=in_features, out_channels=1,
kernel_size=1, padding=0, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | abhay97ps/visual-control-ppo-procgen | LinearAttentionBlock | false | 1,355 | [
"MIT"
] | 0 | 765fe1ddb289d384abddc4df8eb865379c8da76a | https://github.com/abhay97ps/visual-control-ppo-procgen/tree/765fe1ddb289d384abddc4df8eb865379c8da76a |
SimpleModel | import torch
import torch.nn as nn
class SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
def forward(self, x):
return x * 2
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... | JimmyCai91/tensorboardX | SimpleModel | false | 9,148 | [
"MIT"
] | 0 | 9bff602008d71f4bbf6e83e99125033629f4ee6f | https://github.com/JimmyCai91/tensorboardX/tree/9bff602008d71f4bbf6e83e99125033629f4ee6f |
DenseSAGEConv | # 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 math
from torch.nn imp... | douglasrizzo/pytorch_geometric | DenseSAGEConv | false | 12,295 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
BasicNN | import torch
import numpy as np
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
class BasicNN(nn.Module):
def __init__(self):
super(BasicNN, self).__init__()
self.net = nn.Linear(28 * 28, 2)
def forward(self, x):
if isinstance(x, np.ndarray):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DNCoelho/clipper | BasicNN | false | 13,541 | [
"Apache-2.0"
] | 1,403 | 0144078c9da757ee319d60b362d9f51538657ca8 | https://github.com/DNCoelho/clipper/tree/0144078c9da757ee319d60b362d9f51538657ca8 |
Netleaky | import torch
import torch.nn as nn
import torch.nn.functional as F
class Netleaky(nn.Module):
def __init__(self, input_dim, output_dim):
super(Netleaky, self).__init__()
self.linear1 = nn.Linear(input_dim, 32)
self.linear2 = nn.Linear(32, 32)
self.linear3 = nn.Linear(32, 64)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gautam-sharma1/Imitation-Learning | Netleaky | false | 3,529 | [
"MIT"
] | 0 | 20b6fcd2a8d6de8eb95e6831f5b379a083306361 | https://github.com/gautam-sharma1/Imitation-Learning/tree/20b6fcd2a8d6de8eb95e6831f5b379a083306361 |
RPLHead | import torch
import torch.nn as nn
class RPLHead(nn.Module):
def __init__(self, in_channels, num_classes, num_centers=1, init='random'):
super(RPLHead, self).__init__()
self.feat_dim = in_channels
self.num_classes = num_classes
self.num_centers = num_centers
if init == 'ra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Cogito2012/OpenTAL | RPLHead | false | 7,909 | [
"BSD-3-Clause"
] | 16 | a7ab938a52b3fb82163eb1ba5403888359eb7e6a | https://github.com/Cogito2012/OpenTAL/tree/a7ab938a52b3fb82163eb1ba5403888359eb7e6a |
EncoderLayer | # 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.... | BHD233/PaddleOCR2Pytorch | EncoderLayer | false | 13,393 | [
"Apache-2.0"
] | 364 | f114069b3e2669c6adf0adf9596756205f184c9c | https://github.com/BHD233/PaddleOCR2Pytorch/tree/f114069b3e2669c6adf0adf9596756205f184c9c |
Coral | # 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
import torch.nn.init
assert_size_stride = torch._C._dynamo... | TropComplique/associative-domain-adaptation | Coral | false | 18,010 | [
"MIT"
] | 8 | a2ec0a9e678af20624f79e40c8042c969a69e8f3 | https://github.com/TropComplique/associative-domain-adaptation/tree/a2ec0a9e678af20624f79e40c8042c969a69e8f3 |
FTanh | import torch
import torch.nn as nn
class FTanh(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FTanh, self).__init__()
def forward(self, x):
from torch.nn import functional as F
return F.tanh(x)
def get_inputs():
return [torch.rand([4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dawnclaude/onnx2keras | FTanh | false | 15,146 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
NN | import torch
import torch.nn.functional as F
import torch.nn as nn
class NN(nn.Module):
def __init__(self, input_size):
super().__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, 40)
self.fc3 = nn.Linear(40, 20)
self.fc4 = nn.Linear(20, 9)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Meydand2001/Machine-Learning-project | NN | false | 11,705 | [
"MIT"
] | 0 | dc73bc3820024939ba66a1a3e2ae130d6bf35f9a | https://github.com/Meydand2001/Machine-Learning-project/tree/dc73bc3820024939ba66a1a3e2ae130d6bf35f9a |
ScaleLayer | # 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... | Anurag14/STar-framework | ScaleLayer | false | 16,956 | [
"MIT"
] | 4 | 6670499c681fce8d76aae1d1910bc849ec5f56ea | https://github.com/Anurag14/STar-framework/tree/6670499c681fce8d76aae1d1910bc849ec5f56ea |
ChannelSpatialSELayer3D | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelSELayer3D(nn.Module):
"""
3D implementation of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
"""
def __init__(self, num_channels, reduction_ratio=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
import ... | HiLab-git/PyMIC | ChannelSpatialSELayer3D | false | 13,785 | [
"Apache-2.0"
] | 147 | abf5c43de43668b85f4c049c95a8f1b7cf1d9f16 | https://github.com/HiLab-git/PyMIC/tree/abf5c43de43668b85f4c049c95a8f1b7cf1d9f16 |
Normalize01 | # 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... | ScarWar/DeepSTORM3D | Normalize01 | false | 8,736 | [
"MIT"
] | 25 | 8ba5bc61120abedba9c1b24a994e616e280bdda2 | https://github.com/ScarWar/DeepSTORM3D/tree/8ba5bc61120abedba9c1b24a994e616e280bdda2 |
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.... | mbarnig/vits-train | MultiHeadAttention | false | 7,190 | [
"MIT"
] | 1 | cfb8a0fc91daad868fe3d062ebf85d62edbd7506 | https://github.com/mbarnig/vits-train/tree/cfb8a0fc91daad868fe3d062ebf85d62edbd7506 |
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.... | yuanweining/DTI | MultiHeadAttention | false | 4,653 | [
"Apache-2.0"
] | 0 | 11eacb46a221da04d0e9b01d41c89c7ce51ea302 | https://github.com/yuanweining/DTI/tree/11eacb46a221da04d0e9b01d41c89c7ce51ea302 |
Conv_Q | # 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.... | ssimonc/NeoRL | Conv_Q | false | 16,534 | [
"Apache-2.0"
] | 50 | 098c58c8e4c3e43e67803f6384619d3bfe7fce5d | https://github.com/ssimonc/NeoRL/tree/098c58c8e4c3e43e67803f6384619d3bfe7fce5d |
KLDivLoss | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd
class KLDivLoss(Module):
"""
## KL-Divergence loss
This calculates the KL divergence between a given normal distribution and $\\mathcal{N}(0, 1)$
"""
def forward(self, sigma_hat: '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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import M... | mcx/annotated_deep_learning_paper_implementations | KLDivLoss | false | 7,199 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
ReCoNet2 | # 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.... | irsisyphus/reconet | ReCoNet2 | false | 15,690 | [
"MIT"
] | 56 | 863acf8dde4d45c8521634af27878fe04f3b2e56 | https://github.com/irsisyphus/reconet/tree/863acf8dde4d45c8521634af27878fe04f3b2e56 |
MetaAconC | # 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... | GoalballAnalysis/GUI | MetaAconC | false | 2,314 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
NonLocalBlock2D | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo
class NonLocalBlock2D(nn.Module):
def __init__(self, in_channels, inter_channels):
super(NonLocalBlock2D, self).__init__()
self.in_channels = in_channels
self.inter_channels = inter_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
from torch._inductor.runtime.... | zhouhuanxiang/EDSR-PyTorch | NonLocalBlock2D | false | 13,181 | [
"MIT"
] | 0 | ca2f0eea49476a0acde59dd76aa4ae257389d98c | https://github.com/zhouhuanxiang/EDSR-PyTorch/tree/ca2f0eea49476a0acde59dd76aa4ae257389d98c |
tfAvgPool3D | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Jo951128/2021-2-MIP | tfAvgPool3D | false | 2,423 | [
"MIT"
] | 0 | 511e0a38816d16fdba9631f76cf913ba51c43138 | https://github.com/Jo951128/2021-2-MIP/tree/511e0a38816d16fdba9631f76cf913ba51c43138 |
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.... | wu0004in/vedastr | MultiHeadAttention | false | 4,557 | [
"Apache-2.0"
] | 0 | 83511a408b68c264561a30daff5154cd0148bebd | https://github.com/wu0004in/vedastr/tree/83511a408b68c264561a30daff5154cd0148bebd |
SIMSE | # 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... | Columbine21/TFR-Net | SIMSE | false | 17,126 | [
"MIT"
] | 7 | 1da01577542e7f477fdf7323ec0696aebc632357 | https://github.com/Columbine21/TFR-Net/tree/1da01577542e7f477fdf7323ec0696aebc632357 |
AdditiveAttention | import torch
import torch.nn as nn
class AdditiveAttention(nn.Module):
def __init__(self, in_features, att_hidden, out_features, bias=True):
super(AdditiveAttention, self).__init__()
self.out_size = out_features
self.linear1 = nn.Linear(in_features=in_features, out_features=
a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LindgeW/DomainAdaption4DependencyParsing | AdditiveAttention | false | 5,546 | [
"Apache-2.0"
] | 1 | 5de136a37d8fe730e4235ed95bf923763fe21ea6 | https://github.com/LindgeW/DomainAdaption4DependencyParsing/tree/5de136a37d8fe730e4235ed95bf923763fe21ea6 |
GraphAttention | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GraphAttention(nn.Module):
def __init__(self, in_features, out_features, dropout, alpha=0.2,
concat=True, return_attention=False):
super(GraphAttention, self).__init__()
self.dropout = 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
from torch._inductor.runtime.... | Supermaxman/covid19-data | GraphAttention | false | 9,602 | [
"Apache-2.0"
] | 0 | 13e8e0c30a063c60e2160896458cd290a85ea0e2 | https://github.com/Supermaxman/covid19-data/tree/13e8e0c30a063c60e2160896458cd290a85ea0e2 |
ContrastiveLoss | import torch
import torch.nn.functional as F
class ContrastiveLoss(torch.nn.Module):
def __init__(self, margin=2):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pairwise_distance(output1, output2)
... | 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
assert_size_stride = torch._... | e-Neural/OfflineSignatureVerification | ContrastiveLoss | false | 15,283 | [
"MIT"
] | 51 | ea11009a3b2ac82c7091075466c505602a50817a | https://github.com/e-Neural/OfflineSignatureVerification/tree/ea11009a3b2ac82c7091075466c505602a50817a |
AvgPool | import torch
import torch.nn as nn
class AvgPool(nn.Module):
"""
给定形如[batch_size, max_len, hidden_size]的输入，在最后一维进行avg pooling. 输出为[batch_size, hidden_size]
"""
def __init__(self, stride=None, padding=0):
super(AvgPool, self).__init__()
self.stride = stride
self.padding = paddi... | 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... | Raiselimit/TorchBlocks | AvgPool | false | 5,736 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
Invertible1x1Conv | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data
import torch.nn
class Invertible1x1Conv(torch.nn.Module):
"""
The layer outputs both the convolution, and the log determinant
of its weight matrix. If reverse=True it does convolution with
inverse... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch.autograd import Variable
import torch... | leo0519/TensorRT | Invertible1x1Conv | false | 7,075 | [
"Apache-2.0"
] | 1 | 498dcb009fe4c2dedbe9c61044d3de4f3c04a41b | https://github.com/leo0519/TensorRT/tree/498dcb009fe4c2dedbe9c61044d3de4f3c04a41b |
ln | import torch
from torch import nn
import torch.utils.data
class ln(nn.Module):
"""
Layer Normalization
"""
def __init__(self, input):
super(ln, self).__init__()
self.ln = nn.LayerNorm(input.size()[1:])
def forward(self, x):
x = self.ln(x)
return x
def get_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | ZAKAUDD/-GEU-Net | ln | false | 18,201 | [
"MIT"
] | 8 | 5251d329afb80c74328e72fd2fc21ff691ef3353 | https://github.com/ZAKAUDD/-GEU-Net/tree/5251d329afb80c74328e72fd2fc21ff691ef3353 |
AlphaModule | import torch
from torch import nn
from torch.nn.parameter import Parameter
import torch.optim
class AlphaModule(nn.Module):
def __init__(self, shape):
super(AlphaModule, self).__init__()
if not isinstance(shape, tuple):
shape = shape,
self.alpha = Parameter(torch.rand(tuple([1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn.parameter import Parameter
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | albertozurli/mammoth | AlphaModule | false | 1,393 | [
"MIT"
] | 0 | 849234afe084b4f707de5300e953a2a8c104ea36 | https://github.com/albertozurli/mammoth/tree/849234afe084b4f707de5300e953a2a8c104ea36 |
ClipLayer | import torch
import torch.nn as nn
def clip_data(data, max_norm):
norms = torch.norm(data.reshape(data.shape[0], -1), dim=-1)
scale = (max_norm / norms).clamp(max=1.0)
data *= scale.reshape(-1, 1, 1, 1)
return data
class ClipLayer(nn.Module):
def __init__(self, max_norm):
super(ClipLaye... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | skat00sh/Handcrafted-DP | ClipLayer | false | 16,479 | [
"MIT"
] | 48 | d1f8bc004adc240d5c424a10bdcc30fc266c8218 | https://github.com/skat00sh/Handcrafted-DP/tree/d1f8bc004adc240d5c424a10bdcc30fc266c8218 |
AttDec | # 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 ... | IacoSimoncini/tfvaegan | AttDec | false | 13,204 | [
"MIT"
] | 0 | 157b526d65d0b0d5412f4be6fed02fc7d6325827 | https://github.com/IacoSimoncini/tfvaegan/tree/157b526d65d0b0d5412f4be6fed02fc7d6325827 |
AttentionBlock | # 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.... | Anjum48/commonlitreadabilityprize | AttentionBlock | false | 7,707 | [
"MIT"
] | 28 | b310742520b847b452ced0d27f47a934e834e4de | https://github.com/Anjum48/commonlitreadabilityprize/tree/b310742520b847b452ced0d27f47a934e834e4de |
WDV52Linear | # 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 ... | Lollipop321/weight-distillation | WDV52Linear | false | 5,568 | [
"BSD-3-Clause"
] | 1 | cfc76ec58e3e88094dde1825287b2968f9718431 | https://github.com/Lollipop321/weight-distillation/tree/cfc76ec58e3e88094dde1825287b2968f9718431 |
CustomGruCell | # 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 numpy as np
... | NiWaRe/PySyft | CustomGruCell | false | 11,750 | [
"Apache-2.0"
] | 0 | b5abe66ea949d60be14a08d2e4e32e9587c7bf5c | https://github.com/NiWaRe/PySyft/tree/b5abe66ea949d60be14a08d2e4e32e9587c7bf5c |
LargeNN | # 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... | AustinCai/gmaxup-augmentation | LargeNN | false | 86 | [
"MIT"
] | 0 | a64ca0a76eb333e5ce6b217c301d27ca04d73bce | https://github.com/AustinCai/gmaxup-augmentation/tree/a64ca0a76eb333e5ce6b217c301d27ca04d73bce |
SelfAttentionLayer | # 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.... | Darg-Iztech/quote-detection | SelfAttentionLayer | false | 2,133 | [
"MIT"
] | 0 | 14a4731139db859f9672f54400d78d77cca8a008 | https://github.com/Darg-Iztech/quote-detection/tree/14a4731139db859f9672f54400d78d77cca8a008 |
Block | # 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.... | Challyfilio/NAIC2021 | Block | false | 299 | [
"MIT"
] | 0 | 11b38a920dcc902f9b798dc43ae360062862e6e4 | https://github.com/Challyfilio/NAIC2021/tree/11b38a920dcc902f9b798dc43ae360062862e6e4 |
IBNbResInitBlock | # 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.... | earhian/imgclsmob | IBNbResInitBlock | false | 6,629 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
TwoLayerCNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class TwoLayerCNN(nn.Module):
def __init__(self, C, M, embedding, channel, mtc_input):
super(TwoLayerCNN, self).__init__()
self.C = C
self.M = M
self.embedding = embedding
self.mtc_input = C if mtc_input el... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | LFhase/string-embed | TwoLayerCNN | false | 9,211 | [
"MIT"
] | 0 | da8eb60186fcd26a94734f265f79fa5fc5096f76 | https://github.com/LFhase/string-embed/tree/da8eb60186fcd26a94734f265f79fa5fc5096f76 |
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 import triton_helpers
from torch._inductor.runtime.... | ekvall93/tape | Attention | false | 12,344 | [
"BSD-3-Clause"
] | 0 | 1ca4d5a39c72f806f23a36fb7a7c7325f06096ae | https://github.com/ekvall93/tape/tree/1ca4d5a39c72f806f23a36fb7a7c7325f06096ae |
Threshold | import torch
import torch.optim
class Threshold(torch.nn.Module):
CAST_OPS = {'float': lambda t: t.float(), 'byte': lambda t: t.byte()}
def __init__(self, value: 'float', comparison: 'str'='lower', dtype:
'str'='float'):
super(Threshold, self).__init__()
self.threshold = value
... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | Threshold | false | 18,348 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
KLLoss | # 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 ... | abhisheklalwani/ActionCLIP | KLLoss | false | 14,745 | [
"MIT"
] | 141 | dd2ab27db4bf3d5be3a51cd011cb49aa8b679de0 | https://github.com/abhisheklalwani/ActionCLIP/tree/dd2ab27db4bf3d5be3a51cd011cb49aa8b679de0 |
shift | import torch
import torch.nn as nn
class crop(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
N, C, H, W = x.shape
x = x[0:N, 0:C, 0:H - 1, 0:W]
return x
class shift(nn.Module):
def __init__(self):
super().__init__()
self.shift_... | 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... | amonod/udvd | shift | false | 1,428 | [
"MIT"
] | 0 | a1ccb777d205255ac68c40efb93dd3996f562c45 | https://github.com/amonod/udvd/tree/a1ccb777d205255ac68c40efb93dd3996f562c45 |
ConformerFeedForward | # 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.data
import torch.optim
assert_size_stri... | JINHXu/NeMo | ConformerFeedForward | false | 11,615 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
ConvBlock | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
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 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Sid1057/sid1057.github.io | ConvBlock | false | 17,936 | [
"MIT"
] | 4 | 623d1731e308b42b6f86304dcfd671a061b414bf | https://github.com/Sid1057/sid1057.github.io/tree/623d1731e308b42b6f86304dcfd671a061b414bf |
SoftTargetCrossEntropy | # 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... | lovelinability/pytorch_image_models | SoftTargetCrossEntropy | false | 7,150 | [
"Apache-2.0"
] | 1 | 7c54200f3de7611ab1222a37088eb7f66ae2858f | https://github.com/lovelinability/pytorch_image_models/tree/7c54200f3de7611ab1222a37088eb7f66ae2858f |
Conv2dWithConstraint | import torch
from torch import nn
class Conv2dWithConstraint(nn.Conv2d):
def __init__(self, *args, max_norm=1, **kwargs):
self.max_norm = max_norm
super(Conv2dWithConstraint, self).__init__(*args, **kwargs)
def forward(self, x):
self.weight.data = torch.renorm(self.weight.data, p=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.triton_helpers import libdevice
from torch import n... | tomMoral/braindecode | Conv2dWithConstraint | false | 4,440 | [
"BSD-3-Clause"
] | 0 | 09d63b7e32fdfcfbaac7569a003f2611721a78ca | https://github.com/tomMoral/braindecode/tree/09d63b7e32fdfcfbaac7569a003f2611721a78ca |
NumericalFeaturesEmbedding | import torch
import torch.utils.tensorboard
import torch.utils.data
import torch.distributed
class NumericalFeaturesEmbedding(torch.nn.Module):
"""Transform a sequence of numerical feature vectors into a single vector.
Currently, this module simply aggregates the features by averaging, although more
elab... | 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.tensorboard
import torch.utils.data
import torch.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_str... | JoeLambourne/SketchGraphs | NumericalFeaturesEmbedding | false | 1,312 | [
"MIT"
] | 0 | 183c65f82d71d82c62b253092e9b7fa65846a3e6 | https://github.com/JoeLambourne/SketchGraphs/tree/183c65f82d71d82c62b253092e9b7fa65846a3e6 |
ModulatedConv2d | # 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.autograd... | hyunobae/BasicSR | ModulatedConv2d | false | 12,528 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
ECA_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
import math
import torch.nn as nn
import torch.utils.data.distributed
assert_siz... | Erfun76/insightface | ECA_Layer | false | 9,281 | [
"MIT"
] | 0 | 148cef36a43a055f68d2b6a475f4aa38625ad8b4 | https://github.com/Erfun76/insightface/tree/148cef36a43a055f68d2b6a475f4aa38625ad8b4 |
cal_L2Norm | import torch
class cal_L2Norm(torch.nn.Module):
def __init__(self):
super(cal_L2Norm, self).__init__()
self.eps = 1e-10
def forward(self, x):
norm = torch.sqrt(torch.sum(x * x, dim=1) + self.eps)
return norm
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | RiyaoDong/HGSL | cal_L2Norm | false | 2,765 | [
"Apache-2.0"
] | 0 | 19fa984b3bfde0e3b7acbce87dd40177cd64f9b0 | https://github.com/RiyaoDong/HGSL/tree/19fa984b3bfde0e3b7acbce87dd40177cd64f9b0 |
SelfAttentionConv2d | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.modules.utils import _pair
class SelfAttentionConv2d(nn.Module):
def __init__(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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MerHS/SASA-pytorch | SelfAttentionConv2d | false | 14,020 | [
"MIT"
] | 47 | 7d113852dce2e25d4de23caf87ad7d33758c322e | https://github.com/MerHS/SASA-pytorch/tree/7d113852dce2e25d4de23caf87ad7d33758c322e |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""Calculates attention over the input nodes given the current state."""
def __init__(self, encoder_hidden_size, decoder_hidden_size):
super(Attention, self).__init__()
self.v = nn.Parameter(torch.z... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Bachery/Shape-driven-Coordinate-Ordering | Attention | false | 16,976 | [
"MIT"
] | 6 | 6afa933a882cbe7a40ddf1de169537eccfe415b7 | https://github.com/Bachery/Shape-driven-Coordinate-Ordering/tree/6afa933a882cbe7a40ddf1de169537eccfe415b7 |
deepmind | # 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_... | Rowing0914/TF2_RL | deepmind | false | 17,908 | [
"MIT"
] | 8 | c1b7f9b376cbecf01deb17f76f8e761035ed336a | https://github.com/Rowing0914/TF2_RL/tree/c1b7f9b376cbecf01deb17f76f8e761035ed336a |
LinearDrop | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
import torch.nn.init
import torch.optim
class Model(nn.Module):
""" Class representing sampleable neural network model """
def num_params(self):
""" Get the number of model parameters. """
return sum(p.numel() ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CBIIT/NCI-DOE-Collab-Pilot2-Autoencoder_MD_Simulation_Data | LinearDrop | false | 13,431 | [
"MIT"
] | 51 | 2b1213f944cf5f2c60799099a469989a1f0a6d3a | https://github.com/CBIIT/NCI-DOE-Collab-Pilot2-Autoencoder_MD_Simulation_Data/tree/2b1213f944cf5f2c60799099a469989a1f0a6d3a |
CriticNetwork | # 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_... | aishikawa/drl-impl | CriticNetwork | false | 9,711 | [
"MIT"
] | 0 | 1afe7426494cd94990cb4dae247486a25dfe37bf | https://github.com/aishikawa/drl-impl/tree/1afe7426494cd94990cb4dae247486a25dfe37bf |
GlobalAttention | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_le... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Katarina11/PreSumm | GlobalAttention | false | 11,612 | [
"MIT"
] | 0 | 616e72f038d512e9e9112af375d66a0b2e3db6cd | https://github.com/Katarina11/PreSumm/tree/616e72f038d512e9e9112af375d66a0b2e3db6cd |
L1CompositionLoss | import functools
import torch
import torch.nn as nn
from torch.nn import 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".
Returns:
Tensor: Reduced lo... | 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... | Juggernaut93/mmediting | L1CompositionLoss | false | 13,900 | [
"Apache-2.0"
] | 1,884 | 8ef46ace29756dd2df1d92f2f73a33646e33e007 | https://github.com/Juggernaut93/mmediting/tree/8ef46ace29756dd2df1d92f2f73a33646e33e007 |
Maxout | # 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... | shadow2496/KAIST_2019_Deep-Learning_HW4 | Maxout | false | 4,302 | [
"MIT"
] | 0 | f837ee23816c7486952733925b1f338b54d7086f | https://github.com/shadow2496/KAIST_2019_Deep-Learning_HW4/tree/f837ee23816c7486952733925b1f338b54d7086f |
wide_basic | # 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... | JunLi-Galios/JEM | wide_basic | false | 11,598 | [
"Apache-2.0"
] | 0 | dd4d33f64269d3999458f129ac83a3043ad7e63f | https://github.com/JunLi-Galios/JEM/tree/dd4d33f64269d3999458f129ac83a3043ad7e63f |
RNNAgent | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
class RNNAgent(nn.Module):
def __init__(self, input_shape, args):
super(RNNAgent, self).__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, args.rnn_hidden_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Sud0x67/mrmix | RNNAgent | false | 18,379 | [
"Apache-2.0"
] | 4 | 4f4784e421c768509bd007e21b4455b56edc7cd2 | https://github.com/Sud0x67/mrmix/tree/4f4784e421c768509bd007e21b4455b56edc7cd2 |
DenseSAGEConv | import math
import torch
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseSAGEConv(torch.nn.Module):
"""See :class:`torch_geometric... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from torch.nn imp... | rbshi/pytorch_geometric | DenseSAGEConv | false | 4,180 | [
"MIT"
] | 0 | fcfbad49219974689eb5c6e32365939ae09ace84 | https://github.com/rbshi/pytorch_geometric/tree/fcfbad49219974689eb5c6e32365939ae09ace84 |
UnpoolingAsConvolution | # 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... | ColinKohler/ActionDyanmicsNetwork | UnpoolingAsConvolution | false | 330 | [
"MIT"
] | 0 | 9cb6ffca111bfb1e1efb31cbac9201a98739a6ed | https://github.com/ColinKohler/ActionDyanmicsNetwork/tree/9cb6ffca111bfb1e1efb31cbac9201a98739a6ed |
Conv2d | # 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 numpy as np
import torch.utils.data
import torch
import torch.nn as nn
im... | Rayhane-mamah/Efficient-VDVAE | Conv2d | false | 8,682 | [
"MIT"
] | 41 | 07bcb8ba58c228ab0ed62c5cf374c19a10932010 | https://github.com/Rayhane-mamah/Efficient-VDVAE/tree/07bcb8ba58c228ab0ed62c5cf374c19a10932010 |
IdentityMessage | import torch
import torch.fx
import torch.utils.data
class IdentityMessage(torch.nn.Module):
def __init__(self, raw_msg_dim: 'int', memory_dim: 'int', time_dim: 'int'):
super(IdentityMessage, self).__init__()
self.out_channels = raw_msg_dim + 2 * memory_dim + time_dim
def forward(self, z_src... | 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.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | HWSelf/pytorch_geometric | IdentityMessage | false | 508 | [
"MIT"
] | 0 | c1214de674079b5e39e57c045d0f844b60caf590 | https://github.com/HWSelf/pytorch_geometric/tree/c1214de674079b5e39e57c045d0f844b60caf590 |
FloorModule | import torch
class FloorModule(torch.nn.Module):
def __init__(self):
super(FloorModule, self).__init__()
def forward(self, x):
return torch.floor(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | MichaelZhero/nncase | FloorModule | false | 11,934 | [
"Apache-2.0"
] | 0 | 0fae6ce90d7adff386e1a286cd2b42422f4b850a | https://github.com/MichaelZhero/nncase/tree/0fae6ce90d7adff386e1a286cd2b42422f4b850a |
PartialConv | # 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
assert_size_stride = torch._C._dynamo.guards.a... | maayan-myheritage/3d-photo-inpainting | PartialConv | false | 10,597 | [
"MIT"
] | 0 | 6293eecfeb55ceba019655723f6efe31e8ecb177 | https://github.com/maayan-myheritage/3d-photo-inpainting/tree/6293eecfeb55ceba019655723f6efe31e8ecb177 |
ScaleNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | nvski/ST-TR | ScaleNorm | false | 4,100 | [
"MIT"
] | 0 | 75aa9fb872af217f8616c01cee7ca6548846260b | https://github.com/nvski/ST-TR/tree/75aa9fb872af217f8616c01cee7ca6548846260b |
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | DonkeyShot21/cassle | Whitening2d | false | 8,000 | [
"MIT"
] | 13 | d25f9c7cb5e822660dc1ef03e7fac09a33d0b1a8 | https://github.com/DonkeyShot21/cassle/tree/d25f9c7cb5e822660dc1ef03e7fac09a33d0b1a8 |
MaxPool2dDynamicSamePadding | # 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... | Kwongy/Pretrained-backbone-Pytorch | MaxPool2dDynamicSamePadding | false | 2,478 | [
"MIT"
] | 0 | 1b24bb677e0fd420cce32715c1ead8f0c804d707 | https://github.com/Kwongy/Pretrained-backbone-Pytorch/tree/1b24bb677e0fd420cce32715c1ead8f0c804d707 |
CAM_Module | from torch.nn import Module
import torch
import torch.utils.data
import torch
from torch.nn import Parameter
from torch.nn import Softmax
class CAM_Module(Module):
""" Channel attention module"""
def __init__(self, in_dim):
super(CAM_Module, self).__init__()
self.chanel_in = in_dim
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | mlcb-jlu/wsMedSeg | CAM_Module | false | 4,021 | [
"MIT"
] | 0 | 63bd1fd28583f11444f292f4b961870ea1b12635 | https://github.com/mlcb-jlu/wsMedSeg/tree/63bd1fd28583f11444f292f4b961870ea1b12635 |
LRN | # 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
import torch.utils.data
assert_size_stride = torch._C._dy... | VisionLearningGroup/CDS | LRN | false | 18,040 | [
"MIT"
] | 7 | 5b3644c286f19f76acdc03c6f6021a6f6e4ec4fc | https://github.com/VisionLearningGroup/CDS/tree/5b3644c286f19f76acdc03c6f6021a6f6e4ec4fc |
CCAMDec | # 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.... | tousifulhaque/DANet | CCAMDec | false | 4,464 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
AtLocPlusCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C.... | xunshengliuyin/ATwvo | AtLocPlusCriterion | false | 4,611 | [
"MIT"
] | 0 | 7d8b7aeb7893cb59d48864a9a35f7de9dce084b4 | https://github.com/xunshengliuyin/ATwvo/tree/7d8b7aeb7893cb59d48864a9a35f7de9dce084b4 |
ConvMeanPool | # 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.functional as F
import torch.nn as nn
assert_size_stride = torch... | tsirif/cortex | ConvMeanPool | false | 16,627 | [
"BSD-3-Clause"
] | 109 | 2837b220f9fb73279df3815bb18b274106412c08 | https://github.com/tsirif/cortex/tree/2837b220f9fb73279df3815bb18b274106412c08 |
CaffeNormalize | # 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | cynthiamao98/DepthAwareCNN | CaffeNormalize | false | 12,234 | [
"MIT"
] | 0 | 824cffaa4159e3dc7cc251a4a659e35c437bb92c | https://github.com/cynthiamao98/DepthAwareCNN/tree/824cffaa4159e3dc7cc251a4a659e35c437bb92c |
FitNet | # 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_... | ahu-hpt/AOMD | FitNet | false | 3,043 | [
"Apache-2.0"
] | 0 | 8d99dbb803feaef55fc089bfb3399d2fb21d55d8 | https://github.com/ahu-hpt/AOMD/tree/8d99dbb803feaef55fc089bfb3399d2fb21d55d8 |
GatedConv2d | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | autocomic/https-github.com-autocomic-DeepFillv2_Pytorch | GatedConv2d | false | 3,147 | [
"MIT"
] | 0 | 7f6712a9b42dfd827879271f13856f1da5d6a032 | https://github.com/autocomic/https-github.com-autocomic-DeepFillv2_Pytorch/tree/7f6712a9b42dfd827879271f13856f1da5d6a032 |
Clamp | # 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... | kmiec96/mlhep-2021-baseline-track_1 | Clamp | false | 12,678 | [
"Apache-2.0"
] | 0 | 6fd2aa1529734204c522c49dba40fdc4b2bce353 | https://github.com/kmiec96/mlhep-2021-baseline-track_1/tree/6fd2aa1529734204c522c49dba40fdc4b2bce353 |
LossPredictionLoss | # 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... | saksman/deepfish_adaptation | LossPredictionLoss | false | 7,590 | [
"MIT"
] | 1 | 0413def87ec1d3cb67fa043a2fb60ef7e0d73539 | https://github.com/saksman/deepfish_adaptation/tree/0413def87ec1d3cb67fa043a2fb60ef7e0d73539 |
Swish | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | lorylei/DARTS-et | Swish | false | 7,128 | [
"Apache-2.0"
] | 1 | f22cfd53c14afd6ba602b8ecfbff9cdf77fc2ff8 | https://github.com/lorylei/DARTS-et/tree/f22cfd53c14afd6ba602b8ecfbff9cdf77fc2ff8 |
ConvDropoutLayerNorm | import torch
from torch import nn
import torch.utils.checkpoint
class SqueezeBertLayerNorm(nn.LayerNorm):
"""
This is a nn.LayerNorm subclass that accepts NCW data layout and performs normalization in the C dimension.
N = batch C = channels W = sequence length
"""
def __init__(self, hidden_size,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Clemens123/transformers | ConvDropoutLayerNorm | false | 11,490 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
GatedMultiHeadAttn | # 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.... | HKUST-KnowComp/BMGF-RoBERTa | GatedMultiHeadAttn | false | 8,216 | [
"MIT"
] | 16 | 8e9eebd7e9fb6cc2492131fc8eaa5b5b29d999fd | https://github.com/HKUST-KnowComp/BMGF-RoBERTa/tree/8e9eebd7e9fb6cc2492131fc8eaa5b5b29d999fd |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | PanagiotisP/demucs | Decoder | false | 11,799 | [
"MIT"
] | 0 | d115d0773ca08a081f5b6bfe274cf0e4ed9e2677 | https://github.com/PanagiotisP/demucs/tree/d115d0773ca08a081f5b6bfe274cf0e4ed9e2677 |
SelfAttentionLayer2 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import *
class SelfAttentionLayer2(nn.Module):
def __init__(self, dim, da):
super(SelfAttentionLayer2, self).__init__()
self.dim = dim
self.Wq = nn.Parameter(torch.zeros(self.dim, self.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.... | RUCAIBox/TG_CRS_Code | SelfAttentionLayer2 | false | 8,672 | [
"Apache-2.0"
] | 27 | 0428a3a069c4d0d4888f2d476dba2cafd7918524 | https://github.com/RUCAIBox/TG_CRS_Code/tree/0428a3a069c4d0d4888f2d476dba2cafd7918524 |
GLU | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.onnx
class GLU(nn.Module):
def __init__(self):
super(GLU, self).__init__()
def forward(self, x):
nc = x.size(1)
assert nc % 2 == 0, 'channels dont divide 2!'
nc = int(nc / 2)
return x[:, :nc] * t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | Nakachi-S/AttnGAN | GLU | false | 869 | [
"MIT"
] | 0 | 2dfd1e38f78f2a58895d81131cd8c17e74dbacb2 | https://github.com/Nakachi-S/AttnGAN/tree/2dfd1e38f78f2a58895d81131cd8c17e74dbacb2 |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: 'float'=2.0, alpha: 'float'=0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
... | 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... | Chuxwa/OpenPCDet | SigmoidFocalClassificationLoss | false | 5,007 | [
"Apache-2.0"
] | 1 | be064eafee68cb23f4bbe7decf2286ef13a94ebb | https://github.com/Chuxwa/OpenPCDet/tree/be064eafee68cb23f4bbe7decf2286ef13a94ebb |
DQN | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class DQN(nn.Module):
def __init__(self, state_dim, out_dim, capacity, bsz, epsilon):
super().__init__()
self.steps_done = 0
self.position = 0
self.pool = []
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | NeilWangziyu/torch_light | DQN | false | 5,684 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
PatchEmbed | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._C
import torch.serialization
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
Args:
patch_size (tuple[int]): Patch token size. Default: (4, 4, 4).
in_chans (int): Number of input image channels. Default:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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_str... | dkswxd/Swin-Transformer-Semantic-Segmentation | PatchEmbed | false | 1,861 | [
"Apache-2.0"
] | 0 | 6af19736e5492a01d8952d4ee86a6d59b21c2ae1 | https://github.com/dkswxd/Swin-Transformer-Semantic-Segmentation/tree/6af19736e5492a01d8952d4ee86a6d59b21c2ae1 |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | andreaspts/DRL_CartPole | QNetwork | false | 9,733 | [
"MIT"
] | 0 | e4f018ab4adaeeaac2902c541e14933b56957e22 | https://github.com/andreaspts/DRL_CartPole/tree/e4f018ab4adaeeaac2902c541e14933b56957e22 |
MyModel | import torch
import torch.nn as nn
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
def forward(self, x):
return x + 1
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | blagojce95/ai-research-mamo-framework | MyModel | false | 1,554 | [
"Apache-2.0"
] | 0 | 7f3b5a5a9fb8b19c9eef453b81b03b6046a33bf2 | https://github.com/blagojce95/ai-research-mamo-framework/tree/7f3b5a5a9fb8b19c9eef453b81b03b6046a33bf2 |
NeuralNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(NeuralNet, self).__init__()
self.layer1 = nn.Linear(input_size, hidden_size)
self.layer2 = nn.Linear(hidden_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Silent-Zebra/sequential_social_dilemma_games | NeuralNet | false | 2,842 | [
"MIT"
] | 0 | 8cf8faebf7de727bac55bd8020be7cd9cc243ccc | https://github.com/Silent-Zebra/sequential_social_dilemma_games/tree/8cf8faebf7de727bac55bd8020be7cd9cc243ccc |
LossEnergy | import torch
from torch import nn
class WaveFunctionLoss(nn.Module):
"""Base class for all wave function loss functions.
Any such loss must be derived from the local energy and wave function
values, :math:`L(\\{E_\\text{loc}[\\psi],\\ln|\\psi|,w\\})`, using also
importance-sampling weights *w*.
... | 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... | PKUfjh/deepqmc | LossEnergy | false | 14,141 | [
"MIT"
] | 224 | 2a948ce712dd4e40568aa35931527e6c874eba73 | https://github.com/PKUfjh/deepqmc/tree/2a948ce712dd4e40568aa35931527e6c874eba73 |
ShuffleBlock | import torch
import torch.nn as nn
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.size(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AlexHoffman9/HAET-2021-competition-baseline-code | ShuffleBlock | false | 11,164 | [
"MIT"
] | 0 | 1d71c94c68c9903854eceda6caf07442930caa44 | https://github.com/AlexHoffman9/HAET-2021-competition-baseline-code/tree/1d71c94c68c9903854eceda6caf07442930caa44 |
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