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 |
|---|---|---|---|---|---|---|---|---|---|---|
LDEPooling | import torch
import torch.nn
class LDEPooling(torch.nn.Module):
"""A novel learnable dictionary encoding layer.
Reference: Weicheng Cai, etc., "A NOVEL LEARNABLE DICTIONARY ENCODING LAYER FOR END-TO-END
LANGUAGE IDENTIFICATION", icassp, 2018
"""
def __init__(self, input_dim, c_num=64,... | 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
assert... | ishine/asv-subtools | LDEPooling | false | 15,645 | [
"Apache-2.0"
] | 370 | 597dcb29a772b8113dbe7ab64f0d4cc1da298707 | https://github.com/ishine/asv-subtools/tree/597dcb29a772b8113dbe7ab64f0d4cc1da298707 |
co_peak_loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | cj4L/DeepCO3-python | co_peak_loss | false | 6,445 | [
"MIT"
] | 1 | fa28ed7b43a3a236d0cc7bf31ce9fd68c01b5888 | https://github.com/cj4L/DeepCO3-python/tree/fa28ed7b43a3a236d0cc7bf31ce9fd68c01b5888 |
HalfMSELoss | # 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.nn.modules.loss import MSELoss
assert_size_stride = torch._C._dynamo.guards.as... | ThayaFluss/candle | HalfMSELoss | false | 1,132 | [
"MIT"
] | 0 | 4a12fde60ffbbf0cb688617fee81aded94c0b613 | https://github.com/ThayaFluss/candle/tree/4a12fde60ffbbf0cb688617fee81aded94c0b613 |
Encoder | # 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 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... | DeterjoSimon/dtu_mlops | Encoder | false | 11,346 | [
"Apache-2.0"
] | 0 | 6484be509c002690b995f399001704c6b0bb42e4 | https://github.com/DeterjoSimon/dtu_mlops/tree/6484be509c002690b995f399001704c6b0bb42e4 |
CAModule | # 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... | Tarandro/Chexpert | CAModule | false | 11,922 | [
"Apache-2.0"
] | 0 | 6bc51f899a479f8dbad8a64c92f35ed4632377b3 | https://github.com/Tarandro/Chexpert/tree/6bc51f899a479f8dbad8a64c92f35ed4632377b3 |
MyMetric | import torch
import torch.nn as nn
class MyMetric(nn.Module):
def __init__(self):
super().__init__()
def forward(self, output, target):
pred = output.argmax(dim=1, keepdim=True)
return pred.eq(target.view_as(pred)).sum() / output.size(0)
def get_inputs():
return [torch.rand([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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Min-Sheng/template | MyMetric | false | 2,650 | [
"MIT"
] | 0 | c58b2c41383668c45cd7851a88c9b0e222d7a25b | https://github.com/Min-Sheng/template/tree/c58b2c41383668c45cd7851a88c9b0e222d7a25b |
NNTest | import torch
import torch.nn as nn
import torch.nn.functional as F
class NNTest(nn.Module):
def __init__(self, input_size, output_size):
super(NNTest, self).__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, 100)
self.fc3 = nn.Linear(100, 50)
self.fc4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | tassotirap/data-science | NNTest | false | 13,029 | [
"Apache-2.0"
] | 0 | 644bc351740cda90c0d8c907132d9da9630266c9 | https://github.com/tassotirap/data-science/tree/644bc351740cda90c0d8c907132d9da9630266c9 |
DQN | # 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_... | stepinski/machinelearning | DQN | false | 12,998 | [
"MIT"
] | 0 | 1f84883a25616da4cd76bb4655267efd3421e561 | https://github.com/stepinski/machinelearning/tree/1f84883a25616da4cd76bb4655267efd3421e561 |
ConvBlockINEDense | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BaekduChoi/Halftoning | ConvBlockINEDense | false | 191 | [
"BSD-3-Clause"
] | 0 | 9459d202c0b3b4e587e6d89af04c4bcfaa604d31 | https://github.com/BaekduChoi/Halftoning/tree/9459d202c0b3b4e587e6d89af04c4bcfaa604d31 |
MCCLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | CarlosPena00/pytorch-unet | MCCLoss | false | 215 | [
"MIT"
] | 0 | 8365bace23e4b04b9c5b75cd6720807ea8cac5ab | https://github.com/CarlosPena00/pytorch-unet/tree/8365bace23e4b04b9c5b75cd6720807ea8cac5ab |
ElectronicAsymptotic | # 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... | PKUfjh/deepqmc | ElectronicAsymptotic | false | 14,140 | [
"MIT"
] | 224 | 2a948ce712dd4e40568aa35931527e6c874eba73 | https://github.com/PKUfjh/deepqmc/tree/2a948ce712dd4e40568aa35931527e6c874eba73 |
AffineChannel2d | import torch
import torch.nn as nn
class AffineChannel2d(nn.Module):
""" A simple channel-wise affine transformation operation """
def __init__(self, num_features):
super().__init__()
self.num_features = num_features
self.weight = nn.Parameter(torch.Tensor(num_features))
self.... | 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... | FVL2020/2DImage2BMI | AffineChannel2d | false | 5,144 | [
"MIT"
] | 1 | 90783bcb6fce0b91fb5ab70f62f595e3cfff39d0 | https://github.com/FVL2020/2DImage2BMI/tree/90783bcb6fce0b91fb5ab70f62f595e3cfff39d0 |
ReduceMin | import torch
import torch.onnx
import torch.nn as nn
class ReduceMin(nn.Module):
def forward(self, x):
return torch.min(x, -1, keepdim=True)[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.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.asse... | mil-tokyo/webdnn | ReduceMin | false | 16,077 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
ANet | # 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
import torch.utils.data
assert_size_stride = torch._C._dyn... | Neronjust2017/TransferBed | ANet | false | 5,648 | [
"MIT"
] | 1 | eaa703a4bc10eaf6216fe1394cd272f6e75489e2 | https://github.com/Neronjust2017/TransferBed/tree/eaa703a4bc10eaf6216fe1394cd272f6e75489e2 |
RMulInt | # 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... | Ilyabasharov/torch2trt | RMulInt | false | 2,549 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
RpowInt | import torch
class RpowInt(torch.nn.Module):
def __init__(self):
super(RpowInt, self).__init__()
def forward(self, x):
return 2 ** 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... | PogChamper/torch2trt | RpowInt | false | 14,228 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
CAM_Use | import torch
import torch.nn as nn
import torch.utils.data
class CAM_Use(nn.Module):
""" Channel attention module"""
def __init__(self, in_dim):
super(CAM_Use, self).__init__()
self.chanel_in = in_dim
self.gamma = nn.Parameter(torch.zeros(1))
def forward(self, x, attention):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | jbcnrlz/san | CAM_Use | false | 3,847 | [
"MIT"
] | 0 | 1eab20f83d3c7dba5607e22d1c70768905b62b12 | https://github.com/jbcnrlz/san/tree/1eab20f83d3c7dba5607e22d1c70768905b62b12 |
Biaffine | # 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... | KoichiYasuoka/diaparser | Biaffine | false | 9,240 | [
"MIT"
] | 0 | ca11e65ef890cee2fbb23f42ae9c711c89767158 | https://github.com/KoichiYasuoka/diaparser/tree/ca11e65ef890cee2fbb23f42ae9c711c89767158 |
BothContextGate | import torch
import torch.nn as nn
import torch.cuda
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 the input from the target ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AndrewM1998/MultimodalNMT | BothContextGate | false | 7,717 | [
"MIT"
] | 40 | b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b | https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b |
SimpleTextClassifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | mtfelix/pytorch_active_learning | SimpleTextClassifier | false | 4,040 | [
"MIT"
] | 0 | 495f20c9cf5100cf2a100f4a4c6103e05fb62ca2 | https://github.com/mtfelix/pytorch_active_learning/tree/495f20c9cf5100cf2a100f4a4c6103e05fb62ca2 |
AttentionModule | # 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.... | BruceWen120/medical-abbreviation-pretraining | AttentionModule | false | 13,444 | [
"Apache-2.0",
"MIT"
] | 125 | 333e49461f7463e97515f949f441c7ac8af7d980 | https://github.com/BruceWen120/medical-abbreviation-pretraining/tree/333e49461f7463e97515f949f441c7ac8af7d980 |
DPFP | # 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.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.... | ppvalluri09/annotated_deep_learning_paper_implementations | DPFP | false | 11,066 | [
"MIT"
] | 0 | 387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 | https://github.com/ppvalluri09/annotated_deep_learning_paper_implementations/tree/387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 |
ResnetBlock | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
import torch.utils.data.distributed
def actvn(x):
out = F.leaky_relu(x, 0.2)
return out
class ResnetBlock(nn.Module):
def __init__(self, fin, fout, fhidden=None, is_bias=True):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import functional as F
import torch.utils.dat... | arnabgho/GAN_stability | ResnetBlock | false | 12,116 | [
"MIT"
] | 0 | 5037d1d856be58818d1c825cd415e0d90d90aff2 | https://github.com/arnabgho/GAN_stability/tree/5037d1d856be58818d1c825cd415e0d90d90aff2 |
ConvGLU | # 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... | Zenodia/NeMo | ConvGLU | false | 1,303 | [
"Apache-2.0"
] | 0 | 3c288d8a7caf667c95444c39434e3ebc5f53d911 | https://github.com/Zenodia/NeMo/tree/3c288d8a7caf667c95444c39434e3ebc5f53d911 |
BMNLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_ma... | HypnosXC/mmaction2 | BMNLoss | false | 13,826 | [
"Apache-2.0"
] | 549 | a26d5f981449445a5e22a0a60d8b285e06c3dd6e | https://github.com/HypnosXC/mmaction2/tree/a26d5f981449445a5e22a0a60d8b285e06c3dd6e |
Concat4 | import torch
import torch.onnx
import torch.nn as nn
class Concat4(nn.Module):
def __init__(self):
super().__init__()
def forward(self, c0, c1, c2, c3):
return torch.cat([c0, c1, c2, c3], dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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 | Concat4 | false | 16,068 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
PMA | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : 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.... | karayanni/torch-scae | PMA | false | 10,441 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 |
LayerShift | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class LayerShift(nn.Module):
def __init__(self, init=1.0):
super().__init__()
self.bias = torch.nn.Parameter(torch.zeros(1))
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_st... | ptillet/Fixup | LayerShift | false | 10,628 | [
"BSD-3-Clause"
] | 0 | c36dbe7f2cce71c4308afc43ab6e8551e567be30 | https://github.com/ptillet/Fixup/tree/c36dbe7f2cce71c4308afc43ab6e8551e567be30 |
BiDAFAttention | # 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.... | JNXSTJ/squad | BiDAFAttention | false | 11,536 | [
"MIT"
] | 0 | ed875a90b212e1fe2f05144edb5595cedb5dd42b | https://github.com/JNXSTJ/squad/tree/ed875a90b212e1fe2f05144edb5595cedb5dd42b |
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AmitNativ1984/masqr | GlobalAvgPool2d | false | 8,864 | [
"MIT"
] | 0 | a57a60d1011aa70317f5893fc05bfb0f029cafb5 | https://github.com/AmitNativ1984/masqr/tree/a57a60d1011aa70317f5893fc05bfb0f029cafb5 |
MiniBatchDiscrimination | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | danielnflam/GAN-Tests | MiniBatchDiscrimination | false | 3,428 | [
"BSD-3-Clause"
] | 0 | f112e27b802d717f64a8f2cfa79b9898667da14c | https://github.com/danielnflam/GAN-Tests/tree/f112e27b802d717f64a8f2cfa79b9898667da14c |
SpatialAttention | import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | YuanZi1501040205/Ice-detection-for-smart-track-heating-with-normal-only-self-supervised-learning | SpatialAttention | false | 1,283 | [
"MIT"
] | 0 | ac7603dc27cf1cc9ce29cfae6c525dd5dbbcdd83 | https://github.com/YuanZi1501040205/Ice-detection-for-smart-track-heating-with-normal-only-self-supervised-learning/tree/ac7603dc27cf1cc9ce29cfae6c525dd5dbbcdd83 |
BertOutAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertOutAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MarSaKi/Recurrent-VLN-BERT | BertOutAttention | false | 13,219 | [
"MIT"
] | 0 | c1170f9ca48c234a0c3ded19f9273f2fdcd571d6 | https://github.com/MarSaKi/Recurrent-VLN-BERT/tree/c1170f9ca48c234a0c3ded19f9273f2fdcd571d6 |
FastRCNNPredictor | import torch
import torch.nn.functional as F
from torch import nn
class FastRCNNPredictor(nn.Module):
def __init__(self, in_channels, mid_channels, num_classes):
super().__init__()
self.fc1 = nn.Linear(in_channels, mid_channels)
self.fc2 = nn.Linear(mid_channels, mid_channels)
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
from torch import nn
assert_s... | yokosyun/instance-segmentation | FastRCNNPredictor | false | 4,630 | [
"MIT"
] | 0 | 5779ae864b24c28300b0ddc4c314e63490215606 | https://github.com/yokosyun/instance-segmentation/tree/5779ae864b24c28300b0ddc4c314e63490215606 |
SimpleStackModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleStackModel(torch.nn.Module):
def __init__(self, dim):
super(SimpleStackModel, self).__init__()
self.dim = dim
def forward(self, a, b):
c = b + b
return torch.stack((a, c), dim=self.dim)
def get_inpu... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleStackModel | false | 3,368 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
BinaryReg | # 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
... | aarushgupta/pytorch_connectomics | BinaryReg | false | 18,184 | [
"MIT"
] | 5 | eb90ada14dbd425a741f481761d1ed9ea633e67c | https://github.com/aarushgupta/pytorch_connectomics/tree/eb90ada14dbd425a741f481761d1ed9ea633e67c |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | mityanony404/TopGraph | Net | false | 4,019 | [
"MIT"
] | 0 | 23595ca5d3dfcd5bc5ebb771800e3fbe9a0d5eed | https://github.com/mityanony404/TopGraph/tree/23595ca5d3dfcd5bc5ebb771800e3fbe9a0d5eed |
BinaryNLLEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | RoderickGu/Pretraining_GPT | BinaryNLLEntropy | false | 17,851 | [
"Apache-2.0"
] | 4 | 0a3ecd38116dc271e273f57490b9b45b660bf401 | https://github.com/RoderickGu/Pretraining_GPT/tree/0a3ecd38116dc271e273f57490b9b45b660bf401 |
ConvMeanPool | import torch
import torch.nn as nn
class ConvMeanPool(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
adjust_padding=False):
super().__init__()
if not adjust_padding:
conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | samsartor/score_sde | ConvMeanPool | false | 7,597 | [
"Apache-2.0"
] | 1 | d25c8d092a68d643c796d771c55f80075aa041d1 | https://github.com/samsartor/score_sde/tree/d25c8d092a68d643c796d771c55f80075aa041d1 |
CosineLinear | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.modules.module import Module
class CosineLinear(Module):
def __i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JosephKJ/class-incremental-learning | CosineLinear | false | 17,517 | [
"MIT"
] | 8 | 689271b84f2e553930ca6687d036ac99bd84b311 | https://github.com/JosephKJ/class-incremental-learning/tree/689271b84f2e553930ca6687d036ac99bd84b311 |
AUGRUCell | import torch
import torch.nn as nn
import torch.nn.functional as F
class AUGRUCell(nn.Module):
' Effect of GRU with attentional update gate (AUGRU). AUGRU combines attention mechanism and GRU seamlessly.\n\n Formally:\n ..math: \tilde{{u}}_{t}^{\\prime}=a_{t} * {u}_{t}^{\\prime} \\\n {h}_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | MIracleyin/RecBole-notebook | AUGRUCell | false | 9,562 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
BboxHead | # 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 itertools import product as product
import torch.nn as nn
assert_size_strid... | Jung-Jun-Uk/UNPG | BboxHead | false | 17,542 | [
"Apache-2.0"
] | 7 | a6f9c1731a68fc035eb8fe8198f5a5c643825a5b | https://github.com/Jung-Jun-Uk/UNPG/tree/a6f9c1731a68fc035eb8fe8198f5a5c643825a5b |
Swish | import torch
import torch.nn as nn
class Swish(nn.Module):
def __init__(self, beta=1):
super(Swish, self).__init__()
self.beta = beta
def forward(self, x):
return x * torch.sigmoid(self.beta * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
r... | 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... | brandstetter-johannes/ocp | Swish | false | 9,963 | [
"MIT",
"BSD-3-Clause"
] | 0 | 69cc90e6bed8aa09222cd77b926d7a34e96302ed | https://github.com/brandstetter-johannes/ocp/tree/69cc90e6bed8aa09222cd77b926d7a34e96302ed |
Upsample | import torch
import torch.nn.functional as F
import torch.nn as nn
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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 |
Smooth_Loss | import torch
import torch.nn as nn
class Smooth_Loss(nn.Module):
def __init__(self):
super(Smooth_Loss, self).__init__()
def forward(self, x):
loss_smooth = torch.mean(torch.abs(x[:, :, :, :-1] - x[:, :, :, 1:])
) + torch.mean(torch.abs(x[:, :, :-1, :] - x[:, :, 1:, :]))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | NeilDG/SGID-PFF | Smooth_Loss | false | 17,740 | [
"MIT"
] | 8 | e027ac65e63f3c052665290cd0438bb7bdeabf9f | https://github.com/NeilDG/SGID-PFF/tree/e027ac65e63f3c052665290cd0438bb7bdeabf9f |
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.... | footoredo/pymarl | Attention | false | 3,520 | [
"Apache-2.0"
] | 0 | 9c62dda7a7ed984e020f2cafab93601342305af2 | https://github.com/footoredo/pymarl/tree/9c62dda7a7ed984e020f2cafab93601342305af2 |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
import torch.distributions
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
class MultiHeadAttention(nn.M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rexiome/NATSpeech | MultiHeadAttention | false | 14,321 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
_Gate | import torch
import torch.nn as nn
class _Gate(nn.Module):
"""Utility class to implement a standard sigmoid gate"""
def __init__(self, in_features: 'int', out_features: 'int'):
super(_Gate, self).__init__()
self.fc = nn.Linear(in_features=in_features, out_features=out_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DavidChoi76/neuralhydrology | _Gate | false | 13,575 | [
"BSD-3-Clause"
] | 144 | a4c284b92934ee973c8b3fedf8a60df60c8feae1 | https://github.com/DavidChoi76/neuralhydrology/tree/a4c284b92934ee973c8b3fedf8a60df60c8feae1 |
Attention | import torch
import torch.nn as nn
class Attention(nn.Module):
""" Applies attention mechanism on the `context` using the `query`.
**Thank you** to IBM for their initial implementation of :class:`Attention`. Here is
their `License
<https://github.com/IBM/pytorch-seq2seq/blob/master/LICENSE>`__.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JiaqiLiu/PyTorch-NLP | Attention | false | 13,897 | [
"BSD-3-Clause"
] | 2,125 | 71d2ce1e8b8da5ab4e7732d1ebf971150986e6c8 | https://github.com/JiaqiLiu/PyTorch-NLP/tree/71d2ce1e8b8da5ab4e7732d1ebf971150986e6c8 |
ActorCriticContinuous | import torch
from torch import nn
import torch.nn.functional as F
class ActorCriticContinuous(nn.Module):
"""
Actor-Critic for continuous action spaces. The network returns a state_value (critic) and
action mean and action standarddeviation (actor). The action is the sampled from a normal
distribution... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | fschur/Advantage-Actor-Critic-for-OpenAi-gym | ActorCriticContinuous | false | 6,705 | [
"MIT"
] | 1 | c130038789425301684825e09e77f17e89d21859 | https://github.com/fschur/Advantage-Actor-Critic-for-OpenAi-gym/tree/c130038789425301684825e09e77f17e89d21859 |
Biaffine | # 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... | LindgeW/DomainAdaption4DependencyParsing | Biaffine | false | 5,538 | [
"Apache-2.0"
] | 1 | 5de136a37d8fe730e4235ed95bf923763fe21ea6 | https://github.com/LindgeW/DomainAdaption4DependencyParsing/tree/5de136a37d8fe730e4235ed95bf923763fe21ea6 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, kernel_size):
super(Actor, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=kernel_size)
self.conv2 = nn.Conv2d(16, 4, kernel_size=kernel_size)
self.pool1 = nn.M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | zwc662/SequentialAttack | Actor | false | 16,844 | [
"MIT"
] | 116 | 677b19c51ea76d794939ee126fccd75ffa0e6fe6 | https://github.com/zwc662/SequentialAttack/tree/677b19c51ea76d794939ee126fccd75ffa0e6fe6 |
SamePadConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class SamePadConv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
super(SamePadConv2d, self).__init__(in_channels, out_channels,
kernel_size, stride,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | DandelionLau/NetworkCollections | SamePadConv2d | false | 17,218 | [
"Apache-2.0"
] | 8 | 29e5cd2091f7085b3241209ed9447f2baadbce41 | https://github.com/DandelionLau/NetworkCollections/tree/29e5cd2091f7085b3241209ed9447f2baadbce41 |
ActorNetwork | # 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.... | indigoLovee/TD3 | ActorNetwork | false | 6,889 | [
"MIT"
] | 1 | 0e86a40c27ec376b52e9f8e0e70db28e7411276b | https://github.com/indigoLovee/TD3/tree/0e86a40c27ec376b52e9f8e0e70db28e7411276b |
VectorQuantizer | # 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 Tensor
from... | vipavlovic/pyprobml | VectorQuantizer | false | 16,687 | [
"MIT"
] | 4,895 | 59a2edc682d0163955db5e2f27491ad772b60141 | https://github.com/vipavlovic/pyprobml/tree/59a2edc682d0163955db5e2f27491ad772b60141 |
BiaffineAttention | import torch
import torch.nn as nn
import torch.utils.data.dataloader
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.nn
class BiaffineAttention(nn.Module):
"""
Adopted from NeuroNLP2:
https://github.com/XuezheMax/NeuroNLP2/blob/master/neuronlp2/nn/modules/attentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | db-bionlp/CLNER | BiaffineAttention | false | 15,157 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
MaxPool2dDynamicSamePadding | import math
import torch
from torch import nn
from torch.nn import functional as F
class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
The padding is operated in forward function by calculating dynamically.
"""
def __init__(se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | ANI717/effecientnet_b7_pneumonia | MaxPool2dDynamicSamePadding | false | 4,768 | [
"MIT"
] | 1 | f8bf71c92bc1ae5a80b8e37b685bf314004001b3 | https://github.com/ANI717/effecientnet_b7_pneumonia/tree/f8bf71c92bc1ae5a80b8e37b685bf314004001b3 |
SpatialAttentionModule | # 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_... | poppy862/Qnet | SpatialAttentionModule | false | 4,128 | [
"Apache-2.0"
] | 0 | da751bc6eb9ae23e0ff9b96fe0afdfd6bed31f8b | https://github.com/poppy862/Qnet/tree/da751bc6eb9ae23e0ff9b96fe0afdfd6bed31f8b |
CELossWeighted | # 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
... | FANG-Xiaolin/uois | CELossWeighted | false | 2,223 | [
"MIT"
] | 0 | 7489e69d1513faf2f3f030a441abdd33ca22304c | https://github.com/FANG-Xiaolin/uois/tree/7489e69d1513faf2f3f030a441abdd33ca22304c |
MultiHeadAttention | import math
import torch
import typing
import torch.multiprocessing
from torch import nn
from torch.nn import functional as F
import torch.optim
import torch.utils.data
import torch.distributed
class MultiHeadAttention(nn.Module):
def __init__(self, channels: 'int', out_channels: 'int', n_heads: 'int',
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
from torch._inductor.runtime.... | mbarnig/vits-train | MultiHeadAttention | false | 7,190 | [
"MIT"
] | 1 | cfb8a0fc91daad868fe3d062ebf85d62edbd7506 | https://github.com/mbarnig/vits-train/tree/cfb8a0fc91daad868fe3d062ebf85d62edbd7506 |
C3 | # 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
from co... | zjgbz/img_cls | C3 | false | 4,680 | [
"MIT"
] | 0 | 513d5ae423d95e008a82a6ffe443db49f8ed9ac2 | https://github.com/zjgbz/img_cls/tree/513d5ae423d95e008a82a6ffe443db49f8ed9ac2 |
Sparsify1D | # 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... | jmhuer/TCN | Sparsify1D | false | 10,301 | [
"MIT"
] | 0 | 8233b2ff5686ef496b113a6984f5100709a503d3 | https://github.com/jmhuer/TCN/tree/8233b2ff5686ef496b113a6984f5100709a503d3 |
BehlerAngular | # 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.... | Avinashpathapati/gnn_molecule | BehlerAngular | false | 16,943 | [
"MIT"
] | 3 | 84b5e92902c638694b872c42d010676bcd3d7658 | https://github.com/Avinashpathapati/gnn_molecule/tree/84b5e92902c638694b872c42d010676bcd3d7658 |
TimeBlock | # 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_... | SakastLord/STGAT | TimeBlock | false | 11,843 | [
"MIT"
] | 0 | 664843b3a55ac55383de1d5400d731376476ea03 | https://github.com/SakastLord/STGAT/tree/664843b3a55ac55383de1d5400d731376476ea03 |
InputConv | # 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_... | Sanjay-Ganeshan/webcam-mouse | InputConv | false | 2,815 | [
"Apache-2.0"
] | 0 | 240d1ee00816440e971c8c747bef02c12f3e5d57 | https://github.com/Sanjay-Ganeshan/webcam-mouse/tree/240d1ee00816440e971c8c747bef02c12f3e5d57 |
BasicModel4_MultiArgs | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel4_MultiArgs(nn.Module):
"""
Slightly modified example model from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2) / x3)
"""
def __init__(self) ->None:
super().__in... | 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... | aravipati12/captum | BasicModel4_MultiArgs | false | 10,113 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
MetaAconC | import torch
import torch.nn as nn
class MetaAconC(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Aditya239233/MDP | MetaAconC | false | 16,895 | [
"MIT"
] | 4 | 87491e1d67e547c11f4bdd5d784d120473429eae | https://github.com/Aditya239233/MDP/tree/87491e1d67e547c11f4bdd5d784d120473429eae |
SquaredReLU | # 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.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.... | techthiyanes/annotated_deep_learning_paper_implementations | SquaredReLU | false | 16,559 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
PointerSwitch | # 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... | CrafterKolyan/TabularSemanticParsing | PointerSwitch | false | 2,109 | [
"BSD-3-Clause"
] | 0 | 2d75a3b71fa4c58f2c14ac43a33916747e8f4d1f | https://github.com/CrafterKolyan/TabularSemanticParsing/tree/2d75a3b71fa4c58f2c14ac43a33916747e8f4d1f |
T5LayerNorm | import torch
import torch.nn as nn
import torch.utils.checkpoint
class T5LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-06):
"""
Construct a layernorm module in the T5 style No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter... | 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.checkpoint
assert_size_stride = torch.... | longquan0609/bert_seq2seq | T5LayerNorm | false | 12,825 | [
"Apache-2.0"
] | 0 | 3aaeb2ea76cd435d53ebcfedd2a080d0c37c1976 | https://github.com/longquan0609/bert_seq2seq/tree/3aaeb2ea76cd435d53ebcfedd2a080d0c37c1976 |
AffineGridGen | from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.nn
from torch.nn.modules.module import Module
class AffineGridGen(Module):
def __init__(self, out_h=240, out_w=240, out_ch=3, use_cuda=True):
super(AffineGridGen, self).__init__()
self.out_h = out_h
self... | 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
import torch.nn
from torch.nn.modules.module import Module
assert_size_stride = torch._C._dynamo.guards.assert_s... | mcimpoi/ncnet | AffineGridGen | false | 16,030 | [
"MIT"
] | 159 | d801df77154bce9e5653090273aacb0e588fa4ea | https://github.com/mcimpoi/ncnet/tree/d801df77154bce9e5653090273aacb0e588fa4ea |
DeepNN_v2 | # 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 ... | SBIlab/NetBio | DeepNN_v2 | false | 11,818 | [
"MIT"
] | 0 | 7abd24b8989cea381147d912f76a72676750b9d2 | https://github.com/SBIlab/NetBio/tree/7abd24b8989cea381147d912f76a72676750b9d2 |
TrainablePositionalEncoding | import torch
import torch.nn as nn
class TrainablePositionalEncoding(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, max_position_embeddings, hidden_size, dropout=0.1):
super(TrainablePositionalEncoding, self).__init__()
self.p... | 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_... | Worm4047/TVR | TrainablePositionalEncoding | false | 14,604 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
BalancedBinaryCrossEntropyWithLogits | import torch
import torch.nn as nn
from typing import Any
class BalancedBinaryCrossEntropyWithLogits(nn.Module):
"""二分类加权交叉熵"""
def __init__(self, reduction: 'str'='mean', class_weight: 'Any'=None,
loss_weight: 'float'=1.0, activation: 'bool'=False, eposion:
'float'=1e-10) ->None:
sup... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | hehaoming/RSI-ChangeDetection | BalancedBinaryCrossEntropyWithLogits | false | 6,799 | [
"MIT"
] | 1 | f24a1d79c03fb9fefc49bc91bc94b3c120992496 | https://github.com/hehaoming/RSI-ChangeDetection/tree/f24a1d79c03fb9fefc49bc91bc94b3c120992496 |
FcCat | import torch
import torch.nn as nn
class FcCat(nn.Module):
def __init__(self, nIn, nOut):
super(FcCat, self).__init__()
self.fc = nn.Linear(nIn, nOut, bias=False)
def forward(self, x):
out = torch.cat((x, self.fc(x)), 1)
return out
def get_inputs():
return [torch.rand([... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | zwh930712/densenet.pytorch | FcCat | false | 16,834 | [
"Apache-2.0"
] | 826 | d1cd5e1957975628286e516512c6d1c14430f810 | https://github.com/zwh930712/densenet.pytorch/tree/d1cd5e1957975628286e516512c6d1c14430f810 |
CRNNcell | import torch
import torch.nn as nn
class CRNNcell(nn.Module):
"""
Convolutional RNN cell that evolves over both time and iterations
Parameters
-----------------
input: 4d tensor, shape (batch_size, channel, width, height)
hidden: hidden states in temporal dimension, 4d tensor, shape (batch_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | myyaqubpython/https-github.com-cq615-Deep-MRI-Reconstruction | CRNNcell | false | 16,122 | [
"Apache-2.0"
] | 260 | 4484cff9f1e19ff9874c279c5c5d6cf2a317ddbf | https://github.com/myyaqubpython/https-github.com-cq615-Deep-MRI-Reconstruction/tree/4484cff9f1e19ff9874c279c5c5d6cf2a317ddbf |
ItemInferenceNetwork | # 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | gpoesia/variational-item-response-theory-public | ItemInferenceNetwork | false | 12,467 | [
"MIT"
] | 0 | 6a0db81068695422dddec8832ce353879c5acb82 | https://github.com/gpoesia/variational-item-response-theory-public/tree/6a0db81068695422dddec8832ce353879c5acb82 |
Conv2dSamePadding | # 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
assert_size_stride = torch.... | Florian-P-Huber/pycrop-yield-prediction | Conv2dSamePadding | false | 9,053 | [
"MIT"
] | 0 | 9c1a000db55589b3480ae3ac2baab8f461947855 | https://github.com/Florian-P-Huber/pycrop-yield-prediction/tree/9c1a000db55589b3480ae3ac2baab8f461947855 |
DenseGCNConv | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def glorot(tensor):
if tensor is not None:
stdv = math.sqrt(6.0 / (tensor.size(-2) + tensor.size(-1)))
tensor.data.uniform_(-stdv, stdv)
def zeros(tensor):
if tensor is not None:
tensor.data.fill_(0)
cl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CFF-Dream/pytorch_geometric | DenseGCNConv | false | 2,059 | [
"MIT"
] | 0 | 7c19ad74957409ee9e07314ce81524b3113b9c84 | https://github.com/CFF-Dream/pytorch_geometric/tree/7c19ad74957409ee9e07314ce81524b3113b9c84 |
ConcatELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | onlyrico/lightning-tutorials | ConcatELU | false | 12,854 | [
"Apache-2.0"
] | 0 | b5d5c4015422f8c70411e57734d73bb6c1472999 | https://github.com/onlyrico/lightning-tutorials/tree/b5d5c4015422f8c70411e57734d73bb6c1472999 |
MaskedL1Loss | # 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... | lichnost/head2head | MaskedL1Loss | false | 3,914 | [
"MIT"
] | 0 | b0ec8b6965c9a32f3727dee9c164a7aaff027c5f | https://github.com/lichnost/head2head/tree/b0ec8b6965c9a32f3727dee9c164a7aaff027c5f |
Cartesian | # 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
from torch import nn
import torch.utils.data
import torch.utils.data.dist... | aslakey/fastMRI | Cartesian | false | 1,484 | [
"MIT"
] | 0 | e94028aeccfdc70472b453c2ef2f072b40a287c7 | https://github.com/aslakey/fastMRI/tree/e94028aeccfdc70472b453c2ef2f072b40a287c7 |
_ResampleNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
class _TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch... | 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.nn.functional as F
assert_size_stride = torc... | amadejkocbek/darts | _ResampleNorm | false | 12,107 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 |
TLU | import torch
from torch import nn
from torch.nn.parameter import Parameter
from torch.optim.lr_scheduler import *
from torch.optim import *
class TLU(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super(TLU, self).__init__()
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from torch.nn.parameter import Parameter
from torch.optim.lr_schedul... | Challyfilio/NAIC2021 | TLU | false | 235 | [
"MIT"
] | 0 | 11b38a920dcc902f9b798dc43ae360062862e6e4 | https://github.com/Challyfilio/NAIC2021/tree/11b38a920dcc902f9b798dc43ae360062862e6e4 |
SynthWide | # 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_... | dengliming/iotnets | SynthWide | false | 1,842 | [
"MIT"
] | 0 | db744e56769c799dbf765a27fc5aa91e3edeaaa3 | https://github.com/dengliming/iotnets/tree/db744e56769c799dbf765a27fc5aa91e3edeaaa3 |
SoftDetectionModule | # 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
... | UditSinghParihar/d2-net | SoftDetectionModule | false | 18,027 | [
"BSD-3-Clause-Clear"
] | 6 | b3592beebe6759cf4cc1acdfd23d603ef059ef30 | https://github.com/UditSinghParihar/d2-net/tree/b3592beebe6759cf4cc1acdfd23d603ef059ef30 |
Generator | # 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.... | Yixuan-Lee/yixuan-lee.github.io | Generator | false | 2,983 | [
"MIT"
] | 0 | 139dd141544302ca1802a6104f7db7aeb1ace825 | https://github.com/Yixuan-Lee/yixuan-lee.github.io/tree/139dd141544302ca1802a6104f7db7aeb1ace825 |
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_... | jsztompka/DuelDQN | QNetwork | false | 3,778 | [
"MIT"
] | 0 | 3b1234425b66034ef233ac988305dc13ffbf7ace | https://github.com/jsztompka/DuelDQN/tree/3b1234425b66034ef233ac988305dc13ffbf7ace |
CatFusion | # 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... | Asichurter/MalFusionFSL | CatFusion | false | 16,979 | [
"MIT"
] | 4 | 713bf64cc07a3489f42941fd2299837075575ac0 | https://github.com/Asichurter/MalFusionFSL/tree/713bf64cc07a3489f42941fd2299837075575ac0 |
Log1p | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | CPJKU/kagglebirds2020 | Log1p | false | 17,034 | [
"MIT"
] | 4 | f86b459389b1d0b0af96ebc9252ffc8496c272e8 | https://github.com/CPJKU/kagglebirds2020/tree/f86b459389b1d0b0af96ebc9252ffc8496c272e8 |
filtered_softmax | import torch
import torch.nn as nn
class filtered_softmax(nn.Module):
def __init__(self):
super(filtered_softmax, self).__init__()
def forward(self, x, label):
x = torch.softmax(x, dim=1)
x = x * label
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.r... | 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
... | AutumnCrocus/shadow_sim | filtered_softmax | false | 16,948 | [
"MIT"
] | 6 | 79ad13ff9bd7131c82f269af32a3970f3e4bf2ca | https://github.com/AutumnCrocus/shadow_sim/tree/79ad13ff9bd7131c82f269af32a3970f3e4bf2ca |
Attention | import torch
from torch import nn
class Attention(nn.Module):
def __init__(self, heads, dim, hidden_dim):
super().__init__()
self.dim = dim
self.hdim = hidden_dim
self.heads = heads
self.to_q = nn.Linear(dim, hidden_dim * heads)
self.to_k = nn.Linear(dim, hidden_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rish-16/audio-tf-pytorch | Attention | false | 7,562 | [
"MIT"
] | 1 | 397a6e9f1a97cce774202d392eb9706f0483405c | https://github.com/rish-16/audio-tf-pytorch/tree/397a6e9f1a97cce774202d392eb9706f0483405c |
Value | import torch
import torch.nn as nn
import torch.nn.functional as F
class Value(nn.Module):
def __init__(self, num_inputs):
super(Value, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
self.affine2 = nn.Linear(64, 64)
self.value_head = nn.Linear(64, 1)
self.value_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | dragen1860/TRPO-Pytorch | Value | false | 6,597 | [
"MIT"
] | 1 | c5a8e5ac890ec50e331db12fd5885dd4fb753a3b | https://github.com/dragen1860/TRPO-Pytorch/tree/c5a8e5ac890ec50e331db12fd5885dd4fb753a3b |
SelfAttentionWide | # 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.... | wjeliot/former | SelfAttentionWide | false | 13,106 | [
"MIT"
] | 0 | 38bd29b68b110e1e3eddae3106f7db2ffc0e5ce8 | https://github.com/wjeliot/former/tree/38bd29b68b110e1e3eddae3106f7db2ffc0e5ce8 |
ATTA | # 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... | dreamflake/ODI | ATTA | false | 6,601 | [
"MIT"
] | 1 | d58001b96821c8a74d6ebb5402bd2be2b524890a | https://github.com/dreamflake/ODI/tree/d58001b96821c8a74d6ebb5402bd2be2b524890a |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kyteinsky/OmniNet | PositionwiseFeedForward | false | 15,867 | [
"Apache-2.0"
] | 525 | 497dfbeaa9e4bdd8b076152e71ab7999ca5cfc4a | https://github.com/kyteinsky/OmniNet/tree/497dfbeaa9e4bdd8b076152e71ab7999ca5cfc4a |
KdCeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class KdCeLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits_S, logits_T, temperature=1):
"""
Calculate the cross entropy between logits_S and logits_T
:param logits_S: Tensor of... | 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
... | Raiselimit/TorchBlocks | KdCeLoss | false | 5,761 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
WingLoss | # 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
... | Daiver/torch_fuze | WingLoss | false | 5,038 | [
"MIT"
] | 1 | 6b7ad568e2d7549c7f0c0d4c309532ac1b92881d | https://github.com/Daiver/torch_fuze/tree/6b7ad568e2d7549c7f0c0d4c309532ac1b92881d |
SpatialGatherModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class SpatialGatherModule(nn.Module):
"""Aggregate the context features according to the initial predicted
probability distribution.
Employ the soft-weighted method to aggregate the context.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AlexanderDokuchaev/mmsegmentation | SpatialGatherModule | false | 11,205 | [
"Apache-2.0"
] | 0 | 0c443ee370cce6227661b802184072174c4e3f64 | https://github.com/AlexanderDokuchaev/mmsegmentation/tree/0c443ee370cce6227661b802184072174c4e3f64 |
JaccardLoss | # 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.... | KeeratKG/pytorch_connectomics | JaccardLoss | false | 709 | [
"MIT"
] | 0 | ba168da6f077ccfbeffcd8936df90ba413895086 | https://github.com/KeeratKG/pytorch_connectomics/tree/ba168da6f077ccfbeffcd8936df90ba413895086 |
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