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 |
|---|---|---|---|---|---|---|---|---|---|---|
NoisyOneHiddenLayer | # 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.... | MichaelArbel/MMD-gradient-flow | NoisyOneHiddenLayer | false | 17,711 | [
"BSD-3-Clause"
] | 5 | aa7be78c53c1995ae156fb04b6f1b4fcf02dd039 | https://github.com/MichaelArbel/MMD-gradient-flow/tree/aa7be78c53c1995ae156fb04b6f1b4fcf02dd039 |
LinearDiag | # 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... | CSer-Tang-hao/FS-KTN | LinearDiag | false | 7,832 | [
"MIT"
] | 19 | 8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 | https://github.com/CSer-Tang-hao/FS-KTN/tree/8e5b1637e0f86f9d29dad7ff740a9c7a4a292a74 |
BaseFactorizationMachine | import torch
import torch.nn as nn
class BaseFactorizationMachine(nn.Module):
"""Calculate FM result over the embeddings
Args:
reduce_sum: bool, whether to sum the result, default is True.
Input:
input_x: tensor, A 3D tensor with shape:``(batch_size,field_size,embed_dim)``.
Output
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Ahren09/RecBole | BaseFactorizationMachine | false | 1,923 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
AddNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | edchengmoore/pytorch_tabular | AddNorm | false | 3,460 | [
"MIT"
] | 0 | 25f87089fbed95b46f2a1a8a96fba1f581aa8af1 | https://github.com/edchengmoore/pytorch_tabular/tree/25f87089fbed95b46f2a1a8a96fba1f581aa8af1 |
TorchJaccardLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Spiruel/solaris | TorchJaccardLoss | false | 11,889 | [
"Apache-2.0"
] | 0 | eb2ce05265a462d69b01ee2b621a85a3e9082402 | https://github.com/Spiruel/solaris/tree/eb2ce05265a462d69b01ee2b621a85a3e9082402 |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, inS, outS):
super().__init__()
self.input_size = inS
self.fc1 = nn.Linear(in_features=inS, out_features=120)
self.fc2 = nn.Linear(in_features=120, out_features=60)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Thytu/MLOPS | Network | false | 11,940 | [
"MIT"
] | 0 | 08e07e8fbe7621da1407276f68dff2dbcc2d8097 | https://github.com/Thytu/MLOPS/tree/08e07e8fbe7621da1407276f68dff2dbcc2d8097 |
DiracConv | import torch
import torch.utils.data
import torch.nn as nn
class DiracConv(nn.Module):
"""
DiracNetV2 specific convolution block with pre-activation.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | HyperGAN/imgclsmob | DiracConv | false | 17,675 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
FinalTanh | # 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.... | MLforHealth/state_representations_for_RLinHealth | FinalTanh | false | 8,512 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
DeltaGFit | # 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.triton_helpers import math as tl_math
import torch.... | sajetan/PyHDX | DeltaGFit | false | 4,310 | [
"MIT"
] | 0 | f764849e33b2dd1bcae5824795a38c64ef01e13c | https://github.com/sajetan/PyHDX/tree/f764849e33b2dd1bcae5824795a38c64ef01e13c |
ReshapeF | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out ... | 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
import torch.nn as nn
assert_size_stride =... | bronemos/contrastive-unpaired-translation-focal | ReshapeF | false | 3,248 | [
"BSD-3-Clause"
] | 0 | 50b9008d08a86439ede081a910d02df5da8e32df | https://github.com/bronemos/contrastive-unpaired-translation-focal/tree/50b9008d08a86439ede081a910d02df5da8e32df |
CNN | import torch
from torch import nn
import torch.nn.functional as F
class CNN(torch.nn.Module):
"""Basic CNN architecture."""
def __init__(self, in_channels=1):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 64, 8, 1)
self.conv2 = nn.Conv2d(64, 128, 6, 2)
self.c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | jubueche/cleverhans | CNN | false | 10,322 | [
"MIT"
] | 0 | 2e45b75ccc7b04ffec27fd9e6079f00451586266 | https://github.com/jubueche/cleverhans/tree/2e45b75ccc7b04ffec27fd9e6079f00451586266 |
Net | import torch
import torch.nn
import torch.nn.functional as F
import torch.nn as nn
class Net(nn.Module):
def __init__(self, n_feature, n_hidden1, n_hidden2, n_hidden3,
n_hidden4, n_hidden5, n_output):
super(Net, self).__init__()
self.hidden1 = torch.nn.Linear(n_feature, n_hidden1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | smit25/Code-Clone-Detection-Using-Intermediate-Merge-Siamese-Network | Net | false | 10,789 | [
"MIT"
] | 0 | 73298836b565febf67c2144a4cacdd2a039d8677 | https://github.com/smit25/Code-Clone-Detection-Using-Intermediate-Merge-Siamese-Network/tree/73298836b565febf67c2144a4cacdd2a039d8677 |
Selection | import torch
import torch.nn as nn
class Selection(nn.Module):
"""
Selection neurons to sample from a latent representation for a decoder agent.
An abstract representation :math:`l_i` is disturbed by a value :math:`r_i` sampled from a normal
standard distribution which is scaled by the selection neur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | HendrikPN/reinforced_scinet | Selection | false | 17,375 | [
"Apache-2.0"
] | 4 | b57c9d1d997cc56647db4faa0690364e7039a5ee | https://github.com/HendrikPN/reinforced_scinet/tree/b57c9d1d997cc56647db4faa0690364e7039a5ee |
Upsample4x | # 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... | xqterry/lightweight-human-pose-estimation.pytorch | Upsample4x | false | 4,595 | [
"Apache-2.0"
] | 0 | e5ec9452c9bd9683451d3b2f97c6fe9e075b2d48 | https://github.com/xqterry/lightweight-human-pose-estimation.pytorch/tree/e5ec9452c9bd9683451d3b2f97c6fe9e075b2d48 |
ActNorm2D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parame... | lingzenan/invertible-resnet | ActNorm2D | false | 7,102 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
LinearARD | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
class LinearARD(nn.Module):
"""
Dense layer implementation with weights ARD-prior (arxiv:1701.05369)
"""
def __init__(self, in_features, out_features, bias=True, thresh=3,
ard_init=-10):
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
from torch._inductor.runtime.... | x-zho14/pytorch_ard | LinearARD | false | 10,991 | [
"MIT"
] | 0 | 5a9b790f33bf0340b2b3a2108c45d97786a2be86 | https://github.com/x-zho14/pytorch_ard/tree/5a9b790f33bf0340b2b3a2108c45d97786a2be86 |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ago3/VLP | BertSelfAttention | false | 9,980 | [
"Apache-2.0"
] | 0 | 4dec0e04b8592f4a74fe66c253dbb92574e7e2ba | https://github.com/Ago3/VLP/tree/4dec0e04b8592f4a74fe66c253dbb92574e7e2ba |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | DarekGit/neural_style | TVLoss | false | 8,935 | [
"MIT"
] | 0 | 461f0d791f23e82bbf0adcecf5630854ccac9944 | https://github.com/DarekGit/neural_style/tree/461f0d791f23e82bbf0adcecf5630854ccac9944 |
DDPGActorVersion1 | # 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.... | Brandon-HY-Lin/deep-reinforcement-learning | DDPGActorVersion1 | false | 170 | [
"MIT"
] | 0 | d809851b6f98d1089379392d4687e2acaf1c0c79 | https://github.com/Brandon-HY-Lin/deep-reinforcement-learning/tree/d809851b6f98d1089379392d4687e2acaf1c0c79 |
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.... | macg0406/Transformer | EncoderLayer | false | 12,762 | [
"Apache-2.0"
] | 0 | 8c747a6e9f108c63ecc600bf14cde6827b438172 | https://github.com/macg0406/Transformer/tree/8c747a6e9f108c63ecc600bf14cde6827b438172 |
UPChannelBAN | import torch
import torch.nn.functional as F
import torch.nn as nn
def xcorr_fast(x, kernel):
"""group conv2d to calculate cross correlation, fast version
"""
batch = kernel.size()[0]
pk = kernel.view(-1, x.size()[1], kernel.size()[2], kernel.size()[3])
px = x.view(1, -1, x.size()[2], x.size()[3])... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Edwardsoft/siamban | UPChannelBAN | false | 9,207 | [
"Apache-2.0"
] | 0 | f89e70485437fa240bcf4ee4929e3cb6d5211ebc | https://github.com/Edwardsoft/siamban/tree/f89e70485437fa240bcf4ee4929e3cb6d5211ebc |
SeparableConv2d_same | # 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.nn.functional as F
assert_size_stride = torch... | Mnsy-Syl/new_20201103 | SeparableConv2d_same | false | 14,057 | [
"MIT"
] | 46 | 9ee39f1c69a4cba896b30f007560fcbe8ac89c02 | https://github.com/Mnsy-Syl/new_20201103/tree/9ee39f1c69a4cba896b30f007560fcbe8ac89c02 |
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... | NVIDIA-AI-IOT-private/torch2trt | Div | false | 10,505 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
CQLAgent | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | netlab-lcy/CMIX | CQLAgent | false | 7,343 | [
"MIT"
] | 1 | 53e2d8794af2b380295efe06dcb05235089953c1 | https://github.com/netlab-lcy/CMIX/tree/53e2d8794af2b380295efe06dcb05235089953c1 |
RPN_Up | import torch
import torch.nn as nn
import torch.nn.functional as F
class RPN_Up(nn.Module):
"""
For SiamRPN
"""
def __init__(self, anchor_nums=5, inchannels=256, outchannels=256,
cls_type='thicker'):
super(RPN_Up, self).__init__()
self.anchor_nums = anchor_nums
self.in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | Re3write/siamdw | RPN_Up | false | 11,912 | [
"MIT"
] | 0 | f5d7d4bda36cb8c14e93b460fbc77bb225aa8572 | https://github.com/Re3write/siamdw/tree/f5d7d4bda36cb8c14e93b460fbc77bb225aa8572 |
MultiHeadGeometryAttention | # 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.... | jianqingxie/RSTNet | MultiHeadGeometryAttention | false | 15,699 | [
"BSD-3-Clause"
] | 68 | aaa7b5be08e5ec9e79e14ed3e6a04fc3d50483be | https://github.com/jianqingxie/RSTNet/tree/aaa7b5be08e5ec9e79e14ed3e6a04fc3d50483be |
Encoder1 | import torch
import torch.nn as nn
class Encoder1(nn.Module):
def __init__(self, model=None, fixed=False):
super(Encoder1, self).__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv11 = nn.Conv2d(3, 64, 3, 1, 0, dilation=1)
self.relu = nn.ReLU(inpl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MingSun-Tse/Collaborative-Distillation | Encoder1 | false | 14,018 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 |
ReduceSum3 | # 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.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 | ReduceSum3 | false | 16,075 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
OnnxErf | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ENOT-AutoDL/onnx2torch | OnnxErf | false | 13,616 | [
"Apache-2.0"
] | 144 | 2391987b3349bed1670ac3c1bc9062a37323abe3 | https://github.com/ENOT-AutoDL/onnx2torch/tree/2391987b3349bed1670ac3c1bc9062a37323abe3 |
MuSigmaEncoder | import torch
import torch.nn as nn
class MuSigmaEncoder(nn.Module):
"""
Maps a representation r to mu and sigma which will define the normal
distribution from which we sample the latent variable z.
Parameters
----------
r_dim : int
Dimension of output representation r.
z_dim : 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
import torch.nn as nn
assert_... | JeremyAlain/meta_learning_pacoh | MuSigmaEncoder | false | 5,390 | [
"MIT"
] | 1 | b4c2c37d9715e74542bab556ac1f5d778cc3409c | https://github.com/JeremyAlain/meta_learning_pacoh/tree/b4c2c37d9715e74542bab556ac1f5d778cc3409c |
SigSoftmaxV2 | # 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
i... | DingYuan0118/DeepEMD | SigSoftmaxV2 | false | 5,077 | [
"MIT"
] | 1 | a91f77c3da16fecefa62b14aa8b2f195b0e49b84 | https://github.com/DingYuan0118/DeepEMD/tree/a91f77c3da16fecefa62b14aa8b2f195b0e49b84 |
InvConvNear | # 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
assert_size_stride = torch._C._dyna... | entn-at/GradTTS | InvConvNear | false | 15,317 | [
"MIT"
] | 55 | d31cbf41211615a01fffc3812715e3f7f2be214d | https://github.com/entn-at/GradTTS/tree/d31cbf41211615a01fffc3812715e3f7f2be214d |
SoftmaxOutputLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class OutputLayer(nn.Module):
"""
Abstract base class for output layer.
Handles projection to output labels
"""
def __init__(self, hidden_size, output_size):
super(OutputLayer, self).__init__()
self.output_size = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | markiewagner/torchnlp | SoftmaxOutputLayer | false | 16,010 | [
"Apache-2.0"
] | 262 | 92f0a98c7c2b407508810834cbfd544214481695 | https://github.com/markiewagner/torchnlp/tree/92f0a98c7c2b407508810834cbfd544214481695 |
Envelope | # 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.fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | JinheonBaek/pytorch_geometric | Envelope | false | 17,513 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 |
UGRNNLRCell | # 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 ... | Shenzhen-Cloudatawalk-Technology-Co-Ltd/EdgeML | UGRNNLRCell | false | 14,417 | [
"MIT"
] | 719 | ef9f8a77f096acbdeb941014791f8eda1c1bc35b | https://github.com/Shenzhen-Cloudatawalk-Technology-Co-Ltd/EdgeML/tree/ef9f8a77f096acbdeb941014791f8eda1c1bc35b |
IAdd | import torch
class IAdd(torch.nn.Module):
def __init__(self):
super(IAdd, self).__init__()
def forward(self, x, y):
x += y
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | Ilyabasharov/torch2trt | IAdd | false | 2,530 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
SoftCrossEntropyLoss2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
class SoftCrossEntropyLoss2d(nn.Module):
def __init__(self):
super(SoftCrossEntropyLoss2d, self).__init__()
def forward(self, inputs, targets):
loss = 0
inputs = -F.log_softmax(inputs, dim=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Gaussianer/FasterSeg | SoftCrossEntropyLoss2d | false | 17,308 | [
"MIT"
] | 6 | f2e102b433275ac9f3387a8c2ae8439b2687bfda | https://github.com/Gaussianer/FasterSeg/tree/f2e102b433275ac9f3387a8c2ae8439b2687bfda |
MultiRelu | # 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... | ngduduong/captum | MultiRelu | false | 4,068 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
ToRGB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.utils.data
import torch
import torch.nn as nn
import to... | bronemos/contrastive-unpaired-translation-focal | ToRGB | false | 3,251 | [
"BSD-3-Clause"
] | 0 | 50b9008d08a86439ede081a910d02df5da8e32df | https://github.com/bronemos/contrastive-unpaired-translation-focal/tree/50b9008d08a86439ede081a910d02df5da8e32df |
MSELoss | # 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
... | CarlosPena00/pytorch-unet | MSELoss | false | 205 | [
"MIT"
] | 0 | 8365bace23e4b04b9c5b75cd6720807ea8cac5ab | https://github.com/CarlosPena00/pytorch-unet/tree/8365bace23e4b04b9c5b75cd6720807ea8cac5ab |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
.. math::
\\begin{array}{ll}
x = context*output \\\\
attn = exp(x_i) / sum_j exp(x_j) \\\\
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HanSeokhyeon/speech_recognition_for_multi_language | Attention | false | 9,061 | [
"Apache-2.0"
] | 0 | 6219186146ec4e47dcb7ac46cdb74ca49dad7770 | https://github.com/HanSeokhyeon/speech_recognition_for_multi_language/tree/6219186146ec4e47dcb7ac46cdb74ca49dad7770 |
Get_gradient_nopadding | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
import torch.jit
class Get_gradient_nopadding(nn.Module):
def __init__(self):
super(Get_gradient_nopadding, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 0, 0], [-1, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | BlueAmulet/BasicSR | Get_gradient_nopadding | false | 7,811 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
CrossEntropyLossWithOHEM | # 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... | ForrestPi/semanticSegmentation | CrossEntropyLossWithOHEM | false | 17,344 | [
"MIT"
] | 7 | 1e5519279e2a9574f09eaf91439138b74b0f860c | https://github.com/ForrestPi/semanticSegmentation/tree/1e5519279e2a9574f09eaf91439138b74b0f860c |
ChanNorm | import torch
from torch import nn
class ChanNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
std = torch.var(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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Asha-Gutlapalli/StyleGAN2-Art | ChanNorm | false | 16,949 | [
"MIT"
] | 4 | 5a8a8ad61183e82abafe587d755a7fbce28aa8f0 | https://github.com/Asha-Gutlapalli/StyleGAN2-Art/tree/5a8a8ad61183e82abafe587d755a7fbce28aa8f0 |
ParallelAttention | import torch
import torch.nn as nn
class ParallelAttention(nn.Module):
def __init__(self, embedding_size, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.embedding_size = embedding_size
self.ques_linear = nn.Linear(self.embedding_size, 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 import triton_helpers
from torch._inductor.runtime.... | alasin/vqa_pytorch | ParallelAttention | false | 6,169 | [
"MIT"
] | 1 | 8a311226d8eea56ef79f6be3c864ec05768e2895 | https://github.com/alasin/vqa_pytorch/tree/8a311226d8eea56ef79f6be3c864ec05768e2895 |
SoftmaxScaleController | import torch
class ScaleControllerBase(torch.nn.Module):
"""
The base class for ScaleController.
ScaleController is a callable class that re-scale input tensor's value.
Traditional scale method may include:
soft-max, L2 normalize, relu and so on.
Advanced method:
Learnable scale pa... | 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
assert_size_stride = t... | niloofar17/MetaDialog | SoftmaxScaleController | false | 16,196 | [
"Apache-2.0"
] | 204 | d75b84a02807d53d9596e72c2f698e5a4f180369 | https://github.com/niloofar17/MetaDialog/tree/d75b84a02807d53d9596e72c2f698e5a4f180369 |
FeatNet | # 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 ... | DongChengdongHangZhou/adversarial-attack-iris | FeatNet | false | 11,383 | [
"Apache-2.0"
] | 0 | ae7e408c47c332fc876d572acd4701e4b8970487 | https://github.com/DongChengdongHangZhou/adversarial-attack-iris/tree/ae7e408c47c332fc876d572acd4701e4b8970487 |
TripletGCN | # 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.... | Brain03Yao/M2TGCN | TripletGCN | false | 17,024 | [
"MIT"
] | 6 | 72c65687fa52c618740cd6d1db7366116f68398c | https://github.com/Brain03Yao/M2TGCN/tree/72c65687fa52c618740cd6d1db7366116f68398c |
SBlock | import torch
import torch.nn as nn
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
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.... | gaopengcuhk/deit | SBlock | false | 3,524 | [
"Apache-2.0"
] | 0 | de7db8f3a12c35e5e554b385030c574b7c78aaa6 | https://github.com/gaopengcuhk/deit/tree/de7db8f3a12c35e5e554b385030c574b7c78aaa6 |
OutConv | import torch
import torch.nn as nn
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | AtharvBhat/EstimateDepth | OutConv | false | 11,212 | [
"MIT"
] | 0 | f440a9e8372ca2346cae8634f396bac06d004bf7 | https://github.com/AtharvBhat/EstimateDepth/tree/f440a9e8372ca2346cae8634f396bac06d004bf7 |
LinearClassifier | import logging
import random
import torch
import torch.nn.functional as F
import torch.nn as nn
from typing import List
import torch.onnx.operators
from functools import wraps
def singleton(cls):
"""
Singleton decorator
Args:
cls: singleton class
Returns:
- an instance of a singleton... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | godweiyang/ParaGen | LinearClassifier | false | 15,450 | [
"Apache-2.0"
] | 50 | 9665d1244ea38a41fc06b4e0a7f6411985e2221f | https://github.com/godweiyang/ParaGen/tree/9665d1244ea38a41fc06b4e0a7f6411985e2221f |
TripletLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C.... | ketan-lambat/contrastive-unpaired-translation | TripletLoss | false | 3,825 | [
"BSD-3-Clause"
] | 0 | ea71b3a9603a51b97f1fa8426d5a1beae9260a0d | https://github.com/ketan-lambat/contrastive-unpaired-translation/tree/ea71b3a9603a51b97f1fa8426d5a1beae9260a0d |
AconC | import torch
import torch.nn as nn
class AconC(nn.Module):
""" ACON activation (activate or not).
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"""
def __i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | cuiboyuan/plato | AconC | false | 15,086 | [
"Apache-2.0"
] | 135 | 260b785cbbf8588c92331d6343211ff72321f90e | https://github.com/cuiboyuan/plato/tree/260b785cbbf8588c92331d6343211ff72321f90e |
TripletLoss | import torch
from torch import nn
from torch.nn import functional as F
class TripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a postive sample and a negative sample
"""
def __init__(self):
super(TripletLoss, self).__init__()
def forward(self, anchor, pos... | 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
from torch import nn
assert_... | CV-ZMH/human-action-recognition | TripletLoss | false | 7,886 | [
"MIT"
] | 36 | 009bd1da71c087c3071173b325e34ed342599581 | https://github.com/CV-ZMH/human-action-recognition/tree/009bd1da71c087c3071173b325e34ed342599581 |
FitNet | import torch
import torch.nn as nn
class FitNet(nn.Module):
def __init__(self, in_feature, out_feature):
super().__init__()
self.in_feature = in_feature
self.out_feature = out_feature
self.transform = nn.Conv2d(in_feature, out_feature, 1, bias=False)
self.transform.weight.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 |
gen_ba_cf | # 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.... | layel2/layyer-lib | gen_ba_cf | false | 3,903 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
ImageToSequence | import torch
from typing import NamedTuple
from torch.nn.utils.rnn import pack_padded_sequence
def image_to_sequence(x, columnwise=True, return_packed=False):
x, xs = (x.data, x.sizes) if isinstance(x, PaddedTensor) else (x, None)
if x.dim() == 2:
x = x.view(1, 1, x.size(0), x.size(1))
elif x.dim(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import NamedTuple
from torch.nn.utils.rnn import pack_padded_sequence
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | eivtho/PyLaia | ImageToSequence | false | 15,284 | [
"MIT"
] | 89 | 2a7a6e2eeb9b5af68c0faed0c564b02063e72be0 | https://github.com/eivtho/PyLaia/tree/2a7a6e2eeb9b5af68c0faed0c564b02063e72be0 |
PatchEmbed | # 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 chain as chain
import torch.utils.data
import torch.nn as ... | JerryYLi/SlowFast | PatchEmbed | false | 11,569 | [
"Apache-2.0"
] | 0 | 70bbd8d917c49f86b41fdd7c2de5c1231e6d950c | https://github.com/JerryYLi/SlowFast/tree/70bbd8d917c49f86b41fdd7c2de5c1231e6d950c |
VGGSiameseNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class VGGSiameseNet(nn.Module):
def __init__(self):
super(VGGSiameseNet, self).__init__()
self.conv11 = nn.Conv2d(1, 64, 3)
self.conv12 = nn.Conv2d(64, 64, 3)
self.conv21 = nn.Conv2d(64, 128, 3)
self.conv22... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | christnp/comse6998-project | VGGSiameseNet | false | 9,973 | [
"MIT"
] | 0 | 7deffaceb945ae0bd4851ff9478a7efe6e486d39 | https://github.com/christnp/comse6998-project/tree/7deffaceb945ae0bd4851ff9478a7efe6e486d39 |
ScaledDotProductAttention | import torch
import numpy as np
import torch.utils.data
class ScaledDotProductAttention(torch.nn.Module):
"""
Scaled, softmax attention module for Transformer as defined by
Attention(Q, K, V) on pg 4. Returns the final attention vectors as well as
the attention matrices (pairwise scores). """
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | icemansina/protein-transformer | ScaledDotProductAttention | false | 6,853 | [
"BSD-3-Clause"
] | 1 | 4e73b17f2a4b89ba1a9f6703976d1a31b7a8a5eb | https://github.com/icemansina/protein-transformer/tree/4e73b17f2a4b89ba1a9f6703976d1a31b7a8a5eb |
DeepSupervisionModule | import torch
import torch.nn as nn
class DeepSupervisionModule(nn.Module):
def __init__(self, up_sampling_factors=(2, 2, 2)):
super(DeepSupervisionModule, self).__init__()
self.up = nn.UpsamplingBilinear2d(scale_factor=2)
self.up_sampling_factors = up_sampling_factors
def forward(sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | rinkwitz/Thesis_Semantic_Image_Segmentation_on_Satellite_Imagery_using_UNets | DeepSupervisionModule | false | 7,566 | [
"MIT"
] | 1 | 75d3a4a536f6ef81fe0efd4f5fbba32b627a7472 | https://github.com/rinkwitz/Thesis_Semantic_Image_Segmentation_on_Satellite_Imagery_using_UNets/tree/75d3a4a536f6ef81fe0efd4f5fbba32b627a7472 |
BasicModel2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel2(nn.Module):
"""
Example model one from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1) - 1 - ReLU(x2))
"""
def __init__(self) ->None:
super().__init__()
def forward(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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LMdeLiangMi/captum | BasicModel2 | false | 5,469 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
conv_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.triton_helpers import libdevice
import torch.nn as ... | ShaharLutatiPersonal/hyperhypernetworks | conv_block | false | 2,827 | [
"MIT"
] | 0 | 16e2595d89ad0533c9d5a2c62870fb90f1b1dc42 | https://github.com/ShaharLutatiPersonal/hyperhypernetworks/tree/16e2595d89ad0533c9d5a2c62870fb90f1b1dc42 |
GaussionConvF | # 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.... | EdisonLeeeee/GraphGallery | GaussionConvF | false | 13,630 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
HINet | import torch
import torch.nn as nn
class HINet(nn.Module):
def __init__(self, in_ch):
super(HINet, self).__init__()
self.instance_norm = nn.InstanceNorm2d(in_ch - in_ch // 2, affine=True)
def forward(self, x):
channels = x.shape[1]
channels_i = channels - channels // 2
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AmaldevHari/Ghost-DeblurGAN | HINet | false | 7,681 | [
"MIT"
] | 16 | e725e5dad6a5fa5865d317e6644d96d0e800eae6 | https://github.com/AmaldevHari/Ghost-DeblurGAN/tree/e725e5dad6a5fa5865d317e6644d96d0e800eae6 |
Nullifier | import torch
import torch.nn as nn
class Nullifier(nn.Container):
def __init__(self):
super(Nullifier, self).__init__()
def forward(self, inTensor):
outTensor = inTensor.clone()
outTensor.fill_(0.0)
return outTensor
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | haoruilee/DeepSets | Nullifier | false | 15,485 | [
"Apache-2.0"
] | 213 | b405dd6b51a34fb1ef622e25e6685b417b7b7cbb | https://github.com/haoruilee/DeepSets/tree/b405dd6b51a34fb1ef622e25e6685b417b7b7cbb |
MSELoss | import torch
import torch.utils.data
from torch import nn
class MSELoss(nn.Module):
def __init__(self):
super(self.__class__, self).__init__()
def forward(self, input, target):
return torch.mean(torch.sum((input - target) ** 2, 1))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | kejiejiang/UnsupervisedDeepLearning-Pytorch | MSELoss | false | 15,788 | [
"MIT"
] | 87 | 6ea7b7151ae62bf0130b56cc023f2be068aa87f0 | https://github.com/kejiejiang/UnsupervisedDeepLearning-Pytorch/tree/6ea7b7151ae62bf0130b56cc023f2be068aa87f0 |
DeConvNet2 | # 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_... | GloryyrolG/normalized-autoencoders | DeConvNet2 | false | 510 | [
"MIT"
] | 0 | 27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 | https://github.com/GloryyrolG/normalized-autoencoders/tree/27ccb74bb725768f9ba9ea6fa03a7a40867eebb1 |
EPE | import torch
import torch.nn as nn
class EPE(nn.Module):
def __init__(self):
super(EPE, self).__init__()
def forward(self, flow, gt, loss_mask):
loss_map = (flow - gt.detach()) ** 2
loss_map = (loss_map.sum(1, True) + 1e-06) ** 0.5
return loss_map * loss_mask
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Shreyamkmr/Frame-Interpolation | EPE | false | 2,832 | [
"MIT"
] | 0 | bf5eb768e11fdd55d3f322f0a365db3b190a7903 | https://github.com/Shreyamkmr/Frame-Interpolation/tree/bf5eb768e11fdd55d3f322f0a365db3b190a7903 |
GraphLinear | import torch
import torch._utils
class GraphLinear(torch.nn.Module):
"""
Generalization of 1x1 convolutions on Graphs
"""
def __init__(self, in_channels, out_channels):
super(GraphLinear, self).__init__()
self.in_channels = in_channels
self.out_channels = out_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
import torch._utils
assert_size_stride = torch._C._dynamo.guards.assert_size_str... | microsoft/MeshGraphormer | GraphLinear | false | 16,048 | [
"MIT"
] | 135 | 1c489e35e6bd3848ce0702891e4c8365b584bb8e | https://github.com/microsoft/MeshGraphormer/tree/1c489e35e6bd3848ce0702891e4c8365b584bb8e |
CELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | JiYuanFeng/MCTrans | CELoss | false | 13,876 | [
"Apache-2.0"
] | 84 | 9b8b5677eef584b423d5e1630680a4b667cbe823 | https://github.com/JiYuanFeng/MCTrans/tree/9b8b5677eef584b423d5e1630680a4b667cbe823 |
CRFOutputLayer | # 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
import torch.nn as nn
assert_... | yezhengli-Mr9/torchnlp | CRFOutputLayer | false | 13,151 | [
"Apache-2.0"
] | 0 | 0f2ad6d149a413da9f03c6f6694c429746de6551 | https://github.com/yezhengli-Mr9/torchnlp/tree/0f2ad6d149a413da9f03c6f6694c429746de6551 |
MultiheadAttention | import math
import torch
import torch.nn as nn
class MultiheadAttention(nn.Module):
def __init__(self, num_heads=4):
super().__init__()
self.num_heads = num_heads
def forward(self, key, query, value):
b, d, n = key.size()
_, _, m = query.size()
_, do, _ = value.size()... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Jiayuan-Gu/policy-refactorization | MultiheadAttention | false | 17,488 | [
"MIT"
] | 6 | c626c598d735d4c08c2c0553da34196b3fba0b6d | https://github.com/Jiayuan-Gu/policy-refactorization/tree/c626c598d735d4c08c2c0553da34196b3fba0b6d |
Encoder | import torch
from torch import nn
class Encoder(nn.Module):
def __init__(self, embedding_dim, nhead, dropout, k=4):
super(Encoder, self).__init__()
self.transformer = nn.TransformerEncoderLayer(embedding_dim, nhead,
dim_feedforward=k * embedding_dim, dropout=dropout, activation=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | yukiar/distil_wic | Encoder | false | 4,658 | [
"MIT"
] | 0 | 1f9c5c7252105dd9f4f264f8533753f0cd08ca5b | https://github.com/yukiar/distil_wic/tree/1f9c5c7252105dd9f4f264f8533753f0cd08ca5b |
Conv2dWithConstraint | import torch
import torch.nn as 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
import torch.nn as ... | rmpeng/TIE-EEGNet | Conv2dWithConstraint | false | 10,691 | [
"MIT"
] | 0 | 69817fce3edb67f68bf4e85b53596f122dbc78fb | https://github.com/rmpeng/TIE-EEGNet/tree/69817fce3edb67f68bf4e85b53596f122dbc78fb |
Squash | # 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
... | ethanabrooks/teacher-RL | Squash | false | 3,477 | [
"MIT"
] | 0 | 41b44fa4de1e8ce7e0c3eac726919c28ede63538 | https://github.com/ethanabrooks/teacher-RL/tree/41b44fa4de1e8ce7e0c3eac726919c28ede63538 |
GAT | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLay... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | leiloong/PaperRobot | GAT | false | 7,104 | [
"MIT"
] | 1 | 070972dc1548571c28d89d2c54fb379e87d172c7 | https://github.com/leiloong/PaperRobot/tree/070972dc1548571c28d89d2c54fb379e87d172c7 |
HILL | import torch
import torch.nn as nn
class HILL(nn.Module):
def __init__(self, img_size):
super(HILL, self).__init__()
self.img_size = img_size
self.pad_3 = nn.ReplicationPad2d(3)
self.pad = nn.ReplicationPad2d(7)
self.conv1 = nn.Conv2d(1, 1, 3, 1, padding=1, 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.triton_helpers import math as tl_math
import torch.... | Ekko-zn/StegoAdv | HILL | false | 5,132 | [
"MIT"
] | 1 | 2852dbc85d66f30efb7127695c0d75806bf4aa4c | https://github.com/Ekko-zn/StegoAdv/tree/2852dbc85d66f30efb7127695c0d75806bf4aa4c |
Discriminator | # 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_... | ChiragCD/NR-GAN | Discriminator | false | 13,552 | [
"MIT"
] | 54 | fc455c6219b09bc8bf605715504b78b2bb801e48 | https://github.com/ChiragCD/NR-GAN/tree/fc455c6219b09bc8bf605715504b78b2bb801e48 |
EqualizedWeight | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from torch import nn
import torch.utils.data
import torch.nn.functional
from typing import List
import torch.... | Hadryan/nn | EqualizedWeight | false | 9,388 | [
"MIT"
] | 0 | b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d | https://github.com/Hadryan/nn/tree/b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d |
ConstractiveLoss | import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
class ConstractiveLoss(nn.Module):
def __init__(self, margin=2.0, dist_flag='l2'):
super(ConstractiveLoss, self).__init__()
self.margin = margin
self.dist_flag = dist_flag
def various_distance(... | 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 numpy as np
import to... | tommy90191/Find_Tiny_but_Important_Image_Changes | ConstractiveLoss | false | 4,442 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
SoftMaxLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftMaxLayer(nn.Module):
def forward(self, tensor, dim=1):
return F.softmax(tensor, dim=dim)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | nicofirst1/lucent | SoftMaxLayer | false | 12,913 | [
"Apache-2.0"
] | 0 | 1e249918e91cc04117368826cd7a192bd8cf2046 | https://github.com/nicofirst1/lucent/tree/1e249918e91cc04117368826cd7a192bd8cf2046 |
Cosine | # 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.triton_helpers import libdevice
from torch.optim.lr... | posuer/mt-dnn | Cosine | false | 12,899 | [
"MIT"
] | 0 | 5106083238654777838aaab5d1111b3b05c4ce04 | https://github.com/posuer/mt-dnn/tree/5106083238654777838aaab5d1111b3b05c4ce04 |
TransformerBasicHead | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class TransformerBasicHead(nn.Module):
"""
BasicHead. No pool.
"""
def __init__(self, dim_in, num_classes, dropout_rate=0.0, act_func=
'softmax'):
"""
Perform linear projection and ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Drill-D/SlowFast | TransformerBasicHead | false | 2,174 | [
"Apache-2.0"
] | 0 | d55ae1cf30a9415858a9bd5da983790a2b418653 | https://github.com/Drill-D/SlowFast/tree/d55ae1cf30a9415858a9bd5da983790a2b418653 |
Foo | import torch
import torch.jit
import torch.onnx
import torch.nn
class Foo(torch.nn.Module):
def __init__(self):
super(Foo, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 6, 3)
self.relu = torch.nn.ReLU()
self.conv2 = torch.nn.Conv2d(6, 16, 3)
def forward(self, x):
x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | opti-mix/glow | Foo | false | 7,403 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
Net | import torch
import torch.nn.functional as F
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = 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
from torch._inductor.runtime.... | EKami/EzeeML | Net | false | 8,069 | [
"MIT"
] | 35 | 21753a0ede7cc1dc675a2dcd09b6306cea2cad56 | https://github.com/EKami/EzeeML/tree/21753a0ede7cc1dc675a2dcd09b6306cea2cad56 |
_MCLSTMCell | # 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.... | DavidChoi76/neuralhydrology | _MCLSTMCell | false | 15,216 | [
"BSD-3-Clause"
] | 144 | a4c284b92934ee973c8b3fedf8a60df60c8feae1 | https://github.com/DavidChoi76/neuralhydrology/tree/a4c284b92934ee973c8b3fedf8a60df60c8feae1 |
SSWELoss | import torch
import torch.nn as nn
class HingeMarginLoss(nn.Module):
"""
计算hinge loss 接口
"""
def __init__(self):
super(HingeMarginLoss, self).__init__()
def forward(self, t, tr, delt=None, size_average=False):
"""
计算hingle loss
"""
if delt is None:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Aurelius84/SPWE | SSWELoss | false | 16,954 | [
"MIT"
] | 9 | 5f9fc5495e879b5272c118271a69c5adad4ba260 | https://github.com/Aurelius84/SPWE/tree/5f9fc5495e879b5272c118271a69c5adad4ba260 |
TransposeConv2dLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
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_featu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | LenKerr/Semantic-Colorization-GAN | TransposeConv2dLayer | false | 5,530 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
MultiLayerPerceptron | # 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.... | Oktai15/NeMo | MultiLayerPerceptron | false | 5,678 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
BAP | import torch
import torch.nn as nn
class BAP(nn.Module):
def __init__(self, pool='GAP'):
super(BAP, self).__init__()
assert pool in ['GAP', 'GMP']
if pool == 'GAP':
self.pool = nn.AdaptiveAvgPool2d(1)
else:
self.pool = nn.AdaptiveMaxPool2d(1)
def forwa... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | amobiny/hide_and_seek | BAP | false | 6,188 | [
"MIT"
] | 1 | e298d9a352a6ee58e9beedf15ef3d700473b7f27 | https://github.com/amobiny/hide_and_seek/tree/e298d9a352a6ee58e9beedf15ef3d700473b7f27 |
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_... | Jerimat/MITx-6.86-MachineLearning_EdX | DQN | false | 5,394 | [
"MIT"
] | 1 | e454e0646cd923d689d3946ea2ff3432dec920ac | https://github.com/Jerimat/MITx-6.86-MachineLearning_EdX/tree/e454e0646cd923d689d3946ea2ff3432dec920ac |
CAT_TokenEmbedding | import torch
import torch.nn as nn
class CAT_TokenEmbedding(nn.Module):
def __init__(self, c_in=1, d_feature=10):
super(CAT_TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= '1.5.0' else 2
self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_feature,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | mkmysk123456789/Informer2020 | CAT_TokenEmbedding | false | 7,250 | [
"Apache-2.0"
] | 1 | ad4b895169a17db580aab6d2c09fd07e06c9b6fa | https://github.com/mkmysk123456789/Informer2020/tree/ad4b895169a17db580aab6d2c09fd07e06c9b6fa |
ToRGB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
from torch import nn
import torc... | ArashVahabpour/encoder4editing | ToRGB | false | 1,986 | [
"MIT"
] | 0 | 819b90ecd7397fbe2ab7cb30eb451dab0f3149fd | https://github.com/ArashVahabpour/encoder4editing/tree/819b90ecd7397fbe2ab7cb30eb451dab0f3149fd |
BahdanauAttention | import torch
import torch.nn as nn
class BahdanauAttention(nn.Module):
def __init__(self, annot_dim, query_dim, attn_dim):
super(BahdanauAttention, self).__init__()
self.query_layer = nn.Linear(query_dim, attn_dim, bias=True)
self.annot_layer = nn.Linear(annot_dim, attn_dim, bias=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | vigilancetrent/chatbot-advanced | BahdanauAttention | false | 16,667 | [
"Apache-2.0"
] | 52 | 2e0c72c4df2e1434da995b7105f8f0414aba6248 | https://github.com/vigilancetrent/chatbot-advanced/tree/2e0c72c4df2e1434da995b7105f8f0414aba6248 |
GraphConvolution | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn import Parameter
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
from scipy.sparse import *
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
App... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
i... | IBM/graph4nlp | GraphConvolution | false | 8,332 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
TransformerDecoderLayer | # 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.... | Deepest-Project/AlignTTS | TransformerDecoderLayer | false | 13,598 | [
"MIT"
] | 70 | ed9c29d845f65ceb44c87f293b2919b9bbc6a6de | https://github.com/Deepest-Project/AlignTTS/tree/ed9c29d845f65ceb44c87f293b2919b9bbc6a6de |
ActNormFlow | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from typing import Tuple
assert_size_stride = torch... | Tiamat-Tech/VAENAR-TTS | ActNormFlow | false | 14,503 | [
"MIT"
] | 62 | 69b6b5be1ab5168cfd3c6ab902075638e76a3b8d | https://github.com/Tiamat-Tech/VAENAR-TTS/tree/69b6b5be1ab5168cfd3c6ab902075638e76a3b8d |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | faisman/deep-reinforcement-learning-projects | Actor | false | 12,361 | [
"MIT"
] | 0 | cef102ec4019069a22f95d798f6694dce73655ae | https://github.com/faisman/deep-reinforcement-learning-projects/tree/cef102ec4019069a22f95d798f6694dce73655ae |
PcamPool | import torch
import torch.nn as nn
class PcamPool(nn.Module):
def __init__(self):
super(PcamPool, self).__init__()
def forward(self, feat_map, logit_map):
assert logit_map is not None
prob_map = torch.sigmoid(logit_map)
weight_map = prob_map / prob_map.sum(dim=2, keepdim=True... | 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... | C3-ASV-Team/torchxrayvision | PcamPool | false | 4,930 | [
"Apache-2.0"
] | 1 | 7e53f0606986562f17a1ffd9f31d006756eff78d | https://github.com/C3-ASV-Team/torchxrayvision/tree/7e53f0606986562f17a1ffd9f31d006756eff78d |
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