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 |
|---|---|---|---|---|---|---|---|---|---|---|
AttentionNet | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
def conv3x3(in_, out):
return nn.Conv2d(in_, out, 3, padding=1)
class ConvRelu(nn.Module):
def __init__(self, in_, out):
super().__init__()
self.conv = conv3x3(in_, out)
self.activation = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | lvxiuwang/ferattention | AttentionNet | false | 7,188 | [
"MIT"
] | 1 | 02e97df4a12129ed6706bddf0d2109650eae8765 | https://github.com/lvxiuwang/ferattention/tree/02e97df4a12129ed6706bddf0d2109650eae8765 |
ConvNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tens... | Ahmad1s/FastSpeech2 | ConvNorm | false | 8,835 | [
"MIT"
] | 0 | d31802ffcd74bb2c2ca57b53e481917989ded6b9 | https://github.com/Ahmad1s/FastSpeech2/tree/d31802ffcd74bb2c2ca57b53e481917989ded6b9 |
BertLayerNorm | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ArrowLuo/GRACE | BertLayerNorm | false | 7,736 | [
"Apache-2.0"
] | 17 | f27b500ba905685c03eee6d91d87adc9ef78b4d1 | https://github.com/ArrowLuo/GRACE/tree/f27b500ba905685c03eee6d91d87adc9ef78b4d1 |
DecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | csyhhu/attention-is-all-you-need-pytorch | DecoderLayer | false | 6,525 | [
"MIT"
] | 1 | 5792c9714295b1a33d1ca074206ec223f436b954 | https://github.com/csyhhu/attention-is-all-you-need-pytorch/tree/5792c9714295b1a33d1ca074206ec223f436b954 |
BernoulliLayer | # 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 abc
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | BUTSpeechFIT/beer | BernoulliLayer | false | 16,963 | [
"MIT"
] | 6 | 43fb9027a859db28d2f2f8709260ca2ce9501e25 | https://github.com/BUTSpeechFIT/beer/tree/43fb9027a859db28d2f2f8709260ca2ce9501e25 |
BesselBasis | import math
import torch
import torch.jit
import torch.nn.functional
from torch import nn
import torch.nn
import torch.utils.data
class BesselBasis(nn.Module):
r_max: 'float'
prefactor: 'float'
def __init__(self, r_max, num_basis=8, trainable=True):
"""Radial Bessel Basis, as proposed in DimeNet:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import torch.jit
import torch.nn.functional
from torch import... | mir-group/nequip | BesselBasis | false | 16,163 | [
"MIT"
] | 153 | 4e6a0914a289cf000da57a6b6e79678efdf3347f | https://github.com/mir-group/nequip/tree/4e6a0914a289cf000da57a6b6e79678efdf3347f |
SimpleCNN32Filter | import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleCNN32Filter(nn.Module):
"""
Defines a simple CNN arhcitecture with 1 layer
"""
def __init__(self, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=10, stride=2)
self.fc1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | adwaykanhere/df-dn-paper | SimpleCNN32Filter | false | 12,057 | [
"MIT"
] | 0 | 5df413e06ce33c6be5d005e6d1141de9fcd45cb4 | https://github.com/adwaykanhere/df-dn-paper/tree/5df413e06ce33c6be5d005e6d1141de9fcd45cb4 |
SchedulerTestNet | import torch
from torch.nn import functional as F
class SchedulerTestNet(torch.nn.Module):
"""adapted from: https://github.com/pytorch/pytorch/blob/master/test/test_optim.py."""
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(1, 1, 1)
self.conv2 = torch.nn.Conv2d(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
assert_size_stride = torch._C... | bzrry/lightning-bolts | SchedulerTestNet | false | 14,994 | [
"Apache-2.0"
] | 822 | bd392ad858039290c72c20cc3f10df39384e90b9 | https://github.com/bzrry/lightning-bolts/tree/bd392ad858039290c72c20cc3f10df39384e90b9 |
Normalize_one | # 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... | Alibaba-AAIG/Beyond-ImageNet-Attack | Normalize_one | false | 7,667 | [
"MIT"
] | 23 | c14b4844b64a8035b8fe033a617c0567224a9fa4 | https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/tree/c14b4844b64a8035b8fe033a617c0567224a9fa4 |
GridMixupLoss | # 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 math
import ran... | IlyaDobrynin/GridMixup | GridMixupLoss | false | 8,327 | [
"MIT"
] | 42 | 11b741f234832c9a15b4e650e1e4fad0e79dc63b | https://github.com/IlyaDobrynin/GridMixup/tree/11b741f234832c9a15b4e650e1e4fad0e79dc63b |
p_model | # 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... | cenkcorapci/visual-fashion-item-search | p_model | false | 6,407 | [
"MIT"
] | 1 | 47b93f97383c1b7f9ec23bb4ff66f90504db3da8 | https://github.com/cenkcorapci/visual-fashion-item-search/tree/47b93f97383c1b7f9ec23bb4ff66f90504db3da8 |
FPNOutput | # 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... | Xlinford/TDNet | FPNOutput | false | 2,974 | [
"MIT"
] | 0 | e7cb59c40b8751b6dab9691d26ad224fd61c24d1 | https://github.com/Xlinford/TDNet/tree/e7cb59c40b8751b6dab9691d26ad224fd61c24d1 |
BranchNet | # 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_... | aalto-intelligent-robotics/sivl | BranchNet | false | 6,061 | [
"MIT"
] | 1 | a5de0e0dd4fc6b15c9b15cb4ffa8b6f9de12a96d | https://github.com/aalto-intelligent-robotics/sivl/tree/a5de0e0dd4fc6b15c9b15cb4ffa8b6f9de12a96d |
ChannelPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.... | HolmesShuan/OISR-PyTorch | ChannelPool | false | 13,770 | [
"BSD-2-Clause"
] | 141 | bbe0c88f71fe565a2842df7971b62a9bc5a56c48 | https://github.com/HolmesShuan/OISR-PyTorch/tree/bbe0c88f71fe565a2842df7971b62a9bc5a56c48 |
AUGLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | DonghyunAhn/sadvirus | AUGLoss | false | 372 | [
"MIT"
] | 0 | cdcc98812d613962a7003ff0c6013d0805bde024 | https://github.com/DonghyunAhn/sadvirus/tree/cdcc98812d613962a7003ff0c6013d0805bde024 |
TAE_decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HamzaG737/Deep-temporal-clustering---Pytorch | TAE_decoder | false | 8,212 | [
"MIT"
] | 12 | 5ee423d833e655e73b6ba2f1c13be5f1b83f92d2 | https://github.com/HamzaG737/Deep-temporal-clustering---Pytorch/tree/5ee423d833e655e73b6ba2f1c13be5f1b83f92d2 |
LevelVariabilityLoss | import torch
import torch.nn as nn
class LevelVariabilityLoss(nn.Module):
"""Computes the variability penalty for the level.
levels: levels obtained from exponential smoothing component of ESRNN.
tensor with shape (batch, n_time).
level_variability_penalty: float.
return: level_var_loss
"""
... | 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... | cchallu/esrnn | LevelVariabilityLoss | false | 6,392 | [
"MIT"
] | 1 | 543ca365c70be2775a4b5863820b246071ccde3c | https://github.com/cchallu/esrnn/tree/543ca365c70be2775a4b5863820b246071ccde3c |
SelfAttention | import torch
from torch import nn
class SelfAttention(nn.Module):
def __init__(self, dim, heads=8):
super(SelfAttention, self).__init__()
self.dim, self.heads = dim, heads
self.Q = nn.Linear(dim, dim * heads, bias=False)
self.K = nn.Linear(dim, dim * heads, bias=False)
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._inductor.runtime.... | MartrixG/transformer | SelfAttention | false | 829 | [
"MIT"
] | 0 | 8cd1e31d11aff6059fad28d4cfe27e936d611c8c | https://github.com/MartrixG/transformer/tree/8cd1e31d11aff6059fad28d4cfe27e936d611c8c |
GEGLU | import torch
from torch import nn
import torch.nn.functional as F
class GEGLU(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim=-1)
return x * F.gelu(gates)
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | JaireYu/perceiver-pytorch | GEGLU | false | 2,391 | [
"MIT"
] | 0 | 23edd66a057bb0a6fc15126461b4409a522ca09e | https://github.com/JaireYu/perceiver-pytorch/tree/23edd66a057bb0a6fc15126461b4409a522ca09e |
PoolingF | 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | bomtorazek/contrastive-unpaired-translation | PoolingF | false | 12,186 | [
"BSD-3-Clause"
] | 0 | 07c048038375e1b9a4e464154b8dbc49f5e16ede | https://github.com/bomtorazek/contrastive-unpaired-translation/tree/07c048038375e1b9a4e464154b8dbc49f5e16ede |
LogCoshLoss | # 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... | crobbins327/semanticGAN_WSI | LogCoshLoss | false | 1,749 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 |
Swish | import torch
import torch.nn as nn
class Swish(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sigmoid(x) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | CW-Huang/sdeflow-light | Swish | false | 7,830 | [
"MIT"
] | 35 | 524650bc5ad69522b3e0905672deef0650374512 | https://github.com/CW-Huang/sdeflow-light/tree/524650bc5ad69522b3e0905672deef0650374512 |
SpatialDepthWisePerHeadConvolution | # 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.nn import Module
from torch import nn
import torch.utils.data
import ... | mcx/annotated_deep_learning_paper_implementations | SpatialDepthWisePerHeadConvolution | false | 7,212 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
PolicySPG | # 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.... | JimmyMVP/plain_rl | PolicySPG | false | 17,496 | [
"MIT"
] | 10 | 4780f05fffb62533a339197b49de487cdc9d9954 | https://github.com/JimmyMVP/plain_rl/tree/4780f05fffb62533a339197b49de487cdc9d9954 |
AE_2D_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 ... | gitter-badger/HEPAutoencoders | AE_2D_v2 | false | 12,436 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
Pool | # 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
assert_size_stride = torch._C._dynamo.gu... | HibikiJie/MONet | Pool | false | 2,348 | [
"Apache-2.0"
] | 0 | 931400df28cb62aab90662abe00acd1d3688073d | https://github.com/HibikiJie/MONet/tree/931400df28cb62aab90662abe00acd1d3688073d |
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... | Europium248/captum | MultiRelu | false | 429 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
WeightedSmoothL1Loss | import torch
import numpy as np
import torch.nn as nn
class WeightedSmoothL1Loss(nn.Module):
"""
Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
| 0.5 * x ** 2 / beta if abs(... | 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 numpy as np
import torch.nn as nn
assert_size_stride = ... | Jasonkks/mlcnet | WeightedSmoothL1Loss | false | 8,324 | [
"Apache-2.0"
] | 18 | 8f89c860c709733c8baa663607004fc48d76291d | https://github.com/Jasonkks/mlcnet/tree/8f89c860c709733c8baa663607004fc48d76291d |
Normalize | # 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... | Electronicshelf/Few-shot-regularization | Normalize | false | 405 | [
"MIT"
] | 0 | 3fd0fef52684af77a5e574b5d61cfd8dd557b14b | https://github.com/Electronicshelf/Few-shot-regularization/tree/3fd0fef52684af77a5e574b5d61cfd8dd557b14b |
BuildBlock | import torch
import torch.nn.functional as F
from torch import nn
class BuildBlock(nn.Module):
def __init__(self, planes=256):
super(BuildBlock, self).__init__()
self.planes = planes
self.toplayer1 = nn.Conv2d(2048, planes, kernel_size=1, stride=1,
padding=0)
self.topl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.functional as... | YacobBY/ICDAR2019-ArT-Recognition-Alchemy | BuildBlock | false | 14,852 | [
"MIT"
] | 209 | 911c572c2aff4599a74b7974d46ef4cfb17078b9 | https://github.com/YacobBY/ICDAR2019-ArT-Recognition-Alchemy/tree/911c572c2aff4599a74b7974d46ef4cfb17078b9 |
LinearZeros | # 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.... | NirDiamant/pytorch-glow | LinearZeros | false | 907 | [
"MIT"
] | 0 | 2ab11f3a8486b86a279fe4fa64f25aa91226ee8a | https://github.com/NirDiamant/pytorch-glow/tree/2ab11f3a8486b86a279fe4fa64f25aa91226ee8a |
EncoderLayer | import torch
from typing import Tuple
from torch import nn
from typing import Optional
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention
Parameters
----------
scale : float
Scale factor (sqrt(d_k))
dropout : float
Dropout
"""
def __init__(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.... | Renovamen/Text-Classification | EncoderLayer | false | 14,304 | [
"MIT"
] | 72 | 4a4aa4001c402ed4371ebaabe1393b27794e5992 | https://github.com/Renovamen/Text-Classification/tree/4a4aa4001c402ed4371ebaabe1393b27794e5992 |
Net_L2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net_L2(nn.Module):
def __init__(self, inputSize, kernel=64):
super(Net_L2, self).__init__()
self.inputSize = inputSize
self.kernel = kernel
self.fc1 = nn.Linear(self.inputSize, 256)
self.fc2 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | kamomehz/waveletCodingCNN | Net_L2 | false | 3,795 | [
"MIT"
] | 0 | 50c7db9d986039ded38999b7e4f4265e2250fb90 | https://github.com/kamomehz/waveletCodingCNN/tree/50c7db9d986039ded38999b7e4f4265e2250fb90 |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | Blidge/tgn-caw-main | MLP | false | 4,920 | [
"Apache-2.0"
] | 1 | 7a58f22bc7d9f1e2f6e9cbb1a60a18aed81071ee | https://github.com/Blidge/tgn-caw-main/tree/7a58f22bc7d9f1e2f6e9cbb1a60a18aed81071ee |
GlobalAttention | import torch
import torch.nn as nn
import torch.cuda
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments
), 'Not all arguments have the same value: ' + str(args)
def 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.... | vvjn/MultimodalNMT | GlobalAttention | false | 13,074 | [
"MIT"
] | 0 | 2d69588a5b640290602b4f6d7e4120ae9742c1c2 | https://github.com/vvjn/MultimodalNMT/tree/2d69588a5b640290602b4f6d7e4120ae9742c1c2 |
distLinear | # 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 ... | MuawizChaudhary/STARTUP | distLinear | false | 873 | [
"MIT"
] | 0 | 03f39b34a4ec232f132173b4a1e67ea04165e52b | https://github.com/MuawizChaudhary/STARTUP/tree/03f39b34a4ec232f132173b4a1e67ea04165e52b |
wide_basic | import torch
import torch.nn as nn
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == 'layer':
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | tianyi21/JEM | wide_basic | false | 4,437 | [
"Apache-2.0"
] | 0 | 59b4bb87be1b1643731540133df557edd7780a88 | https://github.com/tianyi21/JEM/tree/59b4bb87be1b1643731540133df557edd7780a88 |
SelfCriticCriterion | import torch
import torch.nn as nn
class SelfCriticCriterion(nn.Module):
def __init__(self):
super().__init__()
def forward(self, props, s_words, tgt, advantage):
advantage = (advantage - advantage.mean()) / advantage.std().clamp(min
=1e-08)
s_props = props.gather(2, s_wo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | NeilWangziyu/torch_light | SelfCriticCriterion | false | 5,642 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
Generator_mnist | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | RicoFio/disentangle_mlp | Generator_mnist | false | 5,770 | [
"MIT"
] | 1 | 1fb3b6070b5846051b8b9e9333e8ee61418f4893 | https://github.com/RicoFio/disentangle_mlp/tree/1fb3b6070b5846051b8b9e9333e8ee61418f4893 |
SVHN_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_... | nannullna/deep-active-learning | SVHN_Net | false | 16,157 | [
"MIT"
] | 465 | c54a995640c63ba4679129c5a1fd5cec9a2858e6 | https://github.com/nannullna/deep-active-learning/tree/c54a995640c63ba4679129c5a1fd5cec9a2858e6 |
SeparableConvBlock | import math
import torch
import torch.nn.functional as F
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
class Conv2dSamePadding(torch.nn.Conv2d):
"""
A wrapper around :class:`torch.nn.Conv2d` to support "SAME" padding mode and more features.
"""
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
import math
import torch.nn.functional as F
import torch.utils.data
from itertoo... | WFDetector/WFDetection | SeparableConvBlock | false | 2,957 | [
"Apache-2.0"
] | 0 | b16d35b3a3a5de62de9e0bac83eccd21b6358b53 | https://github.com/WFDetector/WFDetection/tree/b16d35b3a3a5de62de9e0bac83eccd21b6358b53 |
OrModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert... | SpyrosMouselinos/DeltaFormers | OrModule | false | 5,843 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
LinearAttention2d | import torch
class LinearAttention2d(torch.nn.Module):
"""
Linear attention based on parametrized compatibility score function with softmax normalization.
"""
def __init__(self, in_features, out_features):
super(LinearAttention2d, self).__init__()
self.in_features = in_features
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | itsfrank98/CT-COVID | LinearAttention2d | false | 6,911 | [
"MIT"
] | 1 | 3f054000ca0518be2486cf00cfab695b09e39a26 | https://github.com/itsfrank98/CT-COVID/tree/3f054000ca0518be2486cf00cfab695b09e39a26 |
Relu_Caps | # 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
from torch import nn
assert_... | WdBlink/AugMix-3DOCUNet-Brats2019 | Relu_Caps | false | 5,957 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
MyUpsample2 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.... | zigonk/ReSC | MyUpsample2 | false | 16,816 | [
"MIT"
] | 57 | c816365b0410f521974060ef0cc6eaa1dd09b63a | https://github.com/zigonk/ReSC/tree/c816365b0410f521974060ef0cc6eaa1dd09b63a |
BahdanauAttention | import torch
from torch import nn
class BahdanauAttention(nn.Module):
def __init__(self, dim):
super(BahdanauAttention, self).__init__()
self.query_layer = nn.Linear(dim, dim, bias=False)
self.tanh = nn.Tanh()
self.v = nn.Linear(dim, 1, bias=False)
def forward(self, query, pr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | puppyapple/tacotron_pytorch | BahdanauAttention | false | 16,292 | [
"MIT"
] | 278 | 800bf8b0538c91f1104e99d8e7c1b645bb6154d3 | https://github.com/puppyapple/tacotron_pytorch/tree/800bf8b0538c91f1104e99d8e7c1b645bb6154d3 |
LR_PAD | # 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... | zokin/HorizonNet | LR_PAD | false | 4,673 | [
"MIT"
] | 0 | a93a76ec7fdc76a5ba023adaed869e34f7f3cea4 | https://github.com/zokin/HorizonNet/tree/a93a76ec7fdc76a5ba023adaed869e34f7f3cea4 |
SimpleReluModel | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleReluModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleReluModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
other = F.relu(tensor, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | briancoutinho/glow | SimpleReluModel | false | 12,579 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SpatialPyramidPooling | # 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... | janewen134/fyp | SpatialPyramidPooling | false | 10,377 | [
"Apache-2.0"
] | 0 | 8fb93ac22d21d5d862035ba794fe9d264add2e63 | https://github.com/janewen134/fyp/tree/8fb93ac22d21d5d862035ba794fe9d264add2e63 |
RelPositionMultiHeadedAttention | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Mashiro083/wenet-onnx | RelPositionMultiHeadedAttention | false | 8,546 | [
"Apache-2.0"
] | 18 | ae8f8451d73fa9ceac6f7738194543e83959ca86 | https://github.com/Mashiro083/wenet-onnx/tree/ae8f8451d73fa9ceac6f7738194543e83959ca86 |
VarianceNorm2d | # 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_... | Sriram-Ravula/Inverse_Meta | VarianceNorm2d | false | 2,852 | [
"MIT"
] | 0 | c6c1e4ae0d670093156249c60d74373b22d61f01 | https://github.com/Sriram-Ravula/Inverse_Meta/tree/c6c1e4ae0d670093156249c60d74373b22d61f01 |
SEConv2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.modules.utils import _pair
assert_size_strid... | PannenetsF/TQT | SEConv2d | false | 8,655 | [
"BSD-3-Clause"
] | 14 | 3c3125327d00efe6318b28cb1d0a199b734c2c7b | https://github.com/PannenetsF/TQT/tree/3c3125327d00efe6318b28cb1d0a199b734c2c7b |
TransposedConv1d | # 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
import ... | Cogito2012/OpenTAL | TransposedConv1d | false | 7,896 | [
"BSD-3-Clause"
] | 16 | a7ab938a52b3fb82163eb1ba5403888359eb7e6a | https://github.com/Cogito2012/OpenTAL/tree/a7ab938a52b3fb82163eb1ba5403888359eb7e6a |
NeuralNetPartialNoGradModel | # 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
import torch.... | TingGong1/onnxruntime | NeuralNetPartialNoGradModel | false | 5,897 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
LandmarkLoss | # 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | AnthonyF333/FaceLandmark_PFLD_UltraLight | LandmarkLoss | false | 7,719 | [
"Apache-2.0"
] | 38 | c7c9543bd7f44ab434240eab077242f259df21f8 | https://github.com/AnthonyF333/FaceLandmark_PFLD_UltraLight/tree/c7c9543bd7f44ab434240eab077242f259df21f8 |
LabelSmoothingLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class LabelSmoothingLoss(nn.Module):
def __init__(self, smoothing=0.0):
super(LabelSmoothingLoss, self).__init__()
self.smoothing = smoothing
def smooth_one_hot(self, target: 'torch.Tensor', classes: 'int',
smoothing:... | 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
... | jordiae/DeepLearning-MAI | LabelSmoothingLoss | false | 6,982 | [
"MIT"
] | 1 | e12b6975d8de6cbe89f812bf691a7f7e95213552 | https://github.com/jordiae/DeepLearning-MAI/tree/e12b6975d8de6cbe89f812bf691a7f7e95213552 |
ConvTripleBlock | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class ConvBnRelu(nn.Module):
"""
A block of convolution, relu, batchnorm
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SkywalkerAtlas/HRGAN | ConvTripleBlock | false | 5,838 | [
"MIT"
] | 1 | bf6d58c1f3c6e042c7ea70319a25e3420531d552 | https://github.com/SkywalkerAtlas/HRGAN/tree/bf6d58c1f3c6e042c7ea70319a25e3420531d552 |
cell | # 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.... | Cemu0/Network-of-Neural-Network | cell | false | 11,295 | [
"MIT"
] | 0 | 6a4a097a960fbbec6ea0c5946804666b27c2da0f | https://github.com/Cemu0/Network-of-Neural-Network/tree/6a4a097a960fbbec6ea0c5946804666b27c2da0f |
EQConv2D | import torch
import torch.nn as nn
class EQConv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, gain=2):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
stride, padding)
self.scale = (gain... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | AjaybirRandhawa/Face-Generator | EQConv2D | false | 18,442 | [
"Apache-2.0"
] | 2 | 9cac0822b6e6337c3599e949154ce44eeae5746b | https://github.com/AjaybirRandhawa/Face-Generator/tree/9cac0822b6e6337c3599e949154ce44eeae5746b |
MLP_CIFAR10 | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP_CIFAR10(nn.Module):
def __init__(self, save_features=None, bench_model=False):
super(MLP_CIFAR10, self).__init__()
self.fc1 = nn.Linear(3 * 32 * 32, 1024)
self.fc2 = nn.Linear(1024, 512)
self.fc3 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | VITA-Group/SViTE | MLP_CIFAR10 | false | 14,544 | [
"MIT"
] | 50 | b0c62fd153c8b0b99917ab935ee76925c9de1149 | https://github.com/VITA-Group/SViTE/tree/b0c62fd153c8b0b99917ab935ee76925c9de1149 |
SimpleAvgPool1dModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | opti-mix/glow | SimpleAvgPool1dModule | false | 7,379 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
SquashingCosine_Classifier | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class SquashingCosine_Classifier(nn.Module):
def __init__(self, in_dims, out_dims, scale=16, margin=0.5, init_std=0.001
):
super(SquashingCosine_Classifier, self).__init__()
self.in_dims = in_dims
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | JKozerawski/BLT | SquashingCosine_Classifier | false | 17,453 | [
"MIT"
] | 5 | 6f3a6f4dc3c832b62c4ac3f3baf34b6a0bd6e181 | https://github.com/JKozerawski/BLT/tree/6f3a6f4dc3c832b62c4ac3f3baf34b6a0bd6e181 |
ExpPool | import torch
from torch import nn
class ExpPool(nn.Module):
def __init__(self):
super(ExpPool, self).__init__()
def forward(self, feat_map):
"""
Numerically stable implementation of the operation
Arguments:
feat_map(Tensor): tensor with shape (N, C, H, W)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | DavidChenL/Chexpert | ExpPool | false | 13,570 | [
"Apache-2.0"
] | 202 | 0300057d3a51301cff35a65f79729436678b4a79 | https://github.com/DavidChenL/Chexpert/tree/0300057d3a51301cff35a65f79729436678b4a79 |
CharbonnierPenalty | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | ChristinaRunkel/HighSpeedImaging | CharbonnierPenalty | false | 5,012 | [
"MIT"
] | 1 | 392437e6c1f4b125fc4771c98b16c85155684d09 | https://github.com/ChristinaRunkel/HighSpeedImaging/tree/392437e6c1f4b125fc4771c98b16c85155684d09 |
JustConvBody | # 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_... | Louis-Bagot/DeepRL | JustConvBody | false | 9,703 | [
"MIT"
] | 0 | 0b152c52bbba90362c8276c223fee3f9a464eb32 | https://github.com/Louis-Bagot/DeepRL/tree/0b152c52bbba90362c8276c223fee3f9a464eb32 |
GHMC | # 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
... | ChHanXiao/mmdetection | GHMC | false | 9,151 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
"""
compute the bounds [-lim, lim] for subsequent uniform sampling
with lim = 1/sqrt(nb_output)
"""
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bar0net/Udacity_DeepReinforcementLearning | Actor | false | 1,524 | [
"MIT"
] | 0 | 3b5f98b7c2c1911b351be541fda3aa190bf48456 | https://github.com/bar0net/Udacity_DeepReinforcementLearning/tree/3b5f98b7c2c1911b351be541fda3aa190bf48456 |
ScaleNorm | import torch
import torch.nn as nn
class ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.g = nn.Parameter(torch.ones(1))
self.eps = eps
def forward(self, x):
n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps)
return x / n * 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._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | blizda/reformer-pytorch | ScaleNorm | false | 3,232 | [
"MIT"
] | 0 | f7187d887c3522124d265dd11e4bb42b2f2906c6 | https://github.com/blizda/reformer-pytorch/tree/f7187d887c3522124d265dd11e4bb42b2f2906c6 |
GAP | # 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.... | CaptainEven/MCMOT-ByteTrack | GAP | false | 7,835 | [
"MIT"
] | 20 | e014275cfb25147dfa6f49cdbed24e91e5d6c41e | https://github.com/CaptainEven/MCMOT-ByteTrack/tree/e014275cfb25147dfa6f49cdbed24e91e5d6c41e |
SLP | import torch
import torch.nn.functional as F
import torch.utils.data.distributed
import torch
import torch.nn as nn
class SLP(nn.Module):
def __init__(self, input_size, logits):
super(SLP, self).__init__()
self._input_size = input_size
self.fc = nn.Linear(input_size, logits)
def forw... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Pandinosaurus/KungFu | SLP | false | 14,144 | [
"Apache-2.0"
] | 291 | 80dfa463450330e920b413f65cc49d8e013b84a9 | https://github.com/Pandinosaurus/KungFu/tree/80dfa463450330e920b413f65cc49d8e013b84a9 |
TorchModule | import torch
import torch.nn
class TorchLinearModule(torch.nn.Module):
def __init__(self, in_size, out_size):
super(TorchLinearModule, self).__init__()
self._linear = torch.nn.Linear(in_size, out_size)
def forward(self, x):
return self._linear(x)
class TorchModule(torch.nn.Module):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
ass... | JudeDavis1/ivy | TorchModule | false | 2,435 | [
"Apache-2.0"
] | 0 | 0f3dc38f978a6ce65fc1ed11110338d635e5c9f3 | https://github.com/JudeDavis1/ivy/tree/0f3dc38f978a6ce65fc1ed11110338d635e5c9f3 |
IntegIndepenPathLoss | import torch
import numpy as np
import torch.utils.data
import torch
import torch.nn as nn
class IntegralGrad:
grad_norm_scale = 20
def __init__(self):
return
@staticmethod
def grad_merge(grad_x_mtx, grad_y_mtx, dim=-3):
grad_mtx = torch.cat((grad_x_mtx, grad_y_mtx), dim=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 torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.utils.data
import torch
import torch.nn a... | KingOnTheStar/pytorch-CycleGAN-and-pix2pix | IntegIndepenPathLoss | false | 728 | [
"BSD-3-Clause"
] | 0 | 9016b98d09902975b49a07c394bb0d5066e2aa55 | https://github.com/KingOnTheStar/pytorch-CycleGAN-and-pix2pix/tree/9016b98d09902975b49a07c394bb0d5066e2aa55 |
FocalLossSigmoid | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class FocalLossSigmoid(nn.Module):
"""
sigmoid version focal loss
"""
def __init__(self, alpha=0.25, gamma=2, size_average=False):
super(FocalLossSigmoid, self).__init__()
self.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
... | Shi-Yuyao/SSD_Pytorch | FocalLossSigmoid | false | 11,870 | [
"MIT"
] | 0 | 870732682935a8523b5232fac3bdb080c5a59cf9 | https://github.com/Shi-Yuyao/SSD_Pytorch/tree/870732682935a8523b5232fac3bdb080c5a59cf9 |
EqualLinearActModule | import torch
import torch.nn as nn
from copy import deepcopy
from functools import partial
from torch.nn.init import _calculate_correct_fan
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from copy import deepcopy
from functools import partial
fr... | HXWAndCL/mmgeneration | EqualLinearActModule | false | 5,260 | [
"Apache-2.0"
] | 1 | 9afb1d740bf56a4ecde5064d5bb2a4e2d777638b | https://github.com/HXWAndCL/mmgeneration/tree/9afb1d740bf56a4ecde5064d5bb2a4e2d777638b |
UpsampleConvLayer | # 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.... | bruchano/ImageStyler | UpsampleConvLayer | false | 9,919 | [
"MIT"
] | 0 | 7bde13bc954566088c477065adb5c4e4214c28bb | https://github.com/bruchano/ImageStyler/tree/7bde13bc954566088c477065adb5c4e4214c28bb |
SimpleNet | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
class SimpleNet(nn.Module):
def __init__(self):
super(SimpleNet, self).__init__()
self.fc1 = nn.Linear(12288, 84)
self.fc2 = nn.Linear(84, 50)
self.fc3 = nn.Linear(50, 2)
def forward(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
import torch.nn as nn
import ... | aakgun/pytorch-VideoDataset | SimpleNet | false | 3,014 | [
"MIT"
] | 0 | 619e385f37b99bfabca0b814673825ed902242b2 | https://github.com/aakgun/pytorch-VideoDataset/tree/619e385f37b99bfabca0b814673825ed902242b2 |
BalancedLoss | import torch
from torch import nn
import torch.nn.functional as F
class BalancedLoss(nn.Module):
def __init__(self, neg_weight=1.0):
super(BalancedLoss, self).__init__()
self.neg_weight = neg_weight
def forward(self, input, target):
pos_mask = target == 0
neg_mask = target ==... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | gabrielsluz/vince | BalancedLoss | false | 15,391 | [
"Apache-2.0"
] | 61 | f4e17a2cf70c080a7e01e46d15537e33224c869b | https://github.com/gabrielsluz/vince/tree/f4e17a2cf70c080a7e01e46d15537e33224c869b |
distLinear | # 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 ... | VisionLearningGroup/CDS | distLinear | false | 18,054 | [
"MIT"
] | 7 | 5b3644c286f19f76acdc03c6f6021a6f6e4ec4fc | https://github.com/VisionLearningGroup/CDS/tree/5b3644c286f19f76acdc03c6f6021a6f6e4ec4fc |
Bi_Attention | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
class Bi_Attention(nn.Module):
def __init__(self):
super(Bi_Attention, self).__init__()
self.inf = 10000000000000.0
def forward(self, in_x1, in_x2, x1_len, x2_len):
assert in_x1.size()[0] == in_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LindaCY/fastNLP | Bi_Attention | false | 17,614 | [
"Apache-2.0"
] | 4 | 3fa95b6cfc31211453bc21792e3eef87948858da | https://github.com/LindaCY/fastNLP/tree/3fa95b6cfc31211453bc21792e3eef87948858da |
BertSelfAttention | # 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.... | brendon-boldt/minbert-assignment | BertSelfAttention | false | 12,196 | [
"Apache-2.0"
] | 0 | 0b562d791d34a40fd3c0383a0a32b4eeb2171cb5 | https://github.com/brendon-boldt/minbert-assignment/tree/0b562d791d34a40fd3c0383a0a32b4eeb2171cb5 |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | rgreenblatt/path | ResBlock | false | 7,556 | [
"MIT"
] | 1 | 2057618ee3a6067c230c1c1c40856d2c9f5006b0 | https://github.com/rgreenblatt/path/tree/2057618ee3a6067c230c1c1c40856d2c9f5006b0 |
NN_2layer_regression | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | gaseln/FLIX_small_scale_experiments | NN_2layer_regression | false | 6,724 | [
"MIT"
] | 1 | af9ebd7f192fc0f67a6a94af7939fd3d6f548bd6 | https://github.com/gaseln/FLIX_small_scale_experiments/tree/af9ebd7f192fc0f67a6a94af7939fd3d6f548bd6 |
NodeAdaptiveEncoder | # 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.utils.data
import torch.nn as nn
import torch as torch
assert_size_... | ckhui/cogdl | NodeAdaptiveEncoder | false | 12,643 | [
"MIT"
] | 0 | 93bea17c2dc7084857cd0a4af8178c174965127c | https://github.com/ckhui/cogdl/tree/93bea17c2dc7084857cd0a4af8178c174965127c |
hsigmoid | import torch
import torch.nn as nn
import torch.nn.functional as F
class hsigmoid(nn.Module):
def forward(self, x):
out = F.relu6(x + 3, inplace=True) / 6
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Ecalose/dddd_trainer | hsigmoid | false | 13,620 | [
"Apache-2.0"
] | 80 | ef0c6b271cc2898403375f53f813481ffbf6b02c | https://github.com/Ecalose/dddd_trainer/tree/ef0c6b271cc2898403375f53f813481ffbf6b02c |
ResidualBlock_noBN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaimin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | myeldib/Simple-SR | ResidualBlock_noBN | false | 12,828 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
ScaledLeakyReLU | # 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.model_zoo
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Aitical/ADspeech2face | ScaledLeakyReLU | false | 4,801 | [
"MIT"
] | 1 | 2e811ff8cc7333729f4b77d1b1067296253e8e38 | https://github.com/Aitical/ADspeech2face/tree/2e811ff8cc7333729f4b77d1b1067296253e8e38 |
CenterLoss | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
class CenterLoss(nn.Module):
"""Implements the Center loss from https://ydwen.github.io/papers/WenECCV16.pdf"""
def __init__(self, num_classes, embed_size, cos_dist=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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from to... | vadimadr/openvino_training_extensions | CenterLoss | false | 11,044 | [
"Apache-2.0"
] | 0 | 5d64b8423c8eb7b374ed629fad938359d34a07d2 | https://github.com/vadimadr/openvino_training_extensions/tree/5d64b8423c8eb7b374ed629fad938359d34a07d2 |
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.... | ChangyWen/wolpertinger_ddpg | Actor | false | 13,468 | [
"MIT"
] | 46 | 23e1dcf19dd4bed3cc48f898122c3d57cfc296d3 | https://github.com/ChangyWen/wolpertinger_ddpg/tree/23e1dcf19dd4bed3cc48f898122c3d57cfc296d3 |
Early_StyleConv_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 math
import ... | sergkuzn148/stg | Early_StyleConv_Block | false | 16,412 | [
"MIT"
] | 96 | 84d9f53ae3665c423836a4d0176dc3b22de62b19 | https://github.com/sergkuzn148/stg/tree/84d9f53ae3665c423836a4d0176dc3b22de62b19 |
AlexConv | # 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.... | iofthetiger/pkuad | AlexConv | false | 6,899 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
BlendLinear | import torch
import torch.nn as nn
class BlendLinear(nn.Module):
def __init__(self, dim_in, dim_out, layer_type=nn.Linear, **unused_kwargs):
super(BlendLinear, self).__init__()
self._layer0 = layer_type(dim_in, dim_out)
self._layer1 = layer_type(dim_in, dim_out)
def forward(self, t, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | D-hash-code/ffjord-rnode-finalweek-mnist | BlendLinear | false | 2,143 | [
"MIT"
] | 0 | 4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 | https://github.com/D-hash-code/ffjord-rnode-finalweek-mnist/tree/4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 |
DownBlock | import torch
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | Haabibi/RBPN-PyTorch | DownBlock | false | 5,276 | [
"MIT"
] | 1 | 0b04420b384fcc8f78a7b9afeca179fa6c0332c2 | https://github.com/Haabibi/RBPN-PyTorch/tree/0b04420b384fcc8f78a7b9afeca179fa6c0332c2 |
Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bubbliiiing/classification-pytorch | Block | false | 14,998 | [
"MIT"
] | 88 | ee62c05bd3094c3fab48bada5a57cb2ed8b61c11 | https://github.com/bubbliiiing/classification-pytorch/tree/ee62c05bd3094c3fab48bada5a57cb2ed8b61c11 |
ExtendedModel | import torch
import torch.nn as nn
class ExtendedModel(nn.Module):
def __init__(self, D_in, H, D_out):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super(ExtendedModel, self).__init__()
self.linear1 = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | SID262000/BentoML | ExtendedModel | false | 9,419 | [
"Apache-2.0"
] | 0 | 0708a6495e4d1f0ddf639026be768abf2d55410a | https://github.com/SID262000/BentoML/tree/0708a6495e4d1f0ddf639026be768abf2d55410a |
Square | # 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... | Mrswolf/brainda | Square | false | 8,571 | [
"MIT"
] | 24 | cbd2fa6334d9e6243324dbaf086be4eb4047e801 | https://github.com/Mrswolf/brainda/tree/cbd2fa6334d9e6243324dbaf086be4eb4047e801 |
ClassHead | # 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
from itertools import product as produ... | Capetian/FaceX-Zoo | ClassHead | false | 5,028 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
MonotonicMin | # 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... | tiwalayo/monotonic-mlp | MonotonicMin | false | 10,858 | [
"MIT"
] | 0 | 2f519797a753f7f297fac1365125c6da79f7b890 | https://github.com/tiwalayo/monotonic-mlp/tree/2f519797a753f7f297fac1365125c6da79f7b890 |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, stride=2):
super(Upsample, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(1)
... | 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... | Hydroxy-OH/deep_sort_pytorch | Upsample | false | 11,489 | [
"MIT"
] | 0 | 040656566d9f52fefa4ef02ca58f039ff591211b | https://github.com/Hydroxy-OH/deep_sort_pytorch/tree/040656566d9f52fefa4ef02ca58f039ff591211b |
BehlerAngular | import torch
from torch import nn as nn
class BehlerAngular(nn.Module):
"""
Compute Behler type angular contribution of the angle spanned by three atoms:
:math:`2^{(1-\\zeta)} (1 + \\lambda \\cos( {\\theta}_{ijk} ) )^\\zeta`
Sets of zetas with lambdas of -1 and +1 are generated automatically.
A... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | AlexanderDKazakov/schnetpack | BehlerAngular | false | 6 | [
"MIT"
] | 0 | 97b82469d977981b500e439a6c93696d8dac8a3f | https://github.com/AlexanderDKazakov/schnetpack/tree/97b82469d977981b500e439a6c93696d8dac8a3f |
ResidualLinear | import torch
import torch.nn as nn
class ResidualLinear(nn.Module):
def __init__(self, hidden_dim, norm1=None, norm2=None):
super().__init__()
self.linear1 = nn.Linear(hidden_dim, hidden_dim)
self.norm1 = norm1
self.linear2 = nn.Linear(hidden_dim, hidden_dim)
self.norm2 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ashutosh1919/neuro-symbolic-sudoku-solver | ResidualLinear | false | 14,910 | [
"Apache-2.0"
] | 52 | ecb4274ff66d3b6a86f64584e0a767bf785f107f | https://github.com/ashutosh1919/neuro-symbolic-sudoku-solver/tree/ecb4274ff66d3b6a86f64584e0a767bf785f107f |
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