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 |
|---|---|---|---|---|---|---|---|---|---|---|
Hidden2Discrete | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
import torch.jit
class Hidden2Discrete(nn.Module):
def __init__(self, input_size, y_size, k_size, is_lstm=False, has_bias=True
):
super(Hidden2Discrete, self).__init__()
self.y_size = y_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.... | RoderickGu/Pretraining_GPT | Hidden2Discrete | false | 17,855 | [
"Apache-2.0"
] | 4 | 0a3ecd38116dc271e273f57490b9b45b660bf401 | https://github.com/RoderickGu/Pretraining_GPT/tree/0a3ecd38116dc271e273f57490b9b45b660bf401 |
ConvModule | import torch
import warnings
import torch.nn as nn
def build_norm_layer(cfg, num_features, postfix=''):
""" Build normalization layer
Args:
cfg (dict): cfg should contain:
type (str): identify norm layer type.
layer args: args needed to instantiate a norm layer.
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import warnings
import torch.... | Complicateddd/Complicateddd-ROITransformer | ConvModule | false | 11,309 | [
"Apache-2.0"
] | 0 | 2adfbf98892d569c460d100c6e2169c5fa3a9b82 | https://github.com/Complicateddd/Complicateddd-ROITransformer/tree/2adfbf98892d569c460d100c6e2169c5fa3a9b82 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 16, 3, 1)
self.conv2 = nn.Conv2d(16, 40, 2, 1)
self.fc1 = nn.Linear(3 * 3 * 40, 400)
self.f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tkhkaeio/PyTorch-GAN | Net | false | 10,882 | [
"MIT"
] | 0 | 565c67cae168a42c6822c787562a1f7a5b35a2ab | https://github.com/tkhkaeio/PyTorch-GAN/tree/565c67cae168a42c6822c787562a1f7a5b35a2ab |
SiamFC | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | LIANGKE23/Siamese-FC-KF-CF | SiamFC | false | 17,608 | [
"MIT"
] | 10 | 3d9db19c0f39f0588a5061cd182bfbfc37dca76f | https://github.com/LIANGKE23/Siamese-FC-KF-CF/tree/3d9db19c0f39f0588a5061cd182bfbfc37dca76f |
Norm | # 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_... | Hyunseung-Kim/molGCT | Norm | false | 8,236 | [
"Apache-2.0"
] | 10 | 5a2604337cf0a9d3c725295ccb7c8ea4b0144636 | https://github.com/Hyunseung-Kim/molGCT/tree/5a2604337cf0a9d3c725295ccb7c8ea4b0144636 |
Invertible1x1Conv | import torch
import torch.utils.data
from torch import nn
class Flow(nn.Module):
"""
Generic class for flow functions
"""
def __init__(self):
super().__init__()
def forward(self, z):
"""
:param z: input variable, first dimension is batch dim
:return: transformed z... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | pkulwj1994/normalizing-flows | Invertible1x1Conv | false | 16,296 | [
"MIT"
] | 96 | 326321c4aea4a3f6ab703f82e21277a79cd7d9e4 | https://github.com/pkulwj1994/normalizing-flows/tree/326321c4aea4a3f6ab703f82e21277a79cd7d9e4 |
WorldNet | import torch
class WorldNet(torch.nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(WorldNet, self).__init__()
self.fc_in = torch.nn.Linear(input_dim, hidden_dim)
self.fc_1 = torch.nn.Linear(hidden_dim, hidden_dim)
self.fc_2 = torch.nn.Linear(hidden_dim, hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | tim-ts-chu/mbpo | WorldNet | false | 10,854 | [
"MIT"
] | 0 | 0d98e6e80499a82812d3361658e0707c0b489fc5 | https://github.com/tim-ts-chu/mbpo/tree/0d98e6e80499a82812d3361658e0707c0b489fc5 |
SimpleReshapeModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleReshapeModel(torch.nn.Module):
def __init__(self, shape):
super(SimpleReshapeModel, self).__init__()
self.shape = shape
def forward(self, tensor):
combined = tensor + tensor
return combined.reshape(se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleReshapeModel | false | 3,350 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
RelativeSelfAttentionLayer | # 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.... | ishine/tfm-tts | RelativeSelfAttentionLayer | false | 3,703 | [
"MIT"
] | 0 | a964736467851ddec8f8e8933b9550cbe7d7d7eb | https://github.com/ishine/tfm-tts/tree/a964736467851ddec8f8e8933b9550cbe7d7d7eb |
SelfGate | # 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, math as tl_math
im... | awesome-archive/AI-Writer | SelfGate | false | 6,276 | [
"BSD-3-Clause"
] | 1 | abdcd5582f81fca2f677a020360654865bf82065 | https://github.com/awesome-archive/AI-Writer/tree/abdcd5582f81fca2f677a020360654865bf82065 |
FreqUpsample | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
class FreqUpsample(nn.Module):
def __init__(self, factor: 'int', mode='nearest'):
super().__init__()
self.f = float(factor)
self.mode = mode
def forward(self, x: 'Tensor') ->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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Rikorose/DeepFilterNet | FreqUpsample | false | 14,306 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
SoftMaxAvgPoolModel | import torch
import torch.cuda
import torch.nn
import torch.utils.data
import torch.fx
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class SoftMaxAvgPoolModel(torch.nn.Module):
def __init__(self):
super(SoftMaxAvgPoolModel, self).__init__()
self.sfmax = torch.nn.... | 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.cuda
impo... | quic-araha/aimet | SoftMaxAvgPoolModel | false | 10,657 | [
"BSD-3-Clause"
] | 0 | 1afd5ce23f06bed74fec9812d5d2ea256ac4a650 | https://github.com/quic-araha/aimet/tree/1afd5ce23f06bed74fec9812d5d2ea256ac4a650 |
ShiftedConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from numpy import prod
assert_size_stride = to... | raphaelreme/CPC_audio | ShiftedConv | false | 10,843 | [
"MIT"
] | 0 | a2b045d5f03f4a73beaab9b481244e454edacbaa | https://github.com/raphaelreme/CPC_audio/tree/a2b045d5f03f4a73beaab9b481244e454edacbaa |
PMA | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KohavTal/SCAE_Project | PMA | false | 8,421 | [
"Apache-2.0"
] | 40 | bc6d1c3697fcb9327dd96e9657c3299b47cf355e | https://github.com/KohavTal/SCAE_Project/tree/bc6d1c3697fcb9327dd96e9657c3299b47cf355e |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | adriaciurana/udacity-project-3 | Critic | false | 9,679 | [
"MIT"
] | 0 | 806f78e35a6699eeb0a3272e326d0edc199d16be | https://github.com/adriaciurana/udacity-project-3/tree/806f78e35a6699eeb0a3272e326d0edc199d16be |
NHDUnitV2 | # 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_... | YiqunChen1999/NTIRE2021NHDehazing | NHDUnitV2 | false | 1,280 | [
"MIT"
] | 0 | 3341ae561ac8caff7f40ddf6d4408032a28ff13c | https://github.com/YiqunChen1999/NTIRE2021NHDehazing/tree/3341ae561ac8caff7f40ddf6d4408032a28ff13c |
LayerNorm | # 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_... | AntiAegis/PyTorch-GAN | LayerNorm | false | 4,866 | [
"MIT"
] | 1 | 1cb951b3ad3a58b749c1802f84947b85f72c8367 | https://github.com/AntiAegis/PyTorch-GAN/tree/1cb951b3ad3a58b749c1802f84947b85f72c8367 |
RNN_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
from torch._inductor.runtime.... | CMOONCS/DeepLearning | RNN_net | false | 13,439 | [
"MIT"
] | 86 | 748107d27e466bb18559b828642a4cace6431dc2 | https://github.com/CMOONCS/DeepLearning/tree/748107d27e466bb18559b828642a4cace6431dc2 |
BasicModel_ConvNet_MaxPool1d | import torch
import torch.nn as nn
class BasicModel_ConvNet_MaxPool1d(nn.Module):
"""Same as above, but with the MaxPool2d replaced
with a MaxPool1d. This is useful because the MaxPool modules
behave differently to other modules from the perspective
of the DeepLift Attributions
"""
def __init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ngduduong/captum | BasicModel_ConvNet_MaxPool1d | false | 4,092 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
LocallyConnected | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | Rishab26/causalnex | LocallyConnected | false | 14,312 | [
"Apache-2.0"
] | 1,523 | 127d9324a3d68c1795299c7522f22cdea880f344 | https://github.com/Rishab26/causalnex/tree/127d9324a3d68c1795299c7522f22cdea880f344 |
ComplexLinear | import torch
from torch import nn
import torch.utils
class ComplexLinear(nn.Module):
def __init__(self, in_features, out_features):
super(ComplexLinear, self).__init__()
self.fc_r = nn.Linear(in_features, out_features)
self.fc_i = nn.Linear(in_features, out_features)
def forward(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils
assert_size_stride = torch._C._dynamo.gu... | muqiaoy/dl_signal | ComplexLinear | false | 16,123 | [
"MIT"
] | 54 | 3a30d14982016644bfc96a7d1ca0109b441f17fd | https://github.com/muqiaoy/dl_signal/tree/3a30d14982016644bfc96a7d1ca0109b441f17fd |
ConvExpansion | # 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... | Jincheng-Sun/Kylearn-pytorch | ConvExpansion | false | 654 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
Inequality | import math
import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, distribution=None, **kwargs):
super().__init__()
self.distribution = distribution
self.data_ = []
if distribution is None:
pass
elif distribution == 'normal':
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | Inequality | false | 17,129 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
GSA | import torch
from torch import nn
class GSAHelper(nn.Module):
def __init__(self, d):
super().__init__()
self.d = d
self.fc_k = nn.Linear(self.d, self.d)
self.fc_q = nn.Linear(self.d, self.d)
self.fc_kq = nn.Linear(self.d, self.d)
def forward(self, k, q):
m = k... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | VKCOM/TopicsDataset | GSA | false | 5,930 | [
"MIT"
] | 1 | 149919321ba61a8f17b22f62f60f4aedec43d72b | https://github.com/VKCOM/TopicsDataset/tree/149919321ba61a8f17b22f62f60f4aedec43d72b |
G_Small | import torch
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, bn
=False, activation='leakyrelu', dropout=False):
super(Conv2d, self).__init__()
padding = int((kernel_size - 1) / 2)
self.conv = nn.Conv2d(in_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | RQuispeC/pytorch-ACSCP | G_Small | false | 8,762 | [
"MIT"
] | 25 | c83f08632012c2245250ff9c5140814461db575c | https://github.com/RQuispeC/pytorch-ACSCP/tree/c83f08632012c2245250ff9c5140814461db575c |
BottleneckBlock | import math
import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
def init_layer(L):
if isinstance(L, nn.Conv2d):
n = L.kernel_size[0] * L.kernel_size[1] * L.out_channels
L.weight.data.normal_(0, math.sqrt(2.0 / f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | Aamer98/FeatureNorm | BottleneckBlock | false | 7 | [
"MIT"
] | 0 | fbf3d3b4cef81b3351347d272eb51b6cdd9f0cc5 | https://github.com/Aamer98/FeatureNorm/tree/fbf3d3b4cef81b3351347d272eb51b6cdd9f0cc5 |
cnn_7layer_alt | import torch
import torch.nn as nn
import torch.nn.functional as F
class cnn_7layer_alt(nn.Module):
def __init__(self, in_ch, in_dim, width=2, linear_size=128):
super(cnn_7layer_alt, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 4 * width, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Mahoumaru/auto_LiRPA | cnn_7layer_alt | false | 11,682 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
AveragedHausdorffLoss | import torch
import torch.nn as nn
def cdist(x, y):
"""
Input: x is a Nxd Tensor
y is a Mxd Tensor
Output: dist is a NxM matrix where dist[i,j] is the norm
between x[i,:] and y[j,:]
i.e. dist[i,j] = ||x[i,:]-y[j,:]||
"""
differences = x.unsqueeze(1) - y.unsqueeze(0)
d... | 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... | adriangrepo/segmentl | AveragedHausdorffLoss | false | 18,264 | [
"MIT"
] | 5 | 9b520bf6cfd005eef9bba3db36ee6b3bb373b085 | https://github.com/adriangrepo/segmentl/tree/9b520bf6cfd005eef9bba3db36ee6b3bb373b085 |
HuberLoss | import torch
import torch.nn as nn
class HuberLoss(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.SmoothL1Loss()
def forward(self, logits, labels):
loss = self.loss(logits, labels)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | hslrock/Reinforcement-Learning-Implementation | HuberLoss | false | 10,171 | [
"MIT"
] | 0 | 31db7e31c92f8e01609bf51d3f8f22211ec0fd5d | https://github.com/hslrock/Reinforcement-Learning-Implementation/tree/31db7e31c92f8e01609bf51d3f8f22211ec0fd5d |
PowerPropLinear | # 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.... | dlpbc/powerpropagation-pytorch | PowerPropLinear | false | 6,587 | [
"MIT"
] | 1 | 99e29ce25ede9330cb8f624cb1fa7ffef6f82f03 | https://github.com/dlpbc/powerpropagation-pytorch/tree/99e29ce25ede9330cb8f624cb1fa7ffef6f82f03 |
Normalize | # 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_... | jingraham/struct2seq | Normalize | false | 15,691 | [
"MIT"
] | 106 | 22e497a2b565fe82f17e12ea37e89dcf4e50e92f | https://github.com/jingraham/struct2seq/tree/22e497a2b565fe82f17e12ea37e89dcf4e50e92f |
ConLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConLoss(nn.Module):
def __init__(self, device, margin=2):
super(ConLoss, self).__init__()
self.margin = margin
self.device = device
def forward(self, output1, output2, label):
diff = F.pairwise_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 torch.nn as nn
import... | Devanshu-singh-VR/FaceRecognition | ConLoss | false | 2,157 | [
"MIT"
] | 0 | f596d1964f4f43174ffe5bac6d6437a7d22c3593 | https://github.com/Devanshu-singh-VR/FaceRecognition/tree/f596d1964f4f43174ffe5bac6d6437a7d22c3593 |
UnpoolAvgHealpix | import torch
class UnpoolAvgHealpix(torch.nn.Module):
"""Healpix Average Unpooling module
Parameters
----------
kernel_size : int
Pooling kernel width
"""
def __init__(self, kernel_size, *args, **kwargs):
"""kernel_size should be 4, 16, 64, etc."""
super().__init_... | 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
reinterpret... | ownzonefeng/weather_prediction | UnpoolAvgHealpix | false | 7,427 | [
"MIT"
] | 1 | 723c02b6b3c0a40751d87572b66c7a4e040dec92 | https://github.com/ownzonefeng/weather_prediction/tree/723c02b6b3c0a40751d87572b66c7a4e040dec92 |
HuberLoss | # 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... | NagisaZj/ProMP | HuberLoss | false | 11,726 | [
"MIT"
] | 0 | 539739ae2b7d5fdcad00855da695f643b23df4b3 | https://github.com/NagisaZj/ProMP/tree/539739ae2b7d5fdcad00855da695f643b23df4b3 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 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
import torch.nn as nn
assert_... | KuangenZhang/StructuredRL | Critic | false | 5,463 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a |
ChannelSpatialSELayer3D | # 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 ... | YilinLiu97/AmygNet-Pytorch | ChannelSpatialSELayer3D | false | 18,147 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 |
SelectAdaptivePool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn im... | BigFishMaster/tnt | SelectAdaptivePool2d | false | 17,482 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
AnswerModule | import torch
import torch.nn as nn
import torch.nn.init as init
class AnswerModule(nn.Module):
def __init__(self, vocab_size, hidden_size):
super(AnswerModule, self).__init__()
self.z = nn.Linear(2 * hidden_size, vocab_size)
init.xavier_normal_(self.z.state_dict()['weight'])
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init as init
assert_size_stride = torch._C... | kirubarajan/Dynamic-Memory-Network-Plus | AnswerModule | false | 12,674 | [
"Apache-2.0"
] | 0 | 0613287ef5a959c7b260afcea2c31afcfb0ea189 | https://github.com/kirubarajan/Dynamic-Memory-Network-Plus/tree/0613287ef5a959c7b260afcea2c31afcfb0ea189 |
FloorDivAssign | import torch
class FloorDivAssign(torch.nn.Module):
def __init__(self):
super(FloorDivAssign, 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
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
d... | PogChamper/torch2trt | FloorDivAssign | false | 14,186 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
Softmax | # 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... | SaumilShah66/dqn_uav | Softmax | false | 9,584 | [
"MIT"
] | 0 | 2bf780369e964b870624aebcff16c0714cad03c1 | https://github.com/SaumilShah66/dqn_uav/tree/2bf780369e964b870624aebcff16c0714cad03c1 |
PrimaryCapsLayer | # 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 ... | shwetasrsh/MNIST-baselines | PrimaryCapsLayer | false | 16,437 | [
"MIT"
] | 61 | aa888e201a1dddda13e7b278cab8f940d57538db | https://github.com/shwetasrsh/MNIST-baselines/tree/aa888e201a1dddda13e7b278cab8f940d57538db |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(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(... | 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_... | AlansBoyHeart/vit-pytorch | LayerNorm | false | 1,920 | [
"MIT"
] | 0 | 1959adae0bdd7801475bba34d7d61bdc529b4616 | https://github.com/AlansBoyHeart/vit-pytorch/tree/1959adae0bdd7801475bba34d7d61bdc529b4616 |
InnerProductLayer | # 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
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | Sunmyunghan/Final_Project | InnerProductLayer | false | 1,221 | [
"MIT"
] | 0 | 28cde293dc6d07521b2e1c5613b20444aea91d21 | https://github.com/Sunmyunghan/Final_Project/tree/28cde293dc6d07521b2e1c5613b20444aea91d21 |
ResNet | # 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_... | Maosef/easy-to-hard | ResNet | false | 8,555 | [
"MIT"
] | 44 | 711ec0965229444a6c51b1b06a4e2cad3e32d02e | https://github.com/Maosef/easy-to-hard/tree/711ec0965229444a6c51b1b06a4e2cad3e32d02e |
BlendLinear | import torch
import torch.nn as nn
import torch.utils.data
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)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Justin-Tan/ffjord | BlendLinear | false | 681 | [
"MIT"
] | 0 | 2caf8a4ff84933672fe0d94255d665b3dd7a6791 | https://github.com/Justin-Tan/ffjord/tree/2caf8a4ff84933672fe0d94255d665b3dd7a6791 |
SFCN | # 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_... | L597383845/row-col-table-recognition | SFCN | false | 17,548 | [
"MIT"
] | 7 | 617718751861b3f4e35a4b34dde4c898575e6818 | https://github.com/L597383845/row-col-table-recognition/tree/617718751861b3f4e35a4b34dde4c898575e6818 |
ScaleNorm | import torch
from torch import nn
class ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(1))
def forward(self, x):
norm = torch.norm(x, dim=-1, keepdim=True) * self.sc... | 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_... | booydar/x-transformers | ScaleNorm | false | 3,233 | [
"MIT"
] | 0 | 97f0a854fdf4df8a3fbf6a580e2375463af3538c | https://github.com/booydar/x-transformers/tree/97f0a854fdf4df8a3fbf6a580e2375463af3538c |
BMNLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_ma... | Viditagarwal7479/Video-Swin-Transformer | BMNLoss | false | 18,064 | [
"Apache-2.0"
] | 9 | 37910ef3141c7b2eef76544f9ec8bdf26ec94c7d | https://github.com/Viditagarwal7479/Video-Swin-Transformer/tree/37910ef3141c7b2eef76544f9ec8bdf26ec94c7d |
NormedConv2d | import torch
from torch import nn
class NormedConv2d(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Bo396543018/mmdetection | NormedConv2d | false | 7,794 | [
"Apache-2.0"
] | 16 | eb337336d3c239dc1d20534496f69df41ae9a300 | https://github.com/Bo396543018/mmdetection/tree/eb337336d3c239dc1d20534496f69df41ae9a300 |
T5LayerNorm | import torch
import torch.nn as nn
import torch.utils.checkpoint
class T5LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-06):
"""
Construct a layernorm module in the T5 style No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch.... | Hzfinfdu/Black-Box-Tuning | T5LayerNorm | false | 2,458 | [
"MIT"
] | 0 | 64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 | https://github.com/Hzfinfdu/Black-Box-Tuning/tree/64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 |
RGBBlock | import torch
from torch import nn
import torch.nn.functional as F
class Conv2DMod(nn.Module):
def __init__(self, in_chan, out_chan, kernel, demod=True, stride=1,
dilation=1, **kwargs):
super().__init__()
self.filters = out_chan
self.demod = demod
self.kernel = kernel
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import t... | mahmoudnafifi/HistoGAN | RGBBlock | false | 15,997 | [
"MIT"
] | 169 | 50be1482638ace3ec85d733e849dec494ede155b | https://github.com/mahmoudnafifi/HistoGAN/tree/50be1482638ace3ec85d733e849dec494ede155b |
GatedConv1d | import torch
import torch.nn as nn
class MaskedConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
groups=1, bias=True, causal=True):
if causal:
padding = (kernel_size - 1) * dilation
else:
padding = (kernel_size - 1) * dilatio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | lonePatient/TorchBlocks | GatedConv1d | false | 15,963 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
MultiHeadedAttention | 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 | MultiHeadedAttention | false | 8,532 | [
"Apache-2.0"
] | 18 | ae8f8451d73fa9ceac6f7738194543e83959ca86 | https://github.com/Mashiro083/wenet-onnx/tree/ae8f8451d73fa9ceac6f7738194543e83959ca86 |
Highway | # 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... | abhinonymous/MSMARCO-Question-Answering | Highway | false | 14,746 | [
"MIT"
] | 127 | bfdd802d20b63322adca23f1da1f6a5931593920 | https://github.com/abhinonymous/MSMARCO-Question-Answering/tree/bfdd802d20b63322adca23f1da1f6a5931593920 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, alpha: 'float'=0.25, gamma: 'float'=2, reduction:
'str'='none'):
"""
Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | VisualJoyce/ChengyuBERT | FocalLoss | false | 18,063 | [
"MIT"
] | 8 | 605db3a4b3241dd4d02baa41a68bf23b5b00b36d | https://github.com/VisualJoyce/ChengyuBERT/tree/605db3a4b3241dd4d02baa41a68bf23b5b00b36d |
GCNLayer | import torch
import torch.nn as nn
class GCNLayer(nn.Module):
def __init__(self, c_in, c_out):
super().__init__()
self.projection = nn.Linear(c_in, c_out)
def forward(self, node_feats, adj_matrix):
"""
Args:
node_feats: Tensor with node features of shape [batch_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | jiwidi/lightning-tutorials | GCNLayer | false | 15,703 | [
"Apache-2.0"
] | 114 | 70ba437447f345d4d6ba089d5b30fd1da2cbc04b | https://github.com/jiwidi/lightning-tutorials/tree/70ba437447f345d4d6ba089d5b30fd1da2cbc04b |
OptimizedMLP | import torch
import torch.optim
import torch.jit
import torch.nn as nn
class OptimizedMLP(nn.Module):
def __init__(self, num_in_features: 'int', num_out_features: 'int'):
super(OptimizedMLP, self).__init__()
self.act = nn.ELU()
self.l_in = nn.Linear(in_features=num_in_features, out_featur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.optim
... | plaveczlambert/deep_euler_tests | OptimizedMLP | false | 7,501 | [
"MIT"
] | 1 | a3ceef98ba76bd7a00ccd3c773cd9850311b3b1a | https://github.com/plaveczlambert/deep_euler_tests/tree/a3ceef98ba76bd7a00ccd3c773cd9850311b3b1a |
RNN | # 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.... | jrbtaylor/recurrent_pytorch | RNN | false | 10,326 | [
"Apache-2.0"
] | 0 | 09ee203a86b70a32aec3e97d7daa646caf8fd182 | https://github.com/jrbtaylor/recurrent_pytorch/tree/09ee203a86b70a32aec3e97d7daa646caf8fd182 |
GramMatrix | import torch
import torch.nn as nn
import torch.utils.data
class GramMatrix(nn.Module):
def forward(self, input):
b, c, h, w = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
def get_inputs():
return [torch.rand([4,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Reytuag/non-stationary_texture_syn | GramMatrix | false | 14,295 | [
"MIT"
] | 351 | 005d3e4ead3dfa2164b14c5b3bf41cdc15fd3b0b | https://github.com/Reytuag/non-stationary_texture_syn/tree/005d3e4ead3dfa2164b14c5b3bf41cdc15fd3b0b |
BertLayer | # 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 | BertLayer | false | 12,274 | [
"Apache-2.0"
] | 0 | 0b562d791d34a40fd3c0383a0a32b4eeb2171cb5 | https://github.com/brendon-boldt/minbert-assignment/tree/0b562d791d34a40fd3c0383a0a32b4eeb2171cb5 |
CPAMDec | from torch.nn import Module
import torch
from torchvision.datasets import *
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Linear
from torch.nn import Softmax
from torchvision.transforms import *
class CPAMDec(Module):
"""
CPAM decoding module
"""
def __init__(self, i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ruijieren98/DANet | CPAMDec | false | 16,348 | [
"MIT"
] | 2,190 | e38d61e371179833c08888fd5a1ee444cf5bd875 | https://github.com/ruijieren98/DANet/tree/e38d61e371179833c08888fd5a1ee444cf5bd875 |
ConvSig | import math
import torch
import torch.nn.functional as F
from torch.nn import Conv2d
from torch.nn import Sigmoid
class PadSameConv2d(torch.nn.Module):
def __init__(self, kernel_size, stride=1):
"""
Imitates padding_mode="same" from tensorflow.
:param kernel_size: Kernelsize of the convol... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch.nn import Conv2d
from tor... | shlomi-amitai/monorec | ConvSig | false | 10,908 | [
"MIT"
] | 0 | 74571c6cd8d06ae4fb15cbee5a41147c54c78556 | https://github.com/shlomi-amitai/monorec/tree/74571c6cd8d06ae4fb15cbee5a41147c54c78556 |
HardNegativeContrastiveLoss | import torch
import torch.nn as nn
class HardNegativeContrastiveLoss(nn.Module):
def __init__(self, nmax=1, margin=0.2):
super(HardNegativeContrastiveLoss, self).__init__()
self.margin = margin
self.nmax = nmax
def forward(self, imgs, caps):
scores = torch.mm(imgs, caps.t())
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | maxgreat/dsve-loc | HardNegativeContrastiveLoss | false | 16,047 | [
"BSD-3-Clause-Clear"
] | 56 | dd6807d02c0d5fd3e215be8e5c7a88e73102e561 | https://github.com/maxgreat/dsve-loc/tree/dd6807d02c0d5fd3e215be8e5c7a88e73102e561 |
Benefit3 | import torch
import torch.nn as nn
class Benefit3(nn.Module):
def __init__(self):
super(Benefit3, self).__init__()
self.delta = torch.nn.Parameter(torch.FloatTensor([0.03]),
requires_grad=True)
def forward(self, I, A, B):
self.Y = I * self.delta + A * self.delta ** 2 + B ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | DingLyu/Investigating-and-Modeling-the-Dynamics-of-Long-Ties | Benefit3 | false | 11,348 | [
"MIT"
] | 0 | aa37c3d5c85a8d1696db3dda7dcb22782b737d17 | https://github.com/DingLyu/Investigating-and-Modeling-the-Dynamics-of-Long-Ties/tree/aa37c3d5c85a8d1696db3dda7dcb22782b737d17 |
Attention | import math
import torch
from torch import nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self.v = nn.Paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | AmitMY/seq2seq | Attention | false | 8,837 | [
"MIT"
] | 0 | 1ad7c09188537729e5b18356f5c36fad1928d245 | https://github.com/AmitMY/seq2seq/tree/1ad7c09188537729e5b18356f5c36fad1928d245 |
Split | import torch
import torch.nn as nn
class Split(nn.Module):
def __init__(self):
super(Split, self).__init__()
def forward(self, x):
n = int(x.size(1) / 2)
x1 = x[:, :n, :, :].contiguous()
x2 = x[:, n:, :, :].contiguous()
return x1, x2
def inverse(self, x1, x2):
... | 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... | lingzenan/invertible-resnet | Split | false | 7,090 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
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 ... | juyongjiang/Simple-SR | ResidualBlock_noBN | false | 7,009 | [
"MIT"
] | 1 | 76820511abc04fbe6e4a79d23c67aee97406d563 | https://github.com/juyongjiang/Simple-SR/tree/76820511abc04fbe6e4a79d23c67aee97406d563 |
LabelSmoothingCrossEntropy | import torch
from torch import nn
class LabelSmoothingCrossEntropy(nn.Module):
def __init__(self, eps=0.1, reduction='mean', ignore_index=-100):
"""LabelSmoothingCrossEntropy, no-softmax-input
对logits进行smoothing, 即log_softmax后进行操作
args:
ignore_index: (int, optional): Specifies... | 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 ... | dumpmemory/Pytorch-NLU | LabelSmoothingCrossEntropy | false | 15,252 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 |
FastRCNNPredictor | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
from torchvision.transforms import functional as F
class FastRCNNPredictor(nn.Module):
"""
Standard classification + bounding box regression layers
for Fast R-CNN.
Arguments:
in_channels (int): number 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
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | CancerDataScience/NuCLS | FastRCNNPredictor | false | 17,390 | [
"MIT"
] | 7 | c172b55b18d4ea78c3f51a8fd28ee6c2595c8360 | https://github.com/CancerDataScience/NuCLS/tree/c172b55b18d4ea78c3f51a8fd28ee6c2595c8360 |
SpatialAttention2d | # 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
assert_size_stride = torch._C._dynamo.... | elmajdma/seismic-deeplearning | SpatialAttention2d | false | 15,298 | [
"MIT"
] | 270 | bc084abe153509c40b45f8bf0f80dfda1049d7dc | https://github.com/elmajdma/seismic-deeplearning/tree/bc084abe153509c40b45f8bf0f80dfda1049d7dc |
SoftTarget | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class SoftTarget(nn.Module):
"""
Distilling the Knowledge in a Neural Network
https://arxiv.org/pdf/1503.02531.pdf
"""
def __init__(self, T):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | wangxianliang/FaceX-Zoo | SoftTarget | false | 13,088 | [
"Apache-2.0"
] | 0 | b0555c88a0350fa7b59c317f3a171f551fef4e6e | https://github.com/wangxianliang/FaceX-Zoo/tree/b0555c88a0350fa7b59c317f3a171f551fef4e6e |
NasPathBranch | import torch
import torch.utils.data
import torch.nn as nn
def conv1x1(in_channels, out_channels, stride=1, groups=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | HyperGAN/imgclsmob | NasPathBranch | false | 17,685 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
RegLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | BELIEVEfxy/LightSANs | RegLoss | false | 7,772 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
Connect2Model | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Connect2Model(nn.Module):
def __init__(self, board_size, action_size, device):
super(Connect2Model, self).__init__()
self.device = device
self.size = board_size
self.action_size = action_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ShokuninSan/AlphaZeroSimple | Connect2Model | false | 1,074 | [
"MIT"
] | 0 | e32e6a28f872a046705a3f68882139688d5a43c3 | https://github.com/ShokuninSan/AlphaZeroSimple/tree/e32e6a28f872a046705a3f68882139688d5a43c3 |
Normalize | import torch
from torchvision.datasets import *
import torch.nn as nn
import torch.nn.functional as F
from torchvision.transforms import *
class Normalize(nn.Module):
"""Performs :math:`L_p` normalization of inputs over specified dimension.
Does:
.. math::
v = \\frac{v}{\\max(\\lVert v \\rVert_p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torchvision.datasets im... | tousifulhaque/DANet | Normalize | false | 4,457 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
layer_2_to_1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | HyTruongSon/InvariantGraphNetworks-PyTorch | layer_2_to_1 | false | 17,413 | [
"Apache-2.0"
] | 7 | da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | https://github.com/HyTruongSon/InvariantGraphNetworks-PyTorch/tree/da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 |
ContextGate | # 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.cuda
import torch.distributed
assert_size_str... | ChenRocks/Distill-BERT-Textgen-ONMT | ContextGate | false | 17,100 | [
"MIT"
] | 7 | d83dd1a95af7513cbfae4a2768f6effc2f3a589f | https://github.com/ChenRocks/Distill-BERT-Textgen-ONMT/tree/d83dd1a95af7513cbfae4a2768f6effc2f3a589f |
ModuloMapIDList | # 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 abc
import torch.nn
import torch.optim
assert_size_stride = torch._C._dy... | BerenLuthien/ReAgent | ModuloMapIDList | false | 13,387 | [
"BSD-3-Clause"
] | 1,156 | 52f666670a7fa03206812ef48949f6b934d400f7 | https://github.com/BerenLuthien/ReAgent/tree/52f666670a7fa03206812ef48949f6b934d400f7 |
AttDot | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ishine/NISQA | AttDot | false | 15,615 | [
"MIT"
] | 223 | 2c8917f30c4e4bbca3a48e9852301f1e2480a741 | https://github.com/ishine/NISQA/tree/2c8917f30c4e4bbca3a48e9852301f1e2480a741 |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 3, kernel_size=7, stride=1, bias=False,
padding=3)
self.conv2 = nn.Conv2d(3, 3, kernel_size=7, stride=1, bias=False,
padding=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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | alirezadavoudi/tensorflow-vs-pytorch | Net | false | 18,277 | [
"MIT"
] | 4 | 1c0ccda8004591f3f29d4787d7b3bbfbc397523f | https://github.com/alirezadavoudi/tensorflow-vs-pytorch/tree/1c0ccda8004591f3f29d4787d7b3bbfbc397523f |
FocalLossV1 | # 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... | chizhu/pytorch-loss | FocalLossV1 | false | 6,439 | [
"MIT"
] | 1 | c8fbd78771f11a910b0b51ae3697c09761dd9696 | https://github.com/chizhu/pytorch-loss/tree/c8fbd78771f11a910b0b51ae3697c09761dd9696 |
Pooler | # 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 ... | ExtremeViscent/ColossalAI-Examples | Pooler | false | 2,658 | [
"Apache-2.0"
] | 0 | 98ced2435d8d814f06740ab10d3e277ca91a83c7 | https://github.com/ExtremeViscent/ColossalAI-Examples/tree/98ced2435d8d814f06740ab10d3e277ca91a83c7 |
L2Norm | import torch
import torch.nn as nn
from itertools import product as product
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
self.eps = 1e-10
... | 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
from itertools import product as product
import torch.nn.... | AnupKumarGupta/syncnet_python | L2Norm | false | 11,249 | [
"MIT"
] | 0 | 932b4621cf6aa090baac7c7de22d0649bde9fbbd | https://github.com/AnupKumarGupta/syncnet_python/tree/932b4621cf6aa090baac7c7de22d0649bde9fbbd |
SingleGate | import torch
import torch.multiprocessing
from torch import nn
import torch.utils.data
class SingleGate(nn.Module):
def __init__(self, vector_dim, topic_dim):
super().__init__()
assert vector_dim == topic_dim
self.fusion_linear = nn.Linear(vector_dim + topic_dim, 1)
self.sigmoid =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.multiprocessing
from torch import nn
import torch.utils.data
assert... | WuDiDaBinGe/TAKG | SingleGate | false | 1,238 | [
"MIT"
] | 0 | 83e608e677a4ee74722d18cb5ef430f4f6c6ad31 | https://github.com/WuDiDaBinGe/TAKG/tree/83e608e677a4ee74722d18cb5ef430f4f6c6ad31 |
LayerNorm | import torch
import torch.nn as nn
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
class LayerNorm(nn.Module):
def __init__(self, dim, mean=0.0, std=1.0, fixed=False, eps=1e-06, ball
=False):
super(LayerNorm, self).__init__()
self.eps = eps
self.ball ... | 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.optim
import torch.autograd
import torch.nn
... | FilippoC/-deep-syntactic-dependency-parsing-release | LayerNorm | false | 17,280 | [
"MIT"
] | 4 | 30e2571ea930c2fd81559f5a2a971e3738cc6d39 | https://github.com/FilippoC/-deep-syntactic-dependency-parsing-release/tree/30e2571ea930c2fd81559f5a2a971e3738cc6d39 |
Hsigmoid | # 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... | IgorDavidyuk/pytorch-mobilenet-v3 | Hsigmoid | false | 2,359 | [
"Apache-2.0"
] | 0 | 48678f80d9390b530cb97966db492cf01d1c4a43 | https://github.com/IgorDavidyuk/pytorch-mobilenet-v3/tree/48678f80d9390b530cb97966db492cf01d1c4a43 |
SpatialSoftmax | import torch
import torch.utils.data
import torch.random
import torch.nn.functional as F
def marginal_softmax(heatmap, dim):
marginal = torch.mean(heatmap, dim=dim)
sm = F.softmax(marginal, dim=2)
return sm
def prob_to_keypoints(prob, length):
ruler = torch.linspace(0, 1, length).type_as(prob).expan... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | DuaneNielsen/keypoints | SpatialSoftmax | false | 8,032 | [
"MIT"
] | 42 | 302fa02966d4372ac9b5aaa3d8dc24684be0b252 | https://github.com/DuaneNielsen/keypoints/tree/302fa02966d4372ac9b5aaa3d8dc24684be0b252 |
ACNetwork | import torch
import torch.nn as nn
class ACNetwork(nn.Module):
def __init__(self, num_actions, num_states):
super(ACNetwork, self).__init__()
self.fc1 = nn.Linear(num_states, 1024)
self.fc2 = nn.Linear(1024, 512)
self.action = nn.Linear(512, num_actions)
self.softmax = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Devanshu-singh-VR/Reinforcement-Learning_Mixed | ACNetwork | false | 9,040 | [
"MIT"
] | 0 | 6b8b23977864f918ab8958b729d0faabcca720e4 | https://github.com/Devanshu-singh-VR/Reinforcement-Learning_Mixed/tree/6b8b23977864f918ab8958b729d0faabcca720e4 |
ShallowNet | import torch
import torch.nn as nn
class ShallowNet(nn.Module):
def __init__(self, n_features):
super(ShallowNet, self).__init__()
self.a1 = nn.Linear(n_features, 2)
def forward(self, x):
return torch.sigmoid(self.a1(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | SkBlaz/KBNR | ShallowNet | false | 5,830 | [
"MIT"
] | 1 | 4c37fe3fdfa7719572affd617e2dab43a54ba1d5 | https://github.com/SkBlaz/KBNR/tree/4c37fe3fdfa7719572affd617e2dab43a54ba1d5 |
AttentionModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class AttentionModule(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv1 = nn.Conv2d(dim, dim, kernel_size=(3, 3), padding=1)
self.conv2 = nn.Conv2d(dim, dim, kernel_size=(3, 3), padding=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
import torch.nn as nn
import ... | SpyrosMouselinos/DeltaFormers | AttentionModule | false | 5,851 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
JointsMSELoss | import torch
import torch.nn as nn
import torch.utils.data
class JointsMSELoss(nn.Module):
def __init__(self):
super(JointsMSELoss, self).__init__()
self.criterion = nn.MSELoss(reduction='mean')
def forward(self, output, target, target_weight=None):
batch_size = output.size(0)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | shunya-toyokawa/qanet_human_parts_segmentatiom | JointsMSELoss | false | 16,433 | [
"MIT"
] | 72 | 5527b247acd65534b455c26e3692a14b31669602 | https://github.com/shunya-toyokawa/qanet_human_parts_segmentatiom/tree/5527b247acd65534b455c26e3692a14b31669602 |
PredictTargets | # 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... | dmcinerney/ehr-extraction-models | PredictTargets | false | 6,589 | [
"Apache-2.0"
] | 1 | c7e7e176f69a2558d420c607254ed7e98b5e836a | https://github.com/dmcinerney/ehr-extraction-models/tree/c7e7e176f69a2558d420c607254ed7e98b5e836a |
TokenEmbedding | import torch
import torch.nn as nn
class TokenEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= '1.5.0' else 2
self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
kernel_size=3, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Ares-Long/Time | TokenEmbedding | false | 11,285 | [
"Apache-2.0"
] | 0 | 7827463613f45baea82de189a890afb7394e73e4 | https://github.com/Ares-Long/Time/tree/7827463613f45baea82de189a890afb7394e73e4 |
decoder5 | # 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.... | MingSun-Tse/PytorchWCT | decoder5 | false | 9,390 | [
"MIT"
] | 0 | 9d11cc0995c0610c129b78ff5f72a26f4d60e10a | https://github.com/MingSun-Tse/PytorchWCT/tree/9d11cc0995c0610c129b78ff5f72a26f4d60e10a |
Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_inputs, num_outputs, hidden_size=256):
super(Model, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_size)
self.linear2 = nn.Linear(hidden_size, num_outputs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | PacktPublishing/Hands-On-Reinforcement-Learning-for-Games | Model | false | 8,650 | [
"MIT"
] | 41 | 045b8846f2558aa8fb8ac8cef5c71ee098cb9b22 | https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-for-Games/tree/045b8846f2558aa8fb8ac8cef5c71ee098cb9b22 |
TripletLogExpLoss | # 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, math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = ... | zhangxue123/deep-image-retrieval | TripletLogExpLoss | false | 13,170 | [
"BSD-3-Clause"
] | 0 | ac188856fa5a034aed3f7ed3fb617d580da44462 | https://github.com/zhangxue123/deep-image-retrieval/tree/ac188856fa5a034aed3f7ed3fb617d580da44462 |
ConvolModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvolModel(nn.Module):
def __init__(self):
super(ConvolModel, self).__init__()
self.conv1 = nn.Conv2d(1, 5, 2)
self.conv2 = nn.Conv2d(5, 10, 2)
self.conv3 = nn.Conv2d(10, 10, 2)
def forward(self, 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.nn as nn
assert_... | VVKot/mlinseconds-vote-prediction | ConvolModel | false | 2,943 | [
"MIT"
] | 0 | c869ae428fb8d5e83f0a47468722da968aed28c6 | https://github.com/VVKot/mlinseconds-vote-prediction/tree/c869ae428fb8d5e83f0a47468722da968aed28c6 |
Linear | # 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
from torch.nn.parameter import Parameter
assert_size_strid... | THAKAORI/SalsaNext | Linear | false | 11,916 | [
"MIT"
] | 0 | 855cd7e9ebb83ee62538ba4753a011ada7bbfb6c | https://github.com/THAKAORI/SalsaNext/tree/855cd7e9ebb83ee62538ba4753a011ada7bbfb6c |
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
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 |
LayerNorm | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | IBM/context-relevant-pruning-textrl | LayerNorm | false | 17,405 | [
"Apache-2.0"
] | 8 | c8630203af5df64c8e1e3c4624e4a158b40a5f27 | https://github.com/IBM/context-relevant-pruning-textrl/tree/c8630203af5df64c8e1e3c4624e4a158b40a5f27 |
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