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 |
|---|---|---|---|---|---|---|---|---|---|---|
SoftmaxDeepLiftModel | import torch
import torch.nn as nn
class SoftmaxDeepLiftModel(nn.Module):
"""
Model architecture from:
https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
"""
def __init__(self, num_in, num_hidden, num_out):
super().__init__()
self.num_in = num_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.... | Europium248/captum | SoftmaxDeepLiftModel | false | 449 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
BinaryCrossEntropyLoss2d | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
class BinaryCrossEntropyLoss2d(nn.Module):
def __init__(self, weight=None, size_average=True):
"""
Binary cross entropy loss 2D
Args:
weight:
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | jayden-chua/image-mask | BinaryCrossEntropyLoss2d | false | 3,690 | [
"MIT"
] | 0 | ce2c6a32bf13df582e7b57e506d58518258be292 | https://github.com/jayden-chua/image-mask/tree/ce2c6a32bf13df582e7b57e506d58518258be292 |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AlbertiPot/attention-is-all-you-need-pytorch | PositionwiseFeedForward | false | 28 | [
"MIT"
] | 0 | c5ec40907db281b85b3bd7a5dd8016940291add0 | https://github.com/AlbertiPot/attention-is-all-you-need-pytorch/tree/c5ec40907db281b85b3bd7a5dd8016940291add0 |
CecaModule | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch._utils
import torch.optim
class CecaModule(nn.Module):
"""Constructs a circular ECA module.
ECA module where the conv uses circular padding rather than zero padding.
Unlike the spatial dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.nn.parallel
import torch._utils
i... | Alicegaz/torchok | CecaModule | false | 16,929 | [
"Apache-2.0"
] | 8 | 7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 | https://github.com/Alicegaz/torchok/tree/7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 |
waspIntrinsicComposer | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | zhixinshu/DeformingAutoencoders-pytorch | waspIntrinsicComposer | false | 16,849 | [
"BSD-2-Clause"
] | 112 | 72996c5d11ae25dd0051bb51df353fef88e65742 | https://github.com/zhixinshu/DeformingAutoencoders-pytorch/tree/72996c5d11ae25dd0051bb51df353fef88e65742 |
SoftQNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class SoftQNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size=[400, 300],
init_w=0.003):
super(SoftQNetwork, self).__init__()
self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size[0])
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | constancecrozier/CityLearn | SoftQNetwork | false | 9,922 | [
"MIT"
] | 0 | c92f981771d29181cffce448a31d8f367a668175 | https://github.com/constancecrozier/CityLearn/tree/c92f981771d29181cffce448a31d8f367a668175 |
BinaryClassificationHead | from _paritybench_helpers import _mock_config
import torch
class BinaryClassificationHead(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = torch.nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = torch.nn.Dropout(conf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | IgnatovFedor/DeepPavlov | BinaryClassificationHead | false | 10,822 | [
"Apache-2.0"
] | 0 | 02ba9c4b2919384c142c170c7f89c65cf05dd426 | https://github.com/IgnatovFedor/DeepPavlov/tree/02ba9c4b2919384c142c170c7f89c65cf05dd426 |
IoULoss | # 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... | Exdenta/torchsat | IoULoss | false | 13,652 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
ResNetModel | # 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 typing import Dict
from ... | aethersis/VisualEyeTracker | ResNetModel | false | 18,279 | [
"MIT"
] | 7 | 53723bd68972954249b53d6ba0ac1cbe93b8844f | https://github.com/aethersis/VisualEyeTracker/tree/53723bd68972954249b53d6ba0ac1cbe93b8844f |
Swish | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | LucasFidon/MONAI | Swish | false | 2,591 | [
"Apache-2.0"
] | 0 | a7ef9d567775dd7a222f93bab08191c0e3532c92 | https://github.com/LucasFidon/MONAI/tree/a7ef9d567775dd7a222f93bab08191c0e3532c92 |
RankingLoss | import torch
from abc import abstractmethod
import torch.utils.data.dataloader
import torch.nn.functional as F
from torch import nn
import torch.nn
class SimilarityLoss(nn.Module):
def __init__(self):
super(SimilarityLoss, self).__init__()
@abstractmethod
def forward(self, inputs, targets):
... | 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 abc import abstractmethod
import torch.utils.data.dataloader
from torch import nn
im... | OatsProduction/flair | RankingLoss | false | 11,762 | [
"MIT"
] | 0 | 1cf2c9a9ae487e279dce9f6b92c41fa32c4563cf | https://github.com/OatsProduction/flair/tree/1cf2c9a9ae487e279dce9f6b92c41fa32c4563cf |
TotalVariations | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dy... | HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping | TotalVariations | false | 17,462 | [
"MIT"
] | 4 | 1e2dee8d6d1f97722eba91618462537faf9efba7 | https://github.com/HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping/tree/1e2dee8d6d1f97722eba91618462537faf9efba7 |
SIREN_layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | dustlrdk/noise2self | SIREN_layer | false | 3,445 | [
"MIT"
] | 0 | 46e8c4650f7ec4f664448417fecd39b4cae477f7 | https://github.com/dustlrdk/noise2self/tree/46e8c4650f7ec4f664448417fecd39b4cae477f7 |
Loss | import torch
import torch.nn as nn
class Loss(nn.Module):
def __init__(self, lambd):
super(Loss, self).__init__()
self.lambd = lambd
self.lsm = nn.LogSoftmax(dim=1)
def forward(self, O, Y, C):
return (Y * (self.lambd * C - self.lsm(O))).mean(dim=0).sum()
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | DmZhukov/CrossTask | Loss | false | 13,587 | [
"BSD-3-Clause"
] | 58 | 2d79941d687dc8bd100898acd9c71c476b99def1 | https://github.com/DmZhukov/CrossTask/tree/2d79941d687dc8bd100898acd9c71c476b99def1 |
Encoder1 | # 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.... | EndyWon/Texture-Reformer | Encoder1 | false | 8,144 | [
"MIT"
] | 11 | f84f95accb3574c7b759a7f03c0b0b4e150314b5 | https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5 |
Myloss | # 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
... | MoriZSJ/GVB | Myloss | false | 2,656 | [
"MIT"
] | 0 | 9b954660ef377ead81c8e631c4a0f4a17075b2ea | https://github.com/MoriZSJ/GVB/tree/9b954660ef377ead81c8e631c4a0f4a17075b2ea |
MiniBatchStdDev | # 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
import torch.utils.data
import torch.nn.functional
import ... | Hadryan/nn | MiniBatchStdDev | false | 9,375 | [
"MIT"
] | 0 | b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d | https://github.com/Hadryan/nn/tree/b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d |
NearestInterp | import torch
class NearestInterp(torch.nn.Module):
"""
Nearest neighbor interpolation layer.
note:
From the source code, it appears that Darknet uses
nearest neighbor method for its upsampling layer
(darknet master-30 oct 2018).
Internally calls torch.nn.functional.interpolate
to suppress the war... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | jonathanzjl/cam-vision | NearestInterp | false | 3,765 | [
"BSD-2-Clause"
] | 0 | d1bd865b147ea1137979b624c64a6baa4a4b0714 | https://github.com/jonathanzjl/cam-vision/tree/d1bd865b147ea1137979b624c64a6baa4a4b0714 |
DBLoss | import torch
import numpy as np
from torch import nn
class DBLoss(nn.Module):
def __init__(self, alpha=1.0, beta=10.0, ohem_ratio=3):
"""
Implement DB Loss.
:param alpha: loss binary_map 前面的系数
:param beta: loss threshold 前面的系数
:param ohem_ratio: OHEM的比例
"""
... | 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 numpy as np
fro... | SURFZJY/Real-time-Text-Detection | DBLoss | false | 14,374 | [
"Apache-2.0"
] | 65 | b76ee8d840b1fcebf7b9545402907416c7daf24e | https://github.com/SURFZJY/Real-time-Text-Detection/tree/b76ee8d840b1fcebf7b9545402907416c7daf24e |
FFDNN | import torch
import torch as tc
import torch.nn as nn
class FFDNN(nn.Module):
def __init__(self, insize, action_space):
super(FFDNN, self).__init__()
self.input = nn.Linear(insize, 64)
self.layer1 = nn.Linear(64, 32)
self.layer2 = nn.Linear(32, action_space)
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._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | johan-gras/rl-camb-kaggle-connect-x | FFDNN | false | 6,964 | [
"Apache-2.0"
] | 1 | 764463e556c5aea6f61390d2fec83f363510d029 | https://github.com/johan-gras/rl-camb-kaggle-connect-x/tree/764463e556c5aea6f61390d2fec83f363510d029 |
SumNorm | import torch
import torch.nn as nn
class SumNorm(nn.Module):
"""
Normalize dividing by the sum
Shape:
-Input: (N, *)
-Output: (N, *), same shape as the input
Parameters:
-in_features: number of input features
-dim(int): A dimension along witch sum will be comp... | 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... | RosarioAndolina/psychXRF | SumNorm | false | 1,003 | [
"MIT"
] | 0 | e2adadbd17664d7f74c10304f84b3751c571226e | https://github.com/RosarioAndolina/psychXRF/tree/e2adadbd17664d7f74c10304f84b3751c571226e |
PerceptronTanh | import torch
import torch.nn as nn
from typing import Any
import torch.nn.functional as F
class PerceptronTanh(nn.Module):
"""Implements a 1-layer perceptron with Tanh activaton."""
def _forward_unimplemented(self, *input: Any) ->None:
pass
def __init__(self, input_dimension, hidden_dimension, o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | shi27feng/PDP-Solver | PerceptronTanh | false | 12,976 | [
"MIT"
] | 0 | bf6e392f72f8a3572e0987313230943d94d53c95 | https://github.com/shi27feng/PDP-Solver/tree/bf6e392f72f8a3572e0987313230943d94d53c95 |
AdaptiveConcatPool2d | # 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 typing import *
from typing import Optional
from torch import nn
assert_size_stride ... | JacobARose/image-utils | AdaptiveConcatPool2d | false | 592 | [
"MIT"
] | 0 | aa0e005c0b4df5198d188b074f4e21f8d8f97962 | https://github.com/JacobARose/image-utils/tree/aa0e005c0b4df5198d188b074f4e21f8d8f97962 |
down_shifted_conv2d | import torch
import torch.nn as nn
from torch.nn.utils import weight_norm as wn
def down_shift(x, pad=None):
xs = [int(y) for y in x.size()]
x = x[:, :, :xs[2] - 1, :]
pad = nn.ZeroPad2d((0, 0, 1, 0)) if pad is None else pad
return pad(x)
class down_shifted_conv2d(nn.Module):
def __init__(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | andiac/pixel-cnn-pp | down_shifted_conv2d | false | 6,207 | [
"MIT"
] | 1 | 3ba856320e40208cbb6e9cac3e66a739f148903e | https://github.com/andiac/pixel-cnn-pp/tree/3ba856320e40208cbb6e9cac3e66a739f148903e |
SinkhornKnopp | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | TranNhiem/MVAR_SSL | SinkhornKnopp | false | 5,932 | [
"MIT"
] | 1 | 339964db4d40f06a92866675ff99ef67cd968cca | https://github.com/TranNhiem/MVAR_SSL/tree/339964db4d40f06a92866675ff99ef67cd968cca |
AnchorBoxTransform | # 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 Tensor
from typing import Optional
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | TidalPaladin/combustion | AnchorBoxTransform | false | 17,985 | [
"Apache-2.0"
] | 3 | 69b9a2b9baf90b81ed9098b4f0391f5c15efaee7 | https://github.com/TidalPaladin/combustion/tree/69b9a2b9baf90b81ed9098b4f0391f5c15efaee7 |
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
import torch.optim
assert_size_stride = torch._C._dynamo.... | lidayuls/comet-commonsense-v1 | LayerNorm | false | 3,917 | [
"Apache-2.0"
] | 0 | d0c8475b8432358c59c0d957c2d928521741c057 | https://github.com/lidayuls/comet-commonsense-v1/tree/d0c8475b8432358c59c0d957c2d928521741c057 |
RSubFloat | import torch
class RSubFloat(torch.nn.Module):
def __init__(self):
super(RSubFloat, self).__init__()
def forward(self, x):
return 1.0 - 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Akababa/torch2trt | RSubFloat | false | 18,406 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
NormLoss | import torch
class NormLoss(torch.nn.Module):
"""
Norm penalty on function
parameters:
p - dimension of norm
"""
def __init__(self, p):
super(NormLoss, self).__init__()
self.p = p
def forward(self, beta):
return torch.norm(beta, p=self.p)
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | phernst/TopologyLayer | NormLoss | false | 12,879 | [
"MIT"
] | 0 | aad72704114235156a244ddaa14dc805530e3fc7 | https://github.com/phernst/TopologyLayer/tree/aad72704114235156a244ddaa14dc805530e3fc7 |
Modified | import torch
from torch import nn
import torch.nn.functional as F
class Modified(nn.Module):
def __init__(self):
super(Modified, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 10, 3)
self.conv3 = nn.Conv2d(10, 16, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Karin-S/USYD-ELEC5307 | Modified | false | 9,222 | [
"Apache-2.0"
] | 0 | 83cb40adf0c15ee703a880fc7aba5c69b82a5434 | https://github.com/Karin-S/USYD-ELEC5307/tree/83cb40adf0c15ee703a880fc7aba5c69b82a5434 |
SmoothL1Loss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AlphaLFC/mmdetection | SmoothL1Loss | false | 4,853 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 |
Normalization | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class Normalization(nn.Module):
def __init__(self):
super(Normalization, self).__init__()
self.alpha = Parameter(torch.ones(1))
self.beta = Parameter(torch.zeros(1))
def forward(self, x):
x = 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 libdevice
import torch.nn as nn
from t... | Prinsphield/ELEGANT | Normalization | false | 14,250 | [
"MIT"
] | 276 | 26827e679cbef2074693ffb0d3f36426e481f7f5 | https://github.com/Prinsphield/ELEGANT/tree/26827e679cbef2074693ffb0d3f36426e481f7f5 |
GLU | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | Abrantex/finegan | GLU | false | 11,175 | [
"BSD-2-Clause"
] | 0 | 0d60105fd81abaa945cebb2232dbed703fe319f0 | https://github.com/Abrantex/finegan/tree/0d60105fd81abaa945cebb2232dbed703fe319f0 |
deepmind | import torch
import torch.nn as nn
import torch.nn.functional as F
class deepmind(nn.Module):
def __init__(self):
super(deepmind, self).__init__()
self.conv1 = nn.Conv2d(4, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = nn.Conv2d(64, 32, 3, stride=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Rowing0914/TF2_RL | deepmind | false | 17,908 | [
"MIT"
] | 8 | c1b7f9b376cbecf01deb17f76f8e761035ed336a | https://github.com/Rowing0914/TF2_RL/tree/c1b7f9b376cbecf01deb17f76f8e761035ed336a |
CustomBatchNormAutograd | # 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_... | askliar/deep_learning | CustomBatchNormAutograd | false | 1,488 | [
"MIT"
] | 0 | e61b2391a3258d18719bf12d9ed1404620ce6c02 | https://github.com/askliar/deep_learning/tree/e61b2391a3258d18719bf12d9ed1404620ce6c02 |
TLU | # 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 import triton_helpers
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stride = torch... | COATZ/ShapeConv | TLU | false | 13,440 | [
"Apache-2.0"
] | 57 | f34f4e95ee2b69ac645fd5ba608e3c11cfadfded | https://github.com/COATZ/ShapeConv/tree/f34f4e95ee2b69ac645fd5ba608e3c11cfadfded |
CausalConv1d | import torch
import torch.nn as nn
class CausalConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=1,
**kwargs):
super(CausalConv1d, self).__init__(in_channels, out_channels,
kernel_size, padding=dilation * (kernel_size - 1), dilation=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ChesterHuynh/Wavenet-CPC-Music-Translation | CausalConv1d | false | 253 | [
"MIT"
] | 0 | 60632b0330a61a10bac1a129826c55372f685427 | https://github.com/ChesterHuynh/Wavenet-CPC-Music-Translation/tree/60632b0330a61a10bac1a129826c55372f685427 |
Pooling | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride... | 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
from typing import *
assert_size_stride = torch._C... | Johnsonms/NNI_master | Pooling | false | 11,579 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
Codebook | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Codebook(nn.Module):
"""
Codebook mapping: takes in an encoded image and maps each vector onto its closest codebook vector.
Metric: mean squared error = (z_e - z_q)**2 = (z_e**2) - (2*z_e*z_q) + (z_q**2)
"""
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | JiangtaoFeng/MaskGIT-pytorch | Codebook | false | 15,903 | [
"MIT"
] | 163 | 198b32e29a306fae2830a71621befad008500f76 | https://github.com/JiangtaoFeng/MaskGIT-pytorch/tree/198b32e29a306fae2830a71621befad008500f76 |
LogSparsemax | # 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.autograd im... | gililior/qasrl-modeling | LogSparsemax | false | 6,747 | [
"MIT"
] | 1 | 2f9684536f6d5f0283b0e4b90a911ea12fa72f72 | https://github.com/gililior/qasrl-modeling/tree/2f9684536f6d5f0283b0e4b90a911ea12fa72f72 |
ConvSigmoidInplace | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.... | XiaobingSuper/intel-extension-for-pytorch | ConvSigmoidInplace | false | 9,713 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
MLPEncoder | # 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.... | cheng-xie/motionEncode | MLPEncoder | false | 1,672 | [
"MIT"
] | 0 | fa2152b3eaf2e09ad9477d054566db0a7bc4c7b4 | https://github.com/cheng-xie/motionEncode/tree/fa2152b3eaf2e09ad9477d054566db0a7bc4c7b4 |
FSPool | # 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | bostdiek/DarkMachinesAutoEncoder | FSPool | false | 3,261 | [
"MIT"
] | 0 | f05f482b1bbd79cd777221bfe0d37e75b72c3e2b | https://github.com/bostdiek/DarkMachinesAutoEncoder/tree/f05f482b1bbd79cd777221bfe0d37e75b72c3e2b |
ConvAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | ShulingTang/DSC-Net | ConvAE | false | 9,505 | [
"MIT"
] | 0 | 2da1e0c654b045057c654cbcbb8a8c23fb832c9d | https://github.com/ShulingTang/DSC-Net/tree/2da1e0c654b045057c654cbcbb8a8c23fb832c9d |
Classifier | import torch
import torch.distributed
import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, hidden_size):
super(Classifier, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = 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
import torch.distributed
import torch
import torch.nn as nn
assert_size_stride =... | EisakuHiguchi/BertSum | Classifier | false | 9,018 | [
"Apache-2.0"
] | 0 | 67177fe025a26c40707d541bcfa0e723f88110da | https://github.com/EisakuHiguchi/BertSum/tree/67177fe025a26c40707d541bcfa0e723f88110da |
GlobalAvgPool1d | import torch
import torch.nn as nn
class GlobalAvgPool1d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool1d, self).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool1d(inputs, 1).view(inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Neronjust2017/challenge2020_test4 | GlobalAvgPool1d | false | 9,467 | [
"BSD-2-Clause"
] | 0 | 6494107a459b563aa51f8ea75c580c17557b13af | https://github.com/Neronjust2017/challenge2020_test4/tree/6494107a459b563aa51f8ea75c580c17557b13af |
dis_cf | import torch
from torch import nn
import torch.nn.functional as F
class dis_cf(nn.Module):
def __init__(self):
super().__init__()
self.d1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3,
stride=1, padding=1)
self.d2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | layel2/layyer-lib | dis_cf | false | 3,915 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
Pooling | # 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
from typing import *
assert_size_stride = torch._C... | Johnsonms/NNI_master | Pooling | false | 11,579 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
AveragePooling | import torch
import torch.nn as nn
class AveragePooling(nn.Module):
def __init__(self):
super(AveragePooling, self).__init__()
"""
(item, subitem) can be (word, characters), or (sentence, words)
x: num_items x max_subitem_size x input_size
x_mask: num_items x max_subitem_size
retu... | 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... | GingerNg/SDNet | AveragePooling | false | 13,728 | [
"MIT"
] | 112 | 48ad8cc57c9a02aaad10e34d0c91a174ac68f056 | https://github.com/GingerNg/SDNet/tree/48ad8cc57c9a02aaad10e34d0c91a174ac68f056 |
IBNbConvBlock | import torch
import torch.nn as nn
import torch.utils.data
class IBNbConvBlock(nn.Module):
"""
IBN(b)-ResNet specific convolution block with Instance normalization and ReLU activation.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | earhian/imgclsmob | IBNbConvBlock | false | 6,626 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
Conv2dDynamicSamePadding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | BradleyBrown19/CustomObjectDetector | Conv2dDynamicSamePadding | false | 2,092 | [
"Apache-2.0"
] | 0 | 11c14ec6127c553ac365703c768b75dde33d9a4d | https://github.com/BradleyBrown19/CustomObjectDetector/tree/11c14ec6127c553ac365703c768b75dde33d9a4d |
LogSTFTMagnitudeLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | Oktai15/NeMo | LogSTFTMagnitudeLoss | false | 5,675 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
DownShiftedConv2d | import torch
import torch.nn as nn
class DownShiftedConv2d(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.shift_pad = nn.ConstantPad2d((int((self.kernel_size[1] - 1) //
2), int((self.kernel_size[1] - 1) // 2), self.kernel_size[0] -
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | stankevich-mipt/pixiv-tags-to-image | DownShiftedConv2d | false | 4,385 | [
"MIT"
] | 0 | 220a157956296c8a5b183ffe219e7c1929342c39 | https://github.com/stankevich-mipt/pixiv-tags-to-image/tree/220a157956296c8a5b183ffe219e7c1929342c39 |
SymmetricPad2d | # 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... | Lee-Gihun/Micronet_GSJ | SymmetricPad2d | false | 8,449 | [
"MIT"
] | 12 | 72289bb66507b6c3b4d14f2e5916dec718a1b198 | https://github.com/Lee-Gihun/Micronet_GSJ/tree/72289bb66507b6c3b4d14f2e5916dec718a1b198 |
LogicProjection | # 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... | HKUST-KnowComp/EFO-1-QA-benchmark | LogicProjection | false | 17,366 | [
"MIT"
] | 9 | 600fb02c76ab631f93ee362ceb789216ec085790 | https://github.com/HKUST-KnowComp/EFO-1-QA-benchmark/tree/600fb02c76ab631f93ee362ceb789216ec085790 |
CORALLoss | import torch
def covariance(x):
batch_size = x.shape[0]
mm1 = torch.mm(x.t(), x)
cols_summed = torch.sum(x, dim=0)
mm2 = torch.mm(cols_summed.unsqueeze(1), cols_summed.unsqueeze(0))
return 1.0 / (batch_size - 1) * (mm1 - 1.0 / batch_size * mm2)
class CORALLoss(torch.nn.Module):
"""
Imple... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | KevinMusgrave/pytorch-adapt | CORALLoss | false | 13,947 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
PSNR | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | S-aiueo32/srntt-pytorch | PSNR | false | 14,372 | [
"Apache-2.0"
] | 88 | 4ea0aa22a54a2d1b1f19c4a43596a693b9e7c067 | https://github.com/S-aiueo32/srntt-pytorch/tree/4ea0aa22a54a2d1b1f19c4a43596a693b9e7c067 |
SymKlCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | anlewy/mt-dnn | SymKlCriterion | false | 14,870 | [
"MIT"
] | 2,075 | eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 | https://github.com/anlewy/mt-dnn/tree/eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 |
Encoding | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._C
import torch.serialization
class Encoding(nn.Module):
"""Encoding Layer: a learnable residual encoder.
Input is of shape (batch_size, channels, height, width).
Output is of shape (batch_size, num_codes, channels).
Ar... | 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
... | ImportPaddle/APCNet | Encoding | false | 2,367 | [
"MIT"
] | 0 | 68ade1f83827b4cdd60ee4b6ac25454397100316 | https://github.com/ImportPaddle/APCNet/tree/68ade1f83827b4cdd60ee4b6ac25454397100316 |
PyTorchMlp | # 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.... | jasonjabbour/motion_imitation | PyTorchMlp | false | 3,710 | [
"Apache-2.0"
] | 0 | a28e7cd9dca2fbdd6823f19db4f66b496dd29144 | https://github.com/jasonjabbour/motion_imitation/tree/a28e7cd9dca2fbdd6823f19db4f66b496dd29144 |
PredictFC | import torch
import torch.nn as nn
class PredictFC(nn.Module):
def __init__(self, num_params, num_states, in_channels):
super(PredictFC, self).__init__()
self.num_params = num_params
self.fc_param = nn.Conv2d(in_channels, num_params, kernel_size=1,
stride=1, padding=0, bias=Tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | DistinctVision/conditional-lane-detection | PredictFC | false | 11,350 | [
"Apache-2.0"
] | 0 | b118a40738188facf63ec7cd0bb0422fdf562b77 | https://github.com/DistinctVision/conditional-lane-detection/tree/b118a40738188facf63ec7cd0bb0422fdf562b77 |
VGGBase | # 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 torchvision
from torch... | dee-walia20/SSD-Implementation-using-Pytorch | VGGBase | false | 7,169 | [
"MIT"
] | 1 | 2a7dcdcea2787f4bffd45f335819f08af2b525dd | https://github.com/dee-walia20/SSD-Implementation-using-Pytorch/tree/2a7dcdcea2787f4bffd45f335819f08af2b525dd |
RKDAngleLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | DA-southampton/KD_Lib | RKDAngleLoss | false | 5,027 | [
"MIT"
] | 1 | bd4a9b93b9674607ecf467d280d5cab1c516bdc6 | https://github.com/DA-southampton/KD_Lib/tree/bd4a9b93b9674607ecf467d280d5cab1c516bdc6 |
SentenceEmbedding | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class BaseSelfAttention(nn.Module):
def __init__(self):
super(BaseSelfAttention, self).__init__()
def init_linear(self, input_linear):
"""Initialize linear transformation"""
bias = np.sqrt(6.0 / (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.... | Gan-Tu/ganutils | SentenceEmbedding | false | 5,188 | [
"MIT"
] | 1 | 203c703cbba0345f9cfe23b03e1e3981f03e43db | https://github.com/Gan-Tu/ganutils/tree/203c703cbba0345f9cfe23b03e1e3981f03e43db |
LinearZeros | import torch
import torch.nn as nn
class LinearZeros(nn.Linear):
def __init__(self, in_channels, out_channels, logscale_factor=3):
super().__init__(in_channels, out_channels)
self.logscale_factor = logscale_factor
self.register_parameter('logs', nn.Parameter(torch.zeros(out_channels))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | GauriJagatap/glow-pytorch | LinearZeros | false | 2,331 | [
"MIT"
] | 0 | e379f524b7cc0b57a9bc2849f4115f97bda5a1de | https://github.com/GauriJagatap/glow-pytorch/tree/e379f524b7cc0b57a9bc2849f4115f97bda5a1de |
FrameMaxPool | # 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.parallel
impo... | MicroTensor-ai/episodic-memory | FrameMaxPool | false | 11,704 | [
"MIT"
] | 0 | 295a3752ab94c7a6f45355aa2c54bffbf84b574f | https://github.com/MicroTensor-ai/episodic-memory/tree/295a3752ab94c7a6f45355aa2c54bffbf84b574f |
ResConnectionLayer | # 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 ... | MobtgZhang/MWMLNet | ResConnectionLayer | false | 5,603 | [
"MIT"
] | 1 | 125bb39935916b6b4be505c51cb6a04eb49b96d0 | https://github.com/MobtgZhang/MWMLNet/tree/125bb39935916b6b4be505c51cb6a04eb49b96d0 |
DiceLoss | import torch
from torch import nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, pred, target, weight=None):
smooth = 1
size = pred.size(0)
pred_flat = pred.view(size, -1)
target_flat = target.view(size, -1)
i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | CityU-AIM-Group/PRR-Imbalance | DiceLoss | false | 8,914 | [
"MIT"
] | 0 | e893809c72697511897c9100c25f831087fc345f | https://github.com/CityU-AIM-Group/PRR-Imbalance/tree/e893809c72697511897c9100c25f831087fc345f |
CodeLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | KMU-AELAB/DeepHashing | CodeLoss | false | 9,253 | [
"MIT"
] | 0 | c60069884778246c5a6e11161b78af69e5c8c176 | https://github.com/KMU-AELAB/DeepHashing/tree/c60069884778246c5a6e11161b78af69e5c8c176 |
SoftAttention | import torch
import torch.utils.data
import torch.nn as nn
class SoftAttention(torch.nn.Module):
"""
v = tanh(hW + b)
w = softmax(v*u)
out = sum wh
see eqs 5-7 in https://www.sciencedirect.com/science/article/abs/pii/S0924271619300115
"""
def __init__(self, hidden_dim):
super(Sof... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Pratyush1991/crop-type-mapping | SoftAttention | false | 14,253 | [
"MIT"
] | 94 | d9d99ec92c3a090ec5576f9e46c89dfcc6f50cf3 | https://github.com/Pratyush1991/crop-type-mapping/tree/d9d99ec92c3a090ec5576f9e46c89dfcc6f50cf3 |
FullSort | import torch
from torch import nn
import torch.utils.data.distributed
class FullSort(nn.Module):
def forward(self, x):
return torch.sort(x, 1)[0]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data.distributed
assert_size_stride = torch._C._d... | rh-ia/color-information | FullSort | false | 4,283 | [
"MIT"
] | 0 | e912a1667e4fffb339dbc574c85020ec6cf78b02 | https://github.com/rh-ia/color-information/tree/e912a1667e4fffb339dbc574c85020ec6cf78b02 |
MeanSquared | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim
def mean_squared(y, target, mask=None, reduce=True):
y = y.softmax(1)
loss = F.mse_loss(y, target, reduction='none').mean(1)
if mask is not None:
loss = mask * loss
if reduce:
return loss.mean()
e... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | gsaiabhishek/AUTOMATA | MeanSquared | false | 12,479 | [
"MIT"
] | 0 | e944992a7bf3a50bc8951a303294b3a798822176 | https://github.com/gsaiabhishek/AUTOMATA/tree/e944992a7bf3a50bc8951a303294b3a798822176 |
Warp | import torch
import torch.nn as nn
class Warp(torch.nn.Module):
"""Custom warping layer."""
def __init__(self, mode='bilinear', padding_mode='reflection'):
super().__init__()
self.mode = mode
self.padding_mode = padding_mode
def forward(self, x, tform):
"""Warp the 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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size... | vishalbelsare/deepdow | Warp | false | 16,688 | [
"Apache-2.0"
] | 511 | cbb99347fba9a447d4fcae64fe5137c203643e44 | https://github.com/vishalbelsare/deepdow/tree/cbb99347fba9a447d4fcae64fe5137c203643e44 |
TVLoss | import torch
import torch.nn as nn
class TVLoss(nn.Module):
def __init__(self):
super(TVLoss, self).__init__()
def forward(self, x):
h_x, w_x = x.size()[2:]
h_tv = torch.abs(x[:, :, 1:, :] - x[:, :, :h_x - 1, :])
w_tv = torch.abs(x[:, :, :, 1:] - x[:, :, :, :w_x - 1])
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | vlbthambawita/polyp-inpainting | TVLoss | false | 4,504 | [
"MIT"
] | 0 | f1d754f8ffb3f6d991206b2a661933ff32de0d7a | https://github.com/vlbthambawita/polyp-inpainting/tree/f1d754f8ffb3f6d991206b2a661933ff32de0d7a |
GraphConvolution | import torch
from torch import nn
from torch.nn import init
class GraphConvolution(nn.Module):
def __init__(self, window_size, in_features, out_features):
super(GraphConvolution, self).__init__()
self.weights = nn.Parameter(torch.Tensor(window_size, in_features,
out_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 import nn
from torch.nn import init
assert_size_stride = torch._C._dy... | DavidHeSkr/GCN-GAN-pytorch | GraphConvolution | false | 13,589 | [
"MIT"
] | 66 | f8adf82596733464cb63dddf978c244b25aebe46 | https://github.com/DavidHeSkr/GCN-GAN-pytorch/tree/f8adf82596733464cb63dddf978c244b25aebe46 |
NormedLinear | # 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.... | ZonghaiZhu/EZBM | NormedLinear | false | 1,321 | [
"MIT"
] | 0 | b4f6fbd10598c79f144b778ef848554ac62a173a | https://github.com/ZonghaiZhu/EZBM/tree/b4f6fbd10598c79f144b778ef848554ac62a173a |
MyCustomFunctionReluModel | # 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
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_siz... | RyanUnderhill/onnxruntime | MyCustomFunctionReluModel | false | 11,830 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
LearnedPositionalEncoding | import torch
from torch import nn
class LayerNorm(nn.Module):
"""A layernorm module in the TF style (epsilon inside the square root)."""
def __init__(self, d_model, variance_epsilon=1e-12):
super().__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torc... | 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... | longnsl1998/vietocr | LearnedPositionalEncoding | false | 10,411 | [
"Apache-2.0"
] | 0 | 686dd6c9d897e0401c20e7dcadb07a07c1dbc284 | https://github.com/longnsl1998/vietocr/tree/686dd6c9d897e0401c20e7dcadb07a07c1dbc284 |
MaskUpdate | # 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... | Vious/LBAM_Pytorch | MaskUpdate | false | 14,559 | [
"MIT"
] | 112 | b9292440e7a7559c027f48d6fd061dcabc41a6bf | https://github.com/Vious/LBAM_Pytorch/tree/b9292440e7a7559c027f48d6fd061dcabc41a6bf |
BCEFocalLoss | # 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
... | Chizuchizu/riadd | BCEFocalLoss | false | 4,987 | [
"MIT"
] | 1 | c3f55aebc0f582d9fa55dc517b1489963cf0506f | https://github.com/Chizuchizu/riadd/tree/c3f55aebc0f582d9fa55dc517b1489963cf0506f |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.weight_norm as weightNorm
def conv3x3(in_planes, out_planes, stride=1):
return weightNorm(nn.Conv2d(in_planes, out_planes, kernel_size=3,
stride=stride, padding=1, bias=True))
class TReLU(nn.Module):
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.... | HenryOsborne/LearningToPaint | BasicBlock | false | 9,149 | [
"MIT"
] | 0 | d8fdf41c8d193b91c78f73b7a092897e846e19eb | https://github.com/HenryOsborne/LearningToPaint/tree/d8fdf41c8d193b91c78f73b7a092897e846e19eb |
Quantization | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | Geunwoo-Jeon/iclr_17_compression | Quantization | false | 13,710 | [
"MIT"
] | 56 | a28746b1f1c518d91125d8f289d9511cde488c77 | https://github.com/Geunwoo-Jeon/iclr_17_compression/tree/a28746b1f1c518d91125d8f289d9511cde488c77 |
Split | # 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.... | ffraaz/flow_based_priors | Split | false | 3,501 | [
"MIT"
] | 0 | 4f61ecc233a01375c9a069a8baf676152a3e20fa | https://github.com/ffraaz/flow_based_priors/tree/4f61ecc233a01375c9a069a8baf676152a3e20fa |
AttentionPool | import torch
import torch.nn as nn
class AttentionPool(nn.Module):
"""docstring for AttentionPool"""
def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs):
super().__init__()
self.inputdim = inputdim
self.outputdim = outputdim
self.pooldim = pooldim
self.tran... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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/AudioCaption | AttentionPool | false | 15,616 | [
"MIT"
] | 76 | d121cba8247b96aeed9ff77d2fff073f93e0a63f | https://github.com/ishine/AudioCaption/tree/d121cba8247b96aeed9ff77d2fff073f93e0a63f |
RankCrossEntropyLoss | # 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... | amberhuang01/LearningFromFactCheckers | RankCrossEntropyLoss | false | 18,306 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 |
GumbelSoftmaxLayer | # 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 torch.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch... | cjlovering/EGG | GumbelSoftmaxLayer | false | 10,042 | [
"MIT"
] | 0 | cce146e035decbc410e981f8bc7ada32979f3b6d | https://github.com/cjlovering/EGG/tree/cce146e035decbc410e981f8bc7ada32979f3b6d |
LearnedUpsampling1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Barbany/Multi-speaker-Neural-Vocoder | LearnedUpsampling1d | false | 7,764 | [
"MIT"
] | 13 | a3f5c266603b17bcbe264e750947140f302272c8 | https://github.com/Barbany/Multi-speaker-Neural-Vocoder/tree/a3f5c266603b17bcbe264e750947140f302272c8 |
FTanhTest | import torch
import torch.nn as nn
class FTanhTest(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FTanhTest, self).__init__()
def forward(self, x):
from torch.nn import functional as F
return F.tanh(x)
def get_inputs():
return [torch.rand... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | goldbattle/onnx2keras | FTanhTest | false | 12,463 | [
"MIT"
] | 0 | dcf52041299ce4216552d1132ec86eb4debd5303 | https://github.com/goldbattle/onnx2keras/tree/dcf52041299ce4216552d1132ec86eb4debd5303 |
MLPEncoder | import torch
from torch import nn
from torch.nn import functional as F
from typing import *
class MLPEncoder(torch.nn.Module):
def __init__(self, indim, hiddim, outdim):
super(MLPEncoder, self).__init__()
self.fc = nn.Linear(indim, hiddim)
self.fc2 = nn.Linear(hiddim, 2 * outdim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from typ... | HughMun/MultiBench | MLPEncoder | false | 13,811 | [
"MIT"
] | 148 | d5712a0815a9486b0e0c76b54cd63c880188fc8e | https://github.com/HughMun/MultiBench/tree/d5712a0815a9486b0e0c76b54cd63c880188fc8e |
MaxPPVPool1d | # 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
import torch.multiprocessing
import torch
assert_size_stride ... | MOREDataset/tsai | MaxPPVPool1d | false | 9,285 | [
"Apache-2.0"
] | 0 | 54987a579365ca7722475fff2fc4a24dc054e82c | https://github.com/MOREDataset/tsai/tree/54987a579365ca7722475fff2fc4a24dc054e82c |
LabelPropagation | import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelPropagation(nn.Module):
"""label propagation model adapted from https://github.com/CUAI/CorrectAndSmooth
`"Learning from Labeled and
Unlabeled Datawith Label Propagation"
<http://mlg.eng.cam.ac.uk/zoubin/papers/CMU-CALD-02-1... | 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_... | EdisonLeeeee/GraphGallery | LabelPropagation | false | 13,658 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
EncoderImagePrecomp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | Ballester/SCAN | EncoderImagePrecomp | false | 2,020 | [
"Apache-2.0"
] | 0 | 4a003f60d3e45e5dd16969745e4b182fe705e758 | https://github.com/Ballester/SCAN/tree/4a003f60d3e45e5dd16969745e4b182fe705e758 |
WassersteinGeneratorLoss | import torch
import torch.nn as nn
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` the mean value of the
Tensor is re... | 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... | shi-weili/torchgan | WassersteinGeneratorLoss | false | 12,973 | [
"MIT"
] | 0 | 28ffd4026b8c0db2217b667d30a222d6758bfc41 | https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41 |
MaxPoolBranch | import torch
import torch.nn as nn
class MaxPoolBranch(nn.Module):
"""
InceptionV4 specific max pooling branch block.
"""
def __init__(self, kernel_size=3, stride=2, padding=0):
super(MaxPoolBranch, self).__init__()
self.pool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride,
... | 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... | daniil-lyakhov/deep-object-reid | MaxPoolBranch | false | 1,778 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 |
CrossEntropyLossSoft | # 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.... | jiuyecao/Opt-CoInfer | CrossEntropyLossSoft | false | 6,953 | [
"MIT"
] | 1 | 60f29a28c34d3bf9b2f23c98bb8e98caf1abc4f0 | https://github.com/jiuyecao/Opt-CoInfer/tree/60f29a28c34d3bf9b2f23c98bb8e98caf1abc4f0 |
Attention | import torch
import torch.nn as nn
import torch.nn
class Attention(nn.Module):
def __init__(self, dim_i, dim_o):
"""
build the target-aware attention
input schema:
dim_i: the dimension of the input feature vector
dim_o: the dimension of the output feature vector
output schema:
return a agg... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | chencsgit/luoxi_models | Attention | false | 15,025 | [
"Apache-2.0"
] | 58 | ea9e69dfb81b29f41ed92c75faacf81114c69a2f | https://github.com/chencsgit/luoxi_models/tree/ea9e69dfb81b29f41ed92c75faacf81114c69a2f |
AttentionTransfer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | ahu-hpt/AOMD | AttentionTransfer | false | 3,048 | [
"Apache-2.0"
] | 0 | 8d99dbb803feaef55fc089bfb3399d2fb21d55d8 | https://github.com/ahu-hpt/AOMD/tree/8d99dbb803feaef55fc089bfb3399d2fb21d55d8 |
FencepostModule | # 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 ... | FilippoC/-deep-syntactic-dependency-parsing-release | FencepostModule | false | 17,281 | [
"MIT"
] | 4 | 30e2571ea930c2fd81559f5a2a971e3738cc6d39 | https://github.com/FilippoC/-deep-syntactic-dependency-parsing-release/tree/30e2571ea930c2fd81559f5a2a971e3738cc6d39 |
MaxMinGroup | import torch
import torch.nn as nn
import torch.utils.data
def process_maxmin_groupsize(x, group_size, axis=-1):
size = list(x.size())
num_channels = size[axis]
if num_channels % group_size:
raise ValueError(
'number of features({}) is not a multiple of group_size({})'.
for... | 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.data
assert_size_stride = torch._C._dynamo.guard... | XinZhang525/fGAIL | MaxMinGroup | false | 18,102 | [
"MIT"
] | 4 | 682d70286685612558e072d9a1668779b8ae325b | https://github.com/XinZhang525/fGAIL/tree/682d70286685612558e072d9a1668779b8ae325b |
Decoder3 | # 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.... | EndyWon/Texture-Reformer | Decoder3 | false | 8,124 | [
"MIT"
] | 11 | f84f95accb3574c7b759a7f03c0b0b4e150314b5 | https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5 |
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