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 |
|---|---|---|---|---|---|---|---|---|---|---|
CategoricalAccuracy | import torch
class _Metric(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input: 'torch.Tensor', target: 'torch.Tensor'):
raise NotImplementedError()
class Accuracy(_Metric):
def __init__(self):
super().__init__()
def forward(self, input: '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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | NestLakerJasonLIN/MusicTransformer-pytorch | CategoricalAccuracy | false | 5,656 | [
"MIT"
] | 1 | 5f183374833ff6b7e17f3a24e3594dedd93a5fe5 | https://github.com/NestLakerJasonLIN/MusicTransformer-pytorch/tree/5f183374833ff6b7e17f3a24e3594dedd93a5fe5 |
Theta | from torch.autograd import Function
import torch
from typing import Tuple
from typing import Optional
import torch.nn as nn
import torch.utils.data
from typing import Any
class GradientReverseFunction(Function):
@staticmethod
def forward(ctx: 'Any', input: 'torch.Tensor', coeff: 'Optional[float]'=1.0
... | 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.autograd import Function
from typing import Tuple
from typing import ... | Neronjust2017/TransferBed | Theta | false | 5,657 | [
"MIT"
] | 1 | eaa703a4bc10eaf6216fe1394cd272f6e75489e2 | https://github.com/Neronjust2017/TransferBed/tree/eaa703a4bc10eaf6216fe1394cd272f6e75489e2 |
GramMatrix | import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
_, channels, h, w = input.size()
out = input.view(-1, h * w)
out = torch.mm(out, out.t())
return out.div(channels * h * w)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | NeilWangziyu/torch_light | GramMatrix | false | 5,658 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, input_size, action_size):
super(Network, self).__init__()
self.fc1 = nn.Linear(input_size, 256)
self.fc2 = nn.Linear(256, 256)
self.logits_p = nn.Linear(256, action_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
import torch.nn as nn
assert_... | NeuralFlux/rl-analysis | Network | false | 5,659 | [
"MIT"
] | 1 | bb45e1f8bb9da4683cce4bd0a5e687770a4005e2 | https://github.com/NeuralFlux/rl-analysis/tree/bb45e1f8bb9da4683cce4bd0a5e687770a4005e2 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, input_size, action_size):
super(Actor, self).__init__()
self.fc1 = nn.Linear(input_size, 200)
self.output = nn.Linear(200, action_size)
def forward(self, x):
x = F.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 torch.nn as nn
assert_... | NeuralFlux/rl-analysis | Actor | false | 5,660 | [
"MIT"
] | 1 | bb45e1f8bb9da4683cce4bd0a5e687770a4005e2 | https://github.com/NeuralFlux/rl-analysis/tree/bb45e1f8bb9da4683cce4bd0a5e687770a4005e2 |
DoubleResolutionLayer | import torch
import torch.nn as nn
class DoubleResolutionLayer(nn.Module):
def forward(self, x):
x = nn.functional.interpolate(x, scale_factor=2, mode='nearest')
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | NunoEdgarGFlowHub/gandissect | DoubleResolutionLayer | false | 5,661 | [
"MIT"
] | 1 | 1a162a6bd3d4842139feb9f191aa1fad565dee4e | https://github.com/NunoEdgarGFlowHub/gandissect/tree/1a162a6bd3d4842139feb9f191aa1fad565dee4e |
ConvSwishInplace | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvSwishInplace(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size):
super(ConvSwishInplace, 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 import nn
import torch.cuda
import torch.backends.cudnn
import torch.... | Observer007/intel-extension-for-pytorch | ConvSwishInplace | false | 5,662 | [
"Apache-2.0"
] | 1 | f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 | https://github.com/Observer007/intel-extension-for-pytorch/tree/f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 |
PixelNormLayer | import torch
import torch.nn as nn
class PixelNormLayer(nn.Module):
def __init__(self):
super(PixelNormLayer, self).__init__()
def forward(self, x):
return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + 1e-08)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | NunoEdgarGFlowHub/gandissect | PixelNormLayer | false | 5,663 | [
"MIT"
] | 1 | 1a162a6bd3d4842139feb9f191aa1fad565dee4e | https://github.com/NunoEdgarGFlowHub/gandissect/tree/1a162a6bd3d4842139feb9f191aa1fad565dee4e |
ConvSwishOutplace | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvSwishOutplace(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size):
super(ConvSwishOutplace, 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.cuda
import torch.backends.cudnn
import torch.... | Observer007/intel-extension-for-pytorch | ConvSwishOutplace | false | 5,664 | [
"Apache-2.0"
] | 1 | f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 | https://github.com/Observer007/intel-extension-for-pytorch/tree/f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 |
ConvHardtanh | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvHardtanh(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size,
inplace=False):
super(ConvHard... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Observer007/intel-extension-for-pytorch | ConvHardtanh | false | 5,665 | [
"Apache-2.0"
] | 1 | f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 | https://github.com/Observer007/intel-extension-for-pytorch/tree/f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 |
Network | import torch
import torch.nn as nn
class Network(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv3d(in_channels=1, out_channels=3, kernel_size=3)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 1, 64, 64, 64])]
def get_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | NunoEdgarGFlowHub/torchio | Network | false | 5,666 | [
"MIT"
] | 1 | 656e96c8863ecff0bb29bf880af054675bbb30fd | https://github.com/NunoEdgarGFlowHub/torchio/tree/656e96c8863ecff0bb29bf880af054675bbb30fd |
ConvSigmoidInplace | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvSigmoidInplace(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size):
super(ConvSigmoidInplace, 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.cuda
import torch.backends.cudnn
import torch.... | Observer007/intel-extension-for-pytorch | ConvSigmoidInplace | false | 5,667 | [
"Apache-2.0"
] | 1 | f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 | https://github.com/Observer007/intel-extension-for-pytorch/tree/f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 |
ConvUnit | import torch
import torch.nn as nn
class ConvUnit(nn.Module):
def __init__(self):
super(ConvUnit, self).__init__()
self.conv = nn.Conv2d(in_channels=256, out_channels=32, kernel_size
=5, stride=1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | NeilWangziyu/torch_light | ConvUnit | false | 5,668 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
Encoder_H | import torch
import torch.nn as nn
class Encoder_H(nn.Module):
def __init__(self, input_shape=(64, 64), z_dim=10, nc=3, padding=1):
super(Encoder_H, self).__init__()
self.conv2d_1 = nn.Conv2d(nc, 32, 4, 2, padding)
self.conv2d_2 = nn.Conv2d(32, 32, 4, 2, padding)
self.conv2d_3 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | KinWaiCheuk/Beta-VAE | Encoder_H | false | 5,669 | [
"MIT"
] | 1 | 57f538320fed76b54e8489656b11dc83c06d1584 | https://github.com/KinWaiCheuk/Beta-VAE/tree/57f538320fed76b54e8489656b11dc83c06d1584 |
ConvGLU | import torch
from torch import nn
import torch.utils.data
import torch.optim
def str2act(txt):
"""Translates text to neural network activation"""
return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu': nn.SELU()}[txt.
lower()]
class C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | Oktai15/NeMo | ConvGLU | false | 5,670 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
UpdateNodeEmbeddingLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
class UpdateNodeEmbeddingLayer(nn.Module):
def __init__(self, n_features):
super().__init__()
self.message_layer = nn.Linear(2 * n_features, n_features, bias=False)
self.update_layer = nn.Linear(2 * n_features, n_features,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | NinaMaz/eco-dqn | UpdateNodeEmbeddingLayer | false | 5,671 | [
"MIT"
] | 1 | d9ea164c59014e4209ae069005029af818372ade | https://github.com/NinaMaz/eco-dqn/tree/d9ea164c59014e4209ae069005029af818372ade |
GaussianKernel | import torch
from typing import Optional
import torch.nn as nn
import torch.utils.data
class GaussianKernel(nn.Module):
"""Gaussian Kernel Matrix
Gaussian Kernel k is defined by
.. math::
k(x_1, x_2) = \\exp \\left( - \\dfrac{\\| x_1 - x_2 \\|^2}{2\\sigma^2} \\right)
where :math:`x_1, x_2 \... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Opt... | Neronjust2017/TransferBed | GaussianKernel | false | 5,672 | [
"MIT"
] | 1 | eaa703a4bc10eaf6216fe1394cd272f6e75489e2 | https://github.com/Neronjust2017/TransferBed/tree/eaa703a4bc10eaf6216fe1394cd272f6e75489e2 |
ConformerFeedForward | import torch
from torch import nn
import torch.utils.data
import torch.optim
class Swish(nn.Module):
"""
Swish activation function introduced in 'https://arxiv.org/abs/1710.05941'
"""
def forward(self, x):
return x * torch.sigmoid(x)
class ConformerFeedForward(nn.Module):
"""
feed-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 import nn
import torch.utils.data
import torch.optim
assert_size_stri... | Oktai15/NeMo | ConformerFeedForward | false | 5,673 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
BertNonFusedLayerNorm | import torch
from torch import nn
class BertNonFusedLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertNonFusedLayerNorm, self).__init__()
self.gamma = nn.Parameter(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.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Og-ChRoNiC/FasterTransformer | BertNonFusedLayerNorm | false | 5,674 | [
"Apache-2.0"
] | 1 | 05c7e3db209064efec4798a570a488ce08ad211c | https://github.com/Og-ChRoNiC/FasterTransformer/tree/05c7e3db209064efec4798a570a488ce08ad211c |
LogSTFTMagnitudeLoss | import torch
from torch.nn import functional as F
import torch.utils.data
import torch.optim
class LogSTFTMagnitudeLoss(torch.nn.Module):
"""Log STFT magnitude loss module."""
def __init__(self):
"""Initilize los STFT magnitude loss module."""
super(LogSTFTMagnitudeLoss, self).__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
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 |
LayerNorm | import torch
from torch import nn
from torch.nn import LayerNorm
import torch.utils.data
import torch.optim
class LayerNorm(nn.Module):
def __init__(self, channels, eps=0.0001):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channel... | 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.optim
assert_size_str... | Oktai15/NeMo | LayerNorm | false | 5,676 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
LR | import torch
class LR(torch.nn.Module):
def __init__(self, input_size, output_size):
super(LR, self).__init__()
self.lr = torch.ones(input_size)
self.lr = torch.nn.Parameter(self.lr)
def forward(self, grad):
return self.lr * grad
def get_inputs():
return [torch.rand([4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | OliverWang-Au/learn2learn | LR | false | 5,677 | [
"MIT"
] | 1 | df3c3291b4681440a80a69a7815090a4bd3cd661 | https://github.com/OliverWang-Au/learn2learn/tree/df3c3291b4681440a80a69a7815090a4bd3cd661 |
MultiLayerPerceptron | import torch
import torch.utils.data
import torch.optim
class MultiLayerPerceptron(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (int): the size of each layer
num_cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Oktai15/NeMo | MultiLayerPerceptron | false | 5,678 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
SpectralConvergenceLoss | import torch
import torch.utils.data
import torch.optim
class SpectralConvergenceLoss(torch.nn.Module):
"""Spectral convergence loss module."""
def __init__(self):
"""Initilize spectral convergence loss module."""
super(SpectralConvergenceLoss, self).__init__()
def forward(self, x_mag, y... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | Oktai15/NeMo | SpectralConvergenceLoss | false | 5,679 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
BatchSpectralShrinkage | import torch
import torch.nn as nn
import torch.utils.data
class BatchSpectralShrinkage(nn.Module):
"""
The regularization term in `Catastrophic Forgetting Meets Negative Transfer:
Batch Spectral Shrinkage for Safe Transfer Learning (NIPS 2019) <https://proceedings.neurips.cc/paper/2019/file/c6bff625bdb03... | 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.... | Neronjust2017/TransferBed | BatchSpectralShrinkage | false | 5,680 | [
"MIT"
] | 1 | eaa703a4bc10eaf6216fe1394cd272f6e75489e2 | https://github.com/Neronjust2017/TransferBed/tree/eaa703a4bc10eaf6216fe1394cd272f6e75489e2 |
PositionWiseFF | import torch
from torch import nn
from torch.nn.functional import gelu
import torch.utils.data
import torch.optim
class PositionWiseFF(nn.Module):
"""
Position-wise feed-forward network of Transformer block.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
inn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Oktai15/NeMo | PositionWiseFF | false | 5,681 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
LinearBlock | import torch
class LinearBlock(torch.nn.Module):
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super().__init__()
self.layer_1 = torch.nn.Linear(in_features, out_features)
self.layer_2 = torch.nn.Linear(out_features, out_features)
self.activation = torch.nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | OleguerCanal/transplanter | LinearBlock | false | 5,682 | [
"MIT"
] | 1 | 854fa727747a484dedde9092eeee6884d7d1b44b | https://github.com/OleguerCanal/transplanter/tree/854fa727747a484dedde9092eeee6884d7d1b44b |
InvConvNear | import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
class InvConvNear(nn.Module):
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
super().__init__()
assert n_split % 2 == 0
self.channels = 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 import nn
import torch.utils.data
import torch.optim
assert_size_stri... | Oktai15/NeMo | InvConvNear | false | 5,683 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
DQN | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class DQN(nn.Module):
def __init__(self, state_dim, out_dim, capacity, bsz, epsilon):
super().__init__()
self.steps_done = 0
self.position = 0
self.pool = []
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 random
import torch.nn... | NeilWangziyu/torch_light | DQN | false | 5,684 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
CNNCifaro | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class CNNCifaro(nn.Module):
def __init__(self, args):
super(CNNCifaro, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = 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
import torch.nn as nn
assert_... | NaiboWang/Federated-Learning-PyTorch | CNNCifaro | false | 5,685 | [
"MIT"
] | 1 | 6f811ebbb783b9d279e5462789ff242968e17bc0 | https://github.com/NaiboWang/Federated-Learning-PyTorch/tree/6f811ebbb783b9d279e5462789ff242968e17bc0 |
HypergradTransform | import torch
class HypergradTransform(torch.nn.Module):
"""Hypergradient-style per-parameter learning rates"""
def __init__(self, param, lr=0.01):
super(HypergradTransform, self).__init__()
self.lr = lr * torch.ones_like(param, requires_grad=True)
self.lr = torch.nn.Parameter(self.lr)... | 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... | OliverWang-Au/learn2learn | HypergradTransform | false | 5,686 | [
"MIT"
] | 1 | df3c3291b4681440a80a69a7815090a4bd3cd661 | https://github.com/OliverWang-Au/learn2learn/tree/df3c3291b4681440a80a69a7815090a4bd3cd661 |
LinearNet | import torch
import torch.nn
import torch.optim
class LinearNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super().__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.nonlinear = torch.nn.ReLU()
self.linear2 = torch.nn.Linear(H, D_out)
def forward(self, x: 'tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | OregonWebSells/ReAgent | LinearNet | false | 5,687 | [
"BSD-3-Clause"
] | 1 | 866f91785ca86db32fb67744aa063fe77791ff21 | https://github.com/OregonWebSells/ReAgent/tree/866f91785ca86db32fb67744aa063fe77791ff21 |
GRUCell | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class GRUCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=True):
super(GRUCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
... | OlhaKi/PySyft | GRUCell | false | 5,688 | [
"Apache-2.0"
] | 1 | c9e16835ba0f05c3ff391e17a33d56a5c2ceb459 | https://github.com/OlhaKi/PySyft/tree/c9e16835ba0f05c3ff391e17a33d56a5c2ceb459 |
Embedder | import math
import torch
from torch import nn
import torch.nn
import torch.optim
class Embedder(nn.Module):
def __init__(self, dim_in, dim_out):
super(Embedder, self).__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.linear = nn.Linear(self.dim_in, self.dim_out)
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn
import torch.optim
assert_size_stride = tor... | OregonWebSells/ReAgent | Embedder | false | 5,689 | [
"BSD-3-Clause"
] | 1 | 866f91785ca86db32fb67744aa063fe77791ff21 | https://github.com/OregonWebSells/ReAgent/tree/866f91785ca86db32fb67744aa063fe77791ff21 |
ConvElu | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvElu(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size,
inplace=False):
super(ConvElu, 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
from torch import n... | Observer007/intel-extension-for-pytorch | ConvElu | false | 5,690 | [
"Apache-2.0"
] | 1 | f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 | https://github.com/Observer007/intel-extension-for-pytorch/tree/f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 |
Attention_layer | import math
import torch
import torch.nn as nn
def calculate_attention(query: 'torch.Tensor', key: 'torch.Tensor', value:
'torch.Tensor', mask: 'torch.Tensor'):
"""Calclulate Attention
@param:
query: torch.Tensor (Batch_size, max_seq_len, hidden_size)
key: torch.Tensor (Batch_size, max_se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | OpenNLPhub/SynSetMineOnBert | Attention_layer | false | 5,691 | [
"MIT"
] | 1 | 58853363557a2397fd8b04c8e68061f9df159d6a | https://github.com/OpenNLPhub/SynSetMineOnBert/tree/58853363557a2397fd8b04c8e68061f9df159d6a |
_CNN | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class _CNN(nn.Module):
def __init__(self, config):
super(_CNN, self).__init__()
self.config = config
self.in_channels = 1
self.in_height = self.config.max_length
self.in_width = self.config.wo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Lnna/OpenNRE-PyTorch | _CNN | false | 5,692 | [
"MIT"
] | 1 | 907026a8bece7a867558087131cd1e97d41eb3f2 | https://github.com/Lnna/OpenNRE-PyTorch/tree/907026a8bece7a867558087131cd1e97d41eb3f2 |
AttentionBlock | import math
import torch
from torch.nn import functional as F
from torch import nn
import torch.utils.data
import torch.optim
def convert_pad_shape(pad_shape):
"""
Used to get arguments for F.pad
"""
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Oktai15/NeMo | AttentionBlock | false | 5,693 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
MetaCurvatureTransform | import torch
import numpy as np
class MetaCurvatureTransform(torch.nn.Module):
"""
[[Source]](https://github.com/learnables/learn2learn/blob/master/learn2learn/optim/transforms/module_transform.py)
**Description**
Implements the Meta-Curvature transform of Park and Oliva, 2019.
Unlike `ModuleTr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | OliverWang-Au/learn2learn | MetaCurvatureTransform | false | 5,694 | [
"MIT"
] | 1 | df3c3291b4681440a80a69a7815090a4bd3cd661 | https://github.com/OliverWang-Au/learn2learn/tree/df3c3291b4681440a80a69a7815090a4bd3cd661 |
TransformerEncoderLayer | import torch
import torch.nn as nn
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=512, dropout=0.1):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.Linear(d_model, dim_feedforward)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Nial4/Gaze_HybirdModel | TransformerEncoderLayer | false | 5,695 | [
"MIT"
] | 1 | e738179408a45c380ec7de289c84bbd3965ae924 | https://github.com/Nial4/Gaze_HybirdModel/tree/e738179408a45c380ec7de289c84bbd3965ae924 |
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 =... | Omkar-Ranadive/Fine-Tuning-BERT | Classifier | false | 5,696 | [
"Apache-2.0"
] | 1 | b046092ec4007a4a59e1a478576cca7557c18d76 | https://github.com/Omkar-Ranadive/Fine-Tuning-BERT/tree/b046092ec4007a4a59e1a478576cca7557c18d76 |
MaxpoolMatchLay | import torch
import torch.nn as nn
from torch.nn.functional import cosine_similarity
def multi_perspective_expand_for_2D(in_tensor, decompose_params):
"""
Return: [batch_size, decompse_dim, dim]
"""
in_tensor = in_tensor.unsqueeze(1)
decompose_params = decompose_params.unsqueeze(0)
return 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | NeilWangziyu/torch_light | MaxpoolMatchLay | false | 5,697 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
ClassifierDummy | import torch
import torch.distributed
import torch
import torch.nn as nn
class ClassifierDummy(nn.Module):
def __init__(self, hidden_size):
super(ClassifierDummy, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.softmax = nn.Softmax()
def forward(self, x, mask_cls):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Omkar-Ranadive/Fine-Tuning-BERT | ClassifierDummy | false | 5,698 | [
"Apache-2.0"
] | 1 | b046092ec4007a4a59e1a478576cca7557c18d76 | https://github.com/Omkar-Ranadive/Fine-Tuning-BERT/tree/b046092ec4007a4a59e1a478576cca7557c18d76 |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
num_attention_heads: number of hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Oktai15/NeMo | MultiHeadAttention | false | 5,699 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
Conv3D_Block | import torch
import torch.nn as nn
def define_norm(n_channel, norm_type, n_group=None, dim_mode=2):
if norm_type == 'bn':
if dim_mode == 2:
return nn.BatchNorm2d(n_channel)
elif dim_mode == 3:
return nn.BatchNorm3d(n_channel)
elif norm_type == 'gn':
if n_group 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
import torch.nn as nn
assert_... | Ohyeon5/SQM_basis | Conv3D_Block | false | 5,700 | [
"Apache-2.0"
] | 1 | a04662f1a4520128dd347b1e84d14717feb0655a | https://github.com/Ohyeon5/SQM_basis/tree/a04662f1a4520128dd347b1e84d14717feb0655a |
LateralBlock | import torch
import torch.utils.data
import torch
from torch import nn
class LateralBlock(nn.Module):
def __init__(self, conv_dim, alpha):
super(LateralBlock, self).__init__()
self.conv = nn.Conv3d(conv_dim, conv_dim * 2, kernel_size=(5, 1, 1),
stride=(alpha, 1, 1), padding=(2, 0, 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.utils.data
import torch
from torch import nn
assert_size_stride = t... | PANBOHE/Humanpose-fight | LateralBlock | false | 5,701 | [
"Apache-2.0"
] | 1 | 36e6218db526d567922fa528fa7e11497c53ad60 | https://github.com/PANBOHE/Humanpose-fight/tree/36e6218db526d567922fa528fa7e11497c53ad60 |
PositionwiseFeedForward | import math
import torch
import torch.distributed
import torch
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Omkar-Ranadive/Fine-Tuning-BERT | PositionwiseFeedForward | false | 5,702 | [
"Apache-2.0"
] | 1 | b046092ec4007a4a59e1a478576cca7557c18d76 | https://github.com/Omkar-Ranadive/Fine-Tuning-BERT/tree/b046092ec4007a4a59e1a478576cca7557c18d76 |
BCEDiceLoss | import torch
from torch import nn
import torch.utils.data
import torch.nn.functional as F
class BCEDiceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
bce = F.binary_cross_entropy_with_logits(input, target)
smooth = 1e-05
input = 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Ostyk/unet-plus-plus | BCEDiceLoss | false | 5,703 | [
"MIT"
] | 1 | 924edd8b90856650da2f040fa2ae2db6fcda18b1 | https://github.com/Ostyk/unet-plus-plus/tree/924edd8b90856650da2f040fa2ae2db6fcda18b1 |
MedianPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _quadruple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kernel, int ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn.modules.utils import _pair
from torch... | PJ-Steeman/2020_Masterproef | MedianPool2d | false | 5,704 | [
"MIT"
] | 1 | 5bd77b4039a897d328fafe9a0b70dc8e593e2899 | https://github.com/PJ-Steeman/2020_Masterproef/tree/5bd77b4039a897d328fafe9a0b70dc8e593e2899 |
Classifier | import torch
import torch.nn as nn
class Classifier(nn.Module):
"""MLP classifier
Parameters
----------
n_dimensions : int
Embedding dimension
n_classes : int
Number of classes.
"""
def __init__(self, n_dimensions, n_classes):
super().__init__()
self.n_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | OrangeBaoWang/pyannote-audio | Classifier | false | 5,705 | [
"MIT"
] | 1 | ddbdf808f81e100ae8f463144fb7b3c32d8eba58 | https://github.com/OrangeBaoWang/pyannote-audio/tree/ddbdf808f81e100ae8f463144fb7b3c32d8eba58 |
ResidualBlock | import torch
import torch.optim
import torch.nn as nn
import torch.nn.parallel
class ResidualBlock(nn.Module):
def __init__(self, in_f, out_f):
super(ResidualBlock, self).__init__()
self.conv = nn.Conv2d(in_f, out_f, 1, 1, padding=0, bias=False)
def forward(self, x):
residual = 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
import torch.optim
import torch.nn as nn
import torch.nn.parallel
assert_size_st... | PeiKaLunCi/code-cs-fairness | ResidualBlock | false | 5,706 | [
"MIT"
] | 1 | 3c34d32c87ad244f6a9f302ba4f61e0acf886574 | https://github.com/PeiKaLunCi/code-cs-fairness/tree/3c34d32c87ad244f6a9f302ba4f61e0acf886574 |
Intensity | import torch
import torch.nn as nn
from torch.cuda.amp import autocast as autocast
from torch.cuda.amp import GradScaler as GradScaler
class Intensity(nn.Module):
def __init__(self, scale):
super().__init__()
self.scale = scale
def forward(self, x):
r = torch.randn((x.size(0), 1, 1, ... | 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
import torch.nn as nn
from torch.cuda.amp import autocast as aut... | PeppaCat/EfficientZero | Intensity | false | 5,707 | [
"MIT"
] | 1 | b0e98197abfc36ab34faac043ecea9b756b11d54 | https://github.com/PeppaCat/EfficientZero/tree/b0e98197abfc36ab34faac043ecea9b756b11d54 |
LayerNorm | import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, size, eps=1e-06):
super(LayerNorm, self).__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(size, 1, 1))
self.bias = nn.Parameter(torch.zeros(size, 1, 1))
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ParadoxZW/CosAttention2d | LayerNorm | false | 5,708 | [
"Apache-2.0"
] | 1 | 19b3e655cf0ebc40721b806eb46a3132c488a188 | https://github.com/ParadoxZW/CosAttention2d/tree/19b3e655cf0ebc40721b806eb46a3132c488a188 |
CenterLoss | import torch
import torch.nn as nn
class CenterLoss(nn.Module):
def __init__(self):
super(CenterLoss, self).__init__()
self.l2_loss = nn.MSELoss(reduction='sum')
def forward(self, outputs, targets):
return self.l2_loss(outputs, targets) / outputs.size(0)
def get_inputs():
retur... | 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... | Peiqi00/WS-DAN.PyTorch | CenterLoss | false | 5,709 | [
"MIT"
] | 1 | eb1307ad6d3a81ee3c18ff9ef1fb0838dd68223a | https://github.com/Peiqi00/WS-DAN.PyTorch/tree/eb1307ad6d3a81ee3c18ff9ef1fb0838dd68223a |
RelativeMSE | import torch
import torch as th
class RelativeMSE(th.nn.Module):
"""Relative Mean-Squared Error.
:math:`0.5 * \\frac{(x - y)^2}{y^2 + \\epsilon}`
Args:
eps(float): small number to avoid division by 0.
"""
def __init__(self, eps=0.01):
super(RelativeMSE, self).__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
from torch._inductor.runtime import triton_helpers
import torch as th
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | PeterZs/sbmc | RelativeMSE | false | 5,710 | [
"Apache-2.0"
] | 1 | ac3f5452efe0166ea73942f37cc60b1f0e1ee555 | https://github.com/PeterZs/sbmc/tree/ac3f5452efe0166ea73942f37cc60b1f0e1ee555 |
PatchApplier | import torch
import torch.nn as nn
class PatchApplier(nn.Module):
"""PatchApplier: applies adversarial patches to images.
Module providing the functionality necessary to apply a patch to all detections in all images in the batch.
"""
def __init__(self):
super(PatchApplier, self).__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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | PJ-Steeman/2020_Masterproef | PatchApplier | false | 5,711 | [
"MIT"
] | 1 | 5bd77b4039a897d328fafe9a0b70dc8e593e2899 | https://github.com/PJ-Steeman/2020_Masterproef/tree/5bd77b4039a897d328fafe9a0b70dc8e593e2899 |
FilterNorm | import torch
import torch.nn as nn
from torch.nn.init import calculate_gain
import torch.nn.parallel
class FilterNorm(nn.Module):
def __init__(self, in_channels, kernel_size, filter_type, nonlinearity=
'linear', running_std=False, running_mean=False):
assert filter_type in ('spatial', 'channel')
... | 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 torch.nn.init import calculate_gain
import torch.nn.... | OutBreak-hui/ddfnet | FilterNorm | false | 5,712 | [
"MIT"
] | 1 | 65f67692352a2c083b5d7e003e320629a86e8460 | https://github.com/OutBreak-hui/ddfnet/tree/65f67692352a2c083b5d7e003e320629a86e8460 |
SMAPE | import torch
import torch as th
class SMAPE(th.nn.Module):
"""Symmetric Mean Absolute error.
:math:`\\frac{|x - y|} {|x| + |y| + \\epsilon}`
Args:
eps(float): small number to avoid division by 0.
"""
def __init__(self, eps=0.01):
super(SMAPE, self).__init__()
self.eps = ... | 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 as th
ass... | PeterZs/sbmc | SMAPE | false | 5,713 | [
"Apache-2.0"
] | 1 | ac3f5452efe0166ea73942f37cc60b1f0e1ee555 | https://github.com/PeterZs/sbmc/tree/ac3f5452efe0166ea73942f37cc60b1f0e1ee555 |
MulticlassSegmentationLoss | from torch.nn import Module
import torch
from torch import Tensor
from torch.nn import MSELoss
def _split_masks_by_classes(pred: 'Tensor', target: 'Tensor') ->[]:
"""
Split masks by classes
Args:
pred (Tensor): predicted masks of shape [B, C, H, W]
target (Tensor): target masks of shape [... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import Tensor
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = to... | PiePline/PieToolbelt | MulticlassSegmentationLoss | false | 5,714 | [
"MIT"
] | 1 | bcf9cab16bf3dbb19015c074a305f9ea8a8dc48e | https://github.com/PiePline/PieToolbelt/tree/bcf9cab16bf3dbb19015c074a305f9ea8a8dc48e |
DQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
def __init__(self, input_size, nbr_actions):
super(DQN, self).__init__()
self.fc1 = nn.Linear(input_size, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 64)
self.fc4 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Ourdin/Phantom-of-the-Opera | DQN | false | 5,715 | [
"MIT"
] | 1 | c1ade346fadd40f6ca79033b8c6f3f74ce949d08 | https://github.com/Ourdin/Phantom-of-the-Opera/tree/c1ade346fadd40f6ca79033b8c6f3f74ce949d08 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(2970, 1024)
self.fc2 = nn.Linear(1024, 1)
def forward(self, x, y=None):
x = x.view(-1, 2970)
x = self.fc1(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_... | OubaidaOubi/PP-Voice-AS-MPC | Net | false | 5,716 | [
"MIT"
] | 1 | 81542b664a0e5a1ec4ccaf86142820d0c1a29023 | https://github.com/OubaidaOubi/PP-Voice-AS-MPC/tree/81542b664a0e5a1ec4ccaf86142820d0c1a29023 |
RestrictionLoss | import torch
import torch.nn as nn
class RestrictionLoss(nn.Module):
def __init__(self, otherbar=0):
super().__init__()
self.otherbar = otherbar
def forward(self, predict):
loss = torch.sum(((self.otherbar - predict) * (1 - predict)) ** 2)
return loss
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Polarbeartnt/SP-ILC | RestrictionLoss | false | 5,717 | [
"MIT"
] | 1 | 07c812dfe40461409c9714936190ba1470f91fc3 | https://github.com/Polarbeartnt/SP-ILC/tree/07c812dfe40461409c9714936190ba1470f91fc3 |
TotalVariation | import torch
import torch.nn as nn
class TotalVariation(nn.Module):
"""TotalVariation: calculates the total variation of a patch.
Module providing the functionality necessary to calculate the total vatiation (TV) of an adversarial patch.
"""
def __init__(self):
super(TotalVariation, self)._... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | PJ-Steeman/2020_Masterproef | TotalVariation | false | 5,718 | [
"MIT"
] | 1 | 5bd77b4039a897d328fafe9a0b70dc8e593e2899 | https://github.com/PJ-Steeman/2020_Masterproef/tree/5bd77b4039a897d328fafe9a0b70dc8e593e2899 |
CosNorm_Classifier | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class CosNorm_Classifier(nn.Module):
def __init__(self, in_dims, out_dims, scale=16, margin=0.5, init_std=0.001
):
super(CosNorm_Classifier, self).__init__()
self.in_dims = in_dims
self.out_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
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | PiperLiu/AliProducts | CosNorm_Classifier | false | 5,719 | [
"MIT"
] | 1 | f51884c4dae035a879dbaca2c1575797f30ee7d3 | https://github.com/PiperLiu/AliProducts/tree/f51884c4dae035a879dbaca2c1575797f30ee7d3 |
DownConv | import copy
import torch
import torch.nn as nn
def get_conv(dim=3):
"""Chooses an implementation for a convolution layer."""
if dim == 3:
return nn.Conv3d
elif dim == 2:
return nn.Conv2d
else:
raise ValueError('dim has to be 2 or 3')
def planar_kernel(x):
"""Returns a "pl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 copy
import torch.nn a... | PlayWeird/ct-volume-preprocessing | DownConv | false | 5,720 | [
"MIT"
] | 1 | 8bacf58c36c001fcdb809d4f74e9a39acb00bcbe | https://github.com/PlayWeird/ct-volume-preprocessing/tree/8bacf58c36c001fcdb809d4f74e9a39acb00bcbe |
CustomInverse | import torch
class CustomInverse(torch.nn.Module):
def forward(self, x, y):
ress = torch.inverse(x) + x
return ress, torch.all(y)
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 import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | QPC-database/onnxruntime-extensions | CustomInverse | false | 5,721 | [
"MIT"
] | 1 | 7fd96c8e9700425335b479ca042b16fe92f8b8e8 | https://github.com/QPC-database/onnxruntime-extensions/tree/7fd96c8e9700425335b479ca042b16fe92f8b8e8 |
ConvBlock | import torch
import torch.nn.functional as F
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
def forward(self, x):
x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Propaler/FedMA | ConvBlock | false | 5,722 | [
"MIT"
] | 1 | e235d971e192fb0e93abd4ad37ac603552b6484c | https://github.com/Propaler/FedMA/tree/e235d971e192fb0e93abd4ad37ac603552b6484c |
ContrastiveLoss | import torch
import torch.nn.functional as F
from torch import nn
import torch.nn
import torch.optim
class ContrastiveLoss(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
Credit due to: https://github.... | 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
import ... | QPC-database/multimodal-affinities | ContrastiveLoss | false | 5,723 | [
"Apache-2.0"
] | 1 | c3298e8db56a8b41110cc5681852f9f15d6deaa6 | https://github.com/QPC-database/multimodal-affinities/tree/c3298e8db56a8b41110cc5681852f9f15d6deaa6 |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | McGill-NLP/imagecode | BertSelfAttention | false | 5,724 | [
"MIT"
] | 1 | 2c636c6c41d705b4c5861841f29ff689748113d1 | https://github.com/McGill-NLP/imagecode/tree/2c636c6c41d705b4c5861841f29ff689748113d1 |
SimpleCNNContainerConvBlocks | import torch
import torch.nn.functional as F
import torch.nn as nn
class SimpleCNNContainerConvBlocks(nn.Module):
def __init__(self, input_channel, num_filters, kernel_size, output_dim=10):
super(SimpleCNNContainerConvBlocks, self).__init__()
"""
A testing cnn container, which allows 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
import torch.nn as nn
assert_... | Propaler/FedMA | SimpleCNNContainerConvBlocks | false | 5,725 | [
"MIT"
] | 1 | e235d971e192fb0e93abd4ad37ac603552b6484c | https://github.com/Propaler/FedMA/tree/e235d971e192fb0e93abd4ad37ac603552b6484c |
AE | import torch
import torch.nn as nn
class AE(nn.Module):
def __init__(self, input_shape):
super().__init__()
self.encoder_hidden_layer = nn.Linear(in_features=input_shape,
out_features=128)
self.encoder_output_layer = nn.Linear(in_features=128, out_features=128
)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | PtrMan/21V1 | AE | false | 5,726 | [
"MIT"
] | 1 | fbac4deb5bec3a5e50b81e1e91c4a8a9820d6aaa | https://github.com/PtrMan/21V1/tree/fbac4deb5bec3a5e50b81e1e91c4a8a9820d6aaa |
BertMixedLayer | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn
import torch.nn as nn
class BertAttention(nn.Module):
"""BERT attention layer.
Based on: BERT (pytorch-transformer)
https://github.com/huggingface/transformers
"""
def __init__(self, config) ->None:
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Erotemic/MONAI | BertMixedLayer | false | 5,727 | [
"Apache-2.0"
] | 1 | a9cd2d88168107281a2abcc2f63efaed80580e79 | https://github.com/Erotemic/MONAI/tree/a9cd2d88168107281a2abcc2f63efaed80580e79 |
BertSelfOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertSelfOutput(nn.Module):
def __init__(self, config):
super(BertSelfOutput, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | QuLiang132/nlp-notebook | BertSelfOutput | false | 5,728 | [
"MIT"
] | 1 | b7659867b967d1e541bee5617cee017b3b67d9ba | https://github.com/QuLiang132/nlp-notebook/tree/b7659867b967d1e541bee5617cee017b3b67d9ba |
SelfOutput | import torch
import torch.nn as nn
class SelfOutput(nn.Module):
def __init__(self, hidden_size, dropout):
super(SelfOutput, self).__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.Layer_norm = nn.LayerNorm(hidden_size)
self.dropout = nn.Dropout(dropout)
def for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | QuLiang132/nlp-notebook | SelfOutput | false | 5,729 | [
"MIT"
] | 1 | b7659867b967d1e541bee5617cee017b3b67d9ba | https://github.com/QuLiang132/nlp-notebook/tree/b7659867b967d1e541bee5617cee017b3b67d9ba |
FCLayer | import torch
import torch.nn as nn
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.0,
use_activation=True):
super(FCLayer, self).__init__()
self.use_activation = use_activation
self.dropout = nn.Dropout(dropout_rate)
self.linear = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Raiselimit/TorchBlocks | FCLayer | false | 5,730 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
KdMseLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class KdMseLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits_S, logits_T, temperature=1):
"""
Calculate the mse loss between logits_S and logits_T
:param logits_S: Tensor of sha... | 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... | Raiselimit/TorchBlocks | KdMseLoss | false | 5,731 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
ANNDigitDetect | import torch
import torch.nn as nn
import torch.nn.functional as F
class ANNDigitDetect(nn.Module):
def __init__(self):
super(ANNDigitDetect, self).__init__()
self.fc1 = nn.Linear(32 * 32, 120)
self.fc2 = nn.Linear(120, 32)
self.fc3 = nn.Linear(32, 10)
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_... | Quest2GM/timestamp_detection_algorithm | ANNDigitDetect | false | 5,732 | [
"MIT"
] | 1 | 8a5a7fba5a924a37402d7daece90fdf626a6a905 | https://github.com/Quest2GM/timestamp_detection_algorithm/tree/8a5a7fba5a924a37402d7daece90fdf626a6a905 |
AttMseLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttMseLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, attention_S, attention_T, mask=None):
"""
Calculate the mse loss between attention_S and attention_T.
:param logits_S: 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Raiselimit/TorchBlocks | AttMseLoss | false | 5,733 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
MaxPoolWithMask | import torch
import torch.nn as nn
class MaxPoolWithMask(nn.Module):
"""
带mask矩阵的max pooling。在做max-pooling的时候不会考虑mask值为0的位置。
"""
def __init__(self):
super(MaxPoolWithMask, self).__init__()
self.inf = 10000000000000.0
def forward(self, tensor, mask, dim=1):
"""
:pa... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Raiselimit/TorchBlocks | MaxPoolWithMask | false | 5,734 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
CosAttention | import torch
import torch.nn as nn
class CosAttention(nn.Module):
def __init__(self):
super(CosAttention, self).__init__()
def forward(self, q, k, v):
"""
q: (batchsize, hidden_dim)
k: (batchsize, seqlen, hidden_dim)
v: (batchsize, seqlen, hidden_dim)
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Raiselimit/TorchBlocks | CosAttention | false | 5,735 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
AvgPool | import torch
import torch.nn as nn
class AvgPool(nn.Module):
"""
给定形如[batch_size, max_len, hidden_size]的输入，在最后一维进行avg pooling. 输出为[batch_size, hidden_size]
"""
def __init__(self, stride=None, padding=0):
super(AvgPool, self).__init__()
self.stride = stride
self.padding = paddi... | 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... | Raiselimit/TorchBlocks | AvgPool | false | 5,736 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
FeatureAssembler | import torch
from typing import Optional
import torch.nn as nn
import torch.nn
import torch.optim
class FeatureAssembler(nn.Module):
def __init__(self, T: 'int', embed_static: 'Optional[FeatureEmbedder]'=
None, embed_dynamic: 'Optional[FeatureEmbedder]'=None) ->None:
super().__init__()
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Optional
import torch.nn as nn
import torch.nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_siz... | RSNirwan/gluon-ts | FeatureAssembler | false | 5,737 | [
"Apache-2.0"
] | 1 | ae4cfdef539e49f93a87034aa2f2bec194c4b7d8 | https://github.com/RSNirwan/gluon-ts/tree/ae4cfdef539e49f93a87034aa2f2bec194c4b7d8 |
AvgPoolWithMask | import torch
import torch.nn as nn
class AvgPoolWithMask(nn.Module):
"""
给定形如[batch_size, max_len, hidden_size]的输入，在最后一维进行avg pooling. 输出为[batch_size, hidden_size], pooling
的时候只会考虑mask为1的位置
"""
def __init__(self):
super(AvgPoolWithMask, self).__init__()
self.inf = 10000000000000.0... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Raiselimit/TorchBlocks | AvgPoolWithMask | false | 5,738 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
CosLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class CosLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, state_S, state_T, mask=None):
"""
This is the loss used in DistilBERT
:param state_S: Tensor of shape (batch_size, length, h... | 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... | Raiselimit/TorchBlocks | CosLoss | false | 5,739 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
PrimaryCaps | import torch
import torch.nn as nn
def squash(inputs, axis=-1):
"""capsule输出的激活函数"""
norm = torch.norm(inputs, dim=axis, keepdim=True)
scale = norm ** 2 / (1 + norm ** 2) / (norm + 1e-08)
return scale * inputs
class PrimaryCaps(nn.Module):
"""计算第一层capsules的输入，转换成32*6*6个8维的capsule vector
in_c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | RacleRay/-Have_Fun_Doing | PrimaryCaps | false | 5,740 | [
"Apache-2.0"
] | 1 | 8ebb7fcabc6148571d38f2f51eac47952ce54424 | https://github.com/RacleRay/-Have_Fun_Doing/tree/8ebb7fcabc6148571d38f2f51eac47952ce54424 |
KL | import torch
import torch.nn as nn
import torch.nn.functional as F
class KL(nn.Module):
def __init__(self, reduction='batchmean'):
super(KL, self).__init__()
self.reduction = reduction
def forward(self, input, target):
input = input.float()
target = target.float()
los... | 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... | Raiselimit/TorchBlocks | KL | false | 5,741 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
PointWiseFeedForward | import torch
import torch.nn as nn
class PointWiseFeedForward(nn.Module):
def __init__(self, d_model, d_affine, fc_dorpout=0.2):
super().__init__()
self.d_model = d_model
self.d_affine = d_affine
self.linear_1 = nn.Linear(self.d_model, self.d_affine)
self.linear_2 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | RacleRay/TextSummerize | PointWiseFeedForward | false | 5,742 | [
"MIT"
] | 1 | fe2572d26d65bdf849ce89fbb0c5adf5607f06b1 | https://github.com/RacleRay/TextSummerize/tree/fe2572d26d65bdf849ce89fbb0c5adf5607f06b1 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
"""
self attention层
原理可看这篇博客: http://jalammar.github.io/illustrated-transformer/
"""
def __init__(self, config):
super(BertSelfAttention, self).__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.... | QuLiang132/nlp-notebook | BertAttention | false | 5,743 | [
"MIT"
] | 1 | b7659867b967d1e541bee5617cee017b3b67d9ba | https://github.com/QuLiang132/nlp-notebook/tree/b7659867b967d1e541bee5617cee017b3b67d9ba |
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... | Raiselimit/TorchBlocks | GatedConv1d | false | 5,744 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
AttCeLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttCeLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, attention_S, attention_T, mask=None):
"""
Calculate the cross entropy between attention_S and attention_T.
:param logits_S... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Raiselimit/TorchBlocks | AttCeLoss | false | 5,745 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
num_attention_heads: number of hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RPrenger/NeMo | MultiHeadAttention | false | 5,746 | [
"Apache-2.0"
] | 1 | e8912ca6e3321347272a6a7da18e052812fb2062 | https://github.com/RPrenger/NeMo/tree/e8912ca6e3321347272a6a7da18e052812fb2062 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
"""
Softmax and sigmoid focal loss
"""
def __init__(self, num_labels, activation_type='softmax', gamma=2.0,
alpha=0.25, epsilon=1e-09):
super(FocalLoss, self).__init__()
self.num_lab... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Raiselimit/TorchBlocks | FocalLoss | false | 5,747 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
AttCeMeanLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttCeMeanLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, attention_S, attention_T, mask=None):
"""
Calculate the cross entropy between attention_S and attention_T, the dim of num_heads... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Raiselimit/TorchBlocks | AttCeMeanLoss | false | 5,748 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
SKL | import torch
import torch.nn as nn
import torch.nn.functional as F
class SKL(nn.Module):
def __init__(self, epsilon=1e-08):
super(SKL, self).__init__()
self.epsilon = epsilon
def forward(self, input, target):
logit = input.view(-1, input.size(-1)).float()
target = target.view... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Raiselimit/TorchBlocks | SKL | false | 5,749 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
MultiSampleDropout | import torch
import torch.nn as nn
class MultiSampleDropout(nn.Module):
"""
# multisample dropout (wut): https://arxiv.org/abs/1905.09788
"""
def __init__(self, hidden_size, num_labels, K=5, p=0.5):
super().__init__()
self.K = K
self.dropout = nn.Dropout(p)
self.classi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Raiselimit/TorchBlocks | MultiSampleDropout | false | 5,750 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
Gate | import torch
import torch.nn as nn
import torch.nn.functional as F
class Gate(nn.Module):
"""Gate Unit
g = sigmoid(Wx)
x = g * x
"""
def __init__(self, input_size, dropout_rate=0.0):
super(Gate, self).__init__()
self.linear = nn.Linear(input_size, input_size, bias=False)
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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Raiselimit/TorchBlocks | Gate | false | 5,751 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
SpaceToDepth | import torch
from torch import nn
import torch.nn.parallel
class SpaceToDepth(nn.Module):
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, self.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
from torch import nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Randl/TResNet | SpaceToDepth | false | 5,752 | [
"Apache-2.0"
] | 1 | 18514caf61d77c7e000a71dde9d1f86ba792b38d | https://github.com/Randl/TResNet/tree/18514caf61d77c7e000a71dde9d1f86ba792b38d |
_CAEAD | import torch
import torch.nn as nn
import torch.nn.functional as F
class _CAEAD(nn.Module):
def __init__(self, input_size):
super(_CAEAD, self).__init__()
self.en_1 = nn.Conv1d(1, 64, 3, padding=1)
self.pool1 = nn.MaxPool1d(2, 2)
self.en_2 = nn.Conv1d(64, 32, 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
assert_... | Pheobe-Sun/anomaly-detection-challenge-2020 | _CAEAD | false | 5,753 | [
"MIT"
] | 1 | 71e34350023023a17338b7931da70af035b2454c | https://github.com/Pheobe-Sun/anomaly-detection-challenge-2020/tree/71e34350023023a17338b7931da70af035b2454c |
HighwayLayer | import torch
import torch.nn as nn
class HighwayLayer(nn.Module):
def __init__(self, in_units, out_units):
super(HighwayLayer, self).__init__()
self.highway_linear = nn.Linear(in_features=in_units, out_features=
out_units, bias=True)
self.highway_gate = nn.Linear(in_features=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
import torch.nn as nn
assert_... | RandolphVI/HyperNet | HighwayLayer | false | 5,754 | [
"Apache-2.0"
] | 1 | e9f376f5eb087e57360ca41cca2533c3ca967e47 | https://github.com/RandolphVI/HyperNet/tree/e9f376f5eb087e57360ca41cca2533c3ca967e47 |
SEModule | import torch
from torch import nn
import torch.nn.parallel
class GlobalAvgPool2d:
def __init__(self, flatten=False):
self.flatten = flatten
def __call__(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], in_size[1], -1)).mean(dim=2)
else:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Randl/TResNet | SEModule | false | 5,755 | [
"Apache-2.0"
] | 1 | 18514caf61d77c7e000a71dde9d1f86ba792b38d | https://github.com/Randl/TResNet/tree/18514caf61d77c7e000a71dde9d1f86ba792b38d |
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