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 |
|---|---|---|---|---|---|---|---|---|---|---|
C3 | import torch
import torch.nn as nn
from collections import OrderedDict
class C3(nn.Module):
def __init__(self):
super(C3, self).__init__()
self.c3 = nn.Sequential(OrderedDict([('c3', nn.Conv2d(32, 64,
kernel_size=(3, 3), bias=32)), ('relu3', nn.ReLU())]))
def forward(self, img):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 co... | devillove084/DeepSignal | C3 | false | 12,260 | [
"MIT"
] | 0 | 1fe122b32752b11e10ca4bef3d07ddd7de4348b5 | https://github.com/devillove084/DeepSignal/tree/1fe122b32752b11e10ca4bef3d07ddd7de4348b5 |
L2Norm | import torch
import torch.nn.functional as F
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
assert x.dim(
) == 2, 'the input tensor of L2Norm must be the shape of [B, C]'
return F.normalize(x, p=2, dim=-1)
def... | 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... | deokhk/Proxy-Anchor-CVPR2020 | L2Norm | false | 12,261 | [
"MIT"
] | 0 | acb3a16c3ebc8b8777542898ec83de32aa8ba64e | https://github.com/deokhk/Proxy-Anchor-CVPR2020/tree/acb3a16c3ebc8b8777542898ec83de32aa8ba64e |
MaskedMSE | import torch
import torch.nn as nn
class MaskedMSE(nn.Module):
def __init__(self):
super(MaskedMSE, self).__init__()
self.criterion = nn.MSELoss()
def forward(self, input, target, gamma=2.0):
mask = gamma * target / (target + 1e-07)
self.loss = self.criterion(input * mask, ta... | 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... | dhruvramani/MaskedMSE | MaskedMSE | false | 12,262 | [
"MIT"
] | 0 | 76ff94add5659217a3f4f21e60a4f069defede29 | https://github.com/dhruvramani/MaskedMSE/tree/76ff94add5659217a3f4f21e60a4f069defede29 |
C1 | import torch
import torch.nn as nn
from collections import OrderedDict
class C1(nn.Module):
def __init__(self) ->None:
super(C1, self).__init__()
self.c1 = nn.Sequential(OrderedDict([('c1', nn.Conv2d(3, 16,
kernel_size=(3, 3), bias=True)), ('relu1', nn.ReLU()), ('s1',
nn.M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 co... | devillove084/DeepSignal | C1 | false | 12,263 | [
"MIT"
] | 0 | 1fe122b32752b11e10ca4bef3d07ddd7de4348b5 | https://github.com/devillove084/DeepSignal/tree/1fe122b32752b11e10ca4bef3d07ddd7de4348b5 |
ContractingBlock | import torch
from torch import nn
class ContractingBlock(nn.Module):
def __init__(self, input_channels, use_bn=True, kernel_size=3,
activation='relu'):
super(ContractingBlock, self).__init__()
self.conv1 = nn.Conv2d(input_channels, input_channels * 2,
kernel_size=kernel_size, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | diegushko/CycleGAN | ContractingBlock | false | 12,264 | [
"MIT"
] | 0 | 630d1cd00cef3f09f036d3c734d31c772cc0a786 | https://github.com/diegushko/CycleGAN/tree/630d1cd00cef3f09f036d3c734d31c772cc0a786 |
h_swish | import torch
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swish(nn.Module):
def __init__(self, inplace=True)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | dhananjaisharma10/mmdetection | h_swish | false | 12,265 | [
"Apache-2.0"
] | 0 | 6f6db3211c3760cffe9db2350297c42cc29ce140 | https://github.com/dhananjaisharma10/mmdetection/tree/6f6db3211c3760cffe9db2350297c42cc29ce140 |
Mlp | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
def gelu(x):
""" Original Implementation of the gelu activation function in Google Bert repo when initialy created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
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.triton_helpers import libdevice
import math
import ... | denisleonov/pytorch-CycleGAN-and-pix2pix | Mlp | false | 12,266 | [
"BSD-3-Clause"
] | 0 | d1a5f0c5911f70ed896f826619b4067ce737a83d | https://github.com/denisleonov/pytorch-CycleGAN-and-pix2pix/tree/d1a5f0c5911f70ed896f826619b4067ce737a83d |
FeatureMapBlock | import torch
from torch import nn
class FeatureMapBlock(nn.Module):
def __init__(self, input_channels, output_channels):
super(FeatureMapBlock, self).__init__()
self.conv = nn.Conv2d(input_channels, output_channels, kernel_size=
7, padding=3, padding_mode='reflect')
def forward(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.triton_helpers import math as tl_math
from torch im... | diegushko/CycleGAN | FeatureMapBlock | false | 12,267 | [
"MIT"
] | 0 | 630d1cd00cef3f09f036d3c734d31c772cc0a786 | https://github.com/diegushko/CycleGAN/tree/630d1cd00cef3f09f036d3c734d31c772cc0a786 |
PrecomputedNorm | import torch
import torch.nn as nn
class PrecomputedNorm(nn.Module):
"""Normalization using Pre-computed Mean/Std.
Args:
stats: Precomputed (mean, std).
axis: Axis setting used to calculate mean/variance.
"""
def __init__(self, stats, axis=[1, 2]):
super().__init__()
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | czlwang/s3prl | PrecomputedNorm | false | 12,268 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
AMSoftmaxLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AMSoftmaxLoss(nn.Module):
def __init__(self, hidden_dim, speaker_num, s=30.0, m=0.4, **kwargs):
"""
AM Softmax Loss
"""
super(AMSoftmaxLoss, self).__init__()
self.s = s
self.m = m
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 import triton_helpers
from torch._inductor.runtime.... | czlwang/s3prl | AMSoftmaxLoss | false | 12,269 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
ResidualBlock | import torch
from torch import nn
class ResidualBlock(nn.Module):
def __init__(self, input_channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(input_channels, input_channels, kernel_size=
3, padding=1, padding_mode='reflect')
self.conv2 = nn.Conv2d(input_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | diegushko/CycleGAN | ResidualBlock | false | 12,271 | [
"MIT"
] | 0 | 630d1cd00cef3f09f036d3c734d31c772cc0a786 | https://github.com/diegushko/CycleGAN/tree/630d1cd00cef3f09f036d3c734d31c772cc0a786 |
SelfAttentionPooling | import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | czlwang/s3prl | SelfAttentionPooling | false | 12,272 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
AP | import torch
import torch.nn as nn
class AttentivePooling(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, **kwargs):
super(AttentivePooling, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W = nn.Linear(input_dim, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | czlwang/s3prl | AP | false | 12,273 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
BertLayer | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | brendon-boldt/minbert-assignment | BertLayer | false | 12,274 | [
"Apache-2.0"
] | 0 | 0b562d791d34a40fd3c0383a0a32b4eeb2171cb5 | https://github.com/brendon-boldt/minbert-assignment/tree/0b562d791d34a40fd3c0383a0a32b4eeb2171cb5 |
AdMSoftmaxLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdMSoftmaxLoss(nn.Module):
def __init__(self, in_features, out_features, s=30.0, m=0.4):
"""
AM Softmax Loss
"""
super(AdMSoftmaxLoss, self).__init__()
self.s = s
self.m = m
self.in_fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | czlwang/s3prl | AdMSoftmaxLoss | false | 12,275 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
SoftmaxLoss | import torch
import torch.nn as nn
class SoftmaxLoss(nn.Module):
def __init__(self, hidden_dim, speaker_num, **kwargs):
"""
Softmax Loss
"""
super(SoftmaxLoss, self).__init__()
self.fc = nn.Linear(hidden_dim, speaker_num)
self.loss = nn.CrossEntropyLoss()
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | czlwang/s3prl | SoftmaxLoss | false | 12,276 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
CMVN | import torch
import torch.nn as nn
class CMVN(nn.Module):
__constants__ = ['mode', 'dim', 'eps']
def __init__(self, mode='global', dim=2, eps=1e-10):
super(CMVN, self).__init__()
if mode != 'global':
raise NotImplementedError(
'Only support global mean variance nor... | 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_... | czlwang/s3prl | CMVN | false | 12,277 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
ASP | import torch
import torch.nn as nn
class AttentivePooling(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, **kwargs):
super(AttentivePooling, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W = nn.Linear(input_dim, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | czlwang/s3prl | ASP | false | 12,278 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
ChannelNorm | import torch
import torch.nn as nn
class ChannelNorm(nn.Module):
def __init__(self, numFeatures, epsilon=1e-05, affine=True):
super(ChannelNorm, self).__init__()
if affine:
self.weight = nn.parameter.Parameter(torch.Tensor(1,
numFeatures, 1))
self.bias = 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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | czlwang/s3prl | ChannelNorm | false | 12,279 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
Block | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
def gelu(x):
""" Original Implementation of the gelu activation function in Google Bert repo when initialy created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
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.... | denisleonov/pytorch-CycleGAN-and-pix2pix | Block | false | 12,280 | [
"BSD-3-Clause"
] | 0 | d1a5f0c5911f70ed896f826619b4067ce737a83d | https://github.com/denisleonov/pytorch-CycleGAN-and-pix2pix/tree/d1a5f0c5911f70ed896f826619b4067ce737a83d |
AttentivePooling | import torch
import torch.nn as nn
class AttentivePooling(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, **kwargs):
super(AttentivePooling, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W = nn.Linear(input_dim, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | czlwang/s3prl | AttentivePooling | false | 12,281 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
Delta | import torch
import torch.nn as nn
from torchaudio import transforms
class Delta(nn.Module):
def __init__(self, order=2, **kwargs):
super(Delta, self).__init__()
self.order = order
self.compute_delta = transforms.ComputeDeltas(**kwargs)
def forward(self, x):
feats = [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.nn as nn
from torchaudio import transforms
assert_size_stride = tor... | czlwang/s3prl | Delta | false | 12,282 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
ExpandingBlock | import torch
from torch import nn
class ExpandingBlock(nn.Module):
def __init__(self, input_channels, use_bn=True):
super(ExpandingBlock, self).__init__()
self.conv1 = nn.ConvTranspose2d(input_channels, input_channels // 2,
kernel_size=3, stride=2, padding=1, output_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
from torch._inductor.runtime.... | diegushko/CycleGAN | ExpandingBlock | false | 12,283 | [
"MIT"
] | 0 | 630d1cd00cef3f09f036d3c734d31c772cc0a786 | https://github.com/diegushko/CycleGAN/tree/630d1cd00cef3f09f036d3c734d31c772cc0a786 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
def focal_loss(input_values, gamma):
"""Computes the focal loss"""
p = torch.exp(-input_values)
loss = (1 - p) ** gamma * input_values
return loss.mean()
class Focal... | 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
... | dixit-dude7/LDAM-DRW | FocalLoss | false | 12,284 | [
"MIT"
] | 0 | 6366f4756d3ac0c6b6db784b7f20e16066967ed4 | https://github.com/dixit-dude7/LDAM-DRW/tree/6366f4756d3ac0c6b6db784b7f20e16066967ed4 |
NormedLinear | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
from torch.nn import Parameter
class NormedLinear(nn.Module):
def __init__(self, in_features, out_features):
super(NormedLinear, self).__init__()
self.weight = Pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dixit-dude7/LDAM-DRW | NormedLinear | false | 12,285 | [
"MIT"
] | 0 | 6366f4756d3ac0c6b6db784b7f20e16066967ed4 | https://github.com/dixit-dude7/LDAM-DRW/tree/6366f4756d3ac0c6b6db784b7f20e16066967ed4 |
Warp | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
def coords_grid(flow: 'Tensor') ->Tensor:
"""Generate shifted coordinate grid based based input flow.
Args:
flow (Tensor): Estimated optical flow.
Returns:
Tensor: The coordinate that shifted by 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._inductor.runtime.triton_helpers import libdevice
from torch import Tensor
import torch.nn as nn
assert_size_stride = torch._C._d... | dimagrshk/opt_flow_attack | Warp | false | 12,286 | [
"Apache-2.0"
] | 0 | 6bfad92abcf3eaae1a6ca05b865be072361636ed | https://github.com/dimagrshk/opt_flow_attack/tree/6bfad92abcf3eaae1a6ca05b865be072361636ed |
Normalize | import torch
from torch import Tensor
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
import torch.onnx
import torch.optim
import torch.utils.data.distributed
class Normalize(torch.nn.Module):
"""Normalize a tensor image with mean and standard deviation.
This transform does no... | 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.parallel
imp... | dineenai/pytorch_untrained_models | Normalize | false | 12,287 | [
"BSD-3-Clause"
] | 0 | eb301d3b8e3e87b8a79cd8cb4e1cb8d4e44a273a | https://github.com/dineenai/pytorch_untrained_models/tree/eb301d3b8e3e87b8a79cd8cb4e1cb8d4e44a273a |
LowRankResidualDecoderLayer | import torch
import torch.nn as nn
import torch.utils.checkpoint
import torch.nn.functional as F
from torch.cuda.amp import autocast
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bahducoup/factorized_training | LowRankResidualDecoderLayer | false | 12,288 | [
"MIT"
] | 0 | 0af38f16338a9bcfcc11091b1a6b75befd67f234 | https://github.com/bahducoup/factorized_training/tree/0af38f16338a9bcfcc11091b1a6b75befd67f234 |
SelfAttention | import torch
import torch.nn.functional as F
from torch import nn
class SelfAttention(nn.Module):
def __init__(self, embedding_dimension, num_heads):
super().__init__()
assert embedding_dimension % num_heads == 0, f'embedding dimension must be divisible by number of heads, got embedding_dimension... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dimitrios-ebi/gene_symbol_classifier | SelfAttention | false | 12,289 | [
"Apache-2.0"
] | 0 | fe415f719fda4619041b9fe0639996c92e0f12a8 | https://github.com/dimitrios-ebi/gene_symbol_classifier/tree/fe415f719fda4619041b9fe0639996c92e0f12a8 |
MHAttentionMap | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch._utils
class MHAttentionMap(nn.Module):
"""This is a 2D attention module, which only returns the attention softmax (no multiplication by value)"""
def __init__(self, query_dim, hidden_dim, num_heads=1, dropout=0.0,
b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dingmyu/mmclassification | MHAttentionMap | false | 12,290 | [
"Apache-2.0"
] | 0 | c600b22907fb9423899f7c308c659168c2d01cd8 | https://github.com/dingmyu/mmclassification/tree/c600b22907fb9423899f7c308c659168c2d01cd8 |
GNNExplainerProbe | import math
import torch
class AbstractTorchModule(torch.nn.Module):
def __init__(self):
torch.nn.Module.__init__(self)
def save(self, path):
None
torch.save(self.state_dict(), path)
def load(self, path):
None
self.load_state_dict(torch.load(path, map_location=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 torch._inductor.runtime.triton_helpers import math as tl_math
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | djz233/GraphMask | GNNExplainerProbe | false | 12,291 | [
"MIT"
] | 0 | 4b699a1685f0d26973bb90cd75b09d74726cdc2f | https://github.com/djz233/GraphMask/tree/4b699a1685f0d26973bb90cd75b09d74726cdc2f |
DenseGCNConv | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def glorot(tensor):
if tensor is not None:
stdv = math.sqrt(6.0 / (tensor.size(-2) + tensor.size(-1)))
tensor.data.uniform_(-stdv, stdv)
def zeros(tensor):
if tensor is not None:
tensor.data.fill_(0)
cl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | douglasrizzo/pytorch_geometric | DenseGCNConv | false | 12,292 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
LayerNormLSTMCell | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNormLSTMCell(nn.LSTMCell):
def __init__(self, input_size, hidden_size, bias=True):
super().__init__(input_size, hidden_size, bias)
self.ln_ih = nn.LayerNorm(4 * hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.distri... | dimoteo333/TL-DR | LayerNormLSTMCell | false | 12,293 | [
"Apache-2.0"
] | 0 | b3bebc51e70a48294d7762fa73375cf1bf2ff068 | https://github.com/dimoteo333/TL-DR/tree/b3bebc51e70a48294d7762fa73375cf1bf2ff068 |
Linear | import math
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from torch.nn import Parameter
import torch... | douglasrizzo/pytorch_geometric | Linear | false | 12,294 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
DenseSAGEConv | import math
import torch
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseSAGEConv(torch.nn.Module):
"""See :class:`torch_geometric... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from torch.nn imp... | douglasrizzo/pytorch_geometric | DenseSAGEConv | false | 12,295 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
Gate | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class Gate(nn.Module):
def __init__(self, args):
super(Gate, self).__init__()
self.d_model = args.d_model
self.weight_proj = nn.Linear(2 * self.d_model, 1)
self.tanh = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | djz233/GraphMask | Gate | false | 12,296 | [
"MIT"
] | 0 | 4b699a1685f0d26973bb90cd75b09d74726cdc2f | https://github.com/djz233/GraphMask/tree/4b699a1685f0d26973bb90cd75b09d74726cdc2f |
TransformerDecoderLayer | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch._utils
def _get_activation_fn(activation):
"""Return an activation function given a string"""
if activation == 'relu':
return F.relu
if activation == 'gelu':
return F.gelu
if activation == 'glu':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dingmyu/mmclassification | TransformerDecoderLayer | false | 12,297 | [
"Apache-2.0"
] | 0 | c600b22907fb9423899f7c308c659168c2d01cd8 | https://github.com/dingmyu/mmclassification/tree/c600b22907fb9423899f7c308c659168c2d01cd8 |
Envelope | import torch
import torch.utils.data
class Envelope(torch.nn.Module):
def __init__(self, exponent):
super(Envelope, self).__init__()
self.p = exponent
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + 1) / 2
def forw... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | douglasrizzo/pytorch_geometric | Envelope | false | 12,298 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
CategoricalSampler | import torch
import torch.nn as nn
class Sampler(nn.Module):
""" args; logits: (batch, n_nodes)
return; next_node: (batch, 1)
TopKSampler <=> greedy; sample one with biggest probability
CategoricalSampler <=> sampling; randomly sample one from possible distribution based on probability
"""
def __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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | daunfamily/VRP_MHA | CategoricalSampler | false | 12,299 | [
"MIT"
] | 0 | 9c23d181d11dbbacac01299c6e8931b8e266b9b4 | https://github.com/daunfamily/VRP_MHA/tree/9c23d181d11dbbacac01299c6e8931b8e266b9b4 |
Attention | import math
import torch
import torch.nn.functional as F
import torch.utils.data
def restricted_softmax(src, dim=-1, margin=0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).exp())
return out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | douglasrizzo/pytorch_geometric | Attention | false | 12,300 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
Model | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, input_size, dropout=0.5):
super(Model, self).__init__()
self.dropout = dropout
if self.dropout > 0:
self.dropout = nn.Dropout(dropout)
self.encode_w1 = nn.Linear(input_size, 64)
self.e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | dohnlee/qufa2021 | Model | false | 12,301 | [
"MIT"
] | 0 | 5fb42caee09ec228358e49768e32c75e3c0094ce | https://github.com/dohnlee/qufa2021/tree/5fb42caee09ec228358e49768e32c75e3c0094ce |
MaxPoolPad | import torch
import torch.nn as nn
import torch.nn.init
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=1)
def forward(self, x):
x = self.pad(x)
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 import triton_helpers
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.a... | dowhilefalse/DeOldify | MaxPoolPad | false | 12,302 | [
"MIT"
] | 0 | 08f012cdbe36e3f8482460f57e1844b361a7fb16 | https://github.com/dowhilefalse/DeOldify/tree/08f012cdbe36e3f8482460f57e1844b361a7fb16 |
DenseGraphConv | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseGraphConv(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv.GraphConv`.
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch.nn import Parameter
import torch.utils.data
assert_size_s... | douglasrizzo/pytorch_geometric | DenseGraphConv | false | 12,303 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
SelfAttentionUnit | import torch
from torch import nn
class SelfAttentionUnit(nn.Module):
def __init__(self, embed_dim, num_heads, max_len, dropout=0.8, bias=
False, skip_connection=True):
super(SelfAttentionUnit, self).__init__()
self.skip_connection = skip_connection
self.attn = nn.MultiheadAttenti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dohnlee/qufa2021 | SelfAttentionUnit | false | 12,304 | [
"MIT"
] | 0 | 5fb42caee09ec228358e49768e32c75e3c0094ce | https://github.com/dohnlee/qufa2021/tree/5fb42caee09ec228358e49768e32c75e3c0094ce |
AvgPoolPad | import torch
import torch.nn as nn
import torch.nn.init
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_pad=... | 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.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | dowhilefalse/DeOldify | AvgPoolPad | false | 12,305 | [
"MIT"
] | 0 | 08f012cdbe36e3f8482460f57e1844b361a7fb16 | https://github.com/dowhilefalse/DeOldify/tree/08f012cdbe36e3f8482460f57e1844b361a7fb16 |
MultiHead | import math
import torch
from torch import Tensor
from torch.nn import Linear
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | douglasrizzo/pytorch_geometric | MultiHead | false | 12,306 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
ResidualLayer | import math
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from torch.nn import Linear
from torch.nn i... | douglasrizzo/pytorch_geometric | ResidualLayer | false | 12,307 | [
"MIT"
] | 0 | effc617c6ad6daad506038bb79e4407082e74740 | https://github.com/douglasrizzo/pytorch_geometric/tree/effc617c6ad6daad506038bb79e4407082e74740 |
BPRLoss | import torch
import torch.nn as nn
class BPRLoss(nn.Module):
""" BPRLoss, based on Bayesian Personalized Ranking
Args:
- gamma(float): Small value to avoid division by zero
Shape:
- Pos_score: (N)
- Neg_score: (N), same shape as the Pos_score
- Output: scalar.
Exampl... | 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
... | dreaming-qin/RecBole | BPRLoss | false | 12,308 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
ResNetV2 | import torch
import torch.nn as nn
from collections import OrderedDict
import torch.nn.functional as F
def conv1x1(cin, cout, stride=1, bias=False):
return StdConv2d(cin, cout, kernel_size=1, stride=stride, padding=0,
bias=bias)
def conv3x3(cin, cout, stride=1, groups=1, bias=False):
return StdConv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Yifanfanfanfan/ViT-pytorch | ResNetV2 | false | 12,309 | [
"MIT"
] | 0 | 0f975aa7d3fd0aba6f74260c2b5a91786f1211ba | https://github.com/Yifanfanfanfan/ViT-pytorch/tree/0f975aa7d3fd0aba6f74260c2b5a91786f1211ba |
NegSamplingLoss | import torch
import torch.nn as nn
class NegSamplingLoss(nn.Module):
def __init__(self):
super(NegSamplingLoss, self).__init__()
def forward(self, score, sign):
return -torch.mean(torch.sigmoid(sign * score))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | dreaming-qin/RecBole | NegSamplingLoss | false | 12,310 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
InnerProductLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class InnerProductLoss(nn.Module):
"""This is the inner-product loss used in CFKG for optimization.
"""
def __init__(self):
super(InnerProductLoss, self).__init__()
def forward(self, anchor, positive, negative):
pos_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.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | dreaming-qin/RecBole | InnerProductLoss | false | 12,311 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
InnerProductLayer | import torch
import torch.nn as nn
class InnerProductLayer(nn.Module):
"""InnerProduct Layer used in PNN that compute the element-wise
product or inner product between feature vectors.
"""
def __init__(self, num_feature_field, device):
"""
Args:
num_feature_field(int) :nu... | 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... | dreaming-qin/RecBole | InnerProductLayer | false | 12,312 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
ConvNCFBPRLoss | import torch
import torch.nn as nn
class ConvNCFBPRLoss(nn.Module):
""" ConvNCFBPRLoss, based on Bayesian Personalized Ranking,
Shape:
- Pos_score: (N)
- Neg_score: (N), same shape as the Pos_score
- Output: scalar.
Examples::
>>> loss = ConvNCFBPRLoss()
>>> ... | 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
... | dreaming-qin/RecBole | ConvNCFBPRLoss | false | 12,313 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
BaseFactorizationMachine | import torch
import torch.nn as nn
class BaseFactorizationMachine(nn.Module):
"""Calculate FM result over the embeddings
Args:
reduce_sum: bool, whether to sum the result, default is True.
Input:
input_x: tensor, A 3D tensor with shape:``(batch_size,field_size,embed_dim)``.
Output
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | dreaming-qin/RecBole | BaseFactorizationMachine | false | 12,314 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
AGRUCell | import torch
import torch.nn as nn
import torch.nn.functional as F
class AGRUCell(nn.Module):
' Attention based GRU (AGRU). AGRU uses the attention score to replace the update gate of GRU, and changes the\n hidden state directly.\n\n Formally:\n ..math: {h}_{t}^{\\prime}=\\left(1-a_{t}\right) * {h}_{... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | dreaming-qin/RecBole | AGRUCell | false | 12,315 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
RegLoss | import torch
import torch.nn as nn
class RegLoss(nn.Module):
""" RegLoss, L2 regularization on model parameters
"""
def __init__(self):
super(RegLoss, self).__init__()
def forward(self, parameters):
reg_loss = None
for W in parameters:
if reg_loss is None:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dreaming-qin/RecBole | RegLoss | false | 12,316 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
AttLayer | import torch
import torch.nn as nn
import torch.nn.functional as fn
class AttLayer(nn.Module):
"""Calculate the attention signal(weight) according the input tensor.
Args:
infeatures (torch.FloatTensor): A 3D input tensor with shape of[batch_size, M, embed_dim].
Returns:
torch.FloatTensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dreaming-qin/RecBole | AttLayer | false | 12,317 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
Repeat_Explore_Mechanism | import torch
import torch.nn as nn
class Repeat_Explore_Mechanism(nn.Module):
def __init__(self, device, hidden_size, seq_len, dropout_prob):
super(Repeat_Explore_Mechanism, self).__init__()
self.dropout = nn.Dropout(dropout_prob)
self.hidden_size = hidden_size
self.device = devic... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dreaming-qin/RecBole | Repeat_Explore_Mechanism | false | 12,318 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
ItemToInterestAggregation | import torch
import torch.nn as nn
class ItemToInterestAggregation(nn.Module):
def __init__(self, seq_len, hidden_size, k_interests=5):
super().__init__()
self.k_interests = k_interests
self.theta = nn.Parameter(torch.randn([hidden_size, k_interests]))
def forward(self, input_tensor)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dreaming-qin/RecBole | ItemToInterestAggregation | false | 12,319 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
AUGRUCell | import torch
import torch.nn as nn
import torch.nn.functional as F
class AUGRUCell(nn.Module):
' Effect of GRU with attentional update gate (AUGRU). AUGRU combines attention mechanism and GRU seamlessly.\n\n Formally:\n ..math: \tilde{{u}}_{t}^{\\prime}=a_{t} * {u}_{t}^{\\prime} \\\n {h}_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | dreaming-qin/RecBole | AUGRUCell | false | 12,320 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
ComplexLinear | from torch.nn import Module
import torch
from torch.nn import Linear
class ComplexLinear(Module):
def __init__(self, in_features, out_features):
super(ComplexLinear, self).__init__()
self.fc_r = Linear(in_features, out_features)
self.fc_i = Linear(in_features, out_features)
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.nn import Linear
assert_size_stride = tor... | drydenwiebe/complexPyTorch | ComplexLinear | false | 12,321 | [
"MIT"
] | 0 | cea88ba7ee5692dfa1b40f0ba609ef14160d5073 | https://github.com/drydenwiebe/complexPyTorch/tree/cea88ba7ee5692dfa1b40f0ba609ef14160d5073 |
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 ... | BunnyNoBugs/DeepPavlov | BinaryClassificationHead | false | 12,322 | [
"Apache-2.0"
] | 0 | b2213db633a669d27d6f745dd780530574ccf8b5 | https://github.com/BunnyNoBugs/DeepPavlov/tree/b2213db633a669d27d6f745dd780530574ccf8b5 |
ComplexConv2d | from torch.nn import Module
import torch
from torch.nn import Conv2d
class ComplexConv2d(Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(ComplexConv2d, self).__init__()
self.conv_r = Conv2d(in_channels, out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn import Conv2d
assert_size_stride = tor... | drydenwiebe/complexPyTorch | ComplexConv2d | false | 12,323 | [
"MIT"
] | 0 | cea88ba7ee5692dfa1b40f0ba609ef14160d5073 | https://github.com/drydenwiebe/complexPyTorch/tree/cea88ba7ee5692dfa1b40f0ba609ef14160d5073 |
Transition | import torch
import torch.nn as nn
import torch.nn.parallel
class Transition(nn.Module):
def __init__(self, in_features, out_features, act_layer=nn.GELU):
super(Transition, self).__init__()
self.act = act_layer()
self.linear = nn.Linear(in_features, out_features)
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.triton_helpers import libdevice
import torch.nn as ... | druzhkov-paul/T2T-ViT | Transition | false | 12,324 | [
"BSD-3-Clause-Clear"
] | 0 | 819c3ddc4cb6f464d4a9866d8713c7ace42ebf6c | https://github.com/druzhkov-paul/T2T-ViT/tree/819c3ddc4cb6f464d4a9866d8713c7ace42ebf6c |
_TestNetStrided | import torch
import torch.nn.functional as F
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class _TestNetStrided(torch.nn.Module):
def __init__(self):
super(_TestNetStrided, self).__init__()
self.conv1 = torch.nn.Conv2d(1, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | arjunsuresh/aimet | _TestNetStrided | false | 12,325 | [
"BSD-3-Clause"
] | 0 | f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 | https://github.com/arjunsuresh/aimet/tree/f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 |
Concat | import torch
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Concat(torch.nn.Module):
""" Concat module for a functional concat"""
def __init__(self, axis: 'int'=0):
super(Concat, self).__init__()
self.axis = axis
... | 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.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
assert_size_stride =... | arjunsuresh/aimet | Concat | false | 12,326 | [
"BSD-3-Clause"
] | 0 | f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 | https://github.com/arjunsuresh/aimet/tree/f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 |
EdgeCaseModel | import torch
from typing import Any
import torch.nn as nn
class LayerWithRidiculouslyLongNameAndDoesntDoAnything(nn.Module):
""" Model with a very long name. """
def __init__(self) ->None:
super().__init__()
self.identity = nn.Identity()
def forward(self, x: 'Any') ->Any:
return ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
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 Any
import torch.nn as nn
assert_size_stride = torch._C._dyna... | e-dorigatti/torchinfo | EdgeCaseModel | false | 12,327 | [
"MIT"
] | 0 | 9fa0e677fb7002e89afd5b1bb372fe8c1dd813d6 | https://github.com/e-dorigatti/torchinfo/tree/9fa0e677fb7002e89afd5b1bb372fe8c1dd813d6 |
ComplexConvTranspose2d | from torch.nn import Module
import torch
from torch.nn import ConvTranspose2d
class ComplexConvTranspose2d(Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, bias=True, dilation=1,
padding_mode='zeros'):
super(ComplexConvTr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.nn import ConvTranspose2d
assert_size_str... | drydenwiebe/complexPyTorch | ComplexConvTranspose2d | false | 12,328 | [
"MIT"
] | 0 | cea88ba7ee5692dfa1b40f0ba609ef14160d5073 | https://github.com/drydenwiebe/complexPyTorch/tree/cea88ba7ee5692dfa1b40f0ba609ef14160d5073 |
OuterProductLayer | import torch
import torch.nn as nn
class OuterProductLayer(nn.Module):
"""OuterProduct Layer used in PNN. This implementation is
adapted from code that the author of the paper published on https://github.com/Atomu2014/product-nets.
"""
def __init__(self, num_feature_field, embedding_size, device):
... | 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... | dreaming-qin/RecBole | OuterProductLayer | false | 12,329 | [
"MIT"
] | 0 | d6de39521484ded60c387ca604abaf86310acdbe | https://github.com/dreaming-qin/RecBole/tree/d6de39521484ded60c387ca604abaf86310acdbe |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_placeholder, output_size):
super(Net, self).__init__()
self.fc1 = nn.Linear(input_placeholder, 255)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(255, 255)
self.relu2 = nn.ReLU()
self.f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | dylan-albertazzi/Berkely_DeepRL | Net | false | 12,330 | [
"MIT"
] | 0 | 997d066df7b429f6ad365dca8105490dae8f978e | https://github.com/dylan-albertazzi/Berkely_DeepRL/tree/997d066df7b429f6ad365dca8105490dae8f978e |
MPNetAttention | from _paritybench_helpers import _mock_config
import math
import torch
from typing import List
from typing import Tuple
from torch import nn
from typing import Set
import torch.utils.checkpoint
def find_pruneable_heads_and_indices(heads: 'List[int]', n_heads: 'int',
head_size: 'int', already_pruned_heads: 'Set[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.... | Clemens123/transformers | MPNetAttention | false | 12,331 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
NoiseLayer | import torch
import torch.nn as nn
class NoiseLayer(nn.Module):
"""adds noise. noise is per pixel (constant over channels) with per-channel weight"""
def __init__(self, channels):
super().__init__()
self.weight = nn.Parameter(torch.zeros(channels))
self.noise = None
def forward(s... | 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | eitanrich/ganspace-manifold | NoiseLayer | false | 12,332 | [
"Apache-2.0"
] | 0 | 148d5d30001c43794a40bbed885601e7816f5d7d | https://github.com/eitanrich/ganspace-manifold/tree/148d5d30001c43794a40bbed885601e7816f5d7d |
KnowledgeDistillationLoss | import torch
from torch import nn
class KnowledgeDistillationLoss(nn.Module):
def __init__(self, reduction='mean', alpha=1.0):
super().__init__()
self.reduction = reduction
self.alpha = alpha
def forward(self, inputs, targets, mask=None):
inputs = inputs.narrow(1, 0, 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 torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | edoardofantolino/MLDLproject4 | KnowledgeDistillationLoss | false | 12,333 | [
"MIT"
] | 0 | fed0cfd51f5984bbf21205a43ea43dc49f4d289a | https://github.com/edoardofantolino/MLDLproject4/tree/fed0cfd51f5984bbf21205a43ea43dc49f4d289a |
SubpixelConvolutionLayer | import torch
import torch.nn as nn
class SubpixelConvolutionLayer(nn.Module):
def __init__(self, channels: 'int'=64) ->None:
"""
Args:
channels (int): Number of channels in the input image. (Default: 64)
"""
super(SubpixelConvolutionLayer, self).__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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | duylebkHCM/Anime-Face-Generator- | SubpixelConvolutionLayer | false | 12,334 | [
"MIT"
] | 0 | ffcbe22f2073971e81b1bbc61b7ef7970889f8a2 | https://github.com/duylebkHCM/Anime-Face-Generator-/tree/ffcbe22f2073971e81b1bbc61b7ef7970889f8a2 |
RecursiveNet | import torch
from typing import Any
import torch.nn as nn
class RecursiveNet(nn.Module):
""" Model that uses a layer recursively in computation. """
def __init__(self) ->None:
super().__init__()
self.conv1 = nn.Conv2d(64, 64, 3, 1, 1)
def forward(self, x: 'torch.Tensor', args1: 'Any'=Non... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | e-dorigatti/torchinfo | RecursiveNet | false | 12,335 | [
"MIT"
] | 0 | 9fa0e677fb7002e89afd5b1bb372fe8c1dd813d6 | https://github.com/e-dorigatti/torchinfo/tree/9fa0e677fb7002e89afd5b1bb372fe8c1dd813d6 |
MyLinear | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
super().__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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | eitanrich/ganspace-manifold | MyLinear | false | 12,336 | [
"Apache-2.0"
] | 0 | 148d5d30001c43794a40bbed885601e7816f5d7d | https://github.com/eitanrich/ganspace-manifold/tree/148d5d30001c43794a40bbed885601e7816f5d7d |
BCELoss2d | import torch
import torch.nn.functional as F
import torch.nn as nn
class BCELoss2d(nn.Module):
def __init__(self, weight=None, size_average=True):
"""
Imlements Binary Cross Entropy loss function.
"""
super(BCELoss2d, self).__init__()
self.bce_loss = nn.BCELoss(weight,... | 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... | ekalyashov/segmentation-unet | BCELoss2d | false | 12,337 | [
"MIT"
] | 0 | 59dc95419481b2535a52332e0be92b15c7450674 | https://github.com/ekalyashov/segmentation-unet/tree/59dc95419481b2535a52332e0be92b15c7450674 |
StyleMod | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
super().__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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | eitanrich/ganspace-manifold | StyleMod | false | 12,338 | [
"Apache-2.0"
] | 0 | 148d5d30001c43794a40bbed885601e7816f5d7d | https://github.com/eitanrich/ganspace-manifold/tree/148d5d30001c43794a40bbed885601e7816f5d7d |
ProteinResNetPooler | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class ProteinResNetPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.attention_weights = nn.Linear(config.hidden_size, 1)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | ekvall93/tape | ProteinResNetPooler | false | 12,339 | [
"BSD-3-Clause"
] | 0 | 1ca4d5a39c72f806f23a36fb7a7c7325f06096ae | https://github.com/ekvall93/tape/tree/1ca4d5a39c72f806f23a36fb7a7c7325f06096ae |
Accuracy | import torch
import torch.nn as nn
def accuracy(logits, labels, ignore_index: 'int'=-100):
with torch.no_grad():
valid_mask = labels != ignore_index
predictions = logits.float().argmax(-1)
correct = (predictions == labels) * valid_mask
return correct.sum().float() / valid_mask.sum(... | 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... | ekvall93/tape | Accuracy | false | 12,340 | [
"BSD-3-Clause"
] | 0 | 1ca4d5a39c72f806f23a36fb7a7c7325f06096ae | https://github.com/ekvall93/tape/tree/1ca4d5a39c72f806f23a36fb7a7c7325f06096ae |
DNNModel | import torch
from torch import nn
class DNNModel(nn.Module):
def __init__(self, dropout=0.2):
super(DNNModel, self).__init__()
self.fc1 = nn.Linear(4, 4)
self.relu1 = nn.ReLU()
self.dropout1 = nn.Dropout(p=dropout)
self.fc2 = nn.Linear(4, 4)
self.relu2 = nn.ReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | ehsangolshani/workload-to-metric-mapper | DNNModel | false | 12,341 | [
"Apache-2.0"
] | 0 | 4c2825696200748382247909f2f777f49bf62cf0 | https://github.com/ehsangolshani/workload-to-metric-mapper/tree/4c2825696200748382247909f2f777f49bf62cf0 |
SoftDiceLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class SoftDiceLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
"""
Imlements Dice loss function (using Sørensen–Dice coefficient).
"""
super(SoftDiceLoss, self).__init__()
def forward(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ekalyashov/segmentation-unet | SoftDiceLoss | false | 12,342 | [
"MIT"
] | 0 | 59dc95419481b2535a52332e0be92b15c7450674 | https://github.com/ekalyashov/segmentation-unet/tree/59dc95419481b2535a52332e0be92b15c7450674 |
LeNet_300_100 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class LeNet_300_100(nn.Module):
"""Simple NN with hidden layers [300, 100]
Based on https://github.com/mi-lad/snip/blob/master/train.py
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | elony314/sparse_learning | LeNet_300_100 | false | 12,343 | [
"MIT"
] | 0 | fff9ea0267016bda747f2882ef8de508ac1369e7 | https://github.com/elony314/sparse_learning/tree/fff9ea0267016bda747f2882ef8de508ac1369e7 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, device, hidden_size):
super(Attention, self).__init__()
self.device = device
self.hidden_size = hidden_size
self.concat_linear = nn.Linear(self.hidden_size * 2, self.h... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ekvall93/tape | Attention | false | 12,344 | [
"BSD-3-Clause"
] | 0 | 1ca4d5a39c72f806f23a36fb7a7c7325f06096ae | https://github.com/ekvall93/tape/tree/1ca4d5a39c72f806f23a36fb7a7c7325f06096ae |
L2Norm | import torch
import torch.nn as nn
import torch.onnx
class L2Norm(nn.Module):
"""
Scale shall be learnable according to original paper
scale: initial scale number
chan_num: L2Norm channel number (norm over all channels)
"""
def __init__(self, scale=20, chan_num=512):
super(L2... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | ephrem-git/inference | L2Norm | false | 12,345 | [
"Apache-2.0"
] | 0 | bfbda5fc419364c3f71b5b1640f6c00e7675b212 | https://github.com/ephrem-git/inference/tree/bfbda5fc419364c3f71b5b1640f6c00e7675b212 |
Get_gradient_nopadding | import torch
import torch.nn as nn
import torch.nn.functional as F
class Get_gradient_nopadding(nn.Module):
def __init__(self):
super(Get_gradient_nopadding, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
kernel_h = 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.triton_helpers import libdevice
import torch.nn as ... | eqprog/ESRGAN | Get_gradient_nopadding | false | 12,346 | [
"Apache-2.0"
] | 0 | d5eb02531cf0ce4e8df93793f3012486bac8d87a | https://github.com/eqprog/ESRGAN/tree/d5eb02531cf0ce4e8df93793f3012486bac8d87a |
MultiheadAttention | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
from torch.nn import Parameter
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more deta... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | edbltn/fairseq | MultiheadAttention | false | 12,347 | [
"BSD-3-Clause"
] | 0 | e4d25fd96f1e38190400dbbdbc77eeda71ac50a0 | https://github.com/edbltn/fairseq/tree/e4d25fd96f1e38190400dbbdbc77eeda71ac50a0 |
StackTime | import torch
import torch.onnx
class StackTime(torch.nn.Module):
__constants__ = ['factor']
def __init__(self, factor):
super().__init__()
self.factor = int(factor)
def forward(self, x, x_lens):
seq = [x]
for i in range(1, self.factor):
tmp = torch.zeros_like(... | 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.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | ephrem-git/inference | StackTime | false | 12,348 | [
"Apache-2.0"
] | 0 | bfbda5fc419364c3f71b5b1640f6c00e7675b212 | https://github.com/ephrem-git/inference/tree/bfbda5fc419364c3f71b5b1640f6c00e7675b212 |
SharpenedCosineSimilarity | import torch
import torch.nn as nn
import torch.nn.functional as F
def unfold2d(x, kernel_size: 'int', stride: 'int', padding: 'int'):
x = F.pad(x, [padding] * 4)
bs, in_c, h, w = x.size()
ks = kernel_size
strided_x = x.as_strided((bs, in_c, (h - ks) // stride + 1, (w - ks) //
stride + 1, ks, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_s... | enzokro/sharpened_cosine_similarity_torch | SharpenedCosineSimilarity | false | 12,349 | [
"MIT"
] | 0 | 150c84f5cf81721baf097abdc0d4ac772fb39fc4 | https://github.com/enzokro/sharpened_cosine_similarity_torch/tree/150c84f5cf81721baf097abdc0d4ac772fb39fc4 |
PredictionHead | import torch
import torch.nn as nn
import torch.onnx
class PredictionHead(nn.Module):
def __init__(self, in_channels, num_classes, num_anchors):
super(PredictionHead, self).__init__()
self.classification = nn.Conv2d(in_channels, num_classes *
num_anchors, kernel_size=1)
self.r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.onnx
assert_size_stride = torch._C._dynamo.gu... | ephrem-git/inference | PredictionHead | false | 12,350 | [
"Apache-2.0"
] | 0 | bfbda5fc419364c3f71b5b1640f6c00e7675b212 | https://github.com/ephrem-git/inference/tree/bfbda5fc419364c3f71b5b1640f6c00e7675b212 |
ExtClassifier | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ExtClassifier(nn.Module):
def __init__(self, hidden_size):
super(ExtClassifier, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask=None):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.cuda
import torch.distributed
assert_size_str... | eric-zhizu/OpenNMT-kpg-release | ExtClassifier | false | 12,351 | [
"MIT"
] | 0 | 9f15dea6f663425eef2157845c4c8042ad845c11 | https://github.com/eric-zhizu/OpenNMT-kpg-release/tree/9f15dea6f663425eef2157845c4c8042ad845c11 |
SharpenedCosineSimilarity_ConvImpl | import torch
import torch.nn as nn
import torch.nn.functional as F
class SharpenedCosineSimilarity_ConvImpl(nn.Module):
def __init__(self, in_channels=1, out_channels=1, kernel_size=1, stride
=1, padding=0, eps=1e-12):
super(SharpenedCosineSimilarity_ConvImpl, self).__init__()
self.in_cha... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | enzokro/sharpened_cosine_similarity_torch | SharpenedCosineSimilarity_ConvImpl | false | 12,352 | [
"MIT"
] | 0 | 150c84f5cf81721baf097abdc0d4ac772fb39fc4 | https://github.com/enzokro/sharpened_cosine_similarity_torch/tree/150c84f5cf81721baf097abdc0d4ac772fb39fc4 |
SharpenedCosineSimilarityAnnotated | import torch
import torch.nn as nn
import torch.nn.functional as F
class SharpenedCosineSimilarityAnnotated(nn.Module):
def __init__(self, in_channels=1, out_channels=1, kernel_size=1, stride
=1, padding=0, eps=1e-12):
super(SharpenedCosineSimilarityAnnotated, self).__init__()
self.in_cha... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | enzokro/sharpened_cosine_similarity_torch | SharpenedCosineSimilarityAnnotated | false | 12,353 | [
"MIT"
] | 0 | 150c84f5cf81721baf097abdc0d4ac772fb39fc4 | https://github.com/enzokro/sharpened_cosine_similarity_torch/tree/150c84f5cf81721baf097abdc0d4ac772fb39fc4 |
Divide | import torch
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Divide(torch.nn.Module):
""" Divide module for a functional divide"""
def forward(self, x, y):
"""
Forward-pass routine for divide op
"""
... | 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.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
assert_size_stride =... | arjunsuresh/aimet | Divide | false | 12,354 | [
"BSD-3-Clause"
] | 0 | f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 | https://github.com/arjunsuresh/aimet/tree/f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 |
EqualLinear | import torch
import torch.nn.functional as F
from torch import nn
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bias = nn.Parameter(torch.zeros(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | ericguizzo/stylegan2-pytorch | EqualLinear | false | 12,355 | [
"MIT"
] | 0 | d6e5cf4e30247e12d330537676f9ba63867cfaa0 | https://github.com/ericguizzo/stylegan2-pytorch/tree/d6e5cf4e30247e12d330537676f9ba63867cfaa0 |
ScaleNorm | import math
import torch
import torch.nn as nn
class ScaleNorm(nn.Module):
"""ScaleNorm"""
"""All g’s in SCALE NORM are initialized to sqrt(d)"""
def __init__(self, scale, eps=1e-05):
super(ScaleNorm, self).__init__()
self.scale = nn.Parameter(torch.tensor(math.sqrt(scale)))
self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.nn ... | eweiner/MAT_Extension | ScaleNorm | false | 12,356 | [
"MIT"
] | 0 | 505884a67f97bf54e1198077d15a48531fcac7a5 | https://github.com/eweiner/MAT_Extension/tree/505884a67f97bf54e1198077d15a48531fcac7a5 |
Generator | import math
import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""Construct a layernorm module (See citation for details)."""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dynamo.guards.a... | eweiner/MAT_Extension | Generator | false | 12,357 | [
"MIT"
] | 0 | 505884a67f97bf54e1198077d15a48531fcac7a5 | https://github.com/eweiner/MAT_Extension/tree/505884a67f97bf54e1198077d15a48531fcac7a5 |
SAB | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ernoult/set_transformer | SAB | false | 12,358 | [
"MIT"
] | 0 | 4b380106e1f43b7eb6315624c57d4d1d38737b78 | https://github.com/ernoult/set_transformer/tree/4b380106e1f43b7eb6315624c57d4d1d38737b78 |
MAB | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ernoult/set_transformer | MAB | false | 12,359 | [
"MIT"
] | 0 | 4b380106e1f43b7eb6315624c57d4d1d38737b78 | https://github.com/ernoult/set_transformer/tree/4b380106e1f43b7eb6315624c57d4d1d38737b78 |
EdgeFeaturesLayer | import torch
import torch.nn as nn
class EdgeFeaturesLayer(nn.Module):
def __init__(self, d_model, d_edge, h, dropout):
super(EdgeFeaturesLayer, self).__init__()
assert d_model % h == 0
d_model // h
self.linear = nn.Linear(d_edge, 1, bias=False)
with torch.no_grad():
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | eweiner/MAT_Extension | EdgeFeaturesLayer | false | 12,360 | [
"MIT"
] | 0 | 505884a67f97bf54e1198077d15a48531fcac7a5 | https://github.com/eweiner/MAT_Extension/tree/505884a67f97bf54e1198077d15a48531fcac7a5 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.