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 |
|---|---|---|---|---|---|---|---|---|---|---|
MaskedConv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch
a... | B0BBB/seq2seq.pytorch | MaskedConv1d | false | 114 | [
"MIT"
] | 0 | 54bb0e9f3e5c7db7f257841ed652e8ff447b8ee4 | https://github.com/B0BBB/seq2seq.pytorch/tree/54bb0e9f3e5c7db7f257841ed652e8ff447b8ee4 |
EqualizedLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from torch import nn
import torch.utils.data
impo... | Hadryan/nn | EqualizedLinear | false | 9,376 | [
"MIT"
] | 0 | b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d | https://github.com/Hadryan/nn/tree/b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d |
ReturnAsLoss | import torch
import torch.nn as nn
class ReturnAsLoss(nn.Module):
def __init__(self):
super(ReturnAsLoss, self).__init__()
def forward(self, output, y):
"""negative logarithm return"""
return -torch.sum(torch.log(torch.sum(output * (y + 1), dim=1)))
def get_inputs():
return [to... | 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... | yanxurui/portfolio | ReturnAsLoss | false | 4,598 | [
"MIT"
] | 0 | 032cf47ccac1c5815fd4827bf0d5f3cf43cec990 | https://github.com/yanxurui/portfolio/tree/032cf47ccac1c5815fd4827bf0d5f3cf43cec990 |
MatrixVectorScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
class MatrixVectorScaledDotProductAttention(nn.Module):
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
self.softmax = nn.Softmax(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 math as tl_math
import torch.nn as nn
... | michiyasunaga/GreaseLM | MatrixVectorScaledDotProductAttention | false | 16,056 | [
"MIT"
] | 76 | 596aa5047841e3e97730f621a2e4576772733df2 | https://github.com/michiyasunaga/GreaseLM/tree/596aa5047841e3e97730f621a2e4576772733df2 |
PointNetfeat | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class PointNetfeat(nn.Module):
"""
Simple PointNet that extracts point-wise feature by concatenating local and global features.
Uses group norm instead of batch norm.
"""
def __init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | davrempe/caspr | PointNetfeat | false | 15,148 | [
"MIT"
] | 65 | a02edb4be11f5ccfe563b2a7869ee8e731e0f8ff | https://github.com/davrempe/caspr/tree/a02edb4be11f5ccfe563b2a7869ee8e731e0f8ff |
BridgeFeatLoss | import torch
import torch.nn as nn
import torch.utils.data
class BridgeFeatLoss(nn.Module):
"""Bridge loss on feature space.
"""
def __init__(self):
super(BridgeFeatLoss, self).__init__()
def forward(self, f_s, f_t, f_mixed, lam):
dist_mixed2s = ((f_mixed - f_s) ** 2).sum(1, keepdim=... | 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... | XianyuanLiu/Transfer-Learning-Library | BridgeFeatLoss | false | 10,139 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
SimpleSSM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | Junyoungpark/2021-lg-AI-camp | SimpleSSM | false | 17,521 | [
"MIT"
] | 4 | 3c0e5dd689e8e3dd61cc80243ad90cab951c06de | https://github.com/Junyoungpark/2021-lg-AI-camp/tree/3c0e5dd689e8e3dd61cc80243ad90cab951c06de |
HME | import numpy
import torch
class HME(torch.nn.Module):
def __init__(self, in_features, out_features, depth, projection='linear'):
super(HME, self).__init__()
self.proj = projection
self.depth = depth
self.in_features = in_features
self.out_features = out_features
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
import numpy
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | alper111/hmog | HME | false | 6,182 | [
"MIT"
] | 1 | 556da11600c97bcb075a0f19ffc284120d9789d2 | https://github.com/alper111/hmog/tree/556da11600c97bcb075a0f19ffc284120d9789d2 |
GCN | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in_features = in_feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | CaiYufan-sjtu/GCNOIE | GCN | false | 2,122 | [
"MIT"
] | 0 | c84afca5b66d75c7108b2719241e2907700b4111 | https://github.com/CaiYufan-sjtu/GCNOIE/tree/c84afca5b66d75c7108b2719241e2907700b4111 |
NeuralNetNonDifferentiableOutput | import torch
import torch.nn
import torch.onnx
class NeuralNetNonDifferentiableOutput(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetNonDifferentiableOutput, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size)
self.relu = torch.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | thilow/onnxruntime | NeuralNetNonDifferentiableOutput | false | 11,017 | [
"MIT"
] | 0 | 1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 | https://github.com/thilow/onnxruntime/tree/1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 |
PA | import torch
import torch.nn as nn
class PA(nn.Module):
def __init__(self, dim):
super().__init__()
self.pa_conv = nn.Conv3d(dim, dim, kernel_size=3, padding=1, groups=dim
)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
return x * self.sigmoid(self.pa_conv(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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | SLKaMiHi/ResT-UNet-unsupervised-medical-image-registration-network-based-on-Transformer-and-CNN | PA | false | 5,790 | [
"MIT"
] | 1 | 728624f978f345a1e713046a7dde12d6f84fd3dd | https://github.com/SLKaMiHi/ResT-UNet-unsupervised-medical-image-registration-network-based-on-Transformer-and-CNN/tree/728624f978f345a1e713046a7dde12d6f84fd3dd |
OneLayerFCBodyWithAction | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Fieps1/p3-tennis | OneLayerFCBodyWithAction | false | 494 | [
"MIT"
] | 0 | 29f3dab5810d7cd7f84120416a615956d266c256 | https://github.com/Fieps1/p3-tennis/tree/29f3dab5810d7cd7f84120416a615956d266c256 |
BinaryFocalLossWithLogits | import torch
import torch.nn as nn
def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='none', eps: 'float'=1e-08) ->torch.Tensor:
"""Function that computes Binary Focal loss.
.. math::
\\text{FL}(p_t) = -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | JoanFM/kornia | BinaryFocalLossWithLogits | false | 11,557 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
CriticNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | TheCamusean/mushroom-rl | CriticNetwork | false | 2,893 | [
"MIT"
] | 0 | 48585f883e546ea57224b8d446ecb9b8ba90cf73 | https://github.com/TheCamusean/mushroom-rl/tree/48585f883e546ea57224b8d446ecb9b8ba90cf73 |
SimpleAtariNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | aaronmckinstry706/pytorch-practice | SimpleAtariNet | false | 12,064 | [
"MIT"
] | 0 | d3fd28733ea6de6a2e522ec52ff3e748df21b85a | https://github.com/aaronmckinstry706/pytorch-practice/tree/d3fd28733ea6de6a2e522ec52ff3e748df21b85a |
co_peak_loss | import torch
from torch import nn
class co_peak_loss(nn.Module):
def __init__(self):
super(co_peak_loss, self).__init__()
def forward(self, co_peak_value):
a = -1 * co_peak_value
b = torch.max(torch.zeros_like(co_peak_value), a)
t = b + torch.log(torch.exp(-b) + torch.exp(a -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | cj4L/DeepCO3-python | co_peak_loss | false | 6,445 | [
"MIT"
] | 1 | fa28ed7b43a3a236d0cc7bf31ce9fd68c01b5888 | https://github.com/cj4L/DeepCO3-python/tree/fa28ed7b43a3a236d0cc7bf31ce9fd68c01b5888 |
Block | import torch
import torch.nn as nn
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
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.... | Pang-Yatian/Point-MAE | Block | false | 8,690 | [
"MIT"
] | 42 | 61727f76e9d0c28babf422505073bd43c2f517bc | https://github.com/Pang-Yatian/Point-MAE/tree/61727f76e9d0c28babf422505073bd43c2f517bc |
BCEIoULoss | import torch
from typing import Callable
from functools import partial
from torch import nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
def get_activation_fn(activation: 'str'=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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from typing... | litvinich/catalyst | BCEIoULoss | false | 3,925 | [
"Apache-2.0"
] | 0 | b039bb69597d3fe48eed8c34342fa9be968b776e | https://github.com/litvinich/catalyst/tree/b039bb69597d3fe48eed8c34342fa9be968b776e |
MultiHeadSelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Zed-Wu/ManiSkill-Learn | MultiHeadSelfAttention | false | 3,106 | [
"Apache-2.0"
] | 0 | 8056fe327752cd0863f8730672fe62bd85a0ec12 | https://github.com/Zed-Wu/ManiSkill-Learn/tree/8056fe327752cd0863f8730672fe62bd85a0ec12 |
TransposedConv1d | import torch
import torch.nn as nn
import torch.nn.functional as F
class TransposedConv1d(nn.Module):
def __init__(self, in_channels, output_channels, kernel_shape=3, stride
=2, padding=1, output_padding=1, activation_fn=F.relu,
use_batch_norm=False, use_bias=True):
super(TransposedConv1d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Cogito2012/OpenTAL | TransposedConv1d | false | 7,896 | [
"BSD-3-Clause"
] | 16 | a7ab938a52b3fb82163eb1ba5403888359eb7e6a | https://github.com/Cogito2012/OpenTAL/tree/a7ab938a52b3fb82163eb1ba5403888359eb7e6a |
PLCCLoss | import torch
import torch.nn as nn
import torch.utils
class PLCCLoss(nn.Module):
def __init__(self):
super(PLCCLoss, self).__init__()
def forward(self, input, target):
input0 = input - torch.mean(input)
target0 = target - torch.mean(target)
self.loss = torch.sum(input0 * targ... | 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... | adynmiles/DARTS-FQA | PLCCLoss | false | 6,074 | [
"MIT"
] | 1 | a088a0efeb1160d0cdbf2b2a3e30f132c16eb53f | https://github.com/adynmiles/DARTS-FQA/tree/a088a0efeb1160d0cdbf2b2a3e30f132c16eb53f |
UpsampleBlock | import torch
import torch.nn.functional as F
import torch.nn as nn
class UpsampleBlock(nn.Module):
"""
Defines upsampling block performed using bilinear
or nearest-neigbor interpolation followed by 1-by-1 convolution
(the latter can be used to reduce a number of feature channels)
Args:
nd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | miguel-fc/atomai | UpsampleBlock | false | 10,665 | [
"MIT"
] | 0 | f51699ef5e1bfc577781977d38f7414b1b51449d | https://github.com/miguel-fc/atomai/tree/f51699ef5e1bfc577781977d38f7414b1b51449d |
MaxPool1D | import torch
class MaxPool1D(torch.nn.Module):
def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False):
super().__init__()
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.ceil_mode = ceil_mode
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 import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | PogChamper/torch2trt | MaxPool1D | false | 14,193 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
ECA | import torch
from torch import nn
class FastGlobalAvgPool2d:
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:
return 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | cooked-sashimi/Yet-Another-YOLOv4-Pytorch | ECA | false | 15,087 | [
"MIT"
] | 133 | c884ef8849987a75b0e17eba1b739c22d3782e90 | https://github.com/cooked-sashimi/Yet-Another-YOLOv4-Pytorch/tree/c884ef8849987a75b0e17eba1b739c22d3782e90 |
VAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | anandijain/audio | VAE | false | 6,203 | [
"MIT"
] | 1 | 1990de57ebc760cf6c5cc7132119b389cfd2dbfb | https://github.com/anandijain/audio/tree/1990de57ebc760cf6c5cc7132119b389cfd2dbfb |
TripletLoss | import torch
import torch.utils.data
import torch
import torch.nn as nn
class TripletLoss(nn.Module):
def __init__(self, margin=1.0):
super(TripletLoss, self).__init__()
self.margin = margin
def calc_euclidean(self, x1, x2):
return (x1 - x2).pow(2).sum(1)
def forward(self, ancho... | 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.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C.... | ketan-lambat/contrastive-unpaired-translation | TripletLoss | false | 3,825 | [
"BSD-3-Clause"
] | 0 | ea71b3a9603a51b97f1fa8426d5a1beae9260a0d | https://github.com/ketan-lambat/contrastive-unpaired-translation/tree/ea71b3a9603a51b97f1fa8426d5a1beae9260a0d |
GLU | import torch
import torch.nn as nn
import torch.nn.functional
class GLU(nn.Module):
def __init__(self, input_size, gating_size, output_size):
super().__init__()
self.gate = nn.Linear(gating_size, input_size)
self.lin = nn.Linear(input_size, output_size)
def forward(self, x, gating):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._... | MichalOp/StarTrain | GLU | false | 17,720 | [
"MIT"
] | 7 | e8dddf879f103e18239ad37b373c9b51fbbe093b | https://github.com/MichalOp/StarTrain/tree/e8dddf879f103e18239ad37b373c9b51fbbe093b |
MoEHead | import math
import torch
from torch.nn import functional as F
from torch.autograd import Variable
from torch import nn
def softmax(x):
if x.dim() == 3:
return F.softmax(x.transpose(0, 2)).transpose(0, 2)
return F.softmax(x)
def gumbel_softmax(input, beta=0.5, tau=1.0):
noise = input.data.new(*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.... | cclauss/nonauto-nmt | MoEHead | false | 15,014 | [
"BSD-3-Clause"
] | 262 | efcbe4f2329b140ac3ce06abb6409457cebc8e49 | https://github.com/cclauss/nonauto-nmt/tree/efcbe4f2329b140ac3ce06abb6409457cebc8e49 |
ContrastiveLoss | import torch
import torch.utils.data
import torch.nn.functional as F
import torch.nn.parallel
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=2.0):
super(Contra... | 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... | MinesNicaicai/large-scale-pointcloud-matching | ContrastiveLoss | false | 5,598 | [
"MIT"
] | 1 | cfe140f2be1110ed75b6edd27538021e513a31c9 | https://github.com/MinesNicaicai/large-scale-pointcloud-matching/tree/cfe140f2be1110ed75b6edd27538021e513a31c9 |
GlobalWeightedAvgPool2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | huangjiadidi/dfdc_deepfake_challenge | GlobalWeightedAvgPool2d | false | 15,549 | [
"MIT"
] | 499 | 1f78fe93a5a445ced386e43b3b0378ee567eaa77 | https://github.com/huangjiadidi/dfdc_deepfake_challenge/tree/1f78fe93a5a445ced386e43b3b0378ee567eaa77 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Emily0219/distiller | Actor | false | 5,134 | [
"Apache-2.0"
] | 1 | 445ed35b671fb54586acc280b53d951f18bf97ae | https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae |
SoftTargetCrossEntropy | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed
class SoftTargetCrossEntropy(nn.Module):
def forward(self, x, target):
loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1)
return loss.mean()
def get_inputs():
return [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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ccjlovewsy/relabel_imagenet | SoftTargetCrossEntropy | false | 15,005 | [
"Apache-2.0"
] | 344 | 6cd84dffe4ce8005395970b2938b3196d0958351 | https://github.com/ccjlovewsy/relabel_imagenet/tree/6cd84dffe4ce8005395970b2938b3196d0958351 |
DotSelector | from _paritybench_helpers import _mock_config
import torch
import torch as th
from torch.distributions import Categorical
import torch.nn as nn
import torch.nn.functional as F
class DotSelector(nn.Module):
def __init__(self, input_shape, args):
super(DotSelector, self).__init__()
self.args = args... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from torch... | OkYongChoi/smac | DotSelector | false | 18,386 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 |
GAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | g6ling/Pytorch-Cartpole | GAE | false | 15,392 | [
"MIT"
] | 116 | ecb7b622cfefe825ac95388cceb6752413d90a2a | https://github.com/g6ling/Pytorch-Cartpole/tree/ecb7b622cfefe825ac95388cceb6752413d90a2a |
LossAttentionLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class LossAttentionLayer(nn.Module):
def __init__(self):
super(LossAttentionLayer, self).__init__()
def forward(self, features, W_1, b_1):
out_c = F.linear(features, W_1, b_1)
out = out_c - 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.... | AnetaKaczynska/ProtoPNet | LossAttentionLayer | false | 1,952 | [
"MIT"
] | 0 | 7de2aa57833586ccfd8e63dc835c8cc9db727a2f | https://github.com/AnetaKaczynska/ProtoPNet/tree/7de2aa57833586ccfd8e63dc835c8cc9db727a2f |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | dongminlee94/Samsung-DRL-Code | Critic | false | 15,207 | [
"MIT"
] | 116 | c96f8739a09cfd708c265954ee8ecf0ea3b67395 | https://github.com/dongminlee94/Samsung-DRL-Code/tree/c96f8739a09cfd708c265954ee8ecf0ea3b67395 |
Bias | import torch
import torch.nn as nn
class Bias(nn.Module):
def __init__(self, size):
super().__init__()
self.bias = nn.Parameter(torch.Tensor(size))
self.reset_parameters()
def reset_parameters(self):
nn.init.zeros_(self.bias)
def forward(self, x):
return x + 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | daviddavini/cs-260-project | Bias | false | 3,390 | [
"MIT"
] | 0 | 9e1067f8ff85c8c573262589bbe52740ef11275d | https://github.com/daviddavini/cs-260-project/tree/9e1067f8ff85c8c573262589bbe52740ef11275d |
L2Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from itertools import product as product
import torch.nn.... | AnupKumarGupta/syncnet_python | L2Norm | false | 11,249 | [
"MIT"
] | 0 | 932b4621cf6aa090baac7c7de22d0649bde9fbbd | https://github.com/AnupKumarGupta/syncnet_python/tree/932b4621cf6aa090baac7c7de22d0649bde9fbbd |
FociDetector | import torch
import torch.nn as nn
import torch.utils.data
class FociDetector(nn.Module):
def __init__(self, input_channels=3, input_size=17, ksize=5,
hidden_channels=10):
super(FociDetector, self).__init__()
self.conv1 = nn.Conv2d(input_channels, hidden_channels, ksize,
strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | bharath272/centrosome-analysis | FociDetector | false | 6,340 | [
"MIT"
] | 1 | 6ae3744be464812b3767909420d7b78cea9da670 | https://github.com/bharath272/centrosome-analysis/tree/6ae3744be464812b3767909420d7b78cea9da670 |
Dunet_2levels | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | MuhammadIbrahim0/dvae-refiner | Dunet_2levels | false | 9,369 | [
"MIT"
] | 0 | 034241ce6a5aeb19e9f8952ee996b56412a1f95a | https://github.com/MuhammadIbrahim0/dvae-refiner/tree/034241ce6a5aeb19e9f8952ee996b56412a1f95a |
HighwayLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ROCmSoftwarePlatform/translate | HighwayLayer | false | 968 | [
"BSD-3-Clause"
] | 0 | 32a6380d914ebe1a6c38c4992aac9600ed3d9810 | https://github.com/ROCmSoftwarePlatform/translate/tree/32a6380d914ebe1a6c38c4992aac9600ed3d9810 |
WRNBottleneck | import torch
import torch.nn as nn
import torch.utils.data
def wrn_conv1x1(in_channels, out_channels, stride, activate):
"""
1x1 version of the WRN specific convolution block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of outpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | earhian/imgclsmob | WRNBottleneck | false | 6,635 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
RPN_Up | import torch
import torch.nn as nn
import torch.nn.functional as F
class RPN_Up(nn.Module):
"""
For SiamRPN
"""
def __init__(self, anchor_nums=5, inchannels=256, outchannels=256,
cls_type='thicker'):
super(RPN_Up, self).__init__()
self.anchor_nums = anchor_nums
self.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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | FMsunyh/SiamDW | RPN_Up | false | 9,286 | [
"MIT"
] | 0 | ef7a97ee6bdf732edbb7dc2943daf15b92535019 | https://github.com/FMsunyh/SiamDW/tree/ef7a97ee6bdf732edbb7dc2943daf15b92535019 |
DeepHeadModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from ma... | fuankarion/FaceDetection-DSFD | DeepHeadModule | false | 12,403 | [
"Apache-2.0"
] | 0 | f1e464ec5c9d95c2fe73edf44e4d414a464839b1 | https://github.com/fuankarion/FaceDetection-DSFD/tree/f1e464ec5c9d95c2fe73edf44e4d414a464839b1 |
TensorClampOptionMaxMin | import torch
class TensorClampOptionMaxMin(torch.nn.Module):
def forward(self, x):
return x.clamp(min=-0.1, max=0.1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | NVIDIA-AI-IOT-private/torch2trt | TensorClampOptionMaxMin | false | 10,521 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
SEBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | DingXiaoH/GSM-SGD | SEBlock | false | 7,960 | [
"MIT"
] | 40 | ffc605651c4c5115dfb8659ebe48ccf71d3955a0 | https://github.com/DingXiaoH/GSM-SGD/tree/ffc605651c4c5115dfb8659ebe48ccf71d3955a0 |
PointerSwitch | import torch
import torch.nn as nn
class Linear(nn.Linear):
"""
Apply linear projection to the last dimention of a tensor.
"""
def forward(self, x):
size = x.size()
return super().forward(x.contiguous().view(-1, size[-1])).view(*
size[:-1], -1)
class ConcatAndProject(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing | PointerSwitch | false | 9,665 | [
"BSD-3-Clause"
] | 0 | 640858024df444006dfae106a28fdb58f36f687e | https://github.com/aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing/tree/640858024df444006dfae106a28fdb58f36f687e |
Net | import torch
import torch.nn.functional as F
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = 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
from torch._inductor.runtime.... | generall/Torchlite | Net | false | 6,752 | [
"MIT"
] | 1 | 2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a | https://github.com/generall/Torchlite/tree/2eb3e2a20b7619bd58b0b0fca120e2aefca0e79a |
CustomInverse | import torch
class CustomTorchOp(torch.autograd.Function):
@staticmethod
def symbolic(g, input):
return g.op('torchcustom::Add10', input)
@staticmethod
def forward(ctx, x):
return x + 10
class CustomInverse(torch.nn.Module):
def forward(self, x, y):
ress = CustomTorchO... | 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... | shaahji/onnxruntime-extensions | CustomInverse | false | 13,063 | [
"MIT"
] | 0 | c30df08aee69db761b97185be9f87160a4efa6bc | https://github.com/shaahji/onnxruntime-extensions/tree/c30df08aee69db761b97185be9f87160a4efa6bc |
MyNetwork | from torch.nn import Module
import random
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data import TensorDataset
class MyNetwork(Module):
def __init__(self, size_input, size_hidden, size_output):
"""Create simple network"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Thijsvanede/torch-train | MyNetwork | false | 2,909 | [
"MIT"
] | 0 | e10c64b1d61f9cdfb84b2645a196be4379851a1a | https://github.com/Thijsvanede/torch-train/tree/e10c64b1d61f9cdfb84b2645a196be4379851a1a |
DRS | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | manideep1108/DRS | DRS | false | 15,991 | [
"MIT"
] | 62 | 0858c3ffea310e9d504b7c2b06db5f281273df56 | https://github.com/manideep1108/DRS/tree/0858c3ffea310e9d504b7c2b06db5f281273df56 |
AmdimNCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bartolkaruza/pytorch-lightning-bolts | AmdimNCELoss | false | 9,992 | [
"Apache-2.0"
] | 0 | 2e903c333c37ea83394c7da2ce826de1b82fb356 | https://github.com/bartolkaruza/pytorch-lightning-bolts/tree/2e903c333c37ea83394c7da2ce826de1b82fb356 |
Hsigmoid | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
def get_inputs():
re... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | IgorDavidyuk/pytorch-mobilenet-v3 | Hsigmoid | false | 2,359 | [
"Apache-2.0"
] | 0 | 48678f80d9390b530cb97966db492cf01d1c4a43 | https://github.com/IgorDavidyuk/pytorch-mobilenet-v3/tree/48678f80d9390b530cb97966db492cf01d1c4a43 |
SimpleAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RaleLee/conv-emotion | SimpleAttention | false | 11,809 | [
"MIT"
] | 0 | 1b07223cbdfd52eb31e913e982d18ff1ed3daf08 | https://github.com/RaleLee/conv-emotion/tree/1b07223cbdfd52eb31e913e982d18ff1ed3daf08 |
MultiLabelSoftBinaryCrossEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ClementPla/Retinal-Lesions-Segmentation | MultiLabelSoftBinaryCrossEntropy | false | 5,014 | [
"MIT"
] | 1 | 20fa4ac8eae24814470095bb6e7f08d6751c4e11 | https://github.com/ClementPla/Retinal-Lesions-Segmentation/tree/20fa4ac8eae24814470095bb6e7f08d6751c4e11 |
MultiHeadAttention | import torch
import torch.nn as nn
def scaled_dot_product_attention(q, k, v, mask=None):
"""Calculate the attention weights.
q, k, v must have matching leading dimensions.
k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.
The mask has different shapes depending on its type(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.... | ULTR-Community/ULTRA_Pytorch | MultiHeadAttention | false | 14,535 | [
"Apache-2.0"
] | 46 | ec4fe329e4239b588a940cb4bcdd6a321aade679 | https://github.com/ULTR-Community/ULTRA_Pytorch/tree/ec4fe329e4239b588a940cb4bcdd6a321aade679 |
AE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | peterfeifanchen/scGNN | AE | false | 16,230 | [
"MIT"
] | 60 | 4ef9013ad0f44f9f51708e9bb60e5138f5706593 | https://github.com/peterfeifanchen/scGNN/tree/4ef9013ad0f44f9f51708e9bb60e5138f5706593 |
Joiner | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Andrewzh112/experiments | Joiner | false | 47 | [
"MIT"
] | 0 | a35fd9e6157cd9a746f82229c2487539f668716a | https://github.com/Andrewzh112/experiments/tree/a35fd9e6157cd9a746f82229c2487539f668716a |
SoftDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | Project-SwaG/igvc-software | SoftDiceLoss | false | 14,243 | [
"MIT"
] | 100 | cfe5ad5ae06199030544560af7e4ebf732cd3004 | https://github.com/Project-SwaG/igvc-software/tree/cfe5ad5ae06199030544560af7e4ebf732cd3004 |
torch_fakeint8_to_float | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | ozendelait/pytorch-semseg | torch_fakeint8_to_float | false | 7,434 | [
"MIT"
] | 1 | 200491febd653bd26befcd5b3d52c614aa832b7e | https://github.com/ozendelait/pytorch-semseg/tree/200491febd653bd26befcd5b3d52c614aa832b7e |
LocationLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | FadyKhalaf/tacotron2 | LocationLayer | false | 463 | [
"BSD-3-Clause"
] | 0 | d9bf28a6d286aab42bce46df9f26a9a3d7c2f01f | https://github.com/FadyKhalaf/tacotron2/tree/d9bf28a6d286aab42bce46df9f26a9a3d7c2f01f |
Confucius | import torch
import torch.nn as nn
class Confucius(nn.Module):
def __init__(self, output_dim, expose_dim, hidden):
super(Confucius, self).__init__()
self.output_fc = nn.Linear(output_dim, hidden)
self.fc_expose = nn.Linear(expose_dim, hidden)
self.fc_final = nn.Linear(hidden, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Fuchai/FixMatch-pytorch | Confucius | false | 11,434 | [
"MIT"
] | 0 | 105f40678414182d194945b77d24d658b1e84850 | https://github.com/Fuchai/FixMatch-pytorch/tree/105f40678414182d194945b77d24d658b1e84850 |
TripletMarginCosineLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | monkeyhjy/aspect_summarization | TripletMarginCosineLoss | false | 10,572 | [
"MIT"
] | 0 | 3018815cd0aeccb752e9f51a4d49453c4f441650 | https://github.com/monkeyhjy/aspect_summarization/tree/3018815cd0aeccb752e9f51a4d49453c4f441650 |
ResARModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.utils.spectral_norm import spectral_norm
fro... | mansoorcheema/segan_pytorch | ResARModule | false | 10,699 | [
"MIT"
] | 0 | 8f3b401e42cadfd1f8ad57a8ba0e89c16cc7ee65 | https://github.com/mansoorcheema/segan_pytorch/tree/8f3b401e42cadfd1f8ad57a8ba0e89c16cc7ee65 |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
"""Positionwise feed forward
:param int idim: input dimenstion
:param int hidden_units: number of hidden units
:param float dropout_rate: dropout rate
"""
def __init__(self, idim, hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | pkufool/DaVinci-Speech | PositionwiseFeedForward | false | 7,475 | [
"MIT"
] | 1 | 98602363168476356d492852093adbe65c65ac95 | https://github.com/pkufool/DaVinci-Speech/tree/98602363168476356d492852093adbe65c65ac95 |
Get_gradient_nopadding | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | JoeyBallentine/ESRGAN | Get_gradient_nopadding | false | 13,905 | [
"Apache-2.0"
] | 95 | 9000b43e3acf8709626f45951bb91ace1d983359 | https://github.com/JoeyBallentine/ESRGAN/tree/9000b43e3acf8709626f45951bb91ace1d983359 |
encoder_block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ninamiolane/quicksilver | encoder_block | false | 16,188 | [
"Apache-2.0"
] | 126 | 1baf251360dadea0afa3daaa09942d9d2d7c71fb | https://github.com/ninamiolane/quicksilver/tree/1baf251360dadea0afa3daaa09942d9d2d7c71fb |
IndepAnisotropicGaussianUVLoss | import math
import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class IndepAnisotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of independent residuals with anisotropic covariances:
$Sigma_i = sigma_i^2 I + r_i r_i^T$
The loss (negative log likelihood) is ... | 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 math... | TinBacon/FastAutoAugmentation | IndepAnisotropicGaussianUVLoss | false | 5,892 | [
"Apache-2.0"
] | 1 | 011e4e348fd9a937a29df11695dc71410f555d0a | https://github.com/TinBacon/FastAutoAugmentation/tree/011e4e348fd9a937a29df11695dc71410f555d0a |
EQConv2D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AjaybirRandhawa/Face-Generator | EQConv2D | false | 18,442 | [
"Apache-2.0"
] | 2 | 9cac0822b6e6337c3599e949154ce44eeae5746b | https://github.com/AjaybirRandhawa/Face-Generator/tree/9cac0822b6e6337c3599e949154ce44eeae5746b |
ModelRegressionGex2Adt | import torch
import torch.utils.data
import torch.nn.functional as F
import torch.nn as nn
class ModelRegressionGex2Adt(nn.Module):
def __init__(self, dim_mod1, dim_mod2):
super(ModelRegressionGex2Adt, self).__init__()
self.input_ = nn.Linear(dim_mod1, 512)
self.dropout1 = nn.Dropout(p=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 torch.utils.... | Permoment-95/neurips2021_multimodal_topmethods | ModelRegressionGex2Adt | false | 9,479 | [
"MIT"
] | 0 | 017bc23b366a80ba9b1c2a47ea6c44124f77a7ca | https://github.com/Permoment-95/neurips2021_multimodal_topmethods/tree/017bc23b366a80ba9b1c2a47ea6c44124f77a7ca |
GaussianMixtureReconstructionLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import nump... | v-a-s-a/diffvg | GaussianMixtureReconstructionLoss | false | 4,479 | [
"Apache-2.0"
] | 0 | 3685f3d47a5a4e5c76c68643ebf383f809ba59ed | https://github.com/v-a-s-a/diffvg/tree/3685f3d47a5a4e5c76c68643ebf383f809ba59ed |
GroupNorm32 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | GastonMazzei/escher-project-website | GroupNorm32 | false | 17,293 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
Network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | PuzeLiu/mushroom-rl | Network | false | 14,251 | [
"MIT"
] | 344 | 99942b425e66b4ddcc26009d7105dde23841e95d | https://github.com/PuzeLiu/mushroom-rl/tree/99942b425e66b4ddcc26009d7105dde23841e95d |
ThreeLayerSemSegNetWideView | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | benkoger/kasanka | ThreeLayerSemSegNetWideView | false | 12,161 | [
"Apache-2.0"
] | 0 | d5b1d32b7abf54845af0832da577137397089001 | https://github.com/benkoger/kasanka/tree/d5b1d32b7abf54845af0832da577137397089001 |
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 |
GCN | import torch
import torch.nn as nn
import torch.nn.functional as F
class GCN(nn.Module):
def __init__(self, dim_nd, dim_ft, dim_hd, dim_ot, drop_rate=0.5):
super(GCN, self).__init__()
self.lin1 = nn.Linear(dim_ft, dim_hd)
self.lin2 = nn.Linear(dim_hd, dim_ot)
self.act1 = F.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
import torch.nn as nn
import ... | lanseyege/Graph | GCN | false | 12,694 | [
"MIT"
] | 0 | ec94502ea59d2b68de095d8160f37aa22d26f8cb | https://github.com/lanseyege/Graph/tree/ec94502ea59d2b68de095d8160f37aa22d26f8cb |
AdMSoftmaxLoss | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gcambara/s3prl | AdMSoftmaxLoss | false | 15,484 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
VoxelFeatureExtractor | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | eraofelix/PV-RCNN | VoxelFeatureExtractor | false | 6,647 | [
"MIT"
] | 1 | 6361ec99cc1c92120263ef56b2c2b003c2cd7264 | https://github.com/eraofelix/PV-RCNN/tree/6361ec99cc1c92120263ef56b2c2b003c2cd7264 |
AttDec | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.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 torch.nn as ... | IacoSimoncini/tfvaegan | AttDec | false | 13,204 | [
"MIT"
] | 0 | 157b526d65d0b0d5412f4be6fed02fc7d6325827 | https://github.com/IacoSimoncini/tfvaegan/tree/157b526d65d0b0d5412f4be6fed02fc7d6325827 |
GroupLinear | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class GroupLinear(nn.Module):
"""
Group Linear operator
"""
def __init__(self, in_planes, out_channels, groups=1, bias=True):
super(GroupLinear, self).__init__()
assert in_planes % groups == 0
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_si... | dumpmemory/TokenLabeling | GroupLinear | false | 15,256 | [
"Apache-2.0"
] | 367 | 9dbfd59aedecfe83f6f3253db4e99b82359d48ac | https://github.com/dumpmemory/TokenLabeling/tree/9dbfd59aedecfe83f6f3253db4e99b82359d48ac |
L2Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from functools import *
assert_size_stride = torch._C._dynamo.guards... | JaviBite/TFG | L2Loss | false | 2,399 | [
"MIT"
] | 0 | e406580697132f53b63a7c983daaa098af45b52c | https://github.com/JaviBite/TFG/tree/e406580697132f53b63a7c983daaa098af45b52c |
FiLMNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | bblinn2017/IM-NET-pytorch | FiLMNetwork | false | 3,184 | [
"MIT"
] | 0 | 82ff646aaf2f93ae1560debb40fe05f1420ff655 | https://github.com/bblinn2017/IM-NET-pytorch/tree/82ff646aaf2f93ae1560debb40fe05f1420ff655 |
RelativeMSE | import torch
class RelativeMSE(torch.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__()
self.eps = 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Mephisto405/WCMC-Public | RelativeMSE | false | 8,540 | [
"BSD-2-Clause"
] | 19 | bd54f218d5239db84f404fbe1b465f9497bcf9e4 | https://github.com/Mephisto405/WCMC-Public/tree/bd54f218d5239db84f404fbe1b465f9497bcf9e4 |
FixupResidualChain | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | IlyaBizyaev/ttools | FixupResidualChain | false | 8,323 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
Normalization | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Inkln/StyleTransferWithCatalyst | Normalization | false | 8,298 | [
"Apache-2.0"
] | 11 | c3181ecdfd32160907efc2d9d917a55925c25c11 | https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11 |
MixedLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def dice_loss(input, target):
input = torch.sigmoid(input)
smooth = 1.0
iflat = input.view(-1)
tflat = target.view(-1)
intersection = (iflat * tflat).sum()
return (2.0 * intersection + smooth) / (iflat.sum() + tflat.sum() + smo... | 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... | xkp793003821/kaggle-tgs-salt | MixedLoss | false | 4,586 | [
"MIT"
] | 0 | 4acd7f8b6aff914e2c8558677d6dac8b5ddc1f30 | https://github.com/xkp793003821/kaggle-tgs-salt/tree/4acd7f8b6aff914e2c8558677d6dac8b5ddc1f30 |
MultiheadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | wukevin/RoseTTAFold | MultiheadAttention | false | 4,562 | [
"MIT"
] | 0 | e3c15dbf4bc1e4f8726e26c63aca1625188da803 | https://github.com/wukevin/RoseTTAFold/tree/e3c15dbf4bc1e4f8726e26c63aca1625188da803 |
BCELoss | import torch
import torch.distributed
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
def bce_loss(pred, target, use_sigmoid=True):
"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | zhangzhengde0225/SwinTrack | BCELoss | false | 16,795 | [
"MIT"
] | 143 | 526be17f8ef266cb924c6939bd8dda23e9b73249 | https://github.com/zhangzhengde0225/SwinTrack/tree/526be17f8ef266cb924c6939bd8dda23e9b73249 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 5)
self.l2 = nn.Linear(5, 3)
self.l3 = nn.Linear(3, action_dim)
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.... | chenbq1234/CityLearn | Actor | false | 6,422 | [
"MIT"
] | 1 | baa162435954ecd58e7f4769a46fa9046f4d2cf6 | https://github.com/chenbq1234/CityLearn/tree/baa162435954ecd58e7f4769a46fa9046f4d2cf6 |
Attn | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Attn(nn.Module):
def __init__(self, method, hidden_size):
super(Attn, self).__init__()
self.method = method
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, 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 import triton_helpers
from torch._inductor.runtime.... | Aleph0Inc/HDSA-Dialog | Attn | false | 13,264 | [
"MIT"
] | 146 | 88e2604adb5dc38ae32205410b15b2ac39116ecd | https://github.com/Aleph0Inc/HDSA-Dialog/tree/88e2604adb5dc38ae32205410b15b2ac39116ecd |
Discriminate_Loss | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn
class Discriminate_Loss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, real_outputs, fake_outputs):
return torch.mean((real_outputs - 1) ** 2 / 2) + torch.mean(
fake_outputs ** 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
import torch.nn as nn
import torch.nn.functional
import torch.nn
assert_size_stride = tor... | ChmarsLuo/Hero_anomaly_prediction | Discriminate_Loss | false | 4,992 | [
"Apache-2.0"
] | 1 | dba2322dabb3476466e296db6c316fc08e0cb11d | https://github.com/ChmarsLuo/Hero_anomaly_prediction/tree/dba2322dabb3476466e296db6c316fc08e0cb11d |
LatentLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | CSID-DGU/-2020-1-OSSP1-ninetynine-2 | LatentLoss | false | 4,941 | [
"MIT"
] | 1 | b1824254882eeea0ee44e4e60896b72c51ef1d2c | https://github.com/CSID-DGU/-2020-1-OSSP1-ninetynine-2/tree/b1824254882eeea0ee44e4e60896b72c51ef1d2c |
GaussianKLLoss | import torch
import torch.nn as nn
class GaussianKLLoss(nn.Module):
def __init__(self):
super(GaussianKLLoss, self).__init__()
def forward(self, mu1, logvar1, mu2, logvar2):
numerator = logvar1.exp() + torch.pow(mu1 - mu2, 2)
fraction = torch.div(numerator, logvar2.exp())
kl ... | 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... | johnson7788/Info-HCVAE | GaussianKLLoss | false | 12,628 | [
"Apache-2.0"
] | 0 | f43bf705aab3dcdc340ded3be09fb87420a48c51 | https://github.com/johnson7788/Info-HCVAE/tree/f43bf705aab3dcdc340ded3be09fb87420a48c51 |
DisAlignFastRCNNOutputLayers | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
import torch.utils.data
from itertools ... | tonysy/cvpods | DisAlignFastRCNNOutputLayers | false | 16,599 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
ContentLoss | import torch
from torch import nn
class ContentLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return 0.5 * torch.sum((x - y) ** 2)
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Enigmatisms/NeuralStyle | ContentLoss | false | 5,130 | [
"Apache-2.0"
] | 1 | 27b435b5c51b41427e9f465793a0b81ad7248ab8 | https://github.com/Enigmatisms/NeuralStyle/tree/27b435b5c51b41427e9f465793a0b81ad7248ab8 |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | RyanMoussouni/iSeeBetter | TVLoss | false | 14,336 | [
"MIT"
] | 327 | af193ae0852f8e477fcd6875dce874eb5092a24a | https://github.com/RyanMoussouni/iSeeBetter/tree/af193ae0852f8e477fcd6875dce874eb5092a24a |
QNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
def __init__(self, state_size, action_size, seed, num_layers=1,
hidden_size=64):
"""
Initialize parameters and build model.
parameters:
state_size : (int) Dimension of each s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | RevanMacQueen/DRQN | QNetwork | false | 9,410 | [
"MIT"
] | 0 | 7b8a743935679f65817ad4f41d28c2c155e7a62a | https://github.com/RevanMacQueen/DRQN/tree/7b8a743935679f65817ad4f41d28c2c155e7a62a |
MergeLayer | import torch
class MergeLayer(torch.nn.Module):
def __init__(self, dim1, dim2, dim3, dim4):
super().__init__()
self.fc1 = torch.nn.Linear(dim1 + dim2, dim3)
self.fc2 = torch.nn.Linear(dim3, dim4)
self.act = torch.nn.ReLU()
torch.nn.init.xavier_normal_(self.fc1.weight)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | IDSC-io/vre-tgn | MergeLayer | false | 9,138 | [
"Apache-2.0"
] | 0 | 46e8327e3befe67003874fa70b384a511523f8f7 | https://github.com/IDSC-io/vre-tgn/tree/46e8327e3befe67003874fa70b384a511523f8f7 |
AE_4D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow | AE_4D | false | 4,888 | [
"Apache-2.0"
] | 1 | 772ba547c2b7d5d90e79382bf4d8a50e4d733210 | https://github.com/Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow/tree/772ba547c2b7d5d90e79382bf4d8a50e4d733210 |
LeastSquaresGenerativeAdversarialLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | XianyuanLiu/Transfer-Learning-Library | LeastSquaresGenerativeAdversarialLoss | false | 10,143 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
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