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 |
|---|---|---|---|---|---|---|---|---|---|---|
ForgetMult | import torch
from torch.optim import *
class ForgetMult(torch.nn.Module):
"""ForgetMult computes a simple recurrent equation:
h_t = f_t * x_t + (1 - f_t) * h_{t-1}
This equation is equivalent to dynamic weighted averaging.
Inputs: X, hidden
- X (seq_len, batch, input_size): tensor containing... | 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.optim import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empt... | MochizukiShinichi/NeuronBlocks | ForgetMult | false | 9,331 | [
"MIT"
] | 0 | ee15beb564b35900a179fe767745d031124273e9 | https://github.com/MochizukiShinichi/NeuronBlocks/tree/ee15beb564b35900a179fe767745d031124273e9 |
BBoxTransform | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | cosmos1982/pytorch_efficientdet_openvino_demo | BBoxTransform | false | 9,969 | [
"Apache-2.0"
] | 0 | f626af448a827c0df655eb2af52ae3dbd10f2478 | https://github.com/cosmos1982/pytorch_efficientdet_openvino_demo/tree/f626af448a827c0df655eb2af52ae3dbd10f2478 |
PolicyNetwork | # 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.... | chagri/SOTA-RL-Algorithms | PolicyNetwork | false | 1,665 | [
"Apache-2.0"
] | 0 | 58b416e7c706d8426dc402482e72ca7283568e71 | https://github.com/chagri/SOTA-RL-Algorithms/tree/58b416e7c706d8426dc402482e72ca7283568e71 |
WeightedTVLoss | import functools
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
def reduce_loss(loss, reduction):
"""Reduce ... | 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 functools
from torch import nn as nn
from torch.nn import function... | hyunobae/BasicSR | WeightedTVLoss | false | 12,516 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
MockAccuracy | import torch
class _Metric(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input: 'torch.Tensor', target: 'torch.Tensor'):
raise NotImplementedError()
class Accuracy(_Metric):
def __init__(self):
super().__init__()
def forward(self, input: 'torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | HalleyYoung/MusicTransformer-pytorch | MockAccuracy | false | 11,461 | [
"MIT"
] | 0 | bbfb7050f4a81675b089cd826d4476cf29bf19c2 | https://github.com/HalleyYoung/MusicTransformer-pytorch/tree/bbfb7050f4a81675b089cd826d4476cf29bf19c2 |
custom_loss | import torch
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.backends.cudnn
class custom_loss(nn.Module):
def __init__(self):
super(custom_loss, self).__init__()
def forward(self, x):
nc = x.size(1)
assert nc % 2 == 0, 'channels do not divide 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
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.as... | Divyanshu23/model-zoo | custom_loss | false | 8,089 | [
"MIT"
] | 43 | 2eea6df691d302e182bb1ff8ec5af3542de562ba | https://github.com/Divyanshu23/model-zoo/tree/2eea6df691d302e182bb1ff8ec5af3542de562ba |
LearnedPositionalEmbedding | import torch
import torch.nn as nn
import torch.nn.functional as F
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | boxiangliu/esm | LearnedPositionalEmbedding | false | 1,579 | [
"MIT"
] | 0 | 3c143d99103e0ea38a9455f30a73cd9c87376606 | https://github.com/boxiangliu/esm/tree/3c143d99103e0ea38a9455f30a73cd9c87376606 |
RankingLoss | # 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
from abc import abstractmethod
import torch.utils.data.dataloader
i... | GT-SALT/LADA | RankingLoss | false | 8,139 | [
"MIT"
] | 31 | 2838a4c90694bf1054c6bab7f3b60ab5e04a5d4d | https://github.com/GT-SALT/LADA/tree/2838a4c90694bf1054c6bab7f3b60ab5e04a5d4d |
ResidualBlock | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_layer=nn.InstanceNorm2d,
stride=1, dilation=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, dilation=
dilation, padd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | haofeixu/gmflow | ResidualBlock | false | 15,489 | [
"Apache-2.0"
] | 58 | d304e5e516c11df378d63808d6679aea43bc564a | https://github.com/haofeixu/gmflow/tree/d304e5e516c11df378d63808d6679aea43bc564a |
Policy | import torch
import torch.nn.functional as F
import torch.nn as nn
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.conv1 = nn.Conv2d(2, 4, kernel_size=6, stride=2, bias=False)
self.conv2 = nn.Conv2d(4, 16, kernel_size=6, stride=4)
self.size = 9 * 9... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ShirelJosef/deep-reinforcement-learning | Policy | false | 11,877 | [
"MIT"
] | 0 | 63979b975c71e730c9d4c66e39efac210260dd18 | https://github.com/ShirelJosef/deep-reinforcement-learning/tree/63979b975c71e730c9d4c66e39efac210260dd18 |
Attention | # 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.... | xurantju/densecap | Attention | false | 11,049 | [
"BSD-3-Clause"
] | 0 | 2e58501e453bf98b9cc892e5b64997f5c1dfc808 | https://github.com/xurantju/densecap/tree/2e58501e453bf98b9cc892e5b64997f5c1dfc808 |
LqLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | Vanova/argus-freesound | LqLoss | false | 11,946 | [
"MIT"
] | 0 | 55f6e1b5ca1fd95c985f88a3e3fb0c81f8317b9d | https://github.com/Vanova/argus-freesound/tree/55f6e1b5ca1fd95c985f88a3e3fb0c81f8317b9d |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, ignore_target=-1):
super().__init__()
self.ignore_target = ignore_target
def forward(self, input, target):
"""
:param input: (N), logit
:param target: (N), {0, 1}
: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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Benedict0819/pointrcnn_multiclass | DiceLoss | false | 16,984 | [
"MIT"
] | 4 | 61781815920c0a5d44486ed25cf5bed805eb6b89 | https://github.com/Benedict0819/pointrcnn_multiclass/tree/61781815920c0a5d44486ed25cf5bed805eb6b89 |
LMA_Merge | import torch
from torch import nn
class LMA_Merge(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self.lamb = nn.Parameter(torch.zeros(1))
def forward(self, x, y):
return x + self.lamb * y
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | hmdliu/PCGNet | LMA_Merge | false | 3,599 | [
"MIT"
] | 0 | c03f25dc1b138afc52f612c1c517b61874baa02a | https://github.com/hmdliu/PCGNet/tree/c03f25dc1b138afc52f612c1c517b61874baa02a |
Ln_distance | import torch
from torch import nn
import torch.utils.data
class Ln_distance(nn.Module):
"""If dims is None Compute across all dimensions except first"""
def __init__(self, n, dim=None):
super(Ln_distance, self).__init__()
self.n = n
self.dim = dim
def forward(self, x, y):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
import torch.utils.data
assert_size_strid... | carla-recourse/CARLA | Ln_distance | false | 15,000 | [
"MIT"
] | 140 | e9bb3152598a94e700c38d7377825054959dcf48 | https://github.com/carla-recourse/CARLA/tree/e9bb3152598a94e700c38d7377825054959dcf48 |
SigmoidRange | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | LaurenSpiegel/fastai_docs | SigmoidRange | false | 757 | [
"Apache-2.0"
] | 0 | 4fe6b62116d88dea9610548133e6cadb6b260a73 | https://github.com/LaurenSpiegel/fastai_docs/tree/4fe6b62116d88dea9610548133e6cadb6b260a73 |
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.... | albertvillanova/s3prl | AttentivePooling | false | 6,156 | [
"MIT"
] | 1 | b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 | https://github.com/albertvillanova/s3prl/tree/b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 |
Conv2 | import math
import torch
import torch.nn as nn
import torch.utils.data.distributed
class Conv2(nn.Module):
""" A convolution layer with the stride of 2.
Input:
x: (N, 2L+2, in_channels) numeric tensor
global_cond: (N, global_cond_channels) numeric tensor
Output:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | aperquin/Extended_VQVAE | Conv2 | false | 14,888 | [
"MIT"
] | 55 | 46d309643c3fe3663e6fbd2fd6dd6b768341863b | https://github.com/aperquin/Extended_VQVAE/tree/46d309643c3fe3663e6fbd2fd6dd6b768341863b |
Flip | import torch
import torch.nn as nn
import torch.nn.init
class Flip(nn.Module):
"""Does horizontal or vertical flip on a BCHW tensor.
Args:
horizontal (bool): If True, applies horizontal flip. Else, vertical
flip is applied. Default = True
** Not recommended for CPU (Pillow/OpenCV bas... | 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... | Johnson-yue/TensorMONK | Flip | false | 5,406 | [
"MIT"
] | 1 | 1785132b82c685c3b3fc05b00dec46b1fccfc948 | https://github.com/Johnson-yue/TensorMONK/tree/1785132b82c685c3b3fc05b00dec46b1fccfc948 |
distLinear | # 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 ... | artificially-ai/FewShotVision | distLinear | false | 14,902 | [
"MIT"
] | 90 | 02c1132828bc9caba4cadd0b2f731bd63f66b826 | https://github.com/artificially-ai/FewShotVision/tree/02c1132828bc9caba4cadd0b2f731bd63f66b826 |
angularLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class angularLoss(nn.Module):
def __init__(self, margin, l=1):
super(angularLoss, self).__init__()
self.margin = margin
self.l = l
def forward(self, anchor, pos, neg):
distance_pos = (anchor - pos).pow(2).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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | MingzheWu418/plastering | angularLoss | false | 9,322 | [
"MIT"
] | 0 | 322531e934c3acf2ecc8f520b37a6d255b9959c2 | https://github.com/MingzheWu418/plastering/tree/322531e934c3acf2ecc8f520b37a6d255b9959c2 |
SeperableConv | import torch
import torch.nn as nn
import torch.nn.functional as F
def _get_padding(kernel_size, stride, dilation):
padding = (stride - 1 + dilation * (kernel_size - 1)) // 2
return padding
class SeperableConv(nn.Module):
def __init__(self, inp, outp, k=3, stride=1, dilation=1):
super(Seperable... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Sanjay-Ganeshan/webcam-mouse | SeperableConv | false | 2,820 | [
"Apache-2.0"
] | 0 | 240d1ee00816440e971c8c747bef02c12f3e5d57 | https://github.com/Sanjay-Ganeshan/webcam-mouse/tree/240d1ee00816440e971c8c747bef02c12f3e5d57 |
AttentiveStatsPooling | import torch
import torch.nn as nn
class AttentiveStatsPooling(nn.Module):
"""
The attentive statistics pooling layer uses an attention
mechanism to give different weights to different frames and
generates not only weighted means but also weighted variances,
to form utterance-level features from f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Wadaboa/titanet | AttentiveStatsPooling | false | 18,096 | [
"MIT"
] | 4 | b07e3074e79ea8c1129fb0adb8315e06bb4943ea | https://github.com/Wadaboa/titanet/tree/b07e3074e79ea8c1129fb0adb8315e06bb4943ea |
StyleResidual | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | PiotrDabkowski/NeMo | StyleResidual | false | 11,797 | [
"Apache-2.0"
] | 0 | 7c251e9035b24136cf130f3caf760087e5ccf07c | https://github.com/PiotrDabkowski/NeMo/tree/7c251e9035b24136cf130f3caf760087e5ccf07c |
QNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_uniform_
from torch.nn.init import uniform_
import torch.utils.data
def mini_weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(uniform_(m.weight.data, -0.003, 0.003))
m.bias.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | KtechB/machina | QNet | false | 2,472 | [
"MIT"
] | 0 | 24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab | https://github.com/KtechB/machina/tree/24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab |
Discriminator | # 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
from torch import nn as nn
from torch import optim as optim
a... | oke-aditya/pytorch-lightning-bolts | Discriminator | false | 7,374 | [
"Apache-2.0"
] | 1 | 268df20bb442e7385b709b1488d37fd2767aba3c | https://github.com/oke-aditya/pytorch-lightning-bolts/tree/268df20bb442e7385b709b1488d37fd2767aba3c |
Conv2dRelu_Fixed | import torch
from torch import nn
import torch.nn.functional as F
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class Conv2dRelu_Fixed(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(Conv2dRelu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | CaoZhongZ/intel-extension-for-pytorch | Conv2dRelu_Fixed | false | 8,867 | [
"Apache-2.0"
] | 0 | 13c3dcbd6876cc57c08d3db4e50dc435ae96a91d | https://github.com/CaoZhongZ/intel-extension-for-pytorch/tree/13c3dcbd6876cc57c08d3db4e50dc435ae96a91d |
WeightedCE | import torch
from typing import Optional
from typing import List
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class WeightedCE(nn.Module):
"""Mask weighted multi-class cross-entropy (CE) loss.
"""
def __init__(self, class_weight: 'Optional[List[fl... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Opt... | HarshSulakhe/pytorch_connectomics | WeightedCE | false | 9,860 | [
"MIT"
] | 0 | 73402e654afde69a43a5836cc90a32ef75c75dc2 | https://github.com/HarshSulakhe/pytorch_connectomics/tree/73402e654afde69a43a5836cc90a32ef75c75dc2 |
residualUnit | # 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.... | navid0308/medSynthesisV1 | residualUnit | false | 10,686 | [
"MIT"
] | 0 | 6731a67d0eb9bb3e0c1646f01feb24229aa4fe30 | https://github.com/navid0308/medSynthesisV1/tree/6731a67d0eb9bb3e0c1646f01feb24229aa4fe30 |
ATLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class ATLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits, labels):
th_label = torch.zeros_like(labels, dtype=torch.float)
th_label[:, 0] = 1.0
labels[:, 0] = 0.0
p_mask ... | 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
... | seanswyi/R-BERT | ATLoss | false | 4,294 | [
"Apache-2.0"
] | 0 | 4a4aeab3a9314307ce4458bd2b943d94aaf4a706 | https://github.com/seanswyi/R-BERT/tree/4a4aeab3a9314307ce4458bd2b943d94aaf4a706 |
l1normalization | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | kensakurada/SceneChangeDet | l1normalization | false | 15,792 | [
"MIT"
] | 199 | 0530e0162863fec0c5296188526f0d27e0109814 | https://github.com/kensakurada/SceneChangeDet/tree/0530e0162863fec0c5296188526f0d27e0109814 |
SimpleConv2dModule | # 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
import torch.jit
import torch... | YaronBenAtar/glow | SimpleConv2dModule | false | 14,670 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
TripletLogExpLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
from torch import nn
assert_size_stride = t... | ByungHeeCha/visual_localization | TripletLogExpLoss | false | 17,027 | [
"BSD-3-Clause"
] | 3 | 787fb8f6ee5c6e69ece9e83a016d15596e5524bc | https://github.com/ByungHeeCha/visual_localization/tree/787fb8f6ee5c6e69ece9e83a016d15596e5524bc |
PowerLaw_Compressed_Loss | # 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... | giuliacassara/VoiceSplit | PowerLaw_Compressed_Loss | false | 15,439 | [
"Apache-2.0"
] | 84 | 1aa98dce9460db7ec6c5449eb7f92e3902f71a2a | https://github.com/giuliacassara/VoiceSplit/tree/1aa98dce9460db7ec6c5449eb7f92e3902f71a2a |
Join | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NooneBug/entity_typing_framework | Join | false | 2,706 | [
"MIT"
] | 0 | e4c3cf3a6d9c3a3453ce516de855fc22b49ae5c0 | https://github.com/NooneBug/entity_typing_framework/tree/e4c3cf3a6d9c3a3453ce516de855fc22b49ae5c0 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, n_obs, n_actions, hidden_size, init_w=0.003):
super(Actor, self).__init__()
self.linear1 = nn.Linear(n_obs, hidden_size)
self.linear2 = nn.Linear(hidden_size, 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.... | KOWKO1/reinforcement-learning-tutorials | Actor | false | 5,425 | [
"MIT"
] | 1 | 5f29d6eba8b580041f3e82d88dc3e1cd8e4cae10 | https://github.com/KOWKO1/reinforcement-learning-tutorials/tree/5f29d6eba8b580041f3e82d88dc3e1cd8e4cae10 |
Net | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=3)
self.conv2 = nn.Conv2d(10, 2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ygnn123/training_extensions | Net | false | 4,709 | [
"Apache-2.0"
] | 0 | c3aeba9359b0d4e0ef9c054de777d3ec081a9892 | https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892 |
SAP | # 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.... | sumanthd17/s3prl | SAP | false | 13,007 | [
"MIT"
] | 0 | bb74c705295d121c4308ceb6b6a2c8d1814d6f4c | https://github.com/sumanthd17/s3prl/tree/bb74c705295d121c4308ceb6b6a2c8d1814d6f4c |
SymmetricBCELoss | # 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... | thangnx183/kaggle-understanding-clouds | SymmetricBCELoss | false | 16,580 | [
"BSD-2-Clause"
] | 207 | 15ad2a9029958262437b899cb00525579da23911 | https://github.com/thangnx183/kaggle-understanding-clouds/tree/15ad2a9029958262437b899cb00525579da23911 |
MaxPool2dLayer | # 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... | nicofirst1/lucent | MaxPool2dLayer | false | 12,915 | [
"Apache-2.0"
] | 0 | 1e249918e91cc04117368826cd7a192bd8cf2046 | https://github.com/nicofirst1/lucent/tree/1e249918e91cc04117368826cd7a192bd8cf2046 |
SimpleFmodModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleFmodModule(torch.nn.Module):
def __init__(self):
super(SimpleFmodModule, self).__init__()
def forward(self, a, b):
if b.size() == torch.Size([]):
c = a.fmod(b.item())
else:
c = a.fmod(... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | opti-mix/glow | SimpleFmodModule | false | 7,402 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
EncoderLayer | import math
import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, d_model, eps=1e-12):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torch.zeros(d_model))
self.eps = eps
def forward(self, x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | hyunwoongko/transformer | EncoderLayer | false | 15,607 | [
"Apache-2.0"
] | 233 | 8f7aaa19d37b088c156db0512868127ba9bf1a0f | https://github.com/hyunwoongko/transformer/tree/8f7aaa19d37b088c156db0512868127ba9bf1a0f |
SimpleAddMmModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAddMmModule(torch.nn.Module):
def __init__(self, alpha=1, beta=1):
super(SimpleAddMmModule, self).__init__()
self.alpha = alpha
self.beta = beta
def forward(self, a, b, c):
return (a + a).addmm(b, c)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | YaronBenAtar/glow | SimpleAddMmModule | false | 14,651 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
MultiHeadAttn | import torch
from torch import nn
import torch.nn.functional as F
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_head... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | PeganovAnton/transformer-xl | MultiHeadAttn | false | 14,165 | [
"Apache-2.0"
] | 133 | f36428445cc903872fde54d90bc5e61886420a5a | https://github.com/PeganovAnton/transformer-xl/tree/f36428445cc903872fde54d90bc5e61886420a5a |
ChannelSpatialSELayer1d | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelSELayer1d(nn.Module):
def __init__(self, num_channels, reduction_ratio=4):
"""
:param num_channels: No of input channels
:param reduction_ratio: By how much should the num_channels should be reduced
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | ioanvl/1d_squeeze_excitation | ChannelSpatialSELayer1d | false | 10,258 | [
"MIT"
] | 0 | f422dc4b8e7de6239a6fb7d1688048db5053e733 | https://github.com/ioanvl/1d_squeeze_excitation/tree/f422dc4b8e7de6239a6fb7d1688048db5053e733 |
BasicModulationBlock | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | dodoproptit99/WaveGrad | BasicModulationBlock | false | 10,039 | [
"BSD-3-Clause"
] | 0 | d5e3cb5d8c1c3d115eeb5f1673b87bdbb36f79e0 | https://github.com/dodoproptit99/WaveGrad/tree/d5e3cb5d8c1c3d115eeb5f1673b87bdbb36f79e0 |
ANN | import torch
import torch.nn as nn
import torch.nn.functional as F
class ANN(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, use_tanh=True):
super(ANN, self).__init__()
self.l1 = nn.Linear(input_dim, hidden_dim)
self.l2 = nn.Linear(hidden_dim, output_dim)
self.us... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KuangenZhang/StructuredRL | ANN | false | 5,457 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a |
DC | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
i... | ReubenDo/InExtremIS | DC | false | 8,693 | [
"MIT"
] | 17 | 1512ddf9b8c11c4d9f0ebd465d904ef3d539d350 | https://github.com/ReubenDo/InExtremIS/tree/1512ddf9b8c11c4d9f0ebd465d904ef3d539d350 |
Model | # 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... | VashishtMadhavan/pytorch-maml-rl | Model | false | 1,174 | [
"MIT"
] | 0 | d8821b8374d973869bb6a1393f1b2c369c9a664b | https://github.com/VashishtMadhavan/pytorch-maml-rl/tree/d8821b8374d973869bb6a1393f1b2c369c9a664b |
DeConvNet3 | # 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_... | Neural-Diffusion-Research/normalized-autoencoders | DeConvNet3 | false | 8,638 | [
"MIT"
] | 30 | 0c77f7e29289e336c0fe5e941aaec8baa4a4fb82 | https://github.com/Neural-Diffusion-Research/normalized-autoencoders/tree/0c77f7e29289e336c0fe5e941aaec8baa4a4fb82 |
FCMinibatchStd | import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[0]),
ne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
from to... | ishine/GANsNRoses | FCMinibatchStd | false | 15,623 | [
"MIT"
] | 969 | 414e9e77c3df47d4ecf7941b5dcfdffec67403ee | https://github.com/ishine/GANsNRoses/tree/414e9e77c3df47d4ecf7941b5dcfdffec67403ee |
ResnetBlock | import torch
import torch.nn as nn
class ResnetBlock(nn.Module):
def __init__(self, in_channels, out_channels, use_bn=False):
super(ResnetBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.use_bn = use_bn
self.relu = nn.ReLU(inplac... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | VashishtMadhavan/pytorch-maml-rl | ResnetBlock | false | 1,190 | [
"MIT"
] | 0 | d8821b8374d973869bb6a1393f1b2c369c9a664b | https://github.com/VashishtMadhavan/pytorch-maml-rl/tree/d8821b8374d973869bb6a1393f1b2c369c9a664b |
BoxFilter | import torch
from torchvision.transforms import functional as F
from torch import nn
from torch.nn import functional as F
class BoxFilter(nn.Module):
def __init__(self, r):
super(BoxFilter, self).__init__()
self.r = r
def forward(self, x):
kernel_size = 2 * self.r + 1
kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | LightTwist/RobustVideoMatting | BoxFilter | false | 8,508 | [
"Apache-2.0"
] | 11 | 79eb143fef3a4c58b4857c1a5a927a318f528093 | https://github.com/LightTwist/RobustVideoMatting/tree/79eb143fef3a4c58b4857c1a5a927a318f528093 |
EncoderBlock | import torch
from torch import nn
class EncoderBlock(nn.Module):
"""Encoder block"""
def __init__(self, inplanes, outplanes, kernel_size=4, stride=2,
padding=1, norm=True, padding_mode='zeros'):
super().__init__()
self.lrelu = nn.LeakyReLU(0.2, inplace=True)
self.conv = nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | akanametov/CycleGAN | EncoderBlock | false | 6,137 | [
"MIT"
] | 1 | a61e76134cfdda43306e326e3dbba38d8cb21163 | https://github.com/akanametov/CycleGAN/tree/a61e76134cfdda43306e326e3dbba38d8cb21163 |
ZPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | LiChengChen666/DetectDee | ZPool | false | 9,819 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
Refine | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
class Refine(nn.Module):
def __init__(self, inplanes, planes, scale_factor=2):
super(Refine, self).__init__()
self.convFS1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1)
self.convFS2 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | HuaijiaLin/AGSS-VOS | Refine | false | 8,258 | [
"MIT"
] | 11 | e9272365aa45bf098316d7111238fe0ab8df8a17 | https://github.com/HuaijiaLin/AGSS-VOS/tree/e9272365aa45bf098316d7111238fe0ab8df8a17 |
Conv2dZeros | import torch
import torch.utils.data
import torch.nn as nn
class _ActNorm(nn.Module):
"""
Activation Normalization
Initialize the bias and scale with a given minibatch,
so that the output per-channel have zero mean and unit variance for that.
After initialization, `bias` and `logs` will be traine... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | AbnerVictor/HCFlow | Conv2dZeros | false | 9,104 | [
"Apache-2.0"
] | 0 | e55938ac9f58c117898e3d161ddc73b14d15289b | https://github.com/AbnerVictor/HCFlow/tree/e55938ac9f58c117898e3d161ddc73b14d15289b |
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kris927b/ViLT | Block | false | 3,867 | [
"Apache-2.0"
] | 0 | db96f20ebc656f1995aa573cbcbca0fe31f55c42 | https://github.com/kris927b/ViLT/tree/db96f20ebc656f1995aa573cbcbca0fe31f55c42 |
DecoderLayer | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff=2048, dropout=0.1):
super().__init__()
self.linear_1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.linear_2 = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | nlakshmanan/Transformer | DecoderLayer | false | 10,677 | [
"Apache-2.0"
] | 0 | 4562f8e9b282d0a70f26903a7b4410cb6132364b | https://github.com/nlakshmanan/Transformer/tree/4562f8e9b282d0a70f26903a7b4410cb6132364b |
Attention | import torch
class Attention(torch.nn.Module):
""" Applies attention mechanism on the `context` using the `query`.
**Thank you** to IBM for their initial implementation of :class:`Attention`. Here is
their `License
<https://github.com/IBM/pytorch-seq2seq/blob/master/LICENSE>`__.
Args:
di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | choderalab/pisco | Attention | false | 6,446 | [
"MIT"
] | 1 | dccb36edf49960929cfb823f885d38cb84d444d1 | https://github.com/choderalab/pisco/tree/dccb36edf49960929cfb823f885d38cb84d444d1 |
RNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LatifB/char-level-classification | RNN | false | 9,217 | [
"MIT"
] | 0 | 3d0e21e85571efafe0e26c6f27c5fa258a9503da | https://github.com/LatifB/char-level-classification/tree/3d0e21e85571efafe0e26c6f27c5fa258a9503da |
MseCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
assert_siz... | chunhuililili/mt_dnn | MseCriterion | false | 10,208 | [
"MIT"
] | 0 | 4c6efaf21724c7b8103a05e46b5b44d7b246225e | https://github.com/chunhuililili/mt_dnn/tree/4c6efaf21724c7b8103a05e46b5b44d7b246225e |
TverskyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | DeVriesMatt/cellshape-voxel | TverskyLoss | false | 5,059 | [
"BSD-3-Clause"
] | 1 | 64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1 | https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1 |
MetaAconC | import torch
import torch.nn as nn
class MetaAconC(nn.Module):
""" ACON activation (activate or not).
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | GoalballAnalysis/GUI | MetaAconC | false | 2,314 | [
"MIT"
] | 0 | c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 | https://github.com/GoalballAnalysis/GUI/tree/c7f1cc27f4fd7f861c3ca09f5ca25d1a3f19a8a7 |
GumbelSoftMax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_ma... | TayaPenskaya/hair_classifier | GumbelSoftMax | false | 2,883 | [
"MIT"
] | 0 | 669f42b705a4dd0bdde9d330db5214a3213db5a5 | https://github.com/TayaPenskaya/hair_classifier/tree/669f42b705a4dd0bdde9d330db5214a3213db5a5 |
TransitionModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | ishine/Neural-HMM | TransitionModel | false | 15,636 | [
"MIT"
] | 66 | c0bc23ab88f831173d2d4db29a84503b80c5cdc4 | https://github.com/ishine/Neural-HMM/tree/c0bc23ab88f831173d2d4db29a84503b80c5cdc4 |
ActNorm2D | # 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 math as tl_math
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride =... | Schwartz-Zha/My-invertible-resnet | ActNorm2D | false | 1,038 | [
"MIT"
] | 0 | 5415975bb0d640f3bf3ef4a7b986563e84109270 | https://github.com/Schwartz-Zha/My-invertible-resnet/tree/5415975bb0d640f3bf3ef4a7b986563e84109270 |
ScaleNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | imflash217/bumblebee | ScaleNorm | false | 12,530 | [
"MIT"
] | 0 | 09343d42634aa954cac867f7e426eee260b4df57 | https://github.com/imflash217/bumblebee/tree/09343d42634aa954cac867f7e426eee260b4df57 |
LSTMRegressCriterion | # 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... | aluo-x/shape2prog | LSTMRegressCriterion | false | 14,823 | [
"BSD-2-Clause"
] | 109 | 1177e5205b99bb293e353688b564c94a14211c75 | https://github.com/aluo-x/shape2prog/tree/1177e5205b99bb293e353688b564c94a14211c75 |
CDM | import torch
import torch.nn as nn
import torch.utils.data
class CDM(nn.Module):
"""
Implementation of the CDM choice model as a Pytorch module
"""
def __init__(self, n, d):
"""
Initializes a CDM model
Args:
n- number of items in the universe
d- number of dime... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | arjunsesh/lrr-neurips | CDM | false | 6,246 | [
"MIT"
] | 1 | d78106daec1e729b02a0452f74a37bf004ed243c | https://github.com/arjunsesh/lrr-neurips/tree/d78106daec1e729b02a0452f74a37bf004ed243c |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | zwh930712/densenet.pytorch | Net | false | 16,836 | [
"Apache-2.0"
] | 826 | d1cd5e1957975628286e516512c6d1c14430f810 | https://github.com/zwh930712/densenet.pytorch/tree/d1cd5e1957975628286e516512c6d1c14430f810 |
EqualizedWeight | import math
import torch
import numpy as np
from torch import nn
import torch.utils.data
import torch.nn.functional
from typing import List
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rate Equalized Weights Parameter
This is based on equalized... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from torch import nn
import torch.utils.data
import torch.nn.functional
from typing import List
import torch.... | Hadryan/nn | EqualizedWeight | false | 9,388 | [
"MIT"
] | 0 | b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d | https://github.com/Hadryan/nn/tree/b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d |
SpatialPyramidPooling | import torch
import torch.nn as nn
class SpatialPyramidPooling(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPooling, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size //
2) for pool_size in pool_sizes])
def forward(... | 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... | BDeMo/yolov4-pytorch | SpatialPyramidPooling | false | 2,009 | [
"MIT"
] | 0 | 2434afc88d0890bdb19c5655bb7c577d22bf18d3 | https://github.com/BDeMo/yolov4-pytorch/tree/2434afc88d0890bdb19c5655bb7c577d22bf18d3 |
HuberLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | around-star/FLAVR | HuberLoss | false | 14,893 | [
"Apache-2.0"
] | 223 | 3b0b703fd1c67eb053511a3532f539ff468866a8 | https://github.com/around-star/FLAVR/tree/3b0b703fd1c67eb053511a3532f539ff468866a8 |
SqueezeExcite | # 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... | wangxianliang/FaceX-Zoo | SqueezeExcite | false | 13,091 | [
"Apache-2.0"
] | 0 | b0555c88a0350fa7b59c317f3a171f551fef4e6e | https://github.com/wangxianliang/FaceX-Zoo/tree/b0555c88a0350fa7b59c317f3a171f551fef4e6e |
RelativeThreshold_RegLoss | import torch
import torch.nn as nn
import torch.nn.init
class RelativeThreshold_RegLoss(nn.Module):
def __init__(self, threshold, size_average=True):
super(RelativeThreshold_RegLoss, self).__init__()
self.size_average = size_average
self.eps = 1e-07
self.threshold = threshold
... | 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
import torch.nn.init
assert_size_stride = torch._C.... | ginobilinie/medSynthesisV1 | RelativeThreshold_RegLoss | false | 15,428 | [
"MIT"
] | 166 | 1fd202c5928466ef9b11cfebc4490341899312e7 | https://github.com/ginobilinie/medSynthesisV1/tree/1fd202c5928466ef9b11cfebc4490341899312e7 |
DDPGConvBody | # 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 ... | GoingMyWay/DeepRL | DDPGConvBody | false | 13,805 | [
"MIT"
] | 2,857 | 78df98a8eeccc41dacd952932435a5ecc42e1c67 | https://github.com/GoingMyWay/DeepRL/tree/78df98a8eeccc41dacd952932435a5ecc42e1c67 |
DownBlock | import torch
import torch.nn as nn
def get_activation(activation: 'str'):
if activation == 'relu':
return nn.ReLU()
elif activation == 'leaky':
return nn.LeakyReLU(negative_slope=0.1)
elif activation == 'elu':
return nn.ELU()
def conv_layer(dim: 'int'):
if dim == 3:
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | arshadzahangirchowdhury/TomoEncoders | DownBlock | false | 9,761 | [
"BSD-3-Clause"
] | 0 | 9c2b15fd515d864079f198546821faee5d78df17 | https://github.com/arshadzahangirchowdhury/TomoEncoders/tree/9c2b15fd515d864079f198546821faee5d78df17 |
SoftMaxWeightedSum | import torch
from torch import nn
class SoftMaxWeightedSum(nn.Module):
def __init__(self, op_number=2):
super(SoftMaxWeightedSum, self).__init__()
shape = op_number, 1, 1, 1, 1
self.weights = nn.Parameter(torch.ones(shape), requires_grad=True)
def forward(self, x):
return tor... | 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... | Senyaaa/detection-experiments | SoftMaxWeightedSum | false | 17,895 | [
"Apache-2.0"
] | 5 | 5e80dd458e886ca27db5420d25ade8f9d74ae5a8 | https://github.com/Senyaaa/detection-experiments/tree/5e80dd458e886ca27db5420d25ade8f9d74ae5a8 |
MLP | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Conv1D(nn.Module):
def __init__(self, nf, nx):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
from to... | jamessheenworks/GPT2sQA | MLP | false | 10,281 | [
"Apache-2.0"
] | 0 | 14866cb21d229281e8f8b8f88aac9195bca45cd7 | https://github.com/jamessheenworks/GPT2sQA/tree/14866cb21d229281e8f8b8f88aac9195bca45cd7 |
Conv2d | import torch
import torch.nn as nn
from torch.nn import functional as F
class Conv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
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.triton_helpers import libdevice
import torch.nn as ... | chris-rgr/image-background-remove-tool | Conv2d | false | 1,707 | [
"Apache-2.0"
] | 0 | b57b44099e0e35c90833bed010b071aa39efdc80 | https://github.com/chris-rgr/image-background-remove-tool/tree/b57b44099e0e35c90833bed010b071aa39efdc80 |
VirtualBatchNorm | # 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_... | torchgan/torchgan | VirtualBatchNorm | false | 16,610 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
EncoderImageWeightNormPrecomp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from collections im... | BruceW91/CVSE | EncoderImageWeightNormPrecomp | false | 13,428 | [
"MIT"
] | 152 | 20fa1ff50d1dcb4a7b3799071fa78038e52db804 | https://github.com/BruceW91/CVSE/tree/20fa1ff50d1dcb4a7b3799071fa78038e52db804 |
Scale_B | # 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... | sergkuzn148/stg | Scale_B | false | 16,382 | [
"MIT"
] | 96 | 84d9f53ae3665c423836a4d0176dc3b22de62b19 | https://github.com/sergkuzn148/stg/tree/84d9f53ae3665c423836a4d0176dc3b22de62b19 |
GetGradient | # 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 ... | TencentARC/FAIG | GetGradient | false | 14,487 | [
"Apache-2.0"
] | 74 | 14f856a87e3696953304029532e2f84997d12278 | https://github.com/TencentARC/FAIG/tree/14f856a87e3696953304029532e2f84997d12278 |
WeightedBCEFocalLoss | # 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... | Atharva-Peshkar/pytorch_connectomics | WeightedBCEFocalLoss | false | 13,332 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
Sparsemax | # 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.autograd import Function
import torch.nn as nn
assert_size_stride = torch._C._... | roholazandie/entmax | Sparsemax | false | 7,587 | [
"MIT"
] | 1 | 657374e6a792ec6840b6f78bc759cc1f51570aad | https://github.com/roholazandie/entmax/tree/657374e6a792ec6840b6f78bc759cc1f51570aad |
dy_nconv | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | kevin-xuan/Traffic-Benchmark | dy_nconv | false | 15,861 | [
"MIT"
] | 120 | b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 | https://github.com/kevin-xuan/Traffic-Benchmark/tree/b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 |
ConvertTHWCtoTCHW | import torch
import torch.utils.data
class ConvertTHWCtoTCHW(torch.nn.Module):
"""
Convert a torch.FloatTensor of shape (TIME x HEIGHT x WIDTH x CHANNEL) to
a torch.FloatTensor of shape (TIME x CHANNELS x HEIGHT x WIDTH).
"""
def forward(self, tensor):
return tensor.permute(0, 3, 1, 2).co... | 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_... | XianyuanLiu/Transfer-Learning-Library | ConvertTHWCtoTCHW | false | 10,131 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | srlee-ai/claf | LayerNorm | false | 10,885 | [
"MIT"
] | 0 | 89b3e5c5ec0486886876ea3bac381508c6a6bf58 | https://github.com/srlee-ai/claf/tree/89b3e5c5ec0486886876ea3bac381508c6a6bf58 |
AppendDim | # 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... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | AppendDim | false | 17,116 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
FeatureWiseAffine | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | dodoproptit99/WaveGrad | FeatureWiseAffine | false | 10,038 | [
"BSD-3-Clause"
] | 0 | d5e3cb5d8c1c3d115eeb5f1673b87bdbb36f79e0 | https://github.com/dodoproptit99/WaveGrad/tree/d5e3cb5d8c1c3d115eeb5f1673b87bdbb36f79e0 |
SoftmaxBCELoss | import torch
import torch.utils.data
class SoftmaxBCELoss(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self.bce = torch.nn.BCELoss(*args, **kwargs)
def forward(self, output, target):
probs = torch.nn.functional.softmax(output, dim=1)
return self.b... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ELEKTRONN/elektronn3 | SoftmaxBCELoss | false | 13,606 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
SamePadConv3d | # 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... | pointoflight/VideoGPT | SamePadConv3d | false | 7,485 | [
"MIT"
] | 1 | 85f19d8cb0d251238f295f0294e69b9299c13e21 | https://github.com/pointoflight/VideoGPT/tree/85f19d8cb0d251238f295f0294e69b9299c13e21 |
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 |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | raman32/LDAM-DRW | FocalLoss | false | 7,530 | [
"MIT"
] | 1 | 7ce2251c01b94c7259108a1e188457f0b720651d | https://github.com/raman32/LDAM-DRW/tree/7ce2251c01b94c7259108a1e188457f0b720651d |
ClippedValueFunctionLoss | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd
class ClippedValueFunctionLoss(Module):
"""
## Clipped Value Function Loss
Similarly we clip the value function update also.
egin{align}
V^{\\pi_ heta}_{CLIP}(s_t)
&= clip\\Big... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
import tor... | ppvalluri09/annotated_deep_learning_paper_implementations | ClippedValueFunctionLoss | false | 11,065 | [
"MIT"
] | 0 | 387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 | https://github.com/ppvalluri09/annotated_deep_learning_paper_implementations/tree/387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 |
p_model | import torch
from torch import nn
import torch.nn.functional as F
class p_model(nn.Module):
"""
input: N * C * W * H
output: N * 1 * W * H
"""
def __init__(self):
super(p_model, self).__init__()
def forward(self, x):
n, c, w, h = x.size()
x = x.view(n, c, w * h).permu... | 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... | cenkcorapci/visual-fashion-item-search | p_model | false | 6,407 | [
"MIT"
] | 1 | 47b93f97383c1b7f9ec23bb4ff66f90504db3da8 | https://github.com/cenkcorapci/visual-fashion-item-search/tree/47b93f97383c1b7f9ec23bb4ff66f90504db3da8 |
ATANLoss | # 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... | kamomehz/waveletCodingCNN | ATANLoss | false | 3,789 | [
"MIT"
] | 0 | 50c7db9d986039ded38999b7e4f4265e2250fb90 | https://github.com/kamomehz/waveletCodingCNN/tree/50c7db9d986039ded38999b7e4f4265e2250fb90 |
HamidaEtAl | # 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.utils
import tor... | giorgosouz/HSI-classification-using-state-of-the-art-models | HamidaEtAl | false | 12,751 | [
"MIT"
] | 0 | a925972ffe02c2cd1e5dde2b163e1faa854a4966 | https://github.com/giorgosouz/HSI-classification-using-state-of-the-art-models/tree/a925972ffe02c2cd1e5dde2b163e1faa854a4966 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.