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 |
|---|---|---|---|---|---|---|---|---|---|---|
PairwiseNorm | import torch
from torch import nn
class PairwiseNorm(nn.Module):
def __init__(self, order=1, size_average=True):
super().__init__()
self.order = order
self.average = size_average
def forward(self, inp, target=None):
inp = inp.flatten(1)
assert len(inp) % 2 == 0
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | vzinche/inferno | PairwiseNorm | false | 4,508 | [
"Apache-2.0"
] | 0 | 91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 | https://github.com/vzinche/inferno/tree/91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 |
SpaceToDepth | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.... | GuillaumeAI/gia-labeling-ImageNet21K | SpaceToDepth | false | 17,317 | [
"MIT"
] | 4 | 825ff49f1558f848fc8a798e2e393b708e75bb0e | https://github.com/GuillaumeAI/gia-labeling-ImageNet21K/tree/825ff49f1558f848fc8a798e2e393b708e75bb0e |
GeneralizedMeanPooling | # 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
... | ByungHeeCha/visual_localization | GeneralizedMeanPooling | false | 17,020 | [
"BSD-3-Clause"
] | 3 | 787fb8f6ee5c6e69ece9e83a016d15596e5524bc | https://github.com/ByungHeeCha/visual_localization/tree/787fb8f6ee5c6e69ece9e83a016d15596e5524bc |
InnerProductDecoderMLP | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.modules.loss
import torch.utils.data
class InnerProductDecoderMLP(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, input_dim, hidden_dim1, hidden_dim2, dropout, act=
torch.sigmoid):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | WanyuGroup/CVPR2022-OrphicX | InnerProductDecoderMLP | false | 1,209 | [
"MIT"
] | 0 | 98d8d8259439c45661573e575cf956331df16abc | https://github.com/WanyuGroup/CVPR2022-OrphicX/tree/98d8d8259439c45661573e575cf956331df16abc |
GHMC | import torch
import torch.nn.functional as F
import torch.nn as nn
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero((labels >= 0) & (labels < label_channels),
as_tuple=False).squeeze()
if inds.n... | 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
... | ChHanXiao/mmdetection | GHMC | false | 9,151 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 |
GeM | import torch
import torch.nn as nn
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06):
super(GeM, self).__init__()
self.p = nn.Parameter(torch.ones(1) * p)
self.eps = eps
def forward(self, x):
return nn.functional.avg_pool2d(x.clamp(min=self.eps).pow(self.p),
... | 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... | XiaoJake/MinkLocMultimodal | GeM | false | 14,606 | [
"MIT"
] | 49 | 683ef1aae35ab1b60f13cefccfdd0e3f9cb9ea6e | https://github.com/XiaoJake/MinkLocMultimodal/tree/683ef1aae35ab1b60f13cefccfdd0e3f9cb9ea6e |
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
from torch.nn import Parameter
assert_size_stride = torch... | Sheroa/Video_Colorization | LayerNorm | false | 2,867 | [
"MIT"
] | 0 | 5c772ac0ec944814cd8be0a94b0746116b11ac01 | https://github.com/Sheroa/Video_Colorization/tree/5c772ac0ec944814cd8be0a94b0746116b11ac01 |
RAddFloat | # 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._utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | ijinjay/torch2mindspore | RAddFloat | false | 3,652 | [
"MIT"
] | 0 | e4c06bd5e8a3b25b72bf158393a66c5cd1b572d2 | https://github.com/ijinjay/torch2mindspore/tree/e4c06bd5e8a3b25b72bf158393a66c5cd1b572d2 |
PatchMerging | import torch
import torch.nn as nn
import torch.nn.functional as F
class PatchMerging(nn.Module):
""" Patch Merging Layer
Args:
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, dim, norm_layer=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.triton_helpers import libdevice
import torch.nn as ... | luohwu/video-swin-transformer-pytorch | PatchMerging | false | 3,950 | [
"MIT"
] | 0 | ad96877a6db44436183a03e5b9a80c425726c982 | https://github.com/luohwu/video-swin-transformer-pytorch/tree/ad96877a6db44436183a03e5b9a80c425726c982 |
GRUStep | import torch
import torch.nn as nn
import torch.utils.data
import torch.multiprocessing
import torch.nn.modules.loss
from scipy.sparse import *
class GRUStep(nn.Module):
def __init__(self, hidden_size, input_size):
super(GRUStep, self).__init__()
"""GRU module"""
self.linear_z = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | LucasAPayne/graph4nlp | GRUStep | false | 9,695 | [
"Apache-2.0"
] | 0 | 3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 | https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 |
GlobalAveragePooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Chrisfsj2051/my_tools | GlobalAveragePooling | false | 8,918 | [
"MIT"
] | 0 | 67355a46df6290aa2fdc1e0266c61daacced3ba1 | https://github.com/Chrisfsj2051/my_tools/tree/67355a46df6290aa2fdc1e0266c61daacced3ba1 |
SimMaxLoss | import torch
class SimMaxLoss(torch.nn.Module):
def __init__(self, margin=0):
super(SimMaxLoss, self).__init__()
self.margin = margin
def forward(self, x, weights):
return -(torch.log(x + self.margin) * weights).mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | CVI-SZU/CLIMS | SimMaxLoss | false | 17,049 | [
"MIT"
] | 4 | 9d3d0123b625b2c6941069e8fb359019a5cabd59 | https://github.com/CVI-SZU/CLIMS/tree/9d3d0123b625b2c6941069e8fb359019a5cabd59 |
Highway | import torch
from torch import nn
class Highway(nn.Module):
def __init__(self, in_size, out_size):
super(Highway, self).__init__()
self.H = nn.Linear(in_size, out_size)
self.H.bias.data.zero_()
self.T = nn.Linear(in_size, out_size)
self.T.bias.data.fill_(-1)
self.r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Emotional-Text-to-Speech/tacotron_pytorch | Highway | false | 5,136 | [
"MIT"
] | 1 | e6b1a3907afb01fe31bcbd77c677667adf6733f5 | https://github.com/Emotional-Text-to-Speech/tacotron_pytorch/tree/e6b1a3907afb01fe31bcbd77c677667adf6733f5 |
PyTorchLHUC | import torch
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
class PyTorchLHUC(pt.nn.Module):
"""
Learning Hidden Unit Contribution
David Vilar. "Learning Hidden Unit Contribution for Adapting Neural
Machine Translation Models" NAACL 2018
:para... | 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 as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
assert_size_stride = torch._C._dynamo.gu... | blchu/sockeye | PyTorchLHUC | false | 1,556 | [
"Apache-2.0"
] | 0 | 28044a44ee409c9b3df1711c0b16bdebdd463b2e | https://github.com/blchu/sockeye/tree/28044a44ee409c9b3df1711c0b16bdebdd463b2e |
NeuralNerwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class NeuralNerwork(nn.Module):
""" Construct a ReLU-activated NN, set Bias to False
Four hidden layers with sizes [1000, 1000, 500, 200]
Features = 784, Targets = 10 classes
"""
def __init__(self, features, targets):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | jf20541/Pruning-DeepNeuralNetwork | NeuralNerwork | false | 10,283 | [
"MIT"
] | 0 | a78a88616c19aa0f1449eb562b7dd8d7c4f47252 | https://github.com/jf20541/Pruning-DeepNeuralNetwork/tree/a78a88616c19aa0f1449eb562b7dd8d7c4f47252 |
ModulatedToRGB | import torch
import torch.nn.functional as F
from copy import deepcopy
import torch.nn as nn
from functools import partial
from torch.nn.init import _calculate_correct_fan
def upsample(in_tens, out_H=64):
"""Upsamples the input to the given size.
Args:
in_tens (Tensor): Tensor with shape [N, C, H, W]... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional as F
from copy import deepcopy
import torch.nn as nn
... | jiangwenj02/mmgeneration | ModulatedToRGB | false | 12,623 | [
"Apache-2.0"
] | 0 | da9ad377ae19260467fc332ddb88f505c38a915a | https://github.com/jiangwenj02/mmgeneration/tree/da9ad377ae19260467fc332ddb88f505c38a915a |
GuidedBackpropReLUasModule | # 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.autograd import Function
import torch.cuda
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = t... | TigerKinger/pytorch-grad-cam | GuidedBackpropReLUasModule | false | 11,936 | [
"MIT"
] | 0 | adb3c56e274fde782bf84d2a77454046bd4c5be4 | https://github.com/TigerKinger/pytorch-grad-cam/tree/adb3c56e274fde782bf84d2a77454046bd4c5be4 |
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... | Sumeer1/VAE_Impute | VAE | false | 9,532 | [
"MIT"
] | 0 | 803195af20fe54352aedf26147a84a470637d560 | https://github.com/Sumeer1/VAE_Impute/tree/803195af20fe54352aedf26147a84a470637d560 |
ChannelGate2d | import torch
import torch.nn as nn
class ChannelGate2d(nn.Module):
def __init__(self, channels, reduction=2):
super(ChannelGate2d, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
padding=0)
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_... | bantiitnab/kaggle-TGS-salt-identification | ChannelGate2d | false | 1,520 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
GlobalSelfAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda
import torch.distributed
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments
), 'Not ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DenDen047/data2text-macro-plan-py | GlobalSelfAttention | false | 7,967 | [
"MIT"
] | 20 | bb01ec6e23dab28c1e969f23bd55776b597fb995 | https://github.com/DenDen047/data2text-macro-plan-py/tree/bb01ec6e23dab28c1e969f23bd55776b597fb995 |
TorchGloVeModel | # 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
from torch.nn.init import xavier_u... | ammarhusain/cs224u | TorchGloVeModel | false | 6,190 | [
"Apache-2.0"
] | 1 | bbdb0aaa6b7437481e2e1fab8e12bbf1996eecd1 | https://github.com/ammarhusain/cs224u/tree/bbdb0aaa6b7437481e2e1fab8e12bbf1996eecd1 |
UpBlock | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.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
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | HamsterBiz/iSeeBetter | UpBlock | false | 11,674 | [
"MIT"
] | 0 | a71cee61583bdedab1f3b368e2cb7dc5ad969aed | https://github.com/HamsterBiz/iSeeBetter/tree/a71cee61583bdedab1f3b368e2cb7dc5ad969aed |
ConvGLU | # 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... | JINHXu/NeMo | ConvGLU | false | 11,618 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
BPRLoss | import torch
import torch.nn as nn
class BPRLoss(nn.Module):
""" BPRLoss, based on Bayesian Personalized Ranking
Args:
- gamma(float): Small value to avoid division by zero
Shape:
- Pos_score: (N)
- Neg_score: (N), same shape as the Pos_score
- Output: scalar.
Exampl... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | MIracleyin/RecBole-notebook | BPRLoss | false | 9,576 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
Bias | # 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... | daviddavini/cs-260-project | Bias | false | 3,390 | [
"MIT"
] | 0 | 9e1067f8ff85c8c573262589bbe52740ef11275d | https://github.com/daviddavini/cs-260-project/tree/9e1067f8ff85c8c573262589bbe52740ef11275d |
AttMSEloss | import torch
import torch.nn.functional
import torch.nn as nn
class AttMSEloss(nn.Module):
def __init__(self):
super(AttMSEloss, self).__init__()
def forward(self, x_org, y_mask, att):
loss_att = ((x_org * y_mask[:, 1, ...].unsqueeze(dim=1) - att) ** 2
).mean()
loss_att =... | 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.functional
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | HelenGuohx/cv-ferattn-code | AttMSEloss | false | 5,288 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
MaxPool | import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class MaxPool(nn.Module):
def __init__(self, kernel_size, stride=1, padding=1, zero_pad=False):
super(MaxPool, self).__init__()
self.zero_pad = nn.ZeroPad2d((1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
... | Ramstein/Retinopathy2 | MaxPool | false | 974 | [
"MIT"
] | 0 | 669e74206c466e6351d4e3df6087c6aa39b5c6c2 | https://github.com/Ramstein/Retinopathy2/tree/669e74206c466e6351d4e3df6087c6aa39b5c6c2 |
ScaledDotProductAttention | # 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.... | Maxi-0902/DRAN | ScaledDotProductAttention | false | 846 | [
"MIT"
] | 0 | c3dbfcbc018446544150dc4e151442d6a9fcd4d9 | https://github.com/Maxi-0902/DRAN/tree/c3dbfcbc018446544150dc4e151442d6a9fcd4d9 |
AdaptiveInstanceNorm | # 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 ... | williamyang1991/DeepPS | AdaptiveInstanceNorm | false | 11,098 | [
"MIT"
] | 0 | f3eb6ba4b0f2ef068361a4bbbd3d6c2c2f6726b4 | https://github.com/williamyang1991/DeepPS/tree/f3eb6ba4b0f2ef068361a4bbbd3d6c2c2f6726b4 |
BasicModel_ConvNet_MaxPool1d | import torch
from torch import Tensor
import torch.nn as nn
from typing import no_type_check
class BasicModel_ConvNet_MaxPool1d(nn.Module):
"""Same as above, but with the MaxPool2d replaced
with a MaxPool1d. This is useful because the MaxPool modules
behave differently to other modules from the perspectiv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Europium248/captum | BasicModel_ConvNet_MaxPool1d | false | 457 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, base_class_num, nway, dropout):
super(Attention, self).__init__()
self.fc1 = nn.Linear(base_class_num, base_class_num // 2)
self.fc2 = nn.Linear(base_class_num // 2, nway)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Zhen-Tan-dmml/GFCIL | Attention | false | 18,188 | [
"MIT"
] | 7 | 9b78210418711a795280c588f55aef63f7df5b3b | https://github.com/Zhen-Tan-dmml/GFCIL/tree/9b78210418711a795280c588f55aef63f7df5b3b |
Policy | # 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.... | AJSVB/GPBT | Policy | false | 2,082 | [
"MIT"
] | 0 | 746c11d06ecc4c3b62fc0a3290d672d336cbb11e | https://github.com/AJSVB/GPBT/tree/746c11d06ecc4c3b62fc0a3290d672d336cbb11e |
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
from torch._inductor.runtime.... | SheepiesLab/plato | Actor | false | 12,089 | [
"Apache-2.0"
] | 0 | 9f5bbfa4b6952d1b3af24be409982d303d54a169 | https://github.com/SheepiesLab/plato/tree/9f5bbfa4b6952d1b3af24be409982d303d54a169 |
PatchEmbed | # 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 import optim as optim
assert_size_stride = torc... | taokong/ibot | PatchEmbed | false | 16,529 | [
"Apache-2.0"
] | 327 | a2ee1ae7495d4ea8fb9ba100434c062f1bd3d1f0 | https://github.com/taokong/ibot/tree/a2ee1ae7495d4ea8fb9ba100434c062f1bd3d1f0 |
CPUForgetMult | import torch
class CPUForgetMult(torch.nn.Module):
def __init__(self):
super(CPUForgetMult, self).__init__()
def forward(self, f, x, hidden_init=None):
result = []
forgets = f.split(1, dim=0)
prev_h = hidden_init
for i, h in enumerate((f * x).split(1, dim=0)):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | dido1998/cruxeval | CPUForgetMult | false | 3,417 | [
"BSD-3-Clause"
] | 0 | 229f7562c3f5e0da6432728e1c42402f51473a84 | https://github.com/dido1998/cruxeval/tree/229f7562c3f5e0da6432728e1c42402f51473a84 |
ConvBlock | # 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 functools import partial... | derwind/dmfont | ConvBlock | false | 15,175 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 |
ContrastiveDistanceLoss | import torch
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
class ContrastiveDistanceLoss(nn.Module):
"""
Contrastive distance loss
"""
def __init__(self, margin=1.0, 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
from torch.nn.modules.loss import *
from torch.nn.modules import *
... | asmekal/catalyst | ContrastiveDistanceLoss | false | 12,117 | [
"MIT"
] | 0 | e11365c0a9812649ceaef14e53061cd5117d8684 | https://github.com/asmekal/catalyst/tree/e11365c0a9812649ceaef14e53061cd5117d8684 |
CQConcatenate | # 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.... | IsaacChanghau/VSLNet | CQConcatenate | false | 13,865 | [
"MIT"
] | 62 | 3793c625f2e251a5f19a0d59f0c83b12e386f808 | https://github.com/IsaacChanghau/VSLNet/tree/3793c625f2e251a5f19a0d59f0c83b12e386f808 |
MultiheadAttentionWrapper | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.optim.lr_scheduler import *
def linear(x):
return x
def activation(func_a):
"""Activation function wrapper
"""
try:
f = eval(func_a)
except:
f = linear
return ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.optim.lr_scheduler import *
assert_s... | anlewy/mt-dnn | MultiheadAttentionWrapper | false | 14,871 | [
"MIT"
] | 2,075 | eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 | https://github.com/anlewy/mt-dnn/tree/eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 |
LowRankResidualEncoderLayer | 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 | LowRankResidualEncoderLayer | false | 12,167 | [
"MIT"
] | 0 | 0af38f16338a9bcfcc11091b1a6b75befd67f234 | https://github.com/bahducoup/factorized_training/tree/0af38f16338a9bcfcc11091b1a6b75befd67f234 |
ShiftedSoftplus | # 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 torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | bokutotu/cgnet | ShiftedSoftplus | false | 1,561 | [
"BSD-3-Clause"
] | 0 | a35170001d969d51548dd01522b1ab93e43741b4 | https://github.com/bokutotu/cgnet/tree/a35170001d969d51548dd01522b1ab93e43741b4 |
AE_2D | import torch
import torch.nn as nn
import torch.utils.data
class AE_2D(nn.Module):
def __init__(self, n_features=4):
super(AE_2D, self).__init__()
self.en1 = nn.Linear(n_features, 20)
self.en2 = nn.Linear(20, 10)
self.en3 = nn.Linear(10, 6)
self.en4 = nn.Linear(6, 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.triton_helpers import libdevice
import torch.nn as ... | gitter-badger/HEPAutoencoders | AE_2D | false | 12,437 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
Conv | # 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... | Labmem-Zhouyx/Attentron_FastSpeech2 | Conv | false | 5,496 | [
"MIT"
] | 1 | ac1c0bd0d23e8314eb2518f3d5abffc9b4b9f5cb | https://github.com/Labmem-Zhouyx/Attentron_FastSpeech2/tree/ac1c0bd0d23e8314eb2518f3d5abffc9b4b9f5cb |
Accuracy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
from torch import Tensor
assert_size_stride = torch._C._dynam... | vixadd/sparseml | Accuracy | false | 4,501 | [
"Apache-2.0"
] | 0 | e2dcb66bad713542158dfe54cba113a0cc02ed39 | https://github.com/vixadd/sparseml/tree/e2dcb66bad713542158dfe54cba113a0cc02ed39 |
MLP | # 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.nn import Module
import torch.nn as nn
from torch.nn.modules.module i... | gear/gfnn | MLP | false | 15,417 | [
"MIT"
] | 46 | 36667861caacba921469d43917d002896e832c3f | https://github.com/gear/gfnn/tree/36667861caacba921469d43917d002896e832c3f |
PreNet | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim.optimizer
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | anh/ForwardTacotron | PreNet | false | 3,112 | [
"MIT"
] | 0 | a58d9244844b4512f5655e154f08f934760c88b3 | https://github.com/anh/ForwardTacotron/tree/a58d9244844b4512f5655e154f08f934760c88b3 |
ChannelNorm | # 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
... | yubin1219/Semantic-Seg | ChannelNorm | false | 4,633 | [
"BSD-2-Clause"
] | 0 | c40bd43d3d7e44bc995b8d041736580dec084251 | https://github.com/yubin1219/Semantic-Seg/tree/c40bd43d3d7e44bc995b8d041736580dec084251 |
WeightedL2 | # 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... | acrosson/dl | WeightedL2 | false | 1,360 | [
"MIT"
] | 0 | 94e68533b38f53f09e9bef460ba96fa389fc8eb4 | https://github.com/acrosson/dl/tree/94e68533b38f53f09e9bef460ba96fa389fc8eb4 |
DeiTAttention | from _paritybench_helpers import _mock_config
import math
import torch
from typing import List
from typing import Tuple
from torch import nn
from typing import Set
import torch.utils.checkpoint
def find_pruneable_heads_and_indices(heads: 'List[int]', n_heads: 'int',
head_size: 'int', already_pruned_heads: 'Set[in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Clemens123/transformers | DeiTAttention | false | 13,205 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
LargeNN | import torch
from torch import nn
import torch.nn.functional as F
class LargeNN(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.l1 = nn.Linear(in_channels, 1024)
self.l2 = nn.Linear(1024, 1024)
self.l3 = nn.Linear(1024, out_channels)
def for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | AustinCai/gmaxup-augmentation | LargeNN | false | 86 | [
"MIT"
] | 0 | a64ca0a76eb333e5ce6b217c301d27ca04d73bce | https://github.com/AustinCai/gmaxup-augmentation/tree/a64ca0a76eb333e5ce6b217c301d27ca04d73bce |
GaussNorm2D | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.autograd
assert_size_stride = torch._C._dyna... | LLNL/fastcam | GaussNorm2D | false | 8,441 | [
"BSD-3-Clause"
] | 25 | 99cefe37528014247319468cf05f54fef259d3bf | https://github.com/LLNL/fastcam/tree/99cefe37528014247319468cf05f54fef259d3bf |
CausalConv1d | # 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
import torch
assert_size_stride = ... | sagelywizard/snail | CausalConv1d | false | 16,350 | [
"MIT"
] | 100 | 1c64787aa970c82f65c3c9d253531d1c2b1bee08 | https://github.com/sagelywizard/snail/tree/1c64787aa970c82f65c3c9d253531d1c2b1bee08 |
PositionalEmbedding | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | THUNLP-MT/PLM4MT | PositionalEmbedding | false | 1,103 | [
"BSD-3-Clause"
] | 0 | 85bd2ee9d96b07ac827e14d4b3e5b0d0924c3401 | https://github.com/THUNLP-MT/PLM4MT/tree/85bd2ee9d96b07ac827e14d4b3e5b0d0924c3401 |
AdaIN | # 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 torch import n... | shimon-c/Machine-Learning-Collection | AdaIN | false | 16,426 | [
"MIT"
] | 3,094 | ac5dcd03a40a08a8af7e1a67ade37f28cf88db43 | https://github.com/shimon-c/Machine-Learning-Collection/tree/ac5dcd03a40a08a8af7e1a67ade37f28cf88db43 |
Conv_ReLU_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
import torch.nn as nn
assert_... | ypf780732/multi-staged-fusion-sr | Conv_ReLU_Block | false | 13,160 | [
"MIT"
] | 0 | 83d82c4310cc9314544793dc0b299a34956044e0 | https://github.com/ypf780732/multi-staged-fusion-sr/tree/83d82c4310cc9314544793dc0b299a34956044e0 |
ScaleReLU | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, nchannels, bias=True, init_scale=1.0):
super().__init__()
self.nchannels = nchannels
self.weight = nn.Parameter(torch.Tensor(1, nchannels, 1, 1))
if bias:
self.bias = nn.Parameter(torch.Tenso... | 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... | CuongNguyen218/ObjectDetection-OneStageDet | ScaleReLU | false | 339 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
Attention | import torch
import torch.optim
from torch import nn
class Attention(nn.Module):
"""
Attention network for calculate attention value
"""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
"""
:param encoder_dim: input size of encoder network
:param decoder_dim: input... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Fpiotro/MOLECULAR-TRANSLATION | Attention | false | 17,298 | [
"MIT"
] | 5 | 050dd0c093ee4e68326c2404c5b4dbf53ca6c8a0 | https://github.com/Fpiotro/MOLECULAR-TRANSLATION/tree/050dd0c093ee4e68326c2404c5b4dbf53ca6c8a0 |
MultiHeadAttn | # 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.... | hamjam/NeMo | MultiHeadAttn | false | 15,533 | [
"Apache-2.0"
] | 4,145 | b3484d32e1317666151f931bfa39867d88ed8658 | https://github.com/hamjam/NeMo/tree/b3484d32e1317666151f931bfa39867d88ed8658 |
GeneratorBlock | import math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
from typing import Tuple
from typing import Optional
from typing import List
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rate E... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | HubBucket-Team/annotated_deep_learning_paper_implementations | GeneratorBlock | false | 5,339 | [
"MIT"
] | 1 | 4a9716b01e336c57739dfdbdd90648276b53c433 | https://github.com/HubBucket-Team/annotated_deep_learning_paper_implementations/tree/4a9716b01e336c57739dfdbdd90648276b53c433 |
BackboneModel1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | rmfan/nni | BackboneModel1 | false | 10,944 | [
"MIT"
] | 0 | 727ee1ce47e070061fe3dab8a2da5d3cd5e55546 | https://github.com/rmfan/nni/tree/727ee1ce47e070061fe3dab8a2da5d3cd5e55546 |
ConvGLU | import torch
import torch.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
def str2act(txt):
"""Translates text to neural network activation"""
return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu':... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.cuda
from torch import nn
import torch.distributed
import torch.uti... | hamjam/NeMo | ConvGLU | false | 15,488 | [
"Apache-2.0"
] | 4,145 | b3484d32e1317666151f931bfa39867d88ed8658 | https://github.com/hamjam/NeMo/tree/b3484d32e1317666151f931bfa39867d88ed8658 |
DAModule | # 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.... | Nitin-Mane/External-Attention-pytorch | DAModule | false | 14,128 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
LogLog | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | awlange/pysurvival | LogLog | false | 14,927 | [
"Apache-2.0"
] | 242 | 841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 | https://github.com/awlange/pysurvival/tree/841b9bc6ce700ba8898d2a1488aa9cd25ee7a8e6 |
Swish | # 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... | ali-zafari/high-fidelity-generative-compression | Swish | false | 9,777 | [
"Apache-2.0"
] | 0 | 37ab8d6727df48f8ebf4577db0986ccd0ffe404b | https://github.com/ali-zafari/high-fidelity-generative-compression/tree/37ab8d6727df48f8ebf4577db0986ccd0ffe404b |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | congson1293/Transformer | EncoderLayer | false | 1,762 | [
"Apache-2.0"
] | 0 | 249638f3287e0ed11c71496178fe2ceac2d758df | https://github.com/congson1293/Transformer/tree/249638f3287e0ed11c71496178fe2ceac2d758df |
mnistmodel_C | # 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... | layel2/layyer-lib | mnistmodel_C | false | 3,876 | [
"MIT"
] | 0 | db48b5c38098ee93d2d34693d98e5ef4d319d919 | https://github.com/layel2/layyer-lib/tree/db48b5c38098ee93d2d34693d98e5ef4d319d919 |
ToRGB | import math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
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 equa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.nn.functional a... | HubBucket-Team/annotated_deep_learning_paper_implementations | ToRGB | false | 5,323 | [
"MIT"
] | 1 | 4a9716b01e336c57739dfdbdd90648276b53c433 | https://github.com/HubBucket-Team/annotated_deep_learning_paper_implementations/tree/4a9716b01e336c57739dfdbdd90648276b53c433 |
DQN_xy4 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DQN_xy4(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super(DQN_xy4, self).__init__()
self.fc1 = nn.Linear(4, 100)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CoAxLab/azad | DQN_xy4 | false | 17,187 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
LastLevelMaxPool | # 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.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | BorisLestsov/retinamask | LastLevelMaxPool | false | 13,765 | [
"MIT"
] | 706 | 265a65f018c64220bcea946d306fc7b07a692b16 | https://github.com/BorisLestsov/retinamask/tree/265a65f018c64220bcea946d306fc7b07a692b16 |
ChannelSpatialSELayer3D | # 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 ... | EmmanuelleB985/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival | ChannelSpatialSELayer3D | false | 11,407 | [
"MIT"
] | 0 | 347883eb6dd5daebba091119ede7a9f5b78076d1 | https://github.com/EmmanuelleB985/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival/tree/347883eb6dd5daebba091119ede7a9f5b78076d1 |
KLDLoss | # 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_... | Cc618/Feature-Changer | KLDLoss | false | 2,080 | [
"MIT"
] | 0 | 7ab82f525c4b5142afec1819732b0fb5f3983152 | https://github.com/Cc618/Feature-Changer/tree/7ab82f525c4b5142afec1819732b0fb5f3983152 |
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 libdevice, math as tl_math
from torch ... | agrawalshubham01/FracNet | FocalLoss | false | 9,714 | [
"Apache-2.0"
] | 0 | 8b912ca65651ff0ee203d9d73cf6ca18539728ac | https://github.com/agrawalshubham01/FracNet/tree/8b912ca65651ff0ee203d9d73cf6ca18539728ac |
VAE | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.modules.loss
class VAE(nn.Module):
""" Variational Autoencoder for dimensional reduction"""
def __init__(self, dim):
super(VAE, self).__init__()
self.dim = dim
self.fc1 = nn.Linear(dim, 400)
sel... | 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... | hurraygong/scGNN | VAE | false | 6,835 | [
"MIT"
] | 1 | bc555895fbd5740ddd82e03187171116889cc10e | https://github.com/hurraygong/scGNN/tree/bc555895fbd5740ddd82e03187171116889cc10e |
ContrastiveLoss | # 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... | kvswim/kv_jhu_cv | ContrastiveLoss | false | 3,864 | [
"MIT"
] | 0 | 2ddf7a9d497aef116a7c043157b8631cea45000d | https://github.com/kvswim/kv_jhu_cv/tree/2ddf7a9d497aef116a7c043157b8631cea45000d |
Gate | # 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 ... | elsehow/Writing-editing-Network | Gate | false | 15,305 | [
"MIT"
] | 79 | a8551cd224a4987a6eec3cf566bcf0793ad36dfd | https://github.com/elsehow/Writing-editing-Network/tree/a8551cd224a4987a6eec3cf566bcf0793ad36dfd |
Grad_hyper | import torch
import torch.nn.functional
class Grad_hyper(torch.nn.Module):
"""
N-D gradient loss.
"""
def __init__(self, penalty='l1'):
super(Grad_hyper, self).__init__()
self.penalty = penalty
def forward(self, y_pred, wts):
dy = torch.abs(y_pred[:, :, 1:, :] - y_pred[:,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration | Grad_hyper | false | 15,758 | [
"MIT"
] | 82 | dfa24a47a564a000aa9b4eea95a6e83a24568359 | https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration/tree/dfa24a47a564a000aa9b4eea95a6e83a24568359 |
pheramon_output_neuron | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AgamChopra/simulation-in-a-box | pheramon_output_neuron | false | 11,154 | [
"MIT"
] | 0 | 2a346a2fc83d79e542b64f1bd45c338d27a1934d | https://github.com/AgamChopra/simulation-in-a-box/tree/2a346a2fc83d79e542b64f1bd45c338d27a1934d |
NoiseInjection | import torch
import torch.nn as nn
class NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | KwonGihyun/DiagonalGAN | NoiseInjection | false | 8,448 | [
"MIT"
] | 13 | 9e401c00e741d700f85df2c715ee11c1e66e1d1c | https://github.com/KwonGihyun/DiagonalGAN/tree/9e401c00e741d700f85df2c715ee11c1e66e1d1c |
Model | # 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... | crisdeodates/AI-depthai-experiments | Model | false | 6,489 | [
"MIT"
] | 1 | 74b8b84a03cb637d20a7fcd091cce11add78bd2c | https://github.com/crisdeodates/AI-depthai-experiments/tree/74b8b84a03cb637d20a7fcd091cce11add78bd2c |
RGBDiff | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class RGBDiff(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, image):
"""
Args:
image (torch.Tensor): (N x T 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 import nn
from torchvision import models as models
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards... | dqawami/openvino_training_extensions | RGBDiff | false | 15,230 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad |
ModMBStddevLayer | import torch
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as autograd
class AllGatherLayer(autograd.Function):
"""All gather layer with backward propagation path.
Indeed, this module is to make ``dist.all_gather()`` in the backward graph.
Such kind of operation has been wi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as... | HXWAndCL/mmgeneration | ModMBStddevLayer | false | 5,256 | [
"Apache-2.0"
] | 1 | 9afb1d740bf56a4ecde5064d5bb2a4e2d777638b | https://github.com/HXWAndCL/mmgeneration/tree/9afb1d740bf56a4ecde5064d5bb2a4e2d777638b |
BertSelfOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.utils.checkpoint
class BertSelfOutput(nn.Module):
def __init__(self, config, twin=False, merge=False):
super().__init__()
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.
layer_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.triton_helpers import libdevice
import torch.nn as ... | igor0/BLIP | BertSelfOutput | false | 16,250 | [
"BSD-3-Clause"
] | 473 | 6d8c3f1e381ed23acb84c55b4adb80e74c08117a | https://github.com/igor0/BLIP/tree/6d8c3f1e381ed23acb84c55b4adb80e74c08117a |
MatchingNetwork | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.optim
import torch.nn.functional as F
import torch.nn.parallel
class MatchingNetwork(nn.Module):
def __init__(self, opt):
super(MatchingNetwork, self).__init__()
scale_cls = opt['scale_cls'] if 'scale_cl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Basasuya/FewShotWithoutForgetting | MatchingNetwork | false | 3,395 | [
"MIT"
] | 0 | eecc70e416ed82999124ddfca1b145f6dbcd74a6 | https://github.com/Basasuya/FewShotWithoutForgetting/tree/eecc70e416ed82999124ddfca1b145f6dbcd74a6 |
MultiheadAttention | import torch
from torch import nn
from torch.nn import functional as F
from typing import *
from torch.nn.parameter import Parameter
from torch.nn import Parameter
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HughMun/MultiBench | MultiheadAttention | false | 13,804 | [
"MIT"
] | 148 | d5712a0815a9486b0e0c76b54cd63c880188fc8e | https://github.com/HughMun/MultiBench/tree/d5712a0815a9486b0e0c76b54cd63c880188fc8e |
Envelope | # 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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | CFF-Dream/pytorch_geometric | Envelope | false | 2,036 | [
"MIT"
] | 0 | 7c19ad74957409ee9e07314ce81524b3113b9c84 | https://github.com/CFF-Dream/pytorch_geometric/tree/7c19ad74957409ee9e07314ce81524b3113b9c84 |
four_layer_conv | # 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
assert_size_stride = torch._C... | peckjon/detectorch | four_layer_conv | false | 16,270 | [
"Apache-2.0"
] | 627 | 69d31250d79a72b12b7419638ef59163f833bbba | https://github.com/peckjon/detectorch/tree/69d31250d79a72b12b7419638ef59163f833bbba |
CMlp | import torch
from torch import nn
import torch.cuda
def conv_1x1x1(inp, oup, groups=1):
return nn.Conv3d(inp, oup, (1, 1, 1), (1, 1, 1), (0, 0, 0), groups=groups)
class CMlp(nn.Module):
"""
CMlp
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GE... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | LoveEachDay/towhee | CMlp | false | 11,655 | [
"Apache-2.0"
] | 0 | 513c9c2626676cadaaf0a16ac3c828d96bec91a1 | https://github.com/LoveEachDay/towhee/tree/513c9c2626676cadaaf0a16ac3c828d96bec91a1 |
Attention | # 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.... | MIT-Omnipush/omnipush-metalearning-baselines | Attention | false | 17,641 | [
"MIT"
] | 4 | b3ba5db7aa5137f1d259470bc6f4bb7019826ab3 | https://github.com/MIT-Omnipush/omnipush-metalearning-baselines/tree/b3ba5db7aa5137f1d259470bc6f4bb7019826ab3 |
Attention | # 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, math as tl_math
im... | deepframwork/TorchBlocks | Attention | false | 6,542 | [
"MIT"
] | 1 | 35f6e1bb83d2b9b05ba914a21fd365cb26ac4a32 | https://github.com/deepframwork/TorchBlocks/tree/35f6e1bb83d2b9b05ba914a21fd365cb26ac4a32 |
SoftConvNotLearnedMask | import math
import torch
import torch.nn as nn
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if init_type == 'gaussian':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
i... | marcelsan/Deep-HdrReconstruction | SoftConvNotLearnedMask | false | 16,013 | [
"BSD-3-Clause"
] | 80 | 7cb0d93938baa6fbe029116451a661c18dfba49e | https://github.com/marcelsan/Deep-HdrReconstruction/tree/7cb0d93938baa6fbe029116451a661c18dfba49e |
MixActiv | import torch
import torch as th
from torch import nn
def gauss(x, mean=0, std=1):
return th.exp(-(x - mean) ** 2 / (2 * std ** 2))
class MixActiv(nn.Module):
def __init__(self):
super().__init__()
self.activations = th.sin, th.tanh, gauss, th.relu
self.n_activs = len(self.activation... | 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... | JiangZehua/control-pcgrl | MixActiv | false | 8,336 | [
"MIT"
] | 15 | e4fd1bf9670e5855f04941ebca34170517c451b4 | https://github.com/JiangZehua/control-pcgrl/tree/e4fd1bf9670e5855f04941ebca34170517c451b4 |
Conv2dBlock | # 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.... | google/dynamic-video-depth | Conv2dBlock | false | 15,453 | [
"Apache-2.0"
] | 144 | 7dab8f9e156fa35735301695ea020aee7221fb31 | https://github.com/google/dynamic-video-depth/tree/7dab8f9e156fa35735301695ea020aee7221fb31 |
Swish | import torch
from torch.functional import F
class Swish(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return F.sigmoid(x) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | bglick13/multi-agent-emergence-environments | Swish | false | 12,151 | [
"MIT"
] | 0 | e02d66f0734d95470d15a4508ff369a75fa093a4 | https://github.com/bglick13/multi-agent-emergence-environments/tree/e02d66f0734d95470d15a4508ff369a75fa093a4 |
WeightNormTransConv2d | import torch
import torch.nn as nn
from torch.nn.utils.weight_norm import weight_norm
import torch.onnx
class WeightNormTransConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, dilation=1, groups=1, bias=True,
padding_mode='zeros'):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AntixK/Neural-Blocks | WeightNormTransConv2d | false | 17,031 | [
"MIT"
] | 3 | 018a44bbb703fc848234b95a3e604576bd9df88f | https://github.com/AntixK/Neural-Blocks/tree/018a44bbb703fc848234b95a3e604576bd9df88f |
OutlookAttention | # 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.... | Nitin-Mane/External-Attention-pytorch | OutlookAttention | false | 14,114 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
EqualConv2d | # 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 math import sqrt
assert_size_stride = torch._C._dynam... | IW276/IW276SS20-P3 | EqualConv2d | false | 574 | [
"MIT"
] | 0 | 7970bd332cc021cf1879f326c444eff3cf8593a1 | https://github.com/IW276/IW276SS20-P3/tree/7970bd332cc021cf1879f326c444eff3cf8593a1 |
HardSigmoid | # 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... | stepbuystep/LightNAS | HardSigmoid | false | 4,373 | [
"Apache-2.0"
] | 0 | 030d0e13e0c85354ed711e36fc4b91b1541f95e5 | https://github.com/stepbuystep/LightNAS/tree/030d0e13e0c85354ed711e36fc4b91b1541f95e5 |
Inception_Temporal_Layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | WoodSugar/GSTNet | Inception_Temporal_Layer | false | 18,112 | [
"MIT"
] | 8 | 3c21cfc8a873d61336f257030a28fdee12dcee2f | https://github.com/WoodSugar/GSTNet/tree/3c21cfc8a873d61336f257030a28fdee12dcee2f |
RegModel | # 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
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | DineshChauhan/fastai_docs | RegModel | false | 11,345 | [
"Apache-2.0"
] | 0 | cf4d88073fb6f3ef7331b5360618b8dd95eb9345 | https://github.com/DineshChauhan/fastai_docs/tree/cf4d88073fb6f3ef7331b5360618b8dd95eb9345 |
L2Norm | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
from math import sqrt as sqrt
from itertools import product as product
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.... | Abraham-Xu/TF2 | L2Norm | false | 13,237 | [
"Apache-2.0"
] | 144 | a5bc18acb7743dc5b6e85cfbefa8b88c3785ce78 | https://github.com/Abraham-Xu/TF2/tree/a5bc18acb7743dc5b6e85cfbefa8b88c3785ce78 |
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