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 |
|---|---|---|---|---|---|---|---|---|---|---|
ATLoss | # 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 Tens... | techthiyanes/DeepPavlov | ATLoss | false | 16,568 | [
"Apache-2.0"
] | 5,893 | 08555428388fed3c7b036c0a82a70a25efcabcff | https://github.com/techthiyanes/DeepPavlov/tree/08555428388fed3c7b036c0a82a70a25efcabcff |
AE_2D_v50 | import torch
import torch.nn as nn
import torch.utils.data
class AE_2D_v50(nn.Module):
def __init__(self, n_features=4):
super(AE_2D_v50, self).__init__()
self.en1 = nn.Linear(n_features, 50)
self.en2 = nn.Linear(50, 50)
self.en3 = nn.Linear(50, 50)
self.en4 = nn.Linear(50... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_v50 | false | 12,431 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | graciofilipe/deep-learning-v2-pytorch | Net | false | 12,480 | [
"MIT"
] | 0 | b1aa2189c99ecd1b79deb6c499bae9d1fa52fa19 | https://github.com/graciofilipe/deep-learning-v2-pytorch/tree/b1aa2189c99ecd1b79deb6c499bae9d1fa52fa19 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 5)
self.l2 = nn.Linear(5, 3)
self.l3 = nn.Linear(3, action_dim)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LampKang/CityLearn | Actor | false | 2,497 | [
"MIT"
] | 0 | d6c178054c385ca991a5384e287f18a1d6380159 | https://github.com/LampKang/CityLearn/tree/d6c178054c385ca991a5384e287f18a1d6380159 |
BboxHead | import torch
import torch.nn as nn
from itertools import product as product
class BboxHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=2):
super(BboxHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(
1, 1), stride=1, padding=0)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | ai18435136351/facenet-retinaface-pytorch | BboxHead | false | 14,765 | [
"MIT"
] | 48 | f228969e46d7402170b708798a210de552879d16 | https://github.com/ai18435136351/facenet-retinaface-pytorch/tree/f228969e46d7402170b708798a210de552879d16 |
Conv2dSame | import math
import torch
from torch import nn
from typing import List
from typing import Union
import torch.nn.functional as F
from typing import Optional
from typing import Tuple
from torch.nn.common_types import _size_2_t
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int') ->int:
"""
Calculate asym... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from typing import List
from typing import Unio... | SimonCqk/towhee | Conv2dSame | false | 9,624 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
FastAdaptiveAvgPool2d | # 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._utils
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_si... | Alicegaz/torchok | FastAdaptiveAvgPool2d | false | 16,918 | [
"Apache-2.0"
] | 8 | 7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 | https://github.com/Alicegaz/torchok/tree/7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AyushExel/s3prl | Model | false | 1,993 | [
"MIT"
] | 0 | 6531904e9621a778978b9cfef3ba9f582e56639a | https://github.com/AyushExel/s3prl/tree/6531904e9621a778978b9cfef3ba9f582e56639a |
FeatureResizer | import torch
from torch import nn
class FeatureResizer(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
"""
def __init__(self, input_feat_size, output_feat_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | XiaoJake/MTTR | FeatureResizer | false | 14,595 | [
"Apache-2.0"
] | 516 | c383c5b151e3c97aeb45cd2fb4bf08719016498b | https://github.com/XiaoJake/MTTR/tree/c383c5b151e3c97aeb45cd2fb4bf08719016498b |
DiscrimNet | # 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 ... | ven-kyoshiro/PILCO-1 | DiscrimNet | false | 10,956 | [
"MIT"
] | 0 | 61c4ef18a6bbecbeb6a10784a7925d31f46dd23b | https://github.com/ven-kyoshiro/PILCO-1/tree/61c4ef18a6bbecbeb6a10784a7925d31f46dd23b |
AvgPoolPad | import torch
import torch.nn as nn
import torch.nn.init
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_pad=... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | MichoelSnow/data_science | AvgPoolPad | false | 9,866 | [
"MIT"
] | 0 | 7f6c054624268308ec4126a601c9fa8bc5de157c | https://github.com/MichoelSnow/data_science/tree/7f6c054624268308ec4126a601c9fa8bc5de157c |
GCN | import torch
from torchvision.datasets import *
import torch.nn as nn
from torchvision.transforms import *
class GCN(nn.Module):
def __init__(self, num_state, num_node, bias=False):
super(GCN, self).__init__()
self.conv1 = nn.Conv1d(num_node, num_node, kernel_size=1, padding=0,
stride... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 torchvision.datasets imp... | tousifulhaque/DANet | GCN | false | 4,456 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
HardSigmoid | import torch
import torch.nn as nn
class HardSigmoid(nn.Module):
"""hardsigmoid activation func used in squeeze-and-excitation module (see MobileNetV3)"""
def __init__(self):
super(HardSigmoid, self).__init__()
def forward(self, x):
return nn.ReLU6(inplace=True)(x + 3.0) / 6.0
def get_... | 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 |
NTXent | # 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.... | salesforce/CoSeRec | NTXent | false | 10,765 | [
"BSD-3-Clause"
] | 0 | c0bf5e5c3a5fd645efd3d6cdb9ff6a98d1c477ef | https://github.com/salesforce/CoSeRec/tree/c0bf5e5c3a5fd645efd3d6cdb9ff6a98d1c477ef |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | UdbhavPrasad072300/Kaggle-Competition-Templates | MultiHeadAttention | false | 1,177 | [
"MIT"
] | 0 | f3c93ff60ae33af9b6c6d79d30c5099eb250396c | https://github.com/UdbhavPrasad072300/Kaggle-Competition-Templates/tree/f3c93ff60ae33af9b6c6d79d30c5099eb250396c |
MultAttention | import torch
import torch.nn as nn
class MultAttention(nn.Module):
"""
Multiplicative attention similar to Vaswani et al.
"""
def __init__(self, key_dim: 'int', val_dim: 'int', out_dim: 'int'):
super(MultAttention, self).__init__()
self.key_encoder = nn.Linear(key_dim, out_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kage08/CAMul | MultAttention | false | 10,385 | [
"MIT"
] | 0 | 79f8a27f472943229fb087bae8e405e38e5e0b47 | https://github.com/kage08/CAMul/tree/79f8a27f472943229fb087bae8e405e38e5e0b47 |
BPRLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BPRLoss(nn.Module):
def __init__(self):
super(BPRLoss, self).__init__()
def forward(self, logit):
"""
Args:
logit (BxB): Variable that stores the logits for the items in the mini-batch
... | 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... | hungthanhpham94/GRU4REC-pytorch | BPRLoss | false | 15,571 | [
"Apache-2.0"
] | 184 | 666b84264c4afae757fe55c6997dcf0a4da1d44e | https://github.com/hungthanhpham94/GRU4REC-pytorch/tree/666b84264c4afae757fe55c6997dcf0a4da1d44e |
BartClassificationHead | import torch
import torch.nn as nn
import torch.utils.checkpoint
class BartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim: 'int', inner_dim: 'int', num_classes:
'int', pooler_dropout: 'float'):
super().__init__()
self.de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Hzfinfdu/Black-Box-Tuning | BartClassificationHead | false | 2,468 | [
"MIT"
] | 0 | 64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 | https://github.com/Hzfinfdu/Black-Box-Tuning/tree/64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 |
GCNLayer | # 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 ... | Roc-Ng/HANet | GCNLayer | false | 8,710 | [
"MIT"
] | 34 | e679703e9e725205424d87f750358fb4f62ceec5 | https://github.com/Roc-Ng/HANet/tree/e679703e9e725205424d87f750358fb4f62ceec5 |
My_Tanh | import torch
import torch.utils.data
import torch.nn as nn
class My_Tanh(nn.Module):
def __init__(self):
super(My_Tanh, self).__init__()
self.tanh = nn.Tanh()
def forward(self, x):
return 0.5 * (self.tanh(x) + 1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_ini... | 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | SUTDBrainLab/MGP-VAE | My_Tanh | false | 8,731 | [
"MIT"
] | 30 | 0b7c252f9f7bdcdf3c4177ac40585633a0e98a0f | https://github.com/SUTDBrainLab/MGP-VAE/tree/0b7c252f9f7bdcdf3c4177ac40585633a0e98a0f |
MeanNormLoss | import torch
from torch import Tensor
from torch import nn
class MeanNormLoss(nn.Module):
def forward(self, input: 'Tensor', target: 'Tensor') ->Tensor:
size = [input.size(0), input.size(1), -1]
input = input.view(*size)
target = target.view(*size)
diff = target - input
lo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Zinoex/hyperverlet | MeanNormLoss | false | 18,185 | [
"MIT"
] | 7 | 431ef92fa2448ce69c357f01c0862353067bfa8a | https://github.com/Zinoex/hyperverlet/tree/431ef92fa2448ce69c357f01c0862353067bfa8a |
GCNLayer | import torch
import torch.nn as nn
class GCNLayer(nn.Module):
def __init__(self, in_ft, out_ft, act='prelu', bias=True):
super(GCNLayer, self).__init__()
self.fc = nn.Linear(in_ft, out_ft, bias=False)
self.act = nn.PReLU() if act == 'prelu' else nn.ReLU()
if bias:
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | GRAND-Lab/MERIT | GCNLayer | false | 8,116 | [
"MIT"
] | 18 | c1cc62056254b1ea2931eef47ccde1e717ff5afe | https://github.com/GRAND-Lab/MERIT/tree/c1cc62056254b1ea2931eef47ccde1e717ff5afe |
RSubFloat | # 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... | bunderhi/torch2trt | RSubFloat | false | 1,613 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
ResidualBlockNoBN | # 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 as nn
fr... | Lotayou/BasicSR | ResidualBlockNoBN | false | 2,635 | [
"Apache-2.0",
"MIT"
] | 0 | 6cf9a706dd680d54f7dc26e87318ff79f76c0dbf | https://github.com/Lotayou/BasicSR/tree/6cf9a706dd680d54f7dc26e87318ff79f76c0dbf |
DenseCrossEntropy | # 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... | CTPLab/IID_representation_learning | DenseCrossEntropy | false | 4,948 | [
"MIT"
] | 1 | b9dc13536963f9af332b039f7cc772e2f1090c62 | https://github.com/CTPLab/IID_representation_learning/tree/b9dc13536963f9af332b039f7cc772e2f1090c62 |
FourierFeatures | import math
import torch
import torch.nn as nn
class FourierFeatures(nn.Module):
def __init__(self, in_features, out_features, std=1.0):
super().__init__()
assert out_features % 2 == 0
self.weight = nn.Parameter(torch.randn([out_features // 2,
in_features]) * std)
def for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | corajr/diffusion_gen | FourierFeatures | false | 12,228 | [
"MIT"
] | 0 | 724377c8e244120cbd1caa75d474e3e14ded9bfa | https://github.com/corajr/diffusion_gen/tree/724377c8e244120cbd1caa75d474e3e14ded9bfa |
DilConv1dWithGLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class DilConv1dWithGLU(nn.Module):
def __init__(self, num_channels, dilation, lenght=100, kernel_size=2,
activation=F.leaky_relu, residual_connection=True, dropout=0.2):
super(DilConv1dWithGLU, self).__init__()
self.dilati... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | icyray/proGENTRL | DilConv1dWithGLU | false | 6,880 | [
"MIT"
] | 1 | c48305c3411ecb604c4f26f5e6b62f285e42e696 | https://github.com/icyray/proGENTRL/tree/c48305c3411ecb604c4f26f5e6b62f285e42e696 |
cosine_similarity | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class cosine_similarity(nn.Module):
def __init__(self, args):
super(cosine_similarity, self).__init__()
self.row_wise_avgpool = nn.AvgPool1d(kernel_size=3, stride=1)
def forward(self, x, y):
x = x.transp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IBM/fold2seq | cosine_similarity | false | 8,798 | [
"Apache-2.0"
] | 33 | b9a97d81eac329b5259ad10e2a6f4fe80ade542f | https://github.com/IBM/fold2seq/tree/b9a97d81eac329b5259ad10e2a6f4fe80ade542f |
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.... | JiaqiLiu/PyTorch-NLP | Attention | false | 13,897 | [
"BSD-3-Clause"
] | 2,125 | 71d2ce1e8b8da5ab4e7732d1ebf971150986e6c8 | https://github.com/JiaqiLiu/PyTorch-NLP/tree/71d2ce1e8b8da5ab4e7732d1ebf971150986e6c8 |
ActNorm2D | import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
class ActNorm2D(nn.Module):
def __init__(self, num_channels, eps=1e-05):
super(ActNorm2D, self).__init__()
self.eps = eps
self.num_channels = num_channels
self._log_scale = 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.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parame... | lingzenan/invertible-resnet | ActNorm2D | false | 7,102 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
RON | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class RON(nn.Module):
def __init__(self, lat_inC, top_inC, outC):
super(RON, self).__init__()
self.latlayer = nn.Conv2d(lat_inC, outC, 3, 1, padding=1)
self.toplayer = nn.ConvTranspos... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 as sqrt
from itertools import produc... | KaiOtter/pytorch_DSOD_variants | RON | false | 5,422 | [
"MIT"
] | 1 | f29088b13b24f24e2cf20e9a2dc800cd6dbde145 | https://github.com/KaiOtter/pytorch_DSOD_variants/tree/f29088b13b24f24e2cf20e9a2dc800cd6dbde145 |
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
import torch.random
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | HEmile/KENN-PyTorch | Join | false | 17,327 | [
"BSD-3-Clause"
] | 5 | e39386f298587ab70ecea88180121ef8cf6ff9bc | https://github.com/HEmile/KENN-PyTorch/tree/e39386f298587ab70ecea88180121ef8cf6ff9bc |
CriterionAT | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch._utils
import torch.optim
def at(x):
return F.normalize(x.pow(2).mean(0).reshape(1, -1), dim=1)
class CriterionAT(nn.Module):
def __init__(self):
super(CriterionAT, self).__init__()
self.at = at
def fo... | 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
from t... | yubin1219/Semantic-Seg | CriterionAT | false | 4,641 | [
"BSD-2-Clause"
] | 0 | c40bd43d3d7e44bc995b8d041736580dec084251 | https://github.com/yubin1219/Semantic-Seg/tree/c40bd43d3d7e44bc995b8d041736580dec084251 |
Classify | import torch
import torch.nn as nn
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Classify(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
super(Classify, self).__init__()
self.aap = nn.AdaptiveAvgP... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Alex-Beh/hand_tracking | Classify | false | 11,159 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
ChannelSpatialSELayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelSELayer(nn.Module):
"""
Re-implementation of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
"""
def __init__(self, num_channels, reduction_ratio=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
import torch.nn as nn
import ... | HiLab-git/PyMIC | ChannelSpatialSELayer | false | 13,777 | [
"Apache-2.0"
] | 147 | abf5c43de43668b85f4c049c95a8f1b7cf1d9f16 | https://github.com/HiLab-git/PyMIC/tree/abf5c43de43668b85f4c049c95a8f1b7cf1d9f16 |
ClassHead | import torch
import torch.nn as nn
from itertools import product as product
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=2):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 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
import torch.nn as nn
from itertools import product as product
assert_size_strid... | ai18435136351/facenet-retinaface-pytorch | ClassHead | false | 14,770 | [
"MIT"
] | 48 | f228969e46d7402170b708798a210de552879d16 | https://github.com/ai18435136351/facenet-retinaface-pytorch/tree/f228969e46d7402170b708798a210de552879d16 |
RNNCell | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
from torch.nn import Parameter
def clip_grad(v, min, max):
v_tmp = v.expand_as(v)
v_tmp.register_hook(lambda g: g.clamp(min, max))
return v_tmp
class RNNCellBase(Module):
def __repr__(self):
s = '{name}({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.nn import Module
i... | CSLT-THU/Vivi_3.0 | RNNCell | false | 17,042 | [
"Apache-2.0"
] | 3 | 86996d99d662cd33100755501a971c41ce30ca70 | https://github.com/CSLT-THU/Vivi_3.0/tree/86996d99d662cd33100755501a971c41ce30ca70 |
MSBlock | import torch
import torch.nn as nn
class MSBlock(nn.Module):
def __init__(self, c_in, rate=4):
super(MSBlock, self).__init__()
self.rate = rate
self.conv = nn.Conv2d(c_in, 32, 3, stride=1, padding=1)
self.relu = nn.ReLU(inplace=True)
dilation = self.rate * 1 if self.rate >... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | CM-BF/FeatureFlow | MSBlock | false | 13,434 | [
"MIT"
] | 161 | 06642697922f17211e5faa353e24b1a0946885b1 | https://github.com/CM-BF/FeatureFlow/tree/06642697922f17211e5faa353e24b1a0946885b1 |
ConvElu | # 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... | XiaobingSuper/intel-extension-for-pytorch | ConvElu | false | 9,718 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
ME | import torch
class ME(torch.nn.Module):
def __init__(self, in_features, out_features, n_leaf, projection=
'linear', dropout=0.0):
super(ME, self).__init__()
self.proj = projection
self.n_leaf = n_leaf
self.in_features = in_features
self.out_features = out_features
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | alper111/hmog | ME | false | 6,183 | [
"MIT"
] | 1 | 556da11600c97bcb075a0f19ffc284120d9789d2 | https://github.com/alper111/hmog/tree/556da11600c97bcb075a0f19ffc284120d9789d2 |
Encoder | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
""" VAE encoder """
def __init__(self, img_channels, latent_size):
super(Encoder, self).__init__()
self.latent_size = latent_size
self.img_channels = img_channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | FabianSchuetze/world-models | Encoder | false | 13,705 | [
"MIT"
] | 440 | d6abd9ce97409734a766eb67ccf0d1967ba9bf0c | https://github.com/FabianSchuetze/world-models/tree/d6abd9ce97409734a766eb67ccf0d1967ba9bf0c |
FixedNorm | import torch
import torch.nn as nn
class FixedNorm(nn.Module):
def __init__(self, d):
super().__init__()
self.dd = d ** (-1.0 / 2)
def forward(self, x):
norm_x = x.norm(2, dim=-1, keepdim=True)
x_normed = x / (norm_x * self.dd + 1e-12)
return x_normed
def get_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | BlinkDL/RWKV-LM | FixedNorm | false | 13,402 | [
"BSD-2-Clause"
] | 102 | b48aa1d430a71ced8ae6a665c47f5dbd95f6f6ab | https://github.com/BlinkDL/RWKV-LM/tree/b48aa1d430a71ced8ae6a665c47f5dbd95f6f6ab |
Skew | # 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Lezcano/tutorials | Skew | false | 5,512 | [
"BSD-3-Clause"
] | 1 | 24946b2e6d3d825afed6b35c1c4d618a70a88be8 | https://github.com/Lezcano/tutorials/tree/24946b2e6d3d825afed6b35c1c4d618a70a88be8 |
Sigmoid | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import Sigmoid
class Sigmoid(nn.Module):
def __init__(self, inplace: 'bool'=False):
super(Sigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.sigmoid_() if self.inplace else x.sigmoid... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | BigFishMaster/tnt | Sigmoid | false | 17,157 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
FusedLeakyReLU | import torch
from torch import nn
import torch.utils.checkpoint
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)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.checkpoint
from torch.nn import functional as F
assert_size_stride = torch._C._dynamo.guards.assert_... | Dokhyam/StyleCLIP | FusedLeakyReLU | false | 9,172 | [
"MIT"
] | 0 | 3953c6fda14672762897d3ee16c0458dc848c21d | https://github.com/Dokhyam/StyleCLIP/tree/3953c6fda14672762897d3ee16c0458dc848c21d |
FixupResidual | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class FixupResidual(nn.Module):
def __init__(self, depth, num_residual):
super().__init__()
self.conv1 = nn.Conv2d(depth, depth, 3, padding=1, bias=False)
self.conv2 = nn.Conv2d(depth, depth, 3, padding=1, 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 math
import torch.nn a... | PacktPublishing/Hands-On-Reinforcement-Learning-for-Games | FixupResidual | false | 8,665 | [
"MIT"
] | 41 | 045b8846f2558aa8fb8ac8cef5c71ee098cb9b22 | https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-for-Games/tree/045b8846f2558aa8fb8ac8cef5c71ee098cb9b22 |
CrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... | 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
... | Andrew-Zhu/DyFPN | CrossEntropyLoss | false | 7,725 | [
"Apache-2.0"
] | 32 | a74463b59c4ce28253c2449a07c0f6692a0147a1 | https://github.com/Andrew-Zhu/DyFPN/tree/a74463b59c4ce28253c2449a07c0f6692a0147a1 |
GaussianBlock | import math
import torch
import torch.nn as nn
import torch.optim
import torch.multiprocessing
from torch.nn.parameter import Parameter
class FullyConnected(nn.Module):
def __init__(self, in_features, out_features, bias=True):
"""
Fully connected layer of learnable weights with learnable 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 math
import torch.nn a... | bouracha/Gen_Motion | GaussianBlock | false | 9,927 | [
"MIT"
] | 0 | 873caa496d14c9a9723581cdf1464f44db4cf358 | https://github.com/bouracha/Gen_Motion/tree/873caa496d14c9a9723581cdf1464f44db4cf358 |
MaxPool2d | # 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 typing import *
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | cbarrick/csb | MaxPool2d | false | 6,395 | [
"MIT"
] | 1 | 0368036ddb7594c0b6e7cdc704aeec918786e58a | https://github.com/cbarrick/csb/tree/0368036ddb7594c0b6e7cdc704aeec918786e58a |
HyperpriorAnalysis | # 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.... | ahmedfgad/high-fidelity-generative-compression | HyperpriorAnalysis | false | 6,163 | [
"Apache-2.0"
] | 1 | f3c6aa3472e3c629cbc35eefb0957119c913054a | https://github.com/ahmedfgad/high-fidelity-generative-compression/tree/f3c6aa3472e3c629cbc35eefb0957119c913054a |
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... | jth1011/ECE539-Project | Net | false | 12,653 | [
"MIT"
] | 0 | bce6ffd75da92e862d8fda3852be247602b1567e | https://github.com/jth1011/ECE539-Project/tree/bce6ffd75da92e862d8fda3852be247602b1567e |
FeatureEncoder | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | JiwanChung/tapm | FeatureEncoder | false | 8,389 | [
"MIT"
] | 14 | ec42b139d1c012daccc55f85e67744488d526476 | https://github.com/JiwanChung/tapm/tree/ec42b139d1c012daccc55f85e67744488d526476 |
DeconvBlock | import torch
import torch.nn as nn
class DeconvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(DeconvBlock, self).__init__()
self.conv = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size=3, stride=2, padding=1, output_padding=0)
self.pad = nn.Ref... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | maxuanquang/FeatDepth | DeconvBlock | false | 4,063 | [
"MIT"
] | 0 | cc68d9f1f49b65ace8f2918af5b9d552ecd80ba4 | https://github.com/maxuanquang/FeatDepth/tree/cc68d9f1f49b65ace8f2918af5b9d552ecd80ba4 |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, plan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | HappyBelief/ContraD | BasicBlock | false | 13,753 | [
"MIT"
] | 168 | abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f | https://github.com/HappyBelief/ContraD/tree/abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f |
PAM_Module | from torch.nn import Module
import torch
import torch.serialization
import torch
import torch.utils.data
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
class PAM_Module(Module):
""" Position attention module"""
def __init__(self, in_dim):
super(PAM_Module, sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | vis-opt-group/GTANet | PAM_Module | false | 4,494 | [
"MIT"
] | 0 | 269ff4418ee5f0267987e1fa4c69bda13e5cb00d | https://github.com/vis-opt-group/GTANet/tree/269ff4418ee5f0267987e1fa4c69bda13e5cb00d |
DownsampleA | # 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... | QIU023/continual-learning-reproduce | DownsampleA | false | 9,477 | [
"MIT"
] | 0 | 772faa6904b3488fa5deee14f03d86f3b3664a87 | https://github.com/QIU023/continual-learning-reproduce/tree/772faa6904b3488fa5deee14f03d86f3b3664a87 |
InnerProductDecoder | # 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.nn as nn
import torch.nn.modules.loss
assert_size_stride = torch._C... | HongyiZhu/EHI | InnerProductDecoder | false | 540 | [
"MIT"
] | 0 | 9fbbc6046546dd7fc6de5d831b4c941bc4404e02 | https://github.com/HongyiZhu/EHI/tree/9fbbc6046546dd7fc6de5d831b4c941bc4404e02 |
GNNLayer | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GNNLayer(Module):
def __init__(self, in_features, out_features):
super(GNNLayer, self).__init__()
self.in_features = in_features
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
f... | drzhang3/SDCN | GNNLayer | false | 15,229 | [
"Apache-2.0"
] | 146 | 3d11365bcb4af2cbe9625362737f1224aeea3b72 | https://github.com/drzhang3/SDCN/tree/3d11365bcb4af2cbe9625362737f1224aeea3b72 |
BertLayerNorm | # 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
from torch.nn import Module
import torch.nn as nn
assert_size_stride = torch._C... | axiserr/Hetu | BertLayerNorm | false | 14,933 | [
"Apache-2.0"
] | 82 | 0052f727488db0570d6b37f63549b43b0920bc29 | https://github.com/axiserr/Hetu/tree/0052f727488db0570d6b37f63549b43b0920bc29 |
ResidualBlock | import torch
import torch.nn as nn
import torch.nn.parallel
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1,
stride=1):
super(ResidualBlock, self).__init__()
self.padding1 = nn.ReflectionPad2d(padding)
self.conv1 = nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | qiyuqianxai/debvc | ResidualBlock | false | 10,784 | [
"MIT"
] | 0 | 1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 | https://github.com/qiyuqianxai/debvc/tree/1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 |
FeatureCorrelation | # 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.... | JiwonCocoder/matching1 | FeatureCorrelation | false | 648 | [
"MIT"
] | 0 | 75274312a4ab6ba318c3d241abc0eb292f6ce69c | https://github.com/JiwonCocoder/matching1/tree/75274312a4ab6ba318c3d241abc0eb292f6ce69c |
MeanVoxelFeatureExtractor | # 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... | GuilinZ/PCDet | MeanVoxelFeatureExtractor | false | 2,312 | [
"Apache-2.0"
] | 0 | f39769160854871bec9954630b9a4369b603391d | https://github.com/GuilinZ/PCDet/tree/f39769160854871bec9954630b9a4369b603391d |
SkipLastTargetChannelWrapper | # 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... | YinanZYN/pytorch-3dunet | SkipLastTargetChannelWrapper | false | 11,988 | [
"MIT"
] | 0 | d1494f421a836af54c3dde65c54e3e62d5c00800 | https://github.com/YinanZYN/pytorch-3dunet/tree/d1494f421a836af54c3dde65c54e3e62d5c00800 |
RepeatModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class RepeatModule(torch.nn.Module):
def __init__(self, repeats):
super(RepeatModule, self).__init__()
self.repeats = repeats
def forward(self, tensor):
tensor = tensor + tensor
return tensor.repeat(self.repeats)... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | RepeatModule | false | 3,315 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
C3D | import logging
import torch
import torch.nn as nn
class C3D(nn.Module):
def __init__(self, pretrained=None, modality='RGB'):
super(C3D, self).__init__()
self.pretrained = pretrained
self.modality = modality
inplace = True
assert modality in ['RGB']
self.conv1a = 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 logging
import torch.n... | hushunda/mmaction | C3D | false | 10,365 | [
"Apache-2.0"
] | 0 | b599273ddb80fd74ecf51ef5fa0c81639ea723c5 | https://github.com/hushunda/mmaction/tree/b599273ddb80fd74ecf51ef5fa0c81639ea723c5 |
DeepTable3 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class DeepTable3(nn.Module):
"""A deep differentialable 'Table' for learning one-hot input and output.
"""
def __init__(self, in_channels, out_channels, num_hidden1=200,
num_hidden2=100):
super(Deep... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CoAxLab/azad | DeepTable3 | false | 17,181 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 |
Invertible1x1Conv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | mbaddar1/normalizing-flows | Invertible1x1Conv | false | 10,773 | [
"MIT"
] | 0 | d1409464a65234354b29ed9ea0ede2d12100440c | https://github.com/mbaddar1/normalizing-flows/tree/d1409464a65234354b29ed9ea0ede2d12100440c |
ConcatenateChannels | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ClashLuke/memcnn | ConcatenateChannels | false | 288 | [
"MIT"
] | 0 | 1d48132282c02506ca3d35540f819c4c9130eab4 | https://github.com/ClashLuke/memcnn/tree/1d48132282c02506ca3d35540f819c4c9130eab4 |
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
import math
import torch.nn ... | Chahalprincy/deepchem | ScaleNorm | false | 228 | [
"MIT"
] | 0 | 9d1a6a879cc74b065694b3ddb763d52151d57b7a | https://github.com/Chahalprincy/deepchem/tree/9d1a6a879cc74b065694b3ddb763d52151d57b7a |
IMul | import torch
class IMul(torch.nn.Module):
def __init__(self):
super(IMul, self).__init__()
def forward(self, x, y):
x *= y
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | PogChamper/torch2trt | IMul | false | 14,195 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
AvgPoolPad | import torch
import torch.nn as nn
import torch.utils.data
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | BigFishMaster/tnt | AvgPoolPad | false | 17,158 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
Hill | # 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... | tianyu-lu/latent_ode | Hill | false | 13,031 | [
"MIT"
] | 0 | 1a9e9415eda1837ed78e50009752b90eda3ca0db | https://github.com/tianyu-lu/latent_ode/tree/1a9e9415eda1837ed78e50009752b90eda3ca0db |
criticAttention | # 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.... | Bachery/Shape-driven-Coordinate-Ordering | criticAttention | false | 16,983 | [
"MIT"
] | 6 | 6afa933a882cbe7a40ddf1de169537eccfe415b7 | https://github.com/Bachery/Shape-driven-Coordinate-Ordering/tree/6afa933a882cbe7a40ddf1de169537eccfe415b7 |
softCrossEntropy | # 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... | Lingkai-Kong/Calibrated-BERT-Fine-Tuning | softCrossEntropy | false | 8,452 | [
"Apache-2.0"
] | 29 | 34b8dbf1bfb0d1e466621f149622933bfeab1555 | https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning/tree/34b8dbf1bfb0d1e466621f149622933bfeab1555 |
SE | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class SwishEfficient(torch.autograd.Function):
"""Swish activation function: x * sigmoid(x)."""
@staticmethod
def forward(ctx, x):
result = x * torch.sigmoid(x)
ctx.save_for_backward(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 itertools import chain a... | JaywongWang/SlowFast | SE | false | 8,371 | [
"Apache-2.0"
] | 43 | 366467aafc856712fdc3e9c4cce8e90969047ee6 | https://github.com/JaywongWang/SlowFast/tree/366467aafc856712fdc3e9c4cce8e90969047ee6 |
WSDiceLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class WSDiceLoss(nn.Module):
def __init__(self, smooth=100.0, power=2.0, v2=0.85, v1=0.15):
super().__init__()
self.smooth = smooth
self.power = power
self.v2 = v2
self.v1 = v1
def dic... | 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
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | devaansh100/pytorch_connectomics | WSDiceLoss | false | 6,566 | [
"MIT"
] | 1 | b1e4b16b0480546ea806d14876208080815ed964 | https://github.com/devaansh100/pytorch_connectomics/tree/b1e4b16b0480546ea806d14876208080815ed964 |
CosineFastRCNNOutputLayers | import math
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
import torch.nn
class NormalizedLinear(torch.nn.Module):
"""
A advanced Linear layer which supports weight normalization or cosine normalization.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tonysy/cvpods | CosineFastRCNNOutputLayers | false | 16,611 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
DiscriminatorLoss | # 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
import torch.utils.data
import torch.nn.functional
import tor... | mcx/annotated_deep_learning_paper_implementations | DiscriminatorLoss | false | 7,194 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
AttentionLoss | # 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 ... | robtu328/TextBPN | AttentionLoss | false | 16,330 | [
"MIT"
] | 49 | 225844770e0107817be9fb86d53f873fa3eb07ae | https://github.com/robtu328/TextBPN/tree/225844770e0107817be9fb86d53f873fa3eb07ae |
TanhGaussianPolicy | import torch
from typing import List
from typing import Tuple
import torch.nn as nn
class LinearNetwork(nn.Module):
def __init__(self, input_size: 'int', output_size: 'int', hidden_layers:
'List[int]', activation: 'nn.Module'=nn.LeakyReLU):
super(LinearNetwork, self).__init__()
self.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 typing import List
from ... | redvinaa/multiagent-path-finding-continuous | TanhGaussianPolicy | false | 10,722 | [
"MIT"
] | 0 | 2d4ba3388f9b951c443ba72a33bd7af4f461275f | https://github.com/redvinaa/multiagent-path-finding-continuous/tree/2d4ba3388f9b951c443ba72a33bd7af4f461275f |
SubNet | import torch
import torch.nn as nn
class SubNet(nn.Module):
"""
The subnetwork that is used in TFN for video and audio in the pre-fusion stage
"""
def __init__(self, in_size, hidden_size, n_class, dropout, modal_name=
'text'):
"""
Args:
in_size: input dimension
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | dumpmemory/Multimodal-Infomax | SubNet | false | 15,238 | [
"MIT"
] | 57 | 9a6dc8f2bfa861cd447ba65c6a037cd7dd24f473 | https://github.com/dumpmemory/Multimodal-Infomax/tree/9a6dc8f2bfa861cd447ba65c6a037cd7dd24f473 |
ReLU | import torch
import numpy as np
import torch.nn as nn
from numbers import Number
def keep_variance_fn(x):
return x + 0.001
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(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 libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | collector-m/LiDAR-MOS | ReLU | false | 15,061 | [
"MIT"
] | 268 | 7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 | https://github.com/collector-m/LiDAR-MOS/tree/7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 |
ExponentialEnvelope | import torch
class ExponentialEnvelope(torch.nn.Module):
"""
Exponential envelope function that ensures a smooth cutoff,
as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom
and Nonlocal Effects
"""
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | chris-price19/ocp | ExponentialEnvelope | false | 1,696 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e |
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.triton_helpers import libdevice, math as tl_math
im... | gebob19/natural-policy-gradient-reinforcement-learning | Policy | false | 3,571 | [
"MIT"
] | 0 | 23faa28d746521d6291034bc87d750c665934ff7 | https://github.com/gebob19/natural-policy-gradient-reinforcement-learning/tree/23faa28d746521d6291034bc87d750c665934ff7 |
LinearEmbedding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | flyslowly/Trajectory-Transformer | LinearEmbedding | false | 10,073 | [
"MIT"
] | 0 | 8a5772e67366854155eb3f9a0ebff08c3e9f9186 | https://github.com/flyslowly/Trajectory-Transformer/tree/8a5772e67366854155eb3f9a0ebff08c3e9f9186 |
ConvHeadPooling | import torch
import torch.nn as nn
class ConvHeadPooling(nn.Module):
"""Adapted from
https://github.com/naver-ai/pit/blob/9d97a62e6a2a72a86685003998fcae700f952e18/pit.py#L54-L69
"""
def __init__(self, in_feature: 'int', out_feature: 'int', stride: 'int'):
super(ConvHeadPooling, self).__init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Justin900429/vision-transformer | ConvHeadPooling | false | 5,411 | [
"MIT"
] | 1 | e149092efbb83c166449944137db0ee5200f9325 | https://github.com/Justin900429/vision-transformer/tree/e149092efbb83c166449944137db0ee5200f9325 |
Fire | import torch
import torch.nn as nn
class Fire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand1x1_planes,
expand3x3_planes):
super(Fire, self).__init__()
self.inplanes = inplanes
self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
self.squeeze_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Danish-VSL/deep-person-reid | Fire | false | 13,562 | [
"MIT"
] | 244 | 2e3a4b6706b84c77203f9905683b917ab0871b93 | https://github.com/Danish-VSL/deep-person-reid/tree/2e3a4b6706b84c77203f9905683b917ab0871b93 |
EncoderLayer | # 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.... | Roestlab/massformer | EncoderLayer | false | 17,877 | [
"BSD-2-Clause"
] | 6 | c6324970c392f8ee96651679f49d21e430caa0c9 | https://github.com/Roestlab/massformer/tree/c6324970c392f8ee96651679f49d21e430caa0c9 |
MinibatchStdDev | import torch
import torch.nn as nn
class MinibatchStdDev(nn.Module):
"""
Minibatch standard deviation layer for the discriminator, try to prevent mode collapse
From https://github.com/akanimax/BMSG-GAN/
"""
def __init__(self):
"""
derived class constructor
"""
supe... | 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_... | cwza/deep_t2i | MinibatchStdDev | false | 1,769 | [
"Apache-2.0"
] | 0 | 22877fdd28ad407984ddc3bc4d57109c54c22fc0 | https://github.com/cwza/deep_t2i/tree/22877fdd28ad407984ddc3bc4d57109c54c22fc0 |
TransferConv3 | # 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 ... | jozhang97/Side-tuning | TransferConv3 | false | 15,731 | [
"MIT"
] | 56 | dea345691fb7ee0230150fe56ddd644efdffa6ac | https://github.com/jozhang97/Side-tuning/tree/dea345691fb7ee0230150fe56ddd644efdffa6ac |
RankCrossEntropyLoss | import torch
import torch.nn.functional as F
from torch import nn
class RankCrossEntropyLoss(nn.Module):
"""Creates a criterion that measures rank cross entropy loss."""
__constants__ = ['num_neg']
def __init__(self, num_neg: 'int'=1):
"""
:class:`RankCrossEntropyLoss` constructor.
... | 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... | amberhuang01/LearningFromFactCheckers | RankCrossEntropyLoss | false | 18,306 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 |
BertAttention | # 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.... | minjoong507/TVRetrieval | BertAttention | false | 10,754 | [
"MIT"
] | 0 | 919e1766ab8aa1ef267bd3b80d4f87b06cde09a9 | https://github.com/minjoong507/TVRetrieval/tree/919e1766ab8aa1ef267bd3b80d4f87b06cde09a9 |
ResNetBlockGroupNorm | # 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.... | TRUMANCFY/wolf | ResNetBlockGroupNorm | false | 2,965 | [
"Apache-2.0"
] | 0 | 1a21479256e4f51885e2d2fdd449b1faa61277a6 | https://github.com/TRUMANCFY/wolf/tree/1a21479256e4f51885e2d2fdd449b1faa61277a6 |
BertOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn
import torch.nn as nn
class BertOutput(nn.Module):
"""BERT output layer.
Based on: BERT (pytorch-transformer)
https://github.com/huggingface/transformers
"""
def __init__(self, config) ->None:
super(BertOutput, sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
imp... | Erotemic/MONAI | BertOutput | false | 7,613 | [
"Apache-2.0"
] | 1 | a9cd2d88168107281a2abcc2f63efaed80580e79 | https://github.com/Erotemic/MONAI/tree/a9cd2d88168107281a2abcc2f63efaed80580e79 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, 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.... | david-varela/continuous_control | Actor | false | 3,397 | [
"MIT"
] | 0 | 2bce9ea958fb21e88ac2f129ba8911e95dd7b1d2 | https://github.com/david-varela/continuous_control/tree/2bce9ea958fb21e88ac2f129ba8911e95dd7b1d2 |
ModConst | import torch
class ModConst(torch.nn.Module):
def __init__(self):
super(ModConst, self).__init__()
def forward(self, x):
return x % 2.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | NVIDIA-AI-IOT-private/torch2trt | ModConst | false | 10,518 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
DecoderLayer | 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.... | bsgiovanini/transformer | DecoderLayer | false | 1,619 | [
"Apache-2.0"
] | 0 | e128fa862f1b3d17d7b92df169a2bbee3f08366f | https://github.com/bsgiovanini/transformer/tree/e128fa862f1b3d17d7b92df169a2bbee3f08366f |
RewardCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | KunpengLi1994/PsTuts | RewardCriterion | false | 17,537 | [
"Apache-2.0"
] | 4 | 2063bf0aac8d3fd13bf5a14b80ce05586b8365f9 | https://github.com/KunpengLi1994/PsTuts/tree/2063bf0aac8d3fd13bf5a14b80ce05586b8365f9 |
FSP | # 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.nn.functional as F
import torch.nn as nn
import torch._utils
from i... | Capetian/FaceX-Zoo | FSP | false | 4,960 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
M | import torch
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
class M(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
y = torch.cat([x, y])
return y
def get_in... | 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.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | ShuaihuaLu/examples | M | false | 5,820 | [
"BSD-3-Clause"
] | 1 | 2639cf050493df9d3cbf065d45e6025733add0f4 | https://github.com/ShuaihuaLu/examples/tree/2639cf050493df9d3cbf065d45e6025733add0f4 |
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