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 |
|---|---|---|---|---|---|---|---|---|---|---|
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
import torc... | jaredaevans/UltrafastNST | FocalLoss | false | 6,923 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
FloorDiv | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | PogChamper/torch2trt | FloorDiv | false | 14,182 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
InnerProductDecoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class InnerProductDecoder(nn.Module):
def __init__(self, activation=torch.sigmoid, dropout=0.1):
super(InnerProductDecoder, self).__init__()
self.dropout = dropout
self.activation = activation
def forward(self, z):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | LymanSong/suwon_bus_stop_competition | InnerProductDecoder | false | 11,660 | [
"MIT"
] | 0 | 42297c8cfb0f109f28d8aeead097a57bb5d6be53 | https://github.com/LymanSong/suwon_bus_stop_competition/tree/42297c8cfb0f109f28d8aeead097a57bb5d6be53 |
JointsMSELoss | # 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
import torch.utils.data.distributed
assert_size_st... | wszsycn/DarkPose-for-VIP2021 | JointsMSELoss | false | 13,097 | [
"Apache-2.0"
] | 0 | 3658c74ed8bc76c497cb0269dbe10ed6898e07fb | https://github.com/wszsycn/DarkPose-for-VIP2021/tree/3658c74ed8bc76c497cb0269dbe10ed6898e07fb |
AmdimNCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Benjamin-Etheredge/lightning-bolts | AmdimNCELoss | false | 138 | [
"Apache-2.0"
] | 0 | 1971d6a924729940b98793aa7751bdf769350aca | https://github.com/Benjamin-Etheredge/lightning-bolts/tree/1971d6a924729940b98793aa7751bdf769350aca |
SCRM | import torch
import torch.nn.functional as F
import torch.nn as nn
class SCRM(nn.Module):
"""
spatial & channel wise relation loss
"""
def __init__(self, gamma=0.1):
super(SCRM, self).__init__()
self.softmax = nn.Softmax(dim=-1)
self.gamma = gamma
def spatial_wise(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.... | Tiamat-Tech/ZAQ-code | SCRM | false | 14,538 | [
"MIT"
] | 55 | e7e9f55791e36c6784d58c356d3ced76a7583369 | https://github.com/Tiamat-Tech/ZAQ-code/tree/e7e9f55791e36c6784d58c356d3ced76a7583369 |
BertMultiPairPooler | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertMultiPairPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | doduo-anonymous/doduo-submission | BertMultiPairPooler | false | 10,027 | [
"Apache-2.0"
] | 0 | 34d397c14174d64e6a3026d51cc25560a4f1e29f | https://github.com/doduo-anonymous/doduo-submission/tree/34d397c14174d64e6a3026d51cc25560a4f1e29f |
LinearDiag | # 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
import torch.optim
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_stri... | Basasuya/FewShotWithoutForgetting | LinearDiag | false | 2,007 | [
"MIT"
] | 0 | eecc70e416ed82999124ddfca1b145f6dbcd74a6 | https://github.com/Basasuya/FewShotWithoutForgetting/tree/eecc70e416ed82999124ddfca1b145f6dbcd74a6 |
PosLinear2 | # 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.... | JunLi-Galios/CP-Flow | PosLinear2 | false | 11,592 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff |
GlobalAveragePool | # 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.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | jiuntian/onnx2pytorch | GlobalAveragePool | false | 10,294 | [
"Apache-2.0"
] | 0 | fadca10a6045f4373293c9c0854607fb51a47c12 | https://github.com/jiuntian/onnx2pytorch/tree/fadca10a6045f4373293c9c0854607fb51a47c12 |
IndepAnisotropicGaussianUVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | Magixxxxxx/detectron2 | IndepAnisotropicGaussianUVLoss | false | 2,640 | [
"Apache-2.0"
] | 0 | c1ee8cf73777c96cc8a89463d0dca6e0ffe148f4 | https://github.com/Magixxxxxx/detectron2/tree/c1ee8cf73777c96cc8a89463d0dca6e0ffe148f4 |
AttentivePooling | # 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.... | albertvillanova/s3prl | AttentivePooling | false | 6,156 | [
"MIT"
] | 1 | b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 | https://github.com/albertvillanova/s3prl/tree/b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 |
ClassificationTestModel | # 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 typing import Any
from to... | LaudateCorpus1/torchgeo | ClassificationTestModel | false | 2,486 | [
"MIT"
] | 0 | 747a9352b9663e7d0e0c90a8b53533f0bb06c9b3 | https://github.com/LaudateCorpus1/torchgeo/tree/747a9352b9663e7d0e0c90a8b53533f0bb06c9b3 |
OneMinusCosThetaByThetaSq | # 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 cos
from torch import sin
assert_size_stride = torch._C... | darkmatter08/dfa-scales-to-modern-deep-learning | OneMinusCosThetaByThetaSq | false | 6,522 | [
"MIT"
] | 1 | 72bf8a045b4bb7eb81736d8ec1d671c4949fb01e | https://github.com/darkmatter08/dfa-scales-to-modern-deep-learning/tree/72bf8a045b4bb7eb81736d8ec1d671c4949fb01e |
MeanEmbedding | # 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.modules.loss
from scipy.sparse import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | IBM/graph4nlp | MeanEmbedding | false | 8,348 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
UpSample | # 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... | aa10402tw/RealTime-Segmentation | UpSample | false | 1,342 | [
"MIT"
] | 0 | 8c5cf13cd5570c48fa7bae9e6ec014989450889d | https://github.com/aa10402tw/RealTime-Segmentation/tree/8c5cf13cd5570c48fa7bae9e6ec014989450889d |
Expand | import torch
import torch.nn as nn
class Expand(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size()
s = self.gain
x = x.view(N, s, s, C // s ** 2, H, W)
x = x.permute(0, 3, 4, 1, 5, 2).contigu... | 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... | Alex-Beh/hand_tracking | Expand | false | 11,157 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
my_Hingeloss | import torch
import torch.nn as nn
class my_Hingeloss(nn.Module):
def __init__(self):
super(my_Hingeloss, self).__init__()
def forward(self, output, target):
pos = torch.sum(output * target, 2)
neg = torch.max((1 - target) * output, 2)
loss = neg[0] - pos + 1
loss[los... | 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... | carsault/chord_extraction_prediction_lib | my_Hingeloss | false | 3,394 | [
"MIT"
] | 0 | 6de09eef9f2852b56b04874d2e42eb504c96d33f | https://github.com/carsault/chord_extraction_prediction_lib/tree/6de09eef9f2852b56b04874d2e42eb504c96d33f |
Dueling_QNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class Dueling_QNetwork(nn.Module):
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=64):
super().__init__()
self.seed = torch.manual_seed(seed)
self.fc1_a = nn.Linear(state_size, fc1_units)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Brandon-HY-Lin/deep-reinforcement-learning | Dueling_QNetwork | false | 187 | [
"MIT"
] | 0 | d809851b6f98d1089379392d4687e2acaf1c0c79 | https://github.com/Brandon-HY-Lin/deep-reinforcement-learning/tree/d809851b6f98d1089379392d4687e2acaf1c0c79 |
StdConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
w = (w - m) / torch.sqrt(v + 1e-10)
return F.conv2d(x, w, self.bias, self.stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | HazyResearch/domino | StdConv2d | false | 5,297 | [
"Apache-2.0"
] | 1 | 76ef413a9f9ee4a5d9c3fc044d8a0a0ea0cc4dc2 | https://github.com/HazyResearch/domino/tree/76ef413a9f9ee4a5d9c3fc044d8a0a0ea0cc4dc2 |
WeightedPool | import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class WeightedPool(nn.Module):
def __init__(self, dim):
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EGO4D/episodic-memory | WeightedPool | false | 8,066 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
CustomConv2d | # 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... | ChiragCD/NR-GAN | CustomConv2d | false | 13,480 | [
"MIT"
] | 54 | fc455c6219b09bc8bf605715504b78b2bb801e48 | https://github.com/ChiragCD/NR-GAN/tree/fc455c6219b09bc8bf605715504b78b2bb801e48 |
HSwish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert... | Kthyeon/micronet_neurips_challenge | HSwish | false | 8,413 | [
"MIT"
] | 19 | 9f71fb752e8fbd5abca07be530f7fb19e164125c | https://github.com/Kthyeon/micronet_neurips_challenge/tree/9f71fb752e8fbd5abca07be530f7fb19e164125c |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.utils
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
in_size = inputs.size()
inputs = inp... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | jameslong95/FasterSeg | GlobalAvgPool2d | false | 6,908 | [
"MIT"
] | 1 | 872e04964ea46494a6018d9915cee5476e361c27 | https://github.com/jameslong95/FasterSeg/tree/872e04964ea46494a6018d9915cee5476e361c27 |
ResBlockWithFusedBN | # 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
from tor... | vadimadr/openvino_training_extensions | ResBlockWithFusedBN | false | 11,042 | [
"Apache-2.0"
] | 0 | 5d64b8423c8eb7b374ed629fad938359d34a07d2 | https://github.com/vadimadr/openvino_training_extensions/tree/5d64b8423c8eb7b374ed629fad938359d34a07d2 |
FeatNet | import torch
import torch.nn as nn
class FeatNet(nn.Module):
def __init__(self):
super(FeatNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
(3, 7), stride=1, padding=(1, 3), bias=False)
self.tanh1 = nn.Tanh()
self.Pool1 = nn.Avg... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | DongChengdongHangZhou/adversarial-attack-iris | FeatNet | false | 11,383 | [
"Apache-2.0"
] | 0 | ae7e408c47c332fc876d572acd4701e4b8970487 | https://github.com/DongChengdongHangZhou/adversarial-attack-iris/tree/ae7e408c47c332fc876d572acd4701e4b8970487 |
rSoftMax | # 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
f... | Challyfilio/NAIC2021 | rSoftMax | false | 239 | [
"MIT"
] | 0 | 11b38a920dcc902f9b798dc43ae360062862e6e4 | https://github.com/Challyfilio/NAIC2021/tree/11b38a920dcc902f9b798dc43ae360062862e6e4 |
GeneralizedMeanPoolingList | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from abc import ABC
import torch.autograd
assert_size_stride = torc... | CASIA-IVA-Lab/PASS_reID | GeneralizedMeanPoolingList | false | 17,041 | [
"Apache-2.0"
] | 5 | 46dc6d25f4396e35ac1a766ad2dcaa580beccf15 | https://github.com/CASIA-IVA-Lab/PASS_reID/tree/46dc6d25f4396e35ac1a766ad2dcaa580beccf15 |
BartClassificationHead | # 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.utils.... | JuruoMP/gap-exp | BartClassificationHead | false | 9,223 | [
"Apache-2.0"
] | 0 | 2d7af8a1da2f0ff8f9d3a2c6e15cc6383c716c05 | https://github.com/JuruoMP/gap-exp/tree/2d7af8a1da2f0ff8f9d3a2c6e15cc6383c716c05 |
VertexDirectEmbedder | # 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 torch.utils.data
from... | Lele-Zhou/detectron2-based | VertexDirectEmbedder | false | 9,254 | [
"Apache-2.0"
] | 0 | a6f65174c6f11918c8e7600746f9f87baa89ecc0 | https://github.com/Lele-Zhou/detectron2-based/tree/a6f65174c6f11918c8e7600746f9f87baa89ecc0 |
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class ScaledDotProductAttention(nn.Module):
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forw... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | yuanweining/DTI | MultiHeadAttention | false | 4,653 | [
"Apache-2.0"
] | 0 | 11eacb46a221da04d0e9b01d41c89c7ce51ea302 | https://github.com/yuanweining/DTI/tree/11eacb46a221da04d0e9b01d41c89c7ce51ea302 |
Illumination_Alone | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def get_conv2d_layer(in_c, out_c, k, s, p=0, dilation=1, groups=1):
return nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=k,
stride=s, padding=p, dilation=dilation, groups=groups)
class Illumination_Alone(nn.Mo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AndersonYong/URetinex-Net-Retinex-based-Deep-Unfolding-Network-for-Low-light-Image-Enhancem | Illumination_Alone | false | 10,007 | [
"MIT"
] | 0 | 9d837b8df9c761defb1eca390b3a60aa4a6fbb1a | https://github.com/AndersonYong/URetinex-Net-Retinex-based-Deep-Unfolding-Network-for-Low-light-Image-Enhancem/tree/9d837b8df9c761defb1eca390b3a60aa4a6fbb1a |
HouseHolderFlow | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | BratChar/variational-item-response-theory-public | HouseHolderFlow | false | 13,404 | [
"MIT"
] | 52 | 12862157e99506a0ed7018f1b8a485d4e61fb5bf | https://github.com/BratChar/variational-item-response-theory-public/tree/12862157e99506a0ed7018f1b8a485d4e61fb5bf |
MAP_Linear_Layer | import torch
import numpy as np
import torch.nn as nn
class MAP_Linear_Layer(nn.Module):
def __init__(self, n_input, n_output):
super(MAP_Linear_Layer, self).__init__()
self.weight = nn.Parameter(torch.Tensor(n_input, n_output).normal_(
0, 1 / np.sqrt(4 * n_output)))
self.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
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | andrewfoongyk/cs230-code-examples | MAP_Linear_Layer | false | 1,431 | [
"MIT"
] | 0 | 8e12aa3414bdada6ec6002bedf919a6816ba237c | https://github.com/andrewfoongyk/cs230-code-examples/tree/8e12aa3414bdada6ec6002bedf919a6816ba237c |
MulticlassSegmentationLoss | from torch.nn import Module
import torch
from torch import Tensor
from torch.nn import MSELoss
def _split_masks_by_classes(pred: 'Tensor', target: 'Tensor') ->[]:
"""
Split masks by classes
Args:
pred (Tensor): predicted masks of shape [B, C, H, W]
target (Tensor): target masks of shape [... | 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.nn import Module
from torch import Tensor
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = to... | PiePline/PieToolbelt | MulticlassSegmentationLoss | false | 5,714 | [
"MIT"
] | 1 | bcf9cab16bf3dbb19015c074a305f9ea8a8dc48e | https://github.com/PiePline/PieToolbelt/tree/bcf9cab16bf3dbb19015c074a305f9ea8a8dc48e |
InstanceNormLayer | import torch
import torch.utils.data
import torch
from torch import nn
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, x):
if len(x.shape) != 4:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
from torch import nn
assert_size_stride = ... | IVRL/BIGPrior | InstanceNormLayer | false | 569 | [
"MIT"
] | 0 | 6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 | https://github.com/IVRL/BIGPrior/tree/6bf3b18fcbbd3c58bad7a792a8d28b017abb2411 |
ScaledConv2d | import torch
from torch import Tensor
from torch import nn
class ScaledConv2d(nn.Conv2d):
def __init__(self, *args, initial_scale: float=1.0, initial_speed:
float=1.0, **kwargs):
super(ScaledConv2d, self).__init__(*args, **kwargs)
initial_scale = torch.tensor(initial_scale).log()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | glynpu/icefall | ScaledConv2d | false | 3,552 | [
"Apache-2.0"
] | 0 | d766dc5aeea1a8aefab033e581948b07c4ac4bc0 | https://github.com/glynpu/icefall/tree/d766dc5aeea1a8aefab033e581948b07c4ac4bc0 |
Mult | import torch
import torch.utils.data
import torch
from torch import nn
class Mult(nn.Module):
def __init__(self, nc):
super(Mult, self).__init__()
self.register_parameter(name='exp', param=torch.nn.Parameter(torch.
diag(torch.ones(nc)).unsqueeze(-1).unsqueeze(-1)))
"""self.reg... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | jayin92/vae-pix2pix-terrain-generator | Mult | false | 6,933 | [
"BSD-3-Clause"
] | 1 | 805ea0b053dc9d9c22301af7f536a8fb7e2118d1 | https://github.com/jayin92/vae-pix2pix-terrain-generator/tree/805ea0b053dc9d9c22301af7f536a8fb7e2118d1 |
RPA | # 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 as nn
from torch.nn import init as init
from torch.utils im... | aesrgan/A-ESRGAN | RPA | false | 14,754 | [
"BSD-3-Clause"
] | 58 | e1a71deb4a47e332cad6b3d6bbbbb21a56bdd9c6 | https://github.com/aesrgan/A-ESRGAN/tree/e1a71deb4a47e332cad6b3d6bbbbb21a56bdd9c6 |
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.... | rafapi/PMTG | Actor | false | 10,649 | [
"Apache-2.0"
] | 0 | 8a89a3dd9620e2fdf747d20781b46daebd41569c | https://github.com/rafapi/PMTG/tree/8a89a3dd9620e2fdf747d20781b46daebd41569c |
Predict_Network1 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class LayerNorm(nn.Module):
"""
Simple 1D LayerNorm.
""... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ltzheng/CDS | Predict_Network1 | false | 7,130 | [
"Apache-2.0"
] | 1 | 397282147498647a9f26577adfa451e8478de76d | https://github.com/ltzheng/CDS/tree/397282147498647a9f26577adfa451e8478de76d |
HexaLinearScore | import math
import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class HexaLinearScore(nn.Module):
"""
Outer product version of hexalinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, norma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.utils.data.dataloader
import torc... | db-bionlp/CLNER | HexaLinearScore | false | 15,163 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
LabelSmoothCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def one_hot(val: 'torch.LongTensor', num: 'int', num_first: 'bool'=False
) ->torch.FloatTensor:
"""
Overview:
Convert a ``torch.LongTensor`` to one hot encoding.
This implementation can be slightly f... | 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
... | Hcnaeg/DI-engine | LabelSmoothCELoss | false | 2,373 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
D_UpBlock | import torch
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.Conv2d(input_size, output_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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | DengZeshuai/DBPN-Pytorch | D_UpBlock | false | 2,634 | [
"MIT"
] | 0 | a90d241a1c4b07830c6d812ad8389d13e8cf05d1 | https://github.com/DengZeshuai/DBPN-Pytorch/tree/a90d241a1c4b07830c6d812ad8389d13e8cf05d1 |
PatchEmbedding | import torch
import torch.nn as nn
def pair(t):
"""
Parameters
----------
t: tuple[int] or int
"""
return t if isinstance(t, tuple) else (t, t)
class PatchEmbedding(nn.Module):
"""
Parameters
----------
img_size: int
Image Size
patch_size: int
Patch Size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | HrithikNambiar/vformer | PatchEmbedding | false | 552 | [
"MIT"
] | 0 | 5bd902a45e5cae70ab001ca6c217f12f923561f1 | https://github.com/HrithikNambiar/vformer/tree/5bd902a45e5cae70ab001ca6c217f12f923561f1 |
gram_matrix | import torch
import torch.nn as nn
class gram_matrix(nn.Module):
def forward(self, input):
b, c, w, h = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ipjessica/neural-style-transfer | gram_matrix | false | 12,535 | [
"MIT"
] | 0 | ae0fc5e1e69d5d52997e5cab69e880085e04723b | https://github.com/ipjessica/neural-style-transfer/tree/ae0fc5e1e69d5d52997e5cab69e880085e04723b |
RobertaClassificationHead | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | BlackNoodle/TUCORE-GCN | RobertaClassificationHead | false | 8,788 | [
"MIT"
] | 27 | 16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 | https://github.com/BlackNoodle/TUCORE-GCN/tree/16fb37d81c5b1182a31fcf7da08a9c0013b20cd6 |
Downsample | import torch
import torch.nn as nn
class Downsample(nn.Module):
def __init__(self, nIn, nOut, stride):
super(Downsample, self).__init__()
self.avg = nn.AvgPool2d(stride)
assert nOut % nIn == 0
self.expand_ratio = nOut // nIn
def forward(self, x):
x = self.avg(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Richard456/TRADES | Downsample | false | 9,405 | [
"MIT"
] | 0 | 6093dbd92ca548cc1b98306e168842982b281140 | https://github.com/Richard456/TRADES/tree/6093dbd92ca548cc1b98306e168842982b281140 |
TransformerLayer | import torch
from torch import nn
import torch.nn.functional as nnf
from typing import Optional
class MlpTransformer(nn.Module):
def __init__(self, in_dim, h_dim, out_d: 'Optional[int]'=None, act=nnf.
relu, dropout=0.0):
super().__init__()
out_d = out_d if out_d is not None else in_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.... | bpiyush/CLIP_prefix_caption-video | TransformerLayer | false | 12,204 | [
"MIT"
] | 0 | 3f6a4b8c841189e20b82fd4de127681424311599 | https://github.com/bpiyush/CLIP_prefix_caption-video/tree/3f6a4b8c841189e20b82fd4de127681424311599 |
L1NormLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class L1NormLoss(nn.Module):
def __init__(self, loss_weight=0.0005, average=True):
super(L1NormLoss, self).__init__()
self.loss_weight = loss_weight
self.average = average
def forwa... | 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
assert_size_stride = torch._C._dynamo.guards.asser... | Dogacel/mmfashion | L1NormLoss | false | 11,404 | [
"Apache-2.0"
] | 0 | e49613245c8501042edd7aeeaa8fb93e5ea13238 | https://github.com/Dogacel/mmfashion/tree/e49613245c8501042edd7aeeaa8fb93e5ea13238 |
GELU | import torch
from torch import nn
import torch.nn.functional as F
class GELU(nn.Module):
"""
GELU activiation layer.
Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
Described in: https://arxiv.org/abs/1606.08415.
Args:
inplace(`Bool`):
whether use inpl... | 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... | SimonCqk/towhee | GELU | false | 9,627 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
StyledConv | import math
import torch
from torch import nn
from torch.nn import functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
pad_x1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | Liamkuo/SAIR | StyledConv | false | 17,584 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 |
ASPP | import torch
from torch import nn
import torch.nn.functional as F
class ASPP(nn.Module):
"""
Atrous spatial pyramid pooling used in object detection and segmentation.
"""
def __init__(self, in_channel=512, depth=256):
super().__init__()
self.mean = nn.AdaptiveAvgPool2d((1, 1))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | L-Net-1992/towhee | ASPP | false | 14,022 | [
"Apache-2.0"
] | 365 | 471de97bf9c5443efaf3b62fd440b3ebdb6d5903 | https://github.com/L-Net-1992/towhee/tree/471de97bf9c5443efaf3b62fd440b3ebdb6d5903 |
Hflip | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
"""Horizontally flip a tensor image or a batch of tensor images.
.. image:: _static/img/hflip.png
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tens... | 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... | lyhyl/kornia | Hflip | false | 12,742 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 5bd3aeb0d54dedac01e6eaf8bac37779bab0bec5 | https://github.com/lyhyl/kornia/tree/5bd3aeb0d54dedac01e6eaf8bac37779bab0bec5 |
TensorRange | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Minyus/kedex | TensorRange | false | 9,688 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
Hidden2Discrete | # 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.... | haojiepan1/CrossWOZ | Hidden2Discrete | false | 6,786 | [
"Apache-2.0"
] | 1 | 6d7b4c4cfb73a528b76074764687906abecc90b6 | https://github.com/haojiepan1/CrossWOZ/tree/6d7b4c4cfb73a528b76074764687906abecc90b6 |
FCN8s | import torch
import numpy as np
from torch import nn
def get_upsampling_weight(in_channels, out_channels, kernel_size):
"""Make a 2D bilinear kernel suitable for upsampling"""
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | Design-AILab/Attention-Tracker | FCN8s | false | 9,564 | [
"MIT"
] | 0 | 3dfe5edabdff0cb6db9c99ed59afd8c0383b6233 | https://github.com/Design-AILab/Attention-Tracker/tree/3dfe5edabdff0cb6db9c99ed59afd8c0383b6233 |
MaxPoolBranch | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | earhian/imgclsmob | MaxPoolBranch | false | 6,623 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
PSNRLoss | import torch
import torch.nn as nn
from torch.nn.functional import mse_loss
def psnr_loss(input: 'torch.Tensor', target: 'torch.Tensor', max_val: 'float'
) ->torch.Tensor:
"""Function that computes PSNR
See :class:`~kornia.losses.PSNRLoss` for details.
"""
if not torch.is_tensor(input) or not tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | manyids2/kornia-1 | PSNRLoss | false | 3,975 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 47f5e91f502a0819be9b5a843019b37b15aa37f2 | https://github.com/manyids2/kornia-1/tree/47f5e91f502a0819be9b5a843019b37b15aa37f2 |
BackwardCrossAttentionLayer | # 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.... | KirkGuo/HCN | BackwardCrossAttentionLayer | false | 5,481 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 |
FloorDivConst | import torch
class FloorDivConst(torch.nn.Module):
def __init__(self):
super(FloorDivConst, 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... | Ilyabasharov/torch2trt | FloorDivConst | false | 2,520 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
MultiHead | import math
import torch
from torch import Tensor
from torch.nn import Linear
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | pwycl/pytorch_geometric | MultiHead | false | 10,783 | [
"MIT"
] | 0 | ef7b1add2bb5a36a3a68cae7639c42000f629cac | https://github.com/pwycl/pytorch_geometric/tree/ef7b1add2bb5a36a3a68cae7639c42000f629cac |
Warp | # 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
assert_size... | vishalbelsare/deepdow | Warp | false | 16,688 | [
"Apache-2.0"
] | 511 | cbb99347fba9a447d4fcae64fe5137c203643e44 | https://github.com/vishalbelsare/deepdow/tree/cbb99347fba9a447d4fcae64fe5137c203643e44 |
KLCoefficient | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | tommy90191/Find_Tiny_but_Important_Image_Changes | KLCoefficient | false | 4,441 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
Polynomial3 | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | LbsIrving/PyTorch | Polynomial3 | false | 766 | [
"MIT"
] | 0 | 314dbe9efc9e0116a7342d4ae3ab168c1c3afa32 | https://github.com/LbsIrving/PyTorch/tree/314dbe9efc9e0116a7342d4ae3ab168c1c3afa32 |
TopicEmbeddingAttention | # 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.... | WuDiDaBinGe/TAKG | TopicEmbeddingAttention | false | 1,243 | [
"MIT"
] | 0 | 83e608e677a4ee74722d18cb5ef430f4f6c6ad31 | https://github.com/WuDiDaBinGe/TAKG/tree/83e608e677a4ee74722d18cb5ef430f4f6c6ad31 |
NormedConv2d | import torch
from torch import nn
class NormedConv2d(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Parskatt/mmdetection | NormedConv2d | false | 928 | [
"Apache-2.0"
] | 0 | ee4cfa29e7f479b2454b1f1355f8c05be62d8466 | https://github.com/Parskatt/mmdetection/tree/ee4cfa29e7f479b2454b1f1355f8c05be62d8466 |
MeanVarFC | import torch
import torch.nn as nn
class MeanVarFC(nn.Module):
def __init__(self, input_shape):
super(MeanVarFC, self).__init__()
shape = list(input_shape)
shape[0] = 1
shape[1] *= 2
self.param = nn.Parameter(0.01 * torch.randn(shape))
def forward(self, x):
x ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | lingzenan/invertible-resnet | MeanVarFC | false | 7,097 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | NeuralFlux/rl-analysis | Actor | false | 5,660 | [
"MIT"
] | 1 | bb45e1f8bb9da4683cce4bd0a5e687770a4005e2 | https://github.com/NeuralFlux/rl-analysis/tree/bb45e1f8bb9da4683cce4bd0a5e687770a4005e2 |
CrossEntropyLoss | # AOT ID: ['1_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.functi... | Sign-up-soon-after-papapa/DEA-Net | CrossEntropyLoss | false | 9,476 | [
"Apache-2.0"
] | 0 | ed25f30ddedcb77eb0991aeb9e498ef2efd8c635 | https://github.com/Sign-up-soon-after-papapa/DEA-Net/tree/ed25f30ddedcb77eb0991aeb9e498ef2efd8c635 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | deshwalmahesh/CURL---cpu-gpu | ConvBlock | false | 3,404 | [
"BSD-3-Clause"
] | 0 | f4e87275b6cce556b9e04a188cf7ae13d810d82a | https://github.com/deshwalmahesh/CURL---cpu-gpu/tree/f4e87275b6cce556b9e04a188cf7ae13d810d82a |
ConvRelu | import torch
import torch.utils.data
import torch.nn as nn
import torch.onnx
import torch.autograd
import torch.backends.cudnn
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block."""
def __init__(self, num_in, num_out):
super().__init__()
self.block = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | CorentinLemaitre/robosat.pink | ConvRelu | false | 5,051 | [
"MIT"
] | 1 | 6ec29a4dd4c0cbf953e73818d7338ee68b2451d3 | https://github.com/CorentinLemaitre/robosat.pink/tree/6ec29a4dd4c0cbf953e73818d7338ee68b2451d3 |
SubpixelConvolutionLayer | # 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 ... | wuyushuwys/SRGAN-PyTorch | SubpixelConvolutionLayer | false | 4,567 | [
"Apache-2.0"
] | 0 | 3a4aaaf7b55692264fca8451e4401466fcb1f39a | https://github.com/wuyushuwys/SRGAN-PyTorch/tree/3a4aaaf7b55692264fca8451e4401466fcb1f39a |
SpatialCrossMapLRN | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class SpatialCrossMapLRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCrossMapLRN, self).__init__()
self.ACROSS_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.... | shubham1206agra/pretrained-models.pytorch | SpatialCrossMapLRN | false | 12,979 | [
"BSD-3-Clause"
] | 0 | a2940f79dd65656eabe5a0cd6d5d014ef1fc2523 | https://github.com/shubham1206agra/pretrained-models.pytorch/tree/a2940f79dd65656eabe5a0cd6d5d014ef1fc2523 |
CentralV_Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | OkYongChoi/smac | CentralV_Critic | false | 18,373 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 |
FastestBlock | import torch
import torch.nn as nn
def get_operator_from_cfg(operator_cfg):
operator_cfg_copy = operator_cfg.copy()
construct_str = 'nn.'
construct_str += operator_cfg_copy.pop('type') + '('
for k, v in operator_cfg_copy.items():
construct_str += k + '=' + str(v) + ','
construct_str += ')'... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | becauseofAI/DemoHub | FastestBlock | false | 3,187 | [
"Apache-2.0"
] | 0 | 2b7fdd1f1c6f229ba326e8c1b78c4e7f5982f3da | https://github.com/becauseofAI/DemoHub/tree/2b7fdd1f1c6f229ba326e8c1b78c4e7f5982f3da |
Beta | # 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... | RohanPankaj/apex | Beta | false | 986 | [
"MIT"
] | 0 | 74e96386bf9446d1179106d6d65ea0368c1b5b27 | https://github.com/RohanPankaj/apex/tree/74e96386bf9446d1179106d6d65ea0368c1b5b27 |
mlp_layer | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | mlp_layer | false | 17,257 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d |
EncoderImagePrecomp | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(x, dim=-1):
return x / x.norm(2, dim=dim, keepdim=True).clamp(min=1e-06)
class EncoderImagePrecomp(nn.Module):
""" image encoder """
def __init__(self, img_dim, embed_size, no_imgno... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ZhouXing19/VGNSL_multilang | EncoderImagePrecomp | false | 1,317 | [
"MIT"
] | 0 | 097ed7bf978dbff052075a26231984ade5522409 | https://github.com/ZhouXing19/VGNSL_multilang/tree/097ed7bf978dbff052075a26231984ade5522409 |
ConstantODE | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Lauu1023/torchdiffeq | ConstantODE | false | 9,341 | [
"MIT"
] | 0 | f4f3184a4c1b657da959c7d15bc8f727f1c25bd8 | https://github.com/Lauu1023/torchdiffeq/tree/f4f3184a4c1b657da959c7d15bc8f727f1c25bd8 |
Decoder | import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
padding = [((i - 1) // 2) for i in kernel_size]
self.tconv = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size=kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YoshikiMas/YoshikiMas-speech-enhancement-with-pytorch-lightning | Decoder | false | 18,154 | [
"MIT"
] | 5 | 8fcb78cbf64cb61dd9d2dd9e1118a1aa1992dd65 | https://github.com/YoshikiMas/YoshikiMas-speech-enhancement-with-pytorch-lightning/tree/8fcb78cbf64cb61dd9d2dd9e1118a1aa1992dd65 |
_boundary | # 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... | STARBOYsachin/semantic-segmentation | _boundary | false | 1,006 | [
"MIT"
] | 0 | 7f553a93b717641edc6c2d463903dfab67267039 | https://github.com/STARBOYsachin/semantic-segmentation/tree/7f553a93b717641edc6c2d463903dfab67267039 |
LocalResponseNormLayer | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_... | fuzhanrahmanian/lucent | LocalResponseNormLayer | false | 15,375 | [
"Apache-2.0"
] | 449 | 13b24c3c37784185275da73c7a11095b2ae809c5 | https://github.com/fuzhanrahmanian/lucent/tree/13b24c3c37784185275da73c7a11095b2ae809c5 |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
asser... | iDMG-dynamicGCN/DatasetCollection | GCN | false | 10,193 | [
"MIT"
] | 0 | ad761b38bc86af1dd3aee6c72e819d6f00252164 | https://github.com/iDMG-dynamicGCN/DatasetCollection/tree/ad761b38bc86af1dd3aee6c72e819d6f00252164 |
SelfAttentionPooling | import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gcambara/s3prl | SelfAttentionPooling | false | 15,415 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
LSTMCell | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class LSTMCell(Module):
"""
## Long Short-Term Memory Cell
LSTM Cell computes $c$, and $h$. $c$ is like the long-term memory,
and $h$ is like the short term memory.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 impor... | ppvalluri09/annotated_deep_learning_paper_implementations | LSTMCell | false | 11,069 | [
"MIT"
] | 0 | 387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 | https://github.com/ppvalluri09/annotated_deep_learning_paper_implementations/tree/387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 |
NALUCell | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torch.nn.parameter import Parameter
class NeuralAccumulatorCell(nn.Module):
"""A Neural Accumulator (NAC) cell [1].
Attributes:
in_dim: size of the input sample.
out_dim: size of the ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | mikomel/machine-number-sense | NALUCell | false | 7,225 | [
"MIT"
] | 1 | 173b67e4f25bd8249ba4a41904d4cd4af26bae05 | https://github.com/mikomel/machine-number-sense/tree/173b67e4f25bd8249ba4a41904d4cd4af26bae05 |
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.... | Zenodia/NeMo | MultiHeadAttention | false | 1,329 | [
"Apache-2.0"
] | 0 | 3c288d8a7caf667c95444c39434e3ebc5f53d911 | https://github.com/Zenodia/NeMo/tree/3c288d8a7caf667c95444c39434e3ebc5f53d911 |
RMulFloat | import torch
class RMulFloat(torch.nn.Module):
def __init__(self):
super(RMulFloat, self).__init__()
def forward(self, x):
return 10.0 * 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... | bunderhi/torch2trt | RMulFloat | false | 1,606 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
LinearEmbedder | # 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 ... | xiaonanzzz/ProxyAnchorLossSimple | LinearEmbedder | false | 13,109 | [
"MIT"
] | 0 | a501578142fd00bf001c840e8051c67dee873f67 | https://github.com/xiaonanzzz/ProxyAnchorLossSimple/tree/a501578142fd00bf001c840e8051c67dee873f67 |
NormalizedLinear | # 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.... | tonysy/cvpods | NormalizedLinear | false | 16,607 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
MinusRbfHSIC | import torch
import torch.nn as nn
import torch.utils.data.distributed
class HSIC(nn.Module):
"""Base class for the finite sample estimator of Hilbert-Schmidt Independence Criterion (HSIC)
..math:: HSIC (X, Y) := || C_{x, y} ||^2_{HS}, where HSIC (X, Y) = 0 iif X and Y are independent.
Empirically, we us... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | derwind/mxfont | MinusRbfHSIC | false | 10,132 | [
"MIT"
] | 0 | 0b6d4554a1e2208906230d3121d792d450ed28dd | https://github.com/derwind/mxfont/tree/0b6d4554a1e2208906230d3121d792d450ed28dd |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MichiganCOG/Video-Grounding | EncoderLayer | false | 8,552 | [
"MIT"
] | 41 | 3e0ec0b69578a59be583911590354fe77d357cab | https://github.com/MichiganCOG/Video-Grounding/tree/3e0ec0b69578a59be583911590354fe77d357cab |
ConvLayer | # 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_... | Amber-Chaeeunk/Open-Domain-Question-Answering | ConvLayer | false | 18,266 | [
"MIT"
] | 5 | 725e369a4409c54bf11bcfb9db53865d8fc1f935 | https://github.com/Amber-Chaeeunk/Open-Domain-Question-Answering/tree/725e369a4409c54bf11bcfb9db53865d8fc1f935 |
Highway | # 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 ... | LFhase/Learning_CS224N | Highway | false | 17,557 | [
"MIT"
] | 5 | 21af6dd4f7b9dcb3f34aac9c2cebf4a02a17176f | https://github.com/LFhase/Learning_CS224N/tree/21af6dd4f7b9dcb3f34aac9c2cebf4a02a17176f |
GatedConv1d | # 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... | lonePatient/TorchBlocks | GatedConv1d | false | 15,963 | [
"MIT"
] | 82 | 4a65d746cc8a396cb7df73ed4644d97ddf843e29 | https://github.com/lonePatient/TorchBlocks/tree/4a65d746cc8a396cb7df73ed4644d97ddf843e29 |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.LayerNorm):
def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True):
"""Layer Norm."""
super(LayerNorm, self).__init__(normalized_shape, eps=eps,
elementwise_affine=elementwise_affine)
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | lorinczb/pytorch-dc-tts | LayerNorm | false | 7,121 | [
"MIT"
] | 1 | 9dae50678113e2f60ad0752b99b959bb0b11dfc9 | https://github.com/lorinczb/pytorch-dc-tts/tree/9dae50678113e2f60ad0752b99b959bb0b11dfc9 |
M3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, same_padding
=False, stride=1, relu=True, bn=False):
super(Conv2D, self).__init__()
padding = int((kernel_size - 1) / 2) if same_padding e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Juggernaut93/SSH-pytorch | M3 | false | 13,943 | [
"MIT"
] | 63 | 8ea205fb1a3adfc32b5a4e35f68ed4d385ddbc31 | https://github.com/Juggernaut93/SSH-pytorch/tree/8ea205fb1a3adfc32b5a4e35f68ed4d385ddbc31 |
TransformerFFN | # 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 math
import ... | AlexShypula/CodeGen | TransformerFFN | false | 13,286 | [
"MIT"
] | 241 | 2e5f8090c4369fd3f0ebec4a867503edc1362d5d | https://github.com/AlexShypula/CodeGen/tree/2e5f8090c4369fd3f0ebec4a867503edc1362d5d |
Unet | # 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... | BoHuangLab/timeunet | Unet | false | 17,108 | [
"MIT"
] | 7 | 8fd34b18e9c4420db8172a402c243f7d03c853f1 | https://github.com/BoHuangLab/timeunet/tree/8fd34b18e9c4420db8172a402c243f7d03c853f1 |
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