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 |
|---|---|---|---|---|---|---|---|---|---|---|
Encoder | # 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_... | akaprasanga/AutoEncoder | Encoder | false | 3,058 | [
"MIT"
] | 0 | a1562a7a720c199b717796e469b9957eb958264a | https://github.com/akaprasanga/AutoEncoder/tree/a1562a7a720c199b717796e469b9957eb958264a |
ResidualBlock | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class avgpool(nn.Module):
"""
Mean pooling class - downsampling
"""
def __init__(self, up_size=0):
super(avgpool, self).__init__()
def forward(self, x):
out_man = (x[:, :, ::2, ::2] + x[:, :, 1::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 ... | nathalia-kim/nu_gan | ResidualBlock | false | 10,724 | [
"MIT"
] | 0 | c1d0891945bd7ac3d95869db91f490f57f203110 | https://github.com/nathalia-kim/nu_gan/tree/c1d0891945bd7ac3d95869db91f490f57f203110 |
AdaptiveAvgMaxPool2d | import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
from torch import optim as optim
import torch.nn.parallel
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as ... | Exir-lxr/crldr-prune-pytorch | AdaptiveAvgMaxPool2d | false | 2,665 | [
"Apache-2.0"
] | 0 | adeb5e0b24ce66ff9531d4d947f72412c1b5c033 | https://github.com/Exir-lxr/crldr-prune-pytorch/tree/adeb5e0b24ce66ff9531d4d947f72412c1b5c033 |
MemoryUpdater | # 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.... | adymaharana/VLCStoryGan | MemoryUpdater | false | 18,286 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
Message_Passing_Unit_v2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Message_Passing_Unit_v2(nn.Module):
def __init__(self, fea_size, filter_size=128):
super(Message_Passing_Unit_v2, self).__init__()
self.w = nn.Linear(fea_size, filter_size, bias=True)
self.fea_size = fea_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | EricssonResearch/scott-eu | Message_Passing_Unit_v2 | false | 8,064 | [
"Apache-2.0"
] | 19 | aad7fd2f767a3c5e7d89223a593fd979ad596db3 | https://github.com/EricssonResearch/scott-eu/tree/aad7fd2f767a3c5e7d89223a593fd979ad596db3 |
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... | mlepori1/noise2self | Unet | false | 16,177 | [
"MIT"
] | 257 | 78cbda2d0f62973f1ba0232bd48a941307cf78f9 | https://github.com/mlepori1/noise2self/tree/78cbda2d0f62973f1ba0232bd48a941307cf78f9 |
Module_CharbonnierLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | HyeongminLEE/AdaCoF-pytorch | Module_CharbonnierLoss | false | 13,823 | [
"MIT"
] | 149 | f121ee0e8cb403216c7bd5183154dbd1cf6966f4 | https://github.com/HyeongminLEE/AdaCoF-pytorch/tree/f121ee0e8cb403216c7bd5183154dbd1cf6966f4 |
SEBatch | # 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._utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | Zhaoyi-Yan/DCANet | SEBatch | false | 18,181 | [
"MIT"
] | 3 | 1d99481494f4ef3cfe5abf227fa49a51011364bf | https://github.com/Zhaoyi-Yan/DCANet/tree/1d99481494f4ef3cfe5abf227fa49a51011364bf |
QNetwork | # 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... | bluebibi/rl_book_codes | QNetwork | false | 3,227 | [
"MIT"
] | 0 | ef7fc9993eb66618e4b4e80e59cc2879a8db3522 | https://github.com/bluebibi/rl_book_codes/tree/ef7fc9993eb66618e4b4e80e59cc2879a8db3522 |
TripletLoss | import torch
from torch import nn
import torch.nn.functional as F
class TripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, margin=2.0):
super(TripletLoss, self).__init__()
self.margin = marg... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | kmi-robots/object_reasoner | TripletLoss | false | 3,836 | [
"Apache-2.0"
] | 0 | 2d45bdb3ee745e0d866a152e8d81cbb375fa2985 | https://github.com/kmi-robots/object_reasoner/tree/2d45bdb3ee745e0d866a152e8d81cbb375fa2985 |
RelateModule | import torch
from torch import nn
from torch.nn import functional as F
class RelateModule(nn.Module):
"""
A neural module that takes as input a feature map and an attention and produces an attention
as output.
Extended Summary
----------------
A :class:`RelateModule` takes input features and ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | kdexd/probnmn-clevr | RelateModule | false | 15,799 | [
"MIT"
] | 69 | 9c1b2286cf30e9fb045370153c9242a39760e02e | https://github.com/kdexd/probnmn-clevr/tree/9c1b2286cf30e9fb045370153c9242a39760e02e |
Gate | # 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... | CityU-AIM-Group/GFBS | Gate | false | 298 | [
"MIT"
] | 0 | d71361243f1bcf699e1a20b312b05fe0be4dfd6d | https://github.com/CityU-AIM-Group/GFBS/tree/d71361243f1bcf699e1a20b312b05fe0be4dfd6d |
Conv2dLayer | # 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 math
import torch.nn a... | jiyanglii/OpenTransformer | Conv2dLayer | false | 15,705 | [
"MIT"
] | 321 | f37cc8cbbc96ddb67082dd2962d09303551010c8 | https://github.com/jiyanglii/OpenTransformer/tree/f37cc8cbbc96ddb67082dd2962d09303551010c8 |
UpsampleCat | # 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_... | Aoi-hosizora/UNet-pytorch | UpsampleCat | false | 8,863 | [
"MIT"
] | 0 | 96951d5d1fdc6c6266a11e1bd97fbf72010bc87d | https://github.com/Aoi-hosizora/UNet-pytorch/tree/96951d5d1fdc6c6266a11e1bd97fbf72010bc87d |
CELoss | import torch
from torch import nn
class CELoss(nn.Module):
def __init__(self):
super(CELoss, self).__init__()
self.criterionBinary = nn.BCELoss(size_average=True)
self.criterionMulti = nn.NLLLoss(size_average=True)
def __repr__(self):
return 'CE'
def forward(self, output... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | nthuy190991/geoseg | CELoss | false | 7,357 | [
"MIT"
] | 1 | b679af5dc558720df36dddc7abfd4e6ecb46d7de | https://github.com/nthuy190991/geoseg/tree/b679af5dc558720df36dddc7abfd4e6ecb46d7de |
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
from torch._inductor.runtime.... | hishamelreedy/Aucrobotics_QA_AutonomousInspector | Net | false | 12,508 | [
"MIT"
] | 0 | 6bad141a62827fa7a299325c69597f17b162400e | https://github.com/hishamelreedy/Aucrobotics_QA_AutonomousInspector/tree/6bad141a62827fa7a299325c69597f17b162400e |
AdaptiveInstanceNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | KwonGihyun/DiagonalGAN | AdaptiveInstanceNorm | false | 8,437 | [
"MIT"
] | 13 | 9e401c00e741d700f85df2c715ee11c1e66e1d1c | https://github.com/KwonGihyun/DiagonalGAN/tree/9e401c00e741d700f85df2c715ee11c1e66e1d1c |
Shifted_softplus | # 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
import torch.nn.parallel
assert_size_str... | QMrpy/deepchem | Shifted_softplus | false | 2,736 | [
"MIT"
] | 0 | f38a21c71e7bc4fd1fa59601be2b79ce7d744bd6 | https://github.com/QMrpy/deepchem/tree/f38a21c71e7bc4fd1fa59601be2b79ce7d744bd6 |
LayerNorm1D | import torch
import torch.nn as nn
class LayerNorm1D(nn.Module):
def __init__(self, num_outputs, eps=1e-05, affine=True):
super(LayerNorm1D, self).__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(1, num_outputs))
self.bias = nn.Parameter(torch.zeros(1, num_outputs))... | 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_... | Pluriscient/learn-to-learn | LayerNorm1D | false | 11,786 | [
"MIT"
] | 0 | 4aa0143522eb90f6439b83ed424d12b434cb344b | https://github.com/Pluriscient/learn-to-learn/tree/4aa0143522eb90f6439b83ed424d12b434cb344b |
ShakeResNet | # 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 math
from torch import... | AustinCai/gmaxup-augmentation | ShakeResNet | false | 93 | [
"MIT"
] | 0 | a64ca0a76eb333e5ce6b217c301d27ca04d73bce | https://github.com/AustinCai/gmaxup-augmentation/tree/a64ca0a76eb333e5ce6b217c301d27ca04d73bce |
AttentivePooling | import torch
import torch.nn as nn
class AttentivePooling(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, **kwargs):
super(AttentivePooling, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W = nn.Linear(input_dim, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | czlwang/s3prl | AttentivePooling | false | 12,281 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
GeM | # 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.nn as nn
from t... | SamYuen101234/Masked_Face_Recognition | GeM | false | 14,375 | [
"MIT"
] | 60 | 2dc572573ebd9ac208314690b529ed69addf0913 | https://github.com/SamYuen101234/Masked_Face_Recognition/tree/2dc572573ebd9ac208314690b529ed69addf0913 |
TokenEmbedding | # 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Ismail-Mustapha/tutorials | TokenEmbedding | false | 13,853 | [
"BSD-3-Clause"
] | 6,424 | 0ccfbf0047db855e93e2aadb43c89c92e89f52b8 | https://github.com/Ismail-Mustapha/tutorials/tree/0ccfbf0047db855e93e2aadb43c89c92e89f52b8 |
ExponentialDecay | # 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 Tensor
from torch import nn
from torch.jit import Final
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
em... | Rikorose/clc-dns-challenge-2020 | ExponentialDecay | false | 8,705 | [
"Apache-2.0"
] | 12 | 4f1c078691327a75b3a338fe372ba356b450a6da | https://github.com/Rikorose/clc-dns-challenge-2020/tree/4f1c078691327a75b3a338fe372ba356b450a6da |
FHardtanh | import random
import torch
import torch.nn as nn
class FHardtanh(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FHardtanh, self).__init__()
self.min_val = random.random()
self.max_val = self.min_val + random.random()
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | dawnclaude/onnx2keras | FHardtanh | false | 15,125 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
PermEqui1_max | # 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_... | haoruilee/DeepSets | PermEqui1_max | false | 15,497 | [
"Apache-2.0"
] | 213 | b405dd6b51a34fb1ef622e25e6685b417b7b7cbb | https://github.com/haoruilee/DeepSets/tree/b405dd6b51a34fb1ef622e25e6685b417b7b7cbb |
L2Norm | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
self.eps = 1e-10
self.weight = nn.... | 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
import torch.nn.init as init
asse... | Anonymous4604/Self-ADE_SSD | L2Norm | false | 1,969 | [
"MIT"
] | 0 | eb4107e17721e17f2dedbdae654a43fc5d291f8c | https://github.com/Anonymous4604/Self-ADE_SSD/tree/eb4107e17721e17f2dedbdae654a43fc5d291f8c |
ModelWithDuplicates | import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class ModelWithDuplicates(nn.Module):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | saman-aghazadeh/distiller | ModelWithDuplicates | false | 4,258 | [
"Apache-2.0"
] | 0 | 7e8d3e6193c807f7c55d8453f64e1bc3c02eee30 | https://github.com/saman-aghazadeh/distiller/tree/7e8d3e6193c807f7c55d8453f64e1bc3c02eee30 |
LSTMAttentionLayer | # 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.... | PeterouZh/SemiNAS | LSTMAttentionLayer | false | 17,821 | [
"Apache-2.0"
] | 5 | 39731663271b994571160d43d796b2bb93386b3b | https://github.com/PeterouZh/SemiNAS/tree/39731663271b994571160d43d796b2bb93386b3b |
MultiHeadedAttention | # 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.... | ClementNguyen/slt | MultiHeadedAttention | false | 307 | [
"Apache-2.0"
] | 0 | 20ee90349d1ed0655b99612ffcfae6d079116db6 | https://github.com/ClementNguyen/slt/tree/20ee90349d1ed0655b99612ffcfae6d079116db6 |
TorchFloorDiv | import torch
class TorchFloorDiv(torch.nn.Module):
def __init__(self):
super(TorchFloorDiv, self).__init__()
def forward(self, x, y):
return torch.floor_divide(x, y)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | PogChamper/torch2trt | TorchFloorDiv | false | 14,227 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
ClassificationTestModel | from torch.nn import Module
import torch
import torch.nn as nn
from typing import Any
from torch.nn.modules import Module
class ClassificationTestModel(Module):
def __init__(self, in_chans: 'int'=3, num_classes: 'int'=1000, **kwargs:
Any) ->None:
super().__init__()
self.conv1 = nn.Conv2d(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 |
GaussianSmearing | import torch
import torch.nn as nn
class GaussianSmearing(nn.Module):
def __init__(self, in_features, start=0, end=1, num_freqs=50):
super(GaussianSmearing, self).__init__()
self.num_freqs = num_freqs
offset = torch.linspace(start, end, num_freqs)
self.coeff = -0.5 / (offset[1] - ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | chris-price19/ocp | GaussianSmearing | false | 1,703 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e |
AuxiliaryConvolutions | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | adityag6994/pytorch_ssd_training | AuxiliaryConvolutions | false | 3,172 | [
"MIT"
] | 0 | 404f3cbef815e314337ec2c1b4f06a2403a7ce03 | https://github.com/adityag6994/pytorch_ssd_training/tree/404f3cbef815e314337ec2c1b4f06a2403a7ce03 |
AttNLocalNew | import torch
import torch.nn as nn
class AttNLocalNew(nn.Module):
"""
自动限制矩阵
实现斜对角线保留权重，其他的设为-inf
"""
def __init__(self, maxlen=128, limit=20):
super(AttNLocalNew, self).__init__()
self.limit = limit
self.maxlen = maxlen
pass
def forward(self, x):
m... | 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
@triton.jit
def triton_poi_fused_index_put_lift_fres... | napoler/tkit-attnlocal-pytorch | AttNLocalNew | false | 4,044 | [
"Apache-2.0"
] | 0 | ec1c32cb49635824f978b3ec19b4c80505ea735b | https://github.com/napoler/tkit-attnlocal-pytorch/tree/ec1c32cb49635824f978b3ec19b4c80505ea735b |
SplAtConv2d | # 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.... | Exdenta/torchsat | SplAtConv2d | false | 13,662 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
VNMaxPool | import torch
import torch.nn as nn
from itertools import product as product
class VNMaxPool(nn.Module):
def __init__(self, in_channels):
super(VNMaxPool, self).__init__()
self.map_to_dir = nn.Linear(in_channels, in_channels, bias=False)
def forward(self, x):
"""
x: point feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | david1309/SynergyNet_bonseyes | VNMaxPool | false | 3,385 | [
"MIT"
] | 0 | 9d675f6e0c78222e1fa55e6598c3d11aa5dc799b | https://github.com/david1309/SynergyNet_bonseyes/tree/9d675f6e0c78222e1fa55e6598c3d11aa5dc799b |
GELayerv2 | # 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.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_... | SSusantAchary/OctaveConv_pytorch | GELayerv2 | false | 14,348 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
netmodel | import torch
import numpy as np
from torch.nn import Parameter
class netmodel(torch.nn.Module):
def __init__(self):
super(netmodel, self).__init__()
self.w0 = Parameter(torch.Tensor(1))
self.w1 = Parameter(torch.Tensor(1))
self.w0.data.uniform_(-1, 1)
self.w1.data.uniform_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch... | uber-common/safemutations | netmodel | false | 16,634 | [
"MIT"
] | 91 | 40e5fd03a244f89bf157d4bedf79201e706aedc1 | https://github.com/uber-common/safemutations/tree/40e5fd03a244f89bf157d4bedf79201e706aedc1 |
LayerNorm2D | import torch
import torch.nn as nn
class LayerNorm2D(nn.Module):
"""Layer normalization for CNN outputs."""
def __init__(self, channel, idim, eps=1e-12):
super(LayerNorm2D, self).__init__()
self.norm = nn.LayerNorm([channel, idim], eps=eps)
def forward(self, xs):
"""Forward pass.... | 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_... | ishine/neural_sp | LayerNorm2D | false | 15,655 | [
"Apache-2.0"
] | 577 | 7995613541d994976b00d80dcc12e2835163acfb | https://github.com/ishine/neural_sp/tree/7995613541d994976b00d80dcc12e2835163acfb |
RingLoss | import torch
import torch.utils.data
from torch import nn
class RingLoss(nn.Module):
"""Ring loss.
Reference:
Zheng et al. Ring loss: Convex Feature Normalization for Face Recognition. CVPR 2018.
"""
def __init__(self, weight_ring=1.0):
super(RingLoss, self).__init__()
self.r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyn... | Luxios22/Dual_Norm | RingLoss | false | 9,276 | [
"MIT"
] | 0 | b404a03b15fc05749e0c648d9e46ffe70f6b2a80 | https://github.com/Luxios22/Dual_Norm/tree/b404a03b15fc05749e0c648d9e46ffe70f6b2a80 |
SeparableConv1D | import torch
from torch import nn
class SeparableConv1D(nn.Module):
"""Depthwise separable 1D convolution.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_size (int): Size of the convolving kernel.
stride (int): Stride 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | johnjosephmorgan/snowfall | SeparableConv1D | false | 12,739 | [
"Apache-2.0"
] | 0 | 604d789c0aed035626d6745e6d7a427168063cae | https://github.com/johnjosephmorgan/snowfall/tree/604d789c0aed035626d6745e6d7a427168063cae |
MixtureDensityHead | # 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 ... | edchengmoore/pytorch_tabular | MixtureDensityHead | false | 4,749 | [
"MIT"
] | 0 | 25f87089fbed95b46f2a1a8a96fba1f581aa8af1 | https://github.com/edchengmoore/pytorch_tabular/tree/25f87089fbed95b46f2a1a8a96fba1f581aa8af1 |
Gating | import torch
import torch.nn as nn
import torch.nn.functional as F
class Gating(nn.Module):
"""
FCN architecture for large scale scene coordiante regression.
"""
def __init__(self, num_experts, capacity=1):
"""
Constructor.
"""
super(Gating, self).__init__()
self.capacity = cap... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | vislearn/esac | Gating | false | 16,705 | [
"BSD-3-Clause"
] | 62 | 4004b251525fa238a1cb6e1043fb41a4719a4ff2 | https://github.com/vislearn/esac/tree/4004b251525fa238a1cb6e1043fb41a4719a4ff2 |
Divide | import torch
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class Divide(torch.nn.Module):
""" Divide module for a functional divide"""
def forward(self, x, y):
"""
Forward-pass routine for divide op
"""
... | 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
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
assert_size_stride =... | arjunsuresh/aimet | Divide | false | 12,354 | [
"BSD-3-Clause"
] | 0 | f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 | https://github.com/arjunsuresh/aimet/tree/f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 |
FocalLoss | import torch
from torch import nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(FocalLoss, self).__init__()
def forward(self, inputs, targets, alpha=0.8, gamma=2, smooth=1):
inputs = inputs.view(-1)
targets = tar... | 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 ... | DeVriesMatt/cellshape-voxel | FocalLoss | false | 5,062 | [
"BSD-3-Clause"
] | 1 | 64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1 | https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1 |
ReceptiveFieldNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import functional as F
from torch.nn import functional as F
def box_filter(x, k):
if k % 2 == 0:
k = k + 1
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | amazon-research/network-deconvolution-pp | ReceptiveFieldNorm | false | 18,354 | [
"Apache-2.0"
] | 6 | 99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce | https://github.com/amazon-research/network-deconvolution-pp/tree/99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce |
SimpleMinModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleMinModule | false | 14,664 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
PSA_p | # 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.... | realphongha/human-pose-estimation.pytorch | PSA_p | false | 4,189 | [
"MIT"
] | 0 | 29b106d3e6c6e12325a7d4bca4abc56ecbc12b1f | https://github.com/realphongha/human-pose-estimation.pytorch/tree/29b106d3e6c6e12325a7d4bca4abc56ecbc12b1f |
ExponentialLoss | import torch
from torch.nn.modules.loss import _Loss
class ExponentialLoss(_Loss):
def __init__(self):
super(ExponentialLoss, self).__init__()
self.mseCriterion = torch.nn.modules.MSELoss()
def forward(self, img, ref):
return self.mseCriterion(img, ref) + 0.005 * self.mseCriterion(to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.... | HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping | ExponentialLoss | false | 17,367 | [
"MIT"
] | 4 | 1e2dee8d6d1f97722eba91618462537faf9efba7 | https://github.com/HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping/tree/1e2dee8d6d1f97722eba91618462537faf9efba7 |
TwoLinearsModel | import torch
import torch.cuda
from torch import nn
import torch.nn
import torch.utils.data
import torch.fx
import torch.utils.tensorboard._pytorch_graph
class TwoLinearsModel(nn.Module):
def __init__(self, per_sample_shape: 'list', hidden_size: 'int',
output_size: 'int'):
super(TwoLinearsModel, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.cuda
from torch ... | mikeseven/aimet | TwoLinearsModel | false | 11,119 | [
"BSD-3-Clause"
] | 0 | 63211a4f259b6457c58dfae1097c70acb93319fe | https://github.com/mikeseven/aimet/tree/63211a4f259b6457c58dfae1097c70acb93319fe |
Biaffine | # 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.autograd
import torch.nn as nn
assert_size_stride = torch._C._dynam... | dumpmemory/W2NER | Biaffine | false | 15,257 | [
"MIT"
] | 128 | fb1b6eb1111eb001b1c965097d995244b840bdda | https://github.com/dumpmemory/W2NER/tree/fb1b6eb1111eb001b1c965097d995244b840bdda |
SDNE_layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | BruceW91/cogdl | SDNE_layer | false | 209 | [
"MIT"
] | 0 | 1ad524375f5ba062103698a0432fc857572a6933 | https://github.com/BruceW91/cogdl/tree/1ad524375f5ba062103698a0432fc857572a6933 |
DilatedResConv | # 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 ... | ChesterHuynh/Wavenet-CPC-Music-Translation | DilatedResConv | false | 266 | [
"MIT"
] | 0 | 60632b0330a61a10bac1a129826c55372f685427 | https://github.com/ChesterHuynh/Wavenet-CPC-Music-Translation/tree/60632b0330a61a10bac1a129826c55372f685427 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, state_dim, action_dim):
super(Net, self).__init__()
fc1_dim = 32
fc2_dim = 64
fc3_dim = 128
self.fc1 = nn.Linear(state_dim, fc1_dim)
self.fc2 = nn.Linear(fc1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ronekko/study_reinforcement_learning | Net | false | 4,207 | [
"MIT"
] | 0 | ef5201e3eae69c20f29b7f176b5a6de7ecdb856a | https://github.com/ronekko/study_reinforcement_learning/tree/ef5201e3eae69c20f29b7f176b5a6de7ecdb856a |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Bhaskers-Blu-Org1/translucent-answer-prediction | BertSelfAttention | false | 8,794 | [
"Apache-2.0"
] | 23 | 1214e0356f24e1a4b1ad64f2eb0edac0baf37a79 | https://github.com/Bhaskers-Blu-Org1/translucent-answer-prediction/tree/1214e0356f24e1a4b1ad64f2eb0edac0baf37a79 |
GlobalAvgPool2d | # 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.utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | songzijiang/FasterSeg | GlobalAvgPool2d | false | 16,484 | [
"MIT"
] | 334 | 1a14ef6dd665afd229a16ab43b532b5a406512f8 | https://github.com/songzijiang/FasterSeg/tree/1a14ef6dd665afd229a16ab43b532b5a406512f8 |
DistanceMatrixLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | daemon/vizbert | DistanceMatrixLoss | false | 1,773 | [
"MIT"
] | 0 | e40b7d1529f8857050313f8d87ff03b1b7226c9e | https://github.com/daemon/vizbert/tree/e40b7d1529f8857050313f8d87ff03b1b7226c9e |
ConcatConv2d | import torch
import torch.nn as nn
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
module = nn.ConvTranspose2d if transpose else nn.Conv2d
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... | Teemo341/BDNN | ConcatConv2d | false | 2,870 | [
"Apache-2.0"
] | 0 | d53d4634a7a43d038faa049d7dfd10b3578ae267 | https://github.com/Teemo341/BDNN/tree/d53d4634a7a43d038faa049d7dfd10b3578ae267 |
UNet | import torch
from torch import nn
import torch.nn.functional as F
from torchvision import models
class UNet(nn.Module):
"""
The U-Net Convolutional Neural Network for semantic segmentation
Source material for the algorithm:
https://link.springer.com/chapter/10.1007%2F978-3-319-24574-4_28
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | mattesko/torch-toolkit | UNet | false | 10,647 | [
"MIT"
] | 0 | 1b4526640232843bdd4022c86cf1856e2e3248b0 | https://github.com/mattesko/torch-toolkit/tree/1b4526640232843bdd4022c86cf1856e2e3248b0 |
ReferenceWeightBinarizationModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
from torchvision import models as models
import torc... | aalborov/openvino_training_extensions | ReferenceWeightBinarizationModule | false | 6,033 | [
"Apache-2.0"
] | 1 | a0bb39424151a98e1ca80c4aa5c865636d401785 | https://github.com/aalborov/openvino_training_extensions/tree/a0bb39424151a98e1ca80c4aa5c865636d401785 |
Rotate | import torch
import torch.nn as nn
from torchvision import transforms as ttf
class Rotate(nn.Module):
def __init__(self, M):
super().__init__()
self.M = M
self.angle = 359 / 10 * self.M
def forward(self, img):
return ttf.functional.rotate(img, self.angle)
def get_inputs():
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Hayoung93/UDA | Rotate | false | 959 | [
"Apache-2.0"
] | 0 | a587b01c76141d64e7cead55b62e0f3ed75890bf | https://github.com/Hayoung93/UDA/tree/a587b01c76141d64e7cead55b62e0f3ed75890bf |
InfoNCE | import torch
import torch.nn.functional as F
from torch import nn
def normalize(*xs):
return [(None if x is None else F.normalize(x, dim=-1)) for x in xs]
def transpose(x):
return x.transpose(-2, -1)
def info_nce(query, positive_key, negative_keys=None, temperature=0.1,
reduction='mean', negative_mode... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RElbers/info-nce-pytorch | InfoNCE | false | 14,256 | [
"MIT"
] | 59 | 37ceef781b3fb89557c0d2b401a9fadf74be8791 | https://github.com/RElbers/info-nce-pytorch/tree/37ceef781b3fb89557c0d2b401a9fadf74be8791 |
LabelSmoothCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Hcnaeg/DI-engine | LabelSmoothCELoss | false | 2,373 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
TransformerEncoderLayer | # 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.... | RongKaiWeskerMA/INSTA | TransformerEncoderLayer | false | 8,745 | [
"MIT"
] | 22 | 298bec0aeac3c1fde7bbcd4dece72ded1056e478 | https://github.com/RongKaiWeskerMA/INSTA/tree/298bec0aeac3c1fde7bbcd4dece72ded1056e478 |
AsymmetricLoss | 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 libdevice, math as tl_math
import torc... | HumberMe/mmclassification | AsymmetricLoss | false | 571 | [
"Apache-2.0"
] | 0 | 68f1542068d3af4db932c97e6a728181432fff0c | https://github.com/HumberMe/mmclassification/tree/68f1542068d3af4db932c97e6a728181432fff0c |
Prototypes | # 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.... | pmirallesr/Dassl.pytorch | Prototypes | false | 4,126 | [
"MIT"
] | 0 | ec41f816bb60a9af94c9b055c500f0e2e404cfc6 | https://github.com/pmirallesr/Dassl.pytorch/tree/ec41f816bb60a9af94c9b055c500f0e2e404cfc6 |
VAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | Code-Cornelius/libraries | VAE | false | 5,032 | [
"MIT"
] | 1 | 2ebd5f78dcedfdce1416280d7d40de7691906951 | https://github.com/Code-Cornelius/libraries/tree/2ebd5f78dcedfdce1416280d7d40de7691906951 |
DotProductAttention | import math
import torch
import warnings
from typing import Optional
from typing import Tuple
import torch.nn as nn
class DotProductAttention(nn.Module):
"""DotProductAttention.
.. math::
\\mathrm{DotProductAttention}(Q, K, V) &=& \\mathrm{softmax}(qk^T) v
q &=& QW_1 + b_1
k &=& KW... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | masanorihirano/pytorch_extra_mhirano | DotProductAttention | false | 7,167 | [
"MIT"
] | 1 | d19e07445567c069793b7ca1a22a846d7cbce58d | https://github.com/masanorihirano/pytorch_extra_mhirano/tree/d19e07445567c069793b7ca1a22a846d7cbce58d |
SEModule | # 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 ... | MAC-AutoML/XNAS | SEModule | false | 17,643 | [
"MIT"
] | 9 | 2c54ceb09b255cbcabd67f3c39fc777c4b2403f4 | https://github.com/MAC-AutoML/XNAS/tree/2c54ceb09b255cbcabd67f3c39fc777c4b2403f4 |
Shifted_softplus | # 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
import torch.nn.parallel
assert_size_str... | Chahalprincy/deepchem | Shifted_softplus | false | 223 | [
"MIT"
] | 0 | 9d1a6a879cc74b065694b3ddb763d52151d57b7a | https://github.com/Chahalprincy/deepchem/tree/9d1a6a879cc74b065694b3ddb763d52151d57b7a |
PointerSwitch | # 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... | aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing | PointerSwitch | false | 9,665 | [
"BSD-3-Clause"
] | 0 | 640858024df444006dfae106a28fdb58f36f687e | https://github.com/aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing/tree/640858024df444006dfae106a28fdb58f36f687e |
VNet | # 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_... | Stranger469/wrench | VNet | false | 5,863 | [
"Apache-2.0"
] | 1 | ab717ac26a76649c8fdb946a28dffe7e682c80ba | https://github.com/Stranger469/wrench/tree/ab717ac26a76649c8fdb946a28dffe7e682c80ba |
CNNLayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | HikaruHotta/M2M-VC-CycleGAN | CNNLayerNorm | false | 17,368 | [
"MIT"
] | 5 | a93b06221c787cc3e13b2d92fee728b811e5d526 | https://github.com/HikaruHotta/M2M-VC-CycleGAN/tree/a93b06221c787cc3e13b2d92fee728b811e5d526 |
CrossEntropy | # 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... | Gromy1211/torch-light | CrossEntropy | false | 11,450 | [
"MIT"
] | 0 | c7d7a9bc5ab1eab03d800a27d9325859516f01e6 | https://github.com/Gromy1211/torch-light/tree/c7d7a9bc5ab1eab03d800a27d9325859516f01e6 |
ActorNet | # 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.... | cugzj/Adaptive-B | ActorNet | false | 6,497 | [
"Apache-2.0"
] | 1 | cebc965b1dbad93332ae371bfef8640259d940c4 | https://github.com/cugzj/Adaptive-B/tree/cebc965b1dbad93332ae371bfef8640259d940c4 |
Affine | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Affine(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones((1, 1, dim)))
self.beta = nn.Parameter(torch.zeros((1, 1, dim)))
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
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | akarshkumar0101/timm-mlp-shaker | Affine | false | 1,386 | [
"Apache-2.0"
] | 0 | ab211dd137b790ac57f5ed924c2ada148d54a194 | https://github.com/akarshkumar0101/timm-mlp-shaker/tree/ab211dd137b790ac57f5ed924c2ada148d54a194 |
GridAttentionBlock | import torch
import torch.nn.functional as F
import torch.nn as nn
class GridAttentionBlock(nn.Module):
def __init__(self, in_channels):
super(GridAttentionBlock, self).__init__()
self.inter_channels = in_channels
self.in_channels = in_channels
self.gating_channels = in_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.nn as nn
assert_... | WHU-YH-jx/bionetwork_segmentation | GridAttentionBlock | false | 5,956 | [
"MIT"
] | 1 | 556c5b61a1a3784875b31eacb8c6bb418d70ee9a | https://github.com/WHU-YH-jx/bionetwork_segmentation/tree/556c5b61a1a3784875b31eacb8c6bb418d70ee9a |
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.... | Chris0919/Deep-reinforcement-learning-with-pytorch | Actor | false | 5,000 | [
"MIT"
] | 1 | a4f458dde7659654fcae4635d25f6bd05a5d2d6c | https://github.com/Chris0919/Deep-reinforcement-learning-with-pytorch/tree/a4f458dde7659654fcae4635d25f6bd05a5d2d6c |
RPNHead | # 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... | Okery/PyTorch-Simple-MaskRCNN | RPNHead | false | 14,136 | [
"MIT"
] | 147 | 5e57a353f211c7130bfcf1d55cacd80057d81423 | https://github.com/Okery/PyTorch-Simple-MaskRCNN/tree/5e57a353f211c7130bfcf1d55cacd80057d81423 |
AugCNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import functional as F
assert_size_stride = ... | krg-nandu/prj-taxRL | AugCNN | false | 7,054 | [
"MIT"
] | 1 | be65d004c196aff73714dcb346c814ae97db30e2 | https://github.com/krg-nandu/prj-taxRL/tree/be65d004c196aff73714dcb346c814ae97db30e2 |
LogSTFTMagnitudeLoss | import torch
import torch.nn.functional as F
import torch.utils.data
class LogSTFTMagnitudeLoss(torch.nn.Module):
"""Log STFT magnitude loss module."""
def __init__(self):
"""Initilize los STFT magnitude loss module."""
super(LogSTFTMagnitudeLoss, self).__init__()
def forward(self, x_mag... | 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.utils.dat... | tebin/Fre-GAN-pytorch | LogSTFTMagnitudeLoss | false | 10,870 | [
"MIT"
] | 0 | e2f51317ae3953f10b8a0d112fc14991a02ebe91 | https://github.com/tebin/Fre-GAN-pytorch/tree/e2f51317ae3953f10b8a0d112fc14991a02ebe91 |
BCELoss | # 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... | HelenGuohx/cv-ferattn-code | BCELoss | false | 5,329 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
FastRCNNPredictor | # 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... | Jaramies/PyTorch-Simple-MaskRCNN | FastRCNNPredictor | false | 5,370 | [
"MIT"
] | 1 | 21e6c6983b34061800280573ebe705ae17212972 | https://github.com/Jaramies/PyTorch-Simple-MaskRCNN/tree/21e6c6983b34061800280573ebe705ae17212972 |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from co... | ZhiTingXin/pytorch-playground | MLP | false | 9,716 | [
"MIT"
] | 0 | b319eaf290ad6d793e41efc488309cedf24eba96 | https://github.com/ZhiTingXin/pytorch-playground/tree/b319eaf290ad6d793e41efc488309cedf24eba96 |
PatchEmbed3D | # 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... | XiaoJake/MTTR | PatchEmbed3D | false | 14,623 | [
"Apache-2.0"
] | 516 | c383c5b151e3c97aeb45cd2fb4bf08719016498b | https://github.com/XiaoJake/MTTR/tree/c383c5b151e3c97aeb45cd2fb4bf08719016498b |
MILLR | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | mhbl3/PrecursorAnalysis | MILLR | false | 7,370 | [
"MIT"
] | 1 | aaa2fe0219ad579b9126fef9cc8594a59ae66815 | https://github.com/mhbl3/PrecursorAnalysis/tree/aaa2fe0219ad579b9126fef9cc8594a59ae66815 |
BboxHead | import torch
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class BboxHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(BboxHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from itertools import product as produ... | Capetian/FaceX-Zoo | BboxHead | false | 5,025 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
AE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | cerbero94/GAN_CP | AE | false | 1,678 | [
"MIT"
] | 0 | e255f5f5b3733c55d47997c1ffc4161529701f8a | https://github.com/cerbero94/GAN_CP/tree/e255f5f5b3733c55d47997c1ffc4161529701f8a |
Discriminator | import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_h):
super(Discriminator, self).__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Bilin... | 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... | mess-clarifai/DGI | Discriminator | false | 10,496 | [
"MIT"
] | 0 | 3a7c96d59991d448b84d709916d1d5f256e5b9be | https://github.com/mess-clarifai/DGI/tree/3a7c96d59991d448b84d709916d1d5f256e5b9be |
SmoothL1Loss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Fanzhongjie/ARFE | SmoothL1Loss | false | 456 | [
"Apache-2.0"
] | 0 | 4b96b8c5bc0895d3d30acec2a490f81a860fe860 | https://github.com/Fanzhongjie/ARFE/tree/4b96b8c5bc0895d3d30acec2a490f81a860fe860 |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
def _sigmoid_cross_entropy_with_logits(logits, labels):
loss = torch.clamp(logits, min=0) - logits * labels.type_as(logits)
loss += torch.log1p(torch.exp(-torch.abs(logits)))
return loss
class SigmoidFocalClassificationLoss(nn.Module):
"""Sigmoid focal cross entrop... | 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... | liuhuaijjin/rpn_rois_proposals_layers | SigmoidFocalClassificationLoss | false | 7,103 | [
"MIT"
] | 1 | c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 | https://github.com/liuhuaijjin/rpn_rois_proposals_layers/tree/c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 |
ECA | # 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._C
import torch.serialization
from torch import nn
from typing impo... | shuaizzZ/mmsegmentation | ECA | false | 4,322 | [
"Apache-2.0"
] | 0 | a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c | https://github.com/shuaizzZ/mmsegmentation/tree/a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c |
SimpleExpModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | opti-mix/glow | SimpleExpModule | false | 7,393 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
CosineClassifier | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class CosineClassifier(Module):
def __init__(self, in_features, n_classes, sigma=True):
super(CosineClassifier, self).__init__()
self.in_features = in_features
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.... | Danden1/DER-ClassIL.pytorch | CosineClassifier | false | 13,553 | [
"MIT"
] | 79 | 66ccdb45890d3da335f4dcb841160cbea8719c15 | https://github.com/Danden1/DER-ClassIL.pytorch/tree/66ccdb45890d3da335f4dcb841160cbea8719c15 |
FBLoss | import torch
from torch import nn
def fb_loss(preds, trues, beta):
smooth = 0.0001
beta2 = beta * beta
batch = preds.size(0)
classes = preds.size(1)
preds = preds.view(batch, classes, -1)
trues = trues.view(batch, classes, -1)
weights = torch.clamp(trues.sum(-1), 0.0, 1.0)
TP = (preds ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | lRomul/argus-tgs-salt | FBLoss | false | 15,866 | [
"MIT"
] | 74 | 2ba7db4d09256bc025c49860cd79560ced6b8a1b | https://github.com/lRomul/argus-tgs-salt/tree/2ba7db4d09256bc025c49860cd79560ced6b8a1b |
Conv2d | # 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.modules.conv import _ConvNd
from torch.nn.modules.utils import _pa... | THAKAORI/SalsaNext | Conv2d | false | 11,902 | [
"MIT"
] | 0 | 855cd7e9ebb83ee62538ba4753a011ada7bbfb6c | https://github.com/THAKAORI/SalsaNext/tree/855cd7e9ebb83ee62538ba4753a011ada7bbfb6c |
DeConvNet2 | import torch
import torch.nn as nn
import torch.nn.functional as F
def spectral_norm(module, init=True, std=1, bound=False):
if init:
nn.init.normal_(module.weight, 0, std)
if hasattr(module, 'bias') and module.bias is not None:
module.bias.data.zero_()
SpectralNorm.apply(module, 'weight',... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Neural-Diffusion-Research/normalized-autoencoders | DeConvNet2 | false | 8,637 | [
"MIT"
] | 30 | 0c77f7e29289e336c0fe5e941aaec8baa4a4fb82 | https://github.com/Neural-Diffusion-Research/normalized-autoencoders/tree/0c77f7e29289e336c0fe5e941aaec8baa4a4fb82 |
GaussianFilter | import torch
import torch.utils.data
import torch
from torch import nn
class GaussianFilter(nn.Module):
def __init__(self, kernel_size=13, stride=1, padding=6):
super(GaussianFilter, self).__init__()
mean = (kernel_size - 1) / 2.0
variance = ((kernel_size - 1) / 6.0) ** 2.0
x_coor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch import nn
assert_size_stride = t... | zsameem/real-world-sr | GaussianFilter | false | 11,097 | [
"MIT"
] | 0 | ed108f3fd2fe4090c18c871c143f30f480de8fb6 | https://github.com/zsameem/real-world-sr/tree/ed108f3fd2fe4090c18c871c143f30f480de8fb6 |
GridPredictionModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class GridPredictionModel(nn.Module):
def __init__(self):
super(GridPredictionModel, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=100, kernel_size
=3, padding=2)
self.conv2 = nn.Conv2d(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
import torch.nn as nn
assert_... | rickmarson/game_of_life_nn | GridPredictionModel | false | 12,942 | [
"MIT"
] | 0 | 728bb009b9d54268e96f33bb752a3e5ba1ae15d1 | https://github.com/rickmarson/game_of_life_nn/tree/728bb009b9d54268e96f33bb752a3e5ba1ae15d1 |
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