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 |
|---|---|---|---|---|---|---|---|---|---|---|
AvgPool2dSame | import math
import torch
import numpy as np
from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int'):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
def pad_same(x, k: 'List[int]', s: 'List... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
assert_... | Hcnaeg/DI-engine | AvgPool2dSame | false | 2,383 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
CRF | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.... | sgrvinod/a-PyTorch-Tutorial-to-Sequence-Labeling | CRF | false | 16,391 | [
"MIT"
] | 334 | ee3f34b45a6e24dd748a144bfc25b1adf9e1f077 | https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Sequence-Labeling/tree/ee3f34b45a6e24dd748a144bfc25b1adf9e1f077 |
stage_block | import torch
import torch.nn as nn
import torch.utils.data
class dilation_layer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, padding=
'same_padding', dilation=1):
super(dilation_layer, self).__init__()
if padding == 'same_padding':
padding = int((ke... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | kacel33/ActionAI_PC | stage_block | false | 15,787 | [
"MIT"
] | 1,311 | a0528f49ea61cc07d7c1e9a3cd6846e5f50cfae7 | https://github.com/kacel33/ActionAI_PC/tree/a0528f49ea61cc07d7c1e9a3cd6846e5f50cfae7 |
ShiftedConv | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 numpy import prod
assert_size_stride = to... | EyalSel/CPC_audio | ShiftedConv | false | 13,670 | [
"MIT"
] | 260 | b98a1bdf1fe9ea219816db7a6c28115d404a3510 | https://github.com/EyalSel/CPC_audio/tree/b98a1bdf1fe9ea219816db7a6c28115d404a3510 |
VideoBoringModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | SheffieldAI/pykale | VideoBoringModel | false | 14,403 | [
"MIT"
] | 324 | be7670941fb06835883c80477b26702d407017db | https://github.com/SheffieldAI/pykale/tree/be7670941fb06835883c80477b26702d407017db |
TripletLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from to... | shuuchen/siamese_network | TripletLoss | false | 4,328 | [
"Apache-2.0"
] | 0 | 54a952d320800c6bb5618cb40386e4c25bdde6fb | https://github.com/shuuchen/siamese_network/tree/54a952d320800c6bb5618cb40386e4c25bdde6fb |
MinibatchStdDev | import torch
import torch as th
import torch.nn.parallel
import torch.utils.data
class MinibatchStdDev(th.nn.Module):
"""
Minibatch standard deviation layer for the discriminator
"""
def __init__(self):
"""
derived class constructor
"""
super(MinibatchStdDev, self).__i... | 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 as th
import torch.nn.parallel
import torch.utils.data
assert_size... | AshwinRJ/Face-Generation-from-Speech | MinibatchStdDev | false | 16,961 | [
"MIT"
] | 4 | 6d8afe8a61185bfe67cd5fd19c7f993630f481b4 | https://github.com/AshwinRJ/Face-Generation-from-Speech/tree/6d8afe8a61185bfe67cd5fd19c7f993630f481b4 |
SineODE | import math
import torch
class SineODE(torch.nn.Module):
def __init__(self, device):
super(SineODE, self).__init__()
def forward(self, t, y):
return 2 * y / t + t ** 4 * torch.sin(2 * t) - t ** 2 + 4 * t ** 3
def y_exact(self, t):
return -0.5 * t ** 4 * torch.cos(2 * t) + 0.5 * ... | 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 math
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | navaro1/parking_prediction | SineODE | false | 12,888 | [
"MIT"
] | 0 | c532a2f75155abc9c0d4be9c955eabe368591932 | https://github.com/navaro1/parking_prediction/tree/c532a2f75155abc9c0d4be9c955eabe368591932 |
MiniBatchStddevLayer | import torch
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as autograd
class AllGatherLayer(autograd.Function):
"""All gather layer with backward propagation path.
Indeed, this module is to make ``dist.all_gather()`` in the backward graph.
Such kind of operation has been wi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as... | arkel23/mmgeneration | MiniBatchStddevLayer | false | 9,947 | [
"Apache-2.0"
] | 0 | 41a30e2972f2037f6aac60ed761bed3fe47bfe4d | https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d |
ContinousRotReprDecoder | # 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... | ShivamDuggal4/human_body_prior | ContinousRotReprDecoder | false | 11,873 | [
"Xnet",
"X11"
] | 0 | e5544560e98ff3bb6d2492b2b32660dd3defed92 | https://github.com/ShivamDuggal4/human_body_prior/tree/e5544560e98ff3bb6d2492b2b32660dd3defed92 |
DoubleConvRelu | import torch
from torch import nn
from torch.nn import functional as F
class DoubleConvRelu(nn.Module):
def __init__(self, in_dec_filters: 'int', out_filters: 'int'):
super().__init__()
self.conv1 = nn.Conv2d(in_dec_filters, out_filters, kernel_size=3,
padding=1, stride=1)
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 import nn
assert_s... | BloodAxe/Catalyst-CamVid-Segmentation-Example | DoubleConvRelu | false | 17,011 | [
"MIT"
] | 7 | a24ed6301c2f2a97cbd4d5ba4ef2348d7ed1d9f3 | https://github.com/BloodAxe/Catalyst-CamVid-Segmentation-Example/tree/a24ed6301c2f2a97cbd4d5ba4ef2348d7ed1d9f3 |
GaussianFocalLoss | import functools
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 ten... | 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... | CK-er/mmdet | GaussianFocalLoss | false | 2,068 | [
"Apache-2.0"
] | 0 | 9bea4068efbcf7bf739dbe41917a68d525c29868 | https://github.com/CK-er/mmdet/tree/9bea4068efbcf7bf739dbe41917a68d525c29868 |
mix_Linear | # 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.... | snudatalab/SensiMix | mix_Linear | false | 12,995 | [
"Apache-2.0"
] | 0 | e5d790f48a96806e9ae01449bb4a66e8f09c4d3a | https://github.com/snudatalab/SensiMix/tree/e5d790f48a96806e9ae01449bb4a66e8f09c4d3a |
AlexNet | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class AlexNet(nn.Module):
def __init__(self, num_classes=10, out_ch_conv1=64, out_ch_conv2=256,
out_ch_conv3=384, out_ch_conv4=256, ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | FujitsuLaboratories/CAC | AlexNet | false | 17,418 | [
"Apache-2.0"
] | 8 | d12df8e47f61eaf7d7b0ed355e2d1aa296453f86 | https://github.com/FujitsuLaboratories/CAC/tree/d12df8e47f61eaf7d7b0ed355e2d1aa296453f86 |
AE_3D_50_no_last_bias | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | gitter-badger/HEPAutoencoders | AE_3D_50_no_last_bias | false | 12,439 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
LuongAttention | # 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.... | aditya140/ques_gen | LuongAttention | false | 18,212 | [
"MIT"
] | 3 | 57be43de682a384ee4114adb3fbc75a527f2aaff | https://github.com/aditya140/ques_gen/tree/57be43de682a384ee4114adb3fbc75a527f2aaff |
Auto_Encoder_Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | sarahESL/MICCAI19-MedVQA | Auto_Encoder_Model | false | 4,275 | [
"MIT"
] | 0 | aa751cb905f79cd356ad5746f8a0640f1d81b5d2 | https://github.com/sarahESL/MICCAI19-MedVQA/tree/aa751cb905f79cd356ad5746f8a0640f1d81b5d2 |
InputInjection | # 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | CarnoZhao/mmsegmentation | InputInjection | false | 7,845 | [
"Apache-2.0"
] | 18 | bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c | https://github.com/CarnoZhao/mmsegmentation/tree/bdaf3d93c4d33c3f0c15f95879fdd7ab78290c1c |
ESA | import torch
import torch.nn as nn
import torch.nn.functional as F
class ESA(nn.Module):
def __init__(self, channel=64, reduction=4, bias=True):
super(ESA, self).__init__()
self.r_nc = channel // reduction
self.conv1 = nn.Conv2d(channel, self.r_nc, kernel_size=1)
self.conv21 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | WestCityInstitute/KAIR | ESA | false | 14,600 | [
"MIT"
] | 1,521 | 3eb3cc7776fa8c57e8ed7c71bfa8039beb4c6677 | https://github.com/WestCityInstitute/KAIR/tree/3eb3cc7776fa8c57e8ed7c71bfa8039beb4c6677 |
Agreement_Routing_Down | import torch
def squash(s, axis=-1, epsilon=1e-07):
squared_norm = torch.sum(s * s, dim=axis)
safe_norm = torch.sqrt(squared_norm + epsilon)
squash_factor = squared_norm / (1.0 + squared_norm)
unit_vector = torch.div(s, safe_norm.unsqueeze(-1))
return torch.mul(squash_factor.unsqueeze(-1), unit_ve... | 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... | arjunsbalaji/oct | Agreement_Routing_Down | false | 1,480 | [
"Apache-2.0"
] | 0 | f21e11f6dda952cd914444512ddadb4141757951 | https://github.com/arjunsbalaji/oct/tree/f21e11f6dda952cd914444512ddadb4141757951 |
AODnet | import torch
import torch.nn as nn
import torch.nn.functional as F
class AODnet(nn.Module):
def __init__(self):
super(AODnet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=1)
self.conv2 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | misads/cv_template | AODnet | false | 16,106 | [
"MIT"
] | 69 | 9976ee0ada449a494d26f896c598610f233edc10 | https://github.com/misads/cv_template/tree/9976ee0ada449a494d26f896c598610f233edc10 |
Conv3BN | # 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
impor... | CalebEverett/fastai-dl2 | Conv3BN | false | 17,149 | [
"Apache-2.0"
] | 4 | 64d23592eddca6ca1f3647e73c319e97c8eb392b | https://github.com/CalebEverett/fastai-dl2/tree/64d23592eddca6ca1f3647e73c319e97c8eb392b |
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... | ethanwhite/torchgeo | ClassificationTestModel | false | 15,308 | [
"MIT"
] | 678 | cb20e1abfd9213f9ee7700df972385db13568642 | https://github.com/ethanwhite/torchgeo/tree/cb20e1abfd9213f9ee7700df972385db13568642 |
CustomLSTMCell | # 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... | vr100/rl-trading | CustomLSTMCell | false | 10,983 | [
"MIT"
] | 0 | 0e3383e383bdfd46c40df65f3c709ba88169153c | https://github.com/vr100/rl-trading/tree/0e3383e383bdfd46c40df65f3c709ba88169153c |
TransitionUp | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.onnx
import torch.utils.data
assert_size_stride = torch... | kuanhungchen/CenterNet-HarDNet | TransitionUp | false | 15,856 | [
"MIT"
] | 164 | 050d55a532706d989105982c5bc10f1c89edc8d2 | https://github.com/kuanhungchen/CenterNet-HarDNet/tree/050d55a532706d989105982c5bc10f1c89edc8d2 |
selfLatentLoss | import torch
import torch.nn as nn
class selfLatentLoss(nn.Module):
def __init__(self):
super(selfLatentLoss, self).__init__()
def forward(self, z_mean, z_log_sigma_sq):
return torch.mean(torch.sum(torch.pow(z_mean, 2) + torch.exp(
z_log_sigma_sq) - z_log_sigma_sq - 1, 1))
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | FizzerYu/CollaborativeVAE | selfLatentLoss | false | 493 | [
"MIT"
] | 0 | 4714cce49acba258600b1b5bbcd3a1a4762385e6 | https://github.com/FizzerYu/CollaborativeVAE/tree/4714cce49acba258600b1b5bbcd3a1a4762385e6 |
make_residual_dense_ver2 | # 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_... | BJTU-MIMO/Channel_estimation_MRDN | make_residual_dense_ver2 | false | 129 | [
"MIT"
] | 0 | f41972998a5403c901bc3e5d68d4acd05e9a7f6c | https://github.com/BJTU-MIMO/Channel_estimation_MRDN/tree/f41972998a5403c901bc3e5d68d4acd05e9a7f6c |
Descendant | # 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... | lindagaw/Kadara | Descendant | false | 10,471 | [
"MIT"
] | 0 | f1059b69a581344ca460c8df02ac3f73f3fbcba1 | https://github.com/lindagaw/Kadara/tree/f1059b69a581344ca460c8df02ac3f73f3fbcba1 |
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.triton_helpers import libdevice
import math
import ... | VayerMaking/gpt-2-Pytorch | MLP | false | 1,902 | [
"MIT"
] | 0 | 7bc35f3c1d6c87d1ac306c0f789282b9df59182a | https://github.com/VayerMaking/gpt-2-Pytorch/tree/7bc35f3c1d6c87d1ac306c0f789282b9df59182a |
_GateAddNorm | # 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 ... | amadejkocbek/darts | _GateAddNorm | false | 12,108 | [
"Apache-2.0"
] | 0 | 074be2a76eee11258da066878c564badf40834e9 | https://github.com/amadejkocbek/darts/tree/074be2a76eee11258da066878c564badf40834e9 |
IMul | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | PogChamper/torch2trt | IMul | false | 14,195 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
ChannelPool | import torch
from torch import nn
class ChannelPool(nn.Module):
def forward(self, x):
return torch.mean(x, 1).unsqueeze(1)
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | GhadeerElmkaiel/MirrorNet | ChannelPool | false | 489 | [
"BSD-3-Clause"
] | 0 | 1a0389abc5b1ccbe7fde7bd1df772cb9df30c072 | https://github.com/GhadeerElmkaiel/MirrorNet/tree/1a0389abc5b1ccbe7fde7bd1df772cb9df30c072 |
Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Natenumber12/LUDO_QLearning | Model | false | 9,323 | [
"MIT"
] | 0 | 0878b9bce01d0afc5798bdbf96db253302654f33 | https://github.com/Natenumber12/LUDO_QLearning/tree/0878b9bce01d0afc5798bdbf96db253302654f33 |
InvertibleUpsampling2D | from torch.autograd import Function
import torch
import numpy as np
from warnings import warn
from typing import Union
from typing import Tuple
from torch.nn.common_types import _size_2_t
from torch.nn.modules.utils import _pair
import torch.nn.functional as F
def _cayley(A):
I = torch.eye(A.shape[-1], device=A.d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import numpy as np
from warnings import warn... | cetmann/iunets | InvertibleUpsampling2D | false | 15,030 | [
"MIT"
] | 86 | 80ed7cce0e505a0396c42359eaf27819222d71f6 | https://github.com/cetmann/iunets/tree/80ed7cce0e505a0396c42359eaf27819222d71f6 |
EncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | chandar-lab/CriticalGradientOptimization | EncoderLayer | false | 6,444 | [
"MIT"
] | 1 | 1af4b1df40489991289bb50bb69859a00b2c97c6 | https://github.com/chandar-lab/CriticalGradientOptimization/tree/1af4b1df40489991289bb50bb69859a00b2c97c6 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | myunghakLee/GainParallel | Attention | false | 12,814 | [
"MIT"
] | 0 | 63112bd996591ad898cbb88fdb839992227a5b74 | https://github.com/myunghakLee/GainParallel/tree/63112bd996591ad898cbb88fdb839992227a5b74 |
SelfAttention | # 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.... | johnson7788/pymarl2 | SelfAttention | false | 3,909 | [
"Apache-2.0"
] | 0 | 8ec3e58fc3325ae82165cae0a5ea8a391ce42bd5 | https://github.com/johnson7788/pymarl2/tree/8ec3e58fc3325ae82165cae0a5ea8a391ce42bd5 |
SymDecoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | BigkoalaZhu/SCORES | SymDecoder | false | 7,793 | [
"MIT"
] | 16 | 8332733c375ee85c02bd34c2adce6a3213aad3c4 | https://github.com/BigkoalaZhu/SCORES/tree/8332733c375ee85c02bd34c2adce6a3213aad3c4 |
Forward_Grad | import torch
import torch.nn as nn
import torch.nn.functional as F
class Forward_Grad(nn.Module):
def __init__(self):
super(Forward_Grad, self).__init__()
self.x_ker_init = torch.tensor([[[[-1, 1]]]], dtype=torch.float,
requires_grad=True)
self.y_ker_init = torch.tensor([[[[-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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AmazingAng/pytorch-tvnet | Forward_Grad | false | 7,661 | [
"MIT"
] | 12 | e880d3ce15f55e5d9a11b423cfd1e0461de4fedb | https://github.com/AmazingAng/pytorch-tvnet/tree/e880d3ce15f55e5d9a11b423cfd1e0461de4fedb |
CDCM | # 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_... | mgpadalkar/pidinet | CDCM | false | 16,037 | [
"MIT"
] | 137 | 781924fe30469cdc64f63ce6666a3e1f5b4e576f | https://github.com/mgpadalkar/pidinet/tree/781924fe30469cdc64f63ce6666a3e1f5b4e576f |
ClassHead | # 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 itertools import product as product
import torch.nn as nn
assert_size_strid... | Edward1900/Face-Detector-1MB-with-landmark | ClassHead | false | 13,701 | [
"MIT"
] | 907 | 16c16c4efa74b0264e0fd7fe0ddc0160f540a4bf | https://github.com/Edward1900/Face-Detector-1MB-with-landmark/tree/16c16c4efa74b0264e0fd7fe0ddc0160f540a4bf |
mean_norm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | ngohienduong/Deep_GCN_Benchmarking | mean_norm | false | 16,171 | [
"MIT"
] | 70 | 3ee57a265bbfd62d8e6f3ee6e3e9062dd5a44633 | https://github.com/ngohienduong/Deep_GCN_Benchmarking/tree/3ee57a265bbfd62d8e6f3ee6e3e9062dd5a44633 |
BinaryPrimitivesPredefined | import math
import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list ... | 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | BinaryPrimitivesPredefined | false | 17,148 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
ConditionalBottleNeck | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class FiLM(nn.Module):
""" Feature-wise Linear Modulation (FiLM) layer"""
def __init__(self, input_size, output_size, num_film_layers=1,
layer_norm=False):
"""
:param input_size: feature size of x_cond
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Daupler/CA-MTL | ConditionalBottleNeck | false | 4,141 | [
"MIT"
] | 0 | d417b039dee973e32f42ba5c1c346738cd29ab3c | https://github.com/Daupler/CA-MTL/tree/d417b039dee973e32f42ba5c1c346738cd29ab3c |
InstancesAccuracy | import torch
import torch.nn as nn
class InstancesAccuracy(nn.Module):
"""
This class implements the accuracy computation. No gradients supported.
"""
def __init__(self, threshold: 'float'=0.5) ->None:
"""
Constructor method
:param threshold: (float) Threshold to be applied
... | 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... | ChristophReich1996/Cell-DETR | InstancesAccuracy | false | 13,487 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
SpatialCrossMapLRN | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.utils.data
import torch.backends.cudnn
import torch.autograd
import torch.nn
class SpatialCrossMapLRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCros... | 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.utils.data.dataloader
import torch.utils.dat... | CASIA-IVA-Lab/DCFST | SpatialCrossMapLRN | false | 7,814 | [
"Apache-2.0"
] | 22 | ca881ba3aae1ce00e4a7a6db01d99e5f6efff68b | https://github.com/CASIA-IVA-Lab/DCFST/tree/ca881ba3aae1ce00e4a7a6db01d99e5f6efff68b |
MemoryEfficientPFLU | from torch.autograd import Function
import torch
from torch import nn
class PFLUFunction(Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return x * (1 + x / torch.sqrt(1 + x * x)) / 2
@staticmethod
def backward(ctx, grad_output):
x, = ctx.saved_tensors
... | 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.autograd import Function
from torch import nn
assert_size_stride = t... | mengzhu0308/PFLU-FPFLU | MemoryEfficientPFLU | false | 7,219 | [
"Apache-2.0"
] | 1 | 628cd472db2913e555e902bdf35af834f84a284b | https://github.com/mengzhu0308/PFLU-FPFLU/tree/628cd472db2913e555e902bdf35af834f84a284b |
MMTM | import torch
import torch.nn as nn
def init_weights(m):
None
if type(m) == nn.Linear:
None
else:
None
class MMTM(nn.Module):
def __init__(self, dim_visual, dim_skeleton, ratio):
super(MMTM, self).__init__()
dim = dim_visual + dim_skeleton
dim_out = int(2 * di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | haamoon/mmtm | MMTM | false | 15,486 | [
"MIT"
] | 70 | 1c81cfefad5532cfb39193b8af3840ac3346e897 | https://github.com/haamoon/mmtm/tree/1c81cfefad5532cfb39193b8af3840ac3346e897 |
ScaledDotProductAttention | # 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.... | IouJenLiu/AFK | ScaledDotProductAttention | false | 5,347 | [
"MIT"
] | 1 | db2b47bb3a5614b61766114b87f143e4a61a4a8d | https://github.com/IouJenLiu/AFK/tree/db2b47bb3a5614b61766114b87f143e4a61a4a8d |
Minimum | import torch
import torch as th
import torch.nn as nn
def minimum(x, dim=-1, scale_up=False, inplace=False):
if inplace:
x_ = x.clone()
min_x = th.min(x_, dim=dim, keepdim=True)[0]
min_mask = x_ == min_x
x.masked_fill_(min_mask == 0, 0.0)
if scale_up:
x_sum = th... | 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 as th
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.ass... | HKUST-KnowComp/DualMessagePassing | Minimum | false | 8,184 | [
"MIT"
] | 12 | d29d627be2a8c8f24b52e3db2c383e33a059aaa7 | https://github.com/HKUST-KnowComp/DualMessagePassing/tree/d29d627be2a8c8f24b52e3db2c383e33a059aaa7 |
PixelNorm | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ArashVahabpour/encoder4editing-contrastive | PixelNorm | false | 13,279 | [
"MIT"
] | 1,051 | 1b91afe1693e01a41118e1ce2451b7d14bec51f4 | https://github.com/ArashVahabpour/encoder4editing-contrastive/tree/1b91afe1693e01a41118e1ce2451b7d14bec51f4 |
DeResNetBlockGroupNorm | # 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.... | wp03052/wolf | DeResNetBlockGroupNorm | false | 13,194 | [
"Apache-2.0"
] | 0 | 49a582cafb829a2642db360c7d94c21439247ec7 | https://github.com/wp03052/wolf/tree/49a582cafb829a2642db360c7d94c21439247ec7 |
DuelingQNetwork | # 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_... | bfMendonca/deep-reinforcement-learning | DuelingQNetwork | false | 9,808 | [
"MIT"
] | 0 | fa8f68d960542658429a4e1a4b1e9fdfb1af0030 | https://github.com/bfMendonca/deep-reinforcement-learning/tree/fa8f68d960542658429a4e1a4b1e9fdfb1af0030 |
AttentiveStatsPool | # 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.... | SecretKeyTeam/voxceleb_trainer | AttentiveStatsPool | false | 9,552 | [
"MIT"
] | 0 | e235cbc2961d32395d30cf606ee830cd47716383 | https://github.com/SecretKeyTeam/voxceleb_trainer/tree/e235cbc2961d32395d30cf606ee830cd47716383 |
Normalize01 | import torch
import torch.nn as nn
class Normalize01(nn.Module):
def __init__(self):
super().__init__()
def forward(self, result_noisy):
Nbatch = result_noisy.size(0)
result_noisy_01 = torch.zeros_like(result_noisy)
for i in range(Nbatch):
min_val = result_noisy[i... | 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... | ScarWar/DeepSTORM3D | Normalize01 | false | 8,736 | [
"MIT"
] | 25 | 8ba5bc61120abedba9c1b24a994e616e280bdda2 | https://github.com/ScarWar/DeepSTORM3D/tree/8ba5bc61120abedba9c1b24a994e616e280bdda2 |
RelativeScalePredictor | # 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.... | JasonQSY/Associative3D | RelativeScalePredictor | false | 8,342 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
AdaptiveAvgMaxPool2d | # 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.functional as F
import torch.nn.parallel
import tor... | Alicegaz/torchok | AdaptiveAvgMaxPool2d | false | 16,899 | [
"Apache-2.0"
] | 8 | 7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 | https://github.com/Alicegaz/torchok/tree/7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 |
PredictionConvolutions | import torch
from torch import nn
class PredictionConvolutions(nn.Module):
"""
Convolutions to predict class scores and bounding boxes using lower and higher-level feature maps.
The bounding boxes (locations) are predicted as encoded offsets w.r.t each of the 8732 prior (default) boxes.
See 'cxcy_to_g... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | HFAiLab/ffrecord | PredictionConvolutions | false | 14,023 | [
"MIT"
] | 47 | e916dc715ffa38a304a673ade7c5aa1efff5936d | https://github.com/HFAiLab/ffrecord/tree/e916dc715ffa38a304a673ade7c5aa1efff5936d |
BesselBasisLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._d... | akirasosa/pre-training-mol | BesselBasisLayer | false | 6,149 | [
"MIT"
] | 1 | 2fd65a959eee50e2eea260719633042ae37bb92c | https://github.com/akirasosa/pre-training-mol/tree/2fd65a959eee50e2eea260719633042ae37bb92c |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
if target.ndim == 3:
target = target.reshape(-1, target.shape[2])
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | VISLANG-Lab/MGCL | LanguageModelCriterion | false | 1,167 | [
"MIT"
] | 0 | 22da06ffa7410d9632bfda8eefb1b79e4f660de0 | https://github.com/VISLANG-Lab/MGCL/tree/22da06ffa7410d9632bfda8eefb1b79e4f660de0 |
AffineTransform | import torch
from torch import nn
class AffineTransform(nn.Module):
def __init__(self, num_features):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1, 1, num_features))
self.beta = nn.Parameter(torch.zeros(1, 1, num_features))
def forward(self, x):
return self.alpha... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | jaketae/res-mlp | AffineTransform | false | 12,592 | [
"MIT"
] | 0 | 6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd | https://github.com/jaketae/res-mlp/tree/6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd |
Sub | import torch
class Sub(torch.nn.Module):
def __init__(self):
super(Sub, self).__init__()
def forward(self, x, y):
return 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | ahangchen/torch2trt | Sub | false | 6,111 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
TwoWordBilinearLabelProbe | import torch
import torch.nn as nn
import torch.utils.data.dataloader
class TwoWordBilinearLabelProbe(nn.Module):
""" Computes a bilinear function of pairs of vectors.
For a batch of sentences, computes all n^2 pairs of scores
for each sentence in the batch.
"""
def __init__(self, model_dim, rank... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data.dataloader
assert_size_stride = to... | TimO96/NLP2 | TwoWordBilinearLabelProbe | false | 1,142 | [
"MIT"
] | 0 | 83f65a385457f68397c641f38b53df0110282578 | https://github.com/TimO96/NLP2/tree/83f65a385457f68397c641f38b53df0110282578 |
ImageGradients | # 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 as th
import torch.utils.data
assert_size_stride = torch._C._dynamo... | IlyaBizyaev/ttools | ImageGradients | false | 8,300 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
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
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | entn-at/GradTTS | Upsample | false | 15,309 | [
"MIT"
] | 55 | d31cbf41211615a01fffc3812715e3f7f2be214d | https://github.com/entn-at/GradTTS/tree/d31cbf41211615a01fffc3812715e3f7f2be214d |
JSD | # 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
import torch.utils.data
assert_size_stride = torch._... | Joshua-Schroijen/deepproblog | JSD | false | 675 | [
"Apache-2.0"
] | 0 | 4ae56f1e860010b7857b29d5bd76fb1555d5e19d | https://github.com/Joshua-Schroijen/deepproblog/tree/4ae56f1e860010b7857b29d5bd76fb1555d5e19d |
BaseFactorizationMachine | import torch
import torch.nn as nn
class BaseFactorizationMachine(nn.Module):
"""Calculate FM result over the embeddings
Args:
reduce_sum: bool, whether to sum the result, default is True.
Input:
input_x: tensor, A 3D tensor with shape:``(batch_size,field_size,embed_dim)``.
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | MIracleyin/RecBole-notebook | BaseFactorizationMachine | false | 9,574 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
MSBlock | # 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_... | CM-BF/FeatureFlow | MSBlock | false | 13,434 | [
"MIT"
] | 161 | 06642697922f17211e5faa353e24b1a0946885b1 | https://github.com/CM-BF/FeatureFlow/tree/06642697922f17211e5faa353e24b1a0946885b1 |
AlphaModule | # 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 import nn
from torch.nn.parameter import Parameter
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | albertozurli/mammoth | AlphaModule | false | 1,393 | [
"MIT"
] | 0 | 849234afe084b4f707de5300e953a2a8c104ea36 | https://github.com/albertozurli/mammoth/tree/849234afe084b4f707de5300e953a2a8c104ea36 |
Attloss | # 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.functional
assert_size_stride = torch._C._dynamo.gu... | lvxiuwang/ferattention | Attloss | false | 7,141 | [
"MIT"
] | 1 | 02e97df4a12129ed6706bddf0d2109650eae8765 | https://github.com/lvxiuwang/ferattention/tree/02e97df4a12129ed6706bddf0d2109650eae8765 |
TorchPow | # 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... | Akababa/torch2trt | TorchPow | false | 18,441 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
ThreeLayerCNN | # 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
asser... | aleb/pipelines | ThreeLayerCNN | false | 6,178 | [
"Apache-2.0"
] | 1 | 2181b2fb8bdd6cd93e7d677b9840ed1b58a83a85 | https://github.com/aleb/pipelines/tree/2181b2fb8bdd6cd93e7d677b9840ed1b58a83a85 |
InterProbCrossEntropyLoss | # 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.... | tkc-morita/secl | InterProbCrossEntropyLoss | false | 10,951 | [
"MIT"
] | 0 | d0156cea4fd95ea5071126dbf076a6da69752a37 | https://github.com/tkc-morita/secl/tree/d0156cea4fd95ea5071126dbf076a6da69752a37 |
Normalize | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out ... | 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
import torch.nn as nn
assert_size_stride =... | bomtorazek/contrastive-unpaired-translation | Normalize | false | 12,185 | [
"BSD-3-Clause"
] | 0 | 07c048038375e1b9a4e464154b8dbc49f5e16ede | https://github.com/bomtorazek/contrastive-unpaired-translation/tree/07c048038375e1b9a4e464154b8dbc49f5e16ede |
SqueezeExcitation | # 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 Tensor
from... | Genevievekim/semantic-segmentation-1 | SqueezeExcitation | false | 13,714 | [
"BSD-3-Clause"
] | 196 | f28b026e44cff80fe3ca4cac94cea27e4073821b | https://github.com/Genevievekim/semantic-segmentation-1/tree/f28b026e44cff80fe3ca4cac94cea27e4073821b |
GOODLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributions
import torch.utils.data
assert... | AlexMeinke/Provable-OOD-Detection | GOODLoss | false | 7,693 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
ResBlockWithFusedBN | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class ResBlockWithFusedBN(nn.Module):
""" Bottleneck Residual Block """
def __init__(self, inplanes, outplanes, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | JinYAnGHe/openvino_training_extensions | ResBlockWithFusedBN | false | 2,716 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee |
NetVLAD | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, dim, num_clusters=64):
"""
Args:
dim : int
Dimension of descriptors
num_clusters : int
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lulor/project_vg | NetVLAD | false | 7,269 | [
"MIT"
] | 1 | 27b0c3b3038c5a666dde516a0a265ae8ddf2059f | https://github.com/lulor/project_vg/tree/27b0c3b3038c5a666dde516a0a265ae8ddf2059f |
LN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class LN(nn.Module):
def forward(self, x):
return F.layer_norm(x, x.size()[1:], weight=None, bias=None, eps=1e-05)
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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | ID56/OrigamiNet | LN | false | 563 | [
"Apache-2.0"
] | 0 | a71ec4984e3d5da7d635d68260026b749ec44fa9 | https://github.com/ID56/OrigamiNet/tree/a71ec4984e3d5da7d635d68260026b749ec44fa9 |
Conv1D | # 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.parallel
import torch.nn as nn
import torch.utils.data
import to... | EGO4D/episodic-memory | Conv1D | false | 8,059 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
DeltaGFit | import torch
from scipy import constants
import torch.nn as nn
import torch as t
class DeltaGFit(nn.Module):
def __init__(self, deltaG):
super(DeltaGFit, self).__init__()
self.deltaG = deltaG
def forward(self, temperature, X, k_int, timepoints):
"""
# inputs, list of:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | sajetan/PyHDX | DeltaGFit | false | 4,310 | [
"MIT"
] | 0 | f764849e33b2dd1bcae5824795a38c64ef01e13c | https://github.com/sajetan/PyHDX/tree/f764849e33b2dd1bcae5824795a38c64ef01e13c |
NextSentencePrediction | # 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.... | greenstar1151/pytorch-benchmark | NextSentencePrediction | false | 10,446 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b |
LogSTFTMagnitude | # 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
... | SolomidHero/speech-regeneration-enhancer | LogSTFTMagnitude | false | 17,948 | [
"MIT"
] | 8 | eb43907ff085d68a707ff7bc3af14e93ff66fd65 | https://github.com/SolomidHero/speech-regeneration-enhancer/tree/eb43907ff085d68a707ff7bc3af14e93ff66fd65 |
model | import torch
import torch.nn as nn
class model(nn.Module):
def __init__(self, input_shape=28 * 28, nr_classes=10):
super(model, self).__init__()
self.input_shape = input_shape
self.fc1 = nn.Linear(input_shape, 200)
self.fc2 = nn.Linear(200, nr_classes)
self.relu = nn.ReLU(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | vishal-keshav/pytorch-project-template | model | false | 10,898 | [
"MIT"
] | 0 | 526dd5b1036ed9cf592172301a2c85e8425cd154 | https://github.com/vishal-keshav/pytorch-project-template/tree/526dd5b1036ed9cf592172301a2c85e8425cd154 |
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.utils.data
impo... | WenlongZhang0724/mmsr | CharbonnierLoss | false | 11,950 | [
"Apache-2.0"
] | 0 | 375ce9207c2b8586101406577faea285885b8009 | https://github.com/WenlongZhang0724/mmsr/tree/375ce9207c2b8586101406577faea285885b8009 |
C3D | import torch
import torch.nn as nn
class C3D(nn.Module):
def __init__(self, num_classes):
super(C3D, self).__init__()
self.conv1a = nn.Conv3d(in_channels=3, out_channels=64, kernel_size
=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
self.pool1 = nn.MaxPool3d(kernel_size=(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DuyHung21/actionrecognition | C3D | false | 5,214 | [
"MIT"
] | 1 | a095b2e16db249bff97b1eebdab1e90468224fcb | https://github.com/DuyHung21/actionrecognition/tree/a095b2e16db249bff97b1eebdab1e90468224fcb |
ESA | # 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... | samuro95/Prox-PnP | ESA | false | 10,974 | [
"MIT"
] | 0 | c05a48a586f0ef27c8ddc14e0a4c2c3d6814f8c9 | https://github.com/samuro95/Prox-PnP/tree/c05a48a586f0ef27c8ddc14e0a4c2c3d6814f8c9 |
AveragePoolingLayer | # 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... | Twizwei/idinvert_pytorch | AveragePoolingLayer | false | 1,149 | [
"MIT"
] | 0 | 11f1126aab517fbe32b488d92f6fdea339463d04 | https://github.com/Twizwei/idinvert_pytorch/tree/11f1126aab517fbe32b488d92f6fdea339463d04 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CrowdDynamicsLab/InfoMotif | Attention | false | 17,195 | [
"BSD-3-Clause"
] | 7 | cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | https://github.com/CrowdDynamicsLab/InfoMotif/tree/cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b |
SimpleLogSoftmaxModel | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleLogSoftmaxModel(torch.nn.Module):
def __init__(self, dimension):
super(SimpleLogSoftmaxModel, self).__init__()
self.dimension = dimension
def forward(self, tensor):
return F.lo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
impor... | YaronBenAtar/glow | SimpleLogSoftmaxModel | false | 14,672 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
GVPDropout | import torch
from torch import nn
class GVPDropout(nn.Module):
""" Separate dropout for scalars and vectors. """
def __init__(self, rate):
super().__init__()
self.vector_dropout = nn.Dropout2d(rate)
self.feat_dropout = nn.Dropout(rate)
def forward(self, feats, vectors):
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | blazingsiyan/geometric-vector-perceptron | GVPDropout | false | 12,176 | [
"MIT"
] | 0 | eee1ee8e71148cfdb3e02b660d80f12cf1cecd0a | https://github.com/blazingsiyan/geometric-vector-perceptron/tree/eee1ee8e71148cfdb3e02b660d80f12cf1cecd0a |
DepthwiseSeparableConv | import torch
import torch.nn.functional as F
import torch.cuda
import torch.nn as nn
class DepthwiseSeparableConv(nn.Module):
def __init__(self, in_ch, out_ch, k, bias=True):
super().__init__()
self.depthwise_conv = nn.Conv1d(in_channels=in_ch, out_channels=
in_ch, kernel_size=k, grou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import torc... | andy840314/QANet-pytorch- | DepthwiseSeparableConv | false | 14,851 | [
"MIT"
] | 92 | 3c11e2d7139e040eee90dd24b673eb1039957cae | https://github.com/andy840314/QANet-pytorch-/tree/3c11e2d7139e040eee90dd24b673eb1039957cae |
SpeakerIntegrator | import torch
import torch.nn as nn
import torch.utils.data
class SpeakerIntegrator(nn.Module):
def __init__(self):
super(SpeakerIntegrator, self).__init__()
def forward(self, x, spembs):
"""
x shape : (batch, 39, 256)
spembs shape : (batch, 256)
"""
spemb... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | hwRG/FastSpeech2-Pytorch-old-man_city | SpeakerIntegrator | false | 10,183 | [
"MIT"
] | 0 | c32ee3a09bf2a53fcd17a2d0b74e8d1c93586573 | https://github.com/hwRG/FastSpeech2-Pytorch-old-man_city/tree/c32ee3a09bf2a53fcd17a2d0b74e8d1c93586573 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, n_hidden_enc, n_hidden_dec):
super().__init__()
self.h_hidden_enc = n_hidden_enc
self.h_hidden_dec = n_hidden_dec
self.W = nn.Linear(n_hidden_enc + n_hidden_dec, n_hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | VisualJoyce/ChengyuBERT | Attention | false | 18,078 | [
"MIT"
] | 8 | 605db3a4b3241dd4d02baa41a68bf23b5b00b36d | https://github.com/VisualJoyce/ChengyuBERT/tree/605db3a4b3241dd4d02baa41a68bf23b5b00b36d |
ExtResNetBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | YinanZYN/pytorch-3dunet | ExtResNetBlock | false | 12,021 | [
"MIT"
] | 0 | d1494f421a836af54c3dde65c54e3e62d5c00800 | https://github.com/YinanZYN/pytorch-3dunet/tree/d1494f421a836af54c3dde65c54e3e62d5c00800 |
BCELoss4BraTS | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | MargeryLab/nnConRes | BCELoss4BraTS | false | 9,320 | [
"Apache-2.0"
] | 0 | a5aba912d0f0f30490ae820fb6d3dbb8cf1556d4 | https://github.com/MargeryLab/nnConRes/tree/a5aba912d0f0f30490ae820fb6d3dbb8cf1556d4 |
LossBasic | # 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
... | xenbaloch/efficientderain | LossBasic | false | 16,734 | [
"MIT"
] | 109 | d5646815fd14a5a03c859102ecd2f298db7e53be | https://github.com/xenbaloch/efficientderain/tree/d5646815fd14a5a03c859102ecd2f298db7e53be |
MeanPoolConv | import torch
import torch.nn as nn
class CustomConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=None, bias=True, residual_init=True):
super(CustomConv2d, self).__init__()
self.residual_init = residual_init
if padding is None:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 | MeanPoolConv | false | 13,483 | [
"MIT"
] | 54 | fc455c6219b09bc8bf605715504b78b2bb801e48 | https://github.com/ChiragCD/NR-GAN/tree/fc455c6219b09bc8bf605715504b78b2bb801e48 |
ScaledDotProductAttention | import torch
import torch.nn as nn
import torch.optim
class Bottle(nn.Module):
""" Perform the reshape routine before and after an operation """
def forward(self, input):
if len(input.size()) <= 2:
return super(Bottle, self).forward(input)
size = input.size()[:2]
out = 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.... | Blickwinkel1107/NJUNMT-pytorch | ScaledDotProductAttention | false | 17,015 | [
"MIT"
] | 9 | 82f684fe768b137ca0649b7b79a1820077917385 | https://github.com/Blickwinkel1107/NJUNMT-pytorch/tree/82f684fe768b137ca0649b7b79a1820077917385 |
cnn_layer | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class cnn_layer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, bias=True):
super(cnn_layer, self).__init__()
self.conv = torch.nn.Conv1d(in_channels=in_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | ciaochiaociao/CLNER | cnn_layer | false | 3,374 | [
"MIT"
] | 0 | a31fb1c3bfdaa5d62147dc892489d29a85e6b385 | https://github.com/ciaochiaociao/CLNER/tree/a31fb1c3bfdaa5d62147dc892489d29a85e6b385 |
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