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 |
|---|---|---|---|---|---|---|---|---|---|---|
ImagePairEncoderV2 | # 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... | KH-Kyle/rmp_nav | ImagePairEncoderV2 | false | 8,772 | [
"MIT"
] | 30 | d598fe70664a4cdc0e9b9dd4b52e84aa3de1b551 | https://github.com/KH-Kyle/rmp_nav/tree/d598fe70664a4cdc0e9b9dd4b52e84aa3de1b551 |
PyConv3 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1,
dilation=1, groups=1):
"""standard convolution with padding"""
return nn.Conv2d(in_planes, out_plan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | lkf59553/pyconv | PyConv3 | false | 15,944 | [
"MIT"
] | 295 | d8b39cf43014b8fd277dcefc9eb7f8880511e977 | https://github.com/lkf59553/pyconv/tree/d8b39cf43014b8fd277dcefc9eb7f8880511e977 |
L2Norm | import torch
import torch.nn as nn
import torch.nn.init as init
from itertools import product as product
from math import sqrt as sqrt
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.init as init
from itertools import produc... | AndOneDay/PytorchSSD | L2Norm | false | 8,876 | [
"MIT"
] | 0 | a9f2cde8d149e14cab3feb0084b5be3c1e6c97c6 | https://github.com/AndOneDay/PytorchSSD/tree/a9f2cde8d149e14cab3feb0084b5be3c1e6c97c6 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 15, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(15, 30, 5)
self.fc1 = nn.Linear(30 * 9 * 9, 300)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | gg4u/cnc_2017 | Net | false | 10,118 | [
"MIT"
] | 0 | 1a5c52c3207ba131139214d14a2161af2db80a5c | https://github.com/gg4u/cnc_2017/tree/1a5c52c3207ba131139214d14a2161af2db80a5c |
QREmbeddingBag | import torch
import numpy as np
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class QREmbeddingBag(nn.Module):
"""Computes sums or means over two 'bags' of embeddings, one using the quotient
of the indices and the other using the remainder of the indices, witho... | 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 numpy as np
import torch.nn as nn
from torch.nn.parameter import Paramet... | Com1t/dlrm | QREmbeddingBag | false | 8,894 | [
"MIT"
] | 0 | fdbae97a974507758296637e0041e80fe3b00ae5 | https://github.com/Com1t/dlrm/tree/fdbae97a974507758296637e0041e80fe3b00ae5 |
TransposedUpsample | # 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... | poliver269/latent-diffusion | TransposedUpsample | false | 12,900 | [
"MIT"
] | 0 | 08e7c987ad423e3f93125b49980c36302ffe3d82 | https://github.com/poliver269/latent-diffusion/tree/08e7c987ad423e3f93125b49980c36302ffe3d82 |
RFDB | import torch
import torch.nn as nn
import torch.nn.functional as F
class ESA(nn.Module):
def __init__(self, num_feat=50, conv=nn.Conv2d, p=0.25):
super(ESA, self).__init__()
f = num_feat // 4
BSConvS_kwargs = {}
if conv.__name__ == 'BSConvS':
BSConvS_kwargs = {'p': p}
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | YingqiLiulll/scrips_for_SR | RFDB | false | 1,322 | [
"MIT"
] | 0 | 04fa6fdaf157e913d3e2521cd80315a10a2ccedc | https://github.com/YingqiLiulll/scrips_for_SR/tree/04fa6fdaf157e913d3e2521cd80315a10a2ccedc |
NormalizationLayer | # 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.init
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | sidphbot/jina-hub | NormalizationLayer | false | 16,442 | [
"Apache-2.0"
] | 106 | ab195030b72353c9b803874e2c99829fb75e1b17 | https://github.com/sidphbot/jina-hub/tree/ab195030b72353c9b803874e2c99829fb75e1b17 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net_basic(nn.Module):
"""基础网络，仅包含保存、加载模型的功能"""
def __init__(self):
super(Net_basic, self).__init__()
def load(self, path):
"""加载指定模型"""
self.load_state_dict(torch.load(path))
def save(self, path):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | IewNixIl/graduation_project_under | Net | false | 9,278 | [
"MIT"
] | 0 | 67d0345208511bb06c35c3453227b2fa4ebef4a3 | https://github.com/IewNixIl/graduation_project_under/tree/67d0345208511bb06c35c3453227b2fa4ebef4a3 |
MLP_PART | # 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.... | KORguy/PIFu_Part | MLP_PART | false | 9,313 | [
"MIT"
] | 0 | bd199d439a94f8bc8b4036898b0f1ec01e56ab9e | https://github.com/KORguy/PIFu_Part/tree/bd199d439a94f8bc8b4036898b0f1ec01e56ab9e |
MCDropout2d | # 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... | jiwoncpark/ex-con | MCDropout2d | false | 6,949 | [
"MIT"
] | 1 | 6775d11ec1c3e7005890e58d16dd07b711861cdf | https://github.com/jiwoncpark/ex-con/tree/6775d11ec1c3e7005890e58d16dd07b711861cdf |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Module):
def __init__(self, stride=2):
super(Upsample, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4
B = x.data.size(0)
C = x.data.size(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | AmitNativ1984/masqr | Upsample | false | 8,870 | [
"MIT"
] | 0 | a57a60d1011aa70317f5893fc05bfb0f029cafb5 | https://github.com/AmitNativ1984/masqr/tree/a57a60d1011aa70317f5893fc05bfb0f029cafb5 |
AsymmetricLoss | # 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... | kivanctezoren/mmclassification | AsymmetricLoss | false | 15,830 | [
"Apache-2.0"
] | 1,190 | 5c73d4b29f61c47d379bbec4621a465099e64bd7 | https://github.com/kivanctezoren/mmclassification/tree/5c73d4b29f61c47d379bbec4621a465099e64bd7 |
C1Bilinear | # 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.... | PCIHD/Project_Daydream | C1Bilinear | false | 9,762 | [
"MIT"
] | 0 | 94c75ff494e7489a4066e3f9d056a85ff768f40e | https://github.com/PCIHD/Project_Daydream/tree/94c75ff494e7489a4066e3f9d056a85ff768f40e |
SpatialSoftArgmax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import Tens... | kevinzakka/torchkit | SpatialSoftArgmax | false | 15,825 | [
"MIT"
] | 144 | 930dba9560d2473406b59b99a474dce1a6621813 | https://github.com/kevinzakka/torchkit/tree/930dba9560d2473406b59b99a474dce1a6621813 |
Clone | # 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... | Ilyabasharov/torch2trt | Clone | false | 2,524 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
DDPGCritic | import torch
import torch as t
import torch.nn as nn
class DDPGCritic(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim + action_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, 1)
def forward(self, state, a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | ikamensh/machin | DDPGCritic | false | 6,857 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
BinaryLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryLoss(nn.Module):
"""
Computes contrastive loss[1, 2] twice, one time for the distance between query and positive example,
and another for the distance between query and negative example. Both use l2-distance.
[1] http:/... | 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... | jishnujayakumar/specter | BinaryLoss | false | 3,740 | [
"Apache-2.0"
] | 0 | 40e3b5e538004b00b0955f17dd3d71fb1f96b922 | https://github.com/jishnujayakumar/specter/tree/40e3b5e538004b00b0955f17dd3d71fb1f96b922 |
Hflip | # 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... | NickleDave/kornia | Hflip | false | 2,696 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 5392651d0bc268da577fa0a49aa50f957289c7dd | https://github.com/NickleDave/kornia/tree/5392651d0bc268da577fa0a49aa50f957289c7dd |
SelfGated | import torch
import torch.utils.data
import torch.nn.functional as F
class SelfGated(torch.nn.Module):
"""
Self-Gated layer. math: \\sigmoid(W*x) * x
"""
def __init__(self, input_size):
super(SelfGated, self).__init__()
self.linear_g = torch.nn.Linear(input_size, input_size)
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | jamaalhay/Final_Proj | SelfGated | false | 15,666 | [
"MIT"
] | 104 | 3f524a90fee5a3cb21466ab76f630d060792045d | https://github.com/jamaalhay/Final_Proj/tree/3f524a90fee5a3cb21466ab76f630d060792045d |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | rdjdejong/attention-learn-to-route | Attention | false | 16,312 | [
"MIT"
] | 540 | 3b6bbdad677a36df53eabad98b48f436be298ac8 | https://github.com/rdjdejong/attention-learn-to-route/tree/3b6bbdad677a36df53eabad98b48f436be298ac8 |
PixelwiseNorm | import torch
import torch.nn as nn
class PixelwiseNorm(nn.Module):
"""
layer pixelwise normalization
"""
def __init__(self, eps=1e-07):
super(PixelwiseNorm, self).__init__()
self.eps = eps
def forward(self, x):
return x / torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True) ... | 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_... | Tak-jae-ho/RGBD-GAN-pytorch | PixelwiseNorm | false | 1,129 | [
"MIT"
] | 0 | 4fb1bc1de7b7807fd4f2d346d9b688a2d257eedb | https://github.com/Tak-jae-ho/RGBD-GAN-pytorch/tree/4fb1bc1de7b7807fd4f2d346d9b688a2d257eedb |
GeneralizedMeanPooling | import torch
from torch import nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import *
from torch.optim import *
class GeneralizedMeanPooling(nn.Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from to... | Challyfilio/NAIC2021 | GeneralizedMeanPooling | false | 232 | [
"MIT"
] | 0 | 11b38a920dcc902f9b798dc43ae360062862e6e4 | https://github.com/Challyfilio/NAIC2021/tree/11b38a920dcc902f9b798dc43ae360062862e6e4 |
Sharpen_Block | import torch
import numpy as np
import torch.nn as nn
class Sharpen_Block(nn.Module):
def __init__(self):
super(Sharpen_Block, self).__init__()
self.pad = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv = nn.Conv2d(1, 1, 3, 1, 0, bias=False)
self.conv.weight = nn.Parameter(torch.from_n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | MingSun-Tse/pytorch-vdsr | Sharpen_Block | false | 5,610 | [
"MIT"
] | 1 | 597bacb4ec7385c8cc6cdf91e26e64ef2e6808b7 | https://github.com/MingSun-Tse/pytorch-vdsr/tree/597bacb4ec7385c8cc6cdf91e26e64ef2e6808b7 |
BartClassificationHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Hzfinfdu/Black-Box-Tuning | BartClassificationHead | false | 2,468 | [
"MIT"
] | 0 | 64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 | https://github.com/Hzfinfdu/Black-Box-Tuning/tree/64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 |
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
from torch._inductor.select_algorithm import extern_kernels
import 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... | Fanxingye/Informer2020 | TokenEmbedding | false | 440 | [
"Apache-2.0"
] | 0 | 94fd05f82ff0882681a9716ae3e980a574fdcbed | https://github.com/Fanxingye/Informer2020/tree/94fd05f82ff0882681a9716ae3e980a574fdcbed |
AttentiveNet | # 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... | ISYSLAB-HUST/DeepNeuropePred | AttentiveNet | false | 5,344 | [
"MIT"
] | 1 | f87f36fdbbc966f727eb063a0f9984850294ba37 | https://github.com/ISYSLAB-HUST/DeepNeuropePred/tree/f87f36fdbbc966f727eb063a0f9984850294ba37 |
BehaviorClone | # 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_... | mdiephuis/Berkeley-cs294-112 | BehaviorClone | false | 7,209 | [
"MIT"
] | 1 | 99559e046b635ca8d229f19ca4ad45c2c02a1c01 | https://github.com/mdiephuis/Berkeley-cs294-112/tree/99559e046b635ca8d229f19ca4ad45c2c02a1c01 |
Upsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.hub
assert_size_stride = torch._C._dynamo.gua... | Frikallo/YAKbot | Upsample | false | 5,174 | [
"MIT"
] | 1 | bc798fe4ead1f6a3e4828960ea77e2a8f07b5fdc | https://github.com/Frikallo/YAKbot/tree/bc798fe4ead1f6a3e4828960ea77e2a8f07b5fdc |
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | UniSerj/ai-research | GlobalAvgPool2d | false | 14,521 | [
"Apache-2.0"
] | 46 | 79f0093c93408cc5dd7d3f56aafd7dc1f901421c | https://github.com/UniSerj/ai-research/tree/79f0093c93408cc5dd7d3f56aafd7dc1f901421c |
EdgePredictor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | amazon-research/tgl | EdgePredictor | false | 18,302 | [
"Apache-2.0"
] | 9 | 5d852b8ae643b64b591a10dfbe8a1d10f696b200 | https://github.com/amazon-research/tgl/tree/5d852b8ae643b64b591a10dfbe8a1d10f696b200 |
AttnGCNLayer | import math
import torch
import torch.nn as nn
import torch.utils.data
class GCNLayer(nn.Module):
def __init__(self, embed_size, dropout=0.0):
super().__init__()
self.embed_size = embed_size
self.ctx_layer = nn.Linear(self.embed_size, self.embed_size, bias=False
)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Roc-Ng/HANet | AttnGCNLayer | false | 8,713 | [
"MIT"
] | 34 | e679703e9e725205424d87f750358fb4f62ceec5 | https://github.com/Roc-Ng/HANet/tree/e679703e9e725205424d87f750358fb4f62ceec5 |
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.... | Dacrol/WaveRNN-server | Attention | false | 11,328 | [
"MIT"
] | 0 | 5189829cec71938ff7ec2e3eb59e73af1382430a | https://github.com/Dacrol/WaveRNN-server/tree/5189829cec71938ff7ec2e3eb59e73af1382430a |
MAE_loss | import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
class MAE_loss(nn.Module):
def __init__(self):
super(MAE_loss, self).__init__()
def forward(self, prediction, gt, epoch=0):
prediction = prediction[:, 0:1]
abs_err = torch.abs(prediction - gt)
mask ... | 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
import torch.utils.data
import torch.optim
assert_s... | alopezgit/project-adapt | MAE_loss | false | 18,322 | [
"MIT"
] | 8 | e93ab350344a5504f76f4e460002e0163996f88a | https://github.com/alopezgit/project-adapt/tree/e93ab350344a5504f76f4e460002e0163996f88a |
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... | luk1684tw/Precision-Reduction | MLP | false | 12,843 | [
"MIT"
] | 0 | c782e9a121ed176b12eb9a081aa1960fabd40019 | https://github.com/luk1684tw/Precision-Reduction/tree/c782e9a121ed176b12eb9a081aa1960fabd40019 |
MonomialNN | import torch
import torch.nn as nn
from warnings import warn
class MonomialNN(nn.Module):
"""A network that expands its input to a given list of monomials.
Its output shape will be (n_samples, n_input_units * n_degrees)
:param degrees: max degree to be included, or a list of degrees that will be used
... | 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 warnings import warn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._... | DiffEqML/neurodiffeq | MonomialNN | false | 5,069 | [
"MIT"
] | 1 | c5e7404c47a4729578ee2149f289be0a8909d775 | https://github.com/DiffEqML/neurodiffeq/tree/c5e7404c47a4729578ee2149f289be0a8909d775 |
ResidualConv1dGLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | ChanganVR/hifigan-denoiser | ResidualConv1dGLU | false | 13,466 | [
"Apache-2.0"
] | 100 | 9bd77c53556e1372b4bbff8dce8b120297cc4e5c | https://github.com/ChanganVR/hifigan-denoiser/tree/9bd77c53556e1372b4bbff8dce8b120297cc4e5c |
ShrinkageLoss | import torch
import torch.nn as nn
class ShrinkageLoss(nn.Module):
""" ShrinkageLoss class.
Modified version of shrinkage loss tailored to images:
http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiankai_Lu_Deep_Regression_Tracking_ECCV_2018_paper.pdf
It basically computes a point-wis... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | cvpr22sub7201/SpeechDrivenTongueAnimation | ShrinkageLoss | false | 6,501 | [
"MIT"
] | 1 | 82caf9d7f4331e039e3b2f0d31df6393d24ccb1c | https://github.com/cvpr22sub7201/SpeechDrivenTongueAnimation/tree/82caf9d7f4331e039e3b2f0d31df6393d24ccb1c |
MetaBilinear | import re
import torch
import warnings
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
---... | 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 re
import warnings
import torch.nn as nn
from collections import OrderedDict
assert_size_stride = torch._C._dynamo.guards.assert_size... | Steffen-Wolf/pytorch-meta | MetaBilinear | false | 9,554 | [
"MIT"
] | 0 | d2dfb902cfa49574eac898045c8e9cf64ce29f96 | https://github.com/Steffen-Wolf/pytorch-meta/tree/d2dfb902cfa49574eac898045c8e9cf64ce29f96 |
CAM | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._utils
class CAM(nn.Module):
def __init__(self, in_dim):
super(CAM, self).__init__()
self.para_mu = nn.Parameter(torch.zeros(1))
def forward(self, x):
N, C, H, W = x.size()
proj_query = x.view(N, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | henbucuoshanghai/crowed-count- | CAM | false | 15,507 | [
"MIT"
] | 81 | 3353c0a8011b6b83e6e0392258a88706378b443b | https://github.com/henbucuoshanghai/crowed-count-/tree/3353c0a8011b6b83e6e0392258a88706378b443b |
h_swish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Felicia980317/mytorch | h_swish | false | 467 | [
"Apache-2.0"
] | 0 | e463122c0d402878ec5b4c5a823a0feeba8fdbfe | https://github.com/Felicia980317/mytorch/tree/e463122c0d402878ec5b4c5a823a0feeba8fdbfe |
UpSample | import torch
import torch.nn as nn
import torch.nn.functional as F
class UpSample(nn.Sequential):
def __init__(self, skip_input, output_features):
super(UpSample, self).__init__()
self.convA = nn.Conv2d(skip_input, output_features, kernel_size=3,
stride=1, padding=1)
self.leak... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | varun-affinsys/Monocular-Depth-Estimation-with-Transfer-Learning-pretrained-MobileNetV2 | UpSample | false | 16,669 | [
"MIT"
] | 70 | 9b20c5b3d7a9f90e1dc6f40e17ee31d9b3dee684 | https://github.com/varun-affinsys/Monocular-Depth-Estimation-with-Transfer-Learning-pretrained-MobileNetV2/tree/9b20c5b3d7a9f90e1dc6f40e17ee31d9b3dee684 |
VectorQuantizer | # 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.... | imatge-upc/pixelcoordEDL | VectorQuantizer | false | 6,904 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
DHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import *
assert_size_stride = torch._C._dynamo.g... | a8252525/NIID-Bench | DHead | false | 3,038 | [
"MIT"
] | 0 | 33df8d3a7b941884eec3c7bd52adb8a9476eb282 | https://github.com/a8252525/NIID-Bench/tree/33df8d3a7b941884eec3c7bd52adb8a9476eb282 |
EqualLinear | # 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.autograd import Function
import math
import torch.nn as nn
assert_siz... | AsianZeus/Diverse-Facial-Edit | EqualLinear | false | 9,409 | [
"Apache-2.0"
] | 0 | 3d4b1b41546a08a1fa3cb164ade33e319806b12b | https://github.com/AsianZeus/Diverse-Facial-Edit/tree/3d4b1b41546a08a1fa3cb164ade33e319806b12b |
NavACLNetwork | import torch
import torch.nn as nn
class NavACLNetwork(nn.Module):
def __init__(self, task_param_dim, hidden_dim, init_w=0.0005):
super(NavACLNetwork, self).__init__()
self.layer_1 = nn.Linear(task_param_dim, hidden_dim)
self.layer_2 = nn.Linear(hidden_dim, hidden_dim)
self.layer_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ai-lab-science/Deep-Reinforcement-Learning-for-mapless-navigation-in-intralogistics | NavACLNetwork | false | 6,131 | [
"MIT"
] | 1 | ac29a691317c69bc397809b222c0f3cf3f1916bc | https://github.com/ai-lab-science/Deep-Reinforcement-Learning-for-mapless-navigation-in-intralogistics/tree/ac29a691317c69bc397809b222c0f3cf3f1916bc |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | shenyunlong/naru | LayerNorm | false | 16,405 | [
"Apache-2.0"
] | 70 | 264cf4e9c96c9e34422f9eebc455a714aeef0b57 | https://github.com/shenyunlong/naru/tree/264cf4e9c96c9e34422f9eebc455a714aeef0b57 |
Project3D | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HalleyJiang/PLNet | Project3D | false | 8,209 | [
"MIT"
] | 16 | a02bd5f343b9e4766891fd234e3a338c1eaa26ff | https://github.com/HalleyJiang/PLNet/tree/a02bd5f343b9e4766891fd234e3a338c1eaa26ff |
Normalize | import torch
import torch.nn as nn
import torch.nn.functional as functional
class Normalize(nn.Module):
def __init__(self, dim: 'int', p: 'int'):
super().__init__()
self.dim = dim
self.p = p
def forward(self, inputs):
outputs = functional.normalize(inputs, dim=self.dim, p=sel... | 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... | uripatish/torchup | Normalize | false | 4,490 | [
"MIT"
] | 0 | 0b7bee031fc99e536342331ba567c523a790d742 | https://github.com/uripatish/torchup/tree/0b7bee031fc99e536342331ba567c523a790d742 |
SiQU | # 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... | chris-price19/ocp | SiQU | false | 1,700 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e |
ClassificationModel | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=15, num_classes=80,
prior=0.01, feature_size=256):
super(ClassificationM... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | BradleyBrown19/CustomObjectDetector | ClassificationModel | false | 2,127 | [
"Apache-2.0"
] | 0 | 11c14ec6127c553ac365703c768b75dde33d9a4d | https://github.com/BradleyBrown19/CustomObjectDetector/tree/11c14ec6127c553ac365703c768b75dde33d9a4d |
MaskedInstanceNorm1d | import torch
from torch import nn
import torch.utils.data
import torch.cuda
import torch.optim
class MaskedInstanceNorm1d(nn.Module):
"""Instance norm + masking."""
MAX_CNT = 100000.0
def __init__(self, d_channel: 'int', unbiased: 'bool'=True, affine:
'bool'=False):
super().__init__()
... | 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
import ... | Malkovsky/NeMo | MaskedInstanceNorm1d | false | 2,615 | [
"Apache-2.0"
] | 0 | 8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 | https://github.com/Malkovsky/NeMo/tree/8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 |
MidNet4 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | deshwalmahesh/CURL---cpu-gpu | MidNet4 | false | 3,415 | [
"BSD-3-Clause"
] | 0 | f4e87275b6cce556b9e04a188cf7ae13d810d82a | https://github.com/deshwalmahesh/CURL---cpu-gpu/tree/f4e87275b6cce556b9e04a188cf7ae13d810d82a |
SineLayer | import torch
import numpy as np
from torch import nn
class SineLayer(nn.Module):
def __init__(self, in_features, out_features, bias=True, is_first=False,
omega_0=30.0):
super().__init__()
self.omega_0 = omega_0
self.is_first = is_first
self.in_features = in_features
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | afiaka87/text_to_img | SineLayer | false | 6,089 | [
"MIT"
] | 1 | 59c28a9de57d88910f6dfe8ea9a9d40d37b2279a | https://github.com/afiaka87/text_to_img/tree/59c28a9de57d88910f6dfe8ea9a9d40d37b2279a |
Gaussian | # 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.utils.tensorboard
import torch.utils.data
assert_size_stride... | cdever01/torchani | Gaussian | false | 4,042 | [
"MIT"
] | 0 | 3f7e1347a06422f50010c04a65219e22f2179bfa | https://github.com/cdever01/torchani/tree/3f7e1347a06422f50010c04a65219e22f2179bfa |
Critic | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
in_size = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(in_size)
return -lim, lim
class Critic(nn.Module):
def __init__(self, state_size, action_size, seed=0, fc1_size=128,
fc2_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | swastiknath/rl_ud_2 | Critic | false | 13,010 | [
"MIT"
] | 0 | 666e538f967252fa609c6b31cb5d66f9415eae82 | https://github.com/swastiknath/rl_ud_2/tree/666e538f967252fa609c6b31cb5d66f9415eae82 |
TriangularSylvester | # 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.triton_helpers import libdevice, math as tl_math
fr... | boldsort/NeuralDX7 | TriangularSylvester | false | 14,983 | [
"MIT"
] | 119 | 327844cea18a6dfe35e0dc8f5de0832343487366 | https://github.com/boldsort/NeuralDX7/tree/327844cea18a6dfe35e0dc8f5de0832343487366 |
AttentionModuleV2 | import math
import torch
import torch.nn.functional as F
class AttentionModuleV2(torch.nn.Module):
def __init__(self, hidden_size, fc_x_query=None, fc_spt_key=None,
fc_spt_value=None, fc_x_update=None, fc_update=None,
fc_spt_spt_query=None, fc_spt_spt_key=None, fc_spt_spt_value=None,
gamm... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ArmandNM/meta-learning | AttentionModuleV2 | false | 126 | [
"MIT"
] | 0 | 173fcd4b929168e9bd7948581293020a3a932857 | https://github.com/ArmandNM/meta-learning/tree/173fcd4b929168e9bd7948581293020a3a932857 |
AbsLoss | import torch
class AbsLoss(torch.nn.Module):
"""
The mean absolute value.
"""
def forward(self, x):
""""""
return torch.mean(torch.abs(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | KevinMusgrave/pytorch-adapt | AbsLoss | false | 13,942 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
Sine | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | idgmatrix/coin | Sine | false | 15,585 | [
"MIT"
] | 84 | 2f2df0614ed4fc866d4b7715ee206081e08b9424 | https://github.com/idgmatrix/coin/tree/2f2df0614ed4fc866d4b7715ee206081e08b9424 |
LinearLR | # 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.checkpoint
assert_size_stride = torch._... | bahducoup/factorized_training | LinearLR | false | 12,152 | [
"MIT"
] | 0 | 0af38f16338a9bcfcc11091b1a6b75befd67f234 | https://github.com/bahducoup/factorized_training/tree/0af38f16338a9bcfcc11091b1a6b75befd67f234 |
InformedSender | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class InformedSender(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super(InformedSender, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | vengalraoguttha/EGG | InformedSender | false | 16,677 | [
"MIT"
] | 254 | e4f8412f197543ec7f1f00cf89b5a364b038dc57 | https://github.com/vengalraoguttha/EGG/tree/e4f8412f197543ec7f1f00cf89b5a364b038dc57 |
InteractingLayer | # 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.... | Sunmyunghan/Final_Project | InteractingLayer | false | 1,211 | [
"MIT"
] | 0 | 28cde293dc6d07521b2e1c5613b20444aea91d21 | https://github.com/Sunmyunghan/Final_Project/tree/28cde293dc6d07521b2e1c5613b20444aea91d21 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
"""policy-value network module"""
def __init__(self, board_width, board_height):
super(Net, self).__init__()
self.board_width = board_width
self.board_height = board_height
self.conv1 = 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
from torch._inductor.runtime.... | sewon0918/pj4 | Net | false | 4,318 | [
"MIT"
] | 0 | 144996e7f99e7639f1fffb34770ab9713307428d | https://github.com/sewon0918/pj4/tree/144996e7f99e7639f1fffb34770ab9713307428d |
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
from torch import nn
import torch.onnx
import torch.utils.data
import torchvision.transfo... | cagery/pytorch-image-models | AdaptiveAvgMaxPool2d | false | 9,899 | [
"Apache-2.0"
] | 0 | 9211b0bd368cecf970165cfad81770dc14e25d45 | https://github.com/cagery/pytorch-image-models/tree/9211b0bd368cecf970165cfad81770dc14e25d45 |
GEGLU | # 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... | NomadicDaggy/DALLE-pytorch | GEGLU | false | 11,756 | [
"MIT"
] | 0 | ecadc12e8063763ad45de50773e5c746262cdfd3 | https://github.com/NomadicDaggy/DALLE-pytorch/tree/ecadc12e8063763ad45de50773e5c746262cdfd3 |
BILM | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | SeunghwanByun/Real-Time-Road-Detection-Network | BILM | false | 1,046 | [
"MIT"
] | 0 | bc46615adef0e2b1a9a03dd4951559ca5849e6e1 | https://github.com/SeunghwanByun/Real-Time-Road-Detection-Network/tree/bc46615adef0e2b1a9a03dd4951559ca5849e6e1 |
LearnedPositionalEmbedding | import torch
import torch.utils.data
from torch import nn
def create_position_ids_from_input_ids(input_ids, padding_idx):
""" Replace non-padding symbols with their position numbers. Position numbers begin at
padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
`utils.make_positions... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | kev2513/gap-text2sql | LearnedPositionalEmbedding | false | 12,667 | [
"Apache-2.0"
] | 0 | 67c4d6489ac44d4785a0cc1b836c889f00226f1d | https://github.com/kev2513/gap-text2sql/tree/67c4d6489ac44d4785a0cc1b836c889f00226f1d |
TVLoss | # 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 typing import Tuple
from torch.nn.modules.loss import _Loss
from typing im... | hecoding/piq | TVLoss | false | 10,188 | [
"Apache-2.0"
] | 0 | c72143ce9deb30fefaca434a39e4dfc557673e97 | https://github.com/hecoding/piq/tree/c72143ce9deb30fefaca434a39e4dfc557673e97 |
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... | aasensio/neural_hinode | VAE | false | 3,028 | [
"MIT"
] | 0 | 63ec076d920f82343618ce67669c73a3b5209957 | https://github.com/aasensio/neural_hinode/tree/63ec076d920f82343618ce67669c73a3b5209957 |
RobertaClassificationHead | # 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 ... | Masum06/CodeXGLUE | RobertaClassificationHead | false | 4,731 | [
"CC0-1.0",
"MIT"
] | 0 | bf1ab8c8878f978bd4ef3cb5e030e52f03e92854 | https://github.com/Masum06/CodeXGLUE/tree/bf1ab8c8878f978bd4ef3cb5e030e52f03e92854 |
ELUPlus | # 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
import torch.nn
assert_size_stride = torch._C._dynamo.guar... | dennisprangle/nflows | ELUPlus | false | 10,062 | [
"MIT"
] | 0 | d3160c60845a4f22f3bf505dc11210d55848e69f | https://github.com/dennisprangle/nflows/tree/d3160c60845a4f22f3bf505dc11210d55848e69f |
ClassificationModel | # 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_... | HenryOsborne/Rotation | ClassificationModel | false | 9,126 | [
"Apache-2.0"
] | 0 | 417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 | https://github.com/HenryOsborne/Rotation/tree/417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 |
ResBlock | 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
from torchvision.transforms import *
import torch.onnx
class ResBlock(nn.Module):
def __init__(self, num_of_channels):
super(R... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | aalborov/openvino_training_extensions | ResBlock | false | 6,042 | [
"Apache-2.0"
] | 1 | a0bb39424151a98e1ca80c4aa5c865636d401785 | https://github.com/aalborov/openvino_training_extensions/tree/a0bb39424151a98e1ca80c4aa5c865636d401785 |
Emitter | # 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.... | abacoelho/variational-poisson-rnn | Emitter | false | 18,210 | [
"MIT"
] | 5 | abf77f79fc64be75ae9102ec8d537f77ed9c5f8f | https://github.com/abacoelho/variational-poisson-rnn/tree/abf77f79fc64be75ae9102ec8d537f77ed9c5f8f |
PositionWiseFCNetwork | # 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.... | sgrvinod/a-PyTorch-Tutorial-to-Machine-Translation | PositionWiseFCNetwork | false | 16,396 | [
"MIT"
] | 59 | a4dd7bc5554d11ac80355241f603dcaa24bc70ae | https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Machine-Translation/tree/a4dd7bc5554d11ac80355241f603dcaa24bc70ae |
AFMLayer | # 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.... | Sunmyunghan/Final_Project | AFMLayer | false | 1,183 | [
"MIT"
] | 0 | 28cde293dc6d07521b2e1c5613b20444aea91d21 | https://github.com/Sunmyunghan/Final_Project/tree/28cde293dc6d07521b2e1c5613b20444aea91d21 |
AddBroadcastPosEmbed | import torch
import torch.nn as nn
def tensor_slice(x, begin, size):
assert all([(b >= 0) for b in begin])
size = [(l - b if s == -1 else s) for s, b, l in zip(size, begin, x.shape)]
assert all([(s >= 0) for s in size])
slices = [slice(b, b + s) for b, s in zip(begin, size)]
return x[slices]
cla... | 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... | AshBT/VideoGPT | AddBroadcastPosEmbed | false | 13,306 | [
"MIT"
] | 396 | a823bc734af3387129f3bd624caad3db270707f2 | https://github.com/AshBT/VideoGPT/tree/a823bc734af3387129f3bd624caad3db270707f2 |
PositionwiseFeedForward | # 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 ... | RerRayne/learn3d | PositionwiseFeedForward | false | 14,285 | [
"MIT"
] | 335 | 83e4ac657c6538fb4cbed6e00b2e3ed6cbf43555 | https://github.com/RerRayne/learn3d/tree/83e4ac657c6538fb4cbed6e00b2e3ed6cbf43555 |
CTLoss | import torch
import torch.nn as nn
import torch.onnx
def _neg_loss(preds, gt):
pos_inds = gt.eq(1)
neg_inds = gt.lt(1)
neg_weights = torch.pow(1 - gt[neg_inds], 4)
loss = 0
for pred in preds:
pos_pred = pred[pos_inds]
neg_pred = pred[neg_inds]
pos_loss = torch.log(pos_pred)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.asse... | c464851257/extremenet-lite | CTLoss | false | 6,387 | [
"BSD-3-Clause"
] | 1 | 331446f2c5d9524d46d2b33823eff02416f43052 | https://github.com/c464851257/extremenet-lite/tree/331446f2c5d9524d46d2b33823eff02416f43052 |
Unit3D | # 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 ... | TencentYoutuResearch/ActionDetection-AFSD | Unit3D | false | 14,466 | [
"BSD-3-Clause"
] | 112 | ed86a0df91e58baa7d78c796ed29cff82b1f3fa6 | https://github.com/TencentYoutuResearch/ActionDetection-AFSD/tree/ed86a0df91e58baa7d78c796ed29cff82b1f3fa6 |
SphereEmbedded | # 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... | vishalbelsare/geotorch | SphereEmbedded | false | 16,683 | [
"MIT"
] | 422 | ba38d406c245d609fee4b4dac3f6427bf6d73a8e | https://github.com/vishalbelsare/geotorch/tree/ba38d406c245d609fee4b4dac3f6427bf6d73a8e |
RDivInt | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | bunderhi/torch2trt | RDivInt | false | 1,608 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
GlobalAveragePool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | Creation-Labs-AI/onnx2pytorch | GlobalAveragePool | false | 13,559 | [
"Apache-2.0"
] | 147 | eaf70c6b75009efa7d07c6042a62f336194c4786 | https://github.com/Creation-Labs-AI/onnx2pytorch/tree/eaf70c6b75009efa7d07c6042a62f336194c4786 |
InstrShifting | import torch
import torch.nn as nn
class InstrShifting(nn.Module):
""" Sub-Instruction Shifting Module.
Decide whether the current subinstruction will
be completed by the next action or not. """
def __init__(self, rnn_hidden_size, shift_hidden_size, action_emb_size,
max_subinstr_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | IMNearth/Curriculum-Learning-For-VLN | InstrShifting | false | 17,437 | [
"MIT"
] | 8 | d2fe1286eb295dc8c63a0c886b35883f32481d85 | https://github.com/IMNearth/Curriculum-Learning-For-VLN/tree/d2fe1286eb295dc8c63a0c886b35883f32481d85 |
AddReadout | import torch
import torch.nn as nn
class AddReadout(nn.Module):
def __init__(self, start_index=1):
super(AddReadout, self).__init__()
self.start_index = start_index
def forward(self, x):
if self.start_index == 2:
readout = (x[:, 0] + x[:, 1]) / 2
else:
... | 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... | blguweb/Tap-Tap-computer | AddReadout | false | 3,275 | [
"MIT"
] | 0 | 4e2007b5a31e6d5f902b1e3ca58206870331ef07 | https://github.com/blguweb/Tap-Tap-computer/tree/4e2007b5a31e6d5f902b1e3ca58206870331ef07 |
GradientReversal | from torch.autograd import Function
import torch
class GradientReversalFunction(Function):
"""
Gradient Reversal Layer from:
Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
Forward pass is the identity function. In the backward pass,
the upstream gradients are multipli... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | TheElderMindseeker/pytorch-domain-adaptation | GradientReversal | false | 1,164 | [
"MIT"
] | 0 | 70ca862708bd6e59b5eee5d7c8bd808ef3457dc8 | https://github.com/TheElderMindseeker/pytorch-domain-adaptation/tree/70ca862708bd6e59b5eee5d7c8bd808ef3457dc8 |
InstanceNorm | import torch
from torch import nn
from typing import *
class InstanceNorm(nn.Module):
"""Normalize by height and width for images."""
__constants__ = ['eps']
def __init__(self, nf, mom, eps):
super().__init__()
self.eps = eps
self.gamma = nn.Parameter(torch.ones(nf, 1, 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 libdevice
from torch import nn
from typing import *
assert_size_stride = torch._C._dynamo... | ImadDabbura/fastai-courses | InstanceNorm | false | 17,434 | [
"Apache-2.0"
] | 3 | 053637a2dd3b4ad6c35f97a13f3fba87af1d3940 | https://github.com/ImadDabbura/fastai-courses/tree/053637a2dd3b4ad6c35f97a13f3fba87af1d3940 |
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.... | MarcosPampuch/TDNet_CARLA | ScaledDotProductAttention | false | 802 | [
"MIT"
] | 0 | efc1c872966f1cef49b82723170586a6abcfb524 | https://github.com/MarcosPampuch/TDNet_CARLA/tree/efc1c872966f1cef49b82723170586a6abcfb524 |
HorizontalMaxPool2d | import torch
import torch.nn as nn
class HorizontalMaxPool2d(nn.Module):
def __init__(self):
super(HorizontalMaxPool2d, self).__init__()
def forward(self, x):
inp_size = x.size()
return nn.functional.max_pool2d(input=x, kernel_size=(1, inp_size[3]))
def get_inputs():
return [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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | linkserendipity/AlignedReID | HorizontalMaxPool2d | false | 3,910 | [
"MIT"
] | 0 | 142a9ebdc200ef4da001f91c1f592e4ff02b2f77 | https://github.com/linkserendipity/AlignedReID/tree/142a9ebdc200ef4da001f91c1f592e4ff02b2f77 |
folder | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | hav4ik/AdelaiDet | folder | false | 3,712 | [
"BSD-2-Clause"
] | 0 | 6ed9c1e1a25a3e25dddfa858ce0f219a30593ce2 | https://github.com/hav4ik/AdelaiDet/tree/6ed9c1e1a25a3e25dddfa858ce0f219a30593ce2 |
LBM | import torch
import torch.nn as nn
class LBM(nn.Module):
def __init__(self, l_dim, r_dim):
super(LBM, self).__init__()
self.W = nn.Bilinear(l_dim, r_dim, 1, bias=False)
def forward(self, e1, e2):
"""
e1: tensor of size (*, l_dim)
e2: tensor of size (*, r_dim)
... | 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... | aryaman4/TaxoExpan | LBM | false | 9,785 | [
"Apache-2.0"
] | 0 | 3d9b9a21ba7cdd872dc62181dd14ff271e20b245 | https://github.com/aryaman4/TaxoExpan/tree/3d9b9a21ba7cdd872dc62181dd14ff271e20b245 |
AttentionPool2d | import math
import torch
import numpy as np
import torch.nn as nn
import torch as th
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jasperhu13/deit | AttentionPool2d | false | 10,265 | [
"Apache-2.0"
] | 0 | 97b09b1c131a7ee8d01ee0ce27a936ff33cf62fc | https://github.com/jasperhu13/deit/tree/97b09b1c131a7ee8d01ee0ce27a936ff33cf62fc |
Conv1DHighwayLayer | import torch
import torch.nn as nn
class Conv1DHighwayLayer(nn.Module):
def __init__(self, inchannels, outchannels, kernelsize, activation=
'relu', stride=1, bias=-1):
super(Conv1DHighwayLayer, self).__init__()
self.inchannels = inchannels
self.outchannels = outchannels
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | avinashsai/Highway-Networks | Conv1DHighwayLayer | false | 3,148 | [
"MIT"
] | 0 | fe30629e47b919776f981eaa2bea7d21e648a17f | https://github.com/avinashsai/Highway-Networks/tree/fe30629e47b919776f981eaa2bea7d21e648a17f |
WordPredictor | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.jit.quantized
import torch.onnx.operators
class WordPredictor(nn.Module):
def __init__(self, encoder_output_dim, hidden_dim, output_dim,
topk_labels_per_source_token=None, use_self_attention=False):
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | Jeffyrao/translate | WordPredictor | false | 2,445 | [
"BSD-3-Clause"
] | 0 | ab928e0b692f476c0a43ee7f9d0fbd3ecbada2b4 | https://github.com/Jeffyrao/translate/tree/ab928e0b692f476c0a43ee7f9d0fbd3ecbada2b4 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | BCHoagland/attention-learn-to-route | Attention | false | 4,886 | [
"MIT"
] | 1 | c411289c3b42be5b9c89240f665a029dfc51e034 | https://github.com/BCHoagland/attention-learn-to-route/tree/c411289c3b42be5b9c89240f665a029dfc51e034 |
Decoder | # 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.... | samsgood0310/Unsupervised-Defect-Segmentation | Decoder | false | 7,615 | [
"Apache-2.0"
] | 1 | 66af32506cd6e60c356890616e28d679622fd8e6 | https://github.com/samsgood0310/Unsupervised-Defect-Segmentation/tree/66af32506cd6e60c356890616e28d679622fd8e6 |
Model | from torch.nn import Module
import torch
import torch.nn.functional
from torch.nn import Parameter
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
from torch.nn import Module
import torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.nn.functional
from torch.nn import Parameter
from torch.nn.parameter import Parameter
from torch.nn... | Liuhongzhi2018/Person_ReID | Model | false | 4,754 | [
"MIT"
] | 0 | 51c576ed5b4ed960801669d6d59c0a77405b369d | https://github.com/Liuhongzhi2018/Person_ReID/tree/51c576ed5b4ed960801669d6d59c0a77405b369d |
SimplifiedScaledDotProductAttention | # 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.... | LiChengChen666/DetectDee | SimplifiedScaledDotProductAttention | false | 9,820 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
BCEDiceLoss | import torch
import torch.nn as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed = te... | 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... | bounesh/pytorch-3dunet | BCEDiceLoss | false | 14,980 | [
"MIT"
] | 1,236 | 60278d01eaacc69feee731979826a0c26e223427 | https://github.com/bounesh/pytorch-3dunet/tree/60278d01eaacc69feee731979826a0c26e223427 |
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