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 |
|---|---|---|---|---|---|---|---|---|---|---|
MatrixAttention | import math
import torch
import torch.nn as nn
class SimilarityFunction(nn.Module):
"""
A ``SimilarityFunction`` takes a pair of tensors with the same shape, and computes a similarity
function on the vectors in the last dimension. For example, the tensors might both have shape
`(batch_size, sentence_... | 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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | Aunsiels/qagnn | MatrixAttention | false | 11,303 | [
"MIT"
] | 0 | d89a3dd650ac4b8b8aae34e0cce7cfc698892d80 | https://github.com/Aunsiels/qagnn/tree/d89a3dd650ac4b8b8aae34e0cce7cfc698892d80 |
Relu_Caps | import torch
from torch import nn
import torch.nn.functional as F
class Relu_Caps(nn.Module):
def __init__(self, num_C, num_D, theta=0.2, eps=0.0001):
super(Relu_Caps, self).__init__()
self.num_C = num_C
self.num_D = num_D
self.theta = theta
self.eps = eps
def forward... | 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
assert_... | WdBlink/AugMix-3DOCUNet-Brats2019 | Relu_Caps | false | 5,957 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
Block | # 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.... | Junhojuno/vision-transformer | Block | false | 5,440 | [
"MIT"
] | 1 | 38f8a17967e91e98f767c8e5754081ee8bcd72b4 | https://github.com/Junhojuno/vision-transformer/tree/38f8a17967e91e98f767c8e5754081ee8bcd72b4 |
DistilMHAScoresCalculation_v1 | import math
import torch
import torch.nn as nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
class DistilMHAScoresCalculation_v1(nn.Module):
def __init__(self, dim_per_head, softmax_dim=-1):
super(DistilMHAScoresCalculation_v1, self).__init__()
self.softmax = nn.Softmax(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | JudeDavis1/intel-extension-for-pytorch | DistilMHAScoresCalculation_v1 | false | 2,580 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
TorchDiv | import torch
class TorchDiv(torch.nn.Module):
def __init__(self):
super(TorchDiv, self).__init__()
def forward(self, x, y):
return torch.div(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... | Ilyabasharov/torch2trt | TorchDiv | false | 2,570 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
RankingLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
from abc import abstractmethod
import torch.utils.data.dataloader
import torch.nn
class SimilarityLoss(nn.Module):
def __init__(self):
super(SimilarityLoss, self).__init__()
@abstractmethod
def forward(self, inputs, targets):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from abc import abstractmethod
import torch.utils.data.dataloader
i... | GT-SALT/LADA | RankingLoss | false | 8,139 | [
"MIT"
] | 31 | 2838a4c90694bf1054c6bab7f3b60ab5e04a5d4d | https://github.com/GT-SALT/LADA/tree/2838a4c90694bf1054c6bab7f3b60ab5e04a5d4d |
PermEqui2_max | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | haoruilee/DeepSets | PermEqui2_max | false | 15,492 | [
"Apache-2.0"
] | 213 | b405dd6b51a34fb1ef622e25e6685b417b7b7cbb | https://github.com/haoruilee/DeepSets/tree/b405dd6b51a34fb1ef622e25e6685b417b7b7cbb |
MultiHeadAttention | from torch.nn import Module
import torch
import numpy as np
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | CurryYuan/X-Trans2Cap | MultiHeadAttention | false | 7,933 | [
"Apache-2.0"
] | 11 | c78a27209f14fcbbec74fe8b5edc06faea2e7d44 | https://github.com/CurryYuan/X-Trans2Cap/tree/c78a27209f14fcbbec74fe8b5edc06faea2e7d44 |
KlCriterion | # 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.... | johnson7788/mt-dnn | KlCriterion | false | 3,902 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
Downsample | # 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... | GastonMazzei/escher-project-website | Downsample | false | 17,289 | [
"MIT"
] | 5 | b3861eeeca11a7c31502f539ded9ae718f3d9e2e | https://github.com/GastonMazzei/escher-project-website/tree/b3861eeeca11a7c31502f539ded9ae718f3d9e2e |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Ergtou/TextWord | Encoder | false | 2,197 | [
"MIT"
] | 0 | f05cc5a630fc8d05357b8a9bc0da3ec5cc255a30 | https://github.com/Ergtou/TextWord/tree/f05cc5a630fc8d05357b8a9bc0da3ec5cc255a30 |
FocalLossBinary | import torch
import torch.jit
import torch.nn.functional as F
from functools import partial
import torch.utils.data
import torch.nn.functional
from torch.nn.modules.loss import _Loss
def reduced_focal_loss(outputs: 'torch.Tensor', targets: 'torch.Tensor',
threshold: 'float'=0.5, gamma: 'float'=2.0, reduction='mea... | 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... | Magnety/nnUNet | FocalLossBinary | false | 9,593 | [
"Apache-2.0"
] | 0 | f07e6fdf191377550c57bcdc8859798486f60443 | https://github.com/Magnety/nnUNet/tree/f07e6fdf191377550c57bcdc8859798486f60443 |
GLU | import torch
import torch.nn as nn
import torch.utils.data
class GLU(nn.Module):
"""
Overview:
Gating Linear Unit.
This class does a thing like this:
.. code:: python
# Inputs: input, context, output_size
# The gate value is a learnt function of the input.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | L-Net-1992/DI-engine | GLU | false | 5,510 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f |
clip_nonlinear | import torch
import torch.nn as nn
def quantize_a(x):
x = Q_A.apply(x)
return x
def fa(x, bitA):
if bitA == 32:
return x
return quantize_a(x)
class Q_A(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
out = x.new(x.size())
... | 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... | GakkiChen/TWB-Net | clip_nonlinear | false | 2,264 | [
"MIT"
] | 0 | bb4917c697c09585bb3fe163a8b429b6dd250f18 | https://github.com/GakkiChen/TWB-Net/tree/bb4917c697c09585bb3fe163a8b429b6dd250f18 |
DiscriminatorLoss | from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd
class DiscriminatorLoss(Module):
"""
## Discriminator Loss
We want to find $w$ to maximize
$$\\mathbb{E}_{x \\sim \\mathbb{P}_r} [f_w(x)]- \\mathbb{E}_{z \... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
import tor... | mcx/annotated_deep_learning_paper_implementations | DiscriminatorLoss | false | 7,194 | [
"MIT"
] | 1 | f169f3a71dd2d36eb28ad31062d3475efa367b88 | https://github.com/mcx/annotated_deep_learning_paper_implementations/tree/f169f3a71dd2d36eb28ad31062d3475efa367b88 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class Net(nn.Module):
def __init__(self, s_dim, a_dim):
super(Net, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | taomo/pytorch-A3C-1 | Net | false | 4,413 | [
"MIT"
] | 0 | 8e26720c75ca8b7e987b267e5e0e652d0c5d23cf | https://github.com/taomo/pytorch-A3C-1/tree/8e26720c75ca8b7e987b267e5e0e652d0c5d23cf |
VPGELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | DucNguyen183/nfnet_f5 | VPGELU | false | 13,592 | [
"Apache-2.0"
] | 133 | 567a1126ff6ea09b33ffa5dacfac9c983fd48713 | https://github.com/DucNguyen183/nfnet_f5/tree/567a1126ff6ea09b33ffa5dacfac9c983fd48713 |
SELayer | # 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_... | implus/pytorch_image_classification | SELayer | false | 10,284 | [
"MIT"
] | 0 | cac490ed518ad09b0429fc01af060457fb050e68 | https://github.com/implus/pytorch_image_classification/tree/cac490ed518ad09b0429fc01af060457fb050e68 |
QuadrupletLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | VictorCallejas/FB-Similarity-Challenge | QuadrupletLoss | false | 2,932 | [
"MIT"
] | 0 | 0092071f29d5d8fab055d27a1e542e2e64e9cdab | https://github.com/VictorCallejas/FB-Similarity-Challenge/tree/0092071f29d5d8fab055d27a1e542e2e64e9cdab |
LayerNorm | import torch
import torch.utils.data
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, d):
super().__init__()
self.a = nn.Parameter(torch.ones(d).unsqueeze(0).unsqueeze(0))
self.b = nn.Parameter(torch.zeros(d).unsqueeze(0).unsqueeze(0))
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | AntoBcc/benchmarking-gnns | LayerNorm | false | 1,961 | [
"MIT"
] | 0 | c5750054b2f4ba0822f203fa18d382f6a3b16542 | https://github.com/AntoBcc/benchmarking-gnns/tree/c5750054b2f4ba0822f203fa18d382f6a3b16542 |
PositionWiseFFN | import torch
from torch import nn
from torch.nn.functional import relu
class PositionWiseFFN(nn.Module):
def __init__(self, model_dim, dropout=0.0):
super().__init__()
dff = model_dim * 4
self.l = nn.Linear(model_dim, dff)
self.o = nn.Linear(dff, model_dim)
self.dropout = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ruifan831/NLP-Tutorials | PositionWiseFFN | false | 12,949 | [
"MIT"
] | 0 | d1fe27b2891156be4d8054022b762f758e9113a9 | https://github.com/ruifan831/NLP-Tutorials/tree/d1fe27b2891156be4d8054022b762f758e9113a9 |
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.... | OneAdder/hseling-repo-chukchi-type | Attention | false | 921 | [
"MIT"
] | 0 | 5f5e651510bca7cfb89dc2e98b07bcc63b6330a4 | https://github.com/OneAdder/hseling-repo-chukchi-type/tree/5f5e651510bca7cfb89dc2e98b07bcc63b6330a4 |
BondEnergyModule | # 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
import torch.nn as nn
from itertools import repeat
assert_size_... | torchmd/mdgrad | BondEnergyModule | false | 16,626 | [
"MIT"
] | 54 | 77bd7685b74b41acf54a9483546e1e8cb545eb01 | https://github.com/torchmd/mdgrad/tree/77bd7685b74b41acf54a9483546e1e8cb545eb01 |
SelfAttentionPooling | import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttenti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | AyushExel/s3prl | SelfAttentionPooling | false | 1,995 | [
"MIT"
] | 0 | 6531904e9621a778978b9cfef3ba9f582e56639a | https://github.com/AyushExel/s3prl/tree/6531904e9621a778978b9cfef3ba9f582e56639a |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ablou1/dqn-navigation | QNetwork | false | 9,659 | [
"MIT"
] | 0 | c89011220983061685ae4501d0207b8958eafc21 | https://github.com/ablou1/dqn-navigation/tree/c89011220983061685ae4501d0207b8958eafc21 |
SineODE | import math
import torch
class SineODE(torch.nn.Module):
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 * t ** 3 * torch.sin(
2 * t) + 0.25 * t ** 2 * torch.cos(2 * t)... | 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... | MaricelaM/torchdiffeq | SineODE | false | 14,002 | [
"MIT"
] | 4,088 | 4e070fb687167e53082a91f32e102af7f4521058 | https://github.com/MaricelaM/torchdiffeq/tree/4e070fb687167e53082a91f32e102af7f4521058 |
ForwardNet | # 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.... | jamaalhay/Final_Proj | ForwardNet | false | 15,671 | [
"MIT"
] | 104 | 3f524a90fee5a3cb21466ab76f630d060792045d | https://github.com/jamaalhay/Final_Proj/tree/3f524a90fee5a3cb21466ab76f630d060792045d |
MaxPool2dDynamicSamePadding | import math
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils
class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
The padding is operated in forward function by calculating dynamically.
"""
d... | 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.utils
assert_size_stride = torch._C._dynamo.guards.asse... | BlakeDai/FedML-test | MaxPool2dDynamicSamePadding | false | 9,198 | [
"Apache-2.0"
] | 0 | 3cb9a7234f3f0294f3137e4be572153ba7b62f8f | https://github.com/BlakeDai/FedML-test/tree/3cb9a7234f3f0294f3137e4be572153ba7b62f8f |
MultiheadAttention | import torch
import torch.nn as nn
def BuildDropout(dropout_type, **kwargs):
supported_dropouts = {'droppath': DropPath, 'dropout': nn.Dropout,
'dropout2d': nn.Dropout2d, 'dropout3d': nn.Dropout3d}
assert dropout_type in supported_dropouts, 'unsupport dropout type %s...' % dropout_type
return supp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | CharlesPikachu/mcibi | MultiheadAttention | false | 7,903 | [
"MIT"
] | 41 | 6ce453504741c2eed1d290306055258a377a4094 | https://github.com/CharlesPikachu/mcibi/tree/6ce453504741c2eed1d290306055258a377a4094 |
AdaIN | # 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 ... | fpaupier/stargan-v2 | AdaIN | false | 6,697 | [
"MIT"
] | 1 | 18d2e04ed6e6df963b84345e798d94383757aaa2 | https://github.com/fpaupier/stargan-v2/tree/18d2e04ed6e6df963b84345e798d94383757aaa2 |
ScaledDotProductAttention | import math
import torch
from torch import nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, d_k):
super().__init__()
self.dropout = nn.Dropout(0.5)
self.sqrt_d_k = math.sqrt(d_k)
def forward(self, Q, K, V):
attn = torch.bmm(Q, K.transpose(2, 1))
attn ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | DaanG96/breakfastDSNet | ScaledDotProductAttention | false | 8,936 | [
"MIT"
] | 0 | 17a146ef5ad077e935e6f4b773e0a1f605f76a78 | https://github.com/DaanG96/breakfastDSNet/tree/17a146ef5ad077e935e6f4b773e0a1f605f76a78 |
RNN | # 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.... | chenyuntc/practical-pytorch | RNN | false | 12,214 | [
"MIT"
] | 0 | 42cbde5275d37bf3f3623a85fd71f13069d95089 | https://github.com/chenyuntc/practical-pytorch/tree/42cbde5275d37bf3f3623a85fd71f13069d95089 |
MetaLayerNorm | # 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_... | KIMGEONUNG/multi-memory-siren | MetaLayerNorm | false | 716 | [
"MIT"
] | 0 | b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b | https://github.com/KIMGEONUNG/multi-memory-siren/tree/b372e1b9abe2b3fb502d808eb3a47c2ad287ca3b |
Inception3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
x ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Galaxies99/inception-cuda | Inception3 | false | 11,638 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
MultiplyLearned | import torch
import torch.fft
import torch.nn
class MultiplyLearned(torch.nn.Module):
def __init__(self, omega_0: 'float'):
"""
out = omega_0 * x, with a learned omega_0
"""
super().__init__()
self.omega_0 = torch.nn.Parameter(torch.Tensor(1))
with torch.no_grad():... | 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.fft
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | dwromero/ckconv | MultiplyLearned | false | 15,280 | [
"MIT"
] | 74 | d44c6441a98792477d6259368c210089bb33fe7a | https://github.com/dwromero/ckconv/tree/d44c6441a98792477d6259368c210089bb33fe7a |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | xuanyuyt/pytorch-tutorial | Net | false | 4,604 | [
"MIT"
] | 0 | 92076ac56d42da98ea61ce06708bb8c537a49af0 | https://github.com/xuanyuyt/pytorch-tutorial/tree/92076ac56d42da98ea61ce06708bb8c537a49af0 |
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.... | ZagHe568/pytorch-seq2seq | Attention | false | 6,035 | [
"MIT"
] | 1 | 2491c04650b480944c76a15532e5cc89e9dc62fb | https://github.com/ZagHe568/pytorch-seq2seq/tree/2491c04650b480944c76a15532e5cc89e9dc62fb |
SIREN_layer | import torch
import numpy as np
import torch.nn as nn
def act(act_fun='LeakyReLU'):
"""
Either string defining an activation function or module (e.g. nn.ReLU)
"""
if isinstance(act_fun, str):
if act_fun == 'LeakyReLU':
return nn.LeakyReLU(0.2, inplace=True)
elif act_fun... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | dustlrdk/noise2self | SIREN_layer | false | 3,445 | [
"MIT"
] | 0 | 46e8c4650f7ec4f664448417fecd39b4cae477f7 | https://github.com/dustlrdk/noise2self/tree/46e8c4650f7ec4f664448417fecd39b4cae477f7 |
FSM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Genevievekim/semantic-segmentation-1 | FSM | false | 13,709 | [
"BSD-3-Clause"
] | 196 | f28b026e44cff80fe3ca4cac94cea27e4073821b | https://github.com/Genevievekim/semantic-segmentation-1/tree/f28b026e44cff80fe3ca4cac94cea27e4073821b |
DivideMax | import torch
from torch import nn
class DivideMax(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
maxes = x.amax(dim=self.dim, keepdim=True).detach()
return x / maxes
def get_inputs():
return [torch.rand([4, 4, 4, 4, 4])]
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Tiamat-Tech/DALLE-pytorch | DivideMax | false | 11,937 | [
"MIT"
] | 0 | d7bd745b23424e5a47c0db7e7ab093542427b22d | https://github.com/Tiamat-Tech/DALLE-pytorch/tree/d7bd745b23424e5a47c0db7e7ab093542427b22d |
DepthwiseSeparableConv | # 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_... | MobtgZhang/MWMLNet | DepthwiseSeparableConv | false | 5,607 | [
"MIT"
] | 1 | 125bb39935916b6b4be505c51cb6a04eb49b96d0 | https://github.com/MobtgZhang/MWMLNet/tree/125bb39935916b6b4be505c51cb6a04eb49b96d0 |
FocalLoss | import torch
class FocalLoss(torch.nn.Module):
def __init__(self, gamma=2, alpha=0.5, size_average=True):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
self.size_average = size_average
self.elipson = 1e-06
def forward(self, logits, labels):
... | 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... | RuiBai1999/HiMatch | FocalLoss | false | 5,771 | [
"MIT"
] | 1 | 199ebc6b06b3cce2b3f2298cb9e20f81c01dc7a6 | https://github.com/RuiBai1999/HiMatch/tree/199ebc6b06b3cce2b3f2298cb9e20f81c01dc7a6 |
MultiLayerPerceptron | import torch
import torch.utils.data
import torch.optim
class MultiLayerPerceptron(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (int): the size of each layer
num_cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Zenodia/NeMo | MultiLayerPerceptron | false | 1,299 | [
"Apache-2.0"
] | 0 | 3c288d8a7caf667c95444c39434e3ebc5f53d911 | https://github.com/Zenodia/NeMo/tree/3c288d8a7caf667c95444c39434e3ebc5f53d911 |
BinaryCrossEntropyLoss | # 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... | Elameri/ivadomed | BinaryCrossEntropyLoss | false | 9,302 | [
"MIT"
] | 0 | 76b5cea46f90f938aafd5ec26e072d559c764b43 | https://github.com/Elameri/ivadomed/tree/76b5cea46f90f938aafd5ec26e072d559c764b43 |
AvgConsensus | # 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... | scenarios/dev | AvgConsensus | false | 4,453 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
PyramidDown | import torch
import torch.nn as nn
from torch.nn import functional as F
class PyramidDown(nn.Module):
def __init__(self) ->None:
super(PyramidDown, self).__init__()
self.filter = nn.Parameter(torch.tensor([[1, 4, 6, 4, 1], [4, 16,
24, 16, 4], [6, 24, 36, 24, 6], [4, 16, 24, 16, 4], [... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | masanorihirano/pytorch_extra_mhirano | PyramidDown | false | 7,165 | [
"MIT"
] | 1 | d19e07445567c069793b7ca1a22a846d7cbce58d | https://github.com/masanorihirano/pytorch_extra_mhirano/tree/d19e07445567c069793b7ca1a22a846d7cbce58d |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Iceland-Leo/StyleGAN2_PyTorch | PixelNorm | false | 5,318 | [
"MIT"
] | 1 | 3621f5e4ba1c7fde7e2fae1f4700d050656a0b02 | https://github.com/Iceland-Leo/StyleGAN2_PyTorch/tree/3621f5e4ba1c7fde7e2fae1f4700d050656a0b02 |
NodeAdaptiveEncoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | Brickser/cogdl | NodeAdaptiveEncoder | false | 2,259 | [
"MIT"
] | 0 | 3952dd11075634cc0f3b669996cfc780635ce026 | https://github.com/Brickser/cogdl/tree/3952dd11075634cc0f3b669996cfc780635ce026 |
TensorClampOptionMin | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Akababa/torch2trt | TensorClampOptionMin | false | 18,431 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
CriticNetwork | import torch
import torch as T
import torch.nn as nn
import torch.optim as optim
class CriticNetwork(nn.Module):
def __init__(self, beta, state_dim, action_dim, fc1_dim, fc2_dim):
super(CriticNetwork, self).__init__()
self.fc1 = nn.Linear(state_dim + action_dim, fc1_dim)
self.ln1 = nn.Lay... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | indigoLovee/TD3 | CriticNetwork | false | 6,890 | [
"MIT"
] | 1 | 0e86a40c27ec376b52e9f8e0e70db28e7411276b | https://github.com/indigoLovee/TD3/tree/0e86a40c27ec376b52e9f8e0e70db28e7411276b |
GMoF_unscaled | import torch
import torch.nn as nn
class GMoF_unscaled(nn.Module):
def __init__(self, rho=1):
super(GMoF_unscaled, self).__init__()
self.rho = rho
def extra_repr(self):
return 'rho = {}'.format(self.rho)
def forward(self, residual):
squared_res = residual ** 2
di... | 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... | sanweiliti/HMP | GMoF_unscaled | false | 16,374 | [
"MIT"
] | 92 | 3d1a96ec86a72396349daa9f8dde9b2e5a3fc578 | https://github.com/sanweiliti/HMP/tree/3d1a96ec86a72396349daa9f8dde9b2e5a3fc578 |
LayerNorm | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-05):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
se... | 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.utils.data
assert_size_stride = torch._C._dyn... | roedoejet/vits | LayerNorm | false | 10,790 | [
"MIT"
] | 0 | 982e3632c876562563bc74c37d485eaf53715ecc | https://github.com/roedoejet/vits/tree/982e3632c876562563bc74c37d485eaf53715ecc |
CNNLayer | # 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.... | NUSTM/PyTorch-DNN | CNNLayer | false | 17,731 | [
"MIT"
] | 5 | 3cea33380df60e5db307cab50f273efe9ac445c1 | https://github.com/NUSTM/PyTorch-DNN/tree/3cea33380df60e5db307cab50f273efe9ac445c1 |
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_... | Weiyuhong-1998/DI-engine | AvgPool2dSame | false | 14,582 | [
"Apache-2.0"
] | 464 | 88658ea358298c6e61e95a454284b8853a3e9484 | https://github.com/Weiyuhong-1998/DI-engine/tree/88658ea358298c6e61e95a454284b8853a3e9484 |
FrameMaxPool | # 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.parallel
impo... | EGO4D/episodic-memory | FrameMaxPool | false | 8,076 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
FM | import torch
from torch import nn
class FM(nn.Module):
"""Factorization Machine models pairwise (order-2) feature interactions
without linear term and bias.
Input shape
- 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
Output shape
- 2D tensor with shape: ``(batc... | 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... | Jie-Yuan/Deeps | FM | false | 17,486 | [
"MIT"
] | 4 | b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 | https://github.com/Jie-Yuan/Deeps/tree/b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 |
MaxPool2dDynamicSamePadding | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | BigFishMaster/tnt | MaxPool2dDynamicSamePadding | false | 17,509 | [
"BSD-3-Clause"
] | 3 | 8b80bb3b194eb87ac18924428ef0924c2fb263c5 | https://github.com/BigFishMaster/tnt/tree/8b80bb3b194eb87ac18924428ef0924c2fb263c5 |
SimpleTwoLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | euidong/ML | SimpleTwoLayer | false | 10,052 | [
"Apache-2.0"
] | 0 | 7e28b6e52c4c145aa6f8342714f16f7fd8880d9b | https://github.com/euidong/ML/tree/7e28b6e52c4c145aa6f8342714f16f7fd8880d9b |
BalancedL1Loss | # 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 functools
impor... | CityU-AIM-Group/HTD | BalancedL1Loss | false | 17,132 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | tassotirap/data-science | Net | false | 13,032 | [
"Apache-2.0"
] | 0 | 644bc351740cda90c0d8c907132d9da9630266c9 | https://github.com/tassotirap/data-science/tree/644bc351740cda90c0d8c907132d9da9630266c9 |
Qnet | # 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 random
import torch.nn... | rainwangphy/minimalRL | Qnet | false | 10,644 | [
"MIT"
] | 0 | 646cc771107f1b15098d7f52f0e7c4444862fb90 | https://github.com/rainwangphy/minimalRL/tree/646cc771107f1b15098d7f52f0e7c4444862fb90 |
Swish | import torch
import torch.nn
import torch.nn as nn
class Swish(nn.Module):
"""Applies the element-wise function:
.. math::
\\text{Swish}(x) = x * \\text{Sigmoid}(\\alpha * x) for constant value alpha.
Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | danielschulz/MONAI | Swish | false | 1,772 | [
"Apache-2.0"
] | 0 | 54ef6e9e700f0de3d50184c0148f953be871a58e | https://github.com/danielschulz/MONAI/tree/54ef6e9e700f0de3d50184c0148f953be871a58e |
LR | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Brikwerk/learn2learn | LR | false | 13,418 | [
"MIT"
] | 1,774 | 7997c13c26ec627d13ce77ba98427260df78ada8 | https://github.com/Brikwerk/learn2learn/tree/7997c13c26ec627d13ce77ba98427260df78ada8 |
ReflectionPad3d | # 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.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | giuliabaldini/Pix2PixNIfTI | ReflectionPad3d | false | 3,542 | [
"BSD-3-Clause"
] | 0 | 59ff825760f682d2734bd5e95503a03f80d32414 | https://github.com/giuliabaldini/Pix2PixNIfTI/tree/59ff825760f682d2734bd5e95503a03f80d32414 |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | gurucharanmk/Fruits-360_Image_Classification | FocalLoss | false | 10,129 | [
"MIT"
] | 0 | 9d26bba972ed3eca762ff225b33bd70e82edc7f0 | https://github.com/gurucharanmk/Fruits-360_Image_Classification/tree/9d26bba972ed3eca762ff225b33bd70e82edc7f0 |
SelfAttention | import torch
from torch import nn as nn
import torch.utils.data.distributed
class SelfAttention(nn.Module):
"""
Self SelfAttention Layer
Given $X\\in \\mathbb{R}^{n imes in_feature}$, the attention is calculated by: $a=Softmax(W_2tanh(W_1X))$, where
$W_1 \\in \\mathbb{R}^{hidden imes in_fea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | MatthewMasters/grover | SelfAttention | false | 836 | [
"MIT"
] | 0 | 737a340754bc4c63134ef84019a0a84023fd69a3 | https://github.com/MatthewMasters/grover/tree/737a340754bc4c63134ef84019a0a84023fd69a3 |
ShuffleBlock | import torch
import torch.nn as nn
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.size(... | 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... | Fede112/pytorch-cifar | ShuffleBlock | false | 452 | [
"MIT"
] | 0 | 2e89028e9aaabb22dd944f0002d29548059c24df | https://github.com/Fede112/pytorch-cifar/tree/2e89028e9aaabb22dd944f0002d29548059c24df |
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... | DazhiZhong/MiDaS | AddReadout | false | 11,378 | [
"MIT"
] | 0 | e8bafa9c0cf6d2a9d940d2dc36f0ea28a75e5809 | https://github.com/DazhiZhong/MiDaS/tree/e8bafa9c0cf6d2a9d940d2dc36f0ea28a75e5809 |
ExampleBackbone | # 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.nn.parallel
import torch.optim
import torch.u... | BostonCrayfish/mmsegmentation | ExampleBackbone | false | 171 | [
"Apache-2.0"
] | 0 | e8b87242b877bfe0c32ea2630c2fd08977d7dd4b | https://github.com/BostonCrayfish/mmsegmentation/tree/e8b87242b877bfe0c32ea2630c2fd08977d7dd4b |
ActorDiscrete | # 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.... | iffiX/machin | ActorDiscrete | false | 15,597 | [
"MIT"
] | 287 | 7fa986b1bafdefff117d6ff73d14644a5488de9d | https://github.com/iffiX/machin/tree/7fa986b1bafdefff117d6ff73d14644a5488de9d |
CMDS_Loss | # 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
from torch import n... | Gustoaxel/Cells_Cycle | CMDS_Loss | false | 11,456 | [
"MIT"
] | 0 | d211dea8c05a8d5535e6e72d95c781d6bc02baeb | https://github.com/Gustoaxel/Cells_Cycle/tree/d211dea8c05a8d5535e6e72d95c781d6bc02baeb |
Stack | import torch
import torch.utils
import torch.utils.data
class Stack(torch.nn.Module):
def __init__(self, repeats):
super(Stack, self).__init__()
self.repeats = repeats
def forward(self, x):
x = torch.repeat_interleave(x, self.repeats, dim=1)
return x
def get_inputs():
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
import torch.utils
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | angusl95/darts-kbc | Stack | false | 1,437 | [
"Apache-2.0"
] | 0 | 85fc6f4bdb7ba73c07d96ce47e96634599b346f9 | https://github.com/angusl95/darts-kbc/tree/85fc6f4bdb7ba73c07d96ce47e96634599b346f9 |
PreNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
self.p = dropout
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | Rongjiehuang/Multiband-WaveRNN | PreNet | false | 8,733 | [
"MIT"
] | 18 | 432e449678220eed841fcb4971415e2e0ac4d9bb | https://github.com/Rongjiehuang/Multiband-WaveRNN/tree/432e449678220eed841fcb4971415e2e0ac4d9bb |
WordAttentionPool | # 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.... | CFM-MSG/Code_LEORN | WordAttentionPool | false | 6,030 | [
"MIT"
] | 1 | fabea1e1ded973a4db692e51e2df442bde55f626 | https://github.com/CFM-MSG/Code_LEORN/tree/fabea1e1ded973a4db692e51e2df442bde55f626 |
CQAttention | import torch
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
def mask_logits(inputs, mask, mask_value=-1e+30):
mask = mask.type(torch.float32)
return inputs + (1.0 - mask) * mask_value
class Conv1D(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, paddin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | IsaacChanghau/VSLNet | CQAttention | false | 13,885 | [
"MIT"
] | 62 | 3793c625f2e251a5f19a0d59f0c83b12e386f808 | https://github.com/IsaacChanghau/VSLNet/tree/3793c625f2e251a5f19a0d59f0c83b12e386f808 |
MultiHeadAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
def forward(self, query, key, value, mask=None):
dk = query.size()[-1]
scores = query.matmul(key.transpose(-2, -1)) / math.sqrt(dk)
if mask is not None:
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
from torch._inductor.runtime.... | dukeNashor/CaptainStony | MultiHeadAttention | false | 1,878 | [
"MIT"
] | 0 | 6320a27420e686666a4d7172437cf55fe42de2b6 | https://github.com/dukeNashor/CaptainStony/tree/6320a27420e686666a4d7172437cf55fe42de2b6 |
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_... | AdityaKane2001/answersheet_automation | ClassificationModel | false | 8,891 | [
"Apache-2.0"
] | 0 | f7f30a514f94bfbdb68ab43a3dfc6e3fd770e8f1 | https://github.com/AdityaKane2001/answersheet_automation/tree/f7f30a514f94bfbdb68ab43a3dfc6e3fd770e8f1 |
equalized_conv2d | import torch
import torch.nn as nn
from torch.nn.init import normal
import torch.utils.data
def _calculate_fan_in_and_fan_out(tensor):
dimensions = tensor.ndimension()
if dimensions < 2:
raise ValueError(
'Fan in and fan out can not be computed for tensor with less than 2 dimensions'
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.init import normal
import torch.utils.data
a... | mingo-x/pggan-pytorch | equalized_conv2d | false | 7,232 | [
"MIT"
] | 1 | a1dde73cd4df52476fe7c948d81fa9caea8070a5 | https://github.com/mingo-x/pggan-pytorch/tree/a1dde73cd4df52476fe7c948d81fa9caea8070a5 |
ComputeDeltas | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | tbright17/audio | ComputeDeltas | false | 10,938 | [
"BSD-2-Clause"
] | 0 | 00d38203e401b8d9472a8f8394a10e2c309be02c | https://github.com/tbright17/audio/tree/00d38203e401b8d9472a8f8394a10e2c309be02c |
Softplus | import torch
import torch.onnx
import torch.nn as nn
class Softplus(nn.Module):
def forward(self, x):
return torch.nn.Softplus()(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.triton_helpers import libdevice, math as tl_math
import torch.onnx
import torch.nn as nn
assert_size_stride = t... | mil-tokyo/webdnn | Softplus | false | 16,160 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
MinibatchStdLayer | import torch
import torch.nn as nn
class MinibatchStdLayer(nn.Module):
def __init__(self, group_size=4):
super().__init__()
self.group_size = group_size
def forward(self, x):
group_size = min(self.group_size, x.shape[0])
s = x.shape
y = x.view([group_size, -1, s[1], s... | 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_... | bg459/gan-ensembling-loader | MinibatchStdLayer | false | 14,960 | [
"MIT"
] | 86 | 5ff6fae5fd5ced0a48ef2cd3dcb1d74aa1dadce8 | https://github.com/bg459/gan-ensembling-loader/tree/5ff6fae5fd5ced0a48ef2cd3dcb1d74aa1dadce8 |
LittleAdversarialNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = ... | FigaroK/pytorch-CycleGAN-and-pix2pix | LittleAdversarialNetwork | false | 9,070 | [
"BSD-3-Clause"
] | 0 | 74407363baf4626782398040e34a342e20915d41 | https://github.com/FigaroK/pytorch-CycleGAN-and-pix2pix/tree/74407363baf4626782398040e34a342e20915d41 |
PositionwiseFeedForward | import torch
import torch.nn as nn
from torch.nn import functional as F
class PointwiseConv(nn.Module):
"""
Pointwise Convolution (1x1 Conv)
Convolution 1 Dimension (Faster version)
(cf. https://github.com/huggingface/pytorch-openai-transformer-lm/blob/ eafc28abdfadfa0732f03a0fc65805c5bfb2ffe7... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 to... | hamishivi/claf | PositionwiseFeedForward | false | 3,563 | [
"MIT"
] | 0 | 8e35f30e3fc4a45a45cc0766eb6ab55a6ba3f0c2 | https://github.com/hamishivi/claf/tree/8e35f30e3fc4a45a45cc0766eb6ab55a6ba3f0c2 |
OcrPtrNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | junj2ejj/sam-textvqa | OcrPtrNet | false | 15,744 | [
"W3C"
] | 48 | 6bf646d741fb2536e3a8f331c78b594f6199df15 | https://github.com/junj2ejj/sam-textvqa/tree/6bf646d741fb2536e3a8f331c78b594f6199df15 |
NumericLabelEncoder | import torch
from abc import ABC
class BaseOperator(ABC):
"""
Abstract class defining the basic structure for operator implementations in Hummingbird.
"""
def __init__(self, regression=False, classification=False, transformer=
False, anomaly_detection=False, **kwargs):
"""
Arg... | 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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | hannahaih/hummingbird | NumericLabelEncoder | false | 6,781 | [
"MIT"
] | 1 | b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 | https://github.com/hannahaih/hummingbird/tree/b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 |
ScaledLeakyReLU | import math
import torch
from torch.nn import functional as F
from torch import nn
class ScaledLeakyReLU(nn.Module):
def __init__(self, negative_slope=0.2):
super().__init__()
self.negative_slope = negative_slope
def forward(self, input):
out = F.leaky_relu(input, negative_slope=self... | 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... | BillyXYB/TransEditor | ScaledLeakyReLU | false | 17,067 | [
"MIT"
] | 4 | 0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 | https://github.com/BillyXYB/TransEditor/tree/0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 |
Channel_mean | import torch
import torch.nn as nn
class Channel_mean(nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, V):
"""
only V[0]
"""
return torch.sum(V[0], dim=0).squeeze()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | wk989898/ARES-implement | Channel_mean | false | 4,536 | [
"MIT"
] | 0 | b2411be01124feaccbc89d74f6025fbfa584bb3f | https://github.com/wk989898/ARES-implement/tree/b2411be01124feaccbc89d74f6025fbfa584bb3f |
SP | # 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.... | JDAI-CV/CM-NAS | SP | false | 8,305 | [
"Apache-2.0"
] | 31 | bbc77f427b2c8afb9f3865f5a04e86079d33dd28 | https://github.com/JDAI-CV/CM-NAS/tree/bbc77f427b2c8afb9f3865f5a04e86079d33dd28 |
TranspConv3DBlock | # 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... | Siyuan89/self-attention-cv | TranspConv3DBlock | false | 14,427 | [
"MIT"
] | 759 | b39cde2fb68e05351bf3bc8048f4af13bbab256a | https://github.com/Siyuan89/self-attention-cv/tree/b39cde2fb68e05351bf3bc8048f4af13bbab256a |
FusedLeakyReLU | import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
if bias is not None:
rest_dim = [1] * (input.ndim - bias.ndim - 1)
return F.leaky_relu(input + bias.view(1, bias.shape[0], *rest_dim),
nega... | 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 import functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | RaghavaDhanya/stylegan2-pytorch | FusedLeakyReLU | false | 11,800 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | 6a2b0c228c56830a7e669bc66dc92073d3c81ca8 | https://github.com/RaghavaDhanya/stylegan2-pytorch/tree/6a2b0c228c56830a7e669bc66dc92073d3c81ca8 |
MultiheadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(self, embed_dim, num_heads, attn_dropout=0.0, bias=True,
add_bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Redaimao/RERD | MultiheadAttention | false | 991 | [
"MIT"
] | 0 | 40413d4b6743f3e5db0c30ee969d45661d001834 | https://github.com/Redaimao/RERD/tree/40413d4b6743f3e5db0c30ee969d45661d001834 |
Classifier | import torch
import torch.distributed
import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, hidden_size):
super(Classifier, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = 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
import torch.distributed
import torch
import torch.nn as nn
assert_size_stride =... | BoonthichaSaejia/ThaiSum | Classifier | false | 7,809 | [
"Apache-2.0"
] | 23 | fdb99eab23e60a933acf4e84836f53ddf05b7c8b | https://github.com/BoonthichaSaejia/ThaiSum/tree/fdb99eab23e60a933acf4e84836f53ddf05b7c8b |
BilinearConvLayer | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | m-dml/lil2021swe | BilinearConvLayer | false | 7,152 | [
"Apache-2.0"
] | 1 | 45352f214ec28c9f91dd24ed3669f492d8b68382 | https://github.com/m-dml/lil2021swe/tree/45352f214ec28c9f91dd24ed3669f492d8b68382 |
SeparableBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn import Linear
assert_size_stride = tor... | andreasjansson/hyperstyle | SeparableBlock | false | 3,137 | [
"MIT"
] | 0 | d9847c76dd75da129a60bf995534ff6e71cbbaa6 | https://github.com/andreasjansson/hyperstyle/tree/d9847c76dd75da129a60bf995534ff6e71cbbaa6 |
InputInjection | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class InputInjection(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
super(InputInjection, self).__init__()
self.pool = nn.ModuleList()
for i in range(num_downsampling)... | 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... | ImportPaddle/APCNet | InputInjection | false | 2,365 | [
"MIT"
] | 0 | 68ade1f83827b4cdd60ee4b6ac25454397100316 | https://github.com/ImportPaddle/APCNet/tree/68ade1f83827b4cdd60ee4b6ac25454397100316 |
Decoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Decoder(nn.Module):
def __init__(self, input_shape, output_shape):
super(Decoder, self).__init__()
self.input_shape = input_shape
self.decoder_out_shape = output_shape
self.linear_one = nn.Linear(self.input_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 as nn
assert_... | akaprasanga/AutoEncoder | Decoder | false | 3,059 | [
"MIT"
] | 0 | a1562a7a720c199b717796e469b9957eb958264a | https://github.com/akaprasanga/AutoEncoder/tree/a1562a7a720c199b717796e469b9957eb958264a |
ChebConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | phil-hawkins/deepsphere-pytorch | ChebConv | false | 16,261 | [
"MIT"
] | 99 | f23c531445b3ddf234c7e98cdadb010163051e6d | https://github.com/phil-hawkins/deepsphere-pytorch/tree/f23c531445b3ddf234c7e98cdadb010163051e6d |
Softsign | # 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.onnx
import torch.nn as nn
assert_size_stride = torch._C._dy... | mil-tokyo/webdnn | Softsign | false | 16,082 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda
import torch.distributed
import torch.multiprocessing
class FocalLoss(nn.Module):
"""Focal Loss - https://arxiv.org/abs/1708.02002"""
def __init__(self, alpha=0.25, gamma=2):
super().__init__()
self.alpha = a... | 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... | azuredsky/retinanet-examples | FocalLoss | false | 9,794 | [
"BSD-3-Clause"
] | 0 | 1b35d8e7d3360050f25fd80e09ecac3eb2654301 | https://github.com/azuredsky/retinanet-examples/tree/1b35d8e7d3360050f25fd80e09ecac3eb2654301 |
Up | # 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 ... | Smith42/unet-pytorch | Up | false | 9,550 | [
"MIT"
] | 0 | 45a0459da69cee7f57fb369a8e2fc58668d81167 | https://github.com/Smith42/unet-pytorch/tree/45a0459da69cee7f57fb369a8e2fc58668d81167 |
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