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 |
|---|---|---|---|---|---|---|---|---|---|---|
ExgLayer | import torch
import torch.nn as nn
class ExgLayer(nn.Module):
def __init__(self, x_size, h_size, g_size, out_size):
super(ExgLayer, self).__init__()
self.h_size = h_size
self.g_size = g_size
self.out_size = out_size
self.x_size = x_size
self.linear_x2 = nn.Linear(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ayyyq/T-LSTM | ExgLayer | false | 6,301 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
FractionProposalModel | import torch
from torch import nn
from torch.nn import functional as F
class FractionProposalModel(nn.Module):
def __init__(self, in_dim, out_dim):
super(FractionProposalModel, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.layer = nn.Linear(self.in_dim, self.ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | alirezakazemipour/Distributional-RL | FractionProposalModel | false | 1,424 | [
"MIT"
] | 0 | a3de3a1707bdd57a420f85c6d64a3fb84fb075af | https://github.com/alirezakazemipour/Distributional-RL/tree/a3de3a1707bdd57a420f85c6d64a3fb84fb075af |
RelativeScalePredictor | import torch
from torch import nn
class RelativeScalePredictor(nn.Module):
def __init__(self, in_size, out_size):
super(RelativeScalePredictor, self).__init__()
self.predictor = nn.Linear(in_size, out_size)
def forward(self, feat):
predictions = self.predictor.forward(feat) + 1
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JasonQSY/Associative3D | RelativeScalePredictor | false | 8,342 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
Conv2d | from torch.autograd import Function
import torch
import numpy as np
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
def _setup_kernel(k):
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
assert k.ndim == 2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import numpy as np
import torchvision.transf... | DeepTitan/PNDM | Conv2d | false | 13,934 | [
"Apache-2.0"
] | 61 | 4037a4f40011c9a0d47b92303e64d47fcc7ed56a | https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a |
softmax_with_multiuse_input | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.cuda
impo... | JudeDavis1/intel-extension-for-pytorch | softmax_with_multiuse_input | false | 2,593 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
ResnetBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
import torch.utils.data
imp... | MiaoyunZhao/GANTransferLimitedData | ResnetBlock | false | 8,539 | [
"MIT"
] | 41 | 5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 | https://github.com/MiaoyunZhao/GANTransferLimitedData/tree/5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 |
AFTSimple | # 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.... | Datta0/aft-pytorch | AFTSimple | false | 2,134 | [
"MIT"
] | 0 | a0ebad01ea6616b00bde319b0c5e63bea467c400 | https://github.com/Datta0/aft-pytorch/tree/a0ebad01ea6616b00bde319b0c5e63bea467c400 |
ResBlock | # 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.functional as... | derwind/dmfont | ResBlock | false | 15,180 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 |
StyleResidual | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | jinsongpan/NeMo | StyleResidual | false | 10,383 | [
"Apache-2.0"
] | 0 | 27f5f2dc6ecf7e0fd4225eedb2500cee6284e7d7 | https://github.com/jinsongpan/NeMo/tree/27f5f2dc6ecf7e0fd4225eedb2500cee6284e7d7 |
InvertibleChannelMixing1D | from torch.autograd import Function
import torch
from torch import nn
from warnings import warn
def _cayley(A):
I = torch.eye(A.shape[-1], device=A.device)
LU = torch.lu(I + A, pivot=True)
return torch.lu_solve(I - A, *LU)
def _cayley_frechet(A, H, Q=None):
I = torch.eye(A.shape[-1], device=A.device... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
from torch import nn
from warnings import wa... | cetmann/iunets | InvertibleChannelMixing1D | false | 15,015 | [
"MIT"
] | 86 | 80ed7cce0e505a0396c42359eaf27819222d71f6 | https://github.com/cetmann/iunets/tree/80ed7cce0e505a0396c42359eaf27819222d71f6 |
Connect2Model | # 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.... | quangchiem139/AlphaZeroSimple | Connect2Model | false | 16,305 | [
"MIT"
] | 76 | 1b1096cc4b2aded6337a90035aee56b370ea1d3a | https://github.com/quangchiem139/AlphaZeroSimple/tree/1b1096cc4b2aded6337a90035aee56b370ea1d3a |
NormedLinear | # 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.... | raman32/LDAM-DRW | NormedLinear | false | 7,534 | [
"MIT"
] | 1 | 7ce2251c01b94c7259108a1e188457f0b720651d | https://github.com/raman32/LDAM-DRW/tree/7ce2251c01b94c7259108a1e188457f0b720651d |
Residual | # 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.... | RicCu/NeuralStyle | Residual | false | 5,774 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
ConvGLU | # 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.cuda
from torch import nn
import torch.distributed
import torch.uti... | hamjam/NeMo | ConvGLU | false | 15,488 | [
"Apache-2.0"
] | 4,145 | b3484d32e1317666151f931bfa39867d88ed8658 | https://github.com/hamjam/NeMo/tree/b3484d32e1317666151f931bfa39867d88ed8658 |
MHAttentionMap | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch._utils
class MHAttentionMap(nn.Module):
"""This is a 2D attention module, which only returns the attention softmax (no multiplication by value)"""
def __init__(self, query_dim, hidden_dim, num_heads=1, dropout=0.0,
b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | dingmyu/mmclassification | MHAttentionMap | false | 12,290 | [
"Apache-2.0"
] | 0 | c600b22907fb9423899f7c308c659168c2d01cd8 | https://github.com/dingmyu/mmclassification/tree/c600b22907fb9423899f7c308c659168c2d01cd8 |
UpSample | # 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 import nn
import t... | Hadryan/nn | UpSample | false | 9,384 | [
"MIT"
] | 0 | b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d | https://github.com/Hadryan/nn/tree/b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d |
LayerCake | # 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... | Saran-nns/delve | LayerCake | false | 1,021 | [
"MIT"
] | 0 | 3489d8aa13181b392d3c47a19f9d9a47d87f8790 | https://github.com/Saran-nns/delve/tree/3489d8aa13181b392d3c47a19f9d9a47d87f8790 |
BiLinearSim | # 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.optim.lr_scheduler import *
assert_size_stride = torch._C._dynamo.gua... | kiminh/mt-dnn | BiLinearSim | false | 7,022 | [
"MIT"
] | 1 | 133884b380244dbe74acc4d7507e551b2c5035b3 | https://github.com/kiminh/mt-dnn/tree/133884b380244dbe74acc4d7507e551b2c5035b3 |
CXLoss | # 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.... | drgripa1/deepvecfont | CXLoss | false | 15,263 | [
"MIT"
] | 68 | a44d81ba19a22e43b4e576cd8ebc5c2fd961a621 | https://github.com/drgripa1/deepvecfont/tree/a44d81ba19a22e43b4e576cd8ebc5c2fd961a621 |
ImgAttention | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import *
import torch.nn.init
class ImgAttention(nn.Module):
def __init__(self, opt):
super(ImgAttention, self).__init__()
self.rnn_size = opt.rnn_size
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.... | WuJie1010/Fine-Grained-Image-Captioning | ImgAttention | false | 18,066 | [
"MIT"
] | 9 | 340bc1868634f3bf0fdd62d439fec32ee1b45407 | https://github.com/WuJie1010/Fine-Grained-Image-Captioning/tree/340bc1868634f3bf0fdd62d439fec32ee1b45407 |
SoftArgmax2D | import torch
import torch.nn as nn
from typing import Optional
def create_meshgrid(x: 'torch.Tensor', normalized_coordinates: 'Optional[bool]'
) ->torch.Tensor:
assert len(x.shape) == 4, x.shape
_, _, height, width = x.shape
_device, _dtype = x.device, x.dtype
if normalized_coordinates:
xs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | danyayay/ynet_adaptive | SoftArgmax2D | false | 1,792 | [
"MIT"
] | 0 | f1daea6f3d5ec8a7349c2ee72bf742df83786103 | https://github.com/danyayay/ynet_adaptive/tree/f1daea6f3d5ec8a7349c2ee72bf742df83786103 |
BinaryLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.distributions
import torch.... | AlexMeinke/Provable-OOD-Detection | BinaryLoss | false | 7,672 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
ActorCritic | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorCritic(nn.Module):
def __init__(self, num_inputs, num_outputs, hidden=64):
super(ActorCritic, self).__init__()
self.affine1 = nn.Linear(num_inputs, hidden)
self.affine2 = nn.Linear(hidden, hidden)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | tpbarron/pytorch-ppo | ActorCritic | false | 16,617 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
LinearWithGroupNorm | # 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.... | MCZhi/nuplan-devkit | LinearWithGroupNorm | false | 795 | [
"Apache-2.0"
] | 0 | 3c4f5b8dcd517b27cfd258915ca5fe5c54e3cb0c | https://github.com/MCZhi/nuplan-devkit/tree/3c4f5b8dcd517b27cfd258915ca5fe5c54e3cb0c |
Highway | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils
import torch.onnx
class Highway(nn.Module):
def __init__(self, e_word):
super(Highway, self).__init__()
self.embed_size = e_word
self.w_proj = nn.Linear(self.embed_size, self.embed_size, bias=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | evazhang612/honygenerator | Highway | false | 6,662 | [
"MIT"
] | 1 | cafcf1736faba978ecaed624b949ebc1498477ee | https://github.com/evazhang612/honygenerator/tree/cafcf1736faba978ecaed624b949ebc1498477ee |
ConvTemporalGraphical | import torch
import torch.nn as nn
class ConvTemporalGraphical(nn.Module):
"""The basic module for applying a graph convolution.
Args:
in_channels (int): Number of channels in the input sequence data
out_channels (int): Number of channels produced by the convolution
kernel_size (int):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Alexis-Fab/mmaction2 | ConvTemporalGraphical | false | 11,221 | [
"Apache-2.0"
] | 0 | 6f76bb465a7164f907318cf58f77fc3d613f8f0f | https://github.com/Alexis-Fab/mmaction2/tree/6f76bb465a7164f907318cf58f77fc3d613f8f0f |
Anomaly | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | sccc19/anomalydetector | Anomaly | false | 16,440 | [
"MIT"
] | 180 | a963ef8d7f30971e99d21a748d059e26f2163b09 | https://github.com/sccc19/anomalydetector/tree/a963ef8d7f30971e99d21a748d059e26f2163b09 |
_Decoder | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class _Decoder(nn.Module):
def __init__(self, z_dim):
super(_Decoder, self).__init__()
self.fc1 = nn.Linear(z_dim, 600)
self.fc2 = nn.Linear(600, 600)
self.fc3 = nn.Linear(600, 784)
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | mori97/revae | _Decoder | false | 4,034 | [
"MIT"
] | 0 | 465009076a9be78e8ddb9021a0699b32fc695f30 | https://github.com/mori97/revae/tree/465009076a9be78e8ddb9021a0699b32fc695f30 |
CNN | # 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 ... | igorvlnascimento/DeepREF | CNN | false | 3,649 | [
"MIT"
] | 0 | 0fed8120571e44e12ee3d1861289bc101c0a275f | https://github.com/igorvlnascimento/DeepREF/tree/0fed8120571e44e12ee3d1861289bc101c0a275f |
SimpleSumModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleSumModule | false | 14,687 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
GatedConv2d | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
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.triton_helpers import libdevice, math as tl_math
im... | kangzhiq/DeepFillv2_Pytorch | GatedConv2d | false | 10,438 | [
"MIT"
] | 0 | 9c7ed61b25bb995713f89108b712490737abe1b1 | https://github.com/kangzhiq/DeepFillv2_Pytorch/tree/9c7ed61b25bb995713f89108b712490737abe1b1 |
LayerNorm | import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
var = torch.var(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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | psemchyshyn/diffusion_reconstruction | LayerNorm | false | 12,938 | [
"MIT"
] | 0 | c7ccc8c9f47c858606a46c2c29fcb64016565b4e | https://github.com/psemchyshyn/diffusion_reconstruction/tree/c7ccc8c9f47c858606a46c2c29fcb64016565b4e |
SubPixelConvolutionalBlock | # 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... | f74066357/SR | SubPixelConvolutionalBlock | false | 3,497 | [
"MIT"
] | 0 | 374ac141dfbfb4f851379d1c3c7c7f6bf1a21c67 | https://github.com/f74066357/SR/tree/374ac141dfbfb4f851379d1c3c7c7f6bf1a21c67 |
Elu | import torch
import torch.fx
class Elu(torch.nn.Module):
def __init__(self):
super(Elu, self).__init__()
self.elu = torch.nn.ELU()
def forward(self, x):
return self.elu(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
import torch.fx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | NVIDIA/Torch-TensorRT | Elu | false | 14,070 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 |
PositionalEncoding | import math
import torch
from torch import nn
class PositionalEncoding(nn.Module):
def __init__(self, dimension: 'int', dropout: 'float'=0.1):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.dimension = dimension
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | IMDxD/NonAttentiveTacotron | PositionalEncoding | false | 17,416 | [
"MIT"
] | 4 | a227fba1bdfa4c5ec63a0f0364313f3ac0fef1ba | https://github.com/IMDxD/NonAttentiveTacotron/tree/a227fba1bdfa4c5ec63a0f0364313f3ac0fef1ba |
BCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BCELoss(nn.Module):
"""Binary Cross Entropy loss."""
def __init__(self, use_target_weight=False, loss_weight=1.0):
super().__init__()
self.criterion = F.binary_cross_entropy
self.use_target_weight = use_target_we... | 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... | atoaiari/mmpose | BCELoss | false | 6,266 | [
"Apache-2.0"
] | 1 | 256a9117767008e8c33b4038a346aca12233e300 | https://github.com/atoaiari/mmpose/tree/256a9117767008e8c33b4038a346aca12233e300 |
SampaddingConv1D | import torch
import torch.nn as nn
class SampaddingConv1D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, use_bias=True):
super(SampaddingConv1D, self).__init__()
self.use_bias = use_bias
self.padding = nn.ConstantPad1d((int((kernel_size - 1) / 2), int(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | amoonfana/Knowledge_Distillation | SampaddingConv1D | false | 6,192 | [
"Apache-2.0"
] | 1 | 1ee814a8f70ae00d17e1e1ee778d5420d96c43c4 | https://github.com/amoonfana/Knowledge_Distillation/tree/1ee814a8f70ae00d17e1e1ee778d5420d96c43c4 |
Value | # 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 ... | zwc662/Safe_GAIL | Value | false | 13,182 | [
"MIT"
] | 0 | 536dd73c91d277b418ef04efdd42aa6c87fdad33 | https://github.com/zwc662/Safe_GAIL/tree/536dd73c91d277b418ef04efdd42aa6c87fdad33 |
L1_Charbonnier_loss_color | # 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.utils.data
from torch.nn.modules.loss import _Loss
assert_size_str... | YDDDDG/3D2Unet | L1_Charbonnier_loss_color | false | 6,001 | [
"MIT"
] | 1 | daca056958fb2ae319dc18a350e04b3cefe0d99f | https://github.com/YDDDDG/3D2Unet/tree/daca056958fb2ae319dc18a350e04b3cefe0d99f |
LabelwiseAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class LabelwiseAttention(nn.Module):
"""Applies attention technique to summarize the sequence for each label
See `Explainable Prediction of Medical Codes from Clinical Text <https://aclanthology.org/N18-1100.pdf>`_
Args:
input_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JamesLYC88/LibMultiLabel | LabelwiseAttention | false | 9,161 | [
"MIT"
] | 0 | 042b76b3564409d916cf735ace617319009ae118 | https://github.com/JamesLYC88/LibMultiLabel/tree/042b76b3564409d916cf735ace617319009ae118 |
IReLU | # 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 math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | rupumped/DFL | IReLU | false | 4,208 | [
"BSD-3-Clause"
] | 0 | a4e4d96b7ce7522cf7fee3c2cfdbb54eb7a473f2 | https://github.com/rupumped/DFL/tree/a4e4d96b7ce7522cf7fee3c2cfdbb54eb7a473f2 |
AngularPenaltySMLoss | # 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.... | Seb-Good/physionet-challenge-2020 | AngularPenaltySMLoss | false | 8,810 | [
"BSD-2-Clause"
] | 13 | c6f1648a148335babc0a26d8a589120616327548 | https://github.com/Seb-Good/physionet-challenge-2020/tree/c6f1648a148335babc0a26d8a589120616327548 |
SelfAttentionUnit | import torch
from torch import nn
class SelfAttentionUnit(nn.Module):
def __init__(self, embed_dim, num_heads, max_len, dropout=0.8, bias=
False, skip_connection=True):
super(SelfAttentionUnit, self).__init__()
self.skip_connection = skip_connection
self.attn = nn.MultiheadAttenti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | dohnlee/qufa2021 | SelfAttentionUnit | false | 12,304 | [
"MIT"
] | 0 | 5fb42caee09ec228358e49768e32c75e3c0094ce | https://github.com/dohnlee/qufa2021/tree/5fb42caee09ec228358e49768e32c75e3c0094ce |
ClassHead | import torch
from itertools import product as product
import torch.nn as nn
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import product as product
import torch.nn as nn
assert_size_strid... | BossunWang/Pytorch_Retinaface | ClassHead | false | 8,983 | [
"MIT"
] | 0 | 01ec6cfbcced1e8cc8802084e4e566ccaf2513a8 | https://github.com/BossunWang/Pytorch_Retinaface/tree/01ec6cfbcced1e8cc8802084e4e566ccaf2513a8 |
Downsample | import torch
import torch.nn as nn
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Liujingxiu23/guided-diffusion | Downsample | false | 5,545 | [
"MIT"
] | 1 | 0ba878e517b276c45d1195eb29f6f5f72659a05b | https://github.com/Liujingxiu23/guided-diffusion/tree/0ba878e517b276c45d1195eb29f6f5f72659a05b |
C1 | # 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... | devillove084/DeepSignal | C1 | false | 12,263 | [
"MIT"
] | 0 | 1fe122b32752b11e10ca4bef3d07ddd7de4348b5 | https://github.com/devillove084/DeepSignal/tree/1fe122b32752b11e10ca4bef3d07ddd7de4348b5 |
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.... | EVA4-RS-Group/Phase2 | Attention | false | 414 | [
"Apache-2.0"
] | 0 | 7c551e3894979cc425dd51baeddbfa5a51b7878d | https://github.com/EVA4-RS-Group/Phase2/tree/7c551e3894979cc425dd51baeddbfa5a51b7878d |
AE | import torch
import torch.nn as nn
class AE(nn.Module):
def __init__(self):
super(AE, self).__init__()
self.leaky_reLU = nn.LeakyReLU(0.2)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=1,
return_indices=True)
self.unpool = nn.MaxUnpool2d(kernel_size=2, stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | personwhofloat/Line-Segmentation-Model | AE | false | 7,576 | [
"MIT"
] | 1 | f00b65c7914f44fa31e14d41120903d0da2d5496 | https://github.com/personwhofloat/Line-Segmentation-Model/tree/f00b65c7914f44fa31e14d41120903d0da2d5496 |
NeuralNetwork | # 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.... | keyshor/homework | NeuralNetwork | false | 12,679 | [
"MIT"
] | 0 | 687f9edf73bbac8fc492dfd82d634c19a38f5aab | https://github.com/keyshor/homework/tree/687f9edf73bbac8fc492dfd82d634c19a38f5aab |
RelateModule | # 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 ... | SpyrosMouselinos/DeltaFormers | RelateModule | false | 5,858 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
ResidualBlock | import torch
from torch import nn
class ConvRelu(nn.Module):
def __init__(self, in_: 'int', out: 'int', activate=True):
super(ConvRelu, self).__init__()
self.activate = activate
self.conv = nn.Conv2d(in_, out, 3, padding=1)
self.activation = nn.ReLU(inplace=True)
def forward(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | haonguyen1107/style_transfer | ResidualBlock | false | 3,574 | [
"MIT"
] | 0 | 8df9b20ce8ebc446cf2c0a67393001b3cf318fed | https://github.com/haonguyen1107/style_transfer/tree/8df9b20ce8ebc446cf2c0a67393001b3cf318fed |
moving_avg | import torch
import torch.nn as nn
class moving_avg(nn.Module):
"""
Moving average block to highlight the trend of time series
"""
def __init__(self, kernel_size, stride):
super(moving_avg, self).__init__()
self.kernel_size = kernel_size
self.avg = nn.AvgPool1d(kernel_size=ker... | 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... | thuml/Autoformer | moving_avg | false | 16,583 | [
"MIT"
] | 263 | 6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab | https://github.com/thuml/Autoformer/tree/6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab |
SpatialAttention | import torch
import torch.nn as nn
class CompressChannels(nn.Module):
"""
Compresses the input channels to 2 by concatenating the results of
Global Average Pooling(GAP) and Global Max Pooling(GMP).
HxWxC => HxWx2
"""
def forward(self, x):
return torch.cat((torch.max(x, 1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Vrushank264/Low-Light-Enhancement | SpatialAttention | false | 5,946 | [
"MIT"
] | 1 | 3c13a10a16eab8183b8fbd0c063d9815b662259a | https://github.com/Vrushank264/Low-Light-Enhancement/tree/3c13a10a16eab8183b8fbd0c063d9815b662259a |
Wide_Conv | # 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 ... | YJiangcm/Chinese-sentence-pair-modeling | Wide_Conv | false | 14,650 | [
"Apache-2.0"
] | 49 | 90adbc5c121832ce3e4a4057e30417a6ec5e7ebc | https://github.com/YJiangcm/Chinese-sentence-pair-modeling/tree/90adbc5c121832ce3e4a4057e30417a6ec5e7ebc |
Net | import torch
from torch import nn
import torch.nn.functional as F
import torch.optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=
3, padding=1)
self.max2 = nn.MaxPool2d(kernel_size=2, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | kawano8811/deep-learning-v2-pytorch | Net | false | 13,232 | [
"MIT"
] | 0 | b7c453728cb85edf3b30e0aeb66b3861747bc043 | https://github.com/kawano8811/deep-learning-v2-pytorch/tree/b7c453728cb85edf3b30e0aeb66b3861747bc043 |
SimpleCosModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCosModule(torch.nn.Module):
def __init__(self):
super(SimpleCosModule, self).__init__()
def forward(self, a):
return torch.cos(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | briancoutinho/glow | SimpleCosModule | false | 12,564 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
Concat | import torch
from torch import nn
import torch.nn
import torch.optim
class Concat(nn.Module):
def forward(self, state: 'torch.Tensor', action: 'torch.Tensor'):
return torch.cat((state, action), dim=-1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inpu... | 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
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda =... | BerenLuthien/ReAgent | Concat | false | 13,389 | [
"BSD-3-Clause"
] | 1,156 | 52f666670a7fa03206812ef48949f6b934d400f7 | https://github.com/BerenLuthien/ReAgent/tree/52f666670a7fa03206812ef48949f6b934d400f7 |
RKDLoss | import torch
from torch import nn
import torch.nn.functional as F
class RKDLoss(nn.Module):
"""Relational Knowledge Disitllation, CVPR2019"""
def __init__(self, w_d=25, w_a=50):
super(RKDLoss, self).__init__()
self.w_d = w_d
self.w_a = w_a
def forward(self, f_s, f_t):
stu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | UBCDingXin/RepDistiller | RKDLoss | false | 14,529 | [
"BSD-2-Clause"
] | 1,347 | dcc043277f2820efafd679ffb82b8e8195b7e222 | https://github.com/UBCDingXin/RepDistiller/tree/dcc043277f2820efafd679ffb82b8e8195b7e222 |
BlendLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | D-hash-code/ffjord | BlendLinear | false | 11,357 | [
"MIT"
] | 0 | 3647ab35537a8bac3b4dc1e45a593819ac8e2c18 | https://github.com/D-hash-code/ffjord/tree/3647ab35537a8bac3b4dc1e45a593819ac8e2c18 |
SimpleCNN | import torch
import torch.nn as nn
from torch.nn import functional as F
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.fc1 = nn.Linear(28 * 28, 500)
self.fc2 = nn.Linear(500, 256)
self.fc3 = nn.Linear(256, 10)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AnweshCR7/autonomous_greenhouse | SimpleCNN | false | 4,867 | [
"MIT"
] | 1 | a29cfe37d0152001d2544216ed65c3472f572b4e | https://github.com/AnweshCR7/autonomous_greenhouse/tree/a29cfe37d0152001d2544216ed65c3472f572b4e |
IOUloss | import torch
import torch.nn as nn
class IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.shape[0]
... | 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... | ankandrew/YOLOX | IOUloss | false | 12,087 | [
"Apache-2.0"
] | 0 | 28da975944887d550f052ebadd8cbdd82d14aed6 | https://github.com/ankandrew/YOLOX/tree/28da975944887d550f052ebadd8cbdd82d14aed6 |
AugCNN | import torch
import torch.nn as nn
import torch.nn.functional as F
def apply_init_(modules):
"""
Initialize NN modules
"""
for m in modules:
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional as F
assert_size_stride = torch... | agarwl/auto-drac | AugCNN | false | 3,034 | [
"MIT"
] | 0 | d86c480b51929e6e4ec0ae1adba84d9f78e91705 | https://github.com/agarwl/auto-drac/tree/d86c480b51929e6e4ec0ae1adba84d9f78e91705 |
DilatedResidualLayer | # 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_... | anonymous-rubick/ms-tcn-bilinear | DilatedResidualLayer | false | 1,454 | [
"MIT"
] | 0 | b95d3ca834dc4811af563d38185acef975970e82 | https://github.com/anonymous-rubick/ms-tcn-bilinear/tree/b95d3ca834dc4811af563d38185acef975970e82 |
DiceBCELoss | import torch
from torch import nn
from torch.nn import functional as F
class DiceBCELoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceBCELoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = torch.sigmoid(inputs)
inputs = inputs.vi... | 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 ... | Michaelistaken/PathPretrain | DiceBCELoss | false | 839 | [
"MIT"
] | 0 | 650b7eb02e67f6d864d81808eb7230c48fe6946a | https://github.com/Michaelistaken/PathPretrain/tree/650b7eb02e67f6d864d81808eb7230c48fe6946a |
GeM | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.functional a... | esha-singh/DL_project | GeM | false | 3,478 | [
"MIT"
] | 0 | 11ac2874845bc3982435cc37f4e0b8896b95660e | https://github.com/esha-singh/DL_project/tree/11ac2874845bc3982435cc37f4e0b8896b95660e |
PVABlock | # 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.... | wu0004in/vedastr | PVABlock | false | 4,561 | [
"Apache-2.0"
] | 0 | 83511a408b68c264561a30daff5154cd0148bebd | https://github.com/wu0004in/vedastr/tree/83511a408b68c264561a30daff5154cd0148bebd |
PositionwiseFeedForward | import torch
import torch.nn as nn
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
self.layer_norm = nn.LayerN... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Jincheng-Sun/Kylearn-pytorch | PositionwiseFeedForward | false | 661 | [
"MIT"
] | 0 | e72f2ab45a3f4724e843a27bec37664d3612fdca | https://github.com/Jincheng-Sun/Kylearn-pytorch/tree/e72f2ab45a3f4724e843a27bec37664d3612fdca |
AdaptiveInstanceNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | zhengqili/Crowdsampling-the-Plenoptic-Function | AdaptiveInstanceNorm | false | 16,807 | [
"MIT"
] | 70 | 3164e9f9574d597690f83dfdfb34cc470d2dcb88 | https://github.com/zhengqili/Crowdsampling-the-Plenoptic-Function/tree/3164e9f9574d597690f83dfdfb34cc470d2dcb88 |
L2N | import torch
import torch.nn as nn
def l2n(x, eps=1e-06):
return x / (torch.norm(x, p=2, dim=1, keepdim=True) + eps).expand_as(x)
class L2N(nn.Module):
def __init__(self, eps=1e-06):
super(L2N, self).__init__()
self.eps = eps
def forward(self, x):
return l2n(x, eps=self.eps)
... | 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_... | SamSweere/CV-PIRE | L2N | false | 2,804 | [
"MIT"
] | 0 | d857167b3058cb51d10662150c6a4ba3c85f2903 | https://github.com/SamSweere/CV-PIRE/tree/d857167b3058cb51d10662150c6a4ba3c85f2903 |
NormedLinear | import torch
import torch.nn.functional as F
from torch import nn
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | HT-hlf/mmdetection_miner-2.22.0 | NormedLinear | false | 2,323 | [
"Apache-2.0"
] | 0 | 76eb94d6547f9f95cd58f41bb5c91941e82322b9 | https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9 |
Dense | import math
import torch
import torch.nn as nn
from string import ascii_lowercase
import torch.optim
class Dense(nn.Module):
def __init__(self, input_features, output_features=None):
super(Dense, self).__init__()
self.input_features = input_features
self.output_features = (input_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
import math
import torch.nn as nn
from string import ascii_lowercase
import torc... | andrew-xu-monash/UMM-Modified | Dense | false | 18,326 | [
"Apache-2.0"
] | 4 | 18729dc34733c203e8cd3873fec2b9f7d0b56dba | https://github.com/andrew-xu-monash/UMM-Modified/tree/18729dc34733c203e8cd3873fec2b9f7d0b56dba |
SoftDetectionModule | import torch
import torch.utils
import torch.nn as nn
import torch.nn.functional as F
class SoftDetectionModule(nn.Module):
def __init__(self, soft_local_max_size=3):
super(SoftDetectionModule, self).__init__()
self.soft_local_max_size = soft_local_max_size
self.pad = self.soft_local_max_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils
imp... | xmlyqing00/d2-net | SoftDetectionModule | false | 4,588 | [
"BSD-3-Clause-Clear"
] | 0 | 3454a2862088682a6bdb2532ff049fd6cd82729c | https://github.com/xmlyqing00/d2-net/tree/3454a2862088682a6bdb2532ff049fd6cd82729c |
Addition_Module | # 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... | LMdeLiangMi/captum | Addition_Module | false | 5,467 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
ShakeResNet | import math
import torch
from torch.nn import functional as F
from torch import nn
class ShakeShake(torch.autograd.Function):
@staticmethod
def forward(ctx, x1, x2, training=True):
if training:
alpha = torch.FloatTensor(x1.size(0)).uniform_()
alpha = alpha.view(alpha.size(0), ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
from torch.nn imp... | ang421/dda | ShakeResNet | false | 9,912 | [
"MIT"
] | 0 | 391ad696ec8479ce41a0d7d6bfbfae06edaddf67 | https://github.com/ang421/dda/tree/391ad696ec8479ce41a0d7d6bfbfae06edaddf67 |
ATLoss | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
class ATLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits: 'Tensor', labels: 'Tensor') ->float:
"""
Args:
logits: predicted probabilities (shape: bat... | 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... | BunnyNoBugs/DeepPavlov | ATLoss | false | 11,258 | [
"Apache-2.0"
] | 0 | b2213db633a669d27d6f745dd780530574ccf8b5 | https://github.com/BunnyNoBugs/DeepPavlov/tree/b2213db633a669d27d6f745dd780530574ccf8b5 |
AlignDifferential | import torch
from torch import nn
class AlignDifferential(nn.Module):
def __init__(self):
super().__init__()
def new_length(self, length):
return length
def forward(self, states):
"""
:param states: [batch, length, *]
"""
padded_states = torch.cat([states... | 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... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | AlignDifferential | false | 17,119 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
MotionEncoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class MotionEncoder(nn.Module):
"""
Encodes motion features from the correlation levels of the pyramid
and the input flow estimate using convolution layers.
Parameters
----------
corr_radius : int
Correlation radius o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | NeelayS/ezflow | MotionEncoder | false | 14,142 | [
"MIT"
] | 94 | b93a48c4adf5021f7eacbfc43220c7efa5ae55cd | https://github.com/NeelayS/ezflow/tree/b93a48c4adf5021f7eacbfc43220c7efa5ae55cd |
BertSelfAttention | # 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.... | Worm4047/TVR | BertSelfAttention | false | 14,607 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
AFTFull | import torch
from torch import nn
class AFTFull(nn.Module):
def __init__(self, max_seqlen, dim, hidden_dim=64):
super().__init__()
"""
max_seqlen: the maximum number of timesteps (sequence length) to be fed in
dim: the embedding dimension of the tokens
hidden_dim: the hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | Datta0/aft-pytorch | AFTFull | false | 2,138 | [
"MIT"
] | 0 | a0ebad01ea6616b00bde319b0c5e63bea467c400 | https://github.com/Datta0/aft-pytorch/tree/a0ebad01ea6616b00bde319b0c5e63bea467c400 |
DCENetLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | SeongjuLee/DCENet-PyTorch | DCENetLoss | false | 8,757 | [
"MIT"
] | 10 | eb477ce06356ae597c162dd3229285400ebf9168 | https://github.com/SeongjuLee/DCENet-PyTorch/tree/eb477ce06356ae597c162dd3229285400ebf9168 |
GlobalPerceptron | # 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 ... | liuruiyang98/Jittor-MLP | GlobalPerceptron | false | 15,934 | [
"MIT"
] | 49 | b86656b65cf5f18ba9eb760d1f7565ed95e7e96e | https://github.com/liuruiyang98/Jittor-MLP/tree/b86656b65cf5f18ba9eb760d1f7565ed95e7e96e |
EncoderImagePrecomp | # 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 numpy as np
... | Harshdeep1996/jina-hub | EncoderImagePrecomp | false | 2,327 | [
"Apache-2.0"
] | 0 | 880ff719715b89969860c70219d26a931a031d10 | https://github.com/Harshdeep1996/jina-hub/tree/880ff719715b89969860c70219d26a931a031d10 |
EncoderLayer | import torch
import torch as th
import torch.nn as nn
class FeedForwardNetwork(nn.Module):
def __init__(self, hidden_size, ffn_size, dropout_rate):
super(FeedForwardNetwork, self).__init__()
self.layer1 = nn.Linear(hidden_size, ffn_size)
self.gelu = nn.GELU()
self.layer2 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Roestlab/massformer | EncoderLayer | false | 17,877 | [
"BSD-2-Clause"
] | 6 | c6324970c392f8ee96651679f49d21e430caa0c9 | https://github.com/Roestlab/massformer/tree/c6324970c392f8ee96651679f49d21e430caa0c9 |
PositionwiseFeedForward | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from typing import Optional
class PositionwiseFeedForward(nn.Module):
"""Position-wise Feed Forward Network block from Attention is All You Need.
Apply two linear transformations to each 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
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | YanLu-nyu/transferlearning | PositionwiseFeedForward | false | 14,629 | [
"MIT"
] | 9,657 | 037806c6eb8b0c12aefbfbf3e35cbf893093cff9 | https://github.com/YanLu-nyu/transferlearning/tree/037806c6eb8b0c12aefbfbf3e35cbf893093cff9 |
BiaffineAttention | # 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.... | Dadmatech/DadmaTools | BiaffineAttention | false | 7,991 | [
"Apache-2.0"
] | 25 | c1b7add5c33544f69c1ba1c5250a5ea07caf9aa2 | https://github.com/Dadmatech/DadmaTools/tree/c1b7add5c33544f69c1ba1c5250a5ea07caf9aa2 |
BiInteractionPooling | import torch
import torch.nn as nn
from sklearn.metrics import *
class BiInteractionPooling(nn.Module):
"""Bi-Interaction Layer used in Neural FM,compress the
pairwise element-wise product of features into one single vector.
Input shape
- A 3D tensor with shape:``(batch_size,field_size,embeddi... | 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 sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | Fanxingye/DeepRS | BiInteractionPooling | false | 13,836 | [
"Apache-2.0"
] | 1,770 | 06b98cf2cb2781656805eafc577fbd088f37d17d | https://github.com/Fanxingye/DeepRS/tree/06b98cf2cb2781656805eafc577fbd088f37d17d |
DiceLoss | # 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... | Nightmare4214/FracNet | DiceLoss | false | 2,690 | [
"Apache-2.0"
] | 0 | db397adb50f71387155d9d110302a5968f86f756 | https://github.com/Nightmare4214/FracNet/tree/db397adb50f71387155d9d110302a5968f86f756 |
GEGLU | import torch
from torch import nn
import torch.nn.functional as F
class GEGLU(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim=-1)
return x * F.gelu(gates)
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Gitsamshi/DALLE-pytorch | GEGLU | false | 13,718 | [
"MIT"
] | 4,025 | 6cfc43158a4615865e97c839133290afcf289824 | https://github.com/Gitsamshi/DALLE-pytorch/tree/6cfc43158a4615865e97c839133290afcf289824 |
FC_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
import math
import torch.nn as nn
import torch.optim
import torch.multiprocessin... | bouracha/Gen_Motion | FC_Block | false | 9,929 | [
"MIT"
] | 0 | 873caa496d14c9a9723581cdf1464f44db4cf358 | https://github.com/bouracha/Gen_Motion/tree/873caa496d14c9a9723581cdf1464f44db4cf358 |
Vgg16 | # 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... | chaitrasj/GAN-based-Visible-Thermal-Person-ReID | Vgg16 | false | 10,145 | [
"MIT"
] | 0 | 8fd65ce3ab5403056fbe6e3574d1a7d02a315e62 | https://github.com/chaitrasj/GAN-based-Visible-Thermal-Person-ReID/tree/8fd65ce3ab5403056fbe6e3574d1a7d02a315e62 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dongminlee94/Samsung-DRL-Code | Actor | false | 15,198 | [
"MIT"
] | 116 | c96f8739a09cfd708c265954ee8ecf0ea3b67395 | https://github.com/dongminlee94/Samsung-DRL-Code/tree/c96f8739a09cfd708c265954ee8ecf0ea3b67395 |
depthwise_conv | import torch
import torch.nn as nn
import torch.utils.data
class depthwise_conv(nn.Module):
def __init__(self, kernel_size=3, stride=1, padding=1):
super(depthwise_conv, self).__init__()
self.depthwise = nn.Conv2d(1, 1, kernel_size=kernel_size, stride=
stride, padding=padding)
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Zacchaeus14/lang-seg | depthwise_conv | false | 9,786 | [
"MIT"
] | 0 | ad1196a4d33830f3219dbe2260a69364a745f094 | https://github.com/Zacchaeus14/lang-seg/tree/ad1196a4d33830f3219dbe2260a69364a745f094 |
AddCoords | # 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... | hoseDUDEface/AdaptiveWingLoss | AddCoords | false | 12,512 | [
"Apache-2.0"
] | 0 | 9185799d87567044f437147639c3999418529684 | https://github.com/hoseDUDEface/AdaptiveWingLoss/tree/9185799d87567044f437147639c3999418529684 |
ActorCritic | import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorCritic(nn.Module):
""" Actor Critic neural network with shared body.
The Actor maps states (actions) to action, log_probs, entropy.
The Critic maps states to values.
"""
def __init__(self, state_size, action_size, seed=0):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ImmanuelXIV/ppo-self-play | ActorCritic | false | 17,445 | [
"MIT"
] | 7 | 21c000492b2450628b5a506d4101b7b12e5755e0 | https://github.com/ImmanuelXIV/ppo-self-play/tree/21c000492b2450628b5a506d4101b7b12e5755e0 |
DiceLoss | # 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... | PARMAGroup/UNet-Instance-Cell-Segmentation | DiceLoss | false | 8,624 | [
"MIT"
] | 30 | 79655a2c5781d2e20c7d5760f631fbb0be392292 | https://github.com/PARMAGroup/UNet-Instance-Cell-Segmentation/tree/79655a2c5781d2e20c7d5760f631fbb0be392292 |
Whitening2d | # 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... | TranNhiem/solo-learn | Whitening2d | false | 1,151 | [
"MIT"
] | 0 | 7539732b68d153087d09a26a23e1edfdc49bc086 | https://github.com/TranNhiem/solo-learn/tree/7539732b68d153087d09a26a23e1edfdc49bc086 |
SoftmaxLayer | import torch
import torch.nn as nn
class SoftmaxLayer(nn.Module):
""" Naive softmax-layer """
def __init__(self, output_dim, n_class):
"""
:param output_dim: int
:param n_class: int
"""
super(SoftmaxLayer, self).__init__()
self.hidden2tag = nn.Linear(output_dim, n_class)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Sy-Zhang/ELMoForManyLangs | SoftmaxLayer | false | 14,452 | [
"MIT"
] | 1,414 | f82bf0fef80df617e39d34baa3e46d9857e94e65 | https://github.com/Sy-Zhang/ELMoForManyLangs/tree/f82bf0fef80df617e39d34baa3e46d9857e94e65 |
vd_linear_1L | import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def calculate_kl(log_alpha):
return 0.5 * torch.sum(torch.log1p(torch.exp(-log_alpha)))
class VdLinear(nn.Module):
"""
variational dropout
"""
def __init__(self, n_in, n_ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Neronjust2017/Bayesian-neural-networks | vd_linear_1L | false | 17,778 | [
"MIT"
] | 4 | 9d7f781f5c2dfa8fadf26300b4b5b64366c939cd | https://github.com/Neronjust2017/Bayesian-neural-networks/tree/9d7f781f5c2dfa8fadf26300b4b5b64366c939cd |
GAT | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLay... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | EagleW/PaperRobot-Incremental-Draft-Generation-of-Scientific-Ideas | GAT | false | 14,106 | [
"MIT"
] | 453 | a338abf3974ba9ce916ae846835063a42b9e6689 | https://github.com/EagleW/PaperRobot-Incremental-Draft-Generation-of-Scientific-Ideas/tree/a338abf3974ba9ce916ae846835063a42b9e6689 |
LightHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | grok-phantom/pytorch_tempest | LightHead | false | 3,702 | [
"MIT"
] | 0 | 37921b5824f9fcb853da3f54d929c4855672416e | https://github.com/grok-phantom/pytorch_tempest/tree/37921b5824f9fcb853da3f54d929c4855672416e |
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