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 |
|---|---|---|---|---|---|---|---|---|---|---|
FixedSubnetConv | import math
import torch
import torch.multiprocessing
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class FixedSubnetConv(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*args... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.multiprocessing
import torch.nn as nn
import torch.nn.p... | Lasitha-93/CRTIDL_2021 | FixedSubnetConv | false | 11,625 | [
"Apache-2.0"
] | 0 | d6bc6fbe08161c3574511623230a7aa4895f65e1 | https://github.com/Lasitha-93/CRTIDL_2021/tree/d6bc6fbe08161c3574511623230a7aa4895f65e1 |
SecondOrderInteraction | from torch.nn import Module
import torch
from torch.nn import Parameter
class SecondOrderInteraction(Module):
"""
Factorized parameters for the Second Order Interactions
Parameters
----------
n_features: int
Length of the input vector.
n_factors: int, optional
Number of factor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 Parameter
assert_size_stride = ... | DanielMorales9/FactorizationPyTorch | SecondOrderInteraction | false | 17,200 | [
"MIT"
] | 4 | 50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | https://github.com/DanielMorales9/FactorizationPyTorch/tree/50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 |
NeuralNet | import torch
class NeuralNet(torch.nn.Module):
def __init__(self, input_size, output_size, hidden_size, lr=0.0001):
super(NeuralNet, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.HiddenLayer = torch.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.... | bva-bme/Constrained_Policy_Gradient | NeuralNet | false | 1,646 | [
"MIT"
] | 0 | 2331f55ff3bf06e2276662517c34cc45d5a51da8 | https://github.com/bva-bme/Constrained_Policy_Gradient/tree/2331f55ff3bf06e2276662517c34cc45d5a51da8 |
CrossEntropyLoss | # AOT ID: ['1_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
... | LiWentomng/boxlevelset | CrossEntropyLoss | false | 8,469 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
PostPreplayer | import torch
from torch import nn
import torch.nn.functional as F
class PostPreplayer(nn.Module):
def __init__(self, dim, out_dim, num_nodes, seq_l, dropout):
super().__init__()
self.norm1 = torch.nn.LayerNorm((dim, num_nodes, seq_l))
self.end_conv_1 = nn.Conv2d(in_channels=dim, out_chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | abcdefg-dev-dd/asxdcvfg | PostPreplayer | false | 6,075 | [
"Apache-2.0"
] | 1 | 83421d4a133810968d6e04b256a9312895452941 | https://github.com/abcdefg-dev-dd/asxdcvfg/tree/83421d4a133810968d6e04b256a9312895452941 |
L2Norm | import torch
from math import sqrt as sqrt
from itertools import product as product
import torch.nn as nn
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
... | 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 math import sqrt as sqrt
from itertools import product as product
import t... | Kalana304/realtime-action-detection | L2Norm | false | 8,435 | [
"MIT"
] | 26 | a40178c749d60c135290c40a8ac658bac253f0d4 | https://github.com/Kalana304/realtime-action-detection/tree/a40178c749d60c135290c40a8ac658bac253f0d4 |
BasicBlock | # 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.... | musyoku/ffjord | BasicBlock | false | 7,306 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
NonLinearProbe4 | import torch
from torch import nn
class NonLinearProbe4(nn.Module):
def __init__(self, input_dim, num_hidden=300, num_classes=255):
super().__init__()
self.linear1 = nn.Linear(in_features=input_dim, out_features=num_hidden
)
self.relu1 = nn.ReLU()
self.linear2 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | PAL-ML/atari-representation-learning | NonLinearProbe4 | false | 2,790 | [
"MIT"
] | 0 | 11977da174d9ef74c0b2333322b9f0b28e15239e | https://github.com/PAL-ML/atari-representation-learning/tree/11977da174d9ef74c0b2333322b9f0b28e15239e |
PositionalEncodingGenerator | import torch
from torch import nn
class PositionalEncodingGenerator(nn.Module):
def __init__(self, dim):
super().__init__()
self.proj = nn.Conv2d(dim, dim, 3, padding=1, bias=False, groups=dim)
def forward(self, input):
out = input.permute(0, 3, 1, 2)
out = self.proj(out) + o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | rosinality/vision-transformers-pytorch | PositionalEncodingGenerator | false | 16,340 | [
"MIT"
] | 77 | b884b5da79900c96e4ce17fbb575cf1c5cb3cd5f | https://github.com/rosinality/vision-transformers-pytorch/tree/b884b5da79900c96e4ce17fbb575cf1c5cb3cd5f |
ClassificationSubNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | geez0219/ARC | ClassificationSubNet | false | 6,753 | [
"Apache-2.0"
] | 1 | f2176f0d442d4a2d6028f0770b1efc1a9ae982b8 | https://github.com/geez0219/ARC/tree/f2176f0d442d4a2d6028f0770b1efc1a9ae982b8 |
ShiftedConv | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from numpy import prod
assert_size_stride = to... | leo19941227/CPC_audio | ShiftedConv | false | 12,704 | [
"MIT"
] | 0 | 2d0051915f4b4a5f773e4510cd5535e1fcb433d8 | https://github.com/leo19941227/CPC_audio/tree/2d0051915f4b4a5f773e4510cd5535e1fcb433d8 |
HubertFeatureProjection | # 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... | jxhe/unify-parameter-efficient-tuning | HubertFeatureProjection | false | 15,765 | [
"Apache-2.0"
] | 101 | 3222ce2c0079566a28043e22380eb4ab6ad14389 | https://github.com/jxhe/unify-parameter-efficient-tuning/tree/3222ce2c0079566a28043e22380eb4ab6ad14389 |
L1Part | import torch
import torch.nn as nn
from itertools import chain as chain
import torch.utils.data
from collections import OrderedDict
import torch.hub
import torch.nn.parallel
import torch.optim
class concatLayer(nn.Module):
def __init__(self, in_channels, out_channels_perSub, i, j, appendix):
super(concat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import chain as chain
import torch.utils.da... | byeongjokim/LateTemporalModeling3DCNN_for_sign | L1Part | false | 1,682 | [
"MIT"
] | 0 | e3a802fcf91dc3930aea782464ee34d9b747d3ab | https://github.com/byeongjokim/LateTemporalModeling3DCNN_for_sign/tree/e3a802fcf91dc3930aea782464ee34d9b747d3ab |
Get_gradient | # 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
import torch.utils.... | BlueAmulet/BasicSR | Get_gradient | false | 7,834 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
ZeroLayer | import torch
import torch.nn as nn
import torch.utils.data
class ZeroLayer(nn.Module):
def __init__(self, stride):
super(ZeroLayer, self).__init__()
self.stride = stride
def forward(self, x):
"""n, c, h, w = x.size()
h //= self.stride
w //= self.stride
device ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | pkuyym/nni | ZeroLayer | false | 10,994 | [
"MIT"
] | 0 | fe533e3bc65ea27997e16250adb503638548d500 | https://github.com/pkuyym/nni/tree/fe533e3bc65ea27997e16250adb503638548d500 |
SReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_... | THEFASHIONGEEK/Echo | SReLU | false | 11,911 | [
"MIT"
] | 0 | 8dcf279ca528f2bfd255f79de07c1a221512c6a0 | https://github.com/THEFASHIONGEEK/Echo/tree/8dcf279ca528f2bfd255f79de07c1a221512c6a0 |
EuclideanGMM | # 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 math as tl_math
import math
i... | ownzonefeng/Graph-based-text-representations | EuclideanGMM | false | 7,456 | [
"MIT"
] | 1 | 4ddac6980d2741284474778dae2c2afa0adb5cda | https://github.com/ownzonefeng/Graph-based-text-representations/tree/4ddac6980d2741284474778dae2c2afa0adb5cda |
QuaternionMean | import torch
from torch import nn as nn
def quaternions_to_group_matrix(q):
"""Normalises q and maps to group matrix."""
q = q / q.norm(p=2, dim=-1, keepdim=True)
r, i, j, k = q[..., 0], q[..., 1], q[..., 2], q[..., 3]
return torch.stack([r * r - i * i - j * j + k * k, 2 * (r * i + j * k),
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.triton_helpers import libdevice
from torch import n... | pimdh/lie-vae | QuaternionMean | false | 16,253 | [
"MIT"
] | 83 | 0e0cc4d533c064fcfc405e8a75449f8b2f6cf8cf | https://github.com/pimdh/lie-vae/tree/0e0cc4d533c064fcfc405e8a75449f8b2f6cf8cf |
InstanceNorm1d | import torch
from torch import nn
class InstanceNorm1d(nn.Module):
"""
Implementation of instance normalization for a 2D tensor of shape (batch size, features)
"""
def __init__(self) ->None:
super(InstanceNorm1d, self).__init__()
def forward(self, input: '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.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ChristophReich1996/3D_Baggage_Segmentation | InstanceNorm1d | false | 4,997 | [
"MIT"
] | 1 | 00392cb0fde22d3180b6baf81e404d0fcf4e2ebf | https://github.com/ChristophReich1996/3D_Baggage_Segmentation/tree/00392cb0fde22d3180b6baf81e404d0fcf4e2ebf |
TransformerLayer | import math
import torch
import uuid
from torch import Tensor
import torch.nn as nn
from typing import Tuple
import torch.nn.functional as F
from typing import Optional
from typing import Dict
from torch.nn import Parameter
def gelu(x):
"""Implementation of the gelu activation function.
For information: Open... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | boxiangliu/esm | TransformerLayer | false | 1,634 | [
"MIT"
] | 0 | 3c143d99103e0ea38a9455f30a73cd9c87376606 | https://github.com/boxiangliu/esm/tree/3c143d99103e0ea38a9455f30a73cd9c87376606 |
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.... | raznem/rlex | Actor | false | 4,242 | [
"MIT"
] | 0 | d24b964d80067becc81d86f6ce87e5be413b7049 | https://github.com/raznem/rlex/tree/d24b964d80067becc81d86f6ce87e5be413b7049 |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
class MaxPoolStride1(nn.Module):
def __init__(self):
super(MaxPoolStride1, self).__init__()
def forward(self, x):
x = F.max_pool2d(F.pad(x, (0, 1, 0, 1), mod... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | Alin1102/Yolov3_Dartnet2Caffe | MaxPoolStride1 | false | 7,641 | [
"MIT"
] | 21 | b4284b080f53c1ac73c1930b1b1c4e07dcd97559 | https://github.com/Alin1102/Yolov3_Dartnet2Caffe/tree/b4284b080f53c1ac73c1930b1b1c4e07dcd97559 |
Scale2D | import torch
import torch.nn as nn
class Scale2D(nn.Module):
def __init__(self, n):
super().__init__()
self.register_parameter('alpha', torch.nn.Parameter(torch.ones([1,
n, 1, 1])))
self.register_parameter('beta', torch.nn.Parameter(torch.ones([1, n,
1, 1])))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | rafapi/yolo3_pytorch | Scale2D | false | 12,909 | [
"MIT"
] | 0 | a936eb4fa5d4ddac97af8c835b6171d3b9c09b6a | https://github.com/rafapi/yolo3_pytorch/tree/a936eb4fa5d4ddac97af8c835b6171d3b9c09b6a |
GaborConstraint | import math
import torch
from torch import nn
class GaborConstraint(nn.Module):
"""Constraint mu and sigma, in radians.
Mu is constrained in [0,pi], sigma s.t full-width at half-maximum of the
gaussian response is in [1,pi/2]. The full-width at half maximum of the
Gaussian response is 2*sqrt(2*log(2)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | entn-at/leaf-audio-pytorch | GaborConstraint | false | 15,318 | [
"Apache-2.0"
] | 72 | 33f4ba4c8bdf07f125033f8e706d0d0bc6816445 | https://github.com/entn-at/leaf-audio-pytorch/tree/33f4ba4c8bdf07f125033f8e706d0d0bc6816445 |
TensorClamp | import torch
class TensorClamp(torch.nn.Module):
def forward(self, x):
return x.clamp(-0.1, 0.1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Ilyabasharov/torch2trt | TensorClamp | false | 2,531 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
UpsampleLayer | # 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... | jianantian/yolo3-pytorch | UpsampleLayer | false | 3,720 | [
"BSD-3-Clause"
] | 0 | 8966f04c5b514a4f60fcb63b1fc753d0b13ebdcc | https://github.com/jianantian/yolo3-pytorch/tree/8966f04c5b514a4f60fcb63b1fc753d0b13ebdcc |
quadexp | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | pierreglaser/MMD-gradient-flow | quadexp | false | 10,688 | [
"BSD-3-Clause"
] | 0 | 43591137e1d04bed5153887a364fae72621b01ae | https://github.com/pierreglaser/MMD-gradient-flow/tree/43591137e1d04bed5153887a364fae72621b01ae |
ToRGB | from torch.autograd import Function
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def upsample(in_tens, out_H=64):
in_H = in_tens.shape[2]
scale_factor = 1.0 * out_H / in_H
return nn.Upsample(scale_factor=scale_factor, mode='bilinear',
align_corners=False)(in_tens)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
import torch.nn as nn
import tor... | songquanpeng/BlendGAN | ToRGB | false | 16,509 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 67 | cbf7225c50c548ee955614715ae3f8fa4d68ee13 | https://github.com/songquanpeng/BlendGAN/tree/cbf7225c50c548ee955614715ae3f8fa4d68ee13 |
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | GavinHU66/DebugEntity | MaxPool2dDynamicSamePadding | false | 11,436 | [
"MIT"
] | 0 | 21f38f01bdfbbc363a73f640331c6f04a121cf82 | https://github.com/GavinHU66/DebugEntity/tree/21f38f01bdfbbc363a73f640331c6f04a121cf82 |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jeffreykuang/mmocr-1 | TransformerEncoderLayer | false | 15,706 | [
"Apache-2.0"
] | 206 | b17304edeb493b0a4d7224c23d23b952350d0db5 | https://github.com/jeffreykuang/mmocr-1/tree/b17304edeb493b0a4d7224c23d23b952350d0db5 |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
from torch.nn import functional as F
imp... | AvrilCheng/LidarStereoNet | Conv2d | false | 7,749 | [
"MIT"
] | 27 | 96c7cd6d5edb9b2fd302e2edd0c05cbda1ed024e | https://github.com/AvrilCheng/LidarStereoNet/tree/96c7cd6d5edb9b2fd302e2edd0c05cbda1ed024e |
AconC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | HarryPham0123/FPT_data_centric_competition | AconC | false | 5,266 | [
"Apache-2.0"
] | 1 | 3fa1e0ac48fdae2649b639229d9a74f75e461878 | https://github.com/HarryPham0123/FPT_data_centric_competition/tree/3fa1e0ac48fdae2649b639229d9a74f75e461878 |
FourierConv2d | import torch
class FourierConv2d(torch.nn.Module):
def __init__(self, in_channels, out_channels, size_x, size_y, bias=True,
periodic=False):
super(FourierConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
if not periodic:
s... | import torch
from torch import device
import 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... | julian-parker/DAFX22_FNO | FourierConv2d | false | 3,793 | [
"MIT"
] | 0 | 72f30144317a3f8ba8ea23ecf9a0333c81fc87db | https://github.com/julian-parker/DAFX22_FNO/tree/72f30144317a3f8ba8ea23ecf9a0333c81fc87db |
HardMish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation | HardMish | false | 8,295 | [
"Apache-2.0"
] | 34 | 2a546ef946989fc5bac8d819b3c93a9fdc83f241 | https://github.com/JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation/tree/2a546ef946989fc5bac8d819b3c93a9fdc83f241 |
ReverseAttentionLayer | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.multiprocessing
def weights_init(init_type='gaussian'):
def init_func(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasatt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Xiefan-Guo/LBAM | ReverseAttentionLayer | false | 18,118 | [
"MIT"
] | 4 | 9795e2af4677a9f5e8e13b5d89fc6d50534c006a | https://github.com/Xiefan-Guo/LBAM/tree/9795e2af4677a9f5e8e13b5d89fc6d50534c006a |
Hsigmoid | # 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
from torch.quantization import QuantStub
from torch.quantization im... | akosik-anyvision/incubator-tvm | Hsigmoid | false | 18,241 | [
"Apache-2.0"
] | 9 | e1b11712ac09c32614483d24a4c7e0245ee4cb4b | https://github.com/akosik-anyvision/incubator-tvm/tree/e1b11712ac09c32614483d24a4c7e0245ee4cb4b |
RNNModel | # 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
assert_size_stride ... | afermanian/rnn-kernel | RNNModel | false | 18,242 | [
"Apache-2.0"
] | 5 | 8c4400c33e61081bfc162fa26d088827cee1028b | https://github.com/afermanian/rnn-kernel/tree/8c4400c33e61081bfc162fa26d088827cee1028b |
ConvLeaky | # 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... | ivan94fi/fast-sr-unet | ConvLeaky | false | 12,540 | [
"MIT"
] | 0 | 76ff5ee1ca87d8cdd06ce3ec406cfac533041d83 | https://github.com/ivan94fi/fast-sr-unet/tree/76ff5ee1ca87d8cdd06ce3ec406cfac533041d83 |
EnsembleFC | # 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... | si0wang/transfer_dmc | EnsembleFC | false | 12,984 | [
"MIT"
] | 0 | 6bda773244e0b709b3c13add2597f5f1cd01bfd7 | https://github.com/si0wang/transfer_dmc/tree/6bda773244e0b709b3c13add2597f5f1cd01bfd7 |
EmbeddingLayer | import torch
import torch.nn.functional
import torch.cuda
class EmbeddingLayer(torch.nn.Module):
"""Attention layer."""
def __init__(self, feature_number: 'int'):
"""Initialize the relational embedding layer.
:param feature_number: Number of features.
"""
super().__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
from torch._inductor.runtime.... | cthoyt/chemicalx | EmbeddingLayer | false | 6,496 | [
"Apache-2.0"
] | 1 | f48d70bc88e89e9605a5b1c2f006fb8d37b42922 | https://github.com/cthoyt/chemicalx/tree/f48d70bc88e89e9605a5b1c2f006fb8d37b42922 |
HubertFeatureProjection | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class HubertFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.
layer_norm_eps)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Clemens123/transformers | HubertFeatureProjection | false | 13,013 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
AP | import torch
import torch.nn as nn
class AttentivePooling(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, **kwargs):
super(AttentivePooling, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W = nn.Linear(input_dim, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | czlwang/s3prl | AP | false | 12,273 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
HardSigmoid | # 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... | CharlesPikachu/mcibi | HardSigmoid | false | 7,885 | [
"MIT"
] | 41 | 6ce453504741c2eed1d290306055258a377a4094 | https://github.com/CharlesPikachu/mcibi/tree/6ce453504741c2eed1d290306055258a377a4094 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Mohan-Zhang-u/vit-pytorch | LayerNorm | false | 11,710 | [
"MIT"
] | 0 | 76050c812474d7c10d67db4e811f537e26c3996a | https://github.com/Mohan-Zhang-u/vit-pytorch/tree/76050c812474d7c10d67db4e811f537e26c3996a |
IBertClassificationHead | # 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... | Clemens123/transformers | IBertClassificationHead | false | 13,214 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
Sobel | # 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
import torch.nn.init
assert_size_stride = torch._C._dynamo... | TropComplique/WESPE | Sobel | false | 18,023 | [
"MIT"
] | 5 | 84738f1ed802a3f6a4a0549677d8137997fac617 | https://github.com/TropComplique/WESPE/tree/84738f1ed802a3f6a4a0549677d8137997fac617 |
SmallMnist | import torch
import torch.nn as nn
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
class SmallMnist(nn.Module):
def __init__(self):
super(SmallMnist, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.relu1 = nn.ReLU()
self.con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | quic-akhobare/aimet | SmallMnist | false | 10,741 | [
"BSD-3-Clause"
] | 0 | 1811a0ef58a75d103e173731b436876ee5dc4c49 | https://github.com/quic-akhobare/aimet/tree/1811a0ef58a75d103e173731b436876ee5dc4c49 |
Layer | import torch
import torch.nn.functional as F
import torch.nn as nn
class Layer(nn.Module):
def __init__(self, input_dim, output_dim, p, name=None):
super(Layer, self).__init__()
self.name = name
self.register_parameter(name='w', param=nn.Parameter(torch.empty(1,
input_dim, out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | kw-lee/VIforSDEs | Layer | false | 3,866 | [
"MIT"
] | 0 | dcba3832aaad0aebc921a3b0628c43046d651629 | https://github.com/kw-lee/VIforSDEs/tree/dcba3832aaad0aebc921a3b0628c43046d651629 |
ModulatedConv2d | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if len(k.shape) == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, u... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | bronemos/contrastive-unpaired-translation-focal | ModulatedConv2d | false | 3,258 | [
"BSD-3-Clause"
] | 0 | 50b9008d08a86439ede081a910d02df5da8e32df | https://github.com/bronemos/contrastive-unpaired-translation-focal/tree/50b9008d08a86439ede081a910d02df5da8e32df |
BertPooler | # 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 ... | Andr3wis2Cool4School/AI-pro | BertPooler | false | 1,847 | [
"MIT"
] | 0 | dfe5f5959bc187d899a86f13b84158c66f64d1cc | https://github.com/Andr3wis2Cool4School/AI-pro/tree/dfe5f5959bc187d899a86f13b84158c66f64d1cc |
UniformNoiseGenerator | import torch
import torch.distributions
import torch.utils.data
class AdversarialNoiseGenerator(torch.nn.Module):
def __init__(self):
super().__init__()
return
def forward(self, x):
raise NotImplementedError()
class UniformNoiseGenerator(AdversarialNoiseGenerator):
def __init_... | import torch
from torch import device
import 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.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | AlexMeinke/Provable-OOD-Detection | UniformNoiseGenerator | false | 7,699 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
FocalTiLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | xuyangcao/AttD2UNet | FocalTiLoss | false | 11,047 | [
"MIT"
] | 0 | b76ed8104a4183140b3cbd7f9671ca99d36e3b3e | https://github.com/xuyangcao/AttD2UNet/tree/b76ed8104a4183140b3cbd7f9671ca99d36e3b3e |
MultiplyLuminance | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ashwinpn/Computer-Vision | MultiplyLuminance | false | 6,252 | [
"MIT"
] | 1 | 9dc3abfe416385171b76e2bad6872e10f36a12b4 | https://github.com/ashwinpn/Computer-Vision/tree/9dc3abfe416385171b76e2bad6872e10f36a12b4 |
CategoricalAccuracy | import torch
class _Metric(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input: 'torch.Tensor', target: 'torch.Tensor'):
raise NotImplementedError()
class Accuracy(_Metric):
def __init__(self):
super().__init__()
def forward(self, input: 'torc... | 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... | NestLakerJasonLIN/MusicTransformer-pytorch | CategoricalAccuracy | false | 5,656 | [
"MIT"
] | 1 | 5f183374833ff6b7e17f3a24e3594dedd93a5fe5 | https://github.com/NestLakerJasonLIN/MusicTransformer-pytorch/tree/5f183374833ff6b7e17f3a24e3594dedd93a5fe5 |
SimulatorReward | import torch
import torch.nn.functional as F
class SimulatorReward(torch.nn.Module):
def __init__(self):
super(SimulatorReward, self).__init__()
self.conv1 = torch.nn.Conv2d(4, 8, kernel_size=3, padding=1)
self.conv2 = torch.nn.Conv2d(8, 16, kernel_size=3, padding=1)
self.conv3 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | karshtharyani/DeepReinforcementLearningInAction | SimulatorReward | false | 12,663 | [
"MIT"
] | 0 | 9dc40a43b43f05daf9aecb7e3ec7592cf38720e5 | https://github.com/karshtharyani/DeepReinforcementLearningInAction/tree/9dc40a43b43f05daf9aecb7e3ec7592cf38720e5 |
GraphReasoning | # 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.... | Chris-cbc/SGRAF | GraphReasoning | false | 13,507 | [
"Apache-2.0"
] | 110 | 785535168ad417dda523888f2f047359231fcbf7 | https://github.com/Chris-cbc/SGRAF/tree/785535168ad417dda523888f2f047359231fcbf7 |
GDL | # 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 import device
import triton
import triton.language as tl
from 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
fro... | Lixy1997/Group-WSSS | GDL | false | 14,007 | [
"MIT"
] | 80 | 0afcc3a21c3bec69fbc5b6d1d4ee84ffd405d253 | https://github.com/Lixy1997/Group-WSSS/tree/0afcc3a21c3bec69fbc5b6d1d4ee84ffd405d253 |
DenseGraphConv | # 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
from torch.nn import Parameter
import torch.utils.data
assert_size_s... | KuangenZhang/pytorch_geometric | DenseGraphConv | false | 5,464 | [
"MIT"
] | 1 | 0bfc79a5eaccfcd16a82395e8578a90c5e44759f | https://github.com/KuangenZhang/pytorch_geometric/tree/0bfc79a5eaccfcd16a82395e8578a90c5e44759f |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch as th
import torch.nn as nn
from torch.nn import Param... | denizetkar/lstms.pth | LayerNorm | false | 15,167 | [
"Apache-2.0"
] | 130 | c1d6af1e106e17c51604ae8acdb5114828adff19 | https://github.com/denizetkar/lstms.pth/tree/c1d6af1e106e17c51604ae8acdb5114828adff19 |
ReSentenceMatrixLayer | # 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... | ayyyq/T-LSTM | ReSentenceMatrixLayer | false | 6,312 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
Mlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Aalanli/MusicGeneration | Mlp | false | 9 | [
"MIT"
] | 0 | 7d268322d692013d8ac6e70be31741cea519fa28 | https://github.com/Aalanli/MusicGeneration/tree/7d268322d692013d8ac6e70be31741cea519fa28 |
AlternateAttention | import torch
import torch.nn as nn
class AlternateAttention(nn.Module):
def __init__(self, embedding_size, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.embedding_size = embedding_size
self.x_linear = nn.Linear(self.embedding_size, self.hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | alasin/vqa_pytorch | AlternateAttention | false | 6,160 | [
"MIT"
] | 1 | 8a311226d8eea56ef79f6be3c864ec05768e2895 | https://github.com/alasin/vqa_pytorch/tree/8a311226d8eea56ef79f6be3c864ec05768e2895 |
toy_yolov3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class toy_yolov3(nn.Module):
def __init__(self):
super(toy_yolov3, self).__init__()
self.conv1 = nn.Conv2d(3, 128, kernel_size=3, stride=2, padding=1)
self.conv2_1 = nn.Conv2d(128, 128, kernel_size=1, stride=1, padding=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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | yifanpu001/PytorchToCaffe | toy_yolov3 | false | 4,730 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
DecoderBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | HugoPopo/robosat.pink | DecoderBlock | false | 2,353 | [
"MIT"
] | 0 | daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d | https://github.com/HugoPopo/robosat.pink/tree/daa6a0cd6dff68103b9bcc78a8c9a15d8912c42d |
MaxPooling | import torch
import torch.nn as nn
class MaxPooling(nn.Module):
def __init__(self):
super(MaxPooling, self).__init__()
self.MIN = -1000000.0
"""
(item, subitem) can be (word, characters), or (sentence, words)
x: num_items x max_subitem_size x input_size
x_mask: num_items x max_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | GingerNg/SDNet | MaxPooling | false | 13,721 | [
"MIT"
] | 112 | 48ad8cc57c9a02aaad10e34d0c91a174ac68f056 | https://github.com/GingerNg/SDNet/tree/48ad8cc57c9a02aaad10e34d0c91a174ac68f056 |
GeneratorLoss | # 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 ... | akanametov/Pix2Pix-new | GeneratorLoss | false | 6,142 | [
"MIT"
] | 1 | 46aaefc506655dbf918ffdbd1c79174d76a748d0 | https://github.com/akanametov/Pix2Pix-new/tree/46aaefc506655dbf918ffdbd1c79174d76a748d0 |
NeuralNetMultiplePositionalArguments | import torch
import torch.nn
import torch.onnx
class NeuralNetMultiplePositionalArguments(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetMultiplePositionalArguments, self).__init__()
self.fc1 = torch.nn.Linear(input_size, hidden_size)
self.relu ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | carefreekk/onnxruntime | NeuralNetMultiplePositionalArguments | false | 3,271 | [
"MIT"
] | 0 | 484e9de55c109dadbeb552cd6ede21bbdd63b830 | https://github.com/carefreekk/onnxruntime/tree/484e9de55c109dadbeb552cd6ede21bbdd63b830 |
Predict_Network1_combine | # 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.... | ltzheng/CDS | Predict_Network1_combine | false | 7,134 | [
"Apache-2.0"
] | 1 | 397282147498647a9f26577adfa451e8478de76d | https://github.com/ltzheng/CDS/tree/397282147498647a9f26577adfa451e8478de76d |
ReduceSum2 | import torch
import torch.onnx
import torch.nn as nn
class ReduceSum2(nn.Module):
def forward(self, x):
return torch.sum(x, (1, 3), keepdim=True)
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
import torch.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | mil-tokyo/webdnn | ReduceSum2 | false | 16,087 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
LayerNorm | import torch
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
def get_inputs():
return [torch.ran... | 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... | Beximus/ResearchPortfolioCode | LayerNorm | false | 16,985 | [
"MIT"
] | 6 | db8343be6bbac361c3f6d01bbb82e458ff40f44e | https://github.com/Beximus/ResearchPortfolioCode/tree/db8343be6bbac361c3f6d01bbb82e458ff40f44e |
RpowFloat | import torch
class RpowFloat(torch.nn.Module):
def __init__(self):
super(RpowFloat, self).__init__()
def forward(self, x):
return 2.0 ** 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | ahangchen/torch2trt | RpowFloat | false | 6,105 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
LowRankConv2d | # 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
from torch import nn
import torch.cuda
import torch.nn.parallel
impo... | sfox14/butterfly | LowRankConv2d | false | 16,388 | [
"Apache-2.0"
] | 52 | 13cc15cee5bdb7adaf376219aaf20fab0459e9ef | https://github.com/sfox14/butterfly/tree/13cc15cee5bdb7adaf376219aaf20fab0459e9ef |
PoseCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ZiyunClaudeWang/e3d | PoseCriterion | false | 18,213 | [
"MIT"
] | 9 | 2efd01167350c29423babb6233907fa54156268f | https://github.com/ZiyunClaudeWang/e3d/tree/2efd01167350c29423babb6233907fa54156268f |
Classifier | # 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.parallel
import torch.utils.data
import tor... | IdanAzuri/MixMatch-pytorch | Classifier | false | 588 | [
"MIT"
] | 0 | b8de2bc30c09e1256b92e0394403487fc4f90135 | https://github.com/IdanAzuri/MixMatch-pytorch/tree/b8de2bc30c09e1256b92e0394403487fc4f90135 |
PPReLU | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, nchannels, bias=True, init_scale=1.0):
super().__init__()
self.nchannels = nchannels
self.weight = nn.Parameter(torch.Tensor(1, nchannels, 1, 1))
if bias:
self.bias = nn.Parameter(torch.Tenso... | 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... | IrisDinge/YoloV3_DOTA | PPReLU | false | 5,357 | [
"MIT"
] | 1 | cdfe6375a2323e9ee162e50a46478d8a66529e6c | https://github.com/IrisDinge/YoloV3_DOTA/tree/cdfe6375a2323e9ee162e50a46478d8a66529e6c |
Atom_Wise_Convolution | import torch
import torch.nn as nn
import torch.nn.parallel
class Shifted_softplus(nn.Module):
"""
Performs a Shifter softplus loss, which modifies with a value of log(2)
"""
def __init__(self):
super(Shifted_softplus, self).__init__()
self.act = nn.Softplus()
self.shift = nn.Para... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | JoseAntonioSiguenza/deepchem | Atom_Wise_Convolution | false | 9,215 | [
"MIT"
] | 0 | 05fe1b186ec154e18de9aa1b110e9258dc484e21 | https://github.com/JoseAntonioSiguenza/deepchem/tree/05fe1b186ec154e18de9aa1b110e9258dc484e21 |
Sobel | import torch
import torch.nn as nn
import torch.nn.init
import torch.nn.functional as F
class Sobel(nn.Module):
def __init__(self):
super(Sobel, self).__init__()
kernel = [[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], [[-1, -2, -1], [0,
0, 0], [1, 2, 1]]]
kernel = torch.Tensor(kernel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.init
assert_size_stride = torch._C._dynamo... | TropComplique/WESPE | Sobel | false | 18,023 | [
"MIT"
] | 5 | 84738f1ed802a3f6a4a0549677d8137997fac617 | https://github.com/TropComplique/WESPE/tree/84738f1ed802a3f6a4a0549677d8137997fac617 |
Down2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | ChanganVR/hifigan-denoiser | Down2d | false | 13,464 | [
"Apache-2.0"
] | 100 | 9bd77c53556e1372b4bbff8dce8b120297cc4e5c | https://github.com/ChanganVR/hifigan-denoiser/tree/9bd77c53556e1372b4bbff8dce8b120297cc4e5c |
FeatureEmbedding | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
def linear(input, weight, bias=None):
if input.dim() == 2 and bias is not None:
ret = torch.addmm(bias, input, weight.t())
else:
output = input.matmul(weight.t())
if bias is not ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | asifr/armisc | FeatureEmbedding | false | 3,219 | [
"MIT"
] | 0 | 486220ba498353faeb94f70cd8ffe917109526d2 | https://github.com/asifr/armisc/tree/486220ba498353faeb94f70cd8ffe917109526d2 |
ShakeResNeXt | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class ShakeShake(torch.autograd.Function):
@staticmethod
def forward(ctx, x1, x2, training=True):
if training:
alpha = torch.FloatTensor(x1.size(0)).uniform_()
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
import math
import torch.nn a... | DensoITLab/TeachAugment | ShakeResNeXt | false | 7,984 | [
"BSD-2-Clause"
] | 20 | 66ec099a0afab99e18531c5437182cfe17dc30c8 | https://github.com/DensoITLab/TeachAugment/tree/66ec099a0afab99e18531c5437182cfe17dc30c8 |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | # 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.... | TingGong1/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 5,906 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
ConvEncoder | import torch
from torch import nn
class ConvEncoder(nn.Module):
""" Simple convolutional encoder network.
It consists of 5 convolutional layers, each downsampling the input by a
factor of 2, and a final fully-connected layer projecting the output to
c_dim dimensions.
Args:
c_dim (int): 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
from torch import nn
assert_s... | planetceres/differentiable_volumetric_rendering | ConvEncoder | false | 4,221 | [
"MIT"
] | 0 | f2fe46d139244c7642439ced23656db1e7f5c128 | https://github.com/planetceres/differentiable_volumetric_rendering/tree/f2fe46d139244c7642439ced23656db1e7f5c128 |
Custom_dropout | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | JoseAntonioSiguenza/deepchem | Custom_dropout | false | 9,210 | [
"MIT"
] | 0 | 05fe1b186ec154e18de9aa1b110e9258dc484e21 | https://github.com/JoseAntonioSiguenza/deepchem/tree/05fe1b186ec154e18de9aa1b110e9258dc484e21 |
AvgReducePool1d | import torch
from torch import nn
class AvgReducePool1d(nn.Module):
"""A subclass of :torch_nn:`Module`.
Avg Pool layer for 1D inputs. The same as :torch_nn:`AvgPool1d` except that
the pooling dimension is entirely reduced (i.e., `pool_size=input_length`).
"""
def forward(self, input: 'torch.Tens... | 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... | Codle/texar-pytorch | AvgReducePool1d | false | 11,302 | [
"Apache-2.0"
] | 0 | d63556e7a8f48076c396467314a771d56552d595 | https://github.com/Codle/texar-pytorch/tree/d63556e7a8f48076c396467314a771d56552d595 |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=
5, padding=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size
=3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | SGeetansh/dffml | ConvNet | false | 5,810 | [
"MIT"
] | 1 | 04647bdcadef2f7e7b59cdd8ac1e89f17ef1095b | https://github.com/SGeetansh/dffml/tree/04647bdcadef2f7e7b59cdd8ac1e89f17ef1095b |
DGN | # 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.... | jungwoohan72/DGN_pytorch | DGN | false | 10,362 | [
"MIT"
] | 0 | 65fe7ab4df661d97725f2a72a1fdb49df1b2ea44 | https://github.com/jungwoohan72/DGN_pytorch/tree/65fe7ab4df661d97725f2a72a1fdb49df1b2ea44 |
StyleLayer | from torch.autograd import Function
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(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.triton_helpers import libdevice
from torch.autograd... | HappyBelief/ContraD | StyleLayer | false | 13,780 | [
"MIT"
] | 168 | abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f | https://github.com/HappyBelief/ContraD/tree/abb72562ddac8d8ab37fe9af6ac4c44c61e8ea0f |
Tucker | # 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... | AndresPMD/GCN_classification | Tucker | false | 8,823 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
GatedConvTranspose | import torch
import torch.nn as nn
import torch.utils.data
class GatedConvTranspose(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1):
super(GatedConvTranspose, self).__init__()
self.layer_f = nn.ConvTranspose2d(in_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ClaraBing/ffjord | GatedConvTranspose | false | 13,520 | [
"MIT"
] | 518 | a97c34ff546a063316828f53bd041555e663428d | https://github.com/ClaraBing/ffjord/tree/a97c34ff546a063316828f53bd041555e663428d |
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.... | posuer/mt-dnn | KlCriterion | false | 12,906 | [
"MIT"
] | 0 | 5106083238654777838aaab5d1111b3b05c4ce04 | https://github.com/posuer/mt-dnn/tree/5106083238654777838aaab5d1111b3b05c4ce04 |
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.... | anlewy/mt-dnn | KlCriterion | false | 14,868 | [
"MIT"
] | 2,075 | eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 | https://github.com/anlewy/mt-dnn/tree/eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 |
BinaryFocalLossWithLogits | # 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 warn... | shubham-gupta-iitr/mmmlX | BinaryFocalLossWithLogits | false | 4,329 | [
"Apache-2.0"
] | 0 | 3485e6191e0e45bf1c8168e4e928a36ab9264d22 | https://github.com/shubham-gupta-iitr/mmmlX/tree/3485e6191e0e45bf1c8168e4e928a36ab9264d22 |
NALU | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class NAC(Module):
"""Neural Accumulator: :math:`y = Wx` where :math:`W = \\tanh(\\hat{W}) * \\sigma(\\hat{M})`
Args:
in_features: size of each input sample
out_featur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | tanbur/pytorch-nalu | NALU | false | 10,847 | [
"MIT"
] | 0 | 91cb036230144b166137a8f3533850f2d4123d4f | https://github.com/tanbur/pytorch-nalu/tree/91cb036230144b166137a8f3533850f2d4123d4f |
BCEDiceLossWithLogits | import torch
import torch.nn as nn
import torch.utils.data
def flatten_samples(input_):
"""
Flattens a tensor or a variable such that the channel axis is first and the sample axis
is second. The shapes are transformed as follows:
(N, C, H, W) --> (C, N * H * W)
(N, C, D, H, W) --> (C, N * ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | JonasHell/torch-em | BCEDiceLossWithLogits | false | 8,387 | [
"MIT"
] | 13 | 2e008e0cd2f0ea6681581374fce4f9f47b986d55 | https://github.com/JonasHell/torch-em/tree/2e008e0cd2f0ea6681581374fce4f9f47b986d55 |
is_she_mad | import torch
import torch.nn.functional as F
import torch.nn as nn
class is_she_mad(nn.Module):
def __init__(self, modality_size):
super(is_she_mad, self).__init__()
self.fc1 = nn.Linear(modality_size, 200)
self.fc2 = nn.Linear(200, 128)
self.fc3 = nn.Linear(128, 1)
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | jryzkns/IsSheMadAtMe | is_she_mad | false | 12,645 | [
"MIT"
] | 0 | 7776fb9730dab56f42418460efa0c2dec3988e46 | https://github.com/jryzkns/IsSheMadAtMe/tree/7776fb9730dab56f42418460efa0c2dec3988e46 |
Accuracy | # 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... | alessandroferrari/defeatcovid19-net-pytorch | Accuracy | false | 18,239 | [
"MIT"
] | 9 | fe9ed82563709bae92524093c3bc0bb887fbdf6d | https://github.com/alessandroferrari/defeatcovid19-net-pytorch/tree/fe9ed82563709bae92524093c3bc0bb887fbdf6d |
BERTAttention | from _paritybench_helpers import _mock_config
import copy
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.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.... | Chriskuei/FedMatch | BERTAttention | false | 18,382 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 |
ScalarMix | import torch
import torch.nn as nn
class ScalarMix(nn.Module):
"""
Computes a parameterised scalar mixture of :math:`N` tensors, :math:`mixture = \\gamma * \\sum_{k}(s_k * tensor_k)`
where :math:`s = \\mathrm{softmax}(w)`, with :math:`w` and :math:`\\gamma` scalar parameters.
Args:
n_layers (... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | KoichiYasuoka/diaparser | ScalarMix | false | 9,234 | [
"MIT"
] | 0 | ca11e65ef890cee2fbb23f42ae9c711c89767158 | https://github.com/KoichiYasuoka/diaparser/tree/ca11e65ef890cee2fbb23f42ae9c711c89767158 |
MultiChannelCombinedScorer | # 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 ... | bharath272/centrosome-analysis | MultiChannelCombinedScorer | false | 6,362 | [
"MIT"
] | 1 | 6ae3744be464812b3767909420d7b78cea9da670 | https://github.com/bharath272/centrosome-analysis/tree/6ae3744be464812b3767909420d7b78cea9da670 |
FactorizationMachine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn... | cspades/algorithm-toolkit | FactorizationMachine | false | 12,257 | [
"Apache-2.0"
] | 0 | 8731112162fb60f8ef3ab3c38524456ae96f0c2d | https://github.com/cspades/algorithm-toolkit/tree/8731112162fb60f8ef3ab3c38524456ae96f0c2d |
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