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 |
|---|---|---|---|---|---|---|---|---|---|---|
GDiceLossV2 | import torch
import torch.nn as nn
import torch.utils.data
import torch
from torch.autograd import Variable
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torch
assert_size_stride = torch._C.... | ayanglab/HDL | GDiceLossV2 | false | 6,295 | [
"Apache-2.0"
] | 1 | 5ff778d713331671ffa85e9fb63378d8c0a57769 | https://github.com/ayanglab/HDL/tree/5ff778d713331671ffa85e9fb63378d8c0a57769 |
StackedAttention | # 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.... | SpyrosMouselinos/DeltaFormers | StackedAttention | false | 5,865 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
Linear | import math
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 Tensor
from torch.nn import Parameter
import torch... | cshjin/pytorch_geometric | Linear | false | 1,757 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f |
Pairer | import torch
import numpy as np
from torch import Tensor
from torch.functional import Tensor
from typing import Union
class Pairer(torch.nn.Module):
"""
To predict links between segments we will find all possible pairs and estimate the probability that they are linked.
We do this by creating a matrix whe... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | AxlAlm/SegNLP | Pairer | false | 4,868 | [
"Apache-2.0"
] | 1 | 89b8d077952397dfcea089376b373b117bcf6a65 | https://github.com/AxlAlm/SegNLP/tree/89b8d077952397dfcea089376b373b117bcf6a65 |
ResForward | import torch
from torch import nn
import torch.distributed
class ResBlock(nn.Module):
def __init__(self, feature_size, action_size):
super(ResBlock, self).__init__()
self.lin_1 = nn.Linear(feature_size + action_size, feature_size)
self.lin_2 = nn.Linear(feature_size + action_size, feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.distributed
assert_size_stride = torch._C._dyn... | Improbable-AI/curiosity_baselines | ResForward | false | 17,457 | [
"MIT"
] | 5 | 42dca92b2fb66c0790a72206bf48595d3b5b487f | https://github.com/Improbable-AI/curiosity_baselines/tree/42dca92b2fb66c0790a72206bf48595d3b5b487f |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rondagdag/onnx-pected | CNN | false | 12,950 | [
"MIT"
] | 0 | 63eb1c7edf2ddb3127073dc6c09b8edba32a9530 | https://github.com/rondagdag/onnx-pected/tree/63eb1c7edf2ddb3127073dc6c09b8edba32a9530 |
VNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_uniform_
import torch.utils.data
def weight_init(m):
if m.__class__.__name__ == 'Linear':
m.weight.data.copy_(kaiming_uniform_(m.weight.data))
m.bias.data.fill_(0)
class VNet(nn.Module):
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
from to... | AswinRetnakumar/Machina | VNet | false | 13,326 | [
"MIT"
] | 302 | 6519935ca4553192ac99fc1c7c1e7cab9dd72693 | https://github.com/AswinRetnakumar/Machina/tree/6519935ca4553192ac99fc1c7c1e7cab9dd72693 |
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
from torch._inductor.runtime.... | innerverz/CodeTemplate | ResBlock | false | 3,681 | [
"MIT"
] | 0 | a20f5d24b0b79871aa39b5cde33e3bb4d2507d13 | https://github.com/innerverz/CodeTemplate/tree/a20f5d24b0b79871aa39b5cde33e3bb4d2507d13 |
LossBasic | import torch
import torch.nn as nn
import torch.nn.functional as F
class TensorGradient(nn.Module):
"""
the gradient of tensor
"""
def __init__(self, L1=True):
super(TensorGradient, self).__init__()
self.L1 = L1
def forward(self, img):
w, h = img.size(-2), img.size(-1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | xenbaloch/efficientderain | LossBasic | false | 16,734 | [
"MIT"
] | 109 | d5646815fd14a5a03c859102ecd2f298db7e53be | https://github.com/xenbaloch/efficientderain/tree/d5646815fd14a5a03c859102ecd2f298db7e53be |
FCN_Net | import torch
import numpy as np
from torch import nn
import torch._utils
class FCN_Net(nn.Module):
def __init__(self, in_channels=1, n_class=1):
super().__init__()
self.conv1_1 = nn.Conv3d(in_channels, 8, 3, padding=60)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv3d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
from torch... | ilcessadecalcular/segmentation | FCN_Net | false | 10,767 | [
"MIT"
] | 0 | 24ba499a399efdba212ec5e2235b72ed8270cc24 | https://github.com/ilcessadecalcular/segmentation/tree/24ba499a399efdba212ec5e2235b72ed8270cc24 |
Conv1d | import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding='same', dilation=1, groups=1, bias=True):
super(Conv1d, self).__init__(in_channels=in_channels, out_channels=
out_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | debasish-mihup/EfficientConformer | Conv1d | false | 10,344 | [
"Apache-2.0"
] | 0 | bddd927cebcde044a999aaa7766fa6d44dc20576 | https://github.com/debasish-mihup/EfficientConformer/tree/bddd927cebcde044a999aaa7766fa6d44dc20576 |
LearnedPositionalEmbedding | # 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.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | JuruoMP/gap-exp | LearnedPositionalEmbedding | false | 9,228 | [
"Apache-2.0"
] | 0 | 2d7af8a1da2f0ff8f9d3a2c6e15cc6383c716c05 | https://github.com/JuruoMP/gap-exp/tree/2d7af8a1da2f0ff8f9d3a2c6e15cc6383c716c05 |
MixPad2d | import torch
from itertools import product as product
import torch.nn as nn
class MixPad2d(nn.Module):
"""Mixed padding modes for H and W dimensions
Args:
padding (tuple): the size of the padding for x and y, ie (pad_x, pad_y)
modes (tuple): the padding modes for x and y, the values of each c... | 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 itertools import product as product
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | xqyzjl/face_parsing | MixPad2d | false | 4,596 | [
"MIT"
] | 0 | 3d6c7b06d67c8fbf01bce22db199bc94a13a1a7c | https://github.com/xqyzjl/face_parsing/tree/3d6c7b06d67c8fbf01bce22db199bc94a13a1a7c |
L1 | # 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
... | d4l3k/crowds | L1 | false | 12,240 | [
"MIT"
] | 0 | a57eee80d66498474c86cec22dd77be9d627ad97 | https://github.com/d4l3k/crowds/tree/a57eee80d66498474c86cec22dd77be9d627ad97 |
Correlation | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | UBCDingXin/RepDistiller | Correlation | false | 14,510 | [
"BSD-2-Clause"
] | 1,347 | dcc043277f2820efafd679ffb82b8e8195b7e222 | https://github.com/UBCDingXin/RepDistiller/tree/dcc043277f2820efafd679ffb82b8e8195b7e222 |
IIDIsotropicGaussianUVLoss | import math
import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class IIDIsotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of iid residuals with isotropic covariance:
$Sigma_i = sigma_i^2 I$
The loss (negative log likelihood) is then:
$1/2 sum_{i=1}^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 math... | BUPT-PRIV/detectron2 | IIDIsotropicGaussianUVLoss | false | 11,238 | [
"Apache-2.0"
] | 0 | 3163664cd5f43d50ea1966f410dc82410b9ccbf4 | https://github.com/BUPT-PRIV/detectron2/tree/3163664cd5f43d50ea1966f410dc82410b9ccbf4 |
Net | import torch
import torch.nn as nn
import torch.nn.init as init
class Net(nn.Module):
def __init__(self, upscale_factor):
super(Net, self).__init__()
self.upscale_factor = int(upscale_factor)
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(1, 64, kernel_size=5, padding=2)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | PiSchool/esa-superresolution-forecasting | Net | false | 17,905 | [
"MIT"
] | 4 | 3c01770dd64749d6b6c40e1068a96a3307c8c035 | https://github.com/PiSchool/esa-superresolution-forecasting/tree/3c01770dd64749d6b6c40e1068a96a3307c8c035 |
LeNet | import torch
import torch.nn as nn
class LeNet(nn.Module):
def __init__(self):
super().__init__()
self.conv_1 = nn.Conv2d(3, 6, kernel_size=5, padding=2)
self.sigmoid = nn.Sigmoid()
self.avgpool = nn.AvgPool2d(kernel_size=5, stride=2)
self.conv_2 = nn.Conv2d(6, 16, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ducnguyenhuynh/via-trafficsign-classification | LeNet | false | 6,631 | [
"MIT"
] | 1 | e65fccc1ee377603334453eacfc3f65619dc0714 | https://github.com/ducnguyenhuynh/via-trafficsign-classification/tree/e65fccc1ee377603334453eacfc3f65619dc0714 |
HighwayLayer | # 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.... | Acidburn0zzz/translate-1 | HighwayLayer | false | 4,822 | [
"BSD-3-Clause"
] | 1 | 8385a3c95de397fec8ca7a032fe1c215fa4e31f9 | https://github.com/Acidburn0zzz/translate-1/tree/8385a3c95de397fec8ca7a032fe1c215fa4e31f9 |
AlexNet | # 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 ... | Fritingo/AlexNet_on_browser | AlexNet | false | 11,528 | [
"MIT"
] | 0 | 3e674dd84e25ee74f2efde77882b4faa788907c2 | https://github.com/Fritingo/AlexNet_on_browser/tree/3e674dd84e25ee74f2efde77882b4faa788907c2 |
DepthwiseSeparableConv | import torch
import torch.nn.functional as F
import torch.nn as nn
class DepthwiseSeparableConv(nn.Module):
"""
Depth-wise separable convolution uses less parameters to generate output by convolution.
:Examples:
>>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
>>> input_tensor = torch.ra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | IsaacChanghau/ReLoCLNet | DepthwiseSeparableConv | false | 8,314 | [
"MIT"
] | 31 | 56cb666ce516cce9acbcfce78fb4e95d81e11e54 | https://github.com/IsaacChanghau/ReLoCLNet/tree/56cb666ce516cce9acbcfce78fb4e95d81e11e54 |
CmapPafHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn
import torch.optim
assert_size_stride = ... | tucachmo2202/trt_pose | CmapPafHeadAttention | false | 13,082 | [
"MIT"
] | 0 | b847fc197c32219dc2d719c2b42906603da0988a | https://github.com/tucachmo2202/trt_pose/tree/b847fc197c32219dc2d719c2b42906603da0988a |
Conv2dSame | import torch
import torch.utils.data
import torch.utils.data.distributed
from torch import nn
import torch.nn.functional as F
from typing import Optional
from typing import Tuple
import torch.nn.parallel
import torch.optim
def _calc_same_pad(input_: 'int', kernel: 'int', stride: 'int', dilation: 'int'
):
"""c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.utils.data.distributed
from torch import nn... | xmyyzy123/zen_nas | Conv2dSame | false | 4,592 | [
"Apache-2.0"
] | 0 | 4870eb0a030856bd67afe8529f65af8dc3bd81dc | https://github.com/xmyyzy123/zen_nas/tree/4870eb0a030856bd67afe8529f65af8dc3bd81dc |
ExponentialEnvelope | import torch
class ExponentialEnvelope(torch.nn.Module):
"""
Exponential envelope function that ensures a smooth cutoff,
as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom
and Nonlocal Effects
"""
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | krylea/ocp | ExponentialEnvelope | false | 10,500 | [
"MIT"
] | 0 | 00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | https://github.com/krylea/ocp/tree/00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 |
DoubleConv | # 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 ... | junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration | DoubleConv | false | 15,747 | [
"MIT"
] | 82 | dfa24a47a564a000aa9b4eea95a6e83a24568359 | https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration/tree/dfa24a47a564a000aa9b4eea95a6e83a24568359 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self) ->None:
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | BatFresh/Resoure_variation | Net | false | 8,872 | [
"MIT"
] | 0 | a55d182b7bdd2b65d7ad10c9f8cfcb45436ad291 | https://github.com/BatFresh/Resoure_variation/tree/a55d182b7bdd2b65d7ad10c9f8cfcb45436ad291 |
Hsigmoid | import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class Hsigmoid(nn.Module):
def __init__(self, add_stub=False):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
self.add_stub = add_stub
... | 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... | T-head-Semi/tvm | Hsigmoid | false | 17,958 | [
"Apache-2.0"
] | 4 | c1b8e06685c92fb7cacbe989e147b0622aee4503 | https://github.com/T-head-Semi/tvm/tree/c1b8e06685c92fb7cacbe989e147b0622aee4503 |
Conv5x5 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | ArminMasoumian/GCNDepth | Conv5x5 | false | 7,733 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 50, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(50, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | lykasbongbongbong/Pytorch | Net | false | 10,434 | [
"MIT"
] | 0 | f01d89fb51ac939f5a110f5ab6190c11917e66fc | https://github.com/lykasbongbongbong/Pytorch/tree/f01d89fb51ac939f5a110f5ab6190c11917e66fc |
Downsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | FengNiMa/pytorch_diffusion_model_celebahq | Downsample | false | 8,105 | [
"MIT"
] | 17 | b81e57453066e05d71feb8451bbff766df401386 | https://github.com/FengNiMa/pytorch_diffusion_model_celebahq/tree/b81e57453066e05d71feb8451bbff766df401386 |
Invertible1x1Conv | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data
import torch.nn
class Invertible1x1Conv(torch.nn.Module):
"""
The layer outputs both the convolution, and the log determinant
of its weight matrix. If reverse=True it does convolution with
inverse... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional as F
from torch.autograd import Variable
import torch... | GreyZzzzzzXh/TensorRT | Invertible1x1Conv | false | 507 | [
"Apache-2.0"
] | 0 | ba5b1b4f1ade5896c7fae206e43570a2712498d4 | https://github.com/GreyZzzzzzXh/TensorRT/tree/ba5b1b4f1ade5896c7fae206e43570a2712498d4 |
FusedUpsample | # 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
from math import sqrt
assert_size_stride = torch._C._dynamo... | celdeldel/style_conditionnal_gan | FusedUpsample | false | 1,656 | [
"MIT"
] | 0 | 3a4623560af1e12d46e2f9ffa9726c29df9d5680 | https://github.com/celdeldel/style_conditionnal_gan/tree/3a4623560af1e12d46e2f9ffa9726c29df9d5680 |
BasicModel_ConvNet_MaxPool3d | # 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.... | ngduduong/captum | BasicModel_ConvNet_MaxPool3d | false | 4,097 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
KLLoss | import torch
from torch import nn
import torch.nn.functional as F
from math import sqrt as sqrt
from itertools import product as product
class KLLoss(nn.Module):
"""
Kl-loss function for bounding box regression from CVPR 2019 paper:
Bounding Box Regression with Uncertainty for Accurate Object Detection
... | 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
f... | JappaB/Active_Learning_Object_Detection | KLLoss | false | 8,319 | [
"MIT"
] | 21 | 3d9ad367aa872cbf3e9d71c566042c78fe2d0e76 | https://github.com/JappaB/Active_Learning_Object_Detection/tree/3d9ad367aa872cbf3e9d71c566042c78fe2d0e76 |
SelfAttention | import torch
import torch.nn as nn
class SelfAttention(nn.Module):
def __init__(self, embed_size, heads):
super(SelfAttention, self).__init__()
self.embed_size = embed_size
self.heads = heads
self.head_dimension = embed_size // heads
assert self.head_dimension * self.heads... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | shahrukhx01/transformers-bisected | SelfAttention | false | 10,797 | [
"Apache-2.0"
] | 0 | a97647aca7963e6f9d4fce5a067ba68d393072d6 | https://github.com/shahrukhx01/transformers-bisected/tree/a97647aca7963e6f9d4fce5a067ba68d393072d6 |
OutConv | import torch
import torch.utils.data
import torch
import torch.nn as nn
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(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.utils.data
import torch
import torch.nn as nn
assert_size_stride = ... | AzmHmd/RMS | OutConv | false | 1,994 | [
"MIT"
] | 0 | 61d108e118d1e06de324644ebd8d92fc1b091b91 | https://github.com/AzmHmd/RMS/tree/61d108e118d1e06de324644ebd8d92fc1b091b91 |
SpaceToDepth | import torch
from torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class SpaceToDepth(nn.Module):
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distr... | Alibaba-MIIL/ZS_SDL | SpaceToDepth | false | 8,093 | [
"MIT"
] | 20 | 769fe4f57d2d458a7c4b5468a6395c9b296b1dad | https://github.com/Alibaba-MIIL/ZS_SDL/tree/769fe4f57d2d458a7c4b5468a6395c9b296b1dad |
HDRLoss | import torch
import torch.nn as nn
class HDRLoss(nn.Module):
"""High dynamic range loss."""
def __init__(self, eps=0.01):
"""Initializes loss with numerical stability epsilon."""
super(HDRLoss, self).__init__()
self._eps = eps
def forward(self, denoised, target):
"""Compu... | 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... | delldu/Noise2Noise | HDRLoss | false | 15,158 | [
"MIT"
] | 224 | f519f208776a60efadac208c109c9b7f432504b5 | https://github.com/delldu/Noise2Noise/tree/f519f208776a60efadac208c109c9b7f432504b5 |
DenseGraphConv | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseGraphConv(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv.GraphConv`.
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | cshjin/pytorch_geometric | DenseGraphConv | false | 1,763 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f |
BCELabelSmoothingLoss | import torch
from torch import nn
import torch.nn.functional as F
class BCELabelSmoothingLoss(nn.Module):
"""
Binary Cross Entropy Loss with label smoothing, takes logits
"""
def __init__(self, smoothing):
"""
`smoothing` is the smoothing factor. How much less confident than 100%
... | 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 ... | alexander-soare/PyTorch-Custom | BCELabelSmoothingLoss | false | 1,408 | [
"Apache-2.0"
] | 0 | f4f9865f960806f7e05d55ea259e861ee2d7c6dc | https://github.com/alexander-soare/PyTorch-Custom/tree/f4f9865f960806f7e05d55ea259e861ee2d7c6dc |
CNNCifar | # 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.... | ITSEG-MQ/Chain-PPFL | CNNCifar | false | 18,369 | [
"MIT"
] | 8 | 21d4fafcd8e118cc4eaa35348f1204fecce78138 | https://github.com/ITSEG-MQ/Chain-PPFL/tree/21d4fafcd8e118cc4eaa35348f1204fecce78138 |
simple_mlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | broadinstitute/TCRP | simple_mlp | false | 1,580 | [
"MIT"
] | 0 | 9e580dbf0c9d0ec5e5b1a949087df5a3724fa35b | https://github.com/broadinstitute/TCRP/tree/9e580dbf0c9d0ec5e5b1a949087df5a3724fa35b |
CrossEntropyLoss | # 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.functi... | aldakata/ClassConditionalC2D | CrossEntropyLoss | false | 6,161 | [
"MIT"
] | 1 | dd73e1d4d5f0f82438340211e3c479dbd16b8ffc | https://github.com/aldakata/ClassConditionalC2D/tree/dd73e1d4d5f0f82438340211e3c479dbd16b8ffc |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GoldbergData/pytorch-forecasting | ScaledDotProductAttention | false | 2,341 | [
"MIT"
] | 0 | e2ef3794da5d996c9740d932a4f55269bb4003f2 | https://github.com/GoldbergData/pytorch-forecasting/tree/e2ef3794da5d996c9740d932a4f55269bb4003f2 |
ScalePredictor | import torch
from torch import nn
class ScalePredictor(nn.Module):
def __init__(self, nz):
super(ScalePredictor, self).__init__()
self.pred_layer = nn.Linear(nz, 3)
def forward(self, feat):
scale = self.pred_layer.forward(feat) + 1
scale = torch.nn.functional.relu(scale) + 1e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | JasonQSY/Associative3D | ScalePredictor | false | 8,340 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
AFMS | import torch
import torch.nn as nn
import torch.nn.functional as F
class AFMS(nn.Module):
"""
Alpha-Feature map scaling, added to the output of each residual block[1,2].
Reference:
[1] RawNet2 : https://www.isca-speech.org/archive/Interspeech_2020/pdfs/1011.pdf
[2] AMFS : https://www.koreascie... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ishine/RawNet | AFMS | false | 15,633 | [
"MIT"
] | 199 | cddec5afa27049a4b507f3d48bb02b993ea838bb | https://github.com/ishine/RawNet/tree/cddec5afa27049a4b507f3d48bb02b993ea838bb |
FastAttention | # 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.... | ishine/Comprehensive-Transformer-TTS | FastAttention | false | 15,627 | [
"MIT"
] | 147 | dca252cae50a18464ce2410aa85a21c557c72d7a | https://github.com/ishine/Comprehensive-Transformer-TTS/tree/dca252cae50a18464ce2410aa85a21c557c72d7a |
AttentionModuleV2 | # 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.... | ArmandNM/meta-learning | AttentionModuleV2 | false | 126 | [
"MIT"
] | 0 | 173fcd4b929168e9bd7948581293020a3a932857 | https://github.com/ArmandNM/meta-learning/tree/173fcd4b929168e9bd7948581293020a3a932857 |
softCE | import torch
import torch.nn as nn
import torch.nn.init
class softCE(nn.Module):
"""
The objective function for the distant supervised typing.
Parameters
----------
if_average : ``bool``, optional, (default = True).
Whether to average over batches or not.
"""
def __init__(self, i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | s-tatsu/AutoNER | softCE | false | 16,351 | [
"Apache-2.0"
] | 446 | 75f8d092a5bf83fabf4ac4e879fab9120bbcd083 | https://github.com/s-tatsu/AutoNER/tree/75f8d092a5bf83fabf4ac4e879fab9120bbcd083 |
MySoftmax | import torch
import torch.nn as nn
import torch.nn.functional as F
class MySoftmax(nn.Module):
def forward(self, input_):
batch_size = input_.size()[0]
output_ = torch.stack([F.softmax(input_[i]) for i in range(
batch_size)], 0)
return output_
def get_inputs():
return [t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | acrosson/dl | MySoftmax | false | 1,369 | [
"MIT"
] | 0 | 94e68533b38f53f09e9bef460ba96fa389fc8eb4 | https://github.com/acrosson/dl/tree/94e68533b38f53f09e9bef460ba96fa389fc8eb4 |
CoevolExtractor | # 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 ... | wukevin/RoseTTAFold | CoevolExtractor | false | 4,559 | [
"MIT"
] | 0 | e3c15dbf4bc1e4f8726e26c63aca1625188da803 | https://github.com/wukevin/RoseTTAFold/tree/e3c15dbf4bc1e4f8726e26c63aca1625188da803 |
layer_1_to_2 | import torch
import numpy as np
import torch.nn as nn
def contractions_1_to_2(inputs, dim, normalization='inf', normalization_val=1.0
):
sum_all = torch.sum(inputs, dim=2).unsqueeze(dim=2)
op1 = torch.diag_embed(inputs, dim1=2, dim2=3)
op2 = torch.diag_embed(torch.cat([sum_all for d in range(dim)], di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | HyTruongSon/InvariantGraphNetworks-PyTorch | layer_1_to_2 | false | 17,421 | [
"Apache-2.0"
] | 7 | da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | https://github.com/HyTruongSon/InvariantGraphNetworks-PyTorch/tree/da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 |
SegmentationTestModel | # 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 torch.nn as nn
from typing import Any
from to... | LaudateCorpus1/torchgeo | SegmentationTestModel | false | 2,491 | [
"MIT"
] | 0 | 747a9352b9663e7d0e0c90a8b53533f0bb06c9b3 | https://github.com/LaudateCorpus1/torchgeo/tree/747a9352b9663e7d0e0c90a8b53533f0bb06c9b3 |
NonlocalWeightedAverage | # 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.... | qiyuqianxai/debvc | NonlocalWeightedAverage | false | 10,787 | [
"MIT"
] | 0 | 1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 | https://github.com/qiyuqianxai/debvc/tree/1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 |
KLDLoss | # 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
... | kudoNCT/michigan_copy | KLDLoss | false | 12,687 | [
"MIT"
] | 0 | e857b96a65b270ef2506cb9866b7e01f117c4396 | https://github.com/kudoNCT/michigan_copy/tree/e857b96a65b270ef2506cb9866b7e01f117c4396 |
Model | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | ibaiGorordo/depthai-experiments | Model | false | 6,846 | [
"MIT"
] | 1 | cde67e277120ddac815cbad6360695759cca900f | https://github.com/ibaiGorordo/depthai-experiments/tree/cde67e277120ddac815cbad6360695759cca900f |
Alignment | # 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.... | IamHimon/re2 | Alignment | false | 11,817 | [
"Apache-2.0"
] | 0 | d16b0ffc385f7b118a6160d035250da8d6320534 | https://github.com/IamHimon/re2/tree/d16b0ffc385f7b118a6160d035250da8d6320534 |
ScaledDotProductAttention | import torch
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
self.softmax = nn.So... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | YuxiXie/Semantic-Graphs-for-Generating-Deep-Questions | ScaledDotProductAttention | false | 14,703 | [
"MIT"
] | 62 | 6e5ef241c64b5b30a6ff54ddad31e610013b8388 | https://github.com/YuxiXie/Semantic-Graphs-for-Generating-Deep-Questions/tree/6e5ef241c64b5b30a6ff54ddad31e610013b8388 |
EncoderLayer | import math
import torch
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, d_model, eps=1e-12):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torch.zeros(d_model))
self.eps = eps
def forward(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.... | jkimbf/transformer-1 | EncoderLayer | false | 15,732 | [
"Apache-2.0"
] | 233 | 6cd29731197822d6db641cdbfad3b045b8a294e4 | https://github.com/jkimbf/transformer-1/tree/6cd29731197822d6db641cdbfad3b045b8a294e4 |
EdgeFeatures | # 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... | BrandonKates/graph-convnet-tsp | EdgeFeatures | false | 11,247 | [
"MIT"
] | 0 | f6e17e84311c23fd5cab041b7a27b4e0636c44f8 | https://github.com/BrandonKates/graph-convnet-tsp/tree/f6e17e84311c23fd5cab041b7a27b4e0636c44f8 |
TransitionUp | import torch
import torch.nn as nn
def center_crop(layer, max_height, max_width):
_, _, h, w = layer.size()
xy1 = (w - max_width) // 2
xy2 = (h - max_height) // 2
return layer[:, :, xy2:xy2 + max_height, xy1:xy1 + max_width]
class TransitionUp(nn.Module):
def __init__(self, in_channels, out_cha... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Alfo5123/ConcreteDropout | TransitionUp | false | 16,885 | [
"MIT"
] | 7 | c442871553e20a2de078c0fbac7fa52302d50abf | https://github.com/Alfo5123/ConcreteDropout/tree/c442871553e20a2de078c0fbac7fa52302d50abf |
WeightedCE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import Opt... | Atharva-Peshkar/pytorch_connectomics | WeightedCE | false | 13,324 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
TCB | # 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 it... | SaralaSewwandi/refinedet-onnxvalidation | TCB | false | 9,469 | [
"MIT"
] | 0 | 5b71c994fc6ca183dc6cb30b7e21d201c15da490 | https://github.com/SaralaSewwandi/refinedet-onnxvalidation/tree/5b71c994fc6ca183dc6cb30b7e21d201c15da490 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Dookas/Robust-Multitask-RL | Net | false | 13,613 | [
"MIT"
] | 106 | 7970e20cbdf91703c88edcb84568d7354e2525bc | https://github.com/Dookas/Robust-Multitask-RL/tree/7970e20cbdf91703c88edcb84568d7354e2525bc |
PixelNorm | import torch
import torch.nn as nn
import torch.utils.cpp_extension
import torch.utils.data.distributed
class PixelNorm(nn.Module):
def __init__(self, dim):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=2, keepdim=
True) + 1e-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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.cpp_extension
import torch.utils.data.... | Pragyanstha/SummerCamp2021 | PixelNorm | false | 4,480 | [
"MIT"
] | 0 | caa8bba64020ba52bdef2b23a7a54de93e93b8af | https://github.com/Pragyanstha/SummerCamp2021/tree/caa8bba64020ba52bdef2b23a7a54de93e93b8af |
DeepSupervisionModule | # 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... | rinkwitz/Thesis_Semantic_Image_Segmentation_on_Satellite_Imagery_using_UNets | DeepSupervisionModule | false | 7,566 | [
"MIT"
] | 1 | 75d3a4a536f6ef81fe0efd4f5fbba32b627a7472 | https://github.com/rinkwitz/Thesis_Semantic_Image_Segmentation_on_Satellite_Imagery_using_UNets/tree/75d3a4a536f6ef81fe0efd4f5fbba32b627a7472 |
CustomSoftplus | # 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
import torch.utils.data
assert_size_stride = torch.... | SortAnon/BVAE-TTS | CustomSoftplus | false | 14,429 | [
"MIT"
] | 138 | 69c2ee0c8bf30fe6133cfa8be68a36916f15bcff | https://github.com/SortAnon/BVAE-TTS/tree/69c2ee0c8bf30fe6133cfa8be68a36916f15bcff |
TrendNet | # 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... | pixel-ports/PV_prod_predic | TrendNet | false | 7,471 | [
"Apache-2.0"
] | 1 | 2ceb4cf8218f43f3ea94c5520b1904663cfb0de1 | https://github.com/pixel-ports/PV_prod_predic/tree/2ceb4cf8218f43f3ea94c5520b1904663cfb0de1 |
Model | from torch.nn import Module
import torch
import torch.nn.functional
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
import torch.optim
from torch.nn import Parameter
from torch.nn import Module
class Mode... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.nn.functional
from torch.nn.parameter import Parameter
from torch.nn.modules import Module
import t... | Cubbee/apex | Model | false | 16,397 | [
"BSD-3-Clause"
] | 268 | 0a991543846966d5f586540dc2441e512139e9fc | https://github.com/Cubbee/apex/tree/0a991543846966d5f586540dc2441e512139e9fc |
Disc | # 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_... | junhahyung/Pytorch-Sketch-RNN | Disc | false | 12,640 | [
"MIT"
] | 0 | 7aa82755fdfdb9bd36f8a83f1cfc0ade43e50a7a | https://github.com/junhahyung/Pytorch-Sketch-RNN/tree/7aa82755fdfdb9bd36f8a83f1cfc0ade43e50a7a |
ActLog | # 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
... | High-East/BCI-ToolBox | ActLog | false | 17,372 | [
"MIT"
] | 10 | 57015ae5fd008e8636889b9afba49c64c3a35ff3 | https://github.com/High-East/BCI-ToolBox/tree/57015ae5fd008e8636889b9afba49c64c3a35ff3 |
PrimaryCaps | import torch
import torch.nn as nn
def squash(inputs, axis=-1):
"""capsule输出的激活函数"""
norm = torch.norm(inputs, dim=axis, keepdim=True)
scale = norm ** 2 / (1 + norm ** 2) / (norm + 1e-08)
return scale * inputs
class PrimaryCaps(nn.Module):
"""计算第一层capsules的输入，转换成32*6*6个8维的capsule vector
in_c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | RacleRay/-Have_Fun_Doing | PrimaryCaps | false | 5,740 | [
"Apache-2.0"
] | 1 | 8ebb7fcabc6148571d38f2f51eac47952ce54424 | https://github.com/RacleRay/-Have_Fun_Doing/tree/8ebb7fcabc6148571d38f2f51eac47952ce54424 |
BasicModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
input = 1 - F.relu(1 - input)
return input
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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ngduduong/captum | BasicModel | false | 4,064 | [
"BSD-3-Clause"
] | 0 | 6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 | https://github.com/ngduduong/captum/tree/6fe5f0f23ea975e73e0c0dee79bdc01b4223d283 |
OutPutBlock | import torch
import torch.nn as nn
class OutPutBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutPutBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns // 2, kernel_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | tea321000/SSL4MIS | OutPutBlock | false | 16,538 | [
"MIT"
] | 854 | 8d1b0be08cf089943481a47877b36eb6405fffb2 | https://github.com/tea321000/SSL4MIS/tree/8d1b0be08cf089943481a47877b36eb6405fffb2 |
UpConv2x2 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.cuda import *
class UpConv2x2(nn.Module):
def __init__(self, channels):
super(UpConv2x2, self).__init__()
self.conv = nn.Conv2d(channels, channels // 2, kernel_size=2,
stride=1, paddi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch.cuda import *
assert_si... | jabae/detectEM | UpConv2x2 | false | 6,912 | [
"MIT"
] | 1 | 2d1a5116164d0bed0a8ea767a227d05a8970a448 | https://github.com/jabae/detectEM/tree/2d1a5116164d0bed0a8ea767a227d05a8970a448 |
HingeLoss | # 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... | cjhsieh/pecos | HingeLoss | false | 3,295 | [
"Apache-2.0",
"BSD-3-Clause"
] | 0 | 22e88ee544d5a5e891a1d23a578881fdf26dfcf7 | https://github.com/cjhsieh/pecos/tree/22e88ee544d5a5e891a1d23a578881fdf26dfcf7 |
WeightNormConv2d | # 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 ... | eyalbetzalel/GlowGAN | WeightNormConv2d | false | 15,337 | [
"MIT"
] | 54 | 144b8fef60d9dc38ca66c178a18c0c9a2a17c23e | https://github.com/eyalbetzalel/GlowGAN/tree/144b8fef60d9dc38ca66c178a18c0c9a2a17c23e |
DistMultLayer | import torch
import torch.utils.data
import torch.nn as nn
class DistMultLayer(nn.Module):
def __init__(self):
super(DistMultLayer, self).__init__()
def forward(self, sub_emb, obj_emb, rel_emb):
return torch.sum(sub_emb * obj_emb * rel_emb, dim=-1)
def predict(self, sub_emb, obj_emb, re... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | ChengzhiPiao/cogdl | DistMultLayer | false | 4,976 | [
"MIT"
] | 1 | 182e0b95b3dfbe771570037c58aacd8f677b6500 | https://github.com/ChengzhiPiao/cogdl/tree/182e0b95b3dfbe771570037c58aacd8f677b6500 |
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... | musyoku/ffjord | BlendLinear | false | 7,297 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
PMA | import math
import torch
from torch import Tensor
from torch.nn import Linear
from typing import Type
from typing import Optional
from typing import Tuple
from torch.nn import LayerNorm
class MAB(torch.nn.Module):
def __init__(self, dim_Q: 'int', dim_K: 'int', dim_V: 'int', num_heads:
'int', Conv: 'Optio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ClintvanHoesel/MXMNet_adapted | PMA | false | 325 | [
"MIT"
] | 0 | 091aae4a664b5b0944dfe95dbd2f5da441541437 | https://github.com/ClintvanHoesel/MXMNet_adapted/tree/091aae4a664b5b0944dfe95dbd2f5da441541437 |
AttnScore | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IndexFziQ/ASER | AttnScore | false | 2,375 | [
"MIT"
] | 0 | 67dd1a2a25cec175c15675cc1f8a63ca065b447e | https://github.com/IndexFziQ/ASER/tree/67dd1a2a25cec175c15675cc1f8a63ca065b447e |
AdaptiveAvgPool3dOutSize1 | # 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
from abc import abstractmethod
from typing import Tuple
import torch.utils.data
import torch.nn
assert_size_stride = t... | TheShadow29/pytorchvideo | AdaptiveAvgPool3dOutSize1 | false | 9,691 | [
"Apache-2.0"
] | 0 | 39a3e34e33fb0e1ec142288df08f6e8c3585961a | https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a |
RegularizedLinear | # 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.... | dearkafka/inferno | RegularizedLinear | false | 1,818 | [
"Apache-2.0"
] | 0 | e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a | https://github.com/dearkafka/inferno/tree/e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a |
ChannelSqueezeAndSpatialExcitation | import torch
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
import torch.backends
class ChannelSqueezeAndSpatialExcitation(nn.Module):
"""
The sSE (Channel Squeeze and Spatial Ex... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules ... | YaLTeR/catalyst | ChannelSqueezeAndSpatialExcitation | false | 9,671 | [
"Apache-2.0"
] | 0 | 4b875b50b3c63ac2dac1f19399af0c016dfb4e2f | https://github.com/YaLTeR/catalyst/tree/4b875b50b3c63ac2dac1f19399af0c016dfb4e2f |
HuberLoss | import torch
import torch.nn as nn
import torch.utils.data
class HuberLoss(nn.Module):
def __init__(self, delta=1):
super().__init__()
self.huber_loss_delta1 = nn.SmoothL1Loss()
self.delta = delta
def forward(self, x, x_hat):
loss = self.huber_loss_delta1(x / self.delta, x_ha... | 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
... | Altriaex/d4rl_evaluations | HuberLoss | false | 8,948 | [
"Apache-2.0"
] | 0 | ceb34c04e98af9332c6338a1414c0c2aa5fea68b | https://github.com/Altriaex/d4rl_evaluations/tree/ceb34c04e98af9332c6338a1414c0c2aa5fea68b |
Downsample | import torch
import torch.nn as nn
import torch.hub
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels, in_channels,
kernel_size=3, st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.hub
assert_size_stride = torch._C._dynamo.gua... | Frikallo/YAKbot | Downsample | false | 5,173 | [
"MIT"
] | 1 | bc798fe4ead1f6a3e4828960ea77e2a8f07b5fdc | https://github.com/Frikallo/YAKbot/tree/bc798fe4ead1f6a3e4828960ea77e2a8f07b5fdc |
BinaryFocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryFocalLoss(nn.Module):
"""
This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in
'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)'
Focal_Loss= -1*... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | naivepig1998/brain_met_3d_cnn | BinaryFocalLoss | false | 12,816 | [
"MIT"
] | 0 | 6abd783a6e0185c72d64a89713fdaa3bee68a65f | https://github.com/naivepig1998/brain_met_3d_cnn/tree/6abd783a6e0185c72d64a89713fdaa3bee68a65f |
SmoothCrossEntropyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.... | Fuminides/athena | SmoothCrossEntropyLoss | false | 17,284 | [
"MIT"
] | 10 | 78ad7ad5236dc8f12adc0401c52add3931292e69 | https://github.com/Fuminides/athena/tree/78ad7ad5236dc8f12adc0401c52add3931292e69 |
GlobalWeightedAvgPool2d | # 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 im... | theNero93/dfdc_deepfake_challenge | GlobalWeightedAvgPool2d | false | 4,414 | [
"MIT"
] | 0 | ef275206efc6f1b0b7984b370a14bd8db61d1ec1 | https://github.com/theNero93/dfdc_deepfake_challenge/tree/ef275206efc6f1b0b7984b370a14bd8db61d1ec1 |
LipSwish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | shi27feng/torchsde | LipSwish | false | 4,304 | [
"Apache-2.0"
] | 0 | 58105bb6b839766c1d27b73c4fe3f949869d7394 | https://github.com/shi27feng/torchsde/tree/58105bb6b839766c1d27b73c4fe3f949869d7394 |
IOU | # 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.multiprocessing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._e... | intchous/SmartText | IOU | false | 6,886 | [
"MIT"
] | 1 | 81abb84ce135a3859c32257d861c9e87b51f8c3f | https://github.com/intchous/SmartText/tree/81abb84ce135a3859c32257d861c9e87b51f8c3f |
GlobalAveragePooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | PeterouZh/CIPS-3D | GlobalAveragePooling | false | 14,163 | [
"MIT"
] | 308 | 9b8bfa0fb23f642af042e150ccd70408f9d137c6 | https://github.com/PeterouZh/CIPS-3D/tree/9b8bfa0fb23f642af042e150ccd70408f9d137c6 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, n_classes):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 *... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | ArWeHei/edflow | Net | false | 4,891 | [
"MIT"
] | 1 | 3383cfbc42a43e906bc7781ad05714fd4fc9616e | https://github.com/ArWeHei/edflow/tree/3383cfbc42a43e906bc7781ad05714fd4fc9616e |
CriticMlp | import torch
import torch.nn as nn
import torch.nn.functional as F
def init_weights(layer, gain):
for p in layer.parameters():
if len(p.data.shape) >= 2:
nn.init.orthogonal_(p, gain=gain)
else:
p.data.zero_()
def all_init_weights(m, gain=2 ** 0.5):
init_weights(m, gai... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | heavenlysf/thesis | CriticMlp | false | 10,428 | [
"MIT"
] | 0 | 646553c45860f337c91a48ab7f666a174784472f | https://github.com/heavenlysf/thesis/tree/646553c45860f337c91a48ab7f666a174784472f |
SuperPointNet | # 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.... | Sunny-Qin-0314/pytorch-superpoint | SuperPointNet | false | 1,134 | [
"MIT"
] | 0 | 5c5325a1e5917afcc7469e137206990a8cd33725 | https://github.com/Sunny-Qin-0314/pytorch-superpoint/tree/5c5325a1e5917afcc7469e137206990a8cd33725 |
ResidualUnit | # 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.model_zoo
assert_size_stride = torch._C... | NawaNae/ESRT-Huawei | ResidualUnit | false | 2,664 | [
"MIT"
] | 0 | edea1c0bafec940dc7ea8e5110c355a83188665c | https://github.com/NawaNae/ESRT-Huawei/tree/edea1c0bafec940dc7ea8e5110c355a83188665c |
FM | # 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
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | Fanxingye/DeepRS | FM | false | 14,028 | [
"Apache-2.0"
] | 1,770 | 06b98cf2cb2781656805eafc577fbd088f37d17d | https://github.com/Fanxingye/DeepRS/tree/06b98cf2cb2781656805eafc577fbd088f37d17d |
ShiftedSoftplus | import torch
import torch.nn.functional as F
import torch.utils.data
class ShiftedSoftplus(torch.nn.Module):
def __init__(self):
super(ShiftedSoftplus, self).__init__()
self.shift = torch.log(torch.tensor(2.0)).item()
def forward(self, x):
return F.softplus(x) - self.shift
def get_... | 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.utils.data
assert_size_stride = torch._C._dynamo.... | MINATILO/pytroch-geometric | ShiftedSoftplus | false | 9,395 | [
"MIT"
] | 0 | 706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 | https://github.com/MINATILO/pytroch-geometric/tree/706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 |
AttentionPool2d | # 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.... | ZGCTroy/guided-diffusion | AttentionPool2d | false | 1,307 | [
"MIT"
] | 0 | af987bb2b65db2875148a5466df79736ea5ae6a1 | https://github.com/ZGCTroy/guided-diffusion/tree/af987bb2b65db2875148a5466df79736ea5ae6a1 |
ConvChain | # 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... | IlyaBizyaev/ttools | ConvChain | false | 8,317 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
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