entry_point stringlengths 1 65 | original_triton_code stringlengths 4.5k 619k | python_code stringlengths 208 60.9k | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
CosineLinear | # 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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
class CosineLinear(nn.Module):
def __init__(self, in_features, out_features, sigma=True):
super(CosineLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | QIU023/continual-learning-reproduce | CosineLinear | false | 9,482 | [
"MIT"
] | 0 | 772faa6904b3488fa5deee14f03d86f3b3664a87 | https://github.com/QIU023/continual-learning-reproduce/tree/772faa6904b3488fa5deee14f03d86f3b3664a87 |
SplitCosineLinear | # 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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
class CosineLinear(nn.Module):
def __init__(self, in_features, out_features, sigma=True):
super(CosineLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | QIU023/continual-learning-reproduce | SplitCosineLinear | false | 9,483 | [
"MIT"
] | 0 | 772faa6904b3488fa5deee14f03d86f3b3664a87 | https://github.com/QIU023/continual-learning-reproduce/tree/772faa6904b3488fa5deee14f03d86f3b3664a87 |
SoftTargetCrossEntropy | # 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 typing import *
import torch.nn.functional as F
from torch.nn.modules.loss import _WeightedLoss
class SoftTargetCrossEntropy(_WeightedLoss):
def __init__(self, weight=None, reduction='mean'):
super().__init__(weight=weight, reduction=reduction)
self.weight = weight
self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from typing import *
f... | SuHuynh/leaf-disease-classification-kaggle | SoftTargetCrossEntropy | false | 9,484 | [
"MIT"
] | 0 | b1c15881de5a20e590a69f6b2fbb476b003bc077 | https://github.com/SuHuynh/leaf-disease-classification-kaggle/tree/b1c15881de5a20e590a69f6b2fbb476b003bc077 |
PointWiseConvolution | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn as nn
class PointWiseConvolution(nn.Module):
def __init__(self, inChannels, outChannels, stride, expansionFactor,
isNormal):
super(PointWiseConvolution, self).__init__()
if isNormal:
self.layer = nn.Conv2d(in_channels=inChannels * expansionFac... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | Pranshu-Bahadur/g2net | PointWiseConvolution | false | 9,485 | [
"MIT"
] | 0 | a117df7699837c9a3ae21ec59a310d7384369601 | https://github.com/Pranshu-Bahadur/g2net/tree/a117df7699837c9a3ae21ec59a310d7384369601 |
ConvLayer | # 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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
from itertools import product as product
class NormLayer(nn.Module):
"""Normalization Layers.
Args:
channels: input channels, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | Cospel/facexlib | ConvLayer | false | 9,486 | [
"MIT"
] | 0 | 2471ddb44b1d61306c6d7fcf56846b9e4aeea4aa | https://github.com/Cospel/facexlib/tree/2471ddb44b1d61306c6d7fcf56846b9e4aeea4aa |
SelfExpression | # 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 torch.nn as nn
class SelfExpression(nn.Module):
def __init__(self, n):
super(SelfExpression, self).__init__()
self.Coefficient = nn.Parameter(0.0001 * torch.ones(n, n, dtype=
torch.float32), requires_grad=True)
def forward(self, x):
y = torch.matmul(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ShulingTang/DSC-Net | SelfExpression | false | 9,487 | [
"MIT"
] | 0 | 2da1e0c654b045057c654cbcbb8a8c23fb832c9d | https://github.com/ShulingTang/DSC-Net/tree/2da1e0c654b045057c654cbcbb8a8c23fb832c9d |
ModelRegressionGex2Atac | # 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 torch.utils.data
import torch.nn.functional as F
import torch.nn as nn
class ModelRegressionGex2Atac(nn.Module):
def __init__(self, dim_mod1, dim_mod2):
super(ModelRegressionGex2Atac, self).__init__()
self.input_ = nn.Linear(dim_mod1, 1024)
self.fc = nn.Linear(1024, 25... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Permoment-95/neurips2021_multimodal_topmethods | ModelRegressionGex2Atac | false | 9,488 | [
"MIT"
] | 0 | 017bc23b366a80ba9b1c2a47ea6c44124f77a7ca | https://github.com/Permoment-95/neurips2021_multimodal_topmethods/tree/017bc23b366a80ba9b1c2a47ea6c44124f77a7ca |
ModelRegressionAtac2Gex | # 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 torch.utils.data
import torch.nn.functional as F
import torch.nn as nn
class ModelRegressionAtac2Gex(nn.Module):
def __init__(self, dim_mod1, dim_mod2):
super(ModelRegressionAtac2Gex, self).__init__()
self.input_ = nn.Linear(dim_mod1, 2048)
self.fc = nn.Linear(2048, 20... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Permoment-95/neurips2021_multimodal_topmethods | ModelRegressionAtac2Gex | false | 9,489 | [
"MIT"
] | 0 | 017bc23b366a80ba9b1c2a47ea6c44124f77a7ca | https://github.com/Permoment-95/neurips2021_multimodal_topmethods/tree/017bc23b366a80ba9b1c2a47ea6c44124f77a7ca |
D_DownBlock | # 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 torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | EvgeneyZ/RBPN | D_DownBlock | false | 9,490 | [
"MIT"
] | 0 | acfe636cc48a4fbfea78f934a251c32e53367659 | https://github.com/EvgeneyZ/RBPN/tree/acfe636cc48a4fbfea78f934a251c32e53367659 |
BaselineEstimator | # 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 torch.nn.functional as F
from torch import nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class BaselineEstimator(nn.Module):
def __init__(self, input_size):
super(BaselineEstimator, self).__init__()
self.ff1 = 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 import nn
import t... | StDario/fairseq-rl | BaselineEstimator | false | 9,491 | [
"BSD-3-Clause"
] | 0 | 96a0ee4db1a2d1781d565a2539c20ed392dfb608 | https://github.com/StDario/fairseq-rl/tree/96a0ee4db1a2d1781d565a2539c20ed392dfb608 |
CMlp | # 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 torch.nn as nn
class CMlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
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.triton_helpers import libdevice
import torch.nn as ... | SteveTsui/DS-Net | CMlp | false | 9,492 | [
"Apache-2.0"
] | 0 | c54585e7af40002178b7e06fc3ee09160e0d775c | https://github.com/SteveTsui/DS-Net/tree/c54585e7af40002178b7e06fc3ee09160e0d775c |
HardTripletLoss | # 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 torch.nn as nn
import torch.nn.functional as F
def _get_anchor_negative_triplet_mask(labels):
labels_equal = torch.unsqueeze(labels, 0) == torch.unsqueeze(labels, 1)
mask = labels_equal ^ 1
return mask
def _get_anchor_positive_triplet_mask(labels):
torch.device('cuda:0' if torch.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Shubodh/NetVLAD-pytorch | HardTripletLoss | false | 9,493 | [
"MIT"
] | 0 | ea45bac16dbb3e3bec4172df58715bf3526ee502 | https://github.com/Shubodh/NetVLAD-pytorch/tree/ea45bac16dbb3e3bec4172df58715bf3526ee502 |
DepthWiseConvolution | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn as nn
class DepthWiseConvolution(nn.Module):
def __init__(self, channels, kernelSize, stride, expansionFactor):
super(DepthWiseConvolution, self).__init__()
channels = channels * expansionFactor
self.layer = nn.Conv2d(channels, channels, kernelSize, strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | Pranshu-Bahadur/g2net | DepthWiseConvolution | false | 9,494 | [
"MIT"
] | 0 | a117df7699837c9a3ae21ec59a310d7384369601 | https://github.com/Pranshu-Bahadur/g2net/tree/a117df7699837c9a3ae21ec59a310d7384369601 |
UpBlock | # 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 torch.nn as nn
from torch.nn import functional as F
class UpBlock(nn.Module):
"""Upsample block for DRRG and TextSnake."""
def __init__(self, in_channels, out_channels):
super().__init__()
assert isinstance(in_channels, int)
assert isinstance(out_channels, int)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | SamDM/mmocr | UpBlock | false | 9,495 | [
"Apache-2.0"
] | 0 | 4cb69141ff8d28c8b1437bf28242e368a0e6ec4f | https://github.com/SamDM/mmocr/tree/4cb69141ff8d28c8b1437bf28242e368a0e6ec4f |
Mlayer | # 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 torch.nn as nn
class Mlayer(nn.Module):
def __init__(self, in_channel, out_channel, stride=1):
super(Mlayer, self).__init__()
m_s = torch.zeros([1, in_channel, 1, 1], requires_grad=True)
self.m_s = torch.nn.Parameter(m_s)
self.register_parameter('m_scale', self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Sharingsky/resrep | Mlayer | false | 9,496 | [
"MIT"
] | 0 | a173d1bc256b75b2c902024929e406863ce48b9b | https://github.com/Sharingsky/resrep/tree/a173d1bc256b75b2c902024929e406863ce48b9b |
ModulatedConv2d | # 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... | from torch.autograd import Function
import math
import random
import torch
from torch import nn
from torch.nn import 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_corne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | SavvaI/stylegan2-pytorch | ModulatedConv2d | false | 9,497 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | b8e4b605bd951283ef2c9a784e7afa0a486975bb | https://github.com/SavvaI/stylegan2-pytorch/tree/b8e4b605bd951283ef2c9a784e7afa0a486975bb |
ScaledL2Norm | # 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 torch.onnx
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledL2Norm(nn.Module):
def __init__(self, in_channels, initial_scale):
super(ScaledL2Norm, self).__init__()
self.in_channels = in_channels
self.scale = nn.Parameter(torch.Tensor(in_ch... | 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.onnx
import tor... | SoonminHwang/pytorch-ssd | ScaledL2Norm | false | 9,498 | [
"MIT"
] | 0 | 1d6b9427a4b649bc2ce85a82511b9dd299f9d3e8 | https://github.com/SoonminHwang/pytorch-ssd/tree/1d6b9427a4b649bc2ce85a82511b9dd299f9d3e8 |
RobustScannerFusionLayer | # 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 torch.nn as nn
class RobustScannerFusionLayer(nn.Module):
def __init__(self, dim_model, dim=-1):
super().__init__()
self.dim_model = dim_model
self.dim = dim
self.linear_layer = nn.Linear(dim_model * 2, dim_model * 2)
self.glu_layer = nn.GLU(dim=dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | SamDM/mmocr | RobustScannerFusionLayer | false | 9,499 | [
"Apache-2.0"
] | 0 | 4cb69141ff8d28c8b1437bf28242e368a0e6ec4f | https://github.com/SamDM/mmocr/tree/4cb69141ff8d28c8b1437bf28242e368a0e6ec4f |
MeanReweightLayer | # 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 torch.nn as nn
import torch.nn.parallel
from torch.nn.parameter import Parameter
class MeanReweightLayer(nn.Module):
"""Renamed to Attention-Bias (AB) layer in paper"""
def __init__(self, channel):
super(MeanReweightLayer, self).__init__()
self.cfc = Parameter(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
import torch.nn as nn
import torch.nn.parallel
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_... | SanderKlomp/channel-attention | MeanReweightLayer | false | 9,500 | [
"MIT"
] | 0 | 9dfdb28f3ad4de13b4c076d1423f21c05c907bd7 | https://github.com/SanderKlomp/channel-attention/tree/9dfdb28f3ad4de13b4c076d1423f21c05c907bd7 |
Upsampler | # 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 math
import torch
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_si... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch.... | EvgeneyZ/RBPN | Upsampler | false | 9,501 | [
"MIT"
] | 0 | acfe636cc48a4fbfea78f934a251c32e53367659 | https://github.com/EvgeneyZ/RBPN/tree/acfe636cc48a4fbfea78f934a251c32e53367659 |
GAT | # 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 torch.nn as nn
import torch.nn.functional as F
from scipy.sparse import *
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
Apply dropout to input tensor.
Parameters
----------
input_tensor: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_ti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ononoki-Yotsugi/IDGL | GAT | false | 9,502 | [
"Apache-2.0"
] | 0 | a99f840681a4ae26c2740ed9e9302d4e15a68c7f | https://github.com/Ononoki-Yotsugi/IDGL/tree/a99f840681a4ae26c2740ed9e9302d4e15a68c7f |
MLPAttention | # 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 torch.nn.functional as F
from torch import nn
from typing import Optional
import torch.optim
def get_activation_fn(name: 'Optional[str]'):
"""Returns a callable activation function from `torch`."""
if name in (None, 'linear'):
return lambda x: x
elif name in ('sigmoid', 'tanh')... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Nickeilf/pysimt | MLPAttention | false | 9,503 | [
"MIT"
] | 0 | 05c8de92d0e2b930e40939ad3695d8d2c2954dda | https://github.com/Nickeilf/pysimt/tree/05c8de92d0e2b930e40939ad3695d8d2c2954dda |
ECALayer | # 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 torch.nn as nn
import torch.nn.parallel
class ECALayer(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel, k_size=3):
super(ECALayer, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dy... | SanderKlomp/channel-attention | ECALayer | false | 9,504 | [
"MIT"
] | 0 | 9dfdb28f3ad4de13b4c076d1423f21c05c907bd7 | https://github.com/SanderKlomp/channel-attention/tree/9dfdb28f3ad4de13b4c076d1423f21c05c907bd7 |
ConvAE | # 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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dSamePad(nn.Module):
"""
Implement Tensorflow's 'SAME' padding mode in Conv2d.
When an odd number, say `m`, of pixels are need to pad, Tensorflow will pad one more column at right or one more
row at bottom. But P... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | ShulingTang/DSC-Net | ConvAE | false | 9,505 | [
"MIT"
] | 0 | 2da1e0c654b045057c654cbcbb8a8c23fb832c9d | https://github.com/ShulingTang/DSC-Net/tree/2da1e0c654b045057c654cbcbb8a8c23fb832c9d |
DSCNet | # 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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dSamePad(nn.Module):
"""
Implement Tensorflow's 'SAME' padding mode in Conv2d.
When an odd number, say `m`, of pixels are need to pad, Tensorflow will pad one more column at right or one more
row at bottom. But P... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | ShulingTang/DSC-Net | DSCNet | false | 9,506 | [
"MIT"
] | 0 | 2da1e0c654b045057c654cbcbb8a8c23fb832c9d | https://github.com/ShulingTang/DSC-Net/tree/2da1e0c654b045057c654cbcbb8a8c23fb832c9d |
MultiHeadedAttention | # 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 math
import torch
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
"""
def __init__(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Slyne/wenet | MultiHeadedAttention | false | 9,507 | [
"Apache-2.0"
] | 0 | de74d8acf40f47a3c503bff5cf4ed6808a9dad14 | https://github.com/Slyne/wenet/tree/de74d8acf40f47a3c503bff5cf4ed6808a9dad14 |
ZeroModule | # 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 torch as th
from torch import nn
import torch.random
class ZeroModule(nn.Module):
"""Module that always returns zeros of same shape as input."""
def __init__(self, features_dim: 'int'):
"""Builds ZeroModule."""
super().__init__()
self.features_dim = features_dim
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.random
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | TaoHuang13/imitation | ZeroModule | false | 9,508 | [
"MIT"
] | 0 | f979be0fa05106754f6d1e5a98495d0fedbea598 | https://github.com/TaoHuang13/imitation/tree/f979be0fa05106754f6d1e5a98495d0fedbea598 |
MaxPoolStride1 | # 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 torch.nn as nn
import torch.nn.functional as F
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, x):
padded_x = F.pad(x, (0, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | TCC-MonitoramentoInteligente/dev-tool | MaxPoolStride1 | false | 9,509 | [
"MIT"
] | 0 | d3a1d697c4ba7a5fff54be08541da4fc4811ab5e | https://github.com/TCC-MonitoramentoInteligente/dev-tool/tree/d3a1d697c4ba7a5fff54be08541da4fc4811ab5e |
NetVLAD | # 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 torch.nn as nn
import torch.nn.functional as F
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, alpha=100.0,
normalize_input=True):
"""
Args:
num_clusters : int
The number of clust... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Shubodh/NetVLAD-pytorch | NetVLAD | false | 9,510 | [
"MIT"
] | 0 | ea45bac16dbb3e3bec4172df58715bf3526ee502 | https://github.com/Shubodh/NetVLAD-pytorch/tree/ea45bac16dbb3e3bec4172df58715bf3526ee502 |
TemporalDecay | # 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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class TemporalDecay(nn.Module):
def __init__(self, input_size, rnn_hid_size):
super(TemporalDecay, self).__init__()
self.rnn_hid_size = rnn_hid_size
self.build(input_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Sobhan1996/BRITS-master | TemporalDecay | false | 9,511 | [
"MIT"
] | 0 | 66726ec104dad43c6d8367b0c9ef8f19daf65f0e | https://github.com/Sobhan1996/BRITS-master/tree/66726ec104dad43c6d8367b0c9ef8f19daf65f0e |
GCN2 | # 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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.sparse import *
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
Apply dropout to input tensor.
Parameters
----------
input_tensor: ``torch.FloatTensor``
A tensor of shape ``(batch_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 import triton_helpers
import math
import torch.nn a... | Ononoki-Yotsugi/IDGL | GCN2 | false | 9,512 | [
"Apache-2.0"
] | 0 | a99f840681a4ae26c2740ed9e9302d4e15a68c7f | https://github.com/Ononoki-Yotsugi/IDGL/tree/a99f840681a4ae26c2740ed9e9302d4e15a68c7f |
QNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class QNet(nn.Module):
def __init__(self, input_dim, output_dim):
super(QNet, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.fc1 = nn.Linear(input_dim, 64)
self.fc2 = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | SunlightWarrior/q_learning | QNet | false | 9,513 | [
"MIT"
] | 0 | 3c5f0c700fbe84ca4859165513123f404c44937f | https://github.com/SunlightWarrior/q_learning/tree/3c5f0c700fbe84ca4859165513123f404c44937f |
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
import torch.nn as nn
class MultiHeadAttention(nn.Module):
"""Multi-Head Attention module."""
def __init__(self, n_head=8, d_model=512, d_k=64, d_v=64, dropout=0.1,
qkv_bias=False, mask_value=0):
super().__init__()
self.mask_value = mask_value
self.n_head = n_head... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SamDM/mmocr | TransformerEncoderLayer | false | 9,514 | [
"Apache-2.0"
] | 0 | 4cb69141ff8d28c8b1437bf28242e368a0e6ec4f | https://github.com/SamDM/mmocr/tree/4cb69141ff8d28c8b1437bf28242e368a0e6ec4f |
CrossAttentionSublayer | # 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 math
import torch
from torch import nn
import torch.optim
class ScaledDotAttention(torch.nn.Module):
def __init__(self, model_dim, n_heads, dropout=0.0):
"""
Creates a ScaledDotAttention.
:param model_dim: The model dimensions.
:param n_heads: The number of 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.... | Nickeilf/pysimt | CrossAttentionSublayer | false | 9,515 | [
"MIT"
] | 0 | 05c8de92d0e2b930e40939ad3695d8d2c2954dda | https://github.com/Nickeilf/pysimt/tree/05c8de92d0e2b930e40939ad3695d8d2c2954dda |
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
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, 16, 3, padding=1)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | LSaldyt/laser-dog | Net | false | 9,516 | [
"MIT"
] | 0 | 168c8bfea95dcd27a499f00f191232d67ae63c1c | https://github.com/LSaldyt/laser-dog/tree/168c8bfea95dcd27a499f00f191232d67ae63c1c |
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
import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_d):
super(Net, self).__init__()
self.fc1 = nn.Linear(input_d, int(input_d / 2))
def forward(self, x):
x = torch.sigmoid(self.fc1(x))
return x
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Tenoke/models | Net | false | 9,517 | [
"Apache-2.0"
] | 0 | 84baffe34509d2f8b61689e043db2130fec8c171 | https://github.com/Tenoke/models/tree/84baffe34509d2f8b61689e043db2130fec8c171 |
GAT | # 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 torch.nn as nn
import torch.nn.functional as F
class GATLayer(nn.Module):
def __init__(self, input_feature, output_feature, dropout, alpha,
concat=True):
super(GATLayer, self).__init__()
self.input_feature = input_feature
self.output_feature = output_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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | OuYangg/GNNs | GAT | false | 9,518 | [
"Apache-2.0"
] | 0 | ef5b1944490507684d603de3ae0b2aa7b5168f47 | https://github.com/OuYangg/GNNs/tree/ef5b1944490507684d603de3ae0b2aa7b5168f47 |
SEBlock | # 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 torch.nn as nn
import torch.nn.functional as F
class SEBlock(nn.Module):
def __init__(self, input_channels, internal_neurons):
super(SEBlock, self).__init__()
self.down = nn.Conv2d(in_channels=input_channels, out_channels=
internal_neurons, kernel_size=1, stride=1,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Sharingsky/resrep | SEBlock | false | 9,520 | [
"MIT"
] | 0 | a173d1bc256b75b2c902024929e406863ce48b9b | https://github.com/Sharingsky/resrep/tree/a173d1bc256b75b2c902024929e406863ce48b9b |
ToRGB | # 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... | from torch.autograd import Function
import math
import random
import torch
from torch import nn
from torch.nn import 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_corne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 random
from torch import ... | SavvaI/stylegan2-pytorch | ToRGB | false | 9,522 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | b8e4b605bd951283ef2c9a784e7afa0a486975bb | https://github.com/SavvaI/stylegan2-pytorch/tree/b8e4b605bd951283ef2c9a784e7afa0a486975bb |
Encoder_mse | # 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 typing import Iterable
from torch.distributions import Normal
from torch import nn as nn
def reparameterize_gaussian(mu, var):
return Normal(mu, var.sqrt()).rsample()
class Encoder_mse(nn.Module):
"""Encodes data of ``n_input`` dimensions into a latent space of ``n_output``
dimensions ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | Famingzhao/scMVP | Encoder_mse | false | 9,523 | [
"MIT"
] | 0 | fb0d2d2523d0ae10e10725babe8da7de63c2eef4 | https://github.com/Famingzhao/scMVP/tree/fb0d2d2523d0ae10e10725babe8da7de63c2eef4 |
SelfAttentionSublayer | # 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 math
import torch
from torch import nn
import torch.optim
class ScaledDotAttention(torch.nn.Module):
def __init__(self, model_dim, n_heads, dropout=0.0):
"""
Creates a ScaledDotAttention.
:param model_dim: The model dimensions.
:param n_heads: The number of 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.... | Nickeilf/pysimt | SelfAttentionSublayer | false | 9,524 | [
"MIT"
] | 0 | 05c8de92d0e2b930e40939ad3695d8d2c2954dda | https://github.com/Nickeilf/pysimt/tree/05c8de92d0e2b930e40939ad3695d8d2c2954dda |
Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self, planes):
super(Block, self).__init__()
self.conv1 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.prelu1 = nn.PReLU(planes)
self.conv2 = nn.Conv2d(planes, pla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | T-Visor/face-encryption | Block | false | 9,525 | [
"Apache-2.0"
] | 0 | b09c4daecb7c77b4caa8cf898c4b09981260179c | https://github.com/T-Visor/face-encryption/tree/b09c4daecb7c77b4caa8cf898c4b09981260179c |
Polynomial3 | # 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 torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Polynomial3(torch.nn.Module):
def __init__(self):
"""
In the constructor we instantiate four parameters and assi... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.op... | Nayef211/tutorials | Polynomial3 | false | 9,526 | [
"BSD-3-Clause"
] | 0 | faf2c476fc3be855051fbea3cce77eaf7b2a2175 | https://github.com/Nayef211/tutorials/tree/faf2c476fc3be855051fbea3cce77eaf7b2a2175 |
Skew | # 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 torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Skew(nn.Module):
def forward(self, X):
A = X.triu(1)
return A - A.transpose(-1, -2)
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Nayef211/tutorials | Skew | false | 9,527 | [
"BSD-3-Clause"
] | 0 | faf2c476fc3be855051fbea3cce77eaf7b2a2175 | https://github.com/Nayef211/tutorials/tree/faf2c476fc3be855051fbea3cce77eaf7b2a2175 |
TwoMLPHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
class TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(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 import nn
assert_s... | GreenCUBIC/GasBotty | TwoMLPHead | false | 9,528 | [
"MIT"
] | 0 | 158f5991201c80bf4cbbbb9deabc9954ff19bbb1 | https://github.com/GreenCUBIC/GasBotty/tree/158f5991201c80bf4cbbbb9deabc9954ff19bbb1 |
DownBlock | # 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 torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | EvgeneyZ/RBPN | DownBlock | false | 9,529 | [
"MIT"
] | 0 | acfe636cc48a4fbfea78f934a251c32e53367659 | https://github.com/EvgeneyZ/RBPN/tree/acfe636cc48a4fbfea78f934a251c32e53367659 |
GreedyTop1 | # 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 typing import Optional
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
class GreedyTop1(pt.nn.Module):
"""
Implements picking the highest scoring next word with support for vocabulary selection and target factors.
"""
def forward(s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
assert_size_stride = torch._C._dynamo.gu... | SamuelLarkin/sockeye | GreedyTop1 | false | 9,530 | [
"Apache-2.0"
] | 0 | 7fcf6c96b15a887897aa712903ecf93c665ebddf | https://github.com/SamuelLarkin/sockeye/tree/7fcf6c96b15a887897aa712903ecf93c665ebddf |
VAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class VAE(nn.Module):
def __init__(self, x_dim, h_dim1, h_dim2, h_dim3, z_dim):
super(VAE, self).__init__()
self.x_dim = x_dim
self.fc1 = nn.Linear(x_dim, h_dim1)
self.fc2 = nn.Linear(h_dim1, h_dim2)
self.fc3 = nn.Linear(h_dim2, h_dim3)
... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from... | Sumeer1/VAE_Impute | VAE | false | 9,532 | [
"MIT"
] | 0 | 803195af20fe54352aedf26147a84a470637d560 | https://github.com/Sumeer1/VAE_Impute/tree/803195af20fe54352aedf26147a84a470637d560 |
PyTorchLHUC | # 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 torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
class PyTorchLHUC(pt.nn.Module):
"""
Learning Hidden Unit Contribution
David Vilar. "Learning Hidden Unit Contribution for Adapting Neural
Machine Translation Models" NAACL 2018
:para... | 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 as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
assert_size_stride = torch._C._dynamo.gu... | SamuelLarkin/sockeye | PyTorchLHUC | false | 9,533 | [
"Apache-2.0"
] | 0 | 7fcf6c96b15a887897aa712903ecf93c665ebddf | https://github.com/SamuelLarkin/sockeye/tree/7fcf6c96b15a887897aa712903ecf93c665ebddf |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, gamma=2):
super().__init__()
self.gamma = gamma
def forward(self, logit, target):
target = target.float()
max_val = (-logit).clamp(min=0)
loss = logit - lo... | 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 ... | Thagio/kaggle-aptos | FocalLoss | false | 9,534 | [
"MIT"
] | 0 | f565335d34b46b7fa7ca925b7d325397df8e1fee | https://github.com/Thagio/kaggle-aptos/tree/f565335d34b46b7fa7ca925b7d325397df8e1fee |
RecurrentNeuralRegressor | # 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 torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.data import BatchSampler
from torch.utils.data import SubsetRandomSampler
class RecurrentNeuralRegressor(nn.Module):
def __init__(self, sizes, prior, nonlin='relu'):
super(RecurrentNeuralRegre... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | TheCamusean/sds | RecurrentNeuralRegressor | false | 9,535 | [
"MIT"
] | 0 | 65e1736eb27dcd8829f5bff452fc09ccab3e0ae2 | https://github.com/TheCamusean/sds/tree/65e1736eb27dcd8829f5bff452fc09ccab3e0ae2 |
TracedModule | # 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 torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class TracedModule(torch.nn.Module):
def forward(self, x):
x = x.type(torch.float32)
return torch.floor(torch.sqrt(x) ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.quantization
import torch.onnx
import torch.nn.parallel
import tor... | Nayef211/tutorials | TracedModule | false | 9,536 | [
"BSD-3-Clause"
] | 0 | faf2c476fc3be855051fbea3cce77eaf7b2a2175 | https://github.com/Nayef211/tutorials/tree/faf2c476fc3be855051fbea3cce77eaf7b2a2175 |
CustomMSELoss | # 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 torch.nn as nn
class CustomMSELoss(nn.Module):
def __init__(self):
super(CustomMSELoss, self).__init__()
def forward(self, x, y):
return torch.mean(torch.pow(torch.log(torch.exp(x) - torch.exp(y)), 2))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([... | 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
... | TAN-OpenLab/TCSE-net | CustomMSELoss | false | 9,537 | [
"Apache-2.0"
] | 0 | fc6ecf704a9c128a9b5b6853cffa8486ee0f54e8 | https://github.com/TAN-OpenLab/TCSE-net/tree/fc6ecf704a9c128a9b5b6853cffa8486ee0f54e8 |
PyTorchSSRU | # 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 typing import Tuple
from abc import abstractmethod
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
class AutoregressiveLayer(pt.nn.Module):
@property
@abstractmethod
def num_state_tensors(self) ->int:
""" Number of state 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
from torch._inductor.runtime import triton_helpers
from typing import Tuple
from... | SamuelLarkin/sockeye | PyTorchSSRU | false | 9,538 | [
"Apache-2.0"
] | 0 | 7fcf6c96b15a887897aa712903ecf93c665ebddf | https://github.com/SamuelLarkin/sockeye/tree/7fcf6c96b15a887897aa712903ecf93c665ebddf |
Network | # 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 torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
"""Agent network"""
def __init__(self, in_size, out_size):
super().__init__()
self.fc1 = nn.Linear(in_size, 200)
self.fc2 = nn.Linear(200, 100)
self.fc3 = nn.Linear(100, 50)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Thytu/Deep-Q-Learning | Network | false | 9,539 | [
"MIT"
] | 0 | b17fbc66829932a9a3814a8f29d8c8146898b413 | https://github.com/Thytu/Deep-Q-Learning/tree/b17fbc66829932a9a3814a8f29d8c8146898b413 |
TokenEmbedding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class TokenEmbedding(nn.Module):
def __init__(self, vocab_size: 'int', emb_... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Nayef211/tutorials | TokenEmbedding | false | 9,540 | [
"BSD-3-Clause"
] | 0 | faf2c476fc3be855051fbea3cce77eaf7b2a2175 | https://github.com/Nayef211/tutorials/tree/faf2c476fc3be855051fbea3cce77eaf7b2a2175 |
RegWeightedL1Loss | # 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 torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def _gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
feat = feat.gather(1, ind)
if mask is not None:
mask = mask.unsqueeze(2).expand_as(... | 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
... | Ssong24/CenterNet_Custom | RegWeightedL1Loss | false | 9,541 | [
"MIT"
] | 0 | 526ec70f8dfabf9fb9179c9be28ce50fb2a7961c | https://github.com/Ssong24/CenterNet_Custom/tree/526ec70f8dfabf9fb9179c9be28ce50fb2a7961c |
ClusterAssignment | # 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 torch.nn as nn
from torch.nn import Parameter
from typing import Optional
class ClusterAssignment(nn.Module):
def __init__(self, cluster_number: 'int', embedding_dimension: 'int',
alpha: 'float'=1.0, cluster_centers: 'Optional[torch.Tensor]'=None
) ->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
import torch.nn as nn
from torch.nn import Parameter
from typing import Optional
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Vaaaas/OpenNRE | ClusterAssignment | false | 9,542 | [
"MIT"
] | 0 | d43859975ed3523d9a8cea02adff5c7b43f94da0 | https://github.com/Vaaaas/OpenNRE/tree/d43859975ed3523d9a8cea02adff5c7b43f94da0 |
AvgPool | # 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 nn
import torch.nn.functional as F
class AvgPool(nn.Module):
def forward(self, x):
return F.avg_pool2d(x, x.shape[2:])
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Thagio/kaggle-aptos | AvgPool | false | 9,543 | [
"MIT"
] | 0 | f565335d34b46b7fa7ca925b7d325397df8e1fee | https://github.com/Thagio/kaggle-aptos/tree/f565335d34b46b7fa7ca925b7d325397df8e1fee |
RegLoss | # 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 torch.nn as nn
import torch.utils.data
def _gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
feat = feat.gather(1, ind)
if mask is not None:
mask = mask.unsqueeze(2).expand_as(feat)
feat = feat[mask]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Ssong24/CenterNet_Custom | RegLoss | false | 9,544 | [
"MIT"
] | 0 | 526ec70f8dfabf9fb9179c9be28ce50fb2a7961c | https://github.com/Ssong24/CenterNet_Custom/tree/526ec70f8dfabf9fb9179c9be28ce50fb2a7961c |
SeasonalityBasis | # 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 numpy as np
import torch as t
class SeasonalityBasis(t.nn.Module):
"""
Harmonic functions to model seasonality.
"""
def __init__(self, harmonics: 'int', backcast_size: 'int',
forecast_size: 'int'):
super().__init__()
self.frequency = np.append(np.zeros(1, d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch as t
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.g... | TaeniKim/nbeats_reproduce | SeasonalityBasis | false | 9,545 | [
"MIT"
] | 0 | dd9375ad3fb4bb3c6c973391e250b5dd60a219ab | https://github.com/TaeniKim/nbeats_reproduce/tree/dd9375ad3fb4bb3c6c973391e250b5dd60a219ab |
ChebConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class ChebConv(nn.Module):
"""
The ChebNet convolution operation.
Laplacian is motified for direct-graph
:param in_c: int, number of input channels.
:param out_c: int, number of output channels.
:param K: int, the order of Chebyshev Polynomial.
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | TAN-OpenLab/TCSE-net | ChebConv | false | 9,546 | [
"Apache-2.0"
] | 0 | fc6ecf704a9c128a9b5b6853cffa8486ee0f54e8 | https://github.com/TAN-OpenLab/TCSE-net/tree/fc6ecf704a9c128a9b5b6853cffa8486ee0f54e8 |
Remap | # 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 numpy as np
import torch.nn as nn
from abc import abstractmethod
from typing import Union
from typing import Tuple
from typing import List
class BaseModel(nn.Module):
"""
Base class for all models
"""
@abstractmethod
def forward(self, *inputs):
"""
Forward pass... | 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 numpy as np
import torch.nn as nn
from abc import abstractmethod
from typing import Union
from typing import Tuple
from typing import... | SuikaSibyl/ReproduceNSRR | Remap | false | 9,547 | [
"MIT"
] | 0 | 732377413fd44f6e5acf40bfb4ae9e6430f586e3 | https://github.com/SuikaSibyl/ReproduceNSRR/tree/732377413fd44f6e5acf40bfb4ae9e6430f586e3 |
Intensity | # 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 torch.nn as nn
from torch.cuda.amp import autocast as autocast
from torch.cuda.amp import GradScaler as GradScaler
class Intensity(nn.Module):
def __init__(self, scale):
super().__init__()
self.scale = scale
def forward(self, x):
r = torch.randn((x.size(0), 1, 1, ... | 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
import torch.nn as nn
from torch.cuda.amp import autocast as aut... | TomFrederik/EfficientZero | Intensity | false | 9,548 | [
"MIT"
] | 0 | d310ec87602076e2ebc84a79f4e54b248ccbe62e | https://github.com/TomFrederik/EfficientZero/tree/d310ec87602076e2ebc84a79f4e54b248ccbe62e |
MaxFeature | # 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 torch.nn as nn
class MaxFeature(nn.Module):
"""Conv2d or Linear layer with max feature selector
Generate feature maps with double channels, split them and select the max
feature.
Args:
in_channels (int): Channel number of inputs.
out_channels (int): Channel nu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Serene99-09/mmediting | MaxFeature | false | 9,549 | [
"Apache-2.0"
] | 0 | be49e33650627ac26fdd065fbbaff66f726e3fde | https://github.com/Serene99-09/mmediting/tree/be49e33650627ac26fdd065fbbaff66f726e3fde |
Up | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Up(nn.Module):
def __init__(self, in_channels, out_channels, factor=2):
super(Up, self).__init__()
self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size
=2, stride=2)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Smith42/unet-pytorch | Up | false | 9,550 | [
"MIT"
] | 0 | 45a0459da69cee7f57fb369a8e2fc58668d81167 | https://github.com/Smith42/unet-pytorch/tree/45a0459da69cee7f57fb369a8e2fc58668d81167 |
Down | # 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 torch.nn as nn
import torch.nn.functional as F
class Down(nn.Module):
def __init__(self, in_channels, out_channels, factor=2):
super(Down, self).__init__()
self.down = nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=factor, padding=1)
def forward(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Smith42/unet-pytorch | Down | false | 9,551 | [
"MIT"
] | 0 | 45a0459da69cee7f57fb369a8e2fc58668d81167 | https://github.com/Smith42/unet-pytorch/tree/45a0459da69cee7f57fb369a8e2fc58668d81167 |
AttentiveStatsPool | # 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 torch.nn as nn
class AttentiveStatsPool(nn.Module):
def __init__(self, in_dim, bottleneck_dim):
super().__init__()
self.linear1 = nn.Conv1d(in_dim, bottleneck_dim, kernel_size=1)
self.linear2 = nn.Conv1d(bottleneck_dim, in_dim, kernel_size=1)
def forward(self, x):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SecretKeyTeam/voxceleb_trainer | AttentiveStatsPool | false | 9,552 | [
"MIT"
] | 0 | e235cbc2961d32395d30cf606ee830cd47716383 | https://github.com/SecretKeyTeam/voxceleb_trainer/tree/e235cbc2961d32395d30cf606ee830cd47716383 |
CosineLinearLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import Tensor
import torch.nn as nn
from torch.nn import Parameter
class CosineLinearLayer(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super(CosineLinearLayer, self).__init__()
self.in_features = in_features
self.out_features = 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._inductor.runtime.... | SecretKeyTeam/voxceleb_trainer | CosineLinearLayer | false | 9,553 | [
"MIT"
] | 0 | e235cbc2961d32395d30cf606ee830cd47716383 | https://github.com/SecretKeyTeam/voxceleb_trainer/tree/e235cbc2961d32395d30cf606ee830cd47716383 |
MetaBilinear | # 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 re
import torch
import warnings
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
---... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import re
import warnings
import torch.nn as nn
from collections import OrderedDict
assert_size_stride = torch._C._dynamo.guards.assert_size... | Steffen-Wolf/pytorch-meta | MetaBilinear | false | 9,554 | [
"MIT"
] | 0 | d2dfb902cfa49574eac898045c8e9cf64ce29f96 | https://github.com/Steffen-Wolf/pytorch-meta/tree/d2dfb902cfa49574eac898045c8e9cf64ce29f96 |
MLPClassifier | # 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 torch.nn as nn
class MLPClassifier(nn.Module):
def __init__(self, embedding_dim, label_size, hidden_dim):
super(MLPClassifier, self).__init__()
self.layer1 = torch.nn.Linear(embedding_dim, hidden_dim)
self.relu = torch.nn.ReLU()
self.layer2 = torch.nn.Linear(hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | UKPLab/curriculum-annotation | MLPClassifier | false | 9,555 | [
"Apache-2.0"
] | 0 | 1d6ca490ea180019bb09d1d3818874f4321d4d0f | https://github.com/UKPLab/curriculum-annotation/tree/1d6ca490ea180019bb09d1d3818874f4321d4d0f |
MuSigmaEncoder | # 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 torch.nn as nn
class MuSigmaEncoder(nn.Module):
"""
Maps a representation r to mu and sigma which will define the normal
distribution from which we sample the latent variable z.
Parameters
----------
r_dim : int
Dimension of output representation r.
z_dim : in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | TheBonheurs/neural-processes | MuSigmaEncoder | false | 9,556 | [
"MIT"
] | 0 | 5834bc65f406456e53c363ade1cb0f2a5f23a033 | https://github.com/TheBonheurs/neural-processes/tree/5834bc65f406456e53c363ade1cb0f2a5f23a033 |
D_UpBlock | # 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 torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | EvgeneyZ/RBPN | D_UpBlock | false | 9,557 | [
"MIT"
] | 0 | acfe636cc48a4fbfea78f934a251c32e53367659 | https://github.com/EvgeneyZ/RBPN/tree/acfe636cc48a4fbfea78f934a251c32e53367659 |
MLPPolicy | # 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 math
import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
def log_normal_density(x, mean, log_std, std):
"""returns guassian density given x on log scale"""
variance = std.pow(2)
log_density = -(x - mean).pow(2) / (2 * variance) - 0.5 * np.log(2 * np.pi
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Timliang/RL-Competition | MLPPolicy | false | 9,558 | [
"MIT"
] | 0 | 638462b95a5aab0bbae46677a59ffc90ba6cd971 | https://github.com/Timliang/RL-Competition/tree/638462b95a5aab0bbae46677a59ffc90ba6cd971 |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch
import torch.nn as nn
from torch.nn import functional as F
class FocalLoss(nn.Module):
def __init__(self, weight=None, gamma=1.0, num_classes=80):
super(FocalLoss, self).__init__()
assert gamma >= 0
self.gamma = gamma
self.weight =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | WdBlink/Teacher-Student-Faster-Rcnn | FocalLoss | false | 9,559 | [
"MIT"
] | 0 | df8085c61e334abb04bab5e8192de8cb4ce2b2af | https://github.com/WdBlink/Teacher-Student-Faster-Rcnn/tree/df8085c61e334abb04bab5e8192de8cb4ce2b2af |
NegSamplingLoss | # 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 torch.nn as nn
class NegSamplingLoss(nn.Module):
def __init__(self):
super(NegSamplingLoss, self).__init__()
def forward(self, score, sign):
return -torch.mean(torch.sigmoid(sign * score))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]... | 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... | MIracleyin/RecBole-notebook | NegSamplingLoss | false | 9,560 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
UpBlock | # 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 torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | EvgeneyZ/RBPN | UpBlock | false | 9,561 | [
"MIT"
] | 0 | acfe636cc48a4fbfea78f934a251c32e53367659 | https://github.com/EvgeneyZ/RBPN/tree/acfe636cc48a4fbfea78f934a251c32e53367659 |
AUGRUCell | # 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 torch.nn as nn
import torch.nn.functional as F
class AUGRUCell(nn.Module):
' Effect of GRU with attentional update gate (AUGRU). AUGRU combines attention mechanism and GRU seamlessly.\n\n Formally:\n ..math: \tilde{{u}}_{t}^{\\prime}=a_{t} * {u}_{t}^{\\prime} \\\n {h}_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | MIracleyin/RecBole-notebook | AUGRUCell | false | 9,562 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
RegLoss | # 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 torch.nn as nn
class RegLoss(nn.Module):
""" RegLoss, L2 regularization on model parameters
"""
def __init__(self):
super(RegLoss, self).__init__()
def forward(self, parameters):
reg_loss = None
for W in parameters:
if reg_loss is None:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | MIracleyin/RecBole-notebook | RegLoss | false | 9,563 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
FCN8s | # 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 numpy as np
from torch import nn
def get_upsampling_weight(in_channels, out_channels, kernel_size):
"""Make a 2D bilinear kernel suitable for upsampling"""
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | Design-AILab/Attention-Tracker | FCN8s | false | 9,564 | [
"MIT"
] | 0 | 3dfe5edabdff0cb6db9c99ed59afd8c0383b6233 | https://github.com/Design-AILab/Attention-Tracker/tree/3dfe5edabdff0cb6db9c99ed59afd8c0383b6233 |
eSEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
class eSEModule(nn.Module... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | UWO-CCPL/AdelaiDet | eSEModule | false | 9,565 | [
"BSD-2-Clause"
] | 0 | 29a59575697dbbb4cfe7b7ab821805913348cf61 | https://github.com/UWO-CCPL/AdelaiDet/tree/29a59575697dbbb4cfe7b7ab821805913348cf61 |
Symmetric | # 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 torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Symmetric(nn.Module):
def forward(self, X):
return X.triu() + X.triu(1).transpose(-1, -2)
def ge... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Nayef211/tutorials | Symmetric | false | 9,566 | [
"BSD-3-Clause"
] | 0 | faf2c476fc3be855051fbea3cce77eaf7b2a2175 | https://github.com/Nayef211/tutorials/tree/faf2c476fc3be855051fbea3cce77eaf7b2a2175 |
MetaLayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import re
import torch
import warnings
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
---... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import re
import warnings
import torch.nn as nn
from collections import Ordered... | Steffen-Wolf/pytorch-meta | MetaLayerNorm | false | 9,567 | [
"MIT"
] | 0 | d2dfb902cfa49574eac898045c8e9cf64ce29f96 | https://github.com/Steffen-Wolf/pytorch-meta/tree/d2dfb902cfa49574eac898045c8e9cf64ce29f96 |
InnerProductLayer | # 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 torch.nn as nn
class InnerProductLayer(nn.Module):
"""InnerProduct Layer used in PNN that compute the element-wise
product or inner product between feature vectors.
"""
def __init__(self, num_feature_field, device):
"""
Args:
num_feature_field(int) :nu... | 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... | MIracleyin/RecBole-notebook | InnerProductLayer | false | 9,568 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
AttLayer | # 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 torch.nn as nn
import torch.nn.functional as fn
class AttLayer(nn.Module):
"""Calculate the attention signal(weight) according the input tensor.
Args:
infeatures (torch.FloatTensor): A 3D input tensor with shape of[batch_size, M, embed_dim].
Returns:
torch.FloatTensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MIracleyin/RecBole-notebook | AttLayer | false | 9,569 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
GatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.utils.data
class GatedConv2d(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, stride,
padding, dilation=1, activation=None):
super(GatedConv2d, self).__init__()
self.activation = activation
self.sigmoid = 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 import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | RobertYCXu/vae_vampprior | GatedConv2d | false | 9,570 | [
"MIT"
] | 0 | edcec4f5f7af673172c5b5b9aa2a22f993564fab | https://github.com/RobertYCXu/vae_vampprior/tree/edcec4f5f7af673172c5b5b9aa2a22f993564fab |
ResizeConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.utils.data
class ResizeConv2d(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, stride,
padding, dilation=1, scale_factor=2, activation=None):
super(ResizeConv2d, self).__init__()
self.activation = activation
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | RobertYCXu/vae_vampprior | ResizeConv2d | false | 9,571 | [
"MIT"
] | 0 | edcec4f5f7af673172c5b5b9aa2a22f993564fab | https://github.com/RobertYCXu/vae_vampprior/tree/edcec4f5f7af673172c5b5b9aa2a22f993564fab |
ItemToInterestAggregation | # 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 torch.nn as nn
class ItemToInterestAggregation(nn.Module):
def __init__(self, seq_len, hidden_size, k_interests=5):
super().__init__()
self.k_interests = k_interests
self.theta = nn.Parameter(torch.randn([hidden_size, k_interests]))
def forward(self, input_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MIracleyin/RecBole-notebook | ItemToInterestAggregation | false | 9,572 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
SE_Connect | # 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 torch.nn as nn
import torch.nn.functional as F
class SE_Connect(nn.Module):
def __init__(self, channels, s=2):
super().__init__()
assert channels % s == 0, '{} % {} != 0'.format(channesl, s)
self.linear1 = nn.Linear(channels, channels // s)
self.linear2 = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | SecretKeyTeam/voxceleb_trainer | SE_Connect | false | 9,573 | [
"MIT"
] | 0 | e235cbc2961d32395d30cf606ee830cd47716383 | https://github.com/SecretKeyTeam/voxceleb_trainer/tree/e235cbc2961d32395d30cf606ee830cd47716383 |
BaseFactorizationMachine | # 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 torch.nn as nn
class BaseFactorizationMachine(nn.Module):
"""Calculate FM result over the embeddings
Args:
reduce_sum: bool, whether to sum the result, default is True.
Input:
input_x: tensor, A 3D tensor with shape:``(batch_size,field_size,embed_dim)``.
Output
... | 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... | MIracleyin/RecBole-notebook | BaseFactorizationMachine | false | 9,574 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
ConvNCFBPRLoss | # 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 torch.nn as nn
class ConvNCFBPRLoss(nn.Module):
""" ConvNCFBPRLoss, based on Bayesian Personalized Ranking,
Shape:
- Pos_score: (N)
- Neg_score: (N), same shape as the Pos_score
- Output: scalar.
Examples::
>>> loss = ConvNCFBPRLoss()
>>> ... | 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
... | MIracleyin/RecBole-notebook | ConvNCFBPRLoss | false | 9,575 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
BPRLoss | # 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 torch.nn as nn
class BPRLoss(nn.Module):
""" BPRLoss, based on Bayesian Personalized Ranking
Args:
- gamma(float): Small value to avoid division by zero
Shape:
- Pos_score: (N)
- Neg_score: (N), same shape as the Pos_score
- Output: scalar.
Exampl... | 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
... | MIracleyin/RecBole-notebook | BPRLoss | false | 9,576 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
ReLU | # 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 numpy as np
import torch.nn as nn
from numbers import Number
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
v... | 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 numpy as np
import torch.nn as nn
from numbers import N... | SaumilShah66/dqn_uav | ReLU | false | 9,577 | [
"MIT"
] | 0 | 2bf780369e964b870624aebcff16c0714cad03c1 | https://github.com/SaumilShah66/dqn_uav/tree/2bf780369e964b870624aebcff16c0714cad03c1 |
AvgPoolPad | # 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 torch.nn as nn
class AvgPoolPad(nn.Module):
def __init__(self, stride=2, padding=1):
super(AvgPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.AvgPool2d(3, stride=stride, padding=padding,
count_include_pad=False)
def forwa... | 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... | ArronHZG/ABD-Net | AvgPoolPad | false | 9,578 | [
"MIT"
] | 0 | 4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 | https://github.com/ArronHZG/ABD-Net/tree/4f6d15f4d389a55549ea10a2e00d4a5cdecb5753 |
MaxPool2d | # 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 numpy as np
import torch.nn as nn
from numbers import Number
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
v... | 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 numpy as np
import torch.nn as nn
from numbers import N... | SaumilShah66/dqn_uav | MaxPool2d | false | 9,579 | [
"MIT"
] | 0 | 2bf780369e964b870624aebcff16c0714cad03c1 | https://github.com/SaumilShah66/dqn_uav/tree/2bf780369e964b870624aebcff16c0714cad03c1 |
GatedDense | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.utils.data
class GatedDense(nn.Module):
def __init__(self, input_size, output_size, activation=None):
super(GatedDense, self).__init__()
self.activation = activation
self.sigmoid = nn.Sigmoid()
self.h = nn.Linear(input_size, output_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | RobertYCXu/vae_vampprior | GatedDense | false | 9,580 | [
"MIT"
] | 0 | edcec4f5f7af673172c5b5b9aa2a22f993564fab | https://github.com/RobertYCXu/vae_vampprior/tree/edcec4f5f7af673172c5b5b9aa2a22f993564fab |
AGRUCell | # 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 torch.nn as nn
import torch.nn.functional as F
class AGRUCell(nn.Module):
' Attention based GRU (AGRU). AGRU uses the attention score to replace the update gate of GRU, and changes the\n hidden state directly.\n\n Formally:\n ..math: {h}_{t}^{\\prime}=\\left(1-a_{t}\right) * {h}_{... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | MIracleyin/RecBole-notebook | AGRUCell | false | 9,581 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
OuterProductLayer | # 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 torch.nn as nn
class OuterProductLayer(nn.Module):
"""OuterProduct Layer used in PNN. This implementation is
adapted from code that the author of the paper published on https://github.com/Atomu2014/product-nets.
"""
def __init__(self, num_feature_field, embedding_size, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | MIracleyin/RecBole-notebook | OuterProductLayer | false | 9,582 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
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.nn import functional as F
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
class Conv2d(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, keep_variance_fn=None,
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
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pa... | SaumilShah66/dqn_uav | Conv2d | false | 9,583 | [
"MIT"
] | 0 | 2bf780369e964b870624aebcff16c0714cad03c1 | https://github.com/SaumilShah66/dqn_uav/tree/2bf780369e964b870624aebcff16c0714cad03c1 |
Softmax | # 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 torch.nn as nn
class Softmax(nn.Module):
def __init__(self, dim=1, keep_variance_fn=None):
super(Softmax, self).__init__()
self.dim = dim
self._keep_variance_fn = keep_variance_fn
def forward(self, features_mean, features_variance, eps=1e-05):
"""Softmax f... | 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... | SaumilShah66/dqn_uav | Softmax | false | 9,584 | [
"MIT"
] | 0 | 2bf780369e964b870624aebcff16c0714cad03c1 | https://github.com/SaumilShah66/dqn_uav/tree/2bf780369e964b870624aebcff16c0714cad03c1 |
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