entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
ChanNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Rexiome/lightweight-gan | ChanNorm | false | 2,745 | [
"MIT"
] | 0 | 4e5c18046fc105129c33995e0bffeb5f14963f4c | https://github.com/Rexiome/lightweight-gan/tree/4e5c18046fc105129c33995e0bffeb5f14963f4c |
TimeIntervalMultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | nmrenyi/ReChorus | TimeIntervalMultiHeadAttention | false | 16,197 | [
"MIT"
] | 314 | 9ab632579d0464b0aaf365539f87b04866920b66 | https://github.com/nmrenyi/ReChorus/tree/9ab632579d0464b0aaf365539f87b04866920b66 |
KLDivLoss | import torch
import torch.nn as nn
class KLDivLoss(nn.Module):
"""
## KL-Divergence loss
This calculates the KL divergence between a given normal distribution and $\\mathcal{N}(0, 1)$
"""
def forward(self, sigma_hat, mu):
return -0.5 * torch.mean(1 + sigma_hat - mu ** 2 - torch.exp(sigma... | 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
... | chrissarmstrong/PL-Sketch-RNN | KLDivLoss | false | 9,895 | [
"MIT"
] | 0 | 82a34718b10f7a2a1458dbad41ba85f0036267c0 | https://github.com/chrissarmstrong/PL-Sketch-RNN/tree/82a34718b10f7a2a1458dbad41ba85f0036267c0 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | ReyhaneAskari/pytorch_experiments | Net | false | 14,425 | [
"MIT"
] | 60 | 43d2efbc08c9dd6275530c4bf49c68772f8afb75 | https://github.com/ReyhaneAskari/pytorch_experiments/tree/43d2efbc08c9dd6275530c4bf49c68772f8afb75 |
FeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | ModelTC/EOD | FeedForward | false | 14,061 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
ConvWS2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.fun... | CityU-AIM-Group/HTD | ConvWS2d | false | 17,117 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
maxPool23DUinit | import torch
from torch import nn
import torch.utils.data
import torch.nn.init
class maxPool23DUinit(nn.Module):
def __init__(self, kernel_size, stride, padding=1, dilation=1, nd=2):
super(maxPool23DUinit, self).__init__()
assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data
import torch.nn.init
assert_size_stride = to... | ForrestPi/Unsupervised-Defect-Segmentation | maxPool23DUinit | false | 8,199 | [
"MIT"
] | 17 | e366ac7c757bb1b45f38ebbc502dfee7ccb72398 | https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398 |
SimpleAvgPool2dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleAvgPool2dModule(torch.nn.Module):
def __init__(self, kernel_size, stride=None, padding=0):
super(SimpleAvgPool2dModule, self).__init__()
self.kernel_size = kernel_size
self.padding ... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleAvgPool2dModule | false | 3,320 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
DepthwiseSeperableConv1d | import torch
import torch.nn as nn
class DepthwiseSeperableConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size):
super(DepthwiseSeperableConv1d, self).__init__()
self.depthwise_conv1d = nn.Conv1d(in_channels, in_channels,
kernel_size, groups=in_channels, paddi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | allenye0119/pytorch-modules | DepthwiseSeperableConv1d | false | 12,074 | [
"MIT"
] | 0 | c7683ef63478becca3b79a7498840450da33f468 | https://github.com/allenye0119/pytorch-modules/tree/c7683ef63478becca3b79a7498840450da33f468 |
Regression | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | BEOKS/Windows-Machine-Learning | Regression | false | 2,055 | [
"MIT"
] | 0 | e227909baa5ef604d45afa976dc04598f09d76bd | https://github.com/BEOKS/Windows-Machine-Learning/tree/e227909baa5ef604d45afa976dc04598f09d76bd |
CustomKLLoss | import torch
from torch.nn.modules.loss import _Loss
class CustomKLLoss(_Loss):
"""
KL_Loss = (|dot(mean , mean)| + |dot(std, std)| - |log(dot(std, std))| - 1) / N
N is the total number of image voxels
"""
def __init__(self, *args, **kwargs):
super(CustomKLLoss, self).__init__()
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.... | WdBlink/AugMix-3DOCUNet-Brats2019 | CustomKLLoss | false | 5,954 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
PatchEmbed3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | acewjh/Video-Swin-Transformer | PatchEmbed3D | false | 12,054 | [
"Apache-2.0"
] | 0 | bfbc8dde12e991455b34b921ca45a978b4dbfdbc | https://github.com/acewjh/Video-Swin-Transformer/tree/bfbc8dde12e991455b34b921ca45a978b4dbfdbc |
ProjectionHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | jianzhnie/MultimodalTransformer | ProjectionHead | false | 6,963 | [
"Apache-2.0"
] | 1 | 6cd4ca8034a53da361149745aecead68fbe304a0 | https://github.com/jianzhnie/MultimodalTransformer/tree/6cd4ca8034a53da361149745aecead68fbe304a0 |
SelfAttentionPooling | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | albertvillanova/s3prl | SelfAttentionPooling | false | 6,162 | [
"MIT"
] | 1 | b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 | https://github.com/albertvillanova/s3prl/tree/b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 |
LinearVarianceUnif | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from torch.nn import Parameter
assert_size_str... | AlliedToasters/elko_den | LinearVarianceUnif | false | 7,675 | [
"Apache-2.0"
] | 38 | 4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 | https://github.com/AlliedToasters/elko_den/tree/4e69f7f5c0dc7ffad54c7e190a2b75aba2eab7d2 |
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... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
from torch import nn
from torch.... | jchetboun/anycost-gan | ToRGB | false | 10,395 | [
"MIT"
] | 0 | 7e0005e50b915e2dfeb90fe7a9846c5df38d7c06 | https://github.com/jchetboun/anycost-gan/tree/7e0005e50b915e2dfeb90fe7a9846c5df38d7c06 |
Upsample_ | import torch
from torch import nn
class Upsample_(nn.Module):
def __init__(self, scale=2):
super(Upsample_, self).__init__()
self.upsample = nn.Upsample(mode='bilinear', scale_factor=scale)
def forward(self, x):
return self.upsample(x)
def get_inputs():
return [torch.rand([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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Dauriel/weather4cast2021 | Upsample_ | false | 360 | [
"Apache-2.0"
] | 0 | 29e818c4bcd488ec84b51558bf5392e4a887db70 | https://github.com/Dauriel/weather4cast2021/tree/29e818c4bcd488ec84b51558bf5392e4a887db70 |
AffineTransform | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | jaketae/res-mlp | AffineTransform | false | 12,592 | [
"MIT"
] | 0 | 6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd | https://github.com/jaketae/res-mlp/tree/6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd |
LinearEmbedding | import math
import torch
import torch.utils.data
import torch.nn as nn
class LinearEmbedding(nn.Module):
def __init__(self, inp_size, d_model):
super(LinearEmbedding, self).__init__()
self.lut = nn.Linear(inp_size, d_model)
self.d_model = d_model
def forward(self, x):
return ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | swift88-clone/Trajectory-Transformer | LinearEmbedding | false | 13,011 | [
"MIT"
] | 0 | 62983b645ec88d8972bc2c2af1b7b4a299d3feb0 | https://github.com/swift88-clone/Trajectory-Transformer/tree/62983b645ec88d8972bc2c2af1b7b4a299d3feb0 |
SimpleCNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleCNN(nn.Module):
def __init__(self, num_channels, num_classes):
super(SimpleCNN, self).__init__()
C = num_channels
self.conv1 = nn.Conv2d(in_channels=C, out_channels=C * 8,
kernel_size=3, stride=2, 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 torch.nn as nn
assert_... | diogo149/doo | SimpleCNN | false | 6,579 | [
"MIT"
] | 1 | d83a1715fb9d4e5eac9f5d3d384a45cfc26fec2f | https://github.com/diogo149/doo/tree/d83a1715fb9d4e5eac9f5d3d384a45cfc26fec2f |
FFNLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | NExTplusplus/tat-qa | FFNLayer | false | 8,596 | [
"MIT"
] | 23 | 4ce5d8e637b80143de0d2492ecd4b861d6ba9a89 | https://github.com/NExTplusplus/tat-qa/tree/4ce5d8e637b80143de0d2492ecd4b861d6ba9a89 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DIAL-RPI/PIPO-FAN | DiceLoss | false | 13,540 | [
"MIT"
] | 53 | 126c17fbdc4c62806a9d249be355542f3990f305 | https://github.com/DIAL-RPI/PIPO-FAN/tree/126c17fbdc4c62806a9d249be355542f3990f305 |
Attention | import math
import torch
import torch.nn.functional as F
import torch.utils.data
def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MINATILO/pytroch-geometric | Attention | false | 9,414 | [
"MIT"
] | 0 | 706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 | https://github.com/MINATILO/pytroch-geometric/tree/706aba3b4a6477a83a1fb73eb3cf0ee9661b70e4 |
_final_conv_block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Minsoo2022/Pose-Transfer | _final_conv_block | false | 14,050 | [
"MIT"
] | 692 | 10a60bb33d51a06e1200f5726f2367b5be4a6b79 | https://github.com/Minsoo2022/Pose-Transfer/tree/10a60bb33d51a06e1200f5726f2367b5be4a6b79 |
SPP | import torch
import torch.nn as nn
class SPP(nn.Module):
"""
Spatial pyramid pooling layer used in YOLOv3-SPP
"""
def __init__(self, kernels=[5, 9, 13]):
super(SPP, self).__init__()
self.maxpool_layers = nn.ModuleList([nn.MaxPool2d(kernel_size=
kernel, stride=1, padding=ke... | 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... | LANCEREN/simpleAICV-pytorch-ImageNet-COCO-training | SPP | false | 13,976 | [
"MIT"
] | 154 | 86c1b38df3cdcb195ec5b6229c343f07a52aeb7b | https://github.com/LANCEREN/simpleAICV-pytorch-ImageNet-COCO-training/tree/86c1b38df3cdcb195ec5b6229c343f07a52aeb7b |
QNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | rosebin/gymlab | QNet | false | 4,200 | [
"BSD-3-Clause"
] | 0 | de97fc24e0ddf5e328a2aa732cc339b2371d92d1 | https://github.com/rosebin/gymlab/tree/de97fc24e0ddf5e328a2aa732cc339b2371d92d1 |
SimpleStackModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | mlupon/glow | SimpleStackModel | false | 4,018 | [
"Apache-2.0"
] | 0 | aedaa7b98617f1a2db651608e7f7c916a7d2c766 | https://github.com/mlupon/glow/tree/aedaa7b98617f1a2db651608e7f7c916a7d2c766 |
KLD | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | UMBCvision/CompReSS | KLD | false | 14,526 | [
"MIT"
] | 61 | c5e57edce75da96482fd36eac484c5aca9676945 | https://github.com/UMBCvision/CompReSS/tree/c5e57edce75da96482fd36eac484c5aca9676945 |
srcEncoder | import torch
import torch.nn as nn
class srcEncoder(nn.Module):
def __init__(self, in_ch, hid_ch):
super(srcEncoder, self).__init__()
self.act = nn.ReLU()
self.conv1 = nn.Conv2d(in_ch, hid_ch, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(hid_ch, hid_ch, kernel_size=3, padding=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | qbhan/pathembed | srcEncoder | false | 7,504 | [
"MIT"
] | 1 | c21823529840593bf606e10696f5879e5adb51b2 | https://github.com/qbhan/pathembed/tree/c21823529840593bf606e10696f5879e5adb51b2 |
SEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ChangeTheWorld20191008/query2labels | SEModule | false | 2,145 | [
"MIT"
] | 0 | cdca1f3519f75cc91ef2aa166c2534691016f04f | https://github.com/ChangeTheWorld20191008/query2labels/tree/cdca1f3519f75cc91ef2aa166c2534691016f04f |
activation_quantize_fn | # AOT ID: ['1_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | heymesut/SJTU_microe | activation_quantize_fn | false | 6,803 | [
"BSD-3-Clause"
] | 1 | 7a862d03b4d8fe4c8608173a16082f44001f3f13 | https://github.com/heymesut/SJTU_microe/tree/7a862d03b4d8fe4c8608173a16082f44001f3f13 |
MultiHeadAttentionLayer | import math
import torch
import torch.nn as nn
class MultiHeadAttentionLayer(nn.Module):
def __init__(self, hidden_dim, n_heads, dropout=0.1):
super().__init__()
assert hidden_dim % n_heads == 0
self.hidden_dim = hidden_dim
self.n_heads = n_heads
self.head_dim = hidden_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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | wenjunyoung/PAN_PLUS | MultiHeadAttentionLayer | false | 11,027 | [
"Apache-2.0"
] | 0 | c893ff4775c8ff137a21c15d34fb93b9394dbfe5 | https://github.com/wenjunyoung/PAN_PLUS/tree/c893ff4775c8ff137a21c15d34fb93b9394dbfe5 |
SEModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | dercaft/XNAS | SEModule | false | 6,571 | [
"MIT"
] | 1 | d6d0fde0d4475210a41607181939188b177e44b1 | https://github.com/dercaft/XNAS/tree/d6d0fde0d4475210a41607181939188b177e44b1 |
ChannelSELayer3D | import torch
import torch.nn as nn
class ChannelSELayer3D(nn.Module):
"""
3D implementation of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
"""
def __init__(self, num_channels, reduction_ratio=2):
"""
:param num... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | HiLab-git/PyMIC | ChannelSELayer3D | false | 13,788 | [
"Apache-2.0"
] | 147 | abf5c43de43668b85f4c049c95a8f1b7cf1d9f16 | https://github.com/HiLab-git/PyMIC/tree/abf5c43de43668b85f4c049c95a8f1b7cf1d9f16 |
PositionalEncoding1d | import math
import torch
import torch.nn as nn
class PositionalEncoding1d(nn.Module):
"""
Learning positional embeddings.
Args:
shape: Iterable, the shape of the input.
embedding_dim: int, the size of each embedding vector.
"""
def __init__(self, size, embedding_dim):
sup... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | Nintorac/survae_experiments | PositionalEncoding1d | false | 901 | [
"MIT"
] | 0 | d68cc25e2604aab08b53617c1f3ffe4716f166c4 | https://github.com/Nintorac/survae_experiments/tree/d68cc25e2604aab08b53617c1f3ffe4716f166c4 |
NonpositiveLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | GlenHGHUANG/STRODE | NonpositiveLinear | false | 8,142 | [
"MIT"
] | 11 | 91565275dffd4f08738c8a0e5b6c9ad89344623e | https://github.com/GlenHGHUANG/STRODE/tree/91565275dffd4f08738c8a0e5b6c9ad89344623e |
Gaussian | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | fmhoward/pysurvival | Gaussian | false | 12,375 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
CosineClassifier | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
def cosine_fully_connected_layer(x_in, weight, scale=None, bias=None,
normalize_x=True, normalize_w=True):
assert x_in.dim() == 2
assert weight.dim() == 2
assert x_in.size(1) == weight.size(0)
if normalize_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.... | zheang01/FACT | CosineClassifier | false | 16,811 | [
"MIT"
] | 65 | a877cc86acc4d29fb7589c8ac571c8aef09e5fd8 | https://github.com/zheang01/FACT/tree/a877cc86acc4d29fb7589c8ac571c8aef09e5fd8 |
attentionLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import MultiheadAttention
from itertools import product as product
class attentionLayer(nn.Module):
def __init__(self, d_model, nhead, dropout=0.1):
super(attentionLayer, self).__init__()
self.self_attn = MultiheadAt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | TaoRuijie/TalkNet_ASD | attentionLayer | false | 14,460 | [
"MIT"
] | 79 | 4a2bc4859ee192ab450eaf63937a799212f2b021 | https://github.com/TaoRuijie/TalkNet_ASD/tree/4a2bc4859ee192ab450eaf63937a799212f2b021 |
LearnablePositionalEncoding | import torch
import torch.nn as nn
class LearnablePositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=1024):
super(LearnablePositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
self.pe = nn.Parameter(torch.empty(max_len, 1, d_model))
... | 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... | saverymax/mvts_transformer | LearnablePositionalEncoding | false | 10,785 | [
"MIT"
] | 0 | 22796d6977b78d5636f6aad3f7efeb49f2991808 | https://github.com/saverymax/mvts_transformer/tree/22796d6977b78d5636f6aad3f7efeb49f2991808 |
AdjEncoder | import torch
from torch import nn
import torch.utils.data
class AdjEncoder(nn.Module):
def __init__(self, feature_size, hidden_size):
super(AdjEncoder, self).__init__()
self.left = nn.Linear(feature_size, hidden_size)
self.right = nn.Linear(feature_size, hidden_size, bias=False)
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
from torch import n... | kevin-kaixu/grass_pytorch | AdjEncoder | false | 15,816 | [
"Apache-2.0"
] | 85 | 1d8dc6dcc0ab3ca029e449f57c37ba3910a4f90a | https://github.com/kevin-kaixu/grass_pytorch/tree/1d8dc6dcc0ab3ca029e449f57c37ba3910a4f90a |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | kant/open-solution-ship-detection | DiceLoss | false | 12,655 | [
"MIT"
] | 0 | 94fa14fc461d6088d884930cbd8e2a2b99a338b5 | https://github.com/kant/open-solution-ship-detection/tree/94fa14fc461d6088d884930cbd8e2a2b99a338b5 |
ModelBasic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | HeikoSchuett/hard-inference | ModelBasic | false | 9,086 | [
"MIT"
] | 0 | eb850d97458dbbf8a5c434df71c802065c8e348f | https://github.com/HeikoSchuett/hard-inference/tree/eb850d97458dbbf8a5c434df71c802065c8e348f |
ConvKernel | # AOT ID: ['1_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import math
from torch.nn.modules.utils import _pair... | JannerM/spatial-reasoning | ConvKernel | false | 13,868 | [
"MIT"
] | 54 | e163003a33177e41ca02d5feefee3fdfca5ba154 | https://github.com/JannerM/spatial-reasoning/tree/e163003a33177e41ca02d5feefee3fdfca5ba154 |
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
import triton
import triton.language as tl
from 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
... | xuewengeophysics/volo | SoftTargetCrossEntropy | false | 10,953 | [
"Apache-2.0"
] | 0 | 411f367c617b556fd0df450e7844e57541695c4d | https://github.com/xuewengeophysics/volo/tree/411f367c617b556fd0df450e7844e57541695c4d |
FFB | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class FFB(nn.Module):
def __init__(self, c1, c2, size):
super(FFB, self).__init__()
self.conv_y1 = nn.Conv2d(c1, c1, kernel_size=1)
self.act1 = nn.ReLU(inplace=True)
self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 math import sqrt as sqrt
from itertools import produc... | chickenshawama/CFEnet2 | FFB | false | 1,689 | [
"MIT"
] | 0 | f20dda7ad2923bed4af5d63e705b6849a48e73df | https://github.com/chickenshawama/CFEnet2/tree/f20dda7ad2923bed4af5d63e705b6849a48e73df |
Net | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=3)
self.conv2 = nn.Conv2d(10, 2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dqawami/openvino_training_extensions | Net | false | 15,223 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad |
PPReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | CuongNguyen218/ObjectDetection-OneStageDet | PPReLU | false | 347 | [
"MIT"
] | 0 | 60efe8b0ee6782b2aea20a32264b2ce1fc21901f | https://github.com/CuongNguyen218/ObjectDetection-OneStageDet/tree/60efe8b0ee6782b2aea20a32264b2ce1fc21901f |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | davidhtf/drlnd | Critic | false | 6,537 | [
"MIT"
] | 1 | 221601f38659055824763ce41c6d9edd3d476fd4 | https://github.com/davidhtf/drlnd/tree/221601f38659055824763ce41c6d9edd3d476fd4 |
BNMLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | KevinMusgrave/pytorch-adapt | BNMLoss | false | 13,950 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
MultiHeadAttention | import torch
from typing import Tuple
from torch import nn
from typing import Optional
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention
Parameters
----------
scale : float
Scale factor (sqrt(d_k))
dropout : float
Dropout
"""
def __init__(s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Renovamen/Text-Classification | MultiHeadAttention | false | 14,302 | [
"MIT"
] | 72 | 4a4aa4001c402ed4371ebaabe1393b27794e5992 | https://github.com/Renovamen/Text-Classification/tree/4a4aa4001c402ed4371ebaabe1393b27794e5992 |
LSGanLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | ForrestPi/faceSwapProjects | LSGanLoss | false | 17,286 | [
"MIT"
] | 5 | daf2649a2791a25aa541c4d6d3b7e1d6552be5d7 | https://github.com/ForrestPi/faceSwapProjects/tree/daf2649a2791a25aa541c4d6d3b7e1d6552be5d7 |
Block_cls | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TencentYoutuResearch/BaseArchitecture-EAT | Block_cls | false | 18,034 | [
"BSD-3-Clause"
] | 9 | b916738ef9b1314f5fdad780a0839cb4e010a208 | https://github.com/TencentYoutuResearch/BaseArchitecture-EAT/tree/b916738ef9b1314f5fdad780a0839cb4e010a208 |
MAPE | import torch
import torch.nn as nn
class MAPE(nn.Module):
def __init__(self):
super().__init__()
self.l1 = nn.L1Loss(reduction='none')
def forward(self, pred, actual):
mape = 100 * self.l1(pred, actual) / torch.max(pred, actual)
return mape.mean()
def get_inputs():
retu... | 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
... | RosarioAndolina/psychXRF | MAPE | false | 990 | [
"MIT"
] | 0 | e2adadbd17664d7f74c10304f84b3751c571226e | https://github.com/RosarioAndolina/psychXRF/tree/e2adadbd17664d7f74c10304f84b3751c571226e |
ClassicalConv2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | mit-han-lab/pytorch-quantum | ClassicalConv2 | false | 16,103 | [
"MIT"
] | 98 | 05cf000d689307f6b1fe02d12744ad455685935b | https://github.com/mit-han-lab/pytorch-quantum/tree/05cf000d689307f6b1fe02d12744ad455685935b |
CosLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards... | PatrickGui/Face_Pytorch | CosLoss | false | 971 | [
"Apache-2.0"
] | 0 | ff5b820ca3978883f7cf95f0209fba3ee958c939 | https://github.com/PatrickGui/Face_Pytorch/tree/ff5b820ca3978883f7cf95f0209fba3ee958c939 |
SinActv | import torch
import torch.nn as nn
class SinActv(nn.Module):
"""The sin activation function.
"""
def __init__(self):
"""Initializer method.
"""
super().__init__()
def forward(self, input_):
return torch.sin(input_)
def get_inputs():
return [torch.rand([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.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | DiffEqML/neurodiffeq | SinActv | false | 5,065 | [
"MIT"
] | 1 | c5e7404c47a4729578ee2149f289be0a8909d775 | https://github.com/DiffEqML/neurodiffeq/tree/c5e7404c47a4729578ee2149f289be0a8909d775 |
TransformerEncoder | import torch
class TransformerEncoder(torch.nn.Module):
def __init__(self, embed_dim, num_heads, dropout, feedforward_dim):
super().__init__()
self.attn = torch.nn.MultiheadAttention(embed_dim, num_heads,
dropout=dropout)
self.linear_1 = torch.nn.Linear(embed_dim, feedforward_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mamuncseru/Denoise-Transformer-AutoEncoder | TransformerEncoder | false | 16,006 | [
"MIT"
] | 265 | 56b3ff8b252ad24a4ed769158e3f0648090e1ffd | https://github.com/mamuncseru/Denoise-Transformer-AutoEncoder/tree/56b3ff8b252ad24a4ed769158e3f0648090e1ffd |
Envelope | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | beneisner/pytorch_geometric | Envelope | false | 6,321 | [
"MIT"
] | 1 | 53d44a96bd2de2753b1ab1d7153c026c92606a81 | https://github.com/beneisner/pytorch_geometric/tree/53d44a96bd2de2753b1ab1d7153c026c92606a81 |
MetaCurvatureTransform | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | OliverWang-Au/learn2learn | MetaCurvatureTransform | false | 5,694 | [
"MIT"
] | 1 | df3c3291b4681440a80a69a7815090a4bd3cd661 | https://github.com/OliverWang-Au/learn2learn/tree/df3c3291b4681440a80a69a7815090a4bd3cd661 |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Slowika/GameBias-EmeCom2020 | TransformerEncoderLayer | false | 17,980 | [
"MIT"
] | 5 | 5b94c47559f8202bca99c26fc1bcb078dd0509a6 | https://github.com/Slowika/GameBias-EmeCom2020/tree/5b94c47559f8202bca99c26fc1bcb078dd0509a6 |
ActorNN | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def init_hidden(layer):
"""
Initialize NN layers
"""
input_size = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(input_size)
return -lim, lim
class ActorNN(nn.Module):
"""
Actor Class
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | kaustav1987/Tennis-Collaboration-and-Competition-Continuous-Control | ActorNN | false | 3,821 | [
"MIT"
] | 0 | d724e09d7a5948e2023fb86bf977455f3c507054 | https://github.com/kaustav1987/Tennis-Collaboration-and-Competition-Continuous-Control/tree/d724e09d7a5948e2023fb86bf977455f3c507054 |
MAB | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ernoult/set_transformer | MAB | false | 12,359 | [
"MIT"
] | 0 | 4b380106e1f43b7eb6315624c57d4d1d38737b78 | https://github.com/ernoult/set_transformer/tree/4b380106e1f43b7eb6315624c57d4d1d38737b78 |
StatsPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | penguinwang96825/Umigame | StatsPool | false | 10,683 | [
"Apache-2.0"
] | 0 | 98d647ab6f40df08fe31d6b3bc444afe229a914e | https://github.com/penguinwang96825/Umigame/tree/98d647ab6f40df08fe31d6b3bc444afe229a914e |
Conv2dWithConstraint | import torch
import torch.nn as nn
class Conv2dWithConstraint(nn.Conv2d):
def __init__(self, *args, max_norm=1, **kwargs):
self.max_norm = max_norm
super(Conv2dWithConstraint, self).__init__(*args, **kwargs)
def forward(self, x):
self.weight.data = torch.renorm(self.weight.data, p=2,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | jiuney/XAI606-EEGNet | Conv2dWithConstraint | false | 6,952 | [
"MIT"
] | 1 | 45ff28630ed1b09d0853f2cfb148a5dd2693e5ab | https://github.com/jiuney/XAI606-EEGNet/tree/45ff28630ed1b09d0853f2cfb148a5dd2693e5ab |
IOULoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ZiyunClaudeWang/e3d | IOULoss | false | 18,207 | [
"MIT"
] | 9 | 2efd01167350c29423babb6233907fa54156268f | https://github.com/ZiyunClaudeWang/e3d/tree/2efd01167350c29423babb6233907fa54156268f |
IdentityPadding | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | Crisescode/Distributed-DL-Example | IdentityPadding | false | 7,908 | [
"Apache-2.0"
] | 19 | a7ff2b4a6c07a126c30eaa886cc6e8cd02a83949 | https://github.com/Crisescode/Distributed-DL-Example/tree/a7ff2b4a6c07a126c30eaa886cc6e8cd02a83949 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | pippinhio/image-recognition | Net | false | 7,482 | [
"MIT"
] | 1 | 89569a0d66ae144d2f6e6f2d73a8577ef8b2272b | https://github.com/pippinhio/image-recognition/tree/89569a0d66ae144d2f6e6f2d73a8577ef8b2272b |
ResnetBlockFC | import torch
import torch.nn as nn
class ResnetBlockFC(nn.Module):
def __init__(self, size_in, size_out=None, size_h=None):
super().__init__()
if size_out is None:
size_out = size_in
if size_h is None:
size_h = min(size_in, size_out)
self.size_in = size_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_... | DveloperY0115/texture_fields | ResnetBlockFC | false | 13,618 | [
"MIT"
] | 78 | 28c277696e0a658ffff3496892810d5a0ef03f65 | https://github.com/DveloperY0115/texture_fields/tree/28c277696e0a658ffff3496892810d5a0ef03f65 |
UNet | import torch
from torch.functional import F
import torch.nn as nn
import torch.nn.functional as F
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.functional import ... | Thomasedv/AI_Interpolation | UNet | false | 6,045 | [
"MIT"
] | 1 | cee51d92185a43a60797785554ee1ae924e5da0d | https://github.com/Thomasedv/AI_Interpolation/tree/cee51d92185a43a60797785554ee1ae924e5da0d |
BCEDiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, eps: 'float'=1e-09):
super(DiceLoss, self).__init__()
self.smooth = 1.0
self.eps = eps
def forward(self, y_pred, y_true):
num = y_true.size(0)
probability = torch.sigmoid(y_pred)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Yukei7/Multimodal-Segmentation-Network | BCEDiceLoss | false | 1,289 | [
"MIT"
] | 0 | 0a38aa8bbd2eb87e28209c810438248c0464a240 | https://github.com/Yukei7/Multimodal-Segmentation-Network/tree/0a38aa8bbd2eb87e28209c810438248c0464a240 |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | d222nguy/gcn_research | GCN | false | 3,372 | [
"MIT"
] | 0 | 83ced4f7d9f7840e48900e62c1eabec0444c5fa2 | https://github.com/d222nguy/gcn_research/tree/83ced4f7d9f7840e48900e62c1eabec0444c5fa2 |
BertIntermediate | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | VinodS7/sota-music-tagging-models | BertIntermediate | false | 14,584 | [
"MIT"
] | 199 | 6232abe693ebe6a99ea64a3ea1fe65c34d0a9dd0 | https://github.com/VinodS7/sota-music-tagging-models/tree/6232abe693ebe6a99ea64a3ea1fe65c34d0a9dd0 |
Fusion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | TranTony/DFAF-for-VQA.pytorch | Fusion | false | 11,939 | [
"MIT"
] | 0 | eba1a893e8e5d3d8bf85078611b0bcf4d56eea86 | https://github.com/TranTony/DFAF-for-VQA.pytorch/tree/eba1a893e8e5d3d8bf85078611b0bcf4d56eea86 |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | mcoavoux/self-attentive-parser | ScaledDotProductAttention | false | 7,185 | [
"MIT"
] | 1 | fa5814ecfdbf4fde329ea725e1d2ddaa55f247d6 | https://github.com/mcoavoux/self-attentive-parser/tree/fa5814ecfdbf4fde329ea725e1d2ddaa55f247d6 |
MultiplicationInverse | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ClashLuke/memcnn | MultiplicationInverse | false | 301 | [
"MIT"
] | 0 | 1d48132282c02506ca3d35540f819c4c9130eab4 | https://github.com/ClashLuke/memcnn/tree/1d48132282c02506ca3d35540f819c4c9130eab4 |
NormedLinear | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
from torch.nn import Parameter
class NormedLinear(nn.Module):
def __init__(self, in_features, out_features):
super(NormedLinear, self).__init__()
self.weight = Pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | raman32/LDAM-DRW | NormedLinear | false | 7,534 | [
"MIT"
] | 1 | 7ce2251c01b94c7259108a1e188457f0b720651d | https://github.com/raman32/LDAM-DRW/tree/7ce2251c01b94c7259108a1e188457f0b720651d |
CGD | import torch
import torch.nn as nn
import torch.utils.model_zoo
class CGD(nn.Module):
def __init__(self, in_channels, bias=True, nonlinear=True):
super(CGD, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.softmax = nn.Softmax(d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HolmesShuan/AIM2020-Real-Super-Resolution | CGD | false | 8,272 | [
"BSD-2-Clause"
] | 19 | 0ea4d7db0f4f7ed488cc162b90bb08fc02082106 | https://github.com/HolmesShuan/AIM2020-Real-Super-Resolution/tree/0ea4d7db0f4f7ed488cc162b90bb08fc02082106 |
SimpleMLP | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
class SimpleMLP(nn.Module):
def __init__(self, n_inputs, n_outputs, dropout_probability):
super(SimpleMLP, self).__init__()
self.n_inputs = n_inputs
self.n_outputs = n_outputs
self.dropout_pr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | AlexandreGuilbault/CS294-Hws | SimpleMLP | false | 34 | [
"MIT"
] | 0 | 2810323c1560949707b71843039ff7c88ae7e596 | https://github.com/AlexandreGuilbault/CS294-Hws/tree/2810323c1560949707b71843039ff7c88ae7e596 |
GateLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import *
class GateLayer(nn.Module):
def __init__(self, dim, target_dim=None, dropout=None):
super(GateLayer, self).__init__()
if target_dim is None:
target_dim = dim
self.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
import torch.nn as nn
from torch.optim.lr_scheduler import *
assert_size_stride ... | xycforgithub/MultiTask-MRC | GateLayer | false | 16,753 | [
"BSD-3-Clause"
] | 105 | 6e5fe8b3cbc40058784cecad73219390e3c2a922 | https://github.com/xycforgithub/MultiTask-MRC/tree/6e5fe8b3cbc40058784cecad73219390e3c2a922 |
XOR | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | Infi-zc/horovod | XOR | false | 13,828 | [
"Apache-2.0"
] | 5,089 | 94cd8561a21d449fc8c80c8fef422025b84dfc22 | https://github.com/Infi-zc/horovod/tree/94cd8561a21d449fc8c80c8fef422025b84dfc22 |
CVAE | # 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 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... | ekrell/learn-planning-space | CVAE | false | 3,473 | [
"MIT"
] | 0 | 730e448bffa4996b2b1ef3a5b00500dc172962ec | https://github.com/ekrell/learn-planning-space/tree/730e448bffa4996b2b1ef3a5b00500dc172962ec |
NIN4d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | XuezheMax/macow | NIN4d | false | 14,612 | [
"Apache-2.0"
] | 60 | 6de247c09b590a037c9eec2d6b1248845f6efb31 | https://github.com/XuezheMax/macow/tree/6de247c09b590a037c9eec2d6b1248845f6efb31 |
Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Vegetebird/MHFormer | Block | false | 14,562 | [
"MIT"
] | 83 | 68d793414e13c256249431a45ac49949930c8e7f | https://github.com/Vegetebird/MHFormer/tree/68d793414e13c256249431a45ac49949930c8e7f |
ConditionalLayerNorm | import torch
from sklearn.metrics import *
from torch import nn
class ConditionalLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-06):
super(ConditionalLayerNorm, self).__init__()
self.eps = eps
self.gamma_dense = nn.Linear(hidden_size, hidden_size, bias=False)
self.be... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 sklearn.metric... | JiaweiSheng/CasEE | ConditionalLayerNorm | false | 8,408 | [
"MIT"
] | 44 | af69432baf34d150f4721a4b4119002555758601 | https://github.com/JiaweiSheng/CasEE/tree/af69432baf34d150f4721a4b4119002555758601 |
resnet_block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | lwkobe/NMC | resnet_block | false | 15,974 | [
"MIT"
] | 74 | a59c187d35b2f929ea3a94fc2b434061d7f7993a | https://github.com/lwkobe/NMC/tree/a59c187d35b2f929ea3a94fc2b434061d7f7993a |
BPRLoss | import torch
from torch import nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
class PairwiseLoss(nn.Module):
"""Base class for pairwise loss functions.
Pairwise approached looks at a pair of document... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
f... | gavrin-s/catalyst | BPRLoss | false | 12,407 | [
"Apache-2.0"
] | 0 | 81087d8348b359e501d899f7a8350e0bedfc2b7d | https://github.com/gavrin-s/catalyst/tree/81087d8348b359e501d899f7a8350e0bedfc2b7d |
QNet | import torch
import torch as t
import torch.nn as nn
class QNet(nn.Module):
def __init__(self, state_dim, action_num, atom_num=10):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, action_num * atom_num)
self.acti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ikamensh/machin | QNet | false | 6,862 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
NotEqualConst | import torch
class NotEqualConst(torch.nn.Module):
def __init__(self):
super(NotEqualConst, self).__init__()
def forward(self, x):
return x != 13.62
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Ilyabasharov/torch2trt | NotEqualConst | false | 2,556 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
CombineSlices | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
assert_size_stride = torch._C._dynamo.gu... | kapoor1992/fastMRI | CombineSlices | false | 10,495 | [
"MIT"
] | 0 | 6b0af94663faa55a2dd901a6a5cbb7d7b5f4cf6d | https://github.com/kapoor1992/fastMRI/tree/6b0af94663faa55a2dd901a6a5cbb7d7b5f4cf6d |
UpsampleConvLayer | import torch
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.optim
import torch.utils.data.distributed
class UpsampleConvLayer(torch.nn.Module):
"""UpsampleConvLayer
Upsamples the input and then does a convolution. This method gives better results
compared to ConvTranspose2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Arjuna197/examples | UpsampleConvLayer | false | 11,372 | [
"BSD-3-Clause"
] | 0 | f504ea2aafc8a8baa5effb659fc1c20a70aabdda | https://github.com/Arjuna197/examples/tree/f504ea2aafc8a8baa5effb659fc1c20a70aabdda |
_MLP_C | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | GaelKBertrand/Meliora_DeepLearning | _MLP_C | false | 5,185 | [
"MIT"
] | 1 | 5618e01066d4d0afcd7dfe074dda91af22b5857c | https://github.com/GaelKBertrand/Meliora_DeepLearning/tree/5618e01066d4d0afcd7dfe074dda91af22b5857c |
CoordLoss | import torch
import torch.optim
import torch.nn as nn
class CoordLoss(nn.Module):
def __init__(self):
super(CoordLoss, self).__init__()
def forward(self, coord_out, coord_gt, valid, is_3D=None):
loss = torch.abs(coord_out - coord_gt) * valid
if is_3D is not None:
loss_z =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.optim
import torch.nn as nn
assert_size_stride = torch._C._d... | Alan-delete/I2L-MeshNet_RELEASE | CoordLoss | false | 13,253 | [
"MIT"
] | 544 | 22d63becc6f6e558e5180a8718dbaa8dde1cc6e5 | https://github.com/Alan-delete/I2L-MeshNet_RELEASE/tree/22d63becc6f6e558e5180a8718dbaa8dde1cc6e5 |
CELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | PARMAGroup/UNet-Instance-Cell-Segmentation | CELoss | false | 8,620 | [
"MIT"
] | 30 | 79655a2c5781d2e20c7d5760f631fbb0be392292 | https://github.com/PARMAGroup/UNet-Instance-Cell-Segmentation/tree/79655a2c5781d2e20c7d5760f631fbb0be392292 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | pkj415/CityLearn | Actor | false | 10,636 | [
"MIT"
] | 0 | 912d1e28270fba2d11a713dc7f0445d59d620511 | https://github.com/pkj415/CityLearn/tree/912d1e28270fba2d11a713dc7f0445d59d620511 |
ShuffleBlock | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Fede112/pytorch-cifar | ShuffleBlock | false | 452 | [
"MIT"
] | 0 | 2e89028e9aaabb22dd944f0002d29548059c24df | https://github.com/Fede112/pytorch-cifar/tree/2e89028e9aaabb22dd944f0002d29548059c24df |
Downsample | import torch
import torch.nn as nn
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels, in_channels,
kernel_size=3, stride=2, padding=0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Rm1n90/SDEdit | Downsample | false | 9,442 | [
"MIT"
] | 0 | 16bfa4f5d37cd32680359db3405af4ea40a9cd1b | https://github.com/Rm1n90/SDEdit/tree/16bfa4f5d37cd32680359db3405af4ea40a9cd1b |
SpatialGroupEnhance | import torch
from torch import nn
from torch.nn import init
class SpatialGroupEnhance(nn.Module):
def __init__(self, groups):
super().__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.bias ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from torch.nn import init
assert_size_stride = torch._C._d... | rushirajsherlocked/External-Attention-pytorch | SpatialGroupEnhance | false | 4,225 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
ConvRelu | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Bovbene/WSCCSN | ConvRelu | false | 172 | [
"Apache-2.0"
] | 0 | 7f454050218e7f2162b0bdc1cdff938d876efc0b | https://github.com/Bovbene/WSCCSN/tree/7f454050218e7f2162b0bdc1cdff938d876efc0b |
MaskedLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.cuda
from torch.nn.functional import *
assert_size_stride = torch._... | bratao/DeepSpeed | MaskedLinear | false | 6,355 | [
"MIT"
] | 1 | c50d8955e942e5e26cf81835d59ec3f20ef8540d | https://github.com/bratao/DeepSpeed/tree/c50d8955e942e5e26cf81835d59ec3f20ef8540d |
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