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 |
|---|---|---|---|---|---|---|---|---|---|---|
Normalize | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
import torch.nn.parallel
assert_size_s... | abeSanchez/FeatureDecoupling | Normalize | false | 9,655 | [
"MIT"
] | 0 | 2a5ace5d057714b0b8657c75f1cff41e779b0ba4 | https://github.com/abeSanchez/FeatureDecoupling/tree/2a5ace5d057714b0b8657c75f1cff41e779b0ba4 |
UpsampleConvLayer | # 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.... | Ali-ry/azureml-examples | UpsampleConvLayer | false | 1,957 | [
"MIT"
] | 0 | 817ae89d2766dcafd70937a22cb3a80f100a2906 | https://github.com/Ali-ry/azureml-examples/tree/817ae89d2766dcafd70937a22cb3a80f100a2906 |
A2Block | import torch
import torch.nn as nn
class A2Block(nn.Module):
"""
Implementation of A2Block(NIPS 2018)
"""
def __init__(self, inplane, plane):
super(A2Block, self).__init__()
self.down = nn.Conv2d(inplane, plane, 1)
self.up = nn.Conv2d(plane, inplane, 1)
self.gather... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zj1008/GALD-DGCNet | A2Block | false | 16,832 | [
"MIT"
] | 127 | be7ebfe2b3d28ea28a2b4714852999d4af2a785e | https://github.com/zj1008/GALD-DGCNet/tree/be7ebfe2b3d28ea28a2b4714852999d4af2a785e |
UpBlock | import torch
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, norm=None):
super(ConvBlock, self).__init__()
self.conv = nn.Conv2d(input_size, output_size, kernel_size, stride,
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | MatusBako/MakeFacesGreatAgain | UpBlock | false | 842 | [
"MIT"
] | 0 | e4941a8460db79dec566ed02d4b23eafb416a6db | https://github.com/MatusBako/MakeFacesGreatAgain/tree/e4941a8460db79dec566ed02d4b23eafb416a6db |
SqueezeExcitation | import torch
import torch.utils.data
def _make_divisible(width, divisor=8):
new_width = max(divisor, int(width + divisor / 2) // divisor * divisor)
if new_width < 0.9 * width:
new_width += divisor
return new_width
class SqueezeExcitation(torch.nn.Module):
""" [https://arxiv.org/abs/1709.0150... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
asser... | yakhyo/MobileNetV3-pt | SqueezeExcitation | false | 4,599 | [
"MIT"
] | 0 | 1fbc966036ed9f036090b3efe3e700f057aa7dde | https://github.com/yakhyo/MobileNetV3-pt/tree/1fbc966036ed9f036090b3efe3e700f057aa7dde |
NTXent | # 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.... | isaaccorley/contrastive-surface-image-pretraining | NTXent | false | 6,903 | [
"MIT"
] | 1 | a918d4fd3b9cc61ec512af978fb4f086d3b46a70 | https://github.com/isaaccorley/contrastive-surface-image-pretraining/tree/a918d4fd3b9cc61ec512af978fb4f086d3b46a70 |
Conv2d_dilated | # 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 ... | xwjBupt/Counting-ICCV-DSSINet | Conv2d_dilated | false | 11,038 | [
"MIT"
] | 0 | 92e4c56c93572fb2b026d573c3e711ce85a4af8f | https://github.com/xwjBupt/Counting-ICCV-DSSINet/tree/92e4c56c93572fb2b026d573c3e711ce85a4af8f |
LayerNorm | import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Parame... | 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... | Arthur1511/CAD-COVID | LayerNorm | false | 78 | [
"MIT"
] | 0 | daab5d70b9f811da41f702e92179a15ca4809fa5 | https://github.com/Arthur1511/CAD-COVID/tree/daab5d70b9f811da41f702e92179a15ca4809fa5 |
MultiClassSegmentationLoss | # 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
... | ChristophReich1996/Cell-DETR | MultiClassSegmentationLoss | false | 13,513 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
ChannelNorm | import torch
import torch.nn as nn
class ChannelNorm(nn.Module):
def __init__(self, numFeatures, epsilon=1e-05, affine=True):
super(ChannelNorm, self).__init__()
if affine:
self.weight = nn.parameter.Parameter(torch.Tensor(1,
numFeatures, 1))
self.bias = nn... | 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_... | B06901052/s3prl | ChannelNorm | false | 106 | [
"MIT"
] | 0 | 5f63d2df043d2d7c81580cd042fa2cea34746f48 | https://github.com/B06901052/s3prl/tree/5f63d2df043d2d7c81580cd042fa2cea34746f48 |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | YigitGunduc/self-driving-car | ResidualBlock | false | 2,978 | [
"MIT"
] | 0 | 2be31f6473c911cf004236ce0874cb2da8fe8ad1 | https://github.com/YigitGunduc/self-driving-car/tree/2be31f6473c911cf004236ce0874cb2da8fe8ad1 |
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.... | MagazzuGaetano/Weather-Classifier | Block | false | 2,612 | [
"MIT"
] | 0 | 2bfac1918eea4aaa37563ef4ffabdc290e411d76 | https://github.com/MagazzuGaetano/Weather-Classifier/tree/2bfac1918eea4aaa37563ef4ffabdc290e411d76 |
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 torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 |
PositionAttentionModule | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LiChengChen666/DetectDee | PositionAttentionModule | false | 9,875 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.onnx
assert_size_stride = torch._C._dynamo.g... | chandar-lab/CriticalGradientOptimization | Norm | false | 6,411 | [
"MIT"
] | 1 | 1af4b1df40489991289bb50bb69859a00b2c97c6 | https://github.com/chandar-lab/CriticalGradientOptimization/tree/1af4b1df40489991289bb50bb69859a00b2c97c6 |
KLMutualLoss | import torch
import torch.nn as nn
class KLMutualLoss(nn.Module):
def __init__(self):
super(KLMutualLoss, self).__init__()
self.kl_loss = nn.KLDivLoss(size_average=False)
self.log_softmax = nn.functional.log_softmax
self.softmax = nn.functional.softmax
def forward(self, pred1... | 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... | linkserendipity/AlignedReID | KLMutualLoss | false | 3,916 | [
"MIT"
] | 0 | 142a9ebdc200ef4da001f91c1f592e4ff02b2f77 | https://github.com/linkserendipity/AlignedReID/tree/142a9ebdc200ef4da001f91c1f592e4ff02b2f77 |
InnerProductLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch.a... | LaudateCorpus1/LIGA-Stereo | InnerProductLoss | false | 13,985 | [
"Apache-2.0"
] | 56 | aee3731a24a0ab1667e633e520cc89be2f135272 | https://github.com/LaudateCorpus1/LIGA-Stereo/tree/aee3731a24a0ab1667e633e520cc89be2f135272 |
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
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | StephenHausler/Patch-NetVLAD | NetVLAD | false | 9,827 | [
"MIT"
] | 0 | 5d8b68fb7aa686e9c08a48ce504ecc552fff7b0b | https://github.com/StephenHausler/Patch-NetVLAD/tree/5d8b68fb7aa686e9c08a48ce504ecc552fff7b0b |
SFU | import torch
import torch.utils.data
import torch.nn.functional as F
class SFU(torch.nn.Module):
"""
only two input, one input vector and one fusion vector
Args:
- input_size:
- fusions_size:
Inputs:
- input: (seq_len, batch, input_size)
- fusions: (seq_len, batch, fus... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | jamaalhay/Final_Proj | SFU | false | 15,667 | [
"MIT"
] | 104 | 3f524a90fee5a3cb21466ab76f630d060792045d | https://github.com/jamaalhay/Final_Proj/tree/3f524a90fee5a3cb21466ab76f630d060792045d |
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.... | TomerRonen34/MeshCNN | ScaledDotProductAttention | false | 5,907 | [
"MIT"
] | 1 | 8c50f3804c48044b78572d652a42184640e904d9 | https://github.com/TomerRonen34/MeshCNN/tree/8c50f3804c48044b78572d652a42184640e904d9 |
AppendLayer | # 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | hssandriss/pybnn | AppendLayer | false | 15,536 | [
"BSD-3-Clause"
] | 110 | e878553a24ce9ebdde9088f285c7f292e4ee8885 | https://github.com/hssandriss/pybnn/tree/e878553a24ce9ebdde9088f285c7f292e4ee8885 |
TdnnAffine | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | ishine/asv-subtools | TdnnAffine | false | 15,646 | [
"Apache-2.0"
] | 370 | 597dcb29a772b8113dbe7ab64f0d4cc1da298707 | https://github.com/ishine/asv-subtools/tree/597dcb29a772b8113dbe7ab64f0d4cc1da298707 |
SqueezeExcitation | # 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 Tensor
impo... | ayrna/ordinal-cnn-ecoc | SqueezeExcitation | false | 6,307 | [
"BSD-3-Clause"
] | 1 | 2b7909d036612727a45a174c891c4e749c3b60c4 | https://github.com/ayrna/ordinal-cnn-ecoc/tree/2b7909d036612727a45a174c891c4e749c3b60c4 |
Block | import torch
import torch.nn as nn
import torch.utils.data
class Mlp(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 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.... | Vegetebird/MHFormer | Block | false | 14,562 | [
"MIT"
] | 83 | 68d793414e13c256249431a45ac49949930c8e7f | https://github.com/Vegetebird/MHFormer/tree/68d793414e13c256249431a45ac49949930c8e7f |
ResidualDenseBlock_5C | import torch
import torch.nn as nn
class ResidualDenseBlock_5C(nn.Module):
def __init__(self, nf=64, gc=32, bias=True):
super(ResidualDenseBlock_5C, self).__init__()
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | PVjammer/ESRGAN | ResidualDenseBlock_5C | false | 9,393 | [
"Apache-2.0"
] | 0 | a37fda8d4efe58eff4dc0ce1cffd8ee4051a7871 | https://github.com/PVjammer/ESRGAN/tree/a37fda8d4efe58eff4dc0ce1cffd8ee4051a7871 |
HardTripletLoss | 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 |
CausalConv1d | import torch
from torch import nn as nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
**kwargs):
super().__init__()
self.pad = (kernel_size - 1) * dilation
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | kschamplin/astro-classifier-neo | CausalConv1d | false | 3,861 | [
"MIT"
] | 0 | 44fcb8ba41ef549c16360df7fd470f56c42da9b3 | https://github.com/kschamplin/astro-classifier-neo/tree/44fcb8ba41ef549c16360df7fd470f56c42da9b3 |
Concat | import torch
from torch import nn
import torch.nn
import torch.optim
class Concat(nn.Module):
def forward(self, state: 'torch.Tensor', action: 'torch.Tensor'):
return torch.cat((state, action), dim=-1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inpu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda =... | mikaylagawarecki/ReAgent | Concat | false | 10,697 | [
"BSD-3-Clause"
] | 0 | b1a306a9d3641c8adeb03ac272e5774a0009fa88 | https://github.com/mikaylagawarecki/ReAgent/tree/b1a306a9d3641c8adeb03ac272e5774a0009fa88 |
NICEMLPBlock | # 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.... | wp03052/wolf | NICEMLPBlock | false | 13,200 | [
"Apache-2.0"
] | 0 | 49a582cafb829a2642db360c7d94c21439247ec7 | https://github.com/wp03052/wolf/tree/49a582cafb829a2642db360c7d94c21439247ec7 |
GateLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Zilize/CRSLab | GateLayer | false | 1,320 | [
"MIT"
] | 0 | fb357d0dfb7d2cf7b67b892d98e52032a31ca564 | https://github.com/Zilize/CRSLab/tree/fb357d0dfb7d2cf7b67b892d98e52032a31ca564 |
SeperableConv | # 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_... | henningpohl/body-based-ar | SeperableConv | false | 6,797 | [
"MIT"
] | 1 | dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb | https://github.com/henningpohl/body-based-ar/tree/dc7d5d6eaf8dd4427de0f2b1cfdcc415cbfffdfb |
SeqExpandConv | # 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
from math import sqrt as sqrt
assert_size_stride = torch._... | NTech-Lab/deepfake-detection-challenge | SeqExpandConv | false | 14,081 | [
"Apache-2.0"
] | 98 | 52095ce4a49f298faf075a5eb28391722b9e4103 | https://github.com/NTech-Lab/deepfake-detection-challenge/tree/52095ce4a49f298faf075a5eb28391722b9e4103 |
ContrastiveDistanceLoss | import torch
import torch.nn as nn
import torch.distributed
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 ContrastiveDistanceLoss(nn.Module):
"""The Contrastive distance loss.
@TODO: Docs. Contri... | 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.distributed
from torch.nn.modules.loss import *
from t... | Ditwoo/catalyst | ContrastiveDistanceLoss | false | 5,072 | [
"Apache-2.0"
] | 1 | 3126390f9f679ebcfedbe01707b416678a2732ac | https://github.com/Ditwoo/catalyst/tree/3126390f9f679ebcfedbe01707b416678a2732ac |
ESRLoss | import torch
def apply_reduction(losses, reduction='none'):
"""Apply reduction to collection of losses."""
if reduction == 'mean':
losses = losses.mean()
elif reduction == 'sum':
losses = losses.sum()
return losses
class ESRLoss(torch.nn.Module):
"""Error-to-signal ratio loss fun... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | leoauri/auraloss | ESRLoss | false | 15,905 | [
"Apache-2.0"
] | 272 | 0e3362674ae1b53aa61c6a631fb4e6970c5683c1 | https://github.com/leoauri/auraloss/tree/0e3362674ae1b53aa61c6a631fb4e6970c5683c1 |
CrossEntropyWithLogSoftmax | import torch
import torch.nn as nn
class CrossEntropyWithLogSoftmax(nn.Module):
def forward(self, y_hat, y):
return -(y_hat * y).mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | cadurosar/graph_kd_dense_cifar100 | CrossEntropyWithLogSoftmax | false | 1,620 | [
"MIT"
] | 0 | 84054ab4f8f61c9db3460993661ba7bf1d951b36 | https://github.com/cadurosar/graph_kd_dense_cifar100/tree/84054ab4f8f61c9db3460993661ba7bf1d951b36 |
mbr_convex_hull | # 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.... | liuhuaijjin/rpn_rois_proposals_layers | mbr_convex_hull | false | 7,122 | [
"MIT"
] | 1 | c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 | https://github.com/liuhuaijjin/rpn_rois_proposals_layers/tree/c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 |
GlobalAttentionGeneral | import torch
import torch.nn as nn
import torch.nn.parallel
def conv1x1(in_planes, out_planes, bias=False):
"""1x1 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
padding=0, bias=bias)
class GlobalAttentionGeneral(nn.Module):
def __init__(self, idf, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Huy2122k/Project3-AttnGANwCLIP | GlobalAttentionGeneral | false | 9,116 | [
"MIT"
] | 0 | 3fb8c643bf71599e1606ec468e86373ccde1ed20 | https://github.com/Huy2122k/Project3-AttnGANwCLIP/tree/3fb8c643bf71599e1606ec468e86373ccde1ed20 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | jordiriu/MP-DQN | Actor | false | 15,729 | [
"MIT"
] | 75 | eec13eb9b4e2c0099649e0639f2a8b93d7d0d5be | https://github.com/jordiriu/MP-DQN/tree/eec13eb9b4e2c0099649e0639f2a8b93d7d0d5be |
injective_pad | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_st... | jhjacobsen/pytorch-i-revnet | injective_pad | false | 15,680 | [
"MIT"
] | 392 | 307413043e33540cbe9c3746ef420261f8138315 | https://github.com/jhjacobsen/pytorch-i-revnet/tree/307413043e33540cbe9c3746ef420261f8138315 |
TransitionUp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = ... | KshingWang/LesionSeg | TransitionUp | false | 9,347 | [
"BSD-3-Clause"
] | 0 | a3c38aa7481eb7ce6a3b0fe5f9c4b349b8cf0b19 | https://github.com/KshingWang/LesionSeg/tree/a3c38aa7481eb7ce6a3b0fe5f9c4b349b8cf0b19 |
PixelNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | BillyXYB/TransEditor | PixelNorm | false | 17,064 | [
"MIT"
] | 4 | 0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 | https://github.com/BillyXYB/TransEditor/tree/0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 |
ShakeResNet | import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
class ShakeShake(torch.autograd.Function):
@staticmethod
def forward(ctx, x1, x2, training=True):
if training:
alpha = torch.FloatTensor(x1.size(0)).uniform_()
alp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
from torch import... | pemcconnell-anyvision/fast-autoaugment | ShakeResNet | false | 12,874 | [
"MIT"
] | 0 | 047cf4bb9ffb85d0e8266a425347cdfe99d16902 | https://github.com/pemcconnell-anyvision/fast-autoaugment/tree/047cf4bb9ffb85d0e8266a425347cdfe99d16902 |
TripletLoss | import torch
import torch.nn as nn
class TripletLoss(nn.Module):
def __init__(self):
super(TripletLoss, self).__init__()
self.margin = 0.5
def distance(self, x, y):
diff = torch.abs(x - y)
diff = torch.pow(diff, 2).sum(-1)
return diff
def forward(self, anchor, po... | 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
... | Alonso94/Representation-learning | TripletLoss | false | 18 | [
"MIT"
] | 0 | c4410b3bc5d2d1de666fba2958c4a7024e2af79f | https://github.com/Alonso94/Representation-learning/tree/c4410b3bc5d2d1de666fba2958c4a7024e2af79f |
IdentityMappingZero | # 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... | alvarobartt/understanding-resnet | IdentityMappingZero | false | 18,271 | [
"MIT"
] | 6 | 1e95aba607bf3fead740affb9ceafb7fde3ee0c6 | https://github.com/alvarobartt/understanding-resnet/tree/1e95aba607bf3fead740affb9ceafb7fde3ee0c6 |
EPELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | haochen23/GeoProj | EPELoss | false | 10,180 | [
"MIT"
] | 0 | 4b31f51789f9cc41ea7dc977cee057b8bc8a83cc | https://github.com/haochen23/GeoProj/tree/4b31f51789f9cc41ea7dc977cee057b8bc8a83cc |
CriticNN | # 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.... | maxmax1992/Q_learning | CriticNN | false | 3,995 | [
"MIT"
] | 0 | 8b2b8491d6f94b94b2fce608b93cdc31b418c5b0 | https://github.com/maxmax1992/Q_learning/tree/8b2b8491d6f94b94b2fce608b93cdc31b418c5b0 |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | RobertYCXu/vae_vampprior | Conv2d | false | 9,616 | [
"MIT"
] | 0 | edcec4f5f7af673172c5b5b9aa2a22f993564fab | https://github.com/RobertYCXu/vae_vampprior/tree/edcec4f5f7af673172c5b5b9aa2a22f993564fab |
resblock | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
import torch.optim
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if 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
import torch.utils.data
impor... | ananiask8/FFWM | resblock | false | 3,131 | [
"MIT"
] | 0 | 117f593783da67da9dc910a751910760497ef37f | https://github.com/ananiask8/FFWM/tree/117f593783da67da9dc910a751910760497ef37f |
Encoder | import torch
import torch.nn as nn
import torch.nn
import torch.nn.init
import torch.optim
class Model(nn.Module):
""" Class representing sampleable neural network model """
def num_params(self):
""" Get the number of model parameters. """
return sum(p.numel() for p in self.parameters())
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CBIIT/NCI-DOE-Colab-Pilot1-Combo | Encoder | false | 11,269 | [
"MIT"
] | 0 | 8d60900c29618083e0944b5b8ef43a2e98881b32 | https://github.com/CBIIT/NCI-DOE-Colab-Pilot1-Combo/tree/8d60900c29618083e0944b5b8ef43a2e98881b32 |
NetWidth | # 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.... | dustasa/senior_software_HW | NetWidth | false | 3,451 | [
"Apache-2.0"
] | 0 | 767d1d7bbd5e7d7414c17fa14b92b942e53d84ed | https://github.com/dustasa/senior_software_HW/tree/767d1d7bbd5e7d7414c17fa14b92b942e53d84ed |
G_t | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class G_t(nn.Module):
def __init__(self, args):
super(G_t, self).__init__()
self._relu = nn.ReLU()
self._ws1 = nn.Linear(args.image_feature_dim, args.
Vt_middle_feature_dim, bias=False)
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_... | HCShi/IONet | G_t | false | 18,361 | [
"MIT"
] | 4 | 42e3c0455a1ecb610f458e814d7310d685b2be7b | https://github.com/HCShi/IONet/tree/42e3c0455a1ecb610f458e814d7310d685b2be7b |
BertSelfOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertSelfOutput(nn.Module):
def __init__(self, config):
super(BertSelfOutput, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | cjinchao/mner | BertSelfOutput | false | 1,729 | [
"MIT"
] | 0 | 12776280da314eb7ef22511aa18ca9af0764fb32 | https://github.com/cjinchao/mner/tree/12776280da314eb7ef22511aa18ca9af0764fb32 |
Conv1dLinear | # 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
assert_size_s... | fancyliumeng/asv-subtools | Conv1dLinear | false | 6,682 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
IndepAnisotropicGaussianUVLoss | import math
import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class IndepAnisotropicGaussianUVLoss(nn.Module):
"""
Loss for the case of independent residuals with anisotropic covariances:
$Sigma_i = sigma_i^2 I + r_i r_i^T$
The loss (negative log likelihood) is ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | JHMeusener/detectron2-ResNeSt | IndepAnisotropicGaussianUVLoss | false | 582 | [
"Apache-2.0"
] | 0 | 6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 | https://github.com/JHMeusener/detectron2-ResNeSt/tree/6abab6fb9496a528f6aa2d4e1e27f3e7ceb42685 |
SE | # 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... | liormagram/pytorch-cifar | SE | false | 10,422 | [
"MIT"
] | 0 | 2ed0fabe6cbd4a468c5c4d155fb76c5b9ad4a764 | https://github.com/liormagram/pytorch-cifar/tree/2ed0fabe6cbd4a468c5c4d155fb76c5b9ad4a764 |
RandomCrop | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | vitskvara/shape-guided-anomaly-detection | RandomCrop | false | 4,503 | [
"MIT"
] | 0 | 6685b2e0b97968a6d0f478d2920486da107b277f | https://github.com/vitskvara/shape-guided-anomaly-detection/tree/6685b2e0b97968a6d0f478d2920486da107b277f |
ResidualBlock | import torch
from torch import nn
class ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv0 = nn.Conv2d(in_channels=channels, out_channels=channels,
kernel_size=3, padding=1)
self.conv1 = nn.Conv2d(in_channels=channels, out_channels=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 import triton_helpers
from torch import nn
assert_s... | Thaigun/Griddly | ResidualBlock | false | 2,972 | [
"MIT"
] | 0 | de5972a608a2928172510a0ac81a977c48af6b1f | https://github.com/Thaigun/Griddly/tree/de5972a608a2928172510a0ac81a977c48af6b1f |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | IDSC-io/vre-tgn | MLP | false | 9,144 | [
"Apache-2.0"
] | 0 | 46e8327e3befe67003874fa70b384a511523f8f7 | https://github.com/IDSC-io/vre-tgn/tree/46e8327e3befe67003874fa70b384a511523f8f7 |
SAC | import torch
import torch.nn as nn
class SAC(nn.Module):
def __init__(self, input_channel, out_channel):
super(SAC, self).__init__()
self.conv_1 = nn.Conv3d(input_channel, out_channel, kernel_size=3,
stride=1, padding=1)
self.conv_3 = nn.Conv3d(input_channel, out_channel, kern... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Luoxd1996/SCPM-Net | SAC | false | 8,487 | [
"MIT"
] | 26 | 2039ea5253ec831dcae79c2f0caa6e5d2641a1f9 | https://github.com/Luoxd1996/SCPM-Net/tree/2039ea5253ec831dcae79c2f0caa6e5d2641a1f9 |
BertPooler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.fun... | bj1103/FaST-VGS-Family | BertPooler | false | 6,345 | [
"BSD-3-Clause"
] | 1 | 824f987a5bd647fc17aa34b98eb1d9109441d64b | https://github.com/bj1103/FaST-VGS-Family/tree/824f987a5bd647fc17aa34b98eb1d9109441d64b |
EqualConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | Dolorousrtur/style-people | EqualConv2d | false | 8,028 | [
"MIT"
] | 15 | c48b12b245cc50f8230c0654dffe40016f2a69f1 | https://github.com/Dolorousrtur/style-people/tree/c48b12b245cc50f8230c0654dffe40016f2a69f1 |
BasicBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, inplanes, planes, droprate=0.2, attention=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1,
bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Galaxies99/alpha-protein | BasicBlock | false | 17,316 | [
"MIT"
] | 4 | db4b77ab48d5905ade5d4a66004f8387773718fa | https://github.com/Galaxies99/alpha-protein/tree/db4b77ab48d5905ade5d4a66004f8387773718fa |
TripletLossXBM | # 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.... | XianyuanLiu/Transfer-Learning-Library | TripletLossXBM | false | 10,147 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
InvDepth | import torch
import torch.nn as nn
class InvDepth(nn.Module):
"""Inverse depth layer"""
def __init__(self, in_channels, out_channels=1, min_depth=0.5):
"""
Initializes an InvDepth object.
Parameters
----------
in_channels : int
Number of 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | pection/packnet-sfm | InvDepth | false | 7,447 | [
"MIT"
] | 1 | d5673567b649e6bfda292c894cacdeb06aa80913 | https://github.com/pection/packnet-sfm/tree/d5673567b649e6bfda292c894cacdeb06aa80913 |
Sine | import torch
import torch.nn as nn
class Sine(nn.Module):
"""Sine Activation Function."""
def __init__(self):
super().__init__()
def forward(self, x):
return torch.sin(30.0 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | justinjohn0306/CIPS-3D | Sine | false | 7,005 | [
"MIT"
] | 1 | 69a910a7841846419a6b5e03182c8cf061a82584 | https://github.com/justinjohn0306/CIPS-3D/tree/69a910a7841846419a6b5e03182c8cf061a82584 |
WeightedMCEDiceLoss | import torch
import torch.nn.functional
import torch.nn as nn
import torch.nn.functional as F
def centercrop(image, w, h):
_nt, _ct, ht, wt = image.size()
padw, padh = (wt - w) // 2, (ht - h) // 2
if padw > 0 and padh > 0:
image = image[:, :, padh:-padh, padw:-padw]
return image
class 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.nn.functi... | HelenGuohx/cv-ferattn-code | WeightedMCEDiceLoss | false | 5,294 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
BoundaryEntDiscriminator | import torch
import torch.nn as nn
class BoundaryEntDiscriminator(nn.Module):
def __init__(self):
super(BoundaryEntDiscriminator, self).__init__()
filter_num_list = [64, 128, 256, 512, 1]
self.conv1 = nn.Conv2d(3, filter_num_list[0], kernel_size=4, stride
=2, padding=2, bias=F... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | EmmaW8/BEAL | BoundaryEntDiscriminator | false | 13,694 | [
"MIT"
] | 95 | 945cad38a354605b8bca5bc01ae1b65848d605e1 | https://github.com/EmmaW8/BEAL/tree/945cad38a354605b8bca5bc01ae1b65848d605e1 |
QREmbeddingBag | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class QREmbeddingBag(nn.Module):
"""Computes sums or means over two 'bags' of embeddings, one using the quotient
of the indices and the other using the remainder of the indices, witho... | 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 numpy as np
import torch.nn as nn
from torch.nn.parameter import Paramet... | SplitInfinity/dlrm | QREmbeddingBag | false | 5,845 | [
"MIT"
] | 1 | 726dc9059be94b249d41e9b5a399c991fe687edb | https://github.com/SplitInfinity/dlrm/tree/726dc9059be94b249d41e9b5a399c991fe687edb |
ConvSwishOutplace | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.... | Observer007/intel-extension-for-pytorch | ConvSwishOutplace | false | 5,664 | [
"Apache-2.0"
] | 1 | f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 | https://github.com/Observer007/intel-extension-for-pytorch/tree/f8ab25c305c89d5aaf06190a4fec0727aeb4dcd7 |
Encoder | # 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... | georgezefko/dtu_mlops | Encoder | false | 10,091 | [
"Apache-2.0"
] | 0 | 3b715bcb934d0c2827d89395823b7d4768faac97 | https://github.com/georgezefko/dtu_mlops/tree/3b715bcb934d0c2827d89395823b7d4768faac97 |
_ASPP | import torch
import torch.nn as nn
class _ASPP(nn.Module):
"""
Atrous spatial pyramid pooling (ASPP)
"""
def __init__(self, in_ch, out_ch, rates):
super(_ASPP, self).__init__()
self.aspp_num = len(rates)
for i, rate in enumerate(rates):
self.add_module('c{}'.format... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | developfeng/BCM | _ASPP | false | 9,989 | [
"BSD-3-Clause-Attribution"
] | 0 | 8eb5ac950a2d67d10fc707519bb66cd9ea4f14f2 | https://github.com/developfeng/BCM/tree/8eb5ac950a2d67d10fc707519bb66cd9ea4f14f2 |
GLU | import torch
import torch.utils.data
import torch.nn as nn
import torch
from torchvision.transforms import functional as F
import torch.nn.functional as F
import torch.nn.parallel
class GLU(nn.Module):
def __init__(self):
super(GLU, self).__init__()
def forward(self, x):
nc = x.size(1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size... | adymaharana/VLCStoryGan | GLU | false | 18,246 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
Message_Passing_Unit_v1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | SpartaG117/scene_graph_benchmark | Message_Passing_Unit_v1 | false | 1,095 | [
"MIT"
] | 0 | e2e49940dd2f752b1faf9ae26707435ba3441bcb | https://github.com/SpartaG117/scene_graph_benchmark/tree/e2e49940dd2f752b1faf9ae26707435ba3441bcb |
HuberLossWithIgnore | import torch
from torch import Tensor
import torch.nn.functional
from torch import nn
class HuberLossWithIgnore(nn.Module):
def __init__(self, ignore_value: 'int', delta: 'float'=1, fraction:
'float'=1.0):
super().__init__()
self.ignore_value = ignore_value
self.delta = delta
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | drivendataorg/DrivenData-2021-Geopose-Solution | HuberLossWithIgnore | false | 6,606 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
StochasticClassifier | # 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... | KaiyangZhou/ssdg-benchmark | StochasticClassifier | false | 8,385 | [
"MIT"
] | 43 | aaa48be4f93b77347fbadff649be6b3e0f7a8779 | https://github.com/KaiyangZhou/ssdg-benchmark/tree/aaa48be4f93b77347fbadff649be6b3e0f7a8779 |
NeuralSort | import torch
from torch import Tensor
class NeuralSort(torch.nn.Module):
def __init__(self, tau=1.0, hard=False):
super(NeuralSort, self).__init__()
self.hard = hard
self.tau = tau
def forward(self, input: 'Tensor', scores: 'Tensor', cuda=None):
"""
:param input:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MaestroGraph/quicksort | NeuralSort | false | 2,622 | [
"MIT"
] | 0 | 54e1aba3b8a1acf3cd5326f5efab2b0a853f4b40 | https://github.com/MaestroGraph/quicksort/tree/54e1aba3b8a1acf3cd5326f5efab2b0a853f4b40 |
ContinousRotReprDecoder | # 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... | LuckyDC/human_body_prior | ContinousRotReprDecoder | false | 5,565 | [
"Xnet",
"X11"
] | 1 | 6a46613b4cbd9c62d888359f1435cec501643af3 | https://github.com/LuckyDC/human_body_prior/tree/6a46613b4cbd9c62d888359f1435cec501643af3 |
OrthoLoss | import torch
import torch.nn as nn
def ortho(w: 'torch.Tensor') ->torch.Tensor:
"""
Returns the orthogonal loss for weight matrix `m`, from Big GAN.
https://arxiv.org/abs/1809.11096
:math:`R_{\\beta}(W)= ||W^T W \\odot (1 - I)||_F^2`
"""
cosine = torch.einsum('ij,ji->ij', w, w)
no_diag ... | 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... | Vermeille/Torchelie | OrthoLoss | false | 14,545 | [
"MIT"
] | 117 | 43957d83238372ae6436aac90127865c2040b76c | https://github.com/Vermeille/Torchelie/tree/43957d83238372ae6436aac90127865c2040b76c |
AttentionMatrix | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | abhinonymous/MSMARCO-Question-Answering | AttentionMatrix | false | 14,744 | [
"MIT"
] | 127 | bfdd802d20b63322adca23f1da1f6a5931593920 | https://github.com/abhinonymous/MSMARCO-Question-Answering/tree/bfdd802d20b63322adca23f1da1f6a5931593920 |
QKVAttentionLegacy | import math
import torch
import numpy as np
import torch as th
import torch.nn as nn
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
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
from torch._inductor.runtime.... | AranKomat/Diff-DALLE | QKVAttentionLegacy | false | 13,276 | [
"MIT"
] | 53 | 9418e98e97b599c5c65f16ee168fedf76a29095f | https://github.com/AranKomat/Diff-DALLE/tree/9418e98e97b599c5c65f16ee168fedf76a29095f |
TwoLayerFCBodyWithAction | # 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 ... | RaviTej310/mrpvf | TwoLayerFCBodyWithAction | false | 11,835 | [
"MIT"
] | 0 | f026b4704f26b85161de26ada5d6390ab549fbbd | https://github.com/RaviTej310/mrpvf/tree/f026b4704f26b85161de26ada5d6390ab549fbbd |
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... | asiliskender/deep-reinforcement-learning | Critic | false | 12,132 | [
"MIT"
] | 0 | dbf96d67477aa9242128b78b081474193e1e4538 | https://github.com/asiliskender/deep-reinforcement-learning/tree/dbf96d67477aa9242128b78b081474193e1e4538 |
CrossEntropyLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def _is_long(x):
return isinstance(x, torch.LongTensor) or isinstance(x, torch.LongTensor)
def onehot(indexes, N=None, ignore_index=None):
"""
Creates a one-representation of indexes with N possible entries
if N is not specified, it ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | aldakata/ClassConditionalC2D | CrossEntropyLoss | false | 6,161 | [
"MIT"
] | 1 | dd73e1d4d5f0f82438340211e3c479dbd16b8ffc | https://github.com/aldakata/ClassConditionalC2D/tree/dd73e1d4d5f0f82438340211e3c479dbd16b8ffc |
AdaptiveInstanceNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | STomoya/animeface | AdaptiveInstanceNorm | false | 14,373 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
AdaptiveFeatureNorm | import torch
import torch.nn as nn
import torch.utils.data
class AdaptiveFeatureNorm(nn.Module):
"""
The `Stepwise Adaptive Feature Norm loss (ICCV 2019) <https://arxiv.org/pdf/1811.07456v2.pdf>`_
Instead of using restrictive scalar R to match the corresponding feature norm, Stepwise Adaptive Feature Nor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | Neronjust2017/TransferBed | AdaptiveFeatureNorm | false | 5,647 | [
"MIT"
] | 1 | eaa703a4bc10eaf6216fe1394cd272f6e75489e2 | https://github.com/Neronjust2017/TransferBed/tree/eaa703a4bc10eaf6216fe1394cd272f6e75489e2 |
SAGEAggregator | import torch
import torch.nn as nn
class SAGEAggregator(nn.Module):
def __init__(self, in_features, out_features, agg_method='mean', concat
=False, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.concat = concat
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EdisonLeeeee/GraphGallery | SAGEAggregator | false | 13,634 | [
"MIT"
] | 300 | 4eec9c5136bda14809bd22584b26cc346cdb633b | https://github.com/EdisonLeeeee/GraphGallery/tree/4eec9c5136bda14809bd22584b26cc346cdb633b |
Standard | import torch
from torch.nn.functional import softmax
from torch.nn import Linear
from torch.nn import Dropout
import torch.random
class Standard(torch.nn.Module):
def __init__(self, in_features: 'int'):
super().__init__()
self.h1 = Linear(in_features, 50)
self.d1 = Dropout()
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HEmile/KENN-PyTorch | Standard | false | 17,330 | [
"BSD-3-Clause"
] | 5 | e39386f298587ab70ecea88180121ef8cf6ff9bc | https://github.com/HEmile/KENN-PyTorch/tree/e39386f298587ab70ecea88180121ef8cf6ff9bc |
my_MaxPool2d | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.modules.utils import _pair
class my_MaxPool2d(Module):
def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False):
supe... | 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.nn import Module
from torch.nn.modules.module import Module
from torch.nn.modu... | likun97/Low_quality_classification_with_mobilenetv3 | my_MaxPool2d | false | 10,439 | [
"Apache-2.0"
] | 0 | a9e6f66caad937fc7c8e101cddb76f116219b255 | https://github.com/likun97/Low_quality_classification_with_mobilenetv3/tree/a9e6f66caad937fc7c8e101cddb76f116219b255 |
EncoderLayer | # 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.... | hyunwoongko/transformer | EncoderLayer | false | 15,607 | [
"Apache-2.0"
] | 233 | 8f7aaa19d37b088c156db0512868127ba9bf1a0f | https://github.com/hyunwoongko/transformer/tree/8f7aaa19d37b088c156db0512868127ba9bf1a0f |
BinaryCrossEntropyLoss | from torch.nn import Module
import torch
class BinaryCrossEntropyLoss(Module):
def __init__(self):
super().__init__()
def forward(self, groundtruth, distr_params, mask):
groundtruth = (groundtruth - groundtruth.min()) / (groundtruth.max(
) - groundtruth.min())
loss = 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
from torch.nn import M... | HugoSenetaire/vaeac | BinaryCrossEntropyLoss | false | 13,825 | [
"MIT"
] | 70 | 451d34dd4986c52f2f37c508f03ee3db9e7408d3 | https://github.com/HugoSenetaire/vaeac/tree/451d34dd4986c52f2f37c508f03ee3db9e7408d3 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KunpengLi1994/VSRN | Attention | false | 13,995 | [
"Apache-2.0"
] | 238 | 777ae74326fdb6abe69dbd3911d0e545322520d1 | https://github.com/KunpengLi1994/VSRN/tree/777ae74326fdb6abe69dbd3911d0e545322520d1 |
HGNN | # 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 math
from torch import... | young917/HGNN | HGNN | false | 4,636 | [
"MIT"
] | 0 | 41017f4315f459e1250830ca6c498b920d57e80a | https://github.com/young917/HGNN/tree/41017f4315f459e1250830ca6c498b920d57e80a |
Pointer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | dcy2018/QANA | Pointer | false | 3,506 | [
"MIT"
] | 0 | 69d1e4ff408a56317479e22ecc854c91fc0f420f | https://github.com/dcy2018/QANA/tree/69d1e4ff408a56317479e22ecc854c91fc0f420f |
Classify | import torch
import torch.nn as nn
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Flatten(nn.Module):
@staticmethod
def forward(x):
return x.view(x.size(0), -1)
class Classify(nn.Module):
def __init__(self, c1,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JuliannaChaykina/social-distance | Classify | false | 2,433 | [
"Apache-2.0"
] | 0 | 1c8ade043254b78de49a1244d438203ddb38c586 | https://github.com/JuliannaChaykina/social-distance/tree/1c8ade043254b78de49a1244d438203ddb38c586 |
MsgNorm | # 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
assert_size_stride = torch._... | LMZimmer/nasbench301 | MsgNorm | false | 9,221 | [
"Apache-2.0"
] | 0 | 3329d24a41765e87ac7ebf91fbf38269beeda822 | https://github.com/LMZimmer/nasbench301/tree/3329d24a41765e87ac7ebf91fbf38269beeda822 |
Highway | import torch
import torch.nn as nn
class Highway(nn.Module):
def __init__(self, x_hidden):
super(Highway, self).__init__()
self.lin = nn.Linear(x_hidden, x_hidden)
def forward(self, x1, x2):
gate = torch.sigmoid(self.lin(x1))
x = torch.mul(gate, x2) + torch.mul(1 - gate, x1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | LinXueyuanStdio/EchoEA | Highway | false | 5,534 | [
"Apache-2.0"
] | 1 | d9b8564023cca71678dec44cf8cab3f91736448a | https://github.com/LinXueyuanStdio/EchoEA/tree/d9b8564023cca71678dec44cf8cab3f91736448a |
MINCNet | import torch
import torch.utils.data
from torch import nn
import torch.jit
class MINCNet(nn.Module):
def __init__(self):
super(MINCNet, self).__init__()
self.ReLU = nn.ReLU(True)
self.conv11 = nn.Conv2d(3, 64, 3, 1, 1)
self.conv12 = nn.Conv2d(64, 64, 3, 1, 1)
self.maxpool1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | BlueAmulet/BasicSR | MINCNet | false | 7,891 | [
"Apache-2.0"
] | 12 | 7040913d8659a05af4c2428feb71c260efbf1e9c | https://github.com/BlueAmulet/BasicSR/tree/7040913d8659a05af4c2428feb71c260efbf1e9c |
Conv1DSame | import math
import torch
import torch.nn as nn
class Conv1DSame(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, groups=1,
bias=True, stride=1):
super(Conv1DSame, self).__init__()
p = (kernel_size - 1) / 2
self.padding = nn.ConstantPad1d((math.floor(p), 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
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | alexchartrand/IoT | Conv1DSame | false | 1,406 | [
"MIT"
] | 0 | 2cc0d40b7f8305b9f82fc83ad4ed55c83efa1bfd | https://github.com/alexchartrand/IoT/tree/2cc0d40b7f8305b9f82fc83ad4ed55c83efa1bfd |
PPO | # 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.... | g6ling/Pytorch-Cartpole | PPO | false | 15,385 | [
"MIT"
] | 116 | ecb7b622cfefe825ac95388cceb6752413d90a2a | https://github.com/g6ling/Pytorch-Cartpole/tree/ecb7b622cfefe825ac95388cceb6752413d90a2a |
CNN | import torch
import torch.nn as nn
class CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=
3, padding=1)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size
=3, padding=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_... | DavidCarlyn/cnn_visualize | CNN | false | 2,158 | [
"MIT"
] | 0 | 6b4e554e1a6ac3b4951f0e914e0414cfa8bd3686 | https://github.com/DavidCarlyn/cnn_visualize/tree/6b4e554e1a6ac3b4951f0e914e0414cfa8bd3686 |
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