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 |
|---|---|---|---|---|---|---|---|---|---|---|
_Residual_Block_SR | # 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.functional
import torch.nn as nn
assert_size_stride = torch._C._... | CarlosPena00/pytorchvision | _Residual_Block_SR | false | 229 | [
"MIT"
] | 0 | 824b3a5a8940f3ee6b4da5de7a391a88e5aa36a2 | https://github.com/CarlosPena00/pytorchvision/tree/824b3a5a8940f3ee6b4da5de7a391a88e5aa36a2 |
LSTM | # 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 ... | Kelang-Tian/ST-MGAT | LSTM | false | 17,531 | [
"MIT"
] | 8 | f527cb5748d022d9c3b4eddd3481cf641bb0dae3 | https://github.com/Kelang-Tian/ST-MGAT/tree/f527cb5748d022d9c3b4eddd3481cf641bb0dae3 |
SelfAttention2d | import torch
from torch import nn
class SelfAttention2d(nn.Module):
def __init__(self, c_in, n_head=1, dropout_rate=0.1):
super().__init__()
assert c_in % n_head == 0
self.norm = nn.GroupNorm(1, c_in)
self.n_head = n_head
self.qkv_proj = nn.Conv2d(c_in, c_in * 3, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DeepTitan/v-diffusion-pytorch | SelfAttention2d | false | 9,045 | [
"MIT"
] | 0 | 857b6f2a4519973f9a8dc0b6c93f0134cebc3a8d | https://github.com/DeepTitan/v-diffusion-pytorch/tree/857b6f2a4519973f9a8dc0b6c93f0134cebc3a8d |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | amyami187/nngeometry | BasicBlock | false | 14,833 | [
"MIT"
] | 103 | cb516da3f7a019e148f48ff3ef3bed0cdae0d184 | https://github.com/amyami187/nngeometry/tree/cb516da3f7a019e148f48ff3ef3bed0cdae0d184 |
WeightedBCEWithLogitsLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class WeightedBCEWithLogitsLoss(nn.Module):
"""Weighted binary cross-entropy with logits.
"""
def __init__(self, size_average=True, reduce=True, eps=0.0):
super().__init__()
... | 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... | devaansh100/pytorch_connectomics | WeightedBCEWithLogitsLoss | false | 6,561 | [
"MIT"
] | 1 | b1e4b16b0480546ea806d14876208080815ed964 | https://github.com/devaansh100/pytorch_connectomics/tree/b1e4b16b0480546ea806d14876208080815ed964 |
BPR | # 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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | georgezzzh/bpr | BPR | false | 3,556 | [
"MIT"
] | 0 | dd2f39d99f7f06ebb305b66363c89c3606a811a1 | https://github.com/georgezzzh/bpr/tree/dd2f39d99f7f06ebb305b66363c89c3606a811a1 |
Rotate | import torch
from typing import cast
from torch import nn
from torchvision.transforms import functional as F
import torch.nn.functional as F
import torchvision.transforms.functional as F
import torch.autograd
class Rotate(nn.Module):
def __init__(self, angle: 'float') ->None:
super().__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.triton_helpers import libdevice
from torch import n... | pystiche/papers | Rotate | false | 7,503 | [
"BSD-3-Clause"
] | 1 | 0d8179dc51f6eda0b27fa525dc0b86b866bc88e1 | https://github.com/pystiche/papers/tree/0d8179dc51f6eda0b27fa525dc0b86b866bc88e1 |
AsymmetricLoss | # 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... | David-19940718/mmclassification | AsymmetricLoss | false | 5,048 | [
"Apache-2.0"
] | 1 | 987dd45457e38c4787237ea468799849dce11ada | https://github.com/David-19940718/mmclassification/tree/987dd45457e38c4787237ea468799849dce11ada |
Cartesian | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
import torch.fft
class Cartesian(nn.Module):
def forward(self, x):
r, phi = x[..., 0], x[..., 1]
return torch.stack((r * torch.cos(phi), r * torch.sin(phi)), dim=-1)
def get_inputs()... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
import torch.utils.data.dist... | Gaskell-1206/fastMRI | Cartesian | false | 13,697 | [
"MIT"
] | 815 | 1b6d1f9020bc9209afa65ef9b9f2f3fa3348901c | https://github.com/Gaskell-1206/fastMRI/tree/1b6d1f9020bc9209afa65ef9b9f2f3fa3348901c |
ImageProcessingModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class ImageProcessingModule(nn.Module):
def __init__(self, n_filters):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=n_filters,
kernel_size=7, stride=7)
def forward(self, observation):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | bentrevett/task-oriented-language-grounding | ImageProcessingModule | false | 6,325 | [
"MIT"
] | 1 | 812a7bc21ee622030eb0594c576c7d60dc630148 | https://github.com/bentrevett/task-oriented-language-grounding/tree/812a7bc21ee622030eb0594c576c7d60dc630148 |
ConformerFeedForward | # 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
import torch.cuda
import torch.opti... | Malkovsky/NeMo | ConformerFeedForward | false | 2,614 | [
"Apache-2.0"
] | 0 | 8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 | https://github.com/Malkovsky/NeMo/tree/8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 |
ConvMlp | import torch
import torch.nn as nn
import torch.utils.collect_env
class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.ReLU, norm_layer=None, drop=0.0):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | HaotianUpenn/scatterbrain | ConvMlp | false | 13,752 | [
"Apache-2.0"
] | 49 | c026128d7362ae627641d11d4e5627bc1f400eb1 | https://github.com/HaotianUpenn/scatterbrain/tree/c026128d7362ae627641d11d4e5627bc1f400eb1 |
TwoLayerNet | # 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... | benfogelson/provenance | TwoLayerNet | false | 3,204 | [
"MIT"
] | 0 | e61095e767e8786943ea76bef9b5dd6dd9575041 | https://github.com/benfogelson/provenance/tree/e61095e767e8786943ea76bef9b5dd6dd9575041 |
AngleLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
i... | DeVriesMatt/pointMLP-pytorch | AngleLoss | false | 365 | [
"Apache-2.0"
] | 0 | e9c09a2038551e83b072353f3fd7e3294463e892 | https://github.com/DeVriesMatt/pointMLP-pytorch/tree/e9c09a2038551e83b072353f3fd7e3294463e892 |
DiracInitBlock | # 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 ... | earhian/imgclsmob | DiracInitBlock | false | 6,624 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
AngleSimpleLinear | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.onnx
import torch.nn
class AngleSimpleLinear(nn.Module):
"""Computes cos of angles between input vectors and weights ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ygnn123/training_extensions | AngleSimpleLinear | false | 4,681 | [
"Apache-2.0"
] | 0 | c3aeba9359b0d4e0ef9c054de777d3ec081a9892 | https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892 |
Sum | # 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... | AkshayGanesh/yolov5processor | Sum | false | 4,808 | [
"MIT"
] | 1 | 788accfa93798729c002b2c9b4f943284ff97cad | https://github.com/AkshayGanesh/yolov5processor/tree/788accfa93798729c002b2c9b4f943284ff97cad |
WeightConvNet | import torch
import torch.nn as nn
class WeightConvNet(nn.Module):
def __init__(self, in_channels, groups, n_segment):
super(WeightConvNet, self).__init__()
self.lastlayer = nn.Conv1d(in_channels, groups, 3, padding=1)
self.groups = groups
def forward(self, x):
N, _C, T = x.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | eynaij/X-Temporal_catdim | WeightConvNet | false | 6,673 | [
"MIT"
] | 1 | 6a2efba407c09c83ca061c8467c1373b6ed0c7eb | https://github.com/eynaij/X-Temporal_catdim/tree/6a2efba407c09c83ca061c8467c1373b6ed0c7eb |
NeuralAccumulatorCell | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.nn import Parameter
import torch.nn.init as init
from torch.nn.parameter import Parameter
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class NeuralAccumulatorCell(nn.Module):
"""... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | CUMLSec/stateformer | NeuralAccumulatorCell | false | 7,926 | [
"MIT"
] | 41 | 87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c | https://github.com/CUMLSec/stateformer/tree/87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c |
BellMembFunc | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | trituenhantaoio/anfis-pytorch | BellMembFunc | false | 16,618 | [
"MIT"
] | 66 | 7a6bf123d69b550e46abeddd5b4a776243d43aa6 | https://github.com/trituenhantaoio/anfis-pytorch/tree/7a6bf123d69b550e46abeddd5b4a776243d43aa6 |
Conv1DBlock | # 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... | ishine/FastPitchFormant | Conv1DBlock | false | 15,617 | [
"MIT"
] | 54 | dd86032953be04fb526b658b19ecdc5600ff25a5 | https://github.com/ishine/FastPitchFormant/tree/dd86032953be04fb526b658b19ecdc5600ff25a5 |
DepthConv2dv2 | # 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
... | rbodo/pytorch-OpCounter | DepthConv2dv2 | false | 7,539 | [
"MIT"
] | 1 | 1857cbb5f9e53343fb349af84efdfde2554a2691 | https://github.com/rbodo/pytorch-OpCounter/tree/1857cbb5f9e53343fb349af84efdfde2554a2691 |
SimpleCosModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | YaronBenAtar/glow | SimpleCosModule | false | 14,658 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
MultiLayeredConv1d | # 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 | MultiLayeredConv1d | false | 6,680 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
SigmoidRange | from torch.nn import Module
import functools
import torch
import torch.nn as nn
from typing import *
def sigmoid_range(x, low, high):
"""Sigmoid function with range `(low, high)`"""
return torch.sigmoid(x) * (high - low) + low
class PrePostInitMeta(type):
"""A metaclass that calls optional `__pre_init__... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import functools
import torch.nn as nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_... | amaarora/fastai_dev | SigmoidRange | false | 14,826 | [
"Apache-2.0"
] | 380 | ffea51a553e4a7f71bc7240730b370cd0d07cb0a | https://github.com/amaarora/fastai_dev/tree/ffea51a553e4a7f71bc7240730b370cd0d07cb0a |
CausalConv2d | import torch
from torch import nn
class WNConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, bias=True, activation=None):
super().__init__()
self.conv = nn.utils.weight_norm(nn.Conv2d(in_channel, out_channel,
kernel_size, stride=st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | sajjad2014/vq-vae-2-pytorch | CausalConv2d | false | 16,356 | [
"MIT"
] | 1,007 | ef5f67c46f93624163776caec9e0d95063910eca | https://github.com/sajjad2014/vq-vae-2-pytorch/tree/ef5f67c46f93624163776caec9e0d95063910eca |
EncoderImagePrecomp | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(X, dim, eps=1e-08):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
return X
class EncoderImagePrecomp(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
... | Ballester/SCAN | EncoderImagePrecomp | false | 2,020 | [
"Apache-2.0"
] | 0 | 4a003f60d3e45e5dd16969745e4b182fe705e758 | https://github.com/Ballester/SCAN/tree/4a003f60d3e45e5dd16969745e4b182fe705e758 |
RationalHat_transform | import torch
import torch.nn as nn
class RationalHat_transform(nn.Module):
"""
Coordinate function as defined in
/Hofer, C., Kwitt, R., and Niethammer, M.
Learning representations of persistence barcodes.
JMLR, 20(126):1–45, 2019b./
"""
def __init__(self, output_dim, input_dim=1):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | BorgwardtLab/TOGL | RationalHat_transform | false | 17,019 | [
"BSD-3-Clause"
] | 6 | d0c986cf829ca6bbae1a23e5cdab1c99146503cd | https://github.com/BorgwardtLab/TOGL/tree/d0c986cf829ca6bbae1a23e5cdab1c99146503cd |
ResidualAttentionBlock | import torch
import torch.nn as nn
from collections import OrderedDict
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(LayerNorm, self).__init__()
self.weight = nn.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
from torch._inductor.runtime.... | FacePerceiver/FaRL | ResidualAttentionBlock | false | 8,198 | [
"MIT"
] | 23 | 38f1d32f4e63940fae524e9f501b88a947ec09cd | https://github.com/FacePerceiver/FaRL/tree/38f1d32f4e63940fae524e9f501b88a947ec09cd |
CatImgs | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | crisdeodates/AI-depthai-experiments | CatImgs | false | 6,484 | [
"MIT"
] | 1 | 74b8b84a03cb637d20a7fcd091cce11add78bd2c | https://github.com/crisdeodates/AI-depthai-experiments/tree/74b8b84a03cb637d20a7fcd091cce11add78bd2c |
MLB | # 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... | AndresPMD/GCN_classification | MLB | false | 7,702 | [
"MIT"
] | 39 | b005c4256d68f1f90a7f73e7fdb3d066448de28c | https://github.com/AndresPMD/GCN_classification/tree/b005c4256d68f1f90a7f73e7fdb3d066448de28c |
IrisClassifier | # 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 ... | ParikhKadam/mlflow | IrisClassifier | false | 2,723 | [
"Apache-2.0"
] | 0 | 21d64d45c6131b62bb956f77327aa1abd9df66b2 | https://github.com/ParikhKadam/mlflow/tree/21d64d45c6131b62bb956f77327aa1abd9df66b2 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Steffen-Wolf/vit-pytorch | LayerNorm | false | 9,611 | [
"MIT"
] | 0 | 4f590b9bd570091d9070a039ad33301516caa341 | https://github.com/Steffen-Wolf/vit-pytorch/tree/4f590b9bd570091d9070a039ad33301516caa341 |
ParameterLoss | import torch
import torch.nn as nn
class ParameterLoss(nn.Module):
def __init__(self):
"""
SMPL parameter loss module.
"""
super(ParameterLoss, self).__init__()
self.loss_fn = nn.MSELoss(reduction='none')
def forward(self, pred_param: 'torch.Tensor', gt_param: 'torch.... | 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... | michael-p-sachen/ProHMR | ParameterLoss | false | 10,573 | [
"BSD-3-Clause"
] | 0 | 0167d05a9a45939a217d02b4ef8fd67977c15f82 | https://github.com/michael-p-sachen/ProHMR/tree/0167d05a9a45939a217d02b4ef8fd67977c15f82 |
IMul | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | Akababa/torch2trt | IMul | false | 18,411 | [
"MIT"
] | 2 | 03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 | https://github.com/Akababa/torch2trt/tree/03063b74a7eb40f5aac88d49be6b8b5e4e4e92d7 |
KLDivergence | import torch
from torch import nn
def kl_divergence(px, py):
eps = 1e-08
kl_div = px * (torch.log(px + eps) - torch.log(py + eps))
return kl_div
class KLDivergence(nn.Module):
"""
Kullback–Leibler divergence
Args:
- None -
"""
def __init__(self):
super().__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | akanametov/pathgan | KLDivergence | false | 18,293 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d |
GCN | from torch.nn import Module
import torch
import torch.utils.data
from torch.nn import Conv1d
from torch.nn import ReLU
class GCN(Module):
def __init__(self, num_state, num_node, bias=False):
super(GCN, self).__init__()
self.conv1 = Conv1d(num_node, num_node, kernel_size=1, padding=0,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | ZhihuaLiuEd/canetbrats | GCN | false | 18,199 | [
"MIT"
] | 7 | a23f008b2876a21026b2564588f4f51692083ae2 | https://github.com/ZhihuaLiuEd/canetbrats/tree/a23f008b2876a21026b2564588f4f51692083ae2 |
RegionPenaltyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | geoffreyangus/pet-ct | RegionPenaltyLoss | false | 6,738 | [
"Apache-2.0"
] | 1 | fa96a07734afade475f6a1e1587ec14965fe2de3 | https://github.com/geoffreyangus/pet-ct/tree/fa96a07734afade475f6a1e1587ec14965fe2de3 |
EmbedNet | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
import torch
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
class EmbedNet(nn.Module):
def __init__(self, cfg):
super(EmbedNet, self).__init__()
self.embed_c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | ron5569/mega.pytorch | EmbedNet | false | 13,120 | [
"BSD-2-Clause"
] | 0 | b845b7050da307576cd98ab73eb7be4e9a9088bc | https://github.com/ron5569/mega.pytorch/tree/b845b7050da307576cd98ab73eb7be4e9a9088bc |
OuterProductLayer | import torch
import torch.nn as nn
class OuterProductLayer(nn.Module):
"""OuterProduct Layer used in PNN. This implementation is
adapted from code that the author of the paper published on https://github.com/Atomu2014/product-nets.
"""
def __init__(self, num_feature_field, embedding_size, device):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Ahren09/RecBole | OuterProductLayer | false | 1,919 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
EmbeddingLearner | import torch
from torch import nn
class EmbeddingLearner(nn.Module):
def __init__(self):
super(EmbeddingLearner, self).__init__()
def forward(self, h, r, t):
if r.dim() == 1:
r = r.unsqueeze(0)
h = h.view(1, -1, h.shape[-1])
t = t.view(1, -1, t.shape[-1])
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | adonis704/ucas_2021_hc_15 | EmbeddingLearner | false | 18,220 | [
"MIT"
] | 6 | 7308c3b32962ef5430d85ccfcb199ebe40bf4a7f | https://github.com/adonis704/ucas_2021_hc_15/tree/7308c3b32962ef5430d85ccfcb199ebe40bf4a7f |
PSNR | # 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 as th
import to... | IlyaBizyaev/ttools | PSNR | false | 8,306 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
BinaryLoss | # 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... | atypon/specter | BinaryLoss | false | 1,496 | [
"Apache-2.0"
] | 0 | bc1ee723167cf1dbf599603e09539c1823f26c17 | https://github.com/atypon/specter/tree/bc1ee723167cf1dbf599603e09539c1823f26c17 |
XNOR_BinarizeConv2d | from torch.autograd import Function
import torch
import torch.nn as nn
import torch.nn.functional as F
class XNOR_BinaryQuantize(Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
out = torch.sign(input)
return out
@staticmethod
def backward(ctx, g... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd... | RuiLin0212/BATMANN | XNOR_BinarizeConv2d | false | 17,864 | [
"MIT"
] | 6 | 5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 | https://github.com/RuiLin0212/BATMANN/tree/5c5cc3334090fc0442bfd2ffdd41bdcab88cbea2 |
MyMaxPool1dPadSame | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
class MyMaxPool1dPadSame(nn.Module):
"""
extend nn.MaxPool1d to support SAME padding
"""
def __init__(self, kernel_size, stride_size):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
from itertools import product as product
fr... | WFDetector/WFDetection | MyMaxPool1dPadSame | false | 2,963 | [
"Apache-2.0"
] | 0 | b16d35b3a3a5de62de9e0bac83eccd21b6358b53 | https://github.com/WFDetector/WFDetection/tree/b16d35b3a3a5de62de9e0bac83eccd21b6358b53 |
MedianPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _quadruple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kernel, int ... | 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
from torch.nn.modules.utils import _pair
from torch... | vztu/DebandingNet | MedianPool2d | false | 4,517 | [
"MIT"
] | 0 | 4af8e83ffbfc70dc220dd6fea2827fb75796f10c | https://github.com/vztu/DebandingNet/tree/4af8e83ffbfc70dc220dd6fea2827fb75796f10c |
fChannelAttention | import math
import torch
import torch.optim
import torch.utils.data
class fChannelAttention(torch.nn.Module):
def __init__(self, N_in, ratio=1):
super(fChannelAttention, self).__init__()
self.N_in = N_in
self.ratio = ratio
self.weight_fc1 = torch.nn.Parameter(torch.Tensor(self.N_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dyn... | dwromero/att_gconvs | fChannelAttention | false | 15,287 | [
"MIT"
] | 53 | 872259cad49763fdcfa3e96e80b6b5c331adf084 | https://github.com/dwromero/att_gconvs/tree/872259cad49763fdcfa3e96e80b6b5c331adf084 |
PartitionedLinear | # 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... | skulick/self-attentive-parser | PartitionedLinear | false | 4,357 | [
"MIT"
] | 0 | 04a91e80cc05bcfe8f48145517f58e85f0c8ade6 | https://github.com/skulick/self-attentive-parser/tree/04a91e80cc05bcfe8f48145517f58e85f0c8ade6 |
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torchvision.datasets im... | JJavierga/PyTorch-Encoding | Normalize | false | 9,462 | [
"MIT"
] | 0 | 207254b2a60276a31ffa24b76ae84df27c6ebf94 | https://github.com/JJavierga/PyTorch-Encoding/tree/207254b2a60276a31ffa24b76ae84df27c6ebf94 |
HGNN_conv | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
class HGNN_conv(nn.Module):
def __init__(self, in_ft, out_ft, bias=True):
super(HGNN_conv, self).__init__()
self.weight = Parameter(torch.Tensor(in_ft, out_ft))
if bias:
self.bias = Paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch.nn.parameter import Parameter
assert... | DCMMC/HGNN | HGNN_conv | false | 13,534 | [
"MIT"
] | 124 | 4315f27faaffb8f2cf1463049a4dc596694e44e1 | https://github.com/DCMMC/HGNN/tree/4315f27faaffb8f2cf1463049a4dc596694e44e1 |
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.... | yuanweining/DTI | EncoderLayer | false | 4,651 | [
"Apache-2.0"
] | 0 | 11eacb46a221da04d0e9b01d41c89c7ce51ea302 | https://github.com/yuanweining/DTI/tree/11eacb46a221da04d0e9b01d41c89c7ce51ea302 |
WeightedBCE | # 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... | Atharva-Peshkar/pytorch_connectomics | WeightedBCE | false | 13,307 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
DyIntraModalityUpdate | # 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.... | Ruiver/CTCNet | DyIntraModalityUpdate | false | 17,903 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 |
NetVLAD | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from sklearn.neighbors import NearestNeighbors
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, normalize_input=True,
vladv2=False):
"""
Args:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rick0514/VPR_SMCN | NetVLAD | false | 3,089 | [
"MIT"
] | 0 | 7a00dc8e4de0c21438474c05a4a7be18d05367fa | https://github.com/Rick0514/VPR_SMCN/tree/7a00dc8e4de0c21438474c05a4a7be18d05367fa |
SEBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class SEBlock(nn.Module):
def __init__(self, input_channels, internal_neurons):
super(SEBlock, self).__init__()
self.down = nn.Conv2d(in_channels=input_channels, out_channels=
internal_neurons, kernel_size=1, stride=1,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Sharingsky/resrep | SEBlock | false | 9,520 | [
"MIT"
] | 0 | a173d1bc256b75b2c902024929e406863ce48b9b | https://github.com/Sharingsky/resrep/tree/a173d1bc256b75b2c902024929e406863ce48b9b |
BinaryFocalLoss | import torch
class BinaryFocalLoss(torch.nn.Module):
""" from https://github.com/qubvel/segmentation_models"""
def __init__(self, gamma=2.0, alpha=0.25, eps=1e-07):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.eps = eps
def forward(self, pr, gt):
... | 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... | uncharted-distil/d3m-primitives | BinaryFocalLoss | false | 13,054 | [
"Apache-2.0"
] | 0 | e8d37dbe302c0f2bae4e7f7fa241a46faebc9b79 | https://github.com/uncharted-distil/d3m-primitives/tree/e8d37dbe302c0f2bae4e7f7fa241a46faebc9b79 |
BatchedVectorAttention | # 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.... | Crazy-Jack/BigGAN-PyTorch | BatchedVectorAttention | false | 326 | [
"MIT"
] | 0 | 1a5644e9c87cc399580c96cfeb180052076888da | https://github.com/Crazy-Jack/BigGAN-PyTorch/tree/1a5644e9c87cc399580c96cfeb180052076888da |
CrossEntropyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | TimO96/NLP2 | CrossEntropyLoss | false | 1,144 | [
"MIT"
] | 0 | 83f65a385457f68397c641f38b53df0110282578 | https://github.com/TimO96/NLP2/tree/83f65a385457f68397c641f38b53df0110282578 |
TemporalDecay | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Sobhan1996/BRITS-master | TemporalDecay | false | 9,511 | [
"MIT"
] | 0 | 66726ec104dad43c6d8367b0c9ef8f19daf65f0e | https://github.com/Sobhan1996/BRITS-master/tree/66726ec104dad43c6d8367b0c9ef8f19daf65f0e |
BatchLinear | import torch
import torch.nn as nn
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited from `MetaModule` are fully compa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | RisingStockPrices/multi-shape-siren | BatchLinear | false | 2,767 | [
"MIT"
] | 0 | f78d6deb94660fd11ef0caf55f88095b74d3e223 | https://github.com/RisingStockPrices/multi-shape-siren/tree/f78d6deb94660fd11ef0caf55f88095b74d3e223 |
N2 | # 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 typing import Tuple
from abc import ABC
from abc import abstractmethod
fro... | uclnlp/cqd | N2 | false | 16,637 | [
"MIT"
] | 59 | 36148c110f336415250c98873fc27ca847741a78 | https://github.com/uclnlp/cqd/tree/36148c110f336415250c98873fc27ca847741a78 |
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 tnn
assert... | jittor-online-first/jittor | Net | false | 12,620 | [
"Apache-2.0"
] | 0 | 4217359f86cbcf174fab27c3b723487a8d78b729 | https://github.com/jittor-online-first/jittor/tree/4217359f86cbcf174fab27c3b723487a8d78b729 |
LRN | # 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_... | nbswords/Paper-implemention-by-Pytorch | LRN | false | 7,316 | [
"MIT"
] | 1 | 429514c4f51c41ec7b3013683fb79ad4b4ab4638 | https://github.com/nbswords/Paper-implemention-by-Pytorch/tree/429514c4f51c41ec7b3013683fb79ad4b4ab4638 |
TemperatureScaleTrainer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Kageshimasu/temperature-scaling-optimizer | TemperatureScaleTrainer | false | 1,196 | [
"MIT"
] | 0 | 3af562e6c3fefef97aec0431d08b8e8275d275c7 | https://github.com/Kageshimasu/temperature-scaling-optimizer/tree/3af562e6c3fefef97aec0431d08b8e8275d275c7 |
SENet | import torch
import torch.nn as nn
import torch.utils.data
class SENet(nn.Module):
"""support estimation network"""
def __init__(self, input_size: 'int', hidden_size: 'int', output_dims:
'int') ->None:
super(SENet, self).__init__()
self.l_1 = nn.Linear(input_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | L-Net-1992/DI-engine | SENet | false | 5,553 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f |
ResidualConvUnit | import torch
from torch import nn
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(features, features, ke... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | google/dynamic-video-depth | ResidualConvUnit | false | 15,451 | [
"Apache-2.0"
] | 144 | 7dab8f9e156fa35735301695ea020aee7221fb31 | https://github.com/google/dynamic-video-depth/tree/7dab8f9e156fa35735301695ea020aee7221fb31 |
ActorCritic | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class ActorCritic(nn.Module):
def __init__(self, num_states, num_actions, hidden_size):
super(ActorCritic, self).__init__()
self.num_actions = num_actions
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | savan77/nni | ActorCritic | false | 4,280 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
Conv | import torch
import torch.utils.data
from torch import nn
class Conv(nn.Module):
def __init__(self, inp_dim, out_dim, kernel_size=3, stride=1, bn=False,
relu=True):
super(Conv, self).__init__()
self.inp_dim = inp_dim
self.conv = nn.Conv2d(inp_dim, out_dim, kernel_size, stride,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CenIII/pose-ae-train | Conv | false | 13,446 | [
"BSD-3-Clause"
] | 250 | 8780ba9f3d80ca3a724bbee7b815073adc3d3e6e | https://github.com/CenIII/pose-ae-train/tree/8780ba9f3d80ca3a724bbee7b815073adc3d3e6e |
SmallDecoder2_16x | # 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.... | EndyWon/Texture-Reformer | SmallDecoder2_16x | false | 8,145 | [
"MIT"
] | 11 | f84f95accb3574c7b759a7f03c0b0b4e150314b5 | https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5 |
PosConv2d | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn.init as init
import torch.utils.data
class PosConv2d(torch.nn.Conv2d):
def reset_parameters(self) ->None:
super().reset_parameters()
self.fan_in, _ = init._calculate_fan_in_and_fan_out(self.weigh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | JunLi-Galios/CP-Flow | PosConv2d | false | 11,600 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff |
eca_block | import math
import torch
import torch.nn as nn
class eca_block(nn.Module):
def __init__(self, channel, b=1, gamma=2):
super(eca_block, self).__init__()
kernel_size = int(abs((math.log(channel, 2) + b) / gamma))
kernel_size = kernel_size if kernel_size % 2 else kernel_size + 1
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
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | huuthieu/pytorch-yolov4-tiny | eca_block | false | 6,836 | [
"MIT"
] | 1 | fac82da75e161221af74b56242272a42cf64c17e | https://github.com/huuthieu/pytorch-yolov4-tiny/tree/fac82da75e161221af74b56242272a42cf64c17e |
MatrixReduceMin | # 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.autograd
assert_size_stride = torch._C._dynamo.guards.... | RyusukeYamano/nngen | MatrixReduceMin | false | 14,344 | [
"Apache-2.0"
] | 207 | 9ed1f7fb83908794aa94d70287d89545d45fe875 | https://github.com/RyusukeYamano/nngen/tree/9ed1f7fb83908794aa94d70287d89545d45fe875 |
FeatureCorrelation | # 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.... | sebastian-echeverria/ncnet | FeatureCorrelation | false | 10,786 | [
"MIT"
] | 0 | c7249fe8f908813bab6443ebfa4590bd362a0dc2 | https://github.com/sebastian-echeverria/ncnet/tree/c7249fe8f908813bab6443ebfa4590bd362a0dc2 |
AttentionPooling | import torch
import torch.utils.data
import torch.nn.functional as F
def masked_softmax(x, m=None, dim=-1):
"""
Softmax with mask
:param x:
:param m:
:param dim:
:return:
"""
if m is not None:
m = m.float()
x = x * m
e_x = torch.exp(x - torch.max(x, dim=dim, keepdim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jamaalhay/Final_Proj | AttentionPooling | false | 15,668 | [
"MIT"
] | 104 | 3f524a90fee5a3cb21466ab76f630d060792045d | https://github.com/jamaalhay/Final_Proj/tree/3f524a90fee5a3cb21466ab76f630d060792045d |
GroupedMultiHeadAttention | # 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.... | debasish-mihup/EfficientConformer | GroupedMultiHeadAttention | false | 10,343 | [
"Apache-2.0"
] | 0 | bddd927cebcde044a999aaa7766fa6d44dc20576 | https://github.com/debasish-mihup/EfficientConformer/tree/bddd927cebcde044a999aaa7766fa6d44dc20576 |
Blind_UNet | # 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 ... | amonod/udvd | Blind_UNet | false | 1,486 | [
"MIT"
] | 0 | a1ccb777d205255ac68c40efb93dd3996f562c45 | https://github.com/amonod/udvd/tree/a1ccb777d205255ac68c40efb93dd3996f562c45 |
BCEDiceLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.backends.cudnn
import torch.utils.data
def dice_loss(preds, trues, weight=None, is_average=True):
num = preds.size(0)
preds = preds.view(num, -1)
trues = trues.view(num, -1)
if weight is not None:
w = torch.autogra... | 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... | ArmenGhambaryan/kaggle_carvana_segmentation | BCEDiceLoss | false | 13,290 | [
"MIT"
] | 447 | 648a6b5c807cb69011316fe6501241dacc027db2 | https://github.com/ArmenGhambaryan/kaggle_carvana_segmentation/tree/648a6b5c807cb69011316fe6501241dacc027db2 |
BalancedLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BalancedLoss(nn.Module):
def __init__(self, neg_weight=1.0):
super(BalancedLoss, self).__init__()
self.neg_weight = neg_weight
def forward(self, input, target):
pos_mask = target == 1
neg_mask = target =... | 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... | Kingzerd/siamfc_pytorch | BalancedLoss | false | 5,439 | [
"MIT"
] | 1 | fd1dbeb12dd7e2b9190876a1de7ea4b71a7a1166 | https://github.com/Kingzerd/siamfc_pytorch/tree/fd1dbeb12dd7e2b9190876a1de7ea4b71a7a1166 |
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
import torch.optim
import torch.nn as nn
import torch.nn.parallel
assert_size_st... | PeiKaLunCi/code-cs-fairness | ResidualBlock | false | 5,706 | [
"MIT"
] | 1 | 3c34d32c87ad244f6a9f302ba4f61e0acf886574 | https://github.com/PeiKaLunCi/code-cs-fairness/tree/3c34d32c87ad244f6a9f302ba4f61e0acf886574 |
BilinearUpsampler | import torch
import torch as th
import torch.utils.data
class BilinearUpsampler(th.nn.Module):
def __init__(self, scale=2, channels=1):
super(BilinearUpsampler, self).__init__()
ksize = 2 * scale
total_pad = ksize - scale // 2
if scale % 2 == 1:
ksize += 1
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
import torch as th
import torch.utils.data
assert_size_stride = torch._C._dynamo... | IlyaBizyaev/ttools | BilinearUpsampler | false | 8,312 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
SigmoidFocalLoss | # 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.utils.dat... | HRTNet/HRTNet | SigmoidFocalLoss | false | 502 | [
"MIT"
] | 0 | 6a51c9c34568988ea6125a1638794c63d8fadbea | https://github.com/HRTNet/HRTNet/tree/6a51c9c34568988ea6125a1638794c63d8fadbea |
ReRegualizedLinearPosNACLayer | import collections
import math
import torch
import torch.utils.data
def sparsity_error(W):
W_error = torch.min(torch.abs(W), torch.abs(1 - torch.abs(W)))
return torch.max(W_error)
class SummaryWriterNamespaceNoLoggingScope:
def __init__(self, writer):
self._writer = writer
def __enter__(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 collections
import mat... | hoedt/stable-nalu | ReRegualizedLinearPosNACLayer | false | 3,615 | [
"MIT"
] | 0 | 64b3d240db8bff4da857d955f213ef3c7e38e035 | https://github.com/hoedt/stable-nalu/tree/64b3d240db8bff4da857d955f213ef3c7e38e035 |
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
import torch.nn as nn
assert_... | HAXRD/PIC | Actor | false | 8,182 | [
"MIT"
] | 28 | 658b4dd6b01e64413d5f8f0107d9167f1bd78546 | https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546 |
SpatialAttention | # 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 ... | LSH9832/MyPythonModules | SpatialAttention | false | 761 | [
"MIT"
] | 0 | 442566a0fbd6ebe2bc20b6914686a1e2663d10c0 | https://github.com/LSH9832/MyPythonModules/tree/442566a0fbd6ebe2bc20b6914686a1e2663d10c0 |
Conv2dZeros | # 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.... | appuzanova/Glow-PyTorch | Conv2dZeros | false | 12,220 | [
"MIT"
] | 0 | 50316b1b242f0f345b2df9e3e4538cfab5a60895 | https://github.com/appuzanova/Glow-PyTorch/tree/50316b1b242f0f345b2df9e3e4538cfab5a60895 |
OffsetNet | import torch
import torch.nn as nn
class OffsetNet(nn.Module):
"""OffsetNet in Temporal interlace module.
The OffsetNet consists of one convolution layer and two fc layers
with a relu activation following with a sigmoid function. Following
the convolution layer, two fc layers and relu are applied to ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | scenarios/dev | OffsetNet | false | 4,458 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
Decoder | import torch
import torch.nn as nn
import torch.nn.functional as F
class Decoder(nn.Module):
""" Encoder
"""
def __init__(self, n_levels, n_color, n_eccentricity, n_azimuth,
n_theta, n_phase):
super(Decoder, self).__init__()
self.n_levels = n_levels
self.n_color = n_color
... | 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... | bicv/POLO | Decoder | false | 6,333 | [
"MIT"
] | 1 | b8d4f9014796a4eb24c178d8be611a0b3b4c44df | https://github.com/bicv/POLO/tree/b8d4f9014796a4eb24c178d8be611a0b3b4c44df |
InfoNCE_loss_vectorized | # 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... | LIIR-KULeuven/CLDR_CLNER_models | InfoNCE_loss_vectorized | false | 8,429 | [
"MIT"
] | 12 | 5fe47a988b88a36d0ccf4484aff5ab70c59f39d6 | https://github.com/LIIR-KULeuven/CLDR_CLNER_models/tree/5fe47a988b88a36d0ccf4484aff5ab70c59f39d6 |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RoshanTanisha/TVCaption | BertSelfAttention | false | 1,900 | [
"MIT"
] | 0 | 8b14a340134ec69ed87426ee1f0e93e53f6456e5 | https://github.com/RoshanTanisha/TVCaption/tree/8b14a340134ec69ed87426ee1f0e93e53f6456e5 |
NCELoss | # 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.... | salesforce/CoSeRec | NCELoss | false | 10,763 | [
"BSD-3-Clause"
] | 0 | c0bf5e5c3a5fd645efd3d6cdb9ff6a98d1c477ef | https://github.com/salesforce/CoSeRec/tree/c0bf5e5c3a5fd645efd3d6cdb9ff6a98d1c477ef |
AddPositionEmbed | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from functools import partial
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_siz... | STomoya/animeface | AddPositionEmbed | false | 14,367 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.utils.data
import torch.cuda
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
num_attention_he... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Malkovsky/NeMo | MultiHeadAttention | false | 2,627 | [
"Apache-2.0"
] | 0 | 8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 | https://github.com/Malkovsky/NeMo/tree/8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 |
ReLUDropout | import torch
import torch.utils.data
import torch.cuda
import torch.utils.checkpoint
def relu_dropout(x, p=0, training=False, variational=False, batch_first=False):
if not training or p == 0:
return x.clamp_(min=0)
p1m = 1 - p
if variational:
if batch_first:
mask = torch.rand_l... | 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.utils.data
import torch.cuda
import torch.utils.checkpoint
assert_size_strid... | Dan-hbd/NMTGMinor | ReLUDropout | false | 5,148 | [
"MIT"
] | 1 | 84e59ac8391ee78852d7c71afc60c3c8b8e3d44d | https://github.com/Dan-hbd/NMTGMinor/tree/84e59ac8391ee78852d7c71afc60c3c8b8e3d44d |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | tailintalent/hamiltonian-nn | MLP | false | 16,553 | [
"Apache-2.0"
] | 293 | 1f6dd2d58ab84977a30584f0d1dd7f8b234e4049 | https://github.com/tailintalent/hamiltonian-nn/tree/1f6dd2d58ab84977a30584f0d1dd7f8b234e4049 |
SoftmaxOutputLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class OutputLayer(nn.Module):
"""
Abstract base class for output layer.
Handles projection to output labels
"""
def __init__(self, hidden_size, output_size):
super(OutputLayer, self).__init__()
self.output_size = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | oya163/torchnlp | SoftmaxOutputLayer | false | 4,112 | [
"Apache-2.0"
] | 0 | 361caa24d741e47b8bd92af122ae281d6ad72d9d | https://github.com/oya163/torchnlp/tree/361caa24d741e47b8bd92af122ae281d6ad72d9d |
RGBBlock | # 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.functional as... | SongweiGe/DoodlerGAN | RGBBlock | false | 14,454 | [
"MIT"
] | 92 | d435d9b3c0579937cd3c22aa2051960ceb921785 | https://github.com/SongweiGe/DoodlerGAN/tree/d435d9b3c0579937cd3c22aa2051960ceb921785 |
ConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
def forward(self, x):
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
import torch.nn as nn
assert_... | QinbinLi/FedKT | ConvBlock | false | 8,664 | [
"MIT"
] | 14 | 0bb9a89ea266c057990a4a326b586ed3d2fb2df8 | https://github.com/QinbinLi/FedKT/tree/0bb9a89ea266c057990a4a326b586ed3d2fb2df8 |
InceptionD | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
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
import torch.nn as nn
import ... | Galaxies99/inception-cuda | InceptionD | false | 11,447 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
SmoothL1Loss | # AOT ID: ['1_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | Sign-up-soon-after-papapa/DEA-Net | SmoothL1Loss | false | 9,464 | [
"Apache-2.0"
] | 0 | ed25f30ddedcb77eb0991aeb9e498ef2efd8c635 | https://github.com/Sign-up-soon-after-papapa/DEA-Net/tree/ed25f30ddedcb77eb0991aeb9e498ef2efd8c635 |
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
from torch import nn
assert_s... | sithu31296/image_classification | MLP | false | 16,459 | [
"MIT"
] | 57 | 6b8cbce96100225621cee3166a73e852ba216cc3 | https://github.com/sithu31296/image_classification/tree/6b8cbce96100225621cee3166a73e852ba216cc3 |
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