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 |
|---|---|---|---|---|---|---|---|---|---|---|
LovaszHingeLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ChristophReich1996/Cell-DETR | LovaszHingeLoss | false | 13,607 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
VAE | import torch
import torch.nn as nn
import torch.nn.functional as F
class VAE(nn.Module):
def __init__(self, input_dim, latent_dim):
super(VAE, self).__init__()
self.latent_dim = latent_dim
self.hidden2mean = nn.Linear(input_dim, latent_dim)
self.hidden2logv = nn.Linear(input_dim, ... | 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... | LindgeW/DomainAdaption4DependencyParsing | VAE | false | 5,541 | [
"Apache-2.0"
] | 1 | 5de136a37d8fe730e4235ed95bf923763fe21ea6 | https://github.com/LindgeW/DomainAdaption4DependencyParsing/tree/5de136a37d8fe730e4235ed95bf923763fe21ea6 |
BCELoss2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class BCELoss2d(nn.Module):
def __init__(self, weight=None, size_average=True):
super(BCELoss2d, self).__init__()
self.criterion = nn.BCELoss(weight, size_average)
def forward(self, inputs, targets):
probs = F.sigmoid... | 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... | ForrestPi/SegDL | BCELoss2d | false | 5,166 | [
"MIT"
] | 1 | 56f2ff229dfa7540704d6de50292c724693aac75 | https://github.com/ForrestPi/SegDL/tree/56f2ff229dfa7540704d6de50292c724693aac75 |
DepthwiseSeparableConv | # 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_... | Worm4047/TVR | DepthwiseSeparableConv | false | 14,614 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
AttentiveStatsPool | import torch
import torch.nn
import torch.nn as nn
class AttentiveStatsPool(nn.Module):
def __init__(self, in_dim, bottleneck_dim):
super().__init__()
self.linear1 = nn.Conv1d(in_dim, bottleneck_dim, kernel_size=1)
self.linear2 = nn.Conv1d(bottleneck_dim, in_dim, kernel_size=1)
def 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | qlindazm/asv-subtools | AttentiveStatsPool | false | 4,229 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 |
Encoding | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._C
import torch.serialization
class Encoding(nn.Module):
"""Encoding Layer: a learnable residual encoder.
Input is of shape (batch_size, channels, height, width).
Output is of shape (batch_size, num_codes, channels).
Ar... | 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
... | AnonSubmission6150/submission6150 | Encoding | false | 8,996 | [
"Apache-2.0"
] | 0 | 571633d9a12b4fd7a9546947787fc068966dab04 | https://github.com/AnonSubmission6150/submission6150/tree/571633d9a12b4fd7a9546947787fc068966dab04 |
LeNet300 | import torch
from typing import *
import torch.nn as nn
import torch.nn.functional as F
class LeNet300(nn.Module):
def __init__(self, num_classes=10):
super(LeNet300, self).__init__()
self.fc1 = nn.Linear(784, 300)
self.fc2 = nn.Linear(300, 100)
self.fc3 = nn.Linear(100, 10)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 *
import t... | chomd90/snip | LeNet300 | false | 1,702 | [
"MIT"
] | 0 | 04aa8ca76364c61c3f6013832827fa292402652b | https://github.com/chomd90/snip/tree/04aa8ca76364c61c3f6013832827fa292402652b |
PPMConcat | # 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | HusterRC/mmsegmentation | PPMConcat | false | 5,328 | [
"Apache-2.0"
] | 1 | c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 | https://github.com/HusterRC/mmsegmentation/tree/c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 |
MultiHeadDenseLayer | # 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 tensorflow as tf
import torch.nn as nn
import torch.nn.functional as F
as... | ishine/neurst | MultiHeadDenseLayer | false | 16,691 | [
"Apache-2.0"
] | 208 | 2ba322393fcfed4261b33f4a657e12bbe321baaa | https://github.com/ishine/neurst/tree/2ba322393fcfed4261b33f4a657e12bbe321baaa |
SelfGating | # 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.hub
import torch.utils.data
assert_size_stride... | nicholasneo78/wav2vec-demo | SelfGating | false | 12,832 | [
"MIT"
] | 0 | c37db7b8211458dc810a85d4262ef41e3e3e4f12 | https://github.com/nicholasneo78/wav2vec-demo/tree/c37db7b8211458dc810a85d4262ef41e3e3e4f12 |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | aalborov/openvino_training_extensions | ResBlock | false | 6,042 | [
"Apache-2.0"
] | 1 | a0bb39424151a98e1ca80c4aa5c865636d401785 | https://github.com/aalborov/openvino_training_extensions/tree/a0bb39424151a98e1ca80c4aa5c865636d401785 |
ComplexBatchNormalize | import torch
import torch.nn as nn
def cylindricalToPolarConversion(input1, input2=None):
if input2 is None:
"""input1 is tensor of [B,C,H,W,D,2] contains both real and imaginary channels
in the last dims"""
ndims = input1.ndimension()
real_input = input1.narrow(ndims - 1, 0, 1).s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | wizofe/urus-mri-recon | ComplexBatchNormalize | false | 4,535 | [
"MIT"
] | 0 | eab8e48dca31d2b936ce69ccc251ec5a4a10facc | https://github.com/wizofe/urus-mri-recon/tree/eab8e48dca31d2b936ce69ccc251ec5a4a10facc |
CNN | import torch
import torch.nn as nn
class CNN(nn.Module):
"""CNN class - defines model and forward operations"""
def __init__(self):
super(CNN, self).__init__()
self.relu = nn.ReLU()
self.pooling = nn.MaxPool2d(kernel_size=2)
self.conv1 = nn.Conv2d(in_channels=1, out_channels=8... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gnzeleven/Hand-Written-Digits-Recognition-Web-App | CNN | false | 3,560 | [
"Apache-2.0"
] | 0 | b2c654f8b897273323a4930e3064b843b45cd5c6 | https://github.com/gnzeleven/Hand-Written-Digits-Recognition-Web-App/tree/b2c654f8b897273323a4930e3064b843b45cd5c6 |
BitEstimator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Bitparm(nn.Module):
"""
save params
"""
def __init__(self, channel, final=False):
super(Bitparm, self).__init__()
self.final = final
self.h = nn.Parameter(torch.nn.init.normal_(tor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
import t... | wemozj/Image-Compression-based-GMM-and-Attention-Module | BitEstimator | false | 4,527 | [
"Apache-2.0"
] | 0 | 93f804dbcea8ffc1621456f3d104d0342c75373b | https://github.com/wemozj/Image-Compression-based-GMM-and-Attention-Module/tree/93f804dbcea8ffc1621456f3d104d0342c75373b |
TransformerDecoderLayer | import math
import torch
import torch.nn.functional as F
from torch import nn
def _normalize(tensor, norm_layer):
"""
Broadcast layer norm
"""
size = tensor.size()
return norm_layer(tensor.view(-1, size[-1])).view(size)
class MultiHeadAttention(nn.Module):
def __init__(self, n_heads, dim, d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | FrankVerhoef/Persona-Dialogue-Generation | TransformerDecoderLayer | false | 5,209 | [
"MIT"
] | 1 | ffd8413c2e8b6446097902dd1c496aeb24b852b4 | https://github.com/FrankVerhoef/Persona-Dialogue-Generation/tree/ffd8413c2e8b6446097902dd1c496aeb24b852b4 |
InitConv | # 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.optim
assert_size_stride = torch._C._dynamo.g... | felixquinton1/TransBTS | InitConv | false | 10,173 | [
"Apache-2.0"
] | 0 | 6992c902413ba15f40ebfe9f6d5d0e3594051033 | https://github.com/felixquinton1/TransBTS/tree/6992c902413ba15f40ebfe9f6d5d0e3594051033 |
Spatial_Attention_layer | # 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.... | kevin-xuan/Traffic-Benchmark | Spatial_Attention_layer | false | 15,836 | [
"MIT"
] | 120 | b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 | https://github.com/kevin-xuan/Traffic-Benchmark/tree/b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 |
ChannelSELayer3D | # 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_... | Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival | ChannelSELayer3D | false | 655 | [
"MIT"
] | 0 | 257cf17ce6d405166dd8449f3b34e305cb5103b2 | https://github.com/Jianrong-Lu/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival/tree/257cf17ce6d405166dd8449f3b34e305cb5103b2 |
ClsHead | # 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.... | DocYard-ai/UCR | ClsHead | false | 8,030 | [
"Apache-2.0"
] | 10 | 7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 | https://github.com/DocYard-ai/UCR/tree/7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 |
MemoryWriter | # 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 ... | rchavan10/Multiple-Intersection-Traffic-Control-using-Reinforcement-Learning | MemoryWriter | false | 10,742 | [
"MIT"
] | 0 | 3663a1c7a89fe18974d13c9dc78ac7a99dac2300 | https://github.com/rchavan10/Multiple-Intersection-Traffic-Control-using-Reinforcement-Learning/tree/3663a1c7a89fe18974d13c9dc78ac7a99dac2300 |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Policy, self).__init__()
self.affine1 = nn.Linear(input_size, hidden_size, bias=False)
self.affine2 = nn.Linear(hidden_size, output_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LaRiffle/private-RL | Policy | false | 17,564 | [
"MIT"
] | 4 | 05fdcefbc0aa8bddcb5e2eaf64d203d0c0a38a58 | https://github.com/LaRiffle/private-RL/tree/05fdcefbc0aa8bddcb5e2eaf64d203d0c0a38a58 |
KLDivergence | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | gsaiabhishek/AUTOMATA | KLDivergence | false | 12,468 | [
"MIT"
] | 0 | e944992a7bf3a50bc8951a303294b3a798822176 | https://github.com/gsaiabhishek/AUTOMATA/tree/e944992a7bf3a50bc8951a303294b3a798822176 |
lstm_cell | # 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 ... | dreamer121121/action-recognition-models-pytorch | lstm_cell | false | 15,253 | [
"MIT"
] | 200 | 6a8a5e9678c359f795079d1f9f3cbdb9502b363d | https://github.com/dreamer121121/action-recognition-models-pytorch/tree/6a8a5e9678c359f795079d1f9f3cbdb9502b363d |
ThreeNet | # 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... | wangg12/fvcore | ThreeNet | false | 13,079 | [
"Apache-2.0"
] | 0 | aca6e95b3319144ec3c66385ff348c1557a2147f | https://github.com/wangg12/fvcore/tree/aca6e95b3319144ec3c66385ff348c1557a2147f |
SRCNN | # 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.... | rivergold/mmediting | SRCNN | false | 7,579 | [
"Apache-2.0"
] | 1 | fd972635c48bb065db29d1b5090592a87c7263d2 | https://github.com/rivergold/mmediting/tree/fd972635c48bb065db29d1b5090592a87c7263d2 |
SE | import torch
import torch.nn as nn
import torch.nn.functional as F
class SE(nn.Module):
"""Squeeze-and-Excitation block."""
def __init__(self, in_planes, se_planes):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(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_... | BoyuGuan/pytorch-cifar | SE | false | 9,046 | [
"MIT"
] | 0 | b96d0e325c614e8351449d63742fea5d085fdd8e | https://github.com/BoyuGuan/pytorch-cifar/tree/b96d0e325c614e8351449d63742fea5d085fdd8e |
TorchGloVeModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
from torch.nn.init import xavier_u... | tayfuntuna/cs224u | TorchGloVeModel | false | 4,408 | [
"Apache-2.0"
] | 0 | 4368090c679d869f21ed2393b9ca0ef217b5c404 | https://github.com/tayfuntuna/cs224u/tree/4368090c679d869f21ed2393b9ca0ef217b5c404 |
SelfAttention | # 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.... | IBM/graph4nlp | SelfAttention | false | 8,355 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
GAT | import torch
from torch import nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
https://github.com/Diego999/pyGAT/blob/master/layers.py
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, batch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bpilseo/VLP_GAT | GAT | false | 1,623 | [
"MIT"
] | 0 | ca8a2594036ab8fe4a180e5ced87f59f8984e54f | https://github.com/bpilseo/VLP_GAT/tree/ca8a2594036ab8fe4a180e5ced87f59f8984e54f |
GradLoss | # 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
... | SiTae9317/Depth-Estimation-PyTorch | GradLoss | false | 2,836 | [
"MIT"
] | 0 | 03b25d5cd2dff665c4435e72aba605a9d710fe01 | https://github.com/SiTae9317/Depth-Estimation-PyTorch/tree/03b25d5cd2dff665c4435e72aba605a9d710fe01 |
LanguageModelCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | curlG0/videotime | LanguageModelCriterion | false | 9,934 | [
"MIT"
] | 0 | 4eba44d148ba2d11f9bf2e9ba3ea9a3ecac70721 | https://github.com/curlG0/videotime/tree/4eba44d148ba2d11f9bf2e9ba3ea9a3ecac70721 |
PositionwiseFeedForward | # 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
from to... | Clemens123/transformers | PositionwiseFeedForward | false | 11,491 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | CCThompson82/deep-reinforcement-learning | Critic | false | 8,910 | [
"MIT"
] | 0 | f93faf0fb2b2dd8cfafeb8a4480e5520cefe6cb2 | https://github.com/CCThompson82/deep-reinforcement-learning/tree/f93faf0fb2b2dd8cfafeb8a4480e5520cefe6cb2 |
SkipLastTargetChannelWrapper | import torch
import torch.nn as nn
from torch.nn import MSELoss
class SkipLastTargetChannelWrapper(nn.Module):
"""
Loss wrapper which removes additional target channel
"""
def __init__(self, loss, squeeze_channel=False):
super(SkipLastTargetChannelWrapper, self).__init__()
self.loss =... | 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... | bounesh/pytorch-3dunet | SkipLastTargetChannelWrapper | false | 14,971 | [
"MIT"
] | 1,236 | 60278d01eaacc69feee731979826a0c26e223427 | https://github.com/bounesh/pytorch-3dunet/tree/60278d01eaacc69feee731979826a0c26e223427 |
ConvCompress | # 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... | yhgon/cmtf | ConvCompress | false | 13,130 | [
"MIT"
] | 0 | 7a3ffc3a59a7c546a00d3b73be58f7d1c2f1f0cf | https://github.com/yhgon/cmtf/tree/7a3ffc3a59a7c546a00d3b73be58f7d1c2f1f0cf |
OutPutBlock | import torch
import torch.nn as nn
class OutPutBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutPutBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns // 2, kernel_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HiLab-git/WSL4MIS | OutPutBlock | false | 8,269 | [
"MIT"
] | 29 | 9683e7c7409b95c0ac2169fe7964f6ca04c80d9a | https://github.com/HiLab-git/WSL4MIS/tree/9683e7c7409b95c0ac2169fe7964f6ca04c80d9a |
PerceptronTanh | # 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.... | negotiatorvivian/PDP-SP | PerceptronTanh | false | 4,060 | [
"MIT"
] | 0 | 0fa4c1145c2b881c1fde4ed8d9f0845b7967f857 | https://github.com/negotiatorvivian/PDP-SP/tree/0fa4c1145c2b881c1fde4ed8d9f0845b7967f857 |
PlanarNormalizingFlow | # 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, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | NightmareNyx/semi-supervised-pytorch | PlanarNormalizingFlow | false | 2,701 | [
"MIT"
] | 0 | 43bb86bc6757345bd7a4eb37d6948ee62a268f7e | https://github.com/NightmareNyx/semi-supervised-pytorch/tree/43bb86bc6757345bd7a4eb37d6948ee62a268f7e |
PositionwiseFeedforwardLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedforwardLayer(nn.Module):
def __init__(self, hid_dim: 'int', pf_dim: 'int', dropout: 'float') ->None:
super().__init__()
self.fc_1 = nn.Linear(hid_dim, pf_dim)
self.fc_2 = nn.Linear(pf_dim, hid_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bob80333/investigating_extrapolation | PositionwiseFeedforwardLayer | false | 6,343 | [
"MIT"
] | 1 | fc4f72baa46b8490968f7ad546897937feb8b25d | https://github.com/bob80333/investigating_extrapolation/tree/fc4f72baa46b8490968f7ad546897937feb8b25d |
GEGLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | booydar/x-transformers | GEGLU | false | 3,240 | [
"MIT"
] | 0 | 97f0a854fdf4df8a3fbf6a580e2375463af3538c | https://github.com/booydar/x-transformers/tree/97f0a854fdf4df8a3fbf6a580e2375463af3538c |
LayerNorm | import torch
from torch import nn
from torch.nn import functional as F
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-05):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HalimSD/A-eye | LayerNorm | false | 2,332 | [
"MIT"
] | 0 | 502dcdf47d54d93e8745be7c49897064550db8c7 | https://github.com/HalimSD/A-eye/tree/502dcdf47d54d93e8745be7c49897064550db8c7 |
SFU | import torch
import torch.nn as nn
import torch.nn.functional as F
class SFU(nn.Module):
"""Semantic Fusion Unit
The ouput vector is expected to not only retrieve correlative information from fusion vectors,
but also retain partly unchange as the input vector
"""
def __init__(self, input_size, fu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | albert-dot-ai/MnemonicReader | SFU | false | 12,069 | [
"BSD-3-Clause"
] | 0 | eb51eb679a58677a405953c0c579568377c0b0f8 | https://github.com/albert-dot-ai/MnemonicReader/tree/eb51eb679a58677a405953c0c579568377c0b0f8 |
MaxPooling | import torch
class MaxPooling(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
x = torch.cat((x.unsqueeze(dim=1), y.unsqueeze(dim=1)), dim=1)
return x.max(dim=1)[0]
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Qualcomm-AI-research/FrameExit | MaxPooling | false | 8,666 | [
"BSD-3-Clause-Clear"
] | 21 | fc5815fd092019d58bcac5d5e6fcc45ce666311f | https://github.com/Qualcomm-AI-research/FrameExit/tree/fc5815fd092019d58bcac5d5e6fcc45ce666311f |
ResidualUnit | import torch
import torch.nn as nn
def defaultConv(inChannels, outChannels, kernelSize, bias=True):
return nn.Conv2d(inChannels, outChannels, kernelSize, padding=
kernelSize // 2, bias=bias)
class ResidualUnit(nn.Module):
def __init__(self, inChannel, outChannel, reScale, kernelSize=1, bias=True
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | YingqiLiulll/scrips_for_SR | ResidualUnit | false | 1,284 | [
"MIT"
] | 0 | 04fa6fdaf157e913d3e2521cd80315a10a2ccedc | https://github.com/YingqiLiulll/scrips_for_SR/tree/04fa6fdaf157e913d3e2521cd80315a10a2ccedc |
SeparableConv | import torch
import torch.nn as nn
import torch.utils
import torch.nn.parallel
class SeparableConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, bias):
super(SeparableConv, self).__init__()
padding = (kernel_size - 1) // 2
self.depthwise = nn.Conv2d(in_planes, in_plan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
import torch.nn.parallel
assert_size_st... | kcyu2014/eval-nas | SeparableConv | false | 15,790 | [
"MIT"
] | 47 | 385376a3ef96336b54ee7e696af1d02b97aa5c32 | https://github.com/kcyu2014/eval-nas/tree/385376a3ef96336b54ee7e696af1d02b97aa5c32 |
Cauchy | # 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
... | davefiorino/EDSR-PyTorch | Cauchy | false | 1,795 | [
"MIT"
] | 0 | 97ad32a09a71816a36c45d92cdb2ea7ab42ba685 | https://github.com/davefiorino/EDSR-PyTorch/tree/97ad32a09a71816a36c45d92cdb2ea7ab42ba685 |
SmoothContourLoss | import torch
from torch import nn
class SmoothContourLoss(nn.Module):
"""
Loss function that contains smoothness loss derived from ss-with-RIM
and contour-aware loss.
Smoothness loss concerns about smoothness of local patterns, while
contour-aware loss is interested in whether two ... | 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... | yueyu-stu/EdgeAwareSpixel | SmoothContourLoss | false | 11,071 | [
"MIT"
] | 0 | f7f9fcb15bfa8e31bd4ad9473f9058c44a8391d7 | https://github.com/yueyu-stu/EdgeAwareSpixel/tree/f7f9fcb15bfa8e31bd4ad9473f9058c44a8391d7 |
InformedSender | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class InformedSender(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super(InformedSender, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | XeniaOhmer/SystematicRepresentations | InformedSender | false | 1,252 | [
"MIT"
] | 0 | 825208d1be659dc820e61f577cdb53afc47302f4 | https://github.com/XeniaOhmer/SystematicRepresentations/tree/825208d1be659dc820e61f577cdb53afc47302f4 |
AttentionLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Linear(nn.Module):
"""
Linear Module
"""
def __init__(self, in_dim, out_dim, bias=True, w_init='linear'):
"""
:param in_dim: dimension of input
:param out_dim: dimension of output
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Francois-Aubet/AHGP | AttentionLayer | false | 8,132 | [
"MIT"
] | 19 | 3ecdd01d138f013ae8da196fbf3a71632aa2cd88 | https://github.com/Francois-Aubet/AHGP/tree/3ecdd01d138f013ae8da196fbf3a71632aa2cd88 |
ConvWithBatchNorm | import torch
from torch import nn
class ConvWithBatchNorm(nn.Module):
def __init__(self, in_channels, out_channels, spacetime_ndim,
kernel_size=3, normalization=None, activation='ReLU'):
super(ConvWithBatchNorm, self).__init__()
self.in_channels = in_channels
self.out_channels = 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 import nn
assert_s... | royerloic/aydin | ConvWithBatchNorm | false | 16,344 | [
"BSD-3-Clause"
] | 78 | f9c61a24030891d008c318b250da5faec69fcd7d | https://github.com/royerloic/aydin/tree/f9c61a24030891d008c318b250da5faec69fcd7d |
PoseRegHead | # 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.... | mrlooi/PoseCNN | PoseRegHead | false | 10,680 | [
"MIT"
] | 0 | c103bd7dc743edbc9c7cc8a4687b035e3d1150f6 | https://github.com/mrlooi/PoseCNN/tree/c103bd7dc743edbc9c7cc8a4687b035e3d1150f6 |
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.... | Chenny0808/tatk | Attention | false | 13,492 | [
"Apache-2.0"
] | 81 | 1c1a3cb557ba84bbfdbd1f6d8b8ea43ed8b9d7c5 | https://github.com/Chenny0808/tatk/tree/1c1a3cb557ba84bbfdbd1f6d8b8ea43ed8b9d7c5 |
RegressionModel | import torch
import torch.utils.data
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Het-Shah/Monk_Object_Detection | RegressionModel | false | 8,246 | [
"Apache-2.0"
] | 15 | 1d7a07193ea3455221caa41d07c33c81d50c6b3f | https://github.com/Het-Shah/Monk_Object_Detection/tree/1d7a07193ea3455221caa41d07c33c81d50c6b3f |
SubpixelConvolutionLayer | import torch
import torch.nn as nn
import torch.utils.data
class SubpixelConvolutionLayer(nn.Module):
def __init__(self, channels: 'int') ->None:
"""
Args:
channels (int): Number of channels in the input image.
"""
super(SubpixelConvolutionLayer, self).__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 ... | wuyushuwys/SRGAN-PyTorch | SubpixelConvolutionLayer | false | 4,567 | [
"Apache-2.0"
] | 0 | 3a4aaaf7b55692264fca8451e4401466fcb1f39a | https://github.com/wuyushuwys/SRGAN-PyTorch/tree/3a4aaaf7b55692264fca8451e4401466fcb1f39a |
LinearGLUBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearGLUBlock(nn.Module):
"""A linear GLU block.
Args:
size (int): input and output dimension
"""
def __init__(self, size):
super().__init__()
self.fc = nn.Linear(size, size * 2)
def forward(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Park-Jong-Min/neural_sp | LinearGLUBlock | false | 2,725 | [
"Apache-2.0"
] | 0 | a4f300ae9c16c6e9ea3128292fbc141f68f38081 | https://github.com/Park-Jong-Min/neural_sp/tree/a4f300ae9c16c6e9ea3128292fbc141f68f38081 |
LR_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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | lixuran/Room_Layout_Estimation_new | LR_PAD | false | 7,107 | [
"MIT"
] | 1 | 8e73b66e1418675e5bb82f3780091c406fe721d8 | https://github.com/lixuran/Room_Layout_Estimation_new/tree/8e73b66e1418675e5bb82f3780091c406fe721d8 |
My_SmoothL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | LiderMyHand/AWR-Adaptive-Weighting-Regression | My_SmoothL1Loss | false | 13,988 | [
"MIT"
] | 90 | 81c4c98edd98cd03d423d820ca1fe9e01dbbb242 | https://github.com/LiderMyHand/AWR-Adaptive-Weighting-Regression/tree/81c4c98edd98cd03d423d820ca1fe9e01dbbb242 |
PatchEmbed | import torch
import torch.nn as nn
import torch.nn.parallel
class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
Different with ViT use 1 conv layer, we use 4 conv layers to do patch embedding
"""
def __init__(self, img_size=224, stem_conv=False, stem_stride=1,
patch_size=8, in_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dy... | javierrodenas/clearml_javi | PatchEmbed | false | 10,364 | [
"Apache-2.0"
] | 0 | b6326104fe6a6f522223c2ac3d87468990a9e6f2 | https://github.com/javierrodenas/clearml_javi/tree/b6326104fe6a6f522223c2ac3d87468990a9e6f2 |
CosineBasisLinear | # 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 numpy ... | KtechB/pfrl | CosineBasisLinear | false | 2,471 | [
"MIT"
] | 0 | 9be4726d327b7ce32d9008c40119c98c93febad5 | https://github.com/KtechB/pfrl/tree/9be4726d327b7ce32d9008c40119c98c93febad5 |
ChannelGate2d | import torch
import torch.nn as nn
class ChannelGate2d(nn.Module):
def __init__(self, channels, reduction=2):
super(ChannelGate2d, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
padding=0)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | evilidol/kaggle-Steel-Defect-Detection | ChannelGate2d | false | 6,664 | [
"MIT"
] | 1 | 41e3e360f49d706c8c79bcd442342c529648a736 | https://github.com/evilidol/kaggle-Steel-Defect-Detection/tree/41e3e360f49d706c8c79bcd442342c529648a736 |
LSTM | import torch
from typing import Tuple
import torch.nn as nn
class LSTM(nn.Module):
"""Implementation of the standard LSTM.
Parameters
----------
input_size : int
Number of input features
hidden_size : int
Number of hidden/memory cells.
batch_first : bool, optional
If ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | bernharl/CamelsML | LSTM | false | 3,215 | [
"Apache-2.0"
] | 0 | 4ec3ea231ba6ed8c9db68f0aa61aba8da32652b8 | https://github.com/bernharl/CamelsML/tree/4ec3ea231ba6ed8c9db68f0aa61aba8da32652b8 |
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 torch.nn as nn
assert_... | PaulPan00/donkey_wrapper | Critic | false | 17,806 | [
"MIT"
] | 6 | a03cf0f42f65625fbce792b06c98acd153c5d6c8 | https://github.com/PaulPan00/donkey_wrapper/tree/a03cf0f42f65625fbce792b06c98acd153c5d6c8 |
HingeLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | Enderdead/BinaryConnect_PyTorch | HingeLoss | false | 13,635 | [
"MIT"
] | 75 | 990e970b1fbd299ff88200db21a9cc3fe44706d3 | https://github.com/Enderdead/BinaryConnect_PyTorch/tree/990e970b1fbd299ff88200db21a9cc3fe44706d3 |
Base | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Base(nn.Module):
"""docstring for Base"""
def __init__(self, view_space, feature_space, num_actions, hidden_size):
super(Base, self).__init__()
self.view_space = view_space
self.feature_space =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | SJTUwbl/mfrl_pytorch | Base | false | 5,801 | [
"MIT"
] | 1 | 2b385121cc9a8aa16ed6d554d1dc10f02f2fc5d9 | https://github.com/SJTUwbl/mfrl_pytorch/tree/2b385121cc9a8aa16ed6d554d1dc10f02f2fc5d9 |
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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | Dogacel/mmfashion | FeatureCorrelation | false | 11,403 | [
"Apache-2.0"
] | 0 | e49613245c8501042edd7aeeaa8fb93e5ea13238 | https://github.com/Dogacel/mmfashion/tree/e49613245c8501042edd7aeeaa8fb93e5ea13238 |
ChannelNorm | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_... | EyalSel/CPC_audio | ChannelNorm | false | 13,676 | [
"MIT"
] | 260 | b98a1bdf1fe9ea219816db7a6c28115d404a3510 | https://github.com/EyalSel/CPC_audio/tree/b98a1bdf1fe9ea219816db7a6c28115d404a3510 |
MNISTmodel | import torch
import torch.nn.functional as F
import torch.nn as nn
class Evidential_layer(nn.Module):
def __init__(self, in_dim, num_classes):
super(Evidential_layer, self).__init__()
self.num_classes = num_classes
self.fc1 = nn.Linear(in_dim, 2 * self.num_classes)
self.relu = tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | caisr-hh/DEED | MNISTmodel | false | 1,862 | [
"MIT"
] | 0 | 2a9edb1df31d99c1e8da177dec696d7c90c2e7de | https://github.com/caisr-hh/DEED/tree/2a9edb1df31d99c1e8da177dec696d7c90c2e7de |
fusion | import torch
import torch.nn as nn
from torch.nn import Linear
class fusion(nn.Module):
def __init__(self, feature_size=768):
super(fusion, self).__init__()
self.fc1 = Linear(feature_size * 3, 1)
self.fc2 = Linear(feature_size * 3, 1)
self.fc3 = Linear(feature_size * 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
import torch.nn as nn
from torch.nn import Linear
assert_size_stride = torch._C.... | funnyzhou/REFERS | fusion | false | 15,373 | [
"MIT"
] | 46 | 392eddf13cbf3c3a7dc0bf8bfffd108ca4a65a19 | https://github.com/funnyzhou/REFERS/tree/392eddf13cbf3c3a7dc0bf8bfffd108ca4a65a19 |
LR_PAD | import torch
from torch import nn
def lr_pad(x, padding=1):
return torch.cat([x[..., -padding:], x, x[..., :padding]], dim=3)
class LR_PAD(nn.Module):
def __init__(self, padding=1):
super(LR_PAD, self).__init__()
self.padding = padding
def forward(self, x):
return lr_pad(x, sel... | 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... | lixuran/Room_Layout_Estimation_new | LR_PAD | false | 7,107 | [
"MIT"
] | 1 | 8e73b66e1418675e5bb82f3780091c406fe721d8 | https://github.com/lixuran/Room_Layout_Estimation_new/tree/8e73b66e1418675e5bb82f3780091c406fe721d8 |
SimpleASinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleASinModule(torch.nn.Module):
def __init__(self):
super(SimpleASinModule, self).__init__()
def forward(self, a):
return torch.asin(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleASinModule | false | 3,314 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
Fp32LayerNorm | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Fp32LayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input)... | 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
import torch.onnx.operators
impor... | AppleHolic/fairseq | Fp32LayerNorm | false | 13,319 | [
"MIT"
] | 429 | c5b32cb2bde59a7bb7987b22864731fe927523d4 | https://github.com/AppleHolic/fairseq/tree/c5b32cb2bde59a7bb7987b22864731fe927523d4 |
Generator | # 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 ... | Iamsdt/UdacityDeepLearningNanodegree | Generator | false | 5,338 | [
"Apache-2.0"
] | 1 | 507c2ce620f42e36271549471b819d3d7fceb1b6 | https://github.com/Iamsdt/UdacityDeepLearningNanodegree/tree/507c2ce620f42e36271549471b819d3d7fceb1b6 |
SimpleExpModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleExpModule(torch.nn.Module):
def forward(self, input):
other = torch.exp(input)
return torch.exp(other)
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.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | opti-mix/glow | SimpleExpModule | false | 7,393 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
Conv1dBlock | import torch
from torch import nn
class Conv1dBlock(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, stride, padding=
0, norm='none', activation='relu', pad_type='zero'):
super(Conv1dBlock, self).__init__()
self.use_bias = True
if pad_type == 'reflect':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | DK-Jang/human_motion_manifold | Conv1dBlock | false | 7,918 | [
"MIT"
] | 23 | dd3b603b892d66685204909c8818f3e1621ab7dc | https://github.com/DK-Jang/human_motion_manifold/tree/dd3b603b892d66685204909c8818f3e1621ab7dc |
ComplexConv2d | import torch
import torch.nn as nn
class ComplexConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, **kwargs):
super().__init__()
self.conv_re = nn.Conv2d(in_channels, out_channels, kernel_size,
st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | jonashaag/PhoneFortifiedPerceptualLoss | ComplexConv2d | false | 3,768 | [
"MIT"
] | 0 | 1dabdd4203f59c2d1bfe22bffc4c63b204aa50bd | https://github.com/jonashaag/PhoneFortifiedPerceptualLoss/tree/1dabdd4203f59c2d1bfe22bffc4c63b204aa50bd |
JSCriterion | # 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
from torch.... | johnson7788/mt-dnn | JSCriterion | false | 3,900 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, ignore_target=-1):
super().__init__()
self.ignore_target = ignore_target
def forward(self, input, target):
"""
:param input: (N), logit
:param target: (N), {0, 1}
: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... | brudermueller/PointRCNN | DiceLoss | false | 3,257 | [
"MIT"
] | 0 | 430bb45d6d512ad4e3eb509d65377511361c300f | https://github.com/brudermueller/PointRCNN/tree/430bb45d6d512ad4e3eb509d65377511361c300f |
GHMIoU | # 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
... | ShegnkaiWu/IoU-aware-single-stage-object-detector-for-accurate-localization | GHMIoU | false | 14,400 | [
"Apache-2.0"
] | 62 | 67b8955eb59137590dbadc6aac45529ae9459e4a | https://github.com/ShegnkaiWu/IoU-aware-single-stage-object-detector-for-accurate-localization/tree/67b8955eb59137590dbadc6aac45529ae9459e4a |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Prabhu204/MNISTdata | Net | false | 9,381 | [
"MIT"
] | 0 | 1ab3be23a0cec8caacd4adec6cd3c413639a62cc | https://github.com/Prabhu204/MNISTdata/tree/1ab3be23a0cec8caacd4adec6cd3c413639a62cc |
Conv_Q | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Conv_Q(nn.Module):
def __init__(self, frames, num_actions):
super(Conv_Q, self).__init__()
self.c1 = nn.Conv2d(frames, 32, kernel_size=8, stride=4)
self.c2 = nn.Conv2d(32, 64, kernel_size=4, s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Altriaex/d4rl_evaluations | Conv_Q | false | 9,020 | [
"Apache-2.0"
] | 0 | ceb34c04e98af9332c6338a1414c0c2aa5fea68b | https://github.com/Altriaex/d4rl_evaluations/tree/ceb34c04e98af9332c6338a1414c0c2aa5fea68b |
BalancedL1Loss | # 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 numpy as np
imp... | Complicateddd/Complicateddd-ROITransformer | BalancedL1Loss | false | 11,305 | [
"Apache-2.0"
] | 0 | 2adfbf98892d569c460d100c6e2169c5fa3a9b82 | https://github.com/Complicateddd/Complicateddd-ROITransformer/tree/2adfbf98892d569c460d100c6e2169c5fa3a9b82 |
MetaBilinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import 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
reinterpret_tensor = torch._C._dynamo.guards._reinterp... | Bunnycakes62/SIREN | MetaBilinear | false | 4,917 | [
"MIT"
] | 1 | 87c2c9e28411fd6a83d1d0d1bc5141cce30e646b | https://github.com/Bunnycakes62/SIREN/tree/87c2c9e28411fd6a83d1d0d1bc5141cce30e646b |
HamidaEtAl | # 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
import tor... | dikers/DeepHyper | HamidaEtAl | false | 12,413 | [
"Apache-2.0"
] | 0 | 827a8f3077e18b71cf448a2e56e49670428b1bfd | https://github.com/dikers/DeepHyper/tree/827a8f3077e18b71cf448a2e56e49670428b1bfd |
CNN_decoder_attention | import torch
import torch.nn as nn
import torch.nn.init as init
class CNN_decoder_attention(nn.Module):
def __init__(self, input_size, output_size, stride=2):
super(CNN_decoder_attention, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.relu = nn.R... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jonathangomesselman/graph-generation | CNN_decoder_attention | false | 7,011 | [
"MIT"
] | 1 | 72a8be30d54a414fcca9ea0fad1a62e38b85ee2f | https://github.com/jonathangomesselman/graph-generation/tree/72a8be30d54a414fcca9ea0fad1a62e38b85ee2f |
ResizeModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | MichaelZhero/nncase | ResizeModule | false | 11,918 | [
"Apache-2.0"
] | 0 | 0fae6ce90d7adff386e1a286cd2b42422f4b850a | https://github.com/MichaelZhero/nncase/tree/0fae6ce90d7adff386e1a286cd2b42422f4b850a |
MlpBlock | import torch
from torch import nn
class MlpBlock(nn.Module):
def __init__(self, input_dim, mlp_dim=512):
super().__init__()
self.fc1 = nn.Linear(input_dim, mlp_dim)
self.gelu = nn.GELU()
self.fc2 = nn.Linear(mlp_dim, input_dim)
def forward(self, x):
return self.fc2(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | rushirajsherlocked/External-Attention-pytorch | MlpBlock | false | 4,218 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
MockAccuracy | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | HalleyYoung/MusicTransformer-pytorch | MockAccuracy | false | 11,461 | [
"MIT"
] | 0 | bbfb7050f4a81675b089cd826d4476cf29bf19c2 | https://github.com/HalleyYoung/MusicTransformer-pytorch/tree/bbfb7050f4a81675b089cd826d4476cf29bf19c2 |
ScaleNorm | import math
import torch
import torch.nn as nn
class ScaleNorm(nn.Module):
"""ScaleNorm"""
"""All g’s in SCALE NORM are initialized to sqrt(d)"""
def __init__(self, scale, eps=1e-05):
super(ScaleNorm, self).__init__()
self.scale = nn.Parameter(torch.tensor(math.sqrt(scale)))
self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.nn ... | nigelnnk/MATCh-sensitivity | ScaleNorm | false | 7,341 | [
"MIT"
] | 1 | aaf2b924ac98c8c5925bbf431481724d11a102f8 | https://github.com/nigelnnk/MATCh-sensitivity/tree/aaf2b924ac98c8c5925bbf431481724d11a102f8 |
Discriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
class Discriminator(nn.Module):
def __init__(self, n_layersDecod, hidden_size, output_size=2):
super(Discriminator, self).__init__()
self.map1 = nn.Linear(n_layersDecod * hidden_size, hidden_size)
self.map2 = nn.Linear(hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | carsault/chord_sequence_prediction | Discriminator | false | 1,645 | [
"MIT"
] | 0 | 6eb539a963ca6350bcf0c88b8d8756775ad7c488 | https://github.com/carsault/chord_sequence_prediction/tree/6eb539a963ca6350bcf0c88b8d8756775ad7c488 |
AE | # 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
import t... | Minauras/deepdefresneling | AE | false | 5,604 | [
"BSD-2-Clause"
] | 1 | e17168e9a8d322201998c73da54efbd334b0ffb9 | https://github.com/Minauras/deepdefresneling/tree/e17168e9a8d322201998c73da54efbd334b0ffb9 |
LinearMaxPoolLinearModel | # 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_... | LMdeLiangMi/captum | LinearMaxPoolLinearModel | false | 5,483 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
PatchEmbed3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
assert_si... | HarshSulakhe/pytorch_connectomics | PatchEmbed3D | false | 9,874 | [
"MIT"
] | 0 | 73402e654afde69a43a5836cc90a32ef75c75dc2 | https://github.com/HarshSulakhe/pytorch_connectomics/tree/73402e654afde69a43a5836cc90a32ef75c75dc2 |
ScaledDotProductAttentionMemory | import torch
import numpy as np
import torch.utils.data
import torch.nn as nn
class ScaledDotProductAttentionMemory(nn.Module):
"""
Scaled dot-product attention with memory
"""
def __init__(self, *, d_model: int, d_k: int, d_v: int, h: int, m: int):
"""
:param d_model: Output dimensio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YehLi/xmodaler | ScaledDotProductAttentionMemory | false | 14,699 | [
"Apache-2.0"
] | 830 | 5340054398c076cfa717317d151ca595c5e37198 | https://github.com/YehLi/xmodaler/tree/5340054398c076cfa717317d151ca595c5e37198 |
NPIProg | import torch
import torch.nn as nn
import torch.nn.functional as F
class NPIProg(nn.Module):
def __init__(self, input_dim: 'int', prog_key_dim: 'int', prog_num: 'int'):
super(NPIProg, self).__init__()
self._fcn1 = nn.Linear(in_features=input_dim, out_features=prog_key_dim
)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | nienjiuntai/pytorch-npi | NPIProg | false | 4,089 | [
"MIT"
] | 0 | 16b413c152dfb7f1506a85997adc10ddc2d9af35 | https://github.com/nienjiuntai/pytorch-npi/tree/16b413c152dfb7f1506a85997adc10ddc2d9af35 |
TemporalAttentionLayer | # 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.... | lawson-source/mtad-gat-pytorch | TemporalAttentionLayer | false | 15,879 | [
"MIT"
] | 93 | 9e671ea99dedd82ac55f53e53af1d1b56c13ebff | https://github.com/lawson-source/mtad-gat-pytorch/tree/9e671ea99dedd82ac55f53e53af1d1b56c13ebff |
MessageNorm | import torch
from torch import Tensor
import torch.nn.functional as F
from torch.nn import Parameter
import torch.fx
import torch.utils.data
from inspect import Parameter
from torch.nn.parameter import Parameter
class MessageNorm(torch.nn.Module):
"""Applies message normalization over the aggregated messages as d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Paramet... | JinheonBaek/pytorch_geometric | MessageNorm | false | 17,507 | [
"MIT"
] | 4 | dfd32d08a3d8191d6290e53458d4eda515d04fd6 | https://github.com/JinheonBaek/pytorch_geometric/tree/dfd32d08a3d8191d6290e53458d4eda515d04fd6 |
GAT | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SsGood/MMGL | GAT | false | 17,993 | [
"MIT"
] | 6 | ea769e46fffb42559e764e2912c5b1dc17c10af2 | https://github.com/SsGood/MMGL/tree/ea769e46fffb42559e764e2912c5b1dc17c10af2 |
DeConv2d | # 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 functools
from torch import nn
from typing import Optional
import torch.n... | pomelyu/ML_HW | DeConv2d | false | 10,711 | [
"MIT"
] | 0 | b87697f3ee86592a34d80c8dbf167a5767731630 | https://github.com/pomelyu/ML_HW/tree/b87697f3ee86592a34d80c8dbf167a5767731630 |
rbbox_corners_aligned | import torch
import torch.nn as nn
class rbbox_corners_aligned(nn.Module):
def _init_(self, gboxes):
super(rbbox_corners_aligned, self)._init_()
self.corners_gboxes = gboxes
return
def forward(ctx, gboxes):
N = gboxes.shape[0]
center_x = gboxes[:, 0]
center_y ... | 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... | hlesmqh/WS3D | rbbox_corners_aligned | false | 15,538 | [
"MIT"
] | 100 | 6816eeb135923a59de34ee5d94be2d0fd3ec83f9 | https://github.com/hlesmqh/WS3D/tree/6816eeb135923a59de34ee5d94be2d0fd3ec83f9 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self, ignore_target=-1):
super().__init__()
self.ignore_target = ignore_target
def forward(self, input, target):
"""
:param input: (N), logit
:param target: (N), {0, 1}
: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... | liuhuaijjin/rpn_rois_proposals_layers | DiceLoss | false | 7,105 | [
"MIT"
] | 1 | c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 | https://github.com/liuhuaijjin/rpn_rois_proposals_layers/tree/c5f9f09b3ae8c52e4b6fa3fda391f993cb7d42c1 |
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