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 |
|---|---|---|---|---|---|---|---|---|---|---|
DenseLayer | import torch
import torch.nn as nn
def actFunc(act, *args, **kwargs):
act = act.lower()
if act == 'relu':
return nn.ReLU()
elif act == 'relu6':
return nn.ReLU6()
elif act == 'leakyrelu':
return nn.LeakyReLU(0.1)
elif act == 'prelu':
return nn.PReLU()
elif act ==... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | zzh-tech/RSCD | DenseLayer | false | 16,840 | [
"MIT"
] | 57 | b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e | https://github.com/zzh-tech/RSCD/tree/b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e |
BertPredictionHeadTransform | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | IsmaelElsharkawi/new_pororo_repo | BertPredictionHeadTransform | false | 8,817 | [
"MIT"
] | 19 | 4617083b420615b8a3eb0f44d02e4e91a8f407f7 | https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7 |
AddNorm | # 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... | sudarshan85/transformer_tutorial | AddNorm | false | 10,791 | [
"MIT"
] | 0 | a7fc327f0d952d38b3f711fe21ba416616ba8d7e | https://github.com/sudarshan85/transformer_tutorial/tree/a7fc327f0d952d38b3f711fe21ba416616ba8d7e |
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.... | AlbertiPot/attention-is-all-you-need-pytorch | EncoderLayer | false | 37 | [
"MIT"
] | 0 | c5ec40907db281b85b3bd7a5dd8016940291add0 | https://github.com/AlbertiPot/attention-is-all-you-need-pytorch/tree/c5ec40907db281b85b3bd7a5dd8016940291add0 |
FM | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | Holldean/pytorch-models | FM | false | 2,350 | [
"MIT"
] | 0 | 9509d0d462b1a98164b266d49ada199071a855ac | https://github.com/Holldean/pytorch-models/tree/9509d0d462b1a98164b266d49ada199071a855ac |
Multihead_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.... | NilsLusch/Point-Cloud-Transformer | Multihead_Attention_Layer | false | 910 | [
"MIT"
] | 0 | 84a16b45b8949bbf8e7730b10bd5835e2ab4e642 | https://github.com/NilsLusch/Point-Cloud-Transformer/tree/84a16b45b8949bbf8e7730b10bd5835e2ab4e642 |
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.... | zaynmi/semantic-equivalent-da-for-vqa | Attention | false | 16,802 | [
"MIT"
] | 298 | f121fb3e8fee8af5f1935a7526f19e0d884bd95b | https://github.com/zaynmi/semantic-equivalent-da-for-vqa/tree/f121fb3e8fee8af5f1935a7526f19e0d884bd95b |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | LanXiangExcavator/challenge2021_submission_4 | DiceLoss | false | 769 | [
"BSD-2-Clause"
] | 0 | ca0d4d4dd219119f7dc46464c92062ecdb7f9c49 | https://github.com/LanXiangExcavator/challenge2021_submission_4/tree/ca0d4d4dd219119f7dc46464c92062ecdb7f9c49 |
SigmoidFocalClassificationLoss | # 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... | Javier-DlaP/OpenPCDet | SigmoidFocalClassificationLoss | false | 620 | [
"Apache-2.0"
] | 0 | c4d308ea8052dd92948e2377b161b2519254275b | https://github.com/Javier-DlaP/OpenPCDet/tree/c4d308ea8052dd92948e2377b161b2519254275b |
GroupNorm32 | # 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_... | ShivanshuPurohit/Diffusion | GroupNorm32 | false | 1,063 | [
"MIT"
] | 0 | 9a190d9aa4ed9767cf223e4ef57d0c31690f92cc | https://github.com/ShivanshuPurohit/Diffusion/tree/9a190d9aa4ed9767cf223e4ef57d0c31690f92cc |
SimpleModel | # 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 ... | PanJinquan/pytorch-base-trainer | SimpleModel | false | 8,652 | [
"MIT"
] | 11 | 37799c948f72b2f9d3771ff469e06cdbff4a1d07 | https://github.com/PanJinquan/pytorch-base-trainer/tree/37799c948f72b2f9d3771ff469e06cdbff4a1d07 |
Model | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, input_size, dropout=0.5):
super(Model, self).__init__()
self.dropout = dropout
if self.dropout > 0:
self.dropout = nn.Dropout(dropout)
self.encode_w1 = nn.Linear(input_size, 64)
self.e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | dohnlee/qufa2021 | Model | false | 12,301 | [
"MIT"
] | 0 | 5fb42caee09ec228358e49768e32c75e3c0094ce | https://github.com/dohnlee/qufa2021/tree/5fb42caee09ec228358e49768e32c75e3c0094ce |
SigmoidFocalClassificationLoss | import torch
import torch.nn as nn
def _sigmoid_cross_entropy_with_logits(logits, labels):
loss = torch.clamp(logits, min=0) - logits * labels.type_as(logits)
loss += torch.log1p(torch.exp(-torch.abs(logits)))
return loss
class SigmoidFocalClassificationLoss(nn.Module):
"""Sigmoid focal cross entrop... | 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... | JamesWang007/PointRCNN | SigmoidFocalClassificationLoss | false | 11,541 | [
"MIT"
] | 0 | ea0812c52e6767b976fc50fed61e6b72fa6cdf81 | https://github.com/JamesWang007/PointRCNN/tree/ea0812c52e6767b976fc50fed61e6b72fa6cdf81 |
Attention | import torch
import torch.nn.functional as F
class Attention(torch.nn.Module):
def __init__(self, features, attn_dim):
super(Attention, self).__init__()
self.to_q = torch.nn.Linear(features, attn_dim)
self.to_k = torch.nn.Linear(features, attn_dim)
self.to_v = torch.nn.Linear(feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rish-16/pytorch-graphdl | Attention | false | 7,570 | [
"MIT"
] | 1 | 631da8cbf24e67fab2122c507e1935d4acf26e41 | https://github.com/rish-16/pytorch-graphdl/tree/631da8cbf24e67fab2122c507e1935d4acf26e41 |
Conv2d | import torch
from torch import nn
from torch.nn import functional as F
class Conv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d, self).__init__(in_channels, out_channels, 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.triton_helpers import libdevice
from torch import n... | Farzanehkaji/MINet | Conv2d | false | 17,266 | [
"MIT"
] | 9 | cc2852cb2b3b20208f5edf38ec6952363a9b04a7 | https://github.com/Farzanehkaji/MINet/tree/cc2852cb2b3b20208f5edf38ec6952363a9b04a7 |
BPRLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | MIracleyin/RecBole-notebook | BPRLoss | false | 9,576 | [
"MIT"
] | 0 | ef32b3e57a297ff4889dec1f63c7984f8f901a23 | https://github.com/MIracleyin/RecBole-notebook/tree/ef32b3e57a297ff4889dec1f63c7984f8f901a23 |
fire | # 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 itertools import product... | suiguoxin/Pytorch_Retinaface | fire | false | 4,388 | [
"MIT"
] | 0 | d9393bad43103635261b4ec5b03f20e79931d0da | https://github.com/suiguoxin/Pytorch_Retinaface/tree/d9393bad43103635261b4ec5b03f20e79931d0da |
Generator | import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Generator(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Generator, self).__init__()
self.map1 = nn.Linear(input_size, hidden_size)
self.map2 = nn.Linear(hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Dora-The-Kid/culture_network | Generator | false | 2,161 | [
"Apache-2.0"
] | 0 | bc2bac86e821faa797eeb2670d179395724f7922 | https://github.com/Dora-The-Kid/culture_network/tree/bc2bac86e821faa797eeb2670d179395724f7922 |
Actor | import torch
import torch.nn as nn
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, net_width, maxaction):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, net_width)
self.l2 = nn.Linear(net_width, net_width)
self.l3 = nn.Linear(net_width, action_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.triton_helpers import libdevice
import torch.nn as ... | XinJingHao/RL | Actor | false | 18,101 | [
"MIT"
] | 6 | eed54d6602b173e45ede722b0fcf82b5a203f14a | https://github.com/XinJingHao/RL/tree/eed54d6602b173e45ede722b0fcf82b5a203f14a |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
target = target[:, :input.size(1)]
mask = mask[:, :input.size(1)]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | SikandarBakht/Sub-GC | LanguageModelCriterion | false | 14,419 | [
"MIT"
] | 71 | 5b89aff766df0b11446cf970fb285004ebfef672 | https://github.com/SikandarBakht/Sub-GC/tree/5b89aff766df0b11446cf970fb285004ebfef672 |
QuantizableHSigmoid | # 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.quantization
assert_size_stride = torch._C._dynamo.gua... | HwangJohn/model_compression | QuantizableHSigmoid | false | 13,818 | [
"MIT"
] | 216 | 1df40c8a531313cc9e79255f4477f39d66d9b849 | https://github.com/HwangJohn/model_compression/tree/1df40c8a531313cc9e79255f4477f39d66d9b849 |
ScaledDotProductAttention | import torch
import torch.nn as nn
import torch.utils.checkpoint
import torch.nn.functional as F
from torch.cuda.amp import autocast
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bahducoup/factorized_training | ScaledDotProductAttention | false | 12,166 | [
"MIT"
] | 0 | 0af38f16338a9bcfcc11091b1a6b75befd67f234 | https://github.com/bahducoup/factorized_training/tree/0af38f16338a9bcfcc11091b1a6b75befd67f234 |
SigmoidFocalLoss | import torch
import torch.nn as nn
import torch.utils
class SigmoidFocalLoss(nn.Module):
def __init__(self, ignore_label, gamma=2.0, alpha=0.25, reduction='mean'):
super(SigmoidFocalLoss, self).__init__()
self.ignore_label = ignore_label
self.gamma = gamma
self.alpha = alpha
... | 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
... | Gaussianer/FasterSeg | SigmoidFocalLoss | false | 17,304 | [
"MIT"
] | 6 | f2e102b433275ac9f3387a8c2ae8439b2687bfda | https://github.com/Gaussianer/FasterSeg/tree/f2e102b433275ac9f3387a8c2ae8439b2687bfda |
Conv5x5 | import torch
import torch.nn as nn
class Conv5x5(nn.Module):
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv5x5, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(2)
else:
self.pad = nn.ZeroPad2d(2)
self.conv = nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ArminMasoumian/GCNDepth | Conv5x5 | false | 7,733 | [
"MIT"
] | 32 | 9fa77812fa944c2701a45f09acf988815ca50aee | https://github.com/ArminMasoumian/GCNDepth/tree/9fa77812fa944c2701a45f09acf988815ca50aee |
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.... | AmitMY/seq2seq | Attention | false | 8,837 | [
"MIT"
] | 0 | 1ad7c09188537729e5b18356f5c36fad1928d245 | https://github.com/AmitMY/seq2seq/tree/1ad7c09188537729e5b18356f5c36fad1928d245 |
Foo | import torch
import torch.jit
import torch.onnx
import torch.nn
class Foo(torch.nn.Module):
def __init__(self):
super(Foo, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 6, 3)
self.relu = torch.nn.ReLU()
self.conv2 = torch.nn.Conv2d(6, 16, 3)
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.jit
import torch... | briancoutinho/glow | Foo | false | 12,573 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
Interpolate | import torch
import torch.fft
import torch.nn as nn
import torch.utils.cpp_extension
class Interpolate(nn.Module):
"""Interpolation module."""
def __init__(self, size, mode='bilinear', align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): inter... | 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.fft
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_strid... | CeciLyu/projected_gan | Interpolate | false | 11,580 | [
"MIT"
] | 0 | 5e86ee0c88d47164c30ede37448e7ba7f010fa7b | https://github.com/CeciLyu/projected_gan/tree/5e86ee0c88d47164c30ede37448e7ba7f010fa7b |
SA_block | import torch
import torch.nn as nn
class SA_block(nn.Module):
"""Self-Attention block with dot product for point/voxel/pillar context.
A part of the code is from MLCVNet (CVPR 2020).
"""
def __init__(self, inplanes, planes, groups=4):
super().__init__()
self.groups = groups
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.... | reinforcementdriving/SA-Det3D | SA_block | false | 16,327 | [
"MIT"
] | 134 | 682cbf5a3023bd580632435d1e4e0acb0ae08ab8 | https://github.com/reinforcementdriving/SA-Det3D/tree/682cbf5a3023bd580632435d1e4e0acb0ae08ab8 |
MultiHeadAttention | # 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.... | IanYHWu/transformers-for-translation | MultiHeadAttention | false | 2,370 | [
"MIT"
] | 0 | b763e58deb2263507eecd2eb569fbaf5c1dd9df8 | https://github.com/IanYHWu/transformers-for-translation/tree/b763e58deb2263507eecd2eb569fbaf5c1dd9df8 |
QNetwork | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
def __init__(self, device, action_size, observation_size):
super(QNetwork, self).__init__()
self.device = device
self.fc1 = nn.Linear(np.array((observation_size,)).prod() + np.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | faisman/deep-reinforcement-learning-projects | QNetwork | false | 12,364 | [
"MIT"
] | 0 | cef102ec4019069a22f95d798f6694dce73655ae | https://github.com/faisman/deep-reinforcement-learning-projects/tree/cef102ec4019069a22f95d798f6694dce73655ae |
EncoderLayer | import torch
import torch.nn as nn
class FeedForwardNetwork(nn.Module):
def __init__(self, hidden_size, ffn_size, dropout_rate):
super(FeedForwardNetwork, self).__init__()
self.layer1 = nn.Linear(hidden_size, ffn_size)
self.gelu = nn.GELU()
self.layer2 = nn.Linear(ffn_size, hidden... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChantalMP/Graphormer | EncoderLayer | false | 8,968 | [
"MIT"
] | 0 | 5c384d0f2840afc88ee88aeb874f4b1f41d760bf | https://github.com/ChantalMP/Graphormer/tree/5c384d0f2840afc88ee88aeb874f4b1f41d760bf |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Bartolo1024/ignite | ResidualBlock | false | 4,905 | [
"BSD-3-Clause"
] | 1 | b087fef0bc5f97cda415c1c56f1cd589383c54be | https://github.com/Bartolo1024/ignite/tree/b087fef0bc5f97cda415c1c56f1cd589383c54be |
MultiHeadAttention | import math
import torch
from torch import nn
from torch.nn import functional as F
from numpy import inf
from math import inf
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from torch.nn import functional as F
from numpy... | L-Zhe/FasySeq | MultiHeadAttention | false | 8,446 | [
"Apache-2.0"
] | 34 | 2cd2abd290666b1e118d8ad11c973b58ca4f0573 | https://github.com/L-Zhe/FasySeq/tree/2cd2abd290666b1e118d8ad11c973b58ca4f0573 |
ApplyStyle | # 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
from torch import nn
import torch.nn.functional as F
ass... | Archjbald/PoseStylizer | ApplyStyle | false | 1,980 | [
"BSD-3-Clause"
] | 0 | 95aae02d1f4ac83536d91b8db5f78d12e7830f97 | https://github.com/Archjbald/PoseStylizer/tree/95aae02d1f4ac83536d91b8db5f78d12e7830f97 |
GaussMembFunc | # 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 math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | trituenhantaoio/anfis-pytorch | GaussMembFunc | false | 16,613 | [
"MIT"
] | 66 | 7a6bf123d69b550e46abeddd5b4a776243d43aa6 | https://github.com/trituenhantaoio/anfis-pytorch/tree/7a6bf123d69b550e46abeddd5b4a776243d43aa6 |
MGRUCell | # 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.nn import Module
i... | CSLT-THU/Vivi_3.0 | MGRUCell | false | 17,039 | [
"Apache-2.0"
] | 3 | 86996d99d662cd33100755501a971c41ce30ca70 | https://github.com/CSLT-THU/Vivi_3.0/tree/86996d99d662cd33100755501a971c41ce30ca70 |
Conv2dSame | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from typing import List
from typing import Unio... | SimonCqk/towhee | Conv2dSame | false | 9,624 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
Baseblock | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import init
class Baseblock(nn.Module):
def __init__(self, in_channels):
super(Baseblock, self).__init__()
self.p_size = [1, 1, 1, 1]
self.pool1 = nn.MaxPool2d(kernel_size=self.p_size[0], stride=self.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | FENGShuanglang/PyTorch_Feat_Vision | Baseblock | false | 11,426 | [
"MIT"
] | 0 | c45dd001c3354e430e9772ddca6f4ba779656761 | https://github.com/FENGShuanglang/PyTorch_Feat_Vision/tree/c45dd001c3354e430e9772ddca6f4ba779656761 |
SimpleOrModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleOrModule | false | 3,346 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
LayerNorm2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm2d(nn.LayerNorm):
"""LayerNorm on channels for 2d images.
Args:
num_channels (int): The number of channels of the input tensor.
eps (float): a value added to the denominator for numerical stability.
... | 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_... | HumberMe/mmclassification | LayerNorm2d | false | 561 | [
"Apache-2.0"
] | 0 | 68f1542068d3af4db932c97e6a728181432fff0c | https://github.com/HumberMe/mmclassification/tree/68f1542068d3af4db932c97e6a728181432fff0c |
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.... | EgemenGuray/Subgraph-Classifier | Attention | false | 2,189 | [
"MIT"
] | 0 | b85d28c521701f41dcd698aed40d4c80d454e893 | https://github.com/EgemenGuray/Subgraph-Classifier/tree/b85d28c521701f41dcd698aed40d4c80d454e893 |
BoundNot | # 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 math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_siz... | mnmueller/auto_LiRPA | BoundNot | false | 7,281 | [
"BSD-3-Clause"
] | 1 | 55cb270b0b99f07b74541d55706c69fbb9daff66 | https://github.com/mnmueller/auto_LiRPA/tree/55cb270b0b99f07b74541d55706c69fbb9daff66 |
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, use_faiss=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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | carson-sky/Patch-NetVLAD | NetVLAD | false | 15,119 | [
"MIT"
] | 278 | 7b913626b34dbbe250d6921a6a093512ee513eac | https://github.com/carson-sky/Patch-NetVLAD/tree/7b913626b34dbbe250d6921a6a093512ee513eac |
BasicCNN2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicCNN2(nn.Module):
def __init__(self):
super().__init__()
self.layer_names = ['conv11', 'conv12', 'conv21', 'conv22',
'conv31', 'conv32', 'fc1', 'output_layer']
self.conv11 = nn.Conv2d(3, 32, 3, paddin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | fnc11/CosDefence | BasicCNN2 | false | 10,133 | [
"MIT"
] | 0 | 94f451b7d4b36cb3b9fcc85098dae242f311532b | https://github.com/fnc11/CosDefence/tree/94f451b7d4b36cb3b9fcc85098dae242f311532b |
Distance | import torch
import torch.nn as nn
class Distance(nn.Module):
def __init__(self):
super(Distance, self).__init__()
def forward(self, s, t):
n, q = s.shape[0], t.shape[0]
dist = (t.unsqueeze(0).expand(n, q, -1) - s.unsqueeze(1).expand(n,
q, -1)).pow(2).sum(dim=2).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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | msc5/ml-tools | Distance | false | 4,032 | [
"Apache-2.0"
] | 0 | 75ca504bdc0495e8a929ad73501b7de692b3089a | https://github.com/msc5/ml-tools/tree/75ca504bdc0495e8a929ad73501b7de692b3089a |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, 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
from torch._inductor.runtime.... | noble6emc2/MoCo-SSPT | BertAttention | false | 12,850 | [
"MIT"
] | 0 | e6d7cf3f0a3b5a467318dfc32096e4929adbe646 | https://github.com/noble6emc2/MoCo-SSPT/tree/e6d7cf3f0a3b5a467318dfc32096e4929adbe646 |
CNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | MaryZolfaghar/WCSLS | CNN | false | 824 | [
"Apache-2.0"
] | 0 | fcb3bfd11c19bb90690ec772f91bbd107832d636 | https://github.com/MaryZolfaghar/WCSLS/tree/fcb3bfd11c19bb90690ec772f91bbd107832d636 |
SelfAttentionSublayer | import math
import torch
from torch import nn
import torch.optim
class ScaledDotAttention(torch.nn.Module):
def __init__(self, model_dim, n_heads, dropout=0.0):
"""
Creates a ScaledDotAttention.
:param model_dim: The model dimensions.
:param n_heads: The number of heads.
:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Nickeilf/pysimt | SelfAttentionSublayer | false | 9,524 | [
"MIT"
] | 0 | 05c8de92d0e2b930e40939ad3695d8d2c2954dda | https://github.com/Nickeilf/pysimt/tree/05c8de92d0e2b930e40939ad3695d8d2c2954dda |
AllReduceLinear | import torch
from torch import Tensor
import torch.distributed as dist
import torch.nn as nn
from torch.nn import Linear
class ParallelModule(nn.Module):
"""Parents of all parallel layer classes"""
def __init__(self):
super().__init__()
self.mp_group = None
def allreduce(self, outputs):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.distributed as dist
import torch.nn as nn
from torch.nn import Line... | Oaklight/parallelformers | AllReduceLinear | false | 14,134 | [
"Apache-2.0"
] | 454 | 57fc36f81734c29aaf814e092ce13681d3c28ede | https://github.com/Oaklight/parallelformers/tree/57fc36f81734c29aaf814e092ce13681d3c28ede |
DotProductAttention | # 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.... | rupeshshrestha123/end2end-asr-pytorch | DotProductAttention | false | 16,346 | [
"MIT"
] | 250 | 8aada8f7cbe90e1d0b05d505042d9e42b8e4dd52 | https://github.com/rupeshshrestha123/end2end-asr-pytorch/tree/8aada8f7cbe90e1d0b05d505042d9e42b8e4dd52 |
CPAMDec | # 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.... | bfjei2825401/siamban | CPAMDec | false | 10,410 | [
"Apache-2.0"
] | 0 | c41d58742b146dfc8960053453227c6e9fec1bac | https://github.com/bfjei2825401/siamban/tree/c41d58742b146dfc8960053453227c6e9fec1bac |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | ChristophReich1996/3D_Baggage_Segmentation | DiceLoss | false | 5,004 | [
"MIT"
] | 1 | 00392cb0fde22d3180b6baf81e404d0fcf4e2ebf | https://github.com/ChristophReich1996/3D_Baggage_Segmentation/tree/00392cb0fde22d3180b6baf81e404d0fcf4e2ebf |
MultiheadAttention | import torch
import torch.nn.functional as F
import torch.utils.data
import torch.distributed
import torch.nn as nn
from torch.nn import Parameter
import torch.optim
import torch.optim.lr_scheduler
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more detail... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | farhanazareen/Fairseq | MultiheadAttention | false | 6,694 | [
"BSD-3-Clause"
] | 1 | 39c7b6804b4a3426ef23b30f3ca8a3c0a9948079 | https://github.com/farhanazareen/Fairseq/tree/39c7b6804b4a3426ef23b30f3ca8a3c0a9948079 |
DeNormalize | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = ... | STomoya/animeface | DeNormalize | false | 14,363 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
InvertibleChannelMixing2D | from torch.autograd import Function
import torch
from torch import nn
from warnings import warn
def _cayley(A):
I = torch.eye(A.shape[-1], device=A.device)
LU = torch.lu(I + A, pivot=True)
return torch.lu_solve(I - A, *LU)
def _cayley_frechet(A, H, Q=None):
I = torch.eye(A.shape[-1], device=A.device... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
from torch import nn
from warnings import wa... | cetmann/iunets | InvertibleChannelMixing2D | false | 15,017 | [
"MIT"
] | 86 | 80ed7cce0e505a0396c42359eaf27819222d71f6 | https://github.com/cetmann/iunets/tree/80ed7cce0e505a0396c42359eaf27819222d71f6 |
ATT | import torch
import torch.nn as nn
import torch.nn.functional as F
class ATT(nn.Module):
def __init__(self, din):
super(ATT, self).__init__()
self.fc1 = nn.Linear(din, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 1)
def forward(self, x):
y = F.relu(self.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
import torch.nn as nn
assert_... | HuangHaoyu1997/pytorch_DGN | ATT | false | 13,778 | [
"MIT"
] | 48 | f1b1a157a9b1678f9238f64458f44412b796d00e | https://github.com/HuangHaoyu1997/pytorch_DGN/tree/f1b1a157a9b1678f9238f64458f44412b796d00e |
ShakeResNeXt | import math
import torch
from torch import nn
from numpy import int64 as int64
import torch.nn.functional as F
from torch.autograd import Variable
class ShakeShake(torch.autograd.Function):
@staticmethod
def forward(ctx, x1, x2, training=True):
if training:
alpha = torch.FloatTensor(x1.si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
from torch import... | Josie-Li/ZazuML-easy_AutoML | ShakeResNeXt | false | 2,436 | [
"MIT"
] | 0 | e4daabaab9df518c35abdba35a67607d002bee33 | https://github.com/Josie-Li/ZazuML-easy_AutoML/tree/e4daabaab9df518c35abdba35a67607d002bee33 |
ContrastiveLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | kevincao91/SiameseNet_Demo | ContrastiveLoss | false | 10,315 | [
"MIT"
] | 0 | 6ec4384159682a8ee93fb110d6fca33de85fa1ba | https://github.com/kevincao91/SiameseNet_Demo/tree/6ec4384159682a8ee93fb110d6fca33de85fa1ba |
WeightNet_DW | # 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... | Sense-GVT/BigPretrain | WeightNet_DW | false | 17,938 | [
"Apache-2.0"
] | 8 | d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e | https://github.com/Sense-GVT/BigPretrain/tree/d8d9b43d94dd1364c18c1e5ba21b85a31cdbba9e |
ScaleNetwork | import torch
import torch.nn as nn
class ScaleNetwork(nn.Module):
"""Network for parameterizing a scaling function"""
def __init__(self, input_dim):
super(ScaleNetwork, self).__init__()
self.fc1 = nn.Linear(input_dim, 2000)
self.relu1 = nn.ReLU(inplace=True)
self.fc2 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | chawins/adv-exp | ScaleNetwork | false | 6,441 | [
"MIT"
] | 1 | 5423e135c5599e4ec2bf90372916d8d05c89f285 | https://github.com/chawins/adv-exp/tree/5423e135c5599e4ec2bf90372916d8d05c89f285 |
ImageNetNormalizer | import torch
from torch import nn
import torch.autograd
class ImageNetNormalizer(nn.Module):
def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
super().__init__()
self.mean = mean
self.std = std
def forward(self, x):
mean = torch.tensor(self.mean, devi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dy... | cassidylaidlaw/perceptual-advex | ImageNetNormalizer | false | 15,009 | [
"MIT"
] | 45 | d39136eb5b5e950442456ddade6b4f4fba3dd8f6 | https://github.com/cassidylaidlaw/perceptual-advex/tree/d39136eb5b5e950442456ddade6b4f4fba3dd8f6 |
BertAttention | 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.... | minjoong507/TVRetrieval | BertAttention | false | 10,754 | [
"MIT"
] | 0 | 919e1766ab8aa1ef267bd3b80d4f87b06cde09a9 | https://github.com/minjoong507/TVRetrieval/tree/919e1766ab8aa1ef267bd3b80d4f87b06cde09a9 |
UpsampleBlock | # 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 ... | Divyanshu23/model-zoo | UpsampleBlock | false | 8,114 | [
"MIT"
] | 43 | 2eea6df691d302e182bb1ff8ec5af3542de562ba | https://github.com/Divyanshu23/model-zoo/tree/2eea6df691d302e182bb1ff8ec5af3542de562ba |
GEGLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Steffen-Wolf/vit-pytorch | GEGLU | false | 9,607 | [
"MIT"
] | 0 | 4f590b9bd570091d9070a039ad33301516caa341 | https://github.com/Steffen-Wolf/vit-pytorch/tree/4f590b9bd570091d9070a039ad33301516caa341 |
wSummation | # 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... | VincentYCYao/MVC-Net-pytorch | wSummation | false | 9,600 | [
"MIT"
] | 0 | 31f826825cdfe862fbfe0fe19edc78c04d1dec55 | https://github.com/VincentYCYao/MVC-Net-pytorch/tree/31f826825cdfe862fbfe0fe19edc78c04d1dec55 |
L2Norm | import torch
from torch import nn
class L2Norm(nn.Module):
def forward(self, x, eps=1e-06):
norm = x.norm(dim=1, keepdim=True).clamp(min=eps)
return x / norm
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | idolumbantobing/vit-pytorch | L2Norm | false | 15,581 | [
"MIT"
] | 9,373 | eb70d8dca041cc387b3e1f72d965d8814eeab29a | https://github.com/idolumbantobing/vit-pytorch/tree/eb70d8dca041cc387b3e1f72d965d8814eeab29a |
ModulatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | BillyXYB/TransEditor | ModulatedConv2d | false | 17,089 | [
"MIT"
] | 4 | 0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 | https://github.com/BillyXYB/TransEditor/tree/0194cd6f0e96c801d55c0cb9683e1f552bcf6d48 |
Accuracy | import torch
import torch.nn.functional
import torch.nn as nn
import torch.nn.functional as F
def centercrop(image, w, h):
_nt, _ct, ht, wt = image.size()
padw, padh = (wt - w) // 2, (ht - h) // 2
if padw > 0 and padh > 0:
image = image[:, :, padh:-padh, padw:-padw]
return image
class Accura... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | HelenGuohx/cv-ferattn-code | Accuracy | false | 5,306 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
SimpleArgSortModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | mciprian13/glow | SimpleArgSortModule | false | 3,994 | [
"Apache-2.0"
] | 0 | 90f88205d9bf8baff8df5bbda51c9d138e3e668b | https://github.com/mciprian13/glow/tree/90f88205d9bf8baff8df5bbda51c9d138e3e668b |
Mlp | import math
import torch
import warnings
import torch.nn as nn
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
"""Copy & paste from PyTorch official master until it's in a few official releases - RW
Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
"""
def n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | TranNhiem/solo-learn | Mlp | false | 1,146 | [
"MIT"
] | 0 | 7539732b68d153087d09a26a23e1edfdc49bc086 | https://github.com/TranNhiem/solo-learn/tree/7539732b68d153087d09a26a23e1edfdc49bc086 |
BCE_LOSS | import math
import torch
from torch.nn.modules.loss import _Loss
import torch.optim
import torch.nn
class BCE_LOSS(_Loss):
def __init__(self):
super().__init__()
self.bce_loss = torch.nn.BCEWithLogitsLoss()
def forward(self, input, label):
one_hot = torch.zeros_like(input)
C ... | 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.... | YZW-explorer/EOD | BCE_LOSS | false | 6,005 | [
"Apache-2.0"
] | 1 | f10e64de86c0f356ebf5c7e923f4042eec4207b1 | https://github.com/YZW-explorer/EOD/tree/f10e64de86c0f356ebf5c7e923f4042eec4207b1 |
TransitionUp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | adriancampos/road-extraction | TransitionUp | false | 6,084 | [
"MIT"
] | 1 | 3eaf4ed010d71475276d99d4841d67990a967a1b | https://github.com/adriancampos/road-extraction/tree/3eaf4ed010d71475276d99d4841d67990a967a1b |
ResidualBlock | import torch
import torch.nn as nn
import torch.utils.data
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.prelu = nn.PReLU()
self.conv2 = nn.Conv2d(channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dyn... | JaguAroo/SRResCGAN | ResidualBlock | false | 619 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
TransformerEncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn import Dropout
from torch.nn import LayerNorm
from typing import Optional
import torch.utils.data
from typing import Tuple
class InProjContainer(torch.nn.Module):
def __init__(self, query_proj, key_proj, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Hirni-Meshram3/text | TransformerEncoderLayer | false | 5,330 | [
"BSD-3-Clause"
] | 1 | 84e6c7bd99c7fb3c229ff289aa722149e3136094 | https://github.com/Hirni-Meshram3/text/tree/84e6c7bd99c7fb3c229ff289aa722149e3136094 |
Generator | import torch
import torch.nn as nn
import torch.nn.functional as F
class Generator(nn.Module):
def __init__(self, dim, hidden_dim, y_dim, sigma=0.02):
super(Generator, self).__init__()
input_dim = dim
hidden_size = hidden_dim
self.fc1 = nn.Linear(input_dim, hidden_size)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ccha23/miml | Generator | false | 6,400 | [
"MIT"
] | 1 | 6a41de1c0bb41d38e3cdc6e9c27363215b7729b9 | https://github.com/ccha23/miml/tree/6a41de1c0bb41d38e3cdc6e9c27363215b7729b9 |
WeightedCrossEntropyLoss | # 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
... | AbangLZU/OpenPCDet | WeightedCrossEntropyLoss | false | 7,638 | [
"Apache-2.0"
] | 29 | eeea3f24d392f692228c1ad4e28c0dc9d0e25665 | https://github.com/AbangLZU/OpenPCDet/tree/eeea3f24d392f692228c1ad4e28c0dc9d0e25665 |
Swish | import torch
import torch.nn as nn
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
self.beta = nn.Parameter(torch.tensor(1.0))
def forward(self, x):
return x * torch.sigmoid(self.beta * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ali-zafari/high-fidelity-generative-compression | Swish | false | 9,777 | [
"Apache-2.0"
] | 0 | 37ab8d6727df48f8ebf4577db0986ccd0ffe404b | https://github.com/ali-zafari/high-fidelity-generative-compression/tree/37ab8d6727df48f8ebf4577db0986ccd0ffe404b |
ContrastiveLoss | import torch
import torch.nn as nn
class ContrastiveLoss(nn.Module):
def __init__(self, margin=0.2):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, imgs, caps):
scores = torch.mm(imgs, caps.t())
diag = scores.diag()
cost_s = torch.clamp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | maxgreat/dsve-loc | ContrastiveLoss | false | 16,025 | [
"BSD-3-Clause-Clear"
] | 56 | dd6807d02c0d5fd3e215be8e5c7a88e73102e561 | https://github.com/maxgreat/dsve-loc/tree/dd6807d02c0d5fd3e215be8e5c7a88e73102e561 |
Model | # 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.... | ChengUVa/ptan | Model | false | 13,475 | [
"MIT"
] | 492 | f9b3ef2680ff64fad52e600d73ff2bf42eee310d | https://github.com/ChengUVa/ptan/tree/f9b3ef2680ff64fad52e600d73ff2bf42eee310d |
OZELoss | # 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.utils.data
import torch.nn as nn
import torch.nn.parallel
as... | YanLu-nyu/transferlearning | OZELoss | false | 14,627 | [
"MIT"
] | 9,657 | 037806c6eb8b0c12aefbfbf3e35cbf893093cff9 | https://github.com/YanLu-nyu/transferlearning/tree/037806c6eb8b0c12aefbfbf3e35cbf893093cff9 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | HamoolNizar/RumorDetectionSystem | LayerNorm | false | 11,534 | [
"MIT"
] | 0 | 902ae4d705c0a6db470064f0e7f07f3c167d3eac | https://github.com/HamoolNizar/RumorDetectionSystem/tree/902ae4d705c0a6db470064f0e7f07f3c167d3eac |
LanguageModelCriterion | import torch
import torch.nn as nn
class LanguageModelCriterion(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, target, mask):
x = x.contiguous().view(-1, x.size(2))
target = target.contiguous().view(-1, 1)
mask = mask.contiguous().view(-1, 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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | neal2018/torch_learn | LanguageModelCriterion | false | 10,608 | [
"MIT"
] | 0 | 80bda3a44952aca6fce7156fe4aecb48ddd602ee | https://github.com/neal2018/torch_learn/tree/80bda3a44952aca6fce7156fe4aecb48ddd602ee |
MAB | import math
import torch
import torch.nn.functional as F
from torch import nn
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
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.... | ydiller/NoMoreNMS | MAB | false | 4,614 | [
"Apache-2.0"
] | 0 | 1c1557357e5312c287f0971c840060deb1bcd039 | https://github.com/ydiller/NoMoreNMS/tree/1c1557357e5312c287f0971c840060deb1bcd039 |
LinearCombine | # 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 math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import ... | savan77/nni | LinearCombine | false | 4,278 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
ConvNet | # 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_... | AndrewAltimit/Scene-Classification-AWS-Serverless | ConvNet | false | 11,234 | [
"MIT"
] | 0 | caa4bff102987338dcfa597b9ec1638e6e5e63f5 | https://github.com/AndrewAltimit/Scene-Classification-AWS-Serverless/tree/caa4bff102987338dcfa597b9ec1638e6e5e63f5 |
Pad | import torch
import torch.nn as nn
import torch.nn.functional as F
class Pad(nn.Module):
def __init__(self, value: 'float', size: 'int'):
super().__init__()
self.value = value
self.size = size
def forward(self, waveform: 'torch.Tensor') ->torch.Tensor:
return F.pad(waveform, ... | 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... | icyda17/very-deep-CNNs | Pad | false | 10,219 | [
"Apache-2.0"
] | 0 | c275ef222d50dae90e508345ec3be5adfa5e33ce | https://github.com/icyda17/very-deep-CNNs/tree/c275ef222d50dae90e508345ec3be5adfa5e33ce |
VNLinear | import torch
import torch.nn as nn
import torch.utils.data
import torch
import torch.nn.parallel
class VNLinear(nn.Module):
def __init__(self, in_channels, out_channels):
super(VNLinear, self).__init__()
self.map_to_feat = nn.Linear(in_channels, out_channels, bias=False)
def forward(self, x)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch
import torch.nn.paral... | shiyani21/vnn | VNLinear | false | 4,313 | [
"MIT"
] | 0 | 921be51d6651ff32bff895f4da99ef83d50900da | https://github.com/shiyani21/vnn/tree/921be51d6651ff32bff895f4da99ef83d50900da |
GlobalAveragePooling | # 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... | hugovk/EnAET | GlobalAveragePooling | false | 15,546 | [
"MIT"
] | 87 | 596a1be95f4ebfc5fc4f372f251e66fb03e23b5a | https://github.com/hugovk/EnAET/tree/596a1be95f4ebfc5fc4f372f251e66fb03e23b5a |
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.... | LyapunovStability/BRITS | TemporalDecay | false | 11,666 | [
"MIT"
] | 0 | 92a889dd5946aae215d61b1854d9767c6f7fcf2c | https://github.com/LyapunovStability/BRITS/tree/92a889dd5946aae215d61b1854d9767c6f7fcf2c |
SoftmaxDeepLiftModel | import torch
import torch.nn as nn
class SoftmaxDeepLiftModel(nn.Module):
"""
Model architecture from:
https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
"""
def __init__(self, num_in, num_hidden, num_out) ->None:
super().__init__()
self.num_in = num_in
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | aravipati12/captum | SoftmaxDeepLiftModel | false | 10,111 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
EqualLinear | # 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.model_zoo
assert_size_stride = torch._C... | Aitical/ADspeech2face | EqualLinear | false | 4,806 | [
"MIT"
] | 1 | 2e811ff8cc7333729f4b77d1b1067296253e8e38 | https://github.com/Aitical/ADspeech2face/tree/2e811ff8cc7333729f4b77d1b1067296253e8e38 |
UpSampleX2 | import torch
from torchvision.transforms import *
class DeconvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=4, stride=2,
padding=1, bias=True, activation='prelu', norm=None):
super(DeconvBlock, self).__init__()
self.deconv = torch.nn.ConvTranspose2d(input... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | lizatish/My_CNN | UpSampleX2 | false | 10,426 | [
"MIT"
] | 0 | b13818bcce2f8a3697d20e34157e3dce53f953ee | https://github.com/lizatish/My_CNN/tree/b13818bcce2f8a3697d20e34157e3dce53f953ee |
KGCN | # 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.nn import Module
i... | gear/gfnn | KGCN | false | 15,418 | [
"MIT"
] | 46 | 36667861caacba921469d43917d002896e832c3f | https://github.com/gear/gfnn/tree/36667861caacba921469d43917d002896e832c3f |
UpConv | # 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... | gandhisamay/Drone-Cam-Segmentation | UpConv | false | 3,547 | [
"MIT"
] | 0 | 7e93b6bb65300aea94dd5e35bb8ca3bd1efbe043 | https://github.com/gandhisamay/Drone-Cam-Segmentation/tree/7e93b6bb65300aea94dd5e35bb8ca3bd1efbe043 |
ConvertPointsFromHomogeneous | # 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... | aravinho/frankmocap | ConvertPointsFromHomogeneous | false | 3,124 | [
"BSD-3-Clause"
] | 0 | 6a150a9cb96e9b32a60d8047eaa84d0c37e471f5 | https://github.com/aravinho/frankmocap/tree/6a150a9cb96e9b32a60d8047eaa84d0c37e471f5 |
CQAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
def mask_logits(target, mask):
mask = mask.type(torch.float32)
return target * mask + (1 - mask) * -1e+30
class CQAttention(nn.Module):
def __init__(self, d_model, dropout=0.1):
super().__init__()
w4C = torch.empty(d_mod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mirbostani/QA-KD-AL | CQAttention | false | 7,255 | [
"MIT"
] | 1 | 0ec8756ee06ae2a204a5e9110503bc697e9108fb | https://github.com/mirbostani/QA-KD-AL/tree/0ec8756ee06ae2a204a5e9110503bc697e9108fb |
BlendLinear | import torch
import torch.nn as nn
import torch.utils.data
class BlendLinear(nn.Module):
def __init__(self, dim_in, dim_out, layer_type=nn.Linear, **unused_kwargs):
super(BlendLinear, self).__init__()
self._layer0 = layer_type(dim_in, dim_out)
self._layer1 = layer_type(dim_in, dim_out)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dyn... | musyoku/ffjord | BlendLinear | false | 7,297 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
T5LayerNorm | # 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... | WangXinglin/BIT_framework | T5LayerNorm | false | 1,205 | [
"MIT"
] | 0 | 1484874fcd00d052c7536789dec95050b480b25d | https://github.com/WangXinglin/BIT_framework/tree/1484874fcd00d052c7536789dec95050b480b25d |
FixupResidualChain | import torch
import numpy as np
import torch as th
import torch.utils.data
import torch.nn as nn
from collections import OrderedDict
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | IlyaBizyaev/ttools | FixupResidualChain | false | 8,323 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
convBlock_basic | import torch
from torch import nn
class convBlock_basic(nn.Module):
def __init__(self, inChannel, outChannel, kernel, stride, pad,
use_batchnorm=False):
super(convBlock_basic, self).__init__()
self.use_batchnorm = use_batchnorm
self.conv = nn.Conv2d(inChannel, outChannel, kernel, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | YacobBY/ICDAR2019-ArT-Recognition-Alchemy | convBlock_basic | false | 14,624 | [
"MIT"
] | 209 | 911c572c2aff4599a74b7974d46ef4cfb17078b9 | https://github.com/YacobBY/ICDAR2019-ArT-Recognition-Alchemy/tree/911c572c2aff4599a74b7974d46ef4cfb17078b9 |
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