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 |
|---|---|---|---|---|---|---|---|---|---|---|
Conv2dZeroInit | import torch
import torch.nn as nn
class Conv2dZeroInit(nn.Conv2d):
def __init__(self, channels_in, channels_out, filter_size, stride=1,
padding=0, logscale=3.0):
super().__init__(channels_in, channels_out, filter_size, stride=
stride, padding=padding)
self.register_parameter(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | david-klindt/invertible-resnet | Conv2dZeroInit | false | 3,386 | [
"MIT"
] | 0 | ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 | https://github.com/david-klindt/invertible-resnet/tree/ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 |
Fusion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Ruiver/CTCNet | Fusion | false | 17,887 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 |
NIN2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | XuezheMax/macow | NIN2d | false | 14,611 | [
"Apache-2.0"
] | 60 | 6de247c09b590a037c9eec2d6b1248845f6efb31 | https://github.com/XuezheMax/macow/tree/6de247c09b590a037c9eec2d6b1248845f6efb31 |
ARFB | import torch
import torch.nn as nn
import torch.utils.model_zoo
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(in_channels, out_channels, kernel_size, padding=
kernel_size // 2, bias=bias)
class ResidualUnit(nn.Module):
def __init__(self, inChannel, outChannel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | NawaNae/ESRT-Huawei | ARFB | false | 2,668 | [
"MIT"
] | 0 | edea1c0bafec940dc7ea8e5110c355a83188665c | https://github.com/NawaNae/ESRT-Huawei/tree/edea1c0bafec940dc7ea8e5110c355a83188665c |
Hflip | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
return torch.flip(input, [-1])
class Hflip(nn.Module):
"""Horizontally flip a tensor image or a batch of tensor images. Input must
be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
... | 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... | NickleDave/kornia | Hflip | false | 2,696 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 5392651d0bc268da577fa0a49aa50f957289c7dd | https://github.com/NickleDave/kornia/tree/5392651d0bc268da577fa0a49aa50f957289c7dd |
TripletLossXBM | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | neka-nat/Transfer-Learning-Library | TripletLossXBM | false | 16,168 | [
"MIT"
] | 1,474 | a3b27b0d7562fa90a02e914140b37ab438469e6c | https://github.com/neka-nat/Transfer-Learning-Library/tree/a3b27b0d7562fa90a02e914140b37ab438469e6c |
CReLU_IN | import torch
import torch.nn.functional as F
import torch.nn as nn
class CReLU_IN(nn.Module):
def __init__(self, channels):
super(CReLU_IN, self).__init__()
self.bn = nn.InstanceNorm2d(channels * 2, eps=1e-05, momentum=0.1,
affine=True)
def forward(self, x):
cat = torch.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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dipikakhullar/ocr | CReLU_IN | false | 15,188 | [
"MIT"
] | 284 | a55e70d82f42803be5ed63f8f59e4fa597fcf8d6 | https://github.com/dipikakhullar/ocr/tree/a55e70d82f42803be5ed63f8f59e4fa597fcf8d6 |
FrequencyLoss | import torch
import torch.nn as nn
class FrequencyLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=0.001):
super(FrequencyLoss, self).__init__()
self.criterion = torch.nn.L1Loss()
def forward(self, x, y):
x_fft = torch.fft.rfft2(x, dim=(2, 3))
y_fft = 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... | vztu/DebandingNet | FrequencyLoss | false | 4,515 | [
"MIT"
] | 0 | 4af8e83ffbfc70dc220dd6fea2827fb75796f10c | https://github.com/vztu/DebandingNet/tree/4af8e83ffbfc70dc220dd6fea2827fb75796f10c |
Decoder | import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, latent_dim=4, obs_dim=2, nhidden=20):
super(Decoder, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.fc1 = nn.Linear(latent_dim, nhidden)
self.fc2 = nn.Linear(nhidden, obs_dim)
def forward... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | navaro1/parking_prediction | Decoder | false | 12,889 | [
"MIT"
] | 0 | c532a2f75155abc9c0d4be9c955eabe368591932 | https://github.com/navaro1/parking_prediction/tree/c532a2f75155abc9c0d4be9c955eabe368591932 |
LDEPooling | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn
assert... | qlindazm/asv-subtools | LDEPooling | false | 4,234 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 |
AdaptiveConv | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data.distributed
class AdaptiveConv(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, padding=1,
dilation=1, groups=1, bias=False, size=(256, 256)):
super(AdaptiveConv,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SSusantAchary/OctaveConv_pytorch | AdaptiveConv | false | 14,376 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
CriticArchitecture | # 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... | ivallesp/RL_Tennis | CriticArchitecture | false | 3,687 | [
"MIT"
] | 0 | a83933af9c4481d50f735983b4fc3b1f053f71d1 | https://github.com/ivallesp/RL_Tennis/tree/a83933af9c4481d50f735983b4fc3b1f053f71d1 |
MultiHeadAttn | import torch
import torch.cuda
from torch.nn import functional as F
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0.1,
pre_lnorm=False):
super(MultiHeadAttn, sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | hamjam/NeMo | MultiHeadAttn | false | 15,533 | [
"Apache-2.0"
] | 4,145 | b3484d32e1317666151f931bfa39867d88ed8658 | https://github.com/hamjam/NeMo/tree/b3484d32e1317666151f931bfa39867d88ed8658 |
ReGLU | # 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_... | edchengmoore/pytorch_tabular | ReGLU | false | 3,448 | [
"MIT"
] | 0 | 25f87089fbed95b46f2a1a8a96fba1f581aa8af1 | https://github.com/edchengmoore/pytorch_tabular/tree/25f87089fbed95b46f2a1a8a96fba1f581aa8af1 |
SphericalBesselBasis | # 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 math
import numpy as np
assert_size_stride = torch._C._dynamo.guar... | Open-Catalyst-Project/baselines | SphericalBesselBasis | false | 17,804 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
GatedConv2d | import torch
import torch.nn as nn
import torch.utils.data
class GatedConv2d(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, stride,
padding, dilation=1, activation=None):
super(GatedConv2d, self).__init__()
self.activation = activation
self.sigmoid = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 | GatedConv2d | false | 7,303 | [
"MIT"
] | 1 | 9e431e122e59fa9a71f3f301dec8fdd3db51e0ce | https://github.com/musyoku/ffjord/tree/9e431e122e59fa9a71f3f301dec8fdd3db51e0ce |
FCDiscriminator | import torch
import torch.nn as nn
class FCDiscriminator(nn.Module):
def __init__(self, num_classes, ndf=64):
super().__init__()
self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2,
padding=1)
self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ciampluca/unsupervised_counting | FCDiscriminator | false | 3,299 | [
"MIT"
] | 0 | 4445d48f68da75359643bcf3003e90ef61d817e3 | https://github.com/ciampluca/unsupervised_counting/tree/4445d48f68da75359643bcf3003e90ef61d817e3 |
BasicModel_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
from torch._inductor.runtime.... | Europium248/captum | BasicModel_ConvNet | false | 455 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
SimpleFusionGenerator | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class SimpleFusionGenerator(nn.Module):
def __init__(self, decoder_input_size, lm_input_size, output_size):
super(SimpleFusionGenerator, self).__init__()
self.decoder_linear = nn.Linear(decoder_input_size, output_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | fangleai/encoder-agnostic-adaptation | SimpleFusionGenerator | false | 15,347 | [
"MIT"
] | 70 | d917e654152df202dd35bba49c409c3ecd24eaf7 | https://github.com/fangleai/encoder-agnostic-adaptation/tree/d917e654152df202dd35bba49c409c3ecd24eaf7 |
h_swish | import torch
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swish(nn.Module):
def __init__(self, inplace=True)... | 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... | CYHYCY/voice-classification | h_swish | false | 17,071 | [
"Apache-2.0"
] | 8 | a6f62e2f1c39b08323da3632411f4ba6b04d5f37 | https://github.com/CYHYCY/voice-classification/tree/a6f62e2f1c39b08323da3632411f4ba6b04d5f37 |
MMD | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | BetterRaven/Transfer-Learning_vscode | MMD | false | 4,913 | [
"MIT"
] | 1 | 90c9bce630f54fd2322cce8fab5fe1d074ff141c | https://github.com/BetterRaven/Transfer-Learning_vscode/tree/90c9bce630f54fd2322cce8fab5fe1d074ff141c |
PKT | # 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.triton_helpers import libdevice, math as tl_math
im... | RylanSchaeffer/RepDistiller | PKT | false | 5,786 | [
"BSD-2-Clause"
] | 1 | 3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e | https://github.com/RylanSchaeffer/RepDistiller/tree/3612d9d8f6f913527c7aaec7e5ea557e72ed7c5e |
my_MLP1 | import torch
import torch.nn as nn
class my_MLP1(nn.Module):
def __init__(self, input_dim, npdf, h1_dim, h2_dim, norm_type='softmax'):
super().__init__()
self.input = nn.Linear(input_dim, h1_dim)
self.hidden = nn.Linear(h1_dim, h2_dim)
self.output = nn.Linear(h2_dim, npdf)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mtcarilli/CME_approximations | my_MLP1 | false | 4,046 | [
"MIT"
] | 0 | 1ffd1cc0bd17679116964ee33634c0d76c50064e | https://github.com/mtcarilli/CME_approximations/tree/1ffd1cc0bd17679116964ee33634c0d76c50064e |
RegModel | # 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.nn import Module
import functools
import torch.nn as nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_... | davidpfahler/fastai_dev | RegModel | false | 10,054 | [
"Apache-2.0"
] | 0 | a86b15f86138a9902e8649e3f745e76a19139ab3 | https://github.com/davidpfahler/fastai_dev/tree/a86b15f86138a9902e8649e3f745e76a19139ab3 |
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