devbox
IT小白
这个屌丝很懒，什么也没留下！
关注作者
热门标签
热门文章
当前位置:   article > 正文

python-torch加载c++与内核函数

作者：IT小白 | 2024-07-13 12:33:54

赞

踩

python-torch加载c++与内核函数

一、定义

  1. python 在线加载.cu 文件
  2. python 加载.cu 文件方式2

二、实现
python 在线加载.cu 文件
python 加载c++ 方法采用的是torch.utils.cpp_extension包的扩展方法。
直接加载cu文件，则采用torch.utils.cpp_extension.load() 方法。
如：

from torch.utils.cpp_extension import load
#import fused_act as fused
module_path = os.path.dirname(__file__)
fused = load(
    "fused",
    sources=[
        os.path.join(module_path, "fused_bias_act.cpp"),
        os.path.join(module_path, "fused_bias_act_kernel.cu"),
    ],
)

  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10

具体： fused_bias_act.cpp

#include <torch/extension.h>
torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
    int act, int grad, float alpha, float scale);

#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)

torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
    int act, int grad, float alpha, float scale) {
    CHECK_CUDA(input);
    CHECK_CUDA(bias);

    return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
}

  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19

fused_bias_act_kernel.cu

// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
//
// This work is made available under the Nvidia Source Code License-NC.
// To view a copy of this license, visit
// https://nvlabs.github.io/stylegan2/license.html

#include <torch/types.h>

#include <ATen/ATen.h>
#include <ATen/AccumulateType.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDAApplyUtils.cuh>

#include <cuda.h>
#include <cuda_runtime.h>


template <typename scalar_t>
static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref,
    int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) {
    int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x;

    scalar_t zero = 0.0;

    for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) {
        scalar_t x = p_x[xi];

        if (use_bias) {
            x += p_b[(xi / step_b) % size_b];
        }

        scalar_t ref = use_ref ? p_ref[xi] : zero;

        scalar_t y;

        switch (act * 10 + grad) {
            default:
            case 10: y = x; break;
            case 11: y = x; break;
            case 12: y = 0.0; break;

            case 30: y = (x > 0.0) ? x : x * alpha; break;
            case 31: y = (ref > 0.0) ? x : x * alpha; break;
            case 32: y = 0.0; break;
        }

        out[xi] = y * scale;
    }
}


torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
    int act, int grad, float alpha, float scale) {
    int curDevice = -1;
    cudaGetDevice(&curDevice);
    cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);

    auto x = input.contiguous();
    auto b = bias.contiguous();
    auto ref = refer.contiguous();

    int use_bias = b.numel() ? 1 : 0;
    int use_ref = ref.numel() ? 1 : 0;

    int size_x = x.numel();
    int size_b = b.numel();
    int step_b = 1;

    for (int i = 1 + 1; i < x.dim(); i++) {
        step_b *= x.size(i);
    }

    int loop_x = 4;
    int block_size = 4 * 32;
    int grid_size = (size_x - 1) / (loop_x * block_size) + 1;

    auto y = torch::empty_like(x);

    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] {
        fused_bias_act_kernel<scalar_t><<<grid_size, block_size, 0, stream>>>(
            y.data_ptr<scalar_t>(),
            x.data_ptr<scalar_t>(),
            b.data_ptr<scalar_t>(),
            ref.data_ptr<scalar_t>(),
            act,
            grad,
            alpha,
            scale,
            loop_x,
            size_x,
            step_b,
            size_b,
            use_bias,
            use_ref
        );
    });

    return y;
}

  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • 32
  • 33
  • 34
  • 35
  • 36
  • 37
  • 38
  • 39
  • 40
  • 41
  • 42
  • 43
  • 44
  • 45
  • 46
  • 47
  • 48
  • 49
  • 50
  • 51
  • 52
  • 53
  • 54
  • 55
  • 56
  • 57
  • 58
  • 59
  • 60
  • 61
  • 62
  • 63
  • 64
  • 65
  • 66
  • 67
  • 68
  • 69
  • 70
  • 71
  • 72
  • 73
  • 74
  • 75
  • 76
  • 77
  • 78
  • 79
  • 80
  • 81
  • 82
  • 83
  • 84
  • 85
  • 86
  • 87
  • 88
  • 89
  • 90
  • 91
  • 92
  • 93
  • 94
  • 95
  • 96
  • 97
  • 98
  • 99

test.py

import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
#import fused_act as fused

module_path = os.path.dirname(__file__)

fused = load(
    "fused",
    sources=[
        os.path.join(module_path, "fused_bias_act.cpp"),
        os.path.join(module_path, "fused_bias_act_kernel.cu"),
    ],
)

class FusedLeakyReLUFunctionBackward(Function):
    @staticmethod
    def forward(ctx, grad_output, out, negative_slope, scale):
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale

        empty = grad_output.new_empty(0)

        grad_input = fused.fused_bias_act(
            grad_output, empty, out, 3, 1, negative_slope, scale
        )

        dim = [0]

        if grad_input.ndim > 2:
            dim += list(range(2, grad_input.ndim))

        grad_bias = grad_input.sum(dim).detach()

        return grad_input, grad_bias

    @staticmethod
    def backward(ctx, gradgrad_input, gradgrad_bias):
        out, = ctx.saved_tensors
        gradgrad_out = fused.fused_bias_act(
            gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
        )

        return gradgrad_out, None, None, None


class FusedLeakyReLUFunction(Function):
    @staticmethod
    def forward(ctx, input, bias, negative_slope, scale):
        empty = input.new_empty(0)
        out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale

        return out

    @staticmethod
    def backward(ctx, grad_output):
        out, = ctx.saved_tensors

        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
            grad_output, out, ctx.negative_slope, ctx.scale
        )

        return grad_input, grad_bias, None, None


class FusedLeakyReLU(nn.Module):
    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
        super().__init__()

        self.bias = nn.Parameter(torch.zeros(channel))
        self.negative_slope = negative_slope
        self.scale = scale

    def forward(self, input):
        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)


def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
    if input.device.type == "cpu":
        rest_dim = [1] * (input.ndim - bias.ndim - 1)
        return (
            F.leaky_relu(
                input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
            )
            * scale
        )

    else:
        return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)



if __name__ == '__main__':
    x=FusedLeakyReLU(3)
    res=x(torch.tensor(0.5))
    print(res)

  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • 32
  • 33
  • 34
  • 35
  • 36
  • 37
  • 38
  • 39
  • 40
  • 41
  • 42
  • 43
  • 44
  • 45
  • 46
  • 47
  • 48
  • 49
  • 50
  • 51
  • 52
  • 53
  • 54
  • 55
  • 56
  • 57
  • 58
  • 59
  • 60
  • 61
  • 62
  • 63
  • 64
  • 65
  • 66
  • 67
  • 68
  • 69
  • 70
  • 71
  • 72
  • 73
  • 74
  • 75
  • 76
  • 77
  • 78
  • 79
  • 80
  • 81
  • 82
  • 83
  • 84
  • 85
  • 86
  • 87
  • 88
  • 89
  • 90
  • 91
  • 92
  • 93
  • 94
  • 95
  • 96
  • 97
  • 98
  • 99
  • 100
  • 101
  • 102
  • 103

2 python 加载.cu 文件方式2
采用setup 与torch.utils.cpp_extension.CUDAExtension() 方法将c++ 文件编译，然后直接加载。
>> python setup.py install
则该包被安装到环境中。
使用：

import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
import fused_act as fused

module_path = os.path.dirname(__file__)

# fused = load(
#     "fused",
#     sources=[
#         os.path.join(module_path, "fused_bias_act.cpp"),
#         os.path.join(module_path, "fused_bias_act_kernel.cu"),
#     ],
# )

class FusedLeakyReLUFunctionBackward(Function):
    @staticmethod
    def forward(ctx, grad_output, out, negative_slope, scale):
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale

        empty = grad_output.new_empty(0)

        grad_input = fused.fused_bias_act(
            grad_output, empty, out, 3, 1, negative_slope, scale
        )

        dim = [0]

        if grad_input.ndim > 2:
            dim += list(range(2, grad_input.ndim))

        grad_bias = grad_input.sum(dim).detach()

        return grad_input, grad_bias

    @staticmethod
    def backward(ctx, gradgrad_input, gradgrad_bias):
        out, = ctx.saved_tensors
        gradgrad_out = fused.fused_bias_act(
            gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
        )

        return gradgrad_out, None, None, None


class FusedLeakyReLUFunction(Function):
    @staticmethod
    def forward(ctx, input, bias, negative_slope, scale):
        empty = input.new_empty(0)
        out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale

        return out

    @staticmethod
    def backward(ctx, grad_output):
        out, = ctx.saved_tensors

        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
            grad_output, out, ctx.negative_slope, ctx.scale
        )

        return grad_input, grad_bias, None, None


class FusedLeakyReLU(nn.Module):
    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
        super().__init__()

        self.bias = nn.Parameter(torch.zeros(channel))
        self.negative_slope = negative_slope
        self.scale = scale

    def forward(self, input):
        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)


def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
    if input.device.type == "cpu":
        rest_dim = [1] * (input.ndim - bias.ndim - 1)
        return (
            F.leaky_relu(
                input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
            )
            * scale
        )

    else:
        return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)



if __name__ == '__main__':
    x=FusedLeakyReLU(3)
    res=x(torch.tensor(0.5))
    print(res)

  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • 25
  • 26
  • 27
  • 28
  • 29
  • 30
  • 31
  • 32
  • 33
  • 34
  • 35
  • 36
  • 37
  • 38
  • 39
  • 40
  • 41
  • 42
  • 43
  • 44
  • 45
  • 46
  • 47
  • 48
  • 49
  • 50
  • 51
  • 52
  • 53
  • 54
  • 55
  • 56
  • 57
  • 58
  • 59
  • 60
  • 61
  • 62
  • 63
  • 64
  • 65
  • 66
  • 67
  • 68
  • 69
  • 70
  • 71
  • 72
  • 73
  • 74
  • 75
  • 76
  • 77
  • 78
  • 79
  • 80
  • 81
  • 82
  • 83
  • 84
  • 85
  • 86
  • 87
  • 88
  • 89
  • 90
  • 91
  • 92
  • 93
  • 94
  • 95
  • 96
  • 97
  • 98
  • 99
  • 100
  • 101
  • 102
  • 103
声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/IT小白/article/detail/819462
推荐阅读
相关标签

Copyright © 2003-2013 www.wpsshop.cn 版权所有，并保留所有权利。

  

闽ICP备14008679号