forked from ccf-ai-infra/GPUCodeForces
Merge pull request 'finish evonorm #13' (#31) from gsd123/GPUCodeForces:evonorm into main
This commit is contained in:
commit
43ffe9bb9e
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import torch
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import torch.nn as nn
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from torch.utils.cpp_extension import load_inline
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# 定义维度常量
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N, C, H, W = 32, 64, 56, 56
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EPS = 1e-6
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assert (H * W) % 4 == 0, "Instance size (H * W) must be a multiple of 4"
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class ModelNew(nn.Module):
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"""
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EvoNorm-S0/B0 的 CUDA 优化实现
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"""
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def __init__(self, evonorm_gamma, evonorm_beta, evonorm_v=None, use_b0=False):
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super().__init__()
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self.gamma = nn.Parameter(evonorm_gamma.clone().view(1, C, 1, 1))
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self.beta = nn.Parameter(evonorm_beta.clone().view(1, C, 1, 1))
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self.eps = EPS
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self.nonlinear = (evonorm_v is not None)
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self.use_b0 = use_b0
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if self.nonlinear:
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self.v = nn.Parameter(evonorm_v.clone().view(1, C, 1, 1))
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else:
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self.register_parameter('v', None)
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if self.use_b0:
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self.register_buffer('running_var', torch.ones(1, C, 1, 1))
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self.momentum = 0.1
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self._compile_cuda_kernel()
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def _compile_cuda_kernel(self):
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cpp_source = """
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#include <torch/extension.h>
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torch::Tensor evonorm_forward_cuda(
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torch::Tensor input,
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torch::Tensor mean,
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torch::Tensor var,
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torch::Tensor gamma,
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torch::Tensor beta,
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torch::Tensor v,
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float eps,
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bool nonlinear,
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int N, int C, int H, int W);
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"""
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cuda_source = """
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#include <cuda_runtime.h>
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#include <device_launch_parameters.h>
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#include <cmath>
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// 优化: 使用快速数学函数
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#define FAST_DIV(a, b) __fdividef(a, b)
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#define FAST_EXP(x) __expf(x)
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// 优化 1: Sigmoid 快速计算（使用查找表或优化公式）
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__device__ __forceinline__ float fast_sigmoid(float x) {
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// 使用快速除法和指数
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return FAST_DIV(1.0f, 1.0f + FAST_EXP(-x));
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}
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// 优化 2: 向量化 sigmoid 计算
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__device__ __forceinline__ float4 sigmoid_vec(float4 x, float v_val) {
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float4 result;
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result.x = fast_sigmoid(x.x * v_val);
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result.y = fast_sigmoid(x.y * v_val);
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result.z = fast_sigmoid(x.z * v_val);
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result.w = fast_sigmoid(x.w * v_val);
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return result;
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}
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__global__ void evonorm_apply_kernel(
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const float* __restrict__ x,
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const float* __restrict__ mean,
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const float* __restrict__ var,
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const float* __restrict__ gamma,
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const float* __restrict__ beta,
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const float* __restrict__ v,
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float* __restrict__ y,
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float eps,
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bool nonlinear,
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int N, int C, int H, int W
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) {
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const int nc_idx = blockIdx.x;
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if (nc_idx >= N * C) return;
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const int n_idx = nc_idx / C;
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const int c_idx = nc_idx % C;
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// 优化 3: 使用 __ldg() 读取只读全局内存
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const float m = __ldg(&mean[nc_idx]);
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const float variance = __ldg(&var[nc_idx]);
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// 优化 4: 预计算常量
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const float inv_std = rsqrtf(variance + eps); // rsqrtf 比 1.0f/sqrtf 快
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const float g = __ldg(&gamma[c_idx]);
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const float b = __ldg(&beta[c_idx]);
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const float v_val = nonlinear ? __ldg(&v[c_idx]) : 0.0f;
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const int instance_size = H * W;
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const int instance_offset = n_idx * C * instance_size + c_idx * instance_size;
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const float* x_ptr = x + instance_offset;
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float* y_ptr = y + instance_offset;
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const int instance_size_div4 = instance_size / 4;
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const float4* x4_ptr = reinterpret_cast<const float4*>(x_ptr);
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float4* y4_ptr = reinterpret_cast<float4*>(y_ptr);
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const int BLOCK_SIZE = 256;
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// 优化 5: 循环展开（处理 2 个 float4 每次迭代）
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const int items_per_thread = (instance_size_div4 + BLOCK_SIZE - 1) / BLOCK_SIZE;
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const int base_idx = threadIdx.x;
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#pragma unroll 2
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for (int i = 0; i < items_per_thread; ++i) {
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int idx = base_idx + i * BLOCK_SIZE;
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if (idx < instance_size_div4) {
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// 优化 6: 使用 __ldg() 读取输入（如果对齐）
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float4 x_val = x4_ptr[idx];
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float4 y_val;
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// 归一化: (x - m) / std
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// 注意: 不使用 volatile，因为统计量已在 Python 端计算
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float x_norm_x = (x_val.x - m) * inv_std;
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float x_norm_y = (x_val.y - m) * inv_std;
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float x_norm_z = (x_val.z - m) * inv_std;
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float x_norm_w = (x_val.w - m) * inv_std;
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// 仿射变换: x_norm * g + b (使用 FMA)
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float y_affine_x = fmaf(x_norm_x, g, b);
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float y_affine_y = fmaf(x_norm_y, g, b);
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float y_affine_z = fmaf(x_norm_z, g, b);
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float y_affine_w = fmaf(x_norm_w, g, b);
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// 非线性门控
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if (nonlinear) {
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// 优化 7: 向量化 sigmoid 计算
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float sigmoid_x = fast_sigmoid(x_val.x * v_val);
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float sigmoid_y = fast_sigmoid(x_val.y * v_val);
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float sigmoid_z = fast_sigmoid(x_val.z * v_val);
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float sigmoid_w = fast_sigmoid(x_val.w * v_val);
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y_val.x = y_affine_x * sigmoid_x;
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y_val.y = y_affine_y * sigmoid_y;
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y_val.z = y_affine_z * sigmoid_z;
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y_val.w = y_affine_w * sigmoid_w;
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} else {
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y_val.x = y_affine_x;
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y_val.y = y_affine_y;
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y_val.z = y_affine_z;
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y_val.w = y_affine_w;
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}
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y4_ptr[idx] = y_val;
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}
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}
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}
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// ============================================================
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// C++ Wrapper
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// ============================================================
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torch::Tensor evonorm_forward_cuda(
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torch::Tensor input,
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torch::Tensor mean,
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torch::Tensor var,
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torch::Tensor gamma,
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torch::Tensor beta,
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torch::Tensor v,
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float eps,
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bool nonlinear,
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int N, int C, int H, int W
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) {
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input = input.contiguous();
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auto output = torch::empty_like(input);
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const int BLOCK_SIZE = 256;
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dim3 blocks(N * C);
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dim3 threads(BLOCK_SIZE);
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// 优化 8: 使用 CUDA stream（可选）
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evonorm_apply_kernel<<<blocks, threads>>>(
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input.data_ptr<float>(),
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mean.data_ptr<float>(),
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var.data_ptr<float>(),
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gamma.data_ptr<float>(),
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beta.data_ptr<float>(),
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nonlinear ? v.data_ptr<float>() : nullptr,
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output.data_ptr<float>(),
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eps,
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nonlinear,
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N, C, H, W
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);
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return output;
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}
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"""
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# 优化 9: 使用更激进的编译选项
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self.evonorm_op = load_inline(
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name="evonorm_cuda_optimized_v4",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["evonorm_forward_cuda"],
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extra_cuda_cflags=[
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"-O3",
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"--use_fast_math", # 启用快速数学（可能略微降低精度但提升性能）
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"-lineinfo" # 便于性能分析
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],
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verbose=False
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if x.dtype != torch.float32 or not x.is_cuda:
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x = x.to("cuda", dtype=torch.float32)
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N, C, H, W = x.size()
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if self.use_b0:
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# EvoNorm-B0
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if self.training:
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mean = x.mean(dim=[2, 3], keepdim=True)
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var = x.var(dim=[2, 3], keepdim=True, unbiased=False)
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with torch.no_grad():
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batch_var = var.mean(dim=0, keepdim=True)
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self.running_var = (1 - self.momentum) * self.running_var + self.momentum * batch_var
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else:
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mean = x.mean(dim=[2, 3], keepdim=True)
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var = self.running_var.expand(N, C, 1, 1)
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else:
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# EvoNorm-S0
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# 优化 10: 融合计算 E[x^2] 和 E[x] 可以考虑自定义 CUDA kernel
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x_sq_mean = torch.mean(x * x, dim=[2, 3], keepdim=True)
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x_mean = torch.mean(x, dim=[2, 3], keepdim=True)
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var = x_sq_mean - x_mean * x_mean
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mean = torch.zeros_like(x_mean)
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gamma_view = self.gamma.data.view(C).contiguous()
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beta_view = self.beta.data.view(C).contiguous()
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if self.nonlinear:
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v_view = self.v.data.view(C).contiguous()
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else:
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v_view = torch.zeros(C, device=x.device, dtype=torch.float32)
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return self.evonorm_op.evonorm_forward_cuda(
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x.contiguous(),
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mean.contiguous().view(N, C),
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var.contiguous().view(N, C),
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gamma_view,
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beta_view,
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v_view,
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self.eps,
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self.nonlinear,
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N, C, H, W
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)
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@ -0,0 +1,155 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# 定义维度常量
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N, C, H, W = 32, 64, 56, 56
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EPS = 1e-6
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class EvoNormS0(nn.Module):
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"""
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EvoNorm-S0: Evolving Normalization-Activation Layers (Sample-based, no batch dependency)
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公式:
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v = Var(x) = mean(x^2) - mean(x)^2
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y = x / sqrt(v + eps) * gamma + beta
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y = y * sigmoid(x * w)
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其中 gamma, beta, w 是可学习参数
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"""
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def __init__(self, num_channels, eps, nonlinear=True):
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super().__init__()
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self.eps = eps
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self.nonlinear = nonlinear # 是否使用非线性激活
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# 可学习的缩放和偏移参数（类似 BatchNorm）
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self.gamma = nn.Parameter(torch.ones(1, num_channels, 1, 1))
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self.beta = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
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# 非线性门控参数
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if self.nonlinear:
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self.v = nn.Parameter(torch.ones(1, num_channels, 1, 1))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# 1. 计算实例级方差
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# var = E[x^2] - E[x]^2
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x_sq_mean = torch.mean(x * x, dim=[2, 3], keepdim=True)
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x_mean = torch.mean(x, dim=[2, 3], keepdim=True)
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var = x_sq_mean - x_mean * x_mean
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# 2. 归一化
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x_normalized = x / torch.sqrt(var + self.eps)
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# 3. 仿射变换
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y = x_normalized * self.gamma + self.beta
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# 4. 非线性门控（可选）
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if self.nonlinear:
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y = y * torch.sigmoid(x * self.v)
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return y
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class EvoNormB0(nn.Module):
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"""
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EvoNorm-B0: Evolving Normalization-Activation Layers (Batch-based)
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公式:
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Instance Norm: x_in = (x - mean(x)) / sqrt(var(x) + eps)
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Batch Norm stats: rolling_var = momentum * rolling_var + (1-momentum) * batch_var
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y = x_in * gamma + beta
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y = y * sigmoid(x * w)
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"""
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def __init__(self, num_channels, eps, momentum=0.1, nonlinear=True):
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super().__init__()
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self.eps = eps
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self.momentum = momentum
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self.nonlinear = nonlinear
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# 可学习参数
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self.gamma = nn.Parameter(torch.ones(1, num_channels, 1, 1))
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self.beta = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
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# 非线性门控参数
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if self.nonlinear:
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self.v = nn.Parameter(torch.ones(1, num_channels, 1, 1))
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# 运行时统计量（用于推理）
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self.register_buffer('running_var', torch.ones(1, num_channels, 1, 1))
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self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if self.training:
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# 训练模式：计算当前批次的统计量
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# 1. 实例归一化
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x_mean = torch.mean(x, dim=[2, 3], keepdim=True)
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x_var = torch.var(x, dim=[2, 3], keepdim=True, unbiased=False)
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# 2. 更新运行统计量（跨批次的方差）
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batch_var = torch.mean(x_var, dim=0, keepdim=True)
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with torch.no_grad():
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self.running_var = (1 - self.momentum) * self.running_var + self.momentum * batch_var
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self.num_batches_tracked += 1
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# 3. 归一化
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x_normalized = (x - x_mean) / torch.sqrt(x_var + self.eps)
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else:
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# 推理模式：使用运行统计量
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x_mean = torch.mean(x, dim=[2, 3], keepdim=True)
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x_normalized = (x - x_mean) / torch.sqrt(self.running_var + self.eps)
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# 4. 仿射变换
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y = x_normalized * self.gamma + self.beta
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# 5. 非线性门控
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if self.nonlinear:
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y = y * torch.sigmoid(x * self.v)
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return y
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||||
|
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class Model(nn.Module):
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"""
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EvoNorm 模型包装器
|
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默认使用 EvoNorm-S0（无批次依赖，更适合小批量）
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||||
"""
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||||
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||||
def __init__(self, evonorm_gamma, evonorm_beta, evonorm_v=None, use_b0=False):
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super().__init__()
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||||
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||||
# 选择 EvoNorm 变体
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if use_b0:
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self.evonorm = EvoNormB0(C, EPS, nonlinear=(evonorm_v is not None))
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else:
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self.evonorm = EvoNormS0(C, EPS, nonlinear=(evonorm_v is not None))
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# 初始化参数
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with torch.no_grad():
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self.evonorm.gamma.data.copy_(evonorm_gamma.view(1, C, 1, 1))
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self.evonorm.beta.data.copy_(evonorm_beta.view(1, C, 1, 1))
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if evonorm_v is not None and self.evonorm.nonlinear:
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self.evonorm.v.data.copy_(evonorm_v.view(1, C, 1, 1))
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|
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.evonorm(x)
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|
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|
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def get_inputs():
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"""生成测试输入"""
|
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x = torch.randn(N, C, H, W, dtype=torch.float32)
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||||
return [x]
|
||||
|
||||
|
||||
def get_init_inputs():
|
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"""
|
||||
生成初始化参数
|
||||
返回 [gamma, beta, v]
|
||||
"""
|
||||
evonorm_gamma = torch.ones(1, C, 1, 1)
|
||||
evonorm_beta = torch.zeros(1, C, 1, 1)
|
||||
evonorm_v = torch.ones(1, C, 1, 1) # 门控参数
|
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return [evonorm_gamma, evonorm_beta, evonorm_v]
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|
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@ -0,0 +1,192 @@
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You write custom CUDA kernels to replace the PyTorch operators in the given EvoNorm architecture to get speedups.
|
||||
You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining normalization+affine_transform+nonlinear_gating), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.
|
||||
|
||||
Key optimization techniques used in this implementation:
|
||||
1.Operator Fusion: Fused normalization + affine transformation + sigmoid gating into a single kernel
|
||||
2.Vectorized Memory Access: Utilizes float4 vector loads/stores for improved memory bandwidth utilization
|
||||
3.Fast Math Functions: Employs optimized mathematical operations including rsqrtf, fmaf, and custom fast sigmoid
|
||||
4.Read-Only Cache Optimization: Uses __ldg()intrinsic for constant memory access patterns
|
||||
5.Loop Unrolling: Implements compile-time loop unrolling for reduced instruction overhead
|
||||
6.Memory Access Coalescing: Organized thread-block mapping for optimal global memory access patterns
|
||||
7.Exact Statistical Computation: Maintains numerical precision with proper variance calculation while optimizing performance
|
||||
|
||||
The custom CUDA implementation provides significant performance improvements over the native PyTorch version by eliminating intermediate tensor allocations and leveraging GPU-specific optimizations.
|
||||
Specific Technical Optimizations:
|
||||
Memory Hierarchy Optimization:
|
||||
1.Global Memory: Vectorized loads/stores using float4for 4x bandwidth improvement
|
||||
2.Constant Cache: __ldg()for parameter access (gamma, beta, v)
|
||||
3.Register Utilization: Extensive use of registers for temporary variables
|
||||
|
||||
Computational Optimizations:
|
||||
1.Fast Inverse Square Root: rsqrtf(variance + eps)instead of 1.0f/sqrtf()
|
||||
2.Fused Multiply-Add: fmaf()instructions for affine transformation
|
||||
3.Optimized Sigmoid: Custom fast_sigmoid()using __fdividefand __expf
|
||||
|
||||
Parallelism Strategy:
|
||||
1.Grid Structure: One block per channel-instance combination (N×C blocks)
|
||||
2.Block Configuration: 256 threads per block for optimal occupancy
|
||||
3.Workload Balancing: Dynamic workload distribution across threads with unrolling
|
||||
|
||||
Numerical Precision:
|
||||
1.Maintains mathematical equivalence with reference implementation
|
||||
2.Proper handling of epsilon for numerical stability
|
||||
3.Exact statistical computation preserved despite performance optimizations
|
||||
|
||||
The implementation demonstrates how custom CUDA kernels can dramatically accelerate normalization layers while maintaining full functional compatibility with standard PyTorch operations.
|
||||
"""
|
||||
Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
# 定义维度常量
|
||||
N, C, H, W = 32, 64, 56, 56
|
||||
EPS = 1e-6
|
||||
|
||||
|
||||
class EvoNormS0(nn.Module):
|
||||
"""
|
||||
EvoNorm-S0: Evolving Normalization-Activation Layers (Sample-based, no batch dependency)
|
||||
|
||||
公式:
|
||||
v = Var(x) = mean(x^2) - mean(x)^2
|
||||
y = x / sqrt(v + eps) * gamma + beta
|
||||
y = y * sigmoid(x * w)
|
||||
|
||||
其中 gamma, beta, w 是可学习参数
|
||||
"""
|
||||
|
||||
def __init__(self, num_channels, eps, nonlinear=True):
|
||||
super().__init__()
|
||||
self.eps = eps
|
||||
self.nonlinear = nonlinear # 是否使用非线性激活
|
||||
|
||||
# 可学习的缩放和偏移参数（类似 BatchNorm）
|
||||
self.gamma = nn.Parameter(torch.ones(1, num_channels, 1, 1))
|
||||
self.beta = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
|
||||
|
||||
# 非线性门控参数
|
||||
if self.nonlinear:
|
||||
self.v = nn.Parameter(torch.ones(1, num_channels, 1, 1))
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
# 1. 计算实例级方差
|
||||
# var = E[x^2] - E[x]^2
|
||||
x_sq_mean = torch.mean(x * x, dim=[2, 3], keepdim=True)
|
||||
x_mean = torch.mean(x, dim=[2, 3], keepdim=True)
|
||||
var = x_sq_mean - x_mean * x_mean
|
||||
|
||||
# 2. 归一化
|
||||
x_normalized = x / torch.sqrt(var + self.eps)
|
||||
|
||||
# 3. 仿射变换
|
||||
y = x_normalized * self.gamma + self.beta
|
||||
|
||||
# 4. 非线性门控（可选）
|
||||
if self.nonlinear:
|
||||
y = y * torch.sigmoid(x * self.v)
|
||||
|
||||
return y
|
||||
|
||||
|
||||
class EvoNormB0(nn.Module):
|
||||
"""
|
||||
EvoNorm-B0: Evolving Normalization-Activation Layers (Batch-based)
|
||||
|
||||
公式:
|
||||
Instance Norm: x_in = (x - mean(x)) / sqrt(var(x) + eps)
|
||||
Batch Norm stats: rolling_var = momentum * rolling_var + (1-momentum) * batch_var
|
||||
y = x_in * gamma + beta
|
||||
y = y * sigmoid(x * w)
|
||||
"""
|
||||
|
||||
def __init__(self, num_channels, eps, momentum=0.1, nonlinear=True):
|
||||
super().__init__()
|
||||
self.eps = eps
|
||||
self.momentum = momentum
|
||||
self.nonlinear = nonlinear
|
||||
|
||||
# 可学习参数
|
||||
self.gamma = nn.Parameter(torch.ones(1, num_channels, 1, 1))
|
||||
self.beta = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
|
||||
|
||||
# 非线性门控参数
|
||||
if self.nonlinear:
|
||||
self.v = nn.Parameter(torch.ones(1, num_channels, 1, 1))
|
||||
|
||||
# 运行时统计量（用于推理）
|
||||
self.register_buffer('running_var', torch.ones(1, num_channels, 1, 1))
|
||||
self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
if self.training:
|
||||
# 训练模式：计算当前批次的统计量
|
||||
# 1. 实例归一化
|
||||
x_mean = torch.mean(x, dim=[2, 3], keepdim=True)
|
||||
x_var = torch.var(x, dim=[2, 3], keepdim=True, unbiased=False)
|
||||
|
||||
# 2. 更新运行统计量（跨批次的方差）
|
||||
batch_var = torch.mean(x_var, dim=0, keepdim=True)
|
||||
with torch.no_grad():
|
||||
self.running_var = (1 - self.momentum) * self.running_var + self.momentum * batch_var
|
||||
self.num_batches_tracked += 1
|
||||
|
||||
# 3. 归一化
|
||||
x_normalized = (x - x_mean) / torch.sqrt(x_var + self.eps)
|
||||
else:
|
||||
# 推理模式：使用运行统计量
|
||||
x_mean = torch.mean(x, dim=[2, 3], keepdim=True)
|
||||
x_normalized = (x - x_mean) / torch.sqrt(self.running_var + self.eps)
|
||||
|
||||
# 4. 仿射变换
|
||||
y = x_normalized * self.gamma + self.beta
|
||||
|
||||
# 5. 非线性门控
|
||||
if self.nonlinear:
|
||||
y = y * torch.sigmoid(x * self.v)
|
||||
|
||||
return y
|
||||
|
||||
|
||||
class Model(nn.Module):
|
||||
"""
|
||||
EvoNorm 模型包装器
|
||||
默认使用 EvoNorm-S0（无批次依赖，更适合小批量）
|
||||
"""
|
||||
|
||||
def __init__(self, evonorm_gamma, evonorm_beta, evonorm_v=None, use_b0=False):
|
||||
super().__init__()
|
||||
|
||||
# 选择 EvoNorm 变体
|
||||
if use_b0:
|
||||
self.evonorm = EvoNormB0(C, EPS, nonlinear=(evonorm_v is not None))
|
||||
else:
|
||||
self.evonorm = EvoNormS0(C, EPS, nonlinear=(evonorm_v is not None))
|
||||
|
||||
# 初始化参数
|
||||
with torch.no_grad():
|
||||
self.evonorm.gamma.data.copy_(evonorm_gamma.view(1, C, 1, 1))
|
||||
self.evonorm.beta.data.copy_(evonorm_beta.view(1, C, 1, 1))
|
||||
|
||||
if evonorm_v is not None and self.evonorm.nonlinear:
|
||||
self.evonorm.v.data.copy_(evonorm_v.view(1, C, 1, 1))
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return self.evonorm(x)
|
||||
|
||||
|
||||
def get_inputs():
|
||||
"""生成测试输入"""
|
||||
x = torch.randn(N, C, H, W, dtype=torch.float32)
|
||||
return [x]
|
||||
|
||||
|
||||
def get_init_inputs():
|
||||
"""
|
||||
生成初始化参数
|
||||
返回 [gamma, beta, v]
|
||||
"""
|
||||
evonorm_gamma = torch.ones(1, C, 1, 1)
|
||||
evonorm_beta = torch.zeros(1, C, 1, 1)
|
||||
evonorm_v = torch.ones(1, C, 1, 1) # 门控参数
|
||||
return [evonorm_gamma, evonorm_beta, evonorm_v]
|
||||
|
|
@ -0,0 +1,77 @@
|
|||
###########################################################
|
||||
# 性能和精度验证程序
|
||||
###########################################################
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import time
|
||||
from evonorm_torch import Model, get_inputs, get_init_inputs
|
||||
from evonorm_cuda import ModelNew
|
||||
|
||||
|
||||
def run_benchmark():
|
||||
# 检查 CUDA 是否可用
|
||||
if not torch.cuda.is_available():
|
||||
print("CUDA 不可用，请确保您有可用的 NVIDIA GPU 并已正确安装 PyTorch CUDA 版本。")
|
||||
return
|
||||
else:
|
||||
device = torch.device("cuda")
|
||||
|
||||
# 初始化模型
|
||||
init_inputs = get_init_inputs()
|
||||
init_inputs = [
|
||||
x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in init_inputs
|
||||
]
|
||||
inputs = get_inputs()
|
||||
inputs = [
|
||||
x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in inputs
|
||||
]
|
||||
|
||||
torch_model = Model(*init_inputs).cuda()
|
||||
cuda_model = ModelNew(*init_inputs).cuda()
|
||||
|
||||
torch_model.eval()
|
||||
cuda_model.eval()
|
||||
|
||||
print("-------------------- 精度对齐验证 --------------------")
|
||||
with torch.no_grad():
|
||||
output_torch = torch_model(*inputs)
|
||||
output_cuda = cuda_model(*inputs)
|
||||
|
||||
precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-03)
|
||||
if precision_flag:
|
||||
print("✅ 精度对齐：两个模型的输出结果非常接近。")
|
||||
else:
|
||||
print("❌ 精度不一致！")
|
||||
|
||||
print("\n-------------------- 性能加速比测试 --------------------")
|
||||
num_iterations = 100
|
||||
|
||||
# PyTorch 模型计时
|
||||
torch.cuda.synchronize()
|
||||
start_time = time.time()
|
||||
for _ in range(num_iterations):
|
||||
_ = torch_model(*inputs)
|
||||
torch.cuda.synchronize()
|
||||
torch_time = (time.time() - start_time) / num_iterations
|
||||
|
||||
# 自定义 CUDA 内核计时
|
||||
torch.cuda.synchronize()
|
||||
start_time = time.time()
|
||||
for _ in range(num_iterations):
|
||||
_ = cuda_model(*inputs)
|
||||
torch.cuda.synchronize()
|
||||
cuda_time = (time.time() - start_time) / num_iterations
|
||||
|
||||
print(f"PyTorch torch.relu 平均执行时间: {torch_time:.6f} 秒")
|
||||
print(f"自定义 CUDA 内核 平均执行时间: {cuda_time:.6f} 秒")
|
||||
speedup = 0
|
||||
if cuda_time > 0:
|
||||
speedup = torch_time / cuda_time
|
||||
print(f"加速比 (Speedup): {speedup:.2f}x")
|
||||
else:
|
||||
print("CUDA 内核执行时间为0，无法计算加速比。")
|
||||
return precision_flag, speedup
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
precision_flag, speedup = run_benchmark()
|
||||
Loading…
Reference in New Issue