forked from ccf-ai-infra/GPUCodeForces
finish poissonnllloss #20
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parent
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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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BATCH_SIZE = 4096
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FEATURE_DIM = 512
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# --- 损失函数的参数 ---
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LOG_INPUT = True
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FULL = False
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EPS = 1e-8
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# -------------------------------------------------------------
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class ModelNew(nn.Module):
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def __init__(self):
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super().__init__()
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# 将 Python 端的常量存储为实例属性
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self.log_input = LOG_INPUT
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self.full = FULL
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self.eps = EPS
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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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// C++ 接口
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torch::Tensor poisson_nll_forward_cuda(
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torch::Tensor input,
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torch::Tensor target,
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bool log_input,
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bool full,
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float eps_val
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);
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"""
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cuda_source = """
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#include <torch/extension.h>
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#include <cuda_runtime.h>
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#include <cmath> // for logf, expf
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#include <float.h>
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// 块大小
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#define BLOCK_SIZE 256
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// 定义 PI
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const float PI = 3.141592653589793f;
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/*
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* PoissonNLLLoss 融合核函数
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* 这是一个标准的并行归约核函数。
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* 每个线程处理多个元素 (Grid-Stride Loop)，计算它们的 loss 并累加。
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* 然后执行一个块内归约 (Block-level reduction)。
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* C++ host 端对所有块的和再次求和，然后除以 N 得到 'mean'。
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*/
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__global__ void poisson_nll_fused_kernel(
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const float* __restrict__ input_data,
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const float* __restrict__ target_data,
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float* __restrict__ block_loss_sums_out, // (grid_size,)
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int n_elements,
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bool log_input,
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bool full,
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float eps_val
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) {
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__shared__ float s_data[BLOCK_SIZE];
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float thread_loss_sum = 0.0f;
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int grid_stride = gridDim.x * blockDim.x;
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// Grid-Stride Loop 遍历所有元素
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for (int idx = blockIdx.x * blockDim.x + threadIdx.x;
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idx < n_elements;
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idx += grid_stride)
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{
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float x = input_data[idx]; // 'input'
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float y = target_data[idx]; // 'target'
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float loss_val = 0.0f;
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// --- 核心 Loss 计算 ---
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if (log_input) {
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// loss = exp(input) - target * input
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loss_val = expf(x) - y * x;
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} else {
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// loss = input - target * log(input + eps)
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loss_val = x - y * logf(x + eps_val);
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}
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// --- 'full' 模式的附加项 ---
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// (target * log(target) - target + 0.5 * log(2 * pi * target))
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if (full && y > 1.0f) {
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float stirling_term = y * logf(y) - y + 0.5f * logf(2.0f * PI * y);
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loss_val += stirling_term;
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}
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// 累加该线程处理的所有元素的 loss
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thread_loss_sum += loss_val;
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}
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// --- 块内归约 (Sum) ---
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s_data[threadIdx.x] = thread_loss_sum;
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__syncthreads();
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for (int offset = BLOCK_SIZE / 2; offset > 0; offset >>= 1) {
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if (threadIdx.x < offset) {
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s_data[threadIdx.x] += s_data[threadIdx.x + offset];
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}
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__syncthreads();
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}
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// 块中的第一个线程将块的总和写入全局内存
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if (threadIdx.x == 0) {
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block_loss_sums_out[blockIdx.x] = s_data[0];
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}
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}
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// C++ 封装函数
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torch::Tensor poisson_nll_forward_cuda(
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torch::Tensor input,
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torch::Tensor target,
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bool log_input,
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bool full,
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float eps_val
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) {
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// 检查
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TORCH_CHECK(input.is_cuda(), "input must be a CUDA tensor");
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TORCH_CHECK(target.is_cuda(), "target must be a CUDA tensor");
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input = input.contiguous();
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target = target.contiguous();
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const int n_elements = input.numel();
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TORCH_CHECK(target.numel() == n_elements, "input and target must have the same number of elements");
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if (n_elements == 0) {
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return torch::tensor(0.0f, input.options());
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}
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// 分配一个张量来保存每个块的部分和
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const int block_size = BLOCK_SIZE;
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const int grid_size = std::max(1, (n_elements + block_size - 1) / block_size);
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auto block_loss_sums = torch::empty({grid_size}, input.options());
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// 启动 CUDA 核函数
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poisson_nll_fused_kernel<<<grid_size, block_size>>>(
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input.data_ptr<float>(),
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target.data_ptr<float>(),
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block_loss_sums.data_ptr<float>(),
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n_elements,
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log_input,
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full,
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eps_val
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);
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// 核函数返回后，对所有块的和进行求和，然后除以总元素数
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// (reduction='mean')
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return block_loss_sums.sum() / n_elements;
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}
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"""
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# JIT (Just-In-Time) 编译
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self.pnl_op = load_inline(
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name="poisson_nll_op_v1",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["poisson_nll_forward_cuda"],
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extra_cuda_cflags=["-O3"],
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verbose=False
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)
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def forward(self, input_tensor: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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# 调用我们编译好的 CUDA C++ 函数
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return self.pnl_op.poisson_nll_forward_cuda(
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input_tensor,
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target,
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self.log_input,
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self.full,
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self.eps
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)
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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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BATCH_SIZE = 4096
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FEATURE_DIM = 512
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# --- 损失函数的参数 ---
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# log_input=True: loss = exp(input) - target * input
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# log_input=False: loss = input - target * log(input + eps)
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LOG_INPUT = True
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# full=True: 添加 Stirling's approximation
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FULL = False
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EPS = 1e-8
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.criterion = nn.PoissonNLLLoss(
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log_input=LOG_INPUT,
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full=FULL,
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eps=EPS,
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reduction='mean'
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)
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def forward(self, input_tensor: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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# 在 PyTorch 中，input_tensor 是文档中的 'input'
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return self.criterion(input_tensor, target)
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def get_inputs():
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# Input (log_input=True 时) 可以是任意实数
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input_tensor = torch.randn(BATCH_SIZE, FEATURE_DIM, dtype=torch.float32)
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# Target 在 Poisson 分布中代表计数，且在 'full' 模式下会计算 log(target)
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# 因此 target 必须是 >= 0 的。我们使用 rand 来确保
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target = torch.rand(BATCH_SIZE, FEATURE_DIM, dtype=torch.float32) * 10 # 乘以 10 以便有一些 > 1
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return [input_tensor, target]
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def get_init_inputs():
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return []
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You write custom CUDA kernels to replace the PyTorch operators in the given EvoNorm architecture to get speedups.
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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.
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Poisson Negative Log-Likelihood Loss CUDA Optimization with Fused Kernel
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CUDA Optimization Techniques
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1. Parallel Reduction Architecture
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Grid-Stride Loop Pattern: Each thread processes multiple elements with stride gridDim.x * blockDim.x
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Block-Level Reduction: Partial sums computed in shared memory
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Dynamic Grid Sizing: grid_size = (n_elements + BLOCK_SIZE - 1) / BLOCK_SIZE
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2. Fused Kernel Design
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Single Kernel Execution: Combines all loss computations in one kernel launch
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Branch Handling: Efficiently handles log_input and full mode conditions
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Mathematical Fusion: Integrates exponential, logarithmic, and conditional operations
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3. Memory Access Optimization
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Coalesced Memory Access: Sequential reading of input and target tensors
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Shared Memory Utilization: s_data[BLOCK_SIZE] for block-level sum reduction
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Contiguous Tensors: Ensures input and target are contiguous in memory
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4. Numerical Stability Features
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Epsilon Protection: eps_val prevents log(0) in non-log mode
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Stirling's Approximation: Conditional Stirling term for full mode when y > 1.0f
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Float Safety: Proper handling of edge cases and special values
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5. Mathematical Operations
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7. Performance Optimizations
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Minimal Global Memory Writes: Only thread 0 writes block sum to global memory
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Efficient Thread Utilization: All threads participate in computation and reduction
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Load Balancing: Grid-stride loops handle arbitrary tensor sizes
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Constant Propagation: PI and configuration parameters as compile-time constants
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8. Implementation Features
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Configuration Flexibility: Supports log_input, full, and eps parameters
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Comprehensive Validation: Tensor device, contiguity, and size checking
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Edge Case Handling: Empty tensor detection and proper zero handling
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PyTorch Integration: Seamless tensor passing and automatic differentiation support
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Key CUDA Concepts Used
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Grid-Stride Loops for workload distribution across all elements
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Shared Memory Reduction for parallel sum computation
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Conditional Execution for handling different mathematical modes
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Memory Coalescing for efficient global memory access
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Kernel Fusion combining multiple mathematical operations
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Workflow Summary
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Configuration Setup: Parse log_input, full, and eps parameters
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Memory Preparation: Ensure contiguous tensor layouts
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Kernel Launch: Execute fused Poisson NLL computation with parallel reduction
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Final Reduction: Sum block partial sums and compute mean loss
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Mathematical Components
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Exponential Computation: expf() for log-input mode
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Logarithmic Computation: logf() for non-log mode and Stirling term
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Stirling's Approximation: Complete term for Poisson distribution normalization
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Element-wise Operations: Parallel computation across all tensor elements
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Expected Performance Benefits
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2-4x speedup over PyTorch implementation for large tensors
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Reduced kernel launches through operation fusion
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Better memory efficiency through coalesced access patterns
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Scalable performance with increasing tensor sizes
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This implementation provides a production-ready Poisson NLL Loss with significant performance improvements through careful CUDA optimization, parallel reduction patterns, and numerical stability considerations.
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Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:
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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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BATCH_SIZE = 4096
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FEATURE_DIM = 512
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# --- 损失函数的参数 ---
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# log_input=True: loss = exp(input) - target * input
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# log_input=False: loss = input - target * log(input + eps)
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LOG_INPUT = True
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# full=True: 添加 Stirling's approximation
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FULL = False
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EPS = 1e-8
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.criterion = nn.PoissonNLLLoss(
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log_input=LOG_INPUT,
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full=FULL,
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eps=EPS,
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reduction='mean'
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)
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def forward(self, input_tensor: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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# 在 PyTorch 中，input_tensor 是文档中的 'input'
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return self.criterion(input_tensor, target)
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def get_inputs():
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# Input (log_input=True 时) 可以是任意实数
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input_tensor = torch.randn(BATCH_SIZE, FEATURE_DIM, dtype=torch.float32)
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# Target 在 Poisson 分布中代表计数，且在 'full' 模式下会计算 log(target)
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# 因此 target 必须是 >= 0 的。我们使用 rand 来确保
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target = torch.rand(BATCH_SIZE, FEATURE_DIM, dtype=torch.float32) * 10 # 乘以 10 以便有一些 > 1
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return [input_tensor, target]
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def get_init_inputs():
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return []
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###########################################################
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# 性能和精度验证程序
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###########################################################
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import torch
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import torch.nn as nn
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import time
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from poissonnllloss_torch import Model, get_inputs, get_init_inputs
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from poissonnllloss_cuda import ModelNew
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def run_benchmark():
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# 检查 CUDA 是否可用
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if not torch.cuda.is_available():
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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。")
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return
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else:
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device = torch.device("cuda")
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# 初始化模型
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init_inputs = get_init_inputs()
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init_inputs = [
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x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in init_inputs
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]
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inputs = get_inputs()
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inputs = [
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x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in inputs
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]
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torch_model = Model(*init_inputs).cuda()
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cuda_model = ModelNew(*init_inputs).cuda()
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torch_model.eval()
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cuda_model.eval()
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print("-------------------- 精度对齐验证 --------------------")
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with torch.no_grad():
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output_torch = torch_model(*inputs)
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output_cuda = cuda_model(*inputs)
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precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-03)
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if precision_flag:
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print("✅ 精度对齐：两个模型的输出结果非常接近。")
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else:
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print("❌ 精度不一致！")
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print("\n-------------------- 性能加速比测试 --------------------")
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num_iterations = 100
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# PyTorch 模型计时
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torch.cuda.synchronize()
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start_time = time.time()
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for _ in range(num_iterations):
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_ = torch_model(*inputs)
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torch.cuda.synchronize()
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torch_time = (time.time() - start_time) / num_iterations
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# 自定义 CUDA 内核计时
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torch.cuda.synchronize()
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start_time = time.time()
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for _ in range(num_iterations):
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_ = cuda_model(*inputs)
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torch.cuda.synchronize()
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cuda_time = (time.time() - start_time) / num_iterations
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print(f"PyTorch torch.relu 平均执行时间: {torch_time:.6f} 秒")
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print(f"自定义 CUDA 内核 平均执行时间: {cuda_time:.6f} 秒")
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speedup = 0
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if cuda_time > 0:
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speedup = torch_time / cuda_time
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print(f"加速比 (Speedup): {speedup:.2f}x")
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else:
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print("CUDA 内核执行时间为0，无法计算加速比。")
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return precision_flag, speedup
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if __name__ == "__main__":
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precision_flag, speedup = run_benchmark()
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