forked from ccf-ai-infra/GPUCodeForces
finish multilabelmarginloss #44
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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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cpp_source = """
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#include <torch/extension.h>
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#include <string>
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// 函数前向声明
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torch::Tensor multi_label_margin_loss_cuda_forward(
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const torch::Tensor& input,
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const torch::Tensor& target,
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const std::string& reduction
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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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#define BLOCK_SIZE 256
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// 设置一个合理的单个样本最大正类标签数，用于共享内存数组
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#define MAX_POSITIVE_LABELS 32
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template <typename T>
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__global__ void multi_label_margin_loss_kernel(
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T* output, // (N)
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const T* input, // (N, C)
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const long* target, // (N, C)
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const int N,
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const int C)
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{
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int sample_idx = blockIdx.x;
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if (sample_idx >= N) return;
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__shared__ long positive_indices[MAX_POSITIVE_LABELS];
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__shared__ int num_positives;
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// 线程 0 初始化共享内存计数器
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if (threadIdx.x == 0) {
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num_positives = 0;
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}
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__syncthreads();
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for (int i = threadIdx.x; i < C; i += blockDim.x) {
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long label = target[sample_idx * C + i];
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if (label != -1) {
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int index = atomicAdd(&num_positives, 1);
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if (index < MAX_POSITIVE_LABELS) {
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positive_indices[index] = label;
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}
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}
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}
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__syncthreads();
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__shared__ T sdata[BLOCK_SIZE];
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int tid = threadIdx.x;
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T my_sum = 0.0f;
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const T* input_row = input + sample_idx * C;
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for (int neg_class_idx = tid; neg_class_idx < C; neg_class_idx += blockDim.x) {
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// 检查当前类别是否为正类
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bool is_positive = false;
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for (int j = 0; j < num_positives; ++j) {
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if (positive_indices[j] == neg_class_idx) {
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is_positive = true;
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break;
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}
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}
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// 如果是负类，则计算与所有正类的损失
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if (!is_positive) {
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T x_neg = input_row[neg_class_idx];
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for (int j = 0; j < num_positives; ++j) {
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long pos_class_idx = positive_indices[j];
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T x_pos = input_row[pos_class_idx];
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T loss_term = 1.0f - (x_pos - x_neg);
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if (loss_term > 0) {
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my_sum += loss_term;
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}
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}
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}
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}
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sdata[tid] = my_sum;
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__syncthreads();
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for (int s = blockDim.x / 2; s > 0; s >>= 1) {
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if (tid < s) {
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sdata[tid] += sdata[tid + s];
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}
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__syncthreads();
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}
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if (tid == 0) {
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output[sample_idx] = sdata[0] / C;
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}
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}
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torch::Tensor multi_label_margin_loss_cuda_forward(
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const torch::Tensor& input,
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const torch::Tensor& target,
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const std::string& reduction)
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{
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TORCH_CHECK(input.is_cuda() && target.is_cuda(), "Tensors must be on CUDA");
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TORCH_CHECK(input.dim() == 2, "Input must be 2D");
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TORCH_CHECK(target.dim() == 2, "Target must be 2D");
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TORCH_CHECK(input.size(0) == target.size(0), "Batch sizes must match");
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TORCH_CHECK(input.is_contiguous() && target.is_contiguous(), "Tensors must be contiguous");
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const int N = input.size(0);
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const int C = input.size(1);
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auto options = torch::TensorOptions().device(input.device()).dtype(input.dtype());
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auto sample_losses = torch::empty({N}, options);
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dim3 grid(N);
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dim3 block(BLOCK_SIZE);
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AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "multi_label_margin_loss_kernel", ([&] {{
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multi_label_margin_loss_kernel<scalar_t><<<grid, block>>>(
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sample_losses.data_ptr<scalar_t>(),
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input.data_ptr<scalar_t>(),
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target.data_ptr<long>(),
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N, C
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);
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}}));
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if (reduction == "none") {
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return sample_losses;
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} else if (reduction == "sum") {
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return sample_losses.sum();
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} else {{ // "mean"
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return sample_losses.mean();
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}}
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}
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"""
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class ModelNew(nn.Module):
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def __init__(self, reduction='mean'):
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super(ModelNew, self).__init__()
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self.reduction = reduction
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self.op = load_inline(
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name='multi_label_margin_loss_op',
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=['multi_label_margin_loss_cuda_forward'],
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verbose=False
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)
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def forward(self, input_tensor: torch.Tensor, target_tensor: torch.Tensor) -> torch.Tensor:
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return self.op.multi_label_margin_loss_cuda_forward(
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input_tensor,
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target_tensor,
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self.reduction
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)
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@ -0,0 +1,34 @@
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import torch
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import torch.nn as nn
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import numpy as np
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BATCH_SIZE = 512
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NUM_CLASSES = 1024
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REDUCTION = 'mean'
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# 每个样本的正类标签数量范围
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MIN_LABELS = 1
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MAX_LABELS = 10
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class Model(nn.Module):
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def __init__(self, reduction='mean'):
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super(Model, self).__init__()
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self.loss_fn = nn.MultiLabelMarginLoss(reduction=reduction)
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def forward(self, input_tensor: torch.Tensor, target_tensor: torch.Tensor) -> torch.Tensor:
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return self.loss_fn(input_tensor, target_tensor)
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def get_inputs():
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input_tensor = torch.randn(BATCH_SIZE, NUM_CLASSES, dtype=torch.float32)
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# target每行包含正类索引，并用 -1 填充
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target_np = np.full((BATCH_SIZE, NUM_CLASSES), -1, dtype=np.int64)
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for i in range(BATCH_SIZE):
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num_labels = np.random.randint(MIN_LABELS, MAX_LABELS + 1)
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labels = np.random.choice(NUM_CLASSES, num_labels, replace=False)
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target_np[i, :num_labels] = labels
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target_tensor = torch.from_numpy(target_np)
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return [input_tensor.contiguous(), target_tensor.contiguous()]
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def get_init_inputs():
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return [REDUCTION]
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Write a custom CUDA kernel to optimize `torch.nn.MultiLabelMarginLoss`.
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The original operation is defined by the formula:
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`loss(x, y) = sum_{j,i} max(0, 1 - (x[y[j]] - x[i])) / (x.size(0) * x.size(1))`
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where `y[j]` are the positive class indices for a sample and `i` are the negative class indices. This is computed per sample and then reduced.
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**Problem Analysis:**
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`MultiLabelMarginLoss` is notoriously difficult to vectorize efficiently in PyTorch. The performance bottlenecks are severe:
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1. **Irregular Data Access**: Each sample has a variable number of positive labels defined in `y`, which are padded with -1. Identifying the set of positive and negative classes for each sample requires complex, non-vectorized logic (e.g., masks, loops, or boolean indexing), which is slow.
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2. **Massive Intermediate Tensors**: A naive vectorized approach would require gathering scores for positive classes and broadcasting them for subtraction against scores of negative classes. This would create huge intermediate tensors and is highly memory-inefficient.
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3. **Complex Nested Loop Logic**: The core formula is a nested loop (`for each positive class`, `for each negative class`) for every sample, which is antithetical to efficient GPU execution without a custom kernel.
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**Optimization Strategy: Fused Block-Level Parallelism with Shared Memory Caching**
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The strategy is to implement the entire complex logic within a single CUDA kernel, using a block-per-sample parallelization model.
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1. **Parallelization Model**: A grid of `N` blocks is launched, where `N` is the batch size. Each thread block is assigned to compute the total loss for one sample.
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2. **Shared Memory Caching**: For each sample (block), the kernel first collaboratively reads the list of positive class indices from the `target` tensor. These indices (and their count) are cached in **shared memory**. This makes the critical metadata for the sample instantly accessible to all threads in the block.
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3. **Fused Computation Loop**: The threads within a block then work together to iterate through all `C` possible classes. For each class `i`, a thread checks if it's a positive or negative class using the cached shared memory data.
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* If `i` is a negative class, the thread then iterates through the *positive class indices cached in shared memory*.
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* For each positive-negative pair, it calculates the hinge loss term `max(0, 1 - (x_pos - x_neg))` and accumulates it into a thread-local register. This fuses the nested loops, indexing, subtraction, and `max` operations.
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4. **Efficient Intra-Block Reduction**: Once all classes are processed, a fast parallel reduction is performed using shared memory to sum the partial results from all threads within the block into a single total loss for that sample.
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5. **Finalization**: The first thread of each block performs the final division and writes the result to the output tensor. The kernel directly produces the per-sample losses (`reduction='none'`). The final batch reduction (`'mean'` or `'sum'`) is efficiently handled by a single PyTorch call on the small 1D output tensor.
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This approach transforms the complex, memory-bound, and hard-to-vectorize PyTorch operation into a single, efficient, compute-focused CUDA kernel.
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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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```python
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import torch
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import torch.nn as nn
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import numpy as np
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BATCH_SIZE = 512
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NUM_CLASSES = 1024
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REDUCTION = 'mean'
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# 每个样本的正类标签数量范围
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MIN_LABELS = 1
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MAX_LABELS = 10
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class Model(nn.Module):
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def __init__(self, reduction='mean'):
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super(Model, self).__init__()
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self.loss_fn = nn.MultiLabelMarginLoss(reduction=reduction)
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def forward(self, input_tensor: torch.Tensor, target_tensor: torch.Tensor) -> torch.Tensor:
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return self.loss_fn(input_tensor, target_tensor)
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def get_inputs():
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input_tensor = torch.randn(BATCH_SIZE, NUM_CLASSES, dtype=torch.float32)
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# target每行包含正类索引，并用 -1 填充
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target_np = np.full((BATCH_SIZE, NUM_CLASSES), -1, dtype=np.int64)
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for i in range(BATCH_SIZE):
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num_labels = np.random.randint(MIN_LABELS, MAX_LABELS + 1)
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labels = np.random.choice(NUM_CLASSES, num_labels, replace=False)
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target_np[i, :num_labels] = labels
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target_tensor = torch.from_numpy(target_np)
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return [input_tensor.contiguous(), target_tensor.contiguous()]
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def get_init_inputs():
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return [REDUCTION]
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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 multilabelmarginloss_torch import Model, get_inputs, get_init_inputs
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from multilabelmarginloss_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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# 更严格的精度检查
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abs_diff = (output_torch - output_cuda).abs()
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max_diff = abs_diff.max().item()
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mean_diff = abs_diff.mean().item()
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print(f"最大差异: {max_diff:.6f}")
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print(f"平均差异: {mean_diff:.6f}")
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precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-05, atol=1e-05)
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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 = 1000 # 增加迭代次数以获得更准确的时间测量
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# Warm up
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for _ in range(100):
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_ = torch_model(*inputs)
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_ = cuda_model(*inputs)
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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 MultiLabelMarginLoss 平均执行时间: {torch_time:.6f} 秒")
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print(f"自定义 CUDA MultiLabelMarginLoss 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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