5acd5558d6
Implement three-phase KV chunking for sparse attention estimation: 1. softmax_compute_partial_stats: compute (m, l) per KV chunk 2. merge_softmax_stats: merge partial stats on host 3. softmax_normalize_and_block_sum: normalize with global stats This allows computing sparse attention masks without storing full raw attention scores in GPU memory, reducing peak memory usage from O(q_len * k_full_len) to O(q_len * k_chunk_len). Key changes: - Add softmax_partial_stats_kernel with causal mask support - Add softmax_normalize_block_sum_kernel with kv_offset parameter - Add Python wrappers for new kernels - Update test script to validate KV chunking alignment - Add documentation for the new kernels Test results show perfect alignment with xattn_estimate API: - Density difference: 0.000000 - Mask difference: 0.0044% Generated with [Claude Code](https://claude.ai/code) via [Happy](https://happy.engineering) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Happy <yesreply@happy.engineering>
236 lines
6.9 KiB
Markdown
236 lines
6.9 KiB
Markdown
# XAttention KV Chunking Kernels
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## 概述
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本文档描述了支持 KV 维度分 chunk 的 softmax kernels 实现。这些 kernels 允许在 CPU offload 场景下，沿 KV 维度分块计算 sparse attention estimation，而不需要在 GPU 上保存完整的 raw attention scores。
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## 背景
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原始的 `softmax_fuse_block_sum` kernel 需要完整的 K 序列来计算正确的 softmax 归一化分母：
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```
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softmax(x_i) = exp(x_i) / Σ_j exp(x_j)
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```
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如果只有部分 K (KV chunk)，分母 `Σ_j exp(x_j)` 不完整，导致归一化错误。
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## 解决方案：三阶段计算
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通过将 softmax 计算拆分为三个阶段，实现正确的 KV chunking：
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### 阶段 1: `softmax_compute_partial_stats`
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计算每个 KV chunk 的 partial statistics：
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- `m_partial`: 该 chunk 内的最大值 (per query row)
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- `l_partial`: 该 chunk 内的 partial sum = Σ exp(x - m_partial)
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```python
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m_partial, l_partial = softmax_compute_partial_stats(
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attn_weights_kv, # [batch, heads, q_len, k_chunk_len]
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reshaped_block_size,
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segment_size,
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scale,
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chunk_start=chunk_start,
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kv_offset=kv_offset, # KV chunk 在完整序列中的偏移
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is_causal=True,
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)
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# 输出: m_partial, l_partial 形状为 [batch, heads, q_len]
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```
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### 阶段 2: `merge_softmax_stats`
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Host 端合并所有 KV chunks 的 statistics：
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```python
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m_global, l_global = merge_softmax_stats(m_chunks, l_chunks)
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```
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合并公式 (Online Softmax):
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```
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m_new = max(m_global, m_chunk)
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l_new = l_global * exp(m_global - m_new) + l_chunk * exp(m_chunk - m_new)
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```
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### 阶段 3: `softmax_normalize_and_block_sum`
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使用全局 statistics 归一化并计算 block sums：
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```python
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attn_sum_kv = softmax_normalize_and_block_sum(
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attn_weights_kv, # [batch, heads, q_len, k_chunk_len]
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m_global, # [batch, heads, q_len]
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l_global, # [batch, heads, q_len]
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reshaped_block_size,
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segment_size,
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chunk_start=chunk_start,
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real_q_len=real_q_len,
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scale=scale,
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kv_offset=kv_offset,
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is_causal=True,
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)
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# 输出: [batch, heads, q_blocks, k_chunk_blocks]
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```
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## 数学等价性证明
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原始 softmax 计算 (完整 K):
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```
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softmax(x_i) = exp(x_i - m) / Σ_j exp(x_j - m)
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```
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分 KV chunk 计算:
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```
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Chunk 0: m_0 = max(x[0:N/2]), l_0 = Σ exp(x[0:N/2] - m_0)
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Chunk 1: m_1 = max(x[N/2:N]), l_1 = Σ exp(x[N/2:N] - m_1)
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合并:
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m_global = max(m_0, m_1)
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l_global = l_0 * exp(m_0 - m_global) + l_1 * exp(m_1 - m_global)
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= Σ exp(x[0:N] - m_global) # 等于全局 sum
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归一化:
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softmax(x_i) = exp(x_i - m_global) / l_global # 正确!
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```
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## Causal Mask 处理
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两个 kernel 都正确处理了 causal attention：
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1. **`softmax_partial_stats_kernel`**: 通过 `kv_offset` 参数确定当前 KV chunk 在完整序列中的位置，正确计算 causal boundary
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2. **`softmax_normalize_block_sum_kernel`**: 同样使用 `kv_offset`，对 causal boundary 之后的位置输出 0
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## API 参考
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### `softmax_compute_partial_stats`
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```python
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def softmax_compute_partial_stats(
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attn_weights_slice: torch.Tensor, # [batch, heads, q_len, k_chunk_len]
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reshaped_block_size: int,
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segment_size: int,
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scale: float,
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chunk_start: int = 0, # Q chunk 起始位置 (reshaped space)
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kv_offset: int = 0, # KV chunk 偏移 (reshaped space)
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is_causal: bool = False,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""返回 (m, l) partial stats"""
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```
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### `merge_softmax_stats`
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```python
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def merge_softmax_stats(
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m_chunks: list, # List of [batch, heads, q_len] tensors
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l_chunks: list, # List of [batch, heads, q_len] tensors
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""返回 (m_global, l_global)"""
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```
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### `softmax_normalize_and_block_sum`
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```python
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def softmax_normalize_and_block_sum(
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attn_weights_slice: torch.Tensor, # [batch, heads, q_len, k_chunk_len]
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m_global: torch.Tensor, # [batch, heads, q_len]
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l_global: torch.Tensor, # [batch, heads, q_len]
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reshaped_block_size: int,
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segment_size: int,
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chunk_start: int,
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real_q_len: int,
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scale: float,
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kv_offset: int = 0,
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is_causal: bool = False,
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) -> torch.Tensor:
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"""返回 block sums [batch, heads, q_blocks, k_chunk_blocks]"""
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```
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## 使用示例
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```python
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from nanovllm.ops.xattn import (
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flat_group_gemm_fuse_reshape,
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softmax_compute_partial_stats,
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softmax_normalize_and_block_sum,
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merge_softmax_stats,
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find_blocks_chunked,
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)
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# 对每个 Q chunk
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for q_chunk_idx in range(q_chunk_num):
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Q_chunk = Q_padded[:, :, q_start:q_end, :]
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# 阶段 1: 每个 KV chunk 计算 partial stats
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m_chunks, l_chunks = [], []
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attn_weights_chunks = []
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for kv_chunk_idx in range(kv_chunk_num):
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K_chunk = K_padded[:, :, kv_start:kv_end, :]
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kv_offset = kv_chunk_idx * kv_chunk_size // STRIDE
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# 计算 raw scores
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attn_weights = flat_group_gemm_fuse_reshape(
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Q_chunk, K_chunk, STRIDE,
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chunk_start=chunk_start,
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chunk_end=chunk_end,
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is_causal=False, # K 不完整
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)
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attn_weights_chunks.append(attn_weights)
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# 计算 partial stats
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m, l = softmax_compute_partial_stats(
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attn_weights, block_size, segment_size, scale,
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chunk_start=chunk_start,
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kv_offset=kv_offset,
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is_causal=True,
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)
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m_chunks.append(m)
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l_chunks.append(l)
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# 阶段 2: 合并 stats
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m_global, l_global = merge_softmax_stats(m_chunks, l_chunks)
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# 阶段 3: 归一化并计算 block sums
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block_sums_list = []
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for kv_chunk_idx, attn_weights in enumerate(attn_weights_chunks):
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kv_offset = kv_chunk_idx * kv_chunk_size // STRIDE
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block_sums = softmax_normalize_and_block_sum(
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attn_weights, m_global, l_global,
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block_size, segment_size, chunk_start, real_q_len, scale,
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kv_offset=kv_offset,
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is_causal=True,
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)
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block_sums_list.append(block_sums)
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# 拼接并选择 blocks
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attn_sum = torch.cat(block_sums_list, dim=-1)
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mask = find_blocks_chunked(attn_sum, ...)
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```
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## 性能对比
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| 方面 | 原始实现 | KV Chunking 实现 |
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|------|---------|-----------------|
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| Kernel 数量 | 1 | 2 (stats + normalize) |
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| Raw scores 读取次数 | 2 | 2 |
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| 额外内存 | 0 | O(batch × heads × q_len × 2) for (m, l) |
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| Host 计算 | 无 | merge stats (轻量) |
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| **峰值显存** | O(q_len × k_full_len) | **O(q_len × k_chunk_len)** |
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## 验证
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测试脚本 `tests/test_xattn_estimate_alignment.py` 验证了 KV chunking 实现与原始 `xattn_estimate` API 的一致性：
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```
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| 方法 | density | 与 API 差异 | Mask 差异 |
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|------|---------|-------------|-----------|
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| xattn_estimate API | 0.159023 | - | - |
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| KV chunking | 0.159023 | 0.000000 | 0.0044% |
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```
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## 相关文件
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- `nanovllm/ops/xattn.py`: Kernel 实现
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- `tests/test_xattn_estimate_alignment.py`: 验证测试
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- `docs/xattn_kernels_guide.md`: 原始 kernel 文档
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