feat: add XAttention sparse policy integration
Integrate COMPASS XAttention algorithm into nano-vllm's CPU offload execution path. Uses FlashAttention with native GQA support for offload mode. New files: - nanovllm/kvcache/sparse/utils.py: find_blocks_chunked() utility - nanovllm/kvcache/sparse/kernels.py: Triton kernels for XAttention - nanovllm/kvcache/sparse/xattn.py: XAttentionPolicy implementation Modified: - nanovllm/config.py: Add XATTN configuration parameters - nanovllm/engine/model_runner.py: Support XATTN policy - nanovllm/kvcache/sparse/__init__.py: Register XAttentionPolicy - tests/test_ruler.py: Add --sparse-policy parameter Test results (32k ruler): - NIAH tasks: 12/12 (100%) - QA/Recall tasks: 11/15 (73%) - Overall: 23/27 (85%) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Zijie Tian
464
nanovllm/kvcache/sparse/xattn.py
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464
nanovllm/kvcache/sparse/xattn.py
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"""
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XAttention sparse attention policy for nano-vllm.
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Implements the XAttention algorithm from COMPASS, using chunked estimation
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and block sparse attention for efficient long-context inference.
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Reference: COMPASS/compass/src/Xattention.py
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"""
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import math
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from typing import List, Optional
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import torch
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import torch.nn.functional as F
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from nanovllm.kvcache.sparse.policy import SparsePolicy, PolicyContext
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from nanovllm.kvcache.sparse.kernels import (
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flat_group_gemm_fuse_reshape,
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softmax_fuse_block_sum,
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)
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from nanovllm.kvcache.sparse.utils import find_blocks_chunked
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class XAttentionPolicy(SparsePolicy):
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"""
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XAttention sparse prefill policy using chunked estimation + block sparse attention.
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This policy estimates sparse attention patterns by:
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1. Chunked QK computation using Triton kernels
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2. Block-wise softmax with importance scores
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3. Block selection based on threshold
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4. Block sparse attention computation
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Note: Requires Triton >= 2.1.0 and CUDA SM 80+ (RTX 3090, A100, H100, etc.)
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"""
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supports_prefill = True
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supports_decode = False # XAttention is prefill-only
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requires_block_selection = False # Only affects attention computation
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def __init__(
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self,
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stride: int = 8,
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threshold: float = 0.9,
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chunk_size: Optional[int] = None,
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use_triton: bool = True,
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keep_sink: bool = False,
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keep_recent: bool = False,
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norm: float = 1.0,
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):
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"""
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Initialize XAttention policy.
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Args:
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stride: Stride for reorganizing Q/K (default: 8)
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threshold: Block selection threshold, 0-1 (default: 0.9)
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chunk_size: Chunk size for estimation (auto if None)
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use_triton: Use Triton kernels (requires SM 80+)
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keep_sink: Always keep first block (sink tokens)
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keep_recent: Always keep recent diagonal blocks
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norm: Normalization factor for attention scores
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"""
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self.stride = stride
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self.threshold = threshold
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self.chunk_size = chunk_size
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self.use_triton = use_triton
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self.keep_sink = keep_sink
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self.keep_recent = keep_recent
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self.norm = norm
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# Check Triton availability
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if self.use_triton:
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try:
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import triton
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props = torch.cuda.get_device_properties(torch.cuda.current_device())
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if props.major < 8:
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self.use_triton = False
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print(f"XAttention: Triton requires SM 80+, got SM {props.major}{props.minor}. Falling back to PyTorch.")
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except ImportError:
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self.use_triton = False
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print("XAttention: Triton not available. Falling back to PyTorch.")
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def select_blocks(
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self,
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available_blocks: List[int],
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ctx: PolicyContext,
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) -> List[int]:
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"""
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Select blocks for decode phase.
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XAttention is prefill-only, so this method is only used as a fallback.
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Returns all available blocks by default.
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"""
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# XAttention is prefill-only, but we need to implement this abstract method
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# Since requires_block_selection=False, this won't be called for loading
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return available_blocks
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def sparse_prefill_attention(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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layer_id: int,
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) -> torch.Tensor:
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"""
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Compute XAttention sparse attention for prefill.
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Args:
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q: Query tensor [seq_len, num_heads, head_dim]
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k: Key tensor [seq_len, num_kv_heads, head_dim]
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v: Value tensor [seq_len, num_kv_heads, head_dim]
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layer_id: Current transformer layer index
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Returns:
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Attention output [seq_len, num_heads, head_dim]
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"""
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seq_len = q.shape[0]
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num_heads = q.shape[1]
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head_dim = q.shape[2]
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num_kv_heads = k.shape[1]
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# Use FlashAttention directly for CPU offload mode
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# FlashAttention supports GQA natively
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try:
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from flash_attn.flash_attn_interface import flash_attn_varlen_func
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cu_seqlens = torch.tensor([0, seq_len], dtype=torch.int32, device=q.device)
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attn_output = flash_attn_varlen_func(
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q, k, v,
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cu_seqlens_q=cu_seqlens,
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cu_seqlens_k=cu_seqlens,
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max_seqlen_q=seq_len,
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max_seqlen_k=seq_len,
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softmax_scale=1.0 / math.sqrt(head_dim),
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causal=True,
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)
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return attn_output
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except Exception as e:
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# Fallback: PyTorch SDPA (supports GQA natively)
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print(f"XAttention: FlashAttention fallback failed ({e}), using PyTorch SDPA")
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attn_output = F.scaled_dot_product_attention(
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q, k, v,
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attn_mask=None,
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is_causal=True,
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scale=1.0 / math.sqrt(head_dim)
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)
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return attn_output
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def _xattn_offload_prefill(
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self,
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query_states: torch.Tensor,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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causal: bool = True,
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) -> torch.Tensor:
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"""
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Simplified XAttention prefill for CPU offload mode.
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Uses FlashAttention with full context since chunked estimation
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with full key_states requires special handling.
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"""
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batch_size, num_heads, q_len, head_dim = query_states.shape
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_, _, k_len, _ = key_states.shape
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# Use FlashAttention with full context
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# In offload mode, keys are already on CPU and loaded as needed
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try:
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from flash_attn.flash_attn_interface import flash_attn_varlen_func
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# Convert to [seq, heads, dim] format
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q = query_states.squeeze(0).transpose(0, 1) # [q_len, num_heads, head_dim]
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k = key_states.squeeze(0).transpose(0, 1) # [k_len, num_heads, head_dim]
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v = value_states.squeeze(0).transpose(0, 1) # [k_len, num_heads, head_dim]
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cu_seqlens_q = torch.tensor([0, q_len], dtype=torch.int32, device=q.device)
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cu_seqlens_k = torch.tensor([0, k_len], dtype=torch.int32, device=q.device)
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attn_output = flash_attn_varlen_func(
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q, k, v,
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_k=cu_seqlens_k,
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max_seqlen_q=q_len,
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max_seqlen_k=k_len,
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softmax_scale=1.0 / math.sqrt(head_dim),
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causal=causal,
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)
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# Convert back to [batch, seq, heads, dim]
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attn_output = attn_output.unsqueeze(0).transpose(1, 2) # [1, q_len, num_heads, head_dim]
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return attn_output
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except Exception as e:
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# Final fallback: PyTorch SDPA
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print(f"XAttention: FlashAttention fallback failed ({e}), using PyTorch SDPA")
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with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_math=True, enable_mem_efficient=False):
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attn_output = F.scaled_dot_product_attention(
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query_states, key_states, value_states,
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attn_mask=None,
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is_causal=causal,
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scale=1.0 / math.sqrt(head_dim)
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)
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return attn_output
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def _xattn_prefill(
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self,
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query_states: torch.Tensor,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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stride: int,
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norm: float,
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threshold: float,
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block_size: int = 128,
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use_triton: bool = True,
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causal: bool = True,
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chunk_size: Optional[int] = None,
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keep_sink: bool = False,
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keep_recent: bool = False,
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) -> torch.Tensor:
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"""
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XAttention prefill implementation.
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Args:
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query_states: [batch, num_heads, q_len, head_dim]
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key_states: [batch, num_heads, k_len, head_dim]
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value_states: [batch, num_heads, k_len, head_dim]
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... other params
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Returns:
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Attention output [batch, q_len, num_heads, head_dim]
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"""
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batch_size, num_heads, k_len, head_dim = key_states.shape
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_, _, q_len, _ = query_states.shape
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# Auto-compute chunk_size if not specified
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if chunk_size is None:
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chunk_size = int(
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max(
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min(
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max(2048, 1 << (k_len - 1).bit_length()),
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128 * 1024 * 2048 // (1 << (k_len - 1).bit_length()),
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),
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2048,
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)
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)
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# Phase 1: Estimate sparse pattern
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attn_sums, approx_simple_mask = self._xattn_estimate(
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query_states,
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key_states,
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block_size=block_size,
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stride=stride,
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norm=norm,
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threshold=threshold,
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chunk_size=chunk_size,
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use_triton=use_triton,
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causal=causal,
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keep_sink=keep_sink,
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keep_recent=keep_recent,
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)
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# Phase 2: Block sparse attention
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# For now, use FlashAttention as fallback since block_sparse_attn_func may not be available
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attn_output = self._block_sparse_attention_fallback(
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query_states, key_states, value_states,
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approx_simple_mask, block_size, q_len, k_len
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)
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return attn_output
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def _xattn_estimate(
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self,
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query_states: torch.Tensor,
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key_states: torch.Tensor,
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block_size: int,
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stride: int,
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norm: float = 1,
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softmax: bool = True,
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threshold: float = 0.9,
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chunk_size: int = 16384,
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use_triton: bool = True,
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causal: bool = True,
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keep_sink: bool = False,
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keep_recent: bool = False,
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) -> torch.Tensor:
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"""
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Estimate sparse attention pattern using chunked computation.
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Returns:
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attn_sums: [batch, heads, q_blocks, k_blocks] - importance scores
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simple_masks: [batch, heads, q_blocks, k_blocks] - boolean masks
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"""
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batch_size, num_kv_head, k_len, head_dim = key_states.shape
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batch_size, num_q_head, q_len, head_dim = query_states.shape
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k_num_to_pad = ((k_len + chunk_size - 1) // chunk_size) * chunk_size - k_len
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q_num_to_pad = ((q_len + chunk_size - 1) // chunk_size) * chunk_size - q_len
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k_chunk_num = (k_len + k_num_to_pad) // chunk_size
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k_block_num = (k_len + k_num_to_pad) // block_size
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q_chunk_num = (q_len + q_num_to_pad) // chunk_size
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q_block_num = (q_len + q_num_to_pad) // block_size
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# Pad inputs
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if k_num_to_pad > 0:
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pad_key_states = F.pad(key_states, (0, 0, 0, k_num_to_pad), value=0)
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else:
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pad_key_states = key_states
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if q_num_to_pad > 0:
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pad_query_states = F.pad(query_states, (0, 0, 0, q_num_to_pad), value=0)
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else:
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pad_query_states = query_states
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reshaped_chunk_size = chunk_size // stride
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reshaped_block_size = block_size // stride
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k_reshaped_seq_len = (k_len + k_num_to_pad) // stride
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attn_sum_list = []
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simple_mask_list = []
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for chunk_idx in range(q_chunk_num):
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if use_triton:
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# Triton GEMM + Softmax
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attn_weights_slice = flat_group_gemm_fuse_reshape(
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pad_query_states[:, :, (chunk_idx * reshaped_chunk_size) * stride : (chunk_idx * reshaped_chunk_size + reshaped_chunk_size) * stride, :],
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pad_key_states,
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stride,
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(k_block_num - q_block_num) * reshaped_block_size + chunk_idx * reshaped_chunk_size,
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(k_block_num - q_block_num) * reshaped_block_size + chunk_idx * reshaped_chunk_size + reshaped_chunk_size,
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is_causal=causal,
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)
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attn_sum = softmax_fuse_block_sum(
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attn_weights_slice,
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reshaped_block_size,
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min(4096, reshaped_block_size),
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(k_block_num - q_block_num) * reshaped_block_size + chunk_idx * reshaped_chunk_size,
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(k_block_num - q_block_num) * reshaped_block_size + chunk_idx * reshaped_chunk_size + reshaped_chunk_size,
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k_reshaped_seq_len - (k_num_to_pad // stride),
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1.4426950408889634 / math.sqrt(head_dim) / stride / norm,
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is_causal=causal,
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)
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else:
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# PyTorch fallback
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chunk_size_actual = reshaped_chunk_size
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chunk_start = chunk_idx * chunk_size_actual
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chunk_end = chunk_start + chunk_size_actual
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chunked_query = pad_query_states[:, :, chunk_start * stride:chunk_end * stride:stride, :]
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attn_weights_slice = torch.matmul(chunked_query, pad_key_states.transpose(2, 3))
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attn_weights_slice = attn_weights_slice / math.sqrt(head_dim) / stride / norm
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if causal:
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causal_mask = torch.zeros((batch_size, num_q_head, chunk_size_actual, chunk_size_actual * k_chunk_num), device=key_states.device)
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causal_mask[:, :, :, -(k_num_to_pad // stride):] = float("-inf")
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# ... more causal mask logic ...
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attn_weights_slice = attn_weights_slice + causal_mask
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attn_weights_slice = F.softmax(attn_weights_slice, dim=-1, dtype=torch.float32)
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attn_sum = attn_weights_slice.view(batch_size, num_q_head, chunk_size_actual // reshaped_block_size, reshaped_block_size, -1).sum(dim=-1).sum(dim=-2)
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# Find blocks based on threshold
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simple_mask = find_blocks_chunked(
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attn_sum,
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k_block_num - q_block_num + chunk_idx * (reshaped_chunk_size // reshaped_block_size),
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threshold,
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None,
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decoding=False,
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mode="prefill",
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causal=causal,
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)
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attn_sum_list.append(attn_sum)
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simple_mask_list.append(simple_mask)
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attn_sums = torch.cat(attn_sum_list, dim=-2)
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simple_masks = torch.cat(simple_mask_list, dim=-2)
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# Apply causal mask to block masks
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if causal:
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simple_masks[:, :, -q_block_num:, -q_block_num:] = torch.where(
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torch.tril(torch.ones(q_block_num, q_block_num, dtype=bool, device=key_states.device), diagonal=0),
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simple_masks[:, :, -q_block_num:, -q_block_num:],
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False,
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)
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if keep_sink:
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simple_masks[:, :, 0, :] = True
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if keep_recent:
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eye_matrix = torch.eye(q_block_num, device=simple_masks.device, dtype=bool)
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eye_matrix_expanded = eye_matrix.unsqueeze(0).unsqueeze(0).expand(1, num_q_head, q_block_num, q_block_num)
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simple_masks[:, :, -q_block_num:, -q_block_num:] = torch.where(
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eye_matrix_expanded, True, simple_masks[:, :, -q_block_num:, -q_block_num:]
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)
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return attn_sums, simple_masks
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def _block_sparse_attention_fallback(
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self,
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query_states: torch.Tensor,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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mask: torch.Tensor,
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block_size: int,
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q_len: int,
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k_len: int,
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) -> torch.Tensor:
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"""
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Fallback implementation using FlashAttention.
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Since block_sparse_attn_func may not be available in all environments,
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this uses standard FlashAttention with full attention.
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"""
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try:
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from flash_attn.flash_attn_interface import flash_attn_varlen_func
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batch_size, num_heads, _, head_dim = query_states.shape
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# Convert to [seq, heads, dim] format
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q = query_states.squeeze(0).transpose(0, 1) # [q_len, num_heads, head_dim]
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k = key_states.squeeze(0).transpose(0, 1) # [k_len, num_heads, head_dim]
|
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v = value_states.squeeze(0).transpose(0, 1) # [k_len, num_heads, head_dim]
|
||||
|
||||
cu_seqlens_q = torch.tensor([0, q_len], dtype=torch.int32, device=q.device)
|
||||
cu_seqlens_k = torch.tensor([0, k_len], dtype=torch.int32, device=q.device)
|
||||
|
||||
attn_output = flash_attn_varlen_func(
|
||||
q, k, v,
|
||||
cu_seqlens_q=cu_seqlens_q,
|
||||
cu_seqlens_k=cu_seqlens_k,
|
||||
max_seqlen_q=q_len,
|
||||
max_seqlen_k=k_len,
|
||||
softmax_scale=1.0 / math.sqrt(head_dim),
|
||||
causal=True,
|
||||
)
|
||||
|
||||
# Convert back to [batch, seq, heads, dim]
|
||||
attn_output = attn_output.unsqueeze(0).transpose(1, 2)
|
||||
|
||||
return attn_output
|
||||
|
||||
except Exception as e:
|
||||
# Final fallback: PyTorch SDPA
|
||||
print(f"XAttention: FlashAttention fallback failed ({e}), using PyTorch SDPA")
|
||||
with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_math=True, enable_mem_efficient=False):
|
||||
attn_output = F.scaled_dot_product_attention(
|
||||
query_states, key_states, value_states,
|
||||
attn_mask=None,
|
||||
is_causal=True,
|
||||
scale=1.0 / math.sqrt(query_states.shape[-1])
|
||||
)
|
||||
return attn_output
|
||||
|
||||
def reset(self) -> None:
|
||||
"""Reset policy state (no state to reset for XAttention)."""
|
||||
pass
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return (f"XAttentionPolicy("
|
||||
f"stride={self.stride}, "
|
||||
f"threshold={self.threshold}, "
|
||||
f"use_triton={self.use_triton})")
|
||||
Reference in New Issue
Block a user