Merge branch 'tzj/minference' of ssh://git.zijie-tian.site:2222/zijie-tian/nano-vllm into tzj/minference

nanovllm/kvcache/__init__.py

@@ -64,11 +64,24 @@ def create_kvcache_manager(config: "Config") -> KVCacheManager:
     # Create sparse policy from config enum
     # Quest is decode-only: prefill returns all blocks (query=None), decode does Top-K
     sparse_policy_type = getattr(config, 'sparse_policy', SparsePolicyType.FULL)
-    sparse_policy = create_sparse_policy(
-        sparse_policy_type,
-        topk_blocks=getattr(config, 'sparse_topk_blocks', 8),
-        threshold_blocks=getattr(config, 'sparse_threshold_blocks', 4),
-    )
+
+    # Build policy kwargs based on policy type
+    policy_kwargs = {}
+    if sparse_policy_type == SparsePolicyType.QUEST:
+        policy_kwargs = {
+            'topk_blocks': getattr(config, 'sparse_topk_blocks', 8),
+            'threshold_blocks': getattr(config, 'sparse_threshold_blocks', 4),
+        }
+    elif sparse_policy_type == SparsePolicyType.XATTN_BSA:
+        policy_kwargs = {
+            'block_size': getattr(config, 'sparse_block_size', 128),
+            'samples_per_chunk': getattr(config, 'sparse_samples_per_chunk', 128),
+            'threshold': getattr(config, 'sparse_threshold', 0.9),
+            'use_triton': getattr(config, 'sparse_use_triton', True),
+            'stride': getattr(config, 'sparse_stride', 8),
+        }
+
+    sparse_policy = create_sparse_policy(sparse_policy_type, **policy_kwargs)
 
     return HybridKVCacheManager(
         num_gpu_slots=num_gpu_blocks,

nanovllm/kvcache/offload_engine.py

@@ -905,3 +905,60 @@ class OffloadEngine:
     def wait_prefill_offload(self, layer_id: int) -> None:
         """Wait for a specific layer's prefill offload to complete."""
         self.prefill_offload_events[layer_id].synchronize()
+
+    # ========== XAttention BSA Helper Methods ==========
+
+    def load_block_sample_from_cpu(
+        self,
+        cpu_block_id: int,
+        layer_id: int,
+        num_samples: int,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Load sample tokens from a CPU block for XAttention BSA estimation.
+
+        This is used in the estimate phase of XAttention BSA to load a small
+        sample of tokens from each historical chunk for importance estimation.
+
+        Args:
+            cpu_block_id: Source CPU block ID
+            layer_id: Layer index
+            num_samples: Number of tokens to sample
+
+        Returns:
+            (k_sample, v_sample) tensors, shape: [num_samples, kv_heads, head_dim]
+        """
+        # Sample from the beginning of the block
+        k_sample = self.k_cache_cpu[
+            layer_id, cpu_block_id, :num_samples
+        ].clone().cuda()
+        v_sample = self.v_cache_cpu[
+            layer_id, cpu_block_id, :num_samples
+        ].clone().cuda()
+        return k_sample, v_sample
+
+    def load_block_full_from_cpu(
+        self,
+        cpu_block_id: int,
+        layer_id: int,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Load full tokens from a CPU block for XAttention BSA computation.
+
+        This is used in the compute phase of XAttention BSA to load the full
+        data for selected important chunks.
+
+        Args:
+            cpu_block_id: Source CPU block ID
+            layer_id: Layer index
+
+        Returns:
+            (k_full, v_full) tensors, shape: [block_size, kv_heads, head_dim]
+        """
+        k_full = self.k_cache_cpu[
+            layer_id, cpu_block_id
+        ].clone().cuda()
+        v_full = self.v_cache_cpu[
+            layer_id, cpu_block_id
+        ].clone().cuda()
+        return k_full, v_full

nanovllm/kvcache/sparse/__init__.py

@@ -23,6 +23,7 @@ from nanovllm.config import SparsePolicyType
 from nanovllm.kvcache.sparse.policy import SparsePolicy, PolicyContext
 from nanovllm.kvcache.sparse.full_policy import FullAttentionPolicy
 from nanovllm.kvcache.sparse.quest import QuestPolicy, QuestConfig, BlockMetadataManager
+from nanovllm.kvcache.sparse.xattn_bsa import XAttentionBSAPolicy
 
 
 def create_sparse_policy(policy_type: SparsePolicyType, **kwargs) -> SparsePolicy:
@@ -55,6 +56,13 @@ def create_sparse_policy(policy_type: SparsePolicyType, **kwargs) -> SparsePolic
         )
         return QuestPolicy(config)
 
+    elif policy_type == SparsePolicyType.XATTN_BSA:
+        return XAttentionBSAPolicy(
+            block_size=kwargs.get("block_size", 128),
+            samples_per_chunk=kwargs.get("samples_per_chunk", 128),
+            threshold=kwargs.get("threshold", 0.9),
+        )
+
     else:
         raise ValueError(f"Unknown policy type: {policy_type}")
 
@@ -67,5 +75,6 @@ __all__ = [
     "QuestPolicy",
     "QuestConfig",
     "BlockMetadataManager",
+    "XAttentionBSAPolicy",
     "create_sparse_policy",
 ]

nanovllm/kvcache/sparse/full_policy.py

@@ -5,8 +5,19 @@ This serves as a baseline and default policy when sparse
 attention is not needed.
 """
 
-from typing import List
+import logging
+import torch
+from typing import List, Optional, TYPE_CHECKING
+
 from .policy import SparsePolicy, PolicyContext
+from nanovllm.utils.context import get_context
+
+if TYPE_CHECKING:
+    from nanovllm.kvcache.offload_engine import OffloadEngine
+    from nanovllm.kvcache.manager import KVCacheManager
+    from nanovllm.engine.sequence import Sequence
+
+logger = logging.getLogger(__name__)
 
 
 class FullAttentionPolicy(SparsePolicy):
@@ -29,10 +40,157 @@ class FullAttentionPolicy(SparsePolicy):
     def select_blocks(
         self,
         available_blocks: List[int],
+        offload_engine: "OffloadEngine",
         ctx: PolicyContext,
     ) -> List[int]:
         """Return all blocks - no sparsity."""
         return available_blocks
 
+    def compute_chunked_attention(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        layer_id: int,
+        softmax_scale: float,
+        offload_engine: "OffloadEngine",
+        kvcache_manager: "KVCacheManager",
+        current_chunk_idx: int,
+        seq: "Sequence",
+        num_tokens: int,
+    ) -> torch.Tensor:
+        """
+        Compute full attention for chunked prefill.
+
+        This method handles the complete chunked prefill flow:
+        1. Get historical blocks
+        2. Select blocks via select_blocks
+        3. Load and compute attention to historical chunks
+        4. Compute attention to current chunk
+        5. Merge all results
+
+        Args:
+            q: Query tensor [seq_len, num_heads, head_dim]
+            k: Key tensor [seq_len, num_kv_heads, head_dim] (unused, from prefill buffer)
+            v: Value tensor [seq_len, num_kv_heads, head_dim] (unused, from prefill buffer)
+            layer_id: Current layer index
+            softmax_scale: Softmax scaling factor
+            offload_engine: OffloadEngine for loading blocks
+            kvcache_manager: KVCacheManager for block management
+            current_chunk_idx: Current chunk index
+            seq: Sequence object
+            num_tokens: Number of tokens in current chunk
+
+        Returns:
+            Attention output [seq_len, num_heads, head_dim]
+        """
+        from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
+
+        logger.debug(f"[DEBUG] FullPolicy.compute_chunked_attention called, "
+                     f"layer={layer_id}, chunk={current_chunk_idx}, num_tokens={num_tokens}")
+
+        q_batched = q.unsqueeze(0)  # [1, seq_len, num_heads, head_dim]
+        o_acc = None
+        lse_acc = None
+        compute_stream = offload_engine.compute_stream
+
+        # Step 1: Get historical blocks
+        cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)
+
+        # Step 2: Apply select_blocks to filter blocks
+        if cpu_block_table:
+            num_chunks = current_chunk_idx + 1
+            policy_ctx = PolicyContext(
+                query_chunk_idx=current_chunk_idx,
+                num_query_chunks=num_chunks,
+                layer_id=layer_id,
+                query=None,  # Prefill typically doesn't use query for selection
+                is_prefill=True,
+                block_size=kvcache_manager.block_size,
+                total_kv_len=len(cpu_block_table) * kvcache_manager.block_size,
+            )
+            cpu_block_table = self.select_blocks(cpu_block_table, offload_engine, policy_ctx)
+            logger.debug(f"[DEBUG] select_blocks: output={len(cpu_block_table)} blocks")
+
+        if cpu_block_table:
+            load_slots = list(range(offload_engine.num_ring_slots))
+            num_blocks = len(cpu_block_table)
+
+            if len(load_slots) == 1:
+                # Only 1 slot - use synchronous mode
+                slot = load_slots[0]
+                for block_idx in range(num_blocks):
+                    cpu_block_id = cpu_block_table[block_idx]
+                    offload_engine.load_to_slot_layer(slot, layer_id, cpu_block_id)
+                    offload_engine.wait_slot_layer(slot)
+
+                    with torch.cuda.stream(compute_stream):
+                        prev_k, prev_v = offload_engine.get_kv_for_slot(slot)
+                        prev_o, prev_lse = flash_attn_with_lse(
+                            q_batched, prev_k, prev_v,
+                            softmax_scale=softmax_scale,
+                            causal=False,
+                        )
+                        if o_acc is None:
+                            o_acc, lse_acc = prev_o, prev_lse
+                        else:
+                            o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
+                        offload_engine.record_slot_compute_done(slot)
+            else:
+                # Multiple slots - use pipeline
+                num_slots = len(load_slots)
+                num_preload = min(num_slots, num_blocks)
+                for i in range(num_preload):
+                    offload_engine.load_to_slot_layer(load_slots[i], layer_id, cpu_block_table[i])
+
+                for block_idx in range(num_blocks):
+                    current_slot = load_slots[block_idx % num_slots]
+                    cpu_block_id = cpu_block_table[block_idx]
+
+                    offload_engine.wait_slot_layer(current_slot)
+
+                    with torch.cuda.stream(compute_stream):
+                        prev_k, prev_v = offload_engine.get_kv_for_slot(current_slot)
+                        prev_o, prev_lse = flash_attn_with_lse(
+                            q_batched, prev_k, prev_v,
+                            softmax_scale=softmax_scale,
+                            causal=False,
+                        )
+                        offload_engine.record_slot_compute_done(current_slot)
+
+                        if o_acc is None:
+                            o_acc, lse_acc = prev_o, prev_lse
+                        else:
+                            o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
+
+                    # Issue next transfer
+                    next_block_idx = block_idx + num_slots
+                    if next_block_idx < num_blocks:
+                        next_slot = load_slots[next_block_idx % num_slots]
+                        next_cpu_block_id = cpu_block_table[next_block_idx]
+                        offload_engine.load_to_slot_layer(next_slot, layer_id, next_cpu_block_id)
+
+        # Step 4: Compute attention to current chunk (causal mask)
+        with torch.cuda.stream(compute_stream):
+            k_curr, v_curr = offload_engine.get_prefill_buffer_slice(layer_id, num_tokens)
+            current_o, current_lse = flash_attn_with_lse(
+                q_batched, k_curr, v_curr,
+                softmax_scale=softmax_scale,
+                causal=True,
+            )
+
+        # Step 5: Merge historical and current attention
+        with torch.cuda.stream(compute_stream):
+            if o_acc is None:
+                final_o = current_o
+            else:
+                final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
+
+        # Sync default stream with compute_stream before returning
+        torch.cuda.default_stream().wait_stream(compute_stream)
+
+        # Remove batch dimension: [1, seq_len, num_heads, head_dim] -> [seq_len, num_heads, head_dim]
+        return final_o.squeeze(0)
+
     def __repr__(self) -> str:
         return "FullAttentionPolicy()"

nanovllm/kvcache/sparse/policy.py

@@ -7,12 +7,17 @@ from CPU for each query chunk during chunked attention computation.
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import List, Optional, Any
+from typing import List, Optional, Any, TYPE_CHECKING
 import torch
 
 # Import SparsePolicyType from config to avoid circular imports
 from nanovllm.config import SparsePolicyType
 
+if TYPE_CHECKING:
+    from nanovllm.kvcache.offload_engine import OffloadEngine
+    from nanovllm.kvcache.manager import KVCacheManager
+    from nanovllm.engine.sequence import Sequence
+
 
 @dataclass
 class PolicyContext:
@@ -35,8 +40,8 @@ class PolicyContext:
     query: Optional[torch.Tensor]
     """
     Query tensor for current chunk.
-    Shape: [1, num_heads, head_dim] for decode, [1, seq_len, num_heads, head_dim] for prefill.
-    May be None if not available (e.g., some prefill scenarios).
+    Shape: [1, num_heads, head_dim] for decode, [seq_len, num_heads, head_dim] for prefill.
+    Available for both prefill and decode phases.
     """
 
     is_prefill: bool
@@ -107,6 +112,7 @@ class SparsePolicy(ABC):
     def select_blocks(
         self,
         available_blocks: List[int],
+        offload_engine: "OffloadEngine",
         ctx: PolicyContext,
     ) -> List[int]:
         """
@@ -120,6 +126,8 @@ class SparsePolicy(ABC):
             available_blocks: List of CPU block IDs that contain KV cache
                              from previous chunks. These are ordered by
                              their position in the sequence.
+            offload_engine: OffloadEngine for loading KV (some policies need
+                           to load KV to make selection decisions).
             ctx: PolicyContext with information about the current query
                  chunk, layer, phase (prefill/decode), etc.
 
@@ -183,5 +191,47 @@ class SparsePolicy(ABC):
         """
         pass
 
+    @abstractmethod
+    def compute_chunked_attention(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        layer_id: int,
+        softmax_scale: float,
+        offload_engine: "OffloadEngine",
+        kvcache_manager: "KVCacheManager",
+        current_chunk_idx: int,
+        seq: "Sequence",
+        num_tokens: int,
+    ) -> torch.Tensor:
+        """
+        Compute chunked prefill attention (complete flow).
+
+        This is the main entry point for prefill attention computation.
+        It defines the complete prefill flow:
+        1. Get historical blocks
+        2. Select blocks (call select_blocks)
+        3. Load and compute historical blocks via offload_engine
+        4. Get current chunk KV from offload_engine, compute attention
+        5. Merge all results
+
+        Args:
+            q: [seq_len, num_heads, head_dim] query for current chunk
+            k: [seq_len, num_kv_heads, head_dim] key for current chunk (in prefill buffer)
+            v: [seq_len, num_kv_heads, head_dim] value for current chunk (in prefill buffer)
+            layer_id: transformer layer index
+            softmax_scale: softmax scaling factor
+            offload_engine: OffloadEngine for loading blocks
+            kvcache_manager: KVCacheManager for block management
+            current_chunk_idx: current chunk index
+            seq: Sequence object
+            num_tokens: number of tokens in current chunk
+
+        Returns:
+            [seq_len, num_heads, head_dim] final attention output
+        """
+        pass
+
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}()"

nanovllm/kvcache/sparse/xattn_bsa.py

@@ -0,0 +1,70 @@
+"""
+XAttention Block Sparse Attention (BSA) Policy for nano-vllm.
+
+This module implements XAttention-inspired block sparse attention for chunked prefill.
+Current implementation loads all historical blocks (FULL strategy).
+
+Sparse selection to be implemented in next phase.
+"""
+
+import torch
+from typing import List, Optional, Tuple
+
+from nanovllm.kvcache.sparse.policy import SparsePolicy, PolicyContext
+from nanovllm.utils.context import get_context
+
+
+class XAttentionBSAPolicy(SparsePolicy):
+    """
+    XAttention Block Sparse Attention policy for chunked prefill.
+
+    This policy uses block-level estimation to determine which KV blocks
+    are important for the current chunk's queries, enabling sparse computation.
+
+    Note: Current implementation loads all historical chunks (FULL strategy).
+    Sparse selection to be implemented in next phase.
+    """
+
+    supports_prefill = False  # Uses standard select_blocks interface
+    supports_decode = False  # BSA is prefill-only
+    requires_block_selection = False  # Selection happens at chunk level, not block level
+
+    def __init__(
+        self,
+        block_size: int = 128,
+        samples_per_chunk: int = 128,
+        threshold: float = 0.9,
+    ):
+        """
+        Initialize XAttention BSA policy.
+
+        Args:
+            block_size: Number of tokens per block (default: 128)
+            samples_per_chunk: Number of tokens to sample from each historical chunk for estimation
+            threshold: Cumulative attention threshold for chunk selection (0-1)
+        """
+        self.block_size = block_size
+        self.samples_per_chunk = samples_per_chunk
+        self.threshold = threshold
+
+    def select_blocks(self, available_blocks: List[int], ctx: PolicyContext) -> List[int]:
+        """
+        Select blocks to load from CPU.
+
+        Current implementation returns all blocks (FULL strategy).
+        Sparse selection to be implemented in next phase.
+
+        Args:
+            available_blocks: List of all available CPU block IDs
+            ctx: Policy context with query info, chunk index, etc.
+
+        Returns:
+            List of selected block IDs to load
+        """
+        # Current: Return all blocks (FULL strategy)
+        # TODO: Implement sparse selection based on query attention estimation
+        return available_blocks
+
+    def reset(self) -> None:
+        """Reset policy state."""
+        pass