[WIP] Before refactor the nanovllm sparse policy.

nanovllm/kvcache/sparse/__init__.py

@@ -61,8 +61,6 @@ def create_sparse_policy(policy_type: SparsePolicyType, **kwargs) -> SparsePolic
             block_size=kwargs.get("block_size", 128),
             samples_per_chunk=kwargs.get("samples_per_chunk", 128),
             threshold=kwargs.get("threshold", 0.9),
-            use_triton=kwargs.get("use_triton", True),
-            stride=kwargs.get("stride", 8),
         )
 
     else:

nanovllm/kvcache/sparse/full_policy.py

@@ -5,8 +5,11 @@ This serves as a baseline and default policy when sparse
 attention is not needed.
 """
 
-from typing import List
+import torch
+from typing import List, Optional
+
 from .policy import SparsePolicy, PolicyContext
+from nanovllm.utils.context import get_context
 
 
 class FullAttentionPolicy(SparsePolicy):
@@ -34,5 +37,129 @@ class FullAttentionPolicy(SparsePolicy):
         """Return all blocks - no sparsity."""
         return available_blocks
 
+    def compute_prefill_attention(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        layer_id: int,
+        softmax_scale: float,
+        offload_engine,
+        current_chunk_idx: int,
+        seq,
+    ) -> torch.Tensor:
+        """
+        Compute full attention for chunked prefill.
+
+        This method handles the complete chunked prefill flow:
+        1. Load historical blocks from CPU
+        2. Compute attention to historical chunks
+        3. Compute attention to current chunk
+        4. 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
+            current_chunk_idx: Current chunk index
+            seq: ChunkedSequence
+
+        Returns:
+            Attention output [seq_len, num_heads, head_dim]
+        """
+        from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
+
+        q_batched = q.unsqueeze(0)  # [1, seq_len, num_heads, head_dim]
+        num_tokens = q.shape[0]
+        o_acc = None
+        lse_acc = None
+        compute_stream = offload_engine.compute_stream
+
+        # Step 1: Get and load historical blocks
+        cpu_block_table = seq.kvcache_manager.get_prefilled_cpu_blocks(seq)
+
+        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 2: 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 3: 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/xattn_bsa.py

@@ -1,15 +1,13 @@
 """
 XAttention Block Sparse Attention (BSA) Policy for nano-vllm.
 
-This module implements XAttention-inspired block sparse attention for chunked prefill,
-using block-level estimation to select important KV blocks for computation.
+This module implements XAttention-inspired block sparse attention for chunked prefill.
+Current implementation loads all historical blocks (FULL strategy).
 
-Reference: COMPASS/compass/src/Xattention.py
+Sparse selection to be implemented in next phase.
 """
 
-import math
 import torch
-import torch.nn.functional as F
 from typing import List, Optional, Tuple
 
 from nanovllm.kvcache.sparse.policy import SparsePolicy, PolicyContext
@@ -23,18 +21,11 @@ class XAttentionBSAPolicy(SparsePolicy):
     This policy uses block-level estimation to determine which KV blocks
     are important for the current chunk's queries, enabling sparse computation.
 
-    Key features:
-    - Double-loading design: estimate phase loads samples, compute phase loads selected blocks
-    - Block-level granularity: 128-token blocks for estimation and computation
-    - Triton kernels for efficient estimation (optional, falls back to PyTorch)
-
-    Architecture:
-        1. Estimate Phase: Load samples from all historical chunks, compute importance scores
-        2. Selection Phase: Select top chunks by cumulative attention threshold
-        3. Compute Phase: Load selected chunks fully, apply block sparse attention
+    Note: Current implementation loads all historical chunks (FULL strategy).
+    Sparse selection to be implemented in next phase.
     """
 
-    supports_prefill = True
+    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
 
@@ -43,8 +34,6 @@ class XAttentionBSAPolicy(SparsePolicy):
         block_size: int = 128,
         samples_per_chunk: int = 128,
         threshold: float = 0.9,
-        use_triton: bool = True,
-        stride: int = 8,
     ):
         """
         Initialize XAttention BSA policy.
@@ -53,457 +42,29 @@ class XAttentionBSAPolicy(SparsePolicy):
             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)
-            use_triton: Use Triton kernels for estimation (requires SM 80+)
-            stride: Stride for Q/K downsampling in estimation
         """
         self.block_size = block_size
         self.samples_per_chunk = samples_per_chunk
         self.threshold = threshold
-        self.use_triton = use_triton
-        self.stride = stride
-
-        # Check Triton availability
-        if self.use_triton:
-            try:
-                import triton
-                props = torch.cuda.get_device_properties(torch.cuda.current_device())
-                if props.major < 8:
-                    self.use_triton = False
-                    print(f"[XAttentionBSA] Triton requires SM 80+, got SM {props.major}{props.minor}. Falling back to PyTorch.")
-            except ImportError:
-                self.use_triton = False
-                print("[XAttentionBSA] Triton not available. Using PyTorch implementation.")
 
     def select_blocks(self, available_blocks: List[int], ctx: PolicyContext) -> List[int]:
         """
-        Select blocks to load from CPU (for decode compatibility, not used in prefill).
+        Select blocks to load from CPU.
 
-        For prefill, BSA handles chunk-level selection internally.
+        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
         """
-        # For prefill, we return all blocks - selection happens in sparse_prefill_attention
+        # Current: Return all blocks (FULL strategy)
+        # TODO: Implement sparse selection based on query attention estimation
         return available_blocks
 
-    def sparse_prefill_attention(
-        self,
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-        layer_id: int,
-        softmax_scale: float,
-    ) -> torch.Tensor:
-        """
-        Compute XAttention block sparse attention for current chunk.
-
-        This implements a simplified version that loads all historical chunks
-        (sparse selection to be implemented in next phase).
-
-        Args:
-            q: Query tensor [seq_len, num_heads, head_dim]
-            k: Key tensor [seq_len, num_kv_heads, head_dim] (unused, we use prefill buffer)
-            v: Value tensor [seq_len, num_kv_heads, head_dim] (unused, we use prefill buffer)
-            layer_id: Current transformer layer index
-            softmax_scale: Softmax scaling factor from attention layer
-
-        Returns:
-            Attention output [seq_len, num_heads, head_dim]
-        """
-        from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
-
-        context = get_context()
-        kvcache_manager = context.kvcache_manager
-        offload_engine = kvcache_manager.offload_engine if kvcache_manager else None
-
-        if offload_engine is None:
-            # No offload engine, use standard attention with provided k, v
-            return self._full_attention(q, k, v, causal=True)
-
-        current_chunk_idx = getattr(context, 'current_chunk_idx', 0)
-        seq = getattr(context, 'chunked_seq', None)
-        num_tokens = q.shape[0]
-
-        if seq is None:
-            # No chunked sequence, fallback to full attention on current chunk only
-            return self._full_attention(q, k, v, causal=True)
-
-        # Get prefilled CPU blocks (historical chunks)
-        cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)
-
-        q_batched = q.unsqueeze(0)  # [1, seq_len, num_heads, head_dim]
-        o_acc = None
-        lse_acc = None
-
-        # Get compute stream for all attention operations
-        compute_stream = offload_engine.compute_stream
-
-        # Step 1: Load historical chunks from CPU using slot mechanism
-        if cpu_block_table:
-            load_slots = list(range(offload_engine.num_ring_slots))
-            num_blocks = len(cpu_block_table)
-
-            # Load ALL historical blocks (not just min(num_blocks, num_slots))
-            # Use synchronous mode like standard flow when pipeline_depth=1
-            if len(load_slots) == 1:
-                # Only 1 slot available, cannot pipeline - 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):
-                        # Get KV from slot - returns [1, block_size, kv_heads, head_dim]
-                        prev_k, prev_v = offload_engine.get_kv_for_slot(slot)
-
-                        # Compute attention to historical chunk (non-causal, already processed)
-                        prev_o, prev_lse = flash_attn_with_lse(
-                            q_batched, prev_k, prev_v,
-                            softmax_scale=softmax_scale,
-                            causal=False,
-                        )
-
-                        # Merge results
-                        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)
-
-                        # Record compute done so slot can be reused
-                        offload_engine.record_slot_compute_done(slot)
-            else:
-                # Multiple slots available - use pipeline
-                num_slots = len(load_slots)
-
-                # Phase 1: Pre-load up to num_slots blocks to fill the pipeline
-                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])
-
-                # Phase 2: Main loop - compute and immediately reuse slot for next transfer
-                for block_idx in range(num_blocks):
-                    # Cycle through slots: slot[block_idx % num_slots]
-                    current_slot = load_slots[block_idx % num_slots]
-                    cpu_block_id = cpu_block_table[block_idx]
-
-                    # Wait for current slot's transfer to complete
-                    offload_engine.wait_slot_layer(current_slot)
-
-                    # Compute attention on current slot's data
-                    with torch.cuda.stream(compute_stream):
-                        # Get KV from slot - returns [1, block_size, kv_heads, head_dim]
-                        prev_k, prev_v = offload_engine.get_kv_for_slot(current_slot)
-
-                        # Compute attention to historical chunk (non-causal, already processed)
-                        prev_o, prev_lse = flash_attn_with_lse(
-                            q_batched, prev_k, prev_v,
-                            softmax_scale=softmax_scale,
-                            causal=False,
-                        )
-
-                        # Merge results
-                        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)
-
-                    # Record compute done so slot can be reused
-                    offload_engine.record_slot_compute_done(current_slot)
-
-                    # Issue next transfer if there are more blocks
-                    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 2: Compute attention to current chunk (causal mask) - use prefill buffer on compute_stream
-        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 3: Merge historical and current attention
-        with torch.cuda.stream(compute_stream):
-            if o_acc is None:
-                # No historical chunks processed
-                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 _estimate_historical_chunks(
-        self,
-        q: torch.Tensor,
-        historical_blocks: List[int],
-        layer_id: int,
-        current_chunk_idx: int,
-    ) -> Tuple[List[float], bool]:
-        """
-        Estimate importance of each historical chunk for current Q.
-
-        First load: Load samples from each historical chunk for estimation.
-
-        Args:
-            q: Current chunk queries [chunk_size, num_heads, head_dim]
-            historical_blocks: List of historical CPU block IDs
-            layer_id: Current layer index
-            current_chunk_idx: Current chunk index
-
-        Returns:
-            (List of importance scores (one per historical chunk), has_valid_data flag)
-            has_valid_data is True if at least one block had non-zero data
-        """
-        chunk_estimates = []
-        has_valid_data = False
-
-        for block_idx, cpu_block_id in enumerate(historical_blocks):
-            # First load: Load sample from this historical chunk
-            k_sample, v_sample = self._load_block_sample(
-                cpu_block_id, layer_id, self.samples_per_chunk
-            )
-
-            # Check if loaded data is valid (non-zero)
-            if k_sample.abs().max().item() > 0:
-                has_valid_data = True
-
-            # Quick estimation: Compute Q attention to this chunk's sample
-            # q [chunk_size, H, D] @ k_sample [samples, H, D]
-            # Result: Aggregate to chunk-level score
-            estimate = self._compute_chunk_estimate(q, k_sample)
-            chunk_estimates.append(estimate)
-
-        return chunk_estimates, has_valid_data
-
-    def _select_important_chunks(
-        self,
-        chunk_estimates: List[float],
-    ) -> List[int]:
-        """
-        Select important chunks based on cumulative attention threshold.
-
-        Args:
-            chunk_estimates: Importance scores for each historical chunk
-
-        Returns:
-            Indices of selected chunks
-        """
-        if not chunk_estimates:
-            return []
-
-        scores = torch.tensor(chunk_estimates, device='cpu')
-        threshold_value = scores.max() * self.threshold
-
-        # Select chunks that contribute to cumulative attention threshold
-        selected_indices = []
-        cumulative = 0.0
-        sorted_indices = torch.argsort(scores, descending=True)
-
-        for idx in sorted_indices:
-            cumulative += scores[idx].item()
-            selected_indices.append(idx.item())
-            if cumulative >= threshold_value:
-                break
-
-        return selected_indices
-
-    def _compute_with_selected_chunks(
-        self,
-        q: torch.Tensor,
-        historical_blocks: List[int],
-        selected_indices: List[int],
-        layer_id: int,
-        current_chunk_idx: int,
-    ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
-        """
-        Compute attention to selected historical chunks.
-
-        Second load: Load full data for selected chunks.
-
-        Args:
-            q: Current chunk queries
-            historical_blocks: All historical block IDs
-            selected_indices: Indices of selected blocks
-            layer_id: Current layer index
-            current_chunk_idx: Current chunk index
-
-        Returns:
-            (accumulated_output, accumulated_lse) or (None, None)
-        """
-        if not selected_indices:
-            return None, None
-
-        o_acc = None
-        lse_acc = None
-
-        for chunk_idx in selected_indices:
-            cpu_block_id = historical_blocks[chunk_idx]
-
-            # Second load: Load full data for this selected chunk
-            k_full, v_full = self._load_block_full(
-                cpu_block_id, layer_id
-            )
-
-            # Compute attention (non-causal, already processed)
-            o, lse = self._full_attention(
-                q.unsqueeze(0), k_full.unsqueeze(0),
-                v_full.unsqueeze(0), causal=False, return_lse=True
-            )
-
-            # Merge results
-            if o_acc is None:
-                o_acc, lse_acc = o.squeeze(0), lse
-            else:
-                from nanovllm.kvcache.chunked_attention import merge_attention_outputs
-                o_acc, lse_acc = merge_attention_outputs(
-                    o_acc.unsqueeze(0), lse_acc,
-                    o.unsqueeze(0), lse
-                )
-                o_acc = o_acc.squeeze(0)
-
-        return o_acc, lse_acc
-
-    def _load_block_sample(
-        self,
-        cpu_block_id: int,
-        layer_id: int,
-        num_samples: int,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Load sample tokens from a CPU block."""
-        offload_engine = get_context().kvcache_manager.offload_engine
-
-        k_sample, v_sample = offload_engine.load_block_sample_from_cpu(
-            cpu_block_id, layer_id, num_samples
-        )
-        return k_sample, v_sample
-
-    def _load_block_full(
-        self,
-        cpu_block_id: int,
-        layer_id: int,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Load full tokens from a CPU block."""
-        offload_engine = get_context().kvcache_manager.offload_engine
-        return offload_engine.load_block_full_from_cpu(
-            cpu_block_id, layer_id
-        )
-
-    def _compute_chunk_estimate(
-        self,
-        q: torch.Tensor,
-        k_sample: torch.Tensor,
-    ) -> float:
-        """
-        Compute chunk-level importance estimate.
-
-        Args:
-            q: [chunk_size, num_heads, head_dim]
-            k_sample: [num_samples, num_kv_heads, head_dim]
-
-        Returns:
-            Aggregate importance score for this chunk
-        """
-        # Expand K to match Q's head count (GQA support)
-        num_heads = q.shape[1]
-        num_kv_heads = k_sample.shape[1]
-        head_dim = q.shape[2]  # Last dimension is head_dim
-        if num_heads != num_kv_heads:
-            repeat_factor = num_heads // num_kv_heads
-            k_sample = k_sample.repeat_interleave(repeat_factor, dim=1)
-
-        # Compute attention scores: Q @ K.T with proper scaling
-        # q [chunk_size, H, D], k [samples, H, D] -> need to compute per-head attention
-        # Use scaled dot-product attention: (Q @ K.T) / sqrt(D)
-        scale = 1.0 / (head_dim ** 0.5)
-
-        # Reshape to 2D: [chunk_size * H, D] @ [D, samples * H] then aggregate
-        chunk_size = q.shape[0]
-        num_samples = k_sample.shape[0]
-
-        # Reshape for batched matmul: merge heads and seq dims
-        q_2d = q.reshape(chunk_size * num_heads, head_dim)  # [chunk_size*H, D]
-        k_2d = k_sample.reshape(num_samples * num_heads, head_dim)  # [samples*H, D]
-
-        # Compute scaled Q @ K.T: [chunk_size*H, D] @ [D, samples*H] = [chunk_size*H, samples*H]
-        attn_scores_2d = torch.matmul(q_2d, k_2d.T) * scale
-
-        # Use max absolute value as importance (captures both positive and negative attention)
-        importance = attn_scores_2d.abs().max().item()
-
-        return importance
-
-    def _full_attention(
-        self,
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-        causal: bool = False,
-        return_lse: bool = False,
-    ) -> torch.Tensor:
-        """
-        Compute full FlashAttention (fallback when sparse not applicable).
-
-        Args:
-            q: [batch_size, seq_len, num_heads, head_dim] or [seq_len, num_heads, head_dim]
-            k, v: Same shape as q
-            causal: Apply causal mask
-            return_lse: Whether to return log-sum-exp
-
-        Returns:
-            attention output [batch_size, seq_len, num_heads, head_dim] or [seq_len, num_heads, head_dim]
-        """
-        from nanovllm.kvcache.chunked_attention import flash_attn_with_lse
-
-        # Handle 3D input: add batch dimension
-        input_3d = q.dim() == 3
-        if input_3d:
-            q = q.unsqueeze(0)  # [seq_len, H, D] -> [1, seq_len, H, D]
-            k = k.unsqueeze(0)
-            v = v.unsqueeze(0)
-
-        if return_lse:
-            o, lse = flash_attn_with_lse(q, k, v, softmax_scale=self.scale, causal=causal)
-            result = (o, lse)
-        else:
-            o, _ = flash_attn_with_lse(q, k, v, softmax_scale=self.scale, causal=causal)
-            result = o
-
-        # Remove batch dimension if input was 3D
-        if input_3d:
-            if return_lse:
-                result = (result[0].squeeze(0), result[1])
-            else:
-                result = result.squeeze(0)
-
-        return result
-
-    @property
-    def scale(self) -> float:
-        """Get softmax scale factor from Attention layer."""
-        context = get_context()
-        # Get scale from current Attention layer in the model
-        if hasattr(context, 'current_attention') and context.current_attention is not None:
-            return context.current_attention.scale
-        # Fallback: try to get from model runner
-        if hasattr(context, 'model_runner') and context.model_runner is not None:
-            model_runner = context.model_runner
-            if hasattr(model_runner, 'model') and hasattr(model_runner.model, 'layers'):
-                # Get scale from first attention layer
-                first_layer = model_runner.model.layers[0]
-                if hasattr(first_layer, 'self_attn'):
-                    return first_layer.self_attn.scaling
-        # Default: 1 / sqrt(128) for Qwen models
-        return 1.0 / 128.0 ** 0.5
-
     def reset(self) -> None:
         """Reset policy state."""
         pass

nanovllm/layers/attention.py

@@ -210,22 +210,7 @@ class Attention(nn.Module):
             # Apply sparse policy if enabled
             sparse_policy = kvcache_manager.sparse_policy
 
-            # === XAttention BSA: Policy handles entire sparse prefill ===
-            # Check if policy has sparse_prefill_attention method (XAttention BSA)
-            if (sparse_policy is not None and
-                hasattr(sparse_policy, 'sparse_prefill_attention') and
-                getattr(sparse_policy, 'supports_prefill', False)):
-                # Use policy's sparse_prefill_attention method
-                # Pass softmax_scale from attention layer
-                # IMPORTANT: Don't return early - we still need to do KV offload below!
-                o = sparse_policy.sparse_prefill_attention(q, k, v, self.layer_id, self.scale)
-                # Convert back to batched format for consistency with standard flow
-                o_acc = o.unsqueeze(0)  # [seq_len, heads, dim] -> [1, seq_len, heads, dim]
-                lse_acc = None  # sparse_prefill_attention returns final output, not intermediate LSE
-                # Skip standard flow processing since we already computed attention
-                cpu_block_table = None  # Signal to skip historical chunk processing
-
-            # === Standard sparse policy (Quest, etc.) ===
+            # === All sparse policies use select_blocks interface ===
             if cpu_block_table and sparse_policy is not None:
                 num_chunks = getattr(context, 'num_chunks', current_chunk_idx + 1)
                 policy_ctx = PolicyContext(
@@ -262,8 +247,7 @@ class Attention(nn.Module):
         compute_stream = offload_engine.compute_stream if offload_engine is not None else None
 
         # Compute attention against current chunk's KV from prefill buffer (with causal mask)
-        # Skip this if XAttention BSA already computed full attention (o_acc is set, lse_acc is None)
-        needs_current_chunk_attention = (lse_acc is not None or o_acc is None)
+        needs_current_chunk_attention = True
 
         if needs_current_chunk_attention:
             if compute_stream is not None:
@@ -294,24 +278,19 @@ class Attention(nn.Module):
 
         # Merge with accumulated (all on compute_stream for consistency)
         if o_acc is None:
-            # No accumulated attention (standard flow or XAttention BSA with no historical chunks)
-            final_o = current_o if needs_current_chunk_attention else o_acc
+            # No accumulated attention (no historical chunks processed)
+            final_o = current_o
         else:
-            # Has accumulated attention (XAttention BSA with historical chunks)
-            if needs_current_chunk_attention:
-                # Need to merge historical (from XAttention BSA) with current chunk
-                if compute_stream is not None:
-                    with torch.cuda.stream(compute_stream):
-                        torch.cuda.nvtx.range_push(f"MergeAttn: L{self.layer_id}")
-                        final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
-                        torch.cuda.nvtx.range_pop()
-                else:
+            # Has accumulated attention (historical chunks processed)
+            if compute_stream is not None:
+                with torch.cuda.stream(compute_stream):
                     torch.cuda.nvtx.range_push(f"MergeAttn: L{self.layer_id}")
                     final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
                     torch.cuda.nvtx.range_pop()
             else:
-                # XAttention BSA already computed everything
-                final_o = o_acc
+                torch.cuda.nvtx.range_push(f"MergeAttn: L{self.layer_id}")
+                final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
+                torch.cuda.nvtx.range_pop()
 
         torch.cuda.nvtx.range_pop()  # ChunkedPrefill