大改

vllm_npu/worker/block_table.py

@@ -0,0 +1,312 @@
+from typing import Optional, Union
+
+import numpy as np
+import torch
+from vllm.distributed import get_dcp_group
+from vllm.utils import cdiv
+
+
+class BlockTable:
+
+    def __init__(self,
+                 block_size: int,
+                 max_num_reqs: int,
+                 max_num_blocks_per_req: int,
+                 max_num_batched_tokens: int,
+                 pin_memory: bool,
+                 device: torch.device,
+                 kernel_sizes: Union[list[int], None] = None):
+        self.max_num_reqs = max_num_reqs
+        self.max_num_blocks_per_req = max_num_blocks_per_req
+        self.max_num_batched_tokens = max_num_batched_tokens
+        self.pin_memory = pin_memory
+        self.device = device
+        self.physical_block_size = block_size
+        # If kernel_sizes is None or [0], use physical block size (no splitting)
+        if kernel_sizes is None or kernel_sizes == [0]:
+            self.block_size = block_size
+            self.logical_block_size = block_size
+            self.blocks_per_phys_block = 1
+            self.use_hybrid_blocks = False
+        else:
+            # Find the first kernel size that divides physical_block_size evenly
+            selected_kernel_size = None
+            for kernel_size in kernel_sizes:
+                if kernel_size > 0 \
+                    and self.physical_block_size % kernel_size == 0:
+                    selected_kernel_size = kernel_size
+                    break
+
+            if selected_kernel_size is None:
+                raise ValueError(
+                    f"None of the kernel sizes {kernel_sizes} can divide "
+                    f"physical block size {self.physical_block_size} evenly")
+
+            self.block_size = selected_kernel_size
+            self.logical_block_size = selected_kernel_size
+            self.blocks_per_phys_block = (self.physical_block_size //
+                                          self.logical_block_size)
+            if self.blocks_per_phys_block > 1:
+                self.use_hybrid_blocks = True
+            else:
+                self.use_hybrid_blocks = False
+
+        if self.use_hybrid_blocks:
+            logical_table_size = (max_num_blocks_per_req *
+                                  self.blocks_per_phys_block)
+        else:
+            logical_table_size = max_num_blocks_per_req
+
+        self.block_table = torch.zeros(
+            (max_num_reqs, logical_table_size),
+            device=self.device,
+            dtype=torch.int32,
+        )
+        self.block_table_cpu = torch.zeros(
+            (max_num_reqs, logical_table_size),
+            device="cpu",
+            dtype=torch.int32,
+            pin_memory=pin_memory,
+        )
+        self.block_table_np = self.block_table_cpu.numpy()
+        self.num_blocks_per_row = np.zeros(max_num_reqs, dtype=np.int32)
+
+        self.slot_mapping_cpu = torch.zeros(self.max_num_batched_tokens,
+                                            dtype=torch.int64,
+                                            device="cpu",
+                                            pin_memory=self.pin_memory)
+        self.slot_mapping_np = self.slot_mapping_cpu.numpy()
+        self.slot_mapping = torch.zeros(self.max_num_batched_tokens,
+                                        dtype=torch.int64,
+                                        device=self.device)
+        try:
+            self.dcp_world_size = get_dcp_group().world_size
+            self.dcp_rank = get_dcp_group().rank_in_group
+        except AssertionError:
+            # DCP might not be initialized in testing
+            self.dcp_world_size = 1
+            self.dcp_rank = 0
+        self.kernel_sizes = kernel_sizes
+
+    def append_row(
+        self,
+        block_ids,
+        row_idx: int,
+    ) -> None:
+        if not block_ids:
+            return
+        block_ids = np.array(block_ids)
+        if self.use_hybrid_blocks:
+            block_ids = self._convert_physical_to_logical_blocks(block_ids)
+
+        num_blocks = len(block_ids)
+        start = self.num_blocks_per_row[row_idx]
+
+        self.block_table_np[row_idx, start:start + num_blocks] = block_ids
+        self.num_blocks_per_row[row_idx] += num_blocks
+
+    def add_row(self, block_ids: list[int], row_idx: int) -> None:
+        self.num_blocks_per_row[row_idx] = 0
+        self.append_row(block_ids, row_idx)
+
+    def move_row(self, src: int, tgt: int) -> None:
+        num_blocks = self.num_blocks_per_row[src]
+        self.block_table_np[tgt, :num_blocks] = self.block_table_np[
+            src, :num_blocks]
+        self.num_blocks_per_row[tgt] = num_blocks
+
+    def swap_row(self, src: int, tgt: int) -> None:
+        num_blocks_src = self.num_blocks_per_row[src]
+        num_blocks_tgt = self.num_blocks_per_row[tgt]
+        self.num_blocks_per_row[src] = num_blocks_tgt
+        self.num_blocks_per_row[tgt] = num_blocks_src
+
+        self.block_table_np[[src, tgt]] = self.block_table_np[[tgt, src]]
+
+    def compute_slot_mapping(self, req_indices: np.ndarray,
+                             positions: np.ndarray) -> None:
+        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
+        # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
+        # where K is the max_num_blocks_per_req and the block size is 2.
+        # NOTE(woosuk): We can't simply use `token_indices // block_size`
+        # here because M (max_model_len) is not necessarily divisible by
+        # block_size.
+
+        if self.dcp_world_size > 1:
+            # Note(hc): The DCP implement store kvcache with an interleave
+            # style, the kvcache for the token whose token_idx is i is
+            # always stored on the GPU whose dcp_rank equals i % cp_world_size:
+
+            # Use a "virtual block" which equals to world_size * block_size
+            # for block_table_indices calculation.
+            virtual_block_size = self.block_size * self.dcp_world_size
+
+            # IMPORTANT: In hybrid mode, positions are in logical block space,
+            # but we need to map them to the correct logical block table indices
+            logical_block_idx = positions // virtual_block_size
+
+            # Account for the expanded logical table
+            # (always needed with unified tensor)
+            # Each physical block is split into multiple logical blocks
+            # The logical table has been expanded to accommodate this
+            block_table_indices = (req_indices * self.max_num_blocks_per_req *
+                                   self.blocks_per_phys_block +
+                                   logical_block_idx)
+
+            block_numbers = self.block_table_np.ravel()[block_table_indices]
+            # Use virtual_block_size for mask calculation, which marks local
+            # tokens.
+            virtual_block_offsets = positions % virtual_block_size
+            mask = virtual_block_offsets % self.dcp_world_size == self.dcp_rank
+            # Calculate local block_offsets
+            block_offsets = virtual_block_offsets // self.dcp_world_size
+            # Calculate slot_mapping
+            slot_mapping = block_numbers * self.block_size + block_offsets
+            # Write final slots, use -1 for not-local
+            self.slot_mapping_np[:req_indices.shape[0]] = np.where(
+                mask, slot_mapping, -1)
+        else:
+            assert self.kernel_sizes is not None
+            if self.block_size == self.kernel_sizes[0]:
+                # IMPORTANT: In hybrid mode, positions are in logical block space,
+                # but we need to map them to the correct logical block table indices
+                logical_block_idx = positions // self.block_size
+
+                # Account for the expanded logical table
+                # (always needed with unified tensor)
+                # Each physical block is split into multiple logical blocks
+                # The logical table has been expanded to accommodate this
+                block_table_indices = (
+                    req_indices * self.max_num_blocks_per_req *
+                    self.blocks_per_phys_block + logical_block_idx)
+
+                block_numbers = self.block_table_np.ravel(
+                )[block_table_indices]
+                block_offsets = positions % self.block_size
+                np.add(block_numbers * self.block_size,
+                       block_offsets,
+                       out=self.slot_mapping_np[:req_indices.shape[0]])
+
+    def commit_block_table(self, num_reqs: int) -> None:
+        self.block_table[:num_reqs].copy_(self.block_table_cpu[:num_reqs],
+                                          non_blocking=True)
+
+    def commit_slot_mapping(self, num_tokens: int) -> None:
+        self.slot_mapping[:num_tokens].copy_(
+            self.slot_mapping_cpu[:num_tokens], non_blocking=True)
+
+    def clear(self) -> None:
+        self.block_table.fill_(0)
+        self.block_table_cpu.fill_(0)
+
+    def _convert_physical_to_logical_blocks(
+            self, physical_blocks: np.ndarray) -> np.ndarray:
+        """Convert physical block IDs to logical block IDs."""
+        if not self.use_hybrid_blocks:
+            return physical_blocks
+
+        # Create logical block IDs by splitting each physical block
+        logical_blocks: list[int] = []
+        for phys_block in physical_blocks:
+            # Convert physical block to multiple logical blocks
+            # Physical block 1 becomes logical blocks
+            # [1*split_ratio, 1*split_ratio+1, ...]
+            # But we need to account for the fact that block 0 is special
+            base_logical = phys_block * self.blocks_per_phys_block
+            logical_blocks.extend(
+                range(base_logical, base_logical + self.blocks_per_phys_block))
+
+        return np.array(logical_blocks, dtype=np.int32)
+
+    def get_device_tensor(self) -> torch.Tensor:
+        """Returns the device tensor of the block table."""
+        return self.block_table
+
+    def get_cpu_tensor(self) -> torch.Tensor:
+        """Returns the CPU tensor of the block table."""
+        return self.block_table_cpu
+
+    def get_numpy_array(self) -> np.ndarray:
+        """Returns the numpy array of the block table."""
+        return self.block_table_np
+
+
+class MultiGroupBlockTable:
+    """The BlockTables for each KV cache group."""
+
+    def __init__(self,
+                 max_num_reqs: int,
+                 max_model_len: int,
+                 max_num_batched_tokens: int,
+                 pin_memory: bool,
+                 device: torch.device,
+                 block_sizes: list[int],
+                 num_speculative_tokens: int = 0,
+                 kernel_sizes: Optional[list[list[int]]] = None) -> None:
+        # Note(hc): each dcp rank only store
+        # (max_model_len//dcp_world_size) tokens in kvcache,
+        # so the block_size which used for calc max_num_blocks_per_req
+        # must be multiplied by dcp_world_size.
+        try:
+            dcp_world_size = get_dcp_group().world_size
+        except AssertionError:
+            # DCP might not be initialized in testing
+            dcp_world_size = 1
+
+        if kernel_sizes is None:
+            kernel_sizes = [[0]] * len(block_sizes)
+        # Ensure kernel_sizes matches block_sizes length
+        elif len(kernel_sizes) == 1 and len(block_sizes) > 1:
+            kernel_sizes = kernel_sizes * len(block_sizes)
+        elif len(kernel_sizes) != len(block_sizes):
+            raise ValueError(
+                f"kernel_sizes length ({len(kernel_sizes)}) must match "
+                f"block_sizes length ({len(block_sizes)})")
+
+        # Use zip to pair block_sizes with kernel_sizes one-to-one
+        self.block_tables = [
+            BlockTable(
+                block_size, max_num_reqs,
+                max(cdiv(max_model_len, block_size * dcp_world_size),
+                    1 + num_speculative_tokens), max_num_batched_tokens,
+                pin_memory, device, kernel_size_list)
+            for block_size, kernel_size_list in zip(block_sizes, kernel_sizes)
+        ]
+
+    def append_row(self, block_ids: tuple[list[int], ...],
+                   row_idx: int) -> None:
+        for i, block_table in enumerate(self.block_tables):
+            block_table.append_row(block_ids[i], row_idx)
+
+    def add_row(self, block_ids: tuple[list[int], ...], row_idx: int) -> None:
+        for i, block_table in enumerate(self.block_tables):
+            block_table.add_row(block_ids[i], row_idx)
+
+    def move_row(self, src: int, tgt: int) -> None:
+        for block_table in self.block_tables:
+            block_table.move_row(src, tgt)
+
+    def swap_row(self, src: int, tgt: int) -> None:
+        for block_table in self.block_tables:
+            block_table.swap_row(src, tgt)
+
+    def compute_slot_mapping(self, req_indices: np.ndarray,
+                             positions: np.ndarray) -> None:
+        for block_table in self.block_tables:
+            block_table.compute_slot_mapping(req_indices, positions)
+
+    def commit_block_table(self, num_reqs: int) -> None:
+        for block_table in self.block_tables:
+            block_table.commit_block_table(num_reqs)
+
+    def commit_slot_mapping(self, num_tokens: int) -> None:
+        for block_table in self.block_tables:
+            block_table.commit_slot_mapping(num_tokens)
+
+    def clear(self) -> None:
+        for block_table in self.block_tables:
+            block_table.clear()
+
+    def __getitem__(self, idx: int) -> "BlockTable":
+        """Returns the BlockTable for the i-th KV cache group."""
+        return self.block_tables[idx]