xc-llm-ascend/vllm_ascend/worker/block_table.py

import numpy as np
import torch
from vllm.distributed import get_dcp_group, get_pcp_group
from vllm.utils.math_utils import cdiv
from vllm.v1.utils import CpuGpuBuffer
from vllm.v1.worker.cp_utils import get_total_cp_world_size


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: list[int] | None = None,
        cp_kv_cache_interleave_size: int = 1,
        num_speculative_tokens: int = 0,
    ):
        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

        try:
            self.pcp_world_size = get_pcp_group().world_size
            self.pcp_rank = get_pcp_group().rank_in_group if self.pcp_world_size > 1 else 0
            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.pcp_world_size = 1
            self.pcp_rank = 0

        # 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

        duplicate_size = 1
        if self.pcp_world_size * self.dcp_world_size > 1:
            duplicate_size += num_speculative_tokens
        self.block_table = self._make_buffer(max_num_reqs * duplicate_size, logical_table_size, dtype=torch.int32)
        self.num_blocks_per_row = np.zeros(max_num_reqs, dtype=np.int32)
        self.slot_mapping = self._make_buffer(
            self.max_num_batched_tokens + 2 * self.pcp_world_size * self.max_num_reqs, dtype=torch.int32
        )

        self.kernel_sizes = kernel_sizes
        self.cp_kv_cache_interleave_size = cp_kv_cache_interleave_size

    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 * self.pcp_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 % pcp_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 * self.pcp_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
            self.current_rank = self.dcp_world_size * self.pcp_rank + self.dcp_rank
            mask = (
                virtual_block_offsets // self.cp_kv_cache_interleave_size % (self.dcp_world_size * self.pcp_world_size)
                == self.current_rank
            )
            # Calculate local block_offsets
            block_offsets = (
                virtual_block_offsets
                // (self.dcp_world_size * self.pcp_world_size * self.cp_kv_cache_interleave_size)
                * self.cp_kv_cache_interleave_size
                + virtual_block_offsets % self.cp_kv_cache_interleave_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.copy_to_gpu(num_reqs)

    def commit_slot_mapping(self, num_tokens: int) -> None:
        self.slot_mapping.copy_to_gpu(num_tokens)

    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.gpu

    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

    def _make_buffer(self, *size: int | torch.SymInt, dtype: torch.dtype) -> CpuGpuBuffer:
        return CpuGpuBuffer(*size, dtype=dtype, device=self.device, pin_memory=self.pin_memory)


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,
        max_num_blocks: list[int] | None = None,
        kernel_sizes: list[list[int]] | None = None,
        cp_kv_cache_interleave_size: int = 1,
    ) -> None:
        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 block_sizes length ({len(block_sizes)})"
            )

        if max_num_blocks is 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.
            total_cp_world_size = get_total_cp_world_size()
            max_num_blocks = [cdiv(max_model_len, block_size * total_cp_world_size) for block_size in block_sizes]

        if len(max_num_blocks) != len(block_sizes):
            raise ValueError(
                f"max_num_blocks length ({len(max_num_blocks)}) must match 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_num_blocks_per_req,
                max_num_batched_tokens,
                pin_memory,
                device,
                kernel_size_list,
                cp_kv_cache_interleave_size,
                num_speculative_tokens,
            )
            for block_size, kernel_size_list, max_num_blocks_per_req in zip(block_sizes, kernel_sizes, max_num_blocks)
        ]

    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]