xc-llm-ascend/vllm_ascend/kv_offload/cpu_npu.py

import numpy as np
import torch
from vllm.attention import AttentionBackend
from vllm.logger import init_logger
from vllm.utils import is_pin_memory_available
from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
from vllm.v1.kv_offload.worker.worker import (OffloadingHandler,
                                              TransferResult, TransferSpec)

logger = init_logger(__name__)


def expand_block_ids(
    block_ids: np.ndarray,
    block_size_factor: int,
    output: np.ndarray,
    skip_count: int = 0,
):
    """
    Convert a list of block IDs to a list of matching block ids,
    assuming each block is composed of actual block_size_factor blocks.
    Outputs to output tensor.
    The first skip_count blocks will be skipped.
    Note that skip_count must be less than block_size_factor.

    For example, if block_ids = [0, 1, 3] and block_size_factor =  4,
    then it yields [0, 1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15]
    since 0 maps to [0, 1, 2, 3]
    1 maps to [4, 5, 6, 7]
    and 3 maps to [12, 13, 14, 15]
    """
    assert skip_count < block_size_factor

    first_range = np.arange(skip_count, block_size_factor)
    full_range = np.arange(0, block_size_factor)

    output_idx = 0
    for i, block_id in enumerate(block_ids):
        base_block_id = block_id * block_size_factor
        indices = first_range if i == 0 else full_range
        output_end_idx = output_idx + len(indices)
        output[output_idx:output_end_idx] = base_block_id + indices
        output_idx = output_end_idx


class CpuNpuOffloadingHandler(OffloadingHandler):

    def __init__(
        self,
        gpu_block_size: int,
        cpu_block_size: int,
        num_cpu_blocks: int,
        gpu_caches: dict[str, torch.Tensor],
        attn_backends: dict[str, type[AttentionBackend]],
    ):
        assert cpu_block_size % gpu_block_size == 0
        self.block_size_factor = cpu_block_size // gpu_block_size

        # npu streams for npu->cpu and cpu->npu
        self.d2h_stream = torch.npu.Stream()
        self.h2d_stream = torch.npu.Stream()

        # job_id -> transfer npu event
        self.transfer_events: dict[int, torch.npu.Event] = {}
        # list of npu events available for reuse
        self.events_pool: list[torch.npu.Event] = []

        pin_memory = is_pin_memory_available()

        # allocate cpu tensors
        logger.info("Allocating %d CPU tensors...", len(gpu_caches))
        self.npu_tensors: list[torch.Tensor] = []
        self.cpu_tensors: list[torch.Tensor] = []
        for layer_name, gpu_tensor in gpu_caches.items():
            self.npu_tensors.append(gpu_tensor)

            gpu_shape = gpu_tensor[0].shape

            num_blocks_idx = 0
            cpu_shape = list(gpu_shape)
            cpu_shape[num_blocks_idx] = num_cpu_blocks * self.block_size_factor

            logger.debug("Allocating CPU tensor of shape %r", cpu_shape)
            self.cpu_tensors.append((
                torch.zeros(
                    cpu_shape,
                    dtype=gpu_tensor[0].dtype,
                    device="cpu",
                    pin_memory=pin_memory,
                ),
                torch.zeros(
                    cpu_shape,
                    dtype=gpu_tensor[0].dtype,
                    device="cpu",
                    pin_memory=pin_memory,
                ),
            ))

    def transfer_async(self, job_id: int, spec: TransferSpec) -> bool:
        logger.info("start transfer_async...")
        src_spec, dst_spec = spec
        if isinstance(src_spec, CPULoadStoreSpec):
            assert isinstance(dst_spec, GPULoadStoreSpec)
            stream = self.h2d_stream
            src_tensors = self.cpu_tensors
            dst_tensors = self.npu_tensors
            src_block_size_factor = self.block_size_factor
            dst_block_size_factor = 1
        else:
            assert isinstance(src_spec, GPULoadStoreSpec)
            assert isinstance(dst_spec, CPULoadStoreSpec)
            stream = self.d2h_stream
            src_tensors = self.npu_tensors
            dst_tensors = self.cpu_tensors
            src_block_size_factor = 1
            dst_block_size_factor = self.block_size_factor

        src_blocks = src_spec.block_ids
        dst_blocks = dst_spec.block_ids
        assert src_blocks.ndim == 1
        assert dst_blocks.ndim == 1

        dst_sub_blocks_to_skip = -src_blocks.size % dst_block_size_factor
        src_sub_block_count = src_blocks.size * src_block_size_factor

        assert (
            src_sub_block_count == dst_blocks.size * dst_block_size_factor -
            dst_sub_blocks_to_skip)

        src_to_dst = np.empty((src_sub_block_count, 2), dtype=np.int64)
        expand_block_ids(src_blocks, src_block_size_factor, src_to_dst[:, 0])
        expand_block_ids(
            dst_blocks,
            dst_block_size_factor,
            src_to_dst[:, 1],
            skip_count=dst_sub_blocks_to_skip,
        )
        src_to_dst_tensor = torch.from_numpy(src_to_dst)

        event = self.events_pool.pop(
        ) if self.events_pool else torch.npu.Event()
        with torch.npu.stream(stream):
            for src_tensor, dst_tensor in zip(src_tensors, dst_tensors):
                src_key_cache, src_value_cache = src_tensor[0], src_tensor[1]
                dst_key_cache, dst_value_cache = dst_tensor[0], dst_tensor[1]

                torch.ops._C_ascend.swap_blocks(src_key_cache, dst_key_cache,
                                                src_to_dst_tensor)
                torch.ops._C_ascend.swap_blocks(src_value_cache,
                                                dst_value_cache,
                                                src_to_dst_tensor)

            event.record(stream)

        self.transfer_events[job_id] = event

        # success
        return True

    def get_finished(self) -> list[TransferResult]:
        results: list[TransferResult] = []
        for job_id, event in self.transfer_events.items():
            if event.query():
                results.append((job_id, True))
                self.events_pool.append(event)
        for job_id, _ in results:
            del self.transfer_events[job_id]
        return results