vllm/vllm/worker/cache_engine.py

# SPDX-License-Identifier: Apache-2.0
"""CacheEngine class for managing the KV cache."""
from typing import List

import numpy as np
import torch

from vllm import envs
from vllm.attention import get_attn_backend
from vllm.config import CacheConfig, DeviceConfig, ModelConfig, ParallelConfig
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, LayerBlockType,
                        align_to_256bytes, get_dtype_size,
                        is_pin_memory_available)

logger = init_logger(__name__)


class CacheEngine:
    """Manages the KV cache.

    This class is responsible for initializing and managing the GPU and CPU KV
    caches. It also provides methods for performing KV cache operations, such
    as swapping and copying.
    """

    def __init__(
        self,
        cache_config: CacheConfig,
        model_config: ModelConfig,
        parallel_config: ParallelConfig,
        device_config: DeviceConfig,
    ) -> None:
        self.cache_config = cache_config
        self.model_config = model_config
        self.parallel_config = parallel_config
        self.device_config = device_config

        self.head_size = model_config.get_head_size()
        # Models like Jamba, have mixed typed layers, E.g Mamba
        self.num_attention_layers = model_config.get_num_layers_by_block_type(
            parallel_config, LayerBlockType.attention)
        self.num_kv_heads = model_config.get_num_kv_heads(parallel_config)
        self.align_cache = self._align_cache(model_config)

        self.block_size = cache_config.block_size
        self.num_gpu_blocks = cache_config.num_gpu_blocks
        if self.num_gpu_blocks:
            self.num_gpu_blocks //= parallel_config.pipeline_parallel_size
        self.num_cpu_blocks = cache_config.num_cpu_blocks
        if self.num_cpu_blocks:
            self.num_cpu_blocks //= parallel_config.pipeline_parallel_size

        if cache_config.cache_dtype == "auto":
            self.dtype = model_config.dtype
        else:
            self.dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]

        # Get attention backend.
        self.attn_backend = get_attn_backend(self.head_size,
                                             model_config.dtype,
                                             cache_config.cache_dtype,
                                             self.block_size,
                                             model_config.is_attention_free,
                                             use_mla=model_config.use_mla)

        # Initialize the cache.
        self.gpu_cache = self._allocate_kv_cache(
            self.num_gpu_blocks, self.device_config.device_type)
        self.cpu_cache = self._allocate_kv_cache(self.num_cpu_blocks, "cpu")

    def _allocate_kv_cache(
        self,
        num_blocks: int,
        device: str,
    ) -> List[torch.Tensor]:
        """Allocates KV cache on the specified device."""
        kv_cache_shape = self.attn_backend.get_kv_cache_shape(
            num_blocks, self.block_size, self.num_kv_heads, self.head_size)
        pin_memory = is_pin_memory_available() if device == "cpu" else False
        kv_cache: List[torch.Tensor] = []

        # Align entries so they are 256 byte aligned for better performance
        # Primarily targets MLA as this typically only ends up having entries
        # be 128 byte aligned.
        if self.align_cache:
            # We assume the cache shape is:
            #    (TOTAL_PAGES, PAGE_SIZE, entry_shape...)
            # NOTE this assumption currently only holds for MLA so we only apply
            # this optimization when `use_mla` is true
            entry_shape = kv_cache_shape[2:]
            entry_size = np.prod(entry_shape)
            alloc_entry_size = align_to_256bytes(entry_size, self.dtype)
            alloc_shape = (*kv_cache_shape[:2], alloc_entry_size)
        else:
            alloc_shape = kv_cache_shape

        for _ in range(self.num_attention_layers):
            # null block in CpuGpuBlockAllocator requires at least that
            # block to be zeroed-out.
            # We zero-out everything for simplicity.
            layer_kv_cache = torch.zeros(alloc_shape,
                                         dtype=self.dtype,
                                         pin_memory=pin_memory,
                                         device=device)

            # If we allocated with padding for alignment reasons truncate the
            # shape while preserving the aligned stride
            if self.align_cache:
                layer_kv_cache = layer_kv_cache[..., :entry_size]

            # view back to (TOTAL_PAGES, PAGE_SIZE, entry_shape...) for cases
            # when entry_shape is higher than 1D
            kv_cache.append(layer_kv_cache.view(kv_cache_shape))
        return kv_cache

    def swap_in(self, src_to_dst: torch.Tensor) -> None:
        for i in range(self.num_attention_layers):
            self.attn_backend.swap_blocks(self.cpu_cache[i], self.gpu_cache[i],
                                          src_to_dst)

    def swap_out(self, src_to_dst: torch.Tensor) -> None:
        for i in range(self.num_attention_layers):
            self.attn_backend.swap_blocks(self.gpu_cache[i], self.cpu_cache[i],
                                          src_to_dst)

    def copy(self, src_to_dsts: torch.Tensor) -> None:
        self.attn_backend.copy_blocks(self.gpu_cache, src_to_dsts)

    @staticmethod
    def _align_cache(model_config: ModelConfig):
        # Currently align_cache only applies to MLA models since the other
        # cache kernels haven't been updated yet to support non-continguous
        # tensors
        return model_config.use_mla and current_platform.is_cuda() \
            and envs.VLLM_CUDA_MEM_ALIGN_KV_CACHE

    @staticmethod
    def get_cache_block_size(
        cache_config: CacheConfig,
        model_config: ModelConfig,
        parallel_config: ParallelConfig,
    ) -> int:
        head_size = model_config.get_head_size()
        num_heads = model_config.get_num_kv_heads(parallel_config)
        num_attention_layers = model_config.get_num_layers_by_block_type(
            parallel_config, LayerBlockType.attention)

        if cache_config.cache_dtype == "auto":
            dtype = model_config.dtype
        else:
            dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]

        key_cache_entry = num_heads * head_size
        if CacheEngine._align_cache(model_config):
            key_cache_entry = align_to_256bytes(key_cache_entry,
                                                model_config.dtype)

        # For MLA there is no value cache, since the latent vector
        # is joint keys and values.
        value_cache_entry = key_cache_entry if not model_config.use_mla else 0
        total = num_attention_layers * cache_config.block_size * \
            (key_cache_entry + value_cache_entry)

        dtype_size = get_dtype_size(dtype)
        return dtype_size * total