5206e5e28c
Signed-off-by: huanghaoyan.hhy <huanghaoyan.hhy@alibaba-inc.com> Signed-off-by: Chen Zhang <zhangch99@outlook.com> Co-authored-by: Chen Zhang <zhangch99@outlook.com>
336 lines
12 KiB
Python
336 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import abc
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import copy
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from dataclasses import dataclass
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from typing import ClassVar, TypeVar
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import torch
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from vllm.config import VllmConfig
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from vllm.utils.math_utils import cdiv
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from vllm.v1.attention.backend import (
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AttentionCGSupport,
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AttentionMetadataBuilder,
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CommonAttentionMetadata,
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)
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from vllm.v1.attention.backends.utils import (
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PAD_SLOT_ID,
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compute_causal_conv1d_metadata,
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mamba_get_block_table_tensor,
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split_decodes_and_prefills,
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)
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from vllm.v1.kv_cache_interface import AttentionSpec, MambaSpec
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M = TypeVar("M", bound="BaseMambaAttentionMetadata")
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@dataclass
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class BaseMambaAttentionMetadata:
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num_prefills: int
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num_prefill_tokens: int
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num_decodes: int
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num_decode_tokens: int
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num_reqs: int
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# The following tensors only contain prefill requests and will be None if
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# the batch has no prefill request.
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has_initial_states_p: torch.Tensor | None
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query_start_loc_p: torch.Tensor | None
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num_computed_tokens_p: torch.Tensor | None
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state_indices_tensor: torch.Tensor
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# The following tensors are only used for prefix caching in all mode and
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# are None if disabled
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block_idx_last_scheduled_token: torch.Tensor | None
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block_idx_first_scheduled_token_p: torch.Tensor | None
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block_idx_last_computed_token: torch.Tensor | None
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# The following tensor is only used for prefix caching in align mode
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seq_lens: torch.Tensor
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# The following attributes are for triton implementation of causal_conv1d
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nums_dict: dict | None = None
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batch_ptr: torch.Tensor | None = None
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token_chunk_offset_ptr: torch.Tensor | None = None
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class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
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metadata_cls: type[M]
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reorder_batch_threshold: int = 1
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_cudagraph_support: ClassVar[AttentionCGSupport] = (
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AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
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)
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supports_update_block_table: bool = True
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def __init__(
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self,
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kv_cache_spec: AttentionSpec,
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layer_names: list[str],
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vllm_config: VllmConfig,
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device: torch.device,
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):
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super().__init__(kv_cache_spec, layer_names, vllm_config, device)
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assert isinstance(kv_cache_spec, MambaSpec)
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self.compilation_config = vllm_config.compilation_config
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self.decode_cudagraph_max_bs = self.vllm_config.scheduler_config.max_num_seqs
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if self.compilation_config.max_cudagraph_capture_size is not None:
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self.decode_cudagraph_max_bs = min(
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self.decode_cudagraph_max_bs,
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self.compilation_config.max_cudagraph_capture_size,
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)
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if self.vllm_config.cache_config.mamba_cache_mode == "all":
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self.state_indices_tensor = torch.empty(
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(
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self.decode_cudagraph_max_bs,
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cdiv(
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self.vllm_config.model_config.max_model_len,
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self.kv_cache_spec.block_size,
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),
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),
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dtype=torch.int32,
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device=device,
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)
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self.block_idx_last_scheduled_token = torch.empty(
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(self.decode_cudagraph_max_bs,),
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dtype=torch.int32,
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device=device,
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)
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self.block_idx_last_computed_token = torch.empty(
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(self.decode_cudagraph_max_bs,),
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dtype=torch.int32,
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device=device,
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)
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else:
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self.state_indices_tensor = torch.empty(
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(self.decode_cudagraph_max_bs,),
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dtype=torch.int32,
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device=device,
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)
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def build_for_cudagraph_capture(
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self, common_attn_metadata: CommonAttentionMetadata
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) -> M:
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"""
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This method builds the metadata for full cudagraph capture.
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Currently, only decode is supported for full cudagraphs with Mamba.
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"""
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m = common_attn_metadata
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assert m.num_reqs == m.num_actual_tokens, (
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"Mamba only supports decode-only full CUDAGraph capture. "
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"Make sure all cudagraph capture sizes <= max_num_seq."
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)
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m.max_query_len = 1 # decode-only
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return self.build(0, m)
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def build(
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self,
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common_prefix_len: int,
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common_attn_metadata: CommonAttentionMetadata,
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fast_build: bool = False,
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) -> M:
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"""
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Default build implementation for Mamba-like attention backends.
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Subclasses (e.g., Mamba2) can override to add additional metadata.
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"""
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return self._compute_common_metadata(common_attn_metadata)
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def _compute_prefix_caching_block_indices(
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self,
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common_attn_metadata: CommonAttentionMetadata,
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mamba_block_size: int,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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num_computed_tokens = common_attn_metadata.compute_num_computed_tokens()
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# Block index of the last computed token
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block_idx_last_computed_token = cdiv(num_computed_tokens, mamba_block_size) - 1
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# which is <= block index for the first scheduled token
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block_idx_first_scheduled_token = (
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cdiv(num_computed_tokens + 1, mamba_block_size) - 1
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)
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# which is <= block index of the last scheduled token
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block_idx_last_scheduled_token = (
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cdiv(common_attn_metadata.seq_lens, mamba_block_size) - 1
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)
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# -1 in case it's non-computed and causes later issues with indexing
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block_idx_last_computed_token = torch.clamp(
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block_idx_last_computed_token, min=0
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)
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# -1 in the case we have a padded request (0 seq-len)
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block_idx_last_scheduled_token = torch.clamp(
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block_idx_last_scheduled_token, min=0
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)
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return (
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block_idx_last_computed_token,
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block_idx_first_scheduled_token,
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block_idx_last_scheduled_token,
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)
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def _compute_common_metadata(
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self,
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common_attn_metadata: CommonAttentionMetadata,
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) -> M:
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"""
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Compute metadata common to both Mamba1 and Mamba2.
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"""
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num_reqs = common_attn_metadata.num_reqs
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num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
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split_decodes_and_prefills(
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common_attn_metadata, decode_threshold=self.reorder_batch_threshold
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)
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)
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# Need flags to indicate if there are initial states
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has_initial_states_p = None
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query_start_loc_p = None
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num_computed_tokens = None
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num_computed_tokens_p = None
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# for prefix caching
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block_idx_first_scheduled_token = None
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block_idx_first_scheduled_token_p = None
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block_idx_last_computed_token = None
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block_idx_last_scheduled_token = None
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# for causal_conv1d
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nums_dict, batch_ptr, token_chunk_offset_ptr = None, None, None
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if self.vllm_config.cache_config.mamba_cache_mode == "all":
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num_computed_tokens = common_attn_metadata.compute_num_computed_tokens()
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# Return a tensor of shape (#requests, #max blocks)
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state_indices_tensor = common_attn_metadata.block_table_tensor
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# Additional cache-related varaiables:
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mamba_block_size = self.kv_cache_spec.block_size
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(
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block_idx_last_computed_token,
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block_idx_first_scheduled_token,
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block_idx_last_scheduled_token,
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) = self._compute_prefix_caching_block_indices(
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common_attn_metadata, mamba_block_size
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)
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else:
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# Always return just a single block per each request:
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state_indices_tensor = mamba_get_block_table_tensor(
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common_attn_metadata.block_table_tensor,
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common_attn_metadata.seq_lens,
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self.kv_cache_spec,
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self.vllm_config.cache_config.mamba_cache_mode,
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)[:, 0]
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if num_prefills > 0:
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if num_computed_tokens is None:
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num_computed_tokens = common_attn_metadata.compute_num_computed_tokens()
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query_start_loc_p_cpu = (
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common_attn_metadata.query_start_loc_cpu[-num_prefills - 1 :]
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- num_decode_tokens
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)
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query_start_loc_p = (
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common_attn_metadata.query_start_loc[-num_prefills - 1 :]
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- num_decode_tokens
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)
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has_initial_states_p = (
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num_computed_tokens[num_reqs - num_prefills : num_reqs] > 0
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)
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nums_dict, batch_ptr, token_chunk_offset_ptr = (
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compute_causal_conv1d_metadata(
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query_start_loc_p_cpu,
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device=common_attn_metadata.query_start_loc.device,
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)
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)
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if self.vllm_config.cache_config.mamba_cache_mode == "all":
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assert num_computed_tokens is not None
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num_computed_tokens_p = num_computed_tokens[
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num_reqs - num_prefills : num_reqs
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]
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assert block_idx_first_scheduled_token is not None
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block_idx_first_scheduled_token_p = block_idx_first_scheduled_token[
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num_reqs - num_prefills : num_reqs
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]
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elif (
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num_decodes <= self.decode_cudagraph_max_bs
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and self.compilation_config.cudagraph_mode.has_full_cudagraphs()
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):
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self.state_indices_tensor[:num_decodes].copy_(
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state_indices_tensor, non_blocking=True
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)
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state_indices_tensor = self.state_indices_tensor[:num_decode_tokens]
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state_indices_tensor[num_decodes:] = PAD_SLOT_ID
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if self.vllm_config.cache_config.mamba_cache_mode == "all":
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self.block_idx_last_scheduled_token[:num_decodes].copy_(
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block_idx_last_scheduled_token, non_blocking=True
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)
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block_idx_last_scheduled_token = self.block_idx_last_scheduled_token[
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:num_decode_tokens
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]
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self.block_idx_last_computed_token[:num_decodes].copy_(
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block_idx_last_computed_token, non_blocking=True
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)
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block_idx_last_computed_token = self.block_idx_last_computed_token[
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:num_decode_tokens
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]
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return self.metadata_cls(
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num_prefills=num_prefills,
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num_prefill_tokens=num_prefill_tokens,
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num_decodes=num_decodes,
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num_decode_tokens=num_decode_tokens,
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query_start_loc_p=query_start_loc_p,
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has_initial_states_p=has_initial_states_p,
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state_indices_tensor=state_indices_tensor,
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block_idx_last_scheduled_token=block_idx_last_scheduled_token,
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block_idx_first_scheduled_token_p=block_idx_first_scheduled_token_p,
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block_idx_last_computed_token=block_idx_last_computed_token,
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num_computed_tokens_p=num_computed_tokens_p,
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num_reqs=num_reqs,
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seq_lens=common_attn_metadata.seq_lens,
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nums_dict=nums_dict,
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batch_ptr=batch_ptr,
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token_chunk_offset_ptr=token_chunk_offset_ptr,
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)
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def update_block_table(
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self,
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metadata: M,
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blk_table: torch.Tensor,
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slot_mapping: torch.Tensor,
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) -> M:
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new_metadata = copy.copy(metadata)
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state_indices_t = mamba_get_block_table_tensor(
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blk_table,
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metadata.seq_lens,
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self.kv_cache_spec,
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self.vllm_config.cache_config.mamba_cache_mode,
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)
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if self.vllm_config.cache_config.mamba_cache_mode in ("none", "align"):
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# Only needs the block that saves the running state
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state_indices_t = state_indices_t[:, 0]
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num_reqs = blk_table.shape[0]
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# For CUDA graphs, copy to persistent buffer
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if (
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metadata.num_prefills == 0
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and num_reqs <= self.decode_cudagraph_max_bs
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and self.compilation_config.cudagraph_mode.has_full_cudagraphs()
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):
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persistent_state_indices_t = self.state_indices_tensor[:num_reqs]
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persistent_state_indices_t.copy_(state_indices_t, non_blocking=True)
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state_indices_t = persistent_state_indices_t
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new_metadata.state_indices_tensor = state_indices_t
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return new_metadata
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