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355 lines
14 KiB
Python
355 lines
14 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 itertools
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from dataclasses import dataclass
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import torch
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from vllm.attention.backends.abstract import AttentionBackend
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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.backends.mamba_attn import BaseMambaAttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import (
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PAD_SLOT_ID,
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CommonAttentionMetadata,
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compute_causal_conv1d_metadata,
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split_decodes_and_prefills,
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)
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from vllm.v1.kv_cache_interface import AttentionSpec
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def compute_varlen_chunk_metadata(
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query_start_loc: torch.Tensor,
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chunk_size: int,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Build chunk-aligned, variable-length metadata used by Mamba2 SSD kernels.
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Given per-sequence cumulative token starts `query_start_loc` of shape [B+1]
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and a physical `chunk_size`, returns three tensors on the same device:
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- cu_chunk_seqlens: (nchunks+1,) int32 exclusive prefix-sum of
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logical-chunk lengths (each logical chunk never crosses a sequence or
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physical-chunk boundary).
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- last_chunk_indices: (B,) int32 index of the last logical chunk
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for each sequence (=-1 for empty sequences).
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- seq_idx_chunks: (nchunks,) int32 sequence index for each logical
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chunk in order.
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This is intentionally lightweight and CPU-side; it mirrors the metadata
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produced by the V1 Mamba2 meta-data builder and is exported so tests
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(and other callers) can avoid duplicating the logic.
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"""
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assert query_start_loc.ndim == 1, "query_start_loc must be 1-D [B+1]"
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assert int(query_start_loc[0].item()) == 0, "query_start_loc[0] must be 0"
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device = query_start_loc.device
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qsl64 = query_start_loc.to(torch.int64)
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starts = qsl64[:-1].tolist()
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ends = qsl64[1:].tolist()
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total = int(qsl64[-1].item())
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chunk_lens: list[int] = []
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seq_idx_chunks: list[int] = []
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last_chunk_indices: list[int] = [-1] * len(starts)
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for b, (s, e) in enumerate(zip(starts, ends)):
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if e <= s:
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# empty sequence
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continue
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pos = s
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while pos < e:
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# split at both sequence boundaries and physical chunk boundaries
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room = chunk_size - (pos % chunk_size)
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take = min(room, e - pos)
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chunk_lens.append(int(take))
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seq_idx_chunks.append(b)
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last_chunk_indices[b] = len(chunk_lens) - 1
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pos += take
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# Exclusive prefix sum over logical-chunk lengths
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if chunk_lens:
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cu_chunk_seqlens = torch.tensor(
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[0] + list(itertools.accumulate(chunk_lens)),
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device=device,
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dtype=torch.int32,
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)
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# Final boundary must equal total tokens
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assert int(cu_chunk_seqlens[-1].item()) == total
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else:
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cu_chunk_seqlens = torch.tensor([0], device=device, dtype=torch.int32)
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last_chunk_indices_t = (
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torch.tensor(last_chunk_indices, device=device, dtype=torch.int32)
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if len(starts) > 0
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else torch.empty((0,), device=device, dtype=torch.int32)
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)
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seq_idx_chunks_t = torch.tensor(seq_idx_chunks, device=device, dtype=torch.int32)
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return cu_chunk_seqlens, last_chunk_indices_t, seq_idx_chunks_t
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class Mamba2AttentionBackend(AttentionBackend):
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@staticmethod
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def get_builder_cls() -> type["Mamba2AttentionMetadataBuilder"]:
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return Mamba2AttentionMetadataBuilder
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@dataclass
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class Mamba2AttentionMetadata:
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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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query_start_loc_p: torch.Tensor
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seq_lens: torch.Tensor
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prep_initial_states: bool
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chunk_size: 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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seq_idx_p: torch.Tensor | None
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# cu_chunk_seqlen_p is a tensor of shape (nchunks+1,) that contains, for
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# each chunk, its offests into the varlen sequence dimension. It is defined
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# such that the i-th chunk contains tokens from cu_chunk_seqlen_p[i] to
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# cu_chunk_seqlen_p[i+1].
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cu_chunk_seqlen_p: torch.Tensor | None
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# last_chunk_indices_p is a tensor of shape (batch,) that contains the
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# index of the last chunk for every sequence in the (prefill) batch.
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last_chunk_indices_p: torch.Tensor | None
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state_indices_tensor: torch.Tensor # shape: [batch,]
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block_idx_last_scheduled_token: torch.Tensor # shape: [batch,]
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block_idx_first_scheduled_token_p: torch.Tensor # shape: [batch,]
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block_idx_last_computed_token: torch.Tensor # shape: [batch,]
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num_computed_tokens_p: torch.Tensor # shape: [batch,]
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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 Mamba2AttentionMetadataBuilder(
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BaseMambaAttentionMetadataBuilder[Mamba2AttentionMetadata]
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):
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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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self.chunk_size = vllm_config.model_config.get_mamba_chunk_size()
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assert self.chunk_size is not None, (
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"chunk_size needs to be set in the model config for Mamba2 models"
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)
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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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) -> Mamba2AttentionMetadata:
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num_reqs = common_attn_metadata.num_reqs
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seq_lens = common_attn_metadata.seq_lens
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query_start_loc_p = None
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seq_idx_p = None
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cu_chunk_seqlen_p = None
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last_chunk_indices_p = None
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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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prep_initial_states = False
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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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num_computed_tokens, num_computed_tokens_p = None, None
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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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if self.vllm_config.cache_config.enable_prefix_caching:
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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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num_computed_tokens = common_attn_metadata.num_computed_tokens_cpu.to(
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self.device
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)
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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 = common_attn_metadata.block_table_tensor[:, 0]
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# Additional cache-related varaiables:
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block_idx_last_scheduled_token = None
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block_idx_last_computed_token = None
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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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# Compute seq_idx for prefill only
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if num_prefills > 0:
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# [batch,]
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has_initial_states_cpu = (
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common_attn_metadata.num_computed_tokens_cpu[
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num_reqs - num_prefills : num_reqs
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]
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> 0
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)
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prep_initial_states = torch.any(has_initial_states_cpu).item()
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has_initial_states_p = has_initial_states_cpu.to(
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common_attn_metadata.query_start_loc.device
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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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if self.vllm_config.cache_config.enable_prefix_caching:
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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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num_computed_tokens_p_cpu = common_attn_metadata.num_computed_tokens_cpu[
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num_reqs - num_prefills : num_reqs
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]
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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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# The code below carefully constructs the chunks such that:
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# 1. Chunks contain tokens from a *single* sequence only.
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# 2. For every sequence, we are guaranteed that we can
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# retrieve the mamba state *every* chunk_size tokens.
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# Constraint (1) dramatically simplifies the mamba2 kernels.
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# Constraint (2) dramatically simplifies the implementation
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# of prefix caching for mamba2 (wip). We need to take care
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# of the interaction with chunked prefill in order to
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# satisfy constraint (2).
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# TODO (tdoublep): This code could probably be optimized.
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cu_chunk_seqlen = []
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seq_idx = []
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last_chunk_indices = []
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seqlen_pos = 0
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for req_idx in range(num_prefills):
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this_num_computed = num_computed_tokens_p_cpu[req_idx].item()
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this_new_tokens = (
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query_start_loc_p_cpu[req_idx + 1].item()
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- query_start_loc_p_cpu[req_idx].item()
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)
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# if computed tokens are not chunk-aligned, use the first
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# chunk to finish it off
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if this_num_computed % self.chunk_size != 0:
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seq_idx.append(req_idx)
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cu_chunk_seqlen.append(seqlen_pos)
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# how many tokens to finish the chunk?
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chunk_len = (
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cdiv(this_num_computed, self.chunk_size) * self.chunk_size
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- this_num_computed
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)
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# we can only use at most this_new_tokens
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chunk_len = min(chunk_len, this_new_tokens)
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seqlen_pos += chunk_len
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this_new_tokens -= chunk_len
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n_chunks = cdiv(this_new_tokens, self.chunk_size)
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for chunk in range(n_chunks):
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seq_idx.append(req_idx)
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cu_chunk_seqlen.append(seqlen_pos)
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chunk_len = min(self.chunk_size, this_new_tokens)
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seqlen_pos += chunk_len
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this_new_tokens -= chunk_len
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assert this_new_tokens == 0
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last_chunk_indices.append(len(cu_chunk_seqlen) - 1)
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cu_chunk_seqlen.append(seqlen_pos)
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seq_idx_p = torch.as_tensor(
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seq_idx, device=query_start_loc_p.device, dtype=torch.int32
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)
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cu_chunk_seqlen_p = torch.as_tensor(
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cu_chunk_seqlen, device=query_start_loc_p.device, dtype=torch.int32
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)
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last_chunk_indices_p = torch.as_tensor(
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last_chunk_indices, device=query_start_loc_p.device, dtype=torch.int32
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)
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nums_dict, batch_ptr, token_chunk_offset_ptr = (
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compute_causal_conv1d_metadata(query_start_loc_p)
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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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# Pad state tensor for CUDA graph
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num_input_tokens = self.vllm_config.pad_for_cudagraph(num_decodes)
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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_input_tokens]
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state_indices_tensor[num_decodes:] = PAD_SLOT_ID
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if self.vllm_config.cache_config.enable_prefix_caching:
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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_input_tokens
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]
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block_idx_last_scheduled_token[num_decodes:] = 0
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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_input_tokens
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]
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block_idx_last_computed_token[num_decodes:] = 0
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attn_metadata = Mamba2AttentionMetadata(
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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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seq_lens=seq_lens,
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prep_initial_states=prep_initial_states,
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chunk_size=self.chunk_size,
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has_initial_states_p=has_initial_states_p,
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seq_idx_p=seq_idx_p,
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state_indices_tensor=state_indices_tensor,
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cu_chunk_seqlen_p=cu_chunk_seqlen_p,
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last_chunk_indices_p=last_chunk_indices_p,
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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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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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)
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return attn_metadata
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