191 lines
7.4 KiB
Python
191 lines
7.4 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 torch
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from dataclasses import dataclass
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from typing import ClassVar, Optional
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from vllm.logger import init_logger
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from vllm.v1.attention.backends.utils import ( CommonAttentionMetadata,
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split_decodes_and_prefills)
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from vllm.v1.attention.backends.mla.indexer import (split_prefill_chunks,
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DeepseekV32IndexerMetadataBuilder,
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kv_spans_from_batches)
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logger = init_logger(__name__)
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@dataclass
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class DeepseekV32IndexerPrefillChunkMetadata:
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block_table: torch.Tensor
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cu_seqlen_ks: torch.Tensor
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cu_seqlen_ke: torch.Tensor
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cu_seq_lens: torch.Tensor
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total_seq_lens: int
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token_start: int
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token_end: int
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num_reqs: int
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context_q_lens: torch.Tensor
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context_q_lens_cpu: torch.Tensor
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context_k_lens: torch.Tensor
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context_k_lens_cpu: torch.Tensor
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@dataclass
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class DeepseekV32IndexerPrefillMetadata:
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chunks: list[DeepseekV32IndexerPrefillChunkMetadata]
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@dataclass
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class DeepSeekV32IndexerDecodeMetadata:
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block_table: torch.Tensor
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seq_lens: torch.Tensor
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seq_lens_cpu: torch.Tensor
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decode_lens: torch.Tensor
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requires_padding: bool
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schedule_metadata: torch.Tensor
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@dataclass
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class DeepseekV32IndexerMetadata:
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# FIXME (zyongye)
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# hacky way to access the data now, need to be in chunked meta
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seq_lens: torch.Tensor
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seq_lens_cpu: torch.Tensor
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num_reqs: int
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max_query_len: int
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max_seq_len: int
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num_actual_tokens: int # Number of tokens excluding padding.
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query_start_loc: torch.Tensor
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slot_mapping: torch.Tensor
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# The dimension of the attention heads
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head_dim: int
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# New for MLA (compared to FlashAttention)
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# For handling prefill decode split
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num_decodes: int
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num_decode_tokens: int
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num_prefills: int
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num_prefill_tokens: int
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decode: Optional[DeepSeekV32IndexerDecodeMetadata] = None
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prefill: Optional[DeepseekV32IndexerPrefillMetadata] = None
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def kunlun_build_one_prefill_chunk(self, reqs_start, reqs_end,
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query_start_loc_cpu, seq_lens_cpu,
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block_table):
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prefill_query_start_loc = query_start_loc_cpu[
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reqs_start:reqs_end + 1] - query_start_loc_cpu[reqs_start]
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cu_seqlen_ks, cu_seqlen_ke = kv_spans_from_batches(
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prefill_query_start_loc, seq_lens_cpu[reqs_start:reqs_end],
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self.device)
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token_start = query_start_loc_cpu[reqs_start].item()
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token_end = query_start_loc_cpu[reqs_end].item()
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total_seq_lens = seq_lens_cpu[reqs_start:reqs_end].sum()
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assert total_seq_lens <= self.max_prefill_buffer_size
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cu_seq_lens = torch.cat([
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torch.zeros(1, dtype=torch.int32),
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seq_lens_cpu[reqs_start:reqs_end].cumsum(dim=0)
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]).to(torch.int32).to(self.device)
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seq_len_q = token_end - token_start
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seq_len_kv = total_seq_lens
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context_q_lens = torch.tensor([0, seq_len_q], dtype=torch.int32, device=self.device)
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context_k_lens = torch.tensor([0, seq_len_kv], dtype=torch.int32, device=self.device)
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context_q_lens_cpu = torch.tensor([0, seq_len_q], dtype=torch.int32, device="cpu")
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context_k_lens_cpu = torch.tensor([0, seq_len_kv], dtype=torch.int32, device="cpu")
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return DeepseekV32IndexerPrefillChunkMetadata(
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cu_seqlen_ks=cu_seqlen_ks,
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cu_seqlen_ke=cu_seqlen_ke,
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cu_seq_lens=cu_seq_lens,
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total_seq_lens=total_seq_lens,
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block_table=block_table[reqs_start:reqs_end],
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token_start=token_start,
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token_end=token_end,
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num_reqs=reqs_end - reqs_start,
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context_q_lens=context_q_lens,
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context_q_lens_cpu=context_q_lens_cpu,
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context_k_lens=context_k_lens,
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context_k_lens_cpu=context_k_lens_cpu,
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)
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def kunlun_build(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) -> DeepseekV32IndexerMetadata:
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num_reqs = common_attn_metadata.num_reqs
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num_tokens = common_attn_metadata.num_actual_tokens
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query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
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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,
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decode_threshold=self.reorder_batch_threshold)
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assert num_decodes + num_prefills == num_reqs
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assert num_decode_tokens + num_prefill_tokens == num_tokens
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prefill_metadata = None
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if num_prefills > 0:
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chunk_seq_ids = split_prefill_chunks(
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common_attn_metadata.seq_lens_cpu,
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self.max_prefill_buffer_size,
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num_decodes,
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)
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chunks = [
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self.build_one_prefill_chunk(
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reqs_start, reqs_end, query_start_loc_cpu,
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common_attn_metadata.seq_lens_cpu,
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common_attn_metadata.block_table_tensor)
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for reqs_start, reqs_end in chunk_seq_ids
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]
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prefill_metadata = DeepseekV32IndexerPrefillMetadata(
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chunks=chunks, )
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decode_metadata = None
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if num_decodes > 0:
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torch.diff(common_attn_metadata.query_start_loc[:num_decodes + 1],
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out=self.decode_lens_buffer[:num_decodes])
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decode_lens = self.decode_lens_buffer[:num_decodes]
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decode_lens_cpu = torch.diff(
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common_attn_metadata.query_start_loc_cpu[:num_decodes + 1])
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# Use CPU to avoid GPU sync; breaking async scheduling
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requires_padding = (decode_lens_cpu.max()
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> decode_lens_cpu.min()).item()
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seq_lens = common_attn_metadata.seq_lens[:num_decodes]
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decode_metadata = DeepSeekV32IndexerDecodeMetadata(
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block_table=common_attn_metadata.
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block_table_tensor[:num_decodes, ...],
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seq_lens=common_attn_metadata.seq_lens[:num_decodes],
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seq_lens_cpu=common_attn_metadata.seq_lens[:num_decodes].cpu(),
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decode_lens=decode_lens,
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requires_padding=requires_padding,
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schedule_metadata=self.scheduler_metadata_buffer,
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)
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attn_metadata = DeepseekV32IndexerMetadata(
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seq_lens=common_attn_metadata.seq_lens,
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seq_lens_cpu=common_attn_metadata.seq_lens.cpu(),
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num_reqs=common_attn_metadata.num_reqs,
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max_query_len=common_attn_metadata.max_query_len,
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max_seq_len=common_attn_metadata.max_seq_len,
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num_actual_tokens=common_attn_metadata.num_actual_tokens,
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query_start_loc=common_attn_metadata.query_start_loc,
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slot_mapping=common_attn_metadata.slot_mapping,
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head_dim=128,
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num_decodes=num_decodes,
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num_decode_tokens=num_decode_tokens,
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num_prefills=num_prefills,
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num_prefill_tokens=num_prefill_tokens,
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prefill=prefill_metadata,
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decode=decode_metadata,
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)
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# if get_tensor_model_parallel_rank() == 0:
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# logger.info(f"attn_metadata: {attn_metadata}")
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return attn_metadata
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DeepseekV32IndexerMetadataBuilder.build_one_prefill_chunk= kunlun_build_one_prefill_chunk
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DeepseekV32IndexerMetadataBuilder.build = kunlun_build |