343 lines
13 KiB
Python
343 lines
13 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass
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from typing import ClassVar, Optional
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import torch
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from vllm.attention.backends.abstract import (AttentionBackend,
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AttentionMetadata)
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.utils.deep_gemm import get_paged_mqa_logits_metadata
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from vllm.v1.attention.backends.utils import (AttentionCGSupport,
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AttentionMetadataBuilder,
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CommonAttentionMetadata,
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split_decodes_and_prefills)
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logger = init_logger(__name__)
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class DeepseekV32IndexerBackend(AttentionBackend):
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@staticmethod
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def get_metadata_cls() -> type["AttentionMetadata"]:
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return DeepseekV32IndexerMetadata
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@classmethod
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def get_supported_head_sizes(cls) -> list[int]:
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return [32, 64, 128]
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@staticmethod
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def get_builder_cls() -> type["DeepseekV32IndexerMetadataBuilder"]:
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return DeepseekV32IndexerMetadataBuilder
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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cache_dtype_str: str = "auto",
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) -> tuple[int, ...]:
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assert num_kv_heads == 1
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return (num_blocks, block_size, head_size)
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@staticmethod
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def get_kv_cache_stride_order() -> tuple[int, ...]:
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return (0, 1, 2)
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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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@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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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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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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# TODO (zyongye) optimize this, this is now vibe coded
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def kv_spans_from_batches(
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start_seq_loc: torch.Tensor, seq_len_per_batch: torch.Tensor,
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device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Args:
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start_seq_loc: 1D long tensor [B+1], cumulative counts of
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selected tokens per batch.
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Example: [0, 2, 4, 7] ->
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batch sizes (selected) [2, 2, 3], N=7 tokens total.
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seq_len_per_batch: 1D long tensor [B],
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full sequence length (KV length) of each batch.
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Example: [5, 9, 4].
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Returns:
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start_tensor: 1D long tensor [N], start offset in the
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concatenated KV cache for each token's batch.
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end_location: 1D long tensor [N],
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**exclusive** end = start + token's local position.
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(So the attended KV slice is kv[start:end].)
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Assumes each batch contributes its full `seq_len_per_batch[i]`
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keys to the KV cache, andthe selected tokens within a batch
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are the **last** `counts[i]` positions of that sequence.
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"""
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q = start_seq_loc.to(dtype=torch.long)
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L = seq_len_per_batch.to(dtype=torch.long)
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assert q.dim() == 1 and L.dim() == 1
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assert q.numel() == L.numel() + 1, "start_seq_loc must have length B+1"
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# Selected tokens per batch and totals
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counts = q[1:] - q[:-1] # [B]
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N = int(q[-1].item()) # total selected tokens
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B = L.numel()
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if N == 0:
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return (torch.empty(0, dtype=torch.long, device=device),
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torch.empty(0, dtype=torch.long, device=device))
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# KV start offsets per batch in the concatenated KV cache
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kv_starts_per_batch = torch.cumsum(L, dim=0) - L # [B]
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# For each selected token, which batch does it belong to?
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batch_id = torch.repeat_interleave(torch.arange(B), counts) # [N]
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# Map batch KV start to each token
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start_tensor = kv_starts_per_batch[batch_id] # [N]
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# End-align local positions inside each batch:
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# local_pos = L[b] - counts[b] + (1..counts[b]) for each batch b
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L_expand = torch.repeat_interleave(L, counts) # [N]
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m_expand = torch.repeat_interleave(counts, counts) # [N]
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# position within the selected block: 1..counts[b]
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pos_within = (torch.arange(N, dtype=torch.long) -
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torch.repeat_interleave(q[:-1], counts) + 1)
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local_pos = L_expand - m_expand + pos_within # [N], 1-based
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end_location = start_tensor + local_pos # exclusive end
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return start_tensor.int().to(device), end_location.int().to(device)
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def get_max_prefill_buffer_size(vllm_config: VllmConfig):
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max_model_len = vllm_config.model_config.max_model_len
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# NOTE(Chen): 2 is a magic number for controlling the prefill buffer size.
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# May be tuned later.
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return max_model_len * 2
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def split_prefill_chunks(seq_lens_cpu: torch.Tensor,
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max_prefill_buffer_size: int,
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reqs_start: int) -> list[tuple[int, int]]:
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"""
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Split the prefill chunks into a list of tuples of (reqs_start, reqs_end)
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such that the total sequence length of each chunk is less than the
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maximum prefill buffer size.
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Args:
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seq_lens_cpu: The sequence lengths of the prefill requests.
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max_prefill_buffer_size: The maximum prefill buffer size.
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reqs_start: The start index of the prefill requests.
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Returns:
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A list of tuples of (reqs_start, reqs_end).
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"""
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chunk_seq_ids = []
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total_seq_lens = 0
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for i in range(reqs_start, len(seq_lens_cpu)):
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cur_seq_len = seq_lens_cpu[i].item()
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assert cur_seq_len <= max_prefill_buffer_size
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total_seq_lens += cur_seq_len
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if total_seq_lens > max_prefill_buffer_size:
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chunk_seq_ids.append((reqs_start, i))
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reqs_start = i
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total_seq_lens = cur_seq_len
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if total_seq_lens > 0:
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chunk_seq_ids.append((reqs_start, len(seq_lens_cpu)))
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return chunk_seq_ids
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class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
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cudagraph_support: ClassVar[AttentionCGSupport] = \
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AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
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reorder_batch_threshold: int = 1
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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scheduler_config = self.vllm_config.scheduler_config
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#NOTE(Chen):an estimated max size of flattened_kv. Need to double check.
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self.max_prefill_buffer_size = get_max_prefill_buffer_size(
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self.vllm_config)
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self.num_speculative_tokens = (
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self.vllm_config.speculative_config.num_speculative_tokens
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if self.vllm_config.speculative_config else 0)
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# Now deepgemm fp8_paged_mqa_logits does not support next_n > 2
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self.reorder_batch_threshold += min(self.num_speculative_tokens, 1)
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props = torch.cuda.get_device_properties(self.device)
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sm_count = props.multi_processor_count
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self.num_sms = sm_count
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self.decode_lens_buffer = torch.empty(
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(scheduler_config.max_num_seqs, ),
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dtype=torch.int32,
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device=self.device)
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# See: DeepGMM/csrc/apis/attention.hpp
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self.scheduler_metadata_buffer = torch.empty((self.num_sms + 1, 2),
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dtype=torch.int32,
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device=self.device)
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def 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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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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)
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def 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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self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
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seq_lens, self.kv_cache_spec.block_size, self.num_sms)
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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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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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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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