Sync from v0.13

2026-01-19 10:38:50 +08:00
parent b2ef04d792
commit 5aef6c175a
3714 changed files with 854317 additions and 89342 deletions
--- a/vllm/v1/attention/backends/mla/indexer.py
+++ b/vllm/v1/attention/backends/mla/indexer.py
@@ -0,0 +1,345 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import ClassVar
+
+import torch
+
+from vllm.attention.backends.abstract import (
+    AttentionBackend,
+    MultipleOf,
+)
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.utils.deep_gemm import get_paged_mqa_logits_metadata, is_deep_gemm_supported
+from vllm.v1.attention.backends.utils import (
+    AttentionCGSupport,
+    AttentionMetadataBuilder,
+    CommonAttentionMetadata,
+    split_decodes_and_prefills,
+    split_prefill_chunks,
+)
+
+logger = init_logger(__name__)
+
+
+class DeepseekV32IndexerBackend(AttentionBackend):
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [1 if current_platform.is_rocm() else 64]
+
+    @classmethod
+    def get_supported_head_sizes(cls) -> list[int]:
+        return [32, 64, 128]
+
+    @staticmethod
+    def get_builder_cls() -> type["DeepseekV32IndexerMetadataBuilder"]:
+        return DeepseekV32IndexerMetadataBuilder
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+        cache_dtype_str: str = "auto",
+    ) -> tuple[int, ...]:
+        assert num_kv_heads == 1
+        return (num_blocks, block_size, head_size)
+
+    @staticmethod
+    def get_kv_cache_stride_order(
+        include_num_layers_dimension: bool = False,
+    ) -> tuple[int, ...]:
+        if include_num_layers_dimension:
+            return (0, 1, 2, 3)
+        return (0, 1, 2)
+
+
+@dataclass
+class DeepseekV32IndexerPrefillChunkMetadata:
+    block_table: torch.Tensor
+    cu_seqlen_ks: torch.Tensor
+    cu_seqlen_ke: torch.Tensor
+    cu_seq_lens: torch.Tensor
+    total_seq_lens: int
+    token_start: int
+    token_end: int
+    num_reqs: int
+
+
+@dataclass
+class DeepseekV32IndexerPrefillMetadata:
+    chunks: list[DeepseekV32IndexerPrefillChunkMetadata]
+
+
+@dataclass
+class DeepSeekV32IndexerDecodeMetadata:
+    block_table: torch.Tensor
+    seq_lens: torch.Tensor
+    decode_lens: torch.Tensor
+    requires_padding: bool
+    schedule_metadata: torch.Tensor
+
+
+@dataclass
+class DeepseekV32IndexerMetadata:
+    # FIXME (zyongye)
+    # hacky way to access the data now, need to be in chunked meta
+    seq_lens: torch.Tensor
+
+    num_reqs: int
+    max_query_len: int
+    max_seq_len: int
+
+    num_actual_tokens: int  # Number of tokens excluding padding.
+    query_start_loc: torch.Tensor
+    slot_mapping: torch.Tensor
+    # The dimension of the attention heads
+    head_dim: int
+
+    # New for MLA (compared to FlashAttention)
+    # For handling prefill decode split
+    num_decodes: int
+    num_decode_tokens: int
+    num_prefills: int
+    num_prefill_tokens: int
+
+    decode: DeepSeekV32IndexerDecodeMetadata | None = None
+    prefill: DeepseekV32IndexerPrefillMetadata | None = None
+
+
+# TODO (zyongye) optimize this, this is now vibe coded
+def kv_spans_from_batches(
+    start_seq_loc: torch.Tensor, seq_len_per_batch: torch.Tensor, device: torch.device
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Args:
+      start_seq_loc: 1D long tensor [B+1], cumulative counts of
+                     selected tokens per batch.
+            Example: [0, 2, 4, 7] ->
+                     batch sizes (selected) [2, 2, 3], N=7 tokens total.
+      seq_len_per_batch: 1D long tensor [B],
+                         full sequence length (KV length) of each batch.
+                         Example: [5, 9, 4].
+
+    Returns:
+      start_tensor: 1D long tensor [N], start offset in the
+                    concatenated KV cache for each token's batch.
+      end_location: 1D long tensor [N],
+                    **exclusive** end = start + token's local position.
+                    (So the attended KV slice is kv[start:end].)
+
+    Assumes each batch contributes its full `seq_len_per_batch[i]`
+    keys to the KV cache, andthe selected tokens within a batch
+    are the **last** `counts[i]` positions of that sequence.
+    """
+    q = start_seq_loc.to(dtype=torch.long)
+    L = seq_len_per_batch.to(dtype=torch.long)
+    assert q.dim() == 1 and L.dim() == 1
+    assert q.numel() == L.numel() + 1, "start_seq_loc must have length B+1"
+
+    # Selected tokens per batch and totals
+    counts = q[1:] - q[:-1]  # [B]
+    N = int(q[-1].item())  # total selected tokens
+    B = L.numel()
+
+    if N == 0:
+        return (
+            torch.empty(0, dtype=torch.long, device=device),
+            torch.empty(0, dtype=torch.long, device=device),
+        )
+
+    # KV start offsets per batch in the concatenated KV cache
+    kv_starts_per_batch = torch.cumsum(L, dim=0) - L  # [B]
+
+    # For each selected token, which batch does it belong to?
+    batch_id = torch.repeat_interleave(torch.arange(B), counts)  # [N]
+
+    # Map batch KV start to each token
+    start_tensor = kv_starts_per_batch[batch_id]  # [N]
+
+    # End-align local positions inside each batch:
+    # local_pos = L[b] - counts[b] + (1..counts[b])  for each batch b
+    L_expand = torch.repeat_interleave(L, counts)  # [N]
+    m_expand = torch.repeat_interleave(counts, counts)  # [N]
+    # position within the selected block: 1..counts[b]
+    pos_within = (
+        torch.arange(N, dtype=torch.long) - torch.repeat_interleave(q[:-1], counts) + 1
+    )
+
+    local_pos = L_expand - m_expand + pos_within  # [N], 1-based
+    end_location = start_tensor + local_pos  # exclusive end
+
+    return start_tensor.int().to(device), end_location.int().to(device)
+
+
+def get_max_prefill_buffer_size(vllm_config: VllmConfig):
+    max_model_len = vllm_config.model_config.max_model_len
+    # NOTE(Chen): 40 is a magic number for controlling the prefill buffer size.
+    # Each entry is 128 fp8 bytes and 4 scale bytes for a total of 132 bytes.
+    # The flashmla_sparse backend uses a workspace size of 5 * max_model_len.
+    # The memory usage of the workspace there is 576 * 2 bytes; so we size this as
+    # (576 * 2 // 132) * 5 = 40 to maximize this workspace size while still fitting
+    # within the flashmla_sparse workspace.
+    # For DeepSeek-V3.2, the max_model_len is 163840.
+    #   40 * 163840 * 132 = 865075200 bytes = 825 MB
+    return max_model_len * 40
+
+
+class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
+    _cudagraph_support: ClassVar[AttentionCGSupport] = (
+        AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+    )
+
+    reorder_batch_threshold: int = 1
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        scheduler_config = self.vllm_config.scheduler_config
+        # NOTE(Chen):an estimated max size of flattened_kv. Need to double check.
+        self.max_prefill_buffer_size = get_max_prefill_buffer_size(self.vllm_config)
+        self.num_speculative_tokens = (
+            self.vllm_config.speculative_config.num_speculative_tokens
+            if self.vllm_config.speculative_config
+            else 0
+        )
+        # Now deepgemm fp8_paged_mqa_logits does not support next_n > 2
+        self.reorder_batch_threshold += min(self.num_speculative_tokens, 1)
+
+        props = torch.cuda.get_device_properties(self.device)
+        sm_count = props.multi_processor_count
+        self.num_sms = sm_count
+
+        self.decode_lens_buffer = torch.empty(
+            (scheduler_config.max_num_seqs,), dtype=torch.int32, device=self.device
+        )
+
+        # See: DeepGMM/csrc/apis/attention.hpp
+        self.scheduler_metadata_buffer = torch.empty(
+            (self.num_sms + 1, 2), dtype=torch.int32, device=self.device
+        )
+
+    def build_one_prefill_chunk(
+        self, reqs_start, reqs_end, query_start_loc_cpu, seq_lens_cpu, block_table
+    ):
+        prefill_query_start_loc = (
+            query_start_loc_cpu[reqs_start : reqs_end + 1]
+            - query_start_loc_cpu[reqs_start]
+        )
+        cu_seqlen_ks, cu_seqlen_ke = kv_spans_from_batches(
+            prefill_query_start_loc, seq_lens_cpu[reqs_start:reqs_end], self.device
+        )
+        token_start = query_start_loc_cpu[reqs_start].item()
+        token_end = query_start_loc_cpu[reqs_end].item()
+        total_seq_lens = seq_lens_cpu[reqs_start:reqs_end].sum()
+        assert total_seq_lens <= self.max_prefill_buffer_size
+        cu_seq_lens = (
+            torch.cat(
+                [
+                    torch.zeros(1, dtype=torch.int32),
+                    seq_lens_cpu[reqs_start:reqs_end].cumsum(dim=0),
+                ]
+            )
+            .to(torch.int32)
+            .to(self.device)
+        )
+        return DeepseekV32IndexerPrefillChunkMetadata(
+            cu_seqlen_ks=cu_seqlen_ks,
+            cu_seqlen_ke=cu_seqlen_ke,
+            cu_seq_lens=cu_seq_lens,
+            total_seq_lens=total_seq_lens,
+            block_table=block_table[reqs_start:reqs_end],
+            token_start=token_start,
+            token_end=token_end,
+            num_reqs=reqs_end - reqs_start,
+        )
+
+    def build(
+        self,
+        common_prefix_len: int,
+        common_attn_metadata: CommonAttentionMetadata,
+        fast_build: bool = False,
+    ) -> DeepseekV32IndexerMetadata:
+        num_reqs = common_attn_metadata.num_reqs
+        num_tokens = common_attn_metadata.num_actual_tokens
+
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+            split_decodes_and_prefills(
+                common_attn_metadata, decode_threshold=self.reorder_batch_threshold
+            )
+        )
+
+        assert num_decodes + num_prefills == num_reqs
+        assert num_decode_tokens + num_prefill_tokens == num_tokens
+
+        prefill_metadata = None
+        if num_prefills > 0:
+            chunk_seq_ids = split_prefill_chunks(
+                common_attn_metadata.seq_lens_cpu[num_decodes:],
+                self.max_prefill_buffer_size,
+                request_offset=num_decodes,
+            )
+            chunks = [
+                self.build_one_prefill_chunk(
+                    reqs_start,
+                    reqs_end,
+                    query_start_loc_cpu,
+                    common_attn_metadata.seq_lens_cpu,
+                    common_attn_metadata.block_table_tensor,
+                )
+                for reqs_start, reqs_end in chunk_seq_ids
+            ]
+            prefill_metadata = DeepseekV32IndexerPrefillMetadata(
+                chunks=chunks,
+            )
+
+        decode_metadata = None
+        if num_decodes > 0:
+            torch.diff(
+                common_attn_metadata.query_start_loc[: num_decodes + 1],
+                out=self.decode_lens_buffer[:num_decodes],
+            )
+            decode_lens = self.decode_lens_buffer[:num_decodes]
+            decode_lens_cpu = torch.diff(
+                common_attn_metadata.query_start_loc_cpu[: num_decodes + 1]
+            )
+
+            # Use CPU to avoid GPU sync; breaking async scheduling
+            requires_padding = (decode_lens_cpu.max() > decode_lens_cpu.min()).item()
+
+            seq_lens = common_attn_metadata.seq_lens[:num_decodes]
+            if is_deep_gemm_supported():
+                self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
+                    seq_lens, self.kv_cache_spec.block_size, self.num_sms
+                )
+            decode_metadata = DeepSeekV32IndexerDecodeMetadata(
+                block_table=common_attn_metadata.block_table_tensor[:num_decodes, ...],
+                seq_lens=common_attn_metadata.seq_lens[:num_decodes],
+                decode_lens=decode_lens,
+                requires_padding=requires_padding,
+                schedule_metadata=self.scheduler_metadata_buffer,
+            )
+
+        attn_metadata = DeepseekV32IndexerMetadata(
+            seq_lens=common_attn_metadata.seq_lens,
+            num_reqs=common_attn_metadata.num_reqs,
+            max_query_len=common_attn_metadata.max_query_len,
+            max_seq_len=common_attn_metadata.max_seq_len,
+            num_actual_tokens=common_attn_metadata.num_actual_tokens,
+            query_start_loc=common_attn_metadata.query_start_loc,
+            slot_mapping=common_attn_metadata.slot_mapping,
+            head_dim=128,
+            num_decodes=num_decodes,
+            num_decode_tokens=num_decode_tokens,
+            num_prefills=num_prefills,
+            num_prefill_tokens=num_prefill_tokens,
+            prefill=prefill_metadata,
+            decode=decode_metadata,
+        )
+
+        # if get_tensor_model_parallel_rank() == 0:
+        #     logger.info(f"attn_metadata: {attn_metadata}")
+        return attn_metadata