399 lines
16 KiB
Python
399 lines
16 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 typing import TYPE_CHECKING, ClassVar, cast
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import torch
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from vllm.forward_context import get_forward_context
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from vllm.models.deepseek_v4.attention import DeepseekV4Attention
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from vllm.models.deepseek_v4.common.ops import (
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combine_topk_swa_indices,
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compute_global_topk_indices_and_lens,
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dequantize_and_gather_k_cache,
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)
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from vllm.models.deepseek_v4.nvidia.ops.o_proj import (
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compute_fp8_einsum_recipe,
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deep_gemm_fp8_o_proj,
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)
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from vllm.models.deepseek_v4.sparse_mla import (
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DeepseekV4FlashMLABackend,
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DeepseekV4FlashMLAMetadata,
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)
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from vllm.utils.math_utils import round_up
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from vllm.v1.attention.backend import AttentionCGSupport
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from vllm.v1.attention.backends.mla.sparse_swa import (
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DeepseekSparseSWABackend,
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DeepseekSparseSWAMetadataBuilder,
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)
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from vllm.v1.attention.ops.flashmla import (
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flash_mla_sparse_fwd,
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flash_mla_with_kvcache,
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)
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from vllm.v1.worker.workspace import current_workspace_manager
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if TYPE_CHECKING:
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from vllm.v1.attention.backends.mla.sparse_swa import DeepseekSparseSWAMetadata
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class DeepseekSparseSWAFlashMLAMetadataBuilder(DeepseekSparseSWAMetadataBuilder):
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"""SWA metadata for the FlashMLA decode path, which allows varlen decode."""
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_cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.ALWAYS
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class DeepseekSparseSWAFlashMLABackend(DeepseekSparseSWABackend):
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@staticmethod
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def get_builder_cls() -> type[DeepseekSparseSWAFlashMLAMetadataBuilder]:
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return DeepseekSparseSWAFlashMLAMetadataBuilder
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class DeepseekV4FlashMLAAttention(DeepseekV4Attention):
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"""FlashMLA sparse MLA attention layer for DeepSeek V4 (CUDA)."""
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backend_cls = DeepseekV4FlashMLABackend
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swa_backend_cls = DeepseekSparseSWAFlashMLABackend
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def __init__(self, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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self._einsum_recipe, self._tma_aligned_scales = compute_fp8_einsum_recipe()
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def _o_proj(self, o: torch.Tensor, positions: torch.Tensor) -> torch.Tensor:
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return deep_gemm_fp8_o_proj(
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o,
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positions,
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self.rotary_emb.cos_sin_cache,
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self.wo_a,
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self.wo_b,
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n_groups=self.n_local_groups,
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heads_per_group=self.n_local_heads // self.n_local_groups,
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nope_dim=self.nope_head_dim,
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rope_dim=self.rope_head_dim,
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o_lora_rank=self.o_lora_rank,
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einsum_recipe=self._einsum_recipe,
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tma_aligned_scales=self._tma_aligned_scales,
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)
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@classmethod
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def get_padded_num_q_heads(cls, num_heads: int) -> int:
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# FP8 decode kernel only supports h_q = 64 or 128.
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if num_heads > 128:
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raise ValueError(
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f"DeepseekV4 FlashMLA does not support {num_heads} heads "
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"(FP8 decode kernel requires h_q in {64, 128})."
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)
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return 64 if num_heads <= 64 else 128
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def forward_mqa(
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self,
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q: torch.Tensor,
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kv: torch.Tensor,
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positions: torch.Tensor,
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output: torch.Tensor,
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) -> None:
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assert output.shape == q.shape, (
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f"output buffer shape {output.shape} must match q shape {q.shape}"
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)
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assert output.dtype == q.dtype, (
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f"output buffer dtype {output.dtype} must match q dtype {q.dtype}"
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)
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# Get SWA and indexer metadata from forward context
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forward_context = get_forward_context()
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attn_metadata = forward_context.attn_metadata
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if attn_metadata is None:
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# Warmup dummy run: no real metadata. Reserve the same bf16
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# gather workspace _forward_prefill would; the dequantize / topk
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# / sparse_fwd kernels are skipped this step.
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swa_only = self.compress_ratio <= 1
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N = (
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0
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if swa_only
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else (self.max_model_len + self.compress_ratio - 1)
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// self.compress_ratio
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)
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M = N + self.window_size + self.max_num_batched_tokens
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if swa_only:
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top_k = 0
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else:
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assert self.topk_indices_buffer is not None
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top_k = self.topk_indices_buffer.shape[-1]
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combined_topk = round_up(
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top_k + self.window_size + self.max_image_tokens, 128
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)
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current_workspace_manager().get_simultaneous(
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((self.PREFILL_CHUNK_SIZE, M, q.shape[-1]), torch.bfloat16),
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((self.max_num_batched_tokens, combined_topk), torch.int32),
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((self.max_num_batched_tokens,), torch.int32),
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)
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output.zero_()
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return
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assert isinstance(attn_metadata, dict)
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flashmla_metadata = cast(
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DeepseekV4FlashMLAMetadata | None, attn_metadata.get(self.prefix)
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)
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swa_metadata = cast(
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"DeepseekSparseSWAMetadata | None",
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attn_metadata.get(self.swa_cache_layer.prefix),
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)
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assert swa_metadata is not None
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swa_only = self.compress_ratio <= 1
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# SWA-only layers (compress_ratio <= 1) don't have their own KV cache
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# allocation, so self.kv_cache may be empty after profiling cleanup.
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self_kv_cache = self.kv_cache if not swa_only else None
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swa_kv_cache = self.swa_cache_layer.kv_cache
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# Split prefill and decode
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num_decodes = swa_metadata.num_decodes
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num_prefills = swa_metadata.num_prefills
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num_decode_tokens = swa_metadata.num_decode_tokens
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if num_prefills > 0:
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self._forward_prefill(
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q=q[num_decode_tokens:],
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positions=positions[num_decode_tokens:],
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compressed_k_cache=self_kv_cache,
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swa_k_cache=swa_kv_cache,
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output=output[num_decode_tokens:],
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attn_metadata=flashmla_metadata,
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swa_metadata=swa_metadata,
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)
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if num_decodes > 0:
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self._forward_decode(
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q=q[:num_decode_tokens],
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kv_cache=self_kv_cache,
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swa_metadata=swa_metadata,
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attn_metadata=flashmla_metadata,
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swa_only=swa_only,
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output=output[:num_decode_tokens],
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)
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def _forward_decode(
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self,
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q: torch.Tensor,
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kv_cache: torch.Tensor | None, # Only used when compress_ratio > 1
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swa_metadata: "DeepseekSparseSWAMetadata",
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attn_metadata: DeepseekV4FlashMLAMetadata | None,
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swa_only: bool,
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output: torch.Tensor,
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) -> None:
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num_decodes = swa_metadata.num_decodes
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num_decode_tokens = swa_metadata.num_decode_tokens
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topk_indices = None
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topk_lens = None
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if not swa_only:
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assert attn_metadata is not None
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assert swa_metadata.is_valid_token is not None
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block_size = attn_metadata.block_size // self.compress_ratio
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is_valid = swa_metadata.is_valid_token[:num_decode_tokens]
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if self.compress_ratio == 4:
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# C4A: local indices differ per layer (filled by Indexer).
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assert self.topk_indices_buffer is not None
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global_indices, topk_lens = compute_global_topk_indices_and_lens(
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self.topk_indices_buffer[:num_decode_tokens],
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swa_metadata.token_to_req_indices,
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attn_metadata.block_table[:num_decodes],
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block_size,
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is_valid,
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)
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topk_indices = global_indices.view(num_decode_tokens, 1, -1)
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else:
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# C128A: pre-computed during metadata build.
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topk_indices = attn_metadata.c128a_global_decode_topk_indices
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topk_lens = attn_metadata.c128a_decode_topk_lens
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swa_indices = swa_metadata.decode_swa_indices
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swa_lens = swa_metadata.decode_swa_lens
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# We treat queries in the same seq as different queries
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# and later we only attend by generated indices.
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# q arrives pre-padded to self.padded_heads by the outer wrapper.
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q = q.unsqueeze(1)
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# Prepare SWA cache (num_blocks, swa_block_size, 1, head_bytes)
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# Use unsqueeze to preserve strides (handles padded blocks correctly)
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swa_cache = self.swa_cache_layer.kv_cache.unsqueeze(-2)
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# Reshape KV cache to (num_blocks, block_size, 1, head_bytes)
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if kv_cache is not None:
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kv_cache = kv_cache.unsqueeze(-2)
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# One FlashMLASchedMeta per layer type, shared across all same-type
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# layers within this decode step. The first forward call per type
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# triggers the in-kernel planner (allocating tile_scheduler_metadata
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# and num_splits via PyTorch's graph-aware allocator so CUDA graph
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# capture reuses the same addresses on replay); subsequent same-type
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# layers see have_initialized=True and skip the planner.
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if self.compress_ratio <= 1:
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tile_metadata = swa_metadata.tile_sched_swaonly
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elif self.compress_ratio == 4:
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tile_metadata = swa_metadata.tile_sched_c4a
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elif self.compress_ratio == 128:
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tile_metadata = swa_metadata.tile_sched_c128a
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else:
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raise ValueError(
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f"Unsupported compress_ratio={self.compress_ratio}; "
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"expected 1, 4, or 128."
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)
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assert tile_metadata is not None, (
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"swa_metadata missing tile_sched entry for "
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f"compress_ratio={self.compress_ratio}; "
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"DeepseekSparseSWAMetadataBuilder.build_tile_scheduler did not "
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"allocate one for this layer type."
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)
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out, _ = flash_mla_with_kvcache(
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q=q,
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k_cache=swa_cache,
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block_table=None,
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head_dim_v=512,
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tile_scheduler_metadata=tile_metadata,
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cache_seqlens=None,
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is_fp8_kvcache=True,
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indices=swa_indices,
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topk_length=swa_lens,
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softmax_scale=self.scale,
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attn_sink=self.attn_sink,
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extra_k_cache=kv_cache if not swa_only else None,
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extra_indices_in_kvcache=topk_indices,
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extra_topk_length=topk_lens,
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out=output.unsqueeze(1),
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)
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def _forward_prefill(
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self,
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q: torch.Tensor,
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positions: torch.Tensor,
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compressed_k_cache: torch.Tensor | None, # Only used when compress_ratio > 1
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swa_k_cache: torch.Tensor,
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output: torch.Tensor,
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attn_metadata: DeepseekV4FlashMLAMetadata | None,
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swa_metadata: "DeepseekSparseSWAMetadata",
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) -> None:
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swa_only = attn_metadata is None
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num_prefill_tokens = swa_metadata.num_prefill_tokens
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num_decodes = swa_metadata.num_decodes
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num_decode_tokens = swa_metadata.num_decode_tokens
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# Use pre-computed prefill metadata.
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seq_lens = swa_metadata.prefill_seq_lens
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gather_lens = swa_metadata.prefill_gather_lens
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assert seq_lens is not None
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assert gather_lens is not None
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# Derive prefill-local token offsets from the full query_start_loc_cpu.
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query_start_loc_cpu = swa_metadata.query_start_loc_cpu
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query_start_loc = swa_metadata.query_start_loc
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assert query_start_loc_cpu is not None
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assert query_start_loc is not None
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prefill_token_base = query_start_loc_cpu[num_decodes]
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if not swa_only:
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if self.compress_ratio == 4:
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assert self.topk_indices_buffer is not None
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topk_indices = self.topk_indices_buffer[num_decode_tokens:]
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topk_indices = topk_indices[:num_prefill_tokens]
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else:
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# C128A: pre-computed during metadata build.
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assert attn_metadata is not None
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topk_indices = attn_metadata.c128a_prefill_topk_indices
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top_k = topk_indices.shape[-1]
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else:
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# NOTE(woosuk): topk_indices will not be used for SWA-only layers.
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assert self.topk_indices_buffer is not None
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topk_indices = self.topk_indices_buffer[num_decode_tokens:]
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top_k = 0
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chunk_plan = swa_metadata.get_prefill_chunk_plan(
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compress_ratio=self.compress_ratio,
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prefill_chunk_size=self.PREFILL_CHUNK_SIZE,
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)
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assert chunk_plan, "prefill chunk plan must be non-empty when num_prefills > 0"
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workspace_manager = current_workspace_manager()
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combined_topk = round_up(top_k + self.window_size + self.max_image_tokens, 128)
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for chunk_start, chunk_end, chunk_N, chunk_M in chunk_plan:
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chunk_size = chunk_end - chunk_start
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workspace = workspace_manager.get_simultaneous(
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((chunk_size, chunk_M, q.shape[-1]), torch.bfloat16),
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((self.max_num_batched_tokens, combined_topk), torch.int32),
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((self.max_num_batched_tokens,), torch.int32),
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)
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kv, combined_indices_out, combined_lens_out = workspace
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if not swa_only:
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# Gather compressed KV
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assert attn_metadata is not None
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block_table = attn_metadata.block_table[num_decodes:]
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dequantize_and_gather_k_cache(
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kv[:chunk_size],
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compressed_k_cache,
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seq_lens=seq_lens[chunk_start:chunk_end] // self.compress_ratio,
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gather_lens=None,
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block_table=block_table[chunk_start:chunk_end],
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block_size=attn_metadata.block_size // self.compress_ratio,
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offset=0,
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)
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# Gather SWA KV
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swa_block_table = swa_metadata.block_table[num_decodes:]
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dequantize_and_gather_k_cache(
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kv[:chunk_size],
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swa_k_cache,
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seq_lens=seq_lens[chunk_start:chunk_end],
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gather_lens=gather_lens[chunk_start:chunk_end],
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block_table=swa_block_table[chunk_start:chunk_end],
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block_size=swa_metadata.block_size,
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offset=chunk_N,
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)
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# Combine the topk indices and SWA indices for gathered KV cache
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query_start = (
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query_start_loc_cpu[num_decodes + chunk_start] - prefill_token_base
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)
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query_end = (
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query_start_loc_cpu[num_decodes + chunk_end] - prefill_token_base
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)
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combined_indices_out = combined_indices_out[: query_end - query_start]
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combined_lens_out = combined_lens_out[: query_end - query_start]
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combined_indices, combined_lens = combine_topk_swa_indices(
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topk_indices[query_start:query_end],
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query_start_loc[
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num_decodes + chunk_start : num_decodes + chunk_end + 1
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],
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seq_lens[chunk_start:chunk_end],
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gather_lens[chunk_start:chunk_end],
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self.window_size,
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self.compress_ratio,
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top_k,
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chunk_M,
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chunk_N,
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out=(combined_indices_out, combined_lens_out),
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left_visible=(
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swa_metadata.prefill_left_visible[
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num_decode_tokens + query_start : num_decode_tokens + query_end
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]
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if swa_metadata.prefill_left_visible is not None
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else None
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),
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right_visible=(
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swa_metadata.prefill_right_visible[
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num_decode_tokens + query_start : num_decode_tokens + query_end
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]
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if swa_metadata.prefill_right_visible is not None
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else None
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),
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max_image_tokens=self.max_image_tokens,
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)
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flash_mla_sparse_fwd(
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q=q[query_start:query_end],
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kv=kv.view(-1, 1, q.shape[-1]),
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indices=combined_indices.unsqueeze(1),
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sm_scale=self.scale,
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attn_sink=self.attn_sink,
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topk_length=combined_lens,
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out=output[query_start:query_end],
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)
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