403 lines
16 KiB
Python
403 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 abc import abstractmethod
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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.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.v1.attention.backend import (
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AttentionBackend,
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MultipleOf,
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SparseMLAAttentionImpl,
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)
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from vllm.v1.attention.backends.mla.flashmla_sparse import (
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FlashMLASparseBackend,
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FlashMLASparseMetadata,
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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.models.deepseek_v4.nvidia.ops.attention import (
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DeepseekV4MLAAttention,
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)
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from vllm.v1.attention.backends.mla.sparse_swa import DeepseekSparseSWAMetadata
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class DeepseekV4SparseMLAAttentionImpl(SparseMLAAttentionImpl[FlashMLASparseMetadata]):
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"""Abstract parent for DeepseekV4 sparse MLA impls.
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V4 sparse MLA is driven by the layer (``DeepseekV4MLAAttention.forward``)
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rather than the v1 framework, so ``forward_mqa`` is overridden with a
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classmethod that takes the layer as its first argument. This Liskov-broken
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override is intentional: the grandparent's instance-method ``forward_mqa``
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is never called on V4 layers.
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"""
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backend_cls: ClassVar[type[AttentionBackend]]
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# Prefill is processed in fixed-size chunks; this bounds the bf16 kv-gather
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# workspace allocated in _forward_prefill and is also read by the V4 layer's
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# dummy-run path to pre-reserve that workspace.
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PREFILL_CHUNK_SIZE: ClassVar[int] = 4
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@classmethod
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@abstractmethod
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def forward_mqa( # type: ignore[override]
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cls,
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layer: "DeepseekV4MLAAttention",
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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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raise NotImplementedError
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class DeepseekV4FlashMLASparseBackend(FlashMLASparseBackend):
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@staticmethod
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def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
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return [256]
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@staticmethod
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def get_name() -> str:
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return "V4_FLASHMLA_SPARSE"
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@staticmethod
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def get_impl_cls() -> type["DeepseekV4SparseMLAAttentionImpl"]:
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return DeepseekV4FlashMLASparseImpl
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@classmethod
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def get_supported_head_sizes(cls) -> list[int]:
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# DeepSeek V4 layout: 448 NoPE + 64 RoPE = 512 (overrides the
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# V3.2 default of 576 from FlashMLASparseBackend).
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return [512]
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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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if cache_dtype_str == "fp8_ds_mla":
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# DeepseekV4 main MLA: 584B per token (448 NoPE + 128 RoPE + 8 fp8 scale).
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# head_size passed in is the semantic head_dim (512).
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return (num_blocks, block_size, 584)
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else:
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return (num_blocks, block_size, head_size)
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class DeepseekV4FlashMLASparseImpl(DeepseekV4SparseMLAAttentionImpl):
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"""FlashMLA sparse MLA implementation for DeepSeek V4's custom MLA layer."""
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backend_cls = DeepseekV4FlashMLASparseBackend
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@classmethod
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def forward_mqa( # type: ignore[override]
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cls,
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layer: "DeepseekV4MLAAttention",
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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 = layer.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 (layer.max_model_len + layer.compress_ratio - 1)
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// layer.compress_ratio
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)
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M = N + layer.window_size + layer.max_num_batched_tokens
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current_workspace_manager().get_simultaneous(
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((cls.PREFILL_CHUNK_SIZE, M, q.shape[-1]), torch.bfloat16),
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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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FlashMLASparseMetadata | None, attn_metadata.get(layer.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(layer.swa_cache_layer.prefix),
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)
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assert swa_metadata is not None
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swa_only = layer.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 layer.kv_cache may be empty after profiling cleanup.
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self_kv_cache = layer.kv_cache if not swa_only else None
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swa_kv_cache = layer.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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cls._forward_prefill(
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layer=layer,
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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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cls._forward_decode(
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layer=layer,
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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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@classmethod
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def _forward_decode(
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cls,
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layer: "DeepseekV4MLAAttention",
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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: FlashMLASparseMetadata | 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 // layer.compress_ratio
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is_valid = swa_metadata.is_valid_token[:num_decode_tokens]
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if layer.compress_ratio == 4:
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# C4A: local indices differ per layer (filled by Indexer).
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assert layer.topk_indices_buffer is not None
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global_indices, topk_lens = compute_global_topk_indices_and_lens(
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layer.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 layer.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 = layer.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 layer.compress_ratio <= 1:
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tile_metadata = swa_metadata.tile_sched_swaonly
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elif layer.compress_ratio == 4:
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tile_metadata = swa_metadata.tile_sched_c4a
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elif layer.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={layer.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={layer.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=layer.scale,
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attn_sink=layer.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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@classmethod
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def _forward_prefill(
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cls,
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layer: "DeepseekV4MLAAttention",
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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: FlashMLASparseMetadata | 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_prefills = swa_metadata.num_prefills
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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 layer.compress_ratio == 4:
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assert layer.topk_indices_buffer is not None
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topk_indices = layer.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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# Compressed region must fit the full compressed pool (seq_len //
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# compress_ratio), not just top_k. top_k bounds how many indices
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# the indexer selects, not the pool size it indexes into.
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N = (layer.max_model_len + layer.compress_ratio - 1) // layer.compress_ratio
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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 layer.topk_indices_buffer is not None
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topk_indices = layer.topk_indices_buffer[num_decode_tokens:]
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top_k = 0
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N = 0
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M = N + layer.window_size + layer.max_num_batched_tokens
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chunk_size_const = cls.PREFILL_CHUNK_SIZE
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num_chunks = (num_prefills + chunk_size_const - 1) // chunk_size_const
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workspace_manager = current_workspace_manager()
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kv = workspace_manager.get_simultaneous(
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((chunk_size_const, M, q.shape[-1]), torch.bfloat16),
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)[0]
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for chunk_idx in range(num_chunks):
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chunk_start = chunk_idx * chunk_size_const
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chunk_end = min(chunk_start + chunk_size_const, num_prefills)
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chunk_size = chunk_end - chunk_start
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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] // layer.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 // layer.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=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, 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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layer.window_size,
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layer.compress_ratio,
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top_k,
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M,
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N,
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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=layer.scale,
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attn_sink=layer.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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