2e94b9cfbb
Signed-off-by: Yang Chen <yangche@fb.com> Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com> Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> Co-authored-by: Yang Chen <yangche@fb.com>
140 lines
4.9 KiB
Python
140 lines
4.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple, Type
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import torch
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from vllm.attention.backends.abstract import AttentionType
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from vllm.attention.ops.flashmla import (flash_mla_with_kvcache,
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get_mla_metadata,
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is_flashmla_supported)
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from vllm.logger import init_logger
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from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
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MLACommonImpl,
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MLACommonMetadata,
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MLACommonMetadataBuilder)
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logger = init_logger(__name__)
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class FlashMLABackend(MLACommonBackend):
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@staticmethod
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def get_name() -> str:
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return "FLASHMLA_VLLM_V1"
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@staticmethod
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def get_metadata_cls() -> Type["FlashMLAMetadata"]:
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return FlashMLAMetadata
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@staticmethod
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def get_builder_cls() -> Type["FlashMLAMetadataBuilder"]:
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return FlashMLAMetadataBuilder
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@staticmethod
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def get_impl_cls() -> Type["FlashMLAImpl"]:
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return FlashMLAImpl
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@dataclass
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class FlashMLAMetadata(MLACommonMetadata):
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decode_tile_scheduler_metadata: Optional[Tuple[torch.Tensor,
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torch.Tensor]] = None
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decode_num_splits: Optional[torch.Tensor] = None
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class FlashMLAMetadataBuilder(MLACommonMetadataBuilder[FlashMLAMetadata]):
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def __init__(self, runner):
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super().__init__(runner, cls=FlashMLAMetadata)
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self.num_q_heads = self.runner.model_config.get_num_attention_heads(
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self.runner.parallel_config)
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def build(self, num_reqs: int, num_actual_tokens: int, max_query_len: int,
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common_prefix_len: int):
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m = super().build(num_reqs, num_actual_tokens, max_query_len,
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common_prefix_len)
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if m.num_decode_tokens is not None and m.num_decode_tokens > 0:
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m.decode_tile_scheduler_metadata, m.decode_num_splits = \
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get_mla_metadata(
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m.seq_lens[:m.num_decode_tokens],
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self.num_q_heads,
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1, # MQA for the decode path
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)
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return m
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class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: int,
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alibi_slopes: Optional[List[float]],
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sliding_window: Optional[int],
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kv_cache_dtype: str,
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blocksparse_params: Optional[Dict[str, Any]],
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logits_soft_cap: Optional[float],
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attn_type: str,
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# MLA Specific Arguments
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**mla_args) -> None:
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super().__init__(num_heads, head_size, scale, num_kv_heads,
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alibi_slopes, sliding_window, kv_cache_dtype,
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blocksparse_params, logits_soft_cap, attn_type,
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**mla_args)
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assert is_flashmla_supported(), \
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"FlashMLA is not supported on this device"
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unsupported_features = [
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alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
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]
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if any(unsupported_features):
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raise NotImplementedError(
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"FlashMLAImpl does not support one of the following: "
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"alibi_slopes, sliding_window, blocksparse_params, "
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"logits_soft_cap")
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if attn_type != AttentionType.DECODER:
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raise NotImplementedError("Encoder self-attention and "
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"encoder/decoder cross-attention "
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"are not implemented for "
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"FlashMLAImpl")
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def _forward_decode(
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self,
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q_nope: torch.Tensor,
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q_pe: torch.Tensor,
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kv_c_and_k_pe_cache: torch.Tensor,
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attn_metadata: FlashMLAMetadata,
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) -> torch.Tensor:
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assert kv_c_and_k_pe_cache.numel() > 0
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if self.kv_cache_dtype.startswith("fp8"):
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raise NotImplementedError("FP8 FlashMLA not yet supported")
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q = torch.cat([q_nope, q_pe], dim=-1)\
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.unsqueeze(1) # Add seqlen dim of 1 (decode)
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o, _ = flash_mla_with_kvcache(
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q=q,
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k_cache=kv_c_and_k_pe_cache.unsqueeze(-2), # Add head dim of 1
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block_table=attn_metadata.block_table[:attn_metadata.num_decodes,
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...],
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cache_seqlens=attn_metadata.seq_lens[:attn_metadata.
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num_decode_tokens],
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head_dim_v=self.kv_lora_rank,
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tile_scheduler_metadata=attn_metadata.
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decode_tile_scheduler_metadata,
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num_splits=attn_metadata.decode_num_splits,
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softmax_scale=self.scale,
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causal=True,
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)
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return self._v_up_proj_and_o_proj(o)
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