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https://github.com/handsomezhuzhu/vllm-npu-plugin.git
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989 lines
42 KiB
Python
989 lines
42 KiB
Python
from dataclasses import dataclass
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from typing import (TYPE_CHECKING, ClassVar, NamedTuple, Optional, Tuple, Type,
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TypeVar)
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import torch
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import torch_npu
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from torch import nn
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from vllm.attention.backends.abstract import (AttentionBackend,
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AttentionMetadata,
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MLAAttentionImpl)
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from vllm.config import VllmConfig, get_current_vllm_config
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from vllm.distributed import get_tensor_model_parallel_world_size, get_tp_group
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from vllm.model_executor.layers.linear import (LinearBase,
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UnquantizedLinearMethod)
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from vllm.utils import cdiv, round_down
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from vllm.v1.attention.backends.utils import AttentionCGSupport
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from vllm_npu.ascend_config import get_ascend_config
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from vllm_npu.attention.attention_v1 import AscendAttentionState
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from vllm_npu.attention.mla_v1 import AscendMLAMetadata
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from vllm_npu.attention.utils import (AscendCommonAttentionMetadata,
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split_decodes_and_prefills)
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from vllm_npu.multistream.base import MSAttentionMetadataSplitConfig
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from vllm_npu.multistream.ms_split import model_input_split_v1_mla_attn
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from vllm_npu.worker.npu_input_batch import InputBatch
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if TYPE_CHECKING:
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from vllm.v1.core.sched.output import SchedulerOutput
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class AscendSFABackend(AttentionBackend):
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accept_output_buffer: bool = True
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@staticmethod
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def get_name() -> str:
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return "ASCEND_SFA"
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@staticmethod
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def get_metadata_cls() -> type["AttentionMetadata"]:
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return AscendSFAMetadata
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@staticmethod
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def get_builder_cls():
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return AscendSFAMetadataBuilder
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@staticmethod
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def get_kv_cache_shape(num_blocks: int, block_size: int, num_kv_heads: int,
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head_size: int) -> tuple[int, ...]:
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return (num_blocks, block_size, num_kv_heads, head_size)
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@staticmethod
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def get_impl_cls() -> Type["AscendSFAImpl"]:
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return AscendSFAImpl
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@dataclass
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class AscendSFAPrefillMetadata:
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""" Prefill Specific Metadata for Ascend"""
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@dataclass
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class ChunkedContextMetadata:
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# New for MLA (compared to FlashAttention)
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# For handling chunked prefill
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cu_seq_lens: torch.Tensor
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starts: torch.Tensor
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seq_tot: list[int]
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max_seq_lens: list[int]
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workspace: torch.Tensor
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chunk_seq_lens: torch.Tensor
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attn_mask: torch.Tensor
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query_lens: list[int]
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seq_lens: list[int]
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context_lens: torch.Tensor
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input_positions: torch.Tensor
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query_start_loc: torch.Tensor
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block_table: torch.Tensor
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max_query_len: int
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max_seq_lens: int
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sin: torch.Tensor
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cos: torch.Tensor
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chunked_context: Optional[ChunkedContextMetadata] = None
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@dataclass
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class AscendSFADecodeMetadata:
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# Input positions for rotrary embeddings since for MLA the rotary
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# position embeddings are applied inside the attention backend
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input_positions: torch.Tensor
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block_table: torch.Tensor
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seq_lens: torch.Tensor
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max_seq_lens: int
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seq_lens_list: list[int]
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actual_seq_lengths_q: torch.Tensor
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sin: torch.Tensor
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cos: torch.Tensor
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attn_mask: Optional[torch.Tensor] = None
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@dataclass
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class AscendSFAMetadata:
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"""Metadata for MLACommon.
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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# NOTE(sang): Definition of context_len, query_len, and seq_len.
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# |---------- N-1 iteration --------|
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# |---------------- N iteration ---------------------|
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# |- tokenA -|......................|-- newTokens ---|
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# |---------- context_len ----------|
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# |-------------------- seq_len ---------------------|
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# |-- query_len ---|
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num_actual_tokens: int # Number of tokens excluding padding.
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slot_mapping: torch.Tensor
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query_start_loc: torch.Tensor
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seq_lens: torch.Tensor
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block_tables: torch.Tensor
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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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# For logging.
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num_input_tokens: int = 0 # Number of tokens including padding.
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query_lens: Optional[list[int]] = None
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# The dimension of the attention heads
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head_dim: Optional[int] = None
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attn_mask: torch.Tensor = None
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# chunked prefill by default if no attn_states passed
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attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
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decode: Optional[AscendSFADecodeMetadata] = None
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prefill: Optional[AscendSFAPrefillMetadata] = None
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enable_dbo_across_dp: bool = False
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def __post_init__(self):
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pass
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# supported_head_sizes = AscendMLABackend.get_supported_head_sizes()
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# if self.head_dim is not None and self.head_dim \
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# not in supported_head_sizes:
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# raise ValueError(
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# f"Only {supported_head_sizes} are supported for head_dim,",
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# f"received {self.head_dim}.")
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def split_metadata_for_multistream(
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self,
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ms_split_config: MSAttentionMetadataSplitConfig,
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) -> list["AscendSFAMetadata"]:
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"""Split metadata for multi-stream with AscendSFAMetadata"""
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return model_input_split_v1_mla_attn(
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ms_split_config=ms_split_config,
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attn_metadata=self,
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_metadata_cls=AscendMLAMetadata,
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)
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M = TypeVar("M", bound=AscendSFAMetadata)
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class AscendSFAMetadataBuilder:
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# Does this backend/builder support ACL Graphs for attention (default: no).
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aclgraph_support: ClassVar[AttentionCGSupport] = \
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AttentionCGSupport.NEVER
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"""
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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# _attn_mask_builder = None
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def __init__(self,
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kv_cache_spec,
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layer_names,
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vllm_config: VllmConfig,
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device: torch.device,
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metadata_cls: Optional[AscendSFAMetadata] = None):
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self.metadata_cls: Optional[AscendSFAMetadata] = metadata_cls \
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if metadata_cls is not None else AscendSFAMetadata # type: ignore
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self.vllm_config = vllm_config
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self.model_config = vllm_config.model_config
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self.device = device
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scheduler_config = vllm_config.scheduler_config
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self.block_size = vllm_config.cache_config.block_size
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self.max_blocks = (vllm_config.model_config.max_model_len +
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self.block_size - 1) // self.block_size
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self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled
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self.speculative_config = vllm_config.speculative_config
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self.decode_threshold = 1
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if self.speculative_config:
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spec_token_num = self.speculative_config.num_speculative_tokens
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self.decode_threshold += spec_token_num
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assert self.decode_threshold <= 16, f"decode_threshold exceeded \
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npu_fused_infer_attention_score TND layout's limit of 16, \
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got {self.decode_threshold}"
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if self.chunked_prefill_enabled:
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self.chunked_prefill_workspace_size = min(
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# Max sure there is enough for 8 full length request or at least
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# 4 pages of cache per request
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max(8 * self.model_config.max_model_len,
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4 * scheduler_config.max_num_seqs * self.block_size),
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# For long-context models try not to over-allocate limiting
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# kv-cache space, limiting it to 64k tokens,
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# which would result in the workspace being:
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# 2*(576)*(64*1024) = 144mb
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# (assuming 576 MLA head dim, and fp16)
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# which would result in up-projected context being
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# 2*(192*128)*(64*1024) = 3gb
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# (assuming 192 QK head dim, 128 heads, and fp16)
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128 * 1024)
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assert self.chunked_prefill_workspace_size >= \
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scheduler_config.max_num_seqs * self.block_size
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self.chunked_prefill_workspace = torch.empty(
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(self.chunked_prefill_workspace_size,
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self.model_config.get_head_size()),
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dtype=self.model_config.dtype,
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device=device,
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)
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self.rope_dim = self.model_config.hf_text_config.qk_rope_head_dim
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self.cos_cache = None
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self.sin_cache = None
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def reorder_batch(self, input_batch: "InputBatch",
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scheduler_output: "SchedulerOutput") -> bool:
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# We now want to reorder the batch so that the "decode" requests are at
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# the front and the "prefill" requests are at the using the least amount
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# swaps possible. (NOTE for now we loosely use "decode" to mean requests
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# where attention is likely memory-bound and "prefill" to mean requests
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# where attention is likely compute-bound, TODO(lucas): figure out a
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# better naming here)
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decodes = []
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prefills = []
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for i, req_id in enumerate(input_batch.req_ids):
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num_tokens = scheduler_output.num_scheduled_tokens[req_id]
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if num_tokens <= self.decode_threshold:
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decodes.append(i)
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else:
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prefills.append(i)
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# We hope that this is fairly minimal since decodes
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# should be around for a number of iterations so hopefully they are
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# relatively stationary (and new request are generally appended to the
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# persistent batch so already should be at the back)
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# To achieve this we loop over the decodes in descending order and
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# the prefills in ascending order. We swap decodes from the "back"
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# i.e. past where the last decode should be in the reodorered with
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# prefills from the front of the batch.
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# `decodes` and `prefills` are already in ascending order just based on
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# the above loop
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num_decodes = len(decodes)
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num_prefills = len(prefills)
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first_prefill = 0
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modified_batch = False
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for i in range(1, min(num_decodes, num_prefills) + 1):
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# If the decode is at the "back" of the batch, i, we can swap it
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# with the prefill closest to the front of the batch
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if decodes[num_decodes - i] >= num_decodes:
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input_batch.swap_states(prefills[first_prefill],
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decodes[num_decodes - i])
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first_prefill += 1
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modified_batch = True
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else:
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break
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# Save for next `build` call
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# TODO(lucas): this is a bit of a hack, we should probably have a
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# better way of doing this
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return modified_batch
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def build(
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self,
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common_prefix_len: int,
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common_attn_metadata: AscendCommonAttentionMetadata,
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model: nn.Module,
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) -> AscendSFAMetadata:
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num_reqs = common_attn_metadata.num_reqs
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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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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(common_attn_metadata, decode_threshold=self.decode_threshold)
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assert num_decodes + num_prefills == num_reqs
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assert num_decode_tokens + num_prefill_tokens == num_actual_tokens
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# Note(simon): be careful about the CPU <> GPU memory movement in this
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# function. We should avoid GPU -> CPU sync as much as possible because
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# it blocks on all previous kernels.
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device = self.device
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block_table = (common_attn_metadata.block_table_tensor[:num_reqs])
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slot_mapping = common_attn_metadata.slot_mapping[:
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num_actual_tokens].to(
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device,
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non_blocking=True)
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input_positions = common_attn_metadata.positions[:
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num_actual_tokens].long(
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)
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if self.cos_cache is None:
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self.cos_cache = model.model.layers[
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model.model.start_layer].self_attn.rotary_emb.cos_cached
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self.sin_cache = model.model.layers[
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model.model.start_layer].self_attn.rotary_emb.sin_cached
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if self.cos_cache.dtype != self.model_config.dtype: # type: ignore
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self.cos_cache = self.cos_cache.to( # type: ignore
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self.model_config.dtype) # type: ignore
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self.sin_cache = self.sin_cache.to( # type: ignore
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self.model_config.dtype) # type: ignore
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query_seq_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
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query_lens = query_seq_lens_cpu[:num_reqs]
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seq_lens = common_attn_metadata.seq_lens_cpu[:num_reqs]
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num_computed_tokens_cpu = (seq_lens - query_lens)
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prefill_metadata = None
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chunked_context_metadata = None
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if num_prefills > 0:
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reqs_start = num_decodes # prefill_start
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tokens_start = num_decode_tokens
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max_query_len = query_lens[reqs_start:].max().item()
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max_seq_lens = seq_lens[reqs_start:].max().item()
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prefill_query_start_loc = query_start_loc[
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reqs_start:] - query_start_loc[reqs_start]
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context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
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max_context_len_cpu = context_lens_cpu.max().item()
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num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
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if self.chunked_prefill_enabled and max_context_len_cpu > 0:
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max_context_chunk = (self.chunked_prefill_workspace_size //
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num_prefills_with_context_cpu)
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max_context_chunk = round_down(max_context_chunk,
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self.block_size)
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assert max_context_chunk > 0
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num_chunks = cdiv(max_context_len_cpu, max_context_chunk)
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chunk_starts = torch.arange(num_chunks, dtype=torch.int32) \
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.unsqueeze(1).expand(-1, num_prefills) * max_context_chunk
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chunk_ends = torch.min(context_lens_cpu.unsqueeze(0),
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chunk_starts + max_context_chunk)
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chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0)
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cu_seq_lens_cpu = torch.zeros(num_chunks,
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num_prefills + 1,
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dtype=torch.int32,
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pin_memory=True)
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torch.cumsum(chunk_seq_lens,
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dim=1,
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out=cu_seq_lens_cpu[:, 1:],
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dtype=torch.int32)
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chunked_context_metadata = \
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AscendSFAPrefillMetadata.ChunkedContextMetadata(
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cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
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starts=chunk_starts.to(device, non_blocking=True),
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seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
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max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
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chunk_seq_lens=chunk_seq_lens,
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workspace=self.chunked_prefill_workspace,
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)
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prefill_input_positions = input_positions[tokens_start:]
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cos = self.cos_cache[
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prefill_input_positions].unsqueeze( # type: ignore
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1).unsqueeze(2)
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sin = self.sin_cache[
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prefill_input_positions].unsqueeze( # type: ignore
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1).unsqueeze(2)
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actual_query_lens = torch.tensor(query_lens[reqs_start:],
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dtype=torch.int32).npu()
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query_lens_prefill_sfa = torch.cumsum(actual_query_lens,
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dim=0).to(torch.int32)
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seq_lens_prefill_sfa = seq_lens[reqs_start:].to(torch.int32).npu()
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prefill_metadata = AscendSFAPrefillMetadata(
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attn_mask=common_attn_metadata.attn_mask,
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query_lens=query_lens_prefill_sfa,
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seq_lens=seq_lens_prefill_sfa,
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context_lens=seq_lens[reqs_start:],
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input_positions=prefill_input_positions,
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block_table=block_table[reqs_start:, ...],
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max_query_len=max_query_len,
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max_seq_lens=max_seq_lens,
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query_start_loc=prefill_query_start_loc,
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chunked_context=chunked_context_metadata,
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sin=sin,
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cos=cos,
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)
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decode_metadata = None
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if num_decodes > 0:
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# Notice that num_decodes != num_decode_tokens in SpecDecoding Scenario
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actual_seq_lengths_q = query_start_loc[1:num_decodes + 1].to(
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torch.int32).npu()
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max_seq_lens = seq_lens[:num_decodes].max().item()
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seq_lens = seq_lens[:num_decodes].to(torch.int32).npu()
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input_positions = input_positions[:num_decode_tokens]
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block_table = block_table[:num_decodes, ...]
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seq_lens_list = seq_lens.tolist()
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cos = self.cos_cache[input_positions].unsqueeze( # type: ignore
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1).unsqueeze(2)
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sin = self.sin_cache[input_positions].unsqueeze( # type: ignore
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1).unsqueeze(2)
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decode_metadata = AscendSFADecodeMetadata(
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input_positions=input_positions,
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block_table=block_table,
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seq_lens=seq_lens,
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seq_lens_list=seq_lens_list,
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max_seq_lens=max_seq_lens,
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attn_mask=common_attn_metadata.spec_attn_mask,
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actual_seq_lengths_q=actual_seq_lengths_q,
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sin=sin,
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cos=cos)
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return self.metadata_cls( # type: ignore
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num_input_tokens=common_attn_metadata.num_input_tokens,
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num_actual_tokens=num_actual_tokens,
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query_lens=query_lens.tolist(),
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slot_mapping=slot_mapping,
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head_dim=self.model_config.get_head_size(),
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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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attn_mask=common_attn_metadata.attn_mask,
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attn_state=common_attn_metadata.attn_state,
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prefill=prefill_metadata,
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decode=decode_metadata,
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query_start_loc=query_start_loc,
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block_tables=block_table,
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seq_lens=seq_lens,
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enable_dbo_across_dp=common_attn_metadata.enable_dbo_across_dp,
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)
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class PrefillSFAPreprocessResult(NamedTuple):
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q_nope: Optional[torch.Tensor] = None
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q_pe: Optional[torch.Tensor] = None
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k_nope: Optional[torch.Tensor] = None
|
|
k_pe: Optional[torch.Tensor] = None
|
|
topk_indices: Optional[torch.Tensor] = None
|
|
query_states: Optional[torch.Tensor] = None
|
|
key_states: Optional[torch.Tensor] = None
|
|
|
|
|
|
class DecodeSFAPreprocessResult(NamedTuple):
|
|
q_nope: Optional[torch.Tensor] = None
|
|
q_pe: Optional[torch.Tensor] = None
|
|
# nope_cache: Optional[torch.Tensor] = None
|
|
# rope_cache: Optional[torch.Tensor] = None
|
|
topk_indices: Optional[torch.Tensor] = None
|
|
query_states: Optional[torch.Tensor] = None
|
|
key_states: Optional[torch.Tensor] = None
|
|
bsz: Optional[int] = None
|
|
|
|
|
|
class AscendSFAImpl(MLAAttentionImpl):
|
|
"""
|
|
NOTE: Please read the comment at the top of the file before trying to
|
|
understand this class
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
num_heads: int,
|
|
head_size: int,
|
|
scale: float,
|
|
num_kv_heads: int,
|
|
alibi_slopes: Optional[list[float]],
|
|
sliding_window: Optional[int],
|
|
kv_cache_dtype: str,
|
|
logits_soft_cap: Optional[float],
|
|
attn_type: str,
|
|
kv_sharing_target_layer_name: Optional[str],
|
|
**kwargs,
|
|
) -> None:
|
|
self.num_heads = num_heads
|
|
self.head_size = head_size
|
|
self.scale = float(scale)
|
|
self.num_kv_heads = num_kv_heads
|
|
self.kv_cache_dtype = kv_cache_dtype
|
|
|
|
# MLA Args
|
|
self.q_lora_rank = kwargs['q_lora_rank']
|
|
self.kv_lora_rank = kwargs['kv_lora_rank']
|
|
self.qk_nope_head_dim = kwargs['qk_nope_head_dim']
|
|
self.qk_rope_head_dim = kwargs['qk_rope_head_dim']
|
|
self.qk_head_dim = kwargs['qk_head_dim']
|
|
self.v_head_dim = kwargs['v_head_dim']
|
|
self.rotary_emb = kwargs['rotary_emb']
|
|
self.q_proj = kwargs['q_proj']
|
|
self.kv_b_proj = kwargs['kv_b_proj']
|
|
self.o_proj = kwargs['o_proj']
|
|
self.indexer = kwargs['indexer']
|
|
self.kv_a_proj_with_mqa = kwargs.get('kv_a_proj_with_mqa', None)
|
|
self.kv_a_layernorm = kwargs.get('kv_a_layernorm', None)
|
|
self.q_a_proj = kwargs.get('q_a_proj', None)
|
|
self.q_a_layernorm = kwargs.get('q_a_layernorm', None)
|
|
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.num_heads_per_rank = self.num_heads // self.tp_size
|
|
if self.q_a_proj is not None:
|
|
self.q_b_proj = self.q_proj
|
|
else:
|
|
self.q_b_proj = None
|
|
|
|
ascend_config = get_ascend_config()
|
|
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
|
|
self.enable_kv_nz = ascend_config.torchair_graph_config.enable_kv_nz
|
|
|
|
vllm_config = get_current_vllm_config()
|
|
self.ring_mla_mask_size = 512
|
|
self.prefill_mask = None
|
|
|
|
# indexer param
|
|
self.dim = self.indexer.dim
|
|
self.n_heads: int = self.indexer.n_heads # 64
|
|
self.head_dim: int = self.indexer.head_dim # 128
|
|
self.index_topk: int = self.indexer.index_topk # 2048
|
|
self.wq_b = self.indexer.wq_b
|
|
self.wk = self.indexer.wk
|
|
self.weights_proj = self.indexer.weights_proj
|
|
self.k_norm = self.indexer.k_norm
|
|
self.softmax_scale = self.indexer.softmax_scale
|
|
|
|
# Adapt torch air graph mode with spec decoding.
|
|
speculative_config = vllm_config.speculative_config
|
|
if speculative_config is not None:
|
|
self.spec_token_num = speculative_config.num_speculative_tokens
|
|
assert self.spec_token_num > 0
|
|
|
|
self.cp_size = 1
|
|
|
|
def process_weights_after_loading(self, act_dtype: torch.dtype):
|
|
|
|
def get_layer_weight(layer):
|
|
WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
|
|
for attr in WEIGHT_NAMES:
|
|
if hasattr(layer, attr):
|
|
return getattr(layer, attr)
|
|
raise AttributeError(
|
|
f"Layer '{layer}' has no recognized weight attribute:"
|
|
f" {WEIGHT_NAMES}.")
|
|
|
|
def get_and_maybe_dequant_weights(layer: LinearBase):
|
|
if not isinstance(layer.quant_method, UnquantizedLinearMethod):
|
|
# NOTE: This should only be used offline, since it's O(N^3)
|
|
eye = torch.eye(layer.input_size_per_partition,
|
|
dtype=act_dtype,
|
|
device=get_layer_weight(layer).device)
|
|
dequant_weights = layer.quant_method.apply(layer,
|
|
eye,
|
|
bias=None)
|
|
del eye
|
|
# standardize to (output, input)
|
|
return dequant_weights.T
|
|
return layer.weight
|
|
|
|
# we currently do not have quantized bmm's which are needed for
|
|
# `W_UV` and `W_UK_T`, we we just store fp16/bf16 copies and perform
|
|
# the bmm's in 16-bit, the extra memory overhead of this is fairly low
|
|
kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
|
|
assert kv_b_proj_weight.shape == (
|
|
self.kv_lora_rank,
|
|
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim)), (
|
|
f"{kv_b_proj_weight.shape=}, "
|
|
f"{self.kv_lora_rank=}, "
|
|
f"{self.num_heads=}, "
|
|
f"{self.qk_nope_head_dim=}, "
|
|
f"{self.v_head_dim=}")
|
|
kv_b_proj_weight = kv_b_proj_weight.view(
|
|
self.kv_lora_rank,
|
|
self.num_heads,
|
|
self.qk_nope_head_dim + self.v_head_dim,
|
|
)
|
|
|
|
self.kv_b_proj_w_k, self.kv_b_proj_w_v = kv_b_proj_weight.split(
|
|
[self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
|
|
|
# Convert from (L, N, V) to (N, L, V)
|
|
self.kv_b_proj_w_v = self.kv_b_proj_w_v.transpose(0, 1).contiguous()
|
|
# Convert from (L, N, P) to (N, P, L)
|
|
self.kv_b_proj_w_k = self.kv_b_proj_w_k.permute(1, 2, 0).contiguous()
|
|
|
|
# Waiting for BMM NZ support
|
|
# self.W_UV.data = torch_npu.npu_format_cast(self.W_UV.data, 29)
|
|
# self.W_UK_T.data = torch_npu.npu_format_cast(self.W_UK_T.data, 29)
|
|
|
|
def _sfa_preprocess(self, hidden_states, kv_cache, attn_metadata,
|
|
need_gather_q_kv):
|
|
# SFA Preprocess:
|
|
# 1. Perform q_a_proj and q_a_layernorm to obtain q_c
|
|
# 2. Perform kv_a_proj_with_mqa to obtain kv_no_split
|
|
# 3. If need_gather_q_kv, perform all_gather.
|
|
# 4. Preprocess decode tokens, write kv cache and get:
|
|
# decode_ql_nope, decode_q_pe, decode_k_pe, decode_k_nope
|
|
# 5. Preprocess prefill tokens, write kv cache and get:
|
|
# prefill_q_nope, prefill_q_pe, prefill_k_nope, prefill_k_pe, prefill_value
|
|
has_decode = attn_metadata.num_decodes > 0
|
|
has_prefill = attn_metadata.num_prefills > 0
|
|
|
|
num_decode_tokens = attn_metadata.num_decode_tokens
|
|
num_actual_tokens = attn_metadata.num_actual_tokens
|
|
if need_gather_q_kv:
|
|
# q_c = get_tp_group().all_gather(q_c, 0)
|
|
# kv_no_split = get_tp_group().all_gather(kv_no_split, 0)
|
|
hidden_states = get_tp_group().all_gather(hidden_states, 0)
|
|
# hidden_states_decode = hidden_states[:num_decode_tokens]
|
|
# if self.q_a_proj is not None:
|
|
# npu_prefetch(self.q_a_proj.weight,
|
|
# hidden_states,
|
|
# enabled=self.enable_prefetch)
|
|
# ckq = self.q_a_proj(hidden_states) # q down
|
|
# q_c = self.q_a_layernorm(ckq) # q down layernorm
|
|
# else:
|
|
# q_c = hidden_states
|
|
|
|
# kv_no_split = self.kv_a_proj_with_mqa(hidden_states) # c_kv
|
|
# Process for shared_expert_dp
|
|
|
|
decode_preprocess_res = None
|
|
prefill_preprocess_res = None
|
|
# Preprocess for decode tokens
|
|
if has_decode:
|
|
q_len = 1
|
|
hidden_states_decode = hidden_states[:num_decode_tokens]
|
|
decode_kq = self.q_a_proj(hidden_states_decode) # q down
|
|
decode_q_c = self.q_a_layernorm(decode_kq) # q down layernorm
|
|
decode_kv_no_split = self.kv_a_proj_with_mqa(
|
|
hidden_states_decode) # c_kv
|
|
|
|
# decode_q_c = q_c[:num_decode_tokens]
|
|
decode_slot_mapping = attn_metadata.slot_mapping[:
|
|
num_decode_tokens]
|
|
# decode_kv_no_split = decode_kv_no_split[:num_decode_tokens]
|
|
|
|
decode_q = self.q_b_proj(decode_q_c)
|
|
bsz, _ = decode_q.shape
|
|
decode_q = decode_q.view(bsz, self.num_heads, 1, self.qk_head_dim)
|
|
decode_q_nope, decode_q_pe = torch.split(
|
|
decode_q, [self.qk_nope_head_dim, self.qk_rope_head_dim],
|
|
dim=-1)
|
|
decode_q_nope = decode_q_nope.view(
|
|
-1, self.num_heads, self.qk_nope_head_dim).transpose(0, 1)
|
|
decode_q_nope = (torch.matmul(decode_q_nope,
|
|
self.kv_b_proj_w_k).transpose(
|
|
1,
|
|
0).view(bsz, q_len,
|
|
self.num_heads,
|
|
self.kv_lora_rank))
|
|
|
|
# stream2 kv
|
|
key_cache = kv_cache[0]
|
|
value_cache = kv_cache[1]
|
|
cos = attn_metadata.decode.cos
|
|
sin = attn_metadata.decode.sin
|
|
cos_q, sin_q = cos, sin
|
|
cos = cos.view(-1, 1, 1, self.qk_rope_head_dim)
|
|
sin = sin.view(-1, 1, 1, self.qk_rope_head_dim)
|
|
|
|
decode_kv_no_split = decode_kv_no_split.unsqueeze(1).unsqueeze(1)
|
|
decode_k_rope, decode_k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache(
|
|
decode_kv_no_split,
|
|
self.kv_a_layernorm.weight,
|
|
cos,
|
|
sin,
|
|
decode_slot_mapping.to(torch.int64),
|
|
value_cache,
|
|
key_cache,
|
|
c_kv_scale=None,
|
|
epsilon=self.kv_a_layernorm.variance_epsilon,
|
|
cache_mode='PA') # adapter NZ
|
|
# nz_block_size = 16
|
|
# KVCACHE_NZ_DIM = 16
|
|
# decode_k_nope = decode_k_nope.view(block_num, 1, self.kv_lora_rank // nz_block_size, block_size, nz_block_size)
|
|
# decode_k_rope = decode_k_rope.view(block_num, 1, self.qk_rope_head_dim // KVCACHE_NZ_DIM, block_size, KVCACHE_NZ_DIM)
|
|
|
|
decode_q_pe = torch_npu.npu_interleave_rope(decode_q_pe, cos,
|
|
sin) # BNSD
|
|
|
|
decode_q_nope = decode_q_nope.view(bsz, self.num_heads,
|
|
self.kv_lora_rank)
|
|
decode_q_pe = decode_q_pe.view(bsz, self.num_heads, -1)
|
|
|
|
topk_indices = self.indexer_select(hidden_states_decode,
|
|
decode_q_c,
|
|
attn_metadata=attn_metadata,
|
|
cos=cos,
|
|
sin=sin,
|
|
kv_cache=kv_cache)
|
|
|
|
query_states = (decode_q_nope, decode_q_pe)
|
|
key_states = (decode_k_nope, decode_k_rope)
|
|
decode_preprocess_res = DecodeSFAPreprocessResult(
|
|
q_nope=decode_q_nope,
|
|
q_pe=decode_q_pe,
|
|
# nope_cache = nope_cache,
|
|
# rope_cache = rope_cache,
|
|
topk_indices=topk_indices,
|
|
query_states=query_states,
|
|
key_states=key_states,
|
|
bsz=bsz,
|
|
)
|
|
|
|
# Preprocess for prefill tokens
|
|
if has_prefill:
|
|
bsz = 1
|
|
|
|
hidden_states_prefill = hidden_states[
|
|
num_decode_tokens:num_actual_tokens]
|
|
prefill_kq = self.q_a_proj(hidden_states_prefill) # q down
|
|
prefill_q_c = self.q_a_layernorm(prefill_kq) # q down layernorm
|
|
prefill_kv_no_split = self.kv_a_proj_with_mqa(
|
|
hidden_states_prefill) # c_kv
|
|
|
|
# prefill_q_c = q_c[
|
|
# num_decode_tokens:num_actual_tokens]
|
|
prefill_slot_mapping = attn_metadata.slot_mapping[
|
|
num_decode_tokens:num_actual_tokens]
|
|
# decode_kv_no_split = decode_kv_no_split[:num_decode_tokens]
|
|
|
|
prefill_slot_mapping = attn_metadata.slot_mapping[
|
|
num_decode_tokens:num_actual_tokens]
|
|
# prefill_kv_no_split = kv_no_split[
|
|
# num_decode_tokens:num_actual_tokens]
|
|
# prefill_qr = prefill_q_c[num_decode_tokens:num_actual_tokens]
|
|
prefill_qr = prefill_q_c
|
|
prefill_q = self.q_b_proj(prefill_qr)
|
|
prefill_q = prefill_q.view(-1, self.num_heads, self.qk_head_dim)
|
|
prefill_q_nope, prefill_q_pe = torch.split(
|
|
prefill_q, [self.qk_nope_head_dim, self.qk_rope_head_dim],
|
|
dim=-1)
|
|
prefill_q_nope = prefill_q_nope.view(
|
|
-1, self.num_heads, self.qk_nope_head_dim).transpose(0, 1)
|
|
prefill_q_nope = (torch.matmul(prefill_q_nope,
|
|
self.kv_b_proj_w_k).transpose(
|
|
1,
|
|
0).view(-1, self.num_heads,
|
|
self.kv_lora_rank))
|
|
prefill_q_pe = prefill_q_pe.unsqueeze(2)
|
|
|
|
# stream2 kv
|
|
|
|
nope_cache = kv_cache[0]
|
|
rope_cache = kv_cache[1]
|
|
cos = attn_metadata.prefill.cos
|
|
sin = attn_metadata.prefill.sin
|
|
cos_q, sin_q = cos, sin
|
|
|
|
# cos = cos.view(-1, 1, 1, self.qk_rope_head_dim)
|
|
# sin = sin.view(-1, 1, 1, self.qk_rope_head_dim)
|
|
|
|
prefill_q_pe = torch_npu.npu_interleave_rope(
|
|
prefill_q_pe, cos_q, sin_q) # BNSD
|
|
prefill_q_pe = prefill_q_pe.squeeze(2) #BSH
|
|
# q[..., self.qk_nope_head_dim:] = prefill_q_pe # TODO:????
|
|
|
|
prefill_latent_cache = prefill_kv_no_split # (B,S,N,D)
|
|
prefill_k_pe, prefill_k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache(
|
|
prefill_latent_cache.view(
|
|
-1, 1, 1, self.kv_lora_rank + self.qk_rope_head_dim),
|
|
self.kv_a_layernorm.weight,
|
|
cos.view(-1, 1, 1, self.qk_rope_head_dim),
|
|
sin.view(-1, 1, 1, self.qk_rope_head_dim),
|
|
prefill_slot_mapping.to(torch.int64),
|
|
rope_cache,
|
|
nope_cache,
|
|
k_rope_scale=None,
|
|
c_kv_scale=None,
|
|
k_rope_offset=None,
|
|
c_kv_offset=None,
|
|
epsilon=self.kv_a_layernorm.variance_epsilon,
|
|
cache_mode="PA")
|
|
|
|
topk_indices = self.indexer_select(x=hidden_states_prefill,
|
|
qr=prefill_qr,
|
|
kv_cache=kv_cache,
|
|
cos=cos,
|
|
sin=sin,
|
|
attn_metadata=attn_metadata)
|
|
query_states = (prefill_q_nope, prefill_q_pe)
|
|
key_states = (prefill_k_nope, prefill_k_pe)
|
|
prefill_preprocess_res = PrefillSFAPreprocessResult(
|
|
q_nope=prefill_q_nope,
|
|
q_pe=prefill_q_pe,
|
|
topk_indices=topk_indices,
|
|
k_nope=prefill_k_nope,
|
|
k_pe=prefill_k_pe,
|
|
query_states=query_states,
|
|
key_states=key_states,
|
|
)
|
|
|
|
return decode_preprocess_res, prefill_preprocess_res
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor, # query in unified attn
|
|
kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
|
attn_metadata: M,
|
|
need_gather_q_kv: bool = False,
|
|
output: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
assert output is not None, "Output tensor must be provided."
|
|
if attn_metadata is None:
|
|
# Profiling run.
|
|
return output.fill_(0)
|
|
num_actual_tokens = attn_metadata.num_actual_tokens
|
|
assert attn_metadata.num_decodes is not None and \
|
|
attn_metadata.num_prefills is not None and \
|
|
attn_metadata.num_decode_tokens is not None
|
|
num_decode_tokens = attn_metadata.num_decode_tokens
|
|
# Inputs and outputs may be padded for CUDA graphs
|
|
output = output[:num_actual_tokens, ...]
|
|
o_proj_input_shape = (num_actual_tokens,
|
|
self.num_heads * self.v_head_dim)
|
|
o_proj_input = torch.empty(o_proj_input_shape,
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device)
|
|
|
|
# SFA Preprocess
|
|
decode_preprocess_res, prefill_preprocess_res = self._sfa_preprocess(
|
|
hidden_states, kv_cache, attn_metadata, need_gather_q_kv)
|
|
|
|
if decode_preprocess_res is not None:
|
|
# bsz, q_len, _, _ = query_states[0].shape
|
|
decode_attn_output = self.apply_attention_fusion(
|
|
query_states=decode_preprocess_res.query_states,
|
|
key_states=decode_preprocess_res.key_states,
|
|
attn_metadata=attn_metadata,
|
|
topk_indices=decode_preprocess_res.topk_indices)
|
|
o_proj_input[:num_decode_tokens] = decode_attn_output
|
|
|
|
if prefill_preprocess_res is not None:
|
|
prefill_attn_output = self.apply_attention_fusion(
|
|
query_states=prefill_preprocess_res.query_states,
|
|
key_states=prefill_preprocess_res.key_states,
|
|
attn_metadata=attn_metadata,
|
|
topk_indices=prefill_preprocess_res.topk_indices)
|
|
o_proj_input[num_decode_tokens:] = prefill_attn_output
|
|
|
|
output[...] = self.mla_epilog(o_proj_input, absorb=True)
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|
return output
|
|
|
|
def apply_attention_fusion(self, query_states, key_states, topk_indices,
|
|
attn_metadata: M):
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|
# repeat k/v heads if n_kv_heads < n_heads
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|
q_nope, q_pe = query_states
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|
k_nope, k_rope = key_states
|
|
|
|
if attn_metadata.prefill is not None:
|
|
|
|
prefill_metadata = attn_metadata.prefill
|
|
|
|
slc_fa_fusion = torch.ops.custom.npu_sparse_flash_attention(
|
|
query=q_nope,
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|
key=k_nope,
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|
value=k_nope,
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|
sparse_indices=topk_indices,
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|
scale_value=self.scale,
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|
sparse_block_size=1,
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|
block_table=prefill_metadata.block_table,
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|
actual_seq_lengths_query=prefill_metadata.query_lens,
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|
actual_seq_lengths_kv=prefill_metadata.seq_lens,
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|
query_rope=q_pe,
|
|
key_rope=k_rope,
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|
layout_query="TND",
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|
layout_kv="PA_BSND",
|
|
sparse_mode=3,
|
|
)
|
|
|
|
elif attn_metadata.decode is not None:
|
|
decode_metadata = attn_metadata.decode
|
|
|
|
slc_fa_fusion = torch.ops.custom.npu_sparse_flash_attention(
|
|
query=q_nope,
|
|
key=k_nope,
|
|
value=k_nope,
|
|
sparse_indices=topk_indices,
|
|
scale_value=self.scale,
|
|
sparse_block_size=1,
|
|
block_table=attn_metadata.decode.block_table,
|
|
actual_seq_lengths_query=decode_metadata.actual_seq_lengths_q,
|
|
actual_seq_lengths_kv=decode_metadata.seq_lens,
|
|
query_rope=q_pe,
|
|
key_rope=k_rope,
|
|
layout_query="TND",
|
|
layout_kv="PA_BSND",
|
|
sparse_mode=3,
|
|
)
|
|
slc_fa_fusion = slc_fa_fusion.squeeze(1)
|
|
|
|
slc_fa_fusion = slc_fa_fusion.transpose(0, 1)
|
|
|
|
# input shape [N//attn_tp_size, T(bs*q_len), D]
|
|
# output shape [T(bs*q_len), N//attn_tp_size, D]
|
|
attn_output = torch.matmul(slc_fa_fusion,
|
|
self.kv_b_proj_w_v).transpose(1, 0).reshape(
|
|
-1, self.num_heads * self.v_head_dim)
|
|
# Note: Considering the fusion rules of TBMM, attn_output shape requires a 3-dim shape, and
|
|
# with appropriate tensor stride for the later 'view' operation if oproj_tp_size > 1.
|
|
# after reshape: [T(bs*q_len), 1, N//attn_tp_size*D]
|
|
# attn_output = attn_output.reshape(-1, self.num_heads * self.v_head_dim)
|
|
|
|
return attn_output
|
|
|
|
def mla_epilog(self,
|
|
attn_output: torch.Tensor = None,
|
|
absorb: bool = False):
|
|
# TODO: need to check
|
|
attn_output = self.o_proj(attn_output.reshape(attn_output.shape[0],
|
|
-1),
|
|
is_prefill=True,
|
|
is_force_scatter=False)
|
|
|
|
return attn_output
|
|
|
|
def indexer_select(
|
|
self,
|
|
x: torch.Tensor,
|
|
qr: torch.Tensor,
|
|
kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
|
cos,
|
|
sin,
|
|
attn_metadata: M,
|
|
):
|
|
if attn_metadata.prefill is not None:
|
|
actual_seq_lengths_query = attn_metadata.prefill.query_lens
|
|
actual_seq_lengths_key = attn_metadata.prefill.seq_lens
|
|
block_table = attn_metadata.prefill.block_table
|
|
elif attn_metadata.decode is not None:
|
|
actual_seq_lengths_query = attn_metadata.decode.actual_seq_lengths_q
|
|
actual_seq_lengths_key = attn_metadata.decode.seq_lens
|
|
block_table = attn_metadata.decode.block_table
|
|
|
|
cos_q, sin_q = cos, sin
|
|
cos = cos.view(-1, 1, 1, self.qk_rope_head_dim)
|
|
sin = sin.view(-1, 1, 1, self.qk_rope_head_dim)
|
|
|
|
# q process in new stream
|
|
q = self.wq_b(qr) # [b,s,1536] @ [1536,64*128] = [b,s,64*128]
|
|
q = q.view(-1, self.n_heads, self.head_dim) # [b,s,64,128]
|
|
q_pe, q_nope = torch.split(
|
|
q, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim],
|
|
dim=-1) # [b,s,64,64+64]
|
|
|
|
q_pe = q_pe.unsqueeze(2)
|
|
q_pe = torch_npu.npu_interleave_rope(q_pe, cos_q, sin_q)
|
|
q_pe = q_pe.squeeze(2)
|
|
q = torch.cat([q_pe, q_nope], dim=-1) # [b*s,64,128]
|
|
|
|
k_proj = self.wk(x) # [b,s,7168] @ [7168,128] = [b,s,128]
|
|
k = self.k_norm(k_proj).unsqueeze(1)
|
|
k_pe, k_nope = torch.split(
|
|
k, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim],
|
|
dim=-1) # [b,s,64+64]
|
|
|
|
k_pe = k_pe.unsqueeze(2)
|
|
k_pe = torch_npu.npu_interleave_rope(k_pe, cos, sin)
|
|
k_pe = k_pe.squeeze(2)
|
|
|
|
k = torch.cat([k_pe, k_nope], dim=-1) # [b*s,128]
|
|
|
|
if kv_cache is not None:
|
|
torch_npu.npu_scatter_nd_update_(kv_cache[2].view(-1, k.shape[-1]),
|
|
attn_metadata.slot_mapping.view(
|
|
-1, 1),
|
|
k.view(-1,
|
|
k.shape[-1])) # b, s, n, d
|
|
|
|
weights = self.weights_proj(x)
|
|
|
|
topk_indices = torch.ops.custom.npu_lightning_indexer(
|
|
query=q,
|
|
key=kv_cache[2],
|
|
weights=weights,
|
|
actual_seq_lengths_query=actual_seq_lengths_query,
|
|
actual_seq_lengths_key=actual_seq_lengths_key,
|
|
block_table=block_table,
|
|
layout_query="TND",
|
|
layout_key="PA_BSND",
|
|
sparse_count=2048,
|
|
sparse_mode=3)
|
|
return topk_indices
|