18b90b501d
### What this PR does / why we need it? Provide high-performance AscendC operators lightning_indexer and sparse_flash_attention to boost the execution performance of the DeepSeek v3.2 model. Meanwhile, adapt the two AscendC operators to vllm-ascend framework. ### Does this PR introduce _any_ user-facing change? No (only underlying operator optimizations, with no user-facing changes) ### How was this patch tested? - vLLM version: v0.11.2 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2 Signed-off-by: MingYang119 <songmingyang@huawei.com>
569 lines
23 KiB
Python
569 lines
23 KiB
Python
from dataclasses import dataclass
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from typing import TYPE_CHECKING, ClassVar, Optional, Tuple, Type, 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, MLAAttentionImpl
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from vllm.config import VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.linear import (LinearBase,
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UnquantizedLinearMethod)
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from vllm.triton_utils import HAS_TRITON
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from vllm.v1.attention.backends.utils import AttentionCGSupport
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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from vllm_ascend.attention.mla_v1 import MAX_O_PROJ_PREFETCH_SIZE
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from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata,
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wait_for_kv_layer_from_connector)
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from vllm_ascend.ops.triton.rope import rope_forward_triton
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from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
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from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
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is_enable_nz)
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from vllm_ascend.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_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 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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has_prefill: bool
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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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seq_lens: torch.Tensor
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cum_query_lens: torch.Tensor
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block_tables: torch.Tensor
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sin: torch.Tensor
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cos: torch.Tensor
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# For logging.
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num_input_tokens: int = 0 # Number of tokens including padding.
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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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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.UNIFORM_SINGLE_TOKEN_DECODE
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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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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.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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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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# No need to reorder for Ascend SFA
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return False
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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_cpu = common_attn_metadata.query_start_loc_cpu
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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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query_start_loc = common_attn_metadata.query_start_loc
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query_lens = query_start_loc[1:] - query_start_loc[:-1]
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has_prefill = any(query_lens > self.decode_threshold)
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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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cum_query_lens = query_start_loc_cpu[1:num_reqs + 1].to(
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torch.int32).to(device, non_blocking=True)
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seq_lens = common_attn_metadata.seq_lens_cpu[:num_reqs].to(
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torch.int32).to(device, non_blocking=True)
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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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return self.metadata_cls( # type: ignore
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has_prefill=has_prefill,
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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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cum_query_lens=cum_query_lens,
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seq_lens=seq_lens,
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slot_mapping=slot_mapping,
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head_dim=self.model_config.get_head_size(),
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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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block_tables=block_table,
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sin=sin,
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cos=cos)
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def build_for_graph_capture(
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self,
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common_attn_metadata: AscendCommonAttentionMetadata,
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attn_state: AscendAttentionState = AscendAttentionState.DecodeOnly,
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model: Optional[nn.Module] = None,
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):
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if attn_state == AscendAttentionState.DecodeOnly:
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attn_metadata = self.build(
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common_prefix_len=0,
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common_attn_metadata=common_attn_metadata,
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model=model,
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)
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else:
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raise NotImplementedError(
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"Currently we only support building dummy metadata for DecodeOnly state"
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)
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attn_metadata.attn_state = attn_state
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return attn_metadata
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class AscendSFAImpl(MLAAttentionImpl):
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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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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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logits_soft_cap: Optional[float],
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attn_type: str,
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kv_sharing_target_layer_name: Optional[str],
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**kwargs,
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) -> None:
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self.num_heads = num_heads
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self.head_size = head_size
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self.scale = float(scale)
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self.num_kv_heads = num_kv_heads
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self.kv_cache_dtype = kv_cache_dtype
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# MLA Args
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self.q_lora_rank = kwargs['q_lora_rank']
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self.kv_lora_rank = kwargs['kv_lora_rank']
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self.qk_nope_head_dim = kwargs['qk_nope_head_dim']
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self.qk_rope_head_dim = kwargs['qk_rope_head_dim']
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self.qk_head_dim = kwargs['qk_head_dim']
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self.v_head_dim = kwargs['v_head_dim']
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self.rotary_emb = kwargs['rotary_emb']
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self.q_proj = kwargs['q_proj'] if self.q_lora_rank is None else kwargs[
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'q_b_proj']
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self.fused_qkv_a_proj = kwargs.get('fused_qkv_a_proj', None)
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self.kv_b_proj = kwargs['kv_b_proj']
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self.o_proj = kwargs['o_proj']
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self.indexer = kwargs['indexer']
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self.kv_a_proj_with_mqa = kwargs.get('kv_a_proj_with_mqa', None)
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self.kv_a_layernorm = kwargs.get('kv_a_layernorm', None)
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self.q_a_layernorm = kwargs.get('q_a_layernorm', None)
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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self.tp_size = get_tensor_model_parallel_world_size()
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self.num_heads_per_rank = self.num_heads // self.tp_size
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self.q_b_proj = kwargs['q_b_proj']
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ascend_config = get_ascend_config()
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self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
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self.enable_prefetch = ascend_config.weight_prefetch_config.enabled
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self.enable_kv_nz = ascend_config.torchair_graph_config.enable_kv_nz
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assert self.indexer is not None, "Indexer is required for DSA."
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# indexer param
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self.n_head: int = self.indexer.n_head # 64
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self.head_dim: int = self.indexer.head_dim # 128
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self.wq_b = self.indexer.wq_b
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self.wk = self.indexer.wk
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self.weights_proj = self.indexer.weights_proj
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self.k_norm = self.indexer.k_norm
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self.cp_size = 1
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def process_weights_after_loading(self, act_dtype: torch.dtype):
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def get_layer_weight(layer):
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WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
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for attr in WEIGHT_NAMES:
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try:
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return getattr(layer, attr)
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except AttributeError:
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pass
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raise AttributeError(
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f"Layer '{layer}' has no recognized weight attribute:"
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f" {WEIGHT_NAMES}.")
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def get_and_maybe_dequant_weights(layer: LinearBase):
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if not isinstance(layer.quant_method, UnquantizedLinearMethod):
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# NOTE: This should only be used offline, since it's O(N^3)
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eye = torch.eye(layer.input_size_per_partition,
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dtype=act_dtype,
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device=get_layer_weight(layer).device)
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dequant_weights = layer.quant_method.apply(layer,
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eye,
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bias=None)
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del eye
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# standardize to (output, input)
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return dequant_weights.T
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# Weight will be reshaped next. To be on the safe side, the format
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# of the weight should be reverted to FRACTAL_AND.
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layer.weight.data = torch_npu.npu_format_cast(
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layer.weight.data, ACL_FORMAT_FRACTAL_ND)
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return layer.weight
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# we currently do not have quantized bmm's which are needed for
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# `W_UV` and `W_UK_T`, we we just store fp16/bf16 copies and perform
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# the bmm's in 16-bit, the extra memory overhead of this is fairly low
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kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
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assert kv_b_proj_weight.shape == (
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim)), (
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f"{kv_b_proj_weight.shape=}, "
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f"{self.kv_lora_rank=}, "
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f"{self.num_heads=}, "
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f"{self.qk_nope_head_dim=}, "
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f"{self.v_head_dim=}")
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kv_b_proj_weight = kv_b_proj_weight.view(
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self.kv_lora_rank,
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self.num_heads,
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self.qk_nope_head_dim + self.v_head_dim,
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)
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W_UK, W_UV = kv_b_proj_weight.split(
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[self.qk_nope_head_dim, self.v_head_dim], dim=-1)
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# Convert from (L, N, V) to (N, L, V)
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self.W_UV = W_UV.transpose(0, 1).contiguous()
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# Convert from (L, N, P) to (N, P, L)
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self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
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# Function `get_and_maybe_dequant_weights` will cast the weights to
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# FRACTAL_AND. So we need to cast to FRACTAL_NZ again.
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if is_enable_nz():
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self.kv_b_proj.weight.data = torch_npu.npu_format_cast(
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self.kv_b_proj.weight.data, ACL_FORMAT_FRACTAL_NZ)
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# Waiting for BMM NZ support
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# self.W_UV.data = torch_npu.npu_format_cast(self.W_UV.data, 29)
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# self.W_UK_T.data = torch_npu.npu_format_cast(self.W_UK_T.data, 29)
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def _v_up_proj(self, x):
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if self.W_UV.shape[0] * self.W_UV.shape[1] < 65536:
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x = x.view(-1, self.num_heads, self.kv_lora_rank)
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x = torch_npu.npu_transpose_batchmatmul(x,
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self.W_UV,
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perm_x1=[1, 0, 2],
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perm_x2=[0, 1, 2],
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perm_y=[1, 0, 2])
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x = x.reshape(-1, self.num_heads * self.v_head_dim)
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else:
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# Convert from (B, N, L) to (N, B, L)
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x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
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# # Multiply (N, B, L) x (N, L, V) -> (N, B, V)
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x = torch.bmm(x, self.W_UV)
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# # Convert from (N, B, V) to (B, N * V)
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x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
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return x
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# Return `ql_nope`, `q_pe`
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def _q_proj_and_k_up_proj(self, x):
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q_nope, q_pe = self.q_proj(x)[0]\
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.view(-1, self.num_heads, self.qk_head_dim)\
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.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
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# Convert from (B, N, P) to (N, B, P)
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q_nope = q_nope.transpose(0, 1)
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# Multiply (N, B, P) x (N, P, L) -> (N, B, L)
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ql_nope = torch.bmm(q_nope, self.W_UK_T)
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# Convert from (N, B, L) to (B, N, L)
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return ql_nope.transpose(0, 1), q_pe
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def exec_kv(
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self,
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kv_no_split: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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kv_cache: Tuple,
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slots: torch.Tensor,
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):
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B = kv_no_split.shape[0]
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N = self.num_kv_heads
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S = 1
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# npu_kv_rmsnorm_rope_cache needs [B, N, S, D]
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kv_no_split = kv_no_split.view(
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B, N, S, self.kv_lora_rank + self.qk_rope_head_dim)
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cache_mode = "PA_NZ" if self.enable_kv_nz else "PA"
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k_pe, k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache(
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kv_no_split,
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self.kv_a_layernorm.weight,
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cos,
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sin,
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slots.to(torch.int64),
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kv_cache[1],
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kv_cache[0],
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epsilon=self.kv_a_layernorm.variance_epsilon,
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cache_mode=cache_mode,
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)
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return k_pe, k_nope
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def rope_single(
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self,
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x: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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) -> torch.Tensor:
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B, N, D = x.shape
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S = 1
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x = x.view(B, N, S, D)
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x = torch_npu.npu_interleave_rope(x, cos, sin)
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return x.view(B, N, D)
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def forward(
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self,
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layer_name,
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hidden_states: torch.Tensor, # query in unified attn
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kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
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attn_metadata: M,
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need_gather_q_kv: bool = False,
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output: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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assert output is not None, "Output tensor must be provided."
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if attn_metadata is None:
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# Profiling run.
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return output.fill_(0)
|
|
has_prefill = attn_metadata.has_prefill
|
|
num_actual_tokens = attn_metadata.num_actual_tokens
|
|
hidden_states = hidden_states[:num_actual_tokens]
|
|
# Inputs and outputs may be padded for CUDA graphs
|
|
output_padded = output
|
|
output = output[:num_actual_tokens]
|
|
assert self.fused_qkv_a_proj is not None, "q lora is required for DSA."
|
|
maybe_npu_prefetch(inputs=self.fused_qkv_a_proj.weight,
|
|
dependency=hidden_states,
|
|
enabled=self.enable_prefetch)
|
|
qkv_lora = self.fused_qkv_a_proj(hidden_states)[0]
|
|
q_c, kv_no_split = qkv_lora.split(
|
|
[self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
|
|
dim=-1,
|
|
)
|
|
q_c = self.q_a_layernorm(q_c)
|
|
|
|
# Process for Flash Comm V1
|
|
q_c = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
q_c.contiguous(), need_gather_q_kv)
|
|
kv_no_split = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
kv_no_split.contiguous(), need_gather_q_kv)
|
|
|
|
if has_prefill:
|
|
wait_for_kv_layer_from_connector(layer_name)
|
|
|
|
slot_mapping = attn_metadata.slot_mapping[:num_actual_tokens]
|
|
ql_nope, q_pe = \
|
|
self._q_proj_and_k_up_proj(q_c)
|
|
q_pe = self.rope_single(q_pe, attn_metadata.cos, attn_metadata.sin)
|
|
k_pe, k_nope = self.exec_kv(kv_no_split, attn_metadata.cos,
|
|
attn_metadata.sin, kv_cache, slot_mapping)
|
|
|
|
topk_indices = self.indexer_select(x=hidden_states,
|
|
qr=q_c,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
need_gather_q_kv=need_gather_q_kv)
|
|
attn_output = torch.ops._C_ascend.npu_sparse_flash_attention(
|
|
query=ql_nope,
|
|
key=k_nope,
|
|
value=k_nope,
|
|
sparse_indices=topk_indices,
|
|
scale_value=self.scale,
|
|
sparse_block_size=1,
|
|
block_table=attn_metadata.block_tables,
|
|
actual_seq_lengths_query=attn_metadata.cum_query_lens,
|
|
actual_seq_lengths_kv=attn_metadata.seq_lens,
|
|
query_rope=q_pe,
|
|
key_rope=k_pe,
|
|
layout_query="TND",
|
|
layout_kv="PA_BSND",
|
|
sparse_mode=3,
|
|
)
|
|
attn_output = self._v_up_proj(attn_output)
|
|
maybe_npu_prefetch(inputs=self.o_proj.weight,
|
|
dependency=attn_output,
|
|
max_size=MAX_O_PROJ_PREFETCH_SIZE,
|
|
enabled=self.enable_prefetch)
|
|
output[...] = self.o_proj(attn_output)[0]
|
|
return output_padded
|
|
|
|
def indexer_select(
|
|
self,
|
|
x: torch.Tensor,
|
|
qr: torch.Tensor,
|
|
kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
|
attn_metadata: M,
|
|
need_gather_q_kv: bool = False,
|
|
):
|
|
cos = attn_metadata.cos
|
|
sin = attn_metadata.sin
|
|
|
|
# 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_head, self.head_dim) # [n_toks,64,128]
|
|
|
|
k_proj, _ = self.wk(x) # [b,s,7168] @ [7168,128] = [b,s,128]
|
|
k_proj = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
k_proj, need_gather_q_kv)
|
|
k = self.k_norm(k_proj).unsqueeze(1)
|
|
k = k.view(-1, 1, self.head_dim)
|
|
|
|
if HAS_TRITON:
|
|
cos = cos.view(-1, self.qk_rope_head_dim)
|
|
sin = sin.view(-1, self.qk_rope_head_dim)
|
|
q, k = rope_forward_triton(q,
|
|
k,
|
|
cos,
|
|
sin,
|
|
rope_dim=self.qk_rope_head_dim,
|
|
is_neox_style=True)
|
|
else:
|
|
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_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_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)
|
|
weights = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
weights, need_gather_q_kv)
|
|
|
|
block_table = attn_metadata.block_tables
|
|
seq_lens = attn_metadata.seq_lens
|
|
cum_query_lens = attn_metadata.cum_query_lens
|
|
|
|
topk_indices = torch.ops._C_ascend.npu_lightning_indexer(
|
|
query=q,
|
|
key=kv_cache[2],
|
|
weights=weights,
|
|
actual_seq_lengths_query=cum_query_lens,
|
|
actual_seq_lengths_key=seq_lens,
|
|
block_table=block_table,
|
|
layout_query="TND",
|
|
layout_key="PA_BSND",
|
|
sparse_count=2048,
|
|
sparse_mode=3)
|
|
return topk_indices
|