eac72f5f23
### What this PR does / why we need it?
It is mentioned in the [flashcomm2 technical
report](https://gitcode.com/ascend-tribe/ascend-inference-cluster/blob/main/FlashComm/FlashComm2%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E4%B8%AD%E4%BB%A5%E5%AD%98%E6%8D%A2%E4%BC%A0%E7%9A%84%E9%80%9A%E4%BF%A1%E4%BC%98%E5%8C%96%E6%8A%80%E6%9C%AF.pdf)
that FC2 will introduce full redundant storage of the o_proj matrix,
which will put pressure on the memory. Therefore, the technical report
proposed a compromise solution using otp2, but it will introduce
additional reduce-scatter communication.
We propose a shared linear feature (#2931 ) that supports distributing
weights layer by layer to each card, avoiding the need for TP splitting,
and can solve the memory issue.
This PR depends on #3232 and #2931
### Flashcomm2 flowchart
<img width="1142" height="878" alt="PixPin_2025-11-14_13-37-39"
src="https://github.com/user-attachments/assets/d45ea8db-d8ef-4d45-8e18-abd4d82ce3e0"
/>
### Does this PR introduce _any_ user-facing change?
Use environment variables
```bash
export VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE=1
export VLLM_ASCEND_ENABLE_FLASHCOMM2_OSHARED=1
```
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Signed-off-by: zzhxx <2783294813@qq.com>
Co-authored-by: zzh02232027 <zzh02232027@antgroup.com>
Co-authored-by: clrs97 <524936896@qq.com>
Co-authored-by: Levi-JQ <yujinqi2@huawei.com>
2120 lines
98 KiB
Python
2120 lines
98 KiB
Python
from dataclasses import dataclass
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from typing import (TYPE_CHECKING, ClassVar, List, NamedTuple, Optional, Tuple,
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Type, TypeVar)
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import numpy as np
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import torch
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import torch.distributed as dist
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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.attention.backends.utils import PAD_SLOT_ID
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from vllm.config import VllmConfig, get_current_vllm_config
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from vllm.distributed import (get_dcp_group,
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get_decode_context_model_parallel_rank,
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get_decode_context_model_parallel_world_size,
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get_pcp_group, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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get_tp_group)
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.logger import logger
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from vllm.model_executor.layers.linear import (LinearBase,
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UnquantizedLinearMethod)
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from vllm.utils.math_utils import cdiv, round_down
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from vllm.v1.attention.backends.utils import AttentionCGSupport
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from vllm_ascend import envs
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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.utils import (AscendCommonAttentionMetadata,
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maybe_save_kv_layer_to_connector,
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split_decodes_and_prefills,
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trans_rope_weight, transdata,
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wait_for_kv_layer_from_connector)
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from vllm_ascend.compilation.acl_graph import (get_graph_params,
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get_mtp_graph_params,
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update_graph_params_workspaces)
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from vllm_ascend.ops.shared_weight_layer import (
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is_hidden_layer, post_process_after_loading_for_shared_weight_series,
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reach_layer_for_shared_weight_series,
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register_layer_to_shared_weight_series)
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from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
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from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
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from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
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flashcomm2_o_shared_enabled, is_enable_nz,
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weak_ref_tensors)
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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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MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
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class AscendMLABackend(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_MLA"
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@staticmethod
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def get_builder_cls():
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return AscendMLAMetadataBuilder
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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["MLAAttentionImpl"]:
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return AscendMLAImpl
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@dataclass
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class AscendPCPMetadata:
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q_head_idx: torch.Tensor = None
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q_tail_idx: torch.Tensor = None
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kv_with_q_head_nomask_idx: torch.Tensor = None
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kv_with_q_head_mask_idx: torch.Tensor = None
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kv_with_q_tail_nomask_idx: torch.Tensor = None
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kv_with_q_tail_mask_idx: torch.Tensor = None
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attn_mask_seqlens: torch.Tensor = None
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head_attn_nomask_seqlens: torch.Tensor = None
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tail_attn_nomask_seqlens: torch.Tensor = None
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q_full_idx: torch.Tensor = None
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pcp_prefill_mask: torch.Tensor = None
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pcp_allgather_restore_idx: Optional[list[int]] = None
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@dataclass
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class AscendMLAPrefillMetadata:
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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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chunk_seq_lens_npu: torch.Tensor
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# for mla DCP & PCP
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padded_chunk_seq_lens_npu: torch.Tensor = None
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padded_local_chunk_seq_lens: Optional[list[list[int]]] = None
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local_context_lens_allranks: Optional[list[list[int]]] = None
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padded_local_cu_seq_lens: torch.Tensor = None
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cu_seq_lens_lst: Optional[list[list[int]]] = None
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chunk_size: Optional[int] = None
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attn_mask: torch.Tensor
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query_lens: torch.Tensor
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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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chunked_context: Optional[ChunkedContextMetadata] = None
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sin: torch.Tensor = None
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cos: torch.Tensor = None
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pcp_metadata: Optional[AscendPCPMetadata] = None
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@dataclass
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class AscendMLADecodeMetadata:
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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: Optional[list[int]] = None
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attn_mask: Optional[torch.Tensor] = None
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sin: torch.Tensor = None
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cos: torch.Tensor = None
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cp_seq_len: torch.Tensor = None
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batch_seq_mask: torch.Tensor = None
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@dataclass
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class AscendMLAMetadata:
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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_pcp_padded: int
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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[AscendMLADecodeMetadata] = None
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prefill: Optional[AscendMLAPrefillMetadata] = None
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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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M = TypeVar("M", bound=AscendMLAMetadata)
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class AscendMLAMetadataBuilder:
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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_BATCH
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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__(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[AscendMLAMetadata] = None):
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self.metadata_cls: Optional[AscendMLAMetadata] = metadata_cls \
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if metadata_cls is not None else AscendMLAMetadata # 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.enable_chunked_prefill
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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.reorder_batch_threshold = 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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self.pcp_size = get_pcp_group().world_size
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self.pcp_rank = get_pcp_group(
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).rank_in_group if self.pcp_size > 1 else 0
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self.dcp_size = get_decode_context_model_parallel_world_size()
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self.dcp_rank = get_decode_context_model_parallel_rank(
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) if self.dcp_size > 1 else 0
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self.cp_local_block_size = vllm_config.parallel_config.cp_kv_cache_interleave_size
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self.cp_virtual_block_size = self.cp_local_block_size * self.dcp_size * self.pcp_size
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decode_max_num_seqs = getattr(scheduler_config, 'decode_max_num_seqs',
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0)
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max_num_seqs = max(scheduler_config.max_num_seqs, decode_max_num_seqs)
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self.batch_seq_mask_buf = torch.empty(max_num_seqs *
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self.decode_threshold,
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dtype=torch.uint8,
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device=device)
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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 pad_actual_seq_len_q_mtp_enable_pad(self, num_reqs_pad_size, num_reqs,
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actual_seq_lengths_q,
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common_attn_metadata):
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"""
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Pads actual_seq_lengths_q evenly to not exceed 16 tokens per request
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in order to meet the requirement of npu_fused_infer_attention_score.
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In Torchair scenario, the lengths of the queries must be padded to the same length.
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And npu_fused_infer_attention_score constraint requires the last element must equal to batch_size(num_tokens).
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For example:
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batch_size=36, num_reqs_pad_size=2, num_reqs=16
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By default, each request should have inference 2 token, which means actual_seq_lengths_q should be
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[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36].
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However, mtp torchair + PD scenario, the actual_seq_lengths_q may be
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[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16] before padding, since the first decode request only has 1 token.
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In order to meet the requirement of npu_fused_infer_attention_score, we need to pad actual_seq_lengths_q evenly to not exceed 16 tokens per request.
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after padding actual_seq_lengths_q should be similar to [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,32,36]
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"""
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FIA_SEQ_LEN_LIMIT = 16
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need_padding = num_reqs_pad_size != 0 and \
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len(common_attn_metadata.actual_seq_lengths_q) > num_reqs and \
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common_attn_metadata.actual_seq_lengths_q[num_reqs] - actual_seq_lengths_q[-1] > FIA_SEQ_LEN_LIMIT
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if need_padding:
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padding_seq_len_q = common_attn_metadata.actual_seq_lengths_q[
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num_reqs:num_reqs + num_reqs_pad_size]
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start_val = actual_seq_lengths_q[-1]
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end_val = padding_seq_len_q[-1]
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num_step = len(padding_seq_len_q)
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interpolated = np.round(
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np.linspace(start_val, end_val,
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num_step + 1)[1:]).astype(int).tolist()
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assert interpolated[-1] == end_val
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assert len(interpolated) == len(padding_seq_len_q)
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actual_seq_lengths_q = actual_seq_lengths_q + interpolated
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else:
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actual_seq_lengths_q = actual_seq_lengths_q + common_attn_metadata.actual_seq_lengths_q[
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num_reqs:num_reqs + num_reqs_pad_size]
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|
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return actual_seq_lengths_q
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|
|
|
def pad_actual_seq_len_q_mtp_disable_pad(self, num_reqs_pad_size, num_reqs,
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actual_seq_lengths_q):
|
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"""
|
|
Only use for acl full graph mode.
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|
Pad the last element of the actual_seq_lengths_q equal to the TND(T) and
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the num of dimensions equal to the batch_size of main model.
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|
|
|
For example:
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|
batch_size = 8, num_reqs = 4, num_speculative_tokens = 1
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input actual_seq_lengths_q = [1, 2, 4, 5] (the 3rd req was accept a token)
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After padding the actual_seq_lengths_q will be similar to [1, 2, 4, 5, 6, 6, 7, 8]
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"""
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|
need_padding = num_reqs_pad_size > 0
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|
if need_padding:
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|
start_val = actual_seq_lengths_q[-1]
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end_val = num_reqs + num_reqs_pad_size
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|
num_step = num_reqs_pad_size
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|
interpolated = np.round(
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np.linspace(start_val, end_val,
|
|
num_step + 1)[1:]).astype(int).tolist()
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|
assert interpolated[-1] == end_val
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assert len(interpolated) == num_reqs_pad_size
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|
actual_seq_lengths_q = actual_seq_lengths_q + interpolated
|
|
return actual_seq_lengths_q
|
|
|
|
def build(
|
|
self,
|
|
common_prefix_len: int,
|
|
common_attn_metadata: AscendCommonAttentionMetadata,
|
|
model: nn.Module,
|
|
) -> AscendMLAMetadata:
|
|
num_reqs = common_attn_metadata.num_reqs
|
|
num_actual_tokens = common_attn_metadata.num_actual_tokens
|
|
query_start_loc = common_attn_metadata.query_start_loc
|
|
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
|
|
long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
|
|
|
|
num_actual_tokens_pcp_padded = long_seq_metadata.num_actual_tokens_pcp_padded if long_seq_metadata else None
|
|
num_computed_tokens_of_pcp_dcp = long_seq_metadata.num_computed_tokens_of_pcp_dcp if long_seq_metadata else None
|
|
|
|
num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \
|
|
split_decodes_and_prefills(common_attn_metadata, decode_threshold=self.decode_threshold)
|
|
assert num_decodes + num_prefills == num_reqs
|
|
assert num_decode_tokens + num_prefill_tokens == num_actual_tokens
|
|
|
|
# Note(simon): be careful about the CPU <> GPU memory movement in this
|
|
# function. We should avoid GPU -> CPU sync as much as possible because
|
|
# it blocks on all previous kernels.
|
|
device = self.device
|
|
|
|
# If graph_pad_size > -1, mean is running in fullgraph mode.
|
|
graph_pad_size = common_attn_metadata.graph_pad_size
|
|
# NOTE: Maybe this block_table change can be removed when graph_pad_size > 1.
|
|
if graph_pad_size > num_reqs and self.speculative_config.disable_padded_drafter_batch:
|
|
block_table = (
|
|
common_attn_metadata.block_table_tensor[:graph_pad_size])
|
|
else:
|
|
block_table = (common_attn_metadata.block_table_tensor[:num_reqs])
|
|
# NOTE: Currently, MTP-fullgraph is incompatibility pcp
|
|
if self.pcp_size > 1:
|
|
num_decodes_flatten = num_decodes * self.decode_threshold
|
|
block_table = common_attn_metadata.block_table_tensor[:
|
|
num_decodes_flatten
|
|
+
|
|
num_prefills]
|
|
if num_actual_tokens_pcp_padded is None:
|
|
num_actual_tokens_pcp_padded = num_actual_tokens
|
|
|
|
# NOTE: Currently, MTP-fullgraph is incompatibility pcp
|
|
slot_mapping = common_attn_metadata.slot_mapping[:
|
|
num_actual_tokens_pcp_padded]
|
|
input_positions = common_attn_metadata.positions[:
|
|
num_actual_tokens_pcp_padded].long(
|
|
)
|
|
|
|
if self.cos_cache is None:
|
|
self.cos_cache = model.model.layers[
|
|
model.model.start_layer].self_attn.rotary_emb.cos_cached
|
|
self.sin_cache = model.model.layers[
|
|
model.model.start_layer].self_attn.rotary_emb.sin_cached
|
|
if self.cos_cache.dtype != self.model_config.dtype: # type: ignore
|
|
self.cos_cache = self.cos_cache.to( # type: ignore
|
|
self.model_config.dtype) # type: ignore
|
|
self.sin_cache = self.sin_cache.to( # type: ignore
|
|
self.model_config.dtype) # type: ignore
|
|
|
|
query_seq_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
|
|
query_lens = query_seq_lens_cpu[:num_reqs]
|
|
seq_lens = common_attn_metadata.seq_lens_cpu[:num_reqs]
|
|
num_computed_tokens_cpu = (seq_lens - query_lens)
|
|
|
|
prefill_metadata = None
|
|
chunked_context_metadata = None
|
|
if num_prefills > 0:
|
|
pcp_metadata = None
|
|
common_long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
|
|
if common_long_seq_metadata is not None:
|
|
pcp_metadata = AscendPCPMetadata(
|
|
q_head_idx=common_long_seq_metadata.q_head_idx_tensor,
|
|
q_tail_idx=common_long_seq_metadata.q_tail_idx_tensor,
|
|
kv_with_q_head_nomask_idx=common_long_seq_metadata.
|
|
kv_with_q_head_nomask_idx_tensor,
|
|
kv_with_q_head_mask_idx=common_long_seq_metadata.
|
|
kv_with_q_head_mask_idx_tensor,
|
|
kv_with_q_tail_nomask_idx=common_long_seq_metadata.
|
|
kv_with_q_tail_nomask_idx_tensor,
|
|
kv_with_q_tail_mask_idx=common_long_seq_metadata.
|
|
kv_with_q_tail_mask_idx_tensor,
|
|
attn_mask_seqlens=common_long_seq_metadata.
|
|
attn_mask_seqlens,
|
|
head_attn_nomask_seqlens=common_long_seq_metadata.
|
|
head_attn_nomask_seqlens,
|
|
tail_attn_nomask_seqlens=common_long_seq_metadata.
|
|
tail_attn_nomask_seqlens,
|
|
q_full_idx=common_long_seq_metadata.q_full_idx,
|
|
pcp_prefill_mask=common_long_seq_metadata.pcp_prefill_mask
|
|
if long_seq_metadata else None,
|
|
pcp_allgather_restore_idx=long_seq_metadata.
|
|
pcp_allgather_restore_idx if long_seq_metadata else None)
|
|
|
|
reqs_start = num_decodes # prefill_start
|
|
tokens_start = num_decode_tokens
|
|
max_query_len = query_lens[reqs_start:].max().item()
|
|
max_seq_lens = seq_lens[reqs_start:].max().item()
|
|
prefill_query_start_loc = query_start_loc[
|
|
reqs_start:] - query_start_loc[reqs_start]
|
|
|
|
context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
|
|
max_context_len_cpu = context_lens_cpu.max().item()
|
|
num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
|
|
if self.chunked_prefill_enabled and max_context_len_cpu > 0:
|
|
max_context_chunk = (self.chunked_prefill_workspace_size //
|
|
num_prefills_with_context_cpu)
|
|
max_context_chunk = round_down(max_context_chunk,
|
|
self.block_size)
|
|
|
|
assert max_context_chunk > 0
|
|
num_chunks = cdiv(max_context_len_cpu, max_context_chunk)
|
|
chunk_starts = torch.arange(num_chunks, dtype=torch.int32) \
|
|
.unsqueeze(1).expand(-1, num_prefills) * max_context_chunk
|
|
chunk_ends = torch.min(context_lens_cpu.unsqueeze(0),
|
|
chunk_starts + max_context_chunk)
|
|
chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0)
|
|
cu_seq_lens_cpu = torch.zeros(num_chunks,
|
|
num_prefills + 1,
|
|
dtype=torch.int32,
|
|
pin_memory=True)
|
|
torch.cumsum(chunk_seq_lens,
|
|
dim=1,
|
|
out=cu_seq_lens_cpu[:, 1:],
|
|
dtype=torch.int32)
|
|
|
|
if self.dcp_size * self.pcp_size > 1:
|
|
if num_computed_tokens_of_pcp_dcp is not None:
|
|
local_context_lens_allranks = torch.tensor(
|
|
num_computed_tokens_of_pcp_dcp[reqs_start:num_reqs]
|
|
).reshape(-1, self.dcp_size * self.pcp_size)
|
|
# Note(qcs): The max local context lengths
|
|
# padded to `cp_local_block_size`.
|
|
padded_local_context_lens_cpu = (cdiv(
|
|
context_lens_cpu,
|
|
self.cp_virtual_block_size,
|
|
) * self.cp_local_block_size)
|
|
padded_local_max_context_chunk_across_ranks = (cdiv(
|
|
max_context_chunk,
|
|
self.cp_virtual_block_size,
|
|
) * self.cp_local_block_size)
|
|
local_chunk_starts = (
|
|
torch.arange(num_chunks,
|
|
dtype=torch.int32).unsqueeze(1).expand(
|
|
-1, num_prefills) *
|
|
padded_local_max_context_chunk_across_ranks)
|
|
local_chunk_ends = torch.min(
|
|
padded_local_context_lens_cpu.unsqueeze(0),
|
|
local_chunk_starts +
|
|
padded_local_max_context_chunk_across_ranks,
|
|
)
|
|
padded_local_chunk_seq_lens = (local_chunk_ends -
|
|
local_chunk_starts).clamp(
|
|
min=0)
|
|
padded_local_cu_chunk_seq_lens_cpu = torch.zeros(
|
|
num_chunks,
|
|
num_prefills + 1,
|
|
dtype=torch.int32,
|
|
pin_memory=True)
|
|
torch.cumsum(
|
|
padded_local_chunk_seq_lens,
|
|
dim=1,
|
|
out=padded_local_cu_chunk_seq_lens_cpu[:, 1:],
|
|
dtype=torch.int32,
|
|
)
|
|
chunked_context_metadata = AscendMLAPrefillMetadata.ChunkedContextMetadata(
|
|
cu_seq_lens=cu_seq_lens_cpu.pin_memory().to(
|
|
device, non_blocking=True),
|
|
starts=local_chunk_starts.pin_memory().to(
|
|
device, non_blocking=True),
|
|
seq_tot=padded_local_chunk_seq_lens.sum(
|
|
dim=1).tolist(),
|
|
max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
|
|
chunk_seq_lens=chunk_seq_lens,
|
|
chunk_seq_lens_npu=chunk_seq_lens.npu(),
|
|
workspace=self.chunked_prefill_workspace,
|
|
padded_chunk_seq_lens_npu=padded_local_chunk_seq_lens.
|
|
npu(),
|
|
padded_local_chunk_seq_lens=padded_local_chunk_seq_lens
|
|
.tolist(),
|
|
local_context_lens_allranks=local_context_lens_allranks
|
|
.tolist(),
|
|
padded_local_cu_seq_lens=
|
|
padded_local_cu_chunk_seq_lens_cpu.pin_memory().to(
|
|
device, non_blocking=True),
|
|
cu_seq_lens_lst=cu_seq_lens_cpu.tolist(),
|
|
chunk_size=padded_local_max_context_chunk_across_ranks,
|
|
)
|
|
else:
|
|
chunked_context_metadata = (
|
|
AscendMLAPrefillMetadata.ChunkedContextMetadata(
|
|
cu_seq_lens=cu_seq_lens_cpu.pin_memory().to(
|
|
device, non_blocking=True),
|
|
starts=chunk_starts.pin_memory().to(
|
|
device, non_blocking=True),
|
|
seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
|
|
max_seq_lens=chunk_seq_lens.max(
|
|
dim=1).values.tolist(),
|
|
chunk_seq_lens=chunk_seq_lens,
|
|
chunk_seq_lens_npu=chunk_seq_lens.npu(),
|
|
workspace=self.chunked_prefill_workspace,
|
|
))
|
|
prefill_input_positions = input_positions[tokens_start:]
|
|
cos = self.cos_cache[
|
|
prefill_input_positions].unsqueeze( # type: ignore
|
|
1).unsqueeze(2)
|
|
sin = self.sin_cache[
|
|
prefill_input_positions].unsqueeze( # type: ignore
|
|
1).unsqueeze(2)
|
|
prefill_metadata = AscendMLAPrefillMetadata(
|
|
attn_mask=common_attn_metadata.attn_mask,
|
|
query_lens=query_lens[reqs_start:].to(torch.int32),
|
|
seq_lens=seq_lens,
|
|
context_lens=seq_lens[reqs_start:],
|
|
input_positions=prefill_input_positions,
|
|
block_table=block_table[reqs_start:, ...],
|
|
max_query_len=max_query_len,
|
|
max_seq_lens=max_seq_lens,
|
|
query_start_loc=prefill_query_start_loc,
|
|
chunked_context=chunked_context_metadata,
|
|
sin=sin,
|
|
cos=cos,
|
|
pcp_metadata=pcp_metadata,
|
|
)
|
|
if self.pcp_size > 1:
|
|
prefill_metadata.block_table = block_table[
|
|
num_decodes_flatten:, ...]
|
|
|
|
decode_metadata = None
|
|
if num_decodes > 0:
|
|
cos = common_attn_metadata.cos
|
|
sin = common_attn_metadata.sin
|
|
# Notice that num_decodes != num_decode_tokens in SpecDecoding Scenario
|
|
actual_seq_lengths_q = query_start_loc_cpu[1:num_decodes +
|
|
1].tolist()
|
|
max_seq_lens = seq_lens[:num_decodes].max().item()
|
|
seq_lens = seq_lens[:num_decodes]
|
|
input_positions = input_positions[:num_decode_tokens]
|
|
if self.pcp_size > 1:
|
|
# For pcp + spec decode, we flatten seq_lens and block_table
|
|
# to avoid irregular spec_attn_mask shape
|
|
block_table = block_table[:num_decodes_flatten, ...]
|
|
else:
|
|
block_table = block_table[:num_decodes, ...]
|
|
# NOTE: Currently, MTP-fullgraph is incompatibility pcp
|
|
# NOTE: Maybe this block_table change can be removed when graph_pad_size > 1.
|
|
if graph_pad_size > num_decodes and \
|
|
self.speculative_config.disable_padded_drafter_batch:
|
|
block_table = block_table[:graph_pad_size, ...]
|
|
seq_lens_list = seq_lens.tolist()
|
|
|
|
if num_computed_tokens_of_pcp_dcp is not None:
|
|
# [bs, pcp_size, dcp_size]
|
|
num_computed_tokens_of_cp_dcp_array = np.array(
|
|
num_computed_tokens_of_pcp_dcp)[:num_decodes *
|
|
self.decode_threshold]
|
|
|
|
cp_seq_len = num_computed_tokens_of_cp_dcp_array[:,
|
|
self.pcp_rank,
|
|
self.dcp_rank]
|
|
cp_seq_len = torch.tensor(cp_seq_len, dtype=torch.int32)
|
|
batch_seq_mask = (cp_seq_len == 0)
|
|
self.batch_seq_mask_buf[:batch_seq_mask.shape[0]].copy_(
|
|
batch_seq_mask, non_blocking=True)
|
|
batch_seq_mask = self.batch_seq_mask_buf[:batch_seq_mask.
|
|
shape[0]]
|
|
cp_seq_len = torch.where(cp_seq_len == 0, 1, cp_seq_len)
|
|
else:
|
|
cp_seq_len, batch_seq_mask = None, None
|
|
|
|
if graph_pad_size > num_reqs:
|
|
if self.speculative_config.disable_padded_drafter_batch:
|
|
num_reqs_pad_size = graph_pad_size - num_reqs
|
|
actual_seq_lengths_q = self.pad_actual_seq_len_q_mtp_disable_pad(
|
|
num_reqs_pad_size, num_reqs, actual_seq_lengths_q)
|
|
seq_lens_list = seq_lens_list + [0] * (graph_pad_size - \
|
|
num_decodes)
|
|
num_block_pad_size = graph_pad_size - block_table.shape[0]
|
|
if num_block_pad_size > 0:
|
|
block_table_padding = torch.zeros(
|
|
(num_block_pad_size, ) + block_table.shape[1:],
|
|
dtype=block_table.dtype,
|
|
device=block_table.device)
|
|
block_table = torch.cat(
|
|
[block_table, block_table_padding], dim=0)
|
|
else:
|
|
num_token_pad_size = graph_pad_size - num_decode_tokens
|
|
num_reqs_pad_size = (
|
|
graph_pad_size //
|
|
common_attn_metadata.decode_token_per_req - num_reqs)
|
|
num_block_table_pad_size = (
|
|
graph_pad_size //
|
|
common_attn_metadata.decode_token_per_req -
|
|
num_decodes)
|
|
seq_lens_list = seq_lens.tolist() + [0] * num_reqs_pad_size
|
|
slot_padding = torch.full((num_token_pad_size, ),
|
|
PAD_SLOT_ID,
|
|
dtype=slot_mapping.dtype,
|
|
device=slot_mapping.device)
|
|
slot_mapping = torch.cat([slot_mapping, slot_padding])
|
|
block_table_padding = torch.zeros(
|
|
(num_block_table_pad_size, ) + block_table.shape[1:],
|
|
dtype=block_table.dtype,
|
|
device=block_table.device)
|
|
block_table = torch.cat([block_table, block_table_padding],
|
|
dim=0)
|
|
position_padding = torch.zeros(
|
|
num_token_pad_size,
|
|
dtype=input_positions.dtype,
|
|
device=input_positions.device)
|
|
input_positions = torch.cat(
|
|
[input_positions, position_padding])
|
|
actual_seq_lengths_q = self.pad_actual_seq_len_q_mtp_enable_pad(
|
|
num_reqs_pad_size, num_reqs, actual_seq_lengths_q,
|
|
common_attn_metadata)
|
|
|
|
# TODO: After the fullgraph supports MTP, the if branch needs to deleted
|
|
assert self.cos_cache is not None
|
|
assert self.sin_cache is not None
|
|
if cos is None and sin is None:
|
|
cos = self.cos_cache[
|
|
input_positions].unsqueeze( # type: ignore
|
|
1).unsqueeze(2)
|
|
sin = self.sin_cache[
|
|
input_positions].unsqueeze( # type: ignore
|
|
1).unsqueeze(2)
|
|
|
|
decode_metadata = AscendMLADecodeMetadata(
|
|
input_positions=input_positions,
|
|
block_table=block_table,
|
|
seq_lens=seq_lens,
|
|
seq_lens_list=seq_lens_list,
|
|
max_seq_lens=max_seq_lens,
|
|
attn_mask=common_attn_metadata.spec_attn_mask,
|
|
actual_seq_lengths_q=actual_seq_lengths_q,
|
|
sin=sin,
|
|
cos=cos,
|
|
cp_seq_len=cp_seq_len,
|
|
batch_seq_mask=batch_seq_mask)
|
|
else:
|
|
cos[:num_decode_tokens,
|
|
...] = self.cos_cache[input_positions].unsqueeze(
|
|
1).unsqueeze(2)
|
|
sin[:num_decode_tokens,
|
|
...] = self.sin_cache[input_positions].unsqueeze(
|
|
1).unsqueeze(2)
|
|
|
|
decode_metadata = AscendMLADecodeMetadata(
|
|
input_positions=input_positions,
|
|
block_table=block_table,
|
|
seq_lens=seq_lens,
|
|
seq_lens_list=seq_lens_list,
|
|
max_seq_lens=max_seq_lens,
|
|
attn_mask=common_attn_metadata.spec_attn_mask,
|
|
actual_seq_lengths_q=actual_seq_lengths_q,
|
|
sin=sin[:num_decode_tokens, ...],
|
|
cos=cos[:num_decode_tokens, ...],
|
|
cp_seq_len=cp_seq_len,
|
|
batch_seq_mask=batch_seq_mask)
|
|
|
|
return self.metadata_cls( # type: ignore
|
|
num_actual_tokens_pcp_padded=num_actual_tokens_pcp_padded,
|
|
num_input_tokens=common_attn_metadata.num_input_tokens,
|
|
num_actual_tokens=num_actual_tokens,
|
|
query_lens=query_lens.tolist(),
|
|
slot_mapping=slot_mapping,
|
|
head_dim=self.model_config.get_head_size(),
|
|
num_decodes=num_decodes,
|
|
num_decode_tokens=num_decode_tokens,
|
|
num_prefills=num_prefills,
|
|
attn_mask=common_attn_metadata.attn_mask,
|
|
attn_state=common_attn_metadata.attn_state,
|
|
prefill=prefill_metadata,
|
|
decode=decode_metadata,
|
|
query_start_loc=query_start_loc,
|
|
block_tables=block_table,
|
|
seq_lens=seq_lens,
|
|
)
|
|
|
|
def build_for_graph_capture(
|
|
self,
|
|
common_attn_metadata: AscendCommonAttentionMetadata,
|
|
attn_state: AscendAttentionState = AscendAttentionState.DecodeOnly,
|
|
model: Optional[nn.Module] = None,
|
|
):
|
|
if attn_state in {
|
|
AscendAttentionState.DecodeOnly,
|
|
AscendAttentionState.SpecDecoding
|
|
}:
|
|
attn_metadata = self.build(
|
|
common_prefix_len=0,
|
|
common_attn_metadata=common_attn_metadata,
|
|
model=model,
|
|
)
|
|
else:
|
|
raise NotImplementedError(
|
|
"Currently we only support building dummy metadata for DecodeOnly and SpecDecoding state"
|
|
)
|
|
|
|
attn_metadata.attn_state = attn_state
|
|
return attn_metadata
|
|
|
|
|
|
class DecodeMLAPreprocessResult(NamedTuple):
|
|
ql_nope: Optional[torch.Tensor] = None
|
|
q_pe: Optional[torch.Tensor] = None
|
|
k_nope: Optional[torch.Tensor] = None
|
|
k_pe: Optional[torch.Tensor] = None
|
|
decode_q_wo_k_up: Optional[torch.Tensor] = None
|
|
|
|
|
|
class PrefillMLAPreprocessResult(NamedTuple):
|
|
q_nope: Optional[torch.Tensor] = None
|
|
q_pe: Optional[torch.Tensor] = None
|
|
k_nope: Optional[torch.Tensor] = None
|
|
k_pe: Optional[torch.Tensor] = None
|
|
value: Optional[torch.Tensor] = None
|
|
|
|
|
|
class AscendMLAImpl(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.fused_qkv_a_proj = kwargs.get('fused_qkv_a_proj', None)
|
|
self.q_proj = kwargs['q_proj'] if self.q_lora_rank is None else kwargs[
|
|
'q_b_proj']
|
|
self.kv_b_proj = kwargs['kv_b_proj']
|
|
self.o_proj = kwargs['o_proj']
|
|
self.vllm_config = get_current_vllm_config()
|
|
self.fc2_o_shared_enable = flashcomm2_o_shared_enabled()
|
|
|
|
if self.fc2_o_shared_enable and is_hidden_layer(
|
|
self.vllm_config, self.o_proj):
|
|
from vllm_ascend.distributed.parallel_state import \
|
|
get_shared_weight_group
|
|
register_layer_to_shared_weight_series(
|
|
series_name="o_proj",
|
|
group=get_shared_weight_group(),
|
|
layer=self.o_proj,
|
|
prefetch_step=1)
|
|
|
|
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_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()
|
|
|
|
ascend_config = get_ascend_config()
|
|
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
|
|
self.enable_prefetch = ascend_config.weight_prefetch_config.enabled
|
|
|
|
self.ring_mla_mask_size = 512
|
|
|
|
self.speculative_config = self.vllm_config.speculative_config
|
|
self.enable_mlapo = envs.VLLM_ASCEND_ENABLE_MLAPO
|
|
|
|
self.pcp_size = get_pcp_group().world_size
|
|
self.pcp_rank = get_pcp_group(
|
|
).rank_in_group if self.pcp_size > 1 else 0
|
|
self.pcp_group = get_pcp_group(
|
|
).device_group if self.pcp_size > 1 else None
|
|
|
|
self.dcp_size = get_decode_context_model_parallel_world_size()
|
|
self.dcp_rank = get_decode_context_model_parallel_rank(
|
|
) if self.dcp_size > 1 else 0
|
|
self.dcp_group = get_dcp_group(
|
|
).device_group if self.dcp_size > 1 else None
|
|
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.tp_rank = get_tensor_model_parallel_rank()
|
|
self.tp_group = get_tp_group(
|
|
).device_group if self.tp_size > 1 else None
|
|
|
|
def _v_up_proj(self, x):
|
|
if x.dtype in [torch.float16, torch.bfloat16] \
|
|
and hasattr(torch.ops._C_ascend, "batch_matmul_transpose") \
|
|
and not self.dcp_size * self.pcp_size > 1:
|
|
x = x.view(-1, self.num_heads, self.kv_lora_rank)
|
|
b, _, _ = x.shape
|
|
res = torch.empty((b, self.num_heads, self.v_head_dim),
|
|
dtype=x.dtype,
|
|
device=x.device)
|
|
torch.ops._C_ascend.batch_matmul_transpose(x, self.W_UV, res)
|
|
x = res.reshape(-1, self.num_heads * self.v_head_dim)
|
|
else:
|
|
# Convert from (B, N, L) to (N, B, L)
|
|
x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
|
|
# # Multiply (N, B, L) x (N, L, V) -> (N, B, V)
|
|
x = torch.bmm(x, self.W_UV)
|
|
# # Convert from (N, B, V) to (B, N * V)
|
|
x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
|
|
return x
|
|
|
|
# Return `ql_nope`, `q_pe`
|
|
def _q_proj_and_k_up_proj(self, x):
|
|
q_nope, q_pe = self.q_proj(x)[0]\
|
|
.view(-1, self.num_heads, self.qk_head_dim)\
|
|
.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
|
|
|
|
# Convert from (B, N, P) to (N, B, P)
|
|
q_nope = q_nope.transpose(0, 1)
|
|
# Multiply (N, B, P) x (N, P, L) -> (N, B, L)
|
|
ql_nope = torch.bmm(q_nope, self.W_UK_T)
|
|
# Convert from (N, B, L) to (B, N, L)
|
|
return ql_nope.transpose(0, 1), q_pe
|
|
|
|
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:
|
|
try:
|
|
return getattr(layer, attr)
|
|
except AttributeError:
|
|
pass
|
|
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
|
|
# Weight will be reshaped next. To be on the safe side, the format
|
|
# of the weight should be reverted to FRACTAL_AND.
|
|
layer.weight.data = torch_npu.npu_format_cast(
|
|
layer.weight.data, ACL_FORMAT_FRACTAL_ND)
|
|
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,
|
|
)
|
|
|
|
W_UK, W_UV = 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.W_UV = W_UV.transpose(0, 1).contiguous()
|
|
# Convert from (L, N, P) to (N, P, L)
|
|
self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
|
|
|
|
# Function `get_and_maybe_dequant_weights` will cast the weights to
|
|
# FRACTAL_AND. So we need to cast to FRACTAL_NZ again.
|
|
if is_enable_nz():
|
|
self.kv_b_proj.weight.data = torch_npu.npu_format_cast(
|
|
self.kv_b_proj.weight.data, ACL_FORMAT_FRACTAL_NZ)
|
|
|
|
# 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)
|
|
|
|
if self.enable_mlapo:
|
|
# Currently mlapo only supports W8A8 quantization in MLA scenario
|
|
# TODO(whx): modify this limitation when mlapo supports floating point
|
|
if self.fused_qkv_a_proj is None or not isinstance(
|
|
getattr(self.fused_qkv_a_proj.quant_method, 'quant_method',
|
|
None), AscendW8A8LinearMethod):
|
|
self.enable_mlapo = False
|
|
logger.warning_once(
|
|
"Currently mlapo only supports W8A8 quantization in MLA scenario."
|
|
"Some layers in your model are not quantized with W8A8,"
|
|
"thus mlapo is disabled for these layers.")
|
|
if self.enable_mlapo:
|
|
self._process_weights_for_fused_mlapo(act_dtype)
|
|
|
|
if self.fc2_o_shared_enable and is_hidden_layer(
|
|
self.vllm_config, self.o_proj):
|
|
post_process_after_loading_for_shared_weight_series(self.o_proj)
|
|
|
|
def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
|
|
kv_a_proj_wt = self.fused_qkv_a_proj.weight.data[
|
|
..., self.q_lora_rank:].contiguous()
|
|
q_a_proj_wt = self.fused_qkv_a_proj.weight.data[
|
|
..., :self.q_lora_rank].contiguous()
|
|
kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
|
|
kv_a_proj_wt = trans_rope_weight(kv_a_proj_wt, self.qk_rope_head_dim)
|
|
kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
|
|
wd_qkv = torch.cat((kv_a_proj_wt, q_a_proj_wt), dim=-1)
|
|
wd_qkv = wd_qkv.t().contiguous()
|
|
wd_qkv = transdata(wd_qkv,
|
|
block_size=(16, 32)).unsqueeze(0).contiguous()
|
|
self.wd_qkv = torch_npu.npu_format_cast(wd_qkv, 29)
|
|
|
|
kv_a_proj_deq_scl = self.fused_qkv_a_proj.deq_scale[
|
|
self.q_lora_rank:].contiguous()
|
|
q_a_proj_deq_scl = self.fused_qkv_a_proj.deq_scale[:self.
|
|
q_lora_rank].contiguous(
|
|
)
|
|
kv_a_proj_deq_scl = kv_a_proj_deq_scl.reshape(
|
|
self.kv_lora_rank + self.qk_rope_head_dim, -1).contiguous()
|
|
kv_a_proj_deq_scl = trans_rope_weight(kv_a_proj_deq_scl,
|
|
self.qk_rope_head_dim)
|
|
kv_a_proj_deq_scl = kv_a_proj_deq_scl.view(
|
|
self.kv_lora_rank + self.qk_rope_head_dim).contiguous()
|
|
self.deq_scale_qkv = torch.cat((kv_a_proj_deq_scl, q_a_proj_deq_scl),
|
|
dim=-1).contiguous()
|
|
|
|
kv_a_proj_qt_bias = self.fused_qkv_a_proj.quant_bias[
|
|
self.q_lora_rank:].contiguous()
|
|
q_a_proj_qt_bias = self.fused_qkv_a_proj.quant_bias[:self.
|
|
q_lora_rank].contiguous(
|
|
)
|
|
kv_a_proj_qt_bias = kv_a_proj_qt_bias.reshape(
|
|
self.kv_lora_rank + self.qk_rope_head_dim, -1).contiguous()
|
|
kv_a_proj_qt_bias = trans_rope_weight(kv_a_proj_qt_bias,
|
|
self.qk_rope_head_dim)
|
|
kv_a_proj_qt_bias = kv_a_proj_qt_bias.view(
|
|
self.kv_lora_rank + self.qk_rope_head_dim).contiguous()
|
|
self.quant_bias_qkv = torch.cat((kv_a_proj_qt_bias, q_a_proj_qt_bias),
|
|
dim=-1).contiguous()
|
|
|
|
wu_q = self.q_proj.weight.data
|
|
wu_q = wu_q.t().reshape(self.num_heads,
|
|
self.qk_nope_head_dim + self.qk_rope_head_dim,
|
|
-1)
|
|
wu_q = trans_rope_weight(wu_q, self.qk_rope_head_dim)
|
|
wu_q = wu_q.reshape(
|
|
self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim),
|
|
-1)
|
|
wu_q = transdata(wu_q, block_size=(16, 32)).unsqueeze(0).contiguous()
|
|
self.wu_q = torch_npu.npu_format_cast(wu_q, 29)
|
|
|
|
qb_deq_scl = self.q_proj.deq_scale.data
|
|
qb_deq_scl = qb_deq_scl.reshape(
|
|
self.num_heads, self.qk_nope_head_dim + self.qk_rope_head_dim, -1)
|
|
qb_deq_scl = trans_rope_weight(qb_deq_scl, self.qk_rope_head_dim)
|
|
self.qb_deq_scl = qb_deq_scl.reshape(
|
|
self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim))
|
|
|
|
qb_qt_bias = self.q_proj.quant_bias.data
|
|
qb_qt_bias = qb_qt_bias.reshape(
|
|
self.num_heads, self.qk_nope_head_dim + self.qk_rope_head_dim, -1)
|
|
qb_qt_bias = trans_rope_weight(qb_qt_bias, self.qk_rope_head_dim)
|
|
self.qb_qt_bias = qb_qt_bias.reshape(
|
|
self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim))
|
|
|
|
device = self.q_proj.weight.device
|
|
self.gamma1 = self.q_a_layernorm.weight.data
|
|
self.beta1 = torch.zeros_like(self.gamma1) if (
|
|
_bias := self.q_a_layernorm.bias) is None else _bias.data
|
|
self.gamma2 = self.kv_a_layernorm.weight.data
|
|
self.quant_scale0 = self.fused_qkv_a_proj.input_scale.data
|
|
self.quant_offset0 = self.fused_qkv_a_proj.input_offset.data
|
|
self.quant_scale1 = self.q_proj.input_scale.data
|
|
self.quant_offset1 = self.q_proj.input_offset.data
|
|
self.ctkv_scale = torch.tensor([1], dtype=act_dtype, device=device)
|
|
self.q_nope_scale = torch.tensor([1], dtype=act_dtype, device=device)
|
|
|
|
def _compute_prefill_context(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
kv_c_and_k_pe_cache: Tuple[torch.Tensor],
|
|
rope_dim: int,
|
|
attn_metadata: AscendMLAMetadata,
|
|
prefix_output: torch.Tensor,
|
|
prefix_lse: torch.Tensor,
|
|
):
|
|
assert len(kv_c_and_k_pe_cache) > 1
|
|
prefill_metadata = attn_metadata.prefill
|
|
if prefill_metadata is None or prefill_metadata.chunked_context is None:
|
|
return prefix_output, prefix_lse
|
|
|
|
iters = len(prefill_metadata.chunked_context.seq_tot)
|
|
|
|
current_seq_len = torch.tensor(prefill_metadata.query_lens,
|
|
dtype=torch.int32)
|
|
cache_kv_c = kv_c_and_k_pe_cache[0]
|
|
cache_k_pe = kv_c_and_k_pe_cache[1]
|
|
num_heads = cache_k_pe.size(2)
|
|
latent_kv_dim = kv_c_and_k_pe_cache[0].size(-1)
|
|
for i in range(iters):
|
|
toks = prefill_metadata.chunked_context.seq_tot[i]
|
|
# chunk_seq_lens will be padded when pcp&dcp
|
|
context_seq_len = prefill_metadata.chunked_context.chunk_seq_lens[
|
|
i]
|
|
context_seq_len_npu = prefill_metadata.chunked_context.chunk_seq_lens_npu[
|
|
i]
|
|
seq_len = torch.stack([current_seq_len, context_seq_len])
|
|
kv_c_normed = torch.empty(toks,
|
|
num_heads,
|
|
latent_kv_dim,
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
k_pe = torch.empty(toks,
|
|
num_heads,
|
|
rope_dim,
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
|
|
if self.dcp_size * self.pcp_size > 1:
|
|
context_seq_len_npu = prefill_metadata.chunked_context.padded_chunk_seq_lens_npu[
|
|
i]
|
|
|
|
torch_npu.atb.npu_paged_cache_load(
|
|
cache_kv_c,
|
|
cache_k_pe,
|
|
prefill_metadata.block_table,
|
|
context_seq_len_npu,
|
|
seq_starts=prefill_metadata.chunked_context.starts[i],
|
|
key=kv_c_normed,
|
|
value=k_pe,
|
|
)
|
|
|
|
cache_kv_c_k_pe = torch.cat([kv_c_normed, k_pe], dim=-1)
|
|
if self.dcp_size > 1:
|
|
cache_kv_c_k_pe = get_dcp_group().all_gather(
|
|
cache_kv_c_k_pe, 0)
|
|
|
|
if self.pcp_size > 1:
|
|
cache_kv_c_k_pe = get_pcp_group().all_gather(
|
|
cache_kv_c_k_pe, 0)
|
|
|
|
if self.dcp_size * self.pcp_size > 1:
|
|
allgatered_kv_c_normed, allgatered_k_pe = cache_kv_c_k_pe.split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
kv_c_normed, k_pe = self._reorg_kvcache(
|
|
allgatered_kv_c_normed,
|
|
allgatered_k_pe,
|
|
padded_local_chunk_seq_lens_lst=prefill_metadata.
|
|
chunked_context.padded_local_chunk_seq_lens[i],
|
|
local_context_lens_allranks=prefill_metadata.
|
|
chunked_context.local_context_lens_allranks,
|
|
sum_seq_len=prefill_metadata.chunked_context.
|
|
cu_seq_lens_lst[i][-1],
|
|
max_seq_len=prefill_metadata.chunked_context.
|
|
max_seq_lens[i],
|
|
chunk_size=prefill_metadata.chunked_context.chunk_size,
|
|
chunk_idx=i,
|
|
toks=toks,
|
|
)
|
|
|
|
kv_c_normed = kv_c_normed.squeeze()
|
|
kv_nope = self.kv_b_proj(kv_c_normed)[0].view( \
|
|
-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
|
|
k_nope, v = kv_nope\
|
|
.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
|
k_pe = k_pe.expand((*k_nope.shape[:-1], -1))
|
|
|
|
mask = attn_metadata.attn_mask
|
|
torch_npu.atb.npu_ring_mla(
|
|
q_nope=q_nope,
|
|
q_rope=q_pe,
|
|
k_nope=k_nope,
|
|
k_rope=k_pe,
|
|
value=v,
|
|
mask=mask,
|
|
seqlen=seq_len,
|
|
head_num=self.num_heads,
|
|
kv_head_num=self.num_heads,
|
|
pre_out=prefix_output,
|
|
prev_lse=prefix_lse,
|
|
qk_scale=self.scale,
|
|
kernel_type="kernel_type_high_precision",
|
|
mask_type="no_mask",
|
|
input_layout="type_bsnd",
|
|
calc_type="calc_type_default",
|
|
output=prefix_output,
|
|
softmax_lse=prefix_lse)
|
|
return prefix_output, prefix_lse
|
|
|
|
def _forward_prefill(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_c_and_k_pe_cache: Tuple[torch.Tensor],
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
assert attn_metadata.prefill is not None
|
|
assert len(kv_c_and_k_pe_cache) > 1
|
|
num_tokens = q_nope.size(0)
|
|
attn_output = torch.empty(num_tokens,
|
|
self.num_heads,
|
|
self.v_head_dim,
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
attn_lse = torch.empty(self.num_heads,
|
|
num_tokens,
|
|
dtype=torch.float32,
|
|
device=q_nope.device)
|
|
torch_npu.atb.npu_ring_mla(q_nope=q_nope,
|
|
q_rope=q_pe,
|
|
k_nope=k_nope,
|
|
k_rope=k_pe,
|
|
value=value,
|
|
mask=attn_metadata.attn_mask,
|
|
seqlen=attn_metadata.prefill.query_lens,
|
|
head_num=self.num_heads,
|
|
kv_head_num=self.num_heads,
|
|
pre_out=None,
|
|
prev_lse=None,
|
|
qk_scale=self.scale,
|
|
kernel_type="kernel_type_high_precision",
|
|
mask_type="mask_type_triu",
|
|
input_layout="type_bsnd",
|
|
calc_type="calc_type_first_ring",
|
|
output=attn_output,
|
|
softmax_lse=attn_lse)
|
|
attn_output, attn_lse = self._compute_prefill_context( \
|
|
q_nope, q_pe, kv_c_and_k_pe_cache, self.qk_rope_head_dim, attn_metadata, attn_output, attn_lse)
|
|
|
|
attn_output = attn_output.reshape(
|
|
[num_tokens, self.num_heads * self.v_head_dim])
|
|
return attn_output
|
|
|
|
def exec_kv_decode(
|
|
self,
|
|
kv_no_split: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
kv_cache: Tuple,
|
|
slots: torch.Tensor,
|
|
):
|
|
B = kv_no_split.shape[0]
|
|
N = self.num_kv_heads
|
|
S = 1
|
|
# npu_kv_rmsnorm_rope_cache needs [B, N, S, D]
|
|
kv_no_split = kv_no_split.view(
|
|
B, N, S, self.kv_lora_rank + self.qk_rope_head_dim)
|
|
cache_mode = "PA"
|
|
k_pe, k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache(
|
|
kv_no_split,
|
|
self.kv_a_layernorm.weight,
|
|
cos,
|
|
sin,
|
|
slots.to(torch.int64),
|
|
kv_cache[1],
|
|
kv_cache[0],
|
|
epsilon=self.kv_a_layernorm.variance_epsilon,
|
|
cache_mode=cache_mode,
|
|
)
|
|
return k_pe, k_nope
|
|
|
|
def exec_kv_prefill(
|
|
self,
|
|
kv_no_split: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
kv_cache: Tuple,
|
|
slots: torch.Tensor,
|
|
):
|
|
B = kv_no_split.shape[0]
|
|
N = self.num_kv_heads
|
|
S = 1
|
|
# npu_kv_rmsnorm_rope_cache needs [B, N, S, D]
|
|
kv_no_split = kv_no_split.view(
|
|
B, N, S, self.kv_lora_rank + self.qk_rope_head_dim)
|
|
cache_mode = "PA"
|
|
_, _, k_pe, k_nope = torch_npu.npu_kv_rmsnorm_rope_cache(
|
|
kv_no_split,
|
|
self.kv_a_layernorm.weight,
|
|
cos,
|
|
sin,
|
|
slots.to(torch.int64),
|
|
kv_cache[1],
|
|
kv_cache[0],
|
|
epsilon=self.kv_a_layernorm.variance_epsilon,
|
|
cache_mode=cache_mode,
|
|
is_output_kv=True,
|
|
)
|
|
return k_pe, k_nope
|
|
|
|
def rope_single(
|
|
self,
|
|
x: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
B, N, D = x.shape
|
|
S = 1
|
|
x = x.view(B, N, S, D)
|
|
x = torch_npu.npu_interleave_rope(x, cos, sin)
|
|
return x.view(B, N, D)
|
|
|
|
def _forward_decode(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
block_size: int,
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
decode_meta = attn_metadata.decode
|
|
assert decode_meta is not None
|
|
num_tokens = q_nope.size(0)
|
|
# shape of knope/k_pe for npu graph mode should be:
|
|
# [num_blocks, num_kv_heads, block_size, self.kv_lora_rank/self.qk_rope_head_dim]
|
|
actual_seq_lengths = None
|
|
k_nope = k_nope.view(-1, self.num_kv_heads, block_size,
|
|
self.kv_lora_rank)
|
|
k_pe = k_pe.view(-1, self.num_kv_heads, block_size,
|
|
self.qk_rope_head_dim)
|
|
input_layout = "BNSD"
|
|
|
|
if attn_metadata.attn_state in [
|
|
AscendAttentionState.SpecDecoding,
|
|
AscendAttentionState.ChunkedPrefill,
|
|
AscendAttentionState.DecodeOnly,
|
|
] and self.speculative_config is not None:
|
|
# Use TND layout for pure SpecDecoding and SpecDecoding in ChunkedPrefill
|
|
input_layout = "TND"
|
|
# [bs * q_seq_len, num_heads_per_rank, dim]
|
|
# TODO: If the driver is upgraded later, the contiguous function can be deleted.
|
|
q_nope = q_nope.view(num_tokens, self.num_heads, -1).contiguous()
|
|
q_pe = q_pe.view(num_tokens, self.num_heads, -1)
|
|
sparse_mode = 3
|
|
spec_attn_mask = attn_metadata.decode.attn_mask # type:ignore
|
|
actual_seq_lengths = decode_meta.actual_seq_lengths_q
|
|
else:
|
|
q_nope = q_nope.view(num_tokens, self.num_heads, 1,
|
|
-1).contiguous()
|
|
q_pe = q_pe.view(num_tokens, self.num_heads, 1, -1)
|
|
sparse_mode = 0
|
|
spec_attn_mask = None
|
|
|
|
common_kwargs = {
|
|
'query_rope': q_pe,
|
|
'key_rope': k_pe,
|
|
'num_heads': self.num_heads,
|
|
'num_key_value_heads': self.num_kv_heads,
|
|
'input_layout': input_layout,
|
|
'atten_mask': spec_attn_mask,
|
|
'sparse_mode': sparse_mode,
|
|
'scale': self.scale,
|
|
'antiquant_mode': 0,
|
|
'antiquant_scale': None,
|
|
'block_table': decode_meta.block_table,
|
|
'block_size': block_size,
|
|
"actual_seq_lengths": actual_seq_lengths,
|
|
"actual_seq_lengths_kv": decode_meta.seq_lens_list,
|
|
}
|
|
forward_context: ForwardContext = get_forward_context()
|
|
if forward_context.is_mtp_model:
|
|
graph_params = get_mtp_graph_params()
|
|
else:
|
|
graph_params = get_graph_params()
|
|
if forward_context.capturing:
|
|
stream = torch_npu.npu.current_stream()
|
|
|
|
event = torch.npu.ExternalEvent()
|
|
event.wait(stream)
|
|
event.reset(stream)
|
|
graph_params.events[num_tokens].append(event)
|
|
|
|
workspace = graph_params.workspaces.get(num_tokens)
|
|
if workspace is None:
|
|
workspace = torch_npu._npu_fused_infer_attention_score_get_max_workspace(
|
|
q_nope, k_nope, k_nope, **common_kwargs)
|
|
update_graph_params_workspaces(num_tokens, workspace)
|
|
|
|
attn_output = torch.empty_like(q_nope)
|
|
softmax_lse = torch.empty(num_tokens,
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
|
|
graph_params.attn_params[num_tokens].append(
|
|
(weak_ref_tensors(q_nope), weak_ref_tensors(k_nope),
|
|
weak_ref_tensors(q_pe), weak_ref_tensors(k_pe),
|
|
self.num_heads, self.num_kv_heads, input_layout,
|
|
weak_ref_tensors(spec_attn_mask) if spec_attn_mask is not None
|
|
else None, sparse_mode, self.scale, decode_meta.block_table,
|
|
block_size, decode_meta.seq_lens_list, actual_seq_lengths,
|
|
weak_ref_tensors(attn_output), weak_ref_tensors(softmax_lse)))
|
|
|
|
torch.npu.graph_task_group_begin(stream)
|
|
torch_npu.npu_fused_infer_attention_score.out(
|
|
q_nope,
|
|
k_nope,
|
|
k_nope,
|
|
**common_kwargs,
|
|
workspace=workspace,
|
|
out=[attn_output, softmax_lse])
|
|
handle = torch.npu.graph_task_group_end(stream)
|
|
graph_params.handles[num_tokens].append(handle)
|
|
else:
|
|
attn_output, _ = torch_npu.npu_fused_infer_attention_score(
|
|
q_nope, k_nope, k_nope, **common_kwargs)
|
|
|
|
return self._v_up_proj(attn_output)
|
|
|
|
def _mla_decode_preprocess(self, hidden_states, kv_cache, attn_metadata):
|
|
bsz = attn_metadata.num_decode_tokens
|
|
hidden_states = hidden_states[:bsz]
|
|
|
|
cos_shape = attn_metadata.decode.cos.shape
|
|
cos = attn_metadata.decode.cos.view(cos_shape[0], cos_shape[-1])
|
|
sin = attn_metadata.decode.sin.view(cos_shape[0], cos_shape[-1])
|
|
|
|
decode_k_nope, decode_k_pe = kv_cache[0], kv_cache[1]
|
|
decode_q_nope = torch.empty(
|
|
(hidden_states.shape[0], self.W_UK_T.shape[0],
|
|
decode_k_nope.shape[-1]),
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device,
|
|
)
|
|
decode_q_pe = torch.empty(
|
|
(hidden_states.shape[0], self.W_UK_T.shape[0],
|
|
decode_k_pe.shape[-1]),
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device,
|
|
)
|
|
|
|
torch.ops._C_ascend.mla_preprocess(
|
|
hidden_states,
|
|
self.wd_qkv,
|
|
self.deq_scale_qkv,
|
|
self.gamma1,
|
|
self.beta1,
|
|
self.wu_q,
|
|
self.qb_deq_scl,
|
|
self.gamma2,
|
|
cos,
|
|
sin,
|
|
self.W_UK_T,
|
|
decode_k_nope,
|
|
decode_k_pe,
|
|
attn_metadata.slot_mapping[:bsz].flatten(),
|
|
quant_scale0=self.quant_scale0,
|
|
quant_offset0=self.quant_offset0,
|
|
bias0=self.quant_bias_qkv,
|
|
quant_scale1=self.quant_scale1,
|
|
quant_offset1=self.quant_offset1,
|
|
bias1=self.qb_qt_bias,
|
|
ctkv_scale=self.ctkv_scale,
|
|
q_nope_scale=self.q_nope_scale,
|
|
cache_mode="krope_ctkv",
|
|
quant_mode="per_tensor_quant_asymm",
|
|
q_out0=decode_q_nope,
|
|
kv_cache_out0=decode_k_nope,
|
|
q_out1=decode_q_pe,
|
|
kv_cache_out1=decode_k_pe,
|
|
enable_inner_out=False,
|
|
inner_out=torch.tensor([], device=hidden_states.device))
|
|
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)
|
|
|
|
if self.dcp_size > 1:
|
|
decode_q_no_split = torch.cat([decode_q_nope, decode_q_pe], dim=-1)
|
|
decode_q_no_split = get_dcp_group().all_gather(
|
|
decode_q_no_split, 1)
|
|
decode_q_nope, decode_q_pe = decode_q_no_split.split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
|
|
decode_preprocess_res = DecodeMLAPreprocessResult(
|
|
decode_q_nope, decode_q_pe, decode_k_nope, decode_k_pe)
|
|
return decode_preprocess_res, None
|
|
|
|
def _mla_preprocess(self, layer_name, hidden_states, kv_cache,
|
|
attn_metadata, need_gather_q_kv):
|
|
# MLA Preprocess:
|
|
# 1. Perform fused_qkv_a_proj and q_a_layernorm to obtain q_c and kv_no_split
|
|
# or
|
|
# Perform kv_a_proj_with_mqa to obtain kv_no_split
|
|
# 2. If need_gather_q_kv, perform all_gather.
|
|
# 3. Preprocess decode tokens, write kv cache and get:
|
|
# decode_ql_nope, decode_q_pe, decode_k_pe, decode_k_nope
|
|
# 4. 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 self.fused_qkv_a_proj is not None:
|
|
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)
|
|
# allgather need contiguous data
|
|
kv_no_split = kv_no_split.contiguous()
|
|
else:
|
|
q_c = hidden_states
|
|
kv_no_split = self.kv_a_proj_with_mqa(hidden_states)[0]
|
|
|
|
# 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 self.fc2_o_shared_enable and is_hidden_layer(
|
|
self.vllm_config, self.o_proj):
|
|
reach_layer_for_shared_weight_series(self.o_proj)
|
|
|
|
decode_preprocess_res = None
|
|
prefill_preprocess_res = None
|
|
if has_prefill:
|
|
wait_for_kv_layer_from_connector(layer_name)
|
|
# Preprocess for decode tokens
|
|
if has_decode:
|
|
decode_q_c = q_c[:num_decode_tokens]
|
|
cos = attn_metadata.decode.cos
|
|
sin = attn_metadata.decode.sin
|
|
decode_ql_nope, decode_q_pe = \
|
|
self._q_proj_and_k_up_proj(decode_q_c)
|
|
if self.dcp_size > 1:
|
|
decode_q_no_split = torch.cat([decode_ql_nope, decode_q_pe],
|
|
dim=-1)
|
|
decode_q_no_split = get_dcp_group().all_gather(
|
|
decode_q_no_split, 1)
|
|
decode_ql_nope, decode_q_pe = decode_q_no_split.split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
decode_q_pe = self.rope_single(decode_q_pe, cos, sin)
|
|
decode_slots = attn_metadata.slot_mapping[:num_decode_tokens *
|
|
self.pcp_size:self.
|
|
pcp_size]
|
|
decode_kv_no_split = kv_no_split[:num_decode_tokens]
|
|
decode_k_pe, decode_k_nope = self.exec_kv_decode(
|
|
decode_kv_no_split, cos, sin, kv_cache, decode_slots)
|
|
decode_preprocess_res = DecodeMLAPreprocessResult(
|
|
decode_ql_nope, decode_q_pe, decode_k_nope, decode_k_pe)
|
|
# Preprocess for prefill tokens
|
|
if has_prefill:
|
|
if self.pcp_size > 1:
|
|
num_actual_tokens = (attn_metadata.num_actual_tokens_pcp_padded
|
|
- self.pcp_size * num_decode_tokens
|
|
) // self.pcp_size + num_decode_tokens
|
|
prefill_kv_no_split = kv_no_split[
|
|
num_decode_tokens:num_actual_tokens]
|
|
prefill_q_c = q_c[num_decode_tokens:num_actual_tokens]
|
|
prefill_q = self.q_proj(prefill_q_c)[0]\
|
|
.view(-1, self.num_heads, self.qk_head_dim)
|
|
prefill_q_pe = prefill_q[..., self.qk_nope_head_dim:]
|
|
prefill_q_nope = prefill_q[..., :self.qk_nope_head_dim]
|
|
if self.pcp_size > 1:
|
|
cos = attn_metadata.prefill.cos[:num_actual_tokens -
|
|
num_decode_tokens]
|
|
sin = attn_metadata.prefill.sin[:num_actual_tokens -
|
|
num_decode_tokens]
|
|
else:
|
|
cos = attn_metadata.prefill.cos
|
|
sin = attn_metadata.prefill.sin
|
|
prefill_slots = attn_metadata.slot_mapping[
|
|
num_decode_tokens:num_actual_tokens]
|
|
prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin)
|
|
if self.pcp_size > 1:
|
|
prefill_kv_no_split = kv_no_split[:num_actual_tokens]
|
|
kv_c, k_pe = prefill_kv_no_split.split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
|
|
assert len(
|
|
kv_cache
|
|
) > 1, "the number of kv cache should be greater than 1, namely (nope_cache and rope_cache)"
|
|
kv_c_normed = kv_c_normed.view(
|
|
[num_actual_tokens, self.num_kv_heads, -1])
|
|
k_pe = k_pe.unsqueeze(1)
|
|
prefill_k_pe = k_pe
|
|
prefill_k_pe[
|
|
num_decode_tokens:num_actual_tokens] = self.rope_single(
|
|
prefill_k_pe[num_decode_tokens:num_actual_tokens], cos,
|
|
sin)
|
|
prefill_k_c_normed = kv_c_normed[:num_actual_tokens]
|
|
prefill_kv_c_k_pe = torch.cat(
|
|
[prefill_k_c_normed, prefill_k_pe], dim=-1)
|
|
prefill_kv_c_k_pe = get_pcp_group().all_gather(
|
|
prefill_kv_c_k_pe, 0)
|
|
prefill_kv_c_k_pe = torch.index_select(
|
|
prefill_kv_c_k_pe, 0, attn_metadata.prefill.pcp_metadata.
|
|
pcp_allgather_restore_idx)
|
|
prefill_kv_c_k_pe = prefill_kv_c_k_pe[num_decode_tokens *
|
|
self.pcp_size:]
|
|
prefill_k_c_normed, prefill_k_pe = prefill_kv_c_k_pe.split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
kv_c_normed, k_pe = prefill_k_c_normed, prefill_k_pe
|
|
prefill_k_c_normed = prefill_k_c_normed.squeeze()
|
|
slot_mapping = attn_metadata.slot_mapping[self.pcp_size *
|
|
num_decode_tokens:]
|
|
torch_npu._npu_reshape_and_cache(key=kv_c_normed,
|
|
value=k_pe,
|
|
key_cache=kv_cache[0],
|
|
value_cache=kv_cache[1],
|
|
slot_indices=slot_mapping)
|
|
else:
|
|
prefill_k_pe, prefill_k_c_normed = self.exec_kv_prefill(
|
|
prefill_kv_no_split, cos, sin, kv_cache, prefill_slots)
|
|
prefill_k_nope, prefill_value = self.kv_b_proj(
|
|
prefill_k_c_normed)[0].view(
|
|
-1, self.num_heads,
|
|
self.qk_nope_head_dim + self.v_head_dim).split(
|
|
[self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
|
if not self.pcp_size > 1:
|
|
prefill_k_pe = prefill_k_pe.view(prefill_q_c.shape[0],
|
|
self.num_kv_heads, -1)
|
|
prefill_k_pe = prefill_k_pe.expand(
|
|
(*prefill_k_nope.shape[:-1], -1))
|
|
prefill_preprocess_res = PrefillMLAPreprocessResult(
|
|
prefill_q_nope, prefill_q_pe, prefill_k_nope, prefill_k_pe,
|
|
prefill_value)
|
|
return decode_preprocess_res, prefill_preprocess_res
|
|
|
|
def forward(
|
|
self,
|
|
layer_name,
|
|
hidden_states: torch.Tensor, # query in unified attn
|
|
kv_cache: Tuple[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.
|
|
if self.fc2_o_shared_enable and is_hidden_layer(
|
|
self.vllm_config, self.o_proj):
|
|
reach_layer_for_shared_weight_series(self.o_proj)
|
|
return output.fill_(0)
|
|
if self.pcp_size > 1:
|
|
num_actual_tokens = attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
|
|
else:
|
|
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_padded = output
|
|
o_proj_input_shape = (get_forward_context().num_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)
|
|
|
|
# MLA Preprocess
|
|
forward_context = get_forward_context()
|
|
if (self.enable_mlapo and
|
|
(attn_metadata is None or not forward_context.with_prefill)):
|
|
hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
hidden_states.contiguous(), need_gather_q_kv)
|
|
decode_preprocess_res, prefill_preprocess_res = self._mla_decode_preprocess(
|
|
hidden_states, kv_cache, attn_metadata)
|
|
else:
|
|
decode_preprocess_res, prefill_preprocess_res = self._mla_preprocess(
|
|
layer_name, hidden_states, kv_cache, attn_metadata,
|
|
need_gather_q_kv)
|
|
|
|
if decode_preprocess_res is not None:
|
|
# MLA Preprocess for decoding
|
|
if self.pcp_size * self.dcp_size > 1:
|
|
output_decode = self._forward_decode_pcp_dcp(
|
|
decode_preprocess_res.ql_nope,
|
|
decode_preprocess_res.q_pe,
|
|
decode_preprocess_res.k_nope,
|
|
decode_preprocess_res.k_pe,
|
|
kv_cache[0].shape[1],
|
|
attn_metadata,
|
|
)
|
|
else:
|
|
output_decode = self._forward_decode(
|
|
decode_preprocess_res.ql_nope, decode_preprocess_res.q_pe,
|
|
decode_preprocess_res.k_nope, decode_preprocess_res.k_pe,
|
|
kv_cache[0].shape[1], attn_metadata)
|
|
|
|
o_proj_input[:num_decode_tokens] = output_decode
|
|
|
|
if prefill_preprocess_res is not None:
|
|
# FIX: aicore move should be also placed on the comm stream in dbo,
|
|
# otherwise it may affect the accuracy
|
|
# TODO: use an elegant way to overlap
|
|
if self.pcp_size > 1:
|
|
output_prefill = self._forward_prefill_cp(
|
|
prefill_preprocess_res.q_nope, prefill_preprocess_res.q_pe,
|
|
prefill_preprocess_res.k_nope, prefill_preprocess_res.k_pe,
|
|
prefill_preprocess_res.value, kv_cache, attn_metadata)
|
|
else:
|
|
output_prefill = self._forward_prefill(
|
|
prefill_preprocess_res.q_nope, prefill_preprocess_res.q_pe,
|
|
prefill_preprocess_res.k_nope, prefill_preprocess_res.k_pe,
|
|
prefill_preprocess_res.value, kv_cache, attn_metadata)
|
|
|
|
o_proj_input[num_decode_tokens:num_actual_tokens] = output_prefill
|
|
# O proj
|
|
MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
|
|
maybe_npu_prefetch(inputs=self.o_proj.weight,
|
|
dependency=o_proj_input,
|
|
max_size=MAX_O_PROJ_PREFETCH_SIZE,
|
|
enabled=self.enable_prefetch)
|
|
|
|
output[...] = self.o_proj(o_proj_input,
|
|
is_prefill=prefill_preprocess_res
|
|
is not None)[0]
|
|
|
|
del o_proj_input
|
|
|
|
has_prefill = attn_metadata.num_prefills > 0
|
|
if has_prefill:
|
|
maybe_save_kv_layer_to_connector(layer_name, list(kv_cache))
|
|
return output_padded
|
|
|
|
def _forward_prefill_cp(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_c_and_k_pe_cache: Tuple[torch.Tensor],
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
assert attn_metadata.prefill is not None
|
|
assert attn_metadata.prefill.pcp_metadata is not None
|
|
num_tokens = q_nope.size(0)
|
|
# Use precomputed indices from the metadata (already converted to tensors and on device)
|
|
q_head_idx = attn_metadata.prefill.pcp_metadata.q_head_idx
|
|
q_tail_idx = attn_metadata.prefill.pcp_metadata.q_tail_idx
|
|
kv_with_q_head_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_nomask_idx
|
|
kv_with_q_head_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_mask_idx
|
|
kv_with_q_tail_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_nomask_idx
|
|
kv_with_q_tail_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx
|
|
attn_mask_seqlens = attn_metadata.prefill.pcp_metadata.attn_mask_seqlens
|
|
head_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.head_attn_nomask_seqlens
|
|
tail_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.tail_attn_nomask_seqlens
|
|
mask = attn_metadata.prefill.pcp_metadata.pcp_prefill_mask
|
|
output_head, lse_head = self._attention_with_mask_and_nomask(
|
|
q_nope=torch.index_select(q_nope, 0, q_head_idx),
|
|
q_pe=torch.index_select(q_pe, 0, q_head_idx),
|
|
k_nope=k_nope,
|
|
k_pe=k_pe,
|
|
value=value,
|
|
kv_mask_idx=kv_with_q_head_mask_idx,
|
|
kv_nomask_idx=kv_with_q_head_nomask_idx,
|
|
attn_mask_seqlens=attn_mask_seqlens,
|
|
attn_nomask_seqlens=head_attn_nomask_seqlens,
|
|
mask=mask)
|
|
|
|
output_tail, lse_tail = self._attention_with_mask_and_nomask(
|
|
q_nope=torch.index_select(q_nope, 0, q_tail_idx),
|
|
q_pe=torch.index_select(q_pe, 0, q_tail_idx),
|
|
k_nope=k_nope,
|
|
k_pe=k_pe,
|
|
value=value,
|
|
kv_mask_idx=kv_with_q_tail_mask_idx,
|
|
kv_nomask_idx=kv_with_q_tail_nomask_idx,
|
|
attn_mask_seqlens=attn_mask_seqlens,
|
|
attn_nomask_seqlens=tail_attn_nomask_seqlens,
|
|
mask=mask)
|
|
|
|
q_full_idx = attn_metadata.prefill.pcp_metadata.q_full_idx
|
|
attn_output = torch.index_select(
|
|
torch.cat([output_head, output_tail], dim=0), 0, q_full_idx)
|
|
attn_lse = torch.index_select(torch.cat([lse_head, lse_tail], dim=1),
|
|
1, q_full_idx)
|
|
|
|
output, _ = self._compute_prefill_context( \
|
|
q_nope, q_pe, kv_c_and_k_pe_cache, self.qk_rope_head_dim, attn_metadata, attn_output, attn_lse)
|
|
|
|
output = output.reshape([num_tokens, self.num_heads * self.v_head_dim])
|
|
|
|
return output
|
|
|
|
def _attention_with_mask_and_nomask(
|
|
self, q_nope: torch.Tensor, q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor, k_pe: torch.Tensor, value: torch.Tensor,
|
|
kv_mask_idx: torch.Tensor, kv_nomask_idx: torch.Tensor,
|
|
attn_mask_seqlens: torch.Tensor, attn_nomask_seqlens: torch.Tensor,
|
|
mask: torch.Tensor):
|
|
attn_output = torch.empty(q_nope.shape[0],
|
|
self.num_heads,
|
|
self.v_head_dim,
|
|
dtype=k_pe.dtype,
|
|
device=k_pe.device)
|
|
attn_lse = torch.empty(self.num_heads,
|
|
q_pe.shape[0],
|
|
dtype=torch.float32,
|
|
device=k_pe.device)
|
|
# mask
|
|
k_nope_mask = torch.index_select(k_nope, 0, kv_mask_idx)
|
|
value_mask = torch.index_select(value, 0, kv_mask_idx)
|
|
k_pe_mask = torch.index_select(k_pe, 0, kv_mask_idx)
|
|
torch_npu.atb.npu_ring_mla(q_nope=q_nope,
|
|
q_rope=q_pe,
|
|
k_nope=k_nope_mask,
|
|
k_rope=k_pe_mask,
|
|
value=value_mask,
|
|
mask=mask,
|
|
seqlen=attn_mask_seqlens,
|
|
head_num=self.num_heads,
|
|
kv_head_num=self.num_heads,
|
|
pre_out=None,
|
|
prev_lse=None,
|
|
qk_scale=self.scale,
|
|
kernel_type="kernel_type_high_precision",
|
|
mask_type="mask_type_triu",
|
|
input_layout="type_bsnd",
|
|
calc_type="calc_type_first_ring",
|
|
output=attn_output,
|
|
softmax_lse=attn_lse)
|
|
|
|
# nomask
|
|
if kv_nomask_idx.shape[0] == 0:
|
|
return attn_output, attn_lse
|
|
|
|
k_nope_nomask = torch.index_select(k_nope, 0, kv_nomask_idx)
|
|
value_nomask = torch.index_select(value, 0, kv_nomask_idx)
|
|
k_pe_nomask = torch.index_select(k_pe, 0, kv_nomask_idx)
|
|
torch_npu.atb.npu_ring_mla(q_nope=q_nope,
|
|
q_rope=q_pe,
|
|
k_nope=k_nope_nomask,
|
|
k_rope=k_pe_nomask,
|
|
value=value_nomask,
|
|
mask=mask,
|
|
seqlen=attn_nomask_seqlens,
|
|
head_num=self.num_heads,
|
|
kv_head_num=self.num_heads,
|
|
pre_out=attn_output,
|
|
prev_lse=attn_lse,
|
|
qk_scale=self.scale,
|
|
kernel_type="kernel_type_high_precision",
|
|
mask_type="no_mask",
|
|
input_layout="type_bsnd",
|
|
calc_type="calc_type_default",
|
|
output=attn_output,
|
|
softmax_lse=attn_lse)
|
|
return attn_output, attn_lse
|
|
|
|
def _forward_decode_pcp_dcp(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
block_size: int,
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
decode_meta = attn_metadata.decode
|
|
assert decode_meta is not None
|
|
num_tokens = q_nope.size(0)
|
|
# shape of knope/k_pe for npu graph mode should be:
|
|
# [num_blocks, num_kv_heads, block_size, self.kv_lora_rank/self.qk_rope_head_dim]
|
|
if self.dcp_size > 1:
|
|
num_heads = self.num_heads * self.dcp_size
|
|
else:
|
|
num_heads = self.num_heads
|
|
|
|
k_nope = k_nope.view(-1, block_size, self.num_kv_heads,
|
|
self.kv_lora_rank)
|
|
k_pe = k_pe.view(-1, block_size, self.num_kv_heads,
|
|
self.qk_rope_head_dim)
|
|
q_nope = q_nope.view(num_tokens, num_heads, -1)
|
|
q_pe = q_pe.view(num_tokens, num_heads, -1)
|
|
# use pcp & dcp split computed token nums from scheduler to compute actual seq_len and seq_mask
|
|
seq_len = decode_meta.cp_seq_len
|
|
|
|
common_kwargs = {
|
|
"return_lse": True,
|
|
"calc_type": "calc_type_ring",
|
|
}
|
|
graph_params = get_graph_params()
|
|
forward_context: ForwardContext = get_forward_context()
|
|
if forward_context.capturing:
|
|
stream = torch_npu.npu.current_stream()
|
|
event = torch.npu.ExternalEvent()
|
|
event.wait(stream)
|
|
event.reset(stream)
|
|
graph_params.events[num_tokens].append(event)
|
|
workspace = graph_params.workspaces.get(num_tokens)
|
|
if workspace is None:
|
|
workspace = torch_npu.atb._npu_multi_head_latent_attention_get_workspace(
|
|
q_nope, q_pe, k_nope, k_pe, decode_meta.block_table,
|
|
seq_len, num_heads, self.scale, self.num_kv_heads,
|
|
**common_kwargs)
|
|
update_graph_params_workspaces(num_tokens, workspace)
|
|
attn_output = torch.empty_like(q_nope)
|
|
softmax_lse = torch.empty((num_tokens, num_heads, 1),
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
graph_params.attn_params[num_tokens].append(
|
|
(weak_ref_tensors(q_nope), weak_ref_tensors(q_pe),
|
|
weak_ref_tensors(k_nope), weak_ref_tensors(k_pe),
|
|
decode_meta.block_table, seq_len, num_heads, self.scale,
|
|
self.num_kv_heads, weak_ref_tensors(attn_output),
|
|
weak_ref_tensors(softmax_lse)))
|
|
torch.npu.graph_task_group_begin(stream)
|
|
torch_npu.atb.npu_multi_head_latent_attention(
|
|
q_nope,
|
|
q_pe,
|
|
k_nope,
|
|
k_pe,
|
|
decode_meta.block_table,
|
|
seq_len,
|
|
num_heads,
|
|
self.scale,
|
|
self.num_kv_heads,
|
|
**common_kwargs,
|
|
workspace=workspace,
|
|
output=attn_output,
|
|
lse=softmax_lse)
|
|
handle = torch.npu.graph_task_group_end(stream)
|
|
graph_params.handles[num_tokens].append(handle)
|
|
else:
|
|
attn_output = torch.empty_like(q_nope)
|
|
softmax_lse = torch.empty((num_tokens, num_heads, 1),
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
torch_npu.atb.npu_multi_head_latent_attention(
|
|
q_nope,
|
|
q_pe,
|
|
k_nope,
|
|
k_pe,
|
|
decode_meta.block_table,
|
|
seq_len,
|
|
num_heads,
|
|
self.scale,
|
|
self.num_kv_heads,
|
|
return_lse=True,
|
|
calc_type="calc_type_ring",
|
|
output=attn_output,
|
|
lse=softmax_lse)
|
|
|
|
# Update out&lse
|
|
attn_out_lse_list = self._process_attn_out_lse(attn_output,
|
|
softmax_lse,
|
|
decode_meta)
|
|
attn_output = self._npu_attention_update(attn_out_lse_list)
|
|
return self._v_up_proj(attn_output)
|
|
|
|
def _npu_attention_update(
|
|
self, attn_out_lse_list: List[torch.Tensor]) -> torch.Tensor:
|
|
attn_out_split_cp = []
|
|
attn_lse_split_cp = []
|
|
|
|
for attn_out_lse in attn_out_lse_list:
|
|
attn_out_allgather, attn_lse_allgather = self._out_lse_reshape(
|
|
*torch.split(attn_out_lse, [self.kv_lora_rank, 1], dim=-1))
|
|
attn_out_split_cp.append(attn_out_allgather)
|
|
attn_lse_split_cp.append(attn_lse_allgather)
|
|
attn_out, _ = torch_npu.npu_attention_update(attn_lse_split_cp,
|
|
attn_out_split_cp, 0)
|
|
attn_out = attn_out.view(-1, attn_out_lse_list[0].shape[1],
|
|
self.kv_lora_rank)
|
|
return attn_out
|
|
|
|
def _out_lse_reshape(self, attn_out: torch.Tensor,
|
|
attn_lse: torch.Tensor) -> torch.Tensor:
|
|
attn_out = attn_out.contiguous().view(
|
|
attn_out.shape[0] * attn_out.shape[1], attn_out.shape[2])
|
|
attn_lse = attn_lse.contiguous().view(
|
|
attn_lse.shape[0] * attn_lse.shape[1] * attn_lse.shape[2])
|
|
return attn_out, attn_lse
|
|
|
|
def _process_attn_out_lse(
|
|
self,
|
|
attn_output: torch.Tensor,
|
|
softmax_lse: torch.Tensor,
|
|
decode_meta: AscendMLADecodeMetadata,
|
|
) -> List[torch.Tensor]:
|
|
attn_out_lse_list = []
|
|
out_mask = decode_meta.batch_seq_mask[:, None,
|
|
None].expand_as(attn_output)
|
|
attn_output = torch.where(out_mask, 0, attn_output)
|
|
lse_mask = decode_meta.batch_seq_mask[:, None,
|
|
None].expand_as(softmax_lse)
|
|
softmax_lse = torch.where(lse_mask, -torch.inf, softmax_lse)
|
|
|
|
softmax_lse = softmax_lse.to(torch.float32)
|
|
attn_output = attn_output.to(torch.float32)
|
|
# Concat out&lse: [bs,num_heads,v_head_dim] + [bs,num_heads,1] -> [bs,num_heads,v_head_dim+1]
|
|
attn_out_lse = torch.cat([attn_output, softmax_lse], dim=-1)
|
|
if self.dcp_size > 1:
|
|
# permute: [bs, num_heads, v_head_dim+1] -> [num_heads, v_head_dim+1, bs]
|
|
attn_out_lse = attn_out_lse.permute([1, 2, 0]).contiguous()
|
|
attn_out_lse_all2all = torch.empty_like(attn_out_lse)
|
|
dist.all_to_all_single(attn_out_lse_all2all,
|
|
attn_out_lse,
|
|
group=self.dcp_group)
|
|
# permute: [num_heads, v_head_dim+1, bs] -> [bs, num_heads, v_head_dim+1]
|
|
attn_out_lse_all2all = attn_out_lse_all2all.permute([2, 0, 1])
|
|
if self.pcp_size > 1:
|
|
attn_out_lse = attn_out_lse_all2all.contiguous()
|
|
attn_out_lse_list = list(
|
|
torch.chunk(attn_out_lse_all2all, self.dcp_size, dim=1))
|
|
|
|
if self.pcp_size > 1:
|
|
# AllGather out&lse within PCP group
|
|
attn_out_lse_list = [
|
|
torch.empty_like(attn_out_lse) for _ in range(self.pcp_size)
|
|
]
|
|
dist.all_gather(attn_out_lse_list,
|
|
attn_out_lse,
|
|
group=self.pcp_group)
|
|
if self.dcp_size > 1 and self.pcp_size > 1:
|
|
attn_out_lse_list_pcp_dcp = []
|
|
for s in attn_out_lse_list:
|
|
attn_out_lse_list_split = list(
|
|
torch.chunk(s, self.dcp_size, dim=1))
|
|
attn_out_lse_list_pcp_dcp += attn_out_lse_list_split
|
|
attn_out_lse_list = attn_out_lse_list_pcp_dcp
|
|
|
|
return attn_out_lse_list
|
|
|
|
def _reorg_kvcache(
|
|
self,
|
|
allgatered_kv_c_normed: torch.Tensor,
|
|
allgatered_k_pe: torch.Tensor,
|
|
padded_local_chunk_seq_lens_lst: list[int],
|
|
local_context_lens_allranks: list[list[int]],
|
|
sum_seq_len: int,
|
|
max_seq_len: int,
|
|
chunk_size: int,
|
|
chunk_idx: int,
|
|
toks: int,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
reorg and unpad kvcache after cp local gather to tp layout for attn kernel.
|
|
e.g.
|
|
kv_c_normed in rank0 = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...]
|
|
kv_c_normed in rank1 = [T0_4, T0_5, pad, pad, T1_2, pad, ...]
|
|
allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...,
|
|
T0_4, T0_5, pad, pad, T1_2, pad, ...]
|
|
-> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5,
|
|
T1_0, T1_1, T1_2, ...]
|
|
Args:
|
|
padded_local_chunk_seq_lens_lst: local chunk context lengths
|
|
under current CP rank.
|
|
local_context_lens_allranks: local context lengths on each CP rank.
|
|
sum_seq_len: the sum of cp_chunk_seq_lens_lst.
|
|
max_seq_len: the max value of cp_chunk_seq_lens_lst.
|
|
chunk_size: the local padded max context chunk from
|
|
chunked_context_metadata building.
|
|
chunk_idx: chunk idx of chunked_prefill.
|
|
toks: the number of tokens for local gather cache.
|
|
"""
|
|
kv_c_segments = []
|
|
k_pe_segments = []
|
|
src_token_idx = 0
|
|
max_seq_len_check = 0
|
|
for padded_local_chunk_seq_len, local_context_lens in zip(
|
|
padded_local_chunk_seq_lens_lst, local_context_lens_allranks):
|
|
cur_seq_len = 0
|
|
for rank, local_context_len in enumerate(local_context_lens):
|
|
# Note(qcs): We split the context into multiple chunks,
|
|
# depending on the size of the workspace.
|
|
# local_context in dcp0: |-----------------|
|
|
# local_context in dcp1: |--------------|
|
|
# n*padded_local_chunk: |-----|-----|-----|
|
|
# local_chunk_len in dcp1: |-----|-----|--|
|
|
# so we need update the last chunk length in dcp1.
|
|
local_chunk_len = min(
|
|
max(0, local_context_len - chunk_idx * chunk_size),
|
|
padded_local_chunk_seq_len,
|
|
)
|
|
if local_chunk_len != 0:
|
|
kv_c_segment = allgatered_kv_c_normed[rank * toks +
|
|
src_token_idx:rank *
|
|
toks +
|
|
src_token_idx +
|
|
local_chunk_len]
|
|
k_pe_segment = allgatered_k_pe[rank * toks +
|
|
src_token_idx:rank * toks +
|
|
src_token_idx +
|
|
local_chunk_len]
|
|
kv_c_segments.append(kv_c_segment)
|
|
k_pe_segments.append(k_pe_segment)
|
|
cur_seq_len += local_chunk_len
|
|
max_seq_len_check = max(max_seq_len_check, cur_seq_len)
|
|
src_token_idx += padded_local_chunk_seq_len
|
|
reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0)
|
|
reorganized_k_pe = torch.cat(k_pe_segments, dim=0)
|
|
assert reorganized_kv_c_normed.shape[0] == sum_seq_len
|
|
assert reorganized_k_pe.shape[0] == sum_seq_len
|
|
assert max_seq_len_check == max_seq_len
|
|
return reorganized_kv_c_normed, reorganized_k_pe
|