819a4459ce
### What this PR does / why we need it?
Drop vLLM 0.13.0 support, upgrade to 0.14.0
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
1118 lines
47 KiB
Python
1118 lines
47 KiB
Python
from dataclasses import dataclass
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from typing import TYPE_CHECKING, Optional, Tuple, Type, TypeVar
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import torch
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import torch_npu
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import vllm.envs as envs_vllm
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from torch import nn
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from vllm.config import CUDAGraphMode, VllmConfig, get_current_vllm_config
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from vllm.config import VllmConfig, get_current_vllm_config
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from vllm.distributed import get_tensor_model_parallel_world_size, get_tp_group
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from vllm.forward_context import get_forward_context
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from vllm.logger import logger
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from vllm.model_executor.layers.linear import UnquantizedLinearMethod
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from vllm.triton_utils import HAS_TRITON
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from vllm.v1.attention.backend import ( # type: ignore
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AttentionBackend, AttentionCGSupport, MLAAttentionImpl)
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from vllm.v1.attention.backends.mla.common import MLACommonMetadataBuilder
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from vllm.v1.kv_cache_interface import AttentionSpec
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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_mask import AttentionMaskBuilder
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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from vllm_ascend.attention.mla_v1 import MAX_O_PROJ_PREFETCH_SIZE, MLAPO_MAX_SUPPORTED_TOKENS
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from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata,
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ascend_chunked_prefill_workspace_size,
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maybe_save_kv_layer_to_connector,
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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.distributed.utils import all_gather_async
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from vllm_ascend.ops.layer_shard_linear import (
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is_hidden_layer, post_process_after_loading_for_shard_weight_series,
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reach_layer_for_shard_weight_series,
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register_all_layers_to_shard_weight_series)
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from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_mla
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from vllm_ascend.ops.triton.rope import rope_forward_triton
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from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
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from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
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from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, _round_up, dispose_layer,
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enable_dsa_cp, enable_dsa_cp_with_layer_shard, maybe_trans_nz)
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from vllm_ascend.worker.npu_input_batch import NPUInputBatch
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if TYPE_CHECKING:
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from vllm.v1.core.sched.output import SchedulerOutput
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# token count limits within bmm_transpose operator
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BMM_TRANS_MAX_SUPPORTED_TOKENS = 1024
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class AscendSFABackend(AttentionBackend):
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accept_output_buffer: bool = True
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@staticmethod
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def get_name() -> str:
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# HACK(Ronald1995): vllm `initialize_kv_cache` method in model runner v2 make
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# attention name assertion, we just set name to FLASH_ATTN to avoid assertion error.
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# rectify this when vllm disable the assertion.
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return "ASCEND_SFA" if not envs_vllm.VLLM_USE_V2_MODEL_RUNNER else "FLASH_ATTN"
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@staticmethod
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def get_builder_cls():
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return AscendSFAMetadataBuilder
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@staticmethod
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def get_kv_cache_shape(num_blocks: int, block_size: int, num_kv_heads: int,
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head_size: int) -> tuple[int, ...]:
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return (num_blocks, block_size, num_kv_heads, head_size)
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@staticmethod
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def get_impl_cls() -> Type["AscendSFAImpl"]:
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return AscendSFAImpl
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@dataclass
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class DSACPContext:
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num_tokens: int
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num_tokens_pad: int
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local_start: int
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local_end: int
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local_end_with_pad: int
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slot_mapping_cp: torch.Tensor
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actual_seq_lengths_query: torch.Tensor
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actual_seq_lengths_key: torch.Tensor
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@dataclass
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class AscendSFAMetadata:
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"""Metadata for MLACommon.
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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# NOTE(sang): Definition of context_len, query_len, and seq_len.
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# |---------- N-1 iteration --------|
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# |---------------- N iteration ---------------------|
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# |- tokenA -|......................|-- newTokens ---|
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# |---------- context_len ----------|
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# |-------------------- seq_len ---------------------|
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# |-- query_len ---|
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num_actual_tokens: int # Number of tokens excluding padding.
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slot_mapping: torch.Tensor
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seq_lens: torch.Tensor
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cum_query_lens: torch.Tensor
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block_tables: torch.Tensor
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sin: torch.Tensor
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cos: torch.Tensor
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# For logging.
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num_input_tokens: int = 0 # Number of tokens including padding.
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# The dimension of the attention heads
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head_dim: Optional[int] = None
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attn_mask: torch.Tensor = None
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# chunked prefill by default if no attn_states passed
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attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
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dsa_cp_context: Optional[DSACPContext] = None
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reshape_cache_event: torch.npu.Event = None
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M = TypeVar("M", bound=AscendSFAMetadata)
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class AscendSFAMetadataBuilder(MLACommonMetadataBuilder[AscendSFAMetadata]):
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"""
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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def __init__(
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self,
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kv_cache_spec,
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layer_names: list[str],
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vllm_config: VllmConfig,
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device: torch.device,
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metadata_cls: type[AscendSFAMetadata] | None = None,
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supports_dcp_with_varlen: bool = False,
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):
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super().__init__(
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kv_cache_spec, layer_names, vllm_config, device,
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metadata_cls if metadata_cls is not None else AscendSFAMetadata,
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supports_dcp_with_varlen)
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self.block_size = vllm_config.cache_config.block_size
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self.max_blocks = (vllm_config.model_config.max_model_len +
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self.block_size - 1) // self.block_size
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self.speculative_config = vllm_config.speculative_config
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self.decode_threshold = 1
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if self.speculative_config:
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spec_token_num = self.speculative_config.num_speculative_tokens
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self.decode_threshold += spec_token_num
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assert self.decode_threshold <= 16, f"decode_threshold exceeded \
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npu_fused_infer_attention_score TND layout's limit of 16, \
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got {self.decode_threshold}"
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self.attn_mask_builder = AttentionMaskBuilder(self.device)
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self.rope_dim = self.model_config.hf_text_config.qk_rope_head_dim
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self.enable_dsa_cp = enable_dsa_cp()
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max_num_reqs = vllm_config.scheduler_config.max_num_seqs
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self.actual_seq_lengths_query = torch.zeros(max_num_reqs + 1,
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dtype=torch.int32,
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device=device)
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self.actual_seq_lengths_key = torch.empty_like(
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self.actual_seq_lengths_query)
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@staticmethod
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def determine_chunked_prefill_workspace_size(
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vllm_config: VllmConfig) -> int:
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return ascend_chunked_prefill_workspace_size(vllm_config)
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@classmethod
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def get_cudagraph_support(
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cls: type["AscendSFAMetadataBuilder"],
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vllm_config: VllmConfig,
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kv_cache_spec: AttentionSpec,
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) -> AttentionCGSupport:
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# Explicit override in case the underlying builder specialized this getter.
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# @override omitted only because of mypy limitation due to type variable.
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return AttentionCGSupport.UNIFORM_BATCH
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def reorder_batch(self, input_batch: "NPUInputBatch",
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scheduler_output: "SchedulerOutput") -> bool:
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# No need to reorder for Ascend SFA
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return False
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def build(
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self,
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common_prefix_len: int,
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common_attn_metadata: AscendCommonAttentionMetadata,
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fast_build: bool = False,
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) -> AscendSFAMetadata:
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num_reqs = common_attn_metadata.num_reqs
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num_actual_tokens = common_attn_metadata.num_actual_tokens
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num_input_tokens = common_attn_metadata.num_input_tokens
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block_table = common_attn_metadata.block_table_tensor[:num_reqs]
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slot_mapping = common_attn_metadata.slot_mapping[:num_input_tokens]
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input_positions = common_attn_metadata.positions[:
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num_input_tokens].long(
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)
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cum_query_lens = common_attn_metadata.query_start_loc[1:num_reqs + 1]
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seq_lens = common_attn_metadata.seq_lens[:num_reqs]
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cos, sin = get_cos_and_sin_mla(input_positions, True)
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dsa_cp_context = None
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if self.enable_dsa_cp:
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global_tp_size = get_tp_group().world_size
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num_tokens = num_input_tokens
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num_tokens_pad = _round_up(num_tokens, global_tp_size)
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num_tokens_per_device = num_tokens_pad // global_tp_size
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local_start = get_tp_group().rank_in_group * num_tokens_per_device
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local_end_with_pad = local_start + num_tokens_per_device
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local_end = min(local_end_with_pad, num_actual_tokens)
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pad_size = num_tokens_pad - cos.shape[0]
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assert cos.shape == sin.shape, \
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f"cos.shape must be equal to sin.shape, got {cos.shape} and {sin.shape}"
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if pad_size > 0:
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cos = nn.functional.pad(cos, (0, 0, 0, 0, 0, 0, 0, pad_size))
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sin = nn.functional.pad(sin, (0, 0, 0, 0, 0, 0, 0, pad_size))
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pad_size_slot = num_tokens_pad - slot_mapping.shape[0]
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if pad_size_slot > 0:
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slot_mapping = nn.functional.pad(slot_mapping,
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(0, pad_size_slot),
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value=-1)
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else:
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slot_mapping = slot_mapping[:num_tokens_pad]
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slot_mapping_cp = slot_mapping[local_start:local_end_with_pad]
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cos = cos[local_start:local_end_with_pad]
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sin = sin[local_start:local_end_with_pad]
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assert cos.shape[0] == num_tokens_per_device, \
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f"cos.shape[0] must be equal to num_tokens_per_device, \
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got {cos.shape[0]} and {num_tokens_per_device}"
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assert slot_mapping_cp.shape[0] == num_tokens_per_device, \
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f"slot_mapping_cp.shape[0] must be equal to num_tokens_per_device, \
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got {slot_mapping_cp.shape[0]} and {num_tokens_per_device}"
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assert slot_mapping.shape[0] == num_tokens_pad, \
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f"slot_mapping.shape[0] must be equal to num_tokens_pad, \
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got {slot_mapping.shape[0]} and {num_tokens_pad}"
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actual_seq_lengths_query = self.actual_seq_lengths_query
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actual_seq_lengths_key = self.actual_seq_lengths_key
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num_segs = cum_query_lens.shape[0]
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last_token = 0
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cum = 0
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for i in range(0, num_segs):
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global_start = last_token
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global_end = cum_query_lens[i].item()
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last_token = global_end
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req_local_start = max(global_start, local_start)
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req_local_end = min(global_end, local_end_with_pad)
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num_local_tokens = req_local_end - req_local_start
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if num_local_tokens > 0:
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cum += num_local_tokens
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actual_seq_lengths_query[i] = cum
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offset = global_end - req_local_end
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actual_seq_lengths_key[i] = seq_lens[i].item() - offset
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else:
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actual_seq_lengths_query[i] = cum
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actual_seq_lengths_key[i] = 0
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actual_seq_lengths_query = actual_seq_lengths_query[:num_reqs]
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actual_seq_lengths_key = actual_seq_lengths_key[:num_reqs]
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dsa_cp_context = DSACPContext(
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num_tokens=num_tokens,
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num_tokens_pad=num_tokens_pad,
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local_start=local_start,
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local_end=local_end,
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local_end_with_pad=local_end_with_pad,
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slot_mapping_cp=slot_mapping_cp,
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actual_seq_lengths_query=actual_seq_lengths_query,
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actual_seq_lengths_key=actual_seq_lengths_key,
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)
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return self.metadata_cls( # type: ignore
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num_input_tokens=common_attn_metadata.num_input_tokens,
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num_actual_tokens=num_actual_tokens,
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cum_query_lens=cum_query_lens,
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seq_lens=seq_lens,
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slot_mapping=slot_mapping,
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head_dim=self.model_config.get_head_size(),
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attn_mask=self.attn_mask_builder.get_attention_mask(
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self.model_config),
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attn_state=common_attn_metadata.attn_state,
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block_tables=block_table,
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sin=sin[:num_input_tokens],
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cos=cos[:num_input_tokens],
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dsa_cp_context=dsa_cp_context)
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def build_for_graph_capture(
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self,
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common_attn_metadata: AscendCommonAttentionMetadata,
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attn_state: AscendAttentionState = AscendAttentionState.DecodeOnly,
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):
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if attn_state in {
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AscendAttentionState.DecodeOnly,
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AscendAttentionState.SpecDecoding
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}:
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attn_metadata = self.build(
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common_prefix_len=0,
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common_attn_metadata=common_attn_metadata,
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)
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else:
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raise NotImplementedError(
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"Currently we only support building dummy metadata for DecodeOnly state"
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)
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attn_metadata.attn_state = attn_state
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return attn_metadata
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class AscendSFAImpl(MLAAttentionImpl):
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"""
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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# Supports forward using the all-gather o_proj weight for decode requests when Sharded CP is enabled.
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o_proj_full_pool: Optional[torch.Tensor] = None
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: int,
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alibi_slopes: Optional[list[float]],
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sliding_window: Optional[int],
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kv_cache_dtype: str,
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logits_soft_cap: Optional[float],
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attn_type: str,
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kv_sharing_target_layer_name: Optional[str],
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**kwargs,
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) -> None:
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self.num_heads = num_heads
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self.head_size = head_size
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self.scale = float(scale)
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self.num_kv_heads = num_kv_heads
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self.kv_cache_dtype = kv_cache_dtype
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# MLA Args
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self.q_lora_rank = kwargs['q_lora_rank']
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self.kv_lora_rank = kwargs['kv_lora_rank']
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self.qk_nope_head_dim = kwargs['qk_nope_head_dim']
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self.qk_rope_head_dim = kwargs['qk_rope_head_dim']
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self.qk_head_dim = kwargs['qk_head_dim']
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self.v_head_dim = kwargs['v_head_dim']
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self.rotary_emb = kwargs['rotary_emb']
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self.q_proj = kwargs['q_proj'] if self.q_lora_rank is None else kwargs[
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'q_b_proj']
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self.fused_qkv_a_proj = kwargs.get('fused_qkv_a_proj', None)
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self.kv_b_proj = kwargs['kv_b_proj']
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self.o_proj = kwargs['o_proj']
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self.indexer = kwargs['indexer']
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self.kv_a_proj_with_mqa = kwargs.get('kv_a_proj_with_mqa', None)
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self.kv_a_layernorm = kwargs.get('kv_a_layernorm', None)
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self.q_a_layernorm = kwargs.get('q_a_layernorm', None)
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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self.tp_size = get_tensor_model_parallel_world_size()
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self.tp_rank = get_tp_group().rank_in_group
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self.q_b_proj = kwargs['q_b_proj']
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ascend_config = get_ascend_config()
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self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
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self.enable_prefetch = ascend_config.weight_prefetch_config.enabled
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# In sfa, prefill and decode have the same calculation formula,
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# so do not distinguish between prefill and decode here.
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self.enable_mlapo = envs.VLLM_ASCEND_ENABLE_MLAPO
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assert self.indexer is not None, "Indexer is required for DSA."
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self.local_num_heads = self.num_heads
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self.vllm_config = get_current_vllm_config()
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self.is_kv_producer = self.vllm_config.kv_transfer_config is not None and self.vllm_config.kv_transfer_config.is_kv_producer
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# indexer param
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self.n_head: int = self.indexer.n_head # 64
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self.head_dim: int = self.indexer.head_dim # 128
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self.wq_b = self.indexer.wq_b
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self.wk = self.indexer.wk
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self.weights_proj = self.indexer.weights_proj
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self.k_norm = self.indexer.k_norm
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self.cp_size = 1
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self.enable_dsa_cp = enable_dsa_cp()
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self.enable_dsa_cp_prefill_only = enable_dsa_cp_with_layer_shard()
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if self.enable_dsa_cp:
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self.local_num_heads = self.num_heads * self.tp_size
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if self.enable_dsa_cp_prefill_only:
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self.layer_sharding_kwargs = []
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for layer_name in (get_ascend_config().layer_sharding or []):
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if layer_name in kwargs:
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self.layer_sharding_kwargs.append(kwargs[layer_name])
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else:
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logger.warning_once(
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f"[SFAImpl init] Layer '{layer_name}' not found in kwargs for layer sharding, skipping sharding configuration"
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)
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register_all_layers_to_shard_weight_series(
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self.layer_sharding_kwargs)
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def process_weights_after_loading(self, act_dtype: torch.dtype):
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# NOTE: We currently do not support quant kv_b_proj.
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assert isinstance(self.kv_b_proj.quant_method, UnquantizedLinearMethod)
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# NOTE: Weight will be reshaped next, we need to revert and transpose it.
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kv_b_proj_weight = torch_npu.npu_format_cast(
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self.kv_b_proj.weight.data, ACL_FORMAT_FRACTAL_ND).T
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assert kv_b_proj_weight.shape == (
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self.kv_lora_rank, self.local_num_heads *
|
|
(self.qk_nope_head_dim + self.v_head_dim)), (
|
|
f"{kv_b_proj_weight.shape=}, "
|
|
f"{self.kv_lora_rank=}, "
|
|
f"{self.local_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.local_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()
|
|
|
|
# TODO(zzzzwwjj): Currently, torch.ops._C_ascend.batch_matmul_transpose cannot support weight nz
|
|
# self.W_UV = maybe_trans_nz(self.W_UV)
|
|
|
|
# Dispose kv_b_proj since it is replaced by W_UV and W_UK_T to save memory
|
|
dispose_layer(self.kv_b_proj)
|
|
if self.enable_dsa_cp:
|
|
if self.enable_dsa_cp_prefill_only:
|
|
for layer in (self.layer_sharding_kwargs or []):
|
|
if is_hidden_layer(layer):
|
|
post_process_after_loading_for_shard_weight_series(
|
|
layer)
|
|
else:
|
|
self._init_o_proj_tp_full_params()
|
|
|
|
if self.enable_mlapo:
|
|
quant_method = getattr(
|
|
getattr(self.fused_qkv_a_proj, "quant_method", None),
|
|
"quant_method",
|
|
None,
|
|
)
|
|
reasons = []
|
|
if self.fused_qkv_a_proj is None or not isinstance(
|
|
quant_method, AscendW8A8LinearMethod):
|
|
reasons.append(
|
|
"Currently mlapo only supports W8A8 quantization in SFA scenario."
|
|
"Some layers in your model are not quantized with W8A8,"
|
|
"thus mlapo is disabled for these layers.")
|
|
if self.enable_dsa_cp:
|
|
reasons.append("Currently mlapo does not support SFA with CP,"
|
|
"thus mlapo is disabled for these layers.")
|
|
if reasons:
|
|
self.enable_mlapo = False
|
|
for msg in reasons:
|
|
logger.warning_once(msg)
|
|
else:
|
|
self._process_weights_for_fused_mlapo(act_dtype)
|
|
if not self.enable_mlapo:
|
|
# if mlapo, W_UK_T can't trans nz
|
|
self.W_UK_T = maybe_trans_nz(self.W_UK_T)
|
|
|
|
def _v_up_proj(self, x):
|
|
num_input_tokens, _, _ = x.shape
|
|
if x.dtype in [torch.float16, torch.bfloat16] \
|
|
and hasattr(torch.ops._C_ascend, "batch_matmul_transpose") \
|
|
and num_input_tokens <= BMM_TRANS_MAX_SUPPORTED_TOKENS:
|
|
x = x.view(-1, self.local_num_heads, self.kv_lora_rank)
|
|
res = torch.empty((num_input_tokens, self.local_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.local_num_heads * self.v_head_dim)
|
|
else:
|
|
# Convert from (B, N, L) to (N, B, L)
|
|
x = x.view(-1, self.local_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.local_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.local_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 exec_kv(
|
|
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"
|
|
|
|
if self.enable_dsa_cp:
|
|
_, _, 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
|
|
else:
|
|
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 None, None
|
|
|
|
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)
|
|
|
|
# Processing the input parameters for MLAPO by reordering and transposing
|
|
# QKV(and part of Q) weight, applying RoPE-related dimension transformations,
|
|
# and handling quantization parameters.
|
|
def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
|
|
assert self.kv_a_proj_with_mqa is None
|
|
assert self.fused_qkv_a_proj is not None
|
|
|
|
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 = self.q_a_layernorm.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)
|
|
|
|
# On KV consumers (decode-only) MLAPO uses the transformed weights built above;
|
|
# the original fused_qkv_a_proj/q_proj weights and quant params are no longer
|
|
# referenced, so drop them to save memory.
|
|
if self.vllm_config.kv_transfer_config is not None and \
|
|
self.vllm_config.kv_transfer_config.is_kv_consumer and \
|
|
self.vllm_config.scheduler_config.max_num_batched_tokens <= MLAPO_MAX_SUPPORTED_TOKENS:
|
|
self.fused_qkv_a_proj.weight = None
|
|
self.fused_qkv_a_proj.deq_scale = None
|
|
self.fused_qkv_a_proj.quant_bias = None
|
|
self.q_proj.weight = None
|
|
self.q_proj.deq_scale = None
|
|
self.q_proj.quant_bias = None
|
|
torch.npu.empty_cache()
|
|
|
|
def _sfa_preprocess_decode(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
|
attn_metadata: M,
|
|
need_gather_q_kv: bool,
|
|
num_input_tokens: int,
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
|
hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
hidden_states.contiguous(), need_gather_q_kv)
|
|
k_nope, k_pe = kv_cache[0], kv_cache[1]
|
|
ql_nope = torch.empty(
|
|
(num_input_tokens, self.W_UK_T.shape[0], k_nope.shape[-1]),
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device,
|
|
)
|
|
q_pe = torch.empty(
|
|
(num_input_tokens, self.W_UK_T.shape[0], k_pe.shape[-1]),
|
|
dtype=hidden_states.dtype,
|
|
device=hidden_states.device,
|
|
)
|
|
q_c = torch.empty(
|
|
(num_input_tokens, self.q_lora_rank),
|
|
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,
|
|
attn_metadata.cos,
|
|
attn_metadata.sin,
|
|
self.W_UK_T,
|
|
k_nope,
|
|
k_pe,
|
|
attn_metadata.slot_mapping,
|
|
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",
|
|
enable_inner_out=True,
|
|
q_out0=ql_nope,
|
|
kv_cache_out0=k_nope,
|
|
q_out1=q_pe,
|
|
kv_cache_out1=k_pe,
|
|
inner_out=q_c,
|
|
)
|
|
return hidden_states, ql_nope, q_pe, q_c
|
|
|
|
def forward(
|
|
self,
|
|
layer_name,
|
|
hidden_states: torch.Tensor, # query in unified attn
|
|
kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
|
attn_metadata: M,
|
|
need_gather_q_kv: bool = False,
|
|
output: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
assert output is not None, "Output tensor must be provided."
|
|
forward_context = get_forward_context()
|
|
if attn_metadata is None:
|
|
# Profiling run.
|
|
if self.enable_dsa_cp_prefill_only and not forward_context.in_profile_run:
|
|
for layer in (self.layer_sharding_kwargs or []):
|
|
if is_hidden_layer(layer):
|
|
reach_layer_for_shard_weight_series(layer)
|
|
return output.fill_(0)
|
|
|
|
cos = attn_metadata.cos
|
|
sin = attn_metadata.sin
|
|
actual_seq_lengths_query = attn_metadata.cum_query_lens
|
|
actual_seq_lengths_key = attn_metadata.seq_lens
|
|
if self.enable_dsa_cp:
|
|
need_gather_q_kv = False
|
|
# Inputs and outputs may be padded for CUDA graphs
|
|
num_input_tokens = attn_metadata.num_input_tokens
|
|
output_padded = output
|
|
|
|
# all-gather o_proj weight for prefill stage of PD mix node
|
|
o_proj_full_handle = None
|
|
# if is PD mix stage, using original TP o_proj weight, and also need to full gather for o_proj weight for prefill stage.
|
|
should_shard_weight = self.enable_dsa_cp_prefill_only or attn_metadata.attn_state not in {
|
|
AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding
|
|
}
|
|
|
|
|
|
if self.enable_mlapo and num_input_tokens <= MLAPO_MAX_SUPPORTED_TOKENS:
|
|
hidden_states, ql_nope, q_pe, q_c = self._sfa_preprocess_decode(
|
|
hidden_states=hidden_states,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
need_gather_q_kv=need_gather_q_kv,
|
|
num_input_tokens=num_input_tokens,
|
|
)
|
|
q, k = self.indexer_select_pre_process(
|
|
x=hidden_states,
|
|
qr=q_c,
|
|
cos=cos,
|
|
sin=sin,
|
|
need_gather_q_kv=need_gather_q_kv)
|
|
else:
|
|
assert self.fused_qkv_a_proj is not None, "q lora is required for DSA."
|
|
maybe_npu_prefetch(inputs=self.fused_qkv_a_proj.weight,
|
|
dependency=hidden_states,
|
|
enabled=self.enable_prefetch)
|
|
qkv_lora = self.fused_qkv_a_proj(hidden_states)[0]
|
|
q_c, kv_no_split = qkv_lora.split(
|
|
[self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
|
|
dim=-1,
|
|
)
|
|
q_c = self.q_a_layernorm(q_c)
|
|
# Process for Flash Comm V1
|
|
if need_gather_q_kv:
|
|
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)
|
|
|
|
q, k = self.indexer_select_pre_process(
|
|
x=hidden_states,
|
|
qr=q_c,
|
|
cos=cos,
|
|
sin=sin,
|
|
need_gather_q_kv=need_gather_q_kv)
|
|
|
|
wait_for_kv_layer_from_connector(layer_name)
|
|
|
|
slot_mapping = attn_metadata.slot_mapping
|
|
if self.enable_dsa_cp:
|
|
assert attn_metadata.dsa_cp_context is not None
|
|
slot_mapping = attn_metadata.dsa_cp_context.slot_mapping_cp
|
|
actual_seq_lengths_query = attn_metadata.dsa_cp_context.actual_seq_lengths_query
|
|
actual_seq_lengths_key = attn_metadata.dsa_cp_context.actual_seq_lengths_key
|
|
|
|
k_pe, k_nope = self.exec_kv(kv_no_split, cos, sin, kv_cache,
|
|
slot_mapping)
|
|
|
|
if self.enable_dsa_cp:
|
|
assert k_pe is not None
|
|
assert k_nope is not None
|
|
# support all_gather kv async for communication calculation overlap
|
|
fused_kv_no_split, kv_ag_handle = all_gather_async(
|
|
torch.cat([
|
|
k_pe.view(-1, k_pe.shape[-1]),
|
|
k_nope.view(-1, k_nope.shape[-1]),
|
|
k.view(-1, k.shape[-1])
|
|
],
|
|
dim=1),
|
|
get_tp_group(),
|
|
async_op=should_shard_weight)
|
|
|
|
ql_nope, q_pe = self._q_proj_and_k_up_proj(q_c)
|
|
q_pe = self.rope_single(q_pe, cos, sin)
|
|
|
|
if self.enable_dsa_cp:
|
|
if kv_ag_handle is not None:
|
|
kv_ag_handle.wait()
|
|
|
|
if self.enable_dsa_cp_prefill_only:
|
|
for layer in (self.layer_sharding_kwargs or []):
|
|
if is_hidden_layer(layer):
|
|
reach_layer_for_shard_weight_series(layer)
|
|
elif should_shard_weight:
|
|
_, o_proj_full_handle = all_gather_async(
|
|
self.o_proj_tp_weight,
|
|
get_tp_group(),
|
|
output=AscendSFAImpl.o_proj_full_pool)
|
|
|
|
if kv_cache is not None:
|
|
assert fused_kv_no_split is not None
|
|
k_pe, k_nope, k = fused_kv_no_split.split([
|
|
self.qk_rope_head_dim, self.kv_lora_rank, self.head_dim
|
|
],
|
|
dim=-1)
|
|
slot_mapping = attn_metadata.slot_mapping.view(-1, 1)
|
|
torch_npu.npu_scatter_nd_update_(
|
|
kv_cache[0].view(-1, k_nope.shape[-1]), slot_mapping,
|
|
k_nope)
|
|
torch_npu.npu_scatter_nd_update_(
|
|
kv_cache[1].view(-1, k_pe.shape[-1]), slot_mapping,
|
|
k_pe)
|
|
|
|
if kv_cache is not None:
|
|
torch_npu.npu_scatter_nd_update_(
|
|
kv_cache[2].view(-1, k.shape[-1]),
|
|
attn_metadata.slot_mapping.view(-1, 1),
|
|
k.view(-1, k.shape[-1])) # b, s, n, d
|
|
|
|
topk_indices = self.indexer_select_post_process(
|
|
x=hidden_states,
|
|
qr=q_c,
|
|
q=q,
|
|
k=k,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
cos=cos,
|
|
sin=sin,
|
|
actual_seq_lengths_query=actual_seq_lengths_query,
|
|
actual_seq_lengths_key=actual_seq_lengths_key,
|
|
need_gather_q_kv=need_gather_q_kv)
|
|
|
|
attn_output = torch.ops._C_ascend.npu_sparse_flash_attention(
|
|
query=ql_nope,
|
|
key=kv_cache[0],
|
|
value=kv_cache[0],
|
|
sparse_indices=topk_indices,
|
|
scale_value=self.scale,
|
|
sparse_block_size=1,
|
|
block_table=attn_metadata.block_tables,
|
|
actual_seq_lengths_query=actual_seq_lengths_query,
|
|
actual_seq_lengths_kv=actual_seq_lengths_key,
|
|
query_rope=q_pe,
|
|
key_rope=kv_cache[1],
|
|
layout_query="TND",
|
|
layout_kv="PA_BSND",
|
|
sparse_mode=3,
|
|
)
|
|
|
|
attn_output = self._v_up_proj(attn_output)
|
|
maybe_npu_prefetch(inputs=self.o_proj.weight,
|
|
dependency=attn_output,
|
|
max_size=MAX_O_PROJ_PREFETCH_SIZE,
|
|
enabled=self.enable_prefetch)
|
|
|
|
if self.enable_dsa_cp and not self.enable_dsa_cp_prefill_only:
|
|
# When using SFA-CP with pd mixed, o_proj has two cases:
|
|
# 1. prefill: o_proj is a TP weight, we need to all-gather o_proj weight to switch TP=1.
|
|
# 2. decode: all-to-all the hidden_state before the o_proj forward.
|
|
result, require_o_proj_forward = self._handle_o_proj_weight_switch_and_forward(
|
|
attn_output=attn_output,
|
|
output=output,
|
|
o_proj_full_handle=o_proj_full_handle,
|
|
should_shard_weight=should_shard_weight)
|
|
if not require_o_proj_forward:
|
|
return result
|
|
attn_output = result
|
|
|
|
output[...] = self.o_proj(attn_output)[0]
|
|
|
|
maybe_save_kv_layer_to_connector(layer_name, list(kv_cache))
|
|
|
|
return output_padded
|
|
|
|
def indexer_select_pre_process(
|
|
self,
|
|
x: torch.Tensor,
|
|
qr: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
need_gather_q_kv: bool = False,
|
|
):
|
|
k_proj, _ = self.wk(x) # [b,s,7168] @ [7168,128] = [b,s,128]
|
|
k_proj = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
k_proj, need_gather_q_kv)
|
|
k = self.k_norm(k_proj).unsqueeze(1)
|
|
k = k.view(-1, 1, self.head_dim)
|
|
|
|
if HAS_TRITON:
|
|
q, _ = self.wq_b(qr) # [b,s,1536] @ [1536,64*128] = [b,s,64*128]
|
|
q = q.view(-1, self.n_head, self.head_dim) # [n_toks,64,128]
|
|
|
|
cos = cos.view(-1, self.qk_rope_head_dim)
|
|
sin = sin.view(-1, self.qk_rope_head_dim)
|
|
q, k = rope_forward_triton(q,
|
|
k,
|
|
cos,
|
|
sin,
|
|
rope_dim=self.qk_rope_head_dim,
|
|
is_neox_style=True)
|
|
else:
|
|
k_pe, k_nope = torch.split(
|
|
k,
|
|
[self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim],
|
|
dim=-1)
|
|
|
|
cos = cos.view(-1, 1, 1, self.qk_rope_head_dim)
|
|
sin = sin.view(-1, 1, 1, self.qk_rope_head_dim)
|
|
|
|
k_pe = k_pe.unsqueeze(2)
|
|
k_pe = torch_npu.npu_interleave_rope(k_pe, cos, sin)
|
|
k_pe = k_pe.squeeze(2)
|
|
|
|
k = torch.cat([k_pe, k_nope], dim=-1) # [b*s,128]
|
|
q = None
|
|
|
|
return q, k
|
|
|
|
def indexer_select_post_process(
|
|
self,
|
|
x: torch.Tensor,
|
|
qr: torch.Tensor,
|
|
q: Optional[torch.Tensor],
|
|
k: torch.Tensor,
|
|
kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
|
attn_metadata: M,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
actual_seq_lengths_query: torch.Tensor,
|
|
actual_seq_lengths_key: torch.Tensor,
|
|
need_gather_q_kv: bool = False,
|
|
):
|
|
if q is None:
|
|
q, _ = self.wq_b(qr) # [b,s,1536] @ [1536,64*128] = [b,s,64*128]
|
|
q = q.view(-1, self.n_head, self.head_dim) # [n_toks,64,128]
|
|
cos_q, sin_q = cos, sin
|
|
|
|
q_pe, q_nope = torch.split(
|
|
q,
|
|
[self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim],
|
|
dim=-1) # [b,s,64,64+64]
|
|
|
|
q_pe = q_pe.unsqueeze(2)
|
|
q_pe = torch_npu.npu_rotary_mul(q_pe, cos_q, sin_q)
|
|
q_pe = q_pe.squeeze(2)
|
|
q = torch.cat([q_pe, q_nope], dim=-1) # [b*s,64,128]
|
|
|
|
if kv_cache is not None:
|
|
if self.is_kv_producer:
|
|
attn_metadata.reshape_cache_event = torch.npu.Event()
|
|
torch_npu.npu_scatter_nd_update_(kv_cache[2].view(-1, k.shape[-1]),
|
|
attn_metadata.slot_mapping.view(
|
|
-1, 1),
|
|
k.view(-1,
|
|
k.shape[-1])) # b, s, n, d
|
|
if self.is_kv_producer:
|
|
attn_metadata.reshape_cache_event.record()
|
|
|
|
weights, _ = self.weights_proj(x)
|
|
weights = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
|
weights, need_gather_q_kv)
|
|
|
|
block_table = attn_metadata.block_tables
|
|
|
|
topk_indices = torch.ops._C_ascend.npu_lightning_indexer(
|
|
query=q,
|
|
key=kv_cache[2],
|
|
weights=weights,
|
|
actual_seq_lengths_query=actual_seq_lengths_query,
|
|
actual_seq_lengths_key=actual_seq_lengths_key,
|
|
block_table=block_table,
|
|
layout_query="TND",
|
|
layout_key="PA_BSND",
|
|
sparse_count=2048,
|
|
sparse_mode=3)
|
|
return topk_indices
|
|
|
|
def _init_o_proj_tp_full_params(self):
|
|
"""
|
|
Initialize TP-mode and Full-mode parameters for o_proj weight,
|
|
preparing for weight switching in PD mix stage.
|
|
|
|
For PD mix stage:
|
|
- Use original TP o_proj weight for decode phase
|
|
- Need full-gather o_proj weight from all TP ranks for prefill phase
|
|
"""
|
|
if AscendSFAImpl.o_proj_full_pool is None:
|
|
sample = self.o_proj.weight
|
|
AscendSFAImpl.o_proj_full_pool = torch.empty(
|
|
(sample.shape[0] * self.tp_size, sample.shape[1]),
|
|
dtype=sample.dtype,
|
|
device=sample.device)
|
|
|
|
# Save TP-mode parameters (original sharded weights)
|
|
self.o_proj_tp_weight = self.o_proj.weight.clone().detach()
|
|
self.o_proj_tp_aclnn_input_scale = self.o_proj.aclnn_input_scale.clone(
|
|
).detach()
|
|
self.o_proj_tp_aclnn_input_scale_reciprocal = self.o_proj.aclnn_input_scale_reciprocal.clone(
|
|
).detach()
|
|
self.o_proj_tp_aclnn_input_offset = self.o_proj.aclnn_input_offset.clone(
|
|
).detach()
|
|
|
|
# Initially switch to TP mode for graph capture
|
|
self.o_proj.weight.set_(self.o_proj_tp_weight)
|
|
self.o_proj.aclnn_input_scale.set_(self.o_proj_tp_aclnn_input_scale)
|
|
self.o_proj.aclnn_input_scale_reciprocal.set_(
|
|
self.o_proj_tp_aclnn_input_scale_reciprocal)
|
|
self.o_proj.aclnn_input_offset.set_(self.o_proj_tp_aclnn_input_offset)
|
|
|
|
# Precompute Full-mode quantization parameters by repeating TP parameters across all TP ranks
|
|
self.o_proj_full_aclnn_input_scale = self.o_proj.aclnn_input_scale.repeat(
|
|
self.tp_size)
|
|
self.o_proj_full_aclnn_input_scale_reciprocal = self.o_proj.aclnn_input_scale_reciprocal.repeat(
|
|
self.tp_size)
|
|
self.o_proj_full_aclnn_input_offset = self.o_proj.aclnn_input_offset.repeat(
|
|
self.tp_size)
|
|
|
|
def _handle_o_proj_weight_switch_and_forward(
|
|
self, attn_output: torch.Tensor, output: torch.Tensor,
|
|
o_proj_full_handle: Optional[torch.distributed.Work],
|
|
should_shard_weight: bool) -> Tuple[torch.Tensor, bool]:
|
|
"""
|
|
Handle o_proj weight switching between TP-mode and Full-mode, and execute forward computation.
|
|
"""
|
|
# Gather o_proj weight from all TP ranks for Full-mode computation
|
|
if should_shard_weight:
|
|
# Wait for the completion of o_proj weight all-gather operation
|
|
if o_proj_full_handle is not None:
|
|
o_proj_full_handle.wait()
|
|
|
|
# Switch o_proj to Full-mode (gathered weight from all TP ranks)
|
|
self.o_proj.weight.set_(AscendSFAImpl.o_proj_full_pool)
|
|
self.o_proj.aclnn_input_scale.set_(
|
|
self.o_proj_full_aclnn_input_scale)
|
|
self.o_proj.aclnn_input_scale_reciprocal.set_(
|
|
self.o_proj_full_aclnn_input_scale_reciprocal)
|
|
self.o_proj.aclnn_input_offset.set_(
|
|
self.o_proj_full_aclnn_input_offset)
|
|
|
|
# Apply quantization method and execute forward computation
|
|
output[...] = self.o_proj.quant_method.quant_method.apply(
|
|
self.o_proj, attn_output)
|
|
|
|
# Switch o_proj back to TP-mode for subsequent decode operations
|
|
self.o_proj.weight.set_(self.o_proj_tp_weight)
|
|
self.o_proj.aclnn_input_scale.set_(
|
|
self.o_proj_tp_aclnn_input_scale)
|
|
self.o_proj.aclnn_input_scale_reciprocal.set_(
|
|
self.o_proj_tp_aclnn_input_scale_reciprocal)
|
|
self.o_proj.aclnn_input_offset.set_(
|
|
self.o_proj_tp_aclnn_input_offset)
|
|
|
|
return output, False
|
|
else:
|
|
# For decode scenario: perform all-to-all communication on o_proj input activations
|
|
# Reshape for all-to-all: [batch * seq, tp_size, head_dim] -> [tp_size, batch * seq, head_dim]
|
|
send = attn_output.view(-1, self.tp_size, self.num_heads *
|
|
self.v_head_dim).permute(1, 0, 2).reshape(
|
|
-1, self.num_heads * self.v_head_dim)
|
|
|
|
attn_output = torch.empty_like(send)
|
|
torch.distributed.all_to_all_single(
|
|
attn_output, send, group=get_tp_group().device_group)
|
|
|
|
return attn_output, True
|