136ea9ff56
### What this PR does / why we need it?
Currently, there are two paths to judge the chip type in code,
`get_ascend_soc_version` use `get_soc_version` api in torch_npu, and
`is_310p` `use _build_info.__soc_version__`, which generate when
install. We need to unify the two paths.
We need to unify these codes based on the following points:
1. We need to ensure consistency in chip type judgment between compiling
and running states;
2. In compiling state, we need chip type to complete op's compilation,
but in running state, we only need device
type(910B/910_93/310P/910_95/etc) to make code branch judgement;
3. In compiling state, torch_npu may not have been installed yet, so we
can't use torch_npu's api.
Based on the above points, we have made the following changes:
1. When user set env `SOC_VERSION`, use it; when not set, query
soc_version by `npu-smi`;
2. generate device_type based on soc_version when compiling, and write
`__device_type__` instead of `__soc_version__` in `_build_info.py`;
3. In running state, use `__device_type__` to judge code branch.
### Does this PR introduce _any_ user-facing change?
When not set env `SOC_VERSION`, it will not be `ASCEND910B1` by default,
we will query soc_version by `npu-smi`. And env `SOC_VERSION` must be in
the list `soc_to_device` in `setup.py`.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: zzzzwwjj <1183291235@qq.com>
1606 lines
72 KiB
Python
1606 lines
72 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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from dataclasses import dataclass
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from enum import Enum
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from typing import ClassVar, List, Optional, Tuple, Type
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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.nn as nn
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import torch_npu
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionLayer, AttentionType)
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from vllm.config import VllmConfig
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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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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.utils.math_utils import cdiv
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from vllm.v1.attention.backends.utils import AttentionCGSupport
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.kv_cache_interface import AttentionSpec
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from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata,
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filter_chunked_req_indices,
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split_decodes_and_prefills)
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from vllm_ascend.compilation.acl_graph import (get_graph_params,
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update_graph_params_workspaces)
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from vllm_ascend.ops.attention import vanilla_chunked_prefill
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from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, AscendDeviceType,
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aligned_16, get_ascend_device_type, nd_to_nz_2d,
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nd_to_nz_spec, prefill_context_parallel_enable,
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weak_ref_tensors)
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# isort: off
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if prefill_context_parallel_enable():
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from vllm.distributed import (get_pcp_group,
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get_prefill_context_model_parallel_rank,
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get_prefill_context_model_parallel_world_size
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)
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# isort: on
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from vllm.attention.backends.registry import (AttentionBackendEnum,
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register_backend)
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@register_backend(AttentionBackendEnum.CUSTOM, "ASCEND")
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class AscendAttentionBackend(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 "CUSTOM"
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@staticmethod
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def get_impl_cls() -> Type["AscendAttentionBackendImpl"]:
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return AscendAttentionBackendImpl
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@staticmethod
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def get_builder_cls() -> type["AscendAttentionMetadataBuilder"]:
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return AscendAttentionMetadataBuilder
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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) -> Tuple[int, ...]:
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if get_ascend_device_type() == AscendDeviceType._310P:
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return (2, num_blocks, num_kv_heads * head_size // 16, block_size,
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16)
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return (2, num_blocks, block_size, num_kv_heads, head_size)
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@staticmethod
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def get_bsh_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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) -> Tuple[int, ...]:
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return (2, num_blocks, block_size, num_kv_heads * head_size)
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@staticmethod
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def swap_blocks(
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src_kv_cache: List[torch.Tensor],
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dst_kv_cache: List[torch.Tensor],
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src_to_dst: torch.Tensor,
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) -> None:
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src_key_cache, src_value_cache = src_kv_cache[0], src_kv_cache[1]
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dst_key_cache, dst_value_cache = dst_kv_cache[0], dst_kv_cache[1]
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src_indices = src_to_dst[:, 0]
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dst_indices = src_to_dst[:, 1]
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dst_key_cache[dst_indices] = src_key_cache[src_indices].to(
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dst_key_cache.device)
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dst_value_cache[dst_indices] = src_value_cache[src_indices].to(
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dst_key_cache.device)
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@staticmethod
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def copy_blocks(
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kv_caches: List[torch.Tensor],
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src_to_dists: torch.Tensor,
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) -> None:
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src_indices = src_to_dists[:, 0]
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dst_indices = src_to_dists[:, 1]
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for kv_cache in kv_caches:
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key_caches = kv_cache[0]
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value_caches = kv_cache[1]
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key_caches[dst_indices] = key_caches[src_indices]
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value_caches[dst_indices] = value_caches[src_indices]
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@staticmethod
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def get_supported_block_size() -> list[int]:
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return [128]
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class AscendAttentionState(Enum):
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PrefillNoCache = 0
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PrefillCacheHit = 1
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DecodeOnly = 2
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ChunkedPrefill = 3
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SpecDecoding = 4
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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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@dataclass
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class AscendMetadataForPrefill:
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@dataclass
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class ChunkedContextMetadata:
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actual_chunk_seq_lengths: list[int]
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actual_seq_lengths_kv: list[int]
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starts: torch.Tensor
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chunk_seq_mask_filtered_indices: torch.Tensor
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chunked_req_mask: Optional[list[bool]] = None
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local_context_lens_allranks: Optional[list[list[int]]] = None
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cp_kv_recover_idx_for_chunk: Optional[list[int]] = None
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kv_inverse_idx_for_chunk: Optional[list[int]] = None
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batch_chunk_seq_mask: Optional[list[bool]] = None
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""" Prefill Specific Metadata for Ascend"""
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pcp_metadata: Optional[AscendPCPMetadata] = None
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pcp_allgather_restore_idx: Optional[List[int]] = None
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chunked_context: Optional[ChunkedContextMetadata] = None
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block_tables: torch.Tensor = None
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actual_seq_lengths_q: torch.Tensor = None
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@dataclass
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class AscendMetadataForDecode:
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""" Decode Specific Metadata for Ascend"""
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num_computed_tokens_of_pcp_dcp: Optional[list[list[list[int]]]] = None
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batch_seq_mask: torch.Tensor = None
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block_tables: torch.Tensor = None
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@dataclass
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class AscendMetadata:
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# **************************** Basic Properties ************************** #
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attn_mask: Optional[torch.Tensor] = None
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fia_attn_mask: Optional[torch.Tensor] = None
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# Current state of this attention run.
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attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
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# Number of tokens excluding padding.
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num_actual_tokens_pcp_padded: int = 0
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num_actual_tokens: int = 0
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num_decode_tokens: int = 0
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num_prefills: int = 0
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num_decodes: int = 0
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# The sequence length per sequence. Sequence length means the computed
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# tokens + new tokens (is None if it is a decoding).
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# (batch_size,)
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# TODO(Angazenn): The following parameters are quite redundant and
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# contains similar information (such as seq_lens seq_lens_list). We
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# should simplified these parameters once attention schema in vLLM-Ascend
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# is unified.
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seq_lens: torch.Tensor = None
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seq_lens_list: List[int] = None # type: ignore
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actual_seq_lengths_q: List[int] = None # type: ignore
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query_start_loc_list: List[int] = None # type: ignore
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query_start_loc: torch.Tensor = None
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query_lens: torch.Tensor = None
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# Maximum query length in the batch (None for decoding).
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max_query_len: Optional[int] = None
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# ********************** KV Cache Related Properties ********************* #
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# Block addresses per sequence (Seq id -> list of physical block).
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# (batch_size, max_blocks_per_seq)
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block_tables: torch.Tensor = None
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# The indices of the token slots that input tokens will be stored into.
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# E.g., if `slot_mapping` is [35, 2, 17] and the block size is 16, the
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# three tokens are stored in the 3rd slot in block 2, 2nd slot in block 0,
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# and 1st slot in block 1, respectively.
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# (num_tokens,)
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slot_mapping: torch.Tensor = None
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prefill: Optional[AscendMetadataForPrefill] = None
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decode_meta: Optional[AscendMetadataForDecode] = None
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class AscendAttentionMetadataBuilder:
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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.ALWAYS
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# AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
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# Does this backend/builder reorder the batch?
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# If not, set this to None. Otherwise set it to the query
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# length that will be pulled into the front of the batch.
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reorder_batch_threshold: ClassVar[int] = 1
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def __init__(
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self,
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kv_cache_spec: AttentionSpec,
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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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):
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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.compilation_config = vllm_config.compilation_config
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self.device = device
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self.max_num_blocks_per_req = cdiv(
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self.model_config.max_model_len,
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AscendAttentionBackend.get_supported_block_size()[0])
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self.batch_seq_mask_buf = torch.empty(
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vllm_config.scheduler_config.max_num_batched_tokens,
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dtype=torch.uint8,
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device=device)
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self.pcp_size = get_prefill_context_model_parallel_world_size(
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) if prefill_context_parallel_enable() else 1
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self.pcp_rank = get_prefill_context_model_parallel_rank(
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) 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.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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AscendAttentionMetadataBuilder.reorder_batch_threshold = self.decode_threshold
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scheduler_config = vllm_config.scheduler_config
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self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled
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def reorder_batch(self, input_batch,
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scheduler_output: "SchedulerOutput") -> bool:
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return False
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def build(
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self,
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common_prefix_len: int,
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common_attn_metadata: AscendCommonAttentionMetadata,
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model: Optional[nn.Module] = None,
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):
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num_reqs = common_attn_metadata.num_reqs
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num_actual_tokens = common_attn_metadata.num_actual_tokens
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query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[:
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num_reqs
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+ 1]
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num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \
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split_decodes_and_prefills(common_attn_metadata, decode_threshold=self.decode_threshold)
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assert num_decodes + num_prefills == num_reqs
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assert num_decode_tokens + num_prefill_tokens == num_actual_tokens
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block_table = common_attn_metadata.block_table_tensor
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query_lens = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
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seq_lens = common_attn_metadata.seq_lens_cpu[:num_reqs]
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long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
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num_actual_tokens_pcp_padded = long_seq_metadata.num_actual_tokens_pcp_padded if long_seq_metadata else None
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if num_actual_tokens_pcp_padded is None:
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num_actual_tokens_pcp_padded = num_actual_tokens
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slot_mapping = common_attn_metadata.slot_mapping[:
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num_actual_tokens_pcp_padded]
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# slot_mapping = common_attn_metadata.slot_mapping[:num_actual_tokens]
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attn_mask = common_attn_metadata.attn_mask
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fia_attn_mask = common_attn_metadata.fia_attn_mask
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attn_state = common_attn_metadata.attn_state
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query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[:
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num_reqs
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+ 1]
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num_computed_tokens_cpu = (seq_lens - query_lens)
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if attn_state == AscendAttentionState.DecodeOnly and \
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common_attn_metadata.num_input_tokens > num_actual_tokens:
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padded_num_tokens = common_attn_metadata.num_input_tokens - num_actual_tokens
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seq_lens = torch.cat([
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seq_lens,
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torch.ones(padded_num_tokens,
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dtype=seq_lens.dtype,
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device=seq_lens.device)
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])
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block_table_padding = torch.zeros(
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(padded_num_tokens, ) + block_table.shape[1:],
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dtype=block_table.dtype,
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device=block_table.device)
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block_table = torch.cat([block_table, block_table_padding], dim=0)
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query_start_loc_cpu = torch.cat([
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query_start_loc_cpu,
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torch.arange(query_start_loc_cpu[-1] + 1,
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query_start_loc_cpu[-1] + padded_num_tokens,
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dtype=query_start_loc_cpu.dtype,
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device=query_start_loc_cpu.device)
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])
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query_start_loc = query_start_loc_cpu.to(self.device,
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non_blocking=True)
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if get_ascend_device_type() == AscendDeviceType._310P:
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if attn_state == AscendAttentionState.PrefillNoCache:
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mask_nz = nd_to_nz_2d(attn_mask)
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attn_mask = torch_npu.npu_format_cast(mask_nz.contiguous(),
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ACL_FORMAT_FRACTAL_NZ)
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elif attn_state == AscendAttentionState.ChunkedPrefill:
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mask_nz = nd_to_nz_spec(attn_mask)
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attn_mask = torch_npu.npu_format_cast(mask_nz.contiguous(),
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ACL_FORMAT_FRACTAL_NZ)
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common_long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
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prefill_metadata = None
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decode_metadata = None
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if common_long_seq_metadata is not None:
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num_computed_tokens_of_pcp_dcp = common_long_seq_metadata.num_computed_tokens_of_pcp_dcp
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assert num_computed_tokens_of_pcp_dcp is not None
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chunked_context_metadata = None
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if num_prefills > 0:
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query_lens = query_lens[num_decode_tokens:]
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context_lens_cpu = num_computed_tokens_cpu[
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num_decodes:num_reqs]
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max_context_len_cpu = context_lens_cpu.max().item()
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pcp_size = get_prefill_context_model_parallel_world_size(
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) if prefill_context_parallel_enable() else 1
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if self.chunked_prefill_enabled and max_context_len_cpu > 0:
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local_context_lens_allranks = torch.tensor(
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num_computed_tokens_of_pcp_dcp
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)[num_decodes:num_reqs].to(
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self.device).to(dtype=torch.int32)
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local_chunked_kv_lens_rank = local_context_lens_allranks[:,
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self
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.
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pcp_rank,
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self
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.
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dcp_rank]
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actual_seq_lengths_kv = torch.cumsum(
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local_chunked_kv_lens_rank, dim=0).tolist()
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chunked_req_mask = self._get_chunked_req_mask(
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local_context_lens_allranks)
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local_chunk_starts = torch.zeros(
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(len(local_context_lens_allranks)),
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dtype=torch.int32,
|
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device=self.device)
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cp_kv_recover_idx_for_chunk = common_long_seq_metadata.cp_kv_recover_idx_for_chunk
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kv_inverse_idx_for_chunk = torch.argsort(
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cp_kv_recover_idx_for_chunk.to(torch.float32)
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) if cp_kv_recover_idx_for_chunk is not None else None
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|
|
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batch_chunk_seq_mask = (
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local_context_lens_allranks[:, self.pcp_rank,
|
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self.dcp_rank] == 0)
|
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batch_chunk_seq_mask = torch.repeat_interleave(
|
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batch_chunk_seq_mask,
|
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repeats=(query_lens * self.pcp_size).to(self.device))
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chunk_seq_mask_filtered_indices = filter_chunked_req_indices(
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query_lens, chunked_req_mask).to(self.device)
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chunked_context_metadata = \
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AscendMetadataForPrefill.ChunkedContextMetadata(
|
|
actual_chunk_seq_lengths=torch.cumsum(query_lens * pcp_size, dim=0),
|
|
actual_seq_lengths_kv=actual_seq_lengths_kv,
|
|
chunked_req_mask=chunked_req_mask,
|
|
starts=local_chunk_starts,
|
|
local_context_lens_allranks=local_context_lens_allranks,
|
|
cp_kv_recover_idx_for_chunk=cp_kv_recover_idx_for_chunk,
|
|
kv_inverse_idx_for_chunk=kv_inverse_idx_for_chunk,
|
|
batch_chunk_seq_mask=batch_chunk_seq_mask,
|
|
chunk_seq_mask_filtered_indices=chunk_seq_mask_filtered_indices
|
|
)
|
|
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
|
|
if pcp_size > 1:
|
|
attn_mask_seqlens = torch.cumsum(attn_mask_seqlens[0],
|
|
dim=0).tolist()
|
|
head_attn_nomask_seqlens = torch.cumsum(
|
|
head_attn_nomask_seqlens[1], dim=0).tolist()
|
|
tail_attn_nomask_seqlens = torch.cumsum(
|
|
tail_attn_nomask_seqlens[1], dim=0).tolist()
|
|
|
|
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=attn_mask_seqlens,
|
|
head_attn_nomask_seqlens=head_attn_nomask_seqlens,
|
|
tail_attn_nomask_seqlens=tail_attn_nomask_seqlens,
|
|
q_full_idx=common_long_seq_metadata.q_full_idx,
|
|
pcp_prefill_mask=common_long_seq_metadata.pcp_prefill_mask)
|
|
|
|
prefill_metadata = AscendMetadataForPrefill(
|
|
pcp_metadata=pcp_metadata,
|
|
pcp_allgather_restore_idx=common_long_seq_metadata.
|
|
pcp_allgather_restore_idx
|
|
if common_long_seq_metadata is not None else None,
|
|
chunked_context=chunked_context_metadata,
|
|
block_tables=block_table[num_decodes:],
|
|
actual_seq_lengths_q=torch.cumsum(query_lens, dim=0))
|
|
|
|
if num_decodes > 0:
|
|
num_computed_tokens_array = np.array(
|
|
num_computed_tokens_of_pcp_dcp)
|
|
num_computed_tokens_array = num_computed_tokens_array[:
|
|
num_decodes]
|
|
batch_seq_mask = (
|
|
num_computed_tokens_array[:, self.pcp_rank,
|
|
self.dcp_rank] == 0)
|
|
# TODO: numpy array mode of the shared memory is used to improve performance
|
|
self.batch_seq_mask_buf[:batch_seq_mask.shape[0]].copy_(
|
|
torch.from_numpy(batch_seq_mask), non_blocking=True)
|
|
decode_metadata = AscendMetadataForDecode(
|
|
num_computed_tokens_of_pcp_dcp=num_computed_tokens_array,
|
|
batch_seq_mask=self.batch_seq_mask_buf[:batch_seq_mask.
|
|
shape[0]],
|
|
block_tables=block_table[:num_decodes])
|
|
|
|
attn_metadata = AscendMetadata(
|
|
num_actual_tokens=num_actual_tokens,
|
|
num_decode_tokens=num_decode_tokens,
|
|
num_actual_tokens_pcp_padded=num_actual_tokens_pcp_padded,
|
|
block_tables=block_table,
|
|
query_start_loc=query_start_loc,
|
|
query_start_loc_list=query_start_loc_cpu[1:].tolist(),
|
|
query_lens=query_lens,
|
|
seq_lens=seq_lens,
|
|
seq_lens_list=seq_lens.tolist(),
|
|
max_query_len=common_attn_metadata.max_query_len,
|
|
actual_seq_lengths_q=query_start_loc_cpu[1:].tolist(),
|
|
slot_mapping=slot_mapping,
|
|
attn_mask=attn_mask,
|
|
fia_attn_mask=fia_attn_mask,
|
|
attn_state=attn_state,
|
|
num_prefills=num_prefills,
|
|
num_decodes=num_decodes,
|
|
prefill=prefill_metadata,
|
|
decode_meta=decode_metadata)
|
|
return attn_metadata
|
|
|
|
def _get_chunked_req_mask(self, local_context_lens_allranks) -> List[bool]:
|
|
"""
|
|
given 4-d list [req][pcp][dcp], return:
|
|
1. if each req has any chunk (list[bool])
|
|
"""
|
|
assert local_context_lens_allranks is not None
|
|
if len(local_context_lens_allranks) == 0:
|
|
return []
|
|
chunked_req_mask = [(req.sum() > 0).item()
|
|
for req in local_context_lens_allranks
|
|
if req is not None]
|
|
return chunked_req_mask
|
|
|
|
def build_for_graph_capture(
|
|
self,
|
|
common_attn_metadata: AscendCommonAttentionMetadata,
|
|
attn_state: AscendAttentionState = AscendAttentionState.DecodeOnly,
|
|
model: Optional[nn.Module] = None,
|
|
):
|
|
if attn_state == AscendAttentionState.DecodeOnly:
|
|
attn_metadata = self.build(
|
|
common_prefix_len=0,
|
|
common_attn_metadata=common_attn_metadata,
|
|
)
|
|
else:
|
|
raise NotImplementedError(
|
|
"Currently we only support building dummy metadata for DecodeOnly state"
|
|
)
|
|
|
|
attn_metadata.attn_state = attn_state
|
|
return attn_metadata
|
|
|
|
|
|
class AscendAttentionBackendImpl(AttentionImpl):
|
|
|
|
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_heads if num_kv_heads is None else num_kv_heads
|
|
self.hidden_size = self.num_heads * self.head_size
|
|
self.kv_cache_dtype = kv_cache_dtype
|
|
self.sliding_window = sliding_window
|
|
if alibi_slopes is not None:
|
|
alibi_slopes = torch.tensor(alibi_slopes,
|
|
dtype=torch.float32,
|
|
device="npu")
|
|
self.alibi_slopes = alibi_slopes
|
|
self.attn_type = attn_type
|
|
|
|
assert self.num_heads % self.num_kv_heads == 0
|
|
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
|
|
self.key_cache = None
|
|
self.value_cache = None
|
|
self.pcp_size = get_prefill_context_model_parallel_world_size(
|
|
) if prefill_context_parallel_enable() else 1
|
|
self.pcp_rank = get_prefill_context_model_parallel_rank(
|
|
) 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
|
|
|
|
def full_graph_attention(self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_cache: Tuple[torch.Tensor],
|
|
attn_metadata: AscendMetadata,
|
|
output: torch.Tensor,
|
|
num_tokens=0):
|
|
if self.pcp_size * self.dcp_size > 1:
|
|
intermediate_output = self._forward_pcp_dcp(
|
|
query, key, value, kv_cache, attn_metadata, output)
|
|
return intermediate_output, query.shape[0]
|
|
elif attn_metadata.attn_state == AscendAttentionState.PrefillNoCache:
|
|
block_size = 128
|
|
block_table = None
|
|
actual_seq_lengths_kv = attn_metadata.query_start_loc_list
|
|
elif attn_metadata.attn_state == \
|
|
AscendAttentionState.PrefillCacheHit:
|
|
batch_size = attn_metadata.query_lens.shape[0]
|
|
block_table = attn_metadata.block_tables[:batch_size, :]
|
|
num_block, block_size, _, _ = self.key_cache.shape # type: ignore
|
|
key = self.key_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
value = self.value_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
actual_seq_lengths_kv = attn_metadata.seq_lens_list
|
|
elif attn_metadata.attn_state == AscendAttentionState.DecodeOnly:
|
|
num_block, block_size, _, _ = self.key_cache.shape # type: ignore
|
|
key = self.key_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
value = self.value_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
block_table = attn_metadata.block_tables
|
|
actual_seq_lengths_kv = attn_metadata.seq_lens_list
|
|
# Normal V1 situation.
|
|
else:
|
|
num_block, block_size, _, _ = self.key_cache.shape # type: ignore
|
|
key = self.key_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
value = self.value_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
block_table = attn_metadata.block_tables
|
|
actual_seq_lengths_kv = attn_metadata.seq_lens_list
|
|
|
|
num_tokens = attn_metadata.query_start_loc_list[-1]
|
|
query = query[:num_tokens]
|
|
graph_params = get_graph_params()
|
|
query_start_loc = attn_metadata.query_start_loc_list
|
|
# Prepare tensors for attention output
|
|
# TODO: Refactor this to step-level instead of layer-level
|
|
|
|
# Get workspace from cache or calculate it if not present.
|
|
workspace = graph_params.workspaces.get(num_tokens)
|
|
softmax_lse = torch.empty(1, dtype=query.dtype, device=query.device)
|
|
if workspace is None:
|
|
workspace = torch_npu._npu_fused_infer_attention_score_get_max_workspace(
|
|
query=query,
|
|
key=key,
|
|
value=value,
|
|
atten_mask=attn_metadata.fia_attn_mask,
|
|
block_table=block_table,
|
|
input_layout="TND",
|
|
block_size=block_size,
|
|
actual_seq_lengths=query_start_loc,
|
|
actual_seq_lengths_kv=actual_seq_lengths_kv,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
sparse_mode=3,
|
|
scale=self.scale,
|
|
)
|
|
update_graph_params_workspaces(num_tokens, workspace)
|
|
|
|
# Handle graph capturing mode
|
|
stream = torch_npu.npu.current_stream()
|
|
|
|
event = torch.npu.ExternalEvent()
|
|
event.wait(stream)
|
|
event.reset(stream)
|
|
graph_params.events[num_tokens].append(event)
|
|
graph_params.attn_params[num_tokens].append(
|
|
(weak_ref_tensors(query), weak_ref_tensors(key),
|
|
weak_ref_tensors(value), weak_ref_tensors(block_table),
|
|
weak_ref_tensors(attn_metadata.fia_attn_mask), block_size,
|
|
actual_seq_lengths_kv, query_start_loc, self.num_kv_heads,
|
|
self.num_heads, self.scale, weak_ref_tensors(output),
|
|
weak_ref_tensors(softmax_lse)))
|
|
|
|
torch.npu.graph_task_group_begin(stream)
|
|
torch_npu.npu_fused_infer_attention_score.out(
|
|
query=query,
|
|
key=key,
|
|
value=value,
|
|
atten_mask=attn_metadata.fia_attn_mask,
|
|
block_table=block_table,
|
|
input_layout="TND",
|
|
block_size=block_size,
|
|
actual_seq_lengths=query_start_loc,
|
|
actual_seq_lengths_kv=actual_seq_lengths_kv,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
scale=self.scale,
|
|
sparse_mode=3,
|
|
workspace=workspace,
|
|
out=[output, softmax_lse],
|
|
)
|
|
|
|
output = output.view(num_tokens, self.num_heads, self.head_size)
|
|
|
|
handle = torch.npu.graph_task_group_end(stream)
|
|
graph_params.handles[num_tokens].append(handle)
|
|
return output, num_tokens
|
|
|
|
def _forward_prefill_no_cache(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attn_metadata: AscendMetadata,
|
|
output: Optional[torch.Tensor] = None,
|
|
num_tokens=0,
|
|
) -> torch.Tensor:
|
|
assert attn_metadata is not None
|
|
assert attn_metadata.attn_mask is not None
|
|
|
|
mask = attn_metadata.attn_mask
|
|
|
|
if get_ascend_device_type() == AscendDeviceType._310P:
|
|
# align q k v output tensors
|
|
query = aligned_16(query)
|
|
key = aligned_16(key)
|
|
value = aligned_16(value)
|
|
output = aligned_16(output)
|
|
# do reformat in case of broadcasted tensors
|
|
mask = mask.repeat(attn_metadata.seq_lens.size(0), 1, 1, 1)
|
|
mask = torch_npu.npu_format_cast(mask.contiguous(),
|
|
ACL_FORMAT_FRACTAL_NZ)
|
|
|
|
torch_npu._npu_flash_attention(query=query,
|
|
key=key,
|
|
value=value,
|
|
mask=mask,
|
|
seq_len=attn_metadata.seq_lens,
|
|
scale_value=self.scale,
|
|
num_heads=self.num_heads,
|
|
num_kv_heads=self.num_kv_heads,
|
|
out=output)
|
|
assert output is not None
|
|
return output[:num_tokens]
|
|
|
|
def _forward_prefill_cache_hit(
|
|
self,
|
|
query: torch.Tensor,
|
|
attn_metadata: AscendMetadata,
|
|
output: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
assert attn_metadata is not None
|
|
assert attn_metadata.attn_mask is not None
|
|
|
|
compress_mask = attn_metadata.attn_mask
|
|
batch_size = attn_metadata.query_lens.shape[0]
|
|
block_table = attn_metadata.block_tables[:batch_size, :]
|
|
num_block, block_size, _, _ = self.key_cache.shape # type: ignore
|
|
|
|
if block_size == 128:
|
|
# TODO:The npu_fused_infer_attention_score op is planned to
|
|
# be utilized in a wider range in upcoming versions.
|
|
key = self.key_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
value = self.value_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
|
|
output, _ = torch_npu.npu_fused_infer_attention_score(
|
|
query=query,
|
|
key=key,
|
|
value=value,
|
|
atten_mask=compress_mask,
|
|
block_table=block_table,
|
|
input_layout="TND",
|
|
block_size=block_size,
|
|
actual_seq_lengths=attn_metadata.actual_seq_lengths_q,
|
|
actual_seq_lengths_kv=attn_metadata.seq_lens_list,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
scale=self.scale,
|
|
sparse_mode=3,
|
|
)
|
|
else:
|
|
torch_npu._npu_flash_attention_qlens(
|
|
query=query,
|
|
key_cache=self.key_cache,
|
|
value_cache=self.value_cache,
|
|
block_table=block_table,
|
|
mask=compress_mask,
|
|
seq_len=attn_metadata.query_lens,
|
|
context_lens=attn_metadata.seq_lens,
|
|
num_kv_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
scale_value=self.scale,
|
|
out=output)
|
|
return output
|
|
|
|
def _forward_decode_only(
|
|
self,
|
|
query: torch.Tensor,
|
|
attn_metadata: AscendMetadata,
|
|
output: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
if get_ascend_device_type() == AscendDeviceType._310P:
|
|
# seq_lens_tensor needs to be transferred to the device for 310P.
|
|
attn_metadata.seq_lens = \
|
|
attn_metadata.seq_lens.to(device=query.device)
|
|
if self.sliding_window is not None and attn_metadata.seq_lens.shape[
|
|
0] == query.size(0):
|
|
batch_size = attn_metadata.seq_lens.shape[0]
|
|
block_size = 128
|
|
query = query.view(batch_size, 1, self.num_heads * self.head_size)
|
|
key = self.key_cache
|
|
value = self.value_cache
|
|
if self.key_cache is not None and self.value_cache is not None:
|
|
block_size = self.key_cache.shape[1]
|
|
key = self.key_cache.flatten(2, 3).contiguous()
|
|
value = self.value_cache.flatten(2, 3).contiguous()
|
|
|
|
output, _ = torch_npu.npu_fused_infer_attention_score(
|
|
query,
|
|
key,
|
|
value,
|
|
num_heads=self.num_heads,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
input_layout="BSH",
|
|
block_size=block_size,
|
|
pre_tokens=self.sliding_window,
|
|
scale=self.scale,
|
|
block_table=attn_metadata.block_tables,
|
|
actual_seq_lengths=[1] * len(attn_metadata.seq_lens),
|
|
actual_seq_lengths_kv=attn_metadata.seq_lens)
|
|
|
|
output = output.view(batch_size, self.num_heads, self.head_size)
|
|
else:
|
|
torch_npu._npu_paged_attention(
|
|
query=query,
|
|
key_cache=self.key_cache,
|
|
value_cache=self.value_cache,
|
|
num_kv_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
scale_value=self.scale,
|
|
block_table=attn_metadata.block_tables,
|
|
context_lens=attn_metadata.seq_lens,
|
|
out=output)
|
|
return output
|
|
|
|
def _forward_v1_style(
|
|
self,
|
|
query: torch.Tensor,
|
|
attn_metadata: AscendMetadata,
|
|
output: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
# Use chunked prefill for head size 192 scenario, like deepseek
|
|
# paged_attention_splitfuse maybe crash at such scenario.
|
|
# TODO: vanilla path will be removed after the kernel support
|
|
# head_size 192 scenario.
|
|
if self.head_size == 192:
|
|
cu_seqlen_q = [0] + attn_metadata.query_lens.tolist()
|
|
cu_seqlen_k = [0] + attn_metadata.seq_lens.tolist()
|
|
cu_seqlen_q = torch.tensor(cu_seqlen_q, device=query.device)
|
|
cu_seqlen_k = torch.tensor(cu_seqlen_k, device=query.device)
|
|
cu_seqlen_q = torch.cumsum(cu_seqlen_q, dim=0)
|
|
cu_seqlen_k = torch.cumsum(cu_seqlen_k, dim=0)
|
|
max_seqlen_q = torch.max(attn_metadata.query_lens)
|
|
max_seqlen_k = torch.max(attn_metadata.seq_lens)
|
|
vanilla_chunked_prefill(output, query, self.key_cache,
|
|
self.value_cache,
|
|
attn_metadata.block_tables, cu_seqlen_q,
|
|
cu_seqlen_k, max_seqlen_q, max_seqlen_k,
|
|
self.scale, None, True)
|
|
return output
|
|
|
|
# Use paged attention.
|
|
assert attn_metadata is not None
|
|
assert attn_metadata.attn_mask is not None
|
|
|
|
if get_ascend_device_type() == AscendDeviceType._310P:
|
|
# Do reformat in case of broadcasted tensors.
|
|
attn_metadata.attn_mask = \
|
|
torch_npu.npu_format_cast(attn_metadata.attn_mask.contiguous(),
|
|
ACL_FORMAT_FRACTAL_NZ)
|
|
attn_metadata.seq_lens = \
|
|
attn_metadata.seq_lens.to(device=query.device)
|
|
|
|
# TODO:The npu_fused_infer_attention_score op is planned to
|
|
# be utilized in a wider range in upcoming versions.
|
|
num_block, block_size, _, _ = self.key_cache.shape # type: ignore
|
|
key = self.key_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
value = self.value_cache.view( # type: ignore
|
|
num_block, block_size, -1)
|
|
|
|
output, _ = torch_npu.npu_fused_infer_attention_score(
|
|
query=query,
|
|
key=key,
|
|
value=value,
|
|
atten_mask=attn_metadata.attn_mask,
|
|
block_table=attn_metadata.block_tables,
|
|
input_layout="TND",
|
|
block_size=block_size,
|
|
actual_seq_lengths=attn_metadata.actual_seq_lengths_q,
|
|
actual_seq_lengths_kv=attn_metadata.seq_lens_list,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
scale=self.scale,
|
|
sparse_mode=3,
|
|
)
|
|
return output
|
|
|
|
def _attention_with_nomask_and_mask(self, q: torch.Tensor,
|
|
q_seqlens: List[int],
|
|
k_nomask: torch.Tensor,
|
|
v_nomask: torch.Tensor,
|
|
kv_seqlens_nomask: List[int],
|
|
k_mask: torch.Tensor,
|
|
v_mask: torch.Tensor,
|
|
kv_seqlens_mask: List[int],
|
|
mask: torch.Tensor,
|
|
attn_metadata) -> torch.Tensor:
|
|
# nomask Attention
|
|
if k_nomask is not None:
|
|
attn_out_nomask, attn_lse_nomask = torch.ops.npu.npu_fused_infer_attention_score(
|
|
q,
|
|
k_nomask,
|
|
v_nomask,
|
|
num_heads=self.num_heads,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
input_layout="TND",
|
|
atten_mask=None,
|
|
scale=self.scale,
|
|
sparse_mode=0,
|
|
antiquant_mode=0,
|
|
antiquant_scale=None,
|
|
softmax_lse_flag=True,
|
|
actual_seq_lengths_kv=kv_seqlens_nomask,
|
|
actual_seq_lengths=q_seqlens)
|
|
|
|
# mask Attention
|
|
attn_out_mask, attn_lse_mask = torch.ops.npu.npu_fused_infer_attention_score(
|
|
q,
|
|
k_mask,
|
|
v_mask,
|
|
num_heads=self.num_heads,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
input_layout="TND",
|
|
atten_mask=mask,
|
|
scale=self.scale,
|
|
sparse_mode=3,
|
|
antiquant_mode=0,
|
|
antiquant_scale=None,
|
|
softmax_lse_flag=True,
|
|
actual_seq_lengths_kv=kv_seqlens_mask,
|
|
actual_seq_lengths=q_seqlens)
|
|
# update
|
|
output = attn_out_mask
|
|
attn_lse = attn_lse_mask
|
|
if k_nomask is not None:
|
|
if attn_metadata.prefill is not None and attn_metadata.prefill.chunked_context is None:
|
|
output = self._npu_attn_out_lse_update(attn_lse_mask,
|
|
attn_lse_nomask,
|
|
attn_out_mask,
|
|
attn_out_nomask)
|
|
attn_lse = None
|
|
else:
|
|
output, attn_lse = self._update_out_and_lse(
|
|
torch.stack([attn_out_nomask, attn_out_mask], dim=0),
|
|
torch.stack([attn_lse_nomask, attn_lse_mask], dim=0))
|
|
|
|
return output, attn_lse
|
|
|
|
def _npu_attn_out_lse_update(self, attn_lse_mask, attn_lse_nomask,
|
|
attn_out_mask, attn_out_nomask):
|
|
T = attn_out_mask.shape[0]
|
|
N = attn_out_mask.shape[1]
|
|
D = attn_out_mask.shape[2]
|
|
attn_out_mask, attn_lse_mask = self._out_lse_reshape(
|
|
attn_out_mask, attn_lse_mask)
|
|
attn_out_nomask, attn_lse_nomask = self._out_lse_reshape(
|
|
attn_out_nomask, attn_lse_nomask)
|
|
attn_out_mask = attn_out_mask.to(torch.float32)
|
|
attn_out_nomask = attn_out_nomask.to(torch.float32)
|
|
attn_lse_mask = attn_lse_mask.to(torch.float32)
|
|
attn_lse_nomask = attn_lse_nomask.to(torch.float32)
|
|
attn_output = [attn_out_nomask, attn_out_mask]
|
|
attn_lse = [attn_lse_nomask, attn_lse_mask]
|
|
update_type = 0
|
|
output, _ = torch_npu.npu_attention_update(attn_lse, attn_output,
|
|
update_type)
|
|
output = output.view(T, N, D)
|
|
return output
|
|
|
|
def _forward_prefill_cp(self, query: torch.Tensor, key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attn_metadata: AscendMetadata) -> torch.Tensor:
|
|
assert attn_metadata is not None
|
|
assert attn_metadata.prefill is not None
|
|
assert attn_metadata.prefill.pcp_metadata is not None
|
|
# 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
|
|
|
|
# 1. Attention calculation in the first half of Q in load balancing
|
|
output_heads, lse_heads = self._attention_with_nomask_and_mask(
|
|
q=torch.index_select(query, 0, q_head_idx),
|
|
q_seqlens=attn_mask_seqlens,
|
|
k_nomask=torch.index_select(key, 0, kv_with_q_head_nomask_idx)
|
|
if self.pcp_rank > 0 else None,
|
|
v_nomask=torch.index_select(value, 0, kv_with_q_head_nomask_idx)
|
|
if self.pcp_rank > 0 else None,
|
|
kv_seqlens_nomask=head_attn_nomask_seqlens,
|
|
k_mask=torch.index_select(key, 0, kv_with_q_head_mask_idx),
|
|
v_mask=torch.index_select(value, 0, kv_with_q_head_mask_idx),
|
|
kv_seqlens_mask=attn_mask_seqlens,
|
|
mask=mask,
|
|
attn_metadata=attn_metadata)
|
|
|
|
# 2. the Attention calculation in the latter half of Q in load balancing
|
|
# pcp_rank0: Q3*KV0~KV2 + Q3*KV3
|
|
# pcp_rank1: Q2*KV0~KV1 + Q2*KV2
|
|
output_tails, lse_tails = self._attention_with_nomask_and_mask(
|
|
q=torch.index_select(query, 0, q_tail_idx),
|
|
q_seqlens=attn_mask_seqlens,
|
|
k_nomask=torch.index_select(key, 0, kv_with_q_tail_nomask_idx),
|
|
v_nomask=torch.index_select(value, 0, kv_with_q_tail_nomask_idx),
|
|
kv_seqlens_nomask=tail_attn_nomask_seqlens,
|
|
k_mask=torch.index_select(key, 0, kv_with_q_tail_mask_idx),
|
|
v_mask=torch.index_select(value, 0, kv_with_q_tail_mask_idx),
|
|
kv_seqlens_mask=attn_mask_seqlens,
|
|
mask=mask,
|
|
attn_metadata=attn_metadata)
|
|
|
|
q_full_idx = attn_metadata.prefill.pcp_metadata.q_full_idx
|
|
output = torch.index_select(
|
|
torch.cat([output_heads, output_tails], dim=0), 0, q_full_idx)
|
|
attn_lse = None
|
|
if attn_metadata.prefill is not None and attn_metadata.prefill.chunked_context is not None:
|
|
attn_lse = torch.index_select(
|
|
torch.cat([lse_heads, lse_tails], dim=0), 0, q_full_idx)
|
|
return output, attn_lse
|
|
|
|
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 _npu_attention_update(
|
|
self, attn_out_lse_list: List[torch.Tensor]) -> torch.Tensor:
|
|
update_type = 0
|
|
|
|
batch = attn_out_lse_list[0].shape[0]
|
|
num_heads = attn_out_lse_list[0].shape[1]
|
|
head_dim = attn_out_lse_list[0].shape[2] - 1
|
|
|
|
attn_out_split_cp = []
|
|
attn_lse_split_cp = []
|
|
|
|
for i in attn_out_lse_list:
|
|
attn_out_allgather, attn_lse_allgather = self._out_lse_reshape(
|
|
*torch.split(i, [self.head_size, 1], dim=-1))
|
|
attn_out_split_cp.append(attn_out_allgather)
|
|
attn_lse_split_cp.append(attn_lse_allgather)
|
|
|
|
attn_out, attn_lse = torch_npu.npu_attention_update(
|
|
attn_lse_split_cp, attn_out_split_cp, update_type)
|
|
attn_out = attn_out.view(batch, num_heads, head_dim)
|
|
|
|
return attn_out
|
|
|
|
def _forward_decode_pcp_dcp(self, query: torch.Tensor,
|
|
attn_metadata: AscendMetadata) -> torch.Tensor:
|
|
assert self.key_cache is not None
|
|
assert self.value_cache is not None
|
|
|
|
if self.dcp_size > 1:
|
|
query = get_dcp_group().all_gather(query, 1)
|
|
num_heads = self.num_heads * self.dcp_size
|
|
else:
|
|
num_heads = self.num_heads
|
|
|
|
k_nope = self.key_cache.view(self.key_cache.shape[0],
|
|
self.key_cache.shape[1], -1)
|
|
value = self.value_cache.view(self.key_cache.shape[0],
|
|
self.key_cache.shape[1], -1)
|
|
common_kwargs = {
|
|
'num_heads':
|
|
num_heads,
|
|
'num_key_value_heads':
|
|
self.num_kv_heads,
|
|
'input_layout':
|
|
'TND',
|
|
'atten_mask':
|
|
None,
|
|
'scale':
|
|
self.scale,
|
|
'antiquant_mode':
|
|
0,
|
|
'antiquant_scale':
|
|
None,
|
|
'softmax_lse_flag':
|
|
True,
|
|
'block_table':
|
|
attn_metadata.decode_meta.block_tables,
|
|
'block_size':
|
|
self.key_cache.shape[1],
|
|
'actual_seq_lengths_kv':
|
|
attn_metadata.decode_meta.
|
|
num_computed_tokens_of_pcp_dcp[:, self.pcp_rank, self.dcp_rank],
|
|
'actual_seq_lengths':
|
|
attn_metadata.actual_seq_lengths_q[:attn_metadata.num_decodes],
|
|
}
|
|
graph_params = get_graph_params()
|
|
forward_context: ForwardContext = get_forward_context()
|
|
num_tokens = query.shape[0]
|
|
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(
|
|
query, k_nope, value, **common_kwargs)
|
|
update_graph_params_workspaces(num_tokens,
|
|
weak_ref_tensors(workspace))
|
|
attn_out = torch.empty_like(query)
|
|
attn_lse = torch.empty((num_tokens, num_heads, 1),
|
|
dtype=torch.float,
|
|
device=query.device)
|
|
|
|
graph_params.attn_params[num_tokens].append((
|
|
weak_ref_tensors(query), weak_ref_tensors(k_nope),
|
|
weak_ref_tensors(value), self.num_heads, self.num_kv_heads,
|
|
self.scale, attn_metadata.block_tables,
|
|
self.key_cache.shape[1], attn_metadata.decode_meta.
|
|
num_computed_tokens_of_pcp_dcp[:, self.pcp_rank,
|
|
self.dcp_rank],
|
|
attn_metadata.actual_seq_lengths_q[:attn_metadata.num_decodes],
|
|
weak_ref_tensors(attn_out), weak_ref_tensors(attn_lse),
|
|
self.dcp_size, self.pcp_rank, self.dcp_rank))
|
|
torch.npu.graph_task_group_begin(stream)
|
|
torch_npu.npu_fused_infer_attention_score.out(
|
|
query,
|
|
k_nope,
|
|
value,
|
|
**common_kwargs,
|
|
workspace=workspace,
|
|
out=[attn_out, attn_lse])
|
|
handle = torch.npu.graph_task_group_end(stream)
|
|
graph_params.handles[num_tokens].append(handle)
|
|
else:
|
|
attn_out, attn_lse = torch_npu.npu_fused_infer_attention_score(
|
|
query, k_nope, value, **common_kwargs)
|
|
|
|
out_mask = attn_metadata.decode_meta.batch_seq_mask[:, None,
|
|
None].expand_as(
|
|
attn_out)
|
|
attn_out = torch.where(out_mask, 0, attn_out)
|
|
|
|
lse_mask = attn_metadata.decode_meta.batch_seq_mask[:, None,
|
|
None].expand_as(
|
|
attn_lse)
|
|
attn_lse = torch.where(lse_mask, -torch.inf, attn_lse)
|
|
|
|
attn_out_lse_list = []
|
|
# 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_out, attn_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 CP 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
|
|
# Update out&lse
|
|
attn_out = self._npu_attention_update(attn_out_lse_list)
|
|
return attn_out
|
|
|
|
def _update_out_and_lse(self, out_list: torch.Tensor,
|
|
lse_list: torch.Tensor) -> torch.Tensor:
|
|
"""LSE_final = log(sum(exp(LSE_i))), O_final = sum(exp(LSE_i - LSE_final) * O_i)
|
|
Args:
|
|
out_list: shape = [N, batch_size, num_heads, head_size]
|
|
lse_list: shape = [N, batch_size, num_heads, 1]
|
|
Returns:
|
|
out_final: shape = [batch_size, num_heads, head_size]
|
|
lse_final: shape = [batch_size, num_heads, 1]
|
|
"""
|
|
lse_final = torch.logsumexp(lse_list, dim=0, keepdim=False)
|
|
out_final = torch.sum(torch.exp(lse_list - lse_final) * out_list,
|
|
dim=0)
|
|
return out_final, lse_final
|
|
|
|
def _forward_pcp_dcp(self, query: torch.Tensor, key: torch.Tensor,
|
|
value: torch.Tensor, kv_cache: Tuple[torch.Tensor],
|
|
attn_metadata: AscendMetadata,
|
|
output: torch.Tensor) -> torch.Tensor:
|
|
assert attn_metadata is not None
|
|
has_decode = attn_metadata.num_decodes > 0
|
|
has_prefill = attn_metadata.num_prefills > 0
|
|
num_decode_tokens = attn_metadata.num_decode_tokens
|
|
|
|
if has_decode:
|
|
decode_query = query[:num_decode_tokens]
|
|
output_decode = self._forward_decode_pcp_dcp(
|
|
decode_query, attn_metadata)
|
|
output[:num_decode_tokens] = output_decode
|
|
if has_prefill:
|
|
assert attn_metadata.prefill is not None
|
|
num_actual_tokens_pcp_padded = attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
|
|
prefill_query = query[
|
|
num_decode_tokens:num_actual_tokens_pcp_padded]
|
|
key = key[self.pcp_size * num_decode_tokens:]
|
|
value = value[self.pcp_size * num_decode_tokens:]
|
|
if self.pcp_size > 1:
|
|
# Scenario of Enabling PCP or PCP&DCP
|
|
attn_output_prefill, attn_lse_prefill = self._forward_prefill_cp(
|
|
prefill_query, key, value, attn_metadata)
|
|
else:
|
|
# Scenario of Enabling DCP Individually
|
|
attn_output_prefill, attn_lse_prefill = torch.ops.npu.npu_fused_infer_attention_score(
|
|
prefill_query,
|
|
key,
|
|
value,
|
|
num_heads=self.num_heads,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
input_layout="TND",
|
|
atten_mask=attn_metadata.attn_mask,
|
|
scale=self.scale,
|
|
sparse_mode=3,
|
|
antiquant_mode=0,
|
|
antiquant_scale=None,
|
|
softmax_lse_flag=True,
|
|
actual_seq_lengths_kv=attn_metadata.prefill.
|
|
actual_seq_lengths_q,
|
|
actual_seq_lengths=attn_metadata.prefill.
|
|
actual_seq_lengths_q)
|
|
|
|
self._process_chunk_prefill(attn_output_prefill, attn_lse_prefill,
|
|
kv_cache, prefill_query, attn_metadata)
|
|
output[num_decode_tokens:attn_output_prefill.shape[0] +
|
|
num_decode_tokens] = attn_output_prefill
|
|
return output
|
|
|
|
def _process_chunk_prefill(self, current_attn_output_prefill,
|
|
current_attn_lse_prefill, kv_cache,
|
|
prefill_query, attn_metadata):
|
|
if attn_metadata.prefill is not None and attn_metadata.prefill.chunked_context is not None:
|
|
prefill_query_all = self._prefill_query_all_gather(
|
|
attn_metadata, prefill_query)
|
|
attn_output_full_chunk, attn_lse_full_chunk = self._compute_prefill_context(
|
|
prefill_query_all, kv_cache, attn_metadata)
|
|
self._update_chunk_attn_out_lse_with_current_attn_out_lse(
|
|
current_attn_output_prefill, current_attn_lse_prefill,
|
|
attn_output_full_chunk, attn_lse_full_chunk, prefill_query,
|
|
attn_metadata)
|
|
|
|
def _update_chunk_attn_out_lse_with_current_attn_out_lse(
|
|
self, current_attn_output_prefill, current_attn_lse_prefill,
|
|
attn_output_full_chunk, attn_lse_full_chunk, prefill_query,
|
|
attn_metadata):
|
|
if self.pcp_size > 1:
|
|
inverse_idx = attn_metadata.prefill.chunked_context.kv_inverse_idx_for_chunk
|
|
attn_output_full_chunk = torch.index_select(
|
|
attn_output_full_chunk, 0, inverse_idx)
|
|
attn_lse_full_chunk = torch.index_select(attn_lse_full_chunk, 0,
|
|
inverse_idx)
|
|
num_tokens = prefill_query.size(0)
|
|
attn_output_full_chunk = attn_output_full_chunk[
|
|
self.pcp_rank * num_tokens:(self.pcp_rank + 1) * num_tokens, :, :]
|
|
attn_lse_full_chunk = attn_lse_full_chunk[
|
|
self.pcp_rank * num_tokens:(self.pcp_rank + 1) * num_tokens, :, :]
|
|
|
|
assert attn_output_full_chunk.shape == current_attn_output_prefill.shape and attn_lse_full_chunk.shape == current_attn_lse_prefill.shape
|
|
filtered_indices = attn_metadata.prefill.chunked_context.chunk_seq_mask_filtered_indices
|
|
|
|
attn_output_prefill_filtered = current_attn_output_prefill[
|
|
filtered_indices, :, :]
|
|
attn_lse_prefill_filtered = current_attn_lse_prefill[
|
|
filtered_indices, :, :]
|
|
attn_output_full_chunk = attn_output_full_chunk[filtered_indices, :, :]
|
|
attn_lse_full_chunk = attn_lse_full_chunk[filtered_indices, :, :]
|
|
|
|
attn_output_filtered = self._npu_attn_out_lse_update(
|
|
attn_lse_prefill_filtered, attn_lse_full_chunk,
|
|
attn_output_prefill_filtered, attn_output_full_chunk)
|
|
|
|
current_attn_output_prefill[
|
|
filtered_indices, :, :] = attn_output_filtered.to(
|
|
current_attn_output_prefill.dtype)
|
|
|
|
def _prefill_query_all_gather(self, attn_metadata, prefill_query):
|
|
if self.dcp_size > 1:
|
|
prefill_query = get_dcp_group().all_gather(prefill_query, 1)
|
|
|
|
if self.pcp_size > 1:
|
|
prefill_query = get_pcp_group().all_gather(prefill_query, 0)
|
|
|
|
prefill_query_all = torch.index_select(prefill_query,
|
|
0,
|
|
attn_metadata.prefill.chunked_context.cp_kv_recover_idx_for_chunk) \
|
|
if self.pcp_size > 1 else prefill_query
|
|
|
|
return prefill_query_all
|
|
|
|
def _compute_prefill_context(self, query: torch.Tensor,
|
|
kv_cache: Tuple[torch.Tensor],
|
|
attn_metadata: AscendMetadata):
|
|
assert len(kv_cache) > 1
|
|
assert attn_metadata is not None
|
|
assert attn_metadata.prefill is not None
|
|
assert attn_metadata.prefill.chunked_context is not None
|
|
prefill_metadata = attn_metadata.prefill
|
|
local_chunked_kv_lens = prefill_metadata.chunked_context.local_context_lens_allranks
|
|
assert local_chunked_kv_lens is not None
|
|
|
|
local_chunked_kv_lens_rank = local_chunked_kv_lens[:, self.pcp_rank,
|
|
self.dcp_rank]
|
|
total_toks = local_chunked_kv_lens_rank.sum()
|
|
|
|
key, value = self._load_kv_for_chunk(attn_metadata, kv_cache,
|
|
local_chunked_kv_lens_rank, query,
|
|
total_toks)
|
|
if self.dcp_size > 1:
|
|
num_heads = self.num_heads * self.dcp_size
|
|
else:
|
|
num_heads = self.num_heads
|
|
|
|
prefix_chunk_output = torch.full(
|
|
(query.size(0), num_heads, self.head_size),
|
|
fill_value=0,
|
|
dtype=query.dtype,
|
|
device=query.device)
|
|
prefix_chunk_lse = torch.full((query.size(0), num_heads, 1),
|
|
fill_value=-torch.inf,
|
|
dtype=torch.float32,
|
|
device=query.device)
|
|
|
|
if total_toks > 0:
|
|
prefix_chunk_output, prefix_chunk_lse = torch.ops.npu.npu_fused_infer_attention_score(
|
|
query,
|
|
key,
|
|
value,
|
|
num_heads=num_heads,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
input_layout="TND",
|
|
atten_mask=None,
|
|
scale=self.scale,
|
|
sparse_mode=0,
|
|
antiquant_mode=0,
|
|
antiquant_scale=None,
|
|
softmax_lse_flag=True,
|
|
actual_seq_lengths_kv=prefill_metadata.chunked_context.
|
|
actual_seq_lengths_kv,
|
|
actual_seq_lengths=attn_metadata.prefill.chunked_context.
|
|
actual_chunk_seq_lengths)
|
|
batch_chunk_seq_mask = attn_metadata.prefill.chunked_context.batch_chunk_seq_mask
|
|
out_mask = batch_chunk_seq_mask[:, None, None].expand_as(
|
|
prefix_chunk_output)
|
|
prefix_chunk_output = torch.where(out_mask, 0, prefix_chunk_output)
|
|
lse_mask = batch_chunk_seq_mask[:, None,
|
|
None].expand_as(prefix_chunk_lse)
|
|
prefix_chunk_lse = torch.where(lse_mask, -torch.inf,
|
|
prefix_chunk_lse)
|
|
|
|
prefix_output, prefix_lse = self._update_chunk_attn_out_lse(
|
|
prefix_chunk_output, prefix_chunk_lse)
|
|
|
|
return prefix_output, prefix_lse
|
|
|
|
def _update_chunk_attn_out_lse(self, prefix_chunk_output,
|
|
prefix_chunk_lse):
|
|
# CP dimension all_gather and fusion
|
|
chunk_attn_out_lse = torch.cat([prefix_chunk_output, prefix_chunk_lse],
|
|
dim=-1)
|
|
|
|
if self.dcp_size > 1:
|
|
chunk_attn_out_lse = chunk_attn_out_lse.permute([1, 2,
|
|
0]).contiguous()
|
|
attn_out_lse_all2all = torch.empty_like(chunk_attn_out_lse)
|
|
dist.all_to_all_single(attn_out_lse_all2all,
|
|
chunk_attn_out_lse,
|
|
group=self.dcp_group)
|
|
attn_out_lse_all2all = attn_out_lse_all2all.permute([2, 0, 1])
|
|
if self.pcp_size > 1:
|
|
chunk_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:
|
|
attn_out_lse_list = [
|
|
torch.empty_like(chunk_attn_out_lse)
|
|
for _ in range(self.pcp_size)
|
|
]
|
|
dist.all_gather(attn_out_lse_list,
|
|
chunk_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
|
|
|
|
attn_out_lse_allgather = torch.stack(
|
|
attn_out_lse_list,
|
|
dim=0) # [pcp, batch_size, num_heads, head_size+1]
|
|
attn_out_allgather, attn_lse_allgather = torch.split(
|
|
attn_out_lse_allgather, [self.head_size, 1], dim=-1)
|
|
|
|
prefix_output, prefix_lse = self._update_out_and_lse(
|
|
attn_out_allgather, attn_lse_allgather)
|
|
|
|
return prefix_output, prefix_lse
|
|
|
|
def _load_kv_for_chunk(self, attn_metadata, kv_cache,
|
|
local_chunked_kv_lens_rank, query, total_toks):
|
|
cache_key = kv_cache[0]
|
|
cache_value = kv_cache[1]
|
|
num_heads = cache_key.size(2)
|
|
head_size = kv_cache[0].size(-1)
|
|
|
|
key = torch.empty(total_toks,
|
|
num_heads,
|
|
head_size,
|
|
dtype=query.dtype,
|
|
device=query.device)
|
|
value = torch.empty(total_toks,
|
|
num_heads,
|
|
head_size,
|
|
dtype=query.dtype,
|
|
device=query.device)
|
|
if total_toks > 0:
|
|
torch_npu.atb.npu_paged_cache_load(
|
|
cache_key,
|
|
cache_value,
|
|
attn_metadata.prefill.block_tables,
|
|
local_chunked_kv_lens_rank,
|
|
seq_starts=attn_metadata.prefill.chunked_context.
|
|
starts, # slot offsets of current chunk in current iteration
|
|
key=key,
|
|
value=value,
|
|
)
|
|
return key, value
|
|
|
|
def forward(
|
|
self,
|
|
layer: AttentionLayer,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_cache: Tuple[torch.Tensor],
|
|
attn_metadata: AscendMetadata,
|
|
output: Optional[torch.Tensor] = None,
|
|
output_scale: Optional[torch.Tensor] = None,
|
|
output_block_scale: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
"""Forward pass with Ascend attention.
|
|
Args:
|
|
query: shape = [num_tokens, num_heads, head_size]
|
|
key: shape = [num_tokens, num_kv_heads, head_size]
|
|
value: shape = [num_tokens, num_kv_heads, head_size]
|
|
kv_cache: shape =
|
|
[2, num_blocks, block_size, num_kv_heads, head_size]
|
|
attn_metadata: Metadata for attention.
|
|
Returns:
|
|
shape = [num_tokens, num_heads * head_size]
|
|
"""
|
|
assert output is not None, "Output tensor must be provided."
|
|
|
|
if output_scale is not None or output_block_scale is not None:
|
|
raise NotImplementedError(
|
|
"fused output quantization is not yet supported"
|
|
" for AscendAttentionBackendImpl")
|
|
|
|
num_tokens = query.shape[0]
|
|
if attn_metadata is None:
|
|
return output
|
|
|
|
# NOTE: Currently, we have various attention paths for different
|
|
# scenarios, and not all of them are in-place operations. Therefore,
|
|
# we need to create a separate tensor to hold the attention result.
|
|
# In the future, we may consolidate them into fewer paths, which will
|
|
# hopefully allow us to use in-place operation by default.
|
|
intermediate_output: torch.Tensor
|
|
|
|
assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
|
|
attn_type = self.attn_type
|
|
if attn_type != AttentionType.DECODER and attn_type != AttentionType.ENCODER_ONLY:
|
|
raise NotImplementedError("Encoder/decoder cross-attention "
|
|
"are not implemented for "
|
|
"PallasAttentionBackendImpl")
|
|
|
|
num_decode_tokens = attn_metadata.num_decode_tokens
|
|
has_decode = attn_metadata.num_decodes > 0
|
|
has_prefill = attn_metadata.num_prefills > 0
|
|
|
|
if len(kv_cache) > 1:
|
|
if self.key_cache is None:
|
|
self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
|
|
|
|
if has_decode:
|
|
slot_mapping = attn_metadata.slot_mapping[:num_decode_tokens * self.pcp_size: self.pcp_size] \
|
|
if self.pcp_size * self.dcp_size > 1 else attn_metadata.slot_mapping[:num_decode_tokens]
|
|
torch_npu._npu_reshape_and_cache(
|
|
key=key[:num_decode_tokens],
|
|
value=value[:num_decode_tokens],
|
|
key_cache=self.key_cache,
|
|
value_cache=self.value_cache,
|
|
slot_indices=slot_mapping)
|
|
|
|
if has_prefill:
|
|
if self.pcp_size > 1:
|
|
kv = torch.cat([key, value], dim=-1)
|
|
num_actual_tokens_pcp_padded = attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
|
|
all_kv = get_pcp_group().all_gather(
|
|
kv[:num_actual_tokens_pcp_padded].contiguous(), dim=0)
|
|
pcp_allgather_restore_idx = attn_metadata.prefill.pcp_allgather_restore_idx if attn_metadata.prefill else None
|
|
all_kv = torch.index_select(all_kv, 0,
|
|
pcp_allgather_restore_idx)
|
|
key, value = all_kv.split([self.head_size, self.head_size],
|
|
dim=-1)
|
|
|
|
torch_npu._npu_reshape_and_cache(
|
|
key=key[self.pcp_size * num_decode_tokens:attn_metadata.
|
|
num_actual_tokens_pcp_padded],
|
|
value=value[self.pcp_size *
|
|
num_decode_tokens:attn_metadata.
|
|
num_actual_tokens_pcp_padded],
|
|
key_cache=self.key_cache,
|
|
value_cache=self.value_cache,
|
|
slot_indices=attn_metadata.
|
|
slot_mapping[self.pcp_size *
|
|
num_decode_tokens:attn_metadata.
|
|
num_actual_tokens_pcp_padded])
|
|
|
|
forward_context: ForwardContext = get_forward_context()
|
|
if not forward_context.capturing:
|
|
if self.pcp_size * self.dcp_size > 1:
|
|
intermediate_output = self._forward_pcp_dcp(
|
|
query, key, value, kv_cache, attn_metadata, output)
|
|
elif attn_type == AttentionType.ENCODER_ONLY:
|
|
# TODO(zzzwwjj): Deal with this `cum_seq_len` more elegantly.
|
|
cum_seq_len = attn_metadata.query_start_loc[1:].tolist()
|
|
intermediate_output = torch_npu.npu_fusion_attention(
|
|
query,
|
|
key,
|
|
value,
|
|
head_num=self.num_heads,
|
|
input_layout="TND",
|
|
scale=self.scale,
|
|
sparse_mode=4,
|
|
atten_mask=attn_metadata.attn_mask,
|
|
pre_tockens=attn_metadata.max_query_len,
|
|
next_tockens=attn_metadata.max_query_len,
|
|
actual_seq_qlen=cum_seq_len,
|
|
actual_seq_kvlen=cum_seq_len,
|
|
)[0]
|
|
# V0-Style scheduler situation.
|
|
elif attn_metadata.attn_state == AscendAttentionState.PrefillNoCache:
|
|
intermediate_output = self._forward_prefill_no_cache(
|
|
query, key, value, attn_metadata, output, num_tokens)
|
|
elif attn_metadata.attn_state == \
|
|
AscendAttentionState.PrefillCacheHit:
|
|
intermediate_output = self._forward_prefill_cache_hit(
|
|
query, attn_metadata, output)
|
|
elif attn_metadata.attn_state == AscendAttentionState.DecodeOnly:
|
|
intermediate_output = self._forward_decode_only(
|
|
query, attn_metadata, output)
|
|
# Normal V1 situation.
|
|
else:
|
|
# npu_fused_infer_attention_score does not support cases
|
|
# where query.shape[0] != attn_metadata.query_start_loc[-1].
|
|
# Thus we need unpad it here.
|
|
num_tokens = attn_metadata.query_start_loc[-1]
|
|
query = query[:num_tokens]
|
|
intermediate_output = self._forward_v1_style(
|
|
query, attn_metadata, output)
|
|
else:
|
|
intermediate_output, num_tokens = self.full_graph_attention(
|
|
query, key, value, kv_cache, attn_metadata, output)
|
|
output[:num_tokens] = intermediate_output[:num_tokens]
|
|
|
|
return output
|