643e6f5486
### What this PR does / why we need it?
If a small batch of short requests is sent first, forming a chunk with a
length <128, it will corrupt the `attn_mask_cache`, causing subsequent
requests that do not form a chunk to have accuracy issues.
The root cause of this problem is the use of in-place multiplication.
Modifying it to use out-of-place multiplication will resolve the
accuracy problem.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Yes.
- vLLM version: v0.9.2
- vLLM main:
ad6c2e1a0b
---------
Signed-off-by: ApsarasX <apsarax@outlook.com>
1229 lines
53 KiB
Python
1229 lines
53 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 typing import Any, Dict, List, Optional, Tuple, Type
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import numpy as np
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import torch
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import torch_npu
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import torchair._contrib.custom_torch_ops # type: ignore # noqa: F401
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from torch.nn.functional import scaled_dot_product_attention
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionLayer,
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AttentionMetadata, AttentionType,
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MLAAttentionImpl)
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from vllm.attention.backends.utils import (PAD_SLOT_ID, CommonAttentionState,
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CommonMetadataBuilder,
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compute_slot_mapping,
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compute_slot_mapping_start_idx,
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is_block_tables_empty)
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from vllm.utils import async_tensor_h2d, make_tensor_with_pad
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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.ops.cache import concat_and_cache_mla
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from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, aligned_16,
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enable_custom_op, is_310p, nd_to_nz_2d)
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from vllm_ascend.worker.model_runner import (
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ModelInputForNPUBuilder, ModelInputForNPUWithSamplingMetadata)
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_ALLOWED_NUM_QUERIES_PER_KV = [32, 64, 128]
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class AscendAttentionBackend(AttentionBackend):
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@staticmethod
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def get_name() -> str:
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return "ASCEND"
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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_metadata_cls() -> Type["AscendMetadata"]:
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return AscendMetadata
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@staticmethod
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def get_state_cls() -> Type["CommonAttentionState"]:
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return CommonAttentionState
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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 is_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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else:
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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_builder_cls() -> Type["AscendMetadataBuilder"]:
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return AscendMetadataBuilder
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@classmethod
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def make_metadata_builder(cls, *args, **kwargs) -> "AscendMetadataBuilder":
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return cls.get_builder_cls()(*args, **kwargs)
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class AscendMLAAttentionBackend(AscendAttentionBackend):
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@staticmethod
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def get_impl_cls() -> Type["AscendMLAAttentionBackendImpl"]:
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return AscendMLAAttentionBackendImpl
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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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return (num_blocks, block_size, num_kv_heads, head_size)
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@dataclass
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class AscendMetadata(AttentionMetadata):
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"""Metadata for Ascendbackend.
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* modified from XFormersbackend
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NOTE: Any python object stored here is not updated when it is
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cuda-graph replayed. If you have values that need to be changed
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dynamically, it should be stored in tensor. The tensor has to be
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updated from `CUDAGraphRunner.forward` API.
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"""
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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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# FIXME: It is for flash attn.
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# Maximum sequence length among prefill batch. 0 if there are decoding
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# Avoid mypy error
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# Total number of prefill requests.
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num_prefills: int
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# Number of prefill tokens.
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num_prefill_tokens: int
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# (num_tokens,). The indices of the token slots that input tokens will be
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# stored into. E.g., if `slot_mapping` is [35, 2, 17] and the block size
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# is 16, the three tokens are stored in the 3rd slot in block 2, 2nd slot
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# in block 0, and 1st slot in block 1, respectively.
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slot_mapping: torch.Tensor
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# requests only.
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max_prefill_seq_len: int
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# Maximum sequence length among decode batch. 0 if there are prefill
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# requests only.
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max_decode_seq_len: int
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chunked_prefill_enabled: bool
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# (batch_size, max_blocks_per_seq).
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# Block addresses per sequence. (Seq id -> list of physical block)
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block_tables: Optional[torch.Tensor]
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# seq_lens stored as a tensor.
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seq_lens_tensor: Optional[torch.Tensor]
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# (batch_size,). The sequence length per sequence. Sequence length means
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# the computed tokens + new tokens None if it is a decoding.
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seq_lens: Optional[List[int]] = None
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# The query lengths of the input sequences
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query_lens: Optional[List[int]] = 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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# Self-attention prefill/decode metadata cache
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_cached_prefill_metadata: Optional["AscendMetadata"] = None
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_cached_decode_metadata: Optional["AscendMetadata"] = None
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# Begin encoder attn & enc/dec cross-attn fields...
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# Encoder sequence lengths representation
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encoder_seq_lens: Optional[List[int]] = None
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encoder_seq_lens_tensor: Optional[torch.Tensor] = None
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# Maximum sequence length among encoder sequences
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max_encoder_seq_len: Optional[int] = None
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# Number of tokens input to encoder
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num_encoder_tokens: Optional[int] = None
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# Mask for normal situation
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attn_mask: Optional[torch.Tensor] = None
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# Mask for prefix caching
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compress_mask: Optional[torch.Tensor] = None
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# Mask for chunked prefill
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chunk_mask: Optional[torch.Tensor] = None
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# Cross-attention memory-mapping data structures: slot mapping
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# and block tables
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cross_slot_mapping: Optional[torch.Tensor] = None
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cross_block_tables: Optional[torch.Tensor] = None
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@property
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def prefill_metadata(self) -> Optional["AscendMetadata"]:
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if self.num_prefills == 0:
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return None
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if self._cached_prefill_metadata is not None:
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# Recover cached prefill-phase attention
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# metadata structure.
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return self._cached_prefill_metadata
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assert ((self.seq_lens is not None)
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or (self.encoder_seq_lens is not None))
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# Compute some attn_metadata fields which default to None.
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slot_mapping = (None if self.slot_mapping is None else
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self.slot_mapping[:self.num_prefill_tokens])
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seq_lens = (None if self.seq_lens is None else
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self.seq_lens[:self.num_prefills])
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query_lens = (None if self.query_lens is None else
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self.query_lens[:self.num_prefills])
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block_tables = (None if self.block_tables is None else
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self.block_tables[:self.num_prefills])
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seq_lens_tensor = (None if self.seq_lens_tensor is None else
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self.seq_lens_tensor[:self.num_prefills])
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# Construct & cache prefill-phase attention metadata structure.
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self._cached_prefill_metadata = AscendMetadata(
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num_prefills=self.num_prefills,
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num_prefill_tokens=self.num_prefill_tokens,
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num_decode_tokens=0,
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slot_mapping=slot_mapping,
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seq_lens=seq_lens,
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seq_lens_tensor=seq_lens_tensor,
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query_lens=query_lens,
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max_query_len=self.max_query_len,
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max_prefill_seq_len=self.max_prefill_seq_len,
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max_decode_seq_len=0,
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chunked_prefill_enabled=self.chunked_prefill_enabled,
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block_tables=block_tables,
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# Begin encoder & cross attn fields below...
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encoder_seq_lens=self.encoder_seq_lens,
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encoder_seq_lens_tensor=self.encoder_seq_lens_tensor,
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max_encoder_seq_len=self.max_encoder_seq_len,
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multi_modal_placeholder_index_maps=self.
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multi_modal_placeholder_index_maps,
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cross_slot_mapping=self.cross_slot_mapping,
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cross_block_tables=self.cross_block_tables,
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enable_kv_scales_calculation=False)
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return self._cached_prefill_metadata
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@property
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def decode_metadata(self) -> Optional["AscendMetadata"]:
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if self.num_decode_tokens == 0:
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return None
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if self._cached_decode_metadata is not None:
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# Recover cached decode-phase attention
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# metadata structure.
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return self._cached_decode_metadata
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# Compute some attn_metadata fields which default to None.
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slot_mapping = (None if self.slot_mapping is None else
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self.slot_mapping[self.num_prefill_tokens:])
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seq_lens = (None if self.seq_lens is None else
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self.seq_lens[self.num_prefills:])
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query_lens = (None if self.query_lens is None else
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self.query_lens[self.num_prefills:])
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block_tables = (None if self.block_tables is None else
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self.block_tables[self.num_prefills:])
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seq_lens_tensor = (None if self.seq_lens_tensor is None else
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self.seq_lens_tensor[self.num_prefills:])
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# Construct & cache decode-phase attention metadata structure.
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self._cached_decode_metadata = AscendMetadata(
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num_prefills=0,
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num_prefill_tokens=0,
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num_decode_tokens=self.num_decode_tokens,
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slot_mapping=slot_mapping,
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seq_lens=seq_lens,
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seq_lens_tensor=seq_lens_tensor,
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query_lens=query_lens,
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max_query_len=self.max_query_len,
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max_prefill_seq_len=0,
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max_decode_seq_len=self.max_decode_seq_len,
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chunked_prefill_enabled=self.chunked_prefill_enabled,
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block_tables=block_tables,
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# Begin encoder & cross attn fields below...
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encoder_seq_lens=self.encoder_seq_lens,
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encoder_seq_lens_tensor=self.encoder_seq_lens_tensor,
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max_encoder_seq_len=self.max_encoder_seq_len,
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multi_modal_placeholder_index_maps=self.
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multi_modal_placeholder_index_maps,
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cross_slot_mapping=self.cross_slot_mapping,
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cross_block_tables=self.cross_block_tables,
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enable_kv_scales_calculation=False)
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return self._cached_decode_metadata
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def advance_step(self,
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model_input: "ModelInputForNPUWithSamplingMetadata",
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sampled_token_ids: Optional[torch.Tensor],
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block_size: int,
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num_seqs: int,
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num_queries: int,
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turn_prefills_into_decodes: bool = False):
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"""
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Update metadata in-place to advance one decode step.
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"""
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# When using cudagraph, the num_seqs is padded to the next captured
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# batch sized, but num_queries tracks the actual number of requests in
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# the batch. For --enforce-eager mode, num_seqs == num_queries
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if num_seqs != num_queries:
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assert num_seqs > num_queries
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if turn_prefills_into_decodes:
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# When Mutli-Step is enabled with Chunked-Prefill, prefills and
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# decodes are scheduled together. In the first step, all the
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# prefills turn into decodes. This update reflects that
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# conversion.
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assert self.num_decode_tokens + self.num_prefills == num_seqs
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self.num_decode_tokens += self.num_prefills
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self.num_prefills = 0
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self.num_prefill_tokens = 0
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self.max_prefill_seq_len = 0
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self.max_query_len = 1
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self.slot_mapping = self.slot_mapping[:num_seqs]
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else:
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assert self.seq_lens is not None
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assert self.max_decode_seq_len == max(self.seq_lens)
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assert self.num_prefills == 0
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assert self.num_prefill_tokens == 0
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assert self.num_decode_tokens == num_seqs
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assert self.slot_mapping.shape == (num_seqs, )
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assert self.seq_lens is not None
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assert len(self.seq_lens) == num_seqs
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assert self.seq_lens_tensor is not None
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assert self.seq_lens_tensor.shape == (num_seqs, )
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assert self.max_query_len == 1
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assert self.max_prefill_seq_len == 0
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assert self.block_tables is not None
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assert self.block_tables.shape[0] == num_seqs
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# Update query lengths. Note that we update only queries and not seqs,
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# since tensors may be padded due to captured cuda graph batch size
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for i in range(num_queries):
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self.seq_lens[i] += 1
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self.max_decode_seq_len = max(self.seq_lens)
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if enable_custom_op():
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#advance a step on NPU for existing inputs for a multi-step runner if custom ops is enabled
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torch.ops._C.advance_step_flashattn_ascendc(
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num_seqs=num_seqs,
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num_queries=num_queries,
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block_size=block_size,
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input_tokens=model_input.input_tokens,
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sampled_token_ids=sampled_token_ids,
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input_positions=model_input.input_positions,
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seq_lens=self.seq_lens_tensor,
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slot_mapping=self.slot_mapping,
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block_tables=self.block_tables)
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else:
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# use traditional Pytorch method for updating these tensors.
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# update input_tokens
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sampled_token_ids_list = sampled_token_ids[:
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num_queries].squeeze( # type: ignore
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-1)
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model_input.input_tokens[:
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num_queries] = sampled_token_ids_list # type: ignore
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|
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# get seq_lens and input_positions
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seq_lens = self.seq_lens_tensor[:num_queries]
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next_seq_lens = seq_lens + 1
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next_input_pos = next_seq_lens - 1
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|
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# update seq_lens and input_positions
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self.seq_lens_tensor[:num_queries] = next_seq_lens
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model_input.input_positions[:
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num_queries] = next_input_pos # type: ignore
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|
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# 计算 block index 和 offset
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block_idx = next_input_pos // block_size
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block_offset = next_input_pos % block_size
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current_block_table = self.block_tables.gather(
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1, block_idx.unsqueeze(-1)).squeeze(-1)
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slot_num = current_block_table * block_size + block_offset
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|
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# update slot_mapping
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self.slot_mapping[:num_queries] = slot_num
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|
|
|
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class AscendMetadataBuilder(CommonMetadataBuilder[AscendMetadata]):
|
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|
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_attn_mask_builder = None # noqa
|
|
|
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def __init__(self, input_builder: "ModelInputForNPUBuilder"):
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self.input_builder = input_builder
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self.runner = input_builder.runner
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self.sliding_window = input_builder.sliding_window
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self.block_size = input_builder.block_size
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|
|
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self.attn_mask = None
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self.compress_mask = None
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self.chunk_mask = None
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if AscendMetadataBuilder._attn_mask_builder is None:
|
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AscendMetadataBuilder._attn_mask_builder = AttentionMaskBuilder(
|
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128, self.input_builder.runner.model_config.dtype)
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|
|
|
def _add_seq_group(
|
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self, inter_data: ModelInputForNPUBuilder.InterDataForSeqGroup,
|
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chunked_prefill_enabled: bool):
|
|
"""Add a sequence group to the metadata. Specifically update/append
|
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1. context length.
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2. block table.
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3. slot mapping.
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"""
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is_prompt = inter_data.is_prompt
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|
block_tables = inter_data.block_tables
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|
|
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for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
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curr_sliding_window_block) in zip(
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inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
|
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inter_data.orig_seq_lens, inter_data.seq_lens,
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inter_data.query_lens, inter_data.context_lens,
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inter_data.curr_sliding_window_blocks):
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self.context_lens.append(context_len)
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if is_prompt:
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self.num_prefills += 1
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self.num_prefill_tokens += token_len
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self.prefill_seq_lens.append(seq_len)
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else:
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self.num_decode_tokens += query_len
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self.curr_seq_lens.append(curr_seq_len)
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|
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# Compute block table.
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# TODO(sang): Combine chunked prefill and prefix caching by
|
|
# only allowing multiple of block_size chunk size.
|
|
# NOTE: This only works for oooooooxxx style attention.
|
|
block_table: List[int] = []
|
|
prefix_cache_hit = any([
|
|
inter_data.prefix_cache_hit
|
|
for inter_data in self.input_builder.inter_data_list
|
|
])
|
|
if prefix_cache_hit:
|
|
# NOTE(woosuk): For flash-attn, the block table should
|
|
# include the entries for the incoming prefill tokens.
|
|
if block_tables is not None:
|
|
block_table = block_tables[seq_id]
|
|
elif ((chunked_prefill_enabled or not is_prompt)
|
|
and block_tables is not None):
|
|
if curr_sliding_window_block == 0:
|
|
block_table = block_tables[seq_id]
|
|
else:
|
|
block_table = block_tables[seq_id][
|
|
-curr_sliding_window_block:]
|
|
self.block_tables.append(block_table)
|
|
|
|
# Compute slot mapping.
|
|
is_profile_run = is_block_tables_empty(block_tables)
|
|
start_idx = compute_slot_mapping_start_idx(is_prompt, query_len,
|
|
context_len,
|
|
self.sliding_window)
|
|
compute_slot_mapping(
|
|
is_profile_run,
|
|
self.slot_mapping,
|
|
seq_id,
|
|
seq_len,
|
|
context_len,
|
|
start_idx,
|
|
self.block_size,
|
|
inter_data.block_tables,
|
|
)
|
|
|
|
def _get_graph_runner_block_tables(
|
|
self, num_seqs: int,
|
|
block_tables: List[List[int]]) -> torch.Tensor:
|
|
# The shape of graph_block_tables is
|
|
# [max batch size, max context len // block size].
|
|
|
|
max_batch_size, max_blocks = self.runner.graph_block_tables.shape
|
|
assert max_batch_size >= num_seqs
|
|
|
|
graph_block_tables = self.runner.graph_block_tables # [:num_seqs]
|
|
for i, block_table in enumerate(block_tables):
|
|
if block_table:
|
|
num_blocks = len(block_table)
|
|
if num_blocks <= max_blocks:
|
|
graph_block_tables[i, :num_blocks] = block_table
|
|
else:
|
|
graph_block_tables[
|
|
i, :max_blocks] = block_table[:max_blocks]
|
|
|
|
return torch.from_numpy(graph_block_tables).to(
|
|
device=self.runner.device, non_blocking=True)
|
|
|
|
def build(
|
|
self,
|
|
seq_lens: List[int],
|
|
query_lens: List[int],
|
|
graph_pad_size: int,
|
|
):
|
|
"""Build attention metadata with on-device tensors.
|
|
|
|
Args:
|
|
seq_lens: The maybe padded sequence lengths of the input sequences.
|
|
query_lens: The query lengths of the input sequences.
|
|
"""
|
|
for inter_data in self.input_builder.inter_data_list:
|
|
self._add_seq_group(inter_data,
|
|
self.input_builder.chunked_prefill_enabled)
|
|
|
|
device = self.runner.device
|
|
dtype = self.runner.model_config.dtype
|
|
use_npu_graph = graph_pad_size != -1
|
|
|
|
max_query_len = max(query_lens)
|
|
max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
|
|
max_decode_seq_len = max(self.curr_seq_lens, default=0)
|
|
max_seq_len = max(max_prefill_seq_len, max_decode_seq_len)
|
|
num_decode_tokens = self.num_decode_tokens
|
|
|
|
if self.num_prefills == 0 and use_npu_graph:
|
|
num_seqs = len(seq_lens)
|
|
self.slot_mapping.extend([PAD_SLOT_ID] * graph_pad_size)
|
|
self.block_tables.extend([[]] * graph_pad_size)
|
|
block_tables = self._get_graph_runner_block_tables(
|
|
num_seqs, self.block_tables)
|
|
else:
|
|
block_tables = make_tensor_with_pad(
|
|
self.block_tables,
|
|
pad=0,
|
|
dtype=torch.int32,
|
|
device=device,
|
|
)
|
|
|
|
if self.num_prefills > 0:
|
|
if block_tables is None or block_tables.numel() == 0:
|
|
# normal mask
|
|
self.attn_mask = AscendMetadataBuilder._attn_mask_builder.get_attn_mask( # type: ignore
|
|
max_prefill_seq_len, dtype, device)
|
|
if is_310p():
|
|
mask_nz = nd_to_nz_2d(self.attn_mask)
|
|
mask_nz = torch_npu.npu_format_cast(
|
|
mask_nz.contiguous(), ACL_FORMAT_FRACTAL_NZ)
|
|
self.attn_mask = mask_nz
|
|
elif self.num_decode_tokens == 0 and not self.input_builder.chunked_prefill_enabled:
|
|
# compress mask for prefix cache
|
|
self.compress_mask = AscendMetadataBuilder._attn_mask_builder.get_attn_mask( # type: ignore
|
|
128, dtype, device)
|
|
else:
|
|
# chunk_mask for chunk prefill
|
|
attn_mask = AscendMetadataBuilder._attn_mask_builder.get_attn_mask( # type: ignore
|
|
max_seq_len, dtype, device)
|
|
if attn_mask.numel() > 1 and attn_mask[0][1] > 0:
|
|
# Do not use in-place multiplication to avoid modifying `attn_mask_cache`!
|
|
attn_mask = attn_mask * -10000
|
|
chunk_mask_list = []
|
|
for i, seq_len in enumerate(seq_lens):
|
|
context_len = self.context_lens[i]
|
|
chunk_mask_list.append(attn_mask[context_len:seq_len])
|
|
self.chunk_mask = torch.cat(chunk_mask_list, 0)
|
|
else:
|
|
self.attn_mask = None
|
|
self.compress_mask = None
|
|
self.chunk_mask = None
|
|
|
|
assert max_query_len > 0, "query_lens: {}".format(query_lens)
|
|
|
|
assert device is not None
|
|
slot_mapping_tensor = async_tensor_h2d(self.slot_mapping, torch.int32,
|
|
device, self.runner.pin_memory)
|
|
seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
|
|
self.runner.pin_memory)
|
|
placeholder_index_maps = {
|
|
modality: placeholder_map.index_map()
|
|
for modality, placeholder_map in
|
|
self.multimodal_placeholder_maps.items()
|
|
}
|
|
|
|
return AscendMetadata(
|
|
num_prefills=self.num_prefills,
|
|
slot_mapping=slot_mapping_tensor,
|
|
num_prefill_tokens=self.num_prefill_tokens,
|
|
num_decode_tokens=num_decode_tokens,
|
|
seq_lens=seq_lens,
|
|
multi_modal_placeholder_index_maps=placeholder_index_maps,
|
|
enable_kv_scales_calculation=True,
|
|
seq_lens_tensor=seq_lens_tensor,
|
|
query_lens=query_lens,
|
|
max_query_len=max_query_len,
|
|
max_prefill_seq_len=max_prefill_seq_len,
|
|
max_decode_seq_len=max_decode_seq_len,
|
|
block_tables=block_tables,
|
|
attn_mask=self.attn_mask,
|
|
compress_mask=self.compress_mask,
|
|
chunk_mask=self.chunk_mask,
|
|
chunked_prefill_enabled=self.input_builder.chunked_prefill_enabled,
|
|
)
|
|
|
|
|
|
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,
|
|
blocksparse_params: Optional[Dict[str, Any]] = None,
|
|
logits_soft_cap: Optional[float] = None,
|
|
attn_type: str = AttentionType.DECODER,
|
|
kv_sharing_target_layer_name: Optional[str] = None,
|
|
use_irope: bool = False,
|
|
) -> 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.seq_len_cpu_tensor = None
|
|
self.query_len_cpu_tensor = None
|
|
self.key_cache = None
|
|
self.value_cache = None
|
|
|
|
def forward(
|
|
self,
|
|
layer: AttentionLayer,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AscendMetadata,
|
|
attn_type: str = AttentionType.DECODER,
|
|
output: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
"""Forward pass with Ascend attention.
|
|
Args:
|
|
query: shape = [num_tokens, num_heads * head_size]
|
|
num_tokens = batch_size * seq_len
|
|
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]
|
|
key_cache = [num_blocks, block_size,
|
|
num_kv_heads, head_size]
|
|
value_cache = [num_blocks, block_size,
|
|
num_kv_heads, head_size]
|
|
attn_metadata: Metadata for attention.
|
|
Returns:
|
|
shape = [batch_size, seq_len * num_heads * head_size]
|
|
"""
|
|
assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
|
|
# View q k v to BSH.
|
|
num_tokens = query.shape[0]
|
|
query = query.view(-1, self.num_heads, self.head_size)
|
|
key = key.view(-1, self.num_kv_heads, self.head_size)
|
|
value = value.view(-1, self.num_kv_heads, self.head_size)
|
|
# TODO: Remove this contiguous in the future.
|
|
value = value.contiguous()
|
|
attn_type = self.attn_type
|
|
|
|
output = torch.empty(num_tokens,
|
|
self.num_heads,
|
|
self.head_size,
|
|
dtype=query.dtype,
|
|
device=query.device)
|
|
|
|
if kv_cache.numel() > 0:
|
|
if self.key_cache is None:
|
|
self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
|
|
slots = attn_metadata.slot_mapping
|
|
|
|
if hasattr(layer, 'quant_method'):
|
|
isPrefill = True if attn_metadata.num_prefills > 0 else False
|
|
if isPrefill:
|
|
assert attn_metadata.prefill_metadata is not None
|
|
self.seq_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(attn_metadata.prefill_metadata.seq_lens).astype(
|
|
np.int32))
|
|
else:
|
|
assert attn_metadata.decode_metadata is not None
|
|
self.seq_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(attn_metadata.decode_metadata.seq_lens).astype(
|
|
np.int32))
|
|
block_tables = attn_metadata.decode_metadata.block_tables if attn_metadata.decode_metadata else None
|
|
# Details of kv_cache arrangement in attention quantization
|
|
# are implemented by quant_method.
|
|
layer.quant_method.apply(
|
|
layer,
|
|
query,
|
|
key,
|
|
value,
|
|
self.key_cache,
|
|
self.value_cache,
|
|
self.scale,
|
|
block_tables,
|
|
isPrefill,
|
|
attn_metadata,
|
|
output,
|
|
seq_lens_tensor_cpu=self.seq_lens_tensor_cpu)
|
|
else:
|
|
if self.key_cache is not None:
|
|
torch_npu._npu_reshape_and_cache(key=key,
|
|
value=value,
|
|
key_cache=self.key_cache,
|
|
value_cache=self.value_cache,
|
|
slot_indices=slots)
|
|
|
|
if attn_metadata.num_prefills > 0:
|
|
# Prefix cache disabled and chunk prefill disabled or no prefix cache hit
|
|
if (attn_metadata.block_tables is None
|
|
or attn_metadata.block_tables.numel() == 0):
|
|
if attn_type == AttentionType.ENCODER_ONLY:
|
|
# TODO: change to use torch_npu encoder attention op, instead
|
|
# of torch sdpa
|
|
query = query.movedim(0, query.dim() - 2)
|
|
key = key.movedim(0, key.dim() - 2)
|
|
value = value.movedim(0, value.dim() - 2)
|
|
|
|
causal_attn = (attn_type == AttentionType.DECODER)
|
|
if attn_metadata.seq_lens is not None:
|
|
seq_lens_q = seq_lens_kv = attn_metadata.seq_lens
|
|
attn_masks = [None] * len(seq_lens_q)
|
|
start_q, start_kv = 0, 0
|
|
for seq_len_q, seq_len_kv, mask in zip(
|
|
seq_lens_q, seq_lens_kv, attn_masks):
|
|
end_q = start_q + seq_len_q
|
|
end_kv = start_kv + seq_len_kv
|
|
sub_out = scaled_dot_product_attention(
|
|
query[None, :, start_q:end_q, :],
|
|
key[None, :, start_kv:end_kv, :],
|
|
value[None, :, start_kv:end_kv, :],
|
|
attn_mask=mask,
|
|
dropout_p=0.0,
|
|
is_causal=causal_attn and mask is None,
|
|
scale=self.scale).squeeze(0).movedim(
|
|
query.dim() - 2, 0)
|
|
output[start_q:end_q, :, :] = sub_out
|
|
start_q, start_kv = end_q, end_kv
|
|
else:
|
|
assert attn_metadata.attn_mask is not None
|
|
mask = attn_metadata.attn_mask
|
|
assert attn_metadata.prefill_metadata is not None
|
|
self.seq_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(attn_metadata.prefill_metadata.seq_lens).
|
|
astype(np.int32))
|
|
if is_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(
|
|
self.seq_lens_tensor_cpu.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=self.seq_lens_tensor_cpu,
|
|
scale_value=self.scale,
|
|
num_heads=self.num_heads,
|
|
num_kv_heads=self.num_kv_heads,
|
|
out=output)
|
|
output = output[:num_tokens, :, :]
|
|
# Prefix cache only and cache hit
|
|
elif attn_metadata.num_decode_tokens == 0 and not attn_metadata.chunked_prefill_enabled:
|
|
assert kv_cache is not None
|
|
assert attn_metadata.prefill_metadata is not None
|
|
self.seq_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(
|
|
attn_metadata.prefill_metadata.seq_lens).astype(
|
|
np.int32))
|
|
self.query_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(
|
|
attn_metadata.prefill_metadata.query_lens).astype(
|
|
np.int32))
|
|
block_tables = attn_metadata.prefill_metadata.block_tables
|
|
assert attn_metadata.compress_mask is not None
|
|
compress_mask = attn_metadata.compress_mask
|
|
torch_npu._npu_flash_attention_qlens(
|
|
query=query,
|
|
key_cache=self.key_cache,
|
|
value_cache=self.value_cache,
|
|
block_table=block_tables,
|
|
mask=compress_mask,
|
|
seq_len=self.query_lens_tensor_cpu,
|
|
context_lens=self.seq_lens_tensor_cpu,
|
|
num_kv_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
scale_value=self.scale,
|
|
out=output)
|
|
# Splitfuse
|
|
else:
|
|
assert kv_cache is not None
|
|
self.seq_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(attn_metadata.seq_lens).astype(np.int32))
|
|
self.query_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(attn_metadata.query_lens).astype(np.int32))
|
|
block_tables = attn_metadata.block_tables
|
|
assert attn_metadata.chunk_mask is not None
|
|
chunk_mask = attn_metadata.chunk_mask
|
|
torch_npu._npu_paged_attention_splitfuse(
|
|
query=query,
|
|
key_cache=self.key_cache,
|
|
value_cache=self.value_cache,
|
|
block_table=block_tables,
|
|
context_lens=self.seq_lens_tensor_cpu,
|
|
mask=chunk_mask,
|
|
seq_len=self.query_lens_tensor_cpu,
|
|
num_kv_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
scale_value=self.scale,
|
|
out=output)
|
|
# Decode only
|
|
else:
|
|
assert self.key_cache is not None
|
|
assert self.value_cache is not None
|
|
assert attn_metadata.decode_metadata is not None
|
|
self.seq_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(attn_metadata.decode_metadata.seq_lens).astype(
|
|
np.int32))
|
|
if is_310p():
|
|
# # seq_lens_tensor needs to be transferred to the device for 310P
|
|
self.seq_lens_tensor_cpu = self.seq_lens_tensor_cpu.to(
|
|
device=self.key_cache.device)
|
|
block_tables = attn_metadata.decode_metadata.block_tables
|
|
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=block_tables,
|
|
context_lens=self.seq_lens_tensor_cpu,
|
|
out=output)
|
|
|
|
return output.view(num_tokens, self.hidden_size)
|
|
|
|
|
|
class AscendMLAAttentionBackendImpl(MLAAttentionImpl):
|
|
|
|
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,
|
|
blocksparse_params: Optional[Dict[str, Any]] = None,
|
|
logits_soft_cap: Optional[float] = None,
|
|
attn_type: str = AttentionType.DECODER,
|
|
kv_sharing_target_layer_name: Optional[str] = None,
|
|
**extra_impl_args,
|
|
) -> 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.seq_len_cpu_tensor = None
|
|
|
|
# MLA Args
|
|
self.q_lora_rank = extra_impl_args['q_lora_rank']
|
|
self.kv_lora_rank = extra_impl_args['kv_lora_rank']
|
|
self.qk_nope_head_dim = extra_impl_args['qk_nope_head_dim']
|
|
self.qk_rope_head_dim = extra_impl_args['qk_rope_head_dim']
|
|
self.qk_head_dim = extra_impl_args['qk_head_dim']
|
|
self.v_head_dim = extra_impl_args['v_head_dim']
|
|
self.rotary_emb = extra_impl_args['rotary_emb']
|
|
self.q_proj = extra_impl_args['q_proj']
|
|
self.kv_b_proj = extra_impl_args['kv_b_proj']
|
|
self.o_proj = extra_impl_args['o_proj']
|
|
self.kv_a_proj_with_mqa = extra_impl_args.get('kv_a_proj_with_mqa',
|
|
None)
|
|
self.kv_a_layernorm = extra_impl_args.get('kv_a_layernorm', None)
|
|
self.k_pe_cache = None
|
|
self.k_nope_cache = None
|
|
self.w_kc = None
|
|
self.w_vc = None
|
|
|
|
ascend_config = get_ascend_config()
|
|
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
|
|
|
|
# TODO: support numHeads / numKvHeads < 16 in MLA kernel
|
|
if self.torchair_graph_enabled:
|
|
assert self.num_queries_per_kv in _ALLOWED_NUM_QUERIES_PER_KV, \
|
|
("The allowed number of queries per kv when enabling both MLA and Graph mode"
|
|
" only support {32, 64, 128}, Thus this is not supported for DeepSeek-V2-Lite,"
|
|
" as it only has 16 attention heads. And if you're using DeepSeek-V3 or DeepSeek-R1,"
|
|
" please make sure after the tensor parallel split, num_heads / num_kv_heads in "
|
|
"{32, 64, 128}.")
|
|
|
|
def exec_kv(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
kv_cache: Tuple,
|
|
slots: torch.Tensor,
|
|
):
|
|
B = hidden_states.shape[0]
|
|
N = self.num_kv_heads
|
|
S = 1
|
|
kv = self.kv_a_proj_with_mqa(hidden_states)[0]
|
|
# npu_kv_rmsnorm_rope_cache needs [B, N, S, D]
|
|
kv = kv.view(B, N, S, self.kv_lora_rank + self.qk_rope_head_dim)
|
|
|
|
k_pe, k_nope, _, _ = torch.ops.npu_inference.npu_kv_rmsnorm_rope_cache(
|
|
kv,
|
|
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="PA",
|
|
)
|
|
|
|
return k_pe, k_nope
|
|
|
|
def apply_rotary_emb(
|
|
self,
|
|
x: torch.Tensor,
|
|
cos: torch.Tensor,
|
|
sin: torch.Tensor,
|
|
is_neox_style: bool,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Args:
|
|
x: [num_tokens, num_heads, head_size]
|
|
cos: [num_tokens, head_size // 2]
|
|
sin: [num_tokens, head_size // 2]
|
|
is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
|
|
positional embeddings.
|
|
"""
|
|
cos = cos.unsqueeze(-2).to(x.dtype)
|
|
sin = sin.unsqueeze(-2).to(x.dtype)
|
|
if is_neox_style:
|
|
x1, x2 = torch.chunk(x, 2, dim=-1)
|
|
else:
|
|
x1 = x[..., ::2]
|
|
x2 = x[..., 1::2]
|
|
o1 = x1 * cos - x2 * sin
|
|
o2 = x2 * cos + x1 * sin
|
|
if is_neox_style:
|
|
return torch.cat((o1, o2), dim=-1)
|
|
else:
|
|
return torch.stack((o1, o2), dim=-1).flatten(-2)
|
|
|
|
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.ops.npu_inference.npu_interleave_rope(x, cos, sin)
|
|
return x.view(B, N, D)
|
|
|
|
def process_weights_after_loading(self, act_dtype: torch.dtype):
|
|
if self.w_kc is None or self.w_vc is None:
|
|
kv_b_proj_weight = self.kv_b_proj.weight.reshape(
|
|
self.num_heads, self.qk_nope_head_dim + self.v_head_dim,
|
|
self.kv_lora_rank)
|
|
self.w_kc = kv_b_proj_weight[:, :self.
|
|
qk_nope_head_dim, :].contiguous()
|
|
self.w_vc = kv_b_proj_weight[:,
|
|
self.qk_nope_head_dim:, :].transpose(
|
|
1, 2).contiguous()
|
|
|
|
def forward(
|
|
self,
|
|
layer: AttentionLayer,
|
|
hidden_states_or_q_c: torch.Tensor,
|
|
hidden_states_or_kv_c_normed: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AscendMetadata,
|
|
attn_type: str = AttentionType.DECODER,
|
|
output: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
"""Forward pass with Ascend attention.
|
|
Args:
|
|
hidden_states_or_q_c: shape = [num_tokens, num_heads * head_size]
|
|
num_tokens = batch_size * seq_len
|
|
hidden_states_or_kv_c_normed: shape = [num_tokens, num_kv_heads * head_size]
|
|
k_pe: shape = [num_tokens, num_kv_heads * head_size]
|
|
kv_cache: shape = [1, num_blocks, block_size,
|
|
num_kv_heads * head_size]
|
|
attn_metadata: Metadata for attention.
|
|
Returns:
|
|
shape = [batch_size, seq_len * num_heads * head_size]
|
|
"""
|
|
assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
|
|
attn_type = self.attn_type
|
|
if attn_type != AttentionType.DECODER:
|
|
raise NotImplementedError("Encoder self-attention and "
|
|
"encoder/decoder cross-attention "
|
|
"are not implemented for "
|
|
"PallasAttentionBackendImpl")
|
|
|
|
if attn_metadata is None:
|
|
# for profile run
|
|
return hidden_states_or_q_c
|
|
|
|
num_tokens = hidden_states_or_q_c.shape[0]
|
|
q = self.q_proj(hidden_states_or_q_c)[0].view(-1, self.num_heads,
|
|
self.qk_head_dim)
|
|
q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim],
|
|
dim=-1)
|
|
if k_pe is None and attn_metadata.decode_metadata:
|
|
seq_len = self.rotary_emb.max_position_embeddings
|
|
|
|
cos = self.rotary_emb.cos_cached[:seq_len].to(dtype=q_pe.dtype)
|
|
sin = self.rotary_emb.sin_cached[:seq_len].to(dtype=q_pe.dtype)
|
|
cos = cos[attn_metadata.input_positions]
|
|
sin = sin[attn_metadata.input_positions]
|
|
cos = cos[:, None, None, :]
|
|
sin = sin[:, None, None, :]
|
|
|
|
q_pe = self.rope_single(q_pe, cos, sin)
|
|
k_pe, k_nope = self.exec_kv(hidden_states_or_kv_c_normed, cos, sin,
|
|
kv_cache, attn_metadata.slot_mapping)
|
|
else:
|
|
if k_pe is None:
|
|
# NOTE: k_pe is None when graph mode enabled
|
|
kv_c, k_pe = self.kv_a_proj_with_mqa(
|
|
hidden_states_or_kv_c_normed)[0].split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
|
|
else:
|
|
kv_c_normed = hidden_states_or_kv_c_normed
|
|
k_pe = k_pe.view(num_tokens, self.num_kv_heads, -1)
|
|
if self.rotary_emb.__class__.__name__ == 'RotaryEmbedding':
|
|
# NOTE: When scaling not specified
|
|
ori_q_pe_shape, ori_k_pe_shape = q_pe.shape, k_pe.shape
|
|
q_pe = q_pe.reshape(num_tokens, -1)
|
|
k_pe = k_pe.reshape(num_tokens, -1)
|
|
q_pe, k_pe = self.rotary_emb(attn_metadata.input_positions,
|
|
q_pe, k_pe)
|
|
q_pe = q_pe.view(ori_q_pe_shape)
|
|
k_pe = k_pe.view(ori_k_pe_shape)
|
|
else:
|
|
q_pe, k_pe = self.rotary_emb(attn_metadata.input_positions,
|
|
q_pe, k_pe)
|
|
|
|
if attn_metadata.num_prefills > 0:
|
|
kv = self.kv_b_proj(kv_c_normed)[0].view(num_tokens,
|
|
self.num_heads, -1)
|
|
k_nope, value = kv.split([self.qk_nope_head_dim, self.v_head_dim],
|
|
dim=-1)
|
|
else:
|
|
q_nope_t = torch.transpose(q_nope, 0, 1)
|
|
q_nope_out = torch.bmm(q_nope_t, self.w_kc)
|
|
q_nope = torch.transpose(q_nope_out, 0, 1)
|
|
|
|
query = torch.cat([q_nope, q_pe], dim=-1).view(num_tokens,
|
|
self.num_heads, -1)
|
|
|
|
# TODO: Replace the env with more flexible expressions
|
|
if self.torchair_graph_enabled:
|
|
if len(kv_cache) > 0 and kv_cache[0].numel(
|
|
) > 0 and attn_metadata.num_prefills > 0:
|
|
slots = attn_metadata.slot_mapping
|
|
# NOTE: Separate the kv cache in advance to avoid OOM or other issues
|
|
torch_npu._npu_reshape_and_cache(key=kv_c_normed.view(
|
|
num_tokens, self.num_kv_heads, -1),
|
|
value=k_pe,
|
|
key_cache=kv_cache[0],
|
|
value_cache=kv_cache[1],
|
|
slot_indices=slots)
|
|
elif kv_cache.numel() > 0:
|
|
# TODO replace this naive implement with fusion kernel
|
|
concat_and_cache_mla(kv_c_normed, k_pe, kv_cache,
|
|
attn_metadata.slot_mapping)
|
|
|
|
if attn_metadata.num_prefills > 0:
|
|
attn_output = torch.empty(num_tokens,
|
|
self.num_heads,
|
|
self.v_head_dim,
|
|
dtype=query.dtype,
|
|
device=query.device)
|
|
if (attn_metadata.block_tables is None
|
|
or attn_metadata.block_tables.numel() == 0):
|
|
assert attn_metadata.attn_mask is not None
|
|
assert attn_metadata.prefill_metadata is not None
|
|
assert attn_metadata.prefill_metadata.seq_lens is not None
|
|
mask = attn_metadata.attn_mask
|
|
self.seq_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(attn_metadata.prefill_metadata.seq_lens).astype(
|
|
np.int32))
|
|
k_pe = k_pe.repeat(1, self.num_heads, 1)
|
|
key = torch.cat(
|
|
[k_nope.view(num_tokens, self.num_heads, -1), k_pe], dim=2)
|
|
torch_npu._npu_flash_attention(
|
|
query=query,
|
|
key=key,
|
|
value=value,
|
|
mask=mask,
|
|
seq_len=self.seq_lens_tensor_cpu,
|
|
scale_value=self.scale,
|
|
num_heads=self.num_heads,
|
|
num_kv_heads=self.num_heads,
|
|
out=attn_output)
|
|
else:
|
|
# TODO: Will support prefix cache and chunked prefill soon.
|
|
raise RuntimeError(
|
|
"Prefix cache and chunked prefill are currently not supported."
|
|
)
|
|
elif attn_metadata.decode_metadata:
|
|
assert kv_cache is not None
|
|
if self.torchair_graph_enabled:
|
|
# shape of query for npu graph mode should be:
|
|
# [bs, num_heads_per_rank, seq_len, dim]
|
|
q_nope = q_nope.view(num_tokens, self.num_heads, 1, -1)
|
|
q_pe = q_pe.view(num_tokens, self.num_heads, 1, -1)
|
|
# shape of knope/k_pe for npu graph mode should be:
|
|
# [num_blocks, num_kv_heads, block_size, self.kv_lora_rank/self.qk_rope_head_dim]
|
|
block_size = kv_cache[0].shape[1]
|
|
k_nope = k_nope.view(-1, self.num_kv_heads, block_size,
|
|
self.kv_lora_rank)
|
|
k_pe = k_pe.view(-1, self.num_kv_heads, block_size,
|
|
self.qk_rope_head_dim)
|
|
attn_output, _ = torch.ops.npu.npu_fused_infer_attention_score(
|
|
q_nope,
|
|
k_nope,
|
|
k_nope,
|
|
query_rope=q_pe,
|
|
key_rope=k_pe,
|
|
num_heads=self.num_heads,
|
|
num_key_value_heads=self.num_kv_heads,
|
|
input_layout="BNSD",
|
|
atten_mask=attn_metadata.attn_mask,
|
|
scale=self.scale,
|
|
antiquant_mode=0,
|
|
antiquant_scale=None,
|
|
block_table=attn_metadata.block_tables,
|
|
block_size=block_size,
|
|
actual_seq_lengths_kv=attn_metadata.seq_lens,
|
|
)
|
|
attn_output = attn_output.view(num_tokens, -1,
|
|
self.kv_lora_rank).transpose(
|
|
0, 1)
|
|
attn_output = torch.bmm(attn_output, self.w_vc).transpose(0, 1)
|
|
else:
|
|
# if torch.empty is used here, the preemptive scheduling case of
|
|
# test_mtp_correctness.py will fail to run.
|
|
attn_output = torch.randn(
|
|
[num_tokens, self.num_heads, self.kv_lora_rank],
|
|
dtype=query.dtype,
|
|
device=query.device)
|
|
self.seq_lens_tensor_cpu = torch.from_numpy(
|
|
np.array(attn_metadata.decode_metadata.seq_lens).astype(
|
|
np.int32))
|
|
block_tables = attn_metadata.decode_metadata.block_tables
|
|
torch_npu._npu_paged_attention_mla(
|
|
query=query,
|
|
key_cache=kv_cache,
|
|
num_kv_heads=self.num_kv_heads,
|
|
num_heads=self.num_heads,
|
|
scale_value=self.scale,
|
|
block_table=block_tables,
|
|
context_lens=self.seq_lens_tensor_cpu,
|
|
mla_vheadsize=self.kv_lora_rank,
|
|
out=attn_output)
|
|
attn_output_t = torch.transpose(attn_output, 0, 1)
|
|
attn_output_t = torch.bmm(attn_output_t, self.w_vc)
|
|
attn_output = torch.transpose(attn_output_t, 0, 1)
|
|
|
|
output, _ = self.o_proj(attn_output.reshape(num_tokens, -1))
|
|
|
|
return output
|