49838d4bec
### What this PR does / why we need it?
- Add explicit .contiguous() after permute/view to ensure mem-friendly
layout
- Replace nested PCP/DCP Python loops with fully vectorized tensor
operations
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: F.Liu <liufeng248@huawei.com>
Co-authored-by: F.Liu <liufeng248@huawei.com>
896 lines
42 KiB
Python
896 lines
42 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 typing import ClassVar, List, Optional, Tuple
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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.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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get_pcp_group)
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.v1.attention.backends.utils import AttentionCGSupport
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from vllm.v1.kv_cache_interface import AttentionSpec
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from vllm_ascend.attention.attention_v1 import (AscendAttentionBackendImpl,
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AscendAttentionMetadataBuilder,
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AscendMetadata,
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AscendMetadataForDecode,
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AscendMetadataForPrefill)
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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.utils import weak_ref_tensors
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class AscendAttentionCPMetadataBuilder(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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super().__init__(kv_cache_spec, layer_names, vllm_config, device)
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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_pcp_group().world_size
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self.pcp_rank = get_pcp_group(
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).rank_in_group if self.pcp_size > 1 else 0
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self.dcp_size = get_decode_context_model_parallel_world_size()
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self.dcp_rank = get_decode_context_model_parallel_rank(
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) if self.dcp_size > 1 else 0
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def _get_chunked_req_mask(self, local_context_lens_allranks) -> List[bool]:
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"""
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given 4-d list [req][pcp][dcp], return:
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1. if each req has any chunk (list[bool])
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"""
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assert local_context_lens_allranks is not None
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if len(local_context_lens_allranks) == 0:
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return []
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chunked_req_mask = [(req.sum() > 0).item()
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for req in local_context_lens_allranks
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if req is not None]
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return chunked_req_mask
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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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attn_mask = common_attn_metadata.attn_mask
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attn_state = common_attn_metadata.attn_state
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num_computed_tokens_cpu = (seq_lens - query_lens)
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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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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 None:
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raise AssertionError(
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"common_long_seq_metadata should not be 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[num_decodes:num_reqs]
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max_context_len_cpu = context_lens_cpu.max().item()
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pcp_size = get_pcp_group().world_size
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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)[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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pcp_rank,
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self.
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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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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(
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actual_chunk_seq_lengths=torch.cumsum(query_lens * pcp_size, dim=0),
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actual_seq_lengths_kv=actual_seq_lengths_kv,
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chunked_req_mask=chunked_req_mask,
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starts=local_chunk_starts,
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local_context_lens_allranks=local_context_lens_allranks,
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cp_kv_recover_idx_for_chunk=cp_kv_recover_idx_for_chunk,
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kv_inverse_idx_for_chunk=kv_inverse_idx_for_chunk,
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batch_chunk_seq_mask=batch_chunk_seq_mask,
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chunk_seq_mask_filtered_indices=chunk_seq_mask_filtered_indices
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)
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attn_mask_seqlens = common_long_seq_metadata.attn_mask_seqlens
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head_attn_nomask_seqlens = common_long_seq_metadata.head_attn_nomask_seqlens
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tail_attn_nomask_seqlens = common_long_seq_metadata.tail_attn_nomask_seqlens
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if pcp_size > 1:
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attn_mask_seqlens = torch.cumsum(attn_mask_seqlens[0],
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dim=0).tolist()
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head_attn_nomask_seqlens = torch.cumsum(
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head_attn_nomask_seqlens[1], dim=0).tolist()
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tail_attn_nomask_seqlens = torch.cumsum(
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tail_attn_nomask_seqlens[1], dim=0).tolist()
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pcp_metadata = AscendMetadataForPrefill.AscendPCPMetadata(
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q_head_idx=common_long_seq_metadata.q_head_idx_tensor,
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q_tail_idx=common_long_seq_metadata.q_tail_idx_tensor,
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kv_with_q_head_nomask_idx=common_long_seq_metadata.
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kv_with_q_head_nomask_idx_tensor,
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kv_with_q_head_mask_idx=common_long_seq_metadata.
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kv_with_q_head_mask_idx_tensor,
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kv_with_q_tail_nomask_idx=common_long_seq_metadata.
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kv_with_q_tail_nomask_idx_tensor,
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kv_with_q_tail_mask_idx=common_long_seq_metadata.
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kv_with_q_tail_mask_idx_tensor,
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attn_mask_seqlens=attn_mask_seqlens,
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head_attn_nomask_seqlens=head_attn_nomask_seqlens,
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tail_attn_nomask_seqlens=tail_attn_nomask_seqlens,
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q_full_idx=common_long_seq_metadata.q_full_idx,
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pcp_prefill_mask=common_long_seq_metadata.pcp_prefill_mask)
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prefill_metadata = AscendMetadataForPrefill(
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pcp_metadata=pcp_metadata,
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pcp_allgather_restore_idx=common_long_seq_metadata.
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pcp_allgather_restore_idx
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if common_long_seq_metadata is not None else None,
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chunked_context=chunked_context_metadata,
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block_tables=block_table[num_decodes:],
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actual_seq_lengths_q=torch.cumsum(query_lens, dim=0))
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if num_decodes > 0:
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num_computed_tokens_array = np.array(
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num_computed_tokens_of_pcp_dcp)
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num_computed_tokens_array = num_computed_tokens_array[:num_decodes]
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batch_seq_mask = (num_computed_tokens_array[:, self.pcp_rank,
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self.dcp_rank] == 0)
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# TODO: numpy array mode of the shared memory is used to improve performance
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self.batch_seq_mask_buf[:batch_seq_mask.shape[0]].copy_(
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torch.from_numpy(batch_seq_mask), non_blocking=True)
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decode_metadata = AscendMetadataForDecode(
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num_computed_tokens_of_pcp_dcp=num_computed_tokens_array,
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batch_seq_mask=self.batch_seq_mask_buf[:batch_seq_mask.
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shape[0]],
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block_tables=block_table[:num_decodes])
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attn_metadata = AscendMetadata(
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num_actual_tokens=num_actual_tokens,
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num_decode_tokens=num_decode_tokens,
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num_actual_tokens_pcp_padded=num_actual_tokens_pcp_padded,
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block_tables=block_table,
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query_start_loc=query_start_loc,
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seq_lens=seq_lens,
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seq_lens_list=seq_lens.tolist(),
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max_query_len=common_attn_metadata.max_query_len,
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actual_seq_lengths_q=query_start_loc_cpu[1:].tolist(),
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slot_mapping=slot_mapping,
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attn_mask=attn_mask,
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attn_state=attn_state,
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num_prefills=num_prefills,
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num_decodes=num_decodes,
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prefill=prefill_metadata,
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decode_meta=decode_metadata)
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return attn_metadata
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class AscendAttentionCPImpl(AscendAttentionBackendImpl):
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: int,
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alibi_slopes: Optional[List[float]],
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sliding_window: Optional[int],
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kv_cache_dtype: str,
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logits_soft_cap: Optional[float],
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attn_type: str,
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kv_sharing_target_layer_name: Optional[str],
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**kwargs,
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) -> None:
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super().__init__(num_heads, head_size, scale, num_kv_heads,
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alibi_slopes, sliding_window, kv_cache_dtype,
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logits_soft_cap, attn_type,
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kv_sharing_target_layer_name, **kwargs)
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self.pcp_size = get_pcp_group().world_size
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self.pcp_rank = get_pcp_group(
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).rank_in_group if self.pcp_size > 1 else 0
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self.pcp_group = get_pcp_group(
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).device_group if self.pcp_size > 1 else None
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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.dcp_group = get_dcp_group(
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).device_group if self.dcp_size > 1 else None
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def _attention_with_nomask_and_mask(self, q: torch.Tensor,
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q_seqlens: List[int],
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k_nomask: torch.Tensor,
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v_nomask: torch.Tensor,
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kv_seqlens_nomask: List[int],
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k_mask: torch.Tensor,
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v_mask: torch.Tensor,
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kv_seqlens_mask: List[int],
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mask: torch.Tensor,
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attn_metadata) -> torch.Tensor:
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# nomask Attention
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if k_nomask is not None:
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attn_out_nomask, attn_lse_nomask = torch.ops.npu.npu_fused_infer_attention_score(
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q,
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k_nomask,
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v_nomask,
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num_heads=self.num_heads,
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num_key_value_heads=self.num_kv_heads,
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input_layout="TND",
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atten_mask=None,
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scale=self.scale,
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sparse_mode=0,
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antiquant_mode=0,
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antiquant_scale=None,
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softmax_lse_flag=True,
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actual_seq_lengths_kv=kv_seqlens_nomask,
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actual_seq_lengths=q_seqlens)
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# mask Attention
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attn_out_mask, attn_lse_mask = torch.ops.npu.npu_fused_infer_attention_score(
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q,
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k_mask,
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v_mask,
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num_heads=self.num_heads,
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num_key_value_heads=self.num_kv_heads,
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input_layout="TND",
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atten_mask=mask,
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scale=self.scale,
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sparse_mode=3,
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antiquant_mode=0,
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antiquant_scale=None,
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softmax_lse_flag=True,
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actual_seq_lengths_kv=kv_seqlens_mask,
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actual_seq_lengths=q_seqlens)
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# update
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output = attn_out_mask
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attn_lse = attn_lse_mask
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if k_nomask is not None:
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if attn_metadata.prefill is not None and attn_metadata.prefill.chunked_context is None:
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output = self._npu_attn_out_lse_update(attn_lse_mask,
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attn_lse_nomask,
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attn_out_mask,
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attn_out_nomask)
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attn_lse = None
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else:
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output, attn_lse = self._update_out_and_lse(
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torch.stack([attn_out_nomask, attn_out_mask], dim=0),
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torch.stack([attn_lse_nomask, attn_lse_mask], dim=0))
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return output, attn_lse
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def _npu_attn_out_lse_update(self, attn_lse_mask, attn_lse_nomask,
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attn_out_mask, attn_out_nomask):
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T = attn_out_mask.shape[0]
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N = attn_out_mask.shape[1]
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D = attn_out_mask.shape[2]
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attn_out_mask, attn_lse_mask = self._out_lse_reshape(
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attn_out_mask, attn_lse_mask)
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attn_out_nomask, attn_lse_nomask = self._out_lse_reshape(
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attn_out_nomask, attn_lse_nomask)
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attn_out_mask = attn_out_mask.to(torch.float32)
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attn_out_nomask = attn_out_nomask.to(torch.float32)
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attn_lse_mask = attn_lse_mask.to(torch.float32)
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attn_lse_nomask = attn_lse_nomask.to(torch.float32)
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attn_output = [attn_out_nomask, attn_out_mask]
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attn_lse = [attn_lse_nomask, attn_lse_mask]
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update_type = 0
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output, _ = torch_npu.npu_attention_update(attn_lse, attn_output,
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update_type)
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output = output.view(T, N, D)
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return output
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def _forward_prefill_cp(self, query: torch.Tensor, key: torch.Tensor,
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value: torch.Tensor,
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attn_metadata: AscendMetadata) -> torch.Tensor:
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assert attn_metadata is not None
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assert attn_metadata.prefill is not None
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assert attn_metadata.prefill.pcp_metadata is not None
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# Use precomputed indices from the metadata (already converted to tensors and on device)
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q_head_idx = attn_metadata.prefill.pcp_metadata.q_head_idx
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q_tail_idx = attn_metadata.prefill.pcp_metadata.q_tail_idx
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kv_with_q_head_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_nomask_idx
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kv_with_q_head_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_mask_idx
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kv_with_q_tail_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_nomask_idx
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kv_with_q_tail_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx
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attn_mask_seqlens = attn_metadata.prefill.pcp_metadata.attn_mask_seqlens
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head_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.head_attn_nomask_seqlens
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tail_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.tail_attn_nomask_seqlens
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mask = attn_metadata.prefill.pcp_metadata.pcp_prefill_mask
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# 1. Attention calculation in the first half of Q in load balancing
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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: torch.Tensor) -> torch.Tensor:
|
|
B_total, H_total, D_plus_1 = attn_out_lse.shape
|
|
S = B_total // self.pcp_size
|
|
H = H_total // self.dcp_size
|
|
D = self.head_size
|
|
update_type = 0
|
|
assert D_plus_1 == D + 1
|
|
# [PCP, S, DCP, H, D+1]
|
|
x = attn_out_lse.view(self.pcp_size, S, self.dcp_size, H, D_plus_1)
|
|
# [PCP, DCP, S, H, D+1]
|
|
x = x.permute(0, 2, 1, 3, 4).contiguous()
|
|
# Flatten [N, S, H, D+1], N = pcp_size * dcp_size
|
|
x = x.view(-1, S, H, D_plus_1)
|
|
# Split out lse
|
|
# [N, S, H, D], [N, S, H, 1]
|
|
out_flat, lse_flat = torch.split(x, [D, 1], dim=-1)
|
|
# out: [N, S, H, D] -> [N, S*H, D]
|
|
# lse: [N, S, H, 1] -> [N, S*H]
|
|
out_flat = out_flat.flatten(1, 2)
|
|
lse_flat = lse_flat.squeeze(-1).flatten(1)
|
|
# unbind to list
|
|
# [S*H, D]
|
|
out_list = out_flat.unbind(0)
|
|
# [S*H]
|
|
lse_list = lse_flat.unbind(0)
|
|
|
|
attn_out, attn_lse = torch_npu.npu_attention_update(
|
|
lse_list, out_list, update_type)
|
|
attn_out = attn_out.view(S, H, D)
|
|
|
|
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)
|
|
|
|
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)
|
|
# 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)
|
|
attn_out_lse = attn_out_lse_all2all.permute([2, 0, 1])
|
|
|
|
if self.pcp_size > 1:
|
|
# AllGather out&lse within CP group
|
|
attn_out_lse = get_pcp_group().all_gather(
|
|
attn_out_lse.contiguous(), dim=0)
|
|
|
|
attn_out = self._npu_attention_update(attn_out_lse)
|
|
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 _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)
|
|
chunk_attn_out_lse = attn_out_lse_all2all.permute([2, 0, 1])
|
|
|
|
if self.pcp_size > 1:
|
|
# AllGather out&lse within CP group
|
|
chunk_attn_out_lse = get_pcp_group().all_gather(
|
|
chunk_attn_out_lse.contiguous(), dim=0)
|
|
|
|
B_total, H_total, D_plus_1 = chunk_attn_out_lse.shape
|
|
S = B_total // self.pcp_size
|
|
H = H_total // self.dcp_size
|
|
D = self.head_size
|
|
assert D_plus_1 == D + 1
|
|
# [PCP, S, DCP, H, D+1]
|
|
x = chunk_attn_out_lse.view(self.pcp_size, S, self.dcp_size, H,
|
|
D_plus_1)
|
|
# [PCP, DCP, S, H, D+1]
|
|
x = x.permute(0, 2, 1, 3, 4).contiguous()
|
|
# Flatten [N, S, H, D+1], N = pcp_size * dcp_size
|
|
x = x.view(-1, S, H, D_plus_1)
|
|
# Split out lse.
|
|
# [N, S, H, D], [N, S, H, 1]
|
|
attn_out_allgather, attn_lse_allgather = torch.split(x, [D, 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 reshape_and_cache(
|
|
self,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_cache: Tuple[torch.Tensor],
|
|
attn_metadata: AscendMetadata,
|
|
):
|
|
|
|
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]
|
|
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)
|
|
prefill_key = key[self.pcp_size *
|
|
num_decode_tokens:attn_metadata.
|
|
num_actual_tokens_pcp_padded]
|
|
prefill_value = value[self.pcp_size *
|
|
num_decode_tokens:attn_metadata.
|
|
num_actual_tokens_pcp_padded]
|
|
slot_mapping = attn_metadata.slot_mapping[
|
|
self.pcp_size * num_decode_tokens:attn_metadata.
|
|
num_actual_tokens_pcp_padded]
|
|
torch_npu._npu_reshape_and_cache(key=prefill_key,
|
|
value=prefill_value,
|
|
key_cache=self.key_cache,
|
|
value_cache=self.value_cache,
|
|
slot_indices=slot_mapping)
|
|
|
|
return key, value
|
|
|
|
def forward_impl(
|
|
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].contiguous()
|
|
key = key[self.pcp_size * num_decode_tokens:].contiguous()
|
|
value = value[self.pcp_size * num_decode_tokens:].contiguous()
|
|
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
|