3e67e8276c
### What this PR does / why we need it?
We support to use full graph with eagle.
Change list:
1. Distinguish between processing graph_params and draft_graph_params in
attention_v1.
2. Adapt the full-graph mode in eagle_proposer, include:
1). If use full graph, make Fullgraph Wrapper when load model.
2). Build a new meatadata, set running mode in FULL and mark attention
update in dummy_run when in Fullgraph mode.
3). Fixed and fill any attn_metadata, such as
attn_metadata.slot_mapping.
4). Add a descriptor.
5). Set running mode and triggered update metadata.
3. Trans is_mtp_model to is_draft_model, and add the update of
workspace.
NOTE:
When set async_scheduling=True, the draft model will enforce execution
in eager mode.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: Yizhou <136800916+yiz-liu@users.noreply.github.com>
788 lines
37 KiB
Python
788 lines
37 KiB
Python
from typing import ClassVar, Optional, Tuple, TypeVar
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import numpy as np
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import torch
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import torch.distributed as dist
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import torch_npu
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from 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.utils.math_utils import cdiv
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from vllm.v1.attention.backends.utils import AttentionCGSupport
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from vllm.v1.kv_cache_interface import MLAAttentionSpec
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# isort: off
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from vllm_ascend.attention.mla_v1 import (AscendMLADecodeMetadata,
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AscendMLAImpl, AscendMLAMetadata,
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AscendMLAMetadataBuilder,
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AscendMLAPrefillMetadata,
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DecodeMLAPreprocessResult,
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PrefillMLAPreprocessResult)
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#isort: on
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from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata)
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from vllm_ascend.attention.common_cp import (AscendPCPMetadata,
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CPChunkedContextMetadata)
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from vllm_ascend.compilation.acl_graph import (get_draft_graph_params,
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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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MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
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M = TypeVar("M", bound=AscendMLAMetadata)
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class AscendMlaCPMetadataBuilder(AscendMLAMetadataBuilder):
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# Does this backend/builder support ACL Graphs for attention (default: no).
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aclgraph_support: ClassVar[AttentionCGSupport] = \
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AttentionCGSupport.UNIFORM_BATCH
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"""
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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def __init__(
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self,
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kv_cache_spec: MLAAttentionSpec,
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layer_names: list[str],
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vllm_config: VllmConfig,
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device: torch.device,
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metadata_cls: type[AscendMLAMetadata] | None = None,
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supports_dcp_with_varlen: bool = False,
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):
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super().__init__(kv_cache_spec, layer_names, vllm_config, device,
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metadata_cls, supports_dcp_with_varlen)
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self.pcp_size = get_pcp_group().world_size
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self.pcp_rank = get_pcp_group(
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).rank_in_group if self.pcp_size > 1 else 0
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self.dcp_size = get_decode_context_model_parallel_world_size()
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self.dcp_rank = get_decode_context_model_parallel_rank(
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) if self.dcp_size > 1 else 0
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self.cp_local_block_size = vllm_config.parallel_config.cp_kv_cache_interleave_size
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self.cp_virtual_block_size = self.cp_local_block_size * self.dcp_size * self.pcp_size
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scheduler_config = vllm_config.scheduler_config
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decode_max_num_seqs = getattr(scheduler_config, 'decode_max_num_seqs',
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0)
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max_num_seqs = max(scheduler_config.max_num_seqs, decode_max_num_seqs)
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self.batch_seq_mask_buf = torch.empty(max_num_seqs *
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self.decode_threshold,
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dtype=torch.uint8,
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device=device)
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def set_num_actual_tokens(
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self,
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common_attn_metadata: AscendCommonAttentionMetadata,
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):
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long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
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if long_seq_metadata is None:
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raise AssertionError("long_seq_metadata should not be None.")
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self.num_actual_tokens = max(
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long_seq_metadata.num_actual_tokens_pcp_padded,
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common_attn_metadata.num_actual_tokens)
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def build_cp_metadata(
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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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) -> AscendPCPMetadata | None:
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common_long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
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assert common_long_seq_metadata is not None
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return 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=common_long_seq_metadata.attn_mask_seqlens,
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head_attn_nomask_seqlens=common_long_seq_metadata.
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head_attn_nomask_seqlens,
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tail_attn_nomask_seqlens=common_long_seq_metadata.
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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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pcp_allgather_restore_idx=common_long_seq_metadata.
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pcp_allgather_restore_idx)
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def build_chunked_metadata(
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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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):
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chunked_context_metadata = super().build_chunked_metadata(
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common_prefix_len, common_attn_metadata)
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if chunked_context_metadata is None:
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return None
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long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
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assert long_seq_metadata is not None
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num_computed_tokens_of_pcp_dcp = 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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local_context_lens_allranks = torch.tensor(
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num_computed_tokens_of_pcp_dcp[self.num_decodes_flatten:]).reshape(
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-1, self.dcp_size * self.pcp_size)
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# Note(qcs): The max local context lengths
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# padded to `cp_local_block_size`.
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padded_local_context_lens_cpu = (cdiv(
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self.context_lens_cpu,
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self.cp_virtual_block_size,
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) * self.cp_local_block_size)
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padded_local_max_context_chunk_across_ranks = (cdiv(
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self.max_context_chunk,
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self.cp_virtual_block_size,
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) * self.cp_local_block_size)
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local_chunk_starts = (torch.arange(
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self.num_chunks, dtype=torch.int32).unsqueeze(1).expand(
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-1, self.num_prefills) *
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padded_local_max_context_chunk_across_ranks)
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local_chunk_ends = torch.min(
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padded_local_context_lens_cpu.unsqueeze(0),
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local_chunk_starts + padded_local_max_context_chunk_across_ranks,
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)
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padded_local_chunk_seq_lens = (local_chunk_ends -
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local_chunk_starts).clamp(min=0)
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padded_local_cu_chunk_seq_lens_cpu = torch.zeros(self.num_chunks,
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self.num_prefills + 1,
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dtype=torch.int32,
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pin_memory=True)
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torch.cumsum(
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padded_local_chunk_seq_lens,
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dim=1,
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out=padded_local_cu_chunk_seq_lens_cpu[:, 1:],
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dtype=torch.int32,
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)
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chunked_metadata = CPChunkedContextMetadata(
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cu_seq_lens=chunked_context_metadata.cu_seq_lens,
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starts=local_chunk_starts.pin_memory().to(self.device,
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non_blocking=True),
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seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(),
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max_seq_lens=chunked_context_metadata.max_seq_lens,
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chunk_seq_lens=self.chunk_seq_lens,
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chunk_seq_lens_npu=chunked_context_metadata.chunk_seq_lens_npu,
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workspace=chunked_context_metadata.workspace,
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padded_chunk_seq_lens_npu=padded_local_chunk_seq_lens.npu(),
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padded_local_chunk_seq_lens=padded_local_chunk_seq_lens.tolist(),
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local_context_lens_allranks=local_context_lens_allranks.tolist(),
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padded_local_cu_seq_lens=padded_local_cu_chunk_seq_lens_cpu.
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pin_memory().to(self.device, non_blocking=True),
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cu_seq_lens_lst=self.cu_seq_lens_cpu.tolist(),
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chunk_size=padded_local_max_context_chunk_across_ranks,
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)
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return chunked_metadata
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def set_prefill_block_table(
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self,
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common_attn_metadata: AscendCommonAttentionMetadata,
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):
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# For pcp + spec decode, we flatten seq_lens and block_table
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# to avoid irregular spec_attn_mask shape
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self.num_decodes_flatten = self.query_lens[:self.num_decodes].sum(
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).item()
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self.block_table = common_attn_metadata.block_table_tensor[:self.
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num_decodes_flatten
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+ self.
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num_prefills]
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def set_decode_block_table(self):
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self.block_table = self.block_table[:self.num_decodes_flatten, ...]
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def build_prefill_metadata(
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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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) -> AscendMLAPrefillMetadata:
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prefill_metadata = super().build_prefill_metadata(
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common_prefix_len, common_attn_metadata)
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prefill_metadata.pcp_metadata = self.build_cp_metadata(
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common_prefix_len, common_attn_metadata)
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prefill_metadata.block_table = self.block_table[
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self.num_decodes_flatten:, ...]
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return prefill_metadata
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def build_decode_metadata(
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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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) -> AscendMLADecodeMetadata:
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decode_metadata = super().build_decode_metadata(
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common_prefix_len, common_attn_metadata)
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long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
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assert long_seq_metadata is not None
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num_computed_tokens_of_pcp_dcp = 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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# [bs, pcp_size, dcp_size]
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num_computed_tokens_of_cp_dcp_array = np.array(
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num_computed_tokens_of_pcp_dcp)[:self.num_decodes_flatten]
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cp_seq_len = num_computed_tokens_of_cp_dcp_array[:, self.pcp_rank,
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self.dcp_rank]
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cp_seq_len = torch.tensor(cp_seq_len, dtype=torch.int32)
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batch_seq_mask = (cp_seq_len == 0)
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self.batch_seq_mask_buf[:batch_seq_mask.shape[0]].copy_(
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batch_seq_mask, non_blocking=True)
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batch_seq_mask = self.batch_seq_mask_buf[:batch_seq_mask.shape[0]]
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cp_seq_len = torch.where(cp_seq_len == 0, 1, cp_seq_len)
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decode_metadata.cp_seq_len = cp_seq_len
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decode_metadata.batch_seq_mask = batch_seq_mask
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return decode_metadata
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class AscendMlaCPImpl(AscendMLAImpl):
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"""
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NOTE: Please read the comment at the top of the file before trying to
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understand this class
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"""
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def __init__(
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self,
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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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):
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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 get_num_actual_tokens(self, attn_metadata: M):
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if self.pcp_size > 1:
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return attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
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else:
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return attn_metadata.num_actual_tokens
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def _v_up_proj(self, x):
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# Convert from (B, N, L) to (N, B, L)
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x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
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# # Multiply (N, B, L) x (N, L, V) -> (N, B, V)
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x = torch.bmm(x, self.W_UV)
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# # Convert from (N, B, V) to (B, N * V)
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x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
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return x
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def mla_preprocess_prefill(self, q_c, kv_no_split, kv_cache,
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attn_metadata):
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if not self.pcp_size > 1:
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return super().mla_preprocess_prefill(q_c, kv_no_split, kv_cache,
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attn_metadata)
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num_decode_tokens = attn_metadata.num_decode_tokens
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num_actual_tokens = (attn_metadata.num_actual_tokens_pcp_padded -
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self.pcp_size * num_decode_tokens
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) // self.pcp_size + num_decode_tokens
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prefill_q_c = q_c[num_decode_tokens:num_actual_tokens]
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prefill_q = self.q_proj(prefill_q_c)[0] \
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.view(-1, self.num_heads, self.qk_head_dim)
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prefill_q_pe = prefill_q[..., self.qk_nope_head_dim:]
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prefill_q_nope = prefill_q[..., :self.qk_nope_head_dim]
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cos = attn_metadata.prefill.cos[:num_actual_tokens - num_decode_tokens]
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sin = attn_metadata.prefill.sin[:num_actual_tokens - num_decode_tokens]
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prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin)
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prefill_kv_no_split = kv_no_split[:num_actual_tokens]
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kv_c, k_pe = prefill_kv_no_split.split(
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[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
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assert len(
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kv_cache
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) > 1, "the number of kv cache should be greater than 1, namely (nope_cache and rope_cache)"
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kv_c_normed = kv_c_normed.view(
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[num_actual_tokens, self.num_kv_heads, -1])
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k_pe = k_pe.unsqueeze(1)
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prefill_k_pe = k_pe
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prefill_k_pe[num_decode_tokens:num_actual_tokens] = self.rope_single(
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prefill_k_pe[num_decode_tokens:num_actual_tokens], cos, sin)
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prefill_k_c_normed = kv_c_normed[:num_actual_tokens]
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prefill_kv_c_k_pe = torch.cat([prefill_k_c_normed, prefill_k_pe],
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dim=-1)
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prefill_kv_c_k_pe = get_pcp_group().all_gather(prefill_kv_c_k_pe, 0)
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prefill_kv_c_k_pe = torch.index_select(
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prefill_kv_c_k_pe, 0,
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attn_metadata.prefill.pcp_metadata.pcp_allgather_restore_idx)
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prefill_kv_c_k_pe = prefill_kv_c_k_pe[num_decode_tokens *
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self.pcp_size:]
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prefill_k_c_normed, prefill_k_pe = prefill_kv_c_k_pe.split(
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[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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kv_c_normed, k_pe = prefill_k_c_normed, prefill_k_pe
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prefill_k_c_normed = prefill_k_c_normed.squeeze()
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slot_mapping = attn_metadata.slot_mapping[self.pcp_size *
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num_decode_tokens:]
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torch_npu._npu_reshape_and_cache(key=kv_c_normed,
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value=k_pe,
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key_cache=kv_cache[0],
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value_cache=kv_cache[1],
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slot_indices=slot_mapping)
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prefill_k_nope, prefill_value = self.kv_b_proj(
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prefill_k_c_normed)[0].view(
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-1, self.num_heads,
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self.qk_nope_head_dim + self.v_head_dim).split(
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[self.qk_nope_head_dim, self.v_head_dim], dim=-1)
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prefill_k_pe = prefill_k_pe.expand((*prefill_k_nope.shape[:-1], -1))
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return PrefillMLAPreprocessResult(prefill_q_nope, prefill_q_pe,
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prefill_k_nope, prefill_k_pe,
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prefill_value)
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def mla_preprocess_decode(self, q_c, kv_no_split, kv_cache, attn_metadata):
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num_decode_tokens = attn_metadata.num_decode_tokens
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decode_q_c = q_c[:num_decode_tokens]
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cos = attn_metadata.decode.cos
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sin = attn_metadata.decode.sin
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decode_ql_nope, decode_q_pe = \
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self._q_proj_and_k_up_proj(decode_q_c)
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decode_ql_nope, decode_q_pe = self.reorg_decode_q(
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decode_ql_nope, decode_q_pe)
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decode_q_pe = self.rope_single(decode_q_pe, cos, sin)
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decode_slots = attn_metadata.slot_mapping[:num_decode_tokens *
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self.pcp_size:self.pcp_size]
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decode_kv_no_split = kv_no_split[:num_decode_tokens]
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decode_k_pe, decode_k_nope = self.exec_kv_decode(
|
|
decode_kv_no_split, cos, sin, kv_cache, decode_slots)
|
|
return DecodeMLAPreprocessResult(decode_ql_nope, decode_q_pe,
|
|
decode_k_nope, decode_k_pe)
|
|
|
|
def get_context_seq_len_npu(self, index: int,
|
|
attn_metadata: AscendMLAMetadata):
|
|
prefill_metadata = attn_metadata.prefill
|
|
assert prefill_metadata is not None
|
|
assert prefill_metadata.chunked_context is not None
|
|
assert isinstance(prefill_metadata.chunked_context,
|
|
CPChunkedContextMetadata)
|
|
assert prefill_metadata.chunked_context.padded_chunk_seq_lens_npu is not None
|
|
iters = len(prefill_metadata.chunked_context.seq_tot)
|
|
assert 0 <= index < iters
|
|
return prefill_metadata.chunked_context.padded_chunk_seq_lens_npu[
|
|
index]
|
|
|
|
def reorg_decode_q(self, decode_q_nope, decode_q_pe):
|
|
if self.dcp_size > 1:
|
|
decode_q_no_split = torch.cat([decode_q_nope, decode_q_pe], dim=-1)
|
|
decode_q_no_split = get_dcp_group().all_gather(
|
|
decode_q_no_split, 1)
|
|
decode_q_nope, decode_q_pe = decode_q_no_split.split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
return decode_q_nope, decode_q_pe
|
|
|
|
def _forward_prefill(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_c_and_k_pe_cache: Tuple[torch.Tensor],
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
if not self.pcp_size > 1:
|
|
return super()._forward_prefill(q_nope, q_pe, k_nope, k_pe, value,
|
|
kv_c_and_k_pe_cache, attn_metadata)
|
|
assert attn_metadata.prefill is not None
|
|
assert attn_metadata.prefill.pcp_metadata is not None
|
|
num_tokens = q_nope.size(0)
|
|
# Use precomputed indices from the metadata (already converted to tensors and on device)
|
|
q_head_idx = attn_metadata.prefill.pcp_metadata.q_head_idx
|
|
q_tail_idx = attn_metadata.prefill.pcp_metadata.q_tail_idx
|
|
kv_with_q_head_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_nomask_idx
|
|
kv_with_q_head_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_mask_idx
|
|
kv_with_q_tail_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_nomask_idx
|
|
kv_with_q_tail_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx
|
|
attn_mask_seqlens = attn_metadata.prefill.pcp_metadata.attn_mask_seqlens
|
|
head_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.head_attn_nomask_seqlens
|
|
tail_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.tail_attn_nomask_seqlens
|
|
mask = attn_metadata.prefill.pcp_metadata.pcp_prefill_mask
|
|
output_head, lse_head = self._attention_with_mask_and_nomask(
|
|
q_nope=torch.index_select(q_nope, 0, q_head_idx),
|
|
q_pe=torch.index_select(q_pe, 0, q_head_idx),
|
|
k_nope=k_nope,
|
|
k_pe=k_pe,
|
|
value=value,
|
|
kv_mask_idx=kv_with_q_head_mask_idx,
|
|
kv_nomask_idx=kv_with_q_head_nomask_idx,
|
|
attn_mask_seqlens=attn_mask_seqlens,
|
|
attn_nomask_seqlens=head_attn_nomask_seqlens,
|
|
mask=mask)
|
|
|
|
output_tail, lse_tail = self._attention_with_mask_and_nomask(
|
|
q_nope=torch.index_select(q_nope, 0, q_tail_idx),
|
|
q_pe=torch.index_select(q_pe, 0, q_tail_idx),
|
|
k_nope=k_nope,
|
|
k_pe=k_pe,
|
|
value=value,
|
|
kv_mask_idx=kv_with_q_tail_mask_idx,
|
|
kv_nomask_idx=kv_with_q_tail_nomask_idx,
|
|
attn_mask_seqlens=attn_mask_seqlens,
|
|
attn_nomask_seqlens=tail_attn_nomask_seqlens,
|
|
mask=mask)
|
|
|
|
q_full_idx = attn_metadata.prefill.pcp_metadata.q_full_idx
|
|
attn_output = torch.index_select(
|
|
torch.cat([output_head, output_tail], dim=0), 0, q_full_idx)
|
|
attn_lse = torch.index_select(torch.cat([lse_head, lse_tail], dim=1),
|
|
1, q_full_idx)
|
|
|
|
output, _ = self._compute_prefill_context(q_nope, q_pe,
|
|
kv_c_and_k_pe_cache,
|
|
self.qk_rope_head_dim,
|
|
attn_metadata, attn_output,
|
|
attn_lse)
|
|
|
|
output = output.reshape([num_tokens, self.num_heads * self.v_head_dim])
|
|
|
|
return output
|
|
|
|
def _attention_with_mask_and_nomask(
|
|
self, q_nope: torch.Tensor, q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor, k_pe: torch.Tensor, value: torch.Tensor,
|
|
kv_mask_idx: torch.Tensor, kv_nomask_idx: torch.Tensor,
|
|
attn_mask_seqlens: torch.Tensor, attn_nomask_seqlens: torch.Tensor,
|
|
mask: torch.Tensor):
|
|
attn_output = torch.empty(q_nope.shape[0],
|
|
self.num_heads,
|
|
self.v_head_dim,
|
|
dtype=k_pe.dtype,
|
|
device=k_pe.device)
|
|
attn_lse = torch.empty(self.num_heads,
|
|
q_pe.shape[0],
|
|
dtype=torch.float32,
|
|
device=k_pe.device)
|
|
# mask
|
|
k_nope_mask = torch.index_select(k_nope, 0, kv_mask_idx)
|
|
value_mask = torch.index_select(value, 0, kv_mask_idx)
|
|
k_pe_mask = torch.index_select(k_pe, 0, kv_mask_idx)
|
|
torch_npu.atb.npu_ring_mla(q_nope=q_nope,
|
|
q_rope=q_pe,
|
|
k_nope=k_nope_mask,
|
|
k_rope=k_pe_mask,
|
|
value=value_mask,
|
|
mask=mask,
|
|
seqlen=attn_mask_seqlens,
|
|
head_num=self.num_heads,
|
|
kv_head_num=self.num_heads,
|
|
pre_out=None,
|
|
prev_lse=None,
|
|
qk_scale=self.scale,
|
|
kernel_type="kernel_type_high_precision",
|
|
mask_type="mask_type_triu",
|
|
input_layout="type_bsnd",
|
|
calc_type="calc_type_first_ring",
|
|
output=attn_output,
|
|
softmax_lse=attn_lse)
|
|
|
|
# nomask
|
|
if kv_nomask_idx.shape[0] == 0:
|
|
return attn_output, attn_lse
|
|
|
|
k_nope_nomask = torch.index_select(k_nope, 0, kv_nomask_idx)
|
|
value_nomask = torch.index_select(value, 0, kv_nomask_idx)
|
|
k_pe_nomask = torch.index_select(k_pe, 0, kv_nomask_idx)
|
|
torch_npu.atb.npu_ring_mla(q_nope=q_nope,
|
|
q_rope=q_pe,
|
|
k_nope=k_nope_nomask,
|
|
k_rope=k_pe_nomask,
|
|
value=value_nomask,
|
|
mask=mask,
|
|
seqlen=attn_nomask_seqlens,
|
|
head_num=self.num_heads,
|
|
kv_head_num=self.num_heads,
|
|
pre_out=attn_output,
|
|
prev_lse=attn_lse,
|
|
qk_scale=self.scale,
|
|
kernel_type="kernel_type_high_precision",
|
|
mask_type="no_mask",
|
|
input_layout="type_bsnd",
|
|
calc_type="calc_type_default",
|
|
output=attn_output,
|
|
softmax_lse=attn_lse)
|
|
return attn_output, attn_lse
|
|
|
|
def _forward_decode(
|
|
self,
|
|
q_nope: torch.Tensor,
|
|
q_pe: torch.Tensor,
|
|
k_nope: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
block_size: int,
|
|
attn_metadata: AscendMLAMetadata,
|
|
) -> torch.Tensor:
|
|
decode_meta = attn_metadata.decode
|
|
assert decode_meta is not None
|
|
num_tokens = q_nope.size(0)
|
|
# shape of knope/k_pe for npu graph mode should be:
|
|
# [num_blocks, num_kv_heads, block_size, self.kv_lora_rank/self.qk_rope_head_dim]
|
|
if self.dcp_size > 1:
|
|
num_heads = self.num_heads * self.dcp_size
|
|
else:
|
|
num_heads = self.num_heads
|
|
|
|
k_nope = k_nope.view(-1, block_size, self.num_kv_heads,
|
|
self.kv_lora_rank)
|
|
k_pe = k_pe.view(-1, block_size, self.num_kv_heads,
|
|
self.qk_rope_head_dim)
|
|
q_nope = q_nope.view(num_tokens, num_heads, -1)
|
|
q_pe = q_pe.view(num_tokens, num_heads, -1)
|
|
# use pcp & dcp split computed token nums from scheduler to compute actual seq_len and seq_mask
|
|
seq_len = decode_meta.cp_seq_len
|
|
|
|
common_kwargs = {
|
|
"return_lse": True,
|
|
"calc_type": "calc_type_ring",
|
|
}
|
|
forward_context: ForwardContext = get_forward_context()
|
|
if forward_context.is_draft_model:
|
|
graph_params = get_draft_graph_params()
|
|
else:
|
|
graph_params = get_graph_params()
|
|
if forward_context.capturing:
|
|
stream = torch_npu.npu.current_stream()
|
|
event = torch.npu.ExternalEvent()
|
|
event.wait(stream)
|
|
event.reset(stream)
|
|
graph_params.events[num_tokens].append(event)
|
|
workspace = graph_params.workspaces.get(num_tokens)
|
|
if workspace is None:
|
|
workspace = torch_npu.atb._npu_multi_head_latent_attention_get_workspace(
|
|
q_nope, q_pe, k_nope, k_pe, decode_meta.block_table,
|
|
seq_len, num_heads, self.scale, self.num_kv_heads,
|
|
**common_kwargs)
|
|
update_graph_params_workspaces(num_tokens, workspace)
|
|
attn_output = torch.empty_like(q_nope)
|
|
softmax_lse = torch.empty((num_tokens, num_heads, 1),
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
graph_params.attn_params[num_tokens].append(
|
|
(weak_ref_tensors(q_nope), weak_ref_tensors(q_pe),
|
|
weak_ref_tensors(k_nope), weak_ref_tensors(k_pe),
|
|
decode_meta.block_table, seq_len, num_heads, self.scale,
|
|
self.num_kv_heads, weak_ref_tensors(attn_output),
|
|
weak_ref_tensors(softmax_lse)))
|
|
torch.npu.graph_task_group_begin(stream)
|
|
torch_npu.atb.npu_multi_head_latent_attention(
|
|
q_nope,
|
|
q_pe,
|
|
k_nope,
|
|
k_pe,
|
|
decode_meta.block_table,
|
|
seq_len,
|
|
num_heads,
|
|
self.scale,
|
|
self.num_kv_heads,
|
|
**common_kwargs,
|
|
workspace=workspace,
|
|
output=attn_output,
|
|
lse=softmax_lse)
|
|
handle = torch.npu.graph_task_group_end(stream)
|
|
graph_params.handles[num_tokens].append(handle)
|
|
else:
|
|
attn_output = torch.empty_like(q_nope)
|
|
softmax_lse = torch.empty((num_tokens, num_heads, 1),
|
|
dtype=q_nope.dtype,
|
|
device=q_nope.device)
|
|
torch_npu.atb.npu_multi_head_latent_attention(
|
|
q_nope,
|
|
q_pe,
|
|
k_nope,
|
|
k_pe,
|
|
decode_meta.block_table,
|
|
seq_len,
|
|
num_heads,
|
|
self.scale,
|
|
self.num_kv_heads,
|
|
return_lse=True,
|
|
calc_type="calc_type_ring",
|
|
output=attn_output,
|
|
lse=softmax_lse)
|
|
|
|
# Update out&lse
|
|
attn_out_lse = self._process_attn_out_lse(attn_output, softmax_lse,
|
|
decode_meta)
|
|
attn_output = self._npu_attention_update(attn_out_lse)
|
|
return self._v_up_proj(attn_output)
|
|
|
|
def _npu_attention_update(self,
|
|
attn_out_lse: torch.Tensor) -> torch.Tensor:
|
|
# [PCP * S, DCP * H, D+1]
|
|
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.kv_lora_rank
|
|
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
|
|
out_flat, lse_flat = torch.split(x, [D, 1],
|
|
dim=-1) # [N, S, H, D], [N, S, H, 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) # [N, S*H, D]
|
|
lse_flat = lse_flat.flatten(1, -1) # [N, S*H]
|
|
# unbind to list
|
|
out_list = out_flat.unbind(0) # [S*H, D]
|
|
lse_list = lse_flat.unbind(0) # [S*H]
|
|
attn_out, _ = torch_npu.npu_attention_update(lse_list, out_list, 0)
|
|
attn_out = attn_out.view(-1, H, D)
|
|
return attn_out
|
|
|
|
def _out_lse_reshape(self, attn_out: torch.Tensor,
|
|
attn_lse: torch.Tensor) -> torch.Tensor:
|
|
attn_out = attn_out.contiguous().view(
|
|
attn_out.shape[0] * attn_out.shape[1], attn_out.shape[2])
|
|
attn_lse = attn_lse.contiguous().view(
|
|
attn_lse.shape[0] * attn_lse.shape[1] * attn_lse.shape[2])
|
|
return attn_out, attn_lse
|
|
|
|
def _process_attn_out_lse(
|
|
self,
|
|
attn_output: torch.Tensor,
|
|
softmax_lse: torch.Tensor,
|
|
decode_meta: AscendMLADecodeMetadata,
|
|
) -> torch.Tensor:
|
|
out_mask = decode_meta.batch_seq_mask[:, None,
|
|
None].expand_as(attn_output)
|
|
attn_output = torch.where(out_mask, 0, attn_output)
|
|
lse_mask = decode_meta.batch_seq_mask[:, None,
|
|
None].expand_as(softmax_lse)
|
|
softmax_lse = torch.where(lse_mask, -torch.inf, softmax_lse)
|
|
|
|
softmax_lse = softmax_lse.to(torch.float32)
|
|
attn_output = attn_output.to(torch.float32)
|
|
# Concat out&lse: [bs,num_heads,v_head_dim] + [bs,num_heads,1] -> [bs,num_heads,v_head_dim+1]
|
|
attn_out_lse = torch.cat([attn_output, softmax_lse], dim=-1)
|
|
if self.dcp_size > 1:
|
|
# permute: [bs, num_heads, v_head_dim+1] -> [num_heads, v_head_dim+1, bs]
|
|
attn_out_lse = attn_out_lse.permute([1, 2, 0]).contiguous()
|
|
attn_out_lse_all2all = torch.empty_like(attn_out_lse)
|
|
dist.all_to_all_single(attn_out_lse_all2all,
|
|
attn_out_lse,
|
|
group=self.dcp_group)
|
|
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)
|
|
|
|
return attn_out_lse
|
|
|
|
def _reorg_kvcache(
|
|
self,
|
|
kv_c_normed: torch.Tensor,
|
|
k_pe: torch.Tensor,
|
|
chunked_context: CPChunkedContextMetadata,
|
|
chunk_idx: int,
|
|
toks: int,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
reorg and unpad kvcache after cp local gather to tp layout for attn kernel.
|
|
e.g.
|
|
kv_c_normed in rank0 = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...]
|
|
kv_c_normed in rank1 = [T0_4, T0_5, pad, pad, T1_2, pad, ...]
|
|
allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...,
|
|
T0_4, T0_5, pad, pad, T1_2, pad, ...]
|
|
-> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5,
|
|
T1_0, T1_1, T1_2, ...]
|
|
Args:
|
|
padded_local_chunk_seq_lens_lst: local chunk context lengths
|
|
under current CP rank.
|
|
local_context_lens_allranks: local context lengths on each CP rank.
|
|
sum_seq_len: the sum of cp_chunk_seq_lens_lst.
|
|
max_seq_len: the max value of cp_chunk_seq_lens_lst.
|
|
chunk_size: the local padded max context chunk from
|
|
chunked_context_metadata building.
|
|
chunk_idx: chunk idx of chunked_prefill.
|
|
toks: the number of tokens for local gather cache.
|
|
"""
|
|
assert chunked_context is not None
|
|
assert chunked_context.padded_local_chunk_seq_lens is not None
|
|
assert chunked_context.local_context_lens_allranks is not None
|
|
assert chunked_context.cu_seq_lens_lst is not None
|
|
assert chunked_context.max_seq_lens is not None
|
|
assert chunked_context.chunk_size is not None
|
|
|
|
padded_local_chunk_seq_lens_lst = chunked_context.padded_local_chunk_seq_lens[
|
|
chunk_idx]
|
|
local_context_lens_allranks = chunked_context.local_context_lens_allranks
|
|
sum_seq_len = chunked_context.cu_seq_lens_lst[chunk_idx][-1]
|
|
max_seq_len = chunked_context.max_seq_lens[chunk_idx]
|
|
chunk_size: int = chunked_context.chunk_size
|
|
cache_kv_c_k_pe = torch.cat([kv_c_normed, k_pe], dim=-1)
|
|
if self.dcp_size > 1:
|
|
cache_kv_c_k_pe = get_dcp_group().all_gather(cache_kv_c_k_pe, 0)
|
|
|
|
if self.pcp_size > 1:
|
|
cache_kv_c_k_pe = get_pcp_group().all_gather(cache_kv_c_k_pe, 0)
|
|
|
|
allgatered_kv_c_normed, allgatered_k_pe = cache_kv_c_k_pe.split(
|
|
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
|
|
kv_c_segments = []
|
|
k_pe_segments = []
|
|
src_token_idx = 0
|
|
max_seq_len_check = 0
|
|
for padded_local_chunk_seq_len, local_context_lens in zip(
|
|
padded_local_chunk_seq_lens_lst, local_context_lens_allranks):
|
|
cur_seq_len = 0
|
|
for rank, local_context_len in enumerate(local_context_lens):
|
|
# Note(qcs): We split the context into multiple chunks,
|
|
# depending on the size of the workspace.
|
|
# local_context in dcp0: |-----------------|
|
|
# local_context in dcp1: |--------------|
|
|
# n*padded_local_chunk: |-----|-----|-----|
|
|
# local_chunk_len in dcp1: |-----|-----|--|
|
|
# so we need update the last chunk length in dcp1.
|
|
local_chunk_len = min(
|
|
max(0, local_context_len - chunk_idx * chunk_size),
|
|
padded_local_chunk_seq_len,
|
|
)
|
|
if local_chunk_len != 0:
|
|
kv_c_segment = allgatered_kv_c_normed[rank * toks +
|
|
src_token_idx:rank *
|
|
toks +
|
|
src_token_idx +
|
|
local_chunk_len]
|
|
k_pe_segment = allgatered_k_pe[rank * toks +
|
|
src_token_idx:rank * toks +
|
|
src_token_idx +
|
|
local_chunk_len]
|
|
kv_c_segments.append(kv_c_segment)
|
|
k_pe_segments.append(k_pe_segment)
|
|
cur_seq_len += local_chunk_len
|
|
max_seq_len_check = max(max_seq_len_check, cur_seq_len)
|
|
src_token_idx += padded_local_chunk_seq_len
|
|
reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0)
|
|
reorganized_k_pe = torch.cat(k_pe_segments, dim=0)
|
|
assert reorganized_kv_c_normed.shape[0] == sum_seq_len
|
|
assert reorganized_k_pe.shape[0] == sum_seq_len
|
|
assert max_seq_len_check == max_seq_len
|
|
return reorganized_kv_c_normed, reorganized_k_pe
|