1035 lines
48 KiB
Python
1035 lines
48 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 __future__ import annotations
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import math
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from contextlib import contextmanager, nullcontext
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from functools import partial
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from typing import Any, cast
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import numpy as np
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import torch
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import torch.nn as nn
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import torch_npu
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from vllm.config import CUDAGraphMode
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from vllm.distributed.parallel_state import get_pp_group
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from vllm.forward_context import get_forward_context
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from vllm.logger import logger
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from vllm.sequence import IntermediateTensors
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from vllm.utils.math_utils import cdiv
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from vllm.utils.torch_utils import get_dtype_size
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.kv_cache_interface import (
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AttentionSpec,
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EncoderOnlyAttentionSpec,
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KVCacheConfig,
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KVCacheSpec,
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MambaSpec,
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UniformTypeKVCacheSpecs,
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)
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.worker.cp_utils import get_total_cp_world_size
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from vllm_ascend._310p.block_table import MultiGroupBlockTable as MultiGroupBlockTable310
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from vllm_ascend._310p.kv_block_zeroer import AscendKVBlockZeroer310
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from vllm_ascend._310p.npu_input_batch import NPUInputBatch310 as NPUInputBatch
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from vllm_ascend._310p.ops.rotary_embedding import prepare_mrope_cos_sin_slices_from_runner
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from vllm_ascend._310p.sample.rejection_sampler import AscendRejectionSampler310
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from vllm_ascend._310p.sample.sampler import AscendSampler310
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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from vllm_ascend.spec_decode.utils import (
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update_num_computed_tokens_for_batch_change,
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)
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from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, is_rc_device, lmhead_tp_enable
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from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
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from vllm_ascend.worker.utils import copy_snapshot_to_gpu
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_NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN = 1
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_ATTENTION_BLOCK_SIZE_LIMIT = 128 * 128
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class NPUModelRunner310(NPUModelRunner):
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"""
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310P model runner with a distinct ACL graph capture/replay contract from 910B:
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- Capture: ACLGraphWrapper records the full forward inside ``torch.npu.graph``.
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310P attention calls NPU ops directly (paged / splitfuse), without mainline
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``full_graph_fia`` / ``full_graph_pa`` graph_task registration.
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- Replay: refresh shared runner buffers (block_table, seq_lens, query_start_loc,
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slot_mapping via CPU prepare + copy_to_gpu) so tensor addresses stay stable,
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then ``aclgraph.replay()``.
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"""
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# Inherited from parent runner; annotated here to satisfy strict type checks.
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uniform_decode_query_len: int
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_spec_dummy_capture: bool = False
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.input_batch = NPUInputBatch(
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max_num_reqs=self.max_num_reqs,
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max_model_len=max(self.model_config.max_model_len, self.max_encoder_len),
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max_num_batched_tokens=self.max_num_tokens,
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device=self.device,
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pin_memory=self.pin_memory,
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vocab_size=self.model_config.get_vocab_size(),
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block_sizes=[self.block_size],
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kernel_block_sizes=[[self.cache_config.block_size]],
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is_spec_decode=bool(self.vllm_config.speculative_config),
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logitsprocs=self.input_batch.logitsprocs,
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is_pooling_model=self.is_pooling_model,
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num_speculative_tokens=(
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self.vllm_config.speculative_config.num_speculative_tokens if self.vllm_config.speculative_config else 0
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),
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cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
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)
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self._acl_format = ACL_FORMAT_FRACTAL_NZ
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logger.info_once("Weight layout uses FRACTAL_NZ.")
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self.sampler = AscendSampler310()
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if getattr(self, "rejection_sampler", None) is not None:
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self.rejection_sampler = AscendRejectionSampler310(self.sampler)
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if self.speculative_config is not None and self.speculative_config.method == "ngram":
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# 310P ngram requires decode-only graph shapes to be built with q_len=1.
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# Keep dispatcher's internal query_len in sync to avoid key-init assert.
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self.cudagraph_dispatcher.uniform_decode_query_len = _NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN
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logger.info_once("Ngram speculative decoding uses uniform_decode_query_len=1 for graph capture.")
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def _update_states(self, scheduler_output: SchedulerOutput):
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deferred = super()._update_states(scheduler_output)
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if scheduler_output.finished_req_ids:
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# condense() rewrites block_table.np (move_row). Drain the previous
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# step's ACL graph replay on the NPU stream before the condensed
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# CPU layout is uploaded and read as attn_metadata.block_tables.
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# Main-line Ascend relies on the end-of-_prepare_inputs Triton
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# slot-mapping kernel (reads block_table.gpu) for stream ordering;
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# 310P uses CPU NumPy for slot_mapping and needs this barrier on
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# layout-change steps only.
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torch.npu.current_stream().synchronize()
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return deferred
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@contextmanager
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def temporary_modify_uniform_decode_query_len(self):
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# This is only needed for the 310P ngram path where dispatcher uses q_len=1
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# while runner's default uniform_decode_query_len remains 1 + num_spec_tokens.
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# TODO: remove this temporary override after upstream supports independent
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# decode capture query_len for backend-specific paths.
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if self.speculative_config is None or self.speculative_config.method != "ngram":
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yield
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return
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original_uniform_decode_query_len = self.uniform_decode_query_len
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self.uniform_decode_query_len = _NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN
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try:
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yield
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finally:
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self.uniform_decode_query_len = original_uniform_decode_query_len
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def _determine_batch_execution_and_padding(
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self,
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num_tokens: int,
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num_reqs: int,
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num_scheduled_tokens_np: np.ndarray,
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max_num_scheduled_tokens: int,
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use_cascade_attn: bool,
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allow_microbatching: bool = False,
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force_eager: bool = False,
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force_uniform_decode: bool | None = None,
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force_has_lora: bool | None = None,
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force_num_active_loras: int | None = None,
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num_encoder_reqs: int = 0,
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):
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is_all_decode = np.all(self.input_batch.num_computed_tokens_cpu[:num_reqs] > 0)
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if self.attn_state in (AscendAttentionState.ChunkedPrefill, AscendAttentionState.PrefillCacheHit):
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force_eager = True
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# Spec decoding graph replay is only valid for uniform spec-decode batches (q_len = 1 + K).
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if self.speculative_config is not None and (
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self.attn_state != AscendAttentionState.SpecDecoding
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or max_num_scheduled_tokens != self.uniform_decode_query_len
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or num_tokens != max_num_scheduled_tokens * num_reqs
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):
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force_eager = True
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if force_uniform_decode is None and self.attn_state == AscendAttentionState.DecodeOnly:
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decode_query_len = _NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN
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if (
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max_num_scheduled_tokens == decode_query_len
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and num_tokens == max_num_scheduled_tokens * num_reqs
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and is_all_decode
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):
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# Respect explicit caller override: only force when unset.
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force_uniform_decode = True
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return super()._determine_batch_execution_and_padding(
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num_tokens=num_tokens,
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num_reqs=num_reqs,
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num_scheduled_tokens_np=num_scheduled_tokens_np,
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max_num_scheduled_tokens=max_num_scheduled_tokens,
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use_cascade_attn=use_cascade_attn,
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allow_microbatching=allow_microbatching,
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force_eager=force_eager,
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force_uniform_decode=force_uniform_decode,
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force_has_lora=force_has_lora,
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force_num_active_loras=force_num_active_loras,
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num_encoder_reqs=num_encoder_reqs,
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)
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def _build_attention_metadata(self, *args: Any, **kwargs: Any):
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# Parent dummy_run assigns ChunkedPrefill for non-MLA MTP (910B FIA graph).
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# 310P must capture SpecDecoding + splitfuse for SpecDecoding uniform decode graphs.
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if self._spec_dummy_capture:
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self.attn_state = AscendAttentionState.SpecDecoding
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return super()._build_attention_metadata(*args, **kwargs)
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def _pad_query_start_loc_for_fia(
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self,
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query_start_loc: torch.Tensor,
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num_tokens_padded: int,
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num_reqs_padded: int,
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num_reqs: int,
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cudagraph_runtime_mode: CUDAGraphMode | None = None,
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batch_desc_num_reqs: int | None = None,
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) -> int:
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# Keep this aligned with the dispatcher because batch_desc.num_reqs is
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# generated by dispatcher._create_padded_batch_descriptor().
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# For 310P ngram we intentionally set dispatcher q_len=1, while runner's
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# default uniform_decode_query_len may remain 1 + num_spec_tokens.
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uniform_decode_query_len = self.cudagraph_dispatcher.uniform_decode_query_len
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if num_tokens_padded == num_reqs_padded * uniform_decode_query_len:
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# Uniform-batch case: num_reqs must be no greater than num_reqs_padded
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assert num_reqs <= num_reqs_padded
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last_loc = query_start_loc.np[num_reqs]
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query_start_loc.np[num_reqs + 1 : num_reqs_padded + 1] = (
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self.arange_np[1 : num_reqs_padded + 1 - num_reqs] * uniform_decode_query_len + last_loc
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)
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else:
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# Mixed-batch case: num_reqs must equal num_reqs_padded
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assert num_reqs == num_reqs_padded
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# Insert a dummy request instead of setting query_start_loc[num_reqs] = num_tokens_padded directly
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query_start_loc.np[num_reqs_padded + 1] = num_tokens_padded
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num_reqs_padded = num_reqs_padded + 1
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copy_snapshot_to_gpu(query_start_loc)
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return num_reqs_padded
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def _build_attn_state(self, num_reqs, num_scheduled_tokens, num_valid_tokens):
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attn_state = super()._build_attn_state(num_reqs, num_scheduled_tokens, num_valid_tokens)
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if (
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self.speculative_config is not None
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and not np.all(self.input_batch.num_computed_tokens_cpu[:num_reqs] == 0)
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and np.all(num_scheduled_tokens == self.uniform_decode_query_len)
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):
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attn_state = AscendAttentionState.SpecDecoding
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self.attn_state = attn_state
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return attn_state
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def _prepare_inputs( # type: ignore[override]
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self,
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scheduler_output: SchedulerOutput,
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num_scheduled_tokens: np.ndarray,
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) -> tuple[torch.Tensor, SpecDecodeMetadata | None, int]:
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"""
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310P cannot use the Triton slot-mapping kernel or the generic NPU Add
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kernels used by the base runner for decode metadata. Keep those pieces
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on CPU and upload the prepared tensors.
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"""
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total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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assert total_num_scheduled_tokens > 0
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num_reqs = self.input_batch.num_reqs
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assert num_reqs > 0
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self.input_batch.block_table.commit_block_table(num_reqs)
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req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
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if not scheduler_output.scheduled_spec_decode_tokens:
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num_valid_tokens = num_scheduled_tokens
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else:
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num_valid_tokens = np.array(
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[
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scheduler_output.num_scheduled_tokens[i]
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- len(scheduler_output.scheduled_spec_decode_tokens.get(i, []))
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for i in self.input_batch.req_ids
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],
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dtype=np.int32,
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)
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attn_state = self._build_attn_state(num_reqs, num_scheduled_tokens, num_valid_tokens)
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with_prefill = attn_state not in [AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding]
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self.with_prefill = with_prefill
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cu_num_tokens = self._get_cumsum_and_arange(num_scheduled_tokens, self.query_pos.np)
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prev_req_id_to_index = self.input_batch.prev_req_id_to_index
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self._compute_prev_positions(num_reqs)
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if self.num_accepted_tokens_event is not None:
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self.num_accepted_tokens_event.synchronize()
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if self.use_async_scheduling and prev_req_id_to_index:
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prev_idx = self.prev_positions.np[:num_reqs]
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new_mask = prev_idx < 0
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self.num_accepted_tokens.np[:num_reqs] = self.input_batch.num_accepted_tokens_cpu[
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np.where(new_mask, 0, prev_idx)
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]
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self.num_accepted_tokens.np[:num_reqs][new_mask] = 1
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self.input_batch.num_accepted_tokens_cpu[:num_reqs] = self.num_accepted_tokens.np[:num_reqs]
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else:
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self.num_accepted_tokens.np[:num_reqs] = self.input_batch.num_accepted_tokens_cpu[:num_reqs]
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self.num_accepted_tokens.np[num_reqs:].fill(1)
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self.num_accepted_tokens.copy_to_gpu()
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else:
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if is_rc_device():
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self.num_accepted_tokens.np[num_reqs:].fill(1)
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self.num_accepted_tokens.copy_to_gpu()
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else:
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self.num_accepted_tokens.np.fill(1)
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self.num_accepted_tokens.gpu.fill_(1)
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need_async_num_computed_update = (
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self.use_async_spec_decode and self.valid_sampled_token_count_gpu is not None and prev_req_id_to_index
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)
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if need_async_num_computed_update:
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self.prev_positions.copy_to_gpu(num_reqs)
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self.prev_num_draft_tokens.copy_to_gpu()
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cpu_values = self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs].to(
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device=self.device, non_blocking=True
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)
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update_num_computed_tokens_for_batch_change(
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self.num_computed_tokens,
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self.num_accepted_tokens.gpu[:num_reqs],
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self.prev_positions.gpu[:num_reqs],
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self.valid_sampled_token_count_gpu,
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self.prev_num_draft_tokens.gpu,
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cpu_values,
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)
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# Make sure D2H is synchronized.
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self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs].copy_(
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self.num_computed_tokens[:num_reqs], non_blocking=False
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)
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else:
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self.num_computed_tokens[:num_reqs].copy_(
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self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs],
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non_blocking=True,
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)
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# Make sure when you update the positions and slot mapping, num_computed_tokens_cpu
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# has been corrected.
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positions_np = self._positions_np_buf[:total_num_scheduled_tokens]
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np.add(
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self.input_batch.num_computed_tokens_cpu[req_indices],
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self.query_pos.np[: cu_num_tokens[-1]],
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out=positions_np,
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)
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block_table = cast(MultiGroupBlockTable310, self.input_batch.block_table)
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block_table.compute_slot_mapping(
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req_indices,
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positions_np[:total_num_scheduled_tokens],
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)
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if self.use_cp:
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self.pcp_manager.init_batch_info(
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num_scheduled_tokens,
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self.input_batch.num_reqs,
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self.input_batch.num_computed_tokens_cpu,
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self.input_batch.num_prompt_tokens,
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)
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if self.speculative_config and self.use_cp:
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self.pcp_manager.generate_pcp_mtp_input(
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total_num_scheduled_tokens,
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scheduler_output.num_scheduled_tokens,
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with_prefill,
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self.input_batch,
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self.arange_np,
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req_indices,
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positions_np,
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cu_num_tokens,
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self._draft_token_ids, # type: ignore[has-type]
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scheduler_output,
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self.num_spec_tokens,
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)
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if self.pcp_size > 1:
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num_scheduled_tokens[:num_reqs], position_pcp = self.pcp_manager.update_tokens_for_pcp(
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num_scheduled_tokens[:num_reqs], self.arange_np
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)
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total_num_scheduled_tokens = sum(num_scheduled_tokens[:num_reqs])
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req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
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cu_num_tokens = self._get_cumsum_and_arange(num_scheduled_tokens, self.query_pos.np)
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positions_np = self._positions_np_buf[:total_num_scheduled_tokens]
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np.add(
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self.input_batch.num_computed_tokens_cpu[req_indices],
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position_pcp[:total_num_scheduled_tokens],
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out=positions_np,
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)
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if self.pcp_size > 1 and self.pcp_manager.pcp_use_hybrid_attn:
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assert self.pcp_manager.num_scheduled_tokens_padded is not None
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self.query_lens = torch.from_numpy(self.pcp_manager.num_scheduled_tokens_padded)
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else:
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self.query_lens = torch.from_numpy(num_scheduled_tokens)
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token_indices = positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
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token_indices_tensor = torch.from_numpy(token_indices)
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torch.index_select(
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self.input_batch.token_ids_cpu_tensor.flatten(),
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0,
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token_indices_tensor,
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out=self.input_ids.cpu[:total_num_scheduled_tokens],
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)
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if self.enable_prompt_embeds:
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is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
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torch.index_select(
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is_token_ids, 0, token_indices_tensor, out=self.is_token_ids.cpu[:total_num_scheduled_tokens]
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)
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if self.input_batch.req_prompt_embeds and (self.is_multimodal_model or self.enable_prompt_embeds):
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output_idx = 0
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for req_idx in range(num_reqs):
|
|
num_sched = num_scheduled_tokens[req_idx]
|
|
|
|
if req_idx not in self.input_batch.req_prompt_embeds:
|
|
output_idx += num_sched
|
|
continue
|
|
|
|
if num_sched <= 0:
|
|
output_idx += num_sched
|
|
continue
|
|
|
|
req_embeds = self.input_batch.req_prompt_embeds[req_idx]
|
|
if self.pcp_size > 1:
|
|
req_positions_np = positions_np[output_idx : output_idx + num_sched]
|
|
dst_slice = self.inputs_embeds.cpu[output_idx : output_idx + num_sched]
|
|
self.pcp_manager.fill_prompt_embeds_for_pcp(
|
|
req_embeds=req_embeds,
|
|
req_positions_np=req_positions_np,
|
|
dst_slice=dst_slice,
|
|
)
|
|
else:
|
|
start_pos = self.input_batch.num_computed_tokens_cpu[req_idx]
|
|
|
|
if start_pos >= req_embeds.shape[0]:
|
|
output_idx += num_sched
|
|
continue
|
|
|
|
end_pos = start_pos + num_sched
|
|
actual_end = min(end_pos, req_embeds.shape[0])
|
|
actual_num_sched = actual_end - start_pos
|
|
|
|
if actual_num_sched > 0:
|
|
self.inputs_embeds.cpu[output_idx : output_idx + actual_num_sched].copy_(
|
|
req_embeds[start_pos:actual_end]
|
|
)
|
|
|
|
output_idx += num_sched
|
|
|
|
self.query_start_loc.np[0] = 0
|
|
self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
|
|
if is_rc_device():
|
|
self.query_start_loc.np[num_reqs + 1 :].fill(-1)
|
|
copy_snapshot_to_gpu(self.query_start_loc)
|
|
|
|
if self._has_gdn:
|
|
self.gdn_query_start_loc.np[0] = 0
|
|
self.gdn_query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
|
|
self.gdn_query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
|
|
copy_snapshot_to_gpu(self.gdn_query_start_loc)
|
|
|
|
torch.add(
|
|
self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs],
|
|
torch.from_numpy(num_scheduled_tokens),
|
|
out=self.optimistic_seq_lens_cpu[:num_reqs],
|
|
)
|
|
self.optimistic_seq_lens_cpu[num_reqs:].fill_(0)
|
|
|
|
if not is_rc_device():
|
|
self.query_start_loc.gpu[num_reqs + 1 :].fill_(-1)
|
|
|
|
self._prepare_input_ids(scheduler_output, num_reqs, total_num_scheduled_tokens, cu_num_tokens)
|
|
if self.uses_mrope:
|
|
self._calc_mrope_positions(scheduler_output)
|
|
self.mrope_positions.gpu.copy_(
|
|
self.mrope_positions.cpu,
|
|
non_blocking=True,
|
|
)
|
|
elif self.uses_xdrope_dim > 0:
|
|
self._calc_xdrope_positions(scheduler_output)
|
|
self.xdrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
|
|
self.xdrope_positions.cpu[:, :total_num_scheduled_tokens],
|
|
non_blocking=True,
|
|
)
|
|
|
|
num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
|
|
num_tokens_np = np.array(num_tokens, dtype=np.int32)
|
|
base_num_reqs = self.input_batch.num_reqs
|
|
num_reqs = base_num_reqs
|
|
tokens_original = None
|
|
if self.pcp_size > 1:
|
|
tokens_original = [scheduler_output.num_scheduled_tokens[i] for i in self.input_batch.req_ids]
|
|
original_seq_lens_np = self.input_batch.num_computed_tokens_cpu[:num_reqs] + np.array(
|
|
tokens_original, dtype=np.int32
|
|
)
|
|
discard_requests_mask = original_seq_lens_np < num_tokens_np
|
|
else:
|
|
discard_requests_mask = self.optimistic_seq_lens_cpu[:num_reqs].numpy() < num_tokens_np
|
|
|
|
discard_request_indices = np.nonzero(discard_requests_mask)[0]
|
|
self.num_discarded_requests = len(discard_request_indices)
|
|
self.discard_request_indices.np[: self.num_discarded_requests] = discard_request_indices
|
|
self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)
|
|
|
|
self.req_indices.np[:total_num_scheduled_tokens] = req_indices
|
|
self.req_indices.copy_to_gpu(total_num_scheduled_tokens)
|
|
|
|
self.query_pos.copy_to_gpu(total_num_scheduled_tokens)
|
|
self.num_scheduled_tokens.np[:num_reqs] = num_scheduled_tokens
|
|
self.num_scheduled_tokens.copy_to_gpu(num_reqs)
|
|
num_scheduled_tokens_gpu = self.num_scheduled_tokens.gpu[:num_reqs]
|
|
self.positions[:total_num_scheduled_tokens].copy_(
|
|
self._positions_cpu_buf[:total_num_scheduled_tokens],
|
|
non_blocking=True,
|
|
)
|
|
if need_async_num_computed_update:
|
|
self.seq_lens[:num_reqs] = self.num_computed_tokens[:num_reqs] + num_scheduled_tokens_gpu
|
|
if is_rc_device():
|
|
tail_len = self.seq_lens.shape[0] - num_reqs
|
|
if tail_len > 0:
|
|
self.seq_lens[num_reqs:].copy_(
|
|
self.optimistic_seq_lens_cpu[num_reqs:].to(self.device, non_blocking=True),
|
|
)
|
|
else:
|
|
if is_rc_device():
|
|
self.seq_lens.copy_(
|
|
self.optimistic_seq_lens_cpu[: self.seq_lens.shape[0]],
|
|
non_blocking=True,
|
|
)
|
|
else:
|
|
self.seq_lens[:num_reqs].copy_(
|
|
self.optimistic_seq_lens_cpu[:num_reqs],
|
|
non_blocking=True,
|
|
)
|
|
if not is_rc_device():
|
|
self.seq_lens[num_reqs:].fill_(0)
|
|
|
|
if (
|
|
self._needs_seq_lens_cpu_sync
|
|
and self.use_async_spec_decode
|
|
and self.valid_sampled_token_count_gpu is not None
|
|
and prev_req_id_to_index
|
|
):
|
|
# Correct optimistic_seq_lens_cpu on CPU using the asynchronously
|
|
# copied valid-sampled-token counts; avoids an extra NPU->CPU copy
|
|
# of seq_lens and the event.synchronize() in attention metadata.
|
|
# The shared helper synchronizes on the D2H copy event before the
|
|
# host read to avoid consuming stale counts (see its docstring).
|
|
self._correct_optimistic_seq_lens_cpu(num_reqs)
|
|
|
|
use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
|
|
if not use_spec_decode:
|
|
spec_decode_metadata = None
|
|
num_draft_tokens = None
|
|
num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
|
|
if self.use_cp:
|
|
logits_indices = self.pcp_manager.get_logits_indices(cu_num_tokens, num_reqs, tokens_original)
|
|
logits_indices = logits_indices.pin_memory().to(self.device, non_blocking=True)
|
|
else:
|
|
logits_indices = self.query_start_loc.gpu[1 : num_reqs + 1] - 1
|
|
else:
|
|
num_draft_tokens = np.zeros(num_reqs, dtype=np.int32)
|
|
new_schedule_reqs = [x.req_id for x in scheduler_output.scheduled_new_reqs]
|
|
num_decode_draft_tokens = np.full(num_reqs, -1, dtype=np.int32)
|
|
for (
|
|
req_id,
|
|
draft_token_ids,
|
|
) in scheduler_output.scheduled_spec_decode_tokens.items():
|
|
req_idx = self.input_batch.req_id_to_index[req_id]
|
|
draft_len = len(draft_token_ids)
|
|
num_draft_tokens[req_idx] = draft_len
|
|
if (self.is_kv_consumer and req_id in new_schedule_reqs) or (
|
|
self.input_batch.num_computed_tokens_cpu[req_idx] >= self.input_batch.num_prompt_tokens[req_idx]
|
|
):
|
|
num_decode_draft_tokens[req_idx] = draft_len
|
|
else:
|
|
num_decode_draft_tokens[req_idx] = -1
|
|
|
|
spec_decode_metadata = self._calc_spec_decode_metadata(
|
|
num_draft_tokens,
|
|
cu_num_tokens,
|
|
num_pcp_pads=self.pcp_manager.num_pcp_pads_cpu[:num_reqs] if self.pcp_size > 1 else None,
|
|
)
|
|
logits_indices = spec_decode_metadata.logits_indices
|
|
num_sampled_tokens = num_draft_tokens + 1
|
|
|
|
self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
|
|
self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
|
|
self.num_decode_draft_tokens.copy_to_gpu()
|
|
|
|
self.logits_indices = logits_indices
|
|
|
|
if self.lora_config:
|
|
assert np.sum(num_sampled_tokens) <= self.vllm_config.scheduler_config.max_num_batched_tokens
|
|
self.set_active_loras(self.input_batch, num_scheduled_tokens, num_sampled_tokens)
|
|
if lmhead_tp_enable():
|
|
max_num_reqs_across_dp = self.max_num_reqs * self.uniform_decode_query_len
|
|
logits_indices = nn.functional.pad(logits_indices, (0, max_num_reqs_across_dp - logits_indices.shape[0]))
|
|
|
|
if (
|
|
self.pcp_size > 1
|
|
and self.supports_mm_inputs
|
|
and get_pp_group().is_first_rank
|
|
and not self.model_config.is_encoder_decoder
|
|
):
|
|
self.pcp_manager.cache_local_schedule_layout(
|
|
num_scheduled_tokens=num_scheduled_tokens,
|
|
num_reqs=base_num_reqs,
|
|
total_num_scheduled_tokens=total_num_scheduled_tokens,
|
|
)
|
|
|
|
return (
|
|
logits_indices,
|
|
spec_decode_metadata,
|
|
total_num_scheduled_tokens,
|
|
)
|
|
|
|
@torch.inference_mode()
|
|
def _dummy_run(
|
|
self,
|
|
num_tokens: int,
|
|
with_prefill: bool = False,
|
|
cudagraph_runtime_mode=None,
|
|
force_attention: bool = False,
|
|
uniform_decode: bool = False,
|
|
is_profile: bool = False,
|
|
create_mixed_batch: bool = False,
|
|
allow_microbatching: bool = True,
|
|
skip_eplb: bool = False,
|
|
remove_lora: bool = True,
|
|
is_graph_capturing: bool = False,
|
|
num_active_loras: int = 0,
|
|
profile_seq_lens: int | None = None,
|
|
):
|
|
temporary_context = self.temporary_modify_uniform_decode_query_len() if uniform_decode else nullcontext()
|
|
# All the spec decoding cases has to run splitfuse op on 310P.
|
|
is_spec_graph_capture = (
|
|
uniform_decode
|
|
and not is_profile
|
|
and self.speculative_config is not None
|
|
and not self.vllm_config.model_config.use_mla
|
|
)
|
|
with temporary_context:
|
|
self._spec_dummy_capture = is_spec_graph_capture
|
|
try:
|
|
return super()._dummy_run(
|
|
num_tokens=num_tokens,
|
|
with_prefill=with_prefill,
|
|
cudagraph_runtime_mode=cudagraph_runtime_mode,
|
|
force_attention=force_attention,
|
|
uniform_decode=uniform_decode,
|
|
is_profile=is_profile,
|
|
create_mixed_batch=create_mixed_batch,
|
|
allow_microbatching=allow_microbatching,
|
|
skip_eplb=skip_eplb,
|
|
remove_lora=remove_lora,
|
|
is_graph_capturing=is_graph_capturing,
|
|
num_active_loras=num_active_loras,
|
|
profile_seq_lens=profile_seq_lens,
|
|
)
|
|
finally:
|
|
self._spec_dummy_capture = False
|
|
|
|
def _model_forward(
|
|
self,
|
|
num_tokens_padded: int,
|
|
input_ids: torch.Tensor | None = None,
|
|
positions: torch.Tensor | None = None,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**model_kwargs: dict[str, Any],
|
|
):
|
|
if self.uses_mrope:
|
|
assert positions is not None
|
|
prepare_mrope_cos_sin_slices_from_runner(self, positions)
|
|
|
|
assert self.model is not None
|
|
forward_context = get_forward_context()
|
|
assert forward_context is not None
|
|
model_inputs: dict[str, Any] = {
|
|
"input_ids": input_ids,
|
|
"positions": positions,
|
|
"intermediate_tensors": intermediate_tensors,
|
|
"inputs_embeds": inputs_embeds,
|
|
**model_kwargs,
|
|
}
|
|
run_model = partial(self.model, **model_inputs)
|
|
update_before_replay = (
|
|
self.speculative_config is not None
|
|
and not self.enable_enpu
|
|
and forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL
|
|
and not forward_context.capturing
|
|
and hasattr(self, "update_stream")
|
|
)
|
|
|
|
if self.enable_enpu or update_before_replay:
|
|
if update_before_replay:
|
|
torch.npu.current_stream().synchronize()
|
|
self._update_full_graph_params_if_needed(
|
|
forward_context,
|
|
num_tokens_padded,
|
|
positions,
|
|
)
|
|
if update_before_replay:
|
|
torch.npu.current_stream().wait_stream(self.update_stream)
|
|
hidden_states = run_model()
|
|
else:
|
|
hidden_states = run_model()
|
|
self._update_full_graph_params_if_needed(
|
|
forward_context,
|
|
num_tokens_padded,
|
|
positions,
|
|
)
|
|
|
|
if forward_context.flash_comm_v1_enabled and not isinstance(hidden_states, IntermediateTensors):
|
|
hidden_states = self._all_gather_hidden_states_and_aux(hidden_states)
|
|
return hidden_states
|
|
|
|
def _check_and_update_cudagraph_mode(
|
|
self,
|
|
attention_backends,
|
|
kv_cache_groups,
|
|
) -> None:
|
|
# 910B does not need this branch because runner/dispatcher query_len are
|
|
# naturally consistent there. 310P ngram needs temporary alignment.
|
|
with self.temporary_modify_uniform_decode_query_len():
|
|
super()._check_and_update_cudagraph_mode(attention_backends, kv_cache_groups)
|
|
|
|
def _init_kv_zero_meta(self) -> None:
|
|
"""310P uses torch zeroing because Triton is not available."""
|
|
self._kv_block_zeroer = AscendKVBlockZeroer310(self.device, self.pin_memory)
|
|
self._kv_block_zeroer.init_meta(
|
|
attn_groups_iter=self._kv_cache_spec_attn_group_iterator(),
|
|
kernel_block_sizes=self.kernel_block_sizes,
|
|
cache_dtype=self.cache_config.cache_dtype,
|
|
runner_only_attn_layers=self.runner_only_attn_layers,
|
|
static_forward_context=(self.compilation_config.static_forward_context),
|
|
)
|
|
|
|
def initialize_kv_cache_tensors(self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
|
|
"""
|
|
Override the base class method.
|
|
Initialize the memory buffer for KV cache.
|
|
|
|
Args:
|
|
kv_cache_config: The KV cache config
|
|
Returns:
|
|
Dict[str, torch.Tensor]: A map between layer names to their
|
|
corresponding memory buffer for KV cache.
|
|
"""
|
|
# 310P limitation: KV transfer is not supported
|
|
if self.vllm_config.kv_transfer_config is not None:
|
|
logger.error("KV cache transfer is not supported.")
|
|
raise ValueError("KV cache transfer is not supported for 310P.")
|
|
if self.use_sparse:
|
|
logger.error("Deepseek Sparse Attention is not supported.")
|
|
raise ValueError("Deepseek Sparse Attention is not supported for 310P.")
|
|
if self.model_config.use_mla:
|
|
logger.error("MLAAttention is not supported.")
|
|
raise ValueError("MLAAttention is not supported for 310P.")
|
|
# Initialize the memory buffer for KV cache
|
|
kv_caches = self._allocate_kv_cache_tensors(kv_cache_config)
|
|
# Set up cross-layer KV cache sharing
|
|
for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
|
|
logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
|
|
kv_caches[layer_name] = kv_caches[target_layer_name]
|
|
|
|
from vllm.v1.worker.utils import bind_kv_cache
|
|
|
|
bind_kv_cache(kv_caches, self.compilation_config.static_forward_context, self.kv_caches)
|
|
return kv_caches
|
|
|
|
def _allocate_kv_cache_tensors(self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
|
|
"""
|
|
Initializes the KV cache size. The buffer needs to be reshaped to the desired shape before being used by
|
|
the models.
|
|
|
|
Args:
|
|
kv_cache_config: The KV cache config
|
|
Returns:
|
|
dict[str, torch.Tensor]: A map between layer names to their
|
|
corresponding memory buffer.
|
|
"""
|
|
# init kv cache tensors
|
|
kv_cache: dict[str, list[torch.Tensor] | tuple[torch.Tensor, torch.Tensor]] = {}
|
|
# get kv cache spec for each layer
|
|
layer_kv_cache_spec: dict[str, KVCacheSpec] = {}
|
|
for group_kv_cache_spec in kv_cache_config.kv_cache_groups:
|
|
for layer_name in group_kv_cache_spec.layer_names:
|
|
layer_kv_cache_spec[layer_name] = group_kv_cache_spec.kv_cache_spec
|
|
# Allocate kv cache buffers according to the kv_cache_config and kv_cache_spec
|
|
for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
|
|
for idx in range(len(kv_cache_tensor.shared_by)):
|
|
layer_name = kv_cache_tensor.shared_by[idx]
|
|
if layer_name in self.runner_only_attn_layers:
|
|
continue
|
|
if "linear_attn" in layer_name and layer_name not in kv_cache:
|
|
cache_spec = layer_kv_cache_spec[layer_name]
|
|
assert isinstance(cache_spec, MambaSpec)
|
|
assert kv_cache_tensor.size % cache_spec.page_size_bytes == 0
|
|
num_blocks = kv_cache_tensor.size // cache_spec.page_size_bytes
|
|
assert num_blocks >= kv_cache_config.num_blocks
|
|
raw_tensor = torch.zeros(kv_cache_tensor.size, dtype=torch.int8, device=self.device)
|
|
state_tensors = []
|
|
target_idx = 0
|
|
start_idx = 0
|
|
for shape, dtype in zip(cache_spec.shapes, cache_spec.dtypes):
|
|
target_shape = (num_blocks, *shape)
|
|
target_idx += math.prod(target_shape) * get_dtype_size(dtype)
|
|
tensor = raw_tensor[start_idx:target_idx].view(dtype).view(target_shape)
|
|
start_idx = target_idx
|
|
state_tensors.append(tensor)
|
|
for layer_name_inner in kv_cache_tensor.shared_by:
|
|
if "linear_attn" in layer_name_inner:
|
|
kv_cache[layer_name_inner] = state_tensors
|
|
elif "attn" in layer_name and layer_name not in kv_cache:
|
|
kv_cache_spec = layer_kv_cache_spec[layer_name]
|
|
assert isinstance(kv_cache_spec, AttentionSpec)
|
|
assert kv_cache_tensor.size % kv_cache_spec.page_size_bytes == 0
|
|
num_blocks = kv_cache_tensor.size // kv_cache_spec.page_size_bytes
|
|
assert num_blocks >= kv_cache_config.num_blocks
|
|
# Page attention operation on 310P limits block_size * head_size <= 128 * 128
|
|
supported_sizes = [
|
|
support_size
|
|
for support_size in self.attn_backend.get_supported_kernel_block_sizes()
|
|
if support_size * kv_cache_spec.head_size <= _ATTENTION_BLOCK_SIZE_LIMIT
|
|
]
|
|
if supported_sizes:
|
|
block_size = supported_sizes[0]
|
|
block_size_chunk = kv_cache_spec.block_size // block_size
|
|
kv_cache_shape = self.attn_backend.get_kv_cache_shape(
|
|
num_blocks * block_size_chunk,
|
|
block_size,
|
|
kv_cache_spec.num_kv_heads,
|
|
kv_cache_spec.head_size,
|
|
)
|
|
else:
|
|
kv_cache_shape = self.attn_backend.get_kv_cache_shape(
|
|
num_blocks, kv_cache_spec.block_size, kv_cache_spec.num_kv_heads, kv_cache_spec.head_size
|
|
)
|
|
k_shape = kv_cache_shape[1:]
|
|
v_shape = k_shape
|
|
dtype = kv_cache_spec.dtype
|
|
k_cache = torch_npu.empty_with_format(
|
|
size=k_shape, dtype=dtype, device=self.device, acl_format=self._acl_format
|
|
)
|
|
v_cache = torch_npu.empty_with_format(
|
|
size=v_shape, dtype=dtype, device=self.device, acl_format=self._acl_format
|
|
)
|
|
for layer_name_inner in kv_cache_tensor.shared_by:
|
|
# shared the kvcache between the self_attn specs in the same group
|
|
if "attn" in layer_name_inner and "linear_attn" not in layer_name_inner:
|
|
kv_cache[layer_name_inner] = (k_cache, v_cache)
|
|
layer_names = set()
|
|
for group in kv_cache_config.kv_cache_groups:
|
|
for layer_name in group.layer_names:
|
|
if layer_name in self.runner_only_attn_layers:
|
|
continue
|
|
layer_names.add(layer_name)
|
|
assert layer_names == set(kv_cache.keys()), "Some layers are not correctly initialized"
|
|
return kv_cache
|
|
|
|
# Override this function because of tensor.copy_(other) accuracy issue.
|
|
# TODO: This override will be removed after tensor.copy_(other) accuracy issue is resolved.
|
|
def _prepare_input_ids(
|
|
self,
|
|
scheduler_output: SchedulerOutput,
|
|
num_reqs: int,
|
|
total_num_scheduled_tokens: int,
|
|
cu_num_tokens: np.ndarray,
|
|
) -> None:
|
|
"""Prepare the input IDs for the current batch.
|
|
|
|
Carefully handles the `prev_sampled_token_ids` which can be cached
|
|
from the previous engine iteration, in which case those tokens on the
|
|
GPU need to be copied into the corresponding slots into input_ids."""
|
|
|
|
if self.input_batch.prev_sampled_token_ids is None:
|
|
# Normal scheduling case
|
|
self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
|
|
if self.enable_prompt_embeds:
|
|
self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
|
|
self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
|
|
return
|
|
|
|
# Async scheduling case, where some decode requests from the previous
|
|
# iteration won't have entries in input_ids_cpu and need to be copied
|
|
# on the NPU from prev_sampled_token_ids.
|
|
prev_req_id_to_index = self.input_batch.prev_req_id_to_index
|
|
assert prev_req_id_to_index is not None
|
|
sample_flattened_indices: list[int] = []
|
|
spec_flattened_indices: list[int] = []
|
|
prev_common_req_indices: list[int] = []
|
|
prev_draft_token_indices: list[int] = []
|
|
indices_match = True
|
|
max_flattened_index = -1
|
|
total_num_spec_tokens = 0
|
|
scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
|
|
|
|
for req_id, cur_index in self.input_batch.req_id_to_index.items():
|
|
if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
|
|
prev_common_req_indices.append(prev_index)
|
|
draft_len = len(scheduled_spec_tokens.get(req_id, ()))
|
|
total_num_spec_tokens += draft_len
|
|
flattened_index = int(cu_num_tokens[cur_index]) - 1
|
|
sample_flattened_indices.append(flattened_index - draft_len)
|
|
spec_flattened_indices.extend(range(flattened_index - draft_len + 1, flattened_index + 1))
|
|
start = prev_index * self.num_spec_tokens
|
|
prev_draft_token_indices.extend(range(start, start + draft_len))
|
|
indices_match &= prev_index == flattened_index
|
|
max_flattened_index = max(max_flattened_index, flattened_index)
|
|
num_common_tokens = len(sample_flattened_indices)
|
|
total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
|
|
if num_common_tokens < total_without_spec:
|
|
self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
|
|
if self.enable_prompt_embeds:
|
|
self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
|
|
self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
|
|
if num_common_tokens == 0:
|
|
return
|
|
if indices_match and max_flattened_index == (num_common_tokens - 1):
|
|
# NOTE: Override the copy_ function here
|
|
indices = torch.arange(num_common_tokens, device=self.input_ids.gpu.device)
|
|
source = self.input_batch.prev_sampled_token_ids[:num_common_tokens, 0]
|
|
self.input_ids.gpu.index_copy_(0, indices, source)
|
|
if self.enable_prompt_embeds:
|
|
self.is_token_ids.gpu[:num_common_tokens] = True
|
|
return
|
|
# Upload the index tensors asynchronously so the scatter can be non-blocking.
|
|
sampled_tokens_index_tensor = torch.tensor(
|
|
sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
prev_common_req_indices_tensor = torch.tensor(
|
|
prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
self.input_ids.gpu.scatter_(
|
|
dim=0,
|
|
index=sampled_tokens_index_tensor,
|
|
src=self.input_batch.prev_sampled_token_ids[prev_common_req_indices_tensor, 0],
|
|
)
|
|
# Scatter the draft tokens after the sampled tokens are scattered.
|
|
if self._draft_token_ids is None or not spec_flattened_indices:
|
|
return
|
|
assert isinstance(self._draft_token_ids, torch.Tensor)
|
|
draft_tokens_index_tensor = torch.tensor(
|
|
spec_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
prev_draft_token_indices_tensor = torch.tensor(
|
|
prev_draft_token_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
draft_token_ids = self._draft_token_ids.to(dtype=torch.int32)
|
|
self.input_ids.gpu.scatter_(
|
|
dim=0,
|
|
index=draft_tokens_index_tensor,
|
|
src=draft_token_ids.flatten()[prev_draft_token_indices_tensor],
|
|
)
|
|
|
|
def may_reinitialize_input_batch(self, kv_cache_config: KVCacheConfig) -> None:
|
|
"""
|
|
Re-initialize the input batch if the block sizes are different from
|
|
`[self.cache_config.block_size]`. This usually happens when there
|
|
are multiple KV cache groups.
|
|
|
|
Args:
|
|
kv_cache_config: The KV cache configuration.
|
|
"""
|
|
# Generate kernel_block_sizes that matches each block_size
|
|
# For attention backends that support virtual block splitting,
|
|
# use the supported block sizes from the backend
|
|
# For other backends (like Mamba), use [0] (no splitting)
|
|
block_sizes = []
|
|
self.kernel_block_sizes = []
|
|
kv_cache_specs = []
|
|
for kv_cache_group_id, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
|
|
kv_cache_spec = kv_cache_group.kv_cache_spec
|
|
if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
|
|
kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
|
|
if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
|
|
continue
|
|
kv_cache_specs.append(kv_cache_spec)
|
|
block_sizes.append(kv_cache_spec.block_size)
|
|
if isinstance(kv_cache_spec, AttentionSpec):
|
|
try:
|
|
attn_groups = self.attn_groups[kv_cache_group_id]
|
|
backend = attn_groups[0].backend
|
|
# Page attention operation on 310P limits block_size * head_size <= 128 * 128
|
|
supported_sizes = [
|
|
support_size
|
|
for support_size in backend.get_supported_kernel_block_sizes()
|
|
if support_size * kv_cache_spec.head_size <= _ATTENTION_BLOCK_SIZE_LIMIT
|
|
]
|
|
kernel_block_size_list = supported_sizes if supported_sizes else [self.cache_config.block_size]
|
|
except IndexError:
|
|
kernel_block_size_list = [self.cache_config.block_size]
|
|
self.kernel_block_sizes.append(kernel_block_size_list)
|
|
else:
|
|
self.kernel_block_sizes.append([0])
|
|
|
|
max_num_blocks = []
|
|
max_model_len = max(self.max_model_len, self.max_encoder_len)
|
|
total_cp_world_size = get_total_cp_world_size()
|
|
for kv_cache_spec in kv_cache_specs:
|
|
max_num_blocks_per_req = cdiv(max_model_len, kv_cache_spec.block_size * total_cp_world_size)
|
|
if isinstance(kv_cache_spec, MambaSpec):
|
|
mamba_blocks_per_req = (
|
|
max_num_blocks_per_req if self.cache_config.enable_prefix_caching else 1
|
|
) + kv_cache_spec.num_speculative_blocks
|
|
max_num_blocks_per_req = max(max_num_blocks_per_req, mamba_blocks_per_req)
|
|
max_num_blocks.append(max_num_blocks_per_req)
|
|
|
|
if (
|
|
block_sizes != [self.cache_config.block_size]
|
|
or self.kernel_block_sizes != [[self.cache_config.block_size]]
|
|
or len(kv_cache_config.kv_cache_groups) > 1
|
|
):
|
|
assert self.offload_config.uva.cpu_offload_gb == 0, (
|
|
"Cannot re-initialize the input batch when CPU weight "
|
|
"offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 " # noqa: E501
|
|
"for more details."
|
|
)
|
|
self.input_batch = NPUInputBatch(
|
|
max_num_reqs=self.max_num_reqs,
|
|
max_model_len=max_model_len,
|
|
max_num_batched_tokens=self.max_num_tokens,
|
|
device=self.device,
|
|
pin_memory=self.pin_memory,
|
|
vocab_size=self.model_config.get_vocab_size(),
|
|
block_sizes=block_sizes,
|
|
is_spec_decode=bool(self.vllm_config.speculative_config),
|
|
logitsprocs=self.input_batch.logitsprocs,
|
|
is_pooling_model=self.is_pooling_model,
|
|
num_speculative_tokens=(
|
|
self.vllm_config.speculative_config.num_speculative_tokens
|
|
if self.vllm_config.speculative_config
|
|
else 0
|
|
),
|
|
kernel_block_sizes=self.kernel_block_sizes,
|
|
max_num_blocks_per_req=max_num_blocks,
|
|
kv_cache_groups=kv_cache_config.kv_cache_groups,
|
|
cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
|
|
)
|