fe4cad24e9
### What this PR does / why we need it?
This PR fixes a bug in `reshape_kvcache_tensors` when reshaping the
Mamba cache for models like Qwen3.5. The previous implementation did not
correctly handle cases where the KV cache tensors have different data
types. This change ensures that slicing is performed based on byte
offsets before reshaping the tensors, which correctly handles
heterogeneous dtypes.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
3240 lines
155 KiB
Python
3240 lines
155 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2025 The vLLM team.
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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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# Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py
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#
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import math
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import sys
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from collections import defaultdict
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from contextlib import contextmanager, nullcontext
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from copy import copy, deepcopy
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from dataclasses import dataclass
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from multiprocessing import Manager
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from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias
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import numpy as np
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from vllm.compilation.cuda_graph import CUDAGraphStat
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from vllm.config import CompilationMode, CUDAGraphMode, VllmConfig, get_layers_from_vllm_config
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from vllm.distributed import get_tensor_model_parallel_world_size, tensor_model_parallel_all_gather
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from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
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from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
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from vllm.distributed.parallel_state import get_dcp_group, get_dp_group, get_pcp_group, get_pp_group, get_tp_group
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from vllm.forward_context import BatchDescriptor, get_forward_context
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from vllm.logger import logger
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.layers.mamba.abstract import MambaBase
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from vllm.model_executor.model_loader import get_model
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from vllm.sequence import IntermediateTensors
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from vllm.utils.import_utils import LazyLoader
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from vllm.utils.math_utils import cdiv, round_up
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from vllm.utils.mem_utils import DeviceMemoryProfiler
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from vllm.utils.torch_utils import get_dtype_size
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from vllm.v1.attention.backend import AttentionBackend, AttentionMetadata
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import CommonAttentionMetadata
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from vllm.v1.attention.selector import get_attn_backend # type: ignore
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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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KVCacheGroupSpec,
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KVCacheSpec,
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MambaSpec,
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MLAAttentionSpec,
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UniformTypeKVCacheSpecs,
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)
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from vllm.v1.outputs import (
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EMPTY_MODEL_RUNNER_OUTPUT,
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AsyncModelRunnerOutput,
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ECConnectorOutput,
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LogprobsLists,
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LogprobsTensors,
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ModelRunnerOutput,
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SamplerOutput,
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make_empty_encoder_model_runner_output,
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)
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from vllm.v1.sample.logits_processor import build_logitsprocs
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.rejection_sampler import RejectionSampler
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import record_function_or_nullcontext
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from vllm.v1.worker.gpu_model_runner import AsyncGPUModelRunnerOutput, GPUModelRunner
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from vllm.v1.worker.ubatch_utils import (
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UBatchSlices,
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maybe_create_ubatch_slices,
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)
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from vllm.v1.worker.utils import AttentionGroup
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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from vllm_ascend.attention.utils import AscendCommonAttentionMetadata, using_paged_attention
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# yapf conflicts with isort for this block
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# yapf: disable
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from vllm_ascend.compilation.acl_graph import (
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ACLGraphWrapper,
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set_draft_graph_params,
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set_graph_params,
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update_full_graph_params,
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)
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# yapf: enable
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from vllm_ascend.eplb.adaptor.vllm_adaptor import VllmEplbAdaptor
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from vllm_ascend.eplb.core.eplb_device_transfer_loader import D2DExpertWeightLoader
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from vllm_ascend.eplb.core.eplb_worker import EplbProcess
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from vllm_ascend.eplb.eplb_updator import EplbUpdator
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from vllm_ascend.eplb.utils import model_register
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from vllm_ascend.ops.rotary_embedding import set_cos_and_sin, update_cos_sin
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from vllm_ascend.patch.worker.patch_draft_quarot import patch_load_weights
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from vllm_ascend.patch.worker.patch_module import patch_torch_npu_argsort
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from vllm_ascend.sample.sampler import AscendSampler
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from vllm_ascend.spec_decode import get_spec_decode_method
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from vllm_ascend.spec_decode.eagle_proposer import AscendEagleProposer
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from vllm_ascend.spec_decode.medusa_proposer import AscendMedusaProposer
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from vllm_ascend.spec_decode.ngram_proposer import AscendNgramProposer
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from vllm_ascend.spec_decode.suffix_proposer import AscendSuffixDecodingProposer
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from vllm_ascend.utils import (
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check_gdn_layer,
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enable_sp,
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enable_sp_by_pass,
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global_stream,
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is_drafter_moe_model,
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is_moe_model,
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lmhead_tp_enable,
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set_weight_prefetch_method,
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vllm_version_is,
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)
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from vllm_ascend.worker.npu_input_batch import NPUInputBatch
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from vllm_ascend.worker.pcp_utils import PCPManager
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from vllm_ascend.ascend_forward_context import ( # isort: skip
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MoECommType,
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get_mc2_tokens_capacity,
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select_moe_comm_method,
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set_ascend_forward_context,
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set_mc2_mask,
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set_mc2_tokens_capacity,
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)
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from vllm.model_executor.layers.fused_moe.routed_experts_capturer import RoutedExpertsCapturer
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if TYPE_CHECKING:
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import xgrammar as xgr # type: ignore[import-untyped]
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from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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else:
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xgr = LazyLoader("xgr", globals(), "xgrammar")
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from vllm.model_executor.layers.attention import Attention, MLAAttention
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# if true, allow tensor initialization and casting with internal format (e.g., NZ)
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torch.npu.config.allow_internal_format = True
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AttnMetadataDict: TypeAlias = dict[str, AttentionMetadata]
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# list when ubatching is enabled
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PerLayerAttnMetadata: TypeAlias = list[AttnMetadataDict] | AttnMetadataDict
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SEQ_LEN_WITH_MAX_PA_WORKSPACE = 6144
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@dataclass
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class GraphCaptureContext:
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stream: torch.npu.Stream
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@contextmanager
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def graph_capture(device: torch.device):
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"""
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`graph_capture` is a context manager which should surround the code that
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is capturing the NPU graph. Its main purpose is to ensure that the
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some operations will be run after the graph is captured, before the graph
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is replayed. It returns a `GraphCaptureContext` object which contains the
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necessary data for the graph capture. Currently, it only contains the
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stream that the graph capture is running on. This stream is set to the
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current NPU stream when the context manager is entered and reset to the
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default stream when the context manager is exited. This is to ensure that
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the graph capture is running on a separate stream from the default stream,
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in order to explicitly distinguish the kernels to capture
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from other kernels possibly launched on background in the default stream.
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"""
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graph_capture_context = GraphCaptureContext(torch.npu.Stream(device=device))
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stream = graph_capture_context.stream
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# we use nullcontext now
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maybe_ca_context = nullcontext()
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# ensure all initialization operations complete before attempting to
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# capture the graph on another stream
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curr_stream = torch.npu.current_stream()
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if curr_stream != stream:
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stream.wait_stream(curr_stream)
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with torch.npu.stream(stream), maybe_ca_context:
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yield graph_capture_context
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def get_tp_context(drafter):
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return getattr(drafter, "tp_group_context", nullcontext())
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class ExecuteModelState(NamedTuple):
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"""Ephemeral cached state transferred between execute_model() and
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sample_tokens(), after execute_model() returns None."""
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scheduler_output: "SchedulerOutput"
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logits: torch.Tensor
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spec_decode_metadata: SpecDecodeMetadata | None
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spec_decode_common_attn_metadata: AscendCommonAttentionMetadata | None
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hidden_states: torch.Tensor
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sample_hidden_states: torch.Tensor
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aux_hidden_states: list[torch.Tensor] | None
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attn_metadata: "PerLayerAttnMetadata"
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positions: torch.Tensor
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ec_connector_output: "ECConnectorOutput | None"
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cudagraph_stats: CUDAGraphStat | None
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class NPUModelRunner(GPUModelRunner):
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def __init__(self, vllm_config: VllmConfig, device: torch.device):
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# TODO(qcs): These manual pad and unpad for GPUModelRunner are
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# used to expand some buffers, which need to be reverted after
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# the following PR is merged:
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# https://github.com/vllm-project/vllm/pull/28988
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max_pcp_pad_tokens = (
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vllm_config.parallel_config.prefill_context_parallel_size * 2 * vllm_config.scheduler_config.max_num_seqs
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)
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vllm_config.scheduler_config.max_num_batched_tokens += max_pcp_pad_tokens
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with _torch_cuda_wrapper():
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super().__init__(vllm_config, device)
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# NOTE: For FULL mode we change +1 to +2 to reserve extra space for padding.
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# See _pad_query_start_loc_for_fia.
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self.query_start_loc = self._make_buffer(
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self.max_num_reqs + 2, # type: ignore[has-type]
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dtype=torch.int32,
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)
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# Now, query_start_loc is padded.
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# But gdn needs an unpadded one.
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# gdn_query_start_loc is an unpadded version of query_start_loc.
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# TODO delete it if fia's check is removed.
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self._has_gdn = check_gdn_layer(vllm_config)
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if self._has_gdn:
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self.gdn_query_start_loc = self._make_buffer(
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self.max_num_reqs + 1, # type: ignore[has-type]
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dtype=torch.int32,
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)
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vllm_config.scheduler_config.max_num_batched_tokens -= max_pcp_pad_tokens
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self.max_num_tokens = self.scheduler_config.max_num_batched_tokens
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self.max_num_reqs = self.scheduler_config.max_num_seqs
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self.dp_size = vllm_config.parallel_config.data_parallel_size
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self.dp_rank = vllm_config.parallel_config.data_parallel_rank
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self.sampler = AscendSampler()
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self.attn_state: AscendAttentionState | None = None
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# Ascend-specific configurations
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self.ascend_config = get_ascend_config()
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set_weight_prefetch_method(self.ascend_config.weight_prefetch_config)
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# Dump / PrecisionDebugger configuration now comes from AscendConfig
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dump_cfg = self.ascend_config.dump_config_path
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self.debugger = None
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if dump_cfg is not None:
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if self.model_config.enforce_eager:
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from msprobe.pytorch import PrecisionDebugger
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self.debugger = PrecisionDebugger(dump_cfg)
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else:
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raise RuntimeError("Dumping/debugging only works in eager mode.")
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# use_hybrid_blocks: if hybrid blocks is used.
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self.use_hybrid_blocks: bool = False
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self.need_accepted_tokens: bool = False
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self.is_multimodal_model = self.model_config.is_multimodal_model
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self.block_size = vllm_config.cache_config.block_size
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# Set up Attention
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self.use_sparse = hasattr(self.vllm_config.model_config.hf_text_config, "index_topk")
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self.attn_backend = get_attn_backend(
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0,
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self.dtype,
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None,
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self.block_size,
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use_mla=self.model_config.use_mla,
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use_sparse=self.use_sparse,
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use_mm_prefix=self.model_config is not None and self.model_config.is_mm_prefix_lm,
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)
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try:
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self.dcp_size = get_dcp_group().world_size
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self.dcp_rank = get_dcp_group().rank_in_group
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self.pcp_size = get_pcp_group().world_size
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self.pcp_rank = get_pcp_group().rank_in_group if self.pcp_size > 1 else 0
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except Exception:
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self.dcp_size = 1
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self.dcp_rank = 0
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self.pcp_size = 1
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self.pcp_rank = 0
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if self.pcp_size > 1:
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self.model_config.max_model_len += 2 * self.pcp_size * self.max_num_reqs
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max_buffer_num_tokens = self.max_num_tokens
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if self.pcp_size * self.dcp_size > 1:
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max_buffer_num_tokens = self.max_num_tokens + self.max_num_reqs * 2 * self.pcp_size
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self.pcp_manager = PCPManager(
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self.pcp_size,
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self.pcp_rank,
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self.dcp_size,
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self.dcp_rank,
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max_buffer_num_tokens,
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self.max_num_reqs,
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self.device,
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||
self.vllm_config,
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self.use_async_scheduling,
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self.pin_memory,
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self.use_sparse,
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)
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# TODO(zhenwenqi) after https://github.com/vllm-project/vllm/pull/28988 is merged, we can delete this
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self.input_ids = self._make_buffer(max_buffer_num_tokens, dtype=torch.int32)
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self.positions = self._make_buffer(max_buffer_num_tokens, dtype=torch.int64)
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self._set_up_drafter()
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|
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# kv role
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self.is_kv_producer = False
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self.is_kv_consumer = False
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if vllm_config.kv_transfer_config is not None:
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self.is_kv_producer = vllm_config.kv_transfer_config.is_kv_producer
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self.is_kv_consumer = vllm_config.kv_transfer_config.is_kv_consumer
|
||
|
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set_cos_and_sin(vllm_config, self.max_num_reqs, self.uniform_decode_query_len, self.dtype, self.device)
|
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set_mc2_tokens_capacity(vllm_config, self.max_num_reqs, self.uniform_decode_query_len)
|
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set_mc2_mask(vllm_config, self.device)
|
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self.decode_threshold = 1 + (self.speculative_config.num_speculative_tokens if self.speculative_config else 0)
|
||
|
||
self.use_aclgraph = self._use_aclgraph()
|
||
|
||
eplb_config = self.ascend_config.eplb_config
|
||
self.dynamic_eplb = eplb_config.dynamic_eplb
|
||
if self.dynamic_eplb:
|
||
self.is_eplb_warmuped = False
|
||
self.policy_type = eplb_config.eplb_policy_type
|
||
self.eplb_loader = D2DExpertWeightLoader()
|
||
self.manager = Manager()
|
||
self.shared_dict = self.manager.dict({"expert_map": None, "moe_load": None, "expert_maps": None})
|
||
self.eplb_process = EplbProcess(shared_dict=self.shared_dict, policy_type=self.policy_type, enable_d2d=True)
|
||
self.process = self.eplb_process._launch_process()
|
||
self.eplb_updator = EplbUpdator(eplb_config, self.eplb_loader, self.eplb_process, self.process)
|
||
# Input Batch
|
||
# NOTE(Chen): Ideally, we should initialize the input batch inside
|
||
# `initialize_kv_cache` based on the kv cache config. However, as in
|
||
# https://github.com/vllm-project/vllm/pull/18298, due to some unknown
|
||
# reasons, we have to initialize the input batch before `load_model`,
|
||
# quantization + weight offloading will fail otherwise. As a temporary
|
||
# solution, we initialize the input batch here, and re-initialize it
|
||
# in `initialize_kv_cache` if the block_sizes here is different from
|
||
# the block_sizes in the kv cache config.
|
||
self.input_batch = NPUInputBatch(
|
||
max_num_reqs=self.max_num_reqs,
|
||
max_model_len=max(self.model_config.max_model_len, self.max_encoder_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=[self.block_size],
|
||
kernel_block_sizes=[[self.cache_config.block_size]],
|
||
is_spec_decode=bool(self.vllm_config.speculative_config),
|
||
logitsprocs=build_logitsprocs(
|
||
self.vllm_config,
|
||
self.device,
|
||
self.pin_memory,
|
||
self.is_pooling_model,
|
||
self.vllm_config.model_config.logits_processors,
|
||
),
|
||
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
|
||
),
|
||
cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
|
||
)
|
||
self.num_draft_tokens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
|
||
# here we use int32
|
||
self.sampled_token_ids_pinned_cpu = torch.empty(
|
||
(self.max_num_reqs, 1),
|
||
dtype=torch.int32,
|
||
device="cpu",
|
||
pin_memory=self.pin_memory,
|
||
)
|
||
# for cleancode , actually the three attrs is defined in gpu_model_runner
|
||
self.execute_model_state: ExecuteModelState | None = None
|
||
# None in the first PP rank. The rest are set after load_model.
|
||
self.intermediate_tensors: IntermediateTensors | None = None
|
||
self.reorder_batch_threshold: int | None = None
|
||
self.long_seq_metadata = None
|
||
self.query_lens: torch.Tensor | None = None
|
||
self.cpu_slot_mapping = None
|
||
self.sampling_done_event: torch.npu.Event | None = None
|
||
|
||
@property
|
||
def use_cp(self) -> bool:
|
||
return self.pcp_size * self.dcp_size > 1
|
||
|
||
def _init_device_properties(self) -> None:
|
||
self.num_sms = None
|
||
|
||
def _sync_device(self) -> None:
|
||
torch.npu.synchronize()
|
||
|
||
def _set_up_drafter(self):
|
||
# Set up speculative decoding.
|
||
self.drafter: (
|
||
AscendNgramProposer | AscendEagleProposer | AscendSuffixDecodingProposer | AscendMedusaProposer | None
|
||
) = None
|
||
self.actual_seq_lengths_q: list[int] = []
|
||
self.decode_token_per_req = 1
|
||
if self.speculative_config:
|
||
spec_token_num = self.speculative_config.num_speculative_tokens
|
||
assert spec_token_num > 0
|
||
self.decode_token_per_req = 1 + spec_token_num
|
||
if get_pp_group().is_last_rank:
|
||
self.drafter = self._get_drafter()
|
||
if self.speculative_config.method == "eagle3":
|
||
assert isinstance(self.drafter, AscendEagleProposer)
|
||
self.use_aux_hidden_state_outputs = self.drafter.eagle3_use_aux_hidden_state
|
||
self.rejection_sampler = RejectionSampler(self.sampler)
|
||
self.discard_request_indices = self._make_buffer(self.max_num_reqs, dtype=torch.int64)
|
||
self.num_discarded_requests = 0
|
||
|
||
def _get_drafter(self):
|
||
return get_spec_decode_method(self.speculative_config.method, self.vllm_config, self.device, self)
|
||
|
||
def _use_aclgraph(self) -> bool:
|
||
return (
|
||
self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
|
||
and self.compilation_config.mode == CompilationMode.VLLM_COMPILE
|
||
and not self.model_config.enforce_eager
|
||
)
|
||
|
||
def _skip_all_reduce_across_dp_group(self, is_draft_model=False) -> bool:
|
||
"""
|
||
Decide whether to skip the all-reduce across the data-parallel (DP) group.
|
||
|
||
Skipping is applicable for all dense models and for moe models only on ranks
|
||
that act as KV consumers. We skip the DP all-reduce when either:
|
||
- Both the prefill and decode communication methods are MC2 (or FUSED_MC2), or
|
||
- Decode requires MC2 and ascend_config.recompute_scheduler_enable is True.
|
||
"""
|
||
# For dense models, since we don't actually need dp communication, we simply skip it.
|
||
# This usually happens when main model is moe while eagle draft model is dense.
|
||
is_context_moe_model = (
|
||
is_drafter_moe_model(self.vllm_config) if is_draft_model else is_moe_model(self.vllm_config)
|
||
)
|
||
if not is_context_moe_model:
|
||
return True
|
||
|
||
# Only applicable to MoE models on KV consumer ranks.
|
||
if not self.is_kv_consumer:
|
||
return False
|
||
|
||
def needs_mc2(num_tokens: int) -> bool:
|
||
return select_moe_comm_method(num_tokens, self.vllm_config) in {MoECommType.MC2, MoECommType.FUSED_MC2}
|
||
|
||
# Determine whether decode must use MC2. Use max cudagraph capture size
|
||
# if available, otherwise use the maximal uniform decode token count.
|
||
if self.compilation_config.cudagraph_capture_sizes:
|
||
potential_max_tokens = self.compilation_config.max_cudagraph_capture_size
|
||
else:
|
||
potential_max_tokens = self.max_num_reqs * self.uniform_decode_query_len
|
||
decode_must_use_mc2 = needs_mc2(potential_max_tokens)
|
||
|
||
# For prefill, use the scheduler's max_num_batched_tokens for a single
|
||
# batch.
|
||
prefill_must_use_mc2 = needs_mc2(self.vllm_config.scheduler_config.max_num_batched_tokens)
|
||
|
||
# Skip all-reduce if decode requires MC2 and either prefill also
|
||
# requires MC2 or recompute-based scheduler is enabled.
|
||
return decode_must_use_mc2 and (prefill_must_use_mc2 or self.ascend_config.recompute_scheduler_enable)
|
||
|
||
def _sync_metadata_across_dp(
|
||
self, num_tokens: int, with_prefill: bool = False, is_draft_model: bool = False
|
||
) -> tuple[int, torch.Tensor | None, bool]:
|
||
# TODO: In vLLM, the only thing that needs to be synced is num_tokens, but in
|
||
# our case, we still need to sync the other two flags as well. So we need to
|
||
# include them in the all_reduce operation, and more over, we CANNOT skip it
|
||
# even if we are running in eager mode, which harms performance.
|
||
# FIXME: Restore the `or self.vllm_config.model_config.enforce_eager` here
|
||
# immediately once the other two flags are no longer needed.
|
||
if self.dp_size == 1:
|
||
return num_tokens, None, with_prefill
|
||
|
||
if self._skip_all_reduce_across_dp_group(is_draft_model):
|
||
num_tokens_after_padding = torch.tensor([num_tokens] * self.dp_size, device="cpu", dtype=torch.int32)
|
||
return num_tokens, num_tokens_after_padding, with_prefill
|
||
|
||
# Sync num_tokens, with_prefill across dp ranks
|
||
num_tokens_tensor = torch.tensor(
|
||
[num_tokens if i == self.dp_rank else 0 for i in range(self.dp_size)], dtype=torch.int32, device="cpu"
|
||
)
|
||
|
||
flags_tensor = torch.tensor([int(with_prefill)], dtype=torch.int32, device="cpu")
|
||
|
||
packed_tensor = torch.cat([num_tokens_tensor, flags_tensor])
|
||
# use cpu_group to avoid cpu synchronization issue.
|
||
# it can be overlapped with main moell execution on npu.
|
||
dist.all_reduce(packed_tensor, group=get_dp_group().cpu_group)
|
||
|
||
# Unpack the results
|
||
num_tokens_across_dp = packed_tensor[:-1]
|
||
synced_flags = packed_tensor[-1:]
|
||
max_tokens_across_dp = torch.max(num_tokens_across_dp).item()
|
||
global_with_prefill = bool(synced_flags[0])
|
||
|
||
# Create a tensor for num_tokens_after_padding
|
||
num_tokens_after_padding = torch.tensor([max_tokens_across_dp] * self.dp_size, device="cpu", dtype=torch.int32)
|
||
|
||
return max_tokens_across_dp, num_tokens_after_padding, global_with_prefill
|
||
|
||
def get_model(self) -> nn.Module:
|
||
# get raw model out of the aclgraph wrapper.
|
||
if isinstance(self.model, ACLGraphWrapper):
|
||
return self.model.unwrap()
|
||
return self.model
|
||
|
||
def _pad_query_start_loc_for_fia(
|
||
self,
|
||
num_tokens_padded: int,
|
||
num_reqs_padded: int,
|
||
num_reqs: int,
|
||
cudagraph_runtime_mode: CUDAGraphMode | None = None,
|
||
batch_desc_num_reqs: int | None = None,
|
||
) -> int:
|
||
"""
|
||
This function is only designed to satisfied the constraint that when the layout is TND,
|
||
the first dimension of `hidden_states` must equal the last element of `actual_seq_lengths_q`.
|
||
"""
|
||
# TODO: need refactor later, related to vllm PR #34043 this pr delete func
|
||
# relax_for_mixed_batch_cudagraphs, num_reqs no longer equals the actual number of requests.
|
||
if cudagraph_runtime_mode == CUDAGraphMode.FULL:
|
||
num_reqs_padded = num_reqs
|
||
else:
|
||
num_reqs_padded = batch_desc_num_reqs if batch_desc_num_reqs is not None else num_reqs
|
||
|
||
if num_tokens_padded == num_reqs_padded * self.uniform_decode_query_len:
|
||
# Uniform-batch case: num_reqs must be no greater than num_reqs_padded
|
||
assert num_reqs <= num_reqs_padded
|
||
|
||
last_loc = self.query_start_loc.np[num_reqs]
|
||
self.query_start_loc.np[num_reqs + 1 : num_reqs_padded + 1] = (
|
||
self.arange_np[1 : num_reqs_padded + 1 - num_reqs] * self.uniform_decode_query_len + last_loc
|
||
)
|
||
else:
|
||
# Mixed-batch case: num_reqs must equal num_reqs_padded
|
||
assert num_reqs == num_reqs_padded
|
||
|
||
# Insert a dummy request instead of setting query_start_loc[num_reqs] = num_tokens_padded directly
|
||
self.query_start_loc.np[num_reqs_padded + 1] = num_tokens_padded
|
||
num_reqs_padded = num_reqs_padded + 1
|
||
|
||
self.query_start_loc.copy_to_gpu()
|
||
|
||
return num_reqs_padded
|
||
|
||
def _prepare_inputs(
|
||
self,
|
||
scheduler_output: "SchedulerOutput",
|
||
num_scheduled_tokens: np.ndarray,
|
||
) -> tuple[torch.Tensor, SpecDecodeMetadata | None, int]:
|
||
"""
|
||
:return: tuple[
|
||
logits_indices,
|
||
spec_decode_metadata,
|
||
total_num_scheduled_tokens,
|
||
]
|
||
"""
|
||
total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
|
||
assert total_num_scheduled_tokens > 0
|
||
num_reqs = self.input_batch.num_reqs
|
||
assert num_reqs > 0
|
||
|
||
# OPTIMIZATION: Start copying the block table first.
|
||
# This way, we can overlap the copy with the following CPU operations.
|
||
self.input_batch.block_table.commit_block_table(num_reqs)
|
||
|
||
req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
|
||
|
||
# Get the attention state.
|
||
if not scheduler_output.scheduled_spec_decode_tokens:
|
||
num_valid_tokens = num_scheduled_tokens
|
||
else:
|
||
num_valid_tokens = np.array(
|
||
[
|
||
scheduler_output.num_scheduled_tokens[i]
|
||
- len(scheduler_output.scheduled_spec_decode_tokens.get(i, []))
|
||
for i in self.input_batch.req_ids
|
||
],
|
||
dtype=np.int32,
|
||
)
|
||
attn_state = self._build_attn_state(num_reqs, num_scheduled_tokens, num_valid_tokens)
|
||
|
||
# Determine if it's a splitfuse batch
|
||
with_prefill = attn_state not in [AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding]
|
||
self.with_prefill = with_prefill
|
||
|
||
# Get positions.
|
||
positions_np = self.positions.np[:total_num_scheduled_tokens]
|
||
cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
|
||
np.add(self.input_batch.num_computed_tokens_cpu[req_indices], arange, out=positions_np)
|
||
|
||
self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
|
||
self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
|
||
|
||
if self.use_cp:
|
||
self.pcp_manager.init_batch_info(
|
||
num_scheduled_tokens,
|
||
self.input_batch.num_reqs,
|
||
)
|
||
|
||
# for pcp, prefill mtp should use origin scheduleroutput ,
|
||
if self.speculative_config and self.use_cp:
|
||
self.pcp_manager.generate_pcp_mtp_input(
|
||
total_num_scheduled_tokens,
|
||
scheduler_output.num_scheduled_tokens,
|
||
with_prefill,
|
||
self.input_batch,
|
||
self.arange_np,
|
||
req_indices,
|
||
positions_np,
|
||
cu_num_tokens,
|
||
self._draft_token_ids, # type: ignore[has-type]
|
||
scheduler_output,
|
||
self.num_spec_tokens,
|
||
)
|
||
|
||
if self.pcp_size > 1:
|
||
num_scheduled_tokens[:num_reqs], position_pcp = self.pcp_manager.update_tokens_for_pcp(
|
||
num_scheduled_tokens[:num_reqs], self.arange_np
|
||
)
|
||
# Re-update after PCP split sequences.
|
||
total_num_scheduled_tokens = sum(num_scheduled_tokens[:num_reqs])
|
||
req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
|
||
cu_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
|
||
positions_np = self.positions.np[:total_num_scheduled_tokens]
|
||
np.add(
|
||
self.input_batch.num_computed_tokens_cpu[req_indices],
|
||
position_pcp[:total_num_scheduled_tokens],
|
||
out=positions_np,
|
||
)
|
||
if self.pcp_size > 1 and self.pcp_manager.pcp_use_hybrid_attn:
|
||
assert self.pcp_manager.num_scheduled_tokens_padded is not None
|
||
self.query_lens = torch.from_numpy(self.pcp_manager.num_scheduled_tokens_padded)
|
||
else:
|
||
self.query_lens = torch.from_numpy(num_scheduled_tokens)
|
||
|
||
# Get token indices.
|
||
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
|
||
# -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
|
||
# where M is the max_model_len.
|
||
token_indices = positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
|
||
token_indices_tensor = torch.from_numpy(token_indices)
|
||
# Prepare input_ids.
|
||
# NOTE(woosuk): We use torch.index_select instead of np.take here
|
||
# because torch.index_select is much faster than np.take for large
|
||
# tensors.
|
||
torch.index_select(
|
||
self.input_batch.token_ids_cpu_tensor.flatten(),
|
||
0,
|
||
token_indices_tensor,
|
||
out=self.input_ids.cpu[:total_num_scheduled_tokens],
|
||
)
|
||
if self.enable_prompt_embeds:
|
||
is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
|
||
torch.index_select(
|
||
is_token_ids, 0, token_indices_tensor, out=self.is_token_ids.cpu[:total_num_scheduled_tokens]
|
||
)
|
||
|
||
# Because we did not pre-allocate a massive prompt_embeds CPU tensor on
|
||
# the InputBatch, we need to fill in the prompt embeds into the expected
|
||
# spots in the GpuModelRunner's pre-allocated prompt_embeds tensor.
|
||
if self.input_batch.req_prompt_embeds and (self.is_multimodal_model or self.enable_prompt_embeds):
|
||
output_idx = 0
|
||
for req_idx in range(num_reqs):
|
||
num_sched = num_scheduled_tokens[req_idx]
|
||
|
||
# Skip if this request doesn't have embeddings
|
||
if req_idx not in self.input_batch.req_prompt_embeds:
|
||
output_idx += num_sched
|
||
continue
|
||
|
||
# Skip if no tokens scheduled
|
||
if num_sched <= 0:
|
||
output_idx += num_sched
|
||
continue
|
||
|
||
req_embeds = self.input_batch.req_prompt_embeds[req_idx]
|
||
start_pos = self.input_batch.num_computed_tokens_cpu[req_idx]
|
||
|
||
# Skip if trying to read beyond available embeddings
|
||
if start_pos >= req_embeds.shape[0]:
|
||
output_idx += num_sched
|
||
continue
|
||
|
||
# Copy available embeddings
|
||
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
|
||
self.query_start_loc.copy_to_gpu()
|
||
|
||
# Now, query_start_loc is padded.
|
||
# But gdn needs an unpadded one.
|
||
# gdn_query_start_loc is an unpadded version of query_start_loc.
|
||
# TODO delete it if fia's check is removed.
|
||
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])
|
||
self.gdn_query_start_loc.copy_to_gpu()
|
||
|
||
self.seq_lens.np[:num_reqs] = self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
|
||
self.seq_lens.copy_to_gpu()
|
||
|
||
# Fill unused with -1. Needed for reshape_and_cache in attention_cp
|
||
self.query_start_loc.gpu[num_reqs + 1 :].fill_(-1)
|
||
self.seq_lens.gpu[num_reqs:].fill_(0)
|
||
|
||
# Copy the tensors to the NPU.
|
||
self._prepare_input_ids(scheduler_output, total_num_scheduled_tokens, cu_num_tokens)
|
||
# Calculate M-RoPE positions.
|
||
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
|
||
if self.uses_mrope:
|
||
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
|
||
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)
|
||
# Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
|
||
self.xdrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
|
||
self.xdrope_positions.cpu[:, :total_num_scheduled_tokens],
|
||
non_blocking=True,
|
||
)
|
||
else:
|
||
# Common case (1D positions)
|
||
self.positions.copy_to_gpu(total_num_scheduled_tokens)
|
||
|
||
# Record the index of requests that should not be sampled,
|
||
# so that we could clear the sampled tokens before returning
|
||
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:
|
||
# while pcp > 1, we need the original num_scheduled_tokens before split
|
||
# to calculate discard_requests_mask
|
||
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.seq_lens.np[:num_reqs] < 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)
|
||
use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
|
||
if not use_spec_decode:
|
||
# NOTE(woosuk): Due to chunked prefills, the batch may contain
|
||
# partial requests. While we should not sample any token
|
||
# from these partial requests, we do so for simplicity.
|
||
# We will ignore the sampled tokens from the partial requests.
|
||
# TODO: Support prompt logprobs.
|
||
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:
|
||
# Get the number of draft tokens for each request.
|
||
# Iterate over the dictionary rather than all requests since not all
|
||
# requests have draft tokens.
|
||
num_draft_tokens = np.zeros(num_reqs, dtype=np.int32)
|
||
# For chunked prefills, use -1 as mask rather than 0, as guided
|
||
# decoding may rollback speculative tokens.
|
||
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]
|
||
num_draft_tokens[req_idx] = len(draft_token_ids)
|
||
num_decode_draft_tokens[req_idx] = (
|
||
len(draft_token_ids)
|
||
if (
|
||
self.input_batch.num_computed_tokens_cpu[req_idx] >= self.input_batch.num_prompt_tokens[req_idx]
|
||
)
|
||
else -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
|
||
|
||
# For DECODE only cuda graph of some attention backends (e.g., GDN).
|
||
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()
|
||
# save logits_indices for pcp spec decode usage
|
||
self.logits_indices = logits_indices
|
||
|
||
# Hot-Swap lora model
|
||
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]))
|
||
|
||
return (
|
||
logits_indices,
|
||
spec_decode_metadata,
|
||
total_num_scheduled_tokens,
|
||
)
|
||
|
||
def _build_attn_state(self, num_reqs, num_scheduled_tokens, num_valid_tokens):
|
||
if np.all(self.input_batch.num_computed_tokens_cpu[:num_reqs] == 0):
|
||
attn_state = AscendAttentionState.PrefillNoCache
|
||
# We assume it is the decode stage, where prefill occurs but only one token is not hit in cache.
|
||
elif np.all(num_scheduled_tokens == 1):
|
||
attn_state = AscendAttentionState.DecodeOnly
|
||
if self.speculative_config and self.speculative_config.method == "mtp":
|
||
# SpecDecoding now supports seq_len=1 and seq_len=2
|
||
# In Prefilling Decoding Disaggregation scenario, SpecDecoding need to supports seq_len=1
|
||
attn_state = AscendAttentionState.SpecDecoding
|
||
# Speculative decoding.
|
||
elif np.all(num_valid_tokens == 1):
|
||
if self.speculative_config:
|
||
attn_state = AscendAttentionState.SpecDecoding
|
||
else:
|
||
attn_state = AscendAttentionState.ChunkedPrefill
|
||
# splitfuse
|
||
elif self.scheduler_config.enable_chunked_prefill:
|
||
attn_state = AscendAttentionState.ChunkedPrefill
|
||
else:
|
||
attn_state = AscendAttentionState.PrefillCacheHit
|
||
|
||
# For the overlay of the PCP feature and the eagle3, attn_state needs to be recovered
|
||
# TODO: Resolved the conflict between the sunset of attn_state and the PCP that requires this interface.
|
||
if attn_state == AscendAttentionState.SpecDecoding and self.speculative_config.method != "mtp":
|
||
self.attn_state = AscendAttentionState.ChunkedPrefill # type: ignore
|
||
else:
|
||
self.attn_state = attn_state # type: ignore
|
||
|
||
return attn_state
|
||
|
||
def _calc_spec_decode_metadata(
|
||
self,
|
||
num_draft_tokens: np.ndarray,
|
||
cu_num_scheduled_tokens: np.ndarray,
|
||
num_pcp_pads: np.ndarray | None,
|
||
) -> SpecDecodeMetadata:
|
||
# Inputs:
|
||
# cu_num_scheduled_tokens: [ 4, 104, 107, 207, 209]
|
||
# num_draft_tokens: [ 3, 0, 2, 0, 1]
|
||
# Outputs:
|
||
# cu_num_draft_tokens: [ 3, 3, 5, 5, 6]
|
||
# logits_indices: [ 0, 1, 2, 3, 103, 104, 105, 106,
|
||
# 206, 207, 208]
|
||
# target_logits_indices: [ 0, 1, 2, 5, 6, 9]
|
||
# bonus_logits_indices: [ 3, 4, 7, 8, 10]
|
||
|
||
# Compute the logits indices.
|
||
# [4, 1, 3, 1, 2]
|
||
num_sampled_tokens = num_draft_tokens + 1
|
||
# Step 1. [4, 5, 8, 9, 11]
|
||
cu_num_sampled_tokens = np.cumsum(num_sampled_tokens, dtype=np.int32)
|
||
total_num_sampled_tokens = cu_num_sampled_tokens[-1]
|
||
# Step 2. [0, 0, 0, 0, 4, 5, 5, 5, 8, 9, 9]
|
||
cumsums_offsets = np.repeat(cu_num_sampled_tokens - num_sampled_tokens, num_sampled_tokens)
|
||
# Step 3. [0, 1, 2, 3, 0, 0, 1, 2, 0, 0, 1]
|
||
arange = self.arange_np[:total_num_sampled_tokens] - cumsums_offsets
|
||
# Step 4. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
|
||
logits_indices = np.repeat(cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens)
|
||
# Step 5. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
|
||
logits_indices += arange
|
||
|
||
# while pcp > 1, decode results may contain padding (from pcp all-gather),
|
||
# update logits_indices after getting draft_token_ids from ori logits_indices
|
||
if self.pcp_size > 1:
|
||
cu_num_scheduled_tokens = cu_num_scheduled_tokens * self.pcp_size - num_pcp_pads
|
||
logits_indices_pcp = np.repeat(cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens)
|
||
logits_indices_pcp += arange
|
||
logits_indices_pcp = torch.from_numpy(logits_indices_pcp).pin_memory().to(self.device, non_blocking=True)
|
||
|
||
# Compute the bonus logits indices.
|
||
bonus_logits_indices = cu_num_sampled_tokens - 1
|
||
|
||
# Compute the draft logits indices.
|
||
# [3, 3, 5, 5, 6]
|
||
cu_num_draft_tokens = np.cumsum(num_draft_tokens, dtype=np.int32)
|
||
total_num_draft_tokens = cu_num_draft_tokens[-1]
|
||
# [0, 0, 0, 3, 3, 5]
|
||
cumsums_offsets = np.repeat(cu_num_draft_tokens - num_draft_tokens, num_draft_tokens)
|
||
# [0, 1, 2, 0, 1, 0]
|
||
arange = self.arange_np[:total_num_draft_tokens] - cumsums_offsets
|
||
# [0, 0, 0, 5, 5, 9]
|
||
target_logits_indices = np.repeat(cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens)
|
||
# [0, 1, 2, 5, 6, 9]
|
||
target_logits_indices += arange
|
||
|
||
# TODO: Optimize the CPU -> NPU copy.
|
||
cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).pin_memory().to(self.device, non_blocking=True)
|
||
cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).pin_memory().to(self.device, non_blocking=True)
|
||
logits_indices = torch.from_numpy(logits_indices).pin_memory().to(self.device, non_blocking=True)
|
||
target_logits_indices = torch.from_numpy(target_logits_indices).pin_memory().to(self.device, non_blocking=True)
|
||
bonus_logits_indices = torch.from_numpy(bonus_logits_indices).pin_memory().to(self.device, non_blocking=True)
|
||
|
||
# Compute the draft token ids.
|
||
# draft_token_indices: [ 1, 2, 3, 105, 106, 208]
|
||
draft_token_ids = self.input_ids.gpu[logits_indices]
|
||
draft_token_ids = draft_token_ids[target_logits_indices + 1]
|
||
if self.pcp_size > 1:
|
||
logits_indices = logits_indices_pcp
|
||
return SpecDecodeMetadata(
|
||
draft_token_ids=draft_token_ids,
|
||
num_draft_tokens=num_draft_tokens.tolist(),
|
||
cu_num_draft_tokens=cu_num_draft_tokens,
|
||
cu_num_sampled_tokens=cu_num_sampled_tokens,
|
||
target_logits_indices=target_logits_indices,
|
||
bonus_logits_indices=bonus_logits_indices,
|
||
logits_indices=logits_indices,
|
||
)
|
||
|
||
# TODO: Once the PCP features are complete, it will fully inherit the classes from the VLLM community.
|
||
def propose_draft_token_ids(
|
||
self,
|
||
valid_sampled_token_ids: torch.Tensor | list[list[int]],
|
||
sampling_metadata: SamplingMetadata,
|
||
scheduler_output: "SchedulerOutput",
|
||
spec_decode_metadata: SpecDecodeMetadata,
|
||
spec_decode_common_attn_metadata: AscendCommonAttentionMetadata,
|
||
positions: torch.Tensor,
|
||
num_scheduled_tokens: int,
|
||
hidden_states: torch.Tensor,
|
||
aux_hidden_states: torch.Tensor = None,
|
||
sample_hidden_states: torch.Tensor = None,
|
||
) -> list[list[int]] | None:
|
||
if not self.drafter:
|
||
# Speculative decoding is not enabled.
|
||
draft_token_ids = None
|
||
elif isinstance(self.drafter, (AscendNgramProposer, AscendSuffixDecodingProposer)):
|
||
draft_token_ids = self.drafter.propose(valid_sampled_token_ids)
|
||
elif isinstance(self.drafter, AscendMedusaProposer):
|
||
draft_token_ids = self.drafter.propose(
|
||
valid_sampled_token_ids, sampling_metadata, spec_decode_metadata, sample_hidden_states
|
||
)
|
||
elif self.speculative_config.use_eagle():
|
||
common_attn_metadata = spec_decode_common_attn_metadata
|
||
sampled_token_ids = valid_sampled_token_ids
|
||
|
||
if self.vllm_config.speculative_config.disable_padded_drafter_batch:
|
||
# When padded-batch is disabled, the sampled_token_ids should be
|
||
# the cpu-side list[list[int]] of valid sampled tokens for each
|
||
# request, with invalid requests having empty lists.
|
||
assert isinstance(sampled_token_ids, list), (
|
||
"sampled_token_ids should be a python list whenpadded-batch is disabled."
|
||
)
|
||
assert self.drafter is not None
|
||
next_token_ids = self.drafter.prepare_next_token_ids_cpu(
|
||
sampled_token_ids, self.requests, self.input_batch, scheduler_output.num_scheduled_tokens
|
||
)
|
||
else:
|
||
# When using padded-batch, the sampled_token_ids should be
|
||
# the gpu tensor of sampled tokens for each request, of shape
|
||
# (num_reqs, num_spec_tokens + 1) with rejected tokens having
|
||
# value -1.
|
||
assert isinstance(sampled_token_ids, torch.Tensor), (
|
||
"sampled_token_ids should be a torch.Tensor whenpadded-batch is enabled."
|
||
)
|
||
assert self.drafter is not None
|
||
next_token_ids, valid_sampled_tokens_count = self.drafter.prepare_next_token_ids_padded(
|
||
common_attn_metadata,
|
||
sampled_token_ids,
|
||
self.requests,
|
||
self.input_batch,
|
||
self.discard_request_indices.gpu,
|
||
self.num_discarded_requests,
|
||
)
|
||
self._copy_valid_sampled_token_count(next_token_ids, valid_sampled_tokens_count)
|
||
|
||
req_scheduled_tokens = scheduler_output.num_scheduled_tokens
|
||
if self.use_cp:
|
||
long_seq_metadata = self.long_seq_metadata # type: ignore
|
||
input_ids_pcp_full = self.pcp_manager.input_ids_pcp_full.gpu
|
||
query_start_loc_pcp_full = self.pcp_manager.query_start_loc_pcp_full.gpu
|
||
query_start_loc_pcp_full_cpu = self.pcp_manager.query_start_loc_pcp_full.cpu
|
||
num_reqs = self.input_batch.num_reqs
|
||
num_prefill_reqs = self.pcp_manager.num_prefill_reqs
|
||
num_decode_reqs = self.pcp_manager.num_decode_reqs
|
||
else:
|
||
long_seq_metadata = None # type: ignore
|
||
num_prefill_reqs = 0
|
||
num_decode_reqs = 0
|
||
if spec_decode_metadata is None:
|
||
# update pcp related params
|
||
if self.pcp_size > 1:
|
||
token_indices_to_sample = query_start_loc_pcp_full[1 : num_reqs + 1] - 1
|
||
target_token_ids = input_ids_pcp_full[:num_scheduled_tokens]
|
||
target_positions = self._get_positions(num_scheduled_tokens)
|
||
target_hidden_states = hidden_states
|
||
if self.use_aux_hidden_state_outputs:
|
||
target_hidden_states = torch.cat([h for h in aux_hidden_states], dim=-1)
|
||
else:
|
||
token_indices_to_sample = None
|
||
# input_ids can be None for multimodal models.
|
||
target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
|
||
target_positions = self._get_positions(num_scheduled_tokens)
|
||
if self.use_aux_hidden_state_outputs:
|
||
target_hidden_states = torch.cat([h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1)
|
||
else:
|
||
target_hidden_states = hidden_states[:num_scheduled_tokens]
|
||
else:
|
||
if self.pcp_size > 1:
|
||
assert common_attn_metadata is not None
|
||
common_attn_metadata.query_start_loc_cpu[: num_reqs + 1] = query_start_loc_pcp_full_cpu[
|
||
: num_reqs + 1
|
||
]
|
||
assert common_attn_metadata is not None
|
||
common_attn_metadata.query_start_loc[: num_reqs + 1] = query_start_loc_pcp_full[: num_reqs + 1]
|
||
if self.vllm_config.speculative_config.disable_padded_drafter_batch:
|
||
# NOTE: Currently, MTP-fullgraph is incompatibility with pcp
|
||
token_indices_to_sample = None
|
||
assert self.drafter is not None
|
||
common_attn_metadata, token_indices = self.drafter.prepare_inputs(
|
||
common_attn_metadata, sampled_token_ids, spec_decode_metadata.num_draft_tokens
|
||
)
|
||
else:
|
||
assert self.drafter is not None
|
||
common_attn_metadata, token_indices, token_indices_to_sample = self.drafter.prepare_inputs_padded(
|
||
common_attn_metadata, spec_decode_metadata, valid_sampled_tokens_count
|
||
)
|
||
if self.pcp_size > 1:
|
||
target_token_ids = input_ids_pcp_full[token_indices]
|
||
target_positions = positions
|
||
target_hidden_states = hidden_states
|
||
if self.use_aux_hidden_state_outputs:
|
||
target_hidden_states = torch.cat([h for h in aux_hidden_states], dim=-1)
|
||
else:
|
||
target_token_ids = self.input_ids.gpu[token_indices]
|
||
target_positions = self._get_positions(token_indices)
|
||
if self.use_aux_hidden_state_outputs:
|
||
target_hidden_states = torch.cat([h[token_indices] for h in aux_hidden_states], dim=-1)
|
||
else:
|
||
target_hidden_states = hidden_states[token_indices]
|
||
assert self.drafter is not None
|
||
draft_token_ids = self.drafter._propose(
|
||
target_token_ids=target_token_ids,
|
||
target_positions=target_positions,
|
||
target_hidden_states=target_hidden_states,
|
||
next_token_ids=next_token_ids,
|
||
last_token_indices=token_indices_to_sample,
|
||
common_attn_metadata=common_attn_metadata,
|
||
sampling_metadata=sampling_metadata,
|
||
req_scheduled_tokens=req_scheduled_tokens,
|
||
long_seq_metadata=long_seq_metadata,
|
||
num_prefill_reqs=num_prefill_reqs,
|
||
num_decode_reqs=num_decode_reqs,
|
||
scheduler_output=scheduler_output,
|
||
num_scheduled_tokens=num_scheduled_tokens,
|
||
)
|
||
else:
|
||
raise ValueError(f"Unknown speculative decoding method: {self.speculative_config.method}")
|
||
|
||
return draft_token_ids
|
||
|
||
@torch.inference_mode()
|
||
def execute_model(
|
||
self,
|
||
scheduler_output: "SchedulerOutput",
|
||
intermediate_tensors: IntermediateTensors | None = None,
|
||
) -> ModelRunnerOutput | IntermediateTensors | None:
|
||
if self.vllm_config.model_config.enable_return_routed_experts:
|
||
capturer = RoutedExpertsCapturer.get_instance()
|
||
if capturer is not None:
|
||
capturer.clear_buffer()
|
||
else:
|
||
logger.warning("RoutedExpertsCapturer is not initialized.")
|
||
if self.execute_model_state is not None:
|
||
raise RuntimeError("State error: sample_tokens() must be called after execute_model() returns None.")
|
||
# self._draft_token_ids is None when `input_fits_in_drafter=False`
|
||
# and there is no draft tokens scheduled. so it need to update the
|
||
# spec_decoding info in scheduler_output with async_scheduling.
|
||
# use deepcopy to avoid the modification has influence on the
|
||
# scheduler_output in engine core process.
|
||
# TODO(Ronald1995): deepcopy is expensive when there is a large
|
||
# number of requests, optimize it later.
|
||
if (
|
||
self.use_async_scheduling and self.num_spec_tokens and self._draft_token_ids is None # type: ignore[has-type]
|
||
):
|
||
scheduler_output = deepcopy(scheduler_output)
|
||
num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
|
||
with record_function_or_nullcontext("prepare input"):
|
||
with self.synchronize_input_prep():
|
||
# Update persistent batch states.
|
||
self._update_states(scheduler_output)
|
||
|
||
if has_ec_transfer() and get_ec_transfer().is_producer:
|
||
with self.maybe_get_ec_connector_output(
|
||
scheduler_output,
|
||
encoder_cache=self.encoder_cache,
|
||
) as ec_connector_output:
|
||
self._execute_mm_encoder(scheduler_output)
|
||
return make_empty_encoder_model_runner_output(scheduler_output)
|
||
|
||
if not num_scheduled_tokens:
|
||
if (
|
||
self.parallel_config.distributed_executor_backend == "external_launcher"
|
||
and self.parallel_config.data_parallel_size > 1
|
||
):
|
||
# this is a corner case when both external launcher
|
||
# and DP are enabled, num_scheduled_tokens could be
|
||
# 0, and has_unfinished_requests in the outer loop
|
||
# returns True. before returning early here we call
|
||
# dummy run to ensure coordinate_batch_across_dp
|
||
# is called into to avoid out of sync issues.
|
||
self._dummy_run(1)
|
||
if not has_kv_transfer_group():
|
||
# Return empty ModelRunnerOutput if no work to do.
|
||
return EMPTY_MODEL_RUNNER_OUTPUT
|
||
return self.kv_connector_no_forward(scheduler_output, self.vllm_config)
|
||
if self.cache_config.kv_sharing_fast_prefill:
|
||
assert not self.num_prompt_logprobs, (
|
||
"--kv-sharing-fast-prefill produces incorrect "
|
||
"logprobs for prompt tokens, tokens, please disable "
|
||
"it when the requests need prompt logprobs"
|
||
)
|
||
|
||
num_reqs = self.input_batch.num_reqs
|
||
req_ids = self.input_batch.req_ids
|
||
tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
|
||
num_scheduled_tokens_np = np.array(tokens, dtype=np.int32)
|
||
max_num_scheduled_tokens = int(num_scheduled_tokens_np.max())
|
||
|
||
(
|
||
logits_indices,
|
||
spec_decode_metadata,
|
||
total_num_scheduled_tokens,
|
||
) = self._prepare_inputs(
|
||
scheduler_output,
|
||
num_scheduled_tokens_np,
|
||
)
|
||
|
||
num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
|
||
if self.pcp_size > 1:
|
||
num_tokens_unpadded = self.pcp_manager.total_num_sampled_tokens_pcp
|
||
cascade_attn_prefix_lens = None
|
||
# Disable cascade attention when using microbatching (DBO)
|
||
if self.cascade_attn_enabled and not self.parallel_config.enable_dbo:
|
||
# Pre-compute cascade attention prefix lengths
|
||
cascade_attn_prefix_lens = self._compute_cascade_attn_prefix_lens(
|
||
num_scheduled_tokens_np,
|
||
self.input_batch.num_computed_tokens_cpu[:num_reqs],
|
||
scheduler_output.num_common_prefix_blocks,
|
||
)
|
||
|
||
(
|
||
cudagraph_mode,
|
||
batch_desc,
|
||
should_ubatch,
|
||
num_tokens_across_dp,
|
||
cudagraph_stats,
|
||
) = self._determine_batch_execution_and_padding(
|
||
num_tokens=num_tokens_unpadded,
|
||
num_reqs=num_reqs,
|
||
num_scheduled_tokens_np=num_scheduled_tokens_np,
|
||
max_num_scheduled_tokens=max_num_scheduled_tokens,
|
||
use_cascade_attn=cascade_attn_prefix_lens is not None,
|
||
num_encoder_reqs=len(scheduler_output.scheduled_encoder_inputs),
|
||
)
|
||
|
||
logger.debug(
|
||
"Running batch with cudagraph_mode: %s, batch_descriptor: %s, "
|
||
"should_ubatch: %s, num_tokens_across_dp: %s",
|
||
cudagraph_mode,
|
||
batch_desc,
|
||
should_ubatch,
|
||
num_tokens_across_dp,
|
||
)
|
||
|
||
num_tokens_padded = batch_desc.num_tokens
|
||
num_reqs_padded = batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
|
||
ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
|
||
should_ubatch,
|
||
num_scheduled_tokens_np,
|
||
num_tokens_padded,
|
||
num_reqs_padded,
|
||
self.parallel_config.num_ubatches,
|
||
)
|
||
|
||
pad_attn = cudagraph_mode == CUDAGraphMode.FULL
|
||
|
||
use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
|
||
ubatch_slices_attn = ubatch_slices_padded if pad_attn else ubatch_slices
|
||
|
||
if (
|
||
cudagraph_mode == CUDAGraphMode.FULL
|
||
or (enable_sp() and not self.model_config.use_mla)
|
||
and self.pcp_size == 1 # TODO(lxs): fix this
|
||
):
|
||
# Currently, Graph Mode and SP will both pad num_tokens,
|
||
# Another possible condition is num_tokens_padded != num_tokens_unpadded
|
||
# but this scope is way too big and the consequences are unpredictable
|
||
old_num_reqs_padded = num_reqs_padded
|
||
num_reqs_padded = self._pad_query_start_loc_for_fia(
|
||
num_tokens_padded, num_reqs_padded, num_reqs, cudagraph_mode, batch_desc.num_reqs
|
||
)
|
||
if enable_sp() and num_tokens_padded == num_tokens_unpadded:
|
||
if num_reqs_padded > old_num_reqs_padded:
|
||
num_reqs_padded = old_num_reqs_padded
|
||
self.query_start_loc.np[num_reqs_padded + 1] = 0
|
||
|
||
(attn_metadata, spec_decode_common_attn_metadata) = self._build_attention_metadata(
|
||
num_tokens=num_tokens_unpadded
|
||
if not (self.use_cp and self.pcp_manager.pcp_use_hybrid_attn)
|
||
else total_num_scheduled_tokens,
|
||
num_tokens_padded=num_tokens_padded,
|
||
num_reqs=num_reqs,
|
||
num_reqs_padded=num_reqs_padded,
|
||
max_query_len=max_num_scheduled_tokens,
|
||
ubatch_slices=ubatch_slices_attn,
|
||
logits_indices=logits_indices,
|
||
use_spec_decode=use_spec_decode,
|
||
num_scheduled_tokens=scheduler_output.num_scheduled_tokens,
|
||
num_scheduled_tokens_np=num_scheduled_tokens_np,
|
||
cascade_attn_prefix_lens=cascade_attn_prefix_lens,
|
||
)
|
||
|
||
(
|
||
input_ids,
|
||
inputs_embeds,
|
||
positions,
|
||
intermediate_tensors,
|
||
model_kwargs,
|
||
ec_connector_output,
|
||
) = self._preprocess(
|
||
scheduler_output,
|
||
num_tokens_padded
|
||
if not (self.use_cp and self.pcp_manager.pcp_use_hybrid_attn)
|
||
else total_num_scheduled_tokens,
|
||
intermediate_tensors,
|
||
)
|
||
|
||
# update global cos, sin
|
||
update_cos_sin(positions)
|
||
|
||
if self.dynamic_eplb:
|
||
with record_function_or_nullcontext("EPLB weight D2D"):
|
||
self.eplb_updator.forward_before()
|
||
|
||
# Set cudagraph mode to none if calc_kv_scales is true.
|
||
# KV scales calculation involves dynamic operations that are incompatible
|
||
# with CUDA graph capture.
|
||
if self.calculate_kv_scales: # type: ignore[has-type]
|
||
cudagraph_mode = CUDAGraphMode.NONE
|
||
# Mark KV scales as calculated after the first forward pass
|
||
self.calculate_kv_scales = False # type: ignore[has-type]
|
||
# prevent debugger is None
|
||
if self.debugger is not None:
|
||
dbg_cfg = getattr(self.debugger, "config", None)
|
||
dump_level = str(getattr(dbg_cfg, "level", "L1")).upper() if dbg_cfg is not None else "L1"
|
||
if dump_level in ("L0", "MIX"):
|
||
self.debugger.start(model=self.model)
|
||
else:
|
||
self.debugger.start()
|
||
if self.ascend_config.enable_async_exponential:
|
||
self.sampler.do_async_exponential(
|
||
b_s=logits_indices.shape[0],
|
||
head_dim=self.model_config.get_vocab_size(),
|
||
generators=self.input_batch.sampling_metadata.generators,
|
||
)
|
||
|
||
# Encoder-decoder models can only compile the pure decode steps where no
|
||
# encoder inputs are present. Use eager for the first pass.
|
||
num_encoder_reqs = len(scheduler_output.scheduled_encoder_inputs)
|
||
has_encoder_input = self.model_config.is_encoder_decoder and num_encoder_reqs > 0
|
||
|
||
# Run forward pass
|
||
clear_kv_metadata = self.speculative_config is None
|
||
if vllm_version_is("0.16.0"):
|
||
with (
|
||
record_function_or_nullcontext("forward"),
|
||
set_ascend_forward_context(
|
||
attn_metadata,
|
||
self.vllm_config,
|
||
num_tokens=num_tokens_padded,
|
||
num_tokens_across_dp=num_tokens_across_dp,
|
||
aclgraph_runtime_mode=cudagraph_mode,
|
||
batch_descriptor=batch_desc,
|
||
num_actual_tokens=scheduler_output.total_num_scheduled_tokens,
|
||
model_instance=self.model,
|
||
max_tokens_across_pcp=0 if self.pcp_size == 1 else self.pcp_manager.max_num_tokens_across_pcp,
|
||
skip_compiled=has_encoder_input,
|
||
),
|
||
self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
|
||
):
|
||
hidden_states = self._model_forward(
|
||
num_tokens_padded, input_ids, positions, intermediate_tensors, inputs_embeds, **model_kwargs
|
||
)
|
||
else:
|
||
with (
|
||
record_function_or_nullcontext("forward"),
|
||
set_ascend_forward_context(
|
||
attn_metadata,
|
||
self.vllm_config,
|
||
num_tokens=num_tokens_padded,
|
||
num_tokens_across_dp=num_tokens_across_dp,
|
||
aclgraph_runtime_mode=cudagraph_mode,
|
||
batch_descriptor=batch_desc,
|
||
num_actual_tokens=scheduler_output.total_num_scheduled_tokens,
|
||
model_instance=self.model,
|
||
max_tokens_across_pcp=0 if self.pcp_size == 1 else self.pcp_manager.max_num_tokens_across_pcp,
|
||
skip_compiled=has_encoder_input,
|
||
),
|
||
self.maybe_get_kv_connector_output(
|
||
scheduler_output, clear_metadata=clear_kv_metadata
|
||
) as kv_connector_output,
|
||
):
|
||
hidden_states = self._model_forward(
|
||
num_tokens_padded, input_ids, positions, intermediate_tensors, inputs_embeds, **model_kwargs
|
||
)
|
||
with record_function_or_nullcontext("post process"):
|
||
aux_hidden_states = None
|
||
if self.use_aux_hidden_state_outputs:
|
||
hidden_states, aux_hidden_states = hidden_states
|
||
if self.pcp_size > 1:
|
||
# NOTE we must `slice` hidden_states because pcp_allgather_restore_idx
|
||
# ignores the padding from CUDA Graph.
|
||
hidden_states = self.pcp_manager.get_restore_hidden_states(hidden_states)
|
||
if aux_hidden_states is not None:
|
||
aux_hidden_states = [
|
||
self.pcp_manager.get_restore_hidden_states(aux_hidden_states_pcp)
|
||
for aux_hidden_states_pcp in aux_hidden_states
|
||
]
|
||
|
||
if not self.broadcast_pp_output:
|
||
# Common case.
|
||
if not get_pp_group().is_last_rank:
|
||
# Return the intermediate tensors.
|
||
assert isinstance(hidden_states, IntermediateTensors)
|
||
hidden_states.kv_connector_output = kv_connector_output
|
||
self.kv_connector_output = kv_connector_output
|
||
if self.debugger is not None:
|
||
self.debugger.stop()
|
||
self.debugger.step()
|
||
return hidden_states
|
||
if self.is_pooling_model:
|
||
# Return the pooling output.
|
||
output = self._pool(
|
||
hidden_states, num_scheduled_tokens, num_scheduled_tokens_np, kv_connector_output
|
||
)
|
||
output.kv_connector_output = kv_connector_output
|
||
if self.debugger is not None:
|
||
self.debugger.stop()
|
||
self.debugger.step()
|
||
return output
|
||
|
||
sample_hidden_states = hidden_states[logits_indices]
|
||
logits = self.model.compute_logits(sample_hidden_states)
|
||
else:
|
||
# Rare case.
|
||
assert not self.is_pooling_model
|
||
|
||
if not get_pp_group().is_last_rank:
|
||
sample_hidden_states = hidden_states[logits_indices]
|
||
get_pp_group().send_tensor_dict(hidden_states.tensors, all_gather_group=get_tp_group())
|
||
logits = None
|
||
else:
|
||
sample_hidden_states = hidden_states[logits_indices]
|
||
logits = self.model.compute_logits(sample_hidden_states)
|
||
|
||
model_output_broadcast_data: dict[str, Any] = {}
|
||
if logits is not None:
|
||
model_output_broadcast_data["logits"] = logits.contiguous()
|
||
broadcasted = get_pp_group().broadcast_tensor_dict(
|
||
model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
|
||
)
|
||
assert broadcasted is not None
|
||
logits = broadcasted["logits"]
|
||
|
||
# Apply structured output bitmasks if present
|
||
self.execute_model_state = ExecuteModelState(
|
||
scheduler_output,
|
||
logits,
|
||
spec_decode_metadata,
|
||
spec_decode_common_attn_metadata,
|
||
hidden_states,
|
||
sample_hidden_states,
|
||
aux_hidden_states,
|
||
attn_metadata,
|
||
positions,
|
||
ec_connector_output,
|
||
cudagraph_stats,
|
||
)
|
||
self.kv_connector_output = kv_connector_output
|
||
return None
|
||
|
||
@torch.inference_mode()
|
||
def sample_tokens(
|
||
self, grammar_output: "GrammarOutput | None"
|
||
) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
|
||
kv_connector_output = self.kv_connector_output
|
||
self.kv_connector_output = None
|
||
|
||
if self.execute_model_state is None:
|
||
# Nothing to do (PP non-final rank case), output isn't used.
|
||
if not kv_connector_output:
|
||
return None # noqa
|
||
# In case of PP with kv transfer, we need to pass through the
|
||
# kv_connector_output
|
||
if kv_connector_output.is_empty():
|
||
return EMPTY_MODEL_RUNNER_OUTPUT
|
||
|
||
output = copy(EMPTY_MODEL_RUNNER_OUTPUT)
|
||
output.kv_connector_output = kv_connector_output
|
||
return output
|
||
|
||
# Unpack ephemeral state.
|
||
(
|
||
scheduler_output,
|
||
logits,
|
||
spec_decode_metadata,
|
||
spec_decode_common_attn_metadata,
|
||
hidden_states,
|
||
sample_hidden_states,
|
||
aux_hidden_states,
|
||
attn_metadata,
|
||
positions,
|
||
ec_connector_output,
|
||
cudagraph_stats,
|
||
) = self.execute_model_state
|
||
# Clear ephemeral state.
|
||
self.execute_model_state = None
|
||
|
||
# Apply structured output bitmasks if present.
|
||
if grammar_output is not None:
|
||
# here we are different from gpu_model_runner,
|
||
# the apply_grammar_bitmask uses torch.compile to optimize this,ascend does not support it now
|
||
logits_dtype = logits.dtype
|
||
logits = logits.to("cpu").float()
|
||
apply_grammar_bitmask(scheduler_output, grammar_output, self.input_batch, logits)
|
||
logits = logits.to(self.device).to(logits_dtype)
|
||
|
||
with record_function_or_nullcontext("sample_token"):
|
||
sampler_output = self._sample(logits, spec_decode_metadata)
|
||
|
||
if self.need_accepted_tokens:
|
||
if self.sampling_done_event is None:
|
||
self.sampling_done_event = torch.npu.Event()
|
||
|
||
assert self.sampling_done_event is not None
|
||
self.sampling_done_event.record()
|
||
|
||
def propose_draft_token_ids(sampled_token_ids):
|
||
assert spec_decode_common_attn_metadata is not None
|
||
self._draft_token_ids = self.propose_draft_token_ids(
|
||
sampled_token_ids,
|
||
self.input_batch.sampling_metadata,
|
||
scheduler_output,
|
||
spec_decode_metadata,
|
||
spec_decode_common_attn_metadata,
|
||
positions,
|
||
scheduler_output.total_num_scheduled_tokens,
|
||
hidden_states,
|
||
aux_hidden_states,
|
||
sample_hidden_states,
|
||
)
|
||
self._copy_draft_token_ids_to_cpu(scheduler_output)
|
||
|
||
(
|
||
logprobs_lists,
|
||
valid_sampled_token_ids,
|
||
prompt_logprobs_dict,
|
||
req_ids_output_copy,
|
||
req_id_to_index_output_copy,
|
||
invalid_req_indices,
|
||
) = self._bookkeeping_sync(
|
||
scheduler_output,
|
||
sampler_output,
|
||
logits,
|
||
hidden_states,
|
||
scheduler_output.total_num_scheduled_tokens,
|
||
spec_decode_metadata,
|
||
)
|
||
|
||
with record_function_or_nullcontext("draft_token"):
|
||
if self.speculative_config:
|
||
use_padded_batch_for_eagle = (
|
||
self.speculative_config
|
||
and self.speculative_config.use_eagle()
|
||
and not self.speculative_config.disable_padded_drafter_batch
|
||
)
|
||
if use_padded_batch_for_eagle:
|
||
# EAGLE speculative decoding can use the GPU sampled tokens
|
||
# as inputs, and does not need to wait for bookkeeping to finish.
|
||
propose_draft_token_ids(sampler_output.sampled_token_ids)
|
||
if self.speculative_config and not use_padded_batch_for_eagle:
|
||
# ngram and other speculative decoding methods use the sampled
|
||
# tokens on the CPU, so they are run after bookkeeping.
|
||
propose_draft_token_ids(valid_sampled_token_ids)
|
||
|
||
if has_kv_transfer_group():
|
||
get_kv_transfer_group().clear_connector_metadata()
|
||
|
||
if self.model_config.enable_return_routed_experts:
|
||
capturer = RoutedExpertsCapturer.get_instance()
|
||
if capturer is not None:
|
||
capturer.save_captured_experts(indices=self.cpu_slot_mapping)
|
||
else:
|
||
logger.warning("RoutedExpertsCapturer is not initialized.")
|
||
|
||
model_runner_output = ModelRunnerOutput(
|
||
req_ids=req_ids_output_copy,
|
||
req_id_to_index=req_id_to_index_output_copy,
|
||
sampled_token_ids=valid_sampled_token_ids,
|
||
logprobs=logprobs_lists,
|
||
prompt_logprobs_dict=prompt_logprobs_dict,
|
||
kv_connector_output=kv_connector_output,
|
||
pooler_output=[],
|
||
ec_connector_output=ec_connector_output if self.supports_mm_inputs else None,
|
||
cudagraph_stats=cudagraph_stats,
|
||
)
|
||
|
||
if self.dynamic_eplb:
|
||
with record_function_or_nullcontext("EPLB update"):
|
||
self.eplb_updator.forward_end()
|
||
|
||
if self.debugger is not None:
|
||
self.debugger.stop()
|
||
self.debugger.step()
|
||
|
||
if self.need_accepted_tokens:
|
||
assert self.sampling_done_event is not None
|
||
with (
|
||
record_function_or_nullcontext("async_state_update"),
|
||
torch.npu.stream(global_stream()),
|
||
):
|
||
global_stream().wait_event(self.sampling_done_event)
|
||
self._update_states_after_model_execute(sampler_output.sampled_token_ids, scheduler_output)
|
||
|
||
if not self.use_async_scheduling:
|
||
return model_runner_output
|
||
return AsyncGPUModelRunnerOutput(
|
||
model_runner_output=model_runner_output,
|
||
sampled_token_ids=sampler_output.sampled_token_ids,
|
||
logprobs_tensors=sampler_output.logprobs_tensors,
|
||
invalid_req_indices=invalid_req_indices,
|
||
async_output_copy_stream=self.async_output_copy_stream,
|
||
vocab_size=self.input_batch.vocab_size,
|
||
)
|
||
|
||
# overwrite _sample for lmhead_tp_enable and need_accepted_tokens
|
||
def _sample(self, logits, spec_decode_metadata):
|
||
# Sample the next token and get logprobs if needed.
|
||
sampling_metadata = self.input_batch.sampling_metadata
|
||
if spec_decode_metadata is None:
|
||
if lmhead_tp_enable() and logits is not None:
|
||
logits = logits[: self.input_batch.num_reqs]
|
||
return self.sampler(
|
||
logits=logits,
|
||
sampling_metadata=sampling_metadata,
|
||
)
|
||
|
||
if lmhead_tp_enable() and logits is not None:
|
||
logits = logits[: len(spec_decode_metadata.logits_indices)]
|
||
sampler_output = self.rejection_sampler(
|
||
spec_decode_metadata,
|
||
None, # draft_probs
|
||
logits,
|
||
sampling_metadata,
|
||
)
|
||
return sampler_output
|
||
|
||
# TODO: remove this func after eagle_proposer is refactored and
|
||
# _bookkeeping_sync is moved after propose_draft_token_ids
|
||
def _bookkeeping_sync(
|
||
self,
|
||
scheduler_output: "SchedulerOutput",
|
||
sampler_output: SamplerOutput,
|
||
logits: torch.Tensor | None,
|
||
hidden_states: torch.Tensor,
|
||
num_scheduled_tokens: int,
|
||
spec_decode_metadata: SpecDecodeMetadata | None,
|
||
) -> tuple[
|
||
LogprobsLists | None,
|
||
list[list[int]],
|
||
dict[str, LogprobsTensors | None],
|
||
list[str],
|
||
dict[str, int],
|
||
list[int],
|
||
]:
|
||
# TODO: implement PR 28597 from vllm
|
||
discard_sampled_tokens_req_indices = self.discard_request_indices.np[: self.num_discarded_requests]
|
||
for i in discard_sampled_tokens_req_indices:
|
||
gen = self.input_batch.generators.get(int(i))
|
||
if gen is not None:
|
||
gen.set_offset(gen.get_offset() - 4)
|
||
|
||
# Copy some objects so they don't get modified after returning.
|
||
# This is important when using async scheduling.
|
||
req_ids_output_copy = self.input_batch.req_ids.copy()
|
||
req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
|
||
|
||
num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
|
||
sampled_token_ids = sampler_output.sampled_token_ids
|
||
logprobs_tensors = sampler_output.logprobs_tensors
|
||
invalid_req_indices = []
|
||
cu_num_tokens: list[int] | None = None
|
||
if not self.use_async_scheduling:
|
||
# Get the valid generated tokens.
|
||
max_gen_len = sampled_token_ids.shape[-1]
|
||
if max_gen_len == 1:
|
||
# No spec decode tokens.
|
||
valid_sampled_token_ids = self._to_list(sampled_token_ids)
|
||
# Mask out the sampled tokens that should not be sampled.
|
||
for i in discard_sampled_tokens_req_indices:
|
||
valid_sampled_token_ids[int(i)].clear()
|
||
else:
|
||
# Includes spec decode tokens.
|
||
valid_sampled_token_ids, cu_num_tokens = RejectionSampler.parse_output(
|
||
sampled_token_ids,
|
||
self.input_batch.vocab_size,
|
||
discard_sampled_tokens_req_indices,
|
||
logprobs_tensors=logprobs_tensors,
|
||
)
|
||
else:
|
||
valid_sampled_token_ids = []
|
||
invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
|
||
invalid_req_indices_set = set(invalid_req_indices)
|
||
|
||
if self.num_spec_tokens <= 0:
|
||
assert sampled_token_ids.shape[-1] == 1
|
||
# Cache the sampled tokens on the NPU and avoid CPU sync.
|
||
# These will be copied into input_ids in the next step
|
||
# when preparing inputs.
|
||
self.input_batch.prev_sampled_token_ids = sampled_token_ids
|
||
|
||
self.input_batch.prev_req_id_to_index = {
|
||
req_id: i for i, req_id in enumerate(self.input_batch.req_ids) if i not in invalid_req_indices_set
|
||
}
|
||
|
||
# Cache the sampled tokens in the model runner, so that the scheduler
|
||
# doesn't need to send them back.
|
||
# NOTE(woosuk): As an exception, when using PP, the scheduler sends
|
||
# the sampled tokens back, because there's no direct communication
|
||
# between the first-stage worker and the last-stage worker.
|
||
req_ids = self.input_batch.req_ids
|
||
for req_idx in range(num_sampled_tokens):
|
||
if self.use_async_scheduling:
|
||
sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
|
||
else:
|
||
sampled_ids = valid_sampled_token_ids[req_idx]
|
||
|
||
num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
|
||
|
||
if not sampled_ids:
|
||
continue
|
||
|
||
start_idx = self.input_batch.num_tokens_no_spec[req_idx]
|
||
end_idx = start_idx + num_sampled_ids
|
||
assert end_idx <= self.max_model_len, (
|
||
"Sampled token IDs exceed the max model length. "
|
||
f"Total number of tokens: {end_idx} > max_model_len: "
|
||
f"{self.max_model_len}"
|
||
)
|
||
|
||
self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids
|
||
self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
|
||
self.input_batch.num_tokens_no_spec[req_idx] = end_idx
|
||
self.input_batch.num_tokens[req_idx] = end_idx
|
||
|
||
req_id = req_ids[req_idx]
|
||
req_state = self.requests[req_id]
|
||
req_state.output_token_ids.extend(sampled_ids)
|
||
|
||
logprobs_lists = (
|
||
logprobs_tensors.tolists(cu_num_tokens)
|
||
if not self.use_async_scheduling and logprobs_tensors is not None
|
||
else None
|
||
)
|
||
|
||
# Compute prompt logprobs if needed.
|
||
prompt_logprobs_dict = self._get_prompt_logprobs_dict(
|
||
hidden_states[:num_scheduled_tokens],
|
||
scheduler_output.num_scheduled_tokens,
|
||
)
|
||
|
||
return (
|
||
logprobs_lists,
|
||
valid_sampled_token_ids,
|
||
prompt_logprobs_dict,
|
||
req_ids_output_copy,
|
||
req_id_to_index_output_copy,
|
||
invalid_req_indices,
|
||
)
|
||
|
||
# all-gather one hidden-states in sp scene
|
||
@staticmethod
|
||
def _all_gather_hidden_states(hidden_states):
|
||
hidden_states = tensor_model_parallel_all_gather(hidden_states, 0)
|
||
pad_size = get_forward_context().pad_size
|
||
if pad_size > 0:
|
||
hidden_states = hidden_states[:-pad_size, :]
|
||
|
||
return hidden_states
|
||
|
||
# all-gather a list of hidden-states in sp scene
|
||
@staticmethod
|
||
def _all_gather_hidden_states_list(hidden_states_list):
|
||
return [NPUModelRunner._all_gather_hidden_states(hidden_states) for hidden_states in hidden_states_list]
|
||
|
||
# all-gather hidden-states in last layer with aux-hidden-states in sp scene
|
||
@staticmethod
|
||
def _all_gather_hidden_states_and_aux(hidden_states):
|
||
if isinstance(hidden_states, tuple):
|
||
return (
|
||
NPUModelRunner._all_gather_hidden_states(hidden_states[0]),
|
||
NPUModelRunner._all_gather_hidden_states_list(hidden_states[1]),
|
||
)
|
||
return NPUModelRunner._all_gather_hidden_states(hidden_states)
|
||
|
||
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],
|
||
):
|
||
assert self.model is not None
|
||
hidden_states = self.model(
|
||
input_ids=input_ids,
|
||
positions=positions,
|
||
intermediate_tensors=intermediate_tensors,
|
||
inputs_embeds=inputs_embeds,
|
||
**model_kwargs,
|
||
)
|
||
forward_context = get_forward_context()
|
||
assert forward_context is not None
|
||
if (
|
||
forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL
|
||
and not forward_context.capturing
|
||
and not self.use_sparse
|
||
):
|
||
assert positions is not None
|
||
update_full_graph_params(
|
||
self.attn_backend,
|
||
self.update_stream,
|
||
forward_context,
|
||
num_tokens_padded,
|
||
self.vllm_config,
|
||
self.speculative_config,
|
||
positions.shape[0],
|
||
)
|
||
if get_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 _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
|
||
# Pad tokens to multiple of tensor_parallel_size when
|
||
# enabled collective fusion for SP
|
||
tp_size = self.vllm_config.parallel_config.tensor_parallel_size
|
||
if enable_sp(self.vllm_config) or enable_sp_by_pass(self.vllm_config):
|
||
return round_up(num_scheduled_tokens, tp_size)
|
||
return num_scheduled_tokens
|
||
|
||
def _sync_batch_across_dp(
|
||
self,
|
||
num_tokens_padded: int | None = None,
|
||
cudagraph_mode: int = 0,
|
||
) -> tuple[bool, torch.Tensor | None, int]:
|
||
"""
|
||
Coordinates amongst all DP ranks to determine if and how the full batch
|
||
should be split into microbatches.
|
||
|
||
Args:
|
||
num_tokens_padded: Number of tokens including any non-DP padding (CUDA graphs,
|
||
TP, etc)
|
||
cudagraph_mode: The cudagraph mode for this rank (0=NONE, 1=PIECEWISE, 2=FULL)
|
||
|
||
Returns: tuple[
|
||
ubatch_slices: if this is set then all DP ranks have agreed to
|
||
microbatch
|
||
num_tokens_after_padding: A tensor containing the total number of
|
||
tokens per-microbatch for each DP rank including padding. Will be
|
||
padded up to the max value across all DP ranks when allow_dp_padding
|
||
is True.
|
||
synced_cudagraph_mode: The synchronized cudagraph mode (min across ranks)
|
||
]
|
||
|
||
"""
|
||
|
||
# TODO: In vLLM, the only thing that needs to be synced is num_tokens, but in
|
||
# our case, we still need to sync the other two flags as well. So we need to
|
||
# include them in the all_reduce operation, and more over, we CANNOT skip it
|
||
# even if we are running in eager mode, which harms performance.
|
||
# FIXME: Restore the `or self.vllm_config.model_config.enforce_eager` here
|
||
# immediately once the other two flags are no longer needed.
|
||
|
||
if self.dp_size == 1:
|
||
return False, None, cudagraph_mode
|
||
|
||
if self._skip_all_reduce_across_dp_group():
|
||
num_tokens_after_padding = torch.tensor([num_tokens_padded] * self.dp_size, device="cpu", dtype=torch.int32)
|
||
return False, num_tokens_after_padding, cudagraph_mode
|
||
|
||
tensor = torch.zeros(2, self.dp_size, device="cpu", dtype=torch.int32)
|
||
tensor[0][self.dp_rank] = num_tokens_padded
|
||
tensor[1][self.dp_rank] = cudagraph_mode
|
||
dist.all_reduce(tensor, group=get_dp_group().cpu_group)
|
||
|
||
num_tokens_across_dp = tensor[0, :]
|
||
max_num_tokens = int(num_tokens_across_dp.max().item())
|
||
num_tokens_after_padding = torch.tensor(
|
||
[max_num_tokens] * len(num_tokens_across_dp),
|
||
device="cpu",
|
||
dtype=torch.int32,
|
||
)
|
||
# Synchronize cudagraph_mode across ranks (take min)
|
||
synced_cudagraph_mode = _post_process_cudagraph_mode(tensor)
|
||
return False, num_tokens_after_padding, synced_cudagraph_mode
|
||
|
||
def _determine_batch_execution_and_padding(
|
||
self,
|
||
num_tokens: int,
|
||
num_reqs: int,
|
||
num_scheduled_tokens_np: np.ndarray,
|
||
max_num_scheduled_tokens: int,
|
||
use_cascade_attn: bool,
|
||
allow_microbatching: bool = False,
|
||
force_eager: bool = False,
|
||
# For cudagraph capture TODO(lucas): Refactor how we capture cudagraphs (will
|
||
# be improved in model runner v2)
|
||
force_uniform_decode: bool | None = None,
|
||
force_has_lora: bool | None = None,
|
||
force_num_active_loras: int | None = None,
|
||
num_encoder_reqs: int = 0,
|
||
) -> tuple[CUDAGraphMode, BatchDescriptor, bool, torch.Tensor | None, CUDAGraphStat | None]:
|
||
num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
|
||
is_all_decode = np.all(self.input_batch.num_computed_tokens_cpu[:num_reqs] > 0)
|
||
uniform_decode = (
|
||
(
|
||
(is_all_decode if self.speculative_config else True)
|
||
and (max_num_scheduled_tokens == self.uniform_decode_query_len)
|
||
and (num_tokens == max_num_scheduled_tokens * num_reqs)
|
||
)
|
||
if force_uniform_decode is None
|
||
else force_uniform_decode
|
||
)
|
||
# Encoder-decoder models only support CG for decoder_step > 0 (no enc_output
|
||
# is present). Also, chunked-prefill is disabled, so batch are uniform.
|
||
has_encoder_output = self.model_config.is_encoder_decoder and num_encoder_reqs > 0
|
||
num_active_loras = (
|
||
force_num_active_loras
|
||
if force_num_active_loras is not None
|
||
else len(self.input_batch.lora_id_to_lora_request)
|
||
)
|
||
has_lora = num_active_loras > 0 if force_has_lora is None else force_has_lora
|
||
|
||
# ruff: noqa: E731
|
||
def dispatch_cudagraph(num_tokens, disable_full=False, valid_modes=None):
|
||
if force_eager:
|
||
return (CUDAGraphMode.NONE, BatchDescriptor(num_tokens_padded))
|
||
|
||
if vllm_version_is("0.16.0"):
|
||
return self.cudagraph_dispatcher.dispatch(
|
||
num_tokens=num_tokens,
|
||
has_lora=has_lora,
|
||
uniform_decode=uniform_decode,
|
||
disable_full=disable_full,
|
||
num_active_loras=num_active_loras,
|
||
)
|
||
else:
|
||
return self.cudagraph_dispatcher.dispatch(
|
||
num_tokens=num_tokens,
|
||
has_lora=has_lora,
|
||
uniform_decode=uniform_decode,
|
||
valid_modes=valid_modes,
|
||
invalid_modes={CUDAGraphMode.FULL} if disable_full else None,
|
||
num_active_loras=num_active_loras,
|
||
)
|
||
|
||
cudagraph_mode, batch_descriptor = dispatch_cudagraph(num_tokens_padded, use_cascade_attn or has_encoder_output)
|
||
num_tokens_padded = batch_descriptor.num_tokens
|
||
if enable_sp(self.vllm_config):
|
||
assert batch_descriptor.num_tokens % self.vllm_config.parallel_config.tensor_parallel_size == 0, (
|
||
"Sequence parallelism requires num_tokens to be a multiple of tensor parallel size"
|
||
)
|
||
# Extra coordination when running data-parallel since we need to coordinate
|
||
# across ranks
|
||
should_ubatch, num_tokens_across_dp = False, None
|
||
if self.vllm_config.parallel_config.data_parallel_size > 1:
|
||
_, num_tokens_across_dp, synced_cudagraph_mode = self._sync_batch_across_dp(
|
||
num_tokens_padded=num_tokens_padded,
|
||
cudagraph_mode=cudagraph_mode.value,
|
||
)
|
||
|
||
# Extract DP padding if there is any
|
||
if num_tokens_across_dp is not None:
|
||
dp_rank = self.parallel_config.data_parallel_rank
|
||
num_tokens_padded = int(num_tokens_across_dp[dp_rank].item())
|
||
# Re-dispatch with DP padding
|
||
if vllm_version_is("0.16.0"):
|
||
cudagraph_mode, batch_descriptor = dispatch_cudagraph(
|
||
num_tokens_padded,
|
||
disable_full=synced_cudagraph_mode <= CUDAGraphMode.PIECEWISE.value,
|
||
)
|
||
else:
|
||
cudagraph_mode, batch_descriptor = dispatch_cudagraph(
|
||
num_tokens_padded,
|
||
valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
|
||
)
|
||
# Assert to make sure the agreed upon token count is correct otherwise
|
||
# num_tokens_across_dp will no-longer be valid
|
||
assert batch_descriptor.num_tokens == num_tokens_padded
|
||
cudagraph_stats = None
|
||
if self.vllm_config.observability_config.cudagraph_metrics:
|
||
cudagraph_stats = CUDAGraphStat(
|
||
num_unpadded_tokens=num_tokens,
|
||
num_padded_tokens=batch_descriptor.num_tokens,
|
||
num_paddings=batch_descriptor.num_tokens - num_tokens,
|
||
runtime_mode=str(cudagraph_mode),
|
||
)
|
||
|
||
return (
|
||
cudagraph_mode,
|
||
batch_descriptor,
|
||
should_ubatch,
|
||
num_tokens_across_dp,
|
||
cudagraph_stats,
|
||
)
|
||
|
||
def _build_attention_metadata(
|
||
self,
|
||
num_tokens: int,
|
||
num_reqs: int,
|
||
max_query_len: int,
|
||
num_tokens_padded: int | None = None,
|
||
num_reqs_padded: int | None = None,
|
||
ubatch_slices: UBatchSlices | None = None,
|
||
logits_indices: torch.Tensor | None = None,
|
||
use_spec_decode: bool = False,
|
||
for_cudagraph_capture: bool = False,
|
||
num_scheduled_tokens: dict[str, int] | None = None,
|
||
num_scheduled_tokens_np: np.ndarray | None = None,
|
||
cascade_attn_prefix_lens: list[list[int]] | None = None,
|
||
) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
|
||
"""
|
||
:return: tuple[attn_metadata, spec_decode_common_attn_metadata]
|
||
"""
|
||
# Attention metadata is not needed for attention free models
|
||
if len(self.kv_cache_config.kv_cache_groups) == 0:
|
||
return {}, None
|
||
num_tokens_padded = num_tokens_padded or num_tokens
|
||
num_reqs_padded = num_reqs_padded or num_reqs
|
||
attn_metadata: PerLayerAttnMetadata = {}
|
||
if ubatch_slices is not None:
|
||
attn_metadata = [dict() for _ in range(len(ubatch_slices))]
|
||
if for_cudagraph_capture:
|
||
# For some attention backends (e.g. FA) with sliding window models we need
|
||
# to make sure the backend see a max_seq_len that is larger to the sliding
|
||
# window size when capturing to make sure the correct kernel is selected.
|
||
max_seq_len = self.max_model_len
|
||
else:
|
||
max_seq_len = self.seq_lens.np[:num_reqs].max().item()
|
||
if use_spec_decode and self.need_accepted_tokens:
|
||
self.num_accepted_tokens.np[:num_reqs] = self.input_batch.num_accepted_tokens_cpu[:num_reqs]
|
||
self.num_accepted_tokens.np[num_reqs:].fill(1)
|
||
self.num_accepted_tokens.copy_to_gpu()
|
||
|
||
kv_cache_groups = self.kv_cache_config.kv_cache_groups
|
||
|
||
def _get_pcp_metadata(block_table_tensor):
|
||
if not self.use_cp:
|
||
return None, block_table_tensor
|
||
return self.pcp_manager.generate_pcp_metadata(
|
||
num_tokens,
|
||
self.query_lens,
|
||
self.input_batch,
|
||
num_scheduled_tokens_np,
|
||
block_table_tensor,
|
||
num_reqs_padded,
|
||
num_reqs,
|
||
)
|
||
|
||
def _get_block_table_and_slot_mapping(kv_cache_gid: int):
|
||
assert num_reqs_padded is not None and num_tokens_padded is not None
|
||
kv_cache_spec = kv_cache_groups[kv_cache_gid].kv_cache_spec
|
||
if self.pcp_size > 1:
|
||
total_num_pcp_pads = sum(self.pcp_manager.num_pcp_pads_cpu[:num_reqs])
|
||
if self.pcp_manager.pcp_use_hybrid_attn:
|
||
num_scheduled_tokens_padded = self.pcp_manager.num_scheduled_tokens_padded
|
||
assert num_scheduled_tokens_padded is not None
|
||
maybe_pcp_full_tokens = sum(num_scheduled_tokens_padded) * self.pcp_size - total_num_pcp_pads
|
||
else:
|
||
maybe_pcp_full_tokens = num_tokens * self.pcp_size - total_num_pcp_pads
|
||
else:
|
||
maybe_pcp_full_tokens = num_tokens_padded
|
||
if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
|
||
blk_table_tensor = torch.zeros(
|
||
(num_reqs_padded, 1),
|
||
dtype=torch.int32,
|
||
device=self.device,
|
||
)
|
||
slot_mapping = torch.zeros(
|
||
(num_tokens_padded,),
|
||
dtype=torch.int64,
|
||
device=self.device,
|
||
)
|
||
else:
|
||
blk_table = self.input_batch.block_table[kv_cache_gid]
|
||
slot_mapping = blk_table.slot_mapping.gpu[:maybe_pcp_full_tokens]
|
||
maybe_num_reqs_padded = num_reqs_padded * self.decode_token_per_req if self.use_cp else num_reqs_padded
|
||
blk_table_tensor = blk_table.get_device_tensor()[:maybe_num_reqs_padded]
|
||
|
||
# Fill unused with -1. Needed for reshape_and_cache in full cuda
|
||
# graph mode. `blk_table_tensor` -1 to match mamba PAD_SLOT_ID
|
||
if self.pcp_size == 1:
|
||
slot_mapping[num_tokens:num_tokens_padded].fill_(-1)
|
||
blk_table_tensor[num_reqs:num_reqs_padded].fill_(0)
|
||
if self.pcp_size > 1:
|
||
slot_mapping = self.pcp_manager.get_padded_slot_mapping(
|
||
num_tokens,
|
||
num_tokens_padded,
|
||
slot_mapping,
|
||
)
|
||
if self.model_config.enable_return_routed_experts and kv_cache_gid == 0:
|
||
self.cpu_slot_mapping = slot_mapping.cpu().numpy()
|
||
return blk_table_tensor, slot_mapping
|
||
|
||
block_table_gid_0, slot_mapping_gid_0 = _get_block_table_and_slot_mapping(0)
|
||
self.long_seq_metadata, block_table_gid_0 = _get_pcp_metadata(block_table_gid_0)
|
||
|
||
cm_base = AscendCommonAttentionMetadata(
|
||
query_start_loc=self.query_start_loc.gpu[: num_reqs_padded + 1],
|
||
query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs_padded + 1],
|
||
seq_lens=self.seq_lens.gpu[:num_reqs_padded],
|
||
# TODO
|
||
seq_lens_cpu=self.seq_lens.cpu[:num_reqs_padded],
|
||
# TODO
|
||
num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs_padded],
|
||
num_reqs=num_reqs_padded,
|
||
num_actual_tokens=num_tokens,
|
||
max_query_len=max_query_len,
|
||
max_seq_len=max_seq_len,
|
||
block_table_tensor=block_table_gid_0,
|
||
slot_mapping=slot_mapping_gid_0,
|
||
causal=True,
|
||
num_input_tokens=num_tokens_padded,
|
||
actual_seq_lengths_q=self.actual_seq_lengths_q,
|
||
positions=self.positions.gpu,
|
||
attn_state=self.attn_state,
|
||
decode_token_per_req=self.decode_token_per_req,
|
||
prefill_context_parallel_metadata=self.long_seq_metadata,
|
||
)
|
||
|
||
if logits_indices is not None and self.cache_config.kv_sharing_fast_prefill:
|
||
cm_base.num_logits_indices = logits_indices.size(0)
|
||
cm_base.logits_indices_padded = self._prepare_kv_sharing_fast_prefill(logits_indices)
|
||
|
||
def _build_attn_group_metadata(
|
||
kv_cache_gid: int,
|
||
attn_gid: int,
|
||
common_attn_metadata: CommonAttentionMetadata,
|
||
ubid: int | None = None,
|
||
) -> None:
|
||
attn_group = self.attn_groups[kv_cache_gid][attn_gid]
|
||
builder = attn_group.get_metadata_builder(ubid or 0)
|
||
cascade_attn_prefix_len = (
|
||
cascade_attn_prefix_lens[kv_cache_gid][attn_gid] if cascade_attn_prefix_lens else 0
|
||
)
|
||
|
||
extra_attn_metadata_args = {}
|
||
if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
|
||
assert ubid is None, "UBatching not supported with GDN yet"
|
||
patch_torch_npu_argsort()
|
||
extra_attn_metadata_args = dict(
|
||
num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs_padded],
|
||
num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[:num_reqs_padded],
|
||
)
|
||
|
||
if for_cudagraph_capture:
|
||
attn_metadata_i = builder.build_for_cudagraph_capture(common_attn_metadata)
|
||
else:
|
||
attn_metadata_i = builder.build(
|
||
common_prefix_len=cascade_attn_prefix_len,
|
||
common_attn_metadata=common_attn_metadata,
|
||
**extra_attn_metadata_args,
|
||
)
|
||
|
||
if ubid is None:
|
||
assert isinstance(attn_metadata, dict)
|
||
attn_metadata_dict = attn_metadata
|
||
else:
|
||
assert isinstance(attn_metadata, list)
|
||
attn_metadata_dict = attn_metadata[ubid]
|
||
|
||
for layer_name in attn_group.layer_names:
|
||
attn_metadata_dict[layer_name] = attn_metadata_i
|
||
|
||
# Prepare the attention metadata for each KV cache group and make layers
|
||
# in the same group share the same metadata.
|
||
spec_decode_common_attn_metadata = None
|
||
for kv_cache_gid, kv_cache_group in enumerate(self.kv_cache_config.kv_cache_groups):
|
||
cm = copy(cm_base) # shallow copy
|
||
# Basically only the encoder seq_lens, block_table and slot_mapping change
|
||
# for each kv_cache_group.
|
||
cm.encoder_seq_lens, cm.encoder_seq_lens_cpu = self._get_encoder_seq_lens(
|
||
num_scheduled_tokens or {},
|
||
kv_cache_group.kv_cache_spec,
|
||
num_reqs_padded,
|
||
)
|
||
|
||
# Now, query_start_loc is padded.
|
||
# But gdn needs an unpadded one.
|
||
# gdn_query_start_loc is an unpadded version of query_start_loc.
|
||
# TODO delete it if fia's check is removed.
|
||
if self._has_gdn:
|
||
attn_group = self.attn_groups[kv_cache_gid][0]
|
||
builder = attn_group.get_metadata_builder(0)
|
||
if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
|
||
cm.query_start_loc_cpu = self.gdn_query_start_loc.cpu[: num_reqs_padded + 1]
|
||
cm.query_start_loc = self.gdn_query_start_loc.gpu[: num_reqs_padded + 1]
|
||
|
||
if kv_cache_gid > 0:
|
||
cm.block_table_tensor, cm.slot_mapping = _get_block_table_and_slot_mapping(kv_cache_gid)
|
||
if self.speculative_config and spec_decode_common_attn_metadata is None:
|
||
if isinstance(self.drafter, AscendEagleProposer):
|
||
if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
|
||
spec_decode_common_attn_metadata = cm
|
||
else:
|
||
spec_decode_common_attn_metadata = cm
|
||
|
||
for attn_gid in range(len(self.attn_groups[kv_cache_gid])):
|
||
_build_attn_group_metadata(kv_cache_gid, attn_gid, cm)
|
||
if self.is_mm_prefix_lm:
|
||
req_doc_ranges = {}
|
||
for req_id in self.input_batch.req_ids:
|
||
image_doc_ranges = []
|
||
req_state = self.requests[req_id]
|
||
for mm_feature in req_state.mm_features:
|
||
pos_info = mm_feature.mm_position
|
||
img_doc_range = pos_info.extract_embeds_range()
|
||
image_doc_ranges.extend(img_doc_range)
|
||
req_idx = self.input_batch.req_id_to_index[req_id]
|
||
req_doc_ranges[req_idx] = image_doc_ranges
|
||
|
||
if isinstance(attn_metadata, list):
|
||
for ub_metadata in attn_metadata:
|
||
for _metadata in ub_metadata.values():
|
||
_metadata.mm_prefix_range = req_doc_ranges # type: ignore[attr-defined]
|
||
else:
|
||
for _metadata in attn_metadata.values():
|
||
_metadata.mm_prefix_range = req_doc_ranges # type: ignore[attr-defined]
|
||
|
||
if spec_decode_common_attn_metadata is not None and (
|
||
num_reqs != num_reqs_padded or num_tokens != num_tokens_padded
|
||
):
|
||
# Currently the drafter still only uses piecewise cudagraphs (and modifies
|
||
# the attention metadata in directly), and therefore does not want to use
|
||
# padded attention metadata.
|
||
spec_decode_common_attn_metadata = spec_decode_common_attn_metadata.unpadded(num_tokens, num_reqs)
|
||
return attn_metadata, spec_decode_common_attn_metadata
|
||
|
||
def _should_build_dummy_attn_metadata(
|
||
self,
|
||
force_attention: bool = False,
|
||
is_profile: bool = False,
|
||
cudagraph_runtime_mode: CUDAGraphMode | None = None,
|
||
) -> bool:
|
||
"""
|
||
Determine whether attention metadata should be built during dummy_run.
|
||
SubClass can override this to add custom conditions.
|
||
"""
|
||
# If force_attention is True, we always capture attention, Otherwise,
|
||
# it only happens for cudagraph_runtime_mode=FULL.
|
||
return force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL
|
||
|
||
@torch.inference_mode()
|
||
def _dummy_run(
|
||
self,
|
||
num_tokens: int,
|
||
with_prefill: bool = False,
|
||
cudagraph_runtime_mode: CUDAGraphMode | None = 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,
|
||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||
# only support eager mode and piecewise graph now
|
||
assert cudagraph_runtime_mode is None or cudagraph_runtime_mode.valid_runtime_modes()
|
||
# If cudagraph_mode.decode_mode() == FULL and
|
||
# cudagraph_mode.separate_routine(). This means that we are using
|
||
# different graphs and/or modes for mixed prefill-decode batches vs.
|
||
# uniform decode batches. A uniform decode batch means that all
|
||
# requests have identical query length, except a potential virtual
|
||
# request (shorter) in the batch account for padding.
|
||
# Uniform decode batch could either be common pure decode, where
|
||
# max_query_len == 1, or speculative decode, where
|
||
# max_query_len == 1 + num_spec_decode_tokens.
|
||
|
||
# When setting max_query_len = 1, we switch to and capture the optimized
|
||
# routine of FA2 for pure decode, i.e., Flashdecode + an optimization
|
||
# for GQA/MQA.
|
||
max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
|
||
# Set num_scheduled_tokens based on num_tokens and max_num_seqs
|
||
# for dummy run with LoRA so that the num_reqs collectively
|
||
# has num_tokens in total.
|
||
assert num_tokens <= self.scheduler_config.max_num_batched_tokens
|
||
max_num_reqs = self.scheduler_config.max_num_seqs
|
||
if create_mixed_batch:
|
||
raise NotImplementedError("create_mixed_batch is used for warmup deepgemm, vllm-ascend does not need it")
|
||
elif uniform_decode:
|
||
assert not create_mixed_batch
|
||
num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
|
||
num_scheduled_tokens_list = [max_query_len] * num_reqs
|
||
if num_tokens % max_query_len != 0:
|
||
num_scheduled_tokens_list[-1] = num_tokens % max_query_len
|
||
else:
|
||
num_reqs = min(num_tokens, max_num_reqs)
|
||
min_tokens_per_req = num_tokens // num_reqs
|
||
num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
|
||
num_scheduled_tokens_list[-1] += num_tokens % num_reqs
|
||
assert sum(num_scheduled_tokens_list) == num_tokens
|
||
assert len(num_scheduled_tokens_list) == num_reqs
|
||
|
||
if not is_profile and self.dynamic_eplb:
|
||
self.eplb_updator.forward_before()
|
||
|
||
num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
|
||
self.query_lens = torch.from_numpy(num_scheduled_tokens)
|
||
num_tokens_unpadded = int(num_scheduled_tokens.sum())
|
||
num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
|
||
_cudagraph_mode, batch_desc, _, num_tokens_across_dp, _ = self._determine_batch_execution_and_padding(
|
||
num_tokens=num_tokens_unpadded,
|
||
num_reqs=num_reqs,
|
||
num_scheduled_tokens_np=num_scheduled_tokens,
|
||
max_num_scheduled_tokens=max_query_len,
|
||
use_cascade_attn=False,
|
||
allow_microbatching=allow_microbatching,
|
||
force_eager=is_profile or (cudagraph_runtime_mode == CUDAGraphMode.NONE),
|
||
# `force_uniform_decode` is used for cudagraph capture; because for
|
||
# capturing mixed prefill-decode batches, we sometimes use
|
||
# num_tokens == num_reqs which looks like a uniform decode batch to the
|
||
# dispatcher; but we actually want to capture a piecewise cudagraph
|
||
force_uniform_decode=uniform_decode,
|
||
# `force_has_lora` is used for cudagraph capture; because LoRA is
|
||
# activated later in the context manager, but we need to know the
|
||
# LoRA state when determining the batch descriptor for capture
|
||
force_has_lora=num_active_loras > 0,
|
||
force_num_active_loras=num_active_loras,
|
||
)
|
||
if self.use_cp:
|
||
self.pcp_manager.init_batch_info(
|
||
num_scheduled_tokens,
|
||
num_reqs,
|
||
)
|
||
if self.speculative_config:
|
||
self.pcp_manager.query_lens_pcp_full.cpu[:num_reqs] = torch.from_numpy(num_scheduled_tokens)
|
||
self.pcp_manager.query_lens_pcp_full.copy_to_gpu()
|
||
if cudagraph_runtime_mode is None:
|
||
cudagraph_runtime_mode = _cudagraph_mode
|
||
else:
|
||
assert cudagraph_runtime_mode == _cudagraph_mode, (
|
||
f"Cudagraph runtime mode mismatch in dummy_run. "
|
||
f"Expected {_cudagraph_mode}, but got {cudagraph_runtime_mode}."
|
||
)
|
||
num_tokens_padded = batch_desc.num_tokens
|
||
num_reqs_padded = batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
|
||
if num_tokens_across_dp is not None and num_tokens_padded != num_tokens:
|
||
# pad is needed if the pad of `num_tokens` is triggered inside CudagraphDispatcher
|
||
num_tokens_across_dp[:] = num_tokens_padded
|
||
num_scheduled_tokens = num_scheduled_tokens.repeat(num_reqs_padded)
|
||
# vllm-ascend does not support ubatch now
|
||
ubatch_slices, ubatch_slices_padded = None, None
|
||
attn_metadata: PerLayerAttnMetadata | None = None
|
||
# Build attention metadata for dummy_run
|
||
if self._should_build_dummy_attn_metadata(force_attention, is_profile, cudagraph_runtime_mode):
|
||
if create_mixed_batch:
|
||
raise NotImplementedError(
|
||
"create_mixed_batch is used for warmup deepgemm, vllm-ascend does not need it"
|
||
)
|
||
self.attn_state = AscendAttentionState.DecodeOnly
|
||
if self.speculative_config and self.speculative_config.method == "mtp":
|
||
# `AscendAttentionState.SpecDecoding` is only designed for mla
|
||
if self.vllm_config.model_config.use_mla:
|
||
self.attn_state = AscendAttentionState.SpecDecoding
|
||
else:
|
||
self.attn_state = AscendAttentionState.ChunkedPrefill
|
||
# The reason why we use a fixed seq_len rather than max_query_len is that
|
||
# _npu_paged_attention_get_workspace only returns max workspace with specific
|
||
# seq_lens. We use this seq_len only when capturing graph, and still use max_query_len
|
||
# in inference. This will be removed once npu_fused_infer_attention_score
|
||
# outperforms _npu_paged_attention on all cases.
|
||
seq_lens = (
|
||
SEQ_LEN_WITH_MAX_PA_WORKSPACE
|
||
if is_graph_capturing and using_paged_attention(num_tokens, self.vllm_config)
|
||
else max_query_len
|
||
) # type: ignore[assignment]
|
||
self.seq_lens.np[:num_reqs_padded] = seq_lens
|
||
self.seq_lens.np[num_reqs_padded:] = 0
|
||
self.seq_lens.copy_to_gpu()
|
||
|
||
cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
|
||
self.query_start_loc.np[1 : num_reqs_padded + 1] = cum_num_tokens
|
||
self.query_start_loc.copy_to_gpu()
|
||
num_reqs_padded = self._pad_query_start_loc_for_fia(
|
||
num_tokens_padded, num_reqs_padded, num_reqs, cudagraph_runtime_mode, batch_desc.num_reqs
|
||
)
|
||
|
||
pad_attn = cudagraph_runtime_mode == CUDAGraphMode.FULL
|
||
attn_metadata, _ = self._build_attention_metadata(
|
||
num_tokens=num_tokens_unpadded,
|
||
num_tokens_padded=num_tokens_padded,
|
||
num_reqs=num_reqs_padded,
|
||
max_query_len=max_query_len,
|
||
ubatch_slices=ubatch_slices_padded if pad_attn else ubatch_slices,
|
||
for_cudagraph_capture=is_graph_capturing,
|
||
num_scheduled_tokens_np=num_scheduled_tokens,
|
||
)
|
||
|
||
with self.maybe_dummy_run_with_lora(
|
||
self.lora_config,
|
||
num_scheduled_tokens,
|
||
num_sampled_tokens,
|
||
remove_lora,
|
||
# TODO: The next line is a temporary workaround
|
||
# to fix the accuracy issue of test_llama32_lora.py,
|
||
# which is introduced by vllm-project/vllm#32005
|
||
num_active_loras=(self.lora_config.max_loras if self.lora_config is not None else num_active_loras),
|
||
):
|
||
# Make sure padding doesn't exceed max_num_tokens
|
||
assert num_tokens_padded <= self.max_num_tokens
|
||
if self.is_multimodal_model and not self.model_config.is_encoder_decoder or self.enable_prompt_embeds:
|
||
input_ids = None
|
||
inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
|
||
else:
|
||
input_ids = self.input_ids.gpu[:num_tokens_padded]
|
||
inputs_embeds = None
|
||
|
||
if self.uses_mrope:
|
||
positions = self.mrope_positions.gpu[:, :num_tokens_padded]
|
||
elif self.uses_xdrope_dim > 0:
|
||
positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
|
||
else:
|
||
positions = self.positions.gpu[:num_tokens_padded]
|
||
|
||
# update global cos, sin
|
||
update_cos_sin(positions)
|
||
|
||
if get_pp_group().is_first_rank:
|
||
intermediate_tensors = None
|
||
else:
|
||
# When PP and flashcomm1 are enabled, during dummy_run the estimated space should divide num_tokens by
|
||
# tp_size; otherwise, on non-first PP ranks it would effectively perform an extra all-gather, leading
|
||
# to incorrect memory estimation and potentially causing OOM.
|
||
intermediate_tokens = num_tokens_padded
|
||
if enable_sp():
|
||
tp_size = get_tensor_model_parallel_world_size()
|
||
intermediate_tokens = (num_tokens_padded + tp_size - 1) // tp_size
|
||
if self.intermediate_tensors is None:
|
||
max_actual_tokens = self.max_num_tokens
|
||
if enable_sp():
|
||
max_actual_tokens = (self.max_num_tokens + tp_size - 1) // tp_size
|
||
self.intermediate_tensors = self.model.make_empty_intermediate_tensors(
|
||
batch_size=max_actual_tokens, dtype=self.dtype, device=self.device
|
||
)
|
||
intermediate_tensors = IntermediateTensors(
|
||
{k: v[:intermediate_tokens] for k, v in self.intermediate_tensors.items()}
|
||
)
|
||
|
||
need_dummy_logits = not is_profile and lmhead_tp_enable()
|
||
max_num_reqs_across_dp = max_num_reqs * self.uniform_decode_query_len
|
||
dummy_indices = torch.zeros(max_num_reqs_across_dp, dtype=torch.int32)
|
||
|
||
def dummy_compute_logits(hidden_states):
|
||
if not need_dummy_logits:
|
||
return None
|
||
return self.model.compute_logits(hidden_states[dummy_indices])
|
||
|
||
def dummy_drafter_compute_logits(hidden_states):
|
||
if not need_dummy_logits or self.drafter is None:
|
||
return
|
||
if hasattr(self.drafter, "model") and hasattr(self.drafter.model, "compute_logits"):
|
||
return self.drafter.model.compute_logits(hidden_states[dummy_indices])
|
||
|
||
with set_ascend_forward_context(
|
||
attn_metadata,
|
||
self.vllm_config,
|
||
num_tokens=num_tokens_padded,
|
||
num_tokens_across_dp=num_tokens_across_dp,
|
||
in_profile_run=is_profile,
|
||
num_actual_tokens=num_tokens_padded,
|
||
aclgraph_runtime_mode=cudagraph_runtime_mode,
|
||
batch_descriptor=batch_desc,
|
||
model_instance=self.model,
|
||
):
|
||
outputs = self._model_forward(
|
||
num_tokens_padded, input_ids, positions, intermediate_tensors, inputs_embeds
|
||
)
|
||
if self.use_aux_hidden_state_outputs:
|
||
hidden_states, _ = outputs
|
||
else:
|
||
hidden_states = outputs
|
||
dummy_compute_logits(hidden_states)
|
||
|
||
if self.drafter:
|
||
self.drafter.dummy_run(
|
||
num_tokens=num_tokens_padded,
|
||
with_prefill=with_prefill,
|
||
num_reqs=num_reqs_padded,
|
||
num_tokens_across_dp=num_tokens_across_dp,
|
||
aclgraph_runtime_mode=cudagraph_runtime_mode,
|
||
batch_descriptor=batch_desc,
|
||
dummy_compute_logits=dummy_drafter_compute_logits,
|
||
in_graph_capturing=not force_attention,
|
||
is_profile=is_profile,
|
||
)
|
||
if is_profile and self.dynamic_eplb:
|
||
self.model.clear_all_moe_loads()
|
||
if self.dynamic_eplb:
|
||
self.eplb_updator.forward_end()
|
||
return hidden_states, hidden_states
|
||
|
||
@torch.inference_mode()
|
||
def _dummy_sampler_run(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
) -> torch.Tensor:
|
||
output = None
|
||
|
||
# For profile, have maximum num_reqs and that collectively have
|
||
# maximum num_tokens.
|
||
min_tokens_per_req = self.max_num_tokens // self.max_num_reqs
|
||
num_scheduled_tokens_list = [min_tokens_per_req] * self.max_num_reqs
|
||
num_scheduled_tokens_list[-1] += self.max_num_tokens % self.max_num_reqs
|
||
num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
|
||
logit_indices = np.cumsum(num_scheduled_tokens) - 1
|
||
# TODO: need to rum a dummy sampler for generate task
|
||
hidden_states = hidden_states[logit_indices]
|
||
output = self.model.compute_logits(hidden_states)
|
||
return output
|
||
|
||
def profile_run(self) -> None:
|
||
self.eplb_warmup()
|
||
mc2_tokens_capacity = get_mc2_tokens_capacity()
|
||
if self.max_num_tokens > mc2_tokens_capacity and select_moe_comm_method(
|
||
mc2_tokens_capacity, self.vllm_config
|
||
) in {MoECommType.MC2, MoECommType.FUSED_MC2}:
|
||
self._dummy_run(mc2_tokens_capacity, with_prefill=True, is_profile=True)
|
||
origin_max_num_tokens = self.max_num_tokens
|
||
# in the pcp scenario, the split sequence needs to be used for profile run
|
||
# TODO: after the vllm pcp function is launched, this logic needs to be brought up to the community
|
||
if self.pcp_size > 1:
|
||
self.max_num_tokens = math.ceil(self.max_num_tokens / (self.pcp_size * 2)) * 2
|
||
super().profile_run()
|
||
self.max_num_tokens = origin_max_num_tokens
|
||
|
||
def eplb_warmup(self):
|
||
if self.dynamic_eplb and not self.is_eplb_warmuped:
|
||
self.is_eplb_warmuped = True
|
||
self.eplb_adaptor = VllmEplbAdaptor(model=self.model)
|
||
self.eplb_loader.set_adator(self.eplb_adaptor)
|
||
self.eplb_updator.set_adaptor(self.eplb_adaptor)
|
||
self.eplb_updator.warm_up_eplb()
|
||
|
||
def load_model(self) -> None:
|
||
logger.info("Starting to load model %s...", self.model_config.model)
|
||
|
||
with DeviceMemoryProfiler() as m: # noqa: SIM117
|
||
self.model = get_model(vllm_config=self.vllm_config)
|
||
if self.dynamic_eplb:
|
||
model_register(self.model)
|
||
if self.drafter:
|
||
logger.info("Loading drafter model...")
|
||
if self.vllm_config.quant_config is not None:
|
||
patch_load_weights(self.vllm_config)
|
||
with get_tp_context(self.drafter):
|
||
self.drafter.load_model(self.model)
|
||
if self.use_aux_hidden_state_outputs:
|
||
self.model.set_aux_hidden_state_layers(self.model.get_eagle3_aux_hidden_state_layers())
|
||
|
||
if self.lora_config:
|
||
self.model = self.load_lora_model(self.model, self.vllm_config, self.device)
|
||
self.model_memory_usage = m.consumed_memory
|
||
logger.info("Loading model weights took %.4f GB", m.consumed_memory / float(2**30))
|
||
|
||
# wrap the model with full graph wrapper if needed.
|
||
if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
|
||
self.update_stream: torch.npu.Stream = torch.npu.Stream()
|
||
self.model = ACLGraphWrapper(self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL)
|
||
|
||
def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
|
||
"""
|
||
Initialize KV cache based on `kv_cache_config`.
|
||
Args:
|
||
kv_cache_config: Configuration for the KV cache, including the KV
|
||
cache size of each layer
|
||
"""
|
||
kv_cache_config = deepcopy(kv_cache_config)
|
||
self.kv_cache_config = kv_cache_config
|
||
self.may_add_encoder_only_layers_to_kv_cache_config()
|
||
self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
|
||
# NOTE(cmq): initialize_attn_backend must before using self.attn_groups
|
||
self.initialize_attn_backend(kv_cache_config)
|
||
self.use_hybrid_blocks = len(self.attn_groups) > 1
|
||
# NOTE: Currently, we determine whether we need `num_accepted_tokens` through `MambaSpec`.
|
||
self.need_accepted_tokens = any(
|
||
[isinstance(attn_group[0].kv_cache_spec, MambaSpec) for attn_group in self.attn_groups]
|
||
)
|
||
|
||
self.may_reinitialize_input_batch(kv_cache_config)
|
||
kv_caches = self.initialize_kv_cache_tensors(kv_cache_config)
|
||
|
||
if has_kv_transfer_group():
|
||
get_kv_transfer_group().register_kv_caches(kv_caches)
|
||
|
||
if self.model_config.enable_return_routed_experts:
|
||
self.init_routed_experts_capturer()
|
||
|
||
def _align_memory(self, tensor: torch.Tensor, alignment: int) -> torch.Tensor:
|
||
data_ptr = tensor.data_ptr()
|
||
aligned_addr = (data_ptr + alignment - 1) // alignment * alignment
|
||
offset = (aligned_addr - data_ptr) // tensor.element_size()
|
||
return tensor[int(offset) :]
|
||
|
||
def initialize_kv_cache_tensors(self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
|
||
"""
|
||
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.
|
||
"""
|
||
# Initialize the memory buffer for KV cache
|
||
kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)
|
||
# Change the memory buffer to the desired shape
|
||
kv_caches = self._reshape_kv_cache_tensors(kv_cache_config, kv_cache_raw_tensors)
|
||
|
||
# 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
|
||
|
||
num_attn_module = 2 if self.model_config.hf_text_config.model_type == "longcat_flash" else 1
|
||
bind_kv_cache(kv_caches, self.compilation_config.static_forward_context, self.kv_caches, num_attn_module)
|
||
return kv_caches
|
||
|
||
def _allocate_kv_cache_tensors(self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
|
||
"""
|
||
Initializes the KV cache buffer with the correct size. The buffer needs
|
||
to be reshaped to the desired shape before being used by the models.
|
||
|
||
NOTE: To support prefill disaggregation, we need to split kvcache tensor into
|
||
k_cache and v cache, and the addr of both are aligned by 2M
|
||
|
||
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.
|
||
dict[str, tuple(torch.Tensor, torch.Tensor)] A map between layer names
|
||
to their corresponding memory buffer for K cache and V cache.
|
||
"""
|
||
# init kv cache tensors
|
||
kv_cache_raw_tensors: dict[str, torch.Tensor | torch.Tensor | None] = {}
|
||
# prefill disaggregation need the addr of cache tensor be aligned with 2M
|
||
alignment = 2 * 1024 * 1024
|
||
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
|
||
# If some tensors are shared by linear layers and attention layers,
|
||
# the same tensor format must be maintained even if some layers
|
||
# have only linear or attention layers, for example, the mtp layer.
|
||
self.hybrid_with_attn_and_mamba = False
|
||
for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
|
||
use_mamba, use_attn = False, False
|
||
for layer_name in kv_cache_tensor.shared_by:
|
||
if isinstance(layer_kv_cache_spec[layer_name], MambaSpec):
|
||
use_mamba = True
|
||
if isinstance(layer_kv_cache_spec[layer_name], AttentionSpec):
|
||
use_attn = True
|
||
self.hybrid_with_attn_and_mamba = self.hybrid_with_attn_and_mamba or (use_mamba and use_attn)
|
||
for idx in range(len(kv_cache_tensor.shared_by)):
|
||
layer_name = kv_cache_tensor.shared_by[idx]
|
||
if (
|
||
"linear_attn" in layer_name or self.hybrid_with_attn_and_mamba
|
||
) and layer_name not in kv_cache_raw_tensors:
|
||
# for mamba linear attention or attn-linear hybrid
|
||
if self.vllm_config.kv_transfer_config is None:
|
||
tensor = torch.zeros(kv_cache_tensor.size, dtype=torch.int8, device=self.device)
|
||
else:
|
||
cache_size_aligned = kv_cache_tensor.size + alignment
|
||
tensor = torch.zeros(cache_size_aligned, dtype=torch.int8, device=self.device)
|
||
tensor = self._align_memory(tensor, alignment)[: kv_cache_tensor.size]
|
||
|
||
for layer_name_inner in kv_cache_tensor.shared_by:
|
||
# shared the kvcache for all shared layers
|
||
kv_cache_raw_tensors[layer_name_inner] = tensor
|
||
elif "attn" in layer_name and layer_name not in kv_cache_raw_tensors and not use_mamba:
|
||
# NOTE: We need to init k cache tensor (nope cache tensor in mla) and
|
||
# v cache tensor (rope cache tensor in mla) separately to support prefill disaggregation,
|
||
# as it only support the 0-dim of kv_cache is `num_blocks`.
|
||
# For deepseek mla, we need to spilt cache tensor accrodding to the nope head dim
|
||
# and rope head dim.
|
||
if self.model_config.use_mla:
|
||
head_size = (
|
||
self.model_config.hf_text_config.qk_rope_head_dim
|
||
+ self.model_config.hf_text_config.kv_lora_rank
|
||
)
|
||
|
||
dsa_k_cache_factor = None
|
||
dsa_k_cache_size = None
|
||
if not self.model_config.use_mla:
|
||
# for non-mla model, use FullAttentionSpec
|
||
k_tensor_split_factor = 2
|
||
v_tensor_split_factor = 2
|
||
elif self.use_sparse:
|
||
# for deepseek v3.2, we split the kv cache according to the corresponding ratio
|
||
sparse_sum_head_size = sum(self._get_sparse_kv_cache_ratio())
|
||
k_tensor_split_factor, v_tensor_split_factor, dsa_k_cache_factor = [ # type: ignore
|
||
sparse_sum_head_size / ratio for ratio in self._get_sparse_kv_cache_ratio()
|
||
]
|
||
dsa_k_cache_size = int(kv_cache_tensor.size // dsa_k_cache_factor)
|
||
else:
|
||
# for other deepseek models, use MLAAttentionSpec
|
||
k_tensor_split_factor = head_size / self.model_config.hf_text_config.kv_lora_rank
|
||
v_tensor_split_factor = head_size / self.model_config.hf_text_config.qk_rope_head_dim
|
||
|
||
k_tensor_size = int(kv_cache_tensor.size // k_tensor_split_factor)
|
||
v_tensor_size = int(kv_cache_tensor.size // v_tensor_split_factor)
|
||
|
||
# for other attentions, e.g., self_attn, sliding window attn
|
||
if self.vllm_config.kv_transfer_config is None:
|
||
k_tensor = torch.zeros(k_tensor_size, dtype=torch.int8, device=self.device)
|
||
v_tensor = torch.zeros(v_tensor_size, dtype=torch.int8, device=self.device)
|
||
#### k cache: for deepseek sparse attention
|
||
if dsa_k_cache_factor is not None:
|
||
dsa_k_cache_tensor = torch.zeros(dsa_k_cache_size, dtype=torch.int8, device=self.device)
|
||
else:
|
||
k_tensor = torch.zeros(k_tensor_size + alignment, dtype=torch.int8, device=self.device)
|
||
v_tensor = torch.zeros(v_tensor_size + alignment, dtype=torch.int8, device=self.device)
|
||
k_tensor = self._align_memory(k_tensor, alignment)[:k_tensor_size]
|
||
v_tensor = self._align_memory(v_tensor, alignment)[:v_tensor_size]
|
||
#### k cache: for deepseek sparse attention
|
||
if dsa_k_cache_factor is not None and dsa_k_cache_size is not None:
|
||
dsa_k_cache_tensor = torch.zeros(
|
||
dsa_k_cache_size + alignment, dtype=torch.int8, device=self.device
|
||
)
|
||
dsa_k_cache_tensor = self._align_memory(dsa_k_cache_tensor, alignment)[:dsa_k_cache_size]
|
||
|
||
for layer_name_inner in kv_cache_tensor.shared_by:
|
||
# shared the attn kvcache for all shared layers
|
||
if "attn" in layer_name_inner and "linear_attn" not in layer_name_inner:
|
||
kv_cache_raw_tensors[layer_name_inner] = (
|
||
(k_tensor, v_tensor)
|
||
if not self.use_sparse
|
||
else (k_tensor, v_tensor, dsa_k_cache_tensor)
|
||
)
|
||
|
||
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_raw_tensors.keys()), "Some layers are not correctly initialized"
|
||
|
||
return kv_cache_raw_tensors
|
||
|
||
def _reshape_kv_cache_tensors(
|
||
self,
|
||
kv_cache_config: KVCacheConfig,
|
||
kv_cache_raw_tensors: dict[str, torch.Tensor],
|
||
) -> dict[str, torch.Tensor]:
|
||
"""
|
||
Reshape the KV cache tensors to the desired shape and dtype.
|
||
|
||
Args:
|
||
kv_cache_config: The KV cache config
|
||
kv_cache_raw_tensors: The KV cache buffer of each layer, with
|
||
correct size but uninitialized shape.
|
||
Returns:
|
||
Dict[str, torch.Tensor]: A map between layer names to their
|
||
corresponding memory buffer for KV cache.
|
||
"""
|
||
kv_caches: dict[str, torch.Tensor] = {}
|
||
layer_kv_cache_spec = {}
|
||
for group in kv_cache_config.kv_cache_groups:
|
||
for layer_name in group.layer_names:
|
||
layer_kv_cache_spec[layer_name] = group.kv_cache_spec
|
||
for group in self._kv_cache_spec_attn_group_iterator():
|
||
kv_cache_spec = group.kv_cache_spec
|
||
attn_backend = group.backend
|
||
for layer_name in group.layer_names:
|
||
if layer_name in self.runner_only_attn_layers:
|
||
continue
|
||
|
||
# TODO: remove this after the OOM issue is located and fixed, otherwise, some model may
|
||
# encounter OOM issue
|
||
if isinstance(kv_cache_spec, AttentionSpec):
|
||
raw_dsa_k_tensor = None
|
||
if self.use_sparse:
|
||
raw_k_tensor, raw_v_tensor, raw_dsa_k_tensor = kv_cache_raw_tensors[ # type: ignore
|
||
layer_name
|
||
]
|
||
assert raw_dsa_k_tensor is not None
|
||
sum_page_size_bytes = raw_k_tensor.numel() + raw_v_tensor.numel() + raw_dsa_k_tensor.numel()
|
||
elif self.use_hybrid_blocks and self.hybrid_with_attn_and_mamba:
|
||
# Currently, we ensure that the same kvcache format is used even if there
|
||
# is no shared layer, such as the full attention mtp layer of qwen3.5, etc.
|
||
raw_k_tensor, raw_v_tensor = kv_cache_raw_tensors[layer_name], kv_cache_raw_tensors[layer_name]
|
||
sum_page_size_bytes = raw_k_tensor.numel()
|
||
else:
|
||
raw_k_tensor, raw_v_tensor = kv_cache_raw_tensors[ # type: ignore
|
||
layer_name
|
||
]
|
||
sum_page_size_bytes = raw_k_tensor.numel() + raw_v_tensor.numel()
|
||
assert raw_k_tensor is not None
|
||
assert raw_v_tensor is not None
|
||
assert sum_page_size_bytes % kv_cache_spec.page_size_bytes == 0
|
||
num_blocks = sum_page_size_bytes // kv_cache_spec.page_size_bytes
|
||
|
||
# `num_blocks` is the number of blocks the model runner can use.
|
||
# `kv_cache_config.num_blocks` is the number of blocks that
|
||
# KVCacheManager may allocate.
|
||
# Since different GPUs may have different number of layers and
|
||
# different memory capacities, `num_blocks` can be different on
|
||
# different GPUs, and `kv_cache_config.num_blocks` is set to
|
||
# the min of all `num_blocks`. Verify it here.
|
||
assert num_blocks >= kv_cache_config.num_blocks
|
||
|
||
if hasattr(attn_backend, "get_supported_kernel_block_sizes") and self.use_hybrid_blocks:
|
||
block_size = attn_backend.get_supported_kernel_block_sizes()[0]
|
||
|
||
block_size_chunk = kv_cache_spec.block_size // block_size
|
||
kv_cache_shape = attn_backend.get_kv_cache_shape(
|
||
num_blocks * block_size_chunk,
|
||
block_size,
|
||
kv_cache_spec.num_kv_heads,
|
||
kv_cache_spec.head_size,
|
||
)
|
||
if self.hybrid_with_attn_and_mamba:
|
||
attn_tensor_page_size = int(np.prod(kv_cache_shape[1:])) * get_dtype_size(
|
||
kv_cache_spec.dtype
|
||
)
|
||
conv_block_padding_size = raw_k_tensor.numel() - attn_tensor_page_size * 2
|
||
raw_kv_tensor = raw_k_tensor[conv_block_padding_size:]
|
||
raw_k_tensor = raw_kv_tensor[:attn_tensor_page_size]
|
||
raw_v_tensor = raw_kv_tensor[attn_tensor_page_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
|
||
)
|
||
dtype = kv_cache_spec.dtype
|
||
if not self.model_config.use_mla:
|
||
k_shape = kv_cache_shape[1:]
|
||
v_shape = k_shape
|
||
else:
|
||
# k_cache: nope_cache v_cache: rope_cache
|
||
mla_num_blocks, mla_block_size, num_kv_heads, _ = kv_cache_shape
|
||
k_shape = [
|
||
mla_num_blocks,
|
||
mla_block_size,
|
||
num_kv_heads,
|
||
self.model_config.hf_text_config.kv_lora_rank,
|
||
]
|
||
v_shape = [
|
||
mla_num_blocks,
|
||
mla_block_size,
|
||
num_kv_heads,
|
||
self.model_config.hf_text_config.qk_rope_head_dim,
|
||
]
|
||
k_cache = raw_k_tensor.view(dtype).view(k_shape)
|
||
v_cache = raw_v_tensor.view(dtype).view(v_shape)
|
||
|
||
if self.use_sparse and raw_dsa_k_tensor is not None:
|
||
index_head_dim = self._get_sparse_kv_cache_ratio()[-1]
|
||
dsa_k_cache_shape = (
|
||
num_blocks,
|
||
kv_cache_spec.block_size,
|
||
kv_cache_spec.num_kv_heads,
|
||
index_head_dim,
|
||
)
|
||
dsa_k_cache = raw_dsa_k_tensor.view(dtype).view(dsa_k_cache_shape)
|
||
kv_caches[layer_name] = (k_cache, v_cache, dsa_k_cache)
|
||
else:
|
||
kv_caches[layer_name] = (k_cache, v_cache)
|
||
elif isinstance(kv_cache_spec, MambaSpec):
|
||
raw_tensor = kv_cache_raw_tensors[layer_name]
|
||
assert raw_tensor is not None
|
||
assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
|
||
num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
|
||
assert num_blocks >= kv_cache_config.num_blocks
|
||
|
||
# `num_blocks` is the number of blocks the model runner can use.
|
||
# `kv_cache_config.num_blocks` is the number of blocks that
|
||
# KVCacheManager may allocate.
|
||
# Since different GPUs may have different number of layers and
|
||
# different memory capacities, `num_blocks` can be different on
|
||
# different GPUs, and `kv_cache_config.num_blocks` is set to
|
||
# the min of all `num_blocks`. Verify it here.
|
||
|
||
state_tensors = []
|
||
target_idx = 0
|
||
start_idx = 0
|
||
# NOTE(zxr): in order to keep all tensor contiguous, we align ssm and kv block
|
||
# with same page size, so have to add extra padding block for kv, the overall
|
||
# layout of hybrid kv_cache on Ascend is:
|
||
# tensor1: [(kv_padding), conv , ...]
|
||
# tensor2: [k , ssm , ...]
|
||
# tensor3: [v , (mamba_padding), ...]
|
||
for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
|
||
# normally, there is conv state and ssm state in this loop. And there is only
|
||
# a conv state in some special models.
|
||
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)
|
||
kv_caches[layer_name] = state_tensors
|
||
else:
|
||
raise ValueError("Unknown KV cache spec type.")
|
||
|
||
return kv_caches
|
||
|
||
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.
|
||
"""
|
||
block_sizes = [
|
||
kv_cache_group.kv_cache_spec.block_size
|
||
for kv_cache_group in kv_cache_config.kv_cache_groups
|
||
if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
|
||
]
|
||
|
||
# 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)
|
||
kernel_block_sizes = []
|
||
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):
|
||
# All layers in the UniformTypeKVCacheSpecs have the same type,
|
||
# Pick an arbitrary one to dispatch.
|
||
kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
|
||
if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
|
||
continue
|
||
elif isinstance(kv_cache_spec, AttentionSpec):
|
||
# This is an attention backend that supports virtual
|
||
# block splitting. Get the supported block sizes from
|
||
# the backend.
|
||
try:
|
||
attn_groups = self.attn_groups[kv_cache_group_id]
|
||
except IndexError:
|
||
attn_groups = None
|
||
if attn_groups and self.use_hybrid_blocks:
|
||
# Use the backend's supported block size list
|
||
backend = attn_groups[0].backend
|
||
supported_sizes = backend.get_supported_kernel_block_sizes()
|
||
# If no specific sizes supported, use cache config
|
||
# block_size
|
||
kernel_block_size_list = supported_sizes if supported_sizes else [self.cache_config.block_size]
|
||
else:
|
||
# Fallback to cache config block_size if no backend found
|
||
kernel_block_size_list = [self.cache_config.block_size]
|
||
kernel_block_sizes.append(kernel_block_size_list)
|
||
else:
|
||
# This is likely Mamba or other non-attention cache,
|
||
# no splitting.
|
||
# NOTE: set kernel_block_sizes to 0 to disable slotmapping computation
|
||
# of mamba block. In this case, BlockTable.block_size will never equal
|
||
# to kernel_block_sizes[0]
|
||
kernel_block_sizes.append([0])
|
||
if block_sizes != [self.cache_config.block_size] or kernel_block_sizes != [[self.cache_config.block_size]]:
|
||
assert self.cache_config.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(self.model_config.max_model_len, self.max_encoder_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=kernel_block_sizes,
|
||
)
|
||
|
||
def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
|
||
"""
|
||
Initialize the attention backends and attention metadata builders.
|
||
"""
|
||
assert len(self.attn_groups) == 0, "Attention backends are already initialized"
|
||
|
||
class AttentionGroupKey(NamedTuple):
|
||
attn_backend: type[AttentionBackend]
|
||
kv_cache_spec: KVCacheSpec
|
||
|
||
def get_attn_backends_for_group(
|
||
kv_cache_group_spec: KVCacheGroupSpec,
|
||
) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
|
||
layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names)
|
||
attn_backends = {}
|
||
attn_backend_layers = defaultdict(list)
|
||
# Dedupe based on full class name; this is a bit safer than
|
||
# using the class itself as the key because when we create dynamic
|
||
# attention backend subclasses (e.g. ChunkedLocalAttention) unless
|
||
# they are cached correctly, there will be different objects per
|
||
# layer.
|
||
for layer_name in kv_cache_group_spec.layer_names:
|
||
attn_backend = layers[layer_name].get_attn_backend()
|
||
full_cls_name = attn_backend.full_cls_name()
|
||
layer_kv_cache_spec = kv_cache_group_spec.kv_cache_spec
|
||
if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
|
||
layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
|
||
key = (full_cls_name, layer_kv_cache_spec)
|
||
attn_backends[key] = AttentionGroupKey(attn_backend, layer_kv_cache_spec)
|
||
attn_backend_layers[key].append(layer_name)
|
||
return (
|
||
{attn_backends[k]: v for k, v in attn_backend_layers.items()},
|
||
set(group_key.attn_backend for group_key in attn_backends.values()),
|
||
)
|
||
|
||
def create_attn_groups(
|
||
attn_backends_map: dict[AttentionBackend, list[str]], kv_cache_group_id: int
|
||
) -> list[AttentionGroup]:
|
||
attn_groups: list[AttentionGroup] = []
|
||
for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
|
||
attn_metadata_builders = []
|
||
attn_metadata_builders.append(
|
||
attn_backend.get_builder_cls()(
|
||
kv_cache_spec,
|
||
layer_names,
|
||
self.vllm_config,
|
||
self.device,
|
||
)
|
||
)
|
||
attn_group = AttentionGroup(
|
||
attn_backend, layer_names, kv_cache_spec, kv_cache_group_id, attn_metadata_builders
|
||
)
|
||
attn_groups.append(attn_group)
|
||
return attn_groups
|
||
|
||
attention_backend_maps = []
|
||
attention_backend_list = []
|
||
for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
|
||
attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
|
||
attention_backend_maps.append(attn_backends[0])
|
||
attention_backend_list.append(attn_backends[1])
|
||
|
||
self._check_and_update_cudagraph_mode(attention_backend_list, kv_cache_config.kv_cache_groups)
|
||
|
||
for i, kv_cache_group_spec in enumerate(kv_cache_config.kv_cache_groups):
|
||
attn_backends = get_attn_backends_for_group( # type: ignore
|
||
kv_cache_group_spec
|
||
)
|
||
self.attn_groups.append(create_attn_groups(attn_backends[0], i))
|
||
|
||
# Calculate reorder batch threshold (if needed)
|
||
self.calculate_reorder_batch_threshold()
|
||
|
||
def calculate_reorder_batch_threshold(self) -> None:
|
||
"""
|
||
Check that if any backends reorder batches; that the reordering
|
||
is compatible (e.g., decode threshold is the same)
|
||
"""
|
||
for group in self._attn_group_iterator():
|
||
attn_metadata_builder_i = group.get_metadata_builder()
|
||
if hasattr(attn_metadata_builder_i, "reorder_batch_threshold"): # noqa
|
||
# check that if any backends reorder batches; that the reordering
|
||
# is compatible (e.g., decode threshold is the same)
|
||
reorder_batch_threshold_i = attn_metadata_builder_i.reorder_batch_threshold
|
||
if reorder_batch_threshold_i is not None: # noqa
|
||
if self.reorder_batch_threshold is not None:
|
||
if reorder_batch_threshold_i != self.reorder_batch_threshold:
|
||
raise ValueError(
|
||
f"Attention backend reorders decodes with "
|
||
f"threshold {reorder_batch_threshold_i} but other "
|
||
f"backend uses threshold "
|
||
f"{self.reorder_batch_threshold}"
|
||
)
|
||
else:
|
||
self.reorder_batch_threshold = reorder_batch_threshold_i # noqa
|
||
|
||
def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
|
||
"""
|
||
Generates the KVCacheSpec by parsing the kv cache format from each
|
||
Attention module in the static forward context.
|
||
Returns:
|
||
KVCacheSpec: A dictionary mapping layer names to their KV cache
|
||
format. Layers that do not need KV cache are not included.
|
||
"""
|
||
|
||
if has_ec_transfer() and get_ec_transfer().is_producer:
|
||
return {}
|
||
|
||
kv_cache_spec: dict[str, KVCacheSpec] = {}
|
||
attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
|
||
# NOTE: Must process Attention/MLAAttention before MambaBase to maintain
|
||
# ordering expected by graph parameter update logic in attention backends.
|
||
mamba_layers: dict[str, MambaBase] = {}
|
||
attn_layer_names = set()
|
||
for layer_name, attn_module in attn_layers.items():
|
||
if isinstance(attn_module, Attention):
|
||
if (kv_tgt_layer := attn_module.kv_sharing_target_layer_name) is not None:
|
||
# The layer doesn't need its own KV cache and will use that of
|
||
# the target layer. We skip creating a KVCacheSpec for it, so
|
||
# that KV cache management logic will act as this layer does
|
||
# not exist, and doesn't allocate KV cache for the layer. This
|
||
# enables the memory saving of cross-layer kv sharing, allowing
|
||
# a given amount of memory to accommodate longer context lengths
|
||
# or enable more requests to be processed simultaneously.
|
||
self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
|
||
continue
|
||
|
||
if spec := attn_module.get_kv_cache_spec(self.vllm_config):
|
||
kv_cache_spec[layer_name] = spec
|
||
attn_layer_names.add(layer_name)
|
||
|
||
elif isinstance(attn_module, MLAAttention):
|
||
if self.use_sparse:
|
||
# TODO(cmq): This is a hack way to fix deepseek kvcache when
|
||
# using DSA. Fix the spec in vLLM is the final way.
|
||
sparse_sum_head_size = sum(self._get_sparse_kv_cache_ratio())
|
||
kv_cache_spec[layer_name] = MLAAttentionSpec(
|
||
block_size=self.block_size,
|
||
num_kv_heads=1,
|
||
head_size=sparse_sum_head_size,
|
||
dtype=self.kv_cache_dtype,
|
||
cache_dtype_str=self.vllm_config.cache_config.cache_dtype,
|
||
)
|
||
elif spec := attn_module.get_kv_cache_spec(self.vllm_config):
|
||
kv_cache_spec[layer_name] = spec
|
||
|
||
elif isinstance(attn_module, MambaBase):
|
||
mamba_layers[layer_name] = attn_module
|
||
|
||
if len(mamba_layers) > 0:
|
||
if self.vllm_config.cache_config.enable_prefix_caching:
|
||
raise NotImplementedError("Prefix caching is not supported for Mamba yet.")
|
||
mamba_page_size_padded = 0
|
||
for layer_name, mamba_module in mamba_layers.items():
|
||
if spec := mamba_module.get_kv_cache_spec(self.vllm_config):
|
||
kv_cache_spec[layer_name] = spec
|
||
mamba_page_size_padded = spec.page_size_bytes
|
||
# align attn_page_size to mamba_page_size_padded
|
||
for layer_name in attn_layer_names:
|
||
if kv_cache_spec[layer_name].page_size_bytes < mamba_page_size_padded:
|
||
object.__setattr__(kv_cache_spec[layer_name], "page_size_padded", mamba_page_size_padded)
|
||
|
||
return kv_cache_spec
|
||
|
||
def _get_sparse_kv_cache_ratio(self) -> list[int]:
|
||
# TODO:If C8 is supported, we need to consider the number of bytes occupied by different dtypes
|
||
# when calculating the ratio，for example:
|
||
# [kv_lora_rank * torch.int8.itemsize, qk_rope_head_dim * torch.bfloat16.itemsize, ...]
|
||
return [
|
||
self.model_config.hf_text_config.kv_lora_rank,
|
||
self.model_config.hf_text_config.qk_rope_head_dim,
|
||
self.model_config.hf_text_config.index_head_dim,
|
||
]
|
||
|
||
def _check_and_update_cudagraph_mode(
|
||
self,
|
||
attention_backends: list[set[type[AttentionBackend]]],
|
||
kv_cache_groups: list[KVCacheGroupSpec],
|
||
) -> None:
|
||
with update_pass_config(self):
|
||
super()._check_and_update_cudagraph_mode(attention_backends, kv_cache_groups)
|
||
|
||
# NOTE: Since aclgraph_batch_sizes cannot be determined until here,
|
||
# we set the graph params right before initializing the keys.
|
||
if self.use_aclgraph:
|
||
set_graph_params(self.cudagraph_batch_sizes)
|
||
if self.speculative_config:
|
||
set_draft_graph_params(self.cudagraph_batch_sizes)
|
||
|
||
def capture_model(self) -> None:
|
||
gpu_model_runner_cls = next((cls for cls in self.__class__.__mro__ if cls.__name__ == "GPUModelRunner"), None)
|
||
if gpu_model_runner_cls is None:
|
||
raise TypeError("Could not find GPUModelRunner in the MRO. The class hierarchy may have changed.")
|
||
parent_module_name = gpu_model_runner_cls.__module__
|
||
with _torch_cuda_wrapper(), _replace_gpu_model_runner_function_wrapper(parent_module_name):
|
||
GPUModelRunner.capture_model(self)
|
||
|
||
def _prepare_multimodal_fields(self):
|
||
"""
|
||
Ensures specific multimodal tensors are on CPU.
|
||
This is necessary for fields like 'grid_thw' which are converted to numpy
|
||
inside the model's forward pass.
|
||
"""
|
||
if not self.multimodal_cpu_fields:
|
||
return
|
||
|
||
req_ids = self.input_batch.req_ids
|
||
for req_id in req_ids:
|
||
req = self.requests.get(req_id)
|
||
if req is None:
|
||
continue
|
||
|
||
mm_data = getattr(req, "multimodal_data", None)
|
||
if not mm_data:
|
||
continue
|
||
|
||
for field in self.multimodal_cpu_fields:
|
||
if field in mm_data:
|
||
tensor = mm_data[field]
|
||
if isinstance(tensor, torch.Tensor) and tensor.device.type != "cpu":
|
||
mm_data[field] = tensor.cpu()
|
||
|
||
|
||
def _post_process_cudagraph_mode(tensor: torch.Tensor) -> int:
|
||
"""
|
||
Synchronize cudagraph_mode across DP ranks by taking the minimum.
|
||
If any rank has NONE (0), all ranks use NONE.
|
||
This ensures all ranks send consistent values (all padded or all unpadded).
|
||
"""
|
||
return int(tensor[1, :].min().item())
|
||
|
||
|
||
@contextmanager
|
||
def _torch_cuda_wrapper():
|
||
class _EventPlaceholder:
|
||
def __init__(self, *args, **kwargs) -> None:
|
||
self.record = lambda: None
|
||
self.synchronize = lambda: None
|
||
|
||
class _StreamPlaceholder:
|
||
def __init__(self, *args, **kwargs) -> None:
|
||
pass
|
||
|
||
try:
|
||
# replace cuda APIs with xpu APIs, this should work by default
|
||
torch.Event = torch.npu.Event
|
||
torch.cuda.Event = torch.npu.Event
|
||
torch.cuda.Stream = torch.npu.Stream
|
||
torch.cuda.default_stream = torch.npu.default_stream
|
||
torch.cuda.current_stream = torch.npu.current_stream
|
||
torch.cuda.stream = torch.npu.stream
|
||
torch.cuda.synchronize = torch.npu.synchronize
|
||
torch.cuda.mem_get_info = torch.npu.mem_get_info
|
||
yield
|
||
except Exception as e:
|
||
torch.cuda.Event = _EventPlaceholder
|
||
torch.cuda.Stream = _StreamPlaceholder
|
||
torch.cuda.default_stream = _StreamPlaceholder
|
||
torch.cuda.current_stream = _StreamPlaceholder
|
||
torch.cuda.stream = _StreamPlaceholder
|
||
torch.cuda.synchronize = _StreamPlaceholder
|
||
torch.cuda.mem_get_info = _StreamPlaceholder
|
||
raise RuntimeError(f"NPUModelRunner init failed, error is {e}")
|
||
finally:
|
||
# if anything goes wrong, just patch it with a placeholder
|
||
torch.cuda.Event = _EventPlaceholder
|
||
torch.cuda.Stream = torch.cuda.Stream
|
||
torch.cuda.default_stream = torch.npu.default_stream
|
||
torch.cuda.current_stream = torch.npu.current_stream
|
||
torch.cuda.stream = torch.npu.stream
|
||
torch.cuda.synchronize = torch.npu.synchronize
|
||
torch.cuda.mem_get_info = torch.npu.mem_get_info
|
||
|
||
|
||
# TODO: This method will be removed subsequently and implemented in platform.
|
||
@contextmanager
|
||
def _replace_gpu_model_runner_function_wrapper(target_module_name):
|
||
try:
|
||
target_module = sys.modules[target_module_name]
|
||
setattr(target_module, "graph_capture", graph_capture) # noqa: B010
|
||
yield
|
||
except Exception as e:
|
||
raise RuntimeError(f"NPUModelRunner failed, error is {e}")
|
||
finally:
|
||
setattr(target_module, "graph_capture", graph_capture) # noqa: B010
|
||
|
||
|
||
# TODO: remove it when flash_comm1 is removed
|
||
@contextmanager
|
||
def update_pass_config(model_runner):
|
||
try:
|
||
original_pass_config_sp = model_runner.compilation_config.pass_config.enable_sp
|
||
model_runner.compilation_config.pass_config.enable_sp = enable_sp(model_runner.vllm_config)
|
||
yield
|
||
finally:
|
||
model_runner.compilation_config.pass_config.enable_sp = original_pass_config_sp
|