f6af1d2471
logger in vllm-ascend doesn't work. This PR fix the issue. Fix: https://github.com/vllm-project/vllm-ascend/issues/431 Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
825 lines
37 KiB
Python
825 lines
37 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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# Copyright 2023 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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#
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import gc
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import os
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from typing import TYPE_CHECKING, Dict, List, Optional, Union
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import numpy as np
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import numpy.typing as npt
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import torch
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import torch.distributed
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import torch.nn as nn
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from vllm.attention import AttentionType
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from vllm.attention.layer import Attention
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from vllm.config import VllmConfig
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from vllm.distributed.parallel_state import get_pp_group
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from vllm.forward_context import set_forward_context
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from vllm.inputs import INPUT_REGISTRY
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from vllm.logger import logger
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.model_loader import get_model
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from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
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from vllm.platforms import current_platform
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from vllm.sampling_params import SamplingType
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from vllm.sequence import IntermediateTensors
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from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
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LayerBlockType, cdiv, is_pin_memory_available)
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from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
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from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
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KVCacheSpec)
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from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, ModelRunnerOutput
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from vllm.v1.utils import bind_kv_cache
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm_ascend.attention.attention_v1 import (AscendAttentionBackend,
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AscendMetadata)
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if TYPE_CHECKING:
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from vllm.v1.core.sched.output import SchedulerOutput
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NPU_PAGED_ATTENTION_MASK_VALUE = -10000
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class NPUModelRunner:
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def __init__(self, vllm_config: VllmConfig, device: torch.device):
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self.vllm_config = vllm_config
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self.model_config = vllm_config.model_config
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self.cache_config = vllm_config.cache_config
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self.lora_config = vllm_config.lora_config
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self.load_config = vllm_config.load_config
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self.parallel_config = vllm_config.parallel_config
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self.scheduler_config = vllm_config.scheduler_config
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self.speculative_config = vllm_config.speculative_config
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self.prompt_adapter_config = vllm_config.prompt_adapter_config
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self.observability_config = vllm_config.observability_config
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model_config = self.model_config
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cache_config = self.cache_config
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scheduler_config = self.scheduler_config
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parallel_config = self.parallel_config
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self.device = device
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self.pin_memory = is_pin_memory_available()
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self.dtype = self.model_config.dtype
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if cache_config.cache_dtype == "auto":
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self.kv_cache_dtype = self.dtype
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else:
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self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
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cache_config.cache_dtype]
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self.is_multimodal_model = model_config.is_multimodal_model
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self.sliding_window = model_config.get_sliding_window()
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self.block_size = cache_config.block_size
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self.max_model_len = model_config.max_model_len
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self.max_num_blocks_per_req = cdiv(self.max_model_len, self.block_size)
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self.max_num_tokens = scheduler_config.max_num_batched_tokens
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self.max_num_reqs = scheduler_config.max_num_seqs
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# Model-related.
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self.num_attn_layers = model_config.get_num_layers_by_block_type(
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parallel_config, LayerBlockType.attention)
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self.num_query_heads = model_config.get_num_attention_heads(
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parallel_config)
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self.num_kv_heads = model_config.get_num_kv_heads(parallel_config)
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self.head_size = model_config.get_head_size()
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self.hidden_size = model_config.get_hidden_size()
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# Multi-modal data support
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self.input_registry = INPUT_REGISTRY
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self.mm_registry = MULTIMODAL_REGISTRY
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self.uses_mrope = model_config.uses_mrope
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encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
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model_config=model_config,
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scheduler_config=scheduler_config,
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mm_registry=self.mm_registry)
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self.max_num_encoder_input_tokens = encoder_compute_budget
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self.encoder_cache_size = encoder_cache_size
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# Lazy initialization
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# self.model: nn.Module # Set after load_model
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self.kv_caches: List[torch.Tensor] = []
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# req_id -> (input_id -> encoder_output)
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self.encoder_cache: Dict[str, Dict[int, torch.Tensor]] = {}
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# Set up speculative decoding.
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self.use_spec_decode = False
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# Request states.
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self.requests: Dict[str, CachedRequestState] = {}
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# Persistent batch.
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self.input_batch = InputBatch(
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max_num_reqs=self.max_num_reqs,
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max_model_len=self.max_model_len,
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max_num_blocks_per_req=self.max_num_blocks_per_req,
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device=self.device,
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pin_memory=self.pin_memory,
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vocab_size=model_config.get_vocab_size(),
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)
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self.input_ids = torch.zeros(self.max_num_tokens,
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dtype=torch.int32,
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device=self.device)
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self.positions = torch.zeros(self.max_num_tokens,
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dtype=torch.int64,
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device=self.device)
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# None in the first PP rank. The rest are set after load_model.
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self.intermediate_tensors: Optional[IntermediateTensors] = None
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# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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if self.uses_mrope:
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# NOTE: `mrope_positions` is implemented with one additional dummy
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# position on purpose to make it non-contiguous so that it can work
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# with torch compile.
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# See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
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# NOTE: When M-RoPE is enabled, position ids are 3D regardless of
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# the modality of inputs. For text-only inputs, each dimension has
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# identical position IDs, making M-RoPE functionally equivalent to
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# 1D-RoPE.
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# See page 5 of https://arxiv.org/abs/2409.12191
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self.mrope_positions = torch.zeros((3, self.max_num_tokens + 1),
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dtype=torch.int64,
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device=self.device)
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self.mrope_positions_cpu = torch.zeros(
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(3, self.max_num_tokens + 1),
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dtype=torch.int64,
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device="cpu",
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pin_memory=self.pin_memory)
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self.inputs_embeds = torch.zeros(
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(self.max_num_tokens, self.hidden_size),
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dtype=self.dtype,
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device=self.device)
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# OPTIMIZATION: Cache the tensors rather than creating them every step.
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self.arange_np: npt.NDArray[np.int32] = np.arange(max(
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self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
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dtype=np.int32)
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# NOTE(woosuk): These tensors are "stateless", i.e., they are literally
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# a faster version of creating a new tensor every time. Thus, we should
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# not make any assumptions about the values in these tensors.
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self.input_ids_cpu = torch.zeros(self.max_num_tokens,
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dtype=torch.int32,
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device="cpu",
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pin_memory=self.pin_memory)
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self.input_ids_np = self.input_ids_cpu.numpy()
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self.positions_cpu = torch.zeros(self.max_num_tokens,
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dtype=torch.int64,
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device="cpu",
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pin_memory=self.pin_memory)
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self.positions_np = self.positions_cpu.numpy()
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self.slot_mapping_cpu = torch.zeros(self.max_num_tokens,
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dtype=torch.int32,
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device="cpu",
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pin_memory=self.pin_memory)
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self.slot_mapping_np = self.slot_mapping_cpu.numpy()
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self.query_start_loc_cpu = torch.zeros(self.max_num_reqs + 1,
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dtype=torch.int32,
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device="cpu",
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pin_memory=self.pin_memory)
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self.query_start_loc_np = self.query_start_loc_cpu.numpy()
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self.seq_lens_cpu = torch.zeros(self.max_num_reqs,
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dtype=torch.int32,
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device="cpu",
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pin_memory=self.pin_memory)
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self.seq_lens_np = self.seq_lens_cpu.numpy()
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self.input_positions_cpu = torch.arange(0,
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self.max_num_tokens,
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device="cpu")
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# NOTE: Pre-construct a mask matrix to improve the efficiency of
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# attention mask construction during inference.
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# Note that the length of the matrix needs to be carefully balanced: a
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# matrix that is too large will consume excessive VRAM, while a matrix
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# that is too small will require dynamic concatenation during inference,
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# leading to performance degradation.
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# Therefore, an environment variable is added here to dynamically set
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# the size of the pre-constructed mask matrix based on requirements.
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mask_len = os.getenv("PAGED_ATTENTION_MASK_LEN", 10000)
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self.attn_mask_len = min(self.max_model_len, int(mask_len))
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self.attn_mask_npu = torch.full(
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(self.attn_mask_len, self.attn_mask_len),
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NPU_PAGED_ATTENTION_MASK_VALUE,
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device=self.device,
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dtype=self.vllm_config.model_config.dtype)
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self.attn_mask_npu.masked_fill_(
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self.attn_mask_npu.tril() == NPU_PAGED_ATTENTION_MASK_VALUE, 0)
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def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
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"""Update the cached states and the persistent batch with the scheduler
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output.
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The SamplingMetadata is updated and copied to the NPU if there is a
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new/resumed/paused/finished request in the batch.
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"""
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# Remove finished requests from the cached states.
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for req_id in scheduler_output.finished_req_ids:
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self.requests.pop(req_id, None)
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# Remove the finished requests from the persistent batch.
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# NOTE(woosuk): There could be an edge case where finished_req_ids and
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# scheduled_req_ids overlap. This happens when a request is aborted and
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# then resubmitted with the same ID. In this case, we treat them as two
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# distinct requests - clearing the cached states for the first request
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# and handling the second as a new request.
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removed_req_indices: List[int] = []
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for req_id in scheduler_output.finished_req_ids:
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req_index = self.input_batch.remove_request(req_id)
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if req_index is not None:
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removed_req_indices.append(req_index)
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# Remove the unscheduled requests from the persistent batch.
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# NOTE(woosuk): The unscheduled requests are either preempted requests
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# or running requests that are not scheduled in this step. We remove
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# them from the persistent batch but keep their cached states since
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# they will be scheduled again sometime in the future.
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scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
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cached_req_ids = self.input_batch.req_id_to_index.keys()
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unscheduled_req_ids = cached_req_ids - scheduled_req_ids
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# NOTE(woosuk): The persistent batch optimization assumes that
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# consecutive batches contain mostly the same requests. If batches
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# have low request overlap (e.g., alternating between two distinct
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# sets of requests), this optimization becomes very inefficient.
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for req_id in unscheduled_req_ids:
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req_index = self.input_batch.remove_request(req_id)
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assert req_index is not None
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removed_req_indices.append(req_index)
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req_ids_to_add: List[str] = []
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# Add new requests to the cached states.
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for new_req_data in scheduler_output.scheduled_new_reqs:
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req_id = new_req_data.req_id
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sampling_params = new_req_data.sampling_params
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if sampling_params.sampling_type == SamplingType.RANDOM_SEED:
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generator = torch.Generator(device=self.device)
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generator.manual_seed(sampling_params.seed)
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else:
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generator = None
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self.requests[req_id] = CachedRequestState(
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req_id=req_id,
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prompt_token_ids=new_req_data.prompt_token_ids,
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prompt=new_req_data.prompt,
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mm_inputs=new_req_data.mm_inputs,
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mm_positions=new_req_data.mm_positions,
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sampling_params=sampling_params,
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generator=generator,
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block_ids=new_req_data.block_ids,
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num_computed_tokens=new_req_data.num_computed_tokens,
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output_token_ids=[],
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lora_request=new_req_data.lora_request,
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)
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req_ids_to_add.append(req_id)
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# Update the states of the running/resumed requests.
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for req_data in scheduler_output.scheduled_cached_reqs:
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req_id = req_data.req_id
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req_state = self.requests[req_id]
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# Update the cached states.
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num_computed_tokens = req_data.num_computed_tokens
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req_state.num_computed_tokens = num_computed_tokens
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# Add the sampled token(s) from the previous step (if any).
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# This doesn't include "unverified" tokens like spec decode tokens.
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num_new_tokens = (num_computed_tokens +
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len(req_data.new_token_ids) -
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req_state.num_tokens)
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if num_new_tokens == 1:
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# Avoid slicing list in most common case.
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req_state.output_token_ids.append(req_data.new_token_ids[-1])
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elif num_new_tokens > 0:
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req_state.output_token_ids.extend(
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req_data.new_token_ids[-num_new_tokens:])
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# Update the block IDs.
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if not req_data.resumed_from_preemption:
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# Append the new blocks to the existing block IDs.
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req_state.block_ids.extend(req_data.new_block_ids)
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else:
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# The request is resumed from preemption.
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# Replace the existing block IDs with the new ones.
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req_state.block_ids = req_data.new_block_ids
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req_index = self.input_batch.req_id_to_index.get(req_id)
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if req_index is None:
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# The request is not in the persistent batch.
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# The request was either preempted and resumed later, or was not
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# scheduled in the previous step and needs to be added again.
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req_ids_to_add.append(req_id)
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continue
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# Update the persistent batch.
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self.input_batch.num_computed_tokens_cpu[req_index] = (
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num_computed_tokens)
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start_index = (len(req_state.block_ids) -
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len(req_data.new_block_ids))
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self.input_batch.block_table.append_row(req_data.new_block_ids,
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req_index)
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# Add new_token_ids to token_ids_cpu.
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start_token_index = num_computed_tokens
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end_token_index = num_computed_tokens + len(req_data.new_token_ids)
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self.input_batch.token_ids_cpu[
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req_index,
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start_token_index:end_token_index] = req_data.new_token_ids
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self.input_batch.num_tokens_no_spec[req_index] = end_token_index
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# Add spec_token_ids to token_ids_cpu.
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spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
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req_id, ())
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if spec_token_ids:
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start_index = end_token_index
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end_token_index += len(spec_token_ids)
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self.input_batch.token_ids_cpu[
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req_index, start_index:end_token_index] = spec_token_ids
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# NOTE(woosuk): `num_tokens` here may include spec decode tokens.
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self.input_batch.num_tokens[req_index] = end_token_index
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# Check if the batch has changed. If not, we can skip copying the
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# sampling metadata from CPU to GPU.
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batch_changed = len(removed_req_indices) > 0 or len(req_ids_to_add) > 0
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# Add the new or resumed requests to the persistent batch.
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# The smaller empty indices are filled first.
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removed_req_indices = sorted(removed_req_indices, reverse=True)
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for req_id in req_ids_to_add:
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req_state = self.requests[req_id]
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if removed_req_indices:
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# Fill the empty index.
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req_index = removed_req_indices.pop()
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else:
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# Append to the end.
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req_index = None
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self.input_batch.add_request(req_state, req_index)
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# Condense the batched states if there are empty indices.
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if removed_req_indices:
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self.input_batch.condense(removed_req_indices)
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if batch_changed:
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self.input_batch.refresh_sampling_metadata()
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def get_model(self) -> nn.Module:
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return self.model
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def make_attention_mask(self, seq_lens, query_lens,
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position) -> torch.Tensor:
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max_seq_len = max(seq_lens, default=0)
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if max_seq_len <= self.attn_mask_len:
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return torch.index_select(self.attn_mask_npu,
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dim=0,
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index=position)[:, :max_seq_len]
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total_q_len = sum(query_lens)
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attn_mask = torch.zeros((total_q_len, max_seq_len),
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dtype=self.vllm_config.model_config.dtype,
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device="cpu")
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current_row = 0
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for i in range(len(query_lens)):
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seq_len = seq_lens[i]
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q_len = query_lens[i]
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context_len = seq_len - q_len
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assert context_len >= 0
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attn_mask[current_row:current_row + q_len,
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context_len:] = NPU_PAGED_ATTENTION_MASK_VALUE
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right_tensor = attn_mask[current_row:current_row + q_len,
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context_len:seq_len]
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right_tensor.mask_fill_(
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right_tensor.tril() == NPU_PAGED_ATTENTION_MASK_VALUE, 0)
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current_row += q_len
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return attn_mask.to(self.device, non_blocking=True)
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def _process_reqs(
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self,
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scheduler_output: "SchedulerOutput",
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intermediate_tensors: Optional[IntermediateTensors] = None,
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) -> torch.Tensor:
|
|
# Check input valid
|
|
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
|
|
|
|
# Copy the blocks from CPU to NPU.
|
|
# 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(num_reqs)
|
|
|
|
# Get the number of scheduled tokens for each request.
|
|
# TODO: The Python loop can be slow. Optimize.
|
|
num_scheduled_tokens = np.empty(num_reqs, dtype=np.int32)
|
|
max_num_scheduled_tokens = 0
|
|
for i, req_id in enumerate(self.input_batch.req_ids):
|
|
num_tokens = scheduler_output.num_scheduled_tokens[req_id]
|
|
num_scheduled_tokens[i] = num_tokens
|
|
max_num_scheduled_tokens = max(max_num_scheduled_tokens,
|
|
num_tokens)
|
|
|
|
# Prepare positions
|
|
req_indices = np.repeat(self.arange_np[:num_reqs],
|
|
num_scheduled_tokens)
|
|
cu_num_tokens = np.cumsum(num_scheduled_tokens)
|
|
cumsums_offsets = np.repeat(cu_num_tokens - num_scheduled_tokens,
|
|
num_scheduled_tokens)
|
|
arange = self.arange_np[:total_num_scheduled_tokens] - cumsums_offsets
|
|
|
|
positions_np = self.positions_np[:total_num_scheduled_tokens]
|
|
np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
|
|
arange,
|
|
out=positions_np)
|
|
|
|
self.positions[:total_num_scheduled_tokens].copy_(
|
|
self.positions_cpu[:total_num_scheduled_tokens], non_blocking=True)
|
|
positions = self.positions[:total_num_scheduled_tokens]
|
|
|
|
self.seq_lens_np[:num_reqs] = (
|
|
self.input_batch.num_computed_tokens_cpu[:num_reqs] +
|
|
num_scheduled_tokens)
|
|
seq_lens = self.seq_lens_cpu[:num_reqs]
|
|
|
|
query_lens = torch.from_numpy(num_scheduled_tokens)
|
|
|
|
block_table_indices = (req_indices * self.max_num_blocks_per_req +
|
|
positions_np // self.block_size)
|
|
block_table_cpu = self.input_batch.block_table.get_cpu_tensor()
|
|
block_numbers = block_table_cpu.flatten()[block_table_indices].numpy()
|
|
block_offsets = positions_np % self.block_size
|
|
np.add(block_numbers * self.block_size,
|
|
block_offsets,
|
|
out=self.slot_mapping_np[:total_num_scheduled_tokens])
|
|
slot_mapping = self.slot_mapping_cpu[:total_num_scheduled_tokens].to(
|
|
self.device, non_blocking=True)
|
|
|
|
attn_mask = self.make_attention_mask(seq_lens=seq_lens,
|
|
query_lens=num_scheduled_tokens,
|
|
position=positions)
|
|
|
|
attn_metadata = AscendMetadata(
|
|
seq_lens=query_lens,
|
|
context_lens=seq_lens,
|
|
slot_mapping=slot_mapping,
|
|
block_tables=(
|
|
self.input_batch.block_table.get_device_tensor()[:num_reqs]),
|
|
attn_mask=attn_mask,
|
|
)
|
|
|
|
# Prepare input_ids
|
|
token_indices = (positions_np +
|
|
req_indices * self.input_batch.token_ids_cpu.shape[1])
|
|
torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
|
|
0,
|
|
torch.from_numpy(token_indices),
|
|
out=self.input_ids_cpu[:total_num_scheduled_tokens])
|
|
# Copy the tensors to the NPU.
|
|
self.input_ids[:total_num_scheduled_tokens].copy_(
|
|
self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
|
|
input_ids = self.input_ids[:total_num_scheduled_tokens]
|
|
|
|
# Run forward pass
|
|
with set_forward_context(attn_metadata, self.vllm_config):
|
|
assert self.model is not None
|
|
hidden_states = self.model(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=None,
|
|
)
|
|
|
|
return hidden_states[cu_num_tokens - 1]
|
|
|
|
@torch.inference_mode()
|
|
def execute_model(
|
|
self,
|
|
scheduler_output: "SchedulerOutput",
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
) -> Union[ModelRunnerOutput, torch.Tensor]:
|
|
self._update_states(scheduler_output)
|
|
if not scheduler_output.total_num_scheduled_tokens:
|
|
# Return empty ModelRunnerOuptut if there's no work to do.
|
|
return EMPTY_MODEL_RUNNER_OUTPUT
|
|
hidden_states = self._process_reqs(scheduler_output,
|
|
intermediate_tensors)
|
|
logits = self.model.compute_logits(hidden_states, None)
|
|
|
|
# Sample the next token and get logprobs if needed.
|
|
sampling_metadata = self.input_batch.sampling_metadata
|
|
sampler_output = self.model.sample(
|
|
logits=logits,
|
|
sampling_metadata=sampling_metadata,
|
|
)
|
|
|
|
# TODO(woosuk): The following loop can be slow since it iterates over
|
|
# the requests one by one. Optimize.
|
|
for i, req_id in enumerate(self.input_batch.req_ids):
|
|
req_state = self.requests[req_id]
|
|
seq_len = (req_state.num_computed_tokens +
|
|
scheduler_output.num_scheduled_tokens[req_id])
|
|
if seq_len < req_state.num_tokens:
|
|
# Ignore the sampled token.
|
|
# Rewind the generator state as if the token was not sampled.
|
|
generator = self.input_batch.generators.get(i)
|
|
if generator is not None:
|
|
generator.set_offset(generator.get_offset() - 4)
|
|
|
|
# NOTE: NPU -> CPU Sync happens here.
|
|
# Move as many CPU operations as possible before this sync point.
|
|
logprobs_tensors = sampler_output.logprobs_tensors
|
|
logprobs_lists = logprobs_tensors.tolists() \
|
|
if logprobs_tensors is not None else None
|
|
|
|
# Get the valid generated tokens.
|
|
sampled_token_ids = sampler_output.sampled_token_ids
|
|
max_gen_len = sampled_token_ids.shape[-1]
|
|
if max_gen_len == 1:
|
|
# No spec decode tokens.
|
|
valid_sampled_token_ids = sampled_token_ids.tolist()
|
|
|
|
model_runner_output = ModelRunnerOutput(
|
|
req_ids=self.input_batch.req_ids,
|
|
req_id_to_index=self.input_batch.req_id_to_index,
|
|
sampled_token_ids=valid_sampled_token_ids,
|
|
spec_token_ids=None,
|
|
logprobs=logprobs_lists,
|
|
prompt_logprobs_dict={},
|
|
)
|
|
return model_runner_output
|
|
|
|
def _profile_multimodal(self) -> None:
|
|
# TODO: handle encoder-decoder models once we support them.
|
|
# NOTE: Currently model is profiled with a single non-text
|
|
# modality with the max possible input tokens even when
|
|
# it supports multiple.
|
|
|
|
if (not self.is_multimodal_model
|
|
or self.max_num_encoder_input_tokens <= 0
|
|
or self.encoder_cache_size <= 0):
|
|
return
|
|
|
|
max_tokens_by_modality_dict = (
|
|
MULTIMODAL_REGISTRY.get_max_tokens_per_item_by_nonzero_modality(
|
|
self.model_config))
|
|
dummy_data_modality, max_tokens_per_mm_item = max(
|
|
max_tokens_by_modality_dict.items(), key=lambda item: item[1])
|
|
|
|
# Check how many items of this modality can be supported by
|
|
# the encoder budget.
|
|
encoder_budget = min(self.max_num_encoder_input_tokens,
|
|
self.encoder_cache_size)
|
|
|
|
max_num_mm_items_encoder_budget = cdiv(encoder_budget,
|
|
max_tokens_per_mm_item)
|
|
|
|
# Check how many items of this modality can be supported by
|
|
# the decoder budget.
|
|
max_mm_items_per_req = self.mm_registry.get_mm_limits_per_prompt(
|
|
self.model_config)[dummy_data_modality]
|
|
|
|
# NOTE: We do not consider max_num_batched_tokens on purpose
|
|
# because the multimodal embeddings can be generated in advance
|
|
# and chunked prefilled.
|
|
max_num_mm_items_decoder_budget = self.max_num_reqs * \
|
|
max_mm_items_per_req
|
|
|
|
max_num_mm_items = min(max_num_mm_items_encoder_budget,
|
|
max_num_mm_items_decoder_budget)
|
|
|
|
logger.info(
|
|
"Encoder cache will be initialized with a budget of %s tokens,"
|
|
" and profiled with %s %s items of the maximum feature size.",
|
|
encoder_budget, max_num_mm_items, dummy_data_modality)
|
|
|
|
# Create dummy batch of multimodal inputs.
|
|
dummy_request_data = self.input_registry.dummy_data_for_profiling(
|
|
model_config=self.model_config,
|
|
seq_len=self.max_num_tokens,
|
|
mm_registry=self.mm_registry,
|
|
)
|
|
dummy_mm_data = dummy_request_data.multi_modal_data
|
|
|
|
if not isinstance(dummy_mm_data, MultiModalKwargs):
|
|
# TODO: Delete this check once input mapper is fully removed.
|
|
raise RuntimeError("Legacy input mapper is not supported in V1")
|
|
|
|
# Dummy data definition in V0 may contain multiple multimodal items
|
|
# (e.g, multiple images) for a single request, therefore here we
|
|
# always replicate first item by max_num_mm_items times since in V1
|
|
# they are scheduled to be processed separately.
|
|
|
|
dummy_mm_item = dummy_mm_data.get_item(modality=dummy_data_modality,
|
|
item_index=0)
|
|
dummy_mm_kwargs = MultiModalKwargs.from_items([dummy_mm_item])
|
|
|
|
batched_dummy_mm_inputs = MultiModalKwargs.batch([dummy_mm_kwargs] *
|
|
max_num_mm_items)
|
|
batched_dummy_mm_inputs = MultiModalKwargs.as_kwargs(
|
|
batched_dummy_mm_inputs, device=self.device)
|
|
|
|
# Run multimodal encoder.
|
|
dummy_encoder_outputs = self.model.get_multimodal_embeddings(
|
|
**batched_dummy_mm_inputs)
|
|
assert len(dummy_encoder_outputs) == max_num_mm_items, (
|
|
"Expected dimension 0 of encoder outputs to match the number "
|
|
f"of multimodal data items: {max_num_mm_items}, got "
|
|
f"{len(dummy_encoder_outputs)=} instead. This is most likely "
|
|
"due to the 'get_multimodal_embeddings' method of the model "
|
|
"not implemented correctly.")
|
|
|
|
# Cache the dummy encoder outputs.
|
|
self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
|
|
|
|
@torch.inference_mode()
|
|
def _dummy_run(
|
|
self,
|
|
num_tokens: int,
|
|
) -> torch.Tensor:
|
|
model = self.model
|
|
if self.is_multimodal_model:
|
|
input_ids = None
|
|
inputs_embeds = self.inputs_embeds[:num_tokens]
|
|
else:
|
|
input_ids = self.input_ids[:num_tokens]
|
|
inputs_embeds = None
|
|
|
|
if self.uses_mrope:
|
|
positions = self.mrope_positions[:, :num_tokens]
|
|
else:
|
|
positions = self.input_positions_cpu[:num_tokens]
|
|
|
|
if get_pp_group().is_first_rank:
|
|
intermediate_tensors = None
|
|
else:
|
|
if self.intermediate_tensors is None:
|
|
self.intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors(
|
|
batch_size=self.max_num_tokens,
|
|
dtype=self.model_config.dtype,
|
|
device=self.device))
|
|
intermediate_tensors = IntermediateTensors({
|
|
k: v[:num_tokens]
|
|
for k, v in self.intermediate_tensors.items()
|
|
})
|
|
|
|
with set_forward_context(None, self.vllm_config):
|
|
hidden_states = model(input_ids=input_ids,
|
|
positions=positions.to(self.device),
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=inputs_embeds)
|
|
return hidden_states
|
|
|
|
def profile_run(self) -> None:
|
|
# Profile with multimodal encoder & encoder cache.
|
|
self._profile_multimodal()
|
|
|
|
# For profile, have maximum num_reqs and that collectively have
|
|
# maximum num_tokens.
|
|
num_reqs = self.scheduler_config.max_num_seqs
|
|
num_tokens = self.max_num_tokens
|
|
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
|
|
|
|
num_scheduled_tokens = np.array(num_scheduled_tokens_list,
|
|
dtype=np.int32)
|
|
logit_indices = np.cumsum(num_scheduled_tokens) - 1
|
|
|
|
# assert self.lora_manager is not None, "LoRA is not enabled"
|
|
# TODO: call maybe_profile_with_lora()
|
|
|
|
dummy_kv_caches = [
|
|
torch.tensor((), dtype=torch.float32, device=self.device)
|
|
for _ in range(self.num_attn_layers)
|
|
]
|
|
|
|
# Trigger compilation for general shape.
|
|
hidden_states = self._dummy_run(self.max_num_tokens)
|
|
|
|
if get_pp_group().is_last_rank:
|
|
hidden_states = hidden_states[logit_indices]
|
|
logits = self.model.compute_logits(hidden_states, None)
|
|
else:
|
|
logits = None
|
|
|
|
current_platform.synchronize()
|
|
del hidden_states, logits, dummy_kv_caches
|
|
self.encoder_cache.clear()
|
|
gc.collect()
|
|
|
|
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.lora_config:
|
|
raise ValueError("LoRA model is not supported on NPU now.")
|
|
|
|
self.model_memory_usage = m.consumed_memory
|
|
logger.info("Loading model weights took %.4f GB",
|
|
self.model_memory_usage / float(2**30))
|
|
|
|
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_caches: Dict[str, torch.Tensor] = {}
|
|
for kv_cache_group in kv_cache_config.kv_cache_groups:
|
|
kv_cache_spec = kv_cache_group.kv_cache_spec
|
|
for layer_name in kv_cache_group.layer_names:
|
|
tensor_config = kv_cache_config.tensors[layer_name]
|
|
assert tensor_config.size % kv_cache_spec.page_size_bytes == 0
|
|
num_blocks = tensor_config.size // 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 isinstance(kv_cache_spec, FullAttentionSpec):
|
|
kv_cache_shape = AscendAttentionBackend.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
|
|
kv_caches[layer_name] = torch.zeros(kv_cache_shape,
|
|
dtype=dtype,
|
|
device=self.device)
|
|
else:
|
|
# TODO: add new branches when introducing more types of
|
|
# KV cache specs.
|
|
raise ValueError("Unknown KV cache spec type.")
|
|
|
|
bind_kv_cache(
|
|
kv_caches,
|
|
self.vllm_config.compilation_config.static_forward_context,
|
|
self.kv_caches)
|
|
|
|
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.
|
|
"""
|
|
|
|
forward_ctx = self.vllm_config.compilation_config.static_forward_context
|
|
block_size = self.vllm_config.cache_config.block_size
|
|
use_mla = self.vllm_config.model_config.use_mla
|
|
kv_cache_spec: dict[str, KVCacheSpec] = {}
|
|
for layer_name, attn_module in forward_ctx.items():
|
|
if isinstance(attn_module, FusedMoE):
|
|
continue
|
|
|
|
# TODO: Support other attention modules, e.g., sliding window,
|
|
# cross-attention
|
|
assert isinstance(attn_module, Attention)
|
|
if attn_module.attn_type == AttentionType.DECODER:
|
|
kv_cache_spec[layer_name] = FullAttentionSpec(
|
|
block_size=block_size,
|
|
num_kv_heads=attn_module.num_kv_heads,
|
|
head_size=attn_module.head_size,
|
|
dtype=attn_module.dtype,
|
|
use_mla=use_mla)
|
|
elif attn_module.attn_type in (AttentionType.ENCODER,
|
|
AttentionType.ENCODER_ONLY):
|
|
# encoder-only attention does not need KV cache.
|
|
continue
|
|
elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
|
|
raise NotImplementedError
|
|
else:
|
|
raise ValueError(
|
|
f"Unknown attention type: {attn_module.attn_type}")
|
|
|
|
return kv_cache_spec
|