c06af8b2e0
### What this PR does / why we need it?
Add support for V1 Engine.
Please note that this is just the initial version, and there may be some
places need to be fixed or optimized in the future, feel free to leave
some comments to us.
### Does this PR introduce _any_ user-facing change?
To use V1 Engine on NPU device, you need to set the env variable shown
below:
```bash
export VLLM_USE_V1=1
export VLLM_WORKER_MULTIPROC_METHOD=spawn
```
If you are using vllm for offline inferencing, you must add a `__main__`
guard like:
```bash
if __name__ == '__main__':
llm = vllm.LLM(...)
```
Find more details
[here](https://docs.vllm.ai/en/latest/getting_started/troubleshooting.html#python-multiprocessing).
### How was this patch tested?
I have tested the online serving with `Qwen2.5-7B-Instruct` using this
command:
```bash
vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240
```
Query the model with input prompts:
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-7B-Instruct",
"prompt": "The future of AI is",
"max_tokens": 7,
"temperature": 0
}'
```
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
Co-authored-by: didongli182 <didongli@huawei.com>
838 lines
37 KiB
Python
838 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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from typing import TYPE_CHECKING, Dict, List, Optional, Union
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import numpy as np
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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 init_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 ModelRunnerOutput
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from vllm.v1.sample.rejection_sampler import INVALID_TOKEN_ID, RejectionSampler
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from vllm.v1.spec_decode.ngram_proposer import NgramProposer
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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.scheduler_output import SchedulerOutput
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logger = init_logger(__name__)
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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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)
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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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if self.speculative_config:
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self.use_spec_decode = True
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self.rejection_sampler = RejectionSampler()
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# TODO: find a better way to check if we are using ngram.
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assert self.speculative_config.ngram_prompt_lookup_min, \
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"Currently, only ngram spec decode is supported in V1."
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if get_pp_group().is_last_rank:
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self.drafter = NgramProposer()
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# Trigger Numba JIT compilation for N-gram proposer.
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# This usually takes less than 1 second.
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self.drafter.propose(
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np.zeros(1024, dtype=np.int32),
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self.speculative_config.ngram_prompt_lookup_min,
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self.speculative_config.num_speculative_tokens,
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)
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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 = np.arange(max(self.max_num_reqs + 1,
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self.max_model_len,
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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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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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@staticmethod
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def make_attention_mask(kv_dtype, kv_device, max_seq_len, seq_lens,
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query_lens):
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# for paged attention
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atten_mask = np.zeros([0, max_seq_len])
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for i, context_length in enumerate(seq_lens):
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q_len = query_lens[i]
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ones_len = context_length - q_len
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ones = np.ones((q_len, ones_len), dtype=np.float16)
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bias_cache = np.tril(
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np.ones((q_len, max_seq_len - ones_len), dtype=np.float16))
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bias_cache = np.concatenate((ones, bias_cache), axis=1)
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mask_value = -10000
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bias_cache[bias_cache == 0] = mask_value
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bias_cache[bias_cache == 1] = 0
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|
|
|
atten_mask = np.concatenate([atten_mask, bias_cache], axis=0)
|
|
atten_mask = torch.from_numpy(atten_mask).to(kv_dtype).to(kv_device)
|
|
return atten_mask
|
|
|
|
def _process_reqs(
|
|
self,
|
|
scheduler_output: "SchedulerOutput",
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
) -> 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
|
|
|
|
# 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(
|
|
self.vllm_config.model_config.dtype, self.device,
|
|
max(seq_lens, default=0), seq_lens, num_scheduled_tokens)
|
|
|
|
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)
|
|
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()
|
|
else:
|
|
# Includes spec decode tokens.
|
|
valid_mask = sampled_token_ids != INVALID_TOKEN_ID
|
|
gen_lens = valid_mask.sum(dim=1).tolist()
|
|
# TODO(woosuk): Optimize this.
|
|
valid_sampled_token_ids = [
|
|
seq.tolist()
|
|
for seq in sampled_token_ids[valid_mask].split(gen_lens)
|
|
]
|
|
|
|
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 generate_draft_token_ids(
|
|
self,
|
|
sampled_token_ids: list[list[int]],
|
|
) -> list[list[int]]:
|
|
# TODO(woosuk): Optimize.
|
|
draft_token_ids: list[list[int]] = []
|
|
for i, sampled_ids in enumerate(sampled_token_ids):
|
|
num_sampled_ids = len(sampled_ids)
|
|
if not num_sampled_ids:
|
|
# Skip speculative decoding.
|
|
draft_token_ids.append([])
|
|
continue
|
|
|
|
# Add sampled_token_ids to token_ids_cpu.
|
|
start_idx = self.input_batch.num_tokens_no_spec[i]
|
|
end_idx = start_idx + num_sampled_ids
|
|
self.input_batch.token_ids_cpu[i, start_idx:end_idx] = sampled_ids
|
|
drafter_output = self.drafter.propose(
|
|
self.input_batch.token_ids_cpu[i, :end_idx],
|
|
self.speculative_config.ngram_prompt_lookup_min,
|
|
self.speculative_config.num_speculative_tokens,
|
|
)
|
|
if drafter_output is None or len(drafter_output) == 0:
|
|
draft_token_ids.append([])
|
|
else:
|
|
draft_token_ids.append(drafter_output.tolist())
|
|
return draft_token_ids
|
|
|
|
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
|
|
"""
|
|
if len(kv_cache_config.groups) > 1:
|
|
raise NotImplementedError(
|
|
"Hybrid models with more than one KV cache type are not "
|
|
"supported yet.")
|
|
|
|
kv_caches: Dict[str, torch.Tensor] = {}
|
|
|
|
for layer_name, layer_spec in kv_cache_config.kv_cache_spec.items():
|
|
tensor_config = kv_cache_config.tensors[layer_name]
|
|
assert tensor_config.size % layer_spec.page_size_bytes == 0
|
|
num_blocks = tensor_config.size // layer_spec.page_size_bytes
|
|
if isinstance(layer_spec, FullAttentionSpec):
|
|
kv_cache_shape = AscendAttentionBackend.get_kv_cache_shape(
|
|
num_blocks, layer_spec.block_size, layer_spec.num_kv_heads,
|
|
layer_spec.head_size)
|
|
dtype = layer_spec.dtype
|
|
kv_caches[layer_name] = torch.zeros(kv_cache_shape,
|
|
dtype=dtype,
|
|
device=self.device)
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
bind_kv_cache(
|
|
kv_caches,
|
|
self.vllm_config.compilation_config.static_forward_context,
|
|
self.kv_caches)
|
|
|
|
def get_kv_cache_spec(self) -> 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: 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
|