mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 20:02:53 +08:00
144 lines
4.6 KiB
Python
144 lines
4.6 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import gc
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import os
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from typing import List, Optional
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import paddle
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import paddle.nn as nn
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from fastdeploy.config import FDConfig
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from fastdeploy.engine.request import Request
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from fastdeploy.utils import get_logger
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from fastdeploy.worker.iluvatar_model_runner import IluvatarModelRunner
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from fastdeploy.worker.output import ModelRunnerOutput
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from fastdeploy.worker.worker_base import WorkerBase
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logger = get_logger("iluvatar_worker", "iluvatar_worker.log")
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class IluvatarWorker(WorkerBase):
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""" """
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def __init__(
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self,
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fd_config: FDConfig,
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local_rank: int,
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rank: int,
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):
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super().__init__(
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fd_config=fd_config,
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local_rank=local_rank,
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rank=rank,
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)
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pass
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def init_device(self):
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""" Initialize device and Construct model runner
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"""
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if paddle.is_compiled_with_custom_device("iluvatar_gpu"):
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# Set evironment variable
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self.device = f"iluvatar_gpu:{self.local_rank}"
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paddle.device.set_device(self.device)
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paddle.set_default_dtype(self.parallel_config.dtype)
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self.device_ids = self.parallel_config.device_ids.split(",")
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gc.collect()
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else:
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raise RuntimeError(
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f"Not support device type: {self.device_config.device}")
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# Construct model runner
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self.model_runner: IluvatarModelRunner = IluvatarModelRunner(
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fd_config=self.fd_config,
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device=self.device,
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device_id=self.device_ids[self.local_rank],
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rank=self.rank,
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local_rank=self.local_rank)
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def prefill_finished(self):
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"""
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check whether prefill stage finished
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"""
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return self.model_runner.prefill_finished()
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def determine_available_memory(self) -> int:
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"""
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Profiles the peak memory usage of the model to determine how much
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memory can be used for KV cache without OOMs.
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The engine will first conduct a profiling of the existing memory usage.
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Then, it calculate the maximum possible number of GPU and CPU blocks
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that can be allocated with the remaining free memory.
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Tip:
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You may limit the usage of GPU memory
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by adjusting the `gpu_memory_utilization` parameter.
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"""
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# 1. Record memory state before profile run
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return int(float(os.getenv("FD_ILUVATAR_KVCACHE_MEM", "3")) * 1024**3)
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def load_model(self) -> None:
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""" """
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self.model_runner.load_model()
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def get_model(self) -> nn.Layer:
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""" """
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return self.model_runner.get_model()
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def initialize_cache(self, num_gpu_blocks: int,
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num_cpu_blocks: int) -> None:
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""" """
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pass
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def execute_model(
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self,
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model_forward_batch: Optional[List[Request]] = None,
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) -> Optional[ModelRunnerOutput]:
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""" """
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output = self.model_runner.execute_model(model_forward_batch)
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return output
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def preprocess_new_task(self, req_dicts: List[Request]) -> None:
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""" Process new requests and then start the decode loop
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TODO(gongshaotian):The scheduler should schedule the handling of prefill,
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and workers and modelrunners should not perceive it.
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"""
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self.model_runner.insert_prefill_inputs(req_dicts=req_dicts)
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def graph_optimize_and_warm_up_model(self) -> None:
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"""
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Perform the warm-up and the graph optimization
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"""
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# 1. Warm up model
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# NOTE(gongshaotian): may be not need warm_up at this place
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# 2. Triger cuda grpah capture
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self.model_runner.capture_model()
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def check_health(self) -> bool:
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""" """
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return True
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def cal_theortical_kvcache(self) -> int:
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""" """
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return self.model_runner.cal_theortical_kvcache()
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def reinitialize_kv_cache(self, num_gpu_blocks: int) -> None:
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""" """
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self.model_runner.update_share_input_block_num(
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num_gpu_blocks=num_gpu_blocks)
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