mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-09-27 21:02:24 +08:00
288 lines
12 KiB
Python
288 lines
12 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 os
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import time
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from multiprocessing.shared_memory import SharedMemory
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from typing import Any, Dict, List
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import numpy as np
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import paddle
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from paddle import nn
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from paddleformers.utils.log import logger
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from fastdeploy.config import FDConfig
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from fastdeploy.model_executor.load_weight_utils import \
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load_composite_checkpoint
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from fastdeploy.model_executor.model_loader import MODEL_CLASSES
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class DynamicWeightManager:
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"""Manages model weights loading, updating and shared state across processes."""
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def __init__(self, fd_config: FDConfig, model: nn.Layer):
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"""Initialize with config and model instances."""
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self.fd_config = fd_config
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self.load_config = fd_config.load_config
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self.parallel_config = fd_config.parallel_config
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self.state_dict: Dict[str, paddle.Tensor] = {}
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self.rank = fd_config.parallel_config.tensor_parallel_rank
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self.nranks = paddle.distributed.get_world_size()
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self.meta_src_id = self._get_gpu_id()
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self.first_load = True
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self.ipc_path = f"/shared_ipc_meta/ipc_metas_{self.meta_src_id}"
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self.models: List[nn.Layer] = [model]
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self._capture_model_state()
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if self.load_config.load_strategy != "meta":
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self.update_parameters()
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logger.info(
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f"✅ DynamicLoad model built successfully by {self.load_config.load_strategy}, "
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f" rank={self.rank}, ranks={self.nranks}")
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@paddle.no_grad()
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def _capture_model_state(self):
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"""Capture and store initial model parameters state."""
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for model in self.models:
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for name, param in model.state_dict().items():
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logger.debug(
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f"Model param: {name}, shape={param.shape}, dtype={param.dtype}"
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)
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self.state_dict[name] = param
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def add_model(self, model: nn.Layer):
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""""add model"""
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self.models.append(model)
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self._capture_model_state()
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def update_parameters(self, pid: int = 0) -> None:
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"""Core method to update model parameters based on strategy."""
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start_time = time.perf_counter()
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paddle.device.cuda.empty_cache()
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if not self.first_load:
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paddle.distributed.restart_process_group()
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strategy_handlers = {
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"ipc_snapshot": self._update_ipc_snapshot,
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"ipc": self._update_ipc,
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"ipc_no_reshard": self._update_ipc_no_reshard,
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"normal": self.load_model,
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}
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if handler := strategy_handlers.get(self.load_config.load_strategy):
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handler()
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else:
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raise ValueError(
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f"Unsupported strategy: {self.load_config.load_strategy}")
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logger.info(
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f"Update parameters in {time.perf_counter()-start_time:.2f}s")
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self._finalize_update(pid)
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def _update_ipc_snapshot(self):
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"""Update using IPC snapshot strategy for elastic recovery."""
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model_path = os.path.join(
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self.parallel_config.model_name_or_path,
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f"model_state.tp0{self.meta_src_id}.pdparams")
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try:
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ipc_state_dict = paddle.load(model_path)
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except FileNotFoundError:
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fallback_path = f"/shared_ipc_meta/model_state.tp0{self.meta_src_id}.pdparams"
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ipc_state_dict = paddle.load(fallback_path)
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try:
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self._update_model_from_state(ipc_state_dict, "snapshot")
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except Exception:
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self.models[0].set_state_dict(ipc_state_dict)
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logger.warning(
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"load model from no_reshard weight, maybe need more GPU memory"
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)
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logger.info(
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f"IPC snapshot update parameters completed from {model_path}")
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def _update_ipc(self):
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"""Update using standard IPC strategy (requires Training Worker)."""
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ipc_meta = paddle.load(self.ipc_path)
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state_dict = self._convert_ipc_meta_to_tensor(ipc_meta)
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self._update_model_from_state(state_dict, "raw")
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logger.info(
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f"IPC update parameters completed from file: {self.ipc_path}")
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def _update_ipc_no_reshard(self):
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"""Update using no-reshard IPC strategy (faster but uses more memory)."""
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ipc_meta = paddle.load(self.ipc_path)
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state_dict = self._convert_ipc_meta_to_tensor(ipc_meta)
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self.models[0].set_state_dict(state_dict)
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logger.info(
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f"IPC no-reshard update parameters completed from file: {self.ipc_path}"
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)
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def load_model(self) -> nn.Layer:
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"""Standard model loading without IPC."""
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architectures = self.fd_config.model_config.architectures[0]
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model_class = MODEL_CLASSES[architectures]
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state_dict = load_composite_checkpoint(
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self.fd_config.parallel_config.model_name_or_path,
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model_class,
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self.fd_config.model_config,
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return_numpy=True)
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self.models[0].set_state_dict(state_dict)
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logger.info("normal load update parameters completed")
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def clear_parameters(self, pid: int = 0) -> None:
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"""Clear all model parameters and free memory."""
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logger.info("start clear paramaters")
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paddle.device.cuda.empty_cache()
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for model in self.models:
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for param in model.state_dict().values():
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param._clear_data()
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self._verify_parameters("clearance")
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if self.nranks > 1:
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paddle.distributed.barrier()
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paddle.distributed.shutdown_process_group()
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self._update_shared_status(pid, -2)
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def _update_model_from_state(self, state_dict: Dict[str, paddle.Tensor],
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src_type: str):
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"""Update model parameters from given state dictionary."""
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if len(state_dict) == 0:
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raise ValueError(f"No parameter found in state dict {state_dict}")
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update_count = 0
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for name, new_param in state_dict.items():
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if name not in self.state_dict:
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logger.debug(f"Ignoring unmatched {src_type} param: {name}")
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continue
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target_param = self.state_dict[name]
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self._validate_parameter_match(name, new_param, target_param)
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new_param._share_buffer_to(target_param)
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update_count += 1
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logger.info(
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f"🆗 Updated {update_count}/{len(state_dict)} parameters from {src_type} source"
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)
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def _validate_parameter_match(self, name: str, src: paddle.Tensor,
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dst: paddle.Tensor):
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"""验证参数一致性"""
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if src.dtype != dst.dtype:
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raise TypeError(
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f"Type mismatch for {name}: {src.dtype} vs {dst.dtype}")
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if src.shape != dst.shape:
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raise ValueError(
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f"Shape mismatch for {name}: {src.shape} vs {dst.shape}")
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def _finalize_update(self, pid: int):
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"""Finalize update process with verification."""
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self._verify_parameters("update")
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if self.nranks > 1:
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paddle.distributed.barrier()
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if not self.first_load:
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self._update_shared_status(pid, 0)
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self.first_load = False
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def _get_gpu_id(self) -> int:
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"""Get current GPU device ID."""
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visible_devices = os.getenv("CUDA_VISIBLE_DEVICES", "0").split(",")
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return int(visible_devices[int(os.getenv("FLAGS_selected_gpus", "0"))])
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def _verify_parameters(self, operation: str):
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"""Verify parameters are in expected state after operation."""
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expected_initialized = (operation == "update")
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all_valid = True
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for name, param in self.state_dict.items():
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is_initialized = param._is_initialized()
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if is_initialized != expected_initialized:
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logger.error(
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f"Verification failed after {operation}: "
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f"Param {name} initialized={is_initialized} (expected {expected_initialized})"
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)
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all_valid = False
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if all_valid:
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logger.info(f"💡 Model Parameter {operation} verified successfully")
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else:
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raise RuntimeError(
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f"❌ Model Parameter {operation} verification failed")
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@staticmethod
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def _convert_ipc_meta_to_tensor(
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ipc_meta: Dict[str, Any]) -> Dict[str, paddle.Tensor]:
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"""Convert IPC metadata to tensor dictionary."""
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converted = {}
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for name, meta in ipc_meta.items():
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meta[0] = meta[0].encode("latin-1")
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meta[6] = int(os.getenv("FLAGS_selected_gpus", "0"))
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tensor = paddle.base.core.LoDTensor._new_shared_cuda(tuple(meta))
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converted[name] = paddle.to_tensor(tensor)
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return converted
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def _log_memory(self, context: str):
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"""Log current GPU memory usage."""
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max_alloc = paddle.device.cuda.max_memory_allocated() / (1024**3)
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max_reserved = paddle.device.cuda.max_memory_reserved() / (1024**3)
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curr_alloc = paddle.device.cuda.memory_allocated() / (1024**3)
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curr_reserved = paddle.device.cuda.memory_reserved() / (1024**3)
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logger.warning(f"GPU memory usage {context}:"
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f"max_allocated: {max_alloc:.2f}GB\n"
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f"max_reserved: {max_reserved:.2f}GB\n"
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f"current_allocated: {curr_alloc:.2f}GB\n"
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f"current_reserved: {curr_reserved:.2f}GB")
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def _update_shared_status(self, pid: int, status: int) -> None:
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"""Update shared memory status flag for inter-process communication."""
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array = np.zeros([1], dtype=np.int32)
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shm = SharedMemory(create=False,
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size=array.nbytes,
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name=f"model_weights_status.{pid}")
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value = np.ndarray(array.shape, dtype=array.dtype, buffer=shm.buf)
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if self.rank == 0:
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value[self.rank] = status
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@staticmethod
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def check_model_weights_status(model_weights_status, model_runner, pid):
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"""
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check model weights status
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"""
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is_stop = 0
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while model_weights_status.value[0] != 0:
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if model_weights_status.value[0] == 1:
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logger.info(
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"infer engine stopped! start to load new checkpoint...")
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model_runner.update_parameters(pid)
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elif model_weights_status.value[0] == -1:
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logger.info(
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"infer engine stopped! start to clear checkpoint...")
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model_runner.clear_parameters(pid)
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while True:
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if model_weights_status.value[0] == 0:
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logger.info("finished loading new checkpoint")
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break
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elif is_stop == 1 or (model_weights_status.value[0] == -2
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and is_stop == 0):
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if is_stop == 0:
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logger.info("finished clearing checkpoint")
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is_stop = 1
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time.sleep(0.001)
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break
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else:
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time.sleep(0.001)
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