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# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
from pathlib import Path
from typing import List
import numpy as np
import torch
from torch.cuda.amp import GradScaler
from .utils import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SCALER_NAME,
SCHEDULER_NAME,
get_pretty_name,
is_tpu_available,
is_xpu_available,
save,
)
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
from .logging import get_logger
from .state import PartialState
logger = get_logger(__name__)
def save_accelerator_state(
output_dir: str,
model_states: List[dict],
optimizers: list,
schedulers: list,
process_index: int,
scaler: GradScaler = None,
):
"""
Saves the current states of the models, optimizers, scaler, and RNG generators to a given directory.
Args:
output_dir (`str` or `os.PathLike`):
The name of the folder to save all relevant weights and states.
model_states (`List[torch.nn.Module]`):
A list of model states
optimizers (`List[torch.optim.Optimizer]`):
A list of optimizer instances
schedulers (`List[torch.optim.lr_scheduler._LRScheduler]`):
A list of learning rate schedulers
process_index (`int`):
The current process index in the Accelerator state
scaler (`torch.cuda.amp.GradScaler`, *optional*):
An optional gradient scaler instance to save
"""
# Model states
for i, state in enumerate(model_states):
weights_name = f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
output_model_file = os.path.join(output_dir, weights_name)
save(state, output_model_file)
logger.info(f"Model weights saved in {output_model_file}")
# Optimizer states
for i, opt in enumerate(optimizers):
state = opt.state_dict()
optimizer_name = f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
output_optimizer_file = os.path.join(output_dir, optimizer_name)
save(state, output_optimizer_file)
logger.info(f"Optimizer state saved in {output_optimizer_file}")
# Scheduler states
for i, scheduler in enumerate(schedulers):
state = scheduler.state_dict()
scheduler_name = f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
output_scheduler_file = os.path.join(output_dir, scheduler_name)
save(state, output_scheduler_file)
logger.info(f"Scheduler state saved in {output_scheduler_file}")
# GradScaler state
if scaler is not None:
state = scaler.state_dict()
output_scaler_file = os.path.join(output_dir, SCALER_NAME)
torch.save(state, output_scaler_file)
logger.info(f"Gradient scaler state saved in {output_scaler_file}")
# Random number generator states
states = {}
states_name = f"{RNG_STATE_NAME}_{process_index}.pkl"
states["random_state"] = random.getstate()
states["numpy_random_seed"] = np.random.get_state()
states["torch_manual_seed"] = torch.get_rng_state()
if is_xpu_available():
states["torch_xpu_manual_seed"] = torch.xpu.get_rng_state_all()
else:
states["torch_cuda_manual_seed"] = torch.cuda.get_rng_state_all()
if is_tpu_available():
states["xm_seed"] = xm.get_rng_state()
output_states_file = os.path.join(output_dir, states_name)
torch.save(states, output_states_file)
logger.info(f"Random states saved in {output_states_file}")
return output_dir
def load_accelerator_state(
input_dir,
models,
optimizers,
schedulers,
process_index,
scaler=None,
map_location=None,
**load_model_func_kwargs,
):
"""
Loads states of the models, optimizers, scaler, and RNG generators from a given directory.
Args:
input_dir (`str` or `os.PathLike`):
The name of the folder to load all relevant weights and states.
models (`List[torch.nn.Module]`):
A list of model instances
optimizers (`List[torch.optim.Optimizer]`):
A list of optimizer instances
schedulers (`List[torch.optim.lr_scheduler._LRScheduler]`):
A list of learning rate schedulers
process_index (`int`):
The current process index in the Accelerator state
scaler (`torch.cuda.amp.GradScaler`, *optional*):
An optional *GradScaler* instance to load
map_location (`str`, *optional*):
What device to load the optimizer state onto. Should be one of either "cpu" or "on_device".
load_model_func_kwargs (`dict`, *optional*):
Additional arguments that can be passed to the model's `load_state_dict` method.
"""
if map_location not in [None, "cpu", "on_device"]:
raise TypeError(
"Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"
)
if map_location is None:
map_location = "cpu"
elif map_location == "on_device":
map_location = PartialState().device
# Model states
for i, model in enumerate(models):
weights_name = f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
input_model_file = os.path.join(input_dir, weights_name)
models[i].load_state_dict(torch.load(input_model_file, map_location=map_location), **load_model_func_kwargs)
logger.info("All model weights loaded successfully")
# Optimizer states
for i, opt in enumerate(optimizers):
optimizer_name = f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
input_optimizer_file = os.path.join(input_dir, optimizer_name)
optimizer_state = torch.load(input_optimizer_file, map_location=map_location)
optimizers[i].load_state_dict(optimizer_state)
logger.info("All optimizer states loaded successfully")
# Scheduler states
for i, scheduler in enumerate(schedulers):
scheduler_name = f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
input_scheduler_file = os.path.join(input_dir, scheduler_name)
scheduler.load_state_dict(torch.load(input_scheduler_file))
logger.info("All scheduler states loaded successfully")
# GradScaler state
if scaler is not None:
input_scaler_file = os.path.join(input_dir, SCALER_NAME)
scaler.load_state_dict(torch.load(input_scaler_file))
logger.info("GradScaler state loaded successfully")
# Random states
try:
states = torch.load(os.path.join(input_dir, f"{RNG_STATE_NAME}_{process_index}.pkl"))
random.setstate(states["random_state"])
np.random.set_state(states["numpy_random_seed"])
torch.set_rng_state(states["torch_manual_seed"])
if is_xpu_available():
torch.xpu.set_rng_state_all(states["torch_xpu_manual_seed"])
else:
torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"])
if is_tpu_available():
xm.set_rng_state(states["xm_seed"])
logger.info("All random states loaded successfully")
except Exception:
logger.info("Could not load random states")
def save_custom_state(obj, path, index: int = 0):
"""
Saves the state of `obj` to `{path}/custom_checkpoint_{index}.pkl`
"""
# Should this be the right way to get a qual_name type value from `obj`?
save_location = Path(path) / f"custom_checkpoint_{index}.pkl"
logger.info(f"Saving the state of {get_pretty_name(obj)} to {save_location}")
torch.save(obj.state_dict(), save_location)
def load_custom_state(obj, path, index: int = 0):
"""
Loads the state of `obj` at `{path}/custom_checkpoint_{index}.pkl`
"""
load_location = f"{path}/custom_checkpoint_{index}.pkl"
logger.info(f"Loading the state of {get_pretty_name(obj)} from {load_location}")
obj.load_state_dict(torch.load(load_location, map_location="cpu"))
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