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training.py
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1936 lines (1821 loc) · 80.4 KB
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# SPDX-License-Identifier: LGPL-3.0-or-later
import datetime
import functools
import json
import logging
import time
from collections.abc import (
Callable,
Generator,
Iterable,
)
from copy import (
deepcopy,
)
from pathlib import (
Path,
)
from typing import (
Any,
)
import numpy as np
import torch
from deepmd.common import (
symlink_prefix_files,
)
from deepmd.dpmodel.utils import (
compute_total_numb_batch,
resolve_model_prob,
resolve_model_prob_from_epochs,
)
from deepmd.loggers.training import (
format_training_message,
format_training_message_per_task,
)
from deepmd.pt.loss import (
DenoiseLoss,
DOSLoss,
EnergyHessianStdLoss,
EnergySpinLoss,
EnergyStdLoss,
PropertyLoss,
TaskLoss,
TensorLoss,
)
from deepmd.pt.model.model import (
get_model,
get_zbl_model,
)
from deepmd.pt.optimizer import (
AdaMuonOptimizer,
HybridMuonOptimizer,
KFOptimizerWrapper,
LKFOptimizer,
)
from deepmd.pt.train.validation import (
FullValidator,
resolve_full_validation_start_step,
)
from deepmd.pt.train.wrapper import (
ModelWrapper,
)
from deepmd.pt.utils import (
dp_random,
)
from deepmd.pt.utils.dataloader import (
DpLoaderSet,
get_sampler_from_params,
)
from deepmd.pt.utils.env import (
DEVICE,
JIT,
LOCAL_RANK,
NUM_WORKERS,
SAMPLER_RECORD,
)
from deepmd.pt.utils.learning_rate import (
BaseLR,
)
from deepmd.pt.utils.stat import (
make_stat_input,
)
from deepmd.pt.utils.utils import (
to_numpy_array,
)
from deepmd.utils.data import (
DataRequirementItem,
)
if torch.__version__.startswith("2"):
import torch._dynamo
import torch.distributed as dist
from torch.distributed.checkpoint.state_dict import (
StateDictOptions,
get_model_state_dict,
get_optimizer_state_dict,
set_optimizer_state_dict,
)
from torch.distributed.fsdp import (
fully_shard,
)
from torch.distributed.optim import (
ZeroRedundancyOptimizer,
)
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import (
DataLoader,
)
from deepmd.utils.path import (
DPH5Path,
)
log = logging.getLogger(__name__)
class Trainer:
def __init__(
self,
config: dict[str, Any],
training_data: DpLoaderSet,
stat_file_path: str | None = None,
validation_data: DpLoaderSet | None = None,
init_model: str | None = None,
restart_model: str | None = None,
finetune_model: str | None = None,
force_load: bool = False,
shared_links: dict[str, str] | None = None,
finetune_links: dict[str, str] | None = None,
init_frz_model: str | None = None,
) -> None:
"""Construct a DeePMD trainer.
Args:
- config: The Dict-like configuration with training options.
"""
if init_model is not None:
resume_model = init_model
elif restart_model is not None:
resume_model = restart_model
elif finetune_model is not None:
resume_model = finetune_model
else:
resume_model = None
resuming = resume_model is not None
self.restart_training = restart_model is not None
model_params = config["model"]
training_params = config["training"]
optimizer_params = config.get("optimizer", {})
self.multi_task = "model_dict" in model_params
self.finetune_links = finetune_links
self.finetune_update_stat = False
self.model_keys = (
list(model_params["model_dict"]) if self.multi_task else ["Default"]
)
self.is_distributed = dist.is_available() and dist.is_initialized()
self.rank = dist.get_rank() if self.is_distributed else 0
self.world_size = dist.get_world_size() if self.is_distributed else 1
self.num_model = len(self.model_keys)
self.model_prob = None
# Iteration config
self.num_steps = training_params.get("numb_steps")
self.num_epoch = training_params.get("num_epoch")
self.num_epoch_dict = training_params.get("num_epoch_dict")
self.disp_file = training_params.get("disp_file", "lcurve.out")
self.disp_freq = training_params.get("disp_freq", 1000)
self.disp_avg = training_params.get("disp_avg", False)
self.save_ckpt = training_params.get("save_ckpt", "model.ckpt")
self.save_freq = training_params.get("save_freq", 1000)
self.max_ckpt_keep = training_params.get("max_ckpt_keep", 5)
self.display_in_training = training_params.get("disp_training", True)
self.timing_in_training = training_params.get("time_training", True)
self.change_bias_after_training = training_params.get(
"change_bias_after_training", False
)
self.zero_stage = int(training_params.get("zero_stage", 0))
if self.zero_stage not in (0, 1, 2, 3):
raise ValueError(
f"training.zero_stage must be 0, 1, 2, or 3, got {self.zero_stage}"
)
if self.zero_stage > 0 and not self.is_distributed:
raise ValueError(
"training.zero_stage requires distributed launch via torchrun."
)
if self.zero_stage > 0 and self.change_bias_after_training:
raise ValueError(
"training.zero_stage does not support change_bias_after_training."
)
self.lcurve_should_print_header = True
def get_opt_param(params: dict[str, Any]) -> tuple[str, dict[str, Any]]:
"""
Extract optimizer parameters.
Note: Default values are already filled by argcheck.normalize()
before this function is called.
"""
opt_type = params.get("type", "Adam")
if opt_type not in ("Adam", "AdamW", "LKF", "AdaMuon", "HybridMuon"):
raise ValueError(f"Not supported optimizer type '{opt_type}'")
opt_param = dict(params)
opt_param.pop("type", None)
return opt_type, opt_param
def cycle_iterator(iterable: Iterable) -> Generator[Any, None, None]:
"""
Produces an infinite iterator by repeatedly cycling through the given iterable.
Args:
iterable (Iterable): The iterable to cycle through.
Yields
------
Any: The next item from the iterable, cycling back to the beginning when the end is reached.
"""
while True:
with torch.device("cpu"):
it = iter(iterable)
yield from it
def get_data_loader(
_training_data: DpLoaderSet,
_validation_data: DpLoaderSet | None,
_training_params: dict[str, Any],
) -> tuple[
DataLoader,
Generator[Any, None, None],
DataLoader | None,
Generator[Any, None, None] | None,
int,
]:
def get_dataloader_and_iter(
_data: DpLoaderSet, _params: dict[str, Any]
) -> tuple[DataLoader, Generator[Any, None, None]]:
_sampler = get_sampler_from_params(_data, _params)
if _sampler is None:
log.warning(
"Sampler not specified!"
) # None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration.
_dataloader = DataLoader(
_data,
sampler=_sampler,
batch_size=None,
num_workers=NUM_WORKERS
if dist.is_available()
else 0, # setting to 0 diverges the behavior of its iterator; should be >=1
drop_last=False,
collate_fn=lambda batch: batch, # prevent extra conversion
pin_memory=(DEVICE != "cpu"), # pin memory only if not on CPU
)
_data_iter = cycle_iterator(_dataloader)
return _dataloader, _data_iter
training_dataloader, training_data_iter = get_dataloader_and_iter(
_training_data, _training_params["training_data"]
)
if _validation_data is not None:
(
validation_dataloader,
validation_data_iter,
) = get_dataloader_and_iter(
_validation_data, _training_params["validation_data"]
)
valid_numb_batch = _training_params["validation_data"].get(
"numb_btch", 1
)
else:
validation_dataloader = None
validation_data_iter = None
valid_numb_batch = 1
return (
training_dataloader,
training_data_iter,
validation_dataloader,
validation_data_iter,
valid_numb_batch,
)
def single_model_stat(
_model: Any,
_data_stat_nbatch: int,
_training_data: DpLoaderSet,
_stat_file_path: str | None,
finetune_has_new_type: bool = False,
preset_observed_type: list[str] | None = None,
) -> Callable[[], Any]:
@functools.lru_cache
def get_sample() -> Any:
sampled = make_stat_input(
_training_data.systems,
_training_data.dataloaders,
_data_stat_nbatch,
)
return sampled
if (not resuming or finetune_has_new_type) and self.rank == 0:
_model.compute_or_load_stat(
sampled_func=get_sample,
stat_file_path=_stat_file_path,
preset_observed_type=preset_observed_type,
)
if isinstance(_stat_file_path, DPH5Path):
_stat_file_path.root.close()
return get_sample
def get_lr(lr_params: dict[str, Any]) -> BaseLR:
lr_params["num_steps"] = self.num_steps
lr_schedule = BaseLR(**lr_params)
return lr_schedule
# Optimizer
self.opt_type, self.opt_param = get_opt_param(optimizer_params)
if self.zero_stage > 0 and self.multi_task:
raise ValueError(
"training.zero_stage is currently only supported in single-task training."
)
if self.zero_stage > 0 and self.opt_type == "LKF":
raise ValueError("training.zero_stage does not support LKF optimizer.")
# loss_param_tmp for Hessian activation
loss_param_tmp = None
if not self.multi_task:
loss_param_tmp = config["loss"]
else:
loss_param_tmp = {
model_key: config["loss_dict"][model_key]
for model_key in self.model_keys
}
# Model
self.model = get_model_for_wrapper(
model_params,
resuming=resuming,
_loss_params=loss_param_tmp,
)
# Loss
if not self.multi_task:
self.loss = get_loss(
config["loss"],
config["learning_rate"]["start_lr"],
len(model_params["type_map"]),
self.model,
)
else:
self.loss = {}
for model_key in self.model_keys:
loss_param = config["loss_dict"][model_key]
lr_param = config["learning_rate"]["start_lr"]
ntypes = len(model_params["model_dict"][model_key]["type_map"])
self.loss[model_key] = get_loss(
loss_param, lr_param, ntypes, self.model[model_key]
)
# Data
if not self.multi_task:
# add data requirement for labels
data_requirement = self.loss.label_requirement
data_requirement += get_additional_data_requirement(self.model)
training_data.add_data_requirement(data_requirement)
if validation_data is not None:
validation_data.add_data_requirement(data_requirement)
# Preload and apply modifiers to all data before computing statistics
training_data.preload_and_modify_all_data_torch()
if validation_data is not None:
validation_data.preload_and_modify_all_data_torch()
self.get_sample_func = single_model_stat(
self.model,
model_params.get("data_stat_nbatch", 10),
training_data,
stat_file_path,
finetune_has_new_type=self.finetune_links["Default"].get_has_new_type()
if self.finetune_links is not None
else False,
preset_observed_type=model_params.get("info", {}).get("observed_type"),
)
# Persist observed_type from stat into model_params and model_def_script
if not resuming and self.rank == 0:
observed = self.model.atomic_model.observed_type
if observed is not None:
model_params.setdefault("info", {})["observed_type"] = observed
self.model.model_def_script = json.dumps(model_params)
(
self.training_dataloader,
self.training_data,
self.validation_dataloader,
self.validation_data,
self.valid_numb_batch,
) = get_data_loader(training_data, validation_data, training_params)
training_data.print_summary(
"training", to_numpy_array(self.training_dataloader.sampler.weights)
)
if validation_data is not None:
validation_data.print_summary(
"validation",
to_numpy_array(self.validation_dataloader.sampler.weights),
)
else:
(
self.training_dataloader,
self.training_data,
self.validation_dataloader,
self.validation_data,
self.valid_numb_batch,
self.get_sample_func,
) = {}, {}, {}, {}, {}, {}
for model_key in self.model_keys:
# add data requirement for labels
data_requirement = self.loss[model_key].label_requirement
data_requirement += get_additional_data_requirement(
self.model[model_key]
)
training_data[model_key].add_data_requirement(data_requirement)
if validation_data[model_key] is not None:
validation_data[model_key].add_data_requirement(data_requirement)
# Preload and apply modifiers to all data before computing statistics
training_data[model_key].preload_and_modify_all_data_torch()
if validation_data[model_key] is not None:
validation_data[model_key].preload_and_modify_all_data_torch()
_mt_user_observed = (
model_params["model_dict"][model_key]
.get("info", {})
.get("observed_type")
)
self.get_sample_func[model_key] = single_model_stat(
self.model[model_key],
model_params["model_dict"][model_key].get("data_stat_nbatch", 10),
training_data[model_key],
stat_file_path[model_key],
finetune_has_new_type=self.finetune_links[
model_key
].get_has_new_type()
if self.finetune_links is not None
else False,
preset_observed_type=_mt_user_observed,
)
# Persist observed_type into model_params and model_def_script
if not resuming and self.rank == 0:
observed = self.model[model_key].atomic_model.observed_type
if observed is not None:
model_params["model_dict"][model_key].setdefault("info", {})[
"observed_type"
] = observed
self.model[model_key].model_def_script = json.dumps(
model_params["model_dict"][model_key]
)
(
self.training_dataloader[model_key],
self.training_data[model_key],
self.validation_dataloader[model_key],
self.validation_data[model_key],
self.valid_numb_batch[model_key],
) = get_data_loader(
training_data[model_key],
validation_data[model_key],
training_params["data_dict"][model_key],
)
training_data[model_key].print_summary(
f"training in {model_key}",
to_numpy_array(self.training_dataloader[model_key].sampler.weights),
)
if (
validation_data is not None
and validation_data[model_key] is not None
):
validation_data[model_key].print_summary(
f"validation in {model_key}",
to_numpy_array(
self.validation_dataloader[model_key].sampler.weights
),
)
# Resolve training steps
per_task_total = []
if not self.multi_task:
if self.num_steps is None:
if self.num_epoch is None:
raise ValueError(
"Either training.numb_steps or training.num_epoch must be set."
)
if self.num_epoch <= 0:
raise ValueError("training.num_epoch must be positive.")
sampler_weights = to_numpy_array(
self.training_dataloader.sampler.weights
)
total_numb_batch = compute_total_numb_batch(
training_data.index,
sampler_weights,
)
if total_numb_batch <= 0:
raise ValueError(
"Total number of training batches must be positive."
)
self.num_steps = int(np.ceil(self.num_epoch * total_numb_batch))
log.info(
"Computed num_steps=%d from num_epoch=%s and total_numb_batch=%d.",
self.num_steps,
self.num_epoch,
total_numb_batch,
)
else:
if self.num_epoch_dict:
if self.num_steps is not None:
raise ValueError(
"training.numb_steps and training.num_epoch_dict "
"are mutually exclusive."
)
for model_key in self.model_keys:
sampler_weights = to_numpy_array(
self.training_dataloader[model_key].sampler.weights
)
per_task_total.append(
compute_total_numb_batch(
training_data[model_key].index,
sampler_weights,
)
)
(
self.model_prob,
self.num_steps,
per_task_steps,
) = resolve_model_prob_from_epochs(
self.model_keys,
self.num_epoch_dict,
np.asarray(per_task_total, dtype=np.float64),
)
log.info(
"Computed model_prob=%s and num_steps=%d from num_epoch_dict=%s "
"with per-task target steps: %s.",
self.model_prob,
self.num_steps,
self.num_epoch_dict,
{k: int(np.ceil(v)) for k, v in per_task_steps.items()},
)
else:
if self.num_steps is None:
raise ValueError(
"Either training.numb_steps (multi-task only) or "
"training.num_epoch_dict must be set."
)
self.model_prob = resolve_model_prob(
self.model_keys,
training_params.get("model_prob"),
training_data,
rank=self.rank,
)
# Learning rate
self.gradient_max_norm = training_params.get("gradient_max_norm", 0.0)
self.lr_schedule = get_lr(config["learning_rate"])
# JIT
if JIT:
self.model = torch.jit.script(self.model)
# Model Wrapper
self.wrapper = ModelWrapper(self.model, self.loss, model_params=model_params)
self.start_step = 0
# resuming and finetune
optimizer_state_dict = None
if resuming:
log.info(f"Resuming from {resume_model}.")
state_dict = torch.load(
resume_model, map_location=DEVICE, weights_only=True
)
if "model" in state_dict:
optimizer_state_dict = (
state_dict["optimizer"] if finetune_model is None else None
)
state_dict = state_dict["model"]
self.start_step = (
state_dict["_extra_state"]["train_infos"]["step"]
if self.restart_training
else 0
)
if self.rank == 0:
if force_load:
input_keys = list(state_dict.keys())
target_keys = list(self.wrapper.state_dict().keys())
missing_keys = [
item for item in target_keys if item not in input_keys
]
if missing_keys:
target_state_dict = self.wrapper.state_dict()
slim_keys = []
for item in missing_keys:
state_dict[item] = target_state_dict[item].clone().detach()
new_key = True
for slim_key in slim_keys:
if slim_key in item:
new_key = False
break
if new_key:
tmp_keys = ".".join(item.split(".")[:3])
slim_keys.append(tmp_keys)
slim_keys = [i + ".*" for i in slim_keys]
log.warning(
f"Force load mode allowed! These keys are not in ckpt and will re-init: {slim_keys}"
)
# update model params in the pretrained model
if finetune_model is not None:
new_state_dict = {}
target_state_dict = self.wrapper.state_dict()
# pretrained_model
pretrained_model = get_model_for_wrapper(
state_dict["_extra_state"]["model_params"]
)
pretrained_model_wrapper = ModelWrapper(pretrained_model)
pretrained_model_wrapper.load_state_dict(state_dict)
# update type related params
for model_key in self.model_keys:
finetune_rule_single = self.finetune_links[model_key]
_model_key_from = finetune_rule_single.get_model_branch()
# skip if updated
if (
finetune_rule_single.get_finetune_tmap()
!= pretrained_model_wrapper.model[
_model_key_from
].get_type_map()
):
model_with_new_type_stat = None
if finetune_rule_single.get_has_new_type():
self.finetune_update_stat = True
model_with_new_type_stat = self.wrapper.model[model_key]
pretrained_model_wrapper.model[
_model_key_from
].change_type_map(
finetune_rule_single.get_finetune_tmap(),
model_with_new_type_stat=model_with_new_type_stat,
)
state_dict = pretrained_model_wrapper.state_dict()
def collect_single_finetune_params(
_model_key: str,
_finetune_rule_single: Any,
_new_state_dict: dict[str, Any],
_origin_state_dict: dict[str, Any],
_random_state_dict: dict[str, Any],
) -> None:
_new_fitting = _finetune_rule_single.get_random_fitting()
_model_key_from = _finetune_rule_single.get_model_branch()
target_keys = [
i
for i in _random_state_dict.keys()
if i != "_extra_state" and f".{_model_key}." in i
]
for item_key in target_keys:
new_key = item_key.replace(
f".{_model_key}.", f".{_model_key_from}."
)
use_random_initialization = _new_fitting and (
".descriptor." not in item_key
)
if (
not use_random_initialization
and new_key not in _origin_state_dict
):
# for ZBL models finetuning from standard models
if ".models.0." in new_key:
new_key = new_key.replace(".models.0.", ".")
elif ".models.1." in new_key:
use_random_initialization = True
else:
raise KeyError(
f"Key {new_key} not found in pretrained model."
)
if use_random_initialization:
# print(f'Keep {item_key} in old model!')
_new_state_dict[item_key] = (
_random_state_dict[item_key].clone().detach()
)
else:
# print(f'Replace {item_key} with {new_key} in pretrained_model!')
_new_state_dict[item_key] = (
_origin_state_dict[new_key].clone().detach()
)
# collect model params from the pretrained model
for model_key in self.model_keys:
finetune_rule_single = self.finetune_links[model_key]
collect_single_finetune_params(
model_key,
finetune_rule_single,
new_state_dict,
state_dict,
target_state_dict,
)
state_dict = new_state_dict
state_dict["_extra_state"] = self.wrapper.state_dict()[
"_extra_state"
]
self.wrapper.load_state_dict(state_dict)
# change bias for fine-tuning
if finetune_model is not None:
def single_model_finetune(
_model: Any,
_finetune_rule_single: Any,
_sample_func: Callable,
) -> Any:
_model = model_change_out_bias(
_model,
_sample_func,
_bias_adjust_mode="change-by-statistic"
if not _finetune_rule_single.get_random_fitting()
else "set-by-statistic",
)
return _model
if not self.multi_task:
finetune_rule_single = self.finetune_links["Default"]
self.model = single_model_finetune(
self.model, finetune_rule_single, self.get_sample_func
)
else:
for model_key in self.model_keys:
finetune_rule_single = self.finetune_links[model_key]
if not finetune_rule_single.get_resuming():
log.info(
f"Model branch {model_key} will be fine-tuned. This may take a long time..."
)
self.model[model_key] = single_model_finetune(
self.model[model_key],
finetune_rule_single,
self.get_sample_func[model_key],
)
else:
log.info(
f"Model branch {model_key} will resume training."
)
if init_frz_model is not None:
frz_model = torch.jit.load(init_frz_model, map_location=DEVICE)
state = frz_model.state_dict()
missing, unexpected = self.model.load_state_dict(state, strict=False)
if missing or unexpected:
log.warning(
f"Checkpoint loaded non-strictly. Missing keys: {missing}, Unexpected keys: {unexpected}"
)
# Multi-task share params
if shared_links is not None:
_data_stat_protect = np.array(
[
model_params["model_dict"][ii].get("data_stat_protect", 1e-2)
for ii in model_params["model_dict"]
]
)
assert np.allclose(_data_stat_protect, _data_stat_protect[0]), (
"Model key 'data_stat_protect' must be the same in each branch when multitask!"
)
self.wrapper.share_params(
shared_links,
resume=(resuming and not self.finetune_update_stat) or self.rank != 0,
model_key_prob_map=dict(zip(self.model_keys, self.model_prob)),
data_stat_protect=_data_stat_protect[0],
)
if self.is_distributed:
torch.cuda.set_device(LOCAL_RANK)
if self.zero_stage >= 2:
# FSDP2 does NOT broadcast params (unlike DDP constructor).
# Ensure all ranks share identical weights before sharding.
for p in self.wrapper.parameters():
dist.broadcast(p.data, src=0)
for b in self.wrapper.buffers():
dist.broadcast(b.data, src=0)
reshard = self.zero_stage >= 3
self.wrapper = fully_shard(self.wrapper, reshard_after_forward=reshard)
else:
# zero_stage=0 or 1: standard DDP (ZeRO-1 will wrap the optimizer)
self.wrapper = DDP(
self.wrapper,
device_ids=[LOCAL_RANK],
find_unused_parameters=True,
output_device=LOCAL_RANK,
)
# TODO add lr warmups for multitask
# author: iProzd
# TODO add optimizers for multitask
# author: iProzd
initial_lr = self.lr_schedule.value(self.start_step)
if self.opt_type == "LKF":
self.optimizer = LKFOptimizer(
self.wrapper.parameters(), 0.98, 0.99870, self.opt_param["kf_blocksize"]
)
else:
# === Common path for gradient-based optimizers ===
adam_betas = (
float(self.opt_param["adam_beta1"]),
float(self.opt_param["adam_beta2"]),
)
weight_decay = float(self.opt_param["weight_decay"])
if self.opt_type in ("Adam", "AdamW"):
cls = torch.optim.Adam if self.opt_type == "Adam" else torch.optim.AdamW
extra = {"betas": adam_betas, "fused": DEVICE.type != "cpu"}
elif self.opt_type == "AdaMuon":
cls = AdaMuonOptimizer
extra = {
"adam_betas": adam_betas,
"momentum": float(self.opt_param["momentum"]),
"lr_adjust": float(self.opt_param["lr_adjust"]),
"lr_adjust_coeff": float(self.opt_param["lr_adjust_coeff"]),
}
elif self.opt_type == "HybridMuon":
cls = HybridMuonOptimizer
extra = {
"adam_betas": adam_betas,
"momentum": float(self.opt_param["momentum"]),
"lr_adjust": float(self.opt_param["lr_adjust"]),
"lr_adjust_coeff": float(self.opt_param["lr_adjust_coeff"]),
"muon_mode": str(self.opt_param.get("muon_mode", "slice")),
"named_parameters": tuple(self.wrapper.named_parameters()),
"flash_muon": bool(self.opt_param.get("flash_muon", True)),
"magma_muon": bool(self.opt_param.get("magma_muon", False)),
}
else:
raise ValueError(f"Not supported optimizer type '{self.opt_type}'")
self.optimizer = self._create_optimizer(
cls,
lr=initial_lr,
weight_decay=weight_decay,
**extra,
)
self._load_optimizer_state(optimizer_state_dict)
self.scheduler = torch.optim.lr_scheduler.LambdaLR(
self.optimizer,
lambda step: (
self.lr_schedule.value(step + self.start_step) / initial_lr
),
last_epoch=self.start_step - 1,
)
if self.zero_stage > 0 and self.rank == 0:
if self.zero_stage == 1:
log.info("Enabled DDP + ZeRO Stage-1 Optimizer State Sharding.")
else:
stage = (
"FULL_SHARD (Stage 3)"
if self.zero_stage >= 3
else "SHARD_GRAD_OP (Stage 2)"
)
log.info(f"Enabled FSDP2 {stage}.")
# Tensorboard
self.enable_tensorboard = training_params.get("tensorboard", False)
self.tensorboard_log_dir = training_params.get("tensorboard_log_dir", "log")
self.tensorboard_freq = training_params.get("tensorboard_freq", 1)
self.enable_profiler = training_params.get("enable_profiler", False)
self.profiling = training_params.get("profiling", False)
self.profiling_file = training_params.get("profiling_file", "timeline.json")
validating_params = config.get("validating") or {}
self.full_validator = self._create_full_validator(
validating_params=validating_params,
validation_data=validation_data,
)
# Log model parameter count
if self.rank == 0:
self._log_parameter_count()
def _create_full_validator(
self,
*,
validating_params: dict[str, Any],
validation_data: DpLoaderSet | None,
) -> FullValidator | None:
"""Create the runtime full validator when it is active."""
if not self._is_full_validation_requested(validating_params):
return None
self._raise_if_full_validation_unsupported(validation_data)
if validation_data is None:
raise RuntimeError(
"validation_data must be available after full validation checks."
)
return FullValidator(
validating_params=validating_params,
validation_data=validation_data,
model=self.model,
train_infos=self._get_inner_module().train_infos,
num_steps=self.num_steps,
rank=self.rank,
zero_stage=self.zero_stage,
restart_training=self.restart_training,
checkpoint_dir=Path(self.save_ckpt).parent,
)
def _is_full_validation_requested(self, validating_params: dict[str, Any]) -> bool:
"""Check whether full validation can trigger during this training run."""
if not validating_params.get("full_validation", False):
return False
start_step = resolve_full_validation_start_step(
validating_params.get("full_val_start", 0.5),
self.num_steps,
)
return start_step is not None and start_step <= self.num_steps
def _raise_if_full_validation_unsupported(
self,
validation_data: DpLoaderSet | None,
) -> None:
"""Validate runtime full validation constraints."""
if self.multi_task:
raise ValueError(
"validating.full_validation only supports single-task energy "
"training; multi-task training is not supported."
)
has_spin = getattr(self.model, "has_spin", False)
if callable(has_spin):
has_spin = has_spin()
if has_spin or isinstance(self.loss, EnergySpinLoss):
raise ValueError(
"validating.full_validation only supports single-task energy "
"training; spin-energy training is not supported."
)
if not isinstance(self.loss, EnergyStdLoss):
raise ValueError(
"validating.full_validation only supports single-task energy training."
)
if validation_data is None:
raise ValueError(
"validating.full_validation requires `training.validation_data` "
"to be configured."
)
if self.zero_stage >= 2:
raise ValueError(
"validating.full_validation only supports single-task energy "
"training with training.zero_stage < 2."
)
@staticmethod
def _count_parameters(model: torch.nn.Module) -> tuple[int, int]:
"""
Count model parameters.
Parameters
----------
model : torch.nn.Module
The model to count parameters for.
Returns
-------
tuple[int, int]
A tuple of (trainable, total) parameter counts.
"""
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
total = sum(p.numel() for p in model.parameters())
return trainable, total
def _log_parameter_count(self) -> None:
"""Log model parameter count."""
if not self.multi_task:
trainable, total = self._count_parameters(self.model)
log.info(
f"Model Params: {total / 1e6:.3f} M (Trainable: {trainable / 1e6:.3f} M)"
)
else:
log.warning(
"In multitask mode, parameters may be shared across tasks. "
"The following per-task counts may include duplicates."
)
for model_key in self.model_keys:
trainable, total = self._count_parameters(self.model[model_key])
log.info(
f"Model Params [{model_key}]: {total / 1e6:.3f} M (Trainable: {trainable / 1e6:.3f} M)"
)
def _create_optimizer(
self,
optimizer_class: type[torch.optim.Optimizer],
**kwargs: Any,
) -> torch.optim.Optimizer:
"""
Construct optimizer, wrapping with ZeroRedundancyOptimizer when zero_stage=1.
Parameters
----------
optimizer_class : type[torch.optim.Optimizer]
The optimizer class to instantiate.
**kwargs : Any
Keyword arguments forwarded to the optimizer constructor.
Returns
-------