def __init__(self,
context: pytorch.PyTorchTrialContext,
lm_class=OneVarLM) -> None:
self.context = context
lm = lm_class(**context.get_hparams())
self.dm = OneDatasetLDM()
super().__init__(context, lm)
def test_merge_config(
self, context: det_torch.PyTorchTrialContext, trial: mh_mmdet.MMDetTrial
) -> None:
hparams = context.get_hparams()
hparams["merge_config"] = "./tests/fixtures/merge_config.py"
trial.hparams = attrdict.AttrDict(hparams)
new_cfg = trial.build_mmdet_config()
assert new_cfg.optimizer.type == "AdamW"
assert new_cfg.optimizer_config.grad_clip.max_norm == 0.1
def __init__(self, context: det_torch.PyTorchTrialContext) -> None:
self.logger = logging.getLogger(__name__)
self.hparams = attrdict.AttrDict(context.get_hparams())
self.data_config = attrdict.AttrDict(context.get_data_config())
self.context = context
# Load dataset and get metadata.
# This needs to be done before we initialize the HF config, tokenizer, and model
# because we need to know num_labels before doing so.
if self.data_config.train_language is None:
train_dataset = datasets.load_dataset("xnli",
self.data_config.language,
split="train")
else:
train_dataset = datasets.load_dataset(
"xnli", self.data_config.train_language, split="train")
eval_dataset = datasets.load_dataset("xnli",
self.data_config.language,
split="validation")
self.raw_datasets = {
"train": train_dataset,
"validation": eval_dataset
}
label_list = train_dataset.features["label"].names
self.hparams.num_labels = len(label_list)
super(XNLITrial, self).__init__(context)
self.logger.info(self.config)
# We need to create the tokenized dataset after init because we need to model and
# tokenizer to be available.
self.tokenized_datasets = self.build_datasets()
train_length = len(self.tokenized_datasets["train"])
self.logger.info("training records: {}".format(train_length))
if ("records_per_epoch" in self.exp_config
and train_length != self.exp_config["records_per_epoch"]):
self.logger.warning(
"number of train records {} does not match records_per_epoch of {}"
.format(train_length, self.exp_config["records_per_epoch"]))
# Create metric reducer
metric = datasets.load_metric("xnli", timeout=200)
def compute_metrics(pred_labels) -> Dict:
preds, labels = zip(*pred_labels)
preds = utils.expand_like(preds)
labels = utils.expand_like(labels)
preds = np.argmax(preds, axis=1)
return metric.compute(predictions=preds, references=labels)
self.reducer = context.experimental.wrap_reducer(compute_metrics,
for_training=False)
def __init__(self, context: PyTorchTrialContext) -> None:
self.context = context
self.data_config = context.get_data_config()
self.hparams = AttrDict(context.get_hparams())
# Create a unique download directory for each rank so they don't overwrite each
# other when doing distributed training.
self.download_directory = self.data_config["data_download_dir"]
data.download_data(self.download_directory)
corpus = data_util.Corpus(self.download_directory)
self.corpus = corpus
self.ntokens = len(corpus.dictionary)
self.hidden = None
# This is used to store eval history and will switch to ASGD
# once validation perplexity stops improving.
self._last_loss = None
self._eval_history = []
self._last_epoch = -1
# Define the model
genotype = self.get_genotype_from_hps()
self.model = self.context.wrap_model(
RNNModel(
self.ntokens,
self.hparams.emsize,
self.hparams.nhid,
self.hparams.nhidlast,
self.hparams.dropout,
self.hparams.dropouth,
self.hparams.dropoutx,
self.hparams.dropouti,
self.hparams.dropoute,
genotype=genotype,
))
total_params = sum(x.data.nelement() for x in self.model.parameters())
logging.info("Model total parameters: {}".format(total_params))
# Define the optimizer
self._optimizer = self.context.wrap_optimizer(
HybridSGD(
self.model.parameters(),
self.hparams.learning_rate,
self.hparams.weight_decay,
lambd=0,
t0=0,
))
# Define the LR scheduler
self.myLR = MyLR(self._optimizer, self.hparams)
step_mode = LRScheduler.StepMode.MANUAL_STEP
self.wrapped_LR = self.context.wrap_lr_scheduler(self.myLR,
step_mode=step_mode)
def __init__(self, context: PyTorchTrialContext) -> None:
self.context = context
self.data_config = context.get_data_config()
self.hparams = context.get_hparams()
self.criterion = torch.nn.functional.cross_entropy
# The last epoch is only used for logging.
self._last_epoch = -1
self.results = {
"loss": float("inf"),
"top1_accuracy": 0,
"top5_accuracy": 0
}
# Define the model
genotype = self.get_genotype_from_hps()
self.model = self.context.wrap_model(
Network(
self.hparams["init_channels"],
10, # num_classes
self.hparams["layers"],
self.hparams["auxiliary"],
genotype,
))
print("param size = {} MB".format(
utils.count_parameters_in_MB(self.model)))
size = 0
for p in self.model.parameters():
size += p.nelement()
print("param count: {}".format(size))
# Apply constraints if desired
if "use_constraints" in self.hparams and self.hparams[
"use_constraints"]:
apply_constraints(self.hparams, size)
# Define the optimizer
self.optimizer = self.context.wrap_optimizer(
torch.optim.SGD(
self.model.parameters(),
lr=self.context.get_hparam("learning_rate"),
momentum=self.context.get_hparam("momentum"),
weight_decay=self.context.get_hparam("weight_decay"),
))
# Define the LR scheduler
self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
self.optimizer,
self.context.get_hparam("train_epochs"),
)
step_mode = LRScheduler.StepMode.STEP_EVERY_EPOCH
self.wrapped_scheduler = self.context.wrap_lr_scheduler(
self.scheduler, step_mode=step_mode)
def test_override_mmdet_config(
self, context: det_torch.PyTorchTrialContext, trial: mh_mmdet.MMDetTrial
) -> None:
hparams = context.get_hparams()
hparams["override_mmdet_config"] = {
"optimizer_config._delete_": True,
"optimizer_config.grad_clip.max_norm": 35,
"optimizer_config.grad_clip.norm_type": 2,
}
trial.hparams = attrdict.AttrDict(hparams)
new_cfg = trial.build_mmdet_config()
assert new_cfg.optimizer_config.grad_clip.max_norm == 35
assert new_cfg.optimizer_config.grad_clip.norm_type == 2
def __init__(self, context: det_torch.PyTorchTrialContext) -> None:
self.context = context
# A subclass of BaseTransformerTrial may have already set hparams and data_config
# attributes so we only reset them if they do not exist.
if not hasattr(self, "hparams"):
self.hparams = attrdict.AttrDict(context.get_hparams())
if not hasattr(self, "data_config"):
self.data_config = attrdict.AttrDict(context.get_data_config())
if not hasattr(self, "exp_config"):
self.exp_config = attrdict.AttrDict(
context.get_experiment_config())
# Check to make sure all expected hyperparameters are set.
self.check_hparams()
# Parse hparams and data_config.
(
self.config_kwargs,
self.tokenizer_kwargs,
self.model_kwargs,
) = hf_parse.default_parse_config_tokenizer_model_kwargs(self.hparams)
optimizer_kwargs, scheduler_kwargs = hf_parse.default_parse_optimizer_lr_scheduler_kwargs(
self.hparams)
self.config, self.tokenizer, self.model = build_using_auto(
self.config_kwargs,
self.tokenizer_kwargs,
self.hparams.model_mode,
self.model_kwargs,
use_pretrained_weights=self.hparams.use_pretrained_weights,
)
self.model = self.context.wrap_model(self.model)
self.optimizer = self.context.wrap_optimizer(
build_default_optimizer(self.model, optimizer_kwargs))
if self.hparams.use_apex_amp:
self.model, self.optimizer = self.context.configure_apex_amp(
models=self.model,
optimizers=self.optimizer,
)
self.lr_scheduler = self.context.wrap_lr_scheduler(
build_default_lr_scheduler(self.optimizer, scheduler_kwargs),
det_torch.LRScheduler.StepMode.STEP_EVERY_BATCH,
)
self.grad_clip_fn = None
if optimizer_kwargs.max_grad_norm > 0: # type: ignore
self.grad_clip_fn = lambda x: torch.nn.utils.clip_grad_norm_(
x, optimizer_kwargs.max_grad_norm)
def __init__(self, context: det_torch.PyTorchTrialContext) -> None:
self.context = context
self.hparams = attrdict.AttrDict(context.get_hparams())
self.data_config = attrdict.AttrDict(context.get_data_config())
self.cfg = self.build_mmdet_config()
# We will control how data is moved to GPU.
self.context.experimental.disable_auto_to_device()
# Build model and make sure it's compatible with horovod.
self.model = mmdet.models.build_detector(self.cfg.model)
# Initialize model
self.model.init_weights()
# If use_pretrained, try loading pretrained weights for the mmcv config if available.
if self.hparams.use_pretrained:
ckpt_path, ckpt = utils.get_pretrained_ckpt_path("/tmp", self.hparams.config_file)
if ckpt_path is not None:
logging.info("Loading from pretrained weights.")
if "state_dict" in ckpt:
self.model.load_state_dict(ckpt["state_dict"])
else:
self.model.load_state_dict(ckpt)
# If fp16 is specified in the mmdet config, we will use torch native amp.
fp16_cfg = self.cfg.get("fp16", None)
if fp16_cfg is not None:
self.setup_torch_amp(fp16_cfg)
self.model = self.context.wrap_model(self.model)
self.optimizer = self.context.wrap_optimizer(
mmcv.runner.build_optimizer(self.model, self.cfg.optimizer)
)
self.model.zero_grad()
self.clip_grads_fn = None
if self.cfg.optimizer_config.grad_clip is not None:
self.clip_grads_fn = lambda x: torch.nn.utils.clip_grad_norm_(
x,
self.cfg.optimizer_config.grad_clip.max_norm,
self.cfg.optimizer_config.grad_clip.norm_type,
)
# mmdet sets loggers in the package that interrupt with Determined logging.
# We reset the root logger after mmdet models are initialized.
set_logger(bool(self.context.env.experiment_config.get("debug", False)))
def __init__(self, trial_context: PyTorchTrialContext) -> None:
self.context = trial_context
self.data_config = trial_context.get_data_config()
self.hparams = AttrDict(trial_context.get_hparams())
self.last_epoch = 0
self.data_dir = os.path.join(
self.data_config["download_dir"],
f"data-rank{self.context.distributed.get_rank()}",
)
# Initialize the models.
criterion = nn.CrossEntropyLoss()
self.model = self.context.wrap_model(
Network(
self.hparams.init_channels,
self.hparams.n_classes,
self.hparams.layers,
criterion,
self.hparams.nodes,
k=self.hparams.shuffle_factor,
))
# Initialize the optimizers and learning rate scheduler.
self.ws_opt = self.context.wrap_optimizer(
torch.optim.SGD(
self.model.ws_parameters(),
self.hparams.learning_rate,
momentum=self.hparams.momentum,
weight_decay=self.hparams.weight_decay,
))
self.arch_opt = self.context.wrap_optimizer(
EG(
self.model.arch_parameters(),
self.hparams.arch_learning_rate,
lambda p: p / p.sum(dim=-1, keepdim=True),
))
self.lr_scheduler = self.context.wrap_lr_scheduler(
lr_scheduler=CosineAnnealingLR(
self.ws_opt,
self.hparams.scheduler_epochs,
self.hparams.min_learning_rate,
),
step_mode=LRScheduler.StepMode.STEP_EVERY_EPOCH,
)
def __init__(self, context: det_torch.PyTorchTrialContext) -> None:
self.logger = logging.getLogger(__name__)
self.context = context
self.hparams = attrdict.AttrDict(context.get_hparams())
self.data_config = attrdict.AttrDict(context.get_data_config())
# Load dataset and get metadata.
# This needs to be done before we initialize the HF config, tokenizer, and model
# because we need to know num_labels before doing so.
self.raw_datasets = hf.default_load_dataset(self.data_config)
datasets_metadata = ner_utils.get_dataset_metadata(self.raw_datasets, self.hparams)
self.hparams.num_labels = datasets_metadata.num_labels
super(NERTrial, self).__init__(context)
self.logger.info(self.config)
# We need to create the tokenized dataset after init because we need to model and
# tokenizer to be available.
self.tokenized_datasets = ner_utils.build_tokenized_datasets(
self.raw_datasets,
self.model,
self.data_config,
self.tokenizer,
datasets_metadata.text_column_name,
datasets_metadata.label_column_name,
datasets_metadata.label_to_id,
)
train_length = len(self.tokenized_datasets["train"])
self.logger.info("training records: {}".format(train_length))
if (
"records_per_epoch" in self.exp_config
and train_length != self.exp_config["records_per_epoch"]
):
self.logger.warning(
"number of train records {} does not match records_per_epoch of {}".format(
train_length, self.exp_config["records_per_epoch"]
)
)
# Create metric reducer
self.reducer = context.experimental.wrap_reducer(
functools.partial(ner_utils.compute_metrics, datasets_metadata.label_list),
for_training=False,
)
def __init__(self, context: det_torch.PyTorchTrialContext) -> None:
self.logger = logging.getLogger(__name__)
self.hparams = attrdict.AttrDict(context.get_hparams())
self.data_config = attrdict.AttrDict(context.get_data_config())
self.context = context
# Check to make sure the dataset is configured correctly.
if self.data_config.dataset_name is not None:
dataset_name = self.data_config.dataset_name
if dataset_name == "squad":
assert (not self.data_config.version_2_with_negative
), "version_2_with_negative should be false for squad"
elif dataset_name == "squad_v2":
assert (
self.data_config.version_2_with_negative
), "version_2_with_negative should be true for squad_v2"
self.data_processors = data_beam_search
# Get the datasets: you can either provide your own CSV or JSON training and evaluation
# files (see below) or just provide the name of one of the public datasets available on the
# hub at https://huggingface.co/datasets/ (the dataset will be downloaded automatically
# from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if
# no column called 'text' is found. You can easily tweak this behavior (see below).
# See more about loading any type of standard or custom dataset (from files, python dict,
# pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
self.raw_datasets = hf.default_load_dataset(self.data_config)
self.column_names = self.raw_datasets["train"].column_names
# For beam search, we need to use a different model from the default model returned by
# AutoModelForQuestionAnswering. We will use a custom init in this case that is a slight
# modification of the BaseTransformerTrial init method.
self.exp_config = attrdict.AttrDict(context.get_experiment_config())
# Check to make sure all expected hyperparameters are set.
self.check_hparams()
# Parse hparams and data_config.
(
self.config_kwargs,
self.tokenizer_kwargs,
self.model_kwargs,
) = hf.default_parse_config_tokenizer_model_kwargs(self.hparams)
optimizer_kwargs, scheduler_kwargs = hf.default_parse_optimizer_lr_scheduler_kwargs(
self.hparams)
self.config = transformers.XLNetConfig.from_pretrained(
**self.config_kwargs)
self.tokenizer = transformers.XLNetTokenizerFast.from_pretrained(
**self.tokenizer_kwargs)
# We need to use XLNetForQuestionAnswering instead of XLNetForQuestionAnsweringSimple
# which is the default returned by AutoModelForQuestionAnswering.
if self.hparams.use_pretrained_weights:
self.model_kwargs["config"] = self.config
self.model = transformers.XLNetForQuestionAnswering.from_pretrained(
**self.model_kwargs)
else:
self.model = transformers.XLNetForQuestionAnswering(self.config)
self.model = self.context.wrap_model(self.model)
# The rest is the same as the parent init method.
self.optimizer = self.context.wrap_optimizer(
hf.build_default_optimizer(self.model, optimizer_kwargs))
if self.hparams.use_apex_amp:
self.model, self.optimizer = self.context.configure_apex_amp(
models=self.model,
optimizers=self.optimizer,
)
self.lr_scheduler = self.context.wrap_lr_scheduler(
hf.build_default_lr_scheduler(self.optimizer, scheduler_kwargs),
det_torch.LRScheduler.StepMode.STEP_EVERY_BATCH,
)
self.grad_clip_fn = (
lambda x: torch.nn.utils.clip_grad_norm_(
x, optimizer_kwargs.max_grad_norm)
if optimizer_kwargs.max_grad_norm > 0 # type: ignore
else None)
self.logger.info(self.config)
if not isinstance(self.tokenizer,
transformers.PreTrainedTokenizerFast):
raise ValueError(
"This example script only works for models that have a fast tokenizer. Checkout "
"the big table of models at "
"https://huggingface.co/transformers/index.html#bigtable to find the model types "
"that meet this requirement")
# We need to create the tokenized dataset after init because we need to model and
# tokenizer to be available.
self.tokenized_datasets = self.build_datasets()
train_length = len(self.tokenized_datasets["train"])
self.logger.info("training records: {}".format(train_length))
if ("records_per_epoch" in self.exp_config
and train_length != self.exp_config["records_per_epoch"]):
self.logger.warning(
"number of train records {} does not match records_per_epoch of {}"
.format(train_length, self.exp_config["records_per_epoch"]))
# Create metric reducer
metric = datasets.load_metric("squad_v2" if self.data_config.
version_2_with_negative else "squad")
self.reducer = context.experimental.wrap_reducer(
functools.partial(
qa_utils.compute_metrics,
self.data_config,
self.column_names,
self.data_processors.post_processing_function,
self.raw_datasets,
self.tokenized_datasets,
self.model,
metric,
),
for_training=False,
)
def __init__(self, context: det_torch.PyTorchTrialContext) -> None:
self.logger = logging.getLogger(__name__)
self.hparams = attrdict.AttrDict(context.get_hparams())
self.data_config = attrdict.AttrDict(context.get_data_config())
self.context = context
# Load dataset and get metadata.
# This needs to be done before we initialize the HF config, tokenizer, and model
# because we need to know num_labels before doing so.
# For CSV/JSON files, this example will use as labels the column called `label` and as pair
# of sentences the sentences in columns called `sentence1` and `sentence2` if such column
# exists or the first two columns not named label if at least two columns are provided.
#
# If the CSVs/JSONs contain only one non-label column, the example will do single sentence
# classification on this single column.
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
self.raw_datasets = hf.default_load_dataset(self.data_config)
if self.hparams.finetuning_task is not None:
is_regression = self.hparams.finetuning_task == "stsb"
if not is_regression:
label_list = self.raw_datasets["train"].features["label"].names
num_labels = len(label_list)
else:
num_labels = 1
else:
# Trying to have good defaults here, don't hesitate to tweak to your needs.
is_regression = self.raw_datasets["train"].features[
"label"].dtype in [
"float32",
"float64",
]
if is_regression:
num_labels = 1
else:
# A useful fast method is datasets.Dataset.unique from
# https://huggingface.co/docs/datasets/package_reference/main_classes.html
label_list = self.raw_datasets["train"].unique("label")
label_list.sort() # Let's sort it for determinism
num_labels = len(label_list)
self.is_regression = is_regression
self.hparams.num_labels = num_labels
if not self.is_regression:
self.label_list = label_list
super(GLUETrial, self).__init__(context)
self.logger.info(self.config)
# We need to create the tokenized dataset after init because we need to model and
# tokenizer to be available.
self.tokenized_datasets = self.build_datasets()
train_length = len(self.tokenized_datasets["train"])
self.logger.info("training records: {}".format(train_length))
if ("records_per_epoch" in self.exp_config
and train_length != self.exp_config["records_per_epoch"]):
self.logger.warning(
"number of train records {} does not match records_per_epoch of {}"
.format(train_length, self.exp_config["records_per_epoch"]))
# Create metric reducer
metric = datasets.load_metric("glue", self.hparams.finetuning_task)
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object
# (a namedtuple with a predictions and label_ids field) and has to return a dictionary
# mapping string to float.
def compute_metrics(pred_labels) -> Dict:
preds, labels = zip(*pred_labels)
preds = utils.expand_like(preds)
labels = utils.expand_like(labels)
preds = np.squeeze(preds) if is_regression else np.argmax(preds,
axis=1)
if self.hparams.finetuning_task is not None:
result = metric.compute(predictions=preds, references=labels)
if len(result) > 1:
result["combined_score"] = np.mean(list(
result.values())).item()
return result
elif is_regression:
return {"mse": ((preds - labels)**2).mean().item()}
else:
return {
"accuracy":
(preds == labels).astype(np.float32).mean().item()
}
self.reducer = context.wrap_reducer(compute_metrics,
for_training=False)