tfm.nlp.tasks.TranslationTask
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A single-replica view of training procedure.
Inherits From: Task
tfm.nlp.tasks.TranslationTask(
params: tfm.core.config_definitions.TaskConfig ,
logging_dir=None,
name=None
)
Tasks provide artifacts for training/evalution procedures, including loading/iterating over Datasets, initializing the model, calculating the loss and customized metrics with reduction.
Args | |
|---|---|
params
|
the task configuration instance, which can be any of dataclass, ConfigDict, namedtuple, etc. |
logging_dir
|
a string pointing to where the model, summaries etc. will be saved. You can also write additional stuff in this directory. |
name
|
the task name. |
Attributes | |
|---|---|
logging_dir
|
|
task_config
|
|
Methods
aggregate_logs
aggregate_logs(
state=None, step_outputs=None
)
Aggregates over logs returned from a validation step.
build_inputs
build_inputs(
params: tfm.core.config_definitions.DataConfig ,
input_context: Optional[tf.distribute.InputContext] = None
)
Returns a dataset.
build_losses
build_losses(
labels, model_outputs, aux_losses=None
) -> tf.Tensor
Standard interface to compute losses.
| Args | |
|---|---|
labels
|
optional label tensors. |
model_outputs
|
a nested structure of output tensors. |
aux_losses
|
auxiliary loss tensors, i.e. losses in keras.Model.
|
| Returns | |
|---|---|
| The total loss tensor. |
build_metrics
build_metrics(
training: bool = True
)
Gets streaming metrics for training/validation.
build_model
build_model() -> tf.keras.Model
Creates model architecture.
| Returns | |
|---|---|
| A model instance. |
create_optimizer
@classmethodcreate_optimizer( optimizer_config:tfm.optimization.OptimizationConfig, runtime_config: Optional[tfm.core.base_task.RuntimeConfig] = None, dp_config: Optional[tfm.core.base_task.DifferentialPrivacyConfig] = None )
Creates an TF optimizer from configurations.
| Args | |
|---|---|
optimizer_config
|
the parameters of the Optimization settings. |
runtime_config
|
the parameters of the runtime. |
dp_config
|
the parameter of differential privacy. |
| Returns | |
|---|---|
| A tf.optimizers.Optimizer object. |
inference_step
inference_step(
inputs, model: tf.keras.Model
)
Performs the forward step.
With distribution strategies, this method runs on devices.
| Args | |
|---|---|
inputs
|
a dictionary of input tensors. |
model
|
the keras.Model. |
| Returns | |
|---|---|
| Model outputs. |
initialize
initialize(
model: tf.keras.Model
)
[Optional] A callback function used as CheckpointManager's init_fn.
This function will be called when no checkpoint is found for the model. If there is a checkpoint, the checkpoint will be loaded and this function will not be called. You can use this callback function to load a pretrained checkpoint, saved under a directory other than the model_dir.
| Args | |
|---|---|
model
|
The keras.Model built or used by this task. |
process_compiled_metrics
process_compiled_metrics(
compiled_metrics, labels, model_outputs
)
Process and update compiled_metrics.
call when using compile/fit API.
| Args | |
|---|---|
compiled_metrics
|
the compiled metrics (model.compiled_metrics). |
labels
|
a tensor or a nested structure of tensors. |
model_outputs
|
a tensor or a nested structure of tensors. For example, output of the keras model built by self.build_model. |
process_metrics
process_metrics(
metrics, labels, model_outputs, **kwargs
)
Process and update metrics.
Called when using custom training loop API.
| Args | |
|---|---|
metrics
|
a nested structure of metrics objects. The return of function self.build_metrics. |
labels
|
a tensor or a nested structure of tensors. |
model_outputs
|
a tensor or a nested structure of tensors. For example, output of the keras model built by self.build_model. |
**kwargs
|
other args. |
reduce_aggregated_logs
reduce_aggregated_logs(
aggregated_logs, global_step=None
)
Optional reduce of aggregated logs over validation steps.
This function reduces aggregated logs at the end of validation, and can be used to compute the final metrics. It runs on CPU and in each eval_end() in base trainer (see eval_end() function in official/core/base_trainer.py).
| Args | |
|---|---|
aggregated_logs
|
Aggregated logs over multiple validation steps. |
global_step
|
An optional variable of global step. |
| Returns | |
|---|---|
| A dictionary of reduced results. |
train_step
train_step(
inputs,
model: tf.keras.Model,
optimizer: tf.keras.optimizers.Optimizer,
metrics=None
)
Does forward and backward.
With distribution strategies, this method runs on devices.
| Args | |
|---|---|
inputs
|
a dictionary of input tensors. |
model
|
the model, forward pass definition. |
optimizer
|
the optimizer for this training step. |
metrics
|
a nested structure of metrics objects. |
| Returns | |
|---|---|
| A dictionary of logs. |
validation_step
validation_step(
inputs, model: tf.keras.Model, metrics=None
)
Validation step.
With distribution strategies, this method runs on devices.
| Args | |
|---|---|
inputs
|
a dictionary of input tensors. |
model
|
the keras.Model. |
metrics
|
a nested structure of metrics objects. |
| Returns | |
|---|---|
| A dictionary of logs. |
Class Variables | |
|---|---|
| loss |
'loss'
|