Source code for sparse_operation_kit.core.context_scope

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from sparse_operation_kit.core import EmbeddingVariable
from sparse_operation_kit.optimizers.utils import split_embedding_variable_from_others
from tensorflow.python.distribute.values import DistributedVariable, MirroredVariable

[docs]class OptimizerScope(object): """ The context manager used along with TensorFlow optimizers. It is only needed when TensorFlow native optimizers is used. Abbreviated as ``sok.OptimizerScope(variables)``. Parameters ---------- trainable_variables: list, tuple a list or tuple of trainable *tf.Variable*. Returns ------- context_manager: context_manager used to switch handles for embedding variables. Example ------- .. code-block:: python with strategy.scope(): model = ... emb_opt = tf.keras.optimizers.Adam(...) other_opt = tf.keras.optimizers.Adam(...) @tf.function def _train_step(inputs, labels): with tf.GradientTape() as tape: logits = model(inputs) loss = loss_fn(logits, labels) emb_vars, other_vars = sok.split_embedding_variable_from_others(model.trainable_variables) emb_grads, other_grads = tape.gradient(loss, [emb_vars, other_vars]) with sok.OptimizerScope(emb_vars): emb_opt.apply_gradients(zip(emb_grads, emb_vars), experimental_aggregate_gradients=False) dense_opt.apply_gradients(zip(other_grads, other_vars)) Notes ----- This context manager may not be used in next release. """ def __init__(self, trainable_variables): if not (isinstance(trainable_variables, list) or isinstance(trainable_variables, tuple)): raise RuntimeError("trainable_variables must be a list or tuple.") self._trainable_variables = trainable_variables self._embedding_variables, _ = split_embedding_variable_from_others( self._trainable_variables ) def __enter__(self): self.touched_variables = list() for variable in self._embedding_variables: if isinstance(variable, EmbeddingVariable): # When using horovod, type(variable) is EmbeddingVariable variable._handle = variable.tf_handle self.touched_variables.append(variable) else: # When using tf.strategy, type(variable) is DistributedVariable for sub_variable in variable.values: sub_variable._handle = sub_variable.tf_handle self.touched_variables.append(sub_variable) return self def __exit__(self, exc_type, exc_value, exc_tb): """ This scope does not process exception. """ for variable in self.touched_variables: variable._handle = variable.m_handle return False