CODEWITHSUNDEEP
pythontensorflowkerasgoogle-compute-enginegoogle-cloud-tpu
Luke
Luke

Reputation: 7099

How to use tf.keras with bfloat16

I'm trying to get a tf.keras model to run on a TPU using mixed precision. I was wondering how to build the keras model using bfloat16 mixed precision. Is it something like this?

with tf.contrib.tpu.bfloat16_scope():
    inputs = tf.keras.layers.Input(shape=(2,), dtype=tf.bfloat16)
    logits = tf.keras.layers.Dense(2)(inputs)

logits = tf.cast(logits, tf.float32)
model = tf.keras.models.Model(inputs=inputs, outputs=logits)
model.compile(optimizer=tf.keras.optimizers.Adam(.001),
              loss='mean_absolute_error', metrics=[])

tpu_model = tf.contrib.tpu.keras_to_tpu_model(
        model,
        strategy=tf.contrib.tpu.TPUDistributionStrategy(
            tf.contrib.cluster_resolver.TPUClusterResolver(tpu='my_tpu_name')
        )
    )

Upvotes: 13

Views: 2145

Answers (1)

user11530462
user11530462

Reputation:

You can build the Keras model using bfloat16 Mixed Precision (float16 computations and float32 variables) using the code shown below.

tf.keras.mixed_precision.experimental.set_policy('infer_float32_vars')

model = tf.keras.Sequential([
    tf.keras.layers.Inputs(input_shape=(2, ), dtype=tf.float16),    
    tf.keras.layers.Lambda(lambda x: tf.cast(x, 'float32')),
    tf.keras.layers.Dense(10)])

model.compile(optimizer=tf.keras.optimizers.Adam(.001),
              loss='mean_absolute_error', metrics=[])

model.fit(.............)

Once the model is Built and Trained, we can Save the model using the below step:

tf.keras.experimental.export_saved_model(model, path_to_save_model)

We can load the Saved Mixed Precision Keras Model using the code below:

new_model = tf.keras.experimental.load_from_saved_model(path_to_save_model)
new_model.summary()

Upvotes: 4

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