tf.keras.mixed_precision.Policy

A dtype policy for a Keras layer.

tf.keras.mixed_precision.Policy(
    name
)

A dtype policy determines a layer's computation and variable dtypes. Each layer has a policy. Policies can be passed to the dtype argument of layer constructors, or a global policy can be set with tf.keras.mixed_precision.set_global_policy.

Args
name	The policy name, which determines the compute and variable dtypes. Can be any dtype name, such as 'float32' or 'float64', which causes both the compute and variable dtypes will be that dtype. Can also be the string 'mixed_float16' or 'mixed_bfloat16', which causes the compute dtype to be float16 or bfloat16 and the variable dtype to be float32.

Typically you only need to interact with dtype policies when using mixed precision, which is the use of float16 or bfloat16 for computations and float32 for variables. This is why the term mixed_precision appears in the API name. Mixed precision can be enabled by passing 'mixed_float16' or 'mixed_bfloat16' to tf.keras.mixed_precision.set_global_policy. See the mixed precision guide for more information on how to use mixed precision.

tf.keras.mixed_precision.set_global_policy('mixed_float16')
layer1 = tf.keras.layers.Dense(10)
layer1.dtype_policy  # `layer1` will automatically use mixed precision
<Policy "mixed_float16">
# Can optionally override layer to use float32 instead of mixed precision.
layer2 = tf.keras.layers.Dense(10, dtype='float32')
layer2.dtype_policy
<Policy "float32">
# Set policy back to initial float32 for future examples.
tf.keras.mixed_precision.set_global_policy('float32')

In the example above, passing dtype='float32' to the layer is equivalent to passing dtype=tf.keras.mixed_precision.Policy('float32'). In general, passing a dtype to a layer is equivalent to passing the corresponding policy, so it is never necessary to explicitly construct a Policy object.

Note: Model.compile will automatically wrap an optimizer with a tf.keras.mixed_precision.LossScaleOptimizer if you use the 'mixed_float16' policy. If you use a custom training loop instead of calling Model.compile, you should explicitly use a tf.keras.mixed_precision.LossScaleOptimizer to avoid numeric underflow with float16.

How a layer uses its policy's compute dtype

A layer casts its inputs to its compute dtype. This causes the layer's computations and output to also be in the compute dtype. For example:

x = tf.ones((4, 4, 4, 4), dtype='float64')
# `layer`'s policy defaults to float32.
layer = tf.keras.layers.Conv2D(filters=4, kernel_size=2)
layer.compute_dtype  # Equivalent to layer.dtype_policy.compute_dtype
'float32'
# `layer` casts it's inputs to its compute dtype and does computations in
# that dtype.
y = layer(x)
y.dtype
tf.float32

Note that the base tf.keras.layers.Layer class inserts the casts. If subclassing your own layer, you do not have to insert any casts.

Currently, only tensors in the first argument to the layer's call method are casted (although this will likely be changed in a future minor release). For example:

class MyLayer(tf.keras.layers.Layer):
  # Bug! `b` will not be casted.
  def call(self, a, b):
    return a + 1., b + 1.
a = tf.constant(1., dtype="float32")
b = tf.constant(1., dtype="float32")
layer = MyLayer(dtype="float64")
x, y = layer(a, b)
x.dtype
tf.float64
y.dtype
tf.float32

If writing your own layer with multiple inputs, you should either explicitly cast other tensors to self.compute_dtype in call or accept all tensors in the first argument as a list.

The casting only occurs in TensorFlow 2. If tf.compat.v1.disable_v2_behavior() has been called, you can enable the casting behavior with tf.compat.v1.keras.layers.enable_v2_dtype_behavior().

How a layer uses its policy's variable dtype

The default dtype of variables created by tf.keras.layers.Layer.add_weight is the layer's policy's variable dtype.

If a layer's compute and variable dtypes differ, add_weight will wrap floating-point variables with a special wrapper called an AutoCastVariable. AutoCastVariable is identical to the original variable except it casts itself to the layer's compute dtype when used within Layer.call. This means if you are writing a layer, you do not have to explicitly cast the variables to the layer's compute dtype. For example:

class SimpleDense(tf.keras.layers.Layer):

  def build(self, input_shape):
    # With mixed precision, self.kernel is a float32 AutoCastVariable
    self.kernel = self.add_weight('kernel', (input_shape[-1], 10))

  def call(self, inputs):
    # With mixed precision, self.kernel will be casted to float16
    return tf.linalg.matmul(inputs, self.kernel)

dtype_policy = tf.keras.mixed_precision.Policy('mixed_float16')
layer = SimpleDense(dtype=dtype_policy)
y = layer(tf.ones((10, 10)))
y.dtype
tf.float16
layer.kernel.dtype
tf.float32

A layer author can prevent a variable from being wrapped with an AutoCastVariable by passing experimental_autocast=False to add_weight, which is useful if the float32 value of the variable must be accessed within the layer.

How to write a layer that supports mixed precision and float64.

For the most part, layers will automatically support mixed precision and float64 without any additional work, due to the fact the base layer automatically casts inputs, creates variables of the correct type, and in the case of mixed precision, wraps variables with AutoCastVariables.

The primary case where you need extra work to support mixed precision or float64 is when you create a new tensor, such as with tf.ones or tf.random.normal, In such cases, you must create the tensor of the correct dtype. For example, if you call tf.random.normal, you must pass the compute dtype, which is the dtype the inputs have been casted to:

class AddRandom(tf.keras.layers.Layer):

  def call(self, inputs):
    # We must pass `dtype=inputs.dtype`, otherwise a TypeError may
    # occur when adding `inputs` to `rand`.
    rand = tf.random.normal(shape=inputs.shape, dtype=inputs.dtype)
    return inputs + rand

dtype_policy = tf.keras.mixed_precision.Policy('mixed_float16')
layer = AddRandom(dtype=dtype_policy)
y = layer(x)
y.dtype
tf.float16

If you did not pass dtype=inputs.dtype to tf.random.normal, a TypeError would have occurred. This is because the tf.random.normal's dtype defaults to "float32", but the input dtype is float16. You cannot add a float32 tensor with a float16 tensor.

Attributes
compute_dtype	The compute dtype of this policy. This is the dtype layers will do their computations in. Typically layers output tensors with the compute dtype as well. Note that even if the compute dtype is float16 or bfloat16, hardware devices may not do individual adds, multiplies, and other fundamental operations in float16 or bfloat16, but instead may do some of them in float32 for numeric stability. The compute dtype is the dtype of the inputs and outputs of the TensorFlow ops that the layer executes. Internally, many TensorFlow ops will do certain internal calculations in float32 or some other device-internal intermediate format with higher precision than float16/bfloat16, to increase numeric stability. For example, a tf.keras.layers.Dense layer, when run on a GPU with a float16 compute dtype, will pass float16 inputs to tf.linalg.matmul. But, tf.linalg.matmul will do use float32 intermediate math. The performance benefit of float16 is still apparent, due to increased memory bandwidth and the fact modern GPUs have specialized hardware for computing matmuls on float16 inputs while still keeping intermediate computations in float32.
name	Returns the name of this policy.
variable_dtype	The variable dtype of this policy. This is the dtype layers will create their variables in, unless a layer explicitly chooses a different dtype. If this is different than Policy.compute_dtype, Layers will cast variables to the compute dtype to avoid type errors. Variable regularizers are run in the variable dtype, not the compute dtype.

Methods

from_config

View source

@classmethod
from_config(
    config, custom_objects=None
)

get_config

View source

get_config()