# tf.keras.mixed_precision.Policy

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/mixed_precision/policy.py#L31-L322) |

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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/mixed_precision/set_global_policy).

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| ## 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. |

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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](https://www.tensorflow.org/guide/keras/mixed_precision) 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 policy name to a layer is equivalent to passing the
corresponding policy, so it is never necessary to explicitly construct a
`Policy` object.

> [!NOTE]
> **Note:** [`Model.compile`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/Model#compile) will automatically wrap an optimizer with a [`tf.keras.mixed_precision.LossScaleOptimizer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/mixed_precision/LossScaleOptimizer) if you use the `'mixed_float16'` policy. If you use a custom training loop instead of calling [`Model.compile`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/Model#compile), you should explicitly use a [`tf.keras.mixed_precision.LossScaleOptimizer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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 its 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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/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)

    layer = SimpleDense(dtype='mixed_float16')
    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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/ones) or
[`tf.random.normal`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/random/normal), In such cases, you must create the tensor of the correct
dtype. For example, if you call [`tf.random.normal`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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
    layer = AddRandom(dtype='mixed_float16')
    y = layer(x)
    y.dtype
    tf.float16

If you did not pass `dtype=inputs.dtype` to [`tf.random.normal`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/random/normal), a
`TypeError` would have occurred. This is because the [`tf.random.normal`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/random/normal)'s
dtype defaults to `"float32"`, but the input dtype is float16. You cannot
add a float32 tensor with a float16 tensor.

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| ## Attributes ||
|---|---|
| `compute_dtype` | The compute dtype of this policy. <br /> 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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Dense) layer, when run on a GPU with a float16 compute dtype, will pass float16 inputs to [`tf.linalg.matmul`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/linalg/matmul). But, [`tf.linalg.matmul`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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. <br /> 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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/mixed_precision/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. |

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## Methods

### `from_config`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/mixed_precision/policy.py#L314-L322)

    @classmethod
    from_config(
        config, custom_objects=None
    )

### `get_config`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/mixed_precision/policy.py#L311-L312)

    get_config()