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Remove enable_mixed_precision_graph_rewrite from TF2 namespace.
PiperOrigin-RevId: 368700483 Change-Id: If00653a3a3c465fb35bb860b3643f156b9e1c104
This commit is contained in:
Reed Wanderman-Milne
committed by
TensorFlower Gardener
TensorFlower Gardener
parent
5f80881108
commit
3d782b7d47
@@ -4,6 +4,13 @@
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## Breaking Changes
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* `tf.train.experimental.enable_mixed_precision_graph_rewrite` is removed, as
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the API only works in graph mode and is not customizable. The function is
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still accessible under
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`tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite`, but it is
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recommended to use the
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[Keras mixed precision API](https://www.tensorflow.org/guide/mixed_precision)
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instead.
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*<DOCUMENT BREAKING CHANGES HERE>
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*<THIS SECTION SHOULD CONTAIN API, ABI AND BEHAVIORAL BREAKING CHANGES>
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@@ -55,7 +55,7 @@ def register_loss_scale_wrapper(optimizer_cls, wrapper_cls):
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_REGISTERED_WRAPPER_OPTIMIZER_CLS[optimizer_cls] = wrapper_cls
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def _wrap_optimizer(opt, loss_scale, use_v1_behavior):
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def _wrap_optimizer(opt, loss_scale):
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"""Wraps an optimizer with a LossScaleOptimizer."""
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for wrapper_optimizer in _REGISTERED_WRAPPER_OPTIMIZER_CLS.values():
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@@ -70,163 +70,8 @@ def _wrap_optimizer(opt, loss_scale, use_v1_behavior):
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if isinstance(opt, optimizer_cls):
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return wrapper_cls(opt, loss_scale)
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if use_v1_behavior:
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raise ValueError('"opt" must be an instance of a tf.train.Optimizer or a '
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'tf.keras.optimizers.Optimizer, but got: %s' % opt)
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else:
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raise ValueError('"opt" must be an instance of a '
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'tf.keras.optimizers.Optimizer, but got: %s' % opt)
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@deprecation.deprecated(
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'2020-11-30',
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'Use tf.keras.mixed_precision. There is a guide at '
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'https://www.tensorflow.org/guide/mixed_precision. Alternatively, '
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'`tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite` can '
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'be used, but this is not recommended for TF2 code.')
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@tf_export('train.experimental.enable_mixed_precision_graph_rewrite', v1=[])
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def enable_mixed_precision_graph_rewrite(opt, loss_scale='dynamic'):
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"""Enable mixed precision via a graph rewrite.
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Mixed precision is the use of both float32 and float16 data types when
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training a model to improve performance. This is achieved via a graph rewrite
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operation and a loss-scale optimizer.
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Performing arithmetic operations in float16 takes advantage of specialized
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processing units, such as NVIDIA Tensor Cores, for much higher arithmetic
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throughput. However, due to the smaller representable range, performing the
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entire training with float16 can result in gradient underflow, that is, small
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gradient values becoming zeroes. Instead, performing only select arithmetic
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operations in float16 results in higher throughput and decreased training
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time when using compatible hardware accelerators while also reducing memory
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usage, typically without sacrificing model accuracy.
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Note: While the mixed precision rewrite changes the datatype of various
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layers throughout the model, the same accuracy reached in float32 is
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expected. If a `NaN` gradient occurs with dynamic loss scaling, the model
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update for that batch is skipped. In this case, the global step count is not
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incremented, and the `LossScaleOptimizer` attempts to decrease the loss
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scaling value to avoid `NaN` values in subsequent iterations. This approach
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has been shown to achieve the same accuracy as float32 and, in most cases,
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better training throughput.
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Example:
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```python
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model = tf.keras.models.Sequential([
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tf.keras.layers.Dense(64, activation='relu'),
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tf.keras.layers.Dense(64, activation='softmax'),
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])
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opt = tf.keras.optimizers.SGD()
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opt = tf.train.experimental.enable_mixed_precision_graph_rewrite(opt)
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model.compile(loss="mse", optimizer=opt)
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x_train = np.random.random((1024, 64))
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y_train = np.random.random((1024, 64))
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model.fit(x_train, y_train)
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```
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Calling `enable_mixed_precision_graph_rewrite(opt)` enables the graph rewrite
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operation before computing gradients. The function additionally returns an
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`Optimizer` (`opt`) wrapped with a `LossScaleOptimizer`. This prevents
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underflow in the float16 tensors during the backward pass. An optimizer of
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type `tf.keras.optimizers.Optimizer` or `tf.compat.v1.train.Optimizer` must be
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passed to this function, which will then be wrapped to use loss scaling.
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The graph rewrite operation changes the dtype of certain operations in the
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graph from float32 to float16. There are several categories of operations
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that are either included or excluded by this rewrite operation. The following
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categories of Ops are defined inside corresponding functions under the class
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`AutoMixedPrecisionLists` in
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<a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/
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core/grappler/optimizers/auto_mixed_precision_lists.h">
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auto_mixed_precision_lists.h</a>:
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* `ClearList`: Ops that do not have numerically significant adverse effects.
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E.g. `ArgMax` and `Floor`.
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* `AllowList`: Ops that are considered numerically safe for execution in
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float16, and thus are always converted. E.g. `Conv2D`.
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* `DenyList`: Ops that are numerically unsafe to execute in float16 and
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can negatively affect downstream nodes. E.g. `Softmax`.
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* `GrayList`: Ops that are considered numerically safe for execution in
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float16 unless downstream from a DenyList Op. E.g. `Add` and `AvgPool`.
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When this function is used, gradients should be computed and applied with the
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returned optimizer, either by calling `opt.minimize()` or
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`opt.compute_gradients()` followed by `opt.apply_gradients()`. If gradients
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are instead computed with `tf.gradients` or `tf.GradientTape`, loss scaling
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will not be applied, which will likely cause your model not to converge due to
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float16 underflow problems. To apply lossing scaling with `tf.gradients` or
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`tf.GradientTape`, `LossScaleOptimizer.get_scaled_loss` and
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`LossScaleOptimizer.get_unscaled_gradients`. See
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`keras.mixed_precision.experimental.LossScaleOptimizer` for details how to do
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this.
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When eager execution is enabled, the mixed precision graph rewrite is only
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enabled within `tf.function`s, as outside `tf.function`s, there is no graph.
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For NVIDIA GPUs with Tensor cores, as a general performance guide, dimensions
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(such as batch size, input size, output size, and channel counts)
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should be powers of two if under 256, or otherwise divisible by 8 if above
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256. For more information, check out the
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[NVIDIA Deep Learning Performance Guide](
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https://docs.nvidia.com/deeplearning/sdk/dl-performance-guide/index.html).
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Currently, mixed precision is only enabled on NVIDIA Tensor Core GPUs with
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Compute Capability 7.0 and above (Volta, Turing, or newer architectures). The
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parts of the graph on CPUs and TPUs are untouched by the graph rewrite.
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## Comparison with the Keras mixed precision API
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Both this function and the [Keras mixed precision
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API](https://www.tensorflow.org/guide/keras/mixed_precision) enable the use of
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mixed precision in a model. Therefore, only one of the two APIs can be used.
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We recommend using the Keras mixed precision API, as it is more customizable
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and supports Eager execution. However, it only supports models which use Keras
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layers, while the graph rewrite works in any model that uses `tf.function`s.
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The core difference between the two APIs is that this function is a graph
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rewrite, and so it changes the graph to use mixed precision under the hood.
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You still build your graph in float32, and the graph rewrite will change
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certain ops to float16. The Keras mixed precision API directly builds the
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Keras Model using a mix of float16 and float32.
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One core advantage of the Keras API is it supports mixed precision with Eager
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execution, i.e. mixed precision outside `tf.function`s. The graph rewrite will
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only affect ops within `tf.function`s, making it harder to debug if issues
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occur with mixed precision. The Keras API is also more customizable, as you
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can override any layer to run in float32 by passing `dtype="float32"` to the
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layer constructor. Additionally, you can query the dtype of tensors in the
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model by checking `tensor.dtype`. With the graph rewrite, all tensors appear
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to be float32 since the dtype is only changed under the hood.
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The main advantage of the graph rewrite (this function) is that it works even
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if you do not use Keras layers or any other part of Keras. The Keras mixed
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precision API requires models which use Keras layers, as it only inserts casts
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inside Keras layers and models. Another advantage is that the graph rewrite
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never results in a TypeError, which the Keras API may introduce if you do
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certain operations outside Keras. For example, the following will result in a
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TypeError if the Keras mixed precision API is enabled, as a float16 and
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float32 tensor will be added:
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`tf.keras.layers.Dense(2)(x) + tf.keras.layers.Dense(2, dtype="float32")(x)`
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Raises:
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`ValueError`, if the `tf.keras.mixed_precision` API is also used by calling
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`tf.keras.mixed_precision.experimental.set_policy`. Only one mixed precision
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API can be used.
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Args:
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opt: An instance of a `tf.keras.optimizers.Optimizer`.
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loss_scale: Either an int/float, the string `"dynamic"`, or an instance of a
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`tf.mixed_precision.experimental.LossScale`. The loss scale to use. It is
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recommended to keep this as its default value of `"dynamic"`, which will
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adjust the scaling automatically to prevent `Inf` or `NaN` values.
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Returns:
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A version of `opt` that will use loss scaling to prevent underflow.
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"""
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return _enable_mixed_precision_graph_rewrite_base(opt, loss_scale,
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use_v1_behavior=False)
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raise ValueError('"opt" must be an instance of a tf.train.Optimizer or a '
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'tf.keras.optimizers.Optimizer, but got: %s' % opt)
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@deprecation.deprecated_endpoints(
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@@ -285,7 +130,7 @@ def enable_mixed_precision_graph_rewrite_v1(opt, loss_scale='dynamic'):
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The graph rewrite operation changes the `dtype` of certain operations in the
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graph from float32 to float16. There are several categories of operations
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that are either included or excluded by this rewrite operation. The following
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categories of Ops are defined inside corresponding functions under the class
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categories of Ops are defined inside corresponding functions under the class
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`AutoMixedPrecisionLists` in
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<a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/
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core/grappler/optimizers/auto_mixed_precision_lists.h">
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@@ -339,15 +184,6 @@ def enable_mixed_precision_graph_rewrite_v1(opt, loss_scale='dynamic'):
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Returns:
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A version of `opt` that will use loss scaling to prevent underflow.
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"""
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# TODO(reedwm): If a ConfigProto is passed to Session, either assert that
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# auto_mixed_precision is on or turn it on for the user.
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return _enable_mixed_precision_graph_rewrite_base(opt, loss_scale,
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use_v1_behavior=True)
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def _enable_mixed_precision_graph_rewrite_base(opt, loss_scale,
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use_v1_behavior):
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"""Enables mixed precision. See `enable_mixed_precision_graph_rewrite`."""
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if mixed_precision_global_state.is_using_mixed_precision_policy():
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raise ValueError(
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'The mixed precision graph rewrite cannot be enabled, because the '
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@@ -368,43 +204,12 @@ def _enable_mixed_precision_graph_rewrite_base(opt, loss_scale,
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tf_logging.warn('You already have existing Sessions that do not use mixed '
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'precision. enable_mixed_precision_graph_rewrite() will '
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'not affect these Sessions.')
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opt = _wrap_optimizer(opt, loss_scale, use_v1_behavior=use_v1_behavior)
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opt = _wrap_optimizer(opt, loss_scale)
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config.set_optimizer_experimental_options({'auto_mixed_precision': True})
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mixed_precision_global_state.set_mixed_precision_graph_rewrite_enabled(True)
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return opt
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@deprecation.deprecated(
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'2020-11-30',
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'Use tf.keras.mixed_precision. There is a guide at '
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'https://www.tensorflow.org/guide/mixed_precision. Alternatively, '
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'`tf.compat.v1.mixed_precision.disable_mixed_precision_graph_rewrite` can '
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'be used, but this is not recommended for TF2 code.')
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@tf_export('train.experimental.disable_mixed_precision_graph_rewrite', v1=[])
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def disable_mixed_precision_graph_rewrite():
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"""Disables the mixed precision graph rewrite.
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After this is called, the mixed precision graph rewrite will no longer run for
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tf.functions, and so float32 operations will no longer be converted to
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float16.
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This does not undo the effects of loss scaling. Any optimizers wrapped with a
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LossScaleOptimizer will continue to do loss scaling, although this loss
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scaling will no longer be useful, as the graph rewrite no longer converts
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tf.functions to use float16.
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This function is useful for unit testing. A unit test can test using the mixed
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precision graph rewrite, then disable it so future unit tests continue using
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float32.
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"""
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if (not
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mixed_precision_global_state.is_mixed_precision_graph_rewrite_enabled()):
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tf_logging.warn('disable_mixed_precision_graph_rewrite() called when mixed '
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'precision is already disabled.')
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config.set_optimizer_experimental_options({'auto_mixed_precision': False})
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mixed_precision_global_state.set_mixed_precision_graph_rewrite_enabled(False)
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@deprecation.deprecated_endpoints(
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'train.experimental.disable_mixed_precision_graph_rewrite')
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@tf_export(v1=['mixed_precision.disable_mixed_precision_graph_rewrite',
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@@ -432,4 +237,9 @@ def disable_mixed_precision_graph_rewrite_v1():
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"""
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# We only have a separate V1 version of this function, because the V1
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# docstring mentions sessions.
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disable_mixed_precision_graph_rewrite()
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if (not
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mixed_precision_global_state.is_mixed_precision_graph_rewrite_enabled()):
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tf_logging.warn('disable_mixed_precision_graph_rewrite() called when mixed '
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'precision is already disabled.')
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config.set_optimizer_experimental_options({'auto_mixed_precision': False})
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mixed_precision_global_state.set_mixed_precision_graph_rewrite_enabled(False)
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@@ -22,7 +22,6 @@ import os
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from absl.testing import parameterized
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from tensorflow.core.protobuf import config_pb2
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from tensorflow.python import tf2
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from tensorflow.python.client import session
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from tensorflow.python.eager import context
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from tensorflow.python.eager import def_function
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@@ -39,14 +38,6 @@ from tensorflow.python.training.experimental import mixed_precision
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from tensorflow.python.training.experimental import mixed_precision_global_state
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if tf2.enabled():
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enable_mixed_precision_graph_rewrite = (
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mixed_precision.enable_mixed_precision_graph_rewrite)
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else:
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enable_mixed_precision_graph_rewrite = (
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mixed_precision.enable_mixed_precision_graph_rewrite_v1)
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class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
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IGNORE_PERF_VAR = 'TF_AUTO_MIXED_PRECISION_GRAPH_REWRITE_IGNORE_PERFORMANCE'
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@@ -65,13 +56,13 @@ class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
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else:
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del os.environ[self.IGNORE_PERF_VAR]
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mixed_precision.disable_mixed_precision_graph_rewrite()
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mixed_precision.disable_mixed_precision_graph_rewrite_v1()
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super(MixedPrecisionTest, self).tearDown()
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@test_util.run_in_graph_and_eager_modes
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def test_wrap_optimizer(self):
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opt = gradient_descent_v1.GradientDescentOptimizer(1.0)
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opt = enable_mixed_precision_graph_rewrite(opt, 123.)
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opt = mixed_precision.enable_mixed_precision_graph_rewrite_v1(opt, 123.)
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self.assertIsInstance(
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opt, loss_scale_optimizer_v1.MixedPrecisionLossScaleOptimizer)
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self.assertEqual(self.evaluate(opt._loss_scale()), 123.)
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@@ -79,14 +70,10 @@ class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
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@test_util.run_in_graph_and_eager_modes
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def test_optimizer_errors(self):
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opt = 1
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if tf2.enabled():
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expected_regex = ('"opt" must be an instance of a '
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'tf.keras.optimizers.Optimizer, but got')
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else:
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expected_regex = ('"opt" must be an instance of a tf.train.Optimizer or '
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'a tf.keras.optimizers.Optimizer, but got')
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expected_regex = ('"opt" must be an instance of a tf.train.Optimizer or '
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'a tf.keras.optimizers.Optimizer, but got')
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with self.assertRaisesRegex(ValueError, expected_regex):
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enable_mixed_precision_graph_rewrite(opt)
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mixed_precision.enable_mixed_precision_graph_rewrite_v1(opt)
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self.assertFalse(config.get_optimizer_experimental_options()
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.get('auto_mixed_precision', False))
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@@ -96,7 +83,7 @@ class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
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with self.assertRaisesRegex(
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ValueError, '"opt" must not already be an instance of a '
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'MixedPrecisionLossScaleOptimizer.'):
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enable_mixed_precision_graph_rewrite(opt)
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mixed_precision.enable_mixed_precision_graph_rewrite_v1(opt)
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self.assertFalse(config.get_optimizer_experimental_options()
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.get('auto_mixed_precision', False))
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@@ -114,7 +101,7 @@ class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
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mixed_precision.register_loss_scale_wrapper(MyOptimizer,
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MyLossScaleOptimizer)
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opt = MyOptimizer()
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opt = enable_mixed_precision_graph_rewrite(opt, 123.)
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opt = mixed_precision.enable_mixed_precision_graph_rewrite_v1(opt, 123.)
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self.assertIsInstance(opt, MyLossScaleOptimizer)
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self.assertEqual(opt.loss_scale, 123.)
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@@ -123,7 +110,7 @@ class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
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@test_util.disable_tfrt('Grappler rewrite doesn\'t apply to tfrt.')
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def test_grappler_pass_enabled(self):
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opt = gradient_descent_v1.GradientDescentOptimizer(1.0)
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enable_mixed_precision_graph_rewrite(opt, 123.)
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mixed_precision.enable_mixed_precision_graph_rewrite_v1(opt, 123.)
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var = variables.Variable([[1.0]])
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@@ -139,7 +126,7 @@ class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
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self.assertAlmostEqual(overflow_in_float16().numpy(), 2 ** 20)
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# Test disabling mixed precision.
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mixed_precision.disable_mixed_precision_graph_rewrite()
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mixed_precision.disable_mixed_precision_graph_rewrite_v1()
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self.assertEqual(f().numpy(), 2 ** 20)
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else:
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with session.Session() as sess:
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@@ -155,7 +142,7 @@ class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
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self.assertEqual(sess.run(out), float('Inf'))
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# Test disabling mixed precision.
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mixed_precision.disable_mixed_precision_graph_rewrite()
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mixed_precision.disable_mixed_precision_graph_rewrite_v1()
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with session.Session() as sess:
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out = overflow_in_float16()
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sess.run(var.initializer)
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@@ -168,7 +155,7 @@ class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
|
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mixed_precision_global_state.set_non_mixed_precision_session_created(False)
|
||||
|
||||
with session.Session():
|
||||
enable_mixed_precision_graph_rewrite(
|
||||
mixed_precision.enable_mixed_precision_graph_rewrite_v1(
|
||||
gradient_descent_v1.GradientDescentOptimizer(1.0))
|
||||
mock_warn.assert_any_call(
|
||||
'You already have existing Sessions that do not use mixed precision. '
|
||||
@@ -181,7 +168,7 @@ class MixedPrecisionTest(test.TestCase, parameterized.TestCase):
|
||||
# the warning.
|
||||
mixed_precision_global_state.set_non_mixed_precision_session_created(False)
|
||||
|
||||
enable_mixed_precision_graph_rewrite(
|
||||
mixed_precision.enable_mixed_precision_graph_rewrite_v1(
|
||||
gradient_descent_v1.GradientDescentOptimizer(1.0))
|
||||
with session.Session():
|
||||
# Make sure the "You already have existing Sessions" warning was not
|
||||
|
||||
@@ -34,7 +34,6 @@ from tensorflow.python.training.proximal_adagrad import ProximalAdagradOptimizer
|
||||
from tensorflow.python.training.adam import AdamOptimizer
|
||||
from tensorflow.python.training.ftrl import FtrlOptimizer
|
||||
from tensorflow.python.training.experimental.loss_scale_optimizer import MixedPrecisionLossScaleOptimizer
|
||||
from tensorflow.python.training.experimental.mixed_precision import enable_mixed_precision_graph_rewrite
|
||||
from tensorflow.python.training.experimental.mixed_precision import enable_mixed_precision_graph_rewrite_v1
|
||||
from tensorflow.python.training.momentum import MomentumOptimizer
|
||||
from tensorflow.python.training.moving_averages import ExponentialMovingAverage
|
||||
|
||||
@@ -16,12 +16,4 @@ tf_module {
|
||||
name: "PythonState"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member_method {
|
||||
name: "disable_mixed_precision_graph_rewrite"
|
||||
argspec: "args=[], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "enable_mixed_precision_graph_rewrite"
|
||||
argspec: "args=[\'opt\', \'loss_scale\'], varargs=None, keywords=None, defaults=[\'dynamic\'], "
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1502,6 +1502,10 @@ renames = {
|
||||
'tf.compat.v1.train.do_quantize_training_on_graphdef',
|
||||
'tf.train.experimental.MixedPrecisionLossScaleOptimizer':
|
||||
'tf.compat.v1.train.experimental.MixedPrecisionLossScaleOptimizer',
|
||||
'tf.train.experimental.disable_mixed_precision_graph_rewrite':
|
||||
'tf.compat.v1.train.experimental.disable_mixed_precision_graph_rewrite',
|
||||
'tf.train.experimental.enable_mixed_precision_graph_rewrite':
|
||||
'tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite',
|
||||
'tf.train.exponential_decay':
|
||||
'tf.compat.v1.train.exponential_decay',
|
||||
'tf.train.export_meta_graph':
|
||||
|
||||
Reference in New Issue
Block a user