text: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Utilities for multi-gpu training."""
  
  import tensorflow.compat.v2 as tf
  from keras import backend
  from keras.engine.training import Model
  from keras.layers.core.lambda_layer import Lambda
  from keras.layers.merge import concatenate
  
  
  def _get_available_devices():
    return [x.name for x in backend.get_session().list_devices()]
  
  
  def _normalize_device_name(name):
    name = '/' + name.lower().split('device:')[1]
    return name
  
  
  def multi_gpu_model(model, gpus, cpu_merge=True, cpu_relocation=False):
    """Replicates a model on different GPUs.
  
    Specifically, this function implements single-machine
    multi-GPU data parallelism. It works in the following way:
  
    - Divide the model's input(s) into multiple sub-batches.
    - Apply a model copy on each sub-batch. Every model copy
        is executed on a dedicated GPU.
    - Concatenate the results (on CPU) into one big batch.
  
    E.g. if your `batch_size` is 64 and you use `gpus=2`,
    then we will divide the input into 2 sub-batches of 32 samples,
    process each sub-batch on one GPU, then return the full
    batch of 64 processed samples.
  
    This induces quasi-linear speedup on up to 8 GPUs.
  
    This function is only available with the TensorFlow backend
    for the time being.
  
    Args:
        model: A Keras model instance. To avoid OOM errors, this model could have
          been built on CPU, for instance (see usage example below).
        gpus: Integer >= 2, number of on GPUs on which to create model replicas.
        cpu_merge: A boolean value to identify whether to force merging model
          weights under the scope of the CPU or not.
        cpu_relocation: A boolean value to identify whether to create the model's
          weights under the scope of the CPU. If the model is not defined under
          any preceding device scope, you can still rescue it by activating this
          option.
  
    Returns:
        A Keras `Model` instance which can be used just like the initial
        `model` argument, but which distributes its workload on multiple GPUs.
  
    Example 1: Training models with weights merge on CPU
  
    ```python
        import tensorflow as tf
        from keras.applications import Xception
        from keras.utils import multi_gpu_model
        import numpy as np
  
        num_samples = 1000
        height = 224
        width = 224
        num_classes = 1000
  
        # Instantiate the base model (or "template" model).
        # We recommend doing this with under a CPU device scope,
        # so that the model's weights are hosted on CPU memory.
        # Otherwise they may end up hosted on a GPU, which would
        # complicate weight sharing.
        with tf.device('/cpu:0'):
            model = Xception(weights=None,
                             input_shape=(height, width, 3),
                             classes=num_classes)
  
        # Replicates the model on 8 GPUs.
        # This assumes that your machine has 8 available GPUs.
        parallel_model = multi_gpu_model(model, gpus=8)
        parallel_model.compile(loss='categorical_crossentropy',
                               optimizer='rmsprop')
  
        # Generate dummy data.
        x = np.random.random((num_samples, height, width, 3))
        y = np.random.random((num_samples, num_classes))
  
        # This `fit` call will be distributed on 8 GPUs.
        # Since the batch size is 256, each GPU will process 32 samples.
        parallel_model.fit(x, y, epochs=20, batch_size=256)
  
        # Save model via the template model (which shares the same weights):
        model.save('my_model.h5')
    ```
  
    Example 2: Training models with weights merge on CPU using cpu_relocation
  
    ```python
         ..
         # Not needed to change the device scope for model definition:
         model = Xception(weights=None, ..)
  
         try:
             model = multi_gpu_model(model, cpu_relocation=True)
             print("Training using multiple GPUs..")
         except:
             print("Training using single GPU or CPU..")
  
         model.compile(..)
         ..
    ```
  
    Example 3: Training models with weights merge on GPU (recommended for NV-link)
  
    ```python
         ..
         # Not needed to change the device scope for model definition:
         model = Xception(weights=None, ..)
  
         try:
             model = multi_gpu_model(model, cpu_merge=False)
             print("Training using multiple GPUs..")
         except:
             print("Training using single GPU or CPU..")
         model.compile(..)
         ..
    ```
  
    Raises:
      ValueError: if the `gpus` argument does not match available devices.
    """
    if isinstance(gpus, (list, tuple)):
      if len(gpus) <= 1:
        raise ValueError('For multi-gpu usage to be effective, '
                         'call `multi_gpu_model` with `len(gpus) >= 2`. '
                         'Received: `gpus=%s`' % gpus)
      num_gpus = len(gpus)
      target_gpu_ids = gpus
    else:
      if gpus <= 1:
        raise ValueError('For multi-gpu usage to be effective, '
                         'call `multi_gpu_model` with `gpus >= 2`. '
                         'Received: `gpus=%s`' % gpus)
      num_gpus = gpus
      target_gpu_ids = range(num_gpus)
  
    target_devices = ['/cpu:0'] + ['/gpu:%d' % i for i in target_gpu_ids]
    available_devices = _get_available_devices()
    available_devices = [
        _normalize_device_name(name) for name in available_devices
    ]
    for device in target_devices:
      if device not in available_devices:
        raise ValueError('To call `multi_gpu_model` with `gpus=%s`, '
                         'we expect the following devices to be available: %s. '
                         'However this machine only has: %s. '
                         'Try reducing `gpus`.' %
                         (gpus, target_devices, available_devices))
  
    def get_slice(data, i, parts):
      """Slice an array into `parts` slices and return slice `i`.
  
      Args:
        data: array to slice.
        i: index of slice to return.
        parts: number of slices to make.
  
      Returns:
        Slice `i` of `data`.
      """
      shape = tf.shape(data)
      batch_size = shape[:1]
      input_shape = shape[1:]
      step = batch_size // parts
      if i == parts - 1:
        size = batch_size - step * i
      else:
        size = step
      size = tf.concat([size, input_shape], axis=0)
      stride = tf.concat([step, input_shape * 0], axis=0)
      start = stride * i
      return tf.slice(data, start, size)
  
    # Relocate the model definition under CPU device scope if needed
    if cpu_relocation:
      from keras.models import clone_model  # pylint: disable=g-import-not-at-top
      with tf.device('/cpu:0'):
        model = clone_model(model)
  
    all_outputs = [[] for _ in range(len(model.outputs))]
  
    # Place a copy of the model on each GPU,
    # each getting a slice of the inputs.
    for i, gpu_id in enumerate(target_gpu_ids):
      with tf.device('/gpu:%d' % gpu_id):
        with backend.name_scope('replica_%d' % gpu_id):
          inputs = []
          # Retrieve a slice of the input.
          for x in model.inputs:
            input_shape = tuple(x.shape.as_list())[1:]
            slice_i = Lambda(
                get_slice,
                output_shape=input_shape,
                arguments={
                    'i': i,
                    'parts': num_gpus
                })(
                    x)
            inputs.append(slice_i)
  
          # Apply model on slice
          # (creating a model replica on the target device).
          outputs = model(inputs)
          if not isinstance(outputs, list):
            outputs = [outputs]
  
          # Save the outputs for merging back together later.
          for o, output in enumerate(outputs):
            all_outputs[o].append(output)
  
    # Deduplicate output names to handle Siamese networks.
    occurrences = {}
    for n in model.output_names:
      if n not in occurrences:
        occurrences[n] = 1
      else:
        occurrences[n] += 1
    conflict_counter = {n: 0 for n, count in occurrences.items() if count > 1}
    output_names = []
    for n in model.output_names:
      if n in conflict_counter:
        conflict_counter[n] += 1
        n += '_%d' % conflict_counter[n]
      output_names.append(n)
  
    # Merge outputs under expected scope.
    with tf.device('/cpu:0' if cpu_merge else '/gpu:%d' % target_gpu_ids[0]):
      merged = []
      for name, outputs in zip(output_names, all_outputs):
        merged.append(concatenate(outputs, axis=0, name=name))
      return Model(model.inputs, merged)
  