Repository: tensorflow
Issue: autograph become so slow in multi gpu and newest tf version
Labels: stat:awaiting tensorflower,comp:dist-strat,comp:autograph,type:performance,TF 2.13

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.14.0

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

i test autograph speed in different version of tf from 2.5.0 to 2.14.0 with different gpu card num(1 card and 4 cards).
i found multi card and newer tf both will slow down autograph speed. 
autograph speed in 4 card gpu is 4x slower than 1 card.
and autograph seed in tf 2.14.0 is 10x slower than tf 2.5.0.
Any suggestion? 
<img width="649" alt="image" src="https://github.com/tensorflow/tensorflow/assets/39215341/08580964-f401-4ccf-883a-cc71873b1304">


### Standalone code to reproduce the issue

```shell
import os
import sys
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import tensorflow as tf
import time
print("TensorFlow version:", tf.__version__)

physical_devices = tf.config.list_physical_devices('GPU')
for physical_device in physical_devices:
    tf.config.experimental.set_memory_growth(physical_device, True)
gpu_names = [device.name.replace("/physical_device:", "") for device in physical_devices]
strategy = tf.distribute.MirroredStrategy(gpu_names)
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

with strategy.scope():
    mnist = tf.keras.datasets.mnist
    
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0
    
    x_test = x_test[..., tf.newaxis].astype("float32")
    
    train_ds = tf.data.Dataset.from_tensor_slices(
        (x_train, y_train)).shuffle(10000).batch(32)
    
    test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
    
    class MyModel(Model):
      def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation='relu')
        self.flatten = Flatten()
        self.layer = dict()
        for i in range(500):
            self.layer[f"d{i}"]= Dense(128, activation='relu')
        self.d = Dense(10)
    
      def call(self, x):
        x = tf.expand_dims(x, -1)
        x = self.conv1(x)
        x = self.flatten(x)
        for i in range(500):
            x = self.layer[f"d{i}"](x)
        return self.d(x)
    
    model = MyModel()
    """Choose an optimizer and loss function for training:"""
    
    loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    
    optimizer = tf.keras.optimizers.Adam()
    
    """Select metrics to measure the loss and the accuracy of the model. These metrics accumulate the values over epochs and then print the overall result."""
    
    train_loss = tf.keras.metrics.Mean(name='train_loss')
    train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
    
    test_loss = tf.keras.metrics.Mean(name='test_loss')
    test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
    
    """Use `tf.GradientTape` to train the model:"""
    
    def train_step(images, labels):
      print(images)
      with tf.GradientTape() as tape:
        predictions = model(images, training=True)
        loss = tf.reduce_mean(predictions)
        loss = tf.distribute.get_replica_context().all_reduce(tf.distribute.ReduceOp.SUM, loss)
      gradients = tape.gradient(loss, model.trainable_variables)
      optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    
    @tf.function
    def train(images, labels):
        print(images)
        strategy.run(train_step, args=(images, labels,))
    """Test the model:"""
    
    
    for epoch in range(1):
      time_s = time.time()
      time_e = time.time()
      for images, labels in train_ds:
        time_s = time.time()
        train.get_concrete_function(images, labels)
        time_e = time.time()
        break;
      times = time_e - time_s
      print(
        f'tf version:{tf.__version__};  times: {times}; '
      )
```


### Relevant log output

_No response_