Using bfloat16 with TensorFlow models in Python
In this article, we will discuss bfloat16 (Brain Floating Point 16) in Python. It is a numerical format that occupies 16 bits in memory and is used to represent floating-point numbers. It is similar to the more commonly used 32-bit single-precision float (float32) and 64-bit double-precision float (float64), but with a smaller range of values and lower precision.
Let’s understand this by some examples as discussed below:
Example 1
In Tensorflow, you can use bfloat16 data types in your models by casting your tensors to the bfloat16 dtype.
Python3
import tensorflow as tf # Create a float32 tensor with values [1, 2, 3, 4] tensor = tf.constant([ 1 , 2 , 3 , 4 ], dtype = tf.float32) print (tensor) # Cast the tensor to bfloat16 tensor = tf.cast(tensor, dtype = tf.bfloat16) print (tensor) |
Output:
Example 2
You can also specify the dtype when creating tensors using functions such as tf.zeros and tf.ones.
Python3
import tensorflow as tf # Create a bfloat16 tensor with values [1, 2, 3, 4] tensor = tf.constant([ 1 , 2 , 3 , 4 ], dtype = tf.bfloat16) print (tensor) # Create a tensor of ones with shape (2, 2) and dtype bfloat16 ones = tf.ones(( 2 , 2 ), dtype = tf.bfloat16) print (ones) |
Output:
Note that when using bfloat16, you may see a reduction in model accuracy due to the lower precision compared to float32. You should carefully consider the trade-off between model accuracy and memory usage when deciding whether to use bfloat16 in your model.
Example 3
In this example, we use the functional API to define a model with a single hidden layer. Both the input layer and hidden layer have a bfloat16 dtype. We then compile and fit the model using the fit method.
Python3
import tensorflow as tf # Define a simple model with a single bfloat16-typed layer inputs = tf.keras. Input (shape = ( 10 ,), dtype = tf.bfloat16) x = tf.keras.layers.Dense( 10 , dtype = tf.bfloat16)(inputs) outputs = tf.keras.layers.Dense( 1 , activation = 'sigmoid' , dtype = tf.bfloat16)(x) model = tf.keras.Model(inputs, outputs) print (outputs) print (model) |
Output:
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