Map multliple functions parallely

General Discussion
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#1

I have a query regarding how to map parallelly a list of functions across a list of values.
The tf.vectorize_map fn only applies one function across a list parallely. My scenario looks like this:

let’s assume a list of functions, func = [f1, f2, f3] where each function call is call to a keras layer and
list of values x = [x1, x2, x3] ,
And i want the result to be like: [f1(x1), f2(x2), f3(x3)] to be computed parallely . Performing the same in basic ‘for loop’ takes a lot of time as its a sequential process. is there a solution ?

#2

[Google DeepMind Assist]

To achieve parallel mapping of a list of functions to a corresponding list of values in TensorFlow, you might need to adopt a more manual approach since tf.vectorize_map is designed for applying a single function across elements of an input tensor or list. However, TensorFlow’s ability to run operations in parallel on supported hardware can be leveraged to some extent for your use case.

Here’s a strategy that involves using TensorFlow’s computation graph capabilities to parallelize the function applications as much as possible:

  1. Graph Construction: Construct a TensorFlow graph where each function is applied to its corresponding value. This involves defining TensorFlow operations or Keras layers for each function and value pair.
  2. Session Execution: When you execute this graph, TensorFlow will automatically attempt to parallelize the operations based on the available hardware resources (e.g., multi-core CPUs, GPUs).
  3. Using tf.data.Dataset: If your functions and data are compatible, you could also use tf.data.Dataset to create a dataset from your list of values, and then map each function to its corresponding value. This might not inherently parallelize the function application as you described but can be efficient in batch processing.
  4. Custom Parallelization: For more explicit control over parallelism, you could look into TensorFlow’s lower-level APIs or even integrate with Python’s concurrency modules like concurrent.futures for multi-threading. However, mixing TensorFlow operations with Python-level parallelism needs careful handling to ensure efficiency and correctness.

Here’s a simple illustrative example using TensorFlow operations:

pythonCopy code

import tensorflow as tf

# Define your functions, assuming they are compatible with TensorFlow operations
def f1(x):
    # Example function, replace with your actual function
    return x * 2

def f2(x):
    # Example function, replace with your actual function
    return x + 100

def f3(x):
    # Example function, replace with your actual function
    return x - 50

# List of functions
funcs = [f1, f2, f3]

# Example input tensors/values
x1 = tf.constant(1)
x2 = tf.constant(2)
x3 = tf.constant(3)

# Applying each function to each value
results = [func(x) for func, x in zip(funcs, [x1, x2, x3])]

# To evaluate the results, you need to run a TensorFlow session (for TF 1.x)
# or simply evaluate the tensors in TF 2.x (e.g., using .numpy() in eager execution mode)
# For TensorFlow 2.x (Eager Execution)
results_evaluated = [result.numpy() for result in results]

print(results_evaluated)

Remember, the degree of parallelism TensorFlow can achieve depends on the operations used in the functions and the available hardware. Some operations might not be able to be fully parallelized due to dependencies or hardware limitations.

For more complex scenarios or custom layers, you might need to ensure that your functions are defined in a way that TensorFlow can execute them efficiently in parallel, possibly by leveraging tf.function for graph compilation and execution optimization.