Calling External CPU/GPU Code with jax.experimental.host_callback

While JAX is designed for high-performance execution of numerical programs compiled via XLA, there are situations where you might need to temporarily step outside this compiled environment and execute arbitrary Python code on the host CPU during the execution of your JAX computation. This could be for debugging, logging intermediate values, interacting with external hardware, or calling into libraries that don't have JAX equivalents.

The jax.experimental.host_callback module provides functions for this purpose, primarily id_tap and call. However, as the experimental namespace suggests, these tools come with significant caveats and should be used judiciously. They essentially break the JAX/XLA compilation and optimization boundary.

The Mechanism: Stepping Out of XLA

When a JAX function containing host_callback.id_tap or host_callback.call is executed (not just traced), the following happens:

1. Pause Execution: The execution on the accelerator (GPU/TPU) or compiled CPU code pauses when it reaches the callback instruction.
2. Data Transfer (Device -> Host): The JAX array arguments specified for the callback are transferred from the device memory to the host CPU's memory. This involves serialization and potentially costly data movement.
3. Host Python Execution: The designated Python function is executed on the host CPU. It receives the transferred data, typically as NumPy arrays.
4. Data Transfer (Host -> Device): If using host_callback.call, the results returned by the Python function are transferred back from the host CPU to the device memory, involving deserialization. id_tap doesn't transfer results back in the same way, as it's primarily for side effects.
5. Resume Execution: The execution on the accelerator resumes with the original or returned data.

This round trip between the device and host introduces synchronization points and data transfer overhead, potentially negating many of the performance benefits of JAX and XLA.

Using host_callback.id_tap for Side Effects

The id_tap function is designed for executing Python code primarily for its side effects, like printing or logging, without altering the JAX computation's data flow. It acts as an "identity" function within the JAX computation graph, meaning it returns its input JAX arguments unchanged, but triggers the host-side Python execution.

import jax
import jax.numpy as jnp
from jax.experimental import host_callback
import numpy as np

# This function runs on the Python host
def log_intermediate_data(arg, transform_info):
  # arg is expected to be a NumPy array here
  print("\n--- Host Callback ---")
  print(f"Received data of type: {type(arg)}")
  print(f"Data shape: {arg.shape}")
  print(f"Data content (sample): {arg.flatten()[:5]}")
  print(f"JAX transform context: {transform_info}")
  print("--- End Host Callback ---\n")
  # No explicit return value is used by id_tap

@jax.jit
def process_data(x):
  y = jnp.sin(x) * 2.0
  # Tap into the computation after calculating y
  # tap_with_transform=True provides info about jit/vmap/etc.
  host_callback.id_tap(log_intermediate_data, y, tap_with_transform=True)
  # The value of 'y' used below is the original jnp.sin(x) * 2.0
  z = jnp.mean(jnp.cos(y))
  return z

# Example usage
key = jax.random.PRNGKey(42)
input_data = jax.random.uniform(key, (8, 8))

print("Running JIT-compiled function with host_callback...")
result = process_data(input_data)

# IMPORTANT: Callbacks are executed asynchronously by default.
# We need to block until the computation finishes to see the print output.
result.block_until_ready()

print(f"Final computation result: {result}")

When you run this, you'll see the output from log_intermediate_data printed to your console during the execution of the JIT-compiled process_data function. Notice the use of result.block_until_ready(). Without it, the Python script might finish before the asynchronous JAX computation completes and executes the callback, so you wouldn't see the print statements reliably.

A simplified version, id_print, exists specifically for printing JAX arrays from the host:

@jax.jit
def simple_print_example(x):
  y = x + 5.0
  # Directly print y (from the host)
  host_callback.id_print(y, what="Intermediate value y")
  return y * 2.0

data = jnp.arange(3.0)
output = simple_print_example(data)
output.block_until_ready() # Needed to see print output

Using host_callback.call for Returning Values

If your external Python function needs to compute a value that is then used back in the JAX computation, you need host_callback.call. Unlike id_tap, call takes the return value of the host function, transfers it back to the device, and injects it into the JAX data flow.

Because JAX needs to know the shape and dtype of the returned value during tracing (before the function is actually run) to build the computation graph, you must provide the result_shape_dtypes argument.

import jax
import jax.numpy as jnp
from jax.experimental import host_callback
import numpy as np

# Simulate an external library function unavailable in JAX
def external_cpu_calculation(data_np, parameter):
    # This function runs on the Python host
    print(f"\n--- Host Callback (call) ---")
    print(f"Received data shape: {data_np.shape}")
    print(f"Received parameter: {parameter}")
    # Perform some calculation, maybe using a library like SciPy/OpenCV
    result_np = (np.tanh(data_np) + parameter).astype(data_np.dtype)
    print(f"Returning result shape: {result_np.shape}")
    print(f"--- End Host Callback (call) ---\n")
    return result_np

@jax.jit
def jax_workflow(x, ext_param):
    intermediate = jnp.log1p(jnp.abs(x))

    # Call the external function
    external_result = host_callback.call(
        external_cpu_calculation,         # The Python function to call on host
        (intermediate, ext_param),        # Arguments (JAX arrays automatically converted)
        # IMPORTANT: Specify expected shape and dtype of the *return* value
        result_shape_dtypes=intermediate  # Here, result has same shape/dtype as 'intermediate'
        # If different, use: result_shape_dtypes=jax.ShapeDtypeStruct(shape=(...), dtype=jnp.float32)
    )

    # Use the result from the host callback in subsequent JAX operations
    final_output = external_result / (1.0 + jnp.mean(intermediate))
    return final_output

# Example usage
input_array = jnp.linspace(-2.0, 2.0, 5, dtype=jnp.float32)
parameter_value = 0.5 # Static Python value passed as argument

print("Running JIT-compiled function with host_callback.call...")
final_result = jax_workflow(input_array, parameter_value)
final_result.block_until_ready() # Ensure host execution completes

print(f"Final JAX result: {final_result}")

Significant Caveats and Performance Implications

Using host_callback comes with substantial drawbacks:

1. Performance Cost: The data transfer (serialization, device-host-device round trip) and synchronization introduce significant overhead. This can easily become a bottleneck, especially if used inside performance-sensitive loops.
2. Optimization Barrier: host_callback acts as an opaque barrier to the XLA compiler. XLA cannot fuse operations across the callback or perform optimizations that rely on analyzing the entire computational graph.
3. Asynchronous Execution: Callbacks respect JAX's asynchronous dispatch. The Python callback function might not execute immediately when the line is encountered in Python code. Execution typically happens when results are needed (e.g., via block_until_ready() or data transfer back to Python). This can be confusing for debugging.
4. Side Effects: The host function can perform arbitrary I/O or modify Python global state, breaking the functional purity that makes JAX programs easier to reason about and transform.
5. Non-Differentiability: Functions using host_callback.call are not differentiable through the callback by default. Attempting to compute gradients through call will raise an error unless you manually define custom differentiation rules (a complex topic covered later) or explicitly stop gradients using jax.lax.stop_gradient. id_tap is usually placed where gradients aren't needed, but care is still required.
6. Transformation Interactions:
   • jit: Works, but incurs the performance penalties mentioned above.
   • vmap: Behavior can be complex. By default, the host function receives the entire batch of data. You might need vmap-specific logic within your host callback or alternative approaches. Using tap_with_transform=True with id_tap can help inspect how vmap affects the tapped arguments.
   • pmap: The callback executes on the host Python process associated with each JAX device process. In a multi-device setup on a single machine, this might mean the callback runs multiple times on the same host. In a multi-host setup (like TPU Pods), it runs on each participating host. Managing side effects (like writing to the same file) requires careful coordination.

Why jax.experimental?

The experimental status highlights that the API is subject to change and its use cases are somewhat niche or problematic. It signals that you are opting out of some standard JAX guarantees (like end-to-end XLA optimization and easy differentiability).

When Should You Use host_callback?

Given the drawbacks, host_callback should generally be considered a tool of last resort or for specific, non-performance-critical tasks:

• Debugging: Temporarily printing intermediate values inside jit or other transformations can be invaluable, even with the performance cost. id_print or id_tap are useful here.
• Logging: Sending metrics or summaries to external logging systems during training.
• Legacy Code Integration: Calling existing Python/NumPy/SciPy functions when rewriting them in JAX is infeasible and the performance impact is acceptable.
• Hardware Interaction: Interfacing with specialized hardware controlled via standard Python libraries.

Before resorting to host_callback, consider alternatives:

• Can the logic be reimplemented directly in JAX using jax.numpy and jax.lax?
• For side-effect-free external Python calls, is jax.pure_callback (covered next) suitable? It offers a slightly cleaner interface but still has performance costs.
• For integrating high-performance C++ or CUDA code, defining a custom JAX primitive (covered later in this chapter) is the most effective and efficient solution, though significantly more involved.

In summary, jax.experimental.host_callback provides a bridge to execute host-side Python code from within JAX computations. While useful for debugging and specific integration scenarios, its significant performance overhead and interaction complexities with JAX transformations mean it should be used sparingly and with a clear understanding of its implications.