For sharded weights let's not delete explicitly

[amitsrivastava78]

For solving OOM issue in distributed systems

[gemini-code-assist]

Summary of Changes

Hello @amitsrivastava78, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request refines the JAX memory cleanup utility within Keras by implementing a crucial safeguard. It prevents the explicit deletion of sharded weights, which are essential components in distributed computing environments. This adjustment is designed to mitigate Out-Of-Memory (OOM) errors and ensure the integrity of references to sharded arrays within JAX's distributed computation graph, thereby enhancing the stability and resource management for distributed systems.

Highlights

• Sharded Weight Handling: Introduced a conditional check in the JAX memory cleanup process to prevent the explicit deletion of sharded weights.
• Distributed Systems Stability: This change specifically targets and resolves potential issues in distributed setups by ensuring sharded arrays are not prematurely deleted, which could break references.
• OOM Prevention: The modification aims to alleviate Out-Of-Memory (OOM) issues that can arise in distributed systems due to incorrect handling of sharded weights.

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[gemini-code-assist]

Code Review

This pull request modifies the jax_memory_cleanup function to prevent the deletion of sharded weights when using the JAX backend. This is a crucial fix to avoid errors in distributed systems where sharded weights are referenced in the computation graph. My review includes a suggestion to simplify the conditional logic for checking if a weight is sharded, making the code more concise and Pythonic.