I've added unit tests for critical conversion, layer, and inference c…

[stingram]

…omponents.

This commit introduces unit tests for several key modules that previously lacked them:

1. Checkpoint Conversion (MaxText/checkpoint_conversion_utils.py):

   • I refactored core logic from convert_gemma_chkpt.py into the new checkpoint_conversion_utils.py to facilitate testing without heavy dependencies.
   • I then added tests in MaxText/tests/convert_gemma_chkpt_test.py covering parameter name mapping, tensor transformations (scaling, transposition), and model-specific (MQA vs. MHA) logic for Gemma checkpoint conversion.

2. Gemma Layer (MaxText/layers/gemma.py):

   • Next, I added tests in MaxText/tests/layers/gemma_layer_test.py for the GemmaDecoderLayer.
   • These tests verify correct output shapes, dropout behavior (deterministic vs. non-deterministic), and handling of different model modes (TRAIN, PREFILL, AUTOREGRESSIVE), including proper KV cache initialization.
   • I overcame significant challenges in setting up a valid Config object for layer tests by using minimal pyconfig.initialize arguments and manual post-initialization configuration.

3. Paged Attention (MaxText/inference/paged_attention.py):

   • I also added tests in MaxText/tests/inference/paged_attention_test.py for the PagedAttentionOp.
   • These tests verify output shapes for both prefill and decode modes.
   • A basic attention value calculation is tested for the prefill path.
   • The decode path shape test is marked as an @unittest.expectedFailure on CPU due to Pallas kernel incompatibility, documenting this current limitation.
   • The test setup for PagedAttentionOp also involved careful Config and mock PageState creation.

These tests improve the robustness and maintainability of the MaxText codebase by providing automated verification for these critical components.

Description

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Checklist

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