refactor Flux transformer to use scanned blocks, dynamic checkpointing, and decoupled projections#417
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refactor Flux transformer to use scanned blocks, dynamic checkpointing, and decoupled projections#417prishajain1 wants to merge 1 commit into
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Overview
This PR refactors the Flux model architecture in MaxDiffusion to support scanned blocks (nn.scan) for double and single blocks, implements configurable gradient checkpointing (rematerialization) policies, and updates the weights loader to support loading pretrained checkpoints under the scanned format.
Key Changes
MlpAndOutputBlockwrapper) to eliminate redundant recomputation of attention and projection outputs.jnp.splitacross Flux transformer blocks for cleaner layout constraints.nn.scanto optimize compiler tracing and step execution speed on TPUs.FLUX_OPTIMIZEDtoGradientCheckpointTypeto allow configuring block-specific rematerialization policies dynamically via configuration files instead of being hardcoded.util.py) to slice, group, and stack PyTorch checkpoint weights along axis 0 to match the expected format ofnn.scanlayers.