30 lines
757 B
Markdown
30 lines
757 B
Markdown
Work only on the MLX implementations of the custom non-backbone blocks.
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Goal:
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Implement MLX versions of:
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- MLP
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- DecoderHead
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- RefinerBlock
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- CNNRefinerModule
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Requirements:
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- Use MLX idioms and explicit NHWC handling.
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- Centralize tensor layout transforms in one utility module.
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- Use pytorch-compatible GroupNorm behavior where needed for parity.
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- Write parity tests that use saved PyTorch backbone features and saved coarse predictions.
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- Report max abs error and mean abs error in test output or helper scripts.
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Do not:
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- port Hiera yet
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- optimize prematurely
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- spread layout conversions across many files
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Definition of done:
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- decoder parity test exists
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- refiner parity test exists
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- modules are wired into a partial model path for test usage
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