40 lines
1.0 KiB
Markdown
40 lines
1.0 KiB
Markdown
Work only on the PyTorch reference harness.
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Goal:
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Create a deterministic reference pipeline that loads the original CorridorKey checkpoint and dumps intermediate tensors needed for staged MLX parity.
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Deliverables:
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- scripts/dump_pytorch_reference.py
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- reference/fixtures/ sample inputs and outputs
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- tests that validate fixture generation shape contracts
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- README updates describing the fixture format
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Requirements:
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- Load the model via state_dict, not entire-model pickle semantics.
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- Save:
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- 4 backbone feature maps
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- alpha coarse logits
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- fg coarse logits
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- alpha coarse probs
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- fg coarse probs
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- delta logits
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- final alpha
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- final fg
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- Make fixture generation deterministic where practical.
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- Keep one tiny golden example checked in.
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- Print a concise shape report.
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Do not:
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- start MLX implementation
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- refactor unrelated files
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- add training code
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Before editing:
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- inspect the original CorridorKey model code carefully
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- summarize exact tensors that will be dumped
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- define file naming and serialization format first
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