Fix QLoRA and Gemma4-small decoder issues for NNX#4503
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hsuan-lun-chiang wants to merge 6 commits into
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Fix QLoRA and Gemma4-small decoder issues for NNX#4503hsuan-lun-chiang wants to merge 6 commits into
hsuan-lun-chiang wants to merge 6 commits into
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Fix the Errors:
1. DeepSeek Unscanned Checkpoint Mismatch:
When restoring unscanned DeepSeek checkpoints, loading failed with:
'ValueError: Checkpoint structure mismatch: 374 of 377 model parameter paths were not found... Example missing paths: decoder.dense_layer_0...'
Cause: _init_sequential_deepseek registered layers as singular 'dense_layer' and 'moe_layer', producing PyTree paths like 'decoder.dense_layer_0'. However, Linen checkpoints and MaxText converter scripts format layer keys as plural ('decoder.dense_layers_0', 'decoder.moe_layers_0').
2. Generic Unscanned Model Checkpoint Mismatch (GPT-OSS, Llama4, etc.):
When restoring unscanned generic models, loading failed with:
'ValueError: Checkpoint structure mismatch: 456 of 459 model parameter paths were not found... Example missing paths: decoder.layers.0...'
Cause: _init_sequential_generic used _create_and_register_layer which appended to self.layers initialized as nnx.List([]). In Flax NNX, tracking submodules in an nnx.List container forces state PyTree extraction to use list-index paths ('decoder.layers.0') instead of named attribute paths ('decoder.layers_0'), breaking compatibility with on-disk Linen checkpoints.
Summary of Fixes:
- Updated _init_sequential_deepseek to use plural prefixes ('dense_layers', 'moe_layers').
- Used _create_and_register_named_layer in sequential initialization (_init_sequential_deepseek, _init_sequential_generic) so submodules register exclusively under named string attributes ('layers_0', 'layers_1').
- Replaced nnx.List with a plain Python list for self.layers and updated the forward pass to retrieve named attributes via getattr(self, f'layers_{lyr}').
- Sequential Layer Naming & Flax 0.12.0 Compatibility (nnx_decoders.py):
- Fixed DeepSeek sequential layer retrieval in _apply_sequential_layers to look up 'dense_layers_{i}' and 'moe_layers_{i}' attributes when scan_layers=False.
- Removed self.layers.append(layer) in _init_gemma4_small_layers to prevent Flax 0.12.0 from raising a static container attribute ValueError on unannotated Python lists.
- MaxEngine KV-Cache Stacking & Sharding (maxengine.py):
- Updated self.prefill_kv_cache_shardings, _maybe_stack_prefill_result_cache, and _maybe_unstack_prefill_result_cache to handle named per-layer keys ('layers_0', 'layers_1') in addition to stacked 'layers' keys when scan_layers=False. Unstacks KV cache matching decode_state['cache'] keys to prevent PyTree structure mismatches during engine insert.
- LoRA Module Regex Pattern Matching (lora_utils.py & lora_utils_test.py):
- Updated _get_lora_module_path regex replacement to r'layers(?:_[0-9]+|/[0-9]+)?/' so LoRA target verification matches named attributes ('decoder/layers_0/...') as well as indexed ('decoder/layers/0/...') and scanned ('decoder/layers/...') paths.
- Updated expected regex string assertions in test_get_lora_module_path.
- Increase model size in lora_utils_test.py to avoid IndivisibleError on FSDP. - Monkeypatch qwix LoraProvider.dot_general to find parameter names before quantization. - Refactor NNXDecoder to support dynamic graph init with scan_layers=False. - Add assertions to verify quantization in tests. TAG=agy CONV=a499f437-b38f-44a6-99d3-11a38fb2e032
TAG=agy CONV=a499f437-b38f-44a6-99d3-11a38fb2e032
TAG=agy CONV=a499f437-b38f-44a6-99d3-11a38fb2e032
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When scan_layers=False, NNXDecoder updates its layers in a loop.
It was checking `hasattr(self, "layers")` to decide whether to update `self.layers[lyr]` or use `setattr(self, f"layers_{lyr}", new_layer)`.
Because of the newly added `__getattr__` fallback for read-only access to `self.layers`, `hasattr(self, "layers")` was evaluating to True, causing the code to try to mutate the dynamically constructed list in-place. This mutation was lost as it only modified the temporary list returned by `__getattr__`, leaving the actual `layers_{lyr}` attributes unchanged and discarding newly initialized parameters (like LoRA parameters).
This fix checks `hasattr(self, f"layers_{lyr}")` first to prioritize updating the individual layer attributes directly.
TAG=agy
CONV=a499f437-b38f-44a6-99d3-11a38fb2e032
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This PR fixes several issues with the NNX decoders introduced in PR #4464:
NNXDecoderwhere it was mutating a temporary list returned by__getattr__instead of updating the individuallayers_iattributes. This was causing newly initialized parameters (like LoRA parameters) to be discarded.TAG=agy
CONV=a499f437-b38f-44a6-99d3-11a38fb2e032