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[fix](kt-kernel): Drop Python-loaded weights after C++ weight loading in LlamafileMoEWrapper #1695

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[fix](kt-kernel): Drop Python-loaded weights after C++ weight loading in LlamafileMoEWrapper #1695

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3 changes: 3 additions & 0 deletions kt-kernel/python/utils/llamafile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,3 +217,6 @@ def load_weights(self, physical_to_logical_map_cpu: Optional[torch.Tensor] = Non
# Load weights
self.cpu_infer.submit(self.moe.load_weights_task(physical_to_logical_map_cpu.data_ptr()))
self.cpu_infer.sync()

# Drop original weights after loading
self.weights_to_keep = None
Comment on lines +221 to +222
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@gemini-code-assist gemini-code-assist bot Dec 10, 2025

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While setting self.weights_to_keep = None correctly frees up the memory, a cleaner approach would be to remove the self.weights_to_keep member variable entirely.

The tensors (gate_data, up_data, down_data) are local to the load_weights method and will remain in scope until the method returns. Since self.cpu_infer.sync() blocks until the C++ copy is complete, the lifetime of these local variables is sufficient to ensure the memory is valid for the C++ backend.

This makes self.weights_to_keep redundant. You could consider a refactoring to simplify the code:

  1. Remove self.weights_to_keep = None from the __init__ method (line 135).
  2. Remove the assignment self.weights_to_keep = (gate_data, up_data, down_data) on line 182.
  3. Remove this newly added code block.

This would make the lifetime management implicit and rely on standard Python garbage collection, resulting in cleaner, more idiomatic code.