Improve ONNX save performance: cache proto, update weights only

[adamantivm]

ONNX Save Optimization Results

Setup

• Experiment config: deep_quoridor/experiments/B5W3/test_onnx_export.yaml
• finish_after: 10 models, model_save_timing: true, save_onnx: true
• Tested on both MLP and ResNet network types
• Timing reported by trainer covers both PyTorch .pt save and ONNX save together

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MLP Network

Baseline (before)

Save #	Time (s)
1	1.0424
2	0.9356
3	1.0927
4	1.0188
5	1.0655

Average per save: ~1.03 s · Total for 10 saves: ~10.3 s

After (cached proto)

Save #	Time (s)
2	0.0052
3	0.0041
4	0.0041
5	0.0041
6–11	~0.0041

Average per save (saves 2+): ~0.0043 s · Total for 10 saves: ~0.043 s

MLP Comparison

Metric	Before	After	Speedup
First save	~1.03 s	~1.03 s	1×
Subsequent saves (avg)	~1.03 s	~0.0043 s	~240×
Total for 10 saves	~10.3 s	~0.043 s	~240×

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ResNet Network

Baseline (before)

Save #	Time (s)
1	1.0264
2	0.9065
3	1.0306
4	0.9120
5	1.0329
6	0.9685
7	1.0510
8	0.9162
9	1.0497
10	1.0103

Average per save: ~0.987 s · Total for 10 saves: ~9.87 s

After (cached proto)

Save #	Time (s)
2	0.0050
3	0.0043
4	0.0045
5	0.0045
6	0.0111
7	0.0064
8	0.0042
9	0.0040
10	0.0039
11	0.0039

Average per save (saves 2+): ~0.0052 s · Total for 10 saves: ~0.052 s

ResNet Comparison

Metric	Before	After	Speedup
First save	~0.99 s	~0.99 s	1×
Subsequent saves (avg)	~0.99 s	~0.0052 s	~190×
Total for 10 saves	~9.87 s	~0.052 s	~190×

---

Verification

• All 11 MLP .onnx files passed onnx.checker.check_model() ✅
• All 11 ResNet .onnx files passed onnx.checker.check_model() ✅
• All models ran inference via onnxruntime without error ✅
• Output shapes: policy_logits=(1, 57), value=(1, 1) — correct for 5×5 Quoridor ✅
• Value outputs in reasonable range for both architectures ✅

Implementation Summary

• _onnx_proto = None and _onnx_init_name_to_idx = {} added to AlphaZeroAgent.__init__
• First call to save_model_onnx: full torch.onnx.export() as before, then loads the
  written file with onnx.load() and builds a name→index map over all graph initializers
  (21 for MLP, 21 for ResNet with num_blocks=2, num_channels=32).
• Subsequent calls: iterates state_dict(), looks up each weight's index in the map,
  calls CopyFrom(onnx.numpy_helper.from_array(...)) in place, then onnx.save().
  Graph tracing is skipped entirely.
• Works identically for both MLP and ResNet architectures.

[Copilot]

Pull request overview

Optimizes repeated ONNX model saves by caching the exported ONNX graph and updating only initializer tensors (weights/buffers) on subsequent saves, dramatically reducing save time after the first export.

Changes:

• Cache ONNX ModelProto and an initializer name→index map after the first torch.onnx.export().
• On later saves, update cached initializers from state_dict() and re-serialize via onnx.save().
• Add internal documentation/benchmarks describing the optimization plan and measured speedups.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 5 comments.

File	Description
deep_quoridor/src/agents/alphazero/alphazero.py	Implements ONNX proto caching + initializer-only updates for faster subsequent ONNX saves.
deep_quoridor/coding-agents/onnx_save_optimization_results.md	Adds benchmark results and verification notes for the optimization.
deep_quoridor/coding-agents/onnx_save_optimization_plan.md	Adds an implementation plan documenting the intended approach.

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[alejandromarcu]

MSTM (Makes Sense To Me)