Fix SVD forward-mode AD multiplication priority #161027
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/161027
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Signed-off-by: redwrasse <mail@redwrasse.io>
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Multiplication order priority for the SVD JVP appears to have been the opposite of the optimal one. Results from a crude CPU benchmark on my laptop for random matrices of various ratios: ``` Performance Results Table | Test Case | Matrix Size | Aspect Ratio | Before JVP (ms) | After JVP (ms) | Change (ms) | % Change | Status | |----------------------------------|-------------|--------------|-----------------|----------------|-------------|----------|---------------------| | Tall matrix (10:1 ratio) | 1000×100 | 10:1 tall | 3.13 | 3.24 | +0.11 | -3.5% | ❌ Regression | | Tall matrix (10:1 ratio, larger) | 2000×200 | 10:1 tall | 15.72 | 14.66 | -1.06 | +6.7% | ✅ Improvement | | Tall matrix (10:1 ratio, large) | 5000×500 | 10:1 tall | 105.97 | 101.84 | -4.13 | +3.9% | ✅ Improvement | | Wide matrix (1:10 ratio) | 100×1000 | 1:10 wide | 5.90 | 4.64 | -1.26 | +21.4% | ✅ Major Improvement | | Wide matrix (1:10 ratio, larger) | 200×2000 | 1:10 wide | 18.29 | 17.78 | -0.51 | +2.8% | ✅ Improvement | | Wide matrix (1:10 ratio, large) | 500×5000 | 1:10 wide | 137.40 | 128.70 | -8.70 | +6.3% | ✅ Improvement | | Square matrix (baseline) | 1000×1000 | 1:1 square | 116.16 | 106.09 | -10.07 | +8.7% | ✅ Improvement | | Square matrix (larger baseline) | 2000×2000 | 1:1 square | 714.30 | 673.23 | -41.07 | +5.7% | ✅ Improvement | ``` Pull Request resolved: pytorch#161027 Approved by: https://github.com/soulitzer
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Multiplication order priority for the SVD JVP appears to have been the opposite of the optimal one.
Results from a crude CPU benchmark on my laptop for random matrices of various ratios: