[cuda] int4: stabilize two-layer decode test via CUDA-seeded init#20196
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_make_int4_linear built the throwaway nn.Linear on CPU, so reset_parameters() drew from the CPU RNG between the two layer constructions and shifted the stream that seeds the quantized weights. That pushed test_two_layer_mlp's genuine INT4 error from 0.1405 to 0.1556, crossing the 0.15 bound. Build the module with device=cuda so init draws from the CUDA RNG, leaving the CPU stream (and the measured error) deterministic. Test-only; dequant math is unchanged.
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20196
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shoumikhin
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Jun 10, 2026
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_make_int4_linear built the throwaway nn.Linear on CPU, so reset_parameters() drew from the CPU RNG between the two layer constructions and shifted the stream that seeds the quantized weights. That pushed test_two_layer_mlp's genuine INT4 error from 0.1405 to 0.1556, crossing the 0.15 bound. Build the module with device=cuda so init draws from the CUDA RNG, leaving the CPU stream (and the measured error) deterministic. Test-only; dequant math is unchanged.