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Vulkan: convert quantized linear weight scales to the output dtype - #24023
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The quantized linear shaders read the weight scales in the output dtype, but quantized_linear_impl prepacked them in whatever dtype they were serialized in. An fp16 model quantized from a bf16 or fp32 checkpoint can keep fp32 scales (e.g. torchao's weight_scale_dtype=torch.float32), which the partitioner accepts, and the shaders then read the fp32 bytes as fp16. Prepack the scales in the output dtype, as Q4gswLinear already does.
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24023
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New Failure, 2 Unrelated FailuresAs of commit 7947020 with merge base a39d597 ( NEW FAILURE - The following job has failed:
BROKEN TRUNK - The following jobs failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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SS-JIA
approved these changes
Oct 26, 2026
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Summary
The quantized linear shaders read the weight scales in the output dtype, but
quantized_linear_implprepacked them in their serialized dtype. An fp16 model with fp32 scales (e.g. torchaoweight_scale_dtype=torch.float32, or a bf16 checkpoint cast to fp16 around its scales) is accepted by the partitioner and silently produces garbage. This prepacks the scales in the output dtype, asQ4gswLinearalready does.Test plan
Galaxy S26 Ultra (Adreno 840), fp16 8da4w
Linear(1536, 2048)g32, vs the same model in fp32:No unit test: the 8da4w delegate test is disabled on SwiftShader and the
q4gsw_linearop test skips fp16dq8careferences.cc @SS-JIA @manuelcandales @digantdesai @cbilgin @nil-is-all