[ET-VK][q8ta] Add q8ta_linear operator for int8 quantized linear#17565
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[ET-VK][q8ta] Add q8ta_linear operator for int8 quantized linear#17565SS-JIA wants to merge 1 commit intogh/SS-JIA/439/basefrom
SS-JIA wants to merge 1 commit intogh/SS-JIA/439/basefrom
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Add a new q8ta_linear operator that performs fully quantized int8 linear (matmul + bias) with per-tensor activation quantization and per-channel weight quantization, producing int8 output. This enables back-to-back quantized linear layers without intermediate dequantize/quantize steps. The operator reuses the existing tiled int8 linear GLSL headers (input/weight tile loading, int8 dot product accumulation, weight scales/sums/bias loading) and adds output quantization via quantize_and_pack to produce packed int8 output. The fusion pass in quantized_linear.py detects the q→dq→linear→q pattern (where the output quantize node comes from a subsequent quantized op's input) and fuses it into a single q8ta_linear call. This diff was authored with Claude. Differential Revision: [D93768642](https://our.internmc.facebook.com/intern/diff/D93768642/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/17565
Note: Links to docs will display an error until the docs builds have been completed. ❌ 4 New FailuresAs of commit 3a94e2e with merge base 7b843e4 ( NEW FAILURES - The following jobs have failed:
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This was referenced Feb 19, 2026
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manuelcandales
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Feb 19, 2026
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Stack from ghstack (oldest at bottom):
Add a new q8ta_linear operator that performs fully quantized int8
linear (matmul + bias) with per-tensor activation quantization and
per-channel weight quantization, producing int8 output. This enables
back-to-back quantized linear layers without intermediate
dequantize/quantize steps.
The operator reuses the existing tiled int8 linear GLSL headers
(input/weight tile loading, int8 dot product accumulation, weight
scales/sums/bias loading) and adds output quantization via
quantize_and_pack to produce packed int8 output.
The fusion pass in quantized_linear.py detects the
q→dq→linear→q pattern (where the output quantize node comes from a
subsequent quantized op's input) and fuses it into a single
q8ta_linear call.
This diff was authored with Claude.
Differential Revision: D93768642