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@RahulC7 RahulC7 commented Oct 10, 2025

Summary:

Context

We currently only support 8-bit for most operators. We would like to add generic ops for 16-bit activations, for the following ops:

  • quantized_fully_connected
  • quantized_linear
  • quantized_conv (all flavors)
  • quantized_matmul

This Diff

Here, we add support for quantized_linear and quantized_fully_connected. We need to do the following:

  1. Allow 16-bit activations in quantized_fully_connected_out.cpp and quantized_linear_out.cpp.
  2. Allow 16-bit activations in ref_implementations.py, so tests can run with 16-bit activations to validate the quantization is correct.
  3. Add a quantizer(CadenceWith16BitLinearActivationsQuantizer) for checking this works and create a unit test.

Differential Revision: D84284794

@meta-cla meta-cla bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Oct 10, 2025
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meta-codesync bot commented Oct 10, 2025

@RahulC7 has exported this pull request. If you are a Meta employee, you can view the originating Diff in D84284794.

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This PR needs a release notes: label

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pytorch-bot bot commented Oct 10, 2025

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/15010

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RahulC7 added a commit to RahulC7/executorch that referenced this pull request Oct 13, 2025
…d linear (pytorch#15010)

Summary:

# Context
We currently only support 8-bit for most operators. We would like to add generic ops for 16-bit activations, for the following ops:
- quantized_fully_connected
- quantized_linear
- quantized_conv (all flavors)
- quantized_matmul



# This Diff


Here, we add support for `quantized_linear` and `quantized_fully_connected`. We need to do the following: 
1. Allow 16-bit activations in `quantized_fully_connected_out.cpp` and `quantized_linear_out.cpp`. 
2. Allow 16-bit activations in `ref_implementations.py`, so tests can run with 16-bit activations to validate the quantization is correct. 
3. Add a quantizer(`CadenceWith16BitLinearActivationsQuantizer`) for checking this works and create a unit test.

Reviewed By: hsharma35

Differential Revision: D84284794
…d linear (pytorch#15010)

Summary:

# Context
We currently only support 8-bit for most operators. We would like to add generic ops for 16-bit activations, for the following ops:
- quantized_fully_connected
- quantized_linear
- quantized_conv (all flavors)
- quantized_matmul



# This Diff


Here, we add support for `quantized_linear` and `quantized_fully_connected`. We need to do the following: 
1. Allow 16-bit activations in `quantized_fully_connected_out.cpp` and `quantized_linear_out.cpp`. 
2. Allow 16-bit activations in `ref_implementations.py`, so tests can run with 16-bit activations to validate the quantization is correct. 
3. Add a quantizer(`CadenceWith16BitLinearActivationsQuantizer`) for checking this works and create a unit test.

Reviewed By: DrJessop, hsharma35

Differential Revision: D84284794
@meta-codesync meta-codesync bot merged commit 926312e into pytorch:main Oct 14, 2025
223 of 227 checks passed
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2 participants