Using generic implementation for 16-bit activations and 8 bit weights for linear in backends

[RahulC7]

Summary:

Context

We continue from D84284794 to add support for 16-bit activations. Note that right now, all though they support 16-bit activations already, it's only if the weights are also 16-bits. To do this, we need to change the way we template some functions.

Current Behavior

Right now, we're composing two macros together, the ET_FORALL_JARVIS_QUANTIZED_TYPES_WITH_INT16 macro:

https://www.internalfb.com/code/fbsource/[9e8c6d8466107f58aa3de1b9e4ec71c49d670a8f]/fbcode/on_device_ai/Assistant/Jarvis/min_runtime/operators/generic/operators.h?lines=22-25

and the function macro(quantized_linear chosen for example):

https://www.internalfb.com/code/fbsource/[9e8c6d8466107f58aa3de1b9e4ec71c49d670a8f]/fbcode/on_device_ai/Assistant/Jarvis/min_runtime/operators/generic/quantized_linear_out.cpp?lines=30-41

so together, it just becomes a switch statement, calling the quantized_linear function with the correct template parameter.

However, note that it assumes that both the input activations and weights are the same dtype, which is not the case.

This Diff

We finish by using the generic implementation for all the backends and adding e2e tests as well as unit tests.

Differential Revision: D87946776

[pytorch-bot]

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[meta-codesync]

@RahulC7 has exported this pull request. If you are a Meta employee, you can view the originating Diff in D87946776.

[Copilot]

Pull request overview

This PR adds support for 16-bit activations with 8-bit weights in quantized linear operations across backends. Previously, the implementation required both activations and weights to have matching data types.

Key changes:

• Added conditional handling for int16 activation + int8 weight combinations using generic implementations
• Added unit tests for the new int16 activation support in the HiFi backend
• Updated build configuration to include necessary dependencies for int16 support

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 2 comments.

File	Description
backends/cadence/hifi/operators/tests/test_op_quantized_linear_out.cpp	New test file validating int16 activation quantized linear operations
backends/cadence/hifi/operators/targets.bzl	Updated build targets to add dependencies for int16 support in quantized linear operators
backends/cadence/hifi/operators/op_quantized_linear_out.cpp	Added conditional logic to dispatch to generic implementation for int16 activations with int8 weights

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[Copilot]

Trailing comma after the last list element should be removed for consistency with Python style conventions.

Suggested change

	define_operator("quantized_linear_out", deps=["fbcode//on_device_ai/Assistant/Jarvis/min_runtime/operators:quantize_linear_out", "fbcode//on_device_ai/Assistant/Jarvis/min_runtime/operators:headers",])
	define_operator("quantized_linear_out", deps=["fbcode//on_device_ai/Assistant/Jarvis/min_runtime/operators:quantize_linear_out", "fbcode//on_device_ai/Assistant/Jarvis/min_runtime/operators:headers"])

[Copilot]

Trailing comma after the last list element should be removed for consistency with Python style conventions.

Suggested change

	define_operator("quantized_linear_per_tensor_out", deps=["fbcode//on_device_ai/Assistant/Jarvis/min_runtime/operators:quantize_linear_out", "fbcode//on_device_ai/Assistant/Jarvis/min_runtime/operators:headers",])
	define_operator("quantized_linear_per_tensor_out", deps=["fbcode//on_device_ai/Assistant/Jarvis/min_runtime/operators:quantize_linear_out", "fbcode//on_device_ai/Assistant/Jarvis/min_runtime/operators:headers"])