[ET-VK] Quantized Int8 Linear #13816
Open
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Title says it all! This PR adds implementations for int8 linear layers. Convolution is implemented in a later step, computing convolution as matrix multiplication via the im2col procedure. For both linear and convolution, two versions are implemented: 1. `q8ta_q8csw` variant which quantized the input tensor and then performs integer accumulation via the int8 dot product extension 2. `q8csw` variant which dequantized the weight tensor in-shader and performs floating point accumulation. The second one is needed to provide an alternative path for executing quantized models if the target GPU does not support int8 dot product extension. These new ops are tested via the custom op testing + benchmarking framework introduced in the previous diff. Differential Revision: [D81323424](https://our.internmc.facebook.com/intern/diff/D81323424/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/13816
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: ❌ 7 New FailuresAs of commit 29d1118 with merge base e2098f8 ( NEW FAILURES - The following jobs have failed:
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This was referenced Aug 29, 2025
meta-cla
bot
added
the
CLA Signed
This PR needs a
|
|
This pull request was exported from Phabricator. Differential Revision: D81323424 |
Title says it all! This PR adds implementations for int8 linear layers. Convolution is implemented in a later step, computing convolution as matrix multiplication via the im2col procedure. For both linear and convolution, two versions are implemented: 1. `q8ta_q8csw` variant which quantized the input tensor and then performs integer accumulation via the int8 dot product extension 2. `q8csw` variant which dequantized the weight tensor in-shader and performs floating point accumulation. The second one is needed to provide an alternative path for executing quantized models if the target GPU does not support int8 dot product extension. These new ops are tested via the custom op testing + benchmarking framework introduced in the previous diff. Differential Revision: [D81323424](https://our.internmc.facebook.com/intern/diff/D81323424/) [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D81323424 |
Title says it all! This PR adds implementations for int8 linear layers. Convolution is implemented in a later step, computing convolution as matrix multiplication via the im2col procedure. For both linear and convolution, two versions are implemented: 1. `q8ta_q8csw` variant which quantized the input tensor and then performs integer accumulation via the int8 dot product extension 2. `q8csw` variant which dequantized the weight tensor in-shader and performs floating point accumulation. The second one is needed to provide an alternative path for executing quantized models if the target GPU does not support int8 dot product extension. These new ops are tested via the custom op testing + benchmarking framework introduced in the previous diff. Differential Revision: [D81323424](https://our.internmc.facebook.com/intern/diff/D81323424/) [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D81323424 |
Title says it all! This PR adds implementations for int8 linear layers. Convolution is implemented in a later step, computing convolution as matrix multiplication via the im2col procedure. For both linear and convolution, two versions are implemented: 1. `q8ta_q8csw` variant which quantized the input tensor and then performs integer accumulation via the int8 dot product extension 2. `q8csw` variant which dequantized the weight tensor in-shader and performs floating point accumulation. The second one is needed to provide an alternative path for executing quantized models if the target GPU does not support int8 dot product extension. These new ops are tested via the custom op testing + benchmarking framework introduced in the previous diff. Differential Revision: [D81323424](https://our.internmc.facebook.com/intern/diff/D81323424/) [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D81323424 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Stack from ghstack (oldest at bottom):
Title says it all!
This PR adds implementations for int8 linear layers. Convolution is implemented in a later step, computing convolution as matrix multiplication via the im2col procedure.
For both linear and convolution, two versions are implemented:
q8ta_q8cswvariant which quantized the input tensor and then performs integer accumulation via the int8 dot product extensionq8cswvariant which dequantized the weight tensor in-shader and performs floating point accumulation.The second one is needed to provide an alternative path for executing quantized models if the target GPU does not support int8 dot product extension.
These new ops are tested via the custom op testing + benchmarking framework introduced in the previous diff.
Differential Revision: D81323424