Adding Test for CadenceWithLayerNormQuantizer #16355
Conversation
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/16355
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: ❌ 1 New Failure, 1 Unrelated FailureAs of commit afcf441 with merge base 0ee2f49 ( NEW FAILURE - The following job has failed:
UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
meta-cla
bot
added
the
CLA Signed
This PR needs a
|
Summary: Adding test for CadenceWithLayerNormQuantizer. https://www.internalfb.com/code/fbsource/[72237310dfd9016b5f54a44d994c8a7065eda5d1]/fbcode/executorch/backends/cadence/aot/quantizer/quantizer.py?lines=296-306 Differential Revision: D88898823
Summary: Adding test for CadenceWithLayerNormQuantizer. https://www.internalfb.com/code/fbsource/[72237310dfd9016b5f54a44d994c8a7065eda5d1]/fbcode/executorch/backends/cadence/aot/quantizer/quantizer.py?lines=296-306 Differential Revision: D88898823
There was a problem hiding this comment.
Pull request overview
This PR adds comprehensive test coverage for three previously untested Cadence quantizers: CadenceWith16BitConvActivationsQuantizer, CadenceWithSoftmaxQuantizer, and CadenceWithLayerNormQuantizer. The tests verify that these quantizers correctly annotate graph nodes with the expected quantization specifications.
- Removes TODO comments for the three quantizers from the exclusion list
- Adds four new test cases covering conv1d, conv2d, softmax, and layer_norm operations
- Implements corresponding graph builder helper methods following the established pattern
💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.
Summary: Adding test for CadenceWithLayerNormQuantizer. https://www.internalfb.com/code/fbsource/[72237310dfd9016b5f54a44d994c8a7065eda5d1]/fbcode/executorch/backends/cadence/aot/quantizer/quantizer.py?lines=296-306 Differential Revision: D88898823
Summary: Add annotation tests for CadenceWith16BitConvActivationsQuantizer covering both conv1d and conv2d operations. Differential Revision: D88895865
Differential Revision: D88896712
Summary: Adding test for CadenceWithLayerNormQuantizer. https://www.internalfb.com/code/fbsource/[72237310dfd9016b5f54a44d994c8a7065eda5d1]/fbcode/executorch/backends/cadence/aot/quantizer/quantizer.py?lines=296-306 Reviewed By: hsharma35 Differential Revision: D88898823
RahulC7
force-pushed
the
export-D88898823
branch
from
6f4fab8 to
a983619
Compare
Summary: Adding test for CadenceWithLayerNormQuantizer. https://www.internalfb.com/code/fbsource/[72237310dfd9016b5f54a44d994c8a7065eda5d1]/fbcode/executorch/backends/cadence/aot/quantizer/quantizer.py?lines=296-306 Reviewed By: hsharma35 Differential Revision: D88898823
RahulC7
force-pushed
the
export-D88898823
branch
from
a983619 to
71aabe7
Compare
Summary: Pull Request resolved: pytorch#16355 Adding test for CadenceWithLayerNormQuantizer. https://www.internalfb.com/code/fbsource/[72237310dfd9016b5f54a44d994c8a7065eda5d1]/fbcode/executorch/backends/cadence/aot/quantizer/quantizer.py?lines=296-306 Reviewed By: hsharma35 Differential Revision: D88898823
RahulC7
force-pushed
the
export-D88898823
branch
from
71aabe7 to
afcf441
Compare
There was a problem hiding this comment.
Pull request overview
Copilot reviewed 1 out of 1 changed files in this pull request and generated 1 comment.
💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.
| def _build_layer_norm_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]: | ||
| """Build a simple graph with a layer_norm operation.""" | ||
| # Input shape: (batch, features) | ||
| x = torch.randn(1, 10) | ||
| # normalized_shape must match the last dimension(s) of input | ||
| normalized_shape = [10] | ||
| gm = single_op_builder( | ||
| placeholders=(x,), | ||
| op=torch.ops.aten.layer_norm.default, | ||
| args=(x, normalized_shape), | ||
| ) | ||
|
|
||
| layer_norm_nodes = gm.graph.find_nodes( | ||
| op="call_function", | ||
| target=torch.ops.aten.layer_norm.default, | ||
| ) | ||
| self.assertEqual( | ||
| len(layer_norm_nodes), 1, "Should find exactly one layer_norm node" | ||
| ) | ||
| # Add source_fn_stack metadata required by quantizer pattern matching | ||
| layer_norm_nodes[0].meta["source_fn_stack"] = [ | ||
| ("layer_norm", torch.ops.aten.layer_norm.default) | ||
| ] | ||
| return gm, layer_norm_nodes[0] |
There was a problem hiding this comment.
The _build_layer_norm_graph method uses a different approach than the other graph builders (single_op_builder instead of GraphBuilder), and manually adds source_fn_stack metadata after graph construction. While this works, it creates an inconsistency in the codebase.
Consider refactoring to use GraphBuilder directly like the other methods for consistency. The normalized_shape parameter can be passed directly in args since GraphBuilder's call_operator handles both tensor and non-tensor arguments. This would allow adding the source_fn_stack metadata during construction rather than after the fact.
2 participants
Summary:
Adding test for CadenceWithLayerNormQuantizer.
https://www.internalfb.com/code/fbsource/[72237310dfd9016b5f54a44d994c8a7065eda5d1]/fbcode/executorch/backends/cadence/aot/quantizer/quantizer.py?lines=296-306
Differential Revision: D88898823