Add a16w8 per-op test for var (#19596) by christine-long-meta · Pull Request #19596 · pytorch/executorch

christine-long-meta · 2026-05-14T16:46:50Z

Summary:

Add int16 activation / int8 weight (a16w8) quantization tests for aten.var on Ethos-U55 and Ethos-U85.

Changes

Add a16w8_var_test_parameters dict with 4 test configurations covering keepdim/no-keepdim and correction values 0, 0.5, and 1
Add test_var_a16w8_u55_INT using EthosU55PipelineINT with a16w8_quantization=True, symmetric_io_quantization=True, qtol=128, epsilon=2**-16
Add test_var_a16w8_u85_INT using EthosU85PipelineINT with same kwargs
Register ops/test_var.py in fbcode/ and xplat/ targets.bzl

bypass-pytorch-oss-checks

Differential Revision: D104532362

Summary: Add int16 activation / int8 weight (a16w8) quantization tests for `aten.exp` on Ethos-U55 and Ethos-U85. ## Context The `exp` op is part of the softmax decomposition (`softmax(x) = exp(x) / sum(exp(x))`), which is used in the attention mechanism of EMG2Pose Conformer models. This op was identified as the root cause of the U85 SNR regression investigated in SEV T267939669 — without dedicated a16w8 per-op coverage, the numerics issue was only visible at the full-model level. Adding per-op tests allows us to catch int16 precision regressions at the operator granularity before they propagate to end-to-end model accuracy. ## Changes - Add `a16w8_exp_test_parameters` dict with 3 test configurations covering rank-1, rank-2, and rank-3 tensors - Add `test_exp_a16w8_u55_INT` using `EthosU55PipelineINT` with `a16w8_quantization=True, symmetric_io_quantization=True, qtol=128, epsilon=2**-16` - Add `test_exp_a16w8_u85_INT` using `EthosU85PipelineINT` with same kwargs - Register `ops/test_exp.py` in `fbcode/` and `xplat/` `targets.bzl` bypass-pytorch-oss-checks Differential Revision: D104532358

Summary: Add int16 activation / int8 weight (a16w8) quantization tests for `aten.reciprocal` on Ethos-U55 and Ethos-U85. ## Context The `reciprocal` op is the second half of the softmax decomposition (`softmax(x) = exp(x) * reciprocal(sum(exp(x)))`), paired with `exp`. Together they form the attention mechanism in EMG2Pose Conformer models. Like `exp`, this op was implicated in the U85 SNR regression (SEV T267939669) — the division-by-reciprocal path can amplify quantization error when the denominator is itself quantized at int16. Adding dedicated a16w8 coverage isolates reciprocal numerics from the rest of the softmax pipeline. ## Changes - Add `a16w8_reciprocal_test_parameters` dict with 3 test configurations covering rank-1, rank-2, and rank-3 tensors (all shifted by +0.1 to avoid division near zero) - Add `test_reciprocal_a16w8_u55_INT` using `EthosU55PipelineINT` with `a16w8_quantization=True, symmetric_io_quantization=True, qtol=128, epsilon=2**-16` - Add `test_reciprocal_a16w8_u85_INT` using `EthosU85PipelineINT` with same kwargs - Register `ops/test_reciprocal.py` in `fbcode/` and `xplat/` `targets.bzl` bypass-pytorch-oss-checks Differential Revision: D104532357

Summary: Add int16 activation / int8 weight (a16w8) quantization tests for `aten.mean.dim` on Ethos-U55 and Ethos-U85. ## Context The `mean_dim` op is a core component of the LayerNorm decomposition (`LayerNorm = (x - mean) / sqrt(var + eps) * gamma + beta`). It is used across multiple EMG production models including CC, CASCADE, HW, WAKE, and BTD. Despite this wide usage, no a16w8 per-op coverage existed — the int16 quantization path was only exercised indirectly through end-to-end model tests, making it difficult to isolate mean-specific numerics issues from other LayerNorm components. ## Changes - Add `a16w8_mean_test_parameters` dict with 11 test configurations covering keepdim/no-keepdim, positive/negative dims, dim=None, and ranks 1-4 - Add `test_mean_dim_a16w8_u55_INT` using `EthosU55PipelineINT` with `a16w8_quantization=True, symmetric_io_quantization=True, qtol=128, epsilon=2**-16` - Add `test_mean_dim_a16w8_u85_INT` using `EthosU85PipelineINT` with same kwargs - Register `ops/test_mean_dim.py` in `fbcode/` and `xplat/` `targets.bzl` bypass-pytorch-oss-checks Differential Revision: D104532361

pytorch-bot · 2026-05-14T16:46:55Z

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❌ 2 New Failures, 40 Pending, 1 Unrelated Failure

As of commit 76e5af8 with merge base 58b4f26 ():

NEW FAILURES - The following jobs have failed:

pull / unittest-editable / linux / linux-job (gh)
backends/xnnpack/test/ops/test_conv2d.py::TestConv2d::test_dq_conv2d_seq
trunk / test-arm-backend-ethos-u (test_pytest_ops_ethos_u85) / linux-job (gh)
RuntimeError: Command docker exec -t 9d05a2588839e752cce6438ae02e02d8e1c86aeb693156759048bf1fea5bf3da /exec failed with exit code 1

FLAKY - The following job failed but was likely due to flakiness present on trunk:

pull / test-qnn-testsuite-linux / test-backend-linux (qnn, models) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)

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pytorch-bot · 2026-05-14T16:47:09Z

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github-actions · 2026-05-14T16:47:46Z

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meta-codesync · 2026-05-14T16:50:00Z