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Bump torchao to 03ca489dc and nightly wheel to dev20260729 (#21476)
### Summary Advances both torchao pins to the same upstream source. The pip nightly wheel moves from 0.18.0.dev20260715 to 0.18.0.dev20260729, and the third-party/ao submodule moves from 4aa810113 (v0.17.0-178) to 03ca489dc (v0.17.0-224), which is the exact torchao main commit the dev20260729 nightly wheel was built from. Previously the two pins were roughly six weeks apart; keeping them on the same commit avoids source-versus-wheel skew between the built-from-source kernels and the installed package. This change was authored with Claude Code. cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell
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backends/arm/test/models/test_llama.py

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@@ -208,6 +208,11 @@ def test_llama_tosa_FP():
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pipeline.run()
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@pytest.mark.xfail(
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reason="index_put into a preserved fp32 mutable KV cache (torchao pytorch/ao#4466) is "
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"not delegatable by the INT backend, so the cache round-trip forms a partition "
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"dependency cycle. Same root cause as the xfailed static-cache tests: MLETORCH-1971."
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)
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def test_llama_tosa_INT():
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llama_model, llama_inputs, llama_meta = TestLlama().prepare_model()
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pipeline.run()
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@pytest.mark.xfail(
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reason="index_put into a preserved fp32 mutable buffer (torchao pytorch/ao#4466) is "
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"not delegatable by the INT backend, so the KV-cache round-trip forms a partition "
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"dependency cycle. Same root cause as the xfailed static-cache tests: MLETORCH-1971."
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)
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def test_llama_tosa_INT_static():
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llama_model, llama_inputs, _ = TestLlama().prepare_model_hf_static()
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if llama_model is None or llama_inputs is None:
@@ -270,6 +280,11 @@ def test_llama_vgf_no_quant():
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@common.SkipIfNoModelConverter
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@pytest.mark.xfail(
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reason="The KV cache stays fp32 (torchao pytorch/ao#4466), so attention reads it as "
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"float while the query is quantized: MATMUL rejects the int8/float32 operand pair. "
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"Same root cause as the xfailed static-cache tests: MLETORCH-1971."
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)
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def test_llama_vgf_quant():
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llama_model, llama_inputs, llama_meta = TestLlama().prepare_model()
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install_requirements.py

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# This will be dynamically set based on CUDA availability and CUDA backend enabled/disabled.
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TORCH_URL_BASE = "https://download.pytorch.org/whl/test"
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TORCHAO_URL_BASE = "https://download.pytorch.org/whl/nightly"
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TORCHAO_NIGHTLY_VERSION = "0.18.0.dev20260715"
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TORCHAO_NIGHTLY_VERSION = "0.18.0.dev20260729"
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# Since ExecuTorch often uses main-branch features of pytorch, only the nightly
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# pip versions will have the required features.

third-party/ao

Submodule ao updated 103 files

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