Refine error_handle for BatchLinearAlgebra Ops

[CuiYifeng]

This PR is to remove work-around for a functionality regression in oneMKL 2025.2 and fix the error handling code that has never been executed with 2025.2.

[Copilot]

Pull request overview

This PR addresses error handling in BatchLinearAlgebra operations by removing a workaround for a oneMKL 2025.2 regression and fixing indexing bugs in the error handling code. The changes enable proper exception handling that was previously non-functional.

Key Changes:

• Removed temporary skip list entries for inverse operation tests that were disabled due to oneMKL regression
• Fixed incorrect indexing in error handling code to use exception IDs instead of loop indices

Reviewed changes

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

File	Description
test/xpu/skip_list_common.py	Removed 8 test cases from skip list that were temporarily disabled due to oneMKL regression
src/ATen/native/xpu/mkl/BatchLinearAlgebra.cpp	Removed workaround code block and fixed indexing bug in error handlers to use ids[i] instead of i

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[Copilot]

Potential out-of-bounds access: ids[i] is used to index info_cpu, but there's no validation that ids[i] is within the bounds of info_cpu. This could cause a segfault or memory corruption if the batch exception contains an invalid matrix ID.

[Copilot]

Potential out-of-bounds access: Same issue as above - ids[i] is used to index info_cpu without bounds checking. Consider adding validation that ids[i] < info_cpu.size() before indexing.

[github-actions]

Performance outliers, please check!

• 🟡 [80%, 90%), may be fluctuations

Category	Model	Target vs. Baseline [Eager]	Target vs. Baseline [Inductor]
huggingface_float16_training	MT5ForConditionalGeneration	0.904954	0.882927

[CuiYifeng]

Fix the following cases:

        # Exception is temporarily unavailable due to regression in oneMKL
        "test_inv_errors_and_warnings_xpu_float32",
        "test_inv_errors_and_warnings_xpu_float64",
        "test_inverse_errors_large_xpu_float32",
        "test_inverse_errors_large_xpu_float64",
        "test_inverse_errors_xpu_float32",
        "test_inverse_errors_xpu_float64",
        "test_inv_ex_singular_xpu_float32",
        "test_inv_ex_singular_xpu_float64",

[github-actions]

Performance outliers, please check!

• 🟡 [80%, 90%), may be fluctuations

Category	Model	Target vs. Baseline [Eager]	Target vs. Baseline [Inductor]
torchbench_bfloat16_training	dcgan	0.844164	0.827297
torchbench_bfloat16_training	resnet18	0.934244	0.835603
torchbench_bfloat16_training	resnext50_32x4d	0.952235	0.853000
torchbench_bfloat16_training	densenet121	0.804804	0.858987
torchbench_bfloat16_training	mnasnet1_0	1.021068	0.869143
torchbench_bfloat16_training	squeezenet1_1	1.009145	0.880740

[github-actions]

Performance outliers, please check!

• 🟡 [80%, 90%), may be fluctuations

Category	Model	Target vs. Baseline [Eager]	Target vs. Baseline [Inductor]
torchbench_bfloat16_training	resnet18	0.902028	0.805277

[github-actions]

Performance outliers, please check!

• 🔴 [-1, 80%), should be regression

Category	Model	Target vs. Baseline [Eager]	Target vs. Baseline [Inductor]
torchbench_bfloat16_training	densenet121	0.783036	0.80363

• 🟡 [80%, 90%), may be fluctuations

Category	Model	Target vs. Baseline [Eager]	Target vs. Baseline [Inductor]
torchbench_bfloat16_training	dcgan	0.850267	0.801978
torchbench_bfloat16_training	mobilenet_v3_large	0.976237	0.825791
torchbench_bfloat16_training	resnet18	0.921155	0.839705
torchbench_bfloat16_training	squeezenet1_1	1.023057	0.886896
huggingface_bfloat16_training	MT5ForConditionalGeneration	0.915300	0.890694

[github-actions]

Performance outliers, please check!

• 🟡 [80%, 90%), may be fluctuations

Category	Model	Target vs. Baseline [Eager]	Target vs. Baseline [Inductor]
torchbench_bfloat16_training	resnet18	0.964222	0.831755