[Quant][PT2E][X86] Enable annotation of aten.mul.tensor with X86InductorQuantizer

[Xia-Weiwen]

Stack from ghstack (oldest at bottom):

• -> [Quant][PT2E][X86] Enable annotation of aten.mul.tensor with X86InductorQuantizer #150831
• [Quant][X86] add an op to compute uint8 pointwise mul #151112

Summary
This PR adds support of annotation of aten.mul.tensor in X86InductorQuantizer.
mul is not annotated by default. Users need to set the following to enable annotation of mul:

quantizer.set_function_type_qconfig(
    torch.mul, quantizer.get_global_quantization_config()
)

After convert_pt2e, users get patterns like

quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, ...)
dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, ...)
quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(y, ...);
dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, ...)
mul = torch.ops.aten.mul.Tensor(dequantize_per_tensor_default, dequantize_per_tensor_default_1);

Test plan

pytest test/quantization/pt2e/test_x86inductor_quantizer.py -k test_annotate_mul_tensor

cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10

[pytorch-bot]

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✅ You can merge normally! (1 Unrelated Failure)

As of commit 71e079b with merge base 01f226b ():

UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:

• pull / linux-jammy-py3-clang12-executorch / test (executorch, 1, 1, lf.ephemeral.linux.2xlarge) (gh) (#144480)
  examples/models/llama3_2_vision/text_decoder/test/test_text_decoder.py::TextDecoderTest::test_llama3_2_text_decoder_aoti

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[Copilot]

Copilot reviewed 2 out of 2 changed files in this pull request and generated no comments.

Comments suppressed due to low confidence (1)

test/quantization/pt2e/test_x86inductor_quantizer.py:2877

• [nitpick] Avoid using 'type' as a variable name because it shadows the built-in function. Consider renaming it to 'model_type' or a similar descriptive name.

for type in [0, 1, 2]:

[leslie-fang-intel]

LGTM. A small comment.

[Xia-Weiwen]

Hi @jerryzh168 Could you please review this PR? Thanks.

[jerryzh168]

nit: I think we can improve [0, 1, 2, 3] by defining different classes for each test case here

[Xia-Weiwen]

Thanks. Updated.

[Xia-Weiwen]

Close this PR as we need to move to Torchao.