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

[Xia-Weiwen]

Summary
(It's a port from pytorch/pytorch#150831)
This PR enabled annotation of aten.mul.tensor with X86InductorQuantizer. However, 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

[pytorch-bot]

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[leslie-fang-intel]

LGTM

[Xia-Weiwen]

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