[float8] Prevent quantize_affine_float8/dequantize_affine_float8 decomposed on inductor

[shiyang-weng]

Fix #2228

What we want to do now is to enable FP8 quantization in PyTorch. And similar as INT8 quantization, we need to insert quantize and dequantize ops into the graph.
However we met problems with these q/dq ops both in the PyTorch core and Torchao.

PyTorch core:

The quantize_per_tensor op does not support FP8. We want to fix it via pytorch/pytorch#153601. And as you commented, the op is deprecated.
Torchao:

In the fusion pass in Inductor, we want to match the pattern fp8_weight -> torchao.dequantize_affine_float8 -> fp32_op and fuse it as fp8_weight -> weight_pack -> fp8_op. We have done so for INT8 PT2E quantization. However, the pattern matching pass is applied after a constant folding pass in Inductor:
https://github.com/pytorch/pytorch/blob/100ec0b34aeff2b948dae33937857d0c86cf1646/torch/_inductor/fx_passes/freezing_patterns.py#L69C1-L74C1
After constant_fold(gm), the pattern will be folded as fp32_weight -> fp32_op. Then the original pattern cannot be found any more and the FP8 semantics is lost since the pattern is entirely in fp32 now.
For INT8, the int8_weight -> quantized_decomposed.dequantize_per_channel -> fp32_op pattern won't be folded because we mark quantized_decomposed.dequantize_per_channel impure so that it won't be folded: https://github.com/pytorch/pytorch/blob/100ec0b34aeff2b948dae33937857d0c86cf1646/torch/_inductor/constant_folding.py#L139C1-L149C1 . But for the torchao.dequantize_affine_float8, we cannot do this because
It is an op from Torchao, which is unknown to the constant folder
It is decomposed to smaller ops, so we cannot put it in the list as a single op.

Need to prevent q/dq decomposed both on fp8 and int8.
Based on discussion on #2299
Create this PR to prevent fp8 q/dq decomposed firstly.
On this PR,

1. Register quantize_affine_float8 and dequantize_affine_float8 to torch.ops.torchao
2. Aligned dispatch_key with pt. quantize_affine_float8 and dequantize_affine_float8 will not be decomposed.
3. Register meta func for quantize_affine_float8 and dequantize_affine_float8

[pytorch-bot]

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  test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_prepare_for_propagation_comparison
• Run Regression Tests / test (CUDA 2.7, linux.g5.12xlarge.nvidia.gpu, torch==2.7.0, cuda, 12.6) / linux-job (gh)
  test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_prepare_for_propagation_comparison

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[jerryzh168]

nit: can be more explicit, e.g. inductor_decomposed

[shiyang-weng]

Done

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[jerryzh168]

we can probably make quantize_affine_float8 and choose_qparams_affine_float8 public as well since it's used in inductor lowering. cc @jainapurva @drisspg