[PyTorch][torch.compile] Add TensorProto mechanism

[pggPL]

Description

This PR introduces TensorProto — a data-free prototype of a tensor (or quantized tensor) that captures everything needed to reason about and rebuild a tensor without holding any storage: its logical shape/dtype and, for quantized tensors, the value-opaque quantizer defining the layout.

The key property is that TensorProto.create_tensor() materializes a quantized tensor purely in Python (via Quantizer.alloc_tensors + the storage's __tensor_unflatten__), so it traces under torch.compile(fullgraph=True) with no graph break — unlike make_empty, which goes through the opaque C++ tex.create_empty_quantized_tensor. This is the foundation for writing torch.library custom-op fake implementations of quantized ops.

This builds on the value-opaque quantizer work (so a TensorProto is itself safe to treat as a compile-time constant).

Type of change

• Documentation change (change only to the documentation, either a fix or a new content)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Infra/Build change
• Code refactoring

Changes

• dynamo.py: Add TensorProto dataclass (shape, dtype, quantizer, requires_grad, device) with is_quantized, inner_names(), create_metadata() and create_tensor(), plus a to_tensor_proto() helper that builds a proto from a plain torch.Tensor or a QuantizedTensorStorage/QuantizedTensor.
• quantized_tensor.py:
  • Add the PyTorch wrapper-subclass flatten protocol (__tensor_flatten__ / __tensor_unflatten__) to QuantizedTensorStorage, driven by a per-class _FLATTEN_TENSOR_BUFFERS declaration of (attribute_name, constructor_kwarg) pairs.
  • Add a _STORAGE_REGISTRY (populated via __init_subclass__) so __tensor_unflatten__ can resolve a concrete storage/wrapper class from its qualname inside an FX graph.
  • Add pure-Python, traceable allocation hooks to Quantizer: alloc_tensors, create_metadata, and the opt-in overrides _describe_buffers, _storage_scalars, _resolve_storage_cls.
• Quantizers: Implement the allocation hooks for Float8CurrentScalingQuantizer, MXFP8Quantizer and Float8BlockQuantizer.
• Storage classes: Declare _FLATTEN_TENSOR_BUFFERS for Float8TensorStorage, MXFP8TensorStorage and Float8BlockwiseQTensorStorage.
• ops/basic/basic_linear.py: Add allocation-free _functional_forward_fake / _functional_backward_fake that operate on TensorProto and return output/gradient protos, as a basis for custom-op fake impls (single-device only; TP/SP shape effects not yet modeled).
• Tests: Add tests/pytorch/test_tensor_proto.py (CPU smoke tests for _describe_buffers/alloc_tensors/create_metadata, flatten round-trip, and to_tensor_proto) and torch.compile fullgraph tests in test_torch_compile.py.

Checklist:

• I have read and followed the contributing guidelines
• The functionality is complete
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes

[kshitij12345]

Overall looks good

Would it be possible to reduce duplication between _linear_forward_impl_fake and _linear_forward_impl.