Add scale and zero-point dimension validation to quantization primitives

[GiGiKoneti]

Description

This PR implements upfront shape and size validation for the scale and zero_point tensors in low-level affine quantization and dequantization primitives under torchao/quantization/quant_primitives.py.

It resolves several # TODO: validate scale/zero_point dimensions are compatible with block_size comments in the codebase.

By checking these dimensions before entering .view() calls, we prevent generic PyTorch RuntimeError: shape [...] is invalid for input of size ... and instead raise clear, actionable ValueError and AssertionError messages when dimensions or number of elements are incompatible.

Closes #4538

Test Plan

Added unit tests in test/quantization/test_quant_primitives.py (test_validate_scale_zero_point) and updated test_raises to expect the new ValueError instead of the old RuntimeError.

To run the tests:

python3 -m pytest test/quantization/test_quant_primitives.py -k "test_raises or test_validate_scale_zero_point"

Output:

============================= test session starts ==============================
platform darwin -- Python 3.11.9, pytest-9.0.3, pluggy-1.6.0
rootdir: /Users/mynimbus/ao
configfile: pyproject.toml
plugins: zarr-3.1.6, requests-mock-1.12.1, anyio-4.13.0
collected 27 items / 25 deselected / 2 selected

test/quantization/test_quant_primitives.py ..                            [100%]

======================= 2 passed, 25 deselected in 2.05s =======================

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/4539

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