Quantize models on __init__ / from_config - #46324

…ned`)

Constructing a model directly (`cls(config)` / `AutoModel.from_config`) with a
`quantization_config` set on the config currently ignores it and returns a
full-precision model. This wires up on-the-fly quantization for that path,
mirroring `from_pretrained`.

`PreTrainedModel.post_init` now calls `_maybe_quantize_on_init`, which (only on
the construction root, and only for real non-meta weights) builds the
`HfQuantizer`, captures the random-init weights, swaps modules via
`preprocess_model`, and routes the weights back through the exact same
`convert_and_load_state_dict_in_model` loader used by `from_pretrained` so the
result is identical to loading those weights quantized.

Gated by a new opt-in `HfQuantizer.supports_quantize_on_init` flag (enabled for
FineGrainedFP8); quantizers needing calibration stay opt-out. A meta-device
guard keeps the `from_pretrained` path untouched.

…tion

Drop the `supports_quantize_on_init` opt-in flag: all non-calibration
quantizers can quantize freshly-initialized weights, so reuse the existing
`requires_calibration` to gate instead. `_maybe_quantize_on_init` moves out of
`PreTrainedModel` into `quantizers_utils.maybe_quantize_on_init`.

Resolve the quantizer class without instantiating (new `AutoHfQuantizer.resolve`,
factored out of `from_config`) so we can check `requires_calibration` before any
backend import. Calibration-only methods (AWQ, GPTQ, ...) now emit a warning
instead of silently returning a full-precision model.

Read the dtype off the freshly-initialized parameters with a default-dtype
fallback rather than `self.dtype`, which raises StopIteration on a param-less
model; the parameters are the source of truth that matches the captured
state_dict.