Add QuantizationProgressTracker and wire it through calibration,
chunked calibration, multi-GPU phase 2, QEP general and arch-aware
paths. Runner gains quantization_progress flag (default on).

Includes unit tests for ETA formatting and thread-safe stepping.

Co-authored-by: Cursor <cursoragent@cursor.com>

Raise clear error for unsupported QEP quantizers 

See merge request onecomp/onecomp-lab!71

feat: quantization progress logs with ETA

* refactoring : QuantizationProgressTracker

* update CHANGELOG.md

---------

Co-authored-by: FKKimura <50981196+FKKimura@users.noreply.github.com>

- QuantizationProgressTracker: suppress duplicate logs after completion,
  use `is not None` for lock check, expand Google-style docstrings
- Demote per-layer / per-chunk INFO logs to DEBUG so the [progress] line
  is the single canonical per-step INFO signal
  (onecomp/quantizer/_quantizer.py, onecomp/runner_methods/chunked_quantization.py,
   onecomp/qep/_quantize_with_qep_arch.py)
- Chunked path: switch to per-group progress (was per-layer × per-quantizer);
  drop chunk_progress / layer_progress arguments
- Shorten tracker labels: "Quantization", "Chunked quantization", "QEP"
- Per-block MSE log in QEP arch path: drop redundant `[INFO]` prefix,
  switch to lazy `%` formatting, fix "Layer N" -> "Block N"
- Expand QuantizationProgressTracker tests (10 cases, incl. thread-safety
  and overflow suppression)
- Update CHANGELOG for the new [progress] line and INFO -> DEBUG demotion

See merge request onecomp/onecomp-lab!72

Fix three OneComp-side bugs surfaced by Takane VLM (Cohere2Vision) work:
the `save_quantized_model` flow dropping VLM auxiliary configs, the
`load_quantized_model` flow leaving tied `lm_head` and other non-quantized
modules in the wrong dtype after `load_state_dict(..., assign=True)`.

Issue 1 (save_quantized_model misses VLM aux configs):
- Replace the hard-coded allow-list (processor_config.json /
  preprocessor_config.json) with an extension-based whitelist of
  `*.json` / `*.jinja` (excluding weight shards, weight index files,
  `config.json`, and `generation_config.json`)
- Extract source-model-dir resolution into
  `Runner._resolve_source_model_dir()` with a `snapshot_download(
  local_files_only=True)` fallback that warns on failure instead of
  aborting the save (onecomp/runner.py)
- Skip files already produced by `model.save_pretrained` /
  `tokenizer.save_pretrained` and INFO-log both copied and skipped
  entries for traceability

Issue 2 (tied lm_head left at fp16 after load):
- Call `model.tie_weights()` after `load_state_dict(..., assign=True)`
  to restore `lm_head.weight is embed_tokens.weight` identity that
  `assign=True` breaks (onecomp/quantized_model_loader.py)
- Guard the call on `tie_word_embeddings=True` and
  `isinstance(model.lm_head, nn.Linear)` to avoid touching quantized
  layers such as `GPTQLinear`
- Walk one level of nested sub-configs (`text_config` / `language_config`)
  via `_should_retie_word_embeddings` so HF VLM configs that move
  `tie_word_embeddings` under `text_config` are also covered

Issue 3 (non-quantized VLM modules left at fp16):
- Make `_build_empty_model_from_config` honor `torch_dtype` / `dtype`
  from `config.json` when the caller did not pass an explicit dtype,
  so empty modules start in the saved checkpoint's dtype
  (onecomp/quantized_model_loader.py)
- Add `_cast_fp16_to_target_dtype` as a safety net that casts any
  fp16 parameter / buffer left on non-quantized modules to
  `model.config.torch_dtype` after `tie_weights()`, returning the list
  of fully-qualified names that were converted for logging and tests
- Skip `GPTQLinear` / `DoubleBinaryLinear` and limit the cast to fp16
  so mixed-precision fp32 LayerNorms are not disturbed; mutate
  `p.data` in place so tied-weight identity is preserved

Tests (tests/onecomp/runner/, 30 cases, CPU-only, no network):
- test_save_quantized_aux_files.py (7): whitelist copy, existing-file
  skip, `snapshot_download` fallback, subdirectory exclusion, INFO log
  for skipped files
- test_load_tied_embeddings.py (7): tiny LlamaForCausalLM with
  `tie_word_embeddings` True / False, identity restoration, forward
  dtype, `_should_retie_word_embeddings` top-level / nested / all-False
  / unrelated-sub-attrs cases
- test_load_excluded_module_dtype.py (16): config-driven empty-model
  dtype, fp16 safety-net cast, fp32 preservation, quantized-layer
  skip, `_resolve_dtype_from_config` parsing edge cases, returned
  module-name list

Update CHANGELOG.md (v1.1.1 Bug fixes) and verify each fix in isolation
by reverting it and confirming the targeted test fails.

See merge request onecomp/onecomp-lab!69

Fix-tests-examples

See merge request onecomp/onecomp-lab!70