Currently, if a quantization requires an importance matrix and one isn't provided, the program doesn't discover this until it reaches the offending tensor during the main quantization loop. Depending on model size and target type, this can mean wasting minutes to hours before the process aborts with a partial GGUF.

This PR adds a preliminary metadata pass over all tensors that determines target types upfront, enabling early validation. The quantization logic in llama-quant.cpp is refactored for clarity and correctness.

Fail early for missing required imatrix

A preliminary pass now computes each tensor's target quantization type before the main loop begins. If an importance matrix is required but missing, quantization fails immediately with an error identifying the offending tensor and its target type:

The old ftype-based imatrix guard in quantize.cpp is removed in favor of this per-tensor check. tensor_requires_imatrix (renamed from tensor_type_requires_imatrix) now uses a switch on dst_type and correctly exempts per_layer_token_embd.weight in addition to token_embd.weight.

Refactoring

Extracted functions to reduce the size of llama_model_quantize_impl and make the logic reusable across the preliminary and main passes, and to improve clarity and maintainability:

• tensor_allows_quantization: consolidates all "should we quantize this tensor?" checks (norm tensors, RWKV weights, conv1d, positional embeddings, etc.) previously inlined in the main loop
• tensor_category + tensor_get_category: replaces repeated name.find(...) calls with a single categorization pass; used by type selection and the attention-v check (category_is_attn_v)
• llama_tensor_get_type / llama_tensor_get_type_impl: splits type resolution into a wrapper (manual overrides, token_embedding/output overrides, fallbacks) and the core mixture/architecture logic
• tensor_type_fallback: extracted from the inline incompatible-shape handling block; now also handles the rare case where the fallback type itself is incompatible (falls back to F16 with a warning)
• llama_ftype_get_default_type: the ftype -> ggml_type switch, extracted and organized by category
• tensor_name_match_token_embd / tensor_name_match_output_weight: small helpers used across multiple call sites

Other changes

• Regex patterns for --tensor-type compiled once in the quantize_state_impl constructor instead of compiled per-tensor, per-flag
• tensor_quantization struct moved from llama-quant.cpp to the header (shared with quantize.cpp via #include instead of duplicated)
• tensor_category enum and tensor_metadata struct added to the header
• has_output replaced with has_tied_embeddings (clearer semantics, inverted logic, same behavior)
• n_k_quantized counter removed (fallback count now reported against ml.n_tensors)
• Removed dead MXFP4 sanity check code (#if 0 block)
• Some logging cleanup