[Model][GGUF] Add Gemma4 GGUF serving glue

[lesj0610]

Summary

Add the Gemma4-specific GGUF serving glue needed to load local Gemma4 GGUF repositories with sibling HF config/tokenizer files:

• prefer sibling config.json for local GGUF files when available instead of forcing transformers' GGUF parser
• add Gemma4 GGUF tensor name mappings that are missing from gguf-py's current tables
• reshape Gemma4 vision patch embedding GGUF tensors to vLLM's parameter layout
• patch Gemma4 GGUF tokenizer/processor special-token fields from GGUF metadata
• keep multimodal Gemma4 per-layer embeddings on the language-model device/dtype path

This PR intentionally contains no AutoRound quant runtime changes and no MoE activation changes.

Duplicate-work check

Checked open PRs before opening:

• Fix gemma4 core: quantized inference + gguf loading + fused moe gelu_tanh #40281 is my older monolithic Gemma4 PR containing GGUF, quant, and activation changes. This split PR supersedes only its GGUF/tokenizer/processor portion.
• No separate open PR was found for Gemma4 GGUF tokenizer/processor serving glue.
• [Model] Fix Gemma4 MoE activation mismatch #41574 is activation-only and [Bugfix][Gemma4] Fix quantized MoE weight loading and KV cache spec merge #39582 is quantized MoE/KV-cache loading; neither covers this GGUF scope.

Tests

.venv/bin/python -m py_compile \
  tests/models/test_gguf_download.py \
  vllm/model_executor/model_loader/gguf_loader.py \
  vllm/model_executor/models/gemma4_mm.py \
  vllm/tokenizers/registry.py \
  vllm/transformers_utils/config.py \
  vllm/transformers_utils/gguf_utils.py \
  vllm/transformers_utils/processor.py

pre-commit run ruff-check --files \
  tests/models/test_gguf_download.py \
  vllm/model_executor/model_loader/gguf_loader.py \
  vllm/model_executor/models/gemma4_mm.py \
  vllm/tokenizers/registry.py \
  vllm/transformers_utils/config.py \
  vllm/transformers_utils/gguf_utils.py \
  vllm/transformers_utils/processor.py

pre-commit run ruff-format --files \
  tests/models/test_gguf_download.py \
  vllm/model_executor/model_loader/gguf_loader.py \
  vllm/model_executor/models/gemma4_mm.py \
  vllm/tokenizers/registry.py \
  vllm/transformers_utils/config.py \
  vllm/transformers_utils/gguf_utils.py \
  vllm/transformers_utils/processor.py

.venv/bin/python -m pytest tests/models/test_gguf_download.py -q

Result: 8 passed.

AI assistance was used to prepare and split this change; I reviewed the resulting diff and validation output.

[gemini-code-assist]

Code Review

This pull request introduces support for Gemma4 GGUF models, including manual tensor mappings, tokenizer patching for special token IDs, and improved configuration loading that prioritizes local JSON files. It also updates the Gemma4 multimodal model's embedding initialization. A critical bug was identified in the GGUF loader where missing '.weight' suffixes in the manual mapping for MoE expert weights would lead to a runtime error.

[gemini-code-assist]

The handled_name for Gemma4 MoE expert weights (gate/up and down) is missing the .weight suffix. Since normalized_state_names (built at line 441) includes the suffix for weight tensors, this mismatch will cause the manual mapping to be skipped. Consequently, these parameters will be flagged as unmapped in the final check (line 509), leading to a RuntimeError during model loading.

Suggested change

	add_mapping(
	f"blk.{idx}.ffn_gate_up_exps.weight",
	f"{layer_prefix}.moe.gate_up_proj.weight",
	handled_name=f"{layer_prefix}.experts.gate_up_proj",
	)
	add_mapping(
	f"blk.{idx}.ffn_down_exps.weight",
	f"{layer_prefix}.moe.down_proj.weight",
	handled_name=f"{layer_prefix}.experts.down_proj",
	)
	add_mapping(
	f"blk.{idx}.ffn_gate_up_exps.weight",
	f"{layer_prefix}.moe.gate_up_proj.weight",
	handled_name=f"{layer_prefix}.experts.gate_up_proj.weight",
	)
	add_mapping(
	f"blk.{idx}.ffn_down_exps.weight",
	f"{layer_prefix}.moe.down_proj.weight",
	handled_name=f"{layer_prefix}.experts.down_proj.weight",
	)

[lesj0610]

Closing per author workflow correction. This split will be handled in fork-only PRs before any upstream submission.