[Bugfix] Fix Gemma4 tool call parser using vocab key instead of decoded token string

[pens-u]

Why this is not a duplicate

Checked existing open PRs against issue #44522 and the search terms
gemma4 tool_call_start token decode:

• [Bugfix] Fix Gemma4 streaming tool calls with accumulated parser state #42300, [Bugfix] Fix Gemma4 streaming tool calls lost when entire call arrives in one delta #42875, [Bugfix] Fix Gemma4 MTP streaming multi-tool calls #42006 — fix streaming state / speculative decoding bugs
• [SpecDec + Reasoning] Fix race condition when <channel|> reasoning-end #43691 — fixes reasoning-end race condition
• [Bugfix] Allow Gemma4 reasoning parser to have selective tokens to skip #39588 — tool_choice=none leaking reasoning tokens

None address the tokenizer key/decode-string mismatch described in #44522.

Problem

Some Gemma4 tokenizer builds store the canonical vocabulary key
<|tool_call> (12 chars) in get_vocab() but the HuggingFace fast
tokenizer's added_tokens_decoder[id].content path produces a
different string — e.g. <|tool_call|> (13 chars) — when the same
token ID is decoded. vllm.v1.engine.detokenizer.FastIncrementalDetokenizer
uses tok.content for output text, so output.text and therefore
current_text in the streaming parser contains <|tool_call|>, not
<|tool_call>.

The streaming guard:

if self.tool_call_start_token not in current_text:   # always True!
    return DeltaMessage(content=delta_text)           # entire call leaks

always fired, leaking the full raw tool-call block
(<|tool_call|>call:func{…}<tool_call|>) as plain assistant content.
The same mismatch affects the string-quoting delimiter <|"|>, causing
garbled argument values when tool calls were detected.

Observed output (from issue #44522):

<|tool_call|>call:preview_url{explanation:<|">…<|">,url:<|">…<|">}<tool_call|>

Fix

At construction time, call
tokenizer.decode([token_id], skip_special_tokens=False) for the
start token, end token, and string delimiter to detect the actual
decoded form. All text-matching paths (guard check, _buffer_delta_text,
regex, count-based phase tracking, argument parsing) now use the
detected form instead of the module-level constant.

Key changes in vllm/tool_parsers/gemma4_tool_parser.py:

• _decoded_token_str(tokenizer, token_id, fallback) — decodes a
  single token; guards for isinstance(str) so mock tokenizers in
  tests fall back to the constant without breaking
• Gemma4ToolParser.__init__ stores self.tool_call_start_token,
  self.tool_call_end_token, self.string_delim from the tokenizer
• _buffer_delta_text uses the instance attributes
• self.tool_call_regex rebuilt from the detected strings via
  re.escape
• _parse_gemma4_args / _parse_gemma4_array accept a
  string_delim kwarg (default = STRING_DELIM constant) so the
  correct delimiter propagates through all recursive calls

Test plan

# Install deps (first time only)
uv pip install pytest pytest-timeout

# Run the full Gemma4 tool parser suite (54 tests: 51 original + 3 new)
uv run python -m pytest tests/tool_parsers/test_gemma4_tool_parser.py -v

All 54 pass. The three new tests in TestAltTokenStrings directly
reproduce the tokenizer mismatch by wiring mock.decode.side_effect to
return <|tool_call|> and <|"> and asserting:

1. parser.tool_call_start_token uses the decoded form, not the vocab key
2. Streaming emits zero content deltas (no leakage)
3. Non-streaming extract_tool_calls correctly parses the tool call

AI assistance disclosure: This fix was developed with Claude Code
(Anthropic). Every changed line has been reviewed by the submitter.

🤖 Generated with Claude Code

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[elseyu]

Thanks for the quick fix! Apologies for the confusion—I realized I made a mistake in my previous description regarding the exact tokens. The actual raw output from the client is a bit different from what I initially stated.

Instead of <|tool_call|>, the engine actually outputs <|tool_call> at the start, and <tool_call|> at the end.

Here is the exact behavior we observed:

Observed Behavior (Raw Client Output received):

<|tool_call>call:Bash{command:<|"|>/Users/john_doe/.workbuddy/binaries/python/versions/3.13.12/bin/python3 generate_dashboards_v2.py<|"|>,description:<|"|>Execute the updated dashboard generation script.<|"|>}<tool_call|>

Or for bash/array commands:

<|tool_call>call:deliver_attachments{attachments:<|"|>["/Users/john_doe/WorkBuddy/2026-06-02-14-11-40/Monitoring_Dashboard_v2.html"]<|"|>,explanation:<|"|>Delivering the fixed v2 monitoring dashboard.<|"|>}<tool_call|>

Could you double-check if your PR #44532 covers this specific mismatch?

[pens-u]

Hi @elseyu — thanks for the follow-up. To confirm whether this PR covers your case, could you run the snippet below on your server's model?

from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("/data/model/gemma-4-31b-it")
vocab = tok.get_vocab()
for key in ["<|tool_call>", "<tool_call|>", '<|"|>']:
    tid = vocab[key]
    decoded = tok.decode([tid], skip_special_tokens=False)
    print(f"vocab[{key!r}] → id={tid}  decoded={decoded!r}  match={decoded == key}")

If any line prints match=False, your tokenizer has the vocab-key vs decoded-form mismatch this PR fixes — the streaming guard was checking for the vocab-key string but output.text contained the decoded form, so the guard always missed it and leaked the raw tool-call block as content.

If all lines print match=True, your tokens decode canonically and the bug is something else (possibly the model skipping the <|channel>…<channel|> thinking block in some turns). In that case please let us know and we can investigate further.