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[Bugfix][Frontend] GLM-4.7: guided-decode forced tool calls when strict tool-calling is disabled - #47175
[Bugfix][Frontend] GLM-4.7: guided-decode forced tool calls when strict tool-calling is disabled#47175ifaddict1 wants to merge 1 commit into
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GLM-4.7's tool parser hardcoded `supports_required_and_named = False`, so
even with `VLLM_ENFORCE_STRICT_TOOL_CALLING=0` forced (required/named) tool
calls never took the JSON-based parse path. Combined with the structural-tag
encode path being skipped at strict=0, forced calls were emitted with no
grammar at all and came back as unconstrained native syntax that failed
schema validation -- especially for nested `$defs`/`$ref` argument schemas.
- glm47_moe_tool_parser: drop the hardcoded flag and let `__init_subclass__`
govern it via `VLLM_ENFORCE_STRICT_TOOL_CALLING`, like every other
structural-tag parser (strict=0 -> True).
- DelegatingParser: when no structural tag applies (strict=0) but tool_choice
is required/named, build `StructuredOutputsParams(json=...)` from the
request's own tool schema so forced calls guided-decode to schema-valid JSON
that the JSON-based parse path validates. For "required" the parameters are
nested under {name, parameters} and `$defs` are hoisted to the top level so
`#/$defs/...` refs stay resolvable.
Adds unit tests for the flag toggle and the schema builder (named + required
with $defs hoisting).
Signed-off-by: Selim <selim@aikido.dev>
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We are running into this issue as well, thanks for raising! |
GLM required-tool structural decoding can emit <tool_call> and </tool_call> as ordinary subtokens rather than their dedicated special-token IDs. Once any token IDs have appeared, the streaming parser intentionally demotes text-matched token-ID terminals to content. The GLM tool parser then leaks raw tool markup into message.content and emits no tool_calls. Preserve strict token-ID matching by default. Add an explicit lexical fallback and enable it only for GLM TOOL_START and TOOL_END while processing tool_choice="required". Auto and plain GLM generations retain the existing literal-marker protection, and other parser-engine models are unchanged. Configure the fallback through GLM's request-aware tool-parsing hook so both direct ParserEngine streams and ParserEngineToolAdapter streams use it. Add coverage for the default strict behavior, the explicit fallback, and the GLM tool-adapter path that exposed the bug. Duplicate-work check: - vllm-project#48095 reports the same v0.24 user-visible failure. - vllm-project#47175 and vllm-project#47146 address non-strict JSON-guided generation, not streamed marker classification. - vllm-project#48103 was closed and changed request grammar selection rather than parser routing. - No open PR was found for GLM lexical marker routing. Validation: - pre-commit passed for all changed files, including ruff and mypy. - git diff --check passed. - A vLLM 0.24 image carrying the same runtime changes passed all four targeted GLM-5.2 tool-contract tests and produced a full Maru result of 40 passed, 1 unrelated structured-output multipleOf failure, and 4 skipped. - Targeted pytest could not collect in the current lightweight local environment because numpy is not installed; no dependencies were added to the shared environment. AI assistance was used to inspect parser routing, prepare the patch and tests, and run the listed validation. The human submitter must review and defend the change end to end before proposing it upstream. Assisted-by: OpenAI Codex Signed-off-by: Ace Eldeib <aeldeib@coreweave.com>
Stacks the work that accumulated on `dev` since v0.10.0 into the v0.11.0 web release. ## What's changed - Reworked web search around automatic tool choice. Tool-capable models now decide when to search instead of requiring a per-chat toggle, with a one-message **Search** action in the composer for forcing it and a persistent hard-disable under **Settings → Tools**. - Added a per-model **Tool calling** switch (migration 0004) so models without tool support can be excluded from tool-bearing requests. The connection tester now verifies an actual tool call when the switch is on. - Added provider prompt caching: Anthropic cache breakpoints on the system prefix and the latest user turn, and a stable per-chat `promptCacheKey` for OpenAI derived from a hash of the chat ID. The prompt is append-only so prior prefixes stay byte-for-byte reusable. - Surfaced cache accounting in message stats — uncached input, cache read, and cache write tokens. - Replaced the per-request runtime-context block with a date-only `Current date:` line resolved against a validated client time zone, keeping the system prefix cache-stable across turns. - Rewrote the citation instructions and anchor scheme so anchors address the search call and result index (`turnNsearchI`), fixing attribution when a response performs more than one search. - Upgraded the Vercel AI SDK from v6 to v7 across web, mobile, and shared, moving chat execution onto `ToolLoopAgent` with `createUIMessageStreamResponse`. - Expanded model brand icon coverage and model-ID matching to more top providers. - Added a `?q=` shortcut for starting a new chat from a URL, normalizing repeated and blank values and waiting for a valid selected model before submitting. - Restructured the chat title prompt as a single labelled task with the user's message fenced at the end, which stops markdown formatting and conversational replies from leaking into titles. - Updated the default Compose app image to 0.11.0. ## Upgrade note Migration 0004 runs on startup and adds `tool_calling_enabled`, defaulting to on for existing model configurations. **Web search is now automatic and on by default**, where it used to be opt-in per chat. Turn it off under **Settings → Tools**, or switch off **Tool calling** for a model that can't use tools — otherwise its connection test will fail, since the test now requires a real tool call. Older Android clients keep working and will start searching once the server is upgraded. One caveat if you self-host inference: the Search action and the connection test both send `tool_choice: "required"`, and vLLM's XML-format tool parsers (`poolside_v1`, GLM-4) silently downgrade that to `auto` instead of erroring — so on a model like Laguna S2.1, automatic search works but forcing it doesn't, and the connection test can fail on a model whose tool calls are otherwise fine. Nothing to fix on this side; see vllm-project/vllm#47175. ## Validation `npm run test` (166), `lint`, `typecheck`, `build`, `deps:check`, `docker compose config`, `docker build`. Playwright smoke against the standalone server with live Gemini streaming. Upgraded a v0.10.0-era database to confirm 0004 applies and existing model configurations survive. 🤖 Generated with [Claude Code](https://claude.com/claude-code)
…ash#107) Stacks the work that accumulated on `dev` since v0.10.0 into the v0.11.0 web release. ## What's changed - Reworked web search around automatic tool choice. Tool-capable models now decide when to search instead of requiring a per-chat toggle, with a one-message **Search** action in the composer for forcing it and a persistent hard-disable under **Settings → Tools**. - Added a per-model **Tool calling** switch (migration 0004) so models without tool support can be excluded from tool-bearing requests. The connection tester now verifies an actual tool call when the switch is on. - Added provider prompt caching: Anthropic cache breakpoints on the system prefix and the latest user turn, and a stable per-chat `promptCacheKey` for OpenAI derived from a hash of the chat ID. The prompt is append-only so prior prefixes stay byte-for-byte reusable. - Surfaced cache accounting in message stats — uncached input, cache read, and cache write tokens. - Replaced the per-request runtime-context block with a date-only `Current date:` line resolved against a validated client time zone, keeping the system prefix cache-stable across turns. - Rewrote the citation instructions and anchor scheme so anchors address the search call and result index (`turnNsearchI`), fixing attribution when a response performs more than one search. - Upgraded the Vercel AI SDK from v6 to v7 across web, mobile, and shared, moving chat execution onto `ToolLoopAgent` with `createUIMessageStreamResponse`. - Expanded model brand icon coverage and model-ID matching to more top providers. - Added a `?q=` shortcut for starting a new chat from a URL, normalizing repeated and blank values and waiting for a valid selected model before submitting. - Restructured the chat title prompt as a single labelled task with the user's message fenced at the end, which stops markdown formatting and conversational replies from leaking into titles. - Updated the default Compose app image to 0.11.0. ## Upgrade note Migration 0004 runs on startup and adds `tool_calling_enabled`, defaulting to on for existing model configurations. **Web search is now automatic and on by default**, where it used to be opt-in per chat. Turn it off under **Settings → Tools**, or switch off **Tool calling** for a model that can't use tools — otherwise its connection test will fail, since the test now requires a real tool call. Older Android clients keep working and will start searching once the server is upgraded. One caveat if you self-host inference: the Search action and the connection test both send `tool_choice: "required"`, and vLLM's XML-format tool parsers (`poolside_v1`, GLM-4) silently downgrade that to `auto` instead of erroring — so on a model like Laguna S2.1, automatic search works but forcing it doesn't, and the connection test can fail on a model whose tool calls are otherwise fine. Nothing to fix on this side; see vllm-project/vllm#47175. ## Validation `npm run test` (166), `lint`, `typecheck`, `build`, `deps:check`, `docker compose config`, `docker build`. Playwright smoke against the standalone server with live Gemini streaming. Upgraded a v0.10.0-era database to confirm 0004 applies and existing model configurations survive. 🤖 Generated with [Claude Code](https://claude.com/claude-code)
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Thanks for the work! Closing in favor of #47512. On current main, ToolParser.adjust_request already builds the JSON grammar for required/named calls, including moving $defs to the top level, so the encode-side change isn't needed. The actual bug was that grammar being applied to parsers that read their native format. We're fixing that by skipping the JSON constraint for those parsers rather than switching GLM-4.7 to JSON parsing. For enforced forced calls on GLM, keep VLLM_ENFORCE_STRICT_TOOL_CALLING=1 (the default), which uses the structural tag.
Purpose
On GLM-4.7 / GLM-5.2, forced tool calls (
tool_choice="required"or a named function) return unconstrained native tool syntax (or an emptytool_callslist) whenVLLM_ENFORCE_STRICT_TOOL_CALLING=0, which then fails downstream argument-schema validation — most visibly for tool schemas that use nested$defs/$ref.There are two halves to the bug, and fixing only one is not enough:
Glm47MoeModelToolParserhardcodessupports_required_and_named = False, overridingToolParser.__init_subclass__. So even atstrict=0, GLM never takes the JSON-based parse path for forced calls (every other structural-tag parser flips this toTrueatstrict=0). This is the change in Allow GLM-4.7 required tool parsing when strict mode is disabled #47146.strict=0the structural-tag path is skipped (get_structural_tag(...) is None) and nothing else installs a grammar for forced tool calls. The request therefore goes to the engine with no structured-outputs grammar, the model free-generates native tool syntax, and the now-JSON-based parse path cannot validate it. Flipping the flag alone (Allow GLM-4.7 required tool parsing when strict mode is disabled #47146 on its own) helps flat schemas but still leaves nested-schema forced calls unconstrained.This PR fixes both:
glm47_moe_tool_parser: drop the hardcoded flag and let__init_subclass__govern it viaVLLM_ENFORCE_STRICT_TOOL_CALLING, like every other structural-tag parser.DelegatingParser: when no structural tag applies (strict=0) buttool_choiceisrequired/named, buildStructuredOutputsParams(json=...)from the request's own tool schema (mirroring theresponse_formatjson path). Forrequired, the parameters are nested under{name, parameters}to match thelist[FunctionDefinition]shape the parse path validates, and any$defsare hoisted to the top level so#/$defs/...refs stay resolvable after nesting.The grammar is built entirely from the caller's existing tool definitions (no transpilation/heuristics), so a forced call is constrained to exactly the schema the caller already declared. The change only takes effect when
supports_required_and_namedisTrue(i.e.strict=0) andstructured_outputsis not already set, so the structural-tag (strict=1) andresponse_formatpaths are untouched.Builds on #47146 (parse-side flag) by adding the missing encode-side grammar.
Test Plan
Unit tests (
tests/tool_parsers/test_structural_tag_registry.py):End-to-end on a GLM-4.7-family model served with
--enable-auto-tool-choice --tool-call-parser glm47andVLLM_ENFORCE_STRICT_TOOL_CALLING=0, sending a tool whose parameters contain a nested object referenced via$defs/$ref, withtool_choice="required", a namedtool_choice, and aresponse_formatjson_schema control.Test Result
Unit tests: 4 passed (flag toggle is honored; GLM-4.7 no longer pins the flag;
_build_tool_choice_json_schemareturns the function params for a named choice and an array-of-{name, parameters}with hoisted$defsforrequired).E2E (GLM-5.2,
strict=0):tool_choice="required"→ emptytool_calls; namedtool_choice→ arguments contained raw native tool syntax; both failed schema validation on nested fields.requiredand named return schema-valid JSON with the nested object populated (e.g.{"location": {"file": "app.py", "line": 14}, ...}).response_formatjson output is unchanged.Essential Elements of an Effective PR Description Checklist