Fix XGrammar bitmask initialization and add null check for gen_config in generate method

[windreamer]

Motivation

This PR addresses two critical issues in LMDeploy's guided generation and batch inference functionality:

1. XGrammar Bitmask Initialization Bug: The guided decoding mechanism initializes token bitmasks to control which tokens are allowed during constrained generation. Previously, the bitmask was initialized with zeros, which incorrectly blocked all tokens instead of allowing all tokens. This caused generation failures when certain sequences in a batch didn't have grammar constraints applied.

2. Silent Failure on None gen_config: The batch_infer method accepts a list of GenerationConfig objects, but when None was passed for individual items, the code would silently fail or hang due to unhandled null pointer dereference when accessing gen_config.max_new_tokens.

Modifications

1. Guided Decoding Bitmask Fix (src/turbomind/generation/guided_decoding.cc):

   • Changed the bitmask initialization value from 0 to -1 (all bits set to 1 in two's complement representation for int32_t).
   • This ensures that when a sequence doesn't have an active grammar matcher, all tokens are permitted by default rather than none.

2. Null GenerationConfig Guard (lmdeploy/serve/core/async_engine.py):

   • Added an explicit check to instantiate a default GenerationConfig() when gen_config is None.
   • This prevents downstream code from attempting to access attributes on a null object, which was causing the engine to hang silently.

3. Regression Test (tests/test_lmdeploy/test_grammar.py):

   • Added test_mix_guided_matrix to verify that batch inference works correctly when mixing guided and unguided generation in the same batch.
   • The test ensures unguided sequences produce arbitrary text (not necessarily conforming to schema) while guided sequences strictly follow the JSON schema constraints.

[Copilot]

Pull request overview

This pull request fixes a bug where grammar constraints from structured output requests incorrectly persist and apply to subsequent non-structured output requests when ModelRequest instances are reused. The fix introduces a clearGrammar() method to explicitly reset the grammar state after each inference session completes.

Changes:

• Added clearGrammar() C++ method to ModelRequest class for resetting grammar state
• Added Python binding clear_grammar() to expose the cleanup functionality
• Called clear_grammar() in the finally block of async_stream_infer() to ensure cleanup

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 2 comments.

File	Description
src/turbomind/engine/model_request.h	Declares the new clearGrammar() method
src/turbomind/engine/model_request.cc	Implements clearGrammar() to reset the grammar_ shared_ptr
src/turbomind/python/bind.cpp	Adds Python binding for clear_grammar with appropriate GIL handling
lmdeploy/turbomind/turbomind.py	Calls clear_grammar() in finally block after inference completes

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