Add Cache For XGrammar Backend #933
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Summary of Changes
Hello @flyinglandlord, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly optimizes the XGrammarBackend by implementing a caching mechanism for compiled grammars and JSON schemas. It also introduces a new Triton-based kernel for highly efficient, batch-level application of token bitmasks, leading to improved performance for grammar-guided generation.
Highlights
- Caching XGrammar Grammars: Introduced a caching mechanism within
XgrammarBackendto store and reuse compiledxgrammargrammars and JSON schemas, reducing redundant compilation overhead for repeated requests. - XGrammarGrammar Wrapper: Created a new
XGrammarGrammarclass (lines 33-129) to encapsulatexgrammar.GrammarMatcherlogic and state, providing a consistent interface for managing grammar state, token acceptance, and facilitating the new caching strategy. - Optimized Vocab Masking: Transitioned from per-request bitmask allocation to a batch-level
vocab_mask(lines 146, 279-282), which is now allocated once per batch and filled for each request, enabling more efficient processing. - Triton-Accelerated Bitmask Application: Added a new Triton kernel (
apply_token_bitmask_inplace_tritoninbit_mask_ops.py) to efficiently apply the batch-level token bitmask to logits on CUDA devices (lines 187, 233), significantly speeding up the masking process.
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Code Review
This pull request introduces a caching mechanism for compiled XGrammar objects and a new XGrammarGrammar class. Key areas for review include a bug in the JSON schema cache key, the handling of CPU-side vocabulary mask application, and opportunities for code deduplication and adding docstrings.
lightllm/server/router/model_infer/mode_backend/chunked_prefill/impl_for_xgrammar_mode.py
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lightllm/server/router/model_infer/mode_backend/chunked_prefill/impl_for_xgrammar_mode.py
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| if not all_has_no_constraint: | ||
| for i, run_obj in enumerate(run_reqs): | ||
| if ( | ||
| run_obj.sampling_param.guided_grammar is not None | ||
| or run_obj.sampling_param.guided_json is not None | ||
| ): | ||
| if first_grammar is None: | ||
| first_grammar = run_obj.sampling_param.guided_grammar or run_obj.sampling_param.guided_json | ||
| self._mask_req_out_token(i, run_obj, logits[i]) |
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The logic for finding first_grammar and iterating through run_reqs is duplicated in the prefill block (lines 219-227). Consider refactoring this into a helper method to reduce redundancy.
| @@ -137,22 +270,45 @@ def _mask_req_out_token(self, i, run_obj: InferReq, logits): | |||
| if run_obj.get_chuncked_input_token_len() == run_obj.get_cur_total_len(): | |||
| sample_params = run_obj.sampling_param | |||
| if sample_params.guided_grammar is not None or sample_params.guided_json is not None: | |||
| sample_params.xgrammar_matcher.fill_next_token_bitmask(self.xgrammar_token_bitmask) | |||
| xgr.apply_token_bitmask_inplace(logits, self.xgrammar_token_bitmask.to(logits.device)) | |||
| sample_params.xgrammar_matcher.fill_vocab_mask(self.vocab_mask, i) | |||
| return | |||
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| def _init_req_xgrammer_matcher_infos(self, run_reqs: List[InferReq]): | |||
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Typo: _init_req_xgrammer_matcher_infos should be _init_req_xgrammar_matcher_infos (grammar instead of grammer).
| def _init_req_xgrammer_matcher_infos(self, run_reqs: List[InferReq]): | |
| def _init_req_xgrammar_matcher_infos(self, run_reqs: List[InferReq]): |
lightllm/server/router/model_infer/mode_backend/chunked_prefill/impl_for_xgrammar_mode.py
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| MAX_ROLLBACK_TOKENS = 200 | ||
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| class XGrammarGrammar: |
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Add docstrings to the XGrammarGrammar class and its methods to improve code readability.
…l/impl_for_xgrammar_mode.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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