[TRTLLM-11385][chore] Mark TRTLLMSampler as deprecated and update documentation#11938
[TRTLLM-11385][chore] Mark TRTLLMSampler as deprecated and update documentation#11938Funatiq wants to merge 2 commits intoNVIDIA:mainfrom
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📝 WalkthroughWalkthroughThese changes deprecate TRTLLMSampler functionality by removing automatic beam-search activation while retaining explicit invocation with deprecation warnings. Documentation is generalized to reduce sampler-specific references, and test code is updated to remove reliance on the deprecated sampler type. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes 🚥 Pre-merge checks | ✅ 1 | ❌ 2❌ Failed checks (2 warnings)
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Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@docs/source/features/sampling.md`:
- Around line 84-96: The paragraph about FlashInfer should be restricted to the
default/TorchSampler path: update the text so it explicitly states that
FlashInfer optimizations and sorting-free implementations apply to TorchSampler
(or the sampler returned by _util.instantiate_sampler() when it yields a
TorchSampler) and not to other backends like TRTLLMSampler; mention that
TRTLLMSampler remains available until 1.4 and may not use FlashInfer, and keep
the note about disable_flashinfer_sampling scoped to TorchSampler behavior.
Locate references to _util.instantiate_sampler, TorchSampler, and TRTLLMSampler
and adjust wording so the FlashInfer performance notes only apply to
TorchSampler/default sampler.
In `@tensorrt_llm/llmapi/llm_args.py`:
- Around line 3014-3019: The Field declaration for sampler_type currently marks
the entire parameter deprecated via status="deprecated", which incorrectly flags
values like auto and TorchSampler as deprecated; remove the status="deprecated"
argument from the sampler_type Field so the parameter itself is not deprecated,
keep/update the description to explicitly state only SamplerType.TRTLLMSampler
(TRTLLMSampler) is deprecated and ensure any runtime warning continues to
trigger only when sampler_type == SamplerType.TRTLLMSampler; this involves
editing the sampler_type Field call and the description text around
SamplerType/TRTLLMSampler and leaving SamplerType.auto/TorchSampler behavior
unchanged.
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📒 Files selected for processing (6)
docs/source/features/sampling.mdtensorrt_llm/_torch/pyexecutor/_util.pytensorrt_llm/llmapi/llm_args.pytests/unittest/_torch/modeling/test_modeling_nemotron_h.pytests/unittest/_torch/ray_orchestrator/multi_gpu/test_accuracy_with_allreduce_strategy.pytests/unittest/api_stability/references/llm.yaml
💤 Files with no reviewable changes (1)
- tests/unittest/_torch/modeling/test_modeling_nemotron_h.py
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Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>
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Summary by CodeRabbit
Documentation
Deprecations
Description
Test Coverage
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.
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