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[TRTLLM-15621][fix] Reject sampling params the one-model speculative path cannot honor - #17955
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WalkthroughSampler validation now rejects unsupported logits-processing parameters. Beam-width calculation now handles empty arrays with host-side integer logic and includes parameterized coverage. ChangesSampler request validation
Estimated code review effort: 3 (Moderate) | ~15–30 minutes Merge Risk: 🔵 Low · up to The change prevents unsupported sampling parameters from being silently ignored by rejecting them at request admission. Merge readiness has one bounded follow-up: add an explicit test for narrowing beam-width schedules; no broader production risk is indicated. Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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In `@tensorrt_llm/_torch/speculative/spec_sampler_base.py`:
- Line 174: Update the top-p decay check in the relevant sampler method to read
only sampling_config.top_p_decay through the shared top_p_decay activity
predicate, avoiding _request_get_sampling_params(request) and its beam-width
resolution. Add a regression test covering top_p_decay=None with
beam_width_array=[] and verify the check completes without error.
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In `@tests/unittest/_torch/sampler/test_torch_sampler.py`:
- Around line 3618-3629: Add the -> None return annotation to the
test_empty_beam_width_array_does_not_break_validation test method. Do not alter
its parameterization or validation logic.
Apply the same fix in `@tests/unittest/_torch/sampler/test_torch_sampler.py`
around lines 3619 - 3620.
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Inline comments:
In `@tests/unittest/_torch/sampler/test_torch_sampler.py`:
- Line 3620: Update the test_empty_beam_width_array_does_not_break_validation
function signature by annotating beam_width_array as list[int] | None,
preserving the existing return annotation and test behavior.
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Inline comments:
In `@tests/unittest/_torch/sampler/test_torch_sampler.py`:
- Around line 3619-3631: Update
test_empty_beam_width_array_does_not_break_validation to call
_get_max_beam_width() directly and assert that [1, 2] returns 2, while retaining
the no-error coverage for [] and None. Do not rely on validate_request or the
mocked get_beam_width_by_iter behavior to cover this helper.
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In `@tests/unittest/_torch/sampler/test_torch_sampler.py`:
- Line 3624: Update the acceptance data in
test_beam_width_array_max_handles_empty to use the increasing schedule [3, 5,
7], and add a separate assertion covering PyExecutor.validate_request()
rejection of the narrowing schedule [3, 7, 5].
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PR_Github #68775 [ run ] triggered by Bot. Commit: |
…nnot honor TorchSampler implements min_length, bad_words, no_repeat_ngram_size, embedding_bias and top_p_decay by editing the target logits before sampling. The one-model speculative path has no logits-editing hook -- SpecSampler only scatters the worker's already-sampled tokens -- so all five were dropped without a trace: the request was admitted, returned normally, and decoded from a different distribution than the user asked for, with no error and no log line. Reject them at request admission instead, alongside the existing min_p and occurrence-penalty checks. Each check gates on a non-neutral value rather than on presence, because the frontends may forward a default explicitly (min_length=0, an unset ngram size); gating on 'is not None' would fail requests that ask for nothing. top_p_decay reuses the handler's own top_p_decay_active predicate so the notion of 'active' cannot drift between the two paths. The three logits/logprobs warnings are left as warnings: those are missing output rather than a wrong distribution, and promoting them would break existing users. Signed-off-by: zhaoyangw <zhaoyangw@nvidia.com>
_get_max_beam_width gated on 'beam_width_array is not None' and reduced it with torch.tensor(...).max(), which raises on an empty array: 'max(): Expected reduction dim to be specified for input.numel() == 0'. An empty array does reach the sampler -- the executor's checkBeamWidthArray bounds only the array's length, so [] passes admission -- and _get_max_beam_width has 8 call sites, including _request_strategy, which runs for every request. A request that merely carried an empty array therefore failed regardless of what it asked for. Gate on truthiness instead, and reduce on the host: the array holds at most kMaxBeamWidthArrayLength (8) entries, so building a tensor to take its max was never warranted. Non-empty inputs keep the same result. Signed-off-by: zhaoyangw <zhaoyangw@nvidia.com>
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…he list py_min_length mirrors the C++ SamplingConfig field, i.e. an optional singleton list rather than a scalar. The check tested the list for truthiness, so any request carrying the field at all was rejected. The OpenAI frontend always forwards min_tokens (default 0), which reaches the sampler as [0]. Every OpenAI request under one-model speculative decoding therefore failed admission -- the B200 perf sanity workload llama8b_spec_bs1_128_128 reported all 200 requests rejected. Read min_length[0] and reject only a positive floor, matching how TokenBanHandler consumes the same field (token_ban.py:274). Signed-off-by: zhaoyangw <zhaoyangw@nvidia.com>
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The C++ field is OptVec<vector<SizeType32>>, so it may arrive as [maxBeamWidthArrayLength] or [batchSize, maxBeamWidthArrayLength]. The previous torch.tensor(...).max() flattened both forms; map(int, ...) does not, so the nested form raised TypeError: int() argument must be a string, a bytes-like object or a real number, not 'list' Unwrap the per-request row first, matching what PyExecutor._validate_request already does (py_executor.py:5494). The test now sets the array on the C++ SamplingConfig rather than on SamplingParams, whose list[int] annotation cannot express the nested form, and covers both shapes. Signed-off-by: zhaoyangw <zhaoyangw@nvidia.com>
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…ingParams
Assigning request.sampling_config.beam_width_array directly hit the
nanobind setter, which accepts only Sequence[Sequence[int]]:
TypeError: (): incompatible function arguments.
1. (self, arg: collections.abc.Sequence[collections.abc.Sequence[int]], /)
Invoked with types: tensorrt_llm.bindings.SamplingConfig, NoneType
so None and flat lists both raised before _get_max_beam_width ran.
Construct the request through SamplingParams instead, as production and
the existing VBWS tests do: _get_sampling_config() performs the flat to
nested conversion the runtime SamplingConfig expects.
Signed-off-by: zhaoyangw <zhaoyangw@nvidia.com>
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YihuiLu512
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LGTM. Please also check the format of beam_width_array.
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PR_Github #69058 [ run ] triggered by Bot. Commit: |
The single guard tested the outer list, so the nested empty form [[]] unwrapped to [] and then reduced over nothing: TypeError: 'int' object is not iterable That is the very case this change exists to fix: the runtime config stores OptVec<vector<SizeType32>>, so an empty array reaches the sampler wrapped rather than bare. Guard twice, before and after the unwrap, matching the idiom already used by LlmRequest.get_beam_width_by_iter (llm_request.py:1097-1104) and PyExecutor._validate_request (py_executor.py:5492-5497). Adds a direct unit test over the nested shapes, including [[]], which the SamplingParams-based test cannot express. Signed-off-by: zhaoyangw <zhaoyangw@nvidia.com>
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… token-boundary smoke test_short_token_boundary_smoke drives one-model MTP (MTPDecodingConfig) while passing min_tokens=max_tokens. Since NVIDIA#17955 the one-model speculative path rejects min_length at request admission, because its fused sampling kernels take only temperature/top_k/top_p and have no logits-editing hook, so the request fails with "min_length is not supported with one-model speculative decoding". min_tokens is redundant here: ignore_eos=True already removes the only early-stop condition, so the output length is exactly max_tokens, which the existing assert verifies. Drop it and keep MTP enabled, since exercising the MTP deployment config is the point of this test. This is not a flaky test -- all three waves pass min_tokens > 0, so it fails deterministically on every run since NVIDIA#17955 landed. The CI report classified it as flaky because the 30-day window straddles that commit. Unwaive accordingly. Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
Dev Engineer Review
SpecSampler.validate_requestrejects active unsupported parameters:min_length,bad_words,no_repeat_ngram_size,embedding_bias, andtop_p_decay.min_pvalidation remains unchanged._get_max_beam_widthhandles empty arrays and uses host-side Python integers.QA Engineer Review
beam_width_arrayvalues[],None,[1],[1, 2], and varied-width arrays.beam_width, maximum-width calculation, and request admission.tests/integration/test_lists/were modified.Description
TorchSamplerimplementsmin_length,bad_words,no_repeat_ngram_size,embedding_biasand
top_p_decayby editing the target logits before sampling. The one-model speculative pathhas no logits-editing hook at all —
SpecSampleronly scatters the worker's already-sampledtokens — so all five were dropped without a trace: the request was admitted, returned
normally, and decoded from a different distribution than the user asked for, with no error and
no log line.
They are now rejected at request admission, alongside the existing
min_pcheck. Raising fromvalidate_requestfails only the offending request rather than aborting the whole executorstep.
Each check gates on a non-neutral value rather than on presence, because the frontends may
forward a default explicitly (
min_length=0, an unset ngram size); gating onis not Nonewould fail requests that ask for nothing.
top_p_decayreuses the handler's owntop_p_decay_activepredicate so the notion of "active" cannot drift between the two paths.The three existing
return_context_logits/return_generation_logits/return_log_probswarnings are deliberately left as warnings: those are missing output rather than a wrong
distribution, and promoting them to errors would break existing users.
Test Coverage
The rejection checks are exercised through
SpecSampler.validate_requestat request admission.Each param was verified in both directions — a non-neutral value is rejected, and the
neutral/default value is still accepted — so a frontend that forwards defaults is not affected.
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