[None][fix] Fix MTP 1-model sampler

[mikeiovine]

Description

Forgot to pass use_advanced_sampling to MTPSpecMetadata when creating it in the PR that first added this. Also removed a few logs.

Test Coverage

Existing tests.

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Summary by CodeRabbit

• Refactor
  • Streamlined speculative decoding validation and warning messages for clearer execution feedback.
  • Consolidated metadata handling in the execution pipeline.

_{✏️ Tip: You can customize this high-level summary in your review settings.}

[mikeiovine]

/bot run

[coderabbitai]

📝 Walkthrough

Walkthrough

The changes refactor speculative decoding handling by removing type-specific Eagle3 checks and simplifying warning logic. Eagle3OneModelSpecMetadata is removed from imports and its conditional type check is eliminated in favor of unconditional method invocation. Warning logic for one-engine speculative decoding is consolidated into a single condition, and MTPSpecMetadata constructor is updated to accept an allow_advanced_sampling parameter.

Changes

Cohort / File(s)	Summary
Eagle3 Metadata Import and Type Check Removal tensorrt_llm/_torch/pyexecutor/model_engine.py	Removed Eagle3OneModelSpecMetadata from imports; eliminated conditional type check in previous_seq_slots_device path and now unconditionally invokes populate_sampling_params_for_one_model(...) on spec_metadata with a comment noting it is a no-op for non-1-model cases.
Speculative Decoding Warning Logic Simplification tensorrt_llm/_torch/pyexecutor/py_executor_creator.py	Collapsed nested conditional checks for speculative decoding into a single warning condition that fires when spec_dec_mode.use_one_engine() is true and allow_advanced_sampling is false; removed separate MTP-specific warning path.
MTP Metadata Parameter Addition tensorrt_llm/_torch/speculative/utils.py	Added allow_advanced_sampling parameter to MTPSpecMetadata constructor invocation in one-model MTP spec decoding path, sourced from spec_config.allow_advanced_sampling.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 33.33% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies this as a fix for the MTP 1-model sampler, which directly corresponds to the main changes in the pull request.
Description check	✅ Passed	The description explains the core issue (missing use_advanced_sampling parameter) and mentions log removal, matching the summary of changes. Test coverage relies on existing tests.

✨ Finishing touches

• 📝 Generate docstrings

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📥 Commits

Reviewing files that changed from the base of the PR and between 9085021 and 3e7e17f.

📒 Files selected for processing (3)

• tensorrt_llm/_torch/pyexecutor/model_engine.py
• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
• tensorrt_llm/_torch/speculative/utils.py

🧰 Additional context used

📓 Path-based instructions (2)

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Files:

• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
• tensorrt_llm/_torch/pyexecutor/model_engine.py
• tensorrt_llm/_torch/speculative/utils.py

**/*.{cpp,h,cu,cuh,py}

📄 CodeRabbit inference engine (CODING_GUIDELINES.md)

All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the year of its latest meaningful modification

Files:

• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
• tensorrt_llm/_torch/pyexecutor/model_engine.py
• tensorrt_llm/_torch/speculative/utils.py

🧠 Learnings (5)

📚 Learning: 2025-08-19T12:45:11.997Z

Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:0-0
Timestamp: 2025-08-19T12:45:11.997Z
Learning: In tensorrt_llm/_torch/pyexecutor/model_engine.py, DoRA (Delta Orthogonal Rank Adaptation) functionality was removed from the PyTorch flow to eliminate issues with inverted DoRA detection logic. The original is_dora condition was checking if scaling_vec_pointer == 0, which was potentially incorrect.

Applied to files:

• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
• tensorrt_llm/_torch/pyexecutor/model_engine.py

📚 Learning: 2025-08-26T06:07:02.166Z

Learnt from: shaharmor98
Repo: NVIDIA/TensorRT-LLM PR: 7231
File: tensorrt_llm/_torch/pyexecutor/_util.py:504-509
Timestamp: 2025-08-26T06:07:02.166Z
Learning: In tensorrt_llm/_torch/pyexecutor/_util.py, when calling model_engine.set_lora_model_config(), pass model_binding_config.mlp_hidden_size directly without multiplying by mapping.tp_size, as the mlp_hidden_size from get_bindings_model_config() is already the per-TP rank value needed for LoRA weight packaging.

Applied to files:

• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py

📚 Learning: 2025-12-12T03:27:18.859Z

Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 9655
File: tensorrt_llm/_torch/pyexecutor/sampler.py:3031-3031
Timestamp: 2025-12-12T03:27:18.859Z
Learning: In tensorrt_llm/_torch/pyexecutor/sampler.py, when reviewing code that iterates through requests, ensure it does not convert excessive data into Python lists. Instead, the code should use torch.gather or indexing to gather only the data that will be used in the for loop before converting to Python lists. This minimizes data movement and improves performance.

Applied to files:

• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py

📚 Learning: 2025-08-14T15:38:01.771Z

Learnt from: MatthiasKohl
Repo: NVIDIA/TensorRT-LLM PR: 6904
File: cpp/tensorrt_llm/pybind/thop/bindings.cpp:55-57
Timestamp: 2025-08-14T15:38:01.771Z
Learning: In TensorRT-LLM Python bindings, tensor parameter collections like mla_tensor_params and spec_decoding_tensor_params are kept as required parameters without defaults to maintain API consistency, even when it might affect backward compatibility.

Applied to files:

• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
• tensorrt_llm/_torch/speculative/utils.py

📚 Learning: 2025-12-12T03:27:08.565Z

Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 9655
File: tensorrt_llm/_torch/pyexecutor/sampler.py:3031-3031
Timestamp: 2025-12-12T03:27:08.565Z
Learning: In files under tensorrt_llm/_torch/pyexecutor, avoid accessing torch.Tensor objects inside for-loops when iterating over requests. Convert batched tensors to Python lists beforehand using tensor.tolist(), and then iterate over those lists. This improves performance by reducing tensor-bound operations inside hot loops. Apply this pattern to similar code paths that process batches to access simple Python data structures (lists) inside loops.

Applied to files:

• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
• tensorrt_llm/_torch/pyexecutor/model_engine.py

🧬 Code graph analysis (1)

tensorrt_llm/_torch/pyexecutor/model_engine.py (2)

tensorrt_llm/_torch/speculative/eagle3.py (2)

• Eagle3ResourceManager (22-108)
• Eagle3SpecMetadata (112-274)

tensorrt_llm/_torch/speculative/interface.py (1)

• populate_sampling_params_for_one_model (280-358)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (4)

tensorrt_llm/_torch/speculative/utils.py (1)

34-34: LGTM! Fix correctly passes the missing flag.

The addition of allow_advanced_sampling to the MTPSpecMetadata constructor addresses the omission mentioned in the PR objectives. This enables the MTP 1-model sampler to properly support advanced sampling modes.

Note: This is consistent with how Eagle3OneModelSpecMetadata is constructed at line 80, which also passes allow_advanced_sampling.

tensorrt_llm/_torch/pyexecutor/py_executor_creator.py (1)

284-289: LGTM! Clean consolidation of warning logic.

The simplified warning correctly consolidates the one-engine speculative decoding guardrails into a single path. When use_one_engine() is true and allow_advanced_sampling is false, users get a clear, actionable warning about the greedy decoding fallback.

This is cleaner than separate warning paths for each spec decode mode and aligns with the broader sampling-parameter handling mentioned in the AI summary.

tensorrt_llm/_torch/pyexecutor/model_engine.py (2)

51-52: LGTM! Import cleanup aligns with refactored logic.

The removal of Eagle3OneModelSpecMetadata from the import list is correct, as the code no longer performs type-specific checks for this class. The unconditional invocation of populate_sampling_params_for_one_model at lines 2678-2680 replaces the previous conditional type checking.

---

2678-2680: LGTM! Clean design - method self-guards for applicability.

The unconditional invocation of populate_sampling_params_for_one_model is a cleaner design than conditional type checks. Based on the interface definition in the relevant code snippets, the method contains internal guards:

if not self.allow_advanced_sampling or not self.spec_dec_mode.use_one_engine():
    return

This ensures it's a no-op for non-1-model cases (as noted in the comment), while allowing MTP one-model metadata (which now has allow_advanced_sampling from the fix in utils.py) to properly populate sampling parameters.

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