[https://nvbugs/5814914][fix] Fix llama sm120 spec dec

[mikeiovine]

Description

XQA kernels are only unavailable on sm120 if MLA is being used. Make llama use the correct kernels to fix the accuracy issue.

Test Coverage

Existing tests. Nothing was waived when the bug was opened, so nothing needs to be unwaived in this PR.

I verified the relevant tests are passing locally.

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Summary by CodeRabbit

• Refactor
  • Updated speculative decoding logic to better accommodate additional model configurations, improving compatibility and flexibility in decoder behavior across different model types.

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[mikeiovine]

/bot run --disable-fail-fast

[coderabbitai]

📝 Walkthrough

Walkthrough

The changes add MLA (Multi-head Latent Attention) support to speculative decoding by computing an MLA enablement flag in the model engine and passing it to the speculative decoding mode interface, which uses it to conditionally adjust XQA support logic.

Changes

Cohort / File(s)	Summary
MLA Support for Speculative Decoding tensorrt_llm/_torch/pyexecutor/model_engine.py	Adds MLA enablement detection via is_mla() call and passes is_mla flag to attention_need_spec_dec_mode() to influence spec decoding configuration
Speculative Decoding Mode Interface tensorrt_llm/_torch/speculative/interface.py	Updates attention_need_spec_dec_mode() method signature to accept is_mla: bool parameter; modifies xqa_supported logic to disable multi-token mode when MLA is enabled

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 66.67% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies the specific issue (llama sm120 spec dec) and the type of change (fix), with proper NVBugs reference format.
Description check	✅ Passed	The description explains the issue and solution clearly, references test coverage with existing tests, and completes the required PR checklist items.

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✨ Finishing touches

• 📝 Generate docstrings

---

🧹 Recent nitpick comments

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

148-155: Document the new is_mla argument in the docstring.

Keeps the interface self-describing for callers.

✏️ Suggested docstring update

@@
             spec_resource_manager: the resource manager for the spec-dec mode.
             is_draft_model: whether the model is a draft model.
             attention_backend: the attention backend.
             use_chain_drafter: whether to use capturable drafting loops (CDL). For the target model, it is always False.
+            is_mla: whether Multi-head Latent Attention is enabled for this model.

---

📜 Recent review details

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📥 Commits

Reviewing files that changed from the base of the PR and between e87a406 and f0ca935.

📒 Files selected for processing (2)

• tensorrt_llm/_torch/pyexecutor/model_engine.py
• tensorrt_llm/_torch/speculative/interface.py

🧰 Additional context used

📓 Path-based instructions (2)

**/*.py

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Files:

• tensorrt_llm/_torch/speculative/interface.py
• tensorrt_llm/_torch/pyexecutor/model_engine.py

**/*.{cpp,cc,cxx,h,hpp,hxx,cu,cuh,py}

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Files:

• tensorrt_llm/_torch/speculative/interface.py
• tensorrt_llm/_torch/pyexecutor/model_engine.py

🧠 Learnings (5)

📓 Common learnings

Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6767
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-15T06:46:53.813Z
Learning: In the TensorRT-LLM KV cache manager, SWA (Sliding Window Attention) combined with beam search is currently in a broken/non-functional state and is planned for future rework. During preparatory refactoring phases, code related to SWA+beam search may intentionally remain in a non-working state until the broader rework is completed.

📚 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/speculative/interface.py
• tensorrt_llm/_torch/pyexecutor/model_engine.py

📚 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/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/model_engine.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/model_engine.py

🧬 Code graph analysis (2)

tensorrt_llm/_torch/speculative/interface.py (3)

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

• BaseResourceManager (62-85)

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

• AttentionBackend (644-729)

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

• is_mla (12-16)

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

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

• is_mla (12-16)

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

• attention_need_spec_dec_mode (148-177)

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• GitHub Check: Pre-commit Check

🔇 Additional comments (2)

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

3250-3256: LGTM — MLA enablement now flows into spec-dec mode selection.

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

166-168: Clarify MLA/XQA SM120+ gating by being explicit about affected architectures.

The current check get_sm_version() < 120 disables XQA for all SM ≥ 120 when MLA is enabled, which may be overly broad. While SM120 and SM121 are both explicitly excluded in is_sm_version_trtllm_gen_kernel(), it's unclear whether this limitation applies to all future Blackwell variants. Consider replacing the check with an explicit exclusion list (e.g., not in [120, 121]) and document whether future SM12x parts will have the same limitation, or clarify if all SM ≥ 120 are indeed unsupported with MLA+XQA.

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