[#8763][fix] AutoDeploy: correct mamba cache dtype extraction

[lucaslie]

Summary by CodeRabbit

• Bug Fixes

  • Improved data type handling consistency for Mamba model caching and state management.
  • Enhanced metadata preparation for better model compatibility.

• Refactor

  • Streamlined backend architecture for improved code maintainability and reduced complexity.

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[coderabbitai]

📝 Walkthrough

Walkthrough

These changes extend the Mamba SSM caching infrastructure by introducing a dedicated dtype configuration option, updating metadata tuple signatures to include additional state information, and refactoring the Triton backend to inherit from the Torch backend implementation to eliminate duplicate interface methods.

Changes

Cohort / File(s)	Summary
Cache Configuration Enhancement tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py	Added optional mamba_dtype field to CacheConfig dataclass to support dtype-specific caching logic for Mamba operations.
Torch Backend Mamba Updates tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_mamba.py	Updated get_prepare_metadata_op to return 4-tuple including use_initial_states flag. Modified get_cache_initializers to derive dtype from source node metadata and use cache_config.mamba_dtype as fallback for SSM cache construction.
Triton Backend Refactoring tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py	Changed TritonBackendSSM inheritance from AttentionDescriptor to TorchBackendSSM. Removed 8 duplicate methods (is_paged, get_attention_layout, get_num_qkv_args, get_source_attention_op, get_prepare_metadata_op, get_cache_initializers, get_global_buffer_initializers, get_constants). Retained only get_cached_attention_op as the public interface. Updated imports accordingly.

Sequence Diagram(s)

sequenceDiagram
    participant Caller
    participant TorchBackendSSM as get_cache_initializers<br/>(Torch Backend)
    participant NodeMeta as source_attn_node<br/>.meta["val"]
    participant CacheConfig
    participant SSMCache as ssm_state_cache

    Caller->>TorchBackendSSM: get_cache_initializers(source_attn_node, cache_config)
    TorchBackendSSM->>NodeMeta: Extract dtype
    NodeMeta-->>TorchBackendSSM: dtype (from node metadata)
    TorchBackendSSM->>CacheConfig: Check cache_config.mamba_dtype
    alt mamba_dtype available
        CacheConfig-->>TorchBackendSSM: mamba_dtype
        TorchBackendSSM->>SSMCache: Create with mamba_dtype
    else mamba_dtype not set
        CacheConfig-->>TorchBackendSSM: None
        TorchBackendSSM->>SSMCache: Use node dtype as fallback
    end
    SSMCache-->>Caller: Cache with resolved dtype

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Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

• triton_backend_mamba.py — Requires careful verification that inheriting from TorchBackendSSM correctly replaces all 8 removed methods and that the public API contract is maintained. Ensure no unintended behavioral changes from inheritance-based method resolution.
• torch_backend_mamba.py — Verify dtype resolution logic correctly prioritizes cache_config.mamba_dtype over node dtype, and confirm the expanded 4-tuple return from get_prepare_metadata_op is handled correctly by all callers.
• attention_interface.py — Confirm backward compatibility; the optional field should not affect existing code paths that don't use it.

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Description Check	⚠️ Warning	The PR description is largely incomplete and consists primarily of template boilerplate with empty placeholders. The "Description" section contains only a comment placeholder with no explanation of the issue or solution, the "Test Coverage" section is similarly empty with only a comment, and while the final PR Checklist item is checked, none of the substantive guidance items regarding code guidelines, test cases, dependencies, or documentation are addressed. This represents a failure to meet the basic requirement of communicating what changes were made and why they were necessary.	The author should fill in the Description section with a clear explanation of the issue being fixed (mamba cache dtype extraction) and the solution implemented. Additionally, the Test Coverage section must include information about what tests validate these changes, and the PR Checklist items should be verified and commented on as appropriate for the changes made. At minimum, the description should explain the motivation for changing the cache dtype handling and the impact of the triton backend refactoring.
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title Check	✅ Passed	The PR title "[#8763][fix] AutoDeploy: correct mamba cache dtype extraction" clearly and specifically describes the main technical issue being addressed in the changes. While the changeset includes additional refactoring (such as TritonBackendSSM class hierarchy changes), the title appropriately focuses on the primary fix: correcting how mamba cache dtype is extracted and used in the cache configuration system. The title follows the repository template format, is concise and readable, and provides enough specificity for a developer scanning history to understand the core change.

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📥 Commits

Reviewing files that changed from the base of the PR and between b87448b and 9bb6822.

📒 Files selected for processing (3)

• tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (1 hunks)
• tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_mamba.py (3 hunks)
• tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py (2 hunks)

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• tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py
• tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_mamba.py
• tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py

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Files:

• tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py
• tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_mamba.py
• tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py

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

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Files:

• tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py
• tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_mamba.py
• tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py

🧠 Learnings (2)

📓 Common learnings

Learnt from: Fridah-nv
PR: NVIDIA/TensorRT-LLM#6760
File: tensorrt_llm/_torch/auto_deploy/models/quant_config_reader.py:81-98
Timestamp: 2025-08-09T02:04:49.623Z
Learning: In TensorRT-LLM's auto_deploy module, torch.dtype values in configuration dictionaries must be stored as string representations (e.g., "float16" instead of torch.float16) because OmegaConf.merge does not support torch.dtype types. These string representations are converted to actual torch.dtype objects in downstream code.

Learnt from: ixlmar
PR: NVIDIA/TensorRT-LLM#8263
File: examples/models/contrib/sdxl/run_sdxl.py:0-0
Timestamp: 2025-10-13T13:55:04.170Z
Learning: The `diffusers` library (e.g., `DiffusionPipeline`, `StableDiffusionXLPipeline`, `StableDiffusion3Pipeline`) uses the `torch_dtype` parameter in `from_pretrained()` calls, not `dtype`. Only the `transformers` library has migrated to using `dtype`.

📚 Learning: 2025-10-20T16:54:09.824Z

Learnt from: nvchenghaoz
PR: NVIDIA/TensorRT-LLM#8469
File: tensorrt_llm/_torch/auto_deploy/custom_ops/rms_norm.py:6-6
Timestamp: 2025-10-20T16:54:09.824Z
Learning: In tensorrt_llm/_torch/auto_deploy/custom_ops/rms_norm.py, the import `from ...modules.mamba.layernorm_gated import _layer_norm_fwd` is correct and should not be changed to modules.fla.layernorm_gated. The _layer_norm_fwd function exists in both modules/mamba/layernorm_gated.py and modules/fla/layernorm_gated.py, but the mamba version is the intended implementation for this use case.

Applied to files:

• tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py

🧬 Code graph analysis (1)

tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/triton_backend_mamba.py (2)

tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (2)

• AttentionRegistry (859-882)
• MHACallable (695-699)

tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_mamba.py (1)

• TorchBackendSSM (302-375)

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