Adding KvCacheConfig, SchedulerConfig, and DynamicBatchConfig

[nv-kmcgill53]

This PR adds config classes to the llm argument processing for a consistent interface with the TensorRT-LLM API.

Summary by CodeRabbit

• New Features
  • The API now accepts nested configuration for KV cache, scheduling, and dynamic batching via extra config parameters, enabling finer-grained performance tuning without changing existing request formats.
  • These configurations are automatically recognized and merged into runtime settings when provided, streamlining advanced setup.
  • Backward compatible: existing behavior for other advanced options remains unchanged, and requests without these parameters continue to work as before.

Description

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[coderabbitai]

📝 Walkthrough

Walkthrough

Expanded update_llm_args_with_extra_dict to map and instantiate three additional extra API config keys—kv_cache_config, scheduler_config, and dynamic_batch_config—into their corresponding public config classes. Existing handling for speculative_config and build_config remains unchanged. No function signature or control-flow changes.

Changes

Cohort / File(s)	Summary
Extra-dict config mapping updates tensorrt_llm/llmapi/llm_args.py	Added field mappings for kv_cache_config, scheduler_config, dynamic_batch_config; when present, their dicts are instantiated as KvCacheConfig, SchedulerConfig, and DynamicBatchConfig via constructor. Existing speculative_config and build_config handling unchanged.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Possibly related PRs

• [fix] Fixes KV Cache overrides in trtllm-bench #6103 — Also adjusts how kv_cache_config is merged into LlmArgs, focusing on merge order to prevent overwrites.
• fix: Update trtllm args issues with extra nested config (#5996) #6114 — Extends update_llm_args_with_extra_dict to convert nested config dicts into specific config objects.

Suggested reviewers

• chzblych
• Superjomn

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📥 Commits

Reviewing files that changed from the base of the PR and between 25389c9 and 5ab9bb3.

📒 Files selected for processing (1)

• tensorrt_llm/llmapi/llm_args.py (1 hunks)

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• tensorrt_llm/llmapi/llm_args.py

🧠 Learnings (1)

📚 Learning: 2025-08-26T09:37:10.463Z

Learnt from: jiaganc
PR: NVIDIA/TensorRT-LLM#7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which can contain default `cuda_graph_config` values, so `llm_args` may already have this config before the extra options processing.

Applied to files:

• tensorrt_llm/llmapi/llm_args.py

🧬 Code graph analysis (1)

tensorrt_llm/llmapi/llm_args.py (1)

cpp/tensorrt_llm/executor/kvCacheConfig.cpp (1)

• KvCacheConfig (24-73)

🔇 Additional comments (1)

tensorrt_llm/llmapi/llm_args.py (1)

2624-2627: Ignore hoisting of dynamic_batch_config—it’s an explicit top-level field on BaseLlmArgs and valid under StrictBaseModel.extra="forbid".

Likely an incorrect or invalid review comment.

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[pcastonguay]

@nv-kmcgill53 see comment here: https://github.com/NVIDIA/TensorRT-LLM/pull/5610/files#r2178917332

From what I understand, we should not need to explicitly add those. @Superjomn to confirm.

[pcastonguay]

Do you have a case where the args are not parsed as expected?

[nv-kmcgill53]

I do. There is a lot going on in this section of code, but to summarize:

1. We create a KvCacheConfig object on line 161
2. We put this object in our arg_map on line 184
3. If we provide the engine_config.yaml on the command line then we execute line 194, where we call into the trtllm api to parse the yaml file and return to us the updated arg map.
4. If we don't provide a yaml file then we have the KvCacheConfig option that we originally set in line 161.

At the end of step 3, we end up replacing the KvCacheConfig object with a dictionary. Thus we have an inconsistent api interface with TRTLLM and we get errors on lines 195 and below depending on how our dynamo worker gets called.

While you may be able to give a recommended way to interfacing with TRTLLM, it's not very clear from the documentation why some config options get parsed into their own objects while others live in a dictionary.

[pcastonguay]

@Superjomn can you elaborate on that? Why were those configs removed from update_llm_args_with_extra_dict?

[Superjomn]

@Superjomn can you elaborate on that? Why were those configs removed from update_llm_args_with_extra_dict?

Those are Pydantic configs, and can be initialized from a dict natively, no need to map with an additional dict.