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[#9306][refactor] Refactor AutoDeployConfig into LlmArgs #10613

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Merged
lucaslie merged 1 commit into from
Jan 22, 2026
Merged

[#9306][refactor] Refactor AutoDeployConfig into LlmArgs #10613

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11 changes: 5 additions & 6 deletions examples/auto_deploy/build_and_run_ad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@
SettingsConfigDict,
)

from tensorrt_llm._torch.auto_deploy import LLM, AutoDeployConfig, DemoLLM
from tensorrt_llm._torch.auto_deploy import LLM, DemoLLM
from tensorrt_llm._torch.auto_deploy.llm_args import LlmArgs
from tensorrt_llm._torch.auto_deploy.utils._config import (
DynamicYamlMixInForSettings,
Expand Down Expand Up @@ -142,7 +142,6 @@ class ExperimentConfig(DynamicYamlMixInForSettings, BaseSettings):
# The main AutoDeploy arguments - contains model, tokenizer, backend configs, etc.
args: LlmArgs = Field(
description="The main AutoDeploy arguments containing model, tokenizer, backend configs, etc. "
"Contains all the fields from `AutoDeployConfig` and `BaseLlmArgs`. "
"Please check `tensorrt_llm._torch.auto_deploy.llm_args.LlmArgs` for more details."
)

Expand Down Expand Up @@ -213,15 +212,15 @@ def process_extra_cli_args(cls, data: Dict) -> Dict:
def sync_model_with_args(cls, model_value, info):
if "args" not in info.data:
return model_value
args: AutoDeployConfig = info.data["args"]
args: LlmArgs = info.data["args"]
return args.model

@field_validator("prompt", mode="after")
@classmethod
def sync_prompt_batch_size_with_args_max_batch_size(cls, prompt: PromptConfig, info):
if "args" not in info.data:
return prompt
args: AutoDeployConfig = info.data["args"]
args: LlmArgs = info.data["args"]
if args.max_batch_size < prompt.batch_size:
args.max_batch_size = prompt.batch_size
return prompt
Expand All @@ -231,7 +230,7 @@ def sync_prompt_batch_size_with_args_max_batch_size(cls, prompt: PromptConfig, i
def adjust_args_for_benchmark(cls, benchmark: BenchmarkConfig, info):
if "args" not in info.data:
return benchmark
args: AutoDeployConfig = info.data["args"]
args: LlmArgs = info.data["args"]
if benchmark.enabled:
# propagate benchmark settings to args
args.max_batch_size = max(benchmark.bs, args.max_batch_size)
Expand All @@ -246,7 +245,7 @@ def build_llm_from_config(config: ExperimentConfig) -> LLM:
"demollm": DemoLLM,
"trtllm": LLM,
}
llm = llm_lookup[config.args.runtime](**config.args.to_llm_kwargs())
llm = llm_lookup[config.args.runtime](**config.args.model_dump(exclude_unset=True))
return llm


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