Adding chat template to vllm decode.

[NicoGrande]

Description

Adds optional support for chat templates in vllm_decode.py. This helps achieve similar responses to the native vLLM model implementation when compared to MaxText on vLLM.

FIXES: b/476253050

Tests

Running the following command resulted in the following diff with respect to the vLLM native model:

python3 -m MaxText.vllm_decode --model_name=qwen3-8b --load_parameters_path=$CHECKPOINT_PATH --tokenizer_path=Qwen/Qwen3-8B --hf_model_name=Qwen/Qwen3-8B --max_target_length=1024  --prompt="Suggest some famous landmarks in London." --decode_sampling_temperature=0.0 --decode_sampling_nucleus_p=1.0 --decode_sampling_top_k=0 --hf_config_path=src/MaxText/integration/vllm/maxtext_vllm_adapter --use_chat_template=true

https://diff.googleplex.com/#key=j8DNmAFF934Q

Checklist

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[codecov]

Codecov Report

❌ Patch coverage is 0% with 36 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
src/MaxText/vllm_decode.py	0.00%	36 Missing ⚠️

📢 Thoughts on this report? Let us know!

[khatwanimohit]

LGTM

[ChingTsai]

hi @NicoGrande,
I tried to test it with this command.

python3 -m MaxText.vllm_decode --model_name=qwen3-4b --load_parameters_path=<xxx> --tokenizer_path=Qwen/Qwen3-4B --hf_model_name=Qwen/Qwen3-4B --max_target_length=1024  --prompt="Suggest some famous landmarks in London." --decode_sampling_temperature=0.0 --decode_sampling_nucleus_p=1.0 --decode_sampling_top_k=0 --hf_config_path=src/MaxText/integration/vllm/maxtext_vllm_adapter --use_chat_template=true

but I keep getting the error below.

pydantic_core._pydantic_core.ValidationError: 1 validation error for ModelConfig
  Value error, Invalid repository ID or local directory specified: 'src/MaxText/integration/vllm/maxtext_vllm_adapter'.
Please verify the following requirements:
1. Provide a valid Hugging Face repository ID.
2. Specify a local directory that contains a recognized configuration file.
   - For Hugging Face models: ensure the presence of a 'config.json'.
   - For Mistral models: ensure the presence of a 'params.json'.
 [type=value_error, input_value=ArgsKwargs((), {'model': ...rocessor_plugin': None}), input_type=ArgsKwargs]

However, I tried commenting out this line and it works, and the generation looks good to me now.
Is hf_config_path really needed, or did I miss anything?"

[NicoGrande]

hi @NicoGrande, I tried to test it with this command.

python3 -m MaxText.vllm_decode --model_name=qwen3-4b --load_parameters_path=<xxx> --tokenizer_path=Qwen/Qwen3-4B --hf_model_name=Qwen/Qwen3-4B --max_target_length=1024  --prompt="Suggest some famous landmarks in London." --decode_sampling_temperature=0.0 --decode_sampling_nucleus_p=1.0 --decode_sampling_top_k=0 --hf_config_path=src/MaxText/integration/vllm/maxtext_vllm_adapter --use_chat_template=true

but I keep getting the error below.

pydantic_core._pydantic_core.ValidationError: 1 validation error for ModelConfig
  Value error, Invalid repository ID or local directory specified: 'src/MaxText/integration/vllm/maxtext_vllm_adapter'.
Please verify the following requirements:
1. Provide a valid Hugging Face repository ID.
2. Specify a local directory that contains a recognized configuration file.
   - For Hugging Face models: ensure the presence of a 'config.json'.
   - For Mistral models: ensure the presence of a 'params.json'.
 [type=value_error, input_value=ArgsKwargs((), {'model': ...rocessor_plugin': None}), input_type=ArgsKwargs]

However, I tried commenting out this line and it works, and the generation looks good to me now. Is hf_config_path really needed, or did I miss anything?"

Hi @ChingTsai ! To include hf_config_path you need to download the HuggingFace config.json file for the target model and save it in the hf_config_path. You will also need to modify the architectures in config.json to be MaxTextForCausalLM. When you include these changes, the MaxText model implementation will be used for inference. If you do not include hf_config_path then the vLLM native implementation will be used for inference.

[cychiuak]

Hi @NicoGrande ,

I just tested out your PR, it seems like it only supports unscanned model, meaning that only models with scan_layers=false can successfully ran. When using scanned model, I ran into error where the model generates unreasonable tokens. As we use scanned models for SFT, I would like to ask if this PR could also support scanned models?

cc: @ChingTsai

[NicoGrande]

Hi @NicoGrande ,

I just tested out your PR, it seems like it only supports unscanned model, meaning that only models with scan_layers=false can successfully ran. When using scanned model, I ran into error where the model generates unreasonable tokens. As we use scanned models for SFT, I would like to ask if this PR could also support scanned models?

cc: @ChingTsai

Hi @cychiuak! Thank you for flagging this! I think we should be able to add some functionality to support this. The codepath will likely involve unrolling the scanned checkpoint and then using the unscanned version for inference. I can add this to my TODO list and update this PR with this feature request when this is ready.

[ChingTsai]

Thanks @NicoGrande ! Supporting scanned checkpoints would significantly enhance the training user experiment.
Looking forward to this feature becoming available!

[chishuen]

Hi @cychiuak! Thank you for flagging this! I think we should be able to add some functionality to support this. The codepath will likely involve unrolling the scanned checkpoint and then using the unscanned version for inference. I can add this to my TODO list and update this PR with this feature request when this is ready.

+1 Thank you @NicoGrande for taking on this! This feature will unblock us. Otherwise, the current only viable workflow is to convert the ckpt in orbax format to HF (safetensors) format and run the inference, which is a pain in hyperparameter searches. Would be great if you can help to add the support. Thanks!