forked from phoenix-oss/llama-stack-mirror
f34f22f8c7
# What does this PR do? This PR adds two methods to the Inference API: - `batch_completion` - `batch_chat_completion` The motivation is for evaluations targeting a local inference engine (like meta-reference or vllm) where batch APIs provide for a substantial amount of acceleration. Why did I not add this to `Api.batch_inference` though? That just resulted in a _lot_ more book-keeping given the structure of Llama Stack. Had I done that, I would have needed to create a notion of a "batch model" resource, setup routing based on that, etc. This does not sound ideal. So what's the future of the batch inference API? I am not sure. Maybe we can keep it for true _asynchronous_ execution. So you can submit requests, and it can return a Job instance, etc. ## Test Plan Run meta-reference-gpu using: ```bash export INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct export INFERENCE_CHECKPOINT_DIR=../checkpoints/Llama-4-Scout-17B-16E-Instruct-20250331210000 export MODEL_PARALLEL_SIZE=4 export MAX_BATCH_SIZE=32 export MAX_SEQ_LEN=6144 LLAMA_MODELS_DEBUG=1 llama stack run meta-reference-gpu ``` Then run the batch inference test case. |
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| .. | ||
| routers | ||
| server | ||
| store | ||
| ui | ||
| utils | ||
| __init__.py | ||
| access_control.py | ||
| build.py | ||
| build_conda_env.sh | ||
| build_container.sh | ||
| build_venv.sh | ||
| client.py | ||
| common.sh | ||
| configure.py | ||
| datatypes.py | ||
| distribution.py | ||
| inspect.py | ||
| library_client.py | ||
| providers.py | ||
| request_headers.py | ||
| resolver.py | ||
| stack.py | ||
| start_stack.sh | ||