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43c1f39bd6
# What does this PR do?
This commit significantly improves the environment variable substitution
functionality in Llama Stack configuration files:
* The version field in configuration files has been changed from string
to integer type for better type consistency across build and run
configurations.
* The environment variable substitution system for ${env.FOO:} was fixed
and properly returns an error
* The environment variable substitution system for ${env.FOO+} returns
None instead of an empty strings, it better matches type annotations in
config fields
* The system includes automatic type conversion for boolean, integer,
and float values.
* The error messages have been enhanced to provide clearer guidance when
environment variables are missing, including suggestions for using
default values or conditional syntax.
* Comprehensive documentation has been added to the configuration guide
explaining all supported syntax patterns, best practices, and runtime
override capabilities.
* Multiple provider configurations have been updated to use the new
conditional syntax for optional API keys, making the system more
flexible for different deployment scenarios. The telemetry configuration
has been improved to properly handle optional endpoints with appropriate
validation, ensuring that required endpoints are specified when their
corresponding sinks are enabled.
* There were many instances of ${env.NVIDIA_API_KEY:} that should have
caused the code to fail. However, due to a bug, the distro server was
still being started, and early validation wasn’t triggered. As a result,
failures were likely being handled downstream by the providers. I’ve
maintained similar behavior by using ${env.NVIDIA_API_KEY:+}, though I
believe this is incorrect for many configurations. I’ll leave it to each
provider to correct it as needed.
* Environment variable substitution now uses the same syntax as Bash
parameter expansion.
Signed-off-by: Sébastien Han <seb@redhat.com>
68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any
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from pydantic import BaseModel, field_validator
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from llama_stack.apis.inference import QuantizationConfig
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from llama_stack.providers.utils.inference import supported_inference_models
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class MetaReferenceInferenceConfig(BaseModel):
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# this is a placeholder to indicate inference model id
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# the actual inference model id is dtermined by the moddel id in the request
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# Note: you need to register the model before using it for inference
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# models in the resouce list in the run.yaml config will be registered automatically
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model: str | None = None
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torch_seed: int | None = None
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max_seq_len: int = 4096
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max_batch_size: int = 1
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model_parallel_size: int | None = None
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# when this is False, we assume that the distributed process group is setup by someone
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# outside of this code (e.g., when run inside `torchrun`). that is useful for clients
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# (including our testing code) who might be using llama-stack as a library.
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create_distributed_process_group: bool = True
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# By default, the implementation will look at ~/.llama/checkpoints/<model> but you
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# can override by specifying the directory explicitly
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checkpoint_dir: str | None = None
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quantization: QuantizationConfig | None = None
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@field_validator("model")
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@classmethod
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def validate_model(cls, model: str) -> str:
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permitted_models = supported_inference_models()
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descriptors = [m.descriptor() for m in permitted_models]
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repos = [m.huggingface_repo for m in permitted_models]
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if model not in (descriptors + repos):
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model_list = "\n\t".join(repos)
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raise ValueError(f"Unknown model: `{model}`. Choose from [\n\t{model_list}\n]")
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return model
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@classmethod
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def sample_run_config(
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cls,
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model: str = "Llama3.2-3B-Instruct",
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checkpoint_dir: str = "${env.CHECKPOINT_DIR:=null}",
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quantization_type: str = "${env.QUANTIZATION_TYPE:=bf16}",
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model_parallel_size: str = "${env.MODEL_PARALLEL_SIZE:=0}",
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max_batch_size: str = "${env.MAX_BATCH_SIZE:=1}",
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max_seq_len: str = "${env.MAX_SEQ_LEN:=4096}",
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**kwargs,
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) -> dict[str, Any]:
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return {
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"model": model,
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"checkpoint_dir": checkpoint_dir,
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"quantization": {
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"type": quantization_type,
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},
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"model_parallel_size": model_parallel_size,
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"max_batch_size": max_batch_size,
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"max_seq_len": max_seq_len,
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}
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