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https://github.com/meta-llama/llama-stack.git
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9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
63 lines
2.3 KiB
Python
63 lines
2.3 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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import logging
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import warnings
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from typing import Any
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from pydantic import BaseModel
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from llama_stack.apis.post_training import TrainingConfig
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from llama_stack.providers.remote.post_training.nvidia.config import SFTLoRADefaultConfig
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from .config import NvidiaPostTrainingConfig
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logger = logging.getLogger(__name__)
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def warn_unsupported_params(config_dict: Any, supported_keys: set[str], config_name: str) -> None:
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keys = set(config_dict.__annotations__.keys()) if isinstance(config_dict, BaseModel) else config_dict.keys()
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unsupported_params = [k for k in keys if k not in supported_keys]
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if unsupported_params:
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warnings.warn(
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f"Parameters: {unsupported_params} in `{config_name}` not supported and will be ignored.", stacklevel=2
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)
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def validate_training_params(
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training_config: dict[str, Any], supported_keys: set[str], config_name: str = "TrainingConfig"
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) -> None:
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"""
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Validates training parameters against supported keys.
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Args:
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training_config: Dictionary containing training configuration parameters
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supported_keys: Set of supported parameter keys
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config_name: Name of the configuration for warning messages
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"""
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sft_lora_fields = set(SFTLoRADefaultConfig.__annotations__.keys())
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training_config_fields = set(TrainingConfig.__annotations__.keys())
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# Check for not supported parameters:
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# - not in either of configs
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# - in TrainingConfig but not in SFTLoRADefaultConfig
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unsupported_params = []
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for key in training_config:
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if isinstance(key, str) and key not in (supported_keys.union(sft_lora_fields)):
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if key in (not sft_lora_fields or training_config_fields):
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unsupported_params.append(key)
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if unsupported_params:
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warnings.warn(
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f"Parameters: {unsupported_params} in `{config_name}` are not supported and will be ignored.", stacklevel=2
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)
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# ToDo: implement post health checks for customizer are enabled
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async def _get_health(url: str) -> tuple[bool, bool]: ...
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async def check_health(config: NvidiaPostTrainingConfig) -> None: ...
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