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https://github.com/meta-llama/llama-stack.git
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25268854bc
# What does this PR do?
We were not using conditionals correctly, conditionals can only be used
when the env variable is set, so `${env.ENVIRONMENT:+}` would return
None is ENVIRONMENT is not set.
If you want to create a conditional value, you need to do
`${env.ENVIRONMENT:=}`, this will pick the value of ENVIRONMENT if set,
otherwise will return None.
Closes: https://github.com/meta-llama/llama-stack/issues/2564
Signed-off-by: Sébastien Han <seb@redhat.com>
113 lines
3.4 KiB
Python
113 lines
3.4 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 os
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from typing import Any
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from pydantic import BaseModel, Field
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# TODO: add default values for all fields
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class NvidiaPostTrainingConfig(BaseModel):
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"""Configuration for NVIDIA Post Training implementation."""
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api_key: str | None = Field(
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default_factory=lambda: os.getenv("NVIDIA_API_KEY"),
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description="The NVIDIA API key.",
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)
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dataset_namespace: str | None = Field(
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default_factory=lambda: os.getenv("NVIDIA_DATASET_NAMESPACE", "default"),
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description="The NVIDIA dataset namespace.",
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)
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project_id: str | None = Field(
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default_factory=lambda: os.getenv("NVIDIA_PROJECT_ID", "test-example-model@v1"),
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description="The NVIDIA project ID.",
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)
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# ToDO: validate this, add default value
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customizer_url: str | None = Field(
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default_factory=lambda: os.getenv("NVIDIA_CUSTOMIZER_URL"),
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description="Base URL for the NeMo Customizer API",
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)
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timeout: int = Field(
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default=300,
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description="Timeout for the NVIDIA Post Training API",
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)
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max_retries: int = Field(
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default=3,
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description="Maximum number of retries for the NVIDIA Post Training API",
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)
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# ToDo: validate this
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output_model_dir: str = Field(
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default_factory=lambda: os.getenv("NVIDIA_OUTPUT_MODEL_DIR", "test-example-model@v1"),
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description="Directory to save the output model",
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)
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@classmethod
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def sample_run_config(cls, **kwargs) -> dict[str, Any]:
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return {
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"api_key": "${env.NVIDIA_API_KEY:=}",
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"dataset_namespace": "${env.NVIDIA_DATASET_NAMESPACE:=default}",
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"project_id": "${env.NVIDIA_PROJECT_ID:=test-project}",
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"customizer_url": "${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test}",
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}
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class SFTLoRADefaultConfig(BaseModel):
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"""NVIDIA-specific training configuration with default values."""
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# ToDo: split into SFT and LoRA configs??
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# General training parameters
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n_epochs: int = 50
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# NeMo customizer specific parameters
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log_every_n_steps: int | None = None
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val_check_interval: float = 0.25
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sequence_packing_enabled: bool = False
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weight_decay: float = 0.01
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lr: float = 0.0001
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# SFT specific parameters
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hidden_dropout: float | None = None
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attention_dropout: float | None = None
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ffn_dropout: float | None = None
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# LoRA default parameters
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lora_adapter_dim: int = 8
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lora_adapter_dropout: float | None = None
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lora_alpha: int = 16
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# Data config
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batch_size: int = 8
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@classmethod
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def sample_config(cls) -> dict[str, Any]:
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"""Return a sample configuration for NVIDIA training."""
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return {
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"n_epochs": 50,
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"log_every_n_steps": 10,
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"val_check_interval": 0.25,
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"sequence_packing_enabled": False,
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"weight_decay": 0.01,
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"hidden_dropout": 0.1,
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"attention_dropout": 0.1,
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"lora_adapter_dim": 8,
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"lora_alpha": 16,
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"data_config": {
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"dataset_id": "default",
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"batch_size": 8,
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},
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"optimizer_config": {
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"lr": 0.0001,
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},
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}
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