mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-26 20:08:06 +00:00
feat: add auto-generated CI documentation pre-commit hook (#2890)
Our CI is entirely undocumented, this commit adds a README.md file with a table of the current CI and what is does --------- Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
This commit is contained in:
Nathan Weinberg
Mustafa Elbehery
parent
7f834339ba
commit
b381ed6d64
93 changed files with 495 additions and 477 deletions
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@ -6,7 +6,6 @@
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import gc
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import json
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import logging
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import multiprocessing
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from pathlib import Path
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from typing import Any
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@ -28,6 +27,7 @@ from llama_stack.apis.post_training import (
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LoraFinetuningConfig,
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TrainingConfig,
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)
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from llama_stack.log import get_logger
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from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device
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from ..config import HuggingFacePostTrainingConfig
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@ -44,7 +44,7 @@ from ..utils import (
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split_dataset,
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)
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logger = logging.getLogger(__name__)
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log = get_logger(name=__name__, category="core")
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class HFFinetuningSingleDevice:
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@ -69,14 +69,14 @@ class HFFinetuningSingleDevice:
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try:
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messages = json.loads(row["chat_completion_input"])
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if not isinstance(messages, list) or len(messages) != 1:
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logger.warning(f"Invalid chat_completion_input format: {row['chat_completion_input']}")
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log.warning(f"Invalid chat_completion_input format: {row['chat_completion_input']}")
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return None, None
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if "content" not in messages[0]:
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logger.warning(f"Message missing content: {messages[0]}")
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log.warning(f"Message missing content: {messages[0]}")
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return None, None
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return messages[0]["content"], row["expected_answer"]
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except json.JSONDecodeError:
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logger.warning(f"Failed to parse chat_completion_input: {row['chat_completion_input']}")
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log.warning(f"Failed to parse chat_completion_input: {row['chat_completion_input']}")
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return None, None
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return None, None
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@ -86,13 +86,13 @@ class HFFinetuningSingleDevice:
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try:
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dialog = json.loads(row["dialog"])
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if not isinstance(dialog, list) or len(dialog) < 2:
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logger.warning(f"Dialog must have at least 2 messages: {row['dialog']}")
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log.warning(f"Dialog must have at least 2 messages: {row['dialog']}")
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return None, None
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if dialog[0].get("role") != "user":
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logger.warning(f"First message must be from user: {dialog[0]}")
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log.warning(f"First message must be from user: {dialog[0]}")
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return None, None
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if not any(msg.get("role") == "assistant" for msg in dialog):
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logger.warning("Dialog must have at least one assistant message")
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log.warning("Dialog must have at least one assistant message")
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return None, None
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# Convert to human/gpt format
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@ -100,14 +100,14 @@ class HFFinetuningSingleDevice:
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conversations = []
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for msg in dialog:
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if "role" not in msg or "content" not in msg:
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logger.warning(f"Message missing role or content: {msg}")
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log.warning(f"Message missing role or content: {msg}")
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continue
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conversations.append({"from": role_map[msg["role"]], "value": msg["content"]})
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# Format as a single conversation
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return conversations[0]["value"], conversations[1]["value"]
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except json.JSONDecodeError:
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logger.warning(f"Failed to parse dialog: {row['dialog']}")
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log.warning(f"Failed to parse dialog: {row['dialog']}")
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return None, None
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return None, None
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@ -198,7 +198,7 @@ class HFFinetuningSingleDevice:
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"""
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import asyncio
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logger.info("Starting training process with async wrapper")
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log.info("Starting training process with async wrapper")
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asyncio.run(
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self._run_training(
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model=model,
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@ -228,14 +228,14 @@ class HFFinetuningSingleDevice:
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raise ValueError("DataConfig is required for training")
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# Load dataset
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logger.info(f"Loading dataset: {config.data_config.dataset_id}")
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log.info(f"Loading dataset: {config.data_config.dataset_id}")
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rows = await load_rows_from_dataset(self.datasetio_api, config.data_config.dataset_id)
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if not self.validate_dataset_format(rows):
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raise ValueError("Dataset is missing required fields: input_query, expected_answer, chat_completion_input")
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logger.info(f"Loaded {len(rows)} rows from dataset")
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log.info(f"Loaded {len(rows)} rows from dataset")
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# Initialize tokenizer
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logger.info(f"Initializing tokenizer for model: {model}")
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log.info(f"Initializing tokenizer for model: {model}")
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try:
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tokenizer = AutoTokenizer.from_pretrained(model, **provider_config.model_specific_config)
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@ -257,16 +257,16 @@ class HFFinetuningSingleDevice:
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# This ensures consistent sequence lengths across the training process
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tokenizer.model_max_length = provider_config.max_seq_length
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logger.info("Tokenizer initialized successfully")
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log.info("Tokenizer initialized successfully")
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except Exception as e:
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raise RuntimeError(f"Failed to initialize tokenizer: {str(e)}") from e
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# Create and preprocess dataset
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logger.info("Creating and preprocessing dataset")
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log.info("Creating and preprocessing dataset")
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try:
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ds = self._create_dataset(rows, config, provider_config)
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ds = self._preprocess_dataset(ds, tokenizer, provider_config)
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logger.info(f"Dataset created with {len(ds)} examples")
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log.info(f"Dataset created with {len(ds)} examples")
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except Exception as e:
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raise ValueError(f"Failed to create dataset: {str(e)}") from e
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@ -293,11 +293,11 @@ class HFFinetuningSingleDevice:
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Returns:
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Configured SFTConfig object
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"""
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logger.info("Configuring training arguments")
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log.info("Configuring training arguments")
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lr = 2e-5
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if config.optimizer_config:
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lr = config.optimizer_config.lr
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logger.info(f"Using custom learning rate: {lr}")
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log.info(f"Using custom learning rate: {lr}")
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# Validate data config
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if not config.data_config:
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@ -350,17 +350,17 @@ class HFFinetuningSingleDevice:
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peft_config: Optional LoRA configuration
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output_dir_path: Path to save the model
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"""
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logger.info("Saving final model")
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log.info("Saving final model")
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model_obj.config.use_cache = True
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if peft_config:
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logger.info("Merging LoRA weights with base model")
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log.info("Merging LoRA weights with base model")
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model_obj = trainer.model.merge_and_unload()
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else:
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model_obj = trainer.model
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save_path = output_dir_path / "merged_model"
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logger.info(f"Saving model to {save_path}")
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log.info(f"Saving model to {save_path}")
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model_obj.save_pretrained(save_path)
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async def _run_training(
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@ -380,13 +380,13 @@ class HFFinetuningSingleDevice:
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setup_signal_handlers()
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# Convert config dicts back to objects
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logger.info("Initializing configuration objects")
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log.info("Initializing configuration objects")
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provider_config_obj = HuggingFacePostTrainingConfig(**provider_config)
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config_obj = TrainingConfig(**config)
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# Initialize and validate device
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device = setup_torch_device(provider_config_obj.device)
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logger.info(f"Using device '{device}'")
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log.info(f"Using device '{device}'")
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# Load dataset and tokenizer
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train_dataset, eval_dataset, tokenizer = await self.load_dataset(model, config_obj, provider_config_obj)
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@ -409,7 +409,7 @@ class HFFinetuningSingleDevice:
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model_obj = load_model(model, device, provider_config_obj)
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# Initialize trainer
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logger.info("Initializing SFTTrainer")
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log.info("Initializing SFTTrainer")
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trainer = SFTTrainer(
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model=model_obj,
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train_dataset=train_dataset,
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@ -420,9 +420,9 @@ class HFFinetuningSingleDevice:
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try:
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# Train
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logger.info("Starting training")
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log.info("Starting training")
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trainer.train()
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logger.info("Training completed successfully")
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log.info("Training completed successfully")
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# Save final model if output directory is provided
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if output_dir_path:
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@ -430,12 +430,12 @@ class HFFinetuningSingleDevice:
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finally:
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# Clean up resources
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logger.info("Cleaning up resources")
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log.info("Cleaning up resources")
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if hasattr(trainer, "model"):
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evacuate_model_from_device(trainer.model, device.type)
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del trainer
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gc.collect()
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logger.info("Cleanup completed")
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log.info("Cleanup completed")
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async def train(
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self,
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"""Train a model using HuggingFace's SFTTrainer"""
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# Initialize and validate device
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device = setup_torch_device(provider_config.device)
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logger.info(f"Using device '{device}'")
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log.info(f"Using device '{device}'")
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output_dir_path = None
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if output_dir:
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@ -479,7 +479,7 @@ class HFFinetuningSingleDevice:
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raise ValueError("DataConfig is required for training")
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# Train in a separate process
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logger.info("Starting training in separate process")
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log.info("Starting training in separate process")
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try:
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# Setup multiprocessing for device
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if device.type in ["cuda", "mps"]:
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@ -5,7 +5,6 @@
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# the root directory of this source tree.
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import gc
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import logging
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import multiprocessing
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from pathlib import Path
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from typing import Any
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@ -24,6 +23,7 @@ from llama_stack.apis.post_training import (
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DPOAlignmentConfig,
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TrainingConfig,
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)
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from llama_stack.log import get_logger
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from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device
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from ..config import HuggingFacePostTrainingConfig
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@ -40,7 +40,7 @@ from ..utils import (
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split_dataset,
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)
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logger = logging.getLogger(__name__)
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logger = get_logger(__name__, category="core")
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class HFDPOAlignmentSingleDevice:
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@ -4,7 +4,6 @@
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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 os
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import signal
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import sys
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@ -19,10 +18,11 @@ from transformers import AutoConfig, AutoModelForCausalLM
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from llama_stack.apis.datasetio import DatasetIO
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from llama_stack.apis.post_training import Checkpoint, TrainingConfig
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from llama_stack.log import get_logger
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from .config import HuggingFacePostTrainingConfig
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logger = logging.getLogger(__name__)
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logger = get_logger(__name__, category="core")
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def setup_environment():
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