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sam-paech
256
utils/config_loader.py
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256
utils/config_loader.py
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# utils/config_loader.py
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import argparse
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import copy
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import logging
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from pathlib import Path
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from typing import Dict, List, Sequence, Any
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import yaml
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logger = logging.getLogger(__name__)
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_ALWAYS: Sequence[str] = (
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# minimal required keys for the pipeline to run at all
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"experiment_base_dir",
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"human_profile_path",
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"num_iterations",
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"min_word_len_for_analysis",
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"log_level",
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"model_id",
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)
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_VLLM: Sequence[str] = (
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"vllm_model_id",
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"vllm_port",
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"vllm_hf_token",
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"vllm_cuda_visible_devices",
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"vllm_gpu_memory_utilization",
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"vllm_max_model_len",
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"vllm_dtype",
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"vllm_extra_args",
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"vllm_env",
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)
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_GENERATION: Sequence[str] = (
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"generation_api_key",
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"generation_api_base_url", # needed if you do local or remote calls
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"generation_model_id",
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"generation_max_new_tokens",
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"generation_threads",
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"generation_max_prompts",
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"generation_hf_dataset_name",
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"generation_hf_dataset_split",
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"generation_logging_level",
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"generation_chat_template_model_id",
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"generation_param_chunk_size",
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"generation_param_top_logprobs_count",
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"generation_param_temperature",
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"generation_param_top_p",
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"generation_param_top_k",
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"generation_param_min_p",
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"generation_param_timeout",
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"generation_param_stop_sequences",
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"generation_ngram_remove_stopwords",
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"generation_ngram_language",
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"generation_force_backtrack",
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"generation_prompt_template",
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"generation_system_prompt"
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)
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_NGRAM: Sequence[str] = (
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"top_k_bigrams",
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"top_k_trigrams",
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"dict_bigrams_initial",
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"dict_bigrams_subsequent",
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"nodict_bigrams_initial",
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"nodict_bigrams_subsequent",
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"dict_trigrams_initial",
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"dict_trigrams_subsequent",
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"nodict_trigrams_initial",
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"nodict_trigrams_subsequent",
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"extra_ngrams_to_ban",
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)
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_SLOP: Sequence[str] = (
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"min_phrase_freq_to_keep",
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"top_n_initial_slop_ban",
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"top_n_subsequent_slop_ban",
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"extra_slop_phrases_to_ban",
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"banned_slop_phrases_filename",
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)
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_OVERREP: Sequence[str] = (
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"top_k_words_for_overrep_analysis",
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"dict_overrep_initial",
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"dict_overrep_subsequent",
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"nodict_overrep_initial",
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"nodict_overrep_subsequent",
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)
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_FINETUNE: Sequence[str] = (
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"finetune_mode",
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"finetune_ftpo_dataset",
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"finetune_base_model_id",
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"finetune_max_seq_length",
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"finetune_load_in_4bit",
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"finetune_lora_r",
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"finetune_lora_alpha",
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"finetune_lora_dropout",
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"finetune_weight_decay",
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"finetune_target_modules",
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"finetune_gradient_checkpointing",
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"finetune_chat_template",
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"finetune_batch_size",
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"finetune_gradient_accumulation_steps",
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"finetune_warmup_ratio",
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"finetune_num_epochs",
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"finetune_learning_rate",
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"finetune_auto_learning_rate",
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"finetune_beta",
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"finetune_output_dir_suffix",
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"finetune_save_merged_16bit",
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"finetune_save_gguf_q8_0",
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"finetune_max_train_examples",
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"finetune_cuda_visible_devices",
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"ftpo_sample_rejected_regularisation_strength",
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"ftpo_sample_chosen_regularisation_strength",
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"ftpo_sample_min_chosen_tokens",
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)
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def _deep_update(dst: Dict, src: Dict) -> Dict:
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"""Recursively merge src into dst (src wins)."""
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for k, v in src.items():
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if k in dst and isinstance(v, dict) and isinstance(dst[k], dict):
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_deep_update(dst[k], v)
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else:
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dst[k] = copy.deepcopy(v)
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return dst
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def load_pipeline_config(config_path: Path) -> Dict[str, Any]:
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"""Load config from a YAML file, or return empty dict if missing/invalid."""
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if config_path and config_path.exists():
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try:
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with config_path.open('r', encoding='utf-8') as f:
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data = yaml.safe_load(f) or {}
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logger.info("Loaded configuration from %s", config_path)
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return data
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except Exception as e:
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logger.error("Could not load %s: %s – using empty config", config_path, e)
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else:
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logger.info("Config file %s not found – using empty config", config_path)
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return {}
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def merge_config_with_cli_args(config: Dict[str, Any], cli_args: argparse.Namespace) -> Dict[str, Any]:
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"""
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Merges every possible CLI parameter from your old DEFAULT_CONFIG
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into 'config' if the user actually provided it (i.e. it's not None).
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Also merges housekeeping flags (config_file, resume_from_dir, log_level).
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"""
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merged = copy.deepcopy(config)
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# 1. Housekeeping arguments (not originally in DEFAULT_CONFIG, but we keep them if set)
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if getattr(cli_args, 'config_file', None) is not None:
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merged['config_file'] = cli_args.config_file
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if getattr(cli_args, 'resume_from_dir', None) is not None:
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merged['resume_from_dir'] = cli_args.resume_from_dir
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if getattr(cli_args, 'log_level', None) is not None:
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merged['log_level'] = cli_args.log_level
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# 2. Booleans that map from CLI flags to known keys in config
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if getattr(cli_args, 'run_finetune', None) is not None:
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merged['finetune_enabled'] = cli_args.run_finetune
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if getattr(cli_args, 'manage_vllm', None) is not None:
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merged['manage_vllm'] = cli_args.manage_vllm
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if getattr(cli_args, 'generation_step_enabled', None) is not None:
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merged['generation_step_enabled'] = cli_args.generation_step_enabled
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if getattr(cli_args, "finetune_cuda_visible_devices", None) is not None:
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merged["finetune_cuda_visible_devices"] = cli_args.finetune_cuda_visible_devices
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# 3. All remaining keys from the old DEFAULT_CONFIG
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_all_groups: Sequence[Sequence[str]] = (
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_ALWAYS,
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_VLLM,
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_GENERATION,
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_NGRAM,
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_SLOP,
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_OVERREP,
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_FINETUNE,
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)
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all_config_keys: List[str] = [k for group in _all_groups for k in group]
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# 4. fallback for per-stage model IDs <-- add this block
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for key in (
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"vllm_model_id",
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"generation_model_id",
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"generation_chat_template_model_id",
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"finetune_base_model_id",
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):
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if not merged.get(key): # None, "", or missing
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merged[key] = merged.get("model_id")
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# Overwrite config if user specified a value
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for key in all_config_keys:
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cli_val = getattr(cli_args, key, None)
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if cli_val is not None:
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merged[key] = cli_val
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return merged
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# ---------------------------------------------------------------------------
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# Validate with partial requirements depending on which features are enabled
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# ---------------------------------------------------------------------------
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def _missing(cfg: Dict[str, Any], keys: Sequence[str]) -> List[str]:
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return [k for k in keys if k not in cfg or cfg[k] is None]
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def validate_config(cfg: Dict[str, Any]) -> None:
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"""Raise ValueError if any required config is missing based on pipeline flags."""
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missing = []
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# always
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missing.extend(_missing(cfg, _ALWAYS))
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# vllm
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if cfg.get("manage_vllm", False):
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missing.extend(_missing(cfg, _VLLM))
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# generation
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if cfg.get("generation_step_enabled", True):
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missing.extend(_missing(cfg, _GENERATION))
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# n-gram ban
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if cfg.get("enable_ngram_ban", False):
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missing.extend(_missing(cfg, _NGRAM))
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# slop phrase ban
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if cfg.get("enable_slop_phrase_ban", False):
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missing.extend(_missing(cfg, _SLOP))
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# over-rep analysis
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if cfg.get("compute_overrep_words", False):
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missing.extend(_missing(cfg, _OVERREP))
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# finetuning
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if cfg.get("finetune_enabled", False):
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missing.extend(_missing(cfg, _FINETUNE))
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if missing:
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raise ValueError(
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f"Configuration is incomplete; missing these keys: {', '.join(sorted(set(missing)))}"
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)
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logger.info("Configuration validated – all required keys present (for enabled features).")
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def load_merge_validate(config_path: Path, cli_args: argparse.Namespace) -> Dict[str, Any]:
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"""
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1) Load YAML from config_path,
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2) Merge in any CLI flags user typed,
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3) Validate that all needed keys for enabled features are present.
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"""
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cfg = load_pipeline_config(config_path)
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cfg = merge_config_with_cli_args(cfg, cli_args)
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validate_config(cfg)
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return cfg
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