File size: 10,050 Bytes
f440f03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 | """CLI ieeja pilnam Maris AI training pipeline skrējienam."""
from __future__ import annotations
import argparse
import json
import logging
from dataclasses import replace
from maris_core.training.config import list_training_base_models, load_training_config
logger = logging.getLogger(__name__)
def _parse_bool_arg(value: str) -> bool:
"""Parsē CLI boolean vērtību no true/false, yes/no vai 1/0 formāta."""
normalized = value.strip().lower()
if normalized in {"1", "true", "yes", "on"}:
return True
if normalized in {"0", "false", "no", "off"}:
return False
raise argparse.ArgumentTypeError("Izmanto true/false, yes/no vai 1/0.")
def main() -> int:
"""Izpilda vienu pilnu apmācības skrējienu pēc JSON konfigurācijas."""
parser = argparse.ArgumentParser(description="Apmāca Maris AI modeli ar Transformers")
parser.add_argument("--config", help="JSON konfigurācijas fails")
parser.add_argument("--model-name", help="Bāzes modelis fine-tuningam")
parser.add_argument("--model-preset", help="Iepriekš definēts HF bāzes modeļa presets")
parser.add_argument("--dataset-repo", help="HF dataset repo ID")
parser.add_argument("--eval-dataset-repo", help="Atsevišķs HF eval dataset repo ID")
parser.add_argument(
"--benchmark-dataset-path", help="Lokāls JSON benchmarks release gate un score manifestam"
)
parser.add_argument("--benchmark-name", help="Benchmark komplekta nosaukums artefaktiem")
parser.add_argument(
"--benchmark-levels",
help="Comma-separated benchmark līmeņi, piemēram local,ci,release",
)
parser.add_argument(
"--benchmark-min-overall",
type=float,
help="Minimālais overall benchmark score release gate vajadzībām",
)
parser.add_argument(
"--benchmark-gate-enabled",
type=_parse_bool_arg,
help="Vai training skrējiens jāaptur, ja benchmark gate neiziet",
)
parser.add_argument(
"--preference-dataset-path",
help="Lokāls JSON preference-feedback datasets auditējamam artifactam",
)
parser.add_argument(
"--preference-optimization",
help="Preference optimization režīms: none, dpo vai orpo",
)
parser.add_argument("--preference-beta", type=float, help="DPO/ORPO beta parametrs")
parser.add_argument(
"--preference-max-prompt-length",
type=int,
help="Maksimālais prompt tokenu garums preference optimization laikā",
)
parser.add_argument(
"--preference-max-length",
type=int,
help="Maksimālais kopējais tokenu garums preference optimization laikā",
)
parser.add_argument(
"--preference-reference-model",
help="Atsauces modelis DPO preference optimization stadijai",
)
parser.add_argument("--branch-name", help="Maris atzara nosaukums")
parser.add_argument("--branch-focus", help="Atzara specializācijas fokuss")
parser.add_argument("--adapter-type", help="Adapteru stratēģija, piemēram full vai lora")
parser.add_argument("--lora-r", type=int, help="LoRA rank parametrs PEFT adapteriem")
parser.add_argument("--lora-alpha", type=int, help="LoRA alpha parametrs PEFT adapteriem")
parser.add_argument("--lora-dropout", type=float, help="LoRA dropout parametrs")
parser.add_argument("--lora-bias", help="LoRA bias režīms, piemēram none vai all")
parser.add_argument(
"--peft-target-modules",
help="Comma-separated PEFT target modules saraksts",
)
parser.add_argument("--qlora-quant-type", help="QLoRA quant type, piemēram nf4 vai fp4")
parser.add_argument(
"--qlora-use-double-quant",
type=_parse_bool_arg,
help="Vai QLoRA izmantot double quantization",
)
parser.add_argument(
"--qlora-compute-dtype",
help="QLoRA compute dtype, piemēram float16 vai bfloat16",
)
parser.add_argument(
"--distributed-strategy",
help="Distributed režīms: none, fsdp vai deepspeed",
)
parser.add_argument(
"--distributed-config-path",
help="Ceļš uz FSDP vai DeepSpeed JSON konfigurāciju",
)
parser.add_argument(
"--use-accelerate",
type=_parse_bool_arg,
help="Vai palaist treniņu ar accelerate launcher semantiku",
)
parser.add_argument(
"--accelerate-config-path",
help="Ceļš uz accelerate launcher YAML konfigurāciju",
)
parser.add_argument("--num-processes", type=int, help="Procesu/GPU skaits distributed launcham")
parser.add_argument("--num-machines", type=int, help="Mašīnu skaits distributed launcham")
parser.add_argument(
"--machine-rank", type=int, help="Pašreizējās mašīnas ranks distributed launcham"
)
parser.add_argument("--main-process-ip", help="Galvenā procesa IP multi-node launcham")
parser.add_argument(
"--main-process-port", type=int, help="Galvenā procesa ports multi-node launcham"
)
parser.add_argument(
"--fsdp-transformer-layer-cls-to-wrap",
help="Comma-separated transformer layer class saraksts FSDP auto-wrap vajadzībām",
)
parser.add_argument(
"--fsdp-min-num-params",
type=int,
help="Minimālais parametru skaits FSDP wrap aktivēšanai",
)
parser.add_argument("--hub-model-id", help="Maris model repo ID publicētajam rezultātam")
parser.add_argument("--output-dir", help="Kur saglabāt apmācīto modeli")
parser.add_argument("--num-epochs", type=int, help="Epoku skaits")
parser.add_argument("--learning-rate", type=float, help="Learning rate")
parser.add_argument("--max-seq-length", type=int, help="Maksimālais tokenu garums")
parser.add_argument(
"--push-to-hub",
type=_parse_bool_arg,
help="Vai pēc treniņa publicēt pilnu output direktoriju uz Hugging Face Hub",
)
parser.add_argument(
"--all-branches",
action="store_true",
help="Palaist branch-specific training pipeline visiem atzariem",
)
parser.add_argument(
"--validation-split-ratio",
type=float,
help="Validation split proporcija, ja repo nav validation split",
)
parser.add_argument(
"--list-base-models",
action="store_true",
help="Izvada pieejamos bāzes modeļu presetus JSON formātā un beidz darbu",
)
args = parser.parse_args()
logging.basicConfig(level=logging.INFO)
try:
if args.list_base_models:
print(json.dumps(list_training_base_models(), indent=2, ensure_ascii=False))
return 0
from maris_core.training.train import train_branch_suite, train_with_config
config = load_training_config(
args.config,
overrides={
"model_name": args.model_name,
"model_preset": args.model_preset,
"dataset_repo": args.dataset_repo,
"eval_dataset_repo": args.eval_dataset_repo,
"benchmark_dataset_path": args.benchmark_dataset_path,
"benchmark_name": args.benchmark_name,
"benchmark_levels": args.benchmark_levels,
"benchmark_min_overall": args.benchmark_min_overall,
"benchmark_gate_enabled": args.benchmark_gate_enabled,
"preference_dataset_path": args.preference_dataset_path,
"preference_optimization": args.preference_optimization,
"preference_beta": args.preference_beta,
"preference_max_prompt_length": args.preference_max_prompt_length,
"preference_max_length": args.preference_max_length,
"preference_reference_model": args.preference_reference_model,
"branch_name": args.branch_name,
"branch_focus": args.branch_focus,
"adapter_type": args.adapter_type,
"lora_r": args.lora_r,
"lora_alpha": args.lora_alpha,
"lora_dropout": args.lora_dropout,
"lora_bias": args.lora_bias,
"peft_target_modules": args.peft_target_modules,
"qlora_quant_type": args.qlora_quant_type,
"qlora_use_double_quant": args.qlora_use_double_quant,
"qlora_compute_dtype": args.qlora_compute_dtype,
"distributed_strategy": args.distributed_strategy,
"distributed_config_path": args.distributed_config_path,
"use_accelerate": args.use_accelerate,
"accelerate_config_path": args.accelerate_config_path,
"num_processes": args.num_processes,
"num_machines": args.num_machines,
"machine_rank": args.machine_rank,
"main_process_ip": args.main_process_ip,
"main_process_port": args.main_process_port,
"fsdp_transformer_layer_cls_to_wrap": args.fsdp_transformer_layer_cls_to_wrap,
"fsdp_min_num_params": args.fsdp_min_num_params,
"hub_model_id": args.hub_model_id,
"output_dir": args.output_dir,
"num_epochs": args.num_epochs,
"learning_rate": args.learning_rate,
"max_seq_length": args.max_seq_length,
"push_to_hub": args.push_to_hub,
"validation_split_ratio": args.validation_split_ratio,
},
)
execution_config = replace(config, push_to_hub=False) if args.all_branches else config
metrics = (
train_branch_suite(execution_config) if args.all_branches else train_with_config(config)
)
logger.info("Training metrics: %s", metrics)
print(json.dumps(metrics, indent=2, ensure_ascii=False))
return 0
except (FileNotFoundError, ImportError, ValueError) as exc:
parser.exit(2, f"{parser.prog}: error: {exc}\n")
if __name__ == "__main__":
raise SystemExit(main())
|