mirror of https://github.com/razor-ai/soup.git
306 lines
11 KiB
Python
306 lines
11 KiB
Python
"""KTO (Kahneman-Tversky Optimization) trainer — wraps trl.KTOTrainer."""
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import math
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import time
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from pathlib import Path
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from typing import Optional
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from rich.console import Console
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from soup_cli.config.schema import SoupConfig
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from soup_cli.utils.gpu import estimate_batch_size, model_size_from_name
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console = Console()
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class KTOTrainerWrapper:
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"""High-level wrapper for KTO training from SoupConfig.
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KTO uses unpaired preference data with three fields:
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- prompt: the input prompt
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- completion: the model response
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- label: True (desirable) or False (undesirable)
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"""
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def __init__(
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self,
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config: SoupConfig,
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device: str = "cuda",
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report_to: str = "none",
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deepspeed_config: Optional[str] = None,
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fsdp_config: Optional[dict] = None,
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trust_remote_code: bool = False,
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):
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self.config = config
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self.device = device
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self.report_to = report_to
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self.deepspeed_config = deepspeed_config
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self.fsdp_config = fsdp_config
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self.trust_remote_code = trust_remote_code
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from soup_cli.utils.trust_remote import (
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model_requires_trust_remote_code,
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resolve_trust_remote_code,
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)
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requires = model_requires_trust_remote_code(config.base) or False
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self._trust_remote_code = resolve_trust_remote_code(
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config.base,
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requested=trust_remote_code,
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console=console,
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requires_remote_code=requires,
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)
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self.model = None
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self.tokenizer = None
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self.trainer = None
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self._output_dir = None
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def setup(self, dataset: dict) -> None:
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"""Load model, tokenizer, apply LoRA, create KTO trainer."""
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from datasets import Dataset
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from trl import KTOConfig, KTOTrainer
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# Enable Rich progress bar for HuggingFace downloads
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from soup_cli.trainer.sft import _enable_hf_transfer_progress
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_enable_hf_transfer_progress()
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cfg = self.config
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tcfg = cfg.training
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use_unsloth = cfg.backend == "unsloth"
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if use_unsloth:
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self._setup_unsloth(cfg, tcfg)
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else:
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self._setup_transformers(cfg, tcfg)
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trainable, total = self.model.get_nb_trainable_parameters()
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pct = 100 * trainable / total
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console.print(
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f"[green]LoRA applied:[/] {trainable:,} trainable"
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f" / {total:,} total ({pct:.2f}%)"
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)
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# --- Batch size ---
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batch_size = tcfg.batch_size
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if batch_size == "auto":
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from soup_cli.utils.gpu import get_gpu_info
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gpu_info = get_gpu_info()
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model_size = model_size_from_name(cfg.base)
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batch_size = estimate_batch_size(
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model_params_b=model_size,
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seq_length=cfg.data.max_length,
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gpu_memory_bytes=gpu_info["memory_total_bytes"],
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quantization=tcfg.quantization,
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lora_r=tcfg.lora.r,
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)
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# KTO processes unpaired samples — similar memory to DPO
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batch_size = max(1, batch_size // 2)
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console.print(f"[green]Auto batch size (KTO):[/] {batch_size}")
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# --- Dataset ---
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# KTO expects: prompt, completion, label
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train_ds = Dataset.from_list(dataset["train"])
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eval_ds = None
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if "val" in dataset and dataset["val"]:
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eval_ds = Dataset.from_list(dataset["val"])
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# --- Output dir ---
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output_dir = Path(cfg.output)
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if cfg.experiment_name:
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output_dir = output_dir / cfg.experiment_name
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output_dir.mkdir(parents=True, exist_ok=True)
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# --- Calculate warmup steps from ratio ---
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total_steps = (
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math.ceil(len(train_ds) / batch_size / tcfg.gradient_accumulation_steps)
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* tcfg.epochs
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)
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warmup_steps = int(total_steps * tcfg.warmup_ratio)
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# --- KTO config ---
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kto_config = KTOConfig(
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output_dir=str(output_dir),
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num_train_epochs=tcfg.epochs,
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per_device_train_batch_size=batch_size,
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gradient_accumulation_steps=tcfg.gradient_accumulation_steps,
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learning_rate=tcfg.lr,
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warmup_steps=warmup_steps,
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weight_decay=tcfg.weight_decay,
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max_grad_norm=tcfg.max_grad_norm,
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optim=tcfg.optimizer,
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lr_scheduler_type=tcfg.scheduler,
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logging_steps=tcfg.logging_steps,
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save_steps=tcfg.save_steps,
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save_total_limit=3,
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bf16=self.device == "cuda",
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report_to=self.report_to,
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remove_unused_columns=False,
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deepspeed=self.deepspeed_config,
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**(self.fsdp_config or {}),
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beta=tcfg.kto_beta,
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max_length=cfg.data.max_length,
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max_prompt_length=cfg.data.max_length // 2,
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**({"neftune_noise_alpha": tcfg.neftune_alpha}
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if tcfg.neftune_alpha is not None else {}),
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)
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# --- Trainer ---
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self.trainer = KTOTrainer(
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model=self.model,
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args=kto_config,
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train_dataset=train_ds,
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eval_dataset=eval_ds,
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processing_class=self.tokenizer,
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)
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# v0.40.6 #67 — ReLoRA callback (magnitude-prune LoRA every N steps).
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from soup_cli.utils.peft_wiring import attach_relora_callback
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attach_relora_callback(self.trainer, tcfg)
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self._output_dir = str(output_dir)
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def _setup_transformers(self, cfg: SoupConfig, tcfg) -> None:
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"""Load model via standard transformers + peft pipeline."""
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from peft import LoraConfig, TaskType, get_peft_model, prepare_model_for_kbit_training
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from transformers import AutoModelForCausalLM, AutoTokenizer
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console.print(f"[dim]Loading tokenizer: {cfg.base}[/]")
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self.tokenizer = AutoTokenizer.from_pretrained(
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cfg.base, trust_remote_code=self._trust_remote_code
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)
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if self.tokenizer.pad_token is None:
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self.tokenizer.pad_token = self.tokenizer.eos_token
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# Quantization (v0.38.0 Quant Menu — see soup_cli.utils.quant_menu)
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from soup_cli.utils.quant_menu import build_quantization_config_for_loader
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quant_config_obj = build_quantization_config_for_loader(
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tcfg=tcfg, base=cfg.base, console=console,
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)
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console.print(f"[dim]Loading model: {cfg.base}[/]")
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# On CPU, use device_map="cpu" to avoid meta tensors from "auto"
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dev_map = "cpu" if self.device == "cpu" else "auto"
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model_kwargs = {
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"trust_remote_code": self._trust_remote_code, "device_map": dev_map,
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}
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if quant_config_obj is not None:
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model_kwargs["quantization_config"] = quant_config_obj
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self.model = AutoModelForCausalLM.from_pretrained(cfg.base, **model_kwargs)
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if tcfg.quantization in ("4bit", "8bit", "mxfp4"):
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self.model = prepare_model_for_kbit_training(self.model)
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target_modules = tcfg.lora.target_modules
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if target_modules == "auto":
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target_modules = None
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lora_config = LoraConfig(
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r=tcfg.lora.r,
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lora_alpha=tcfg.lora.alpha,
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lora_dropout=tcfg.lora.dropout,
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target_modules=target_modules,
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task_type=TaskType.CAUSAL_LM,
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bias="none",
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use_dora=tcfg.lora.use_dora,
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use_rslora=tcfg.lora.use_rslora,
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)
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# v0.40.6 #67 — surgical PEFT patches.
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from soup_cli.utils.peft_wiring import (
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apply_post_lora_patches,
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apply_pre_lora_patches,
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)
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apply_pre_lora_patches(self.model, cfg.base)
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self.model = get_peft_model(self.model, lora_config)
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apply_post_lora_patches(self.model)
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# QAT — insert fake quantization ops after LoRA. The "fp8" variant
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# is FP8 training (handled by apply_v028_speed_memory), not int8 QAT.
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if tcfg.quantization_aware and tcfg.quantization_aware != "fp8":
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from soup_cli.utils.qat import prepare_model_for_qat
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self.model = prepare_model_for_qat(self.model)
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# v0.35.0 #60 — multi-trainer wiring of v0.28.0 speed/memory features.
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from soup_cli.utils.v028_features import apply_v028_speed_memory
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apply_v028_speed_memory(
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model=self.model, tcfg=tcfg, base_model=cfg.base,
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console=console, device=self.device, backend=cfg.backend,
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)
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def _setup_unsloth(self, cfg: SoupConfig, tcfg) -> None:
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"""Load model via unsloth FastLanguageModel (2-5x faster)."""
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from soup_cli.utils.unsloth import load_model_and_tokenizer
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console.print(f"[dim]Loading model via [bold]unsloth[/]: {cfg.base}[/]")
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self.model, self.tokenizer = load_model_and_tokenizer(
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model_name=cfg.base,
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max_seq_length=cfg.data.max_length,
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quantization=tcfg.quantization,
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lora_r=tcfg.lora.r,
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lora_alpha=tcfg.lora.alpha,
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lora_dropout=tcfg.lora.dropout,
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target_modules=tcfg.lora.target_modules,
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)
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if self.tokenizer.pad_token is None:
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self.tokenizer.pad_token = self.tokenizer.eos_token
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def train(
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self,
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display: Optional[object] = None,
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tracker: Optional[object] = None,
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run_id: str = "",
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resume_from_checkpoint: Optional[str] = None,
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) -> dict:
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"""Run KTO training and return results summary."""
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if self.trainer is None:
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raise RuntimeError(
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"KTOTrainerWrapper.train() called before setup(). "
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"Call setup(dataset) first."
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)
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start = time.time()
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# Add callback for live display and experiment tracking
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if display:
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from soup_cli.monitoring.callback import SoupTrainerCallback
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self.trainer.add_callback(
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SoupTrainerCallback(
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display, tracker=tracker, run_id=run_id,
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loss_watchdog=self.config.training.loss_watchdog,
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loss_watchdog_threshold=self.config.training.loss_watchdog_threshold,
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loss_watchdog_patience=self.config.training.loss_watchdog_patience,
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)
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)
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from soup_cli.utils.v028_features import activation_offloading_context
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with activation_offloading_context(
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self.config.training, self._output_dir,
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):
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self.trainer.train(resume_from_checkpoint=resume_from_checkpoint)
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duration = time.time() - start
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# Save final model (LoRA adapter)
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self.trainer.save_model(self._output_dir)
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self.tokenizer.save_pretrained(self._output_dir)
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# Extract metrics
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logs = self.trainer.state.log_history
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train_losses = [entry["loss"] for entry in logs if "loss" in entry]
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hours = int(duration // 3600)
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minutes = int((duration % 3600) // 60)
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duration_str = f"{hours}h {minutes}m" if hours > 0 else f"{minutes}m"
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return {
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"initial_loss": train_losses[0] if train_losses else 0,
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"final_loss": train_losses[-1] if train_losses else 0,
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"duration": duration_str,
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"duration_secs": duration,
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"output_dir": self._output_dir,
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"total_steps": self.trainer.state.global_step,
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}
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