187144d4e7Trim and tighten code comments and docstrings across the repository. Comment-only: every changed file verified code-identical to main via AST/token comparison.
296 lines
8.5 KiB
Python
296 lines
8.5 KiB
Python
from unsloth import FastLanguageModel, FastVisionModel, UnslothVisionDataCollator
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from unsloth.chat_templates import get_chat_template
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from trl import SFTTrainer, SFTConfig
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from transformers import (
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DataCollatorForLanguageModeling,
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DataCollatorForSeq2Seq,
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TrainingArguments,
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)
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from datasets import load_dataset, Dataset
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import torch
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from tqdm import tqdm
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import pandas as pd
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import multiprocessing as mp
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from multiprocessing import Process, Queue
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import gc
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# ruff: noqa
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import sys
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from pathlib import Path
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REPO_ROOT = Path(__file__).parents[3]
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sys.path.insert(0, str(REPO_ROOT))
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from tests.utils.cleanup_utils import safe_remove_directory
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from tests.utils.perplexity_eval import (
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ppl_model,
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add_to_comparison,
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print_model_comparison,
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)
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def load_and_compute_8bit_ppl(
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result_queue,
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load_in_4bit = False,
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load_in_8bit = False,
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):
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"""Load model and compute perplexity in subprocess"""
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from unsloth import FastLanguageModel
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from tests.utils.perplexity_eval import ppl_model
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merged_model, merged_tokenizer = FastLanguageModel.from_pretrained(
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model_name = "./unsloth_out/merged_mistral_text_model",
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max_seq_length = 2048,
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load_in_4bit = load_in_4bit,
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load_in_8bit = load_in_8bit,
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)
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# merged_tokenizer = get_chat_template(
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# merged_tokenizer,
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# chat_template="llama-3.1",
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# )
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# Load dataset fresh in subprocess.
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dataset_ppl = load_dataset("allenai/openassistant-guanaco-reformatted", split = "eval")
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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{}
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### Input:
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{}
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### Response:
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{}"""
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EOS_TOKEN = merged_tokenizer.eos_token
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def formatting_prompts_func(examples):
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instructions = []
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inputs = []
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outputs = []
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texts = []
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for conversation in examples["messages"]:
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user_message = ""
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assistant_message = ""
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for turn in conversation:
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if turn["role"] == "user":
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user_message = turn["content"]
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elif turn["role"] == "assistant":
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assistant_message = turn["content"]
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instruction = "Complete the statement"
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instructions.append(instruction)
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inputs.append(user_message)
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outputs.append(assistant_message)
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text = alpaca_prompt.format(instruction, user_message, assistant_message) + EOS_TOKEN
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texts.append(text)
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return {
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"instruction": instructions,
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"input": inputs,
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"output": outputs,
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"text": texts,
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}
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dataset_ppl = dataset_ppl.map(formatting_prompts_func, batched = True)
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ppl_value = ppl_model(merged_model, merged_tokenizer, dataset_ppl)
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# Convert to Python float if it's a tensor.
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if torch.is_tensor(ppl_value):
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ppl_value = ppl_value.cpu().item()
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elif hasattr(ppl_value, "item"):
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ppl_value = ppl_value.item()
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else:
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ppl_value = float(ppl_value)
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result_queue.put(ppl_value)
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del merged_model
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del merged_tokenizer
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del dataset_ppl
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torch.cuda.empty_cache()
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gc.collect()
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if __name__ == "__main__":
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mp.set_start_method("spawn", force = True)
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if torch.cuda.is_bf16_supported():
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compute_dtype = torch.bfloat16
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attn_implementation = "flash_attention_2"
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else:
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compute_dtype = torch.float16
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attn_implementation = "sdpa"
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "unsloth/mistral-7b-v0.3",
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max_seq_length = 2048,
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dtype = compute_dtype,
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load_in_4bit = True,
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load_in_8bit = False,
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full_finetuning = False,
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attn_implementation = attn_implementation,
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)
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EOS_TOKEN = tokenizer.eos_token
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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{}
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### Input:
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{}
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### Response:
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{}"""
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def formatting_prompts_func(examples):
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instructions = []
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inputs = []
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outputs = []
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texts = []
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for conversation in examples["messages"]:
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user_message = ""
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assistant_message = ""
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for turn in conversation:
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if turn["role"] == "user":
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user_message = turn["content"]
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elif turn["role"] == "assistant":
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assistant_message = turn["content"]
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instruction = "Complete the statement"
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instructions.append(instruction)
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inputs.append(user_message)
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outputs.append(assistant_message)
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text = alpaca_prompt.format(instruction, user_message, assistant_message) + EOS_TOKEN
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texts.append(text)
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return {
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"instruction": instructions,
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"input": inputs,
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"output": outputs,
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"text": texts,
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}
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dataset_train = load_dataset("allenai/openassistant-guanaco-reformatted", split = "train")
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dataset_ppl = load_dataset("allenai/openassistant-guanaco-reformatted", split = "eval")
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dataset_train = dataset_train.map(formatting_prompts_func, batched = True)
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dataset_ppl = dataset_ppl.map(formatting_prompts_func, batched = True)
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add_to_comparison("Base model 4 bits", ppl_model(model, tokenizer, dataset_ppl))
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model = FastLanguageModel.get_peft_model(
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model,
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r = 16,
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target_modules = [
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"k_proj",
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"q_proj",
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"v_proj",
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"o_proj",
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"gate_proj",
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"down_proj",
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"up_proj",
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],
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lora_alpha = 16,
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lora_dropout = 0,
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bias = "none",
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use_gradient_checkpointing = "unsloth",
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random_state = 3407,
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use_rslora = False,
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loftq_config = None,
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)
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from unsloth import is_bfloat16_supported
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trainer = SFTTrainer(
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model = model,
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tokenizer = tokenizer,
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train_dataset = dataset_train,
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dataset_text_field = "text",
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max_seq_length = 2048,
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dataset_num_proc = 2,
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packing = False,
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args = TrainingArguments(
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per_device_train_batch_size = 2,
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gradient_accumulation_steps = 4,
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warmup_ratio = 0.1,
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max_steps = 200,
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learning_rate = 2e-4,
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fp16 = not is_bfloat16_supported(),
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bf16 = is_bfloat16_supported(),
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logging_steps = 50,
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optim = "adamw_8bit",
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lr_scheduler_type = "linear",
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seed = 3407,
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output_dir = "outputs",
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report_to = "none",
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),
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)
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trainer_stats = trainer.train()
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add_to_comparison("Qlora model", ppl_model(model, tokenizer, dataset_ppl))
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# Merge and save to local disk.
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print("merge and save to local disk")
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model.save_pretrained_merged(
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save_directory = "./unsloth_out/merged_mistral_text_model", tokenizer = tokenizer
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)
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# print("cleaning")
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# del model
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# del tokenizer
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# torch.cuda.empty_cache()
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# gc.collect()
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# Load merged model from disk and test.
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print("Loading merged model in 4 bit for perplexity test")
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merged_model, merged_tokenizer = FastLanguageModel.from_pretrained(
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model_name = "./unsloth_out/merged_mistral_text_model",
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max_seq_length = 2048,
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load_in_4bit = True,
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load_in_8bit = False,
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)
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add_to_comparison(
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"merged model load 4bit", ppl_model(merged_model, merged_tokenizer, dataset_ppl)
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)
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print("Computing 8-bit model perplexity in subprocess...")
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result_queue = mp.Queue()
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p = mp.Process(target = load_and_compute_8bit_ppl, args = (result_queue, False, True))
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p.start()
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p.join()
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ppl_8bit = result_queue.get()
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add_to_comparison("merged model loaded 8bits", ppl_8bit)
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print("Loading merged model in 16 bit for perplexity test")
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merged_model, merged_tokenizer = FastLanguageModel.from_pretrained(
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model_name = "./unsloth_out/merged_mistral_text_model",
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max_seq_length = 2048,
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load_in_4bit = False,
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load_in_8bit = False,
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)
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add_to_comparison(
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"merged model loaded 16bits",
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ppl_model(merged_model, merged_tokenizer, dataset_ppl),
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)
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print_model_comparison()
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safe_remove_directory("./outputs")
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safe_remove_directory("./unsloth_compiled_cache")
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safe_remove_directory("./unsloth_out")
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