Change loss and dataset format with SFTTrainer (TRL & QLoRA )
I want to finetune thefalcon-7bmodel using a triplet loss function. However I don’t understand how to do so.I’m not very familiar with those new models, I did not find any ressource for this. I’m following this post:Fine-tune Falcon-7B on Your GPU with TRL and QLoRa | by Benjamin Marie | Jun, 2023 | MediumThe code I have so far, which does not work is:import torch, einops
from datasets import load_dataset
from peft import LoraConfig, TaskType
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    AutoTokenizer,
    TrainingArguments
)
from peft.tuners.lora import LoraLayer
from trl import SFTTrainer
from transformers import  FalconModel
from datasets import Dataset as hfd

def create_and_prepare_model():
    """ create the configuration for peft, bitsandbytes, and instanciate the tokenizer"""
    compute_dtype = getattr(torch, "float16")

    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=compute_dtype,
        bnb_4bit_use_double_quant=True,
    )
    peft_config = LoraConfig(
        lora_alpha=16,
        lora_dropout=0.1,
        r=64,
        bias="none",
        task_type= TaskType.FEATURE_EXTRACTION,
        target_modules=[
            "query_key_value"
        ],
    )

    tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b", trust_remote_code=True, return_token_type_ids=False)
    tokenizer.pad_token = tokenizer.eos_token

    return  peft_config, bnb_config, tokenizer

def preprocess_function(examples):
    result={}
    anchor = examples['anchor']
    positive = examples['positive'] 
    negative = examples['negative'] 
    result_anchor = tokenizer(anchor,return_token_type_ids=False,   return_tensors="pt")
    result_positive = tokenizer(positive,return_token_type_ids=False, return_tensors="pt")
    result_negative = tokenizer(negative,return_token_type_ids=False,   return_tensors="pt")
    for k,v in result_anchor.items():
        result[f"anchor_{k}"]= v
    for k,v in result_positive.items():
        result[f"positive_{k}"] = v
    for k,v in result_negative.items():
        result[f"negative_{k}"] = v
    return result

def formatting_func(inputs):
    anchor_inputs = {key.replace('anchor_', ''): val for key, val in inputs.items() if key.startswith('anchor_')}
    positive_inputs = {key.replace('positive_', ''): val for key, val in inputs.items() if key.startswith('positive_')}
    negative_inputs = {key.replace('negative_', ''): val for key, val in inputs.items() if key.startswith('negative_')}
    return {"anchor":anchor_inputs, "positive":positive_inputs,"negative":negative_inputs}

class CustomTrainer(SFTTrainer):
    def loss_function(embeddings, margin=1.0):
        anchor, positive, negative = embeddings
        pos_dist = F.pairwise_distance(anchor, positive)
        neg_dist = F.pairwise_distance(anchor, negative)
        return F.relu(pos_dist - neg_dist + margin)
    def compute_loss(self, inputs, return_outputs=False):
        anchor_embeddings = self.model(inputs['anchor'])
        positive_embeddings = self.model(inputs['positive'])
        negative_embeddings = self.model(inputs['negative'])
        loss = self.loss_function((anchor_embeddings, positive_embeddings, negative_embeddings))
        return (loss, torch.tensor([0])) if return_outputs else loss


model_id="tiiuae/falcon-7b"

#dummy dataset
datasethf = hfd.from_list([{"anchor": "hi", "positive":"hello", "negative":"sweet donuts with sugar"}]*1000)

# instanciate the training argument for the SFTTrainer
training_arguments = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,
    optim="paged_adamw_32bit",
    save_steps=100,
    logging_steps=10,
    learning_rate=2e-4,
    fp16=True,
    max_grad_norm=0.3,
    max_steps=1000,
    warmup_ratio=0.03,
    lr_scheduler_type="constant",
)

peft_config,bnb_config, tokenizer = create_and_prepare_model()
model = FalconModel.from_pretrained(model_id, quantization_config=bnb_config, device_map={"": 0}, trust_remote_code=True)

dataset = datasethf.train_test_split()
processed_datasets = dataset.map(
        preprocess_function,
        remove_columns=dataset["train"].column_names,
        desc="Running tokenizer on dataset",
    )
trainer = CustomTrainer(
    model=model,
    train_dataset=processed_datasets["train"],
    eval_dataset=processed_datasets["train"],
    peft_config=peft_config,
    max_seq_length=512,
    tokenizer=tokenizer,
    args=training_arguments,
    formatting_func=formatting_func,
    packing=False,
)
trainer.train()I can’t get to make it work because I don’t fully understand how to make the right data_collator, preprocessing_function, formatting_function, etc., so that they all work together.So I’m not even sure if this Is this the right approach. Do you have ideas or ressources share ? Thank you !