--- a
+++ b/finetune.py
@@ -0,0 +1,478 @@
+import os
+import pickle
+import random
+import sys
+from typing import List, Optional
+
+import fire
+import torch
+import transformers
+from datasets import load_dataset
+import wandb
+
+from torch import nn
+from torch.utils.data import Sampler
+from transformers.modeling_utils import unwrap_model
+
+from local_config import WANDB_ENTITY
+from utils.datacollator import MyDataCollatorForSeq2Seq
+from model.lavis.models.blip2_models.modeling_llama_imgemb import LlamaForCausalLM
+
+from peft import (
+    LoraConfig,
+    get_peft_model,
+    get_peft_model_state_dict,
+    prepare_model_for_int8_training,
+    set_peft_model_state_dict,
+)
+from transformers import AutoTokenizer, PreTrainedModel
+
+from utils.prompter import Prompter
+
+import logging
+logger = logging.getLogger(__name__)
+
+#how are input and instruction put together:
+'''
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:
+
+or
+
+Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Input:
+{input}
+
+### Response:
+'''
+
+class BalancedSampler(Sampler):
+    def __init__(self, true_indices, false_indices):
+        self.true_indices = true_indices
+        self.false_indices = false_indices
+        self.num_samples = 2 * min(len(self.true_indices), len(self.false_indices))
+
+    def __iter__(self):
+        # Randomly sample from true_indices
+        sampled_true_indices = random.sample(self.true_indices, len(self.false_indices))
+        # Merge and shuffle the two lists of indices
+        indices = sampled_true_indices + self.false_indices
+        random.shuffle(indices)
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+class InstructTrainer(transformers.Trainer):
+    def __init__(self, *args, rep_idxs=None, inst_idxs=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.rep_idxs = rep_idxs
+        self.inst_idxs = inst_idxs
+
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+        return BalancedSampler(self.rep_idxs, self.inst_idxs)
+
+WEIGHTS_NAME = "pytorch_model.bin"
+WEIGHTS_NAME_FINAL = "adapter_model.bin"
+TRAINING_ARGS_NAME = "training_args.bin"
+class ImgTrainer(transformers.Trainer): #also save img projector
+    def _save(self, output_dir: Optional[str] = None, state_dict=None):
+        # If we are executing this function, we are the process zero, so we don't check for that.
+        output_dir = output_dir if output_dir is not None else self.args.output_dir
+        os.makedirs(output_dir, exist_ok=True)
+        logger.info(f"Saving model checkpoint to {output_dir}")
+        # Save a trained model and configuration using `save_pretrained()`.
+        # They can then be reloaded using `from_pretrained()`
+        if not isinstance(self.model, PreTrainedModel):
+            if state_dict is None:
+                state_dict = self.model.state_dict()
+                base_state_dict = self.model.base_model.state_dict()
+                if 'model.model.img_proj_layer.weight' in base_state_dict:
+                    state_dict['base_model.model.model.img_proj_layer.weight'] = base_state_dict['model.model.img_proj_layer.weight']
+                    state_dict['base_model.model.model.img_proj_layer.bias'] = base_state_dict['model.model.img_proj_layer.bias']
+
+            if isinstance(unwrap_model(self.model), PreTrainedModel):
+                unwrap_model(self.model).save_pretrained(
+                    output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
+                )
+            else:
+                logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
+                torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
+        else:
+            self.model.save_pretrained(
+                output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
+            )
+
+        if self.tokenizer is not None:
+            self.tokenizer.save_pretrained(output_dir)
+
+        # Good practice: save your training arguments together with the trained model
+        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
+
+
+def save_pretrained(model, save_directory, **kwargs):
+    r"""
+    This function saves the adapter model and the adapter configuration files to a directory, so that it can be
+    reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`]
+    method.
+
+    Args:
+        save_directory (`str`):
+            Directory where the adapter model and configuration files will be saved (will be created if it does not
+            exist).
+        kwargs (additional keyword arguments, *optional*):
+            Additional keyword arguments passed along to the `push_to_hub` method.
+    """
+    if os.path.isfile(save_directory):
+        raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
+    os.makedirs(save_directory, exist_ok=True)
+
+    # save only the trainable weights
+    output_state_dict = get_peft_model_state_dict(model, kwargs.get("state_dict", None))
+    base_state_dict = model.base_model.state_dict()
+    if 'model.model.img_proj_layer.weight' in base_state_dict:
+        output_state_dict['base_model.model.model.img_proj_layer.weight'] = base_state_dict['model.model.img_proj_layer.weight']
+        output_state_dict['base_model.model.model.img_proj_layer.bias'] = base_state_dict['model.model.img_proj_layer.bias']
+
+    torch.save(output_state_dict, os.path.join(save_directory, WEIGHTS_NAME_FINAL))
+
+    inference_mode = model.peft_config.inference_mode
+    model.peft_config.inference_mode = True
+    model.peft_config.save_pretrained(save_directory)
+    model.peft_config.inference_mode = inference_mode
+
+
+def train(
+    # model/data params
+    base_model: str = "",  # the only required argument
+    lora_weights: str = None,
+    data_path: str = "yahma/alpaca-cleaned",
+    output_dir: str = "./lora-cxr",
+    # training hyperparams
+    batch_size: int = 128,
+    micro_batch_size: int = 2,
+    num_epochs: int = 10,
+    learning_rate: float = 3e-4,
+    cutoff_len: int = 1024, #256 -> need much more with examples in prompt (1024), 512 for without examples but long IG labels
+    val_set_size: int = 5,
+    # lora hyperparams
+    lora_r: int = 8,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.05,
+    lora_target_modules: List[str] = [ #default is for llama models
+        "q_proj",
+        "v_proj",
+    ],
+    # llm hyperparams
+    train_on_inputs: bool = False,  # if False, masks out inputs in loss
+    add_eos_token: bool = False,
+    group_by_length: bool = False,  # faster, but produces an odd training loss curve
+    # wandb params
+    wandb_project: str = "lora_training",
+    wandb_run_name: str = "lora_mimic_cxr",
+    wandb_entity: str = WANDB_ENTITY,
+    wandb_watch: str = "",  # options: false | gradients | all
+    wandb_log_model: str = "",  # options: false | true
+    resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
+    prompt_template_name: str = "alpaca",  # The prompt template to use, will default to alpaca.
+    use_embs=False,
+    use_instruct_data=False
+):
+    if int(os.environ.get("LOCAL_RANK", 0)) == 0:
+        print(
+            f"Training Alpaca-LoRA model with params:\n"
+            f"base_model: {base_model}\n"
+            f"lora_weights: {lora_weights}\n"
+            f"data_path: {data_path}\n"
+            f"output_dir: {output_dir}\n"
+            f"batch_size: {batch_size}\n"
+            f"micro_batch_size: {micro_batch_size}\n"
+            f"num_epochs: {num_epochs}\n"
+            f"learning_rate: {learning_rate}\n"
+            f"cutoff_len: {cutoff_len}\n"
+            f"val_set_size: {val_set_size}\n"
+            f"lora_r: {lora_r}\n"
+            f"lora_alpha: {lora_alpha}\n"
+            f"lora_dropout: {lora_dropout}\n"
+            f"lora_target_modules: {lora_target_modules}\n"
+            f"train_on_inputs: {train_on_inputs}\n"
+            f"add_eos_token: {add_eos_token}\n"
+            f"group_by_length: {group_by_length}\n"
+            f"wandb_project: {wandb_project}\n"
+            f"wandb_run_name: {wandb_run_name}\n"
+            f"wandb_entity: {wandb_entity}\n"
+            f"wandb_watch: {wandb_watch}\n"
+            f"wandb_log_model: {wandb_log_model}\n"
+            f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
+            f"prompt template: {prompt_template_name}\n"
+        )
+    assert (
+        base_model
+    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
+    gradient_accumulation_steps = batch_size // micro_batch_size
+
+    prompter = Prompter(prompt_template_name)
+
+    device_map = "auto"
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    ddp = world_size != 1
+    if ddp:
+        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
+        gradient_accumulation_steps = gradient_accumulation_steps // world_size
+
+    # Check if parameter passed or if set within environ
+    use_wandb = len(wandb_project) > 0 or (
+        "WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0
+    )
+    # Only overwrite environ if wandb param passed
+    if len(wandb_project) > 0:
+        os.environ["WANDB_PROJECT"] = wandb_project
+    if len(wandb_watch) > 0:
+        os.environ["WANDB_WATCH"] = wandb_watch
+    if len(wandb_log_model) > 0:
+        os.environ["WANDB_LOG_MODEL"] = wandb_log_model
+
+
+    if base_model == 'vicuna_v13':
+        model = LlamaForCausalLM.from_pretrained("lmsys/vicuna-13b-v1.3", torch_dtype=torch.float16, device_map='auto', load_in_8bit=False)
+        tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-13b-v1.3", use_fast=False, truncation_side="right", padding_side="right")
+    else: #7b
+        model = LlamaForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3", torch_dtype=torch.float16, device_map='auto', load_in_8bit=False)
+        tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.3", use_fast=False, truncation_side="right", padding_side="right")
+
+    tokenizer.pad_token = tokenizer.unk_token
+
+    if use_embs:
+        model.base_model.img_proj_layer = nn.Linear(768, model.base_model.config.hidden_size).to(model.base_model.device)
+
+    # add special token to tokenizer
+    tokenizer.add_special_tokens({"additional_special_tokens": ["<IMG>"]})
+    model.resize_token_embeddings(len(tokenizer))
+
+
+    def tokenize(prompt, add_eos_token=True):
+        # there's probably a way to do this with the tokenizer settings
+        # but again, gotta move fast
+        result = tokenizer(
+            prompt,
+            truncation=True,
+            max_length=cutoff_len,
+            padding=False,
+            return_tensors=None,
+        )
+        if (
+            result["input_ids"][-1] != tokenizer.eos_token_id
+            and len(result["input_ids"]) < cutoff_len
+            and add_eos_token
+        ):
+            result["input_ids"].append(tokenizer.eos_token_id)
+            result["attention_mask"].append(1)
+
+        result["labels"] = result["input_ids"].copy()
+
+        return result
+
+    def generate_and_tokenize_prompt(data_point):
+        full_prompt = prompter.generate_prompt(
+            data_point["instruction"],
+            data_point["input"],
+            data_point["output"],
+        )
+        tokenized_full_prompt = tokenize(full_prompt)
+        if not train_on_inputs:
+            user_prompt = prompter.generate_prompt(
+                data_point["instruction"], data_point["input"]
+            )
+            tokenized_user_prompt = tokenize(
+                user_prompt, add_eos_token=add_eos_token
+            )
+            user_prompt_len = len(tokenized_user_prompt["input_ids"])
+
+            if add_eos_token:
+                user_prompt_len -= 1
+
+            tokenized_full_prompt["labels"] = [
+                -100
+            ] * user_prompt_len + tokenized_full_prompt["labels"][
+                user_prompt_len:
+            ]  # could be sped up, probably
+        return tokenized_full_prompt
+
+    model = prepare_model_for_int8_training(model)
+
+    config = LoraConfig(
+        r=lora_r,
+        lora_alpha=lora_alpha,
+        target_modules=lora_target_modules,
+        lora_dropout=lora_dropout,
+        bias="none",
+        task_type="CAUSAL_LM",
+    )
+
+    model = get_peft_model(model, config) #this sets requires_grad for all params to False
+    # unfreeze the img_proj_layer
+    model.model.base_model.img_proj_layer.weight.requires_grad = True
+    model.model.base_model.img_proj_layer.bias.requires_grad = True
+
+    print("Loading data from ", data_path)
+    if data_path.endswith(".json") or data_path.endswith(".jsonl"):
+        data = load_dataset("json", data_files=data_path)
+    else:
+        data = load_dataset(data_path)
+
+    if resume_from_checkpoint:
+        # Check the available weights and load them
+        checkpoint_name = os.path.join(
+            resume_from_checkpoint, "pytorch_model.bin"
+        )  # Full checkpoint
+        if not os.path.exists(checkpoint_name):
+            checkpoint_name = os.path.join(
+                resume_from_checkpoint, "adapter_model.bin"
+            )  # only LoRA model - LoRA config above has to fit
+            resume_from_checkpoint = (
+                False  # So the trainer won't try loading its state
+            )
+        # The two files above have a different name depending on how they were saved, but are actually the same.
+        if os.path.exists(checkpoint_name):
+            print(f"Restarting from {checkpoint_name}")
+            adapters_weights = torch.load(checkpoint_name)
+            set_peft_model_state_dict(model, adapters_weights)
+        else:
+            print(f"Checkpoint {checkpoint_name} not found")
+
+    model.print_trainable_parameters()  # Be more transparent about the % of trainable params.
+
+    if val_set_size > 0:
+        train_val = data["train"].train_test_split(
+            test_size=val_set_size, shuffle=True, seed=42
+        )
+        train_data = (
+            train_val["train"].shuffle().map(generate_and_tokenize_prompt)
+        )
+        val_data = (
+            train_val["test"].shuffle().map(generate_and_tokenize_prompt)
+        )
+    else:
+        train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
+        val_data = None
+
+    if use_instruct_data:
+        report_indices = [i for i, item in enumerate(train_data) if item['is_report']][:5]
+        instruct_indices = [i for i, item in enumerate(train_data) if not item['is_report']][:5]
+
+    if not ddp and torch.cuda.device_count() > 1:
+        # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
+        model.is_parallelizable = True
+        model.model_parallel = True
+
+    wandb.init(
+        project=wandb_project,
+        entity=wandb_entity,
+        name=wandb_run_name
+    )
+
+    if use_instruct_data:
+        trainer = InstructTrainer(
+            model=model,
+            train_dataset=train_data,
+            eval_dataset=val_data,
+            rep_idxs = report_indices,
+            inst_idxs = instruct_indices,
+            args=transformers.TrainingArguments(
+                per_device_train_batch_size=micro_batch_size,
+                gradient_accumulation_steps=gradient_accumulation_steps,
+                warmup_steps=100,
+                num_train_epochs=num_epochs,
+                learning_rate=learning_rate,
+                fp16=True,
+                logging_steps=10,
+                optim="adamw_torch",
+                evaluation_strategy="steps" if val_set_size > 0 else "no",
+                save_strategy="steps",
+                eval_steps=200 if val_set_size > 0 else None,
+                save_steps=200,
+                output_dir=output_dir,
+                save_total_limit=None,
+                load_best_model_at_end=True if val_set_size > 0 else False,
+                ddp_find_unused_parameters=False if ddp else None,
+                group_by_length=group_by_length,
+                report_to="wandb" if use_wandb else None,
+                run_name=wandb_run_name if use_wandb else None,
+                max_steps=-1,
+                dataloader_num_workers=8,
+                remove_unused_columns=False if use_embs else True,
+            ),
+            data_collator=MyDataCollatorForSeq2Seq(
+                tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+            ) if use_embs else
+            transformers.DataCollatorForSeq2Seq(
+                tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+            ),
+        )
+    else:
+        trainer = ImgTrainer(
+            model=model,
+            train_dataset=train_data,
+            eval_dataset=val_data,
+            args=transformers.TrainingArguments(
+                per_device_train_batch_size=micro_batch_size,
+                gradient_accumulation_steps=gradient_accumulation_steps,
+                warmup_steps=100,
+                num_train_epochs=num_epochs,
+                learning_rate=learning_rate,
+                fp16=True,
+                logging_steps=10,
+                optim="adamw_torch",
+                evaluation_strategy="steps" if val_set_size > 0 else "no",
+                save_strategy="steps",
+                eval_steps=400 if val_set_size > 0 else None,
+                save_steps=400,
+                output_dir=output_dir,
+                save_total_limit=None,
+                load_best_model_at_end=True if val_set_size > 0 else False,
+                ddp_find_unused_parameters=False if ddp else None,
+                group_by_length=group_by_length,
+                report_to="wandb" if use_wandb else None,
+                run_name=wandb_run_name if use_wandb else None,
+                max_steps=-1,
+                dataloader_num_workers=8,
+                remove_unused_columns=False if use_embs else True
+            ),
+            data_collator=MyDataCollatorForSeq2Seq(
+                tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+            ) if use_embs else
+            transformers.DataCollatorForSeq2Seq(
+                tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+            ),
+        )
+    model.config.use_cache = False
+
+    old_state_dict = model.state_dict
+    model.state_dict = (
+        lambda self, *_, **__: get_peft_model_state_dict(
+            self, old_state_dict()
+        )
+    ).__get__(model, type(model))
+
+    if torch.__version__ >= "2" and sys.platform != "win32":
+        model = torch.compile(model)
+
+    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+
+    save_pretrained(model, output_dir)
+
+    print(
+        "\n If there's a warning about missing keys above, please disregard :)"
+    )
+
+
+if __name__ == "__main__":
+    fire.Fire(train)