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+ "# Fine-tuning Florence-2 on DocVQA\n",
+ "\n",
+ "In this notebook, we will fine-tune Florence-2 by MSFT, a new vision language model capable of various tasks, on document question answering.\n",
+ "\n",
+ "Let's start by installing the dependencies and loading the dataset."
+ ],
+ "metadata": {
+ "id": "KVmYbqqUSlCH"
+ }
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+ "source": [
+ "!pip install -q datasets flash_attn timm einops"
+ ],
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+ "id": "Y6b1dvjgYXOD",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
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+ "outputId": "c1213f07-cd8d-4aaf-eb80-f7936faacce0"
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+ "\u001b[?25h Building wheel for flash_attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
+ "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
+ "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0m"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "data = load_dataset(\"HuggingFaceM4/DocumentVQA\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 465,
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+ },
+ "id": "CpCRb7PfYZP-",
+ "outputId": "71d770b7-1317-413b-8e18-b052e73d77a1"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading readme: 0%| | 0.00/806 [00:00"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'DocVQA': 'unanswerable'}\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'DocVQA': '499150498'}\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We need to construct our dataset. Note how we are adding a new task prefix `` before the question when constructing the prompt."
+ ],
+ "metadata": {
+ "id": "YQKWM3P6Ncx_"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from torch.utils.data import Dataset\n",
+ "\n",
+ "class DocVQADataset(Dataset):\n",
+ " def __init__(self, data):\n",
+ " self.data = data\n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.data)\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " example = self.data[idx]\n",
+ " question = \"\" + example['question']\n",
+ " first_answer = example['answers'][0]\n",
+ " image = example['image']\n",
+ " if image.mode != \"RGB\":\n",
+ " image = image.convert(\"RGB\")\n",
+ " return question, first_answer, image\n"
+ ],
+ "metadata": {
+ "id": "ax4KEL3ceiSO"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's get to fine-tuning. We will create our dataset, the data collator, and start training. In A100 with 40GB memory, we can fit in 6 examples. If you're training on T4, you can use batch size of 1."
+ ],
+ "metadata": {
+ "id": "GtkPrXzkODGx"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "from torch.utils.data import DataLoader\n",
+ "from tqdm import tqdm\n",
+ "from transformers import (AdamW, AutoProcessor, get_scheduler)\n",
+ "\n",
+ "def collate_fn(batch):\n",
+ " questions, answers, images = zip(*batch)\n",
+ " inputs = processor(text=list(questions), images=list(images), return_tensors=\"pt\", padding=True).to(device)\n",
+ " return inputs, answers\n",
+ "\n",
+ "# Create datasets\n",
+ "train_dataset = DocVQADataset(data['train'])\n",
+ "val_dataset = DocVQADataset(data['validation'])\n",
+ "\n",
+ "# Create DataLoader\n",
+ "batch_size = 6\n",
+ "num_workers = 0\n",
+ "\n",
+ "train_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, num_workers=num_workers, shuffle=True)\n",
+ "val_loader = DataLoader(val_dataset, batch_size=batch_size, collate_fn=collate_fn, num_workers=num_workers)"
+ ],
+ "metadata": {
+ "id": "ilMb0ivGdt9l"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def train_model(train_loader, val_loader, model, processor, epochs=10, lr=1e-6):\n",
+ " optimizer = AdamW(model.parameters(), lr=lr)\n",
+ " num_training_steps = epochs * len(train_loader)\n",
+ " lr_scheduler = get_scheduler(\n",
+ " name=\"linear\",\n",
+ " optimizer=optimizer,\n",
+ " num_warmup_steps=0,\n",
+ " num_training_steps=num_training_steps,\n",
+ " )\n",
+ "\n",
+ " for epoch in range(epochs):\n",
+ " model.train()\n",
+ " train_loss = 0\n",
+ " i = -1\n",
+ " for batch in tqdm(train_loader, desc=f\"Training Epoch {epoch + 1}/{epochs}\"):\n",
+ " i += 1\n",
+ " inputs, answers = batch\n",
+ "\n",
+ " input_ids = inputs[\"input_ids\"]\n",
+ " pixel_values = inputs[\"pixel_values\"]\n",
+ " labels = processor.tokenizer(text=answers, return_tensors=\"pt\", padding=True, return_token_type_ids=False).input_ids.to(device)\n",
+ "\n",
+ " outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)\n",
+ " loss = outputs.loss\n",
+ "\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " lr_scheduler.step()\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " train_loss += loss.item()\n",
+ "\n",
+ " avg_train_loss = train_loss / len(train_loader)\n",
+ " print(f\"Average Training Loss: {avg_train_loss}\")\n",
+ "\n",
+ " # Validation phase\n",
+ " model.eval()\n",
+ " val_loss = 0\n",
+ " with torch.no_grad():\n",
+ " for batch in tqdm(val_loader, desc=f\"Validation Epoch {epoch + 1}/{epochs}\"):\n",
+ " inputs, answers = batch\n",
+ "\n",
+ " input_ids = inputs[\"input_ids\"]\n",
+ " pixel_values = inputs[\"pixel_values\"]\n",
+ " labels = processor.tokenizer(text=answers, return_tensors=\"pt\", padding=True, return_token_type_ids=False).input_ids.to(device)\n",
+ "\n",
+ " outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)\n",
+ " loss = outputs.loss\n",
+ "\n",
+ " val_loss += loss.item()\n",
+ "\n",
+ " avg_val_loss = val_loss / len(val_loader)\n",
+ " print(f\"Average Validation Loss: {avg_val_loss}\")\n",
+ "\n",
+ " # Save model checkpoint\n",
+ " output_dir = f\"./model_checkpoints/epoch_{epoch+1}\"\n",
+ " os.makedirs(output_dir, exist_ok=True)\n",
+ " model.save_pretrained(output_dir)\n",
+ " processor.save_pretrained(output_dir)\n"
+ ],
+ "metadata": {
+ "id": "bC06Mc7jOdpY"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "After training, we will push the model to Hugging Face Hub. To do so, we need to login first with write access. Make sure to pass either write token or fine-grained token (by first creating the repository and setting up fine-grained token access)."
+ ],
+ "metadata": {
+ "id": "A6whoCNXR2Hn"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 359,
+ "referenced_widgets": [
+ "5ea5d7a767e9405db0c12b184104fad7",
+ "a28a4879bba845488a059d455840f7df",
+ "774a9597328c429ebe856762d4cac46f",
+ "df6100e810db453f9baca6aa2f0e5b3d",
+ "5a71cc16b2ca4243b0abfd36d6887a50",
+ "6d0062d900bf458792746066546e67c0",
+ "3de2c8c0b0674ceb8f9f8165282f8382",
+ "885858d51c0045efb685996af82dd919",
+ "2d7761357d1444b8bc145255388bbb21",
+ "edca5834a1fe4ef0996fc6d357c3f483",
+ "69d144137e4449f087b3b3fb11e8fcd9",
+ "f2412043f7f94b1bad2c6a0bdf894cce",
+ "3038099151b445279ab600eee329abf9",
+ "31c07e72d49e4dd383730d6bb2f13b2d",
+ "bbe052c524864d5faef734b831353d1f",
+ "0f77e1f0fd4e4839a05231fa61dd5b18",
+ "2457df1786e94920be8687211c86414a"
+ ]
+ },
+ "id": "LjpWNSXePbWx",
+ "outputId": "eeafcd1f-9336-41ab-de96-d161a6e2581d"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value=' --epochs --eval-steps
+```
+
+Example:
+
+```bash
+python distributed_train.py --dataset docvqa --epochs 10 --eval-steps 1000
+```
+
+- dataset_name: Name of the dataset to use (docvqa or cauldron).
+- num_epochs: Number of epochs to train (default is 10).
+- evaluation_steps: Frequency of evaluation during training (default is 10000 steps).
diff --git a/Experiments/CV/florence2-finetuning/data.py b/Experiments/CV/florence2-finetuning/data.py
new file mode 100644
index 0000000..768c889
--- /dev/null
+++ b/Experiments/CV/florence2-finetuning/data.py
@@ -0,0 +1,165 @@
+import concurrent.futures
+import io
+
+import pandas as pd
+from datasets import get_dataset_config_names, load_dataset, load_from_disk
+from PIL import Image
+from torch.utils.data import Dataset
+from tqdm import tqdm
+import random
+
+class BaseDataset(Dataset):
+ def __init__(self, split):
+ self._split = split
+ self.name = "BaseDataset"
+ self.data = []
+ self.task_prompt = ""
+
+ def __len__(self):
+ return len(self.data)
+
+ def correct_casing_finqa(self, text, is_question=False):
+ if text and text[0].islower():
+ text = text.capitalize()
+ if not text.endswith(".") and not is_question:
+ text += "."
+ if not text.endswith("?") and is_question:
+ text += "?"
+ return text
+
+
+class DocVQADataset(BaseDataset):
+ def __init__(self, split):
+ super().__init__(split)
+ self.name = "DocVQA"
+ self.data = load_dataset("HuggingFaceM4/DocumentVQA", split=split)
+ self.task_prompt = ""
+
+ def __getitem__(self, idx):
+ example = self.data[idx]
+ question = self.task_prompt + self.correct_casing_finqa(
+ example["question"], True
+ )
+ first_answer = example["answers"][0]
+ answers = first_answer
+ image = example["image"] # The image is already a PIL Image object
+ if image.mode != "RGB":
+ image = image.convert("RGB")
+ return question, answers, image
+
+class VQAInstructDataset(BaseDataset):
+ def __init__(self, split, max_length=1024):
+ super().__init__(split)
+ self.name = "VQA-Instruct"
+ self._max_length = max_length
+ self.vqa_data = load_from_disk("HuggingFaceM4/Docmatix_single_images")
+ split_actions = {
+ 'train': lambda data: data.train_test_split(test_size=0.05, seed=42)['train'],
+ 'validation': lambda data: data.train_test_split(test_size=0.05, seed=42)['test'].train_test_split(test_size=0.5, seed=42)['train'],
+ 'test': lambda data: data.train_test_split(test_size=0.05, seed=42)['test'].train_test_split(test_size=0.5, seed=42)['test']
+ }
+
+ if split not in split_actions:
+ raise ValueError(f"Unknown split: {split}")
+
+ self.vqa_data = split_actions[split](self.vqa_data)
+ self.task_prompt = ""
+
+ def __len__(self):
+ return len(self.vqa_data)
+
+ def __getitem__(self, idx):
+ example = self.vqa_data[idx]
+ texts = random.choice(example['texts'])
+
+ question = self.task_prompt + texts["user"]
+ answer = texts["assistant"]
+
+ image = example['images']
+
+ if image.mode != "RGB":
+ image = image.convert("RGB")
+
+ return question, answer, image
+
+class TheCauldronDataset(BaseDataset):
+ def __init__(self, split):
+ super().__init__(split)
+ self.name = "The-Cauldron"
+ self.images_df, self.texts_df = self.load_all_configs(split)
+ self.task_prompt = ""
+
+ def __len__(self):
+ return len(self.texts_df)
+
+ def load_config(self, config_name, split):
+ print(f"Loading config: {config_name}")
+ dataset = load_dataset("HuggingFaceM4/the_cauldron", config_name, split=split)
+ print(f"Finished loading config: {config_name}")
+
+ df_data = dataset.to_pandas()
+
+ # Create the images DataFrame
+ df_images = df_data[['images']].copy()
+ df_images['image_index'] = df_images.index
+
+ # Explode the texts into separate rows and create a DataFrame
+ df_texts = df_data[['texts']].explode('texts').reset_index()
+ df_texts.rename(columns={'index': 'image_index'}, inplace=True)
+
+ # Extract 'user', 'assistant', and 'source' from the 'texts' column
+ df_texts['question'] = df_texts['texts'].apply(lambda x: x.get('user'))
+ df_texts['answer'] = df_texts['texts'].apply(lambda x: x.get('assistant'))
+ df_texts['source'] = df_texts['texts'].apply(lambda x: x.get('source'))
+
+ # Drop the original 'texts' column
+ df_texts.drop(columns=['texts'], inplace=True)
+
+ # Copy the 'source' column to the images df, using the first source per image index
+ df_images = df_images.merge(df_texts[['image_index', 'source']], on='image_index', how='left')
+ print(f"Finished processing config: {config_name}")
+
+ return df_images, df_texts
+
+ def load_all_configs(self, split):
+ cauldron_config_names = get_dataset_config_names("HuggingFaceM4/the_cauldron")
+
+ images_dfs = []
+ texts_dfs = []
+
+ # Use ThreadPoolExecutor for parallel processing and tqdm for progress tracking
+ with concurrent.futures.ThreadPoolExecutor(max_workers=16) as executor: # Limit the number of workers
+ with tqdm(total=len(cauldron_config_names), desc="Total Progress") as total_pbar:
+ futures = {executor.submit(self.load_config, config_name, split): config_name for config_name in cauldron_config_names}
+ for future in concurrent.futures.as_completed(futures):
+ config_name = futures[future]
+ try:
+ df_images, df_texts = future.result()
+ images_dfs.append(df_images)
+ texts_dfs.append(df_texts)
+ except Exception as exc:
+ print(f"{config_name} generated an exception: {exc}")
+ total_pbar.update(1)
+
+ # Merge all the loaded DataFrames
+ print("Merging DataFrames...")
+ merged_images_df = pd.concat(images_dfs, ignore_index=True)
+ merged_texts_df = pd.concat(texts_dfs, ignore_index=True)
+ print("Finished merging DataFrames")
+
+ return merged_images_df, merged_texts_df
+
+ def __getitem__(self, idx):
+ example = self.texts_df.iloc[idx]
+ question = example["question"]
+ answer = example["answer"]
+ source = example["source"]
+ image_idx = example["image_index"]
+
+ image_data = self.images_df.loc[(self.images_df['image_index'] == image_idx) & (self.images_df['source'] == source), 'images'].values[0][0]['bytes']
+ image = Image.open(io.BytesIO(image_data))
+
+ if image.mode != "RGB":
+ image = image.convert("RGB")
+
+ return question, answer, image
diff --git a/Experiments/CV/florence2-finetuning/distributed_train.py b/Experiments/CV/florence2-finetuning/distributed_train.py
new file mode 100644
index 0000000..2748036
--- /dev/null
+++ b/Experiments/CV/florence2-finetuning/distributed_train.py
@@ -0,0 +1,291 @@
+import argparse
+import os
+from functools import partial
+
+import friendlywords as fw
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm
+from transformers import (AdamW, AutoModelForCausalLM, AutoProcessor,
+ get_scheduler)
+
+import wandb
+from data import DocVQADataset, TheCauldronDataset, VQAInstructDataset
+from peft import LoraConfig, get_peft_model
+
+
+def setup(rank, world_size):
+ os.environ["MASTER_ADDR"] = "localhost"
+ os.environ["MASTER_PORT"] = "12355"
+ dist.init_process_group("nccl", rank=rank, world_size=world_size)
+ torch.cuda.set_device(rank)
+
+
+def cleanup():
+ dist.destroy_process_group()
+
+
+def collate_fn(batch, processor, device):
+ questions, answers, images = zip(*batch)
+ inputs = processor(
+ text=list(questions), images=list(images), return_tensors="pt", padding=True, truncation=True, max_length=800
+ ).to(device)
+ return inputs, answers
+
+
+def create_data_loaders(
+ train_dataset,
+ val_datasets,
+ batch_size,
+ num_workers,
+ rank,
+ world_size,
+ processor,
+ device,
+):
+ train_sampler = DistributedSampler(
+ train_dataset, num_replicas=world_size, rank=rank
+ )
+
+ train_loader = DataLoader(
+ train_dataset,
+ batch_size=batch_size,
+ collate_fn=partial(collate_fn, processor=processor, device=device),
+ num_workers=num_workers,
+ sampler=train_sampler,
+ )
+
+ val_loaders = {}
+ for name, val_dataset in val_datasets.items():
+ val_sampler = DistributedSampler(val_dataset, num_replicas=world_size, rank=rank)
+ val_loader = DataLoader(
+ val_dataset,
+ batch_size=batch_size//2,
+ collate_fn=partial(collate_fn, processor=processor, device=device),
+ num_workers=num_workers,
+ sampler=val_sampler,
+ )
+ val_loaders[name] = val_loader
+
+ return train_loader, val_loaders
+
+def evaluate_model(rank, world_size, model, val_loaders, device, train_loss, processor, global_step, batch_size, max_val_item_count):
+ if rank == 0:
+ avg_train_loss = train_loss / (global_step*batch_size*world_size)
+ wandb.log({"step": global_step, "train_loss": avg_train_loss})
+ print(f"Rank {rank} - Average Training Loss: {avg_train_loss}")
+
+ # Evaluation phase
+ model.eval()
+ for val_name, val_loader in val_loaders.items():
+ val_loss = 0
+ with torch.no_grad():
+ val_item_count = 0
+ for batch in tqdm(val_loader, desc=f"Evaluation on {val_name} at step {global_step}", position=rank):
+ val_item_count += len(batch)
+ inputs, answers = batch
+
+ # Prepare the input and target tensors
+ input_ids = inputs["input_ids"].to(device)
+ pixel_values = inputs["pixel_values"].to(device)
+ labels = processor.tokenizer(
+ text=answers,
+ return_tensors="pt",
+ padding=True,
+ return_token_type_ids=False,
+ truncation=True,
+ max_length=800,
+ ).input_ids.to(device)
+
+ outputs = model(
+ input_ids=input_ids, pixel_values=pixel_values, labels=labels
+ )
+ loss = outputs.loss
+
+ val_loss += loss.item()
+ if val_item_count > max_val_item_count:
+ break
+
+ avg_val_loss = val_loss / val_item_count
+ print(f"Rank {rank} - Step {global_step} - Average Validation Loss ({val_name}): {avg_val_loss}")
+
+ # Log metrics to wandb
+ if rank == 0:
+ wandb.log({f"{val_name}_val_loss": avg_val_loss, "step": global_step})
+
+ model.train()
+
+def train_model(rank, world_size, dataset_name, batch_size=6, use_lora=False, epochs=10, lr=1e-6, eval_steps=10, run_name=None, max_val_item_count=1000):
+ setup(rank, world_size)
+ device = torch.device(f"cuda:{rank}")
+ if run_name is None:
+ run_name = fw.generate(2, separator="_")
+
+ # Initialize wandb
+ if rank == 0: # Only initialize wandb in the main process
+ wandb.init(project="DocVQA-instruct", name=run_name)
+ wandb.config.update({
+ "dataset": dataset_name,
+ "batch_size": batch_size,
+ "use_lora": use_lora,
+ "epochs": epochs,
+ "learning_rate": lr,
+ "eval_steps": eval_steps,
+ "world_size": world_size,
+ })
+
+ # Load the dataset based on the dataset_name argument
+ if dataset_name == "docvqa":
+ train_dataset = DocVQADataset(split='train')
+ val_datasets = {"docvqa": DocVQADataset(split='validation')}
+ elif dataset_name == "cauldron":
+ train_dataset = TheCauldronDataset(split='train')
+ val_datasets = {
+ "cauldron": TheCauldronDataset(split='validation'),
+ "docvqa": DocVQADataset(split='validation')
+ }
+ elif dataset_name == 'vqainstruct':
+ train_dataset = VQAInstructDataset(split='train')
+ val_datasets = {
+ "vqainstruct": VQAInstructDataset(split='validation'),
+ "docvqa": DocVQADataset(split='validation')
+ }
+ else:
+ raise ValueError(f"Unknown dataset: {dataset_name}")
+
+ # Load the model and processor
+ model = AutoModelForCausalLM.from_pretrained(
+ "andito/Florence-2-large-ft", trust_remote_code=True
+ ).to(device)
+ processor = AutoProcessor.from_pretrained(
+ "andito/Florence-2-large-ft", trust_remote_code=True
+ )
+
+ if use_lora:
+ TARGET_MODULES = [
+ "q_proj", "o_proj", "k_proj", "v_proj",
+ "linear", "Conv2d", "lm_head", "fc2"
+ ]
+
+ config = LoraConfig(
+ r=8,
+ lora_alpha=8,
+ target_modules=TARGET_MODULES,
+ task_type="CAUSAL_LM",
+ lora_dropout=0.05,
+ bias="none",
+ inference_mode=False,
+ use_rslora=True,
+ init_lora_weights="gaussian",
+ )
+ model = get_peft_model(model, config)
+
+ model = DDP(model, device_ids=[rank])
+
+ # Create DataLoaders
+ num_workers = 0
+ train_loader, val_loaders = create_data_loaders(
+ train_dataset,
+ val_datasets,
+ batch_size,
+ num_workers,
+ rank,
+ world_size,
+ processor,
+ device,
+ )
+
+ optimizer = AdamW(model.parameters(), lr=lr)
+ num_training_steps = epochs * len(train_loader)
+ lr_scheduler = get_scheduler(
+ name="linear",
+ optimizer=optimizer,
+ num_warmup_steps=0,
+ num_training_steps=num_training_steps,
+ )
+ global_step = 0
+
+ for epoch in range(epochs):
+ # Training phase
+ model.train()
+ train_loss = 0
+ for batch in tqdm(
+ train_loader, desc=f"Training Epoch {epoch + 1}/{epochs}", position=rank
+ ):
+ inputs, answers = batch
+
+ # Prepare the input and target tensors
+ input_ids = inputs["input_ids"].to(device)
+ pixel_values = inputs["pixel_values"].to(device)
+ labels = processor.tokenizer(
+ text=answers,
+ return_tensors="pt",
+ padding=True,
+ return_token_type_ids=False,
+ truncation=True,
+ max_length=800,
+ ).input_ids.to(device)
+
+ outputs = model(
+ input_ids=input_ids, pixel_values=pixel_values, labels=labels
+ )
+ loss = outputs.loss
+
+ loss.backward()
+ optimizer.step()
+ lr_scheduler.step()
+ optimizer.zero_grad()
+
+ train_loss += loss.item()
+ global_step += 1
+
+ if global_step % eval_steps == 0:
+ evaluate_model(rank, world_size, model, val_loaders, device, train_loss, processor, global_step, batch_size, max_val_item_count)
+
+ evaluate_model(rank, world_size, model, val_loaders, device, train_loss, processor, global_step, batch_size, max_val_item_count)
+
+ # Log training loss to wandb
+ avg_train_loss = train_loss / len(train_loader)
+ if rank == 0:
+ wandb.log({"epoch": epoch + 1, "epoch_train_loss": avg_train_loss})
+
+ # Save model checkpoint
+ if rank == 0: # Only the main process saves the checkpoint
+ output_dir = f"./model_checkpoints/{run_name}/epoch_{epoch + 1}"
+ os.makedirs(output_dir, exist_ok=True)
+ model.module.save_pretrained(output_dir)
+ processor.save_pretrained(output_dir)
+
+ # Finish the wandb run
+ if rank == 0:
+ wandb.finish()
+
+ cleanup()
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Train Florence-2 model on specified dataset")
+ parser.add_argument("--dataset", type=str, required=True, choices=["docvqa", "cauldron", "vqainstruct"], help="Dataset to train on")
+ parser.add_argument("--batch-size", type=int, default=6, help="Batch size for training")
+ parser.add_argument("--use-lora", action='store_true', help="Use LoRA if this flag is passed")
+ parser.add_argument("--epochs", type=int, default=10, help="Number of epochs to train for")
+ parser.add_argument("--lr", type=float, default=1e-6, help="Learning rate")
+ parser.add_argument("--eval-steps", type=int, default=1000, help="Number of steps between evaluations")
+ parser.add_argument("--run-name", type=str, default=None, help="Run name for wandb")
+ parser.add_argument("--max-val-item-count", type=int, default=1000, help="Maximum number of items to evaluate on during validation")
+ args = parser.parse_args()
+
+ world_size = torch.cuda.device_count()
+ mp.spawn(
+ train_model,
+ args=(world_size, args.dataset, args.batch_size, args.use_lora, args.epochs, args.lr, args.eval_steps, args.run_name, args.max_val_item_count),
+ nprocs=world_size,
+ join=True
+ )
+
+if __name__ == "__main__":
+ main()
diff --git a/Experiments/CV/florence2-finetuning/evaluate.py b/Experiments/CV/florence2-finetuning/evaluate.py
new file mode 100644
index 0000000..ddacccc
--- /dev/null
+++ b/Experiments/CV/florence2-finetuning/evaluate.py
@@ -0,0 +1,119 @@
+import logging
+import random
+
+import torch
+from torch.utils.data import DataLoader, Subset
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+from data import DocVQADataset
+from metrics import average_normalized_levenshtein_similarity
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the model and processor
+model = AutoModelForCausalLM.from_pretrained(
+ "model_checkpoints/gigantic_fukuiraptor/epoch_9/", trust_remote_code=True
+).to(device)
+processor = AutoProcessor.from_pretrained(
+ "model_checkpoints/gigantic_fukuiraptor/epoch_9/", trust_remote_code=True
+)
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# Function to run the model on an example
+def run_example(task_prompt, text_input, image):
+ prompt = task_prompt + text_input
+
+ # Ensure the image is in RGB mode
+ if image.mode != "RGB":
+ image = image.convert("RGB")
+
+ inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
+ generated_ids = model.generate(
+ input_ids=inputs["input_ids"],
+ pixel_values=inputs["pixel_values"],
+ max_new_tokens=1024,
+ num_beams=3,
+ )
+ generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+ parsed_answer = processor.post_process_generation(
+ generated_text, task=task_prompt, image_size=(image.width, image.height)
+ )
+ return parsed_answer
+
+
+def collate_fn(batch):
+ questions, answers, images = zip(*batch)
+ inputs = processor(
+ text=list(questions), images=list(images), return_tensors="pt", padding=True
+ ).to(device)
+ return inputs, answers
+
+
+# Create DataLoader
+batch_size = 4 # Adjust the batch size based on your GPU memory
+num_workers = 0 # Number of worker processes to use for data loading
+prefetch_factor = None # Number of batches to prefetch
+
+test_dataset = DocVQADataset("validation")
+
+# Create a subset of the dataset
+subset_size = int(0.2 * len(test_dataset)) # 10% of the dataset
+indices = random.sample(range(len(test_dataset)), subset_size)
+subset_dataset = Subset(test_dataset, indices)
+
+test_loader = DataLoader(
+ subset_dataset,
+ batch_size=batch_size,
+ collate_fn=collate_fn,
+ num_workers=num_workers,
+ prefetch_factor=prefetch_factor,
+)
+
+
+def run_batch(inputs):
+ generated_ids = model.generate(
+ input_ids=inputs["input_ids"],
+ pixel_values=inputs["pixel_values"],
+ max_new_tokens=1024,
+ num_beams=3,
+ )
+ generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=False)
+ return generated_texts
+
+
+def evaluate_model(test_loader):
+ task_prompt = ""
+ predicted_answers = []
+ ground_truth = []
+
+ for inputs, batch_answers in tqdm(test_loader, desc="Evaluating"):
+ generated_texts = run_batch(inputs)
+
+ for generated_text, answers in zip(generated_texts, batch_answers):
+ parsed_answer = processor.post_process_generation(
+ generated_text,
+ task=task_prompt,
+ image_size=(
+ inputs["pixel_values"].shape[-2],
+ inputs["pixel_values"].shape[-1],
+ ),
+ )
+ predicted_answers.append(parsed_answer[task_prompt].replace("", ""))
+ ground_truth.append(answers)
+ # print("Ans:", parsed_answer[task_prompt])
+ # print("GT:", answers)
+
+ avg_levenshtein_similarity = average_normalized_levenshtein_similarity(
+ ground_truth, predicted_answers
+ )
+ return answers, avg_levenshtein_similarity
+
+
+# Run the evaluation
+answers, average_similarity = evaluate_model(test_loader)
+print(f"Average Normalized Levenshtein Similarity: {average_similarity:.4f}")
diff --git a/Experiments/CV/florence2-finetuning/metrics.py b/Experiments/CV/florence2-finetuning/metrics.py
new file mode 100644
index 0000000..02c1398
--- /dev/null
+++ b/Experiments/CV/florence2-finetuning/metrics.py
@@ -0,0 +1,44 @@
+import logging
+
+import Levenshtein
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# Levenshtein similarity functions
+def normalized_levenshtein(s1, s2):
+ len_s1, len_s2 = len(s1), len(s2)
+ distance = Levenshtein.distance(s1, s2)
+ return distance / max(len_s1, len_s2)
+
+
+def similarity_score(a_ij, o_q_i, tau=0.5):
+ if o_q_i.endswith('.'):
+ o_q_i = o_q_i[:-1]
+ if a_ij.endswith('.'):
+ a_ij = a_ij[:-1]
+ nl = normalized_levenshtein(a_ij, o_q_i)
+ return 1 - nl if nl < tau else 0
+
+
+def average_normalized_levenshtein_similarity(ground_truth, predicted_answers):
+ assert len(ground_truth) == len(
+ predicted_answers
+ ), "Length of ground_truth and predicted_answers must match."
+
+ N = len(ground_truth)
+ total_score = 0
+
+ for i in range(N):
+ a_i = ground_truth[i]
+ o_q_i = predicted_answers[i]
+ if o_q_i == "":
+ logger.warning("Skipped an empty prediction.")
+ max_score = 0
+ else:
+ max_score = max(similarity_score(a_ij.lower(), o_q_i.lower()) for a_ij in a_i)
+ total_score += max_score
+
+ return total_score / N
diff --git a/Experiments/CV/florence2-finetuning/requirements.txt b/Experiments/CV/florence2-finetuning/requirements.txt
new file mode 100644
index 0000000..3842927
--- /dev/null
+++ b/Experiments/CV/florence2-finetuning/requirements.txt
@@ -0,0 +1,150 @@
+accelerate==0.31.0
+aiohttp==3.9.5
+aiosignal==1.3.1
+anyio==4.4.0
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==2.4.1
+async-lru==2.0.4
+attrs==23.2.0
+babel==2.15.0
+beautifulsoup4==4.12.3
+bleach==6.1.0
+certifi==2024.6.2
+cffi==1.16.0
+charset-normalizer==3.3.2
+comm==0.2.2
+datasets==2.20.0
+debugpy==1.8.1
+decorator==5.1.1
+defusedxml==0.7.1
+dill==0.3.8
+einops==0.8.0
+executing==2.0.1
+fastjsonschema==2.20.0
+filelock==3.15.3
+flash-attn==2.5.9.post1
+fqdn==1.5.1
+friendlywords==1.1.3
+frozenlist==1.4.1
+fsspec==2024.6.0
+h11==0.14.0
+httpcore==1.0.5
+httpx==0.27.0
+huggingface-hub==0.23.4
+idna==3.7
+ipykernel==6.29.4
+ipython==8.25.0
+ipywidgets==8.1.3
+isoduration==20.11.0
+jedi==0.19.1
+jinja2==3.1.4
+json5==0.9.25
+jsonpointer==3.0.0
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+jupyter==1.0.0
+jupyter-client==8.6.2
+jupyter-console==6.6.3
+jupyter-core==5.7.2
+jupyter-events==0.10.0
+jupyter-lsp==2.2.5
+jupyter-server==2.14.1
+jupyter-server-terminals==0.5.3
+jupyterlab==4.2.2
+jupyterlab-pygments==0.3.0
+jupyterlab-server==2.27.2
+jupyterlab-widgets==3.0.11
+levenshtein==0.25.1
+markupsafe==2.1.5
+matplotlib-inline==0.1.7
+mistune==3.0.2
+mpmath==1.3.0
+multidict==6.0.5
+multiprocess==0.70.16
+nbclient==0.10.0
+nbconvert==7.16.4
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.3
+notebook==7.2.1
+notebook-shim==0.2.4
+numpy==2.0.0
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==8.9.2.26
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.20.5
+nvidia-nvjitlink-cu12==12.5.40
+nvidia-nvtx-cu12==12.1.105
+overrides==7.7.0
+packaging==24.1
+pandas==2.2.2
+pandocfilters==1.5.1
+parso==0.8.4
+pexpect==4.9.0
+pillow==10.3.0
+pip==24.0
+platformdirs==4.2.2
+prometheus-client==0.20.0
+prompt-toolkit==3.0.47
+psutil==6.0.0
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyarrow==16.1.0
+pyarrow-hotfix==0.6
+pycparser==2.22
+pygments==2.18.0
+python-dateutil==2.9.0.post0
+python-json-logger==2.0.7
+pytz==2024.1
+pyyaml==6.0.1
+pyzmq==26.0.3
+qtconsole==5.5.2
+qtpy==2.4.1
+rapidfuzz==3.9.3
+referencing==0.35.1
+regex==2024.5.15
+requests==2.32.3
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.18.1
+ruff==0.4.9
+safetensors==0.4.3
+send2trash==1.8.3
+setuptools==70.1.0
+six==1.16.0
+sniffio==1.3.1
+soupsieve==2.5
+stack-data==0.6.3
+sympy==1.12.1
+terminado==0.18.1
+timm==1.0.7
+tinycss2==1.3.0
+tokenizers==0.19.1
+torch==2.3.1
+torchvision==0.18.1
+tornado==6.4.1
+tqdm==4.66.4
+traitlets==5.14.3
+transformers==4.41.2
+triton==2.3.1
+types-python-dateutil==2.9.0.20240316
+typing-extensions==4.12.2
+tzdata==2024.1
+uri-template==1.3.0
+urllib3==2.2.2
+wcwidth==0.2.13
+webcolors==24.6.0
+webencodings==0.5.1
+websocket-client==1.8.0
+wheel==0.43.0
+widgetsnbextension==4.0.11
+xxhash==3.4.1
+yarl==1.9.4
diff --git a/Experiments/CV/florence2-finetuning/train.py b/Experiments/CV/florence2-finetuning/train.py
new file mode 100644
index 0000000..4d8d0fa
--- /dev/null
+++ b/Experiments/CV/florence2-finetuning/train.py
@@ -0,0 +1,153 @@
+import os
+
+import torch
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import (AdamW, AutoModelForCausalLM, AutoProcessor,
+ get_scheduler)
+
+from data import DocVQADataset
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the model and processor
+model = AutoModelForCausalLM.from_pretrained(
+ "microsoft/Florence-2-base-ft", trust_remote_code=True, revision="refs/pr/6"
+).to(device)
+processor = AutoProcessor.from_pretrained(
+ "microsoft/Florence-2-base-ft", trust_remote_code=True, revision="refs/pr/6"
+)
+
+
+def collate_fn(batch):
+ questions, answers, images = zip(*batch)
+ inputs = processor(
+ text=list(questions), images=list(images), return_tensors="pt", padding=True
+ ).to(device)
+ return inputs, answers
+
+
+# Create datasets
+train_dataset = DocVQADataset("train")
+val_dataset = DocVQADataset("validation")
+
+# Create DataLoader
+batch_size = 8
+num_workers = 0
+
+train_loader = DataLoader(
+ train_dataset,
+ batch_size=batch_size,
+ collate_fn=collate_fn,
+ num_workers=num_workers,
+ shuffle=True,
+)
+val_loader = DataLoader(
+ val_dataset, batch_size=batch_size, collate_fn=collate_fn, num_workers=num_workers
+)
+
+
+def train_model(train_loader, val_loader, model, processor, epochs=10, lr=1e-6):
+ optimizer = AdamW(model.parameters(), lr=lr)
+ num_training_steps = epochs * len(train_loader)
+ lr_scheduler = get_scheduler(
+ name="linear",
+ optimizer=optimizer,
+ num_warmup_steps=0,
+ num_training_steps=num_training_steps,
+ )
+
+ for epoch in range(epochs):
+ # Training phase
+ model.train()
+ train_loss = 0
+ i = -1
+ for batch in tqdm(train_loader, desc=f"Training Epoch {epoch + 1}/{epochs}"):
+ i += 1
+ inputs, answers = batch
+
+ input_ids = inputs["input_ids"]
+ pixel_values = inputs["pixel_values"]
+ labels = processor.tokenizer(
+ text=answers,
+ return_tensors="pt",
+ padding=True,
+ return_token_type_ids=False,
+ ).input_ids.to(device)
+
+ outputs = model(
+ input_ids=input_ids, pixel_values=pixel_values, labels=labels
+ )
+ loss = outputs.loss
+
+ if i % 200 == 0:
+ print(loss)
+
+ generated_ids = model.generate(
+ input_ids=inputs["input_ids"],
+ pixel_values=inputs["pixel_values"],
+ max_new_tokens=1024,
+ num_beams=3,
+ )
+ generated_texts = processor.batch_decode(
+ generated_ids, skip_special_tokens=False
+ )
+
+ for generated_text, answer in zip(generated_texts, answers):
+ parsed_answer = processor.post_process_generation(
+ generated_text,
+ task="",
+ image_size=(
+ inputs["pixel_values"].shape[-2],
+ inputs["pixel_values"].shape[-1],
+ ),
+ )
+ print("GT:", answer)
+ print("Pred:", parsed_answer[""])
+
+ loss.backward()
+ optimizer.step()
+ lr_scheduler.step()
+ optimizer.zero_grad()
+
+ train_loss += loss.item()
+
+ avg_train_loss = train_loss / len(train_loader)
+ print(f"Average Training Loss: {avg_train_loss}")
+
+ # Validation phase
+ model.eval()
+ val_loss = 0
+ with torch.no_grad():
+ for batch in tqdm(
+ val_loader, desc=f"Validation Epoch {epoch + 1}/{epochs}"
+ ):
+ inputs, answers = batch
+
+ input_ids = inputs["input_ids"]
+ pixel_values = inputs["pixel_values"]
+ labels = processor.tokenizer(
+ text=answers,
+ return_tensors="pt",
+ padding=True,
+ return_token_type_ids=False,
+ ).input_ids.to(device)
+
+ outputs = model(
+ input_ids=input_ids, pixel_values=pixel_values, labels=labels
+ )
+ loss = outputs.loss
+
+ val_loss += loss.item()
+
+ avg_val_loss = val_loss / len(val_loader)
+ print(f"Average Validation Loss: {avg_val_loss}")
+
+ # Save model checkpoint
+ output_dir = f"./model_checkpoints/epoch_{epoch+1}"
+ os.makedirs(output_dir, exist_ok=True)
+ model.save_pretrained(output_dir)
+ processor.save_pretrained(output_dir)
+
+train_model(train_loader, val_loader, model, processor, epochs=3)
diff --git a/Experiments/CV/florence2-finetuning/train_job.slurm b/Experiments/CV/florence2-finetuning/train_job.slurm
new file mode 100644
index 0000000..d729693
--- /dev/null
+++ b/Experiments/CV/florence2-finetuning/train_job.slurm
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+#SBATCH --partition=hopper-prod
+#SBATCH --gpus=8
+#SBATCH -o training_logs/%x_%A_%a.out
+#SBATCH --time=24:00:00
+#SBATCH --qos=normal
+#SBATCH --array=0-3
+
+source /admin/home/andres_marafioti/.bashrc
+
+cd /fsx/andi/florence2-finetuning/
+source .venv/bin/activate
+
+# Define datasets and LoRA configurations
+datasets=("docvqa" "docvqa" "vqainstruct" "vqainstruct")
+lora_flags=(0 1 0 1)
+
+# Determine the configuration based on the SLURM_ARRAY_TASK_ID
+dataset=${datasets[$SLURM_ARRAY_TASK_ID]}
+use_lora=${lora_flags[$SLURM_ARRAY_TASK_ID]}
+batch_size=6
+job_name="${dataset}_no_lora"
+if [ $use_lora -eq 1 ]; then
+ batch_size=10
+ lora_arg="--use-lora"
+ job_name="${dataset}_lora"
+else
+ lora_arg=""
+fi
+
+# Run the Python script with the appropriate parameters
+python distributed_train.py --dataset $dataset --batch-size $batch_size --epochs 10 --lr 1e-6 --eval-steps 1000 --max-val-item-count 1000 $lora_arg