Adds VLM Training support to SFTTrainer + VSFT script

docs/source/example_overview.md

@@ -35,6 +35,7 @@ Then, it is encouraged to launch jobs with `accelerate launch`!
 | File                                                                                           | Description                                                                                                              |
 |------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------|
 | [`examples/scripts/sft.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py) | This script shows how to use the `SFTTrainer` to fine tune a model or adapters into a target dataset.                     |
+| [`examples/scripts/vsft_llava.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/vsft_llava.py) | This script shows how to use the `SFTTrainer` to fine tune a Vision Language Model in a chat setting, the script has been tested on a llava1.5 model so users may see unexpected behaviour in other model architectures.                     |
 | [`examples/scripts/reward_modeling.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/reward_modeling.py) | This script shows how to use the `RewardTrainer` to train a reward model on your own dataset.                            |
 | [`examples/scripts/ppo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo.py) | This script shows how to use the `PPOTrainer` to fine-tune a sentiment analysis model using IMDB dataset                 |
 | [`examples/scripts/ppo_multi_adapter.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo_multi_adapter.py) | This script shows how to use the `PPOTrainer` to train a single base model with multiple adapters. Requires you to run the example script with the reward model training beforehand. |

docs/source/sft_trainer.mdx

@@ -3,6 +3,7 @@
 Supervised fine-tuning (or SFT for short) is a crucial step in RLHF. In TRL we provide an easy-to-use API to create your SFT models and train them with few lines of code on your dataset.
 
 Check out a complete flexible example at [`examples/scripts/sft.py`](https://github.com/huggingface/trl/tree/main/examples/scripts/sft.py).
+Experimental support for Vision Language Models is also included in the example [`examples/scripts/vsft_llava.py`](https://github.com/huggingface/trl/tree/main/examples/scripts/vsft_llava.py).
 
 ## Quickstart
 

examples/scripts/vsft_llava.py

@@ -0,0 +1,207 @@
+# flake8: noqa
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+# regular:
+python examples/scripts/vsft.py \
+    --model_name_or_path="llava-hf/llava-1.5-7b-hf" \
+    --report_to="wandb" \
+    --learning_rate=1.4e-5 \
+    --per_device_train_batch_size=8 \
+    --gradient_accumulation_steps=1 \
+    --output_dir="data/vsft-llava-1.5-7b-hf" \
+    --logging_steps=5 \
+    --num_train_epochs=1 \
+    --push_to_hub \
+    --gradient_checkpointing \
+    --remove_unused_columns=False \
+    --torch_dtype=float16 \
+    --fp16=True \ 
+    --dataset_name=HuggingFaceH4/llava-instruct-mix-vsft \
+
+# peft:
+python examples/scripts/vsft.py \
+    --model_name_or_path="llava-hf/llava-1.5-7b-hf" \
+    --report_to="wandb" \
+    --learning_rate=1.4e-5 \
+    --per_device_train_batch_size=8 \
+    --gradient_accumulation_steps=1 \
+    --output_dir="data/vsft-llava-1.5-7b-hf" \
+    --logging_steps=5 \
+    --num_train_epochs=1 \
+    --push_to_hub \
+    --gradient_checkpointing \
+    --remove_unused_columns=False \
+    --torch_dtype=float16 \
+    --fp16=True \ 
+    --dataset_name=HuggingFaceH4/llava-instruct-mix-vsft \    
+    --use_peft=True \
+    --lora_r=64 \
+    --lora_alpha=16 \
+    --lora_target_modules=all-linear"
+
+# evaluation:
+
+To evaluate, first install the lmms-eval framework: pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+then run:
+accelerate launch --num_processes=8 -m lmms_eval \
+        --model llava_hf \
+        --model_args pretrained=llava-hf/llava-1.5-7b-hf \
+        --tasks mmbench \
+        --batch_size 1 \
+        --output_path ./logs/ \
+        --log_sample    
+"""
+import logging
+import os
+from contextlib import nullcontext
+
+TRL_USE_RICH = os.environ.get("TRL_USE_RICH", False)
+
+from trl.commands.cli_utils import init_zero_verbose, SftScriptArguments, TrlParser
+
+if TRL_USE_RICH:
+    init_zero_verbose()
+    FORMAT = "%(message)s"
+
+    from rich.console import Console
+    from rich.logging import RichHandler
+
+import torch
+from datasets import load_dataset
+
+from tqdm.rich import tqdm
+from transformers import AutoTokenizer, AutoProcessor, TrainingArguments, LlavaForConditionalGeneration
+
+from trl import (
+    ModelConfig,
+    RichProgressCallback,
+    SFTTrainer,
+    get_peft_config,
+    get_quantization_config,
+    get_kbit_device_map,
+)
+
+tqdm.pandas()
+
+if TRL_USE_RICH:
+    logging.basicConfig(format=FORMAT, datefmt="[%X]", handlers=[RichHandler()], level=logging.INFO)
+
+
+if __name__ == "__main__":
+    parser = TrlParser((SftScriptArguments, TrainingArguments, ModelConfig))
+    args, training_args, model_config = parser.parse_args_and_config()
+    training_args.gradient_checkpointing_kwargs = dict(use_reentrant=False)
+    # Force use our print callback
+    if TRL_USE_RICH:
+        training_args.disable_tqdm = True
+        console = Console()
+
+    ################
+    # Model, Tokenizer & Processor
+    ################
+    LLAVA_CHAT_TEMPLATE = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. {% for message in messages %}{% if message['role'] == 'user' %}USER: {% else %}ASSISTANT: {% endif %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<image>{% endif %}{% endfor %}{% if message['role'] == 'user' %} {% else %}{{eos_token}}{% endif %}{% endfor %}"""
+
+    torch_dtype = (
+        model_config.torch_dtype
+        if model_config.torch_dtype in ["auto", None]
+        else getattr(torch, model_config.torch_dtype)
+    )
+    quantization_config = get_quantization_config(model_config)
+    model_kwargs = dict(
+        revision=model_config.model_revision,
+        trust_remote_code=model_config.trust_remote_code,
+        attn_implementation=model_config.attn_implementation,
+        torch_dtype=torch_dtype,
+        device_map=get_kbit_device_map() if quantization_config is not None else None,
+        quantization_config=quantization_config,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_config.model_name_or_path, use_fast=True)
+    tokenizer.chat_template = LLAVA_CHAT_TEMPLATE
+    processor = AutoProcessor.from_pretrained(model_config.model_name_or_path)
+    processor.tokenizer = tokenizer
+
+    model = LlavaForConditionalGeneration.from_pretrained(model_config.model_name_or_path, **model_kwargs)
+
+    ################
+    # Create a data collator to encode text and image pairs
+    ################
+
+    class LLavaDataCollator:
+        def __init__(self, processor):
+            self.processor = processor
+
+        def __call__(self, examples):
+            texts = []
+            images = []
+            for example in examples:
+                if len(example["images"]) > 1:
+                    raise ValueError("This collator only supports one image per example")
+                messages = example["messages"]
+                text = self.processor.tokenizer.apply_chat_template(
+                    messages, tokenize=False, add_generation_prompt=False
+                )
+                texts.append(text)
+                images.append(example["images"][0])
+
+            batch = self.processor(texts, images, return_tensors="pt", padding=True)
+
+            labels = batch["input_ids"].clone()
+            if self.processor.tokenizer.pad_token_id is not None:
+                labels[labels == self.processor.tokenizer.pad_token_id] = -100
+            batch["labels"] = labels
+
+            return batch
+
+    data_collator = LLavaDataCollator(processor)
+
+    ################
+    # Dataset
+    ################
+    raw_datasets = load_dataset(args.dataset_name)
+    train_dataset = raw_datasets["train"]
+    eval_dataset = raw_datasets["test"]
+
+    ################
+    # Optional rich context managers
+    ###############
+    init_context = nullcontext() if not TRL_USE_RICH else console.status("[bold green]Initializing the SFTTrainer...")
+    save_context = (
+        nullcontext()
+        if not TRL_USE_RICH
+        else console.status(f"[bold green]Training completed! Saving the model to {training_args.output_dir}")
+    )
+
+    ################
+    # Training
+    ################
+    with init_context:
+        trainer = SFTTrainer(
+            model=model,
+            args=training_args,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            dataset_text_field="text",  # need a dummy field
+            tokenizer=tokenizer,
+            peft_config=get_peft_config(model_config),
+            callbacks=[RichProgressCallback] if TRL_USE_RICH else None,
+            data_collator=data_collator,
+            dataset_kwargs={"skip_prepare_dataset": True},
+        )
+
+    trainer.train()
+
+    with save_context:
+        trainer.save_model(training_args.output_dir)
+        trainer.push_to_hub()