add llava and instructblip

Former-commit-id: 142fb6f4541a1acfefe66ff2574dabde53b00c06

scripts/test_mllm.py

@@ -0,0 +1,84 @@
+import os.path
+
+import fire
+import torch
+from datasets import load_dataset
+from peft import PeftModel
+from transformers import AutoTokenizer, AutoModelForVision2Seq, AutoProcessor
+
+"""usage
+python3 scripts/test_mllm.py \
+--base_model_path llava-hf/llava-1.5-7b-hf \
+--lora_model_path saves/llava-1.5-7b/lora/sft \
+--model_path saves/llava-1.5-7b/lora/merged \
+--dataset_name data/mllm_example_dataset \
+--do_merge 1
+"""
+
+
+def get_processor(model_path):
+    CHAT_TEMPLATE = """{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}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 %}{% if add_generation_prompt %}ASSISTANT: {% endif %}"""
+    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+    tokenizer.chat_template = CHAT_TEMPLATE
+    processor = AutoProcessor.from_pretrained(model_path)
+    processor.tokenizer = tokenizer
+    return processor
+
+
+def apply_lora(base_model_path, model_path, lora_path):
+    print(f"Loading the base model from {base_model_path}")
+    base_model = AutoModelForVision2Seq.from_pretrained(
+        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="cuda",
+    )
+    processor = get_processor(base_model_path)
+    tokenizer = processor.tokenizer
+    print(f"Loading the LoRA adapter from {lora_path}")
+
+    lora_model = PeftModel.from_pretrained(
+        base_model,
+        lora_path,
+        torch_dtype=torch.float16,
+    )
+
+    print("Applying the LoRA")
+    model = lora_model.merge_and_unload()
+
+    print(f"Saving the target model to {model_path}")
+    model.save_pretrained(model_path)
+    tokenizer.save_pretrained(model_path)
+    processor.image_processor.save_pretrained(model_path)
+
+
+def main(
+        model_path: str,
+        dataset_name: str,
+        base_model_path: str = "",
+        lora_model_path: str = "",
+        do_merge: bool = False,
+):
+    if not os.path.exists(model_path) or do_merge:
+        apply_lora(base_model_path, model_path, lora_model_path)
+    model = AutoModelForVision2Seq.from_pretrained(
+        model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="cuda"
+    )
+    processor = get_processor(model_path)
+    raw_datasets = load_dataset(dataset_name)
+    train_dataset = raw_datasets['train']
+    examples = train_dataset.select(range(3))
+    texts = []
+    images = []
+    for example in examples:
+        messages = example["messages"][:1]
+        text = processor.tokenizer.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=False
+        )
+        texts.append(text)
+        images.append(example["images"][0])
+    batch = processor(texts, images, return_tensors="pt", padding=True).to("cuda")
+    output = model.generate(**batch, max_new_tokens=100)
+    res = processor.batch_decode(output, skip_special_tokens=True)
+    print(res)
+
+
+if __name__ == '__main__':
+    fire.Fire(main)