add blip2

README.md

@@ -274,6 +274,12 @@ An example is provided in `~examples/causal_language_modeling/peft_lora_clm_acce
 | ViT           | ✅  |   |   |   | 
 | Swin           | ✅  |   |   |   | 
 
+### Image to text (Multi-modal models)
+
+|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | 
+| --------- | ---- | ---- | ---- | ----  |
+| Blip-2           | ✅  |   |   |   | 
+
 ___Note that we have tested LoRA for [ViT](https://huggingface.co/docs/transformers/model_doc/vit) and [Swin](https://huggingface.co/docs/transformers/model_doc/swin) for fine-tuning on image classification. However, it should be possible to use LoRA for any compatible model [provided](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads&search=vit) by 🤗 Transformers. Check out the respective
 examples to learn more. If you run into problems, please open an issue.___
 

examples/int8_training/fine_tune_blip2_int8.py

@@ -0,0 +1,88 @@
+import torch
+from datasets import load_dataset
+from torch.utils.data import DataLoader, Dataset
+from transformers import AutoModelForVision2Seq, AutoProcessor
+
+from peft import LoraConfig, get_peft_model
+
+
+config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="VISION_2_SEQ",
+)
+
+model = AutoModelForVision2Seq.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0})
+processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
+model = get_peft_model(model, config)
+
+model.print_trainable_parameters()
+
+dataset = load_dataset("ybelkada/football-dataset", split="train")
+
+
+class ImageCaptioningDataset(Dataset):
+    def __init__(self, dataset, processor):
+        self.dataset = dataset
+        self.processor = processor
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        item = self.dataset[idx]
+        encoding = self.processor(images=item["image"], padding="max_length", return_tensors="pt")
+        # remove batch dimension
+        encoding = {k: v.squeeze() for k, v in encoding.items()}
+        encoding["text"] = item["text"]
+        return encoding
+
+
+def collator(batch):
+    # pad the input_ids and attention_mask
+    processed_batch = {}
+    for key in batch[0].keys():
+        if key != "text":
+            processed_batch[key] = torch.stack([example[key] for example in batch])
+        else:
+            text_inputs = processor.tokenizer(
+                [example["text"] for example in batch], padding=True, return_tensors="pt"
+            )
+            processed_batch["input_ids"] = text_inputs["input_ids"]
+            processed_batch["attention_mask"] = text_inputs["attention_mask"]
+    return processed_batch
+
+
+train_dataset = ImageCaptioningDataset(dataset, processor)
+train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=2, collate_fn=collator)
+
+optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+model.train()
+
+for epoch in range(50):
+    print("Epoch:", epoch)
+    for idx, batch in enumerate(train_dataloader):
+        input_ids = batch.pop("input_ids").to(device)
+        pixel_values = batch.pop("pixel_values").to(device, torch.float16)
+
+        outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=input_ids)
+
+        loss = outputs.loss
+
+        print("Loss:", loss.item())
+
+        loss.backward()
+
+        optimizer.step()
+        optimizer.zero_grad()
+
+        if idx % 10 == 0:
+            generated_output = model.generate(pixel_values=pixel_values)
+            print(processor.batch_decode(generated_output, skip_special_tokens=True))

src/peft/peft_model.py

@@ -1102,4 +1102,4 @@ class PeftModelForVision2Seq(PeftModel):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
             **kwargs,
-        )
+        )

src/peft/tuners/lora.py

@@ -394,7 +394,6 @@ class Linear(nn.Linear, LoraLayer):
         self.lora_B.eval()
 
     def forward(self, x: torch.Tensor):
-
         if self.disable_adapters:
             if self.r > 0 and self.merged:
                 self.weight.data -= (