support qwen2-vl

data/dataset_info.json

@@ -38,6 +38,20 @@
       "assistant_tag": "assistant"
     }
   },
+  "qwen2vl_demo": {
+    "file_name": "qwen2vl_demo.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "images": "images"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
   "alpaca_en": {
     "hf_hub_url": "llamafactory/alpaca_en",
     "ms_hub_url": "llamafactory/alpaca_en"

data/qwen2vl_demo.json

@@ -0,0 +1,140 @@
+[
+    {
+        "messages": [
+            {
+                "content": "<|image_pad|>Who are they?",
+                "role": "user"
+            },
+            {
+                "content": "They're Kane and Gretzka from Bayern Munich.",
+                "role": "assistant"
+            },
+            {
+                "content": "What are they doing?",
+                "role": "user"
+            },
+            {
+                "content": "They are celebrating on the soccer field.",
+                "role": "assistant"
+            }
+        ],
+        "images": [
+            "mllm_demo_data/1.jpg"
+        ]
+    },
+    {
+        "messages": [
+            {
+                "content": "<|image_pad|>Who is he?",
+                "role": "user"
+            },
+            {
+                "content": "He's Thomas Muller from Bayern Munich.",
+                "role": "assistant"
+            },
+            {
+                "content": "<|image_pad|>Why is he on the ground?",
+                "role": "user"
+            },
+            {
+                "content": "Because he's sliding on his knees to celebrate.",
+                "role": "assistant"
+            }
+        ],
+        "images": [
+            "mllm_demo_data/2.jpg","mllm_demo_data/2.jpg"
+        ]
+    },
+    {
+        "messages": [
+            {
+                "content": "<|image_pad|>Please describe this image",
+                "role": "user"
+            },
+            {
+                "content": "Chinese astronaut Gui Haichao is giving a speech.",
+                "role": "assistant"
+            },
+            {
+                "content": "What has he accomplished?",
+                "role": "user"
+            },
+            {
+                "content": "He was appointed to be a payload specialist on Shenzhou 16 mission in June 2022, thus becoming the first Chinese civilian of Group 3 in space on 30 May 2023. He is responsible for the on-orbit operation of space science experimental payloads.",
+                "role": "assistant"
+            }
+        ],
+        "images": [
+            "mllm_demo_data/3.jpg"
+        ]
+    },
+    {
+        "messages": [
+            {
+                "content": "<|image_pad|>他们是谁？",
+                "role": "user"
+            },
+            {
+                "content": "他们是拜仁慕尼黑的凯恩和格雷茨卡。",
+                "role": "assistant"
+            },
+            {
+                "content": "<|image_pad|>他们在做什么？",
+                "role": "user"
+            },
+            {
+                "content": "他们在足球场上庆祝。",
+                "role": "assistant"
+            }
+        ],
+        "images": [
+            "mllm_demo_data/1.jpg","mllm_demo_data/1.jpg"
+        ]
+    },
+    {
+        "messages": [
+            {
+                "content": "<|image_pad|>他是谁？",
+                "role": "user"
+            },
+            {
+                "content": "他是来自拜仁慕尼黑的托马斯·穆勒。",
+                "role": "assistant"
+            },
+            {
+                "content": "他为什么在地上？",
+                "role": "user"
+            },
+            {
+                "content": "因为他正在双膝跪地滑行庆祝。",
+                "role": "assistant"
+            }
+        ],
+        "images": [
+            "mllm_demo_data/2.jpg"
+        ]
+    },
+    {
+        "messages": [
+            {
+                "content": "<|image_pad|>请描述这张图片",
+                "role": "user"
+            },
+            {
+                "content": "中国宇航员桂海潮正在讲话。",
+                "role": "assistant"
+            },
+            {
+                "content": "他取得过哪些成就？",
+                "role": "user"
+            },
+            {
+                "content": "他于2022年6月被任命为神舟十六号任务的有效载荷专家，从而成为2023年5月30日进入太空的首位平民宇航员。他负责在轨操作空间科学实验有效载荷。",
+                "role": "assistant"
+            }
+        ],
+        "images": [
+            "mllm_demo_data/3.jpg"
+        ]
+    }
+]

examples/train_full/qwen2vl_full_sft.yaml

@@ -0,0 +1,40 @@
+### model
+model_name_or_path: qwen2-vl-hf/qwen2-vl-7b-hf
+visual_inputs: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+deepspeed: examples/deepspeed/ds_z3_config.json
+
+### dataset
+dataset: qwen2vl_demo
+template: qwen2vl
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/qwen2-vl-7b/full/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 1
+learning_rate: 1.0e-5
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/train_lora/qwen2vl_lora_sft.yaml

@@ -0,0 +1,40 @@
+### model
+model_name_or_path: qwen2-vl-hf/qwen2-vl-7b-hf
+visual_inputs: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: qwen2vl_demo
+template: qwen2vl
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/qwen2-vl-7b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 1
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

src/llamafactory/data/collator.py

@@ -72,9 +72,37 @@ class SFTDataCollatorWith4DAttentionMask(DataCollatorForSeq2Seq):
     compute_dtype: "torch.dtype" = torch.float32
 
     def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, "torch.Tensor"]:
+        image_grid_thw = None
+        if "image_grid_thw" in features[0]:
+            image_grid_thw_list = [
+                torch.Tensor(feature["image_grid_thw"]).long()
+                for feature in features
+                if feature["image_grid_thw"][0][0] > 0
+            ]
+            pixel_values_list = [
+                torch.Tensor(feature["pixel_values"]) for feature in features if feature["image_grid_thw"][0][0] > 0
+            ]
+            if image_grid_thw_list:
+                image_grid_thw = torch.cat(image_grid_thw_list, 0)
+            else:
+                # Handle the case where the list is empty, for example:
+                image_grid_thw = None
+            if pixel_values_list:
+                pixel_values = torch.cat(pixel_values_list, 0)
+            else:
+                # Handle the case where the list is empty, for example:
+                pixel_values = None
+            features = [
+                {key: feature[key] for key in feature if key not in ["image_grid_thw", "pixel_values"]}
+                for feature in features
+            ]
+
         features = super().__call__(features)
         if self.block_diag_attn and self.attn_implementation != "flash_attention_2":
             features["attention_mask"] = prepare_4d_attention_mask(features["attention_mask"], self.compute_dtype)
+        if image_grid_thw is not None:
+            features["image_grid_thw"] = image_grid_thw
+            features["pixel_values"] = pixel_values
 
         return features
 

src/llamafactory/data/processors/processor_utils.py

@@ -78,6 +78,20 @@ def get_paligemma_token_type_ids(input_len: int, processor: "ProcessorMixin") ->
     return [0] * image_seq_length + [1] * (input_len - image_seq_length)
 
 
+def get_qwen2vl_image_inputs(images: Sequence["ImageObject"], processor: "ProcessorMixin") -> "NDArray":
+    r"""
+    Processes visual inputs. support multi images
+    """
+    image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+    if len(images) != 0:
+        image_inputs = image_processor(images=images, return_tensors="pt")
+    else:
+        image = Image.new("RGB", (56, 56), (255, 255, 255))
+        image_inputs = image_processor(images=[image], return_tensors="pt")
+        image_inputs["image_grid_thw"][0][0] = 0
+    return {"pixel_values": image_inputs["pixel_values"], "image_grid_thw": image_inputs["image_grid_thw"]}
+
+
 def infer_seqlen(source_len: int, target_len: int, cutoff_len: int) -> Tuple[int, int]:
     r"""
     Computes the real sequence length after truncation by the cutoff_len.

src/llamafactory/data/processors/supervised.py

@@ -17,10 +17,17 @@
 
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
-from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, greedy_knapsack, infer_seqlen
+from .processor_utils import (
+    get_paligemma_token_type_ids,
+    get_pixel_values,
+    get_qwen2vl_image_inputs,
+    greedy_knapsack,
+    infer_seqlen,
+)
 
 
 if TYPE_CHECKING:
+    from PIL.Image import Image as ImageObject
     from transformers import PreTrainedTokenizer, ProcessorMixin
 
     from ...hparams import DataArguments
@@ -36,13 +43,32 @@ def _encode_supervised_example(
     system: Optional[str],
     tools: Optional[str],
     template: "Template",
+    images: Sequence["ImageObject"],
     tokenizer: "PreTrainedTokenizer",
     processor: Optional["ProcessorMixin"],
     cutoff_len: int,
     train_on_prompt: bool,
     mask_history: bool,
 ) -> Tuple[List[int], List[int]]:
-    if processor is not None and not hasattr(processor, "image_seq_length"):  # llava-like models
+    if processor is not None and "image_grid_thw" in processor.model_input_names:  # qwen2_vl models
+        image_processor = getattr(processor, "image_processor")
+        merge_length = image_processor.merge_size**2
+        if len(images) > 0:
+            image_grid_thw = get_qwen2vl_image_inputs(images, processor)["image_grid_thw"]
+        index = 0
+        for message in prompt:
+            content = message["content"]
+            while "<|image_pad|>" in content:
+                content = content.replace(
+                    "<|image_pad|>",
+                    template.vision_start_token
+                    + "<|placeholder|>" * (image_grid_thw[index].prod() // merge_length)
+                    + template.vision_end_token,
+                    1,
+                )
+                index += 1
+            message["content"] = content.replace("<|placeholder|>", "<|image_pad|>")
+    elif processor is not None and not hasattr(processor, "image_seq_length"):  # llava-like models
         prompt[0]["content"] = template.image_token + prompt[0]["content"]
 
     messages = prompt + response
@@ -107,6 +133,8 @@ def preprocess_supervised_dataset(
         model_inputs["pixel_values"] = []
         if hasattr(processor, "image_seq_length"):  # paligemma models
             model_inputs["token_type_ids"] = []
+        if "image_grid_thw" in processor.model_input_names:  # qwen2_vl models
+            model_inputs["image_grid_thw"] = []
 
     for i in range(len(examples["prompt"])):
         if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
@@ -118,6 +146,7 @@ def preprocess_supervised_dataset(
             response=examples["response"][i],
             system=examples["system"][i],
             tools=examples["tools"][i],
+            images=examples["images"][i],
             template=template,
             tokenizer=tokenizer,
             processor=processor,
@@ -129,9 +158,14 @@ def preprocess_supervised_dataset(
         model_inputs["attention_mask"].append([1] * len(input_ids))
         model_inputs["labels"].append(labels)
         if processor is not None:
-            model_inputs["pixel_values"].append(get_pixel_values(examples["images"][i], processor))
-            if hasattr(processor, "image_seq_length"):  # paligemma models
-                model_inputs["token_type_ids"].append(get_paligemma_token_type_ids(len(input_ids), processor))
+            if "image_grid_thw" in processor.model_input_names:  # qwen2_vl models
+                image_inputs = get_qwen2vl_image_inputs(examples["images"][i], processor)
+                model_inputs["pixel_values"].append(image_inputs["pixel_values"])
+                model_inputs["image_grid_thw"].append(image_inputs["image_grid_thw"])
+            else:
+                model_inputs["pixel_values"].append(get_pixel_values(examples["images"][i], processor))
+                if hasattr(processor, "image_seq_length"):  # paligemma models
+                    model_inputs["token_type_ids"].append(get_paligemma_token_type_ids(len(input_ids), processor))
 
     return model_inputs