release v0.7.0

data/dataset_info.json

@@ -60,7 +60,7 @@
   },
   "mllm_demo": {
     "file_name": "mllm_demo.json",
-    "file_sha1": "b6709b23657d5c42a701f1c5574f3a6edaa40a20",
+    "file_sha1": "d626cc0ad88a26d0dc9fcb47336821cf486d8bcc",
     "formatting": "sharegpt",
     "columns": {
       "messages": "messages",

data/mllm_demo.json

@@ -2,7 +2,7 @@
   {
     "messages": [
       {
-        "content": "Who are they?<image>",
+        "content": "Who are they?",
         "role": "user"
       },
       {
@@ -14,18 +14,18 @@
         "role": "user"
       },
       {
-        "content": "They are celebrating on the soccer field",
+        "content": "They are celebrating on the soccer field.",
         "role": "assistant"
       }
     ],
     "images": [
-      "images/1.jpg"
+      "mllm_demo_data/1.jpg"
     ]
   },
   {
     "messages": [
       {
-        "content": "Who is he?<image>",
+        "content": "Who is he?",
         "role": "user"
       },
       {
@@ -42,13 +42,13 @@
       }
     ],
     "images": [
-      "images/2.jpg"
+      "mllm_demo_data/2.jpg"
     ]
   },
   {
     "messages": [
       {
-        "content": "Please describe this image<image>",
+        "content": "Please describe this image",
         "role": "user"
       },
       {
@@ -65,7 +65,76 @@
       }
     ],
     "images": [
-      "images/3.jpg"
+      "mllm_demo_data/3.jpg"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "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": "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"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "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": "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"
     ]
   }
 ]

setup.py

@@ -25,7 +25,7 @@ def get_requires():
     "unsloth": ["torch==2.2.0", "unsloth[cu121-ampere-torch220]"],
     "galore": ["galore-torch"],
     "badam": ["badam"],
-    "vllm": ["vllm>=0.3.3"],
+    "vllm": ["vllm>=0.4.0"],
     "bitsandbytes": ["bitsandbytes>=0.39.0"],
     "gptq": ["optimum>=1.16.0", "auto-gptq>=0.5.0"],
     "awq": ["autoawq"],

src/llmtuner/__init__.py

@@ -7,5 +7,5 @@
 from .webui import create_ui, create_web_demo
 
 
-__version__ = "0.6.4.dev0"
+__version__ = "0.7.0"
 __all__ = ["create_app", "ChatModel", "Evaluator", "export_model", "run_exp", "create_ui", "create_web_demo"]

src/llmtuner/chat/hf_engine.py

@@ -56,7 +56,7 @@ def _process_args(
         input_kwargs: Optional[Dict[str, Any]] = {},
     ) -> Tuple[Dict[str, Any], int]:
         if processor is not None and image is not None and "<image>" not in messages[0]["content"]:
-            messages[0]["content"] = messages[0]["content"] + "<image>"
+            messages[0]["content"] = "<image>" + messages[0]["content"]
 
         paired_messages = messages + [{"role": "assistant", "content": ""}]
         prompt_ids, _ = template.encode_oneturn(

src/llmtuner/chat/vllm_engine.py

@@ -11,10 +11,13 @@
 if is_vllm_available():
     from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
     from vllm.lora.request import LoRARequest
+    from vllm.sequence import MultiModalData
 
 
 if TYPE_CHECKING:
+    import torch
     from numpy.typing import NDArray
+    from transformers.image_processing_utils import BaseImageProcessor
 
     from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
 
@@ -39,20 +42,30 @@ def __init__(
         self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
         self.generating_args = generating_args.to_dict()
 
-        engine_args = AsyncEngineArgs(
-            model=model_args.model_name_or_path,
-            trust_remote_code=True,
-            download_dir=model_args.cache_dir,
-            dtype=infer_dtype,
-            max_model_len=model_args.vllm_maxlen,
-            tensor_parallel_size=get_device_count() or 1,
-            gpu_memory_utilization=model_args.vllm_gpu_util,
-            disable_log_stats=True,
-            disable_log_requests=True,
-            enforce_eager=model_args.vllm_enforce_eager,
-            enable_lora=model_args.adapter_name_or_path is not None,
-        )
-        self.model = AsyncLLMEngine.from_engine_args(engine_args)
+        engine_args = {
+            "model": model_args.model_name_or_path,
+            "trust_remote_code": True,
+            "download_dir": model_args.cache_dir,
+            "dtype": infer_dtype,
+            "max_model_len": model_args.vllm_maxlen,
+            "tensor_parallel_size": get_device_count() or 1,
+            "gpu_memory_utilization": model_args.vllm_gpu_util,
+            "disable_log_stats": True,
+            "disable_log_requests": True,
+            "enforce_eager": model_args.vllm_enforce_eager,
+            "enable_lora": model_args.adapter_name_or_path is not None,
+        }
+
+        if model_args.visual_inputs:
+            # TODO: auto derive from config
+            # https://github.com/vllm-project/vllm/pull/3042#issuecomment-1984893549
+            self.image_feature_size = 576
+            engine_args["image_input_type"] = "pixel_values"
+            engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids("<image>")
+            engine_args["image_input_shape"] = "1,3,336,336"
+            engine_args["image_feature_size"] = self.image_feature_size
+
+        self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))
         if model_args.adapter_name_or_path is not None:
             self.lora_request = LoRARequest("default", 1, model_args.adapter_name_or_path[0])
         else:
@@ -67,6 +80,9 @@ async def _generate(
         **input_kwargs,
     ) -> AsyncIterator["RequestOutput"]:
         request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+        if self.processor is not None and image is not None and "<image>" not in messages[0]["content"]:
+            messages[0]["content"] = "<image>" * self.image_feature_size + messages[0]["content"]
+
         paired_messages = messages + [{"role": "assistant", "content": ""}]
         prompt_ids, _ = self.template.encode_oneturn(
             tokenizer=self.tokenizer, messages=paired_messages, system=system, tools=tools
@@ -110,12 +126,21 @@ async def _generate(
             max_tokens=generating_args["max_new_tokens"],
             skip_special_tokens=True,
         )
+
+        if self.processor is not None and image is not None:
+            image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
+            pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
+            multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
+        else:
+            multi_modal_data = None
+
         result_generator = self.model.generate(
             prompt=None,
             sampling_params=sampling_params,
             request_id=request_id,
             prompt_token_ids=prompt_ids,
             lora_request=self.lora_request,
+            multi_modal_data=multi_modal_data,
         )
         return result_generator
 

src/llmtuner/data/preprocess.py

@@ -1,14 +1,20 @@
 from functools import partial
 from itertools import chain
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Tuple
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple
 
 from ..extras.constants import IGNORE_INDEX
 from ..extras.logging import get_logger
+from ..extras.packages import is_pillow_available
 from .utils import Role
 
 
+if is_pillow_available():
+    from PIL import Image
+
+
 if TYPE_CHECKING:
-    from PIL.Image import Image
+    from numpy.typing import NDArray
+    from PIL.Image import Image as ImageObject
     from transformers import ProcessorMixin, Seq2SeqTrainingArguments
     from transformers.image_processing_utils import BaseImageProcessor
     from transformers.tokenization_utils import PreTrainedTokenizer
@@ -20,12 +26,11 @@
 logger = get_logger(__name__)
 
 
-def _preprocess_visual_inputs(model_inputs: Dict[str, Any], processor: "ProcessorMixin", image: "Image") -> None:
+def _preprocess_visual_inputs(images: Sequence["ImageObject"], processor: "ProcessorMixin") -> "NDArray":
+    # process visual inputs (currently only supports a single image)
     image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
-    pixel_values = image_processor(image, return_tensors="pt")["pixel_values"][0]
-    if "pixel_values" not in model_inputs:
-        model_inputs["pixel_values"] = []
-    model_inputs["pixel_values"].append(pixel_values)
+    image = images[0] if len(images) != 0 else Image.new("RGB", (100, 100), (255, 255, 255))
+    return image_processor(image, return_tensors="pt")["pixel_values"][0]
 
 
 def preprocess_pretrain_dataset(
@@ -66,11 +71,17 @@ def preprocess_supervised_dataset(
     # build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
     # for multiturn examples, we only mask the prompt part in each prompt-response pair.
     model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
+    if processor is not None:
+        model_inputs["pixel_values"] = []
+        preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
 
     for i in range(len(examples["prompt"])):
         if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
             continue
 
+        if processor is not None:
+            examples["prompt"][i][0]["content"] = "<image>" + examples["prompt"][i][0]["content"]
+
         messages = examples["prompt"][i] + examples["response"][i]
         input_ids, labels = [], []
         for turn_idx, (source_ids, target_ids) in enumerate(
@@ -100,8 +111,8 @@ def preprocess_supervised_dataset(
         model_inputs["input_ids"].append(input_ids)
         model_inputs["attention_mask"].append([1] * len(input_ids))
         model_inputs["labels"].append(labels)
-        if processor is not None and "images" in examples:
-            _preprocess_visual_inputs(model_inputs, processor, examples["images"][i][0])
+        if processor is not None:
+            model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
 
     return model_inputs
 
@@ -161,11 +172,17 @@ def preprocess_unsupervised_dataset(
 ) -> Dict[str, List[List[int]]]:
     # build inputs with format `<bos> X` and labels with format `Y <eos>`
     model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
+    if processor is not None:
+        model_inputs["pixel_values"] = []
+        preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
 
     for i in range(len(examples["prompt"])):
         if len(examples["prompt"][i]) % 2 != 1:
             continue
 
+        if processor is not None:
+            examples["prompt"][i][0]["content"] = "<image>" + examples["prompt"][i][0]["content"]
+
         if len(examples["response"][i]) == 1:
             messages = examples["prompt"][i] + examples["response"][i]
         else:
@@ -186,8 +203,8 @@ def preprocess_unsupervised_dataset(
         model_inputs["input_ids"].append(input_ids)
         model_inputs["attention_mask"].append([1] * len(input_ids))
         model_inputs["labels"].append(labels)
-        if processor is not None and "images" in examples:
-            _preprocess_visual_inputs(model_inputs, processor, examples["images"][i][0])
+        if processor is not None:
+            model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
 
     return model_inputs
 
@@ -201,10 +218,17 @@ def preprocess_pairwise_dataset(
 ) -> Dict[str, List[List[int]]]:
     # build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
     model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
+    if processor is not None:
+        model_inputs["pixel_values"] = []
+        preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
+
     for i in range(len(examples["prompt"])):
         if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) < 2:
             continue
 
+        if processor is not None:
+            examples["prompt"][i][0]["content"] = "<image>" + examples["prompt"][i][0]["content"]
+
         chosen_messages = examples["prompt"][i] + [examples["response"][i][0]]
         rejected_messages = examples["prompt"][i] + [examples["response"][i][1]]
         prompt_ids, chosen_ids = template.encode_oneturn(
@@ -231,8 +255,8 @@ def preprocess_pairwise_dataset(
         model_inputs["prompt_ids"].append(prompt_ids)
         model_inputs["chosen_ids"].append(chosen_ids)
         model_inputs["rejected_ids"].append(rejected_ids)
-        if processor is not None and "images" in examples:
-            _preprocess_visual_inputs(model_inputs, processor, examples["images"][i][0])
+        if processor is not None:
+            model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
 
     return model_inputs
 

src/llmtuner/extras/packages.py

@@ -48,6 +48,10 @@ def is_nltk_available():
     return _is_package_available("nltk")
 
 
+def is_pillow_available():
+    return _is_package_available("PIL")
+
+
 def is_requests_available():
     return _is_package_available("requests")