[Feature] Support LLaVA 1.5

configs/llava/README.md

@@ -16,46 +16,28 @@ Instruction tuning large language models (LLMs) using machine-generated instruct
 
 <!-- [TABS-BEGIN] -->
 
-**Prepare the checkpoint**
-
-According to the license of LLaMA, we cannot provide the merged checkpoint directly. Please use the below
-script to download and get the merged the checkpoint.
-
-```shell
-python tools/model_converters/llava-delta2mmpre.py huggyllama/llama-7b liuhaotian/LLaVA-Lightning-7B-delta-v1-1 ./LLaVA-Lightning-7B-delta-v1-1.pth
-```
-
 **Use the model**
 
 ```python
 import torch
 from mmpretrain import get_model, inference_model
 
-model = get_model('llava-7b-v1_caption', pretrained='MERGED_CHECKPOINT_PATH', device='cuda')
-out = inference_model(model, 'demo/cat-dog.png')
+out = inference_model('llava-7b-v1_caption', 'demo/cat-dog.png', device='cuda')
 print(out)
 # {'pred_caption': 'In the image, there are two cats sitting on a blanket.'}
 ```
 
-**Test Command**
-
-Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
-
-Test:
-
-```shell
-python tools/test.py configs/llava/llava-7b-v1_caption.py MERGED_CHECKPOINT_PATH
-```
-
 <!-- [TABS-END] -->
 
 ## Models and results
 
 ### Image Caption on COCO
 
-| Model                 | Params (M) |  BLEU-4  |  CIDER   |              Config              |        Download        |
-| :-------------------- | :--------: | :------: | :------: | :------------------------------: | :--------------------: |
-| `llava-7b-v1_caption` |  7045.82   | Upcoming | Upcoming | [config](llava-7b-v1_caption.py) | See the above tutorial |
+| Model                   | Params (M) |               Config               |                                                    Download                                                     |
+| :---------------------- | :--------: | :--------------------------------: | :-------------------------------------------------------------------------------------------------------------: |
+| `llava-7b-v1_caption`   |  7045.82   |  [config](llava-7b-v1_caption.py)  |  [ckpt](https://download.openmmlab.com/mmclassification/v1/llava/llava-7b-v1_liuhaotian_20231025-c9e119b6.pth)  |
+| `llava-7b-v1.5_caption` |  7062.90   | [config](llava-7b-v1.5_caption.py) | [ckpt](https://download.openmmlab.com/mmclassification/v1/llava/llava-7b-v1.5_liuhaotian_20231025-5828aa5a.pth) |
+| `llava-7b-v1.5_vqa`     |  7062.90   |   [config](llava-7b-v1.5_vqa.py)   | [ckpt](https://download.openmmlab.com/mmclassification/v1/llava/llava-7b-v1.5_liuhaotian_20231025-5828aa5a.pth) |
 
 ## Citation
 

configs/llava/llava-7b-v1.5_caption.py

@@ -0,0 +1,76 @@
+_base_ = '../_base_/default_runtime.py'
+
+meta_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."  # noqa: E501
+image_size = 336
+prompt_tmpl = f'''{meta_prompt} User: <image>
+Describe the image in detail. ASSISTANT:'''
+
+# model settings
+model = dict(
+    type='Llava',
+    tokenizer=dict(
+        type='AutoTokenizer', name_or_path='liuhaotian/llava-v1.5-7b'),
+    vision_encoder=dict(
+        type='VisionTransformer',
+        arch='l',
+        patch_size=14,
+        img_size=image_size,
+        pre_norm=True,
+        norm_cfg=dict(type='LN', eps=1e-5),
+        layer_cfgs=dict(act_cfg=dict(type='mmpretrain.QuickGELU')),
+        final_norm=False,
+        out_type='raw',
+        pretrained='https://download.openmmlab.com/mmclassification/v0/clip/'
+        'vit-large-p14_clip-openai-pre_336px_20231025-fb1315ed.pth',
+    ),
+    mm_hidden_size=1024,
+    use_im_patch=False,
+    use_im_start_end=False,
+    mm_proj_depth=2,
+    lang_encoder=dict(
+        type='AutoModelForCausalLM',
+        name_or_path='huggyllama/llama-7b',
+    ),
+    task='caption',
+    prompt_tmpl=prompt_tmpl,
+    generation_cfg=dict(num_beams=3, max_new_tokens=50, length_penalty=-1.0),
+)
+
+# data settings
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(image_size, image_size),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='PackInputs', meta_keys=['image_id']),
+]
+
+test_dataloader = dict(
+    batch_size=8,
+    num_workers=5,
+    dataset=dict(
+        type='COCOCaption',
+        data_root='data/coco',
+        ann_file='annotations/coco_karpathy_val.json',
+        pipeline=test_pipeline,
+    ),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+
+test_evaluator = dict(
+    type='COCOCaption',
+    ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
+)
+
+# schedule settings
+test_cfg = dict()

configs/llava/llava-7b-v1.5_vqa.py

@@ -0,0 +1,76 @@
+_base_ = '../_base_/default_runtime.py'
+
+meta_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."  # noqa: E501
+image_size = 336
+prompt_tmpl = f'''{meta_prompt} User: <image>
+{{question}} ASSISTANT:'''
+
+# model settings
+model = dict(
+    type='Llava',
+    tokenizer=dict(
+        type='AutoTokenizer', name_or_path='liuhaotian/llava-v1.5-7b'),
+    vision_encoder=dict(
+        type='VisionTransformer',
+        arch='l',
+        patch_size=14,
+        img_size=image_size,
+        pre_norm=True,
+        norm_cfg=dict(type='LN', eps=1e-5),
+        layer_cfgs=dict(act_cfg=dict(type='mmpretrain.QuickGELU')),
+        final_norm=False,
+        out_type='raw',
+        pretrained='https://download.openmmlab.com/mmclassification/v0/clip/'
+        'vit-large-p14_clip-openai-pre_336px_20231025-fb1315ed.pth',
+    ),
+    mm_hidden_size=1024,
+    use_im_patch=False,
+    use_im_start_end=False,
+    mm_proj_depth=2,
+    lang_encoder=dict(
+        type='AutoModelForCausalLM',
+        name_or_path='huggyllama/llama-7b',
+    ),
+    task='vqa',
+    prompt_tmpl=prompt_tmpl,
+    generation_cfg=dict(max_new_tokens=100),
+)
+
+# data settings
+data_preprocessor = dict(
+    type='MultiModalDataPreprocessor',
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(image_size, image_size),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='PackInputs', meta_keys=['image_id', 'question']),
+]
+
+test_dataloader = dict(
+    batch_size=8,
+    num_workers=5,
+    dataset=dict(
+        type='COCOCaption',
+        data_root='data/coco',
+        ann_file='annotations/coco_karpathy_val.json',
+        pipeline=test_pipeline,
+    ),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+
+test_evaluator = dict(
+    type='COCOCaption',
+    ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
+)
+
+# schedule settings
+test_cfg = dict()

configs/llava/llava-7b-v1_caption.py

@@ -1,16 +1,9 @@
 _base_ = '../_base_/default_runtime.py'
 
 meta_prompt = 'You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab.You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.Follow the instructions carefully and explain your answers in detail.'  # noqa: E501
-im_patch_token = '<im_patch>'
-patch_size = 14
 image_size = 224
-num_patches = (image_size // patch_size)**2
-caption_prompt = ' '.join([
-    meta_prompt,
-    'User: a photo of\n',
-    im_patch_token * num_patches,
-    'ASSISTANT:',
-])
+prompt_tmpl = f'''{meta_prompt} User: <im_start><image><im_end>
+Describe the image in detail. ASSISTANT:'''
 
 # model settings
 model = dict(
@@ -22,6 +15,7 @@
         type='VisionTransformer',
         arch='l',
         patch_size=14,
+        img_size=image_size,
         pre_norm=True,
         norm_cfg=dict(type='LN', eps=1e-5),
         layer_cfgs=dict(act_cfg=dict(type='mmpretrain.QuickGELU')),
@@ -32,15 +26,16 @@
             'vit-large-p14_clip-openai-pre_3rdparty_20230517-95e2af0b.pth'),
     ),
     mm_hidden_size=1024,
-    use_im_start_end=False,
-    use_mm_proj=True,
+    use_im_patch=False,
+    use_im_start_end=True,
+    mm_proj_depth=1,
     lang_encoder=dict(
         type='AutoModelForCausalLM',
         name_or_path='huggyllama/llama-7b',
     ),
     task='caption',
-    prompt_tmpl=caption_prompt,
-    generation_cfg=dict(num_beams=3, max_new_tokens=20, length_penalty=-2.0),
+    prompt_tmpl=prompt_tmpl,
+    generation_cfg=dict(max_new_tokens=50),
 )
 
 # data settings

configs/llava/metafile.yml

@@ -21,5 +21,31 @@ Models:
         Metrics:
           BLEU-4: null
           CIDER: null
-    Weights: null
+    Weights: https://download.openmmlab.com/mmclassification/v1/llava/llava-7b-v1_liuhaotian_20231025-c9e119b6.pth
     Config: configs/llava/llava-7b-v1_caption.py
+  - Name: llava-7b-v1.5_caption
+    Metadata:
+      FLOPs: null
+      Parameters: 7062900736
+    In Collection: LLaVA
+    Results:
+      - Task: Image Caption
+        Dataset: COCO
+        Metrics:
+          BLEU-4: null
+          CIDER: null
+    Weights: https://download.openmmlab.com/mmclassification/v1/llava/llava-7b-v1.5_liuhaotian_20231025-5828aa5a.pth
+    Config: configs/llava/llava-7b-v1.5_caption.py
+  - Name: llava-7b-v1.5_vqa
+    Metadata:
+      FLOPs: null
+      Parameters: 7062900736
+    In Collection: LLaVA
+    Results:
+      - Task: Visual Question Answering
+        Dataset: COCO
+        Metrics:
+          BLEU-4: null
+          CIDER: null
+    Weights: https://download.openmmlab.com/mmclassification/v1/llava/llava-7b-v1.5_liuhaotian_20231025-5828aa5a.pth
+    Config: configs/llava/llava-7b-v1.5_vqa.py

mmpretrain/models/multimodal/llava/llava.py

@@ -24,8 +24,8 @@ class Llava(BaseModel):
         use_im_start_end (bool): Whether to use the im_start and im_end tokens
         mm_vision_select_layer (int): The index from vision encoder output.
             Defaults to -1.
-        use_mm_proj (bool): Whether to enable multi-modal projection.
-            Defaults to True.
+        mm_proj_depth (int): The number of linear layers for multi-modal
+            projection. Defaults to 1.
         load_lang_pretrained (bool): Whether to load the pretrained model of
             language encoder. Defaults to False.
         generation_cfg (dict): The extra generation config, accept the keyword
@@ -51,9 +51,10 @@ def __init__(self,
                  mm_hidden_size: int,
                  prompt_tmpl: str,
                  task: str = 'caption',
+                 use_im_patch: bool = True,
                  use_im_start_end: bool = False,
                  mm_vision_select_layer: int = -1,
-                 use_mm_proj: bool = True,
+                 mm_proj_depth: int = 1,
                  generation_cfg: dict = dict(),
                  load_lang_pretrained: bool = False,
                  data_preprocessor: Optional[dict] = None,
@@ -75,7 +76,9 @@ def __init__(self,
         # init tokenizer
         self.tokenizer = TOKENIZER.build(tokenizer)
         # add Llava special tokens to the tokenizer
-        self.tokenizer.add_tokens([self.im_patch_token], special_tokens=True)
+        if use_im_patch:
+            self.tokenizer.add_tokens([self.im_patch_token],
+                                      special_tokens=True)
         if use_im_start_end:
             self.tokenizer.add_tokens([self.im_start_token, self.im_end_token],
                                       special_tokens=True)
@@ -108,14 +111,12 @@ def __init__(self,
             vision_encoder=vision_encoder,
             lang_encoder=lang_encoder,
             mm_hidden_size=mm_hidden_size,
-            use_mm_proj=use_mm_proj,
+            mm_proj_depth=mm_proj_depth,
             use_im_start_end=use_im_start_end,
             im_start_token=self.tokenizer.convert_tokens_to_ids(
                 self.im_start_token),
             im_end_token=self.tokenizer.convert_tokens_to_ids(
                 self.im_end_token),
-            im_patch_token=self.tokenizer.convert_tokens_to_ids(
-                self.im_patch_token),
             mm_vision_select_layer=mm_vision_select_layer)
 
         self.generation_cfg = generation_cfg
@@ -207,16 +208,24 @@ def preprocess_text(self, data_samples: List[DataSample],
         Returns:
             List[DataSample]: Return list of data samples.
         """
-        prompts = []
+        tokens = []
         for sample in data_samples:
-            final_prompt = self.prompt_tmpl.format(**sample.to_dict())
-            prompts.append(final_prompt)
+            prompt = self.prompt_tmpl.format(**sample.to_dict())
+            input_ids = []
+            while '<image>' in prompt:
+                prefix, _, prompt = prompt.partition('<image>')
+                input_ids.extend(
+                    self.tokenizer(prefix, add_special_tokens=False).input_ids)
+                input_ids.append(-200)
+            if prompt:
+                input_ids.extend(
+                    self.tokenizer(prompt, add_special_tokens=False).input_ids)
+            tokens.append(dict(input_ids=input_ids))
 
         self.tokenizer.padding_side = 'left'
-        input_text = self.tokenizer(
-            prompts,
+        input_text = self.tokenizer.pad(
+            tokens,
             padding='longest',
-            truncation=True,
             return_tensors='pt',
             max_length=2000,
         ).to(device)