[MiniGPT4] Add MiniGPT4 to SHARK

-- This is the first installment of MiniGPT4 in SHARK.

Signed-off-by: Abhishek Varma <[email protected]>

apps/language_models/src/pipelines/minigpt4_utils/blip_processors.py

@@ -0,0 +1,68 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+from omegaconf import OmegaConf
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+
+
+class BaseProcessor:
+    def __init__(self):
+        self.transform = lambda x: x
+        return
+
+    def __call__(self, item):
+        return self.transform(item)
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        return cls()
+
+    def build(self, **kwargs):
+        cfg = OmegaConf.create(kwargs)
+
+        return self.from_config(cfg)
+
+
+class BlipImageBaseProcessor(BaseProcessor):
+    def __init__(self, mean=None, std=None):
+        if mean is None:
+            mean = (0.48145466, 0.4578275, 0.40821073)
+        if std is None:
+            std = (0.26862954, 0.26130258, 0.27577711)
+
+        self.normalize = transforms.Normalize(mean, std)
+
+
+class Blip2ImageEvalProcessor(BlipImageBaseProcessor):
+    def __init__(self, image_size=224, mean=None, std=None):
+        super().__init__(mean=mean, std=std)
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(
+                    (image_size, image_size),
+                    interpolation=InterpolationMode.BICUBIC,
+                ),
+                transforms.ToTensor(),
+                self.normalize,
+            ]
+        )
+
+    def __call__(self, item):
+        return self.transform(item)
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        if cfg is None:
+            cfg = OmegaConf.create()
+
+        image_size = cfg.get("image_size", 224)
+
+        mean = cfg.get("mean", None)
+        std = cfg.get("std", None)
+
+        return cls(image_size=image_size, mean=mean, std=std)

apps/language_models/src/pipelines/minigpt4_utils/configs/cc_sbu_align.yaml

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+datasets:
+  cc_sbu_align:
+    data_type: images
+    build_info:
+      storage: /path/to/cc_sbu_align/

apps/language_models/src/pipelines/minigpt4_utils/configs/minigpt4.yaml

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+model:
+  arch: mini_gpt4
+
+  # vit encoder
+  image_size: 224
+  drop_path_rate: 0
+  use_grad_checkpoint: False
+  vit_precision: "fp16"
+  freeze_vit: True
+  freeze_qformer: True
+
+  # Q-Former
+  num_query_token: 32
+
+  # Vicuna
+  llama_model: "lmsys/vicuna-7b-v1.3"
+
+  # generation configs
+  prompt: ""
+
+preprocess:
+    vis_processor:
+        train:
+          name: "blip2_image_train"
+          image_size: 224
+        eval:
+          name: "blip2_image_eval"
+          image_size: 224
+    text_processor:
+        train:
+          name: "blip_caption"
+        eval:
+          name: "blip_caption"

apps/language_models/src/pipelines/minigpt4_utils/configs/minigpt4_eval.yaml

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+model:
+  arch: mini_gpt4
+  model_type: pretrain_vicuna
+  freeze_vit: True
+  freeze_qformer: True
+  max_txt_len: 160
+  end_sym: "###"
+  low_resource: False
+  prompt_path: "apps/language_models/src/pipelines/minigpt4_utils/prompts/alignment.txt"
+  prompt_template: '###Human: {} ###Assistant: '
+  ckpt: 'prerained_minigpt4_7b.pth'
+
+
+datasets:
+  cc_sbu_align:
+    vis_processor:
+      train:
+        name: "blip2_image_eval"
+        image_size: 224
+    text_processor:
+      train:
+        name: "blip_caption"
+
+run:
+  task: image_text_pretrain

apps/language_models/src/pipelines/minigpt4_utils/dist_utils.py

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+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import os
+
+import torch
+import torch.distributed as dist
+import timm.models.hub as timm_hub
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def download_cached_file(url, check_hash=True, progress=False):
+    """
+    Download a file from a URL and cache it locally. If the file already exists, it is not downloaded again.
+    If distributed, only the main process downloads the file, and the other processes wait for the file to be downloaded.
+    """
+
+    def get_cached_file_path():
+        # a hack to sync the file path across processes
+        parts = torch.hub.urlparse(url)
+        filename = os.path.basename(parts.path)
+        cached_file = os.path.join(timm_hub.get_cache_dir(), filename)
+
+        return cached_file
+
+    if is_main_process():
+        timm_hub.download_cached_file(url, check_hash, progress)
+
+    if is_dist_avail_and_initialized():
+        dist.barrier()
+
+    return get_cached_file_path()