Create builder.py

builder.py

@@ -0,0 +1,170 @@
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+
+
+import os
+import shutil
+import pdb
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
+import torch
+
+CONTROLLER_HEART_BEAT_EXPIRATION = 30
+WORKER_HEART_BEAT_INTERVAL = 15
+
+LOGDIR = "."
+
+# Model Constants
+IGNORE_INDEX = -100
+IMAGE_TOKEN_INDEX = -200
+DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
+DEFAULT_IM_START_TOKEN = "<im_start>"
+DEFAULT_IM_END_TOKEN = "<im_end>"
+IMAGE_PLACEHOLDER = "<image-placeholder>"
+
+# Added by Ferret
+DEFAULT_REGION_FEA_TOKEN = "<region_fea>"
+VOCAB_IMAGE_W = 1000
+VOCAB_IMAGE_H = 1000
+
+# GROUNDING PROMPTS
+GROUNDING_TEMPLATES = [
+  '\nProvide the bounding boxes of the mentioned objects.',
+  '\nInclude the coordinates for each mentioned object.',
+  '\nLocate the objects with their coordinates.',
+  '\nAnswer in [x1, y1, x2, y2] format.',
+  '\nMention the objects and their locations using the format [x1, y1, x2, y2].',
+  '\nDraw boxes around the mentioned objects.',
+  '\nUse boxes to show where each thing is.',
+  '\nTell me where the objects are with coordinates.',
+  '\nList where each object is with boxes.',
+  '\nShow me the regions with boxes.'
+]
+DEFAULT_REGION_FEA_TOKEN = "<region_fea>"
+
+def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto"):
+    kwargs = {"device_map": device_map}
+
+    if load_8bit:
+        kwargs['load_in_8bit'] = True
+    elif load_4bit:
+        kwargs['load_in_4bit'] = True
+        kwargs['quantization_config'] = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type='nf4'
+        )
+    else:
+        kwargs['torch_dtype'] = torch.float16
+
+    if 'llava' in model_name.lower() or 'ferret' in model_name.lower():
+        # Load LLaVA/FERRET model
+        if 'lora' in model_name.lower() and model_base is not None:
+            lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
+            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+            print('Loading LLaVA/FERRET from base model...')
+            model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
+            token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
+            if model.lm_head.weight.shape[0] != token_num:
+                model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
+                model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
+
+            print('Loading additional LLaVA/FERRET weights...')
+            if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
+                non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
+            else:
+                # this is probably from HF Hub
+                from huggingface_hub import hf_hub_download
+                def load_from_hf(repo_id, filename, subfolder=None):
+                    cache_file = hf_hub_download(
+                        repo_id=repo_id,
+                        filename=filename,
+                        subfolder=subfolder)
+                    return torch.load(cache_file, map_location='cpu')
+                non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
+            non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
+            if any(k.startswith('model.model.') for k in non_lora_trainables):
+                non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
+            model.load_state_dict(non_lora_trainables, strict=False)
+
+            from peft import PeftModel
+            print('Loading LoRA weights...')
+            model = PeftModel.from_pretrained(model, model_path)
+            print('Merging LoRA weights...')
+            model = model.merge_and_unload()
+            print('Model is loaded...')
+        elif model_base is not None:
+            # this may be mm projector only
+            print('Loading LLaVA/FERRET from base model...')
+            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+            cfg_pretrained = AutoConfig.from_pretrained(model_path)
+            model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
+
+            mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
+            mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
+            model.load_state_dict(mm_projector_weights, strict=False)
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+            model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+    else:
+        # Load language model
+        if model_base is not None:
+            # PEFT model
+            from peft import PeftModel
+            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+            model = AutoModelForCausalLM.from_pretrained(model_base, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
+            print(f"Loading LoRA weights from {model_path}")
+            model = PeftModel.from_pretrained(model, model_path)
+            print(f"Merging weights")
+            model = model.merge_and_unload()
+            print('Convert to FP16...')
+            model.to(torch.float16)
+        else:
+            use_fast = False
+            tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+            model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+
+    image_processor = None
+
+    if 'llava' in model_name.lower() or 'ferret' in model_name.lower():
+        mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
+        mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
+        mm_im_region_fea_token = getattr(model.config, "im_region_fea_token", None)
+        if mm_use_im_patch_token:
+            tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+        if mm_im_region_fea_token is not None:
+            tokenizer.add_tokens([DEFAULT_REGION_FEA_TOKEN], special_tokens=True)
+        if mm_use_im_start_end:
+            tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+        model.resize_token_embeddings(len(tokenizer))
+
+        vision_tower = model.get_vision_tower()
+        vision_tower_path = os.path.join(model_path, 'vision_tower')
+        if not vision_tower.is_loaded or os.path.exists(vision_tower_path):
+            if os.path.exists(vision_tower_path):
+                print(f'Start Loading vision tower from {vision_tower_path}')
+                vision_tower.load_model(vision_tower_path=vision_tower_path)
+                print(f'Finish Loading vision tower from {vision_tower_path}')
+            else:
+                vision_tower.load_model()
+
+        vision_tower.to(device='cuda', dtype=torch.float16)
+        image_processor = vision_tower.image_processor
+
+    if hasattr(model.config, "max_sequence_length"):
+        context_len = model.config.max_sequence_length
+    else:
+        context_len = 2048
+
+    return tokenizer, model, image_processor, context_len