add new checkpoint

.vscode/launch.json

@@ -14,6 +14,21 @@
                 "CUDA_VISIBLE_DEVICES": "1",
             },
             "args": []
+        },
+        {
+            "name": "merge_lora_weights",
+            "type": "python",
+            "request": "launch",
+            "module": "scripts.merge_lora_weights",
+            "justMyCode": false,
+            "env": {
+                "CUDA_VISIBLE_DEVICES": "1",
+            },
+            "args": [
+                "--model-base=/data/jianingy/3d_grand_demo_hf_space/checkpoints/llava-llama-2-7b-chat-lightning-preview",
+                "--model-path=/data/jianingy/3d_grand_demo_hf_space/checkpoints/llava_lora_grounded_obj_ref_checkpoint-4896",
+                "--save-model-path=/data/jianingy/3d_grand_demo_hf_space/checkpoints/merged_weights_grounded_obj_ref"
+            ]
         }
     ]
 }

app.py

@@ -27,8 +27,8 @@ from session import Session
 from model import load_model_and_dataloader, get_model_response
 
 # Load model and tokenizer once at the start
-model_path = "checkpoints/llava_lora_grounded_obj_ref_checkpoint-4896"
-model_base = "checkpoints/llava-llama-2-7b-chat-lightning-preview"
+model_path = "checkpoints/merged_weights_grounded_obj_ref"
+model_base = None
 load_8bit = False
 load_4bit = False
 load_bf16 = True
@@ -38,37 +38,16 @@ max_new_tokens = 5000
 obj_context_feature_type = "text"
 
 
-class TemporaryDisableCUDA:
-    def __enter__(self):
-        # Store the current CUDA_VISIBLE_DEVICES environment variable
-        self.original_cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES")
-        # Disable CUDA by setting CUDA_VISIBLE_DEVICES to an invalid value
-        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-        # Reinitialize CUDA state in PyTorch
-        torch.cuda.device_count()
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        # Restore the original CUDA_VISIBLE_DEVICES environment variable
-        if self.original_cuda_visible_devices is not None:
-            os.environ["CUDA_VISIBLE_DEVICES"] = self.original_cuda_visible_devices
-        else:
-            del os.environ["CUDA_VISIBLE_DEVICES"]
-        # Reinitialize CUDA state in PyTorch
-        torch.cuda.device_count()
-
-
-# HACK: we need to temporarily disable CUDA b/c when loading model,
-# PEFT somehow always uses CUDA even if we set device_map to 'cpu'
-with TemporaryDisableCUDA():
-    tokenizer, model, data_loader = load_model_and_dataloader(
-        model_path=model_path,
-        model_base=model_base,
-        load_8bit=load_8bit,
-        load_4bit=load_4bit,
-        load_bf16=load_bf16,
-        scene_to_obj_mapping=scene_to_obj_mapping,
-        device_map='cpu',
-    )  # Huggingface Zero-GPU has to use .to(device) to set the device, otherwise it will fail
+
+tokenizer, model, data_loader = load_model_and_dataloader(
+    model_path=model_path,
+    model_base=model_base,
+    load_8bit=load_8bit,
+    load_4bit=load_4bit,
+    load_bf16=load_bf16,
+    scene_to_obj_mapping=scene_to_obj_mapping,
+    device_map='cpu',
+)  # Huggingface Zero-GPU has to use .to(device) to set the device, otherwise it will fail
 
 model.to("cuda")  # Huggingface Zero-GPU requires explicit device placement
 
@@ -509,7 +488,7 @@ for x in range(1, 10):  # try 8 times
             # debug=True,
             # server_name="0.0.0.0",
             # server_port=port,
-            # share=True
+            share=True
         )
     except OSError:
         for proc in process_iter():

checkpoints/merged_weights_grounded_obj_ref/config.json

@@ -0,0 +1,54 @@
+{
+  "_name_or_path": "/data/jianingy/3d_grand_demo_hf_space/checkpoints/llava-llama-2-7b-chat-lightning-preview",
+  "added_special_token_to_input_id": {},
+  "architectures": [
+    "LlavaLlamaForCausalLM"
+  ],
+  "bbox_ce_loss_weight": 0.0,
+  "bbox_iou_loss_weight": 0.0,
+  "bbox_mse_loss_weight": 0.0,
+  "bbox_tokenization_type": null,
+  "bos_token_id": 1,
+  "d_latents": 1024,
+  "eos_token_id": 2,
+  "freeze_mm_mlp_adapter": false,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "image_aspect_ratio": "square",
+  "image_grid_pinpoints": null,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "lm_loss_weight": 1.0,
+  "max_position_embeddings": 2048,
+  "mm_hidden_size": 1024,
+  "mm_resampler_type": null,
+  "mm_use_im_patch_token": false,
+  "mm_use_im_start_end": false,
+  "mm_vision_select_feature": "patch",
+  "mm_vision_select_layer": -2,
+  "mm_vision_tower": "bbox-ground-truth",
+  "model_type": "llava",
+  "normalize_points_to_unit_cube": false,
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "num_latents": 256,
+  "num_voxels_per_axis_for_location_tokens": 10,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "tune_mm_mlp_adapter": false,
+  "tune_mm_vision_resampler": false,
+  "tune_vision_tower": false,
+  "use_bbox_ce_loss": false,
+  "use_bbox_iou_loss": false,
+  "use_bbox_mse_loss": false,
+  "use_cache": false,
+  "use_mm_proj": true,
+  "vision_tower": "bbox-ground-truth",
+  "vocab_size": 32000
+}

checkpoints/merged_weights_grounded_obj_ref/generation_config.json

@@ -0,0 +1,10 @@
+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.31.0"
+}

checkpoints/merged_weights_grounded_obj_ref/pytorch_model-00001-of-00002.bin

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+}

checkpoints/merged_weights_grounded_obj_ref/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<unk>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoints/merged_weights_grounded_obj_ref/tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

checkpoints/merged_weights_grounded_obj_ref/tokenizer_config.json

@@ -0,0 +1,36 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": false,
+  "model_max_length": 4096,
+  "pad_token": null,
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

scripts/merge_lora_weights.py

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+import argparse
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path
+
+
+def merge_lora(args):
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, device_map='cpu')
+
+    model.save_pretrained(args.save_model_path)
+    tokenizer.save_pretrained(args.save_model_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, required=True)
+    parser.add_argument("--model-base", type=str, required=True)
+    parser.add_argument("--save-model-path", type=str, required=True)
+
+    args = parser.parse_args()
+
+    merge_lora(args)