Spaces:
Running
on
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Running
on
Zero
initial demo
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- .gitattributes +1 -0
- .gitignore +0 -0
- .vscode/launch.json +19 -0
- .vscode/settings.json +32 -0
- __pycache__/app.cpython-310.pyc +0 -0
- __pycache__/model.cpython-310.pyc +0 -0
- __pycache__/session.cpython-310.pyc +0 -0
- __pycache__/settings.cpython-310.pyc +0 -0
- __pycache__/utils.cpython-310.pyc +0 -0
- app.py +497 -0
- checkpoints/llava-llama-2-7b-chat-lightning-preview/config.json +39 -0
- checkpoints/llava-llama-2-7b-chat-lightning-preview/generation_config.json +10 -0
- checkpoints/llava-llama-2-7b-chat-lightning-preview/pytorch_model-00001-of-00002.bin +3 -0
- checkpoints/llava-llama-2-7b-chat-lightning-preview/pytorch_model-00002-of-00002.bin +3 -0
- checkpoints/llava-llama-2-7b-chat-lightning-preview/pytorch_model.bin.index.json +723 -0
- checkpoints/llava-llama-2-7b-chat-lightning-preview/special_tokens_map.json +23 -0
- checkpoints/llava-llama-2-7b-chat-lightning-preview/tokenizer.model +3 -0
- checkpoints/llava-llama-2-7b-chat-lightning-preview/tokenizer_config.json +36 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/README.md +9 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/adapter_config.json +26 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/adapter_model.bin +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/config.json +54 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/latest +1 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/non_lora_trainables.bin +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_0.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_1.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_10.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_11.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_2.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_3.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_4.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_5.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_6.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_7.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_8.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/rng_state_9.pth +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/special_tokens_map.json +24 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/tokenizer.model +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/tokenizer_config.json +36 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/trainer_state.json +0 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/training_args.bin +3 -0
- checkpoints/lora_grounded_obj_ref_checkpoint-4896/zero_to_fp32.py +578 -0
- convert_mesh.ipynb +111 -0
- data/predicted_scene_data_update_5.json +0 -0
- data/scanrefer_ground_truth_scene_graph.json +0 -0
- data/scene0025_00/scene0025_00.obj +3 -0
- data/scene0426_00/scene0426_00.obj +3 -0
- data/scene0643_00/scene0643_00.obj +3 -0
- llava/__init__.py +1 -0
- llava/__pycache__/__init__.cpython-310.pyc +0 -0
.gitattributes
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.obj filter=lfs diff=lfs merge=lfs -text
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__pycache__/app.cpython-310.pyc
ADDED
Binary file (11.9 kB). View file
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__pycache__/model.cpython-310.pyc
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__pycache__/session.cpython-310.pyc
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app.py
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import spaces
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import os
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import gradio as gr
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from time import sleep
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from signal import SIGTERM
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from psutil import process_iter
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from settings import GRAND3D_Settings
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from utils import list_dirs
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import open3d as o3d
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from copy import deepcopy
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import numpy as np
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import re
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from bs4 import BeautifulSoup
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import logging
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# The following line sets the root logger level as well.
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# It's equivalent to both previous statements combined:
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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from session import Session
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from model import load_model_and_dataloader, get_model_response
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# Load model and tokenizer once at the start
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model_path = "checkpoints/lora_grounded_obj_ref_checkpoint-4896"
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model_base = "checkpoints/llava-llama-2-7b-chat-lightning-preview"
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load_8bit = False
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load_4bit = False
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load_bf16 = True
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scene_to_obj_mapping = "data/predicted_scene_data_update_5.json"
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# scene_to_obj_mapping = "data/scanrefer_ground_truth_scene_graph.json"
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max_new_tokens = 5000
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obj_context_feature_type = "text"
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tokenizer, model, data_loader = load_model_and_dataloader(
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model_path=model_path,
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model_base=model_base,
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load_8bit=load_8bit,
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load_4bit=load_4bit,
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load_bf16=load_bf16,
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scene_to_obj_mapping=scene_to_obj_mapping,
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)
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def get_chatbot_response(user_chat_input, scene_id):
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# Get the response from the model
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prompt, response = get_model_response(
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model=model,
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tokenizer=tokenizer,
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data_loader=data_loader,
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scene_id=scene_id,
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user_input=user_chat_input,
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max_new_tokens=max_new_tokens,
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temperature=0.2,
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top_p=0.9
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)
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return scene_id, prompt, response
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63 |
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# def get_chatbot_response(user_chat_input):
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64 |
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# # Get the response from the chatbot
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65 |
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# scene_id = "scene0643_00"
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66 |
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# scene_graph = """
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67 |
+
# Object-centric context: <obj_0>: {'category': 'door', 'centroid': '[0.35, 1.99, 1.11]', 'extent': '[0.68, 0.65, 2.11]'}; <obj_1>: {'category': 'ceiling', 'centroid': '[1.04, -1.39, 2.68]', 'extent': '[0.18, 0.90, 0.05]'}; <obj_2>: {'category': 'ceiling', 'centroid': '[0.77, 2.09, 2.65]', 'extent': '[0.94, 0.86, 0.11]'}; <obj_3>: {'category': 'trash can', 'centroid': '[-0.61, -2.16, 0.21]', 'extent': '[0.42, 0.36, 0.41]'}; <obj_4>: {'category': 'chair', 'centroid': '[0.35, -1.35, 0.50]', 'extent': '[0.46, 0.47, 0.94]'}; <obj_5>: {'category': 'trash can', 'centroid': '[-0.22, -2.13, 0.24]', 'extent': '[0.40, 0.28, 0.39]'}; <obj_6>: {'category': 'cabinet', 'centroid': '[-1.24, 0.00, 0.58]', 'extent': '[0.61, 0.57, 0.79]'}; <obj_7>: {'category': 'cup', 'centroid': '[0.62, 0.23, 0.77]', 'extent': '[0.14, 0.14, 0.08]'}; <obj_8>: {'category': 'window', 'centroid': '[-0.35, -2.87, 1.13]', 'extent': '[2.05, 0.60, 1.07]'}; <obj_9>: {'category': 'file cabinet', 'centroid': '[0.40, -1.97, 0.39]', 'extent': '[0.40, 0.66, 0.73]'}; <obj_10>: {'category': 'monitor', 'centroid': '[0.92, -1.51, 0.97]', 'extent': '[0.25, 0.57, 0.47]'}; <obj_11>: {'category': 'chair', 'centroid': '[0.34, 0.59, 0.43]', 'extent': '[0.65, 0.64, 0.94]'}; <obj_12>: {'category': 'desk', 'centroid': '[0.64, 0.75, 0.57]', 'extent': '[0.76, 1.60, 0.82]'}; <obj_13>: {'category': 'chair', 'centroid': '[0.55, -0.33, 0.48]', 'extent': '[0.60, 0.60, 0.87]'}; <obj_14>: {'category': 'office chair', 'centroid': '[-0.28, 1.56, 0.46]', 'extent': '[0.67, 0.55, 1.02]'}; <obj_15>: {'category': 'office chair', 'centroid': '[-0.86, -1.53, 0.43]', 'extent': '[0.54, 0.64, 0.97]'}; <obj_16>: {'category': 'chair', 'centroid': '[-0.28, 1.56, 0.46]', 'extent': '[0.67, 0.55, 1.02]'}; <obj_17>: {'category': 'monitor', 'centroid': '[0.98, 0.56, 1.05]', 'extent': '[0.21, 0.60, 0.54]'}; <obj_18>: {'category': 'doorframe', 'centroid': '[-0.17, 2.42, 1.01]', 'extent': '[0.16, 0.18, 1.70]'}; <obj_19>: {'category': 'chair', 'centroid': '[-0.86, -1.53, 0.43]', 'extent': '[0.54, 0.64, 0.97]'}; <obj_20>: {'category': 'bookshelf', 'centroid': '[0.93, 2.00, 1.34]', 'extent': '[0.73, 0.99, 2.60]'}; <obj_21>: {'category': 'office chair', 'centroid': '[0.35, -1.35, 0.50]', 'extent': '[0.46, 0.47, 0.94]'}; <obj_22>: {'category': 'desk', 'centroid': '[-1.23, 1.60, 0.70]', 'extent': '[0.80, 2.01, 0.51]'}; <obj_23>: {'category': 'book', 'centroid': '[0.91, 1.31, 0.89]', 'extent': '[0.34, 0.32, 0.30]'}; <obj_24>: {'category': 'desk', 'centroid': '[-1.24, -1.12, 0.54]', 'extent': '[0.79, 1.88, 0.85]'}; <obj_25>: {'category': 'desk', 'centroid': '[0.63, -1.51, 0.53]', 'extent': '[0.81, 1.97, 0.85]'}; <obj_26>: {'category': 'calendar', 'centroid': '[-1.72, -0.44, 1.40]', 'extent': '[0.07, 0.88, 0.83]'}; <obj_27>: {'category': 'office chair', 'centroid': '[0.34, 0.59, 0.43]', 'extent': '[0.65, 0.64, 0.94]'}; <obj_28>: {'category': 'file cabinet', 'centroid': '[-1.02, -0.76, 0.47]', 'extent': '[0.58, 0.75, 0.81]'}; <obj_29>: {'category': 'cup', 'centroid': '[-1.26, -1.65, 0.78]', 'extent': '[0.10, 0.12, 0.04]'}; <obj_30>: {'category': 'keyboard', 'centroid': '[0.55, 0.84, 0.73]', 'extent': '[0.22, 0.15, 0.03]'}
|
68 |
+
# """
|
69 |
+
# response = """
|
70 |
+
# <detailed_grounding>a <p>brown wooden office desk</p>[<obj_12>] on the left to the <p>gray shelf</p>[<obj_20>].</detailed_grounding> <refer_expression_grounding>These sentences refer to <p>the brown wooden office desk</p>[<obj_12>].</refer_expression_grounding>
|
71 |
+
# """
|
72 |
+
# return scene_id, scene_graph, response
|
73 |
+
|
74 |
+
# Resetting to blank
|
75 |
+
def reset_textbox():
|
76 |
+
return gr.update(value="")
|
77 |
+
|
78 |
+
|
79 |
+
# to set a component as visible=False
|
80 |
+
def set_visible_false():
|
81 |
+
return gr.update(visible=False)
|
82 |
+
|
83 |
+
|
84 |
+
# to set a component as visible=True
|
85 |
+
def set_visible_true():
|
86 |
+
return gr.update(visible=True)
|
87 |
+
|
88 |
+
|
89 |
+
def change_scene_or_system_prompt(dropdown_scene_selection: str):
|
90 |
+
# reset model_3d, chatbot_for_display, chat_counter, server_status_code
|
91 |
+
new_session_state = Session.create_for_scene(dropdown_scene_selection)
|
92 |
+
file_name = f"{dropdown_scene_selection}.obj"
|
93 |
+
print(os.path.join(GRAND3D_Settings.data_path, dropdown_scene_selection, file_name))
|
94 |
+
return (
|
95 |
+
new_session_state,
|
96 |
+
os.path.join(GRAND3D_Settings.data_path, dropdown_scene_selection, file_name),
|
97 |
+
None,
|
98 |
+
new_session_state.chat_history_for_display,
|
99 |
+
)
|
100 |
+
|
101 |
+
|
102 |
+
def cylinder_frame(p0, p1):
|
103 |
+
"""Calculate the transformation matrix to position a unit cylinder between two points."""
|
104 |
+
direction = np.asarray(p1) - np.asarray(p0)
|
105 |
+
length = np.linalg.norm(direction)
|
106 |
+
direction /= length
|
107 |
+
# Computing rotation matrix using Rodrigues' formula
|
108 |
+
rot_axis = np.cross([0, 0, 1], direction)
|
109 |
+
rot_angle = np.arccos(np.dot([0, 0, 1], direction))
|
110 |
+
rot_matrix = o3d.geometry.get_rotation_matrix_from_axis_angle(rot_axis * rot_angle)
|
111 |
+
|
112 |
+
# Translation
|
113 |
+
translation = (np.asarray(p0) + np.asarray(p1)) / 2
|
114 |
+
|
115 |
+
transformation = np.eye(4)
|
116 |
+
transformation[:3, :3] = rot_matrix
|
117 |
+
transformation[:3, 3] = translation
|
118 |
+
scaling = np.eye(4)
|
119 |
+
scaling[2, 2] = length
|
120 |
+
transformation = np.matmul(transformation, scaling)
|
121 |
+
return transformation
|
122 |
+
|
123 |
+
|
124 |
+
def create_cylinder_mesh(p0, p1, color, radius=0.02, resolution=20, split=1):
|
125 |
+
"""Create a colored cylinder mesh between two points p0 and p1."""
|
126 |
+
cylinder = o3d.geometry.TriangleMesh.create_cylinder(
|
127 |
+
radius=radius, height=1, resolution=resolution, split=split
|
128 |
+
)
|
129 |
+
transformation = cylinder_frame(p0, p1)
|
130 |
+
cylinder.transform(transformation)
|
131 |
+
# Apply color
|
132 |
+
cylinder.paint_uniform_color(color)
|
133 |
+
return cylinder
|
134 |
+
|
135 |
+
|
136 |
+
def prettify_mesh_for_gradio(mesh):
|
137 |
+
# Define the transformation matrix
|
138 |
+
T = np.array([[0, -1, 0, 0], [0, 0, 1, 0], [-1, 0, 0, 0], [0, 0, 0, 1]])
|
139 |
+
|
140 |
+
# Apply the transformation
|
141 |
+
mesh.transform(T)
|
142 |
+
|
143 |
+
mesh.scale(10.0, center=mesh.get_center())
|
144 |
+
|
145 |
+
bright_factor = 1 # Adjust this factor to get the desired brightness
|
146 |
+
mesh.vertex_colors = o3d.utility.Vector3dVector(
|
147 |
+
np.clip(np.asarray(mesh.vertex_colors) * bright_factor, 0, 1)
|
148 |
+
)
|
149 |
+
|
150 |
+
return mesh
|
151 |
+
|
152 |
+
|
153 |
+
def create_bbox(center, extents, color=[1, 0, 0], radius=0.02):
|
154 |
+
"""Create a colored bounding box with given center, extents, and line thickness."""
|
155 |
+
# ... [The same code as before to define corners and lines] ...
|
156 |
+
print(extents)
|
157 |
+
print(type(extents))
|
158 |
+
extents = extents.replace("[", "").replace("]", "")
|
159 |
+
center = center.replace("[", "").replace("]", "")
|
160 |
+
extents = [float(x.strip()) for x in extents.split(",")]
|
161 |
+
center = [float(x.strip()) for x in center.split(",")]
|
162 |
+
|
163 |
+
sx, sy, sz = float(extents[0]), float(extents[1]), float(extents[2])
|
164 |
+
x_corners = [sx / 2, sx / 2, -sx / 2, -sx / 2, sx / 2, sx / 2, -sx / 2, -sx / 2]
|
165 |
+
y_corners = [sy / 2, -sy / 2, -sy / 2, sy / 2, sy / 2, -sy / 2, -sy / 2, sy / 2]
|
166 |
+
z_corners = [sz / 2, sz / 2, sz / 2, sz / 2, -sz / 2, -sz / 2, -sz / 2, -sz / 2]
|
167 |
+
corners_3d = np.vstack([x_corners, y_corners, z_corners])
|
168 |
+
corners_3d[0, :] = corners_3d[0, :] + float(center[0])
|
169 |
+
corners_3d[1, :] = corners_3d[1, :] + float(center[1])
|
170 |
+
corners_3d[2, :] = corners_3d[2, :] + float(center[2])
|
171 |
+
corners_3d = np.transpose(corners_3d)
|
172 |
+
|
173 |
+
lines = [
|
174 |
+
[0, 1],
|
175 |
+
[1, 2],
|
176 |
+
[2, 3],
|
177 |
+
[3, 0],
|
178 |
+
[4, 5],
|
179 |
+
[5, 6],
|
180 |
+
[6, 7],
|
181 |
+
[7, 4],
|
182 |
+
[0, 4],
|
183 |
+
[1, 5],
|
184 |
+
[2, 6],
|
185 |
+
[3, 7],
|
186 |
+
]
|
187 |
+
cylinders = []
|
188 |
+
for line in lines:
|
189 |
+
p0, p1 = corners_3d[line[0]], corners_3d[line[1]]
|
190 |
+
cylinders.append(create_cylinder_mesh(p0, p1, color, radius))
|
191 |
+
return cylinders
|
192 |
+
|
193 |
+
|
194 |
+
def highlight_clusters_in_mesh(
|
195 |
+
centroids_extents_detailed,
|
196 |
+
centroids_extends_refer,
|
197 |
+
mesh,
|
198 |
+
output_dir,
|
199 |
+
output_file_name="highlighted_mesh.glb",
|
200 |
+
):
|
201 |
+
print("*" * 50)
|
202 |
+
# Visualize the highlighted points by drawing 3D bounding boxes overlay on a mesh
|
203 |
+
old_mesh = deepcopy(mesh)
|
204 |
+
output_path = os.path.join(output_dir, "mesh_vis")
|
205 |
+
if not os.path.exists(output_path):
|
206 |
+
os.makedirs(output_path)
|
207 |
+
|
208 |
+
# Create a combined mesh to hold both the original and the bounding boxes
|
209 |
+
combined_mesh = o3d.geometry.TriangleMesh()
|
210 |
+
combined_mesh += old_mesh
|
211 |
+
|
212 |
+
# Draw bounding boxes for each centroid and extent
|
213 |
+
for center, extent in centroids_extents_detailed:
|
214 |
+
print("center: ", center)
|
215 |
+
print("extent: ", extent)
|
216 |
+
bbox = create_bbox(center, extent, color=[0, 0, 1]) # Red color for all boxes
|
217 |
+
for b in bbox:
|
218 |
+
combined_mesh += b
|
219 |
+
|
220 |
+
for center, extent in centroids_extends_refer:
|
221 |
+
bbox = create_bbox(center, extent, color=[0, 1, 0])
|
222 |
+
for b in bbox:
|
223 |
+
combined_mesh += b
|
224 |
+
|
225 |
+
combined_mesh = prettify_mesh_for_gradio(combined_mesh)
|
226 |
+
# Save the combined mesh
|
227 |
+
output_file_path = os.path.join(output_path, output_file_name)
|
228 |
+
o3d.io.write_triangle_mesh(
|
229 |
+
output_file_path, combined_mesh, write_vertex_colors=True
|
230 |
+
)
|
231 |
+
print("*" * 50)
|
232 |
+
return output_file_path
|
233 |
+
|
234 |
+
|
235 |
+
def extract_objects(text):
|
236 |
+
return re.findall(r"<obj_\d+>", text)
|
237 |
+
|
238 |
+
|
239 |
+
# Parse the scene graph into a dictionary
|
240 |
+
def parse_scene_graph(scene_graph):
|
241 |
+
scene_dict = {}
|
242 |
+
matches = re.findall(r"<obj_(\d+)>: (\{.*?\})", scene_graph)
|
243 |
+
for match in matches:
|
244 |
+
obj_id = f"<obj_{match[0]}>"
|
245 |
+
obj_data = eval(match[1])
|
246 |
+
scene_dict[obj_id] = obj_data
|
247 |
+
return scene_dict
|
248 |
+
|
249 |
+
|
250 |
+
def get_centroids_extents(obj_list, scene_dict):
|
251 |
+
centroids_extents = []
|
252 |
+
for obj in obj_list:
|
253 |
+
if obj in scene_dict:
|
254 |
+
centroid = scene_dict[obj]["centroid"]
|
255 |
+
extent = scene_dict[obj]["extent"]
|
256 |
+
centroids_extents.append((centroid, extent))
|
257 |
+
return centroids_extents
|
258 |
+
|
259 |
+
@spaces.GPU
|
260 |
+
def language_model_forward(
|
261 |
+
session_state, user_chat_input, top_p, temperature, dropdown_scene
|
262 |
+
):
|
263 |
+
session_state = Session.create_for_scene(dropdown_scene)
|
264 |
+
session_state.chat_history_for_display.append(
|
265 |
+
(user_chat_input, None)
|
266 |
+
) # append in a tuple format, first is user input, second is assistant response
|
267 |
+
|
268 |
+
yield session_state, None, session_state.chat_history_for_display
|
269 |
+
|
270 |
+
# Load in a 3D model
|
271 |
+
file_name = f"{session_state.scene}.obj"
|
272 |
+
original_model_path = os.path.join(
|
273 |
+
GRAND3D_Settings.data_path, session_state.scene, file_name
|
274 |
+
)
|
275 |
+
print("original_model_path: ", original_model_path)
|
276 |
+
|
277 |
+
# Load the GLB mesh
|
278 |
+
mesh = o3d.io.read_triangle_mesh(original_model_path)
|
279 |
+
|
280 |
+
# get chatbot response
|
281 |
+
scene_id, scene_graph, response = get_chatbot_response(user_chat_input, session_state.scene)
|
282 |
+
|
283 |
+
assert scene_id == session_state.scene # Ensure the scene ID matches
|
284 |
+
|
285 |
+
# use scene_graph and response to get centroids and extents
|
286 |
+
# Parse the scene graph into a dictionary
|
287 |
+
scene_dict = parse_scene_graph(scene_graph)
|
288 |
+
print("Model Input: " + str(scene_dict))
|
289 |
+
print("=" * 50)
|
290 |
+
print("Model Response: " + response)
|
291 |
+
|
292 |
+
# Parse the response to get detailed and refer expression groundings
|
293 |
+
soup = BeautifulSoup(response, "html.parser")
|
294 |
+
detailed_grounding_html = str(soup.find("detailed_grounding"))
|
295 |
+
refer_expression_grounding_html = str(soup.find("refer_expression_grounding"))
|
296 |
+
|
297 |
+
# Extract objects from both sections
|
298 |
+
detailed_objects = extract_objects(detailed_grounding_html)
|
299 |
+
refer_objects = extract_objects(refer_expression_grounding_html)
|
300 |
+
|
301 |
+
# Extract objects from both sections
|
302 |
+
print("detailed_objects: ", detailed_objects)
|
303 |
+
print("refer_objects: ", refer_objects)
|
304 |
+
|
305 |
+
# Perform set subtraction to get remaining objects
|
306 |
+
remaining_objects = list(set(detailed_objects) - set(refer_objects))
|
307 |
+
print("remaining_objects: ", remaining_objects)
|
308 |
+
|
309 |
+
centroids_extents_detailed = get_centroids_extents(remaining_objects, scene_dict)
|
310 |
+
print("centroids_extents_detailed: ", centroids_extents_detailed)
|
311 |
+
centroids_extents_refer = get_centroids_extents(refer_objects, scene_dict)
|
312 |
+
print("centroids_extents_refer: ", centroids_extents_refer)
|
313 |
+
# Define your centroids and extents here (example data)
|
314 |
+
# Highlight clusters in the mesh and save it
|
315 |
+
session_output_dir = session_state.get_session_output_dir()
|
316 |
+
highlighted_model_path = highlight_clusters_in_mesh(
|
317 |
+
centroids_extents_detailed,
|
318 |
+
centroids_extents_refer,
|
319 |
+
mesh,
|
320 |
+
session_output_dir,
|
321 |
+
output_file_name="highlighted_model.glb",
|
322 |
+
)
|
323 |
+
|
324 |
+
# Update the chat history with the response
|
325 |
+
last_turn = session_state.chat_history_for_display[-1] # first is user input, second is assistant response
|
326 |
+
last_turn = (last_turn[0], response)
|
327 |
+
session_state.chat_history_for_display[-1] = last_turn
|
328 |
+
session_state.save() # save the session state
|
329 |
+
|
330 |
+
yield session_state, highlighted_model_path, session_state.chat_history_for_display
|
331 |
+
|
332 |
+
|
333 |
+
title = """<h1 align="center">🤖 3D-GRAND: Towards Better Grounding and Less Hallucination for 3D-LLMs 🚀</h1>
|
334 |
+
<p><center>
|
335 |
+
<a href="https://3d-grand.github.io/" target="_blank">[Project Page]</a>
|
336 |
+
<a href="https://www.dropbox.com/scl/fo/5p9nb4kalnz407sbqgemg/AG1KcxeIS_SUoJ1hoLPzv84?rlkey=weunabtbiz17jitfv3f4jpmm1&dl=0" target="_blank">[3D-GRAND Data]</a>
|
337 |
+
<a href="https://www.dropbox.com/scl/fo/inemjtgqt2nkckymn65rp/AGi2KSYU9AHbnpuj7TWYihs?rlkey=ldbn36b1z6nqj74yv5ph6cqwc&dl=0" target="_blank">[3D-POPE Data]</a>
|
338 |
+
</center></p>
|
339 |
+
"""
|
340 |
+
|
341 |
+
# Modifying existing Gradio Theme
|
342 |
+
# theme = gr.themes.Soft(
|
343 |
+
# primary_hue=gr.themes.colors.blue, secondary_hue=gr.themes.colors.pink
|
344 |
+
# )
|
345 |
+
|
346 |
+
with gr.Blocks(theme=gr.themes.Soft()) as demo:
|
347 |
+
session_state = gr.State(Session.create)
|
348 |
+
|
349 |
+
gr.HTML(title)
|
350 |
+
|
351 |
+
with gr.Column():
|
352 |
+
with gr.Row():
|
353 |
+
with gr.Column(scale=5):
|
354 |
+
dropdown_scene = gr.Dropdown(
|
355 |
+
choices=list_dirs(GRAND3D_Settings.data_path),
|
356 |
+
value=GRAND3D_Settings.default_scene,
|
357 |
+
interactive=True,
|
358 |
+
label="Select a scene",
|
359 |
+
)
|
360 |
+
model_3d = gr.Model3D(
|
361 |
+
value=os.path.join(
|
362 |
+
GRAND3D_Settings.data_path,
|
363 |
+
GRAND3D_Settings.default_scene,
|
364 |
+
f"{GRAND3D_Settings.default_scene}.obj",
|
365 |
+
),
|
366 |
+
clear_color=[0.0, 0.0, 0.0, 0.0],
|
367 |
+
label="3D Model",
|
368 |
+
camera_position=(-50, 65, 10),
|
369 |
+
zoom_speed=10.0,
|
370 |
+
)
|
371 |
+
gr.HTML(
|
372 |
+
"""<center><strong>
|
373 |
+
👆 SCROLL or DRAG on the 3D Model
|
374 |
+
to zoom in/out and rotate. Press CTRL and DRAG to pan.
|
375 |
+
</strong></center>
|
376 |
+
"""
|
377 |
+
)
|
378 |
+
gr.HTML(
|
379 |
+
"""<center><strong>
|
380 |
+
👇 When grounding finishes,
|
381 |
+
the grounding result will be displayed below.
|
382 |
+
</strong></center>
|
383 |
+
"""
|
384 |
+
)
|
385 |
+
model_3d_grounding_result = gr.Model3D(
|
386 |
+
clear_color=[0.0, 0.0, 0.0, 0.0],
|
387 |
+
label="Grounding Result",
|
388 |
+
zoom_speed=15.0,
|
389 |
+
)
|
390 |
+
gr.HTML(
|
391 |
+
"""<center><strong>
|
392 |
+
<div style="display:inline-block; color:blue">■</div> = Landmark
|
393 |
+
<div style="display:inline-block; color:green">■</div> = Chosen Target
|
394 |
+
</strong></center>
|
395 |
+
"""
|
396 |
+
)
|
397 |
+
with gr.Column(scale=5):
|
398 |
+
chat_history_for_display = gr.Chatbot(
|
399 |
+
value=[(None, GRAND3D_Settings.INITIAL_MSG_FOR_DISPLAY)],
|
400 |
+
label="Chat Assistant",
|
401 |
+
height=510,
|
402 |
+
render_markdown=False,
|
403 |
+
sanitize_html=False,
|
404 |
+
)
|
405 |
+
with gr.Row():
|
406 |
+
with gr.Column(scale=8):
|
407 |
+
user_chat_input = gr.Textbox(
|
408 |
+
placeholder="I want to find the chair near the table",
|
409 |
+
show_label=False,
|
410 |
+
)
|
411 |
+
with gr.Column(scale=1, min_width=0):
|
412 |
+
send_button = gr.Button("Send", variant="primary")
|
413 |
+
with gr.Column(scale=1, min_width=0):
|
414 |
+
clear_button = gr.Button("Clear")
|
415 |
+
with gr.Row():
|
416 |
+
with gr.Accordion(label="Examples for user message:", open=True):
|
417 |
+
gr.Examples(
|
418 |
+
examples=[
|
419 |
+
["The TV on the drawer, opposing the bed."],
|
420 |
+
["the desk next to the window"]
|
421 |
+
],
|
422 |
+
inputs=user_chat_input,
|
423 |
+
)
|
424 |
+
|
425 |
+
with gr.Accordion("Parameters", open=False, visible=False):
|
426 |
+
top_p = gr.Slider(
|
427 |
+
minimum=0,
|
428 |
+
maximum=1.0,
|
429 |
+
value=1.0,
|
430 |
+
step=0.05,
|
431 |
+
interactive=True,
|
432 |
+
label="Top-p (nucleus sampling)",
|
433 |
+
)
|
434 |
+
temperature = gr.Slider(
|
435 |
+
minimum=0,
|
436 |
+
maximum=5.0,
|
437 |
+
value=1.0,
|
438 |
+
step=0.1,
|
439 |
+
interactive=True,
|
440 |
+
label="Temperature",
|
441 |
+
)
|
442 |
+
# gr.Markdown("### Terms of Service")
|
443 |
+
# gr.HTML(
|
444 |
+
# """By using this service, users are required to agree to the following terms:
|
445 |
+
# The service is a research preview intended for non-commercial use only.
|
446 |
+
# The service may collect user dialogue data for future research."""
|
447 |
+
# )
|
448 |
+
|
449 |
+
# Event handling
|
450 |
+
dropdown_scene.change(
|
451 |
+
fn=change_scene_or_system_prompt,
|
452 |
+
inputs=[dropdown_scene],
|
453 |
+
outputs=[session_state, model_3d, model_3d_grounding_result, chat_history_for_display],
|
454 |
+
)
|
455 |
+
clear_button.click(
|
456 |
+
fn=change_scene_or_system_prompt,
|
457 |
+
inputs=[dropdown_scene],
|
458 |
+
outputs=[session_state, model_3d, model_3d_grounding_result, chat_history_for_display],
|
459 |
+
)
|
460 |
+
user_chat_input.submit(
|
461 |
+
fn=language_model_forward,
|
462 |
+
inputs=[session_state, user_chat_input, top_p, temperature, dropdown_scene],
|
463 |
+
outputs=[session_state, model_3d_grounding_result, chat_history_for_display],
|
464 |
+
)
|
465 |
+
send_button.click(
|
466 |
+
fn=language_model_forward,
|
467 |
+
inputs=[session_state, user_chat_input, top_p, temperature, dropdown_scene],
|
468 |
+
outputs=[session_state, model_3d_grounding_result, chat_history_for_display],
|
469 |
+
)
|
470 |
+
|
471 |
+
send_button.click(reset_textbox, [], [user_chat_input])
|
472 |
+
user_chat_input.submit(reset_textbox, [], [user_chat_input])
|
473 |
+
|
474 |
+
sleep_time = 2
|
475 |
+
port = 7011
|
476 |
+
for x in range(1, 10): # try 8 times
|
477 |
+
try:
|
478 |
+
# put your logic here
|
479 |
+
gr.close_all()
|
480 |
+
demo.queue(
|
481 |
+
max_size=20,
|
482 |
+
).launch(
|
483 |
+
# debug=True,
|
484 |
+
# server_name="0.0.0.0",
|
485 |
+
# server_port=port,
|
486 |
+
# share=True
|
487 |
+
)
|
488 |
+
except OSError:
|
489 |
+
for proc in process_iter():
|
490 |
+
for conns in proc.connections(kind="inet"):
|
491 |
+
if conns.laddr.port == port:
|
492 |
+
proc.send_signal(SIGTERM) # or SIGKILL
|
493 |
+
print(f"Retrying {x} time...")
|
494 |
+
pass
|
495 |
+
|
496 |
+
sleep(sleep_time) # wait for 2 seconds before trying to fetch the data again
|
497 |
+
sleep_time *= 2 # exponential backoff
|
checkpoints/llava-llama-2-7b-chat-lightning-preview/config.json
ADDED
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "/nfs/turbo/coe-chaijy/pre-trained-weights/LLaMA-2-hf/Llama-2-7b-chat-hf",
|
3 |
+
"architectures": [
|
4 |
+
"LlavaLlamaForCausalLM"
|
5 |
+
],
|
6 |
+
"bos_token_id": 1,
|
7 |
+
"eos_token_id": 2,
|
8 |
+
"freeze_mm_mlp_adapter": true,
|
9 |
+
"hidden_act": "silu",
|
10 |
+
"hidden_size": 4096,
|
11 |
+
"image_aspect_ratio": "square",
|
12 |
+
"image_grid_pinpoints": null,
|
13 |
+
"initializer_range": 0.02,
|
14 |
+
"intermediate_size": 11008,
|
15 |
+
"max_position_embeddings": 2048,
|
16 |
+
"mm_hidden_size": 1024,
|
17 |
+
"mm_resampler_type": null,
|
18 |
+
"mm_use_im_patch_token": false,
|
19 |
+
"mm_use_im_start_end": false,
|
20 |
+
"mm_vision_select_feature": "patch",
|
21 |
+
"mm_vision_select_layer": -2,
|
22 |
+
"mm_vision_tower": "openai/clip-vit-large-patch14",
|
23 |
+
"model_type": "llava",
|
24 |
+
"num_attention_heads": 32,
|
25 |
+
"num_hidden_layers": 32,
|
26 |
+
"num_key_value_heads": 32,
|
27 |
+
"pad_token_id": 0,
|
28 |
+
"pretraining_tp": 1,
|
29 |
+
"rms_norm_eps": 1e-06,
|
30 |
+
"rope_scaling": null,
|
31 |
+
"tie_word_embeddings": false,
|
32 |
+
"torch_dtype": "float16",
|
33 |
+
"transformers_version": "4.31.0",
|
34 |
+
"tune_mm_mlp_adapter": false,
|
35 |
+
"tune_mm_vision_resampler": false,
|
36 |
+
"use_cache": false,
|
37 |
+
"use_mm_proj": true,
|
38 |
+
"vocab_size": 32000
|
39 |
+
}
|
checkpoints/llava-llama-2-7b-chat-lightning-preview/generation_config.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token_id": 1,
|
3 |
+
"do_sample": true,
|
4 |
+
"eos_token_id": 2,
|
5 |
+
"max_length": 4096,
|
6 |
+
"pad_token_id": 0,
|
7 |
+
"temperature": 0.6,
|
8 |
+
"top_p": 0.9,
|
9 |
+
"transformers_version": "4.31.0"
|
10 |
+
}
|
checkpoints/llava-llama-2-7b-chat-lightning-preview/pytorch_model-00001-of-00002.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fea481e9db9e4fb55e25d1e3ff4593f447698caf64bb1a7406ebdedc6d0a85a0
|
3 |
+
size 9976634558
|
checkpoints/llava-llama-2-7b-chat-lightning-preview/pytorch_model-00002-of-00002.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f21f72ef8bea769962054e485b1584f3be9f7683ffa4e7df12580965d50f83d6
|
3 |
+
size 4115226220
|
checkpoints/llava-llama-2-7b-chat-lightning-preview/pytorch_model.bin.index.json
ADDED
@@ -0,0 +1,723 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
|
|
|
|
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checkpoints/llava-llama-2-7b-chat-lightning-preview/special_tokens_map.json
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|
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|
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checkpoints/lora_grounded_obj_ref_checkpoint-4896/README.md
ADDED
@@ -0,0 +1,9 @@
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1 |
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---
|
2 |
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library_name: peft
|
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---
|
4 |
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## Training procedure
|
5 |
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|
6 |
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### Framework versions
|
7 |
+
|
8 |
+
|
9 |
+
- PEFT 0.4.0
|
checkpoints/lora_grounded_obj_ref_checkpoint-4896/adapter_config.json
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}
|
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+
"padding_side": "right",
|
26 |
+
"sp_model_kwargs": {},
|
27 |
+
"tokenizer_class": "LlamaTokenizer",
|
28 |
+
"unk_token": {
|
29 |
+
"__type": "AddedToken",
|
30 |
+
"content": "<unk>",
|
31 |
+
"lstrip": false,
|
32 |
+
"normalized": false,
|
33 |
+
"rstrip": false,
|
34 |
+
"single_word": false
|
35 |
+
}
|
36 |
+
}
|
checkpoints/lora_grounded_obj_ref_checkpoint-4896/trainer_state.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
checkpoints/lora_grounded_obj_ref_checkpoint-4896/training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b3f6df39eb8c3a59e2e18f97752b27792c12a3be4e8cf2474f50e4a4a62371cc
|
3 |
+
size 5755
|
checkpoints/lora_grounded_obj_ref_checkpoint-4896/zero_to_fp32.py
ADDED
@@ -0,0 +1,578 @@
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
# Copyright (c) Microsoft Corporation.
|
4 |
+
# SPDX-License-Identifier: Apache-2.0
|
5 |
+
|
6 |
+
# DeepSpeed Team
|
7 |
+
|
8 |
+
# This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets
|
9 |
+
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
|
10 |
+
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
11 |
+
# application.
|
12 |
+
#
|
13 |
+
# example: python zero_to_fp32.py . pytorch_model.bin
|
14 |
+
|
15 |
+
import argparse
|
16 |
+
import torch
|
17 |
+
import glob
|
18 |
+
import math
|
19 |
+
import os
|
20 |
+
import re
|
21 |
+
from collections import OrderedDict
|
22 |
+
from dataclasses import dataclass
|
23 |
+
|
24 |
+
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
25 |
+
# DeepSpeed data structures it has to be available in the current python environment.
|
26 |
+
from deepspeed.utils import logger
|
27 |
+
from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
|
28 |
+
FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
|
29 |
+
FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
|
30 |
+
|
31 |
+
|
32 |
+
@dataclass
|
33 |
+
class zero_model_state:
|
34 |
+
buffers: dict()
|
35 |
+
param_shapes: dict()
|
36 |
+
shared_params: list
|
37 |
+
ds_version: int
|
38 |
+
frozen_param_shapes: dict()
|
39 |
+
frozen_param_fragments: dict()
|
40 |
+
|
41 |
+
|
42 |
+
debug = 0
|
43 |
+
|
44 |
+
# load to cpu
|
45 |
+
device = torch.device('cpu')
|
46 |
+
|
47 |
+
|
48 |
+
def atoi(text):
|
49 |
+
return int(text) if text.isdigit() else text
|
50 |
+
|
51 |
+
|
52 |
+
def natural_keys(text):
|
53 |
+
'''
|
54 |
+
alist.sort(key=natural_keys) sorts in human order
|
55 |
+
http://nedbatchelder.com/blog/200712/human_sorting.html
|
56 |
+
(See Toothy's implementation in the comments)
|
57 |
+
'''
|
58 |
+
return [atoi(c) for c in re.split(r'(\d+)', text)]
|
59 |
+
|
60 |
+
|
61 |
+
def get_model_state_file(checkpoint_dir, zero_stage):
|
62 |
+
if not os.path.isdir(checkpoint_dir):
|
63 |
+
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
64 |
+
|
65 |
+
# there should be only one file
|
66 |
+
if zero_stage == 2:
|
67 |
+
file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
|
68 |
+
elif zero_stage == 3:
|
69 |
+
file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
|
70 |
+
|
71 |
+
if not os.path.exists(file):
|
72 |
+
raise FileNotFoundError(f"can't find model states file at '{file}'")
|
73 |
+
|
74 |
+
return file
|
75 |
+
|
76 |
+
|
77 |
+
def get_checkpoint_files(checkpoint_dir, glob_pattern):
|
78 |
+
# XXX: need to test that this simple glob rule works for multi-node setup too
|
79 |
+
ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
|
80 |
+
|
81 |
+
if len(ckpt_files) == 0:
|
82 |
+
raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
|
83 |
+
|
84 |
+
return ckpt_files
|
85 |
+
|
86 |
+
|
87 |
+
def get_optim_files(checkpoint_dir):
|
88 |
+
return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
|
89 |
+
|
90 |
+
|
91 |
+
def get_model_state_files(checkpoint_dir):
|
92 |
+
return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
|
93 |
+
|
94 |
+
|
95 |
+
def parse_model_states(files):
|
96 |
+
zero_model_states = []
|
97 |
+
for file in files:
|
98 |
+
state_dict = torch.load(file, map_location=device)
|
99 |
+
|
100 |
+
if BUFFER_NAMES not in state_dict:
|
101 |
+
raise ValueError(f"{file} is not a model state checkpoint")
|
102 |
+
buffer_names = state_dict[BUFFER_NAMES]
|
103 |
+
if debug:
|
104 |
+
print("Found buffers:", buffer_names)
|
105 |
+
|
106 |
+
# recover just the buffers while restoring them to fp32 if they were saved in fp16
|
107 |
+
buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
|
108 |
+
param_shapes = state_dict[PARAM_SHAPES]
|
109 |
+
|
110 |
+
# collect parameters that are included in param_shapes
|
111 |
+
param_names = []
|
112 |
+
for s in param_shapes:
|
113 |
+
for name in s.keys():
|
114 |
+
param_names.append(name)
|
115 |
+
|
116 |
+
# update with frozen parameters
|
117 |
+
frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
|
118 |
+
if frozen_param_shapes is not None:
|
119 |
+
if debug:
|
120 |
+
print(f"Found frozen_param_shapes: {frozen_param_shapes}")
|
121 |
+
param_names += list(frozen_param_shapes.keys())
|
122 |
+
|
123 |
+
# handle shared params
|
124 |
+
shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
|
125 |
+
|
126 |
+
ds_version = state_dict.get(DS_VERSION, None)
|
127 |
+
|
128 |
+
frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
|
129 |
+
|
130 |
+
z_model_state = zero_model_state(buffers=buffers,
|
131 |
+
param_shapes=param_shapes,
|
132 |
+
shared_params=shared_params,
|
133 |
+
ds_version=ds_version,
|
134 |
+
frozen_param_shapes=frozen_param_shapes,
|
135 |
+
frozen_param_fragments=frozen_param_fragments)
|
136 |
+
zero_model_states.append(z_model_state)
|
137 |
+
|
138 |
+
return zero_model_states
|
139 |
+
|
140 |
+
|
141 |
+
def parse_optim_states(files, ds_checkpoint_dir):
|
142 |
+
|
143 |
+
total_files = len(files)
|
144 |
+
state_dicts = []
|
145 |
+
for f in files:
|
146 |
+
state_dicts.append(torch.load(f, map_location=device))
|
147 |
+
|
148 |
+
if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
|
149 |
+
raise ValueError(f"{files[0]} is not a zero checkpoint")
|
150 |
+
zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
|
151 |
+
world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
|
152 |
+
|
153 |
+
# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
|
154 |
+
# parameters can be different from data parallelism for non-expert parameters. So we can just
|
155 |
+
# use the max of the partition_count to get the dp world_size.
|
156 |
+
|
157 |
+
if type(world_size) is list:
|
158 |
+
world_size = max(world_size)
|
159 |
+
|
160 |
+
if world_size != total_files:
|
161 |
+
raise ValueError(
|
162 |
+
f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
|
163 |
+
"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
|
164 |
+
)
|
165 |
+
|
166 |
+
# the groups are named differently in each stage
|
167 |
+
if zero_stage == 2:
|
168 |
+
fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
|
169 |
+
elif zero_stage == 3:
|
170 |
+
fp32_groups_key = FP32_FLAT_GROUPS
|
171 |
+
else:
|
172 |
+
raise ValueError(f"unknown zero stage {zero_stage}")
|
173 |
+
|
174 |
+
if zero_stage == 2:
|
175 |
+
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
|
176 |
+
elif zero_stage == 3:
|
177 |
+
# if there is more than one param group, there will be multiple flattened tensors - one
|
178 |
+
# flattened tensor per group - for simplicity merge them into a single tensor
|
179 |
+
#
|
180 |
+
# XXX: could make the script more memory efficient for when there are multiple groups - it
|
181 |
+
# will require matching the sub-lists of param_shapes for each param group flattened tensor
|
182 |
+
|
183 |
+
fp32_flat_groups = [
|
184 |
+
torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
|
185 |
+
]
|
186 |
+
|
187 |
+
return zero_stage, world_size, fp32_flat_groups
|
188 |
+
|
189 |
+
|
190 |
+
def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
|
191 |
+
"""
|
192 |
+
Returns fp32 state_dict reconstructed from ds checkpoint
|
193 |
+
|
194 |
+
Args:
|
195 |
+
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
|
196 |
+
|
197 |
+
"""
|
198 |
+
print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
|
199 |
+
|
200 |
+
optim_files = get_optim_files(ds_checkpoint_dir)
|
201 |
+
zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
|
202 |
+
print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
|
203 |
+
|
204 |
+
model_files = get_model_state_files(ds_checkpoint_dir)
|
205 |
+
|
206 |
+
zero_model_states = parse_model_states(model_files)
|
207 |
+
print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
|
208 |
+
|
209 |
+
if zero_stage == 2:
|
210 |
+
return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
|
211 |
+
elif zero_stage == 3:
|
212 |
+
return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
|
213 |
+
|
214 |
+
|
215 |
+
def _zero2_merge_frozen_params(state_dict, zero_model_states):
|
216 |
+
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
217 |
+
return
|
218 |
+
|
219 |
+
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
220 |
+
frozen_param_fragments = zero_model_states[0].frozen_param_fragments
|
221 |
+
|
222 |
+
if debug:
|
223 |
+
num_elem = sum(s.numel() for s in frozen_param_shapes.values())
|
224 |
+
print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
225 |
+
|
226 |
+
wanted_params = len(frozen_param_shapes)
|
227 |
+
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
228 |
+
avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
|
229 |
+
print(f'Frozen params: Have {avail_numel} numels to process.')
|
230 |
+
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
231 |
+
|
232 |
+
total_params = 0
|
233 |
+
total_numel = 0
|
234 |
+
for name, shape in frozen_param_shapes.items():
|
235 |
+
total_params += 1
|
236 |
+
unpartitioned_numel = shape.numel()
|
237 |
+
total_numel += unpartitioned_numel
|
238 |
+
|
239 |
+
state_dict[name] = frozen_param_fragments[name]
|
240 |
+
|
241 |
+
if debug:
|
242 |
+
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
243 |
+
|
244 |
+
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
245 |
+
|
246 |
+
|
247 |
+
def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
248 |
+
param_shapes = zero_model_states[0].param_shapes
|
249 |
+
|
250 |
+
# Reconstruction protocol:
|
251 |
+
#
|
252 |
+
# XXX: document this
|
253 |
+
|
254 |
+
if debug:
|
255 |
+
for i in range(world_size):
|
256 |
+
for j in range(len(fp32_flat_groups[0])):
|
257 |
+
print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
|
258 |
+
|
259 |
+
# XXX: memory usage doubles here (zero2)
|
260 |
+
num_param_groups = len(fp32_flat_groups[0])
|
261 |
+
merged_single_partition_of_fp32_groups = []
|
262 |
+
for i in range(num_param_groups):
|
263 |
+
merged_partitions = [sd[i] for sd in fp32_flat_groups]
|
264 |
+
full_single_fp32_vector = torch.cat(merged_partitions, 0)
|
265 |
+
merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
|
266 |
+
avail_numel = sum(
|
267 |
+
[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
|
268 |
+
|
269 |
+
if debug:
|
270 |
+
wanted_params = sum([len(shapes) for shapes in param_shapes])
|
271 |
+
wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
|
272 |
+
# not asserting if there is a mismatch due to possible padding
|
273 |
+
print(f"Have {avail_numel} numels to process.")
|
274 |
+
print(f"Need {wanted_numel} numels in {wanted_params} params.")
|
275 |
+
|
276 |
+
# params
|
277 |
+
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
278 |
+
# out-of-core computing solution
|
279 |
+
total_numel = 0
|
280 |
+
total_params = 0
|
281 |
+
for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
|
282 |
+
offset = 0
|
283 |
+
avail_numel = full_single_fp32_vector.numel()
|
284 |
+
for name, shape in shapes.items():
|
285 |
+
|
286 |
+
unpartitioned_numel = shape.numel()
|
287 |
+
total_numel += unpartitioned_numel
|
288 |
+
total_params += 1
|
289 |
+
|
290 |
+
if debug:
|
291 |
+
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
292 |
+
state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
|
293 |
+
offset += unpartitioned_numel
|
294 |
+
|
295 |
+
# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
|
296 |
+
# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
|
297 |
+
# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
|
298 |
+
# live optimizer object, so we are checking that the numbers are within the right range
|
299 |
+
align_to = 2 * world_size
|
300 |
+
|
301 |
+
def zero2_align(x):
|
302 |
+
return align_to * math.ceil(x / align_to)
|
303 |
+
|
304 |
+
if debug:
|
305 |
+
print(f"original offset={offset}, avail_numel={avail_numel}")
|
306 |
+
|
307 |
+
offset = zero2_align(offset)
|
308 |
+
avail_numel = zero2_align(avail_numel)
|
309 |
+
|
310 |
+
if debug:
|
311 |
+
print(f"aligned offset={offset}, avail_numel={avail_numel}")
|
312 |
+
|
313 |
+
# Sanity check
|
314 |
+
if offset != avail_numel:
|
315 |
+
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
316 |
+
|
317 |
+
print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
|
318 |
+
|
319 |
+
|
320 |
+
def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states):
|
321 |
+
state_dict = OrderedDict()
|
322 |
+
|
323 |
+
# buffers
|
324 |
+
buffers = zero_model_states[0].buffers
|
325 |
+
state_dict.update(buffers)
|
326 |
+
if debug:
|
327 |
+
print(f"added {len(buffers)} buffers")
|
328 |
+
|
329 |
+
_zero2_merge_frozen_params(state_dict, zero_model_states)
|
330 |
+
|
331 |
+
_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
332 |
+
|
333 |
+
# recover shared parameters
|
334 |
+
for pair in zero_model_states[0].shared_params:
|
335 |
+
if pair[1] in state_dict:
|
336 |
+
state_dict[pair[0]] = state_dict[pair[1]]
|
337 |
+
|
338 |
+
return state_dict
|
339 |
+
|
340 |
+
|
341 |
+
def zero3_partitioned_param_info(unpartitioned_numel, world_size):
|
342 |
+
remainder = unpartitioned_numel % world_size
|
343 |
+
padding_numel = (world_size - remainder) if remainder else 0
|
344 |
+
partitioned_numel = math.ceil(unpartitioned_numel / world_size)
|
345 |
+
return partitioned_numel, padding_numel
|
346 |
+
|
347 |
+
|
348 |
+
def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
|
349 |
+
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
350 |
+
return
|
351 |
+
|
352 |
+
if debug:
|
353 |
+
for i in range(world_size):
|
354 |
+
num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
|
355 |
+
print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
356 |
+
|
357 |
+
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
358 |
+
wanted_params = len(frozen_param_shapes)
|
359 |
+
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
360 |
+
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
361 |
+
print(f'Frozen params: Have {avail_numel} numels to process.')
|
362 |
+
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
363 |
+
|
364 |
+
total_params = 0
|
365 |
+
total_numel = 0
|
366 |
+
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
367 |
+
total_params += 1
|
368 |
+
unpartitioned_numel = shape.numel()
|
369 |
+
total_numel += unpartitioned_numel
|
370 |
+
|
371 |
+
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
372 |
+
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
373 |
+
|
374 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
375 |
+
|
376 |
+
if debug:
|
377 |
+
print(
|
378 |
+
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
379 |
+
)
|
380 |
+
|
381 |
+
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
382 |
+
|
383 |
+
|
384 |
+
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
385 |
+
param_shapes = zero_model_states[0].param_shapes
|
386 |
+
avail_numel = fp32_flat_groups[0].numel() * world_size
|
387 |
+
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
388 |
+
# param, re-consolidating each param, while dealing with padding if any
|
389 |
+
|
390 |
+
# merge list of dicts, preserving order
|
391 |
+
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
392 |
+
|
393 |
+
if debug:
|
394 |
+
for i in range(world_size):
|
395 |
+
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
396 |
+
|
397 |
+
wanted_params = len(param_shapes)
|
398 |
+
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
399 |
+
# not asserting if there is a mismatch due to possible padding
|
400 |
+
avail_numel = fp32_flat_groups[0].numel() * world_size
|
401 |
+
print(f"Trainable params: Have {avail_numel} numels to process.")
|
402 |
+
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
403 |
+
|
404 |
+
# params
|
405 |
+
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
406 |
+
# out-of-core computing solution
|
407 |
+
offset = 0
|
408 |
+
total_numel = 0
|
409 |
+
total_params = 0
|
410 |
+
for name, shape in param_shapes.items():
|
411 |
+
|
412 |
+
unpartitioned_numel = shape.numel()
|
413 |
+
total_numel += unpartitioned_numel
|
414 |
+
total_params += 1
|
415 |
+
|
416 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
417 |
+
|
418 |
+
if debug:
|
419 |
+
print(
|
420 |
+
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
421 |
+
)
|
422 |
+
|
423 |
+
# XXX: memory usage doubles here
|
424 |
+
state_dict[name] = torch.cat(
|
425 |
+
tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
|
426 |
+
0).narrow(0, 0, unpartitioned_numel).view(shape)
|
427 |
+
offset += partitioned_numel
|
428 |
+
|
429 |
+
offset *= world_size
|
430 |
+
|
431 |
+
# Sanity check
|
432 |
+
if offset != avail_numel:
|
433 |
+
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
434 |
+
|
435 |
+
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
436 |
+
|
437 |
+
|
438 |
+
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
|
439 |
+
state_dict = OrderedDict()
|
440 |
+
|
441 |
+
# buffers
|
442 |
+
buffers = zero_model_states[0].buffers
|
443 |
+
state_dict.update(buffers)
|
444 |
+
if debug:
|
445 |
+
print(f"added {len(buffers)} buffers")
|
446 |
+
|
447 |
+
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
448 |
+
|
449 |
+
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
450 |
+
|
451 |
+
# recover shared parameters
|
452 |
+
for pair in zero_model_states[0].shared_params:
|
453 |
+
if pair[1] in state_dict:
|
454 |
+
state_dict[pair[0]] = state_dict[pair[1]]
|
455 |
+
|
456 |
+
return state_dict
|
457 |
+
|
458 |
+
|
459 |
+
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
|
460 |
+
"""
|
461 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
462 |
+
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
463 |
+
via a model hub.
|
464 |
+
|
465 |
+
Args:
|
466 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder
|
467 |
+
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
|
468 |
+
|
469 |
+
Returns:
|
470 |
+
- pytorch ``state_dict``
|
471 |
+
|
472 |
+
Note: this approach may not work if your application doesn't have sufficient free CPU memory and
|
473 |
+
you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
474 |
+
the checkpoint.
|
475 |
+
|
476 |
+
A typical usage might be ::
|
477 |
+
|
478 |
+
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
479 |
+
# do the training and checkpoint saving
|
480 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
481 |
+
model = model.cpu() # move to cpu
|
482 |
+
model.load_state_dict(state_dict)
|
483 |
+
# submit to model hub or save the model to share with others
|
484 |
+
|
485 |
+
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
486 |
+
application. i.e. you will need to re-initialize the deepspeed engine, since
|
487 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
488 |
+
|
489 |
+
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
490 |
+
|
491 |
+
"""
|
492 |
+
if tag is None:
|
493 |
+
latest_path = os.path.join(checkpoint_dir, 'latest')
|
494 |
+
if os.path.isfile(latest_path):
|
495 |
+
with open(latest_path, 'r') as fd:
|
496 |
+
tag = fd.read().strip()
|
497 |
+
else:
|
498 |
+
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
499 |
+
|
500 |
+
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
501 |
+
|
502 |
+
if not os.path.isdir(ds_checkpoint_dir):
|
503 |
+
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
504 |
+
|
505 |
+
return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
|
506 |
+
|
507 |
+
|
508 |
+
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
|
509 |
+
"""
|
510 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
511 |
+
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
512 |
+
|
513 |
+
Args:
|
514 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
515 |
+
- ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
|
516 |
+
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
517 |
+
"""
|
518 |
+
|
519 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
520 |
+
print(f"Saving fp32 state dict to {output_file}")
|
521 |
+
torch.save(state_dict, output_file)
|
522 |
+
|
523 |
+
|
524 |
+
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
525 |
+
"""
|
526 |
+
1. Put the provided model to cpu
|
527 |
+
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
528 |
+
3. Load it into the provided model
|
529 |
+
|
530 |
+
Args:
|
531 |
+
- ``model``: the model object to update
|
532 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
533 |
+
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
534 |
+
|
535 |
+
Returns:
|
536 |
+
- ``model`: modified model
|
537 |
+
|
538 |
+
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
539 |
+
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
540 |
+
conveniently placed for you in the checkpoint folder.
|
541 |
+
|
542 |
+
A typical usage might be ::
|
543 |
+
|
544 |
+
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
545 |
+
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
546 |
+
# submit to model hub or save the model to share with others
|
547 |
+
|
548 |
+
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
549 |
+
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
550 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
551 |
+
|
552 |
+
"""
|
553 |
+
logger.info(f"Extracting fp32 weights")
|
554 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
555 |
+
|
556 |
+
logger.info(f"Overwriting model with fp32 weights")
|
557 |
+
model = model.cpu()
|
558 |
+
model.load_state_dict(state_dict, strict=False)
|
559 |
+
|
560 |
+
return model
|
561 |
+
|
562 |
+
|
563 |
+
if __name__ == "__main__":
|
564 |
+
|
565 |
+
parser = argparse.ArgumentParser()
|
566 |
+
parser.add_argument("checkpoint_dir",
|
567 |
+
type=str,
|
568 |
+
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
569 |
+
parser.add_argument(
|
570 |
+
"output_file",
|
571 |
+
type=str,
|
572 |
+
help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
|
573 |
+
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
574 |
+
args = parser.parse_args()
|
575 |
+
|
576 |
+
debug = args.debug
|
577 |
+
|
578 |
+
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file)
|
convert_mesh.ipynb
ADDED
@@ -0,0 +1,111 @@
|
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|
|
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|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 2,
|
6 |
+
"metadata": {},
|
7 |
+
"outputs": [],
|
8 |
+
"source": [
|
9 |
+
"import trimesh\n",
|
10 |
+
"\n",
|
11 |
+
"def convert_ply_to_format(ply_file, output_file):\n",
|
12 |
+
" # Load the PLY file\n",
|
13 |
+
" mesh = trimesh.load(ply_file)\n",
|
14 |
+
"\n",
|
15 |
+
" # Export the mesh to the specified format\n",
|
16 |
+
" mesh.export(output_file)\n",
|
17 |
+
" print(f\"Converted {ply_file} to {output_file}\")"
|
18 |
+
]
|
19 |
+
},
|
20 |
+
{
|
21 |
+
"cell_type": "code",
|
22 |
+
"execution_count": 3,
|
23 |
+
"metadata": {},
|
24 |
+
"outputs": [
|
25 |
+
{
|
26 |
+
"name": "stdout",
|
27 |
+
"output_type": "stream",
|
28 |
+
"text": [
|
29 |
+
"Converted /nfs/turbo/coe-chaijy-unreplicated/datasets/ScanNet/raw_uncompressed/scans/scene0643_00/scene0643_00_vh_clean_2_centered.ply to /home/jianingy/research/LLaVA-original/3d_grand_demo/data/scene0643_00/scene0643_00.obj\n"
|
30 |
+
]
|
31 |
+
}
|
32 |
+
],
|
33 |
+
"source": [
|
34 |
+
"ply_file_path = '/nfs/turbo/coe-chaijy-unreplicated/datasets/ScanNet/raw_uncompressed/scans/scene0643_00/scene0643_00_vh_clean_2_centered.ply'\n",
|
35 |
+
"obj_file_path = '/home/jianingy/research/LLaVA-original/3d_grand_demo/data/scene0643_00/scene0643_00.obj'\n",
|
36 |
+
"\n",
|
37 |
+
"convert_ply_to_format(ply_file_path, obj_file_path)"
|
38 |
+
]
|
39 |
+
},
|
40 |
+
{
|
41 |
+
"cell_type": "code",
|
42 |
+
"execution_count": 4,
|
43 |
+
"metadata": {},
|
44 |
+
"outputs": [
|
45 |
+
{
|
46 |
+
"name": "stdout",
|
47 |
+
"output_type": "stream",
|
48 |
+
"text": [
|
49 |
+
"Converted /nfs/turbo/coe-chaijy-unreplicated/datasets/ScanNet/raw_uncompressed/scans/scene0025_00/scene0025_00_vh_clean_2_centered.ply to /home/jianingy/research/LLaVA-original/3d_grand_demo/data/scene0025_00/scene0025_00.obj\n"
|
50 |
+
]
|
51 |
+
}
|
52 |
+
],
|
53 |
+
"source": [
|
54 |
+
"scene_id = 'scene0025_00'\n",
|
55 |
+
"ply_file_path = f'/nfs/turbo/coe-chaijy-unreplicated/datasets/ScanNet/raw_uncompressed/scans/{scene_id}/{scene_id}_vh_clean_2_centered.ply'\n",
|
56 |
+
"obj_file_path = f'/home/jianingy/research/LLaVA-original/3d_grand_demo/data/{scene_id}/{scene_id}.obj'\n",
|
57 |
+
"\n",
|
58 |
+
"convert_ply_to_format(ply_file_path, obj_file_path)"
|
59 |
+
]
|
60 |
+
},
|
61 |
+
{
|
62 |
+
"cell_type": "code",
|
63 |
+
"execution_count": 5,
|
64 |
+
"metadata": {},
|
65 |
+
"outputs": [
|
66 |
+
{
|
67 |
+
"name": "stdout",
|
68 |
+
"output_type": "stream",
|
69 |
+
"text": [
|
70 |
+
"Converted /nfs/turbo/coe-chaijy-unreplicated/datasets/ScanNet/raw_uncompressed/scans/scene0426_00/scene0426_00_vh_clean_2_centered.ply to /home/jianingy/research/LLaVA-original/3d_grand_demo/data/scene0426_00/scene0426_00.obj\n"
|
71 |
+
]
|
72 |
+
}
|
73 |
+
],
|
74 |
+
"source": [
|
75 |
+
"scene_id = 'scene0426_00'\n",
|
76 |
+
"ply_file_path = f'/nfs/turbo/coe-chaijy-unreplicated/datasets/ScanNet/raw_uncompressed/scans/{scene_id}/{scene_id}_vh_clean_2_centered.ply'\n",
|
77 |
+
"obj_file_path = f'/home/jianingy/research/LLaVA-original/3d_grand_demo/data/{scene_id}/{scene_id}.obj'\n",
|
78 |
+
"\n",
|
79 |
+
"convert_ply_to_format(ply_file_path, obj_file_path)"
|
80 |
+
]
|
81 |
+
},
|
82 |
+
{
|
83 |
+
"cell_type": "code",
|
84 |
+
"execution_count": null,
|
85 |
+
"metadata": {},
|
86 |
+
"outputs": [],
|
87 |
+
"source": []
|
88 |
+
}
|
89 |
+
],
|
90 |
+
"metadata": {
|
91 |
+
"kernelspec": {
|
92 |
+
"display_name": "llava",
|
93 |
+
"language": "python",
|
94 |
+
"name": "python3"
|
95 |
+
},
|
96 |
+
"language_info": {
|
97 |
+
"codemirror_mode": {
|
98 |
+
"name": "ipython",
|
99 |
+
"version": 3
|
100 |
+
},
|
101 |
+
"file_extension": ".py",
|
102 |
+
"mimetype": "text/x-python",
|
103 |
+
"name": "python",
|
104 |
+
"nbconvert_exporter": "python",
|
105 |
+
"pygments_lexer": "ipython3",
|
106 |
+
"version": "3.10.11"
|
107 |
+
}
|
108 |
+
},
|
109 |
+
"nbformat": 4,
|
110 |
+
"nbformat_minor": 2
|
111 |
+
}
|
data/predicted_scene_data_update_5.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
data/scanrefer_ground_truth_scene_graph.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
data/scene0025_00/scene0025_00.obj
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6cfe6e6a671560d01aaf7e9cb6bb69872aa7867c79ca9cc1a87fa1594d59aa21
|
3 |
+
size 18953438
|
data/scene0426_00/scene0426_00.obj
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f8832e13adccb571f351f561c13bf34c824c7a9e9f39f3f4e701430d9b612920
|
3 |
+
size 14657919
|
data/scene0643_00/scene0643_00.obj
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:31bf1def086561935a3e8900378cb127dc723ef8c98b191596fd2ce3e78fbbd9
|
3 |
+
size 18171881
|
llava/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
from .model import LlavaLlamaForCausalLM
|
llava/__pycache__/__init__.cpython-310.pyc
ADDED
Binary file (199 Bytes). View file
|
|