diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..271605e9cbc6bf6a8cc45433a9902bcceb29ffdf 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.obj filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000000000000000000000000000000000000..b7f7432e4b94affabe2d397c212ebb93c2a8a556 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,19 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "3d_grand_demo", + "type": "python", + "request": "launch", + "module": "app", + "justMyCode": false, + "env": { + "CUDA_VISIBLE_DEVICES": "1", + }, + "args": [] + } + ] +} diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000000000000000000000000000000000000..adf3e8f94df876eb92b87784db6fcb2a20fae134 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,32 @@ +{ + "editor.rulers": [ + 100 + ], + "flake8.args": [ + "--max-line-length=100" + ], + "black-formatter.args": [ + "--line-length=100", + ], + "editor.formatOnSave": true, + "workbench.colorCustomizations": { + "activityBar.activeBackground": "#4b668e", + "activityBar.background": "#4b668e", + "activityBar.foreground": "#e7e7e7", + "activityBar.inactiveForeground": "#e7e7e799", + "activityBarBadge.background": "#532c3c", + "activityBarBadge.foreground": "#e7e7e7", + "commandCenter.border": "#e7e7e799", + "sash.hoverBorder": "#4b668e", + 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b/__pycache__/session.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6a7111477d850ca0b6d67974086e64922619ca2d Binary files /dev/null and b/__pycache__/session.cpython-310.pyc differ diff --git a/__pycache__/settings.cpython-310.pyc b/__pycache__/settings.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e9f402a1323346d3a9bd936e5abdc900392186e9 Binary files /dev/null and b/__pycache__/settings.cpython-310.pyc differ diff --git a/__pycache__/utils.cpython-310.pyc b/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a8bd0fb68786819ab1afb11924148ddf239ff35d Binary files /dev/null and b/__pycache__/utils.cpython-310.pyc differ diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..151d312fa3c1799429537ff70c24318af76ab40c --- /dev/null +++ b/app.py @@ -0,0 +1,497 @@ +import spaces +import os +import gradio as gr +from time import sleep +from signal import SIGTERM +from psutil import process_iter +from settings import GRAND3D_Settings +from utils import list_dirs +import open3d as o3d +from copy import deepcopy +import numpy as np +import re +from bs4 import BeautifulSoup + + +import logging + + +# The following line sets the root logger level as well. +# It's equivalent to both previous statements combined: +logging.basicConfig(level=logging.INFO) + +logger = logging.getLogger(__name__) + +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/lora_grounded_obj_ref_checkpoint-4896" +model_base = "checkpoints/llava-llama-2-7b-chat-lightning-preview" +load_8bit = False +load_4bit = False +load_bf16 = True +scene_to_obj_mapping = "data/predicted_scene_data_update_5.json" +# scene_to_obj_mapping = "data/scanrefer_ground_truth_scene_graph.json" +max_new_tokens = 5000 +obj_context_feature_type = "text" + + +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, +) + +def get_chatbot_response(user_chat_input, scene_id): + # Get the response from the model + prompt, response = get_model_response( + model=model, + tokenizer=tokenizer, + data_loader=data_loader, + scene_id=scene_id, + user_input=user_chat_input, + max_new_tokens=max_new_tokens, + temperature=0.2, + top_p=0.9 + ) + return scene_id, prompt, response + +# def get_chatbot_response(user_chat_input): +# # Get the response from the chatbot +# scene_id = "scene0643_00" +# scene_graph = """ +# Object-centric context: : {'category': 'door', 'centroid': '[0.35, 1.99, 1.11]', 'extent': '[0.68, 0.65, 2.11]'}; : {'category': 'ceiling', 'centroid': '[1.04, -1.39, 2.68]', 'extent': '[0.18, 0.90, 0.05]'}; : {'category': 'ceiling', 'centroid': '[0.77, 2.09, 2.65]', 'extent': '[0.94, 0.86, 0.11]'}; : {'category': 'trash can', 'centroid': '[-0.61, -2.16, 0.21]', 'extent': '[0.42, 0.36, 0.41]'}; : {'category': 'chair', 'centroid': '[0.35, -1.35, 0.50]', 'extent': '[0.46, 0.47, 0.94]'}; : {'category': 'trash can', 'centroid': '[-0.22, -2.13, 0.24]', 'extent': '[0.40, 0.28, 0.39]'}; : {'category': 'cabinet', 'centroid': '[-1.24, 0.00, 0.58]', 'extent': '[0.61, 0.57, 0.79]'}; : {'category': 'cup', 'centroid': '[0.62, 0.23, 0.77]', 'extent': '[0.14, 0.14, 0.08]'}; : {'category': 'window', 'centroid': '[-0.35, -2.87, 1.13]', 'extent': '[2.05, 0.60, 1.07]'}; : {'category': 'file cabinet', 'centroid': '[0.40, -1.97, 0.39]', 'extent': '[0.40, 0.66, 0.73]'}; : {'category': 'monitor', 'centroid': '[0.92, -1.51, 0.97]', 'extent': '[0.25, 0.57, 0.47]'}; : {'category': 'chair', 'centroid': '[0.34, 0.59, 0.43]', 'extent': '[0.65, 0.64, 0.94]'}; : {'category': 'desk', 'centroid': '[0.64, 0.75, 0.57]', 'extent': '[0.76, 1.60, 0.82]'}; : {'category': 'chair', 'centroid': '[0.55, -0.33, 0.48]', 'extent': '[0.60, 0.60, 0.87]'}; : {'category': 'office chair', 'centroid': '[-0.28, 1.56, 0.46]', 'extent': '[0.67, 0.55, 1.02]'}; : {'category': 'office chair', 'centroid': '[-0.86, -1.53, 0.43]', 'extent': '[0.54, 0.64, 0.97]'}; : {'category': 'chair', 'centroid': '[-0.28, 1.56, 0.46]', 'extent': '[0.67, 0.55, 1.02]'}; : {'category': 'monitor', 'centroid': '[0.98, 0.56, 1.05]', 'extent': '[0.21, 0.60, 0.54]'}; : {'category': 'doorframe', 'centroid': '[-0.17, 2.42, 1.01]', 'extent': '[0.16, 0.18, 1.70]'}; : {'category': 'chair', 'centroid': '[-0.86, -1.53, 0.43]', 'extent': '[0.54, 0.64, 0.97]'}; : {'category': 'bookshelf', 'centroid': '[0.93, 2.00, 1.34]', 'extent': '[0.73, 0.99, 2.60]'}; : {'category': 'office chair', 'centroid': '[0.35, -1.35, 0.50]', 'extent': '[0.46, 0.47, 0.94]'}; : {'category': 'desk', 'centroid': '[-1.23, 1.60, 0.70]', 'extent': '[0.80, 2.01, 0.51]'}; : {'category': 'book', 'centroid': '[0.91, 1.31, 0.89]', 'extent': '[0.34, 0.32, 0.30]'}; : {'category': 'desk', 'centroid': '[-1.24, -1.12, 0.54]', 'extent': '[0.79, 1.88, 0.85]'}; : {'category': 'desk', 'centroid': '[0.63, -1.51, 0.53]', 'extent': '[0.81, 1.97, 0.85]'}; : {'category': 'calendar', 'centroid': '[-1.72, -0.44, 1.40]', 'extent': '[0.07, 0.88, 0.83]'}; : {'category': 'office chair', 'centroid': '[0.34, 0.59, 0.43]', 'extent': '[0.65, 0.64, 0.94]'}; : {'category': 'file cabinet', 'centroid': '[-1.02, -0.76, 0.47]', 'extent': '[0.58, 0.75, 0.81]'}; : {'category': 'cup', 'centroid': '[-1.26, -1.65, 0.78]', 'extent': '[0.10, 0.12, 0.04]'}; : {'category': 'keyboard', 'centroid': '[0.55, 0.84, 0.73]', 'extent': '[0.22, 0.15, 0.03]'} +# """ +# response = """ +# a

brown wooden office desk

[] on the left to the

gray shelf

[]. These sentences refer to

the brown wooden office desk

[]. +# """ +# return scene_id, scene_graph, response + +# Resetting to blank +def reset_textbox(): + return gr.update(value="") + + +# to set a component as visible=False +def set_visible_false(): + return gr.update(visible=False) + + +# to set a component as visible=True +def set_visible_true(): + return gr.update(visible=True) + + +def change_scene_or_system_prompt(dropdown_scene_selection: str): + # reset model_3d, chatbot_for_display, chat_counter, server_status_code + new_session_state = Session.create_for_scene(dropdown_scene_selection) + file_name = f"{dropdown_scene_selection}.obj" + print(os.path.join(GRAND3D_Settings.data_path, dropdown_scene_selection, file_name)) + return ( + new_session_state, + os.path.join(GRAND3D_Settings.data_path, dropdown_scene_selection, file_name), + None, + new_session_state.chat_history_for_display, + ) + + +def cylinder_frame(p0, p1): + """Calculate the transformation matrix to position a unit cylinder between two points.""" + direction = np.asarray(p1) - np.asarray(p0) + length = np.linalg.norm(direction) + direction /= length + # Computing rotation matrix using Rodrigues' formula + rot_axis = np.cross([0, 0, 1], direction) + rot_angle = np.arccos(np.dot([0, 0, 1], direction)) + rot_matrix = o3d.geometry.get_rotation_matrix_from_axis_angle(rot_axis * rot_angle) + + # Translation + translation = (np.asarray(p0) + np.asarray(p1)) / 2 + + transformation = np.eye(4) + transformation[:3, :3] = rot_matrix + transformation[:3, 3] = translation + scaling = np.eye(4) + scaling[2, 2] = length + transformation = np.matmul(transformation, scaling) + return transformation + + +def create_cylinder_mesh(p0, p1, color, radius=0.02, resolution=20, split=1): + """Create a colored cylinder mesh between two points p0 and p1.""" + cylinder = o3d.geometry.TriangleMesh.create_cylinder( + radius=radius, height=1, resolution=resolution, split=split + ) + transformation = cylinder_frame(p0, p1) + cylinder.transform(transformation) + # Apply color + cylinder.paint_uniform_color(color) + return cylinder + + +def prettify_mesh_for_gradio(mesh): + # Define the transformation matrix + T = np.array([[0, -1, 0, 0], [0, 0, 1, 0], [-1, 0, 0, 0], [0, 0, 0, 1]]) + + # Apply the transformation + mesh.transform(T) + + mesh.scale(10.0, center=mesh.get_center()) + + bright_factor = 1 # Adjust this factor to get the desired brightness + mesh.vertex_colors = o3d.utility.Vector3dVector( + np.clip(np.asarray(mesh.vertex_colors) * bright_factor, 0, 1) + ) + + return mesh + + +def create_bbox(center, extents, color=[1, 0, 0], radius=0.02): + """Create a colored bounding box with given center, extents, and line thickness.""" + # ... [The same code as before to define corners and lines] ... + print(extents) + print(type(extents)) + extents = extents.replace("[", "").replace("]", "") + center = center.replace("[", "").replace("]", "") + extents = [float(x.strip()) for x in extents.split(",")] + center = [float(x.strip()) for x in center.split(",")] + + sx, sy, sz = float(extents[0]), float(extents[1]), float(extents[2]) + x_corners = [sx / 2, sx / 2, -sx / 2, -sx / 2, sx / 2, sx / 2, -sx / 2, -sx / 2] + y_corners = [sy / 2, -sy / 2, -sy / 2, sy / 2, sy / 2, -sy / 2, -sy / 2, sy / 2] + z_corners = [sz / 2, sz / 2, sz / 2, sz / 2, -sz / 2, -sz / 2, -sz / 2, -sz / 2] + corners_3d = np.vstack([x_corners, y_corners, z_corners]) + corners_3d[0, :] = corners_3d[0, :] + float(center[0]) + corners_3d[1, :] = corners_3d[1, :] + float(center[1]) + corners_3d[2, :] = corners_3d[2, :] + float(center[2]) + corners_3d = np.transpose(corners_3d) + + lines = [ + [0, 1], + [1, 2], + [2, 3], + [3, 0], + [4, 5], + [5, 6], + [6, 7], + [7, 4], + [0, 4], + [1, 5], + [2, 6], + [3, 7], + ] + cylinders = [] + for line in lines: + p0, p1 = corners_3d[line[0]], corners_3d[line[1]] + cylinders.append(create_cylinder_mesh(p0, p1, color, radius)) + return cylinders + + +def highlight_clusters_in_mesh( + centroids_extents_detailed, + centroids_extends_refer, + mesh, + output_dir, + output_file_name="highlighted_mesh.glb", +): + print("*" * 50) + # Visualize the highlighted points by drawing 3D bounding boxes overlay on a mesh + old_mesh = deepcopy(mesh) + output_path = os.path.join(output_dir, "mesh_vis") + if not os.path.exists(output_path): + os.makedirs(output_path) + + # Create a combined mesh to hold both the original and the bounding boxes + combined_mesh = o3d.geometry.TriangleMesh() + combined_mesh += old_mesh + + # Draw bounding boxes for each centroid and extent + for center, extent in centroids_extents_detailed: + print("center: ", center) + print("extent: ", extent) + bbox = create_bbox(center, extent, color=[0, 0, 1]) # Red color for all boxes + for b in bbox: + combined_mesh += b + + for center, extent in centroids_extends_refer: + bbox = create_bbox(center, extent, color=[0, 1, 0]) + for b in bbox: + combined_mesh += b + + combined_mesh = prettify_mesh_for_gradio(combined_mesh) + # Save the combined mesh + output_file_path = os.path.join(output_path, output_file_name) + o3d.io.write_triangle_mesh( + output_file_path, combined_mesh, write_vertex_colors=True + ) + print("*" * 50) + return output_file_path + + +def extract_objects(text): + return re.findall(r"", text) + + +# Parse the scene graph into a dictionary +def parse_scene_graph(scene_graph): + scene_dict = {} + matches = re.findall(r": (\{.*?\})", scene_graph) + for match in matches: + obj_id = f"" + obj_data = eval(match[1]) + scene_dict[obj_id] = obj_data + return scene_dict + + +def get_centroids_extents(obj_list, scene_dict): + centroids_extents = [] + for obj in obj_list: + if obj in scene_dict: + centroid = scene_dict[obj]["centroid"] + extent = scene_dict[obj]["extent"] + centroids_extents.append((centroid, extent)) + return centroids_extents + +@spaces.GPU +def language_model_forward( + session_state, user_chat_input, top_p, temperature, dropdown_scene +): + session_state = Session.create_for_scene(dropdown_scene) + session_state.chat_history_for_display.append( + (user_chat_input, None) + ) # append in a tuple format, first is user input, second is assistant response + + yield session_state, None, session_state.chat_history_for_display + + # Load in a 3D model + file_name = f"{session_state.scene}.obj" + original_model_path = os.path.join( + GRAND3D_Settings.data_path, session_state.scene, file_name + ) + print("original_model_path: ", original_model_path) + + # Load the GLB mesh + mesh = o3d.io.read_triangle_mesh(original_model_path) + + # get chatbot response + scene_id, scene_graph, response = get_chatbot_response(user_chat_input, session_state.scene) + + assert scene_id == session_state.scene # Ensure the scene ID matches + + # use scene_graph and response to get centroids and extents + # Parse the scene graph into a dictionary + scene_dict = parse_scene_graph(scene_graph) + print("Model Input: " + str(scene_dict)) + print("=" * 50) + print("Model Response: " + response) + + # Parse the response to get detailed and refer expression groundings + soup = BeautifulSoup(response, "html.parser") + detailed_grounding_html = str(soup.find("detailed_grounding")) + refer_expression_grounding_html = str(soup.find("refer_expression_grounding")) + + # Extract objects from both sections + detailed_objects = extract_objects(detailed_grounding_html) + refer_objects = extract_objects(refer_expression_grounding_html) + + # Extract objects from both sections + print("detailed_objects: ", detailed_objects) + print("refer_objects: ", refer_objects) + + # Perform set subtraction to get remaining objects + remaining_objects = list(set(detailed_objects) - set(refer_objects)) + print("remaining_objects: ", remaining_objects) + + centroids_extents_detailed = get_centroids_extents(remaining_objects, scene_dict) + print("centroids_extents_detailed: ", centroids_extents_detailed) + centroids_extents_refer = get_centroids_extents(refer_objects, scene_dict) + print("centroids_extents_refer: ", centroids_extents_refer) + # Define your centroids and extents here (example data) + # Highlight clusters in the mesh and save it + session_output_dir = session_state.get_session_output_dir() + highlighted_model_path = highlight_clusters_in_mesh( + centroids_extents_detailed, + centroids_extents_refer, + mesh, + session_output_dir, + output_file_name="highlighted_model.glb", + ) + + # Update the chat history with the response + last_turn = session_state.chat_history_for_display[-1] # first is user input, second is assistant response + last_turn = (last_turn[0], response) + session_state.chat_history_for_display[-1] = last_turn + session_state.save() # save the session state + + yield session_state, highlighted_model_path, session_state.chat_history_for_display + + +title = """

🤖 3D-GRAND: Towards Better Grounding and Less Hallucination for 3D-LLMs 🚀

+

+[Project Page] +[3D-GRAND Data] +[3D-POPE Data] +

+""" + +# Modifying existing Gradio Theme +# theme = gr.themes.Soft( +# primary_hue=gr.themes.colors.blue, secondary_hue=gr.themes.colors.pink +# ) + +with gr.Blocks(theme=gr.themes.Soft()) as demo: + session_state = gr.State(Session.create) + + gr.HTML(title) + + with gr.Column(): + with gr.Row(): + with gr.Column(scale=5): + dropdown_scene = gr.Dropdown( + choices=list_dirs(GRAND3D_Settings.data_path), + value=GRAND3D_Settings.default_scene, + interactive=True, + label="Select a scene", + ) + model_3d = gr.Model3D( + value=os.path.join( + GRAND3D_Settings.data_path, + GRAND3D_Settings.default_scene, + f"{GRAND3D_Settings.default_scene}.obj", + ), + clear_color=[0.0, 0.0, 0.0, 0.0], + label="3D Model", + camera_position=(-50, 65, 10), + zoom_speed=10.0, + ) + gr.HTML( + """
+ 👆 SCROLL or DRAG on the 3D Model + to zoom in/out and rotate. Press CTRL and DRAG to pan. +
+ """ + ) + gr.HTML( + """
+ 👇 When grounding finishes, + the grounding result will be displayed below. +
+ """ + ) + model_3d_grounding_result = gr.Model3D( + clear_color=[0.0, 0.0, 0.0, 0.0], + label="Grounding Result", + zoom_speed=15.0, + ) + gr.HTML( + """
+
■
= Landmark   +
■
= Chosen Target +
+ """ + ) + with gr.Column(scale=5): + chat_history_for_display = gr.Chatbot( + value=[(None, GRAND3D_Settings.INITIAL_MSG_FOR_DISPLAY)], + label="Chat Assistant", + height=510, + render_markdown=False, + sanitize_html=False, + ) + with gr.Row(): + with gr.Column(scale=8): + user_chat_input = gr.Textbox( + placeholder="I want to find the chair near the table", + show_label=False, + ) + with gr.Column(scale=1, min_width=0): + send_button = gr.Button("Send", variant="primary") + with gr.Column(scale=1, min_width=0): + clear_button = gr.Button("Clear") + with gr.Row(): + with gr.Accordion(label="Examples for user message:", open=True): + gr.Examples( + examples=[ + ["The TV on the drawer, opposing the bed."], + ["the desk next to the window"] + ], + inputs=user_chat_input, + ) + + with gr.Accordion("Parameters", open=False, visible=False): + top_p = gr.Slider( + minimum=0, + maximum=1.0, + value=1.0, + step=0.05, + interactive=True, + label="Top-p (nucleus sampling)", + ) + temperature = gr.Slider( + minimum=0, + maximum=5.0, + value=1.0, + step=0.1, + interactive=True, + label="Temperature", + ) + # gr.Markdown("### Terms of Service") + # gr.HTML( + # """By using this service, users are required to agree to the following terms: + # The service is a research preview intended for non-commercial use only. + # The service may collect user dialogue data for future research.""" + # ) + + # Event handling + dropdown_scene.change( + fn=change_scene_or_system_prompt, + inputs=[dropdown_scene], + outputs=[session_state, model_3d, model_3d_grounding_result, chat_history_for_display], + ) + clear_button.click( + fn=change_scene_or_system_prompt, + inputs=[dropdown_scene], + outputs=[session_state, model_3d, model_3d_grounding_result, chat_history_for_display], + ) + user_chat_input.submit( + fn=language_model_forward, + inputs=[session_state, user_chat_input, top_p, temperature, dropdown_scene], + outputs=[session_state, model_3d_grounding_result, chat_history_for_display], + ) + send_button.click( + fn=language_model_forward, + inputs=[session_state, user_chat_input, top_p, temperature, dropdown_scene], + outputs=[session_state, model_3d_grounding_result, chat_history_for_display], + ) + + send_button.click(reset_textbox, [], [user_chat_input]) + user_chat_input.submit(reset_textbox, [], [user_chat_input]) + +sleep_time = 2 +port = 7011 +for x in range(1, 10): # try 8 times + try: + # put your logic here + gr.close_all() + demo.queue( + max_size=20, + ).launch( + # debug=True, + # server_name="0.0.0.0", + # server_port=port, + # share=True + ) + except OSError: + for proc in process_iter(): + for conns in proc.connections(kind="inet"): + if conns.laddr.port == port: + proc.send_signal(SIGTERM) # or SIGKILL + print(f"Retrying {x} time...") + pass + + sleep(sleep_time) # wait for 2 seconds before trying to fetch the data again + sleep_time *= 2 # exponential backoff diff --git a/checkpoints/llava-llama-2-7b-chat-lightning-preview/config.json 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"lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/checkpoints/llava-llama-2-7b-chat-lightning-preview/tokenizer.model b/checkpoints/llava-llama-2-7b-chat-lightning-preview/tokenizer.model new file mode 100755 index 0000000000000000000000000000000000000000..6c00c742ce03c627d6cd5b795984876fa49fa899 --- /dev/null +++ b/checkpoints/llava-llama-2-7b-chat-lightning-preview/tokenizer.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 +size 499723 diff --git a/checkpoints/llava-llama-2-7b-chat-lightning-preview/tokenizer_config.json b/checkpoints/llava-llama-2-7b-chat-lightning-preview/tokenizer_config.json new file mode 100755 index 0000000000000000000000000000000000000000..a0024735c8dd7afe47fe72792b2c4edaff63bd3b --- /dev/null +++ b/checkpoints/llava-llama-2-7b-chat-lightning-preview/tokenizer_config.json @@ -0,0 +1,36 @@ +{ + "add_bos_token": true, 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%}", + "clean_up_tokenization_spaces": false, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "legacy": false, + "model_max_length": 1000000000000000019884624838656, + "pad_token": null, + "padding_side": "right", + "sp_model_kwargs": {}, + "tokenizer_class": "LlamaTokenizer", + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/checkpoints/lora_grounded_obj_ref_checkpoint-4896/README.md b/checkpoints/lora_grounded_obj_ref_checkpoint-4896/README.md new file mode 100755 index 0000000000000000000000000000000000000000..d4576fe074287232d3836bf69c21d3f2593290d9 --- /dev/null +++ b/checkpoints/lora_grounded_obj_ref_checkpoint-4896/README.md @@ -0,0 +1,9 @@ +--- +library_name: peft +--- +## Training procedure + +### Framework versions + + +- PEFT 0.4.0 diff --git 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a/checkpoints/lora_grounded_obj_ref_checkpoint-4896/adapter_model.bin b/checkpoints/lora_grounded_obj_ref_checkpoint-4896/adapter_model.bin new file mode 100755 index 0000000000000000000000000000000000000000..62467b2bc19eaccdf8a5eee665ee121dbc22bb3b --- /dev/null +++ b/checkpoints/lora_grounded_obj_ref_checkpoint-4896/adapter_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5d2570b823cb3c260a51788481c1e0b06afd458be512097e47a3c1c2c2f58a93 +size 319970957 diff --git a/checkpoints/lora_grounded_obj_ref_checkpoint-4896/config.json b/checkpoints/lora_grounded_obj_ref_checkpoint-4896/config.json new file mode 100755 index 0000000000000000000000000000000000000000..5bceca6c2de97e79af2a9c0a80549a4341811024 --- /dev/null +++ b/checkpoints/lora_grounded_obj_ref_checkpoint-4896/config.json @@ -0,0 +1,54 @@ +{ + "_name_or_path": "/nfs/turbo/coe-chaijy-unreplicated/pre-trained-weights/LLaVA/llava-llama-2-7b-chat-lightning-preview", + 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0000000000000000000000000000000000000000..002e2db70a13f4f7f9c94c7fe57f834c337aee34 --- /dev/null +++ b/checkpoints/lora_grounded_obj_ref_checkpoint-4896/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3f6df39eb8c3a59e2e18f97752b27792c12a3be4e8cf2474f50e4a4a62371cc +size 5755 diff --git a/checkpoints/lora_grounded_obj_ref_checkpoint-4896/zero_to_fp32.py b/checkpoints/lora_grounded_obj_ref_checkpoint-4896/zero_to_fp32.py new file mode 100755 index 0000000000000000000000000000000000000000..c5246ff52274e1d6142001ccf085186d3545ce57 --- /dev/null +++ b/checkpoints/lora_grounded_obj_ref_checkpoint-4896/zero_to_fp32.py @@ -0,0 +1,578 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: python zero_to_fp32.py . pytorch_model.bin + +import argparse +import torch +import glob +import math +import os +import re +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage == 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + + total_files = len(files) + state_dicts = [] + for f in files: + state_dicts.append(torch.load(f, map_location=device)) + + if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage == 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + if zero_stage == 2: + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + elif zero_stage == 3: + # if there is more than one param group, there will be multiple flattened tensors - one + # flattened tensor per group - for simplicity merge them into a single tensor + # + # XXX: could make the script more memory efficient for when there are multiple groups - it + # will require matching the sub-lists of param_shapes for each param group flattened tensor + + fp32_flat_groups = [ + torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts)) + ] + + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage == 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = fp32_flat_groups[0].numel() * world_size + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + for name, shape in param_shapes.items(): + + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # XXX: memory usage doubles here + state_dict[name] = torch.cat( + tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)), + 0).narrow(0, 0, unpartitioned_numel).view(shape) + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``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`` + + Returns: + - pytorch ``state_dict`` + + Note: this approach may not work if your application doesn't have sufficient free CPU memory and + you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin) + - ``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`` + """ + + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + print(f"Saving fp32 state dict to {output_file}") + torch.save(state_dict, output_file) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``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`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info(f"Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info(f"Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument( + "output_file", + type=str, + help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file) diff --git a/convert_mesh.ipynb b/convert_mesh.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5e124cfd74650e0a4fced9125164255a4dee328b --- /dev/null +++ b/convert_mesh.ipynb @@ -0,0 +1,111 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import trimesh\n", + "\n", + "def convert_ply_to_format(ply_file, output_file):\n", + " # Load the PLY file\n", + " mesh = trimesh.load(ply_file)\n", + "\n", + " # Export the mesh to the specified format\n", + " mesh.export(output_file)\n", + " print(f\"Converted {ply_file} to {output_file}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + } + ], + "source": [ + "ply_file_path = '/nfs/turbo/coe-chaijy-unreplicated/datasets/ScanNet/raw_uncompressed/scans/scene0643_00/scene0643_00_vh_clean_2_centered.ply'\n", + "obj_file_path = '/home/jianingy/research/LLaVA-original/3d_grand_demo/data/scene0643_00/scene0643_00.obj'\n", + "\n", + "convert_ply_to_format(ply_file_path, obj_file_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + } + ], + "source": [ + "scene_id = 'scene0025_00'\n", + "ply_file_path = f'/nfs/turbo/coe-chaijy-unreplicated/datasets/ScanNet/raw_uncompressed/scans/{scene_id}/{scene_id}_vh_clean_2_centered.ply'\n", + "obj_file_path = f'/home/jianingy/research/LLaVA-original/3d_grand_demo/data/{scene_id}/{scene_id}.obj'\n", + "\n", + "convert_ply_to_format(ply_file_path, obj_file_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + } + ], + "source": [ + "scene_id = 'scene0426_00'\n", + "ply_file_path = f'/nfs/turbo/coe-chaijy-unreplicated/datasets/ScanNet/raw_uncompressed/scans/{scene_id}/{scene_id}_vh_clean_2_centered.ply'\n", + "obj_file_path = f'/home/jianingy/research/LLaVA-original/3d_grand_demo/data/{scene_id}/{scene_id}.obj'\n", + "\n", + "convert_ply_to_format(ply_file_path, obj_file_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llava", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/data/predicted_scene_data_update_5.json b/data/predicted_scene_data_update_5.json 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-0,0 +1,18 @@ +CONTROLLER_HEART_BEAT_EXPIRATION = 30 +WORKER_HEART_BEAT_INTERVAL = 15 + +LOGDIR = "." + +# Model Constants +IGNORE_INDEX = -100 +IMAGE_TOKEN_INDEX = -200 +DEFAULT_IMAGE_TOKEN = "" +DEFAULT_IMAGE_PATCH_TOKEN = "" +DEFAULT_IM_START_TOKEN = "" +DEFAULT_IM_END_TOKEN = "" + +GROUND_TOKEN = "" + + +# Debug flags +PROFILE_RUNTIME = False diff --git a/llava/conversation.py b/llava/conversation.py new file mode 100644 index 0000000000000000000000000000000000000000..7a87a0b0536f0748bd4d17674e9fdf147477b537 --- /dev/null +++ b/llava/conversation.py @@ -0,0 +1,436 @@ +import dataclasses +from enum import auto, Enum +from typing import List, Tuple + + +class SeparatorStyle(Enum): + """Different separator style.""" + + SINGLE = auto() + TWO = auto() + MPT = auto() + PLAIN = auto() + LLAMA_2 = auto() + + +@dataclasses.dataclass +class Conversation: + """A class that keeps all conversation history.""" + + system: str + roles: List[str] + messages: List[List[str]] + offset: int + max_extent: float = None + mesh_centroid: List[float] = None + sep_style: SeparatorStyle = SeparatorStyle.SINGLE + sep: str = "###" + sep2: str = None + version: str = "Unknown" + + skip_next: bool = False + bbox_tokenization_type: bool = "numerical" + num_voxels_per_axis_for_location_tokens: int = 10 + normalize_points_to_unit_cube: bool = True + + def get_prompt(self): + messages = self.messages + if len(messages) > 0 and type(messages[0][1]) is tuple: + messages = self.messages.copy() + init_role, init_msg = messages[0].copy() + init_msg = init_msg[0].replace("", "").strip() + if "mmtag" in self.version: + messages[0] = (init_role, init_msg) + messages.insert(0, (self.roles[0], "")) + messages.insert(1, (self.roles[1], "Received.")) + else: + messages[0] = (init_role, "\n" + init_msg) + + if self.sep_style == SeparatorStyle.SINGLE: + ret = self.system + self.sep + for role, message in messages: + if message: + if type(message) is tuple: + message = message[ + 0 + ] # (text, image, image_process_mode) for image input; (text, scene_selector) + ret += role + ": " + message + self.sep + else: + ret += role + ":" + elif self.sep_style == SeparatorStyle.TWO: + seps = [self.sep, self.sep2] + ret = self.system + seps[0] + for i, (role, message) in enumerate(messages): + if message: + if type(message) is tuple: + message = message[ + 0 + ] # (text, image, image_process_mode) for image input; (text, scene_selector) for 3d input + ret += role + ": " + message + seps[i % 2] + else: + ret += role + ":" + elif self.sep_style == SeparatorStyle.MPT: + ret = self.system + self.sep + for role, message in messages: + if message: + if type(message) is tuple: + message = message[ + 0 + ] # (text, image, image_process_mode) for image input; (text, scene_selector) + ret += role + message + self.sep + else: + ret += role + elif self.sep_style == SeparatorStyle.LLAMA_2: + wrap_sys = lambda msg: f"<>\n{msg}\n<>\n\n" + wrap_inst = lambda msg: f"[INST] {msg} [/INST]" + ret = "" + + for i, (role, message) in enumerate(messages): + if i == 0: + assert message, "first message should not be none" + assert role == self.roles[0], "first message should come from user" + if message: + if type(message) is tuple: + message = message[ + 0 + ] # (text, image, image_process_mode) for image input; (text, scene_selector) + if i == 0: + message = wrap_sys(self.system) + message + if i % 2 == 0: + message = wrap_inst(message) + ret += self.sep + message + else: + ret += " " + message + " " + self.sep2 + else: + ret += "" + ret = ret.lstrip(self.sep) + elif self.sep_style == SeparatorStyle.PLAIN: + seps = [self.sep, self.sep2] + ret = self.system + for i, (role, message) in enumerate(messages): + if message: + if type(message) is tuple: + message = message[ + 0 + ] # (text, image, image_process_mode) for image input; (text, scene_selector) + ret += message + seps[i % 2] + else: + ret += "" + else: + raise ValueError(f"Invalid style: {self.sep_style}") + + return ret + + def append_message(self, role, message): + # for image input, message is (text, image, image_process_mode) + # for 3d input, message is (text, scene_selector) + self.messages.append([role, message]) + + def get_scene(self): + for i, (role, msg) in enumerate(self.messages[self.offset :]): + if i % 2 == 0: + if type(msg) is tuple: + return msg[1] + return None + + def get_images(self, return_pil=False): + images = [] + for i, (role, msg) in enumerate(self.messages[self.offset :]): + if i % 2 == 0: + if type(msg) is tuple: + import base64 + from io import BytesIO + from PIL import Image + + msg, image, image_process_mode = msg + if image_process_mode == "Pad": + + def expand2square(pil_img, background_color=(122, 116, 104)): + width, height = pil_img.size + if width == height: + return pil_img + elif width > height: + result = Image.new(pil_img.mode, (width, width), background_color) + result.paste(pil_img, (0, (width - height) // 2)) + return result + else: + result = Image.new(pil_img.mode, (height, height), background_color) + result.paste(pil_img, ((height - width) // 2, 0)) + return result + + image = expand2square(image) + elif image_process_mode == "Crop": + pass + elif image_process_mode == "Resize": + image = image.resize((336, 336)) + else: + raise ValueError(f"Invalid image_process_mode: {image_process_mode}") + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 800, 400 + shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) + longest_edge = int(shortest_edge * aspect_ratio) + W, H = image.size + if H > W: + H, W = longest_edge, shortest_edge + else: + H, W = shortest_edge, longest_edge + image = image.resize((W, H)) + if return_pil: + images.append(image) + else: + buffered = BytesIO() + image.save(buffered, format="PNG") + img_b64_str = base64.b64encode(buffered.getvalue()).decode() + images.append(img_b64_str) + return images + + def to_gradio_chatbot(self): + ret = [] + for i, (role, msg) in enumerate(self.messages[self.offset :]): + if i % 2 == 0: + if type(msg) is tuple: + if len(msg) == 3: # input is image, msg is (text, image, image_process_mode) + import base64 + from io import BytesIO + + msg, image, image_process_mode = msg + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 800, 400 + shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) + longest_edge = int(shortest_edge * aspect_ratio) + W, H = image.size + if H > W: + H, W = longest_edge, shortest_edge + else: + H, W = shortest_edge, longest_edge + image = image.resize((W, H)) + buffered = BytesIO() + image.save(buffered, format="JPEG") + img_b64_str = base64.b64encode(buffered.getvalue()).decode() + img_str = f'user upload image' + ret.append([img_str, None]) + elif len(msg) == 2: # input is 3d, msg is (text, scene_selector) + msg, scene = msg + + msg = msg.replace("", "").strip() + if len(msg) > 0: + ret.append([msg, None]) + else: + ret.append([msg, None]) + else: + ret[-1][-1] = msg + return ret + + def copy(self): + return Conversation( + system=self.system, + roles=self.roles, + messages=[[x, y] for x, y in self.messages], + offset=self.offset, + sep_style=self.sep_style, + sep=self.sep, + sep2=self.sep2, + version=self.version, + ) + + def dict(self, is_input_3d=False): + if is_input_3d or len(self.get_images()) > 0: + return { + "system": self.system, + "roles": self.roles, + "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages], + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + } + return { + "system": self.system, + "roles": self.roles, + "messages": self.messages, + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + } + + +conv_vicuna_v0 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant"), + messages=( + ( + "Human", + "What are the key differences between renewable and non-renewable energy sources?", + ), + ( + "Assistant", + "Renewable energy sources are those that can be replenished naturally in a relatively " + "short amount of time, such as solar, wind, hydro, geothermal, and biomass. " + "Non-renewable energy sources, on the other hand, are finite and will eventually be " + "depleted, such as coal, oil, and natural gas. Here are some key differences between " + "renewable and non-renewable energy sources:\n" + "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable " + "energy sources are finite and will eventually run out.\n" + "2. Environmental impact: Renewable energy sources have a much lower environmental impact " + "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, " + "and other negative effects.\n" + "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically " + "have lower operational costs than non-renewable sources.\n" + "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote " + "locations than non-renewable sources.\n" + "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different " + "situations and needs, while non-renewable sources are more rigid and inflexible.\n" + "6. Sustainability: Renewable energy sources are more sustainable over the long term, while " + "non-renewable sources are not, and their depletion can lead to economic and social instability.\n", + ), + ), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_vicuna_v1 = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the user's questions.", + roles=("USER", "ASSISTANT"), + version="v1", + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", +) + +conv_llama_2 = Conversation( + system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. + +If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""", + roles=("USER", "ASSISTANT"), + version="llama_v2", + messages=(), + offset=0, + sep_style=SeparatorStyle.LLAMA_2, + sep="", + sep2="", +) + +conv_llava_llama_2 = Conversation( + system="You are a helpful language and vision assistant. " + "You are able to understand the visual content that the user provides, " + "and assist the user with a variety of tasks using natural language.", + roles=("USER", "ASSISTANT"), + version="llama_v2", + messages=(), + offset=0, + sep_style=SeparatorStyle.LLAMA_2, + sep="", + sep2="", +) + +conv_llava_llama_2_obj_identifier = Conversation( + system="You are a helpful language and vision assistant that helps human reason about a 3D scene. " + "You will be provided with a 3D scene context that contains a set of objects. " + "You are able to understand the 3D scene, and answer the user's questions truthfully with respect to the 3D scene. " + "In the 3D scene context, each object is represented by a unique identifier, such as , , etc. " + "In all of your responses, you should explicitly ground each object noun phrase to the corresponding object identifier in the 3D scene." + "For example, when the user asks 'Describe this room.', you should respond with this format: 'This is a living room with a

brown sofa

[] and

three chairs

[, , ].'" + "Note that your answer should always enclose object noun phrases with

and

tags, followed by the corresponding object identifiers in square brackets.", + roles=("USER", "ASSISTANT"), + version="llama_v2", + messages=(), + offset=0, + sep_style=SeparatorStyle.LLAMA_2, + sep="", + sep2="", +) + +conv_mpt = Conversation( + system="""<|im_start|>system +A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""", + roles=("<|im_start|>user\n", "<|im_start|>assistant\n"), + version="mpt", + messages=(), + offset=0, + sep_style=SeparatorStyle.MPT, + sep="<|im_end|>", +) + +conv_llava_plain = Conversation( + system="", + roles=("", ""), + messages=(), + offset=0, + sep_style=SeparatorStyle.PLAIN, + sep="\n", +) + +conv_llava_v0 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant"), + messages=(("Human", "Hi!"), ("Assistant", "Hi there! How can I help you today?")), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_llava_v0_mmtag = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language." + "The visual content will be provided with the following format: visual content.", + roles=("Human", "Assistant"), + messages=(), + offset=0, + sep_style=SeparatorStyle.SINGLE, + sep="###", + version="v0_mmtag", +) + +conv_llava_v1 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("USER", "ASSISTANT"), + version="v1", + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", +) + +conv_llava_v1_mmtag = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language." + "The visual content will be provided with the following format: visual content.", + roles=("USER", "ASSISTANT"), + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", + version="v1_mmtag", +) + +default_conversation = conv_vicuna_v0 +conv_templates = { + "default": conv_vicuna_v0, + "v0": conv_vicuna_v0, + "v1": conv_vicuna_v1, + "vicuna_v1": conv_vicuna_v1, + "llama_2": conv_llama_2, + "plain": conv_llava_plain, + "v0_plain": conv_llava_plain, + "llava_v0": conv_llava_v0, + "v0_mmtag": conv_llava_v0_mmtag, + "llava_v1": conv_llava_v1, + "v1_mmtag": conv_llava_v1_mmtag, + "llava_llama_2": conv_llava_llama_2, + "llava_llama_2_obj_identifier": conv_llava_llama_2_obj_identifier, + "mpt": conv_mpt, +} + + +if __name__ == "__main__": + print(default_conversation.get_prompt()) diff --git a/llava/dataset/__pycache__/obj_identifier_dataset.cpython-310.pyc b/llava/dataset/__pycache__/obj_identifier_dataset.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..01e568030c809ba33eef4e8fd91c19cdc7848bc8 Binary files /dev/null and b/llava/dataset/__pycache__/obj_identifier_dataset.cpython-310.pyc differ diff --git a/llava/dataset/obj_identifier_dataset.py b/llava/dataset/obj_identifier_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..b41eeba62e818faa7fbd3119e20695852e552bc2 --- /dev/null +++ b/llava/dataset/obj_identifier_dataset.py @@ -0,0 +1,259 @@ +from collections import OrderedDict +import torch +import os +import copy +from dataclasses import dataclass +import json +import re +from typing import Dict, Optional, Sequence + + +import transformers + +from llava.constants import ( + IGNORE_INDEX, +) +from torch.utils.data import Dataset + +from llava.util.tokenization import ( + preprocess_llama_2, + preprocess_llama_2_obj_identifier, + preprocess_multimodal, + preprocess, +) + +from llava import conversation as conversation_lib + + +class ObjIdentifierDataset(Dataset): + """Dataset for supervised fine-tuning.""" + + def __init__( + self, + tokenizer: transformers.PreTrainedTokenizer, + data_path: str | list, + scene_to_obj_mapping: str, + obj_context_feature_type: str = "text", + mode: str = "train", + **kwargs, + ): + super(ObjIdentifierDataset, self).__init__() + + self.tokenizer = tokenizer + self.scene_to_obj_mapping = json.load(open(scene_to_obj_mapping, "r")) + self.update_data(data_path) + self.obj_context_feature_type = obj_context_feature_type + self.mode = mode + + def __len__(self): + return len(self.list_data_dict) + + def update_data(self, data_path: str): + assert self.scene_to_obj_mapping is not None, "scene_to_obj_mapping needs to be set first." + if isinstance(data_path, str): + self.list_data_dict = json.load(open(data_path, "r")) + elif isinstance(data_path, list): + self.list_data_dict = data_path + + def __getitem__(self, i) -> Dict[str, torch.Tensor]: + sources = copy.deepcopy(self.list_data_dict[i]) + if isinstance(i, int): + sources = [sources] + assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME + + ############ + scene_id = sources[0]["scene_id"] + input_obj_dict = copy.deepcopy(self.scene_to_obj_mapping[scene_id]) + + # prepare the object-centric features + # we want the LLM to see: + # "%%%% Object-centric context: : , : , ..." + # where will later be replaced by the actual feature in vector form, + # everything else is pure text string. + # 1. We need to first change the object_id to a new object_id, e.g., 'obj_0', 'obj_1', ..., + # and replace the old object_id with new object_id in the text conversation + # 2. Tokenize the conversation, and add object context to the tokenized conversation + # 3. Gather and return the necessary information for each object, + # so that it can be later embeded into vector + + # 1. change the object_id to a new object_id + # original object_id: 'wardrobe-0', 'three-seat/multi-seat sofa-1', ... + # convert to obj_id: 'obj_0', 'obj_1', ... + # and remember the mapping + old_id_to_new_id_mapping = {} + result_obj_dict = OrderedDict() + # first pass, map the old object_id to new object_id + for old_id, obj_info in input_obj_dict.items(): + # make sure old_id doesn't contain < or > + assert ( + "<" not in old_id and ">" not in old_id + ), "object_id in scene graph should not contain < or >" + new_id = f"obj_{len(old_id_to_new_id_mapping)}" + old_id_to_new_id_mapping[old_id] = new_id + # second pass, create the new object-centric context, modify the object_id in the text content + for old_id, obj_info in input_obj_dict.items(): + new_id = old_id_to_new_id_mapping[old_id] + # TODO: Determine what information to include in the object-centric context + result_obj_info_dict = {} + result_obj_info_dict["category"] = obj_info["category"] + # result_obj_info_dict["category_id"] = obj_info["category_id"] + # for relations, we need to replace the old object_id with new object_id + # result_obj_info_dict["relations"] = [] + # for relation in obj_info["relations"]: + # for local_old_id, local_new_id in old_id_to_new_id_mapping.items(): + # if local_old_id in relation: + # result_obj_info_dict["relations"].append( + # re.sub(rf"<{local_old_id}>", f"<{local_new_id}>", relation) + # ) + if "description" in obj_info: + result_obj_info_dict["description"] = obj_info["description"] + else: + # print(f"WARNING: Object {old_id} does not have a description.") + pass + + # use two decimal places for the centroid and extent + result_obj_info_dict["centroid"] = ( + f"[{obj_info['centroid'][0]:.2f}, {obj_info['centroid'][1]:.2f}, {obj_info['centroid'][2]:.2f}]" + ) + result_obj_info_dict["extent"] = ( + f"[{obj_info['extent'][0]:.2f}, {obj_info['extent'][1]:.2f}, {obj_info['extent'][2]:.2f}]" + ) + result_obj_dict[new_id] = result_obj_info_dict + + # replace the old object_id with new object_id in the text content + # text conversation example: + # { + # "id": "55f2b905-d367-443d-8f88-ef71b958c81f@LivingRoom-3973@1", + # "scene_id": "55f2b905-d367-443d-8f88-ef71b958c81f@LivingRoom-3973", + # "conversations": [ + # { + # "from": "human", + # "value": "Can you describe the ambiance of this room?" + # }, + # { + # "from": "gpt", + # "value": "In this Living Room, the arrangement of furniture caters to both style and function. The

warm wooden hue wardrobe

[] stands with a retro flair, while the

neutral grey, sleek rectangular form multi-seat sofa

[] and

neutral grey, sleek rectangular form three-seat

[] provide modern and comfortable seating options. The

sleek black, dark grey, brown, rectangular coffee table

[] and

sleek black, dark grey, brown coffee table

[] in minimalist style serve as focal points and functional pieces for gatherings. The

light grey and dark grey (two-tone), shell-like backrest, smooth armchair

[] and

light grey and dark grey (two-tone), smooth armchair

[] add additional seating, complemented by the

rich walnut brown top and contrasting light grey base side table

[] for convenience. Suspended above, the

gradient of grey to bronze hues, cylindrical with abstract cityscape cutouts pendant lamp

[] offers a decorative element with its unique Chinoiserie design. The room's setup is ideal for hosting guests or enjoying quiet evenings, with thoughtful placement of each piece to enhance the living experience." + # } + # ] + # }, + for conv in sources[0]["conversations"]: + for old_id, new_id in old_id_to_new_id_mapping.items(): + conv["value"] = re.sub(rf"<{old_id}>", f"<{new_id}>", conv["value"]) + + # if in generate mode, shave off the last conversation if it is from the assistant + if self.mode == "generate" and sources[0]["conversations"][-1]["from"] == "gpt": + sources[0]["conversations"] = sources[0]["conversations"][:-1] + + # 2. Tokenize the conversation, and add object context to the tokenized conversation + sources = preprocess_multimodal( + copy.deepcopy([e["conversations"] for e in sources]), + is_multimodal=True, + mm_use_im_start_end=False, + ) + data_dict = preprocess_llama_2_obj_identifier( + sources=sources, + tokenizer=self.tokenizer, + obj_dict=result_obj_dict, + obj_context_feature_type=self.obj_context_feature_type, + mode=self.mode, + ) + + # if in generate mode, add the obj context and bbox label to the data_dict + # so that we can use them later to compute the metrics + if self.mode == "generate": + data_dict["obj_context"] = result_obj_dict + if "bbox" in self.list_data_dict[i]: + data_dict["bbox_label"] = self.list_data_dict[i]["bbox"] + # full_info_dict is the full information of this data sample + # {'id': 'scene0643_00$desk-0@0', 'scene_id': 'scene0643_00', 'conversations': [{...}], + # 'referred_object_id': '0', 'referred_object_text': 'desk', + # 'grounded_object_reference': 'a brown wooden office desk on the left to the gray shelf.', + # 'bbox': [0.3769365990161897, -0.06906220592784873, -0.020513275327205656, 1.1370925301275254, 1.5355764355778696, 0.8130822017173767] + # } + data_dict["full_info_dict"] = self.list_data_dict[i] + + return data_dict + + +@dataclass +class DataCollatorForObjIdentifierDataset(object): + """Collate examples for supervised fine-tuning.""" + + def __init__(self, tokenizer: transformers.PreTrainedTokenizer, **kwargs): + self.tokenizer = tokenizer + + def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: + input_ids, labels = tuple( + [instance[key] for instance in instances] for key in ("input_ids", "labels") + ) + input_ids = torch.nn.utils.rnn.pad_sequence( + input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id + ) + labels = torch.nn.utils.rnn.pad_sequence( + labels, batch_first=True, padding_value=IGNORE_INDEX + ) + input_ids = input_ids[:, : self.tokenizer.model_max_length] + labels = labels[:, : self.tokenizer.model_max_length] + batch = dict( + input_ids=input_ids, + labels=labels, + attention_mask=input_ids.ne(self.tokenizer.pad_token_id), + ) + + return batch + + +@dataclass +class DataCollatorForBatchDecodingObjIdentifierDataset(object): + """Collate examples for batch decoding.""" + + def __init__(self, tokenizer: transformers.PreTrainedTokenizer, **kwargs): + self.tokenizer = tokenizer + + def pad_sequence(self, input_ids, batch_first, padding_value): + if self.tokenizer.padding_side == "right": + input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] + input_ids = torch.nn.utils.rnn.pad_sequence( + input_ids, batch_first=batch_first, padding_value=padding_value + ) + if self.tokenizer.padding_side == "right": + input_ids = torch.flip(input_ids, [1]) + return input_ids + + def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: + input_ids = [instance["input_ids"] for instance in instances] + input_ids = self.pad_sequence( + input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id + ) + + batch = dict(input_ids=input_ids) + if "bbox_label" in instances[0].keys(): + batch["bbox_label"] = [instance["bbox_label"] for instance in instances] + + if "obj_context" in instances[0].keys(): + batch["obj_context"] = [instance["obj_context"] for instance in instances] + + if "full_info_dict" in instances[0].keys(): + batch["full_info_dict"] = [instance["full_info_dict"] for instance in instances] + + return batch + + +# test the dataset +if __name__ == "__main__": + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained( + "/data/jianingy/3d-llama/checkpoints/llava-llama-2-7b-chat-lightning-preview", + use_fast=False, + ) + tokenizer.pad_token = tokenizer.unk_token + conversation_lib.default_conversation = conversation_lib.conv_templates["llava_llama_2"] + + dataset = ObjIdentifierDataset( + tokenizer, + data_path="/home/jianingy/research/LLaVA-original/llava/dataset/3dfront/grounded_scene_description_gpt_format.json", + scene_to_obj_mapping="/home/jianingy/research/LLaVA-original/llava/dataset/3dfront/compressed_organized_data.json", + ) + print(len(dataset)) + print(dataset[0]) diff --git a/llava/mm_utils.py b/llava/mm_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..41c5b70d7d20fb6b039409d9cd097a41e5da9f67 --- /dev/null +++ b/llava/mm_utils.py @@ -0,0 +1,145 @@ +from PIL import Image +from io import BytesIO +import base64 + +import torch +from transformers import StoppingCriteria +from llava.constants import IMAGE_TOKEN_INDEX +import numpy as np + +from llava.model.data_3d_util import ( + compute_max_extent_and_centroid, + unit_cube_normalization_in_place, +) + + +def prepare_3d_input_minkowski(pcd_file: str, input_color: bool, voxelizer): + pcd_data_np = np.load(pcd_file) # (num_points, 6) + locs_in = pcd_data_np[:, 3:] + rgb = np.rint(pcd_data_np[:, :3] * 255).astype(int) + # common trick to change the range of [-1, 1] + feats_in = rgb / 127.5 - 1 + # keep this operation if we have other data format + feats_in = (feats_in + 1.0) * 127.5 + labels_in = torch.ones(locs_in.shape[0]).int() + + locs, feats, labels, inds_reconstruct = voxelizer.voxelize(locs_in, feats_in, labels_in) + coords = torch.from_numpy(locs).int() + coords = torch.cat((torch.zeros(coords.shape[0], 1, dtype=torch.int), coords), dim=1) + + if input_color: + feats = torch.from_numpy(feats).float() / 127.5 - 1.0 + else: + feats = torch.ones(coords.shape[0], 3) + + return coords, feats, inds_reconstruct + + +def prepare_3d_input( + pcd_file: str, + max_num_points: int, + is_normalize_points_to_unit_cube: bool, + mm_vision_tower: str, +) -> torch.Tensor: + # TODO: add support for MinkNet + if mm_vision_tower == "pointcloud-perceiver": + pcd_data = np.load(pcd_file) # (num_points, 768) + # note that the convention of the last 3 dimension of any npy flle is x, y, z + pcd_data_xyz = pcd_data[:, -3:] + max_extent, centroid = compute_max_extent_and_centroid(pcd_data_xyz, epsilon=1e-4) + if is_normalize_points_to_unit_cube: + unit_cube_normalization_in_place(pcd_data_xyz, max_extent, centroid) + pcd_data = torch.from_numpy(pcd_data).float() + pcd_attention_mask = torch.ones(pcd_data.shape[0], dtype=pcd_data.dtype) + + # truncate or pad to NUM_POINTS datapoints along dim 0 + if pcd_data.shape[0] > max_num_points: + pcd_data = pcd_data[:max_num_points, :] + pcd_attention_mask = pcd_attention_mask[:max_num_points] + elif pcd_data.shape[0] < max_num_points: + padding = torch.zeros( + ( + max_num_points - pcd_data.shape[0], + pcd_data.shape[1], + ) + ) + pcd_data = torch.cat((pcd_data, padding), dim=0) + + # extend the attention mask with zeros for the padding points + attention_mask_padding = torch.zeros(padding.shape[0], dtype=torch.bool) + pcd_attention_mask = torch.cat((pcd_attention_mask, attention_mask_padding)) + + # output shape: (num_points, 768 + 1) where the last dimension is the attention mask + output_tensor = torch.cat((pcd_data, pcd_attention_mask.unsqueeze(1)), dim=1) + return output_tensor.unsqueeze(0) # add batch dimension + + +def load_image_from_base64(image): + return Image.open(BytesIO(base64.b64decode(image))) + + +def process_images(images, image_processor, model_cfg): + return image_processor(images, return_tensors="pt")["pixel_values"] + + +def tokenizer_image_token( + prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None +): + prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split("")] + + def insert_separator(X, sep): + return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1] + + input_ids = [] + offset = 0 + if ( + len(prompt_chunks) > 0 + and len(prompt_chunks[0]) > 0 + and prompt_chunks[0][0] == tokenizer.bos_token_id + ): + offset = 1 + input_ids.append(prompt_chunks[0][0]) + + for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)): + input_ids.extend(x[offset:]) + + if return_tensors is not None: + if return_tensors == "pt": + return torch.tensor(input_ids, dtype=torch.long) + raise ValueError(f"Unsupported tensor type: {return_tensors}") + return input_ids + + +def get_model_name_from_path(model_path): + model_path = model_path.strip("/") + model_paths = model_path.split("/") + if model_paths[-1].startswith("checkpoint-"): + return model_paths[-2] + "_" + model_paths[-1] + else: + return model_paths[-1] + + +class KeywordsStoppingCriteria(StoppingCriteria): + def __init__(self, keywords, tokenizer, input_ids): + self.keywords = keywords + self.keyword_ids = [] + for keyword in keywords: + cur_keyword_ids = tokenizer(keyword).input_ids + if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id: + cur_keyword_ids = cur_keyword_ids[1:] + self.keyword_ids.append(torch.tensor(cur_keyword_ids)) + self.tokenizer = tokenizer + self.start_len = input_ids.shape[1] + + def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: + assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)" # TODO + offset = min(output_ids.shape[1] - self.start_len, 3) + self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids] + for keyword_id in self.keyword_ids: + if output_ids[0, -keyword_id.shape[0] :] == keyword_id: + return True + outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0] + for keyword in self.keywords: + if keyword in outputs: + return True + return False diff --git a/llava/model/__init__.py b/llava/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2137cedf47934e093b22feab4530c93bc0e50392 --- /dev/null +++ b/llava/model/__init__.py @@ -0,0 +1,3 @@ +from .language_model.llava_llama import LlavaLlamaForCausalLM, LlavaConfig + +# from .language_model.llava_mpt import LlavaMPTForCausalLM, LlavaMPTConfig diff --git 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mode 100644 index 0000000000000000000000000000000000000000..e0e1c8f254e524a266fbd08339b7c5a6465bf26f Binary files /dev/null and b/llava/model/__pycache__/utils.cpython-310.pyc differ diff --git a/llava/model/apply_delta.py b/llava/model/apply_delta.py new file mode 100644 index 0000000000000000000000000000000000000000..666dd9691bde7d54ddf2871e311d6f621e29f099 --- /dev/null +++ b/llava/model/apply_delta.py @@ -0,0 +1,48 @@ +""" +Usage: +python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta +""" +import argparse + +import torch +from tqdm import tqdm +from transformers import AutoTokenizer, AutoModelForCausalLM +from llava import LlavaLlamaForCausalLM + + +def apply_delta(base_model_path, target_model_path, delta_path): + print("Loading base model") + base = AutoModelForCausalLM.from_pretrained( + base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) + + print("Loading delta") + delta = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) + delta_tokenizer = AutoTokenizer.from_pretrained(delta_path) + + print("Applying delta") + for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"): + if name not in base.state_dict(): + assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model' + continue + if param.data.shape == base.state_dict()[name].shape: + param.data += base.state_dict()[name] + else: + assert name in ['model.embed_tokens.weight', 'lm_head.weight'], \ + f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}' + bparam = base.state_dict()[name] + param.data[:bparam.shape[0], :bparam.shape[1]] += bparam + + print("Saving target model") + delta.save_pretrained(target_model_path) + delta_tokenizer.save_pretrained(target_model_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--base-model-path", type=str, required=True) + parser.add_argument("--target-model-path", type=str, required=True) + parser.add_argument("--delta-path", type=str, required=True) + + args = parser.parse_args() + + apply_delta(args.base_model_path, args.target_model_path, args.delta_path) diff --git a/llava/model/bbox_head.py b/llava/model/bbox_head.py new file mode 100644 index 0000000000000000000000000000000000000000..5b105e5ef08cc7c9be14f6e4df7e5a7b00259a10 --- /dev/null +++ b/llava/model/bbox_head.py @@ -0,0 +1,873 @@ +import torch +import torch.nn as nn + +from llava.model.multimodal_encoder.three_detr_model.models.transformer import ( + TransformerEncoder, + TransformerEncoderLayer, + TransformerDecoder, + TransformerDecoderLayer, +) + +from torch.nn.utils.rnn import pad_sequence +from llava.model.multimodal_encoder.mask3d_model.position_embedding import ( + PositionEmbeddingCoordsSine, +) +from torch.nn.init import xavier_uniform_ + + +class SimpleBBoxHead(nn.Module): + def __init__( + self, + lm_feat_dim_in: int, + vision_feat_dim_in: int, + num_vision_feat: int, + dim_feedforward: int = 1024, + ): + super().__init__() + + self.activation = nn.ReLU() + + # # round up to the nearest multiple of 4 + # new_vision_feat_dim_in = (vision_feat_dim_in + 3) // 4 * 4 + # self.vision_projection_mlp = nn.Sequential( + # nn.Linear(vision_feat_dim_in, new_vision_feat_dim_in), + # self.activation, + # nn.Linear(new_vision_feat_dim_in, new_vision_feat_dim_in), + # self.activation, + # nn.Linear(new_vision_feat_dim_in, new_vision_feat_dim_in), + # ) + + # encoder_layer = TransformerEncoderLayer( + # d_model=new_vision_feat_dim_in, + # nhead=4, + # dim_feedforward=dim_feedforward, + # dropout=0.0, + # activation="relu", + # normalize_before=False, + # ) + # self.encoder = TransformerEncoder(encoder_layer=encoder_layer, num_layers=1) + + self.box_mlp = nn.Sequential( + nn.Linear(vision_feat_dim_in * num_vision_feat + lm_feat_dim_in, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, 6), + ) + + def forward( + self, + grd_token_hidden_states_list: list[torch.Tensor], + vision_features_before_mm_projection: torch.Tensor, + ): + """_summary_ + + Args: + grd_token_hidden_states_list (list[torch.Tensor]): each element in this list + contains the hidden states of the ground tokens in one sample, list[[varying N, D]] + vision_features_before_mm_projection (torch.Tensor): [B, num_latents, d_latents] + + Returns: + _type_: _description_ + """ + + # pre_encoder_vision_feat = self.vision_projection_mlp( + # vision_features_before_mm_projection + # ) # (B, num_latents, new_vision_feat_dim_in) + + # # get padding mask by checking where zero vectors are + # src_key_padding_mask = vision_features_before_mm_projection.eq(0).all( + # dim=-1 + # ) # (B, num_latents) + + # # nn.MultiHeadAttention in encoder expects npoints x batch x channel features + # # note that vision_features_before_mm_projection already contains positional embeddings + # _, encoder_output, _ = self.encoder( + # src=pre_encoder_vision_feat.permute(1, 0, 2), + # src_key_padding_mask=src_key_padding_mask, + # ) # [num_latents, B, d_latents] + + # encoder_output = encoder_output.permute(1, 0, 2) # [B, num_latents, d_latents] + + bbox_preds = [] + for batch_idx, grd_token_hidden_states in enumerate(grd_token_hidden_states_list): + # vision_feat = encoder_output[batch_idx].flatten() # (1024 * 96,) + vision_feat = vision_features_before_mm_projection[batch_idx].flatten() # (1024 * 96,) + for i in range(len(grd_token_hidden_states)): + langauge_feat = grd_token_hidden_states[i] # (D,) + concat_feat = torch.cat((vision_feat, langauge_feat), dim=-1) + bbox_pred = self.box_mlp(concat_feat) + bbox_preds.append(bbox_pred) + + bbox_preds = torch.stack(bbox_preds, dim=0) # (N, 6) + return bbox_preds + + +class BBoxHead(nn.Module): + """A simple MLP head for bounding box regression""" + + def __init__(self, lm_feat_dim_in: int, vision_feat_dim_in: int, dim_feedforward: int = 128): + super().__init__() + + encoder_layer = TransformerEncoderLayer( + d_model=vision_feat_dim_in, + nhead=4, + dim_feedforward=dim_feedforward, + dropout=0.0, + activation="relu", + # normalize_before=False, + ) + self.encoder = TransformerEncoder(encoder_layer=encoder_layer, num_layers=4) + + decoder_layer = TransformerDecoderLayer( + d_model=vision_feat_dim_in, + nhead=4, + dim_feedforward=dim_feedforward, + dropout=0.0, + normalize_before=False, + ) + + self.decoder = TransformerDecoder( + decoder_layer=decoder_layer, num_layers=4, return_intermediate=False + ) + + self.language_projection = nn.Sequential( + nn.Linear(lm_feat_dim_in, vision_feat_dim_in), + # nn.ReLU(), + # nn.Linear(256, 256), + # nn.ReLU(), + # nn.Linear(256, vision_feat_dim_in), + ) + + self.activation = nn.GELU() + self.box_mlp = nn.Sequential( + nn.Linear(vision_feat_dim_in, 256), + self.activation, + nn.Linear(256, 256), + self.activation, + nn.Linear(256, 6), + ) + + def forward( + self, + grd_token_hidden_states_list: list[torch.Tensor], + vision_features_before_mm_projection: torch.Tensor, + ): + """_summary_ + + Args: + grd_token_hidden_states_list (list[torch.Tensor]): each element in this list + contains the hidden states of the ground tokens in one sample, list[[varying N, D]] + vision_features_before_mm_projection (torch.Tensor): [B, num_latents, d_latents] + + Returns: + _type_: _description_ + """ + + # nn.MultiHeadAttention in encoder expects npoints x batch x channel features + # note that vision_features_before_mm_projection already contains positional embeddings + _, encoder_output, _ = self.encoder( + src=vision_features_before_mm_projection.permute(1, 0, 2) + ) # [num_latents, B, d_latents] + + # we need to mask out the attention between different ground tokens + # because each ground token is independent of each other + + # Pad the list of hidden states to the longest sample + grd_token_hidden_states_padded = pad_sequence( + grd_token_hidden_states_list, batch_first=True, padding_value=0 + ) # (B, N', D), where N' is the number of ground tokens in the sample with the most ground tokens in the batch + # Create a mask for the padding tokens, True means there will be no attention + tgt_key_padding_mask = grd_token_hidden_states_padded.eq(0).all(dim=-1) # (B, N') + tgt_mask = self.create_diag_mask(grd_token_hidden_states_padded.shape[1]).to( + grd_token_hidden_states_padded.device + ) # (N', N') + + # decoder expects: npoints x batch x channel + language_projected = self.language_projection( + grd_token_hidden_states_padded + ) # (B, N', d_latents) + decoder_output, decoder_attns = self.decoder( + tgt=language_projected.permute(1, 0, 2), # [N', B, d_latents] + memory=encoder_output, + tgt_mask=tgt_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + ) # output, attns, output shape: [N', B, d_latents] + + # predict the bounding boxes + bbox_preds = self.box_mlp(decoder_output) # (N', B, 6) + # flatten the first two dimensions, remove padded locations + bbox_preds = bbox_preds.permute(1, 0, 2) # (B, N', 6) + # discard the padded locations + bbox_preds = bbox_preds[~tgt_key_padding_mask] # (num_boxes_in_batch, 6) + + return bbox_preds + + @staticmethod + def create_diag_mask(size): + # for transformer, a binary ``True`` value indicates that the corresponding position is NOT + # allowed to attend, while a ``False`` value indicates that the position is allowed to attend. + mask = torch.ones(size, size, dtype=torch.bool) + mask.fill_diagonal_(0) + return mask + + +class BBoxHeadForGroundTruthBboxRegressionV2(nn.Module): + """A simple MLP head for bounding box regression""" + + def __init__( + self, + lm_feat_dim_in: int, + vision_feat_dim_in: int, + num_vision_feat: int, + dim_feedforward: int = 1024, + ): + super().__init__() + + # round up to the nearest multiple of 4 + new_vision_feat_dim_in = (vision_feat_dim_in + 3) // 4 * 4 + self.vision_projection_mlp = nn.Sequential( + nn.Linear(vision_feat_dim_in, new_vision_feat_dim_in), + ) + + self.activation = nn.ReLU() + self.language_projection_mlp = nn.Sequential( + nn.Linear(lm_feat_dim_in, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, new_vision_feat_dim_in), + ) + + encoder_layer = TransformerEncoderLayer( + d_model=new_vision_feat_dim_in, + nhead=4, + dim_feedforward=dim_feedforward, + dropout=0.0, + activation="relu", + normalize_before=True, + ) + self.encoder = TransformerEncoder(encoder_layer=encoder_layer, num_layers=4) + + self.activation = nn.ReLU() + self.box_mlp = nn.Sequential( + nn.Linear(new_vision_feat_dim_in, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, 6), + ) + + def forward( + self, + grd_token_hidden_states_list: list[torch.Tensor], + vision_features_before_mm_projection: torch.Tensor, + ): + """_summary_ + + Args: + grd_token_hidden_states_list (list[torch.Tensor]): each element in this list + contains the hidden states of the ground tokens in one sample, list[[varying N, D]] + vision_features_before_mm_projection (torch.Tensor): [B, num_latents, d_latents] + + Returns: + _type_: _description_ + """ + + bbox_preds = [] + for batch_idx, grd_token_hidden_states in enumerate(grd_token_hidden_states_list): + # vision_feat = encoder_output[batch_idx].flatten() # (1024 * 96,) + vision_feat = vision_features_before_mm_projection[batch_idx].unsqueeze( + 0 + ) # (1, num_vision_feat, vision_feat_dim_in) + vision_feat = self.vision_projection_mlp( + vision_feat + ) # (1, num_vision_feat, new_vision_feat_dim_in) + for i in range(len(grd_token_hidden_states)): + language_feat = grd_token_hidden_states[i] # (D,) + + language_feat = self.language_projection_mlp( + language_feat + ) # (new_vision_feat_dim_in,) + + language_feat = language_feat[None, None, :] # (1, 1, new_vision_feat_dim_in) + + language_concat_vision_feat = torch.cat( + (language_feat, vision_feat), dim=1 + ) # (1, 1 + new_vision_feat_dim_in, new_vision_feat_dim_in) + + # # nn.MultiHeadAttention in encoder expects seqlen x batch x channel features + _, encoder_output, _ = self.encoder( + src=language_concat_vision_feat.permute(1, 0, 2) + ) # [1 + new_vision_feat_dim_in, 1, new_vision_feat_dim_in] + + fused_feat = encoder_output[0][0] # (new_vision_feat_dim_in,) + + bbox_pred = self.box_mlp(fused_feat) # (6,) + bbox_preds.append(bbox_pred) + + bbox_preds = torch.stack(bbox_preds, dim=0) # (N, 6) + return bbox_preds + + +class BBoxHeadForGroundTruthBboxRegressionV1(nn.Module): + """A simple MLP head for bounding box regression""" + + def __init__( + self, + lm_feat_dim_in: int, + vision_feat_dim_in: int, + num_vision_feat: int, + dim_feedforward: int = 1024, + ): + super().__init__() + + self.bbox_pos_embedding = PositionEmbeddingCoordsSine( + d_pos=10, + pos_type="fourier", + ) + self.obj_class_embedding = nn.Embedding( + 265, 64 + ) # 265 classes in ScanNet, learnable embedding size 64 + + self.activation = nn.ReLU() + + encoder_layer = TransformerEncoderLayer( + d_model=10 * 2 + 64, + nhead=4, + dim_feedforward=dim_feedforward, + dropout=0.0, + activation="relu", + normalize_before=False, + ) + self.encoder = TransformerEncoder(encoder_layer=encoder_layer, num_layers=2) + + self.box_mlp = nn.Sequential( + nn.Linear((10 * 2 + 64) * num_vision_feat + lm_feat_dim_in, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, 6), + ) + + def forward( + self, + grd_token_hidden_states_list: list[torch.Tensor], + vision_features_before_mm_projection: torch.Tensor, + ): + """_summary_ + + Args: + grd_token_hidden_states_list (list[torch.Tensor]): each element in this list + contains the hidden states of the ground tokens in one sample, list[[varying N, D]] + vision_features_before_mm_projection (torch.Tensor): [B, num_latents, 6 + 1] + + Returns: + _type_: _description_ + """ + # get bbox position embeddings + # xyz is batch x npoints x 3 + min_xyz_pos_embeddings = self.bbox_pos_embedding( + xyz=vision_features_before_mm_projection[:, :, 0:3] + ) # (B, 96, num_latents) + min_xyz_pos_embeddings = min_xyz_pos_embeddings.permute(0, 2, 1) # (B, num_latents, 96) + max_xyz_pos_embeddings = self.bbox_pos_embedding( + xyz=vision_features_before_mm_projection[:, :, 3:6] + ) # (B, 96, num_latents) + max_xyz_pos_embeddings = max_xyz_pos_embeddings.permute(0, 2, 1) # (B, num_latents, 96) + # get the object class embeddings + obj_classes = vision_features_before_mm_projection[:, :, -1].long() + obj_class_embeddings = self.obj_class_embedding(obj_classes) # (B, num_latents, 64) + + vision_feat = torch.concat( + (min_xyz_pos_embeddings, max_xyz_pos_embeddings, obj_class_embeddings), dim=-1 + ) # (B, num_vision_feat, 96*2+64) + + # get padding mask by checking where zero vectors are + src_key_padding_mask = vision_features_before_mm_projection.eq(0).all( + dim=-1 + ) # (B, num_latents) + + # nn.MultiHeadAttention in encoder expects npoints x batch x channel features + # note that vision_features_before_mm_projection already contains positional embeddings + _, encoder_output, _ = self.encoder( + src=vision_feat.permute(1, 0, 2), + src_key_padding_mask=src_key_padding_mask, + ) # [num_latents, B, d_latents] + + encoder_output = encoder_output.permute(1, 0, 2) # [B, num_latents, d_latents] + + bbox_preds = [] + for batch_idx, grd_token_hidden_states in enumerate(grd_token_hidden_states_list): + vision_feat = encoder_output[batch_idx].flatten() # (1024 * 96,) + # vision_feat = vision_features_before_mm_projection[batch_idx].flatten() # (1024 * 96,) + for i in range(len(grd_token_hidden_states)): + langauge_feat = grd_token_hidden_states[i] # (D,) + concat_feat = torch.cat((vision_feat, langauge_feat), dim=-1) + bbox_pred = self.box_mlp(concat_feat) + bbox_preds.append(bbox_pred) + + bbox_preds = torch.stack(bbox_preds, dim=0) # (N, 6) + return bbox_preds + + +class BBoxHeadForGroundTruthBboxSelectionTransformerLateFusion(nn.Module): + """A simple MLP head for bounding box selection, for training on CE loss""" + + def __init__( + self, + lm_feat_dim_in: int, + vision_feat_dim_in: int, + num_vision_feat: int, + dim_feedforward: int = 2048, + ): + super().__init__() + + class_emb_dim = 256 + pos_emb_dim = 16 + self.bbox_pos_embedding = PositionEmbeddingCoordsSine( + d_pos=pos_emb_dim, + pos_type="fourier", + ) + self.obj_class_embedding = nn.Embedding( + 265, class_emb_dim + ) # 265 classes in ScanNet, learnable embedding size 64 + + self.activation = nn.GELU() + self.language_vision_fusion_mlp = nn.Sequential( + nn.Linear(class_emb_dim + pos_emb_dim + lm_feat_dim_in, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + ) + + # encoder_layer = TransformerEncoderLayer( + # d_model=dim_feedforward, + # nhead=8, + # dim_feedforward=dim_feedforward, + # dropout=0.0, + # activation="relu", + # normalize_before=True, + # ) + # self.encoder = TransformerEncoder(encoder_layer=encoder_layer, num_layers=2) + encoder_layer = nn.TransformerEncoderLayer( + d_model=class_emb_dim + pos_emb_dim, + nhead=8, + dim_feedforward=dim_feedforward, + norm_first=True, + ) + self.encoder = nn.TransformerEncoder(encoder_layer=encoder_layer, num_layers=2) + + self.scoring_mlp = nn.Sequential( + nn.Linear(dim_feedforward, 1), + ) + + self._reset_parameters() + + def _reset_parameters(self): + r"""Initiate parameters in the transformer model.""" + + for p in self.parameters(): + if p.dim() > 1: + xavier_uniform_(p) + + def forward( + self, + grd_token_hidden_states_list: list[torch.Tensor], + vision_features_before_mm_projection: torch.Tensor, + ): + """_summary_ + + Args: + grd_token_hidden_states_list (list[torch.Tensor]): each element in this list + contains the hidden states of the ground tokens in one sample, list[[varying N, D]] + vision_features_before_mm_projection (torch.Tensor): [B, num_latents, 6 + 1] + + Returns: + _type_: _description_ + """ + + # get bbox position embeddings + # xyz is batch x npoints x 3 + # get the center of the bbox + bbox_center = ( + vision_features_before_mm_projection[:, :, 0:3] + + vision_features_before_mm_projection[:, :, 3:6] + ) / 2.0 + bbox_pos_embeddings = self.bbox_pos_embedding( + xyz=bbox_center + ) # (B, pos_emb_dim, num_latents) + bbox_pos_embeddings = bbox_pos_embeddings.permute(0, 2, 1) # (B, num_latents, pos_emb_dim) + # get the object class embeddings + obj_classes = vision_features_before_mm_projection[:, :, -1].long() + obj_class_embeddings = self.obj_class_embedding( + obj_classes + ) # (B, num_latents, class_emb_dim) + + vision_feat = torch.concat( + (obj_class_embeddings, bbox_pos_embeddings), dim=-1 + ) # (B, class_emb_dim + pos_emb_dim, class_emb_dim) + + # get padding mask by checking where zero vectors are + src_key_padding_mask = vision_features_before_mm_projection.eq(0).all( + dim=-1 + ) # (B, num_latents) + + bbox_scores = [] + for batch_idx, grd_token_hidden_states in enumerate(grd_token_hidden_states_list): + # vision_feat = vision_features_before_mm_projection[ + # batch_idx + # ] # (num_latents, d_latents) + cur_vision_feat = vision_feat[batch_idx] # (num_latents, class_emb_dim) + cur_vision_feat = cur_vision_feat.unsqueeze(0) # (1, num_latents, class_emb_dim) + cur_vision_feat = cur_vision_feat.permute(1, 0, 2) # (num_latents, 1, class_emb_dim) + # nn.MultiHeadAttention in encoder expects seqlen x batch x channel features + cur_encoder_output = self.encoder( + cur_vision_feat, + src_key_padding_mask=src_key_padding_mask[batch_idx].unsqueeze(0), + ) # [num_latents, 1, class_emb_dim] + cur_encoder_output = cur_encoder_output.squeeze(1) # (num_latents, class_emb_dim) + + for i in range(len(grd_token_hidden_states)): + langauge_feat = grd_token_hidden_states[i] # (lm_feat_dim_in,) + # concat the language feat with each vision feat + langauge_feat_repeat = langauge_feat.repeat( + cur_encoder_output.shape[0], 1 + ) # (num_latents, lm_feat_dim_in) + concat_feat = torch.cat( + (cur_encoder_output, langauge_feat_repeat), dim=-1 + ) # (num_latents, class_emb_dim + lm_feat_dim_in) + fused_feat = self.language_vision_fusion_mlp( + concat_feat + ) # (num_latents, dim_feedforward) + + bbox_score = self.scoring_mlp(fused_feat).squeeze(-1) # (num_latents,) + bbox_scores.append(bbox_score) # (num_latents) + + bbox_scores = torch.stack(bbox_scores, dim=0) # (N, num_latents) + return bbox_scores + + +class BBoxHeadForGroundTruthBboxSelectionTransformerEarlyFusion(nn.Module): + """A simple MLP head for bounding box selection, for training on CE loss""" + + def __init__( + self, + lm_feat_dim_in: int, + vision_feat_dim_in: int, + num_vision_feat: int, + dim_feedforward: int = 2048, + ): + super().__init__() + + class_emb_dim = 256 + pos_emb_dim = 16 + self.bbox_pos_embedding = PositionEmbeddingCoordsSine( + d_pos=pos_emb_dim, + pos_type="fourier", + ) + self.obj_class_embedding = nn.Embedding( + 265, class_emb_dim + ) # 265 classes in ScanNet, learnable embedding size class_emb_dim + + self.activation = nn.GELU() + self.language_projection_mlp = nn.Sequential( + nn.Linear(lm_feat_dim_in, class_emb_dim), + ) + + # encoder_layer = TransformerEncoderLayer( + # d_model=dim_feedforward, + # nhead=8, + # dim_feedforward=dim_feedforward, + # dropout=0.0, + # activation="relu", + # normalize_before=True, + # ) + # self.encoder = TransformerEncoder(encoder_layer=encoder_layer, num_layers=2) + encoder_layer = nn.TransformerEncoderLayer( + d_model=class_emb_dim + pos_emb_dim + class_emb_dim, + nhead=8, + dim_feedforward=dim_feedforward, + norm_first=False, + ) + self.encoder = nn.TransformerEncoder(encoder_layer=encoder_layer, num_layers=2) + + self.scoring_mlp = nn.Sequential( + nn.Linear(class_emb_dim + pos_emb_dim + class_emb_dim, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, 1), + ) + + self._reset_parameters() + + def _reset_parameters(self): + r"""Initiate parameters in the transformer model.""" + + for p in self.parameters(): + if p.dim() > 1: + xavier_uniform_(p) + + def forward( + self, + grd_token_hidden_states_list: list[torch.Tensor], + vision_features_before_mm_projection: torch.Tensor, + ): + """_summary_ + + Args: + grd_token_hidden_states_list (list[torch.Tensor]): each element in this list + contains the hidden states of the ground tokens in one sample, list[[varying N, D]] + vision_features_before_mm_projection (torch.Tensor): [B, num_latents, 6 + 1] + + Returns: + _type_: _description_ + """ + + # get bbox position embeddings + # xyz is batch x npoints x 3 + # get the center of the bbox + bbox_center = ( + vision_features_before_mm_projection[:, :, 0:3] + + vision_features_before_mm_projection[:, :, 3:6] + ) / 2.0 + bbox_pos_embeddings = self.bbox_pos_embedding( + xyz=bbox_center + ) # (B, pos_emb_dim, num_latents) + bbox_pos_embeddings = bbox_pos_embeddings.permute(0, 2, 1) # (B, num_latents, pos_emb_dim) + # get the object class embeddings + obj_classes = vision_features_before_mm_projection[:, :, -1].long() + obj_class_embeddings = self.obj_class_embedding( + obj_classes + ) # (B, num_latents, class_emb_dim) + + vision_feat = torch.concat( + (obj_class_embeddings, bbox_pos_embeddings), dim=-1 + ) # (B, num_latents, class_emb_dim) + + # get padding mask by checking where zero vectors are + src_key_padding_mask = vision_features_before_mm_projection.eq(0).all( + dim=-1 + ) # (B, num_latents) + + bbox_scores = [] + for batch_idx, grd_token_hidden_states in enumerate(grd_token_hidden_states_list): + # vision_feat = vision_features_before_mm_projection[ + # batch_idx + # ] # (num_latents, d_latents) + cur_vision_feat = vision_feat[batch_idx] # (num_latents, class_emb_dim + pos_emb_dim) + + for i in range(len(grd_token_hidden_states)): + langauge_feat = grd_token_hidden_states[i] # (lm_feat_dim_in,) + langauge_feat = self.language_projection_mlp(langauge_feat) # (lm_feat_dim_in,) + langauge_feat_repeat = langauge_feat.repeat( + cur_vision_feat.shape[0], 1 + ) # (num_latents, lm_feat_dim_in) + + concat_feat = torch.cat( + (cur_vision_feat, langauge_feat_repeat), dim=-1 + ) # (num_latents, class_emb_dim + pos_emb_dim + lm_feat_dim_in) + + concat_feat = concat_feat.unsqueeze( + 0 + ) # (1, num_latents, class_emb_dim + pos_emb_dim + lm_feat_dim_in) + concat_feat = concat_feat.permute( + 1, 0, 2 + ) # (num_latents, 1, class_emb_dim + pos_emb_dim + lm_feat_dim_in) + # nn.MultiHeadAttention in encoder expects seqlen x batch x channel features + cur_encoder_output = self.encoder( + concat_feat, + src_key_padding_mask=src_key_padding_mask[batch_idx].unsqueeze(0), + ) # [num_latents, 1, class_emb_dim + pos_emb_dim + lm_feat_dim_in] + cur_encoder_output = cur_encoder_output.squeeze(1) # (num_latents, class_emb_dim) + + bbox_score = self.scoring_mlp(cur_encoder_output).squeeze(-1) # (num_latents,) + bbox_scores.append(bbox_score) # (num_latents) + + bbox_scores = torch.stack(bbox_scores, dim=0) # (N, num_latents) + return bbox_scores + + +class BBoxHeadForGroundTruthBboxSelectionMLPPosEmbAndFusionOneHot(nn.Module): + """A simple MLP head for bounding box selection, for training on CE loss""" + + def __init__( + self, + lm_feat_dim_in: int, + vision_feat_dim_in: int, + num_vision_feat: int, + dim_feedforward: int = 4096, + ): + super().__init__() + + self.class_emb_dim = class_emb_dim = 265 # 265 classes in ScanRefer + pos_emb_dim = 16 + self.bbox_pos_embedding = PositionEmbeddingCoordsSine( + d_pos=pos_emb_dim, + pos_type="fourier", + ) + + self.activation = nn.ReLU() + self.language_vision_fusion_mlp = nn.Sequential( + nn.Linear(class_emb_dim + pos_emb_dim + lm_feat_dim_in, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + ) + + self.scoring_mlp = nn.Sequential( + nn.Linear(dim_feedforward, 1), + ) + + def forward( + self, + grd_token_hidden_states_list: list[torch.Tensor], + vision_features_before_mm_projection: torch.Tensor, + ): + """_summary_ + Args: + grd_token_hidden_states_list (list[torch.Tensor]): each element in this list + contains the hidden states of the ground tokens in one sample, list[[varying N, D]] + vision_features_before_mm_projection (torch.Tensor): [B, num_latents, 6 + 1] + Returns: + _type_: _description_ + """ + + # get bbox position embeddings + # xyz is batch x npoints x 3 + # get the center of the bbox + bbox_center = ( + vision_features_before_mm_projection[:, :, 0:3] + + vision_features_before_mm_projection[:, :, 3:6] + ) / 2.0 + bbox_pos_embeddings = self.bbox_pos_embedding( + xyz=bbox_center + ) # (B, pos_emb_dim, num_latents) + bbox_pos_embeddings = bbox_pos_embeddings.permute(0, 2, 1) # (B, num_latents, pos_emb_dim) + # get the object class embeddings, one-hot encoding of self.class_emb_dim classes + obj_classes = vision_features_before_mm_projection[:, :, -1].long() + obj_class_embeddings = torch.eye( + self.class_emb_dim, + device=vision_features_before_mm_projection.device, + dtype=vision_features_before_mm_projection.dtype, + )[ + obj_classes + ] # (B, num_latents, class_emb_dim) + + vision_feat = torch.concat( + (obj_class_embeddings, bbox_pos_embeddings), dim=-1 + ) # (B, num_latents, class_emb_dim + pos_emb_dim) + + # get padding mask by checking where zero vectors are + src_key_padding_mask = vision_features_before_mm_projection.eq(0).all( + dim=-1 + ) # (B, num_latents) + # for the padded locations, we set the vision_feat to be zero + vision_feat[src_key_padding_mask] = 0 + + bbox_scores = [] + for batch_idx, grd_token_hidden_states in enumerate(grd_token_hidden_states_list): + cur_vision_feat = vision_feat[batch_idx] # (num_latents, d_latents) + for i in range(len(grd_token_hidden_states)): + langauge_feat = grd_token_hidden_states[i] # (lm_feat_dim_in),) + # concat the language feat with each vision feat + langauge_feat_repeat = langauge_feat.repeat( + cur_vision_feat.shape[0], 1 + ) # (num_latents, lm_feat_dim_in) + concat_feat = torch.cat( + (cur_vision_feat, langauge_feat_repeat), dim=-1 + ) # (num_latents, d_latents + lm_feat_dim_in) + fused_feat = self.language_vision_fusion_mlp(concat_feat) + bbox_score = self.scoring_mlp(fused_feat).squeeze(-1) # (num_latents,) + bbox_scores.append(bbox_score) # (num_latents) + + bbox_scores = torch.stack(bbox_scores, dim=0) # (N, num_latents) + return bbox_scores + + +class BBoxHeadForGroundTruthBboxSelectionMLPFusionBoxCoordsAndClassID(nn.Module): + """A simple MLP head for bounding box selection, for training on CE loss""" + + def __init__( + self, + lm_feat_dim_in: int, + vision_feat_dim_in: int, + num_vision_feat: int, + dim_feedforward: int = 1024, + ): + super().__init__() + + self.activation = nn.ReLU() + self.language_vision_fusion_mlp = nn.Sequential( + nn.Linear(vision_feat_dim_in + lm_feat_dim_in, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + self.activation, + nn.Linear(dim_feedforward, dim_feedforward), + ) + + self.scoring_mlp = nn.Sequential( + nn.Linear(dim_feedforward, 1), + ) + + def forward( + self, + grd_token_hidden_states_list: list[torch.Tensor], + vision_features_before_mm_projection: torch.Tensor, + ): + """_summary_ + Args: + grd_token_hidden_states_list (list[torch.Tensor]): each element in this list + contains the hidden states of the ground tokens in one sample, list[[varying N, D]] + vision_features_before_mm_projection (torch.Tensor): [B, num_latents, 6 + 1] + Returns: + _type_: _description_ + """ + + bbox_scores = [] + for batch_idx, grd_token_hidden_states in enumerate(grd_token_hidden_states_list): + vision_feat = vision_features_before_mm_projection[ + batch_idx + ] # (num_latents, d_latents) + for i in range(len(grd_token_hidden_states)): + langauge_feat = grd_token_hidden_states[i] # (lm_feat_dim_in),) + # concat the language feat with each vision feat + langauge_feat_repeat = langauge_feat.repeat( + vision_feat.shape[0], 1 + ) # (num_latents, lm_feat_dim_in) + concat_feat = torch.cat( + (vision_feat, langauge_feat_repeat), dim=-1 + ) # (num_latents, d_latents + lm_feat_dim_in) + fused_feat = self.language_vision_fusion_mlp(concat_feat) + bbox_score = self.scoring_mlp(fused_feat).squeeze(-1) # (num_latents,) + bbox_scores.append(bbox_score) # (num_latents) + + bbox_scores = torch.stack(bbox_scores, dim=0) # (N, num_latents) + return bbox_scores diff --git a/llava/model/builder.py b/llava/model/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..c7711b1aded3b6fb38ffc789eb216e36aac6db7e --- /dev/null +++ b/llava/model/builder.py @@ -0,0 +1,246 @@ +# Copyright 2023 Haotian Liu +# +# 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 + +from transformers import ( + AutoTokenizer, + AutoModelForCausalLM, + AutoConfig, + BitsAndBytesConfig, +) +import torch +from llava.model import * +from llava.constants import ( + DEFAULT_IMAGE_PATCH_TOKEN, + DEFAULT_IM_START_TOKEN, + DEFAULT_IM_END_TOKEN, +) + + +def load_pretrained_model( + model_path, + model_base, + model_name, + load_8bit=False, + load_4bit=False, + device_map="auto", + load_bf16=False, +): + 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", + ) + elif load_bf16: + kwargs["torch_dtype"] = torch.bfloat16 + else: + kwargs["torch_dtype"] = torch.float16 + + if "llava" in model_name.lower(): + # Load LLaVA 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_path, use_fast=False) + print("Loading LLaVA from base model...") + model = LlavaLlamaForCausalLM.from_pretrained( + model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs + ) + if model.get_vision_tower() is not None and not model.get_vision_tower().is_loaded: + model.get_vision_tower().load_model() + + # if the parameters have been ever modified during model training, + # then for some reason, the layer will have the correct shape + # but the weight will have a wrong shape + # the code below fix this weird shape mismatch issue + 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) + ) + + # if the parameters have been ever modified during model training, + # then for some reason, the layer will have the correct shape + # but the weight will have a wrong shape + # the code below fix this weird shape mismatch issue + if model.get_vision_tower() is not None: + mm_projector_in, mm_projector_out = ( + model.model.mm_projector.in_features, + model.model.mm_projector.out_features, + ) + if ( + model.model.mm_projector.weight.shape[1] != mm_projector_in + or model.model.mm_projector.weight.shape[0] != mm_projector_out + ): + model.model.mm_projector.weight = torch.nn.Parameter( + torch.empty( + mm_projector_out, + mm_projector_in, + device=model.device, + dtype=model.dtype, + ) + ) + model.model.mm_projector.bias = torch.nn.Parameter( + torch.empty(mm_projector_out, device=model.device, dtype=model.dtype) + ) + + print("Loading additional LLaVA 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 from base model...") + if "mpt" in model_name.lower(): + if not os.path.isfile(os.path.join(model_path, "configuration_mpt.py")): + shutil.copyfile( + os.path.join(model_base, "configuration_mpt.py"), + os.path.join(model_path, "configuration_mpt.py"), + ) + tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) + cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True) + model = LlavaMPTForCausalLM.from_pretrained( + model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs + ) + else: + tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) + cfg_pretrained = AutoConfig.from_pretrained(model_path) + model = LlavaLlamaForCausalLM.from_pretrained( + model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs + ) + + # load mm projector weights (this include the vision tower weights too) + if model.get_vision_tower() is not None: + if not model.get_vision_tower().is_loaded: + model.get_vision_tower().load_model() + + mm_projector_weights = torch.load( + os.path.join(model_path, "mm_projector.bin"), map_location="cpu" + ) + mm_projector_weights = {k: v for k, v in mm_projector_weights.items()} + model.load_state_dict( + mm_projector_weights, strict=False + ) # for 3d point cloud, this will load the vision tower too. + else: + if "mpt" in model_name.lower(): + tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) + model = LlavaMPTForCausalLM.from_pretrained( + model_path, low_cpu_mem_usage=True, **kwargs + ) + else: + tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) + model = LlavaLlamaForCausalLM.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_path, use_fast=False) + model = AutoModelForCausalLM.from_pretrained( + model_base, + torch_dtype=torch.bfloat16, + 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 BF16...") + model.to(torch.bfloat16) + else: + use_fast = False + if "mpt" in model_name.lower(): + tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) + model = AutoModelForCausalLM.from_pretrained( + model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs + ) + else: + 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(): + 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) + if mm_use_im_patch_token: + tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_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() + if vision_tower is not None: + if not vision_tower.is_loaded: + vision_tower.load_model() + vision_tower.to(device=model.device, dtype=model.dtype) + 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 diff --git a/llava/model/consolidate.py b/llava/model/consolidate.py new file mode 100644 index 0000000000000000000000000000000000000000..653a21c98957d869deb87af4145ae8778a101db8 --- /dev/null +++ b/llava/model/consolidate.py @@ -0,0 +1,29 @@ +""" +Usage: +python3 -m llava.model.consolidate --src ~/model_weights/llava-7b --dst ~/model_weights/llava-7b_consolidate +""" +import argparse + +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM +from llava.model import * +from llava.model.utils import auto_upgrade + + +def consolidate_ckpt(src_path, dst_path): + print("Loading model") + auto_upgrade(src_path) + src_model = AutoModelForCausalLM.from_pretrained(src_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True) + src_tokenizer = AutoTokenizer.from_pretrained(src_path, use_fast=False) + src_model.save_pretrained(dst_path) + src_tokenizer.save_pretrained(dst_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--src", type=str, required=True) + parser.add_argument("--dst", type=str, required=True) + + args = parser.parse_args() + + consolidate_ckpt(args.src, args.dst) diff --git a/llava/model/data_3d_util.py b/llava/model/data_3d_util.py new file mode 100644 index 0000000000000000000000000000000000000000..791a43e8378751b273efde085b5c60fefc9baa21 --- /dev/null +++ b/llava/model/data_3d_util.py @@ -0,0 +1,280 @@ +import numpy as np +import logging +import torch +import re + + +def random_per_point_translation_in_place(pcd_data) -> None: + """ + Jittering the point cloud data by a random value between -0.02 and 0.02 + + Args: + pcd_data: point cloud data in the form x, y, z + + """ + translations = ( + np.random.rand(pcd_data.shape[0], 3) - 0.5 + ) * 0.04 # Random values between -0.02 and 0.02 + pcd_data[:, -3:] += translations + + +def compute_max_extent_and_centroid(pcd_data, epsilon=1e-4) -> tuple[np.ndarray, np.ndarray]: + """_summary_ + + Args: + pcd_data : point cloud data in the form x, y, z + epsilon (float, optional): buffer for the max_extent. Defaults to 1e-4. + + Returns: + max_extent: maximum extent of the point cloud data in terms of the largest dimension + centroid: centroid of the point cloud data + """ + min_vals = pcd_data.min(axis=0) + max_vals = pcd_data.max(axis=0) + centroid = (min_vals + max_vals) / 2 + max_extent = np.max(max_vals - min_vals) + epsilon + return max_extent, centroid + + +def unit_cube_normalization_in_place( + pcd_data, + max_extent, + centroid, +): + """ + Normalized data point in a unit cube between 0 and 1 for each x, y, z in-place + + Args: + pcd_data: point cloud data in the form x, y, z + + """ + # translate the centroid to the origin + pcd_data -= centroid + + # scale the data to fit within [-0.5, 0.5] + pcd_data /= max_extent + + # translate it back to within [0, 1] + pcd_data += 0.5 + + +def point_to_index(point, grid_size): + """ + Maps a point in the unit cube to a unique index based on the grid size. + + Args: + point (tuple): a tuple of (x, y, z) coordinates of the point. Each coordinate should be in [0, 1]. + grid_size (int): the number of divisions along each axis. + + Returns: + int: a unique index for the point. + """ + + xi = int(point[0] * grid_size) + yi = int(point[1] * grid_size) + zi = int(point[2] * grid_size) + + # Ensure that the point is inside the unit cube + if not (0 <= xi < grid_size) or not (0 <= yi < grid_size) or not (0 <= zi < grid_size): + logging.warning( + f"The point is outside the unit cube: point: {point}, grid_index: ({xi}, {yi}, {zi})" + ) + + # Clamp the point to be inside the unit cube + xi = min(max(xi, 0), grid_size - 1) + yi = min(max(yi, 0), grid_size - 1) + zi = min(max(zi, 0), grid_size - 1) + + # Compute the unique voxel ID, row-major order + voxel_id = xi + yi * grid_size + zi * grid_size * grid_size + + return voxel_id + + +def scale_bbox(bbox_str, max_extent, centroid): + """ + Scale the bounding box to be within a unit cube and output numerically tokenized bounding box. + + Args: + bbox_str (str): A string representing a bounding box, in the format "". + max_extent (float): The maximum extent of the bounding box. + centroid (np.array): The centroid of the bounding box. + + Returns: + str: A string representing the scaled bounding box, in the same format as the input. + """ + # Remove < and > from the bounding box string + bbox_str = bbox_str.strip("<>") + + bbox_values = bbox_str.split(",") + # Convert each string to a float and store in a list + bbox_floats = [float(value) for value in bbox_values] + # Convert the list to a numpy array + bbox_array = np.array(bbox_floats) + bbox_array[:3] -= centroid + bbox_array[3:] -= centroid + bbox_array /= max_extent + bbox_array += 0.5 + x_min, y_min, z_min, x_max, y_max, z_max = bbox_array + x_min, y_min, z_min, x_max, y_max, z_max = ( + x_min.item(), + y_min.item(), + z_min.item(), + x_max.item(), + y_max.item(), + z_max.item(), + ) + x_min, y_min, z_min, x_max, y_max, z_max = ( + round(x_min, 3), + round(y_min, 3), + round(z_min, 3), + round(x_max, 3), + round(y_max, 3), + round(z_max, 3), + ) + new_bbox_str = f"< {x_min}, {y_min}, {z_min}, {x_max}, {y_max}, {z_max}>" # adding space after < because tokenizer will not merge < and first digit or negative sign + return new_bbox_str + + +def voxelize_points( + xyz_to_be_voxelized: np.array, + scene_min_xyz: np.array, + scene_max_xyz: np.array, + num_voxels_per_axis: int, +): + """Convert points to voxel indexes + + Args: + xyz_to_be_voxelized (np.array): shape (num_points, 3) + scene_min_xyz (np.array): shape (3,) + scene_max_xyz (np.array): shape (3,) + num_voxels_per_axis (int): number of voxels per axis + + Returns: + voxel_id (np.array): shape (num_points,) + """ + voxel_index = np.floor( + (xyz_to_be_voxelized - scene_min_xyz) + / (scene_max_xyz - scene_min_xyz) + * num_voxels_per_axis + ).astype( + int + ) # range after this overations: [0, num_voxels_per_axis] + voxel_index = np.clip( + voxel_index, 0, num_voxels_per_axis - 1 + ) # clamp range to [0, num_voxels_per_axis - 1] + # calculate index using row-major order + voxel_id = ( + voxel_index[:, 0] + + voxel_index[:, 1] * num_voxels_per_axis + + voxel_index[:, 2] * num_voxels_per_axis * num_voxels_per_axis + ) # range after this operation: [0, num_voxels_per_axis ** 3 - 1] + return voxel_id + + +def process_one_bbox_minkowski_loc_token( + bbox_str, scene_min_xyz, scene_max_xyz, num_voxels_per_axis +): + # Remove < and > from the bounding box string + bbox_str = bbox_str.strip("<>") + bbox_values = bbox_str.split(",") + # Convert each string to a float and store in a list + bbox_floats = [float(value) for value in bbox_values] + # Convert the list to a numpy array + bbox_array = np.array(bbox_floats) # shape: (6,) + bbox_array = bbox_array.reshape(2, 3) # shape: (2, 3) + + voxel_indices = voxelize_points( + bbox_array, scene_min_xyz, scene_max_xyz, num_voxels_per_axis + ) # shape: (2,) + + new_bbox_str = f"" + return new_bbox_str + + +def scale_bbox_special_token(bbox_str, max_extent, centroid, num_grid_cells): + """ + Special token for the bbox. The bbox is scaled to the unit cube and then converted + to a unique index based on the grid size. + + Args: + bbox_str (str): bbox string in the form "" + max_extent (float): max extent of the point cloud data in terms of the largest dimension + centroid (np.array): centroid of the point cloud data + num_grid_cells (int): number of grids along each axis + + Returns: + two unique special tokens for the bbox as string + """ + # Remove < and > from the bounding box string + bbox_str = bbox_str.strip("<>") + + bbox_values = bbox_str.split(",") + # Convert each string to a float and store in a list + bbox_floats = [float(value) for value in bbox_values] + # Convert the list to a numpy array + bbox_floats = np.array(bbox_floats) + bbox_floats[:3] -= centroid + bbox_floats[3:] -= centroid + bbox_floats /= max_extent + bbox_floats += 0.5 + min_point = bbox_floats[:3] + max_point = bbox_floats[3:] + index_min = point_to_index(min_point, num_grid_cells) + index_max = point_to_index(max_point, num_grid_cells) + + new_bbox_str = f"" + return new_bbox_str + + +def rotate_point_cloud_90_degrees(pcd_data): + """ + Rotate the point cloud data by 90 degrees in the x-y plane + + Args: + pcd_data: point cloud data in the form x, y, z + + Returns: + pcd_data: rotated point cloud data in the form x, y, z + """ + # Randomly select among no change, clockwise, and counterclockwise + rotation_choices = ["no change", "clockwise", "counterclockwise"] + direction = np.random.choice(rotation_choices) + + if direction == "clockwise": + rotation_matrix = torch.tensor([[0, 1], [-1, 0]]) + # Apply rotation on x-y plane + pcd_data[:, -3:-1] = torch.matmul(pcd_data[:, -3:-1], rotation_matrix) + elif direction == "counterclockwise": + rotation_matrix = torch.tensor([[0, -1], [1, 0]]) + # Apply rotation on x-y plane + pcd_data[:, -3:-1] = torch.matmul(pcd_data[:, -3:-1], rotation_matrix) + + return pcd_data, direction + + +def adjust_bbox_after_rotation(bbox_str, direction): + """_summary_ + + Args: + bbox_str (_type_): _description_ + direction (_type_): _description_ + + Returns: + _type_: _description_ + """ + + if direction == "no change": + return bbox_str + + values = list(map(float, re.findall(r"[-+]?\d*\.\d+|\d+", bbox_str))) + x_min, y_min, z_min, x_max, y_max, z_max = values + + if direction == "clockwise": + # adding space after < because tokenizer will not merge < and first digit or negative sign + new_bbox_str = f"< {y_min}, {x_min}, {z_min}, {y_max}, {x_max}, {z_max}>" + else: # counterclockwise + # adding space after < because tokenizer will not merge < and first digit or negative sign + new_bbox_str = f"< {x_max}, {y_min}, {z_min}, {x_min}, {y_max}, {z_max}>" + + return new_bbox_str diff --git a/llava/model/iou_3d_loss.py b/llava/model/iou_3d_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..92e6d5be23a389987f99774fb73e748db8140e49 --- /dev/null +++ b/llava/model/iou_3d_loss.py @@ -0,0 +1,143 @@ +import torch +from typing import Tuple + +from torchvision.ops._utils import _upcast_non_float + + +def distance_box_iou_loss_3d( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + reduction: str = "mean", + eps: float = 1e-7, + return_iou: bool = False, +) -> torch.Tensor: + """Gradient-friendly IoU loss with an additional penalty that is non-zero when the + distance between boxes' centers isn't zero. Indeed, for two exactly overlapping + boxes, the distance IoU is the same as the IoU loss. + This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable. + + Both sets of boxes are expected to be in ``(x1, y1, z1, x2, y2, z2)`` format with + ``0 <= x1 < x2``, ``0 <= y1 < y2`` and ``0 <= z1 < z2`` and The two boxes should have the + same dimensions. + + range: [0, 1 + normalized_center_distance_squared] + + Args: + boxes1 (Tensor[N, 6]): first set of boxes + boxes2 (Tensor[N, 6]): second set of boxes + reduction (string, optional): Specifies the reduction to apply to the output: + ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: No reduction will be + applied to the output. ``'mean'``: The output will be averaged. + ``'sum'``: The output will be summed. Default: ``'mean'`` + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + return_iou (bool, optional): If True, also return the IoU loss. Default: False + + Returns: + Tensor: Loss tensor with the reduction option applied. + + Reference: + Zhaohui Zheng et al.: Distance Intersection over Union Loss: + https://arxiv.org/abs/1911.08287 + and + https://pytorch.org/vision/main/_modules/torchvision/ops/diou_loss.html#distance_box_iou_loss + """ + boxes1 = _upcast_non_float(boxes1) + boxes2 = _upcast_non_float(boxes2) + + loss, iou = _diou_iou_loss_3d(boxes1, boxes2, eps) + + # Check reduction option and return loss accordingly + if reduction == "none": + pass + elif reduction == "mean": + loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() + iou = iou.mean() if iou.numel() > 0 else 0.0 * iou.sum() + elif reduction == "sum": + loss = loss.sum() + iou = iou.sum() + else: + raise ValueError( + f"Invalid Value for arg 'reduction': '{reduction} \n Supported reduction modes: 'none', 'mean', 'sum'" + ) + + if return_iou: + return loss, iou + else: + return loss + + +def _diou_iou_loss_3d( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + eps: float = 1e-7, +) -> Tuple[torch.Tensor, torch.Tensor]: + intsct, union = _loss_inter_union_3d(boxes1, boxes2) + iou = intsct / (union + eps) + + # Smallest enclosing box + x1, y1, z1, x2, y2, z2 = boxes1.unbind(dim=-1) + x1g, y1g, z1g, x2g, y2g, z2g = boxes2.unbind(dim=-1) + xc1 = torch.min(x1, x1g) + yc1 = torch.min(y1, y1g) + zc1 = torch.min(z1, z1g) + xc2 = torch.max(x2, x2g) + yc2 = torch.max(y2, y2g) + zc2 = torch.max(z2, z2g) + + # Diagonal distance of the smallest enclosing box squared + diagonal_distance_squared = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + ((zc2 - zc1) ** 2) + eps + + # Centers of boxes + x_c = (x2 + x1) / 2 + y_c = (y2 + y1) / 2 + z_c = (z2 + z1) / 2 + x_cg = (x2g + x1g) / 2 + y_cg = (y2g + y1g) / 2 + z_cg = (z2g + z1g) / 2 + + # Distance between boxes' centers squared + centers_distance_squared = ((x_c - x_cg) ** 2) + ((y_c - y_cg) ** 2) + ((z_c - z_cg) ** 2) + + # The distance IoU is the IoU penalized by a normalized + # distance between boxes' centers squared. + loss = 1 - iou + (centers_distance_squared / diagonal_distance_squared) + return loss, iou + + +def _loss_inter_union_3d( + boxes1: torch.Tensor, + boxes2: torch.Tensor, +) -> Tuple[torch.Tensor, torch.Tensor]: + x1, y1, z1, x2, y2, z2 = boxes1.unbind(dim=-1) + x1g, y1g, z1g, x2g, y2g, z2g = boxes2.unbind(dim=-1) + + # Intersection keypoints + xkis1 = torch.max(x1, x1g) + ykis1 = torch.max(y1, y1g) + zkis1 = torch.max(z1, z1g) + xkis2 = torch.min(x2, x2g) + ykis2 = torch.min(y2, y2g) + zkis2 = torch.min(z2, z2g) + + intsctk = torch.zeros_like(x1) + mask = (xkis2 > xkis1) & (ykis2 > ykis1) & (zkis2 > zkis1) + intsctk[mask] = ( + (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) * (zkis2[mask] - zkis1[mask]) + ) + unionk = (x2 - x1) * (y2 - y1) * (z2 - z1) + (x2g - x1g) * (y2g - y1g) * (z2g - z1g) - intsctk + + return intsctk, unionk + + +if __name__ == "__main__": + # Example usage + boxes1 = torch.tensor([[0.2715, 0.3398, 0.2793, 0.4160, 0.4121, 0.7070]], dtype=torch.float32) + boxes2 = torch.tensor([[0.2402, 0.2246, -0.1689, 0.7422, 0.7109, 0.7734]], dtype=torch.float32) + + loss = distance_box_iou_loss_3d(boxes1, boxes2) + print(loss) + # Output: tensor(0.0000) + # The boxes are exactly overlapping, so the distance IoU is the same as the IoU loss. + # The distance between boxes' centers is zero, so the distance IoU loss is zero. + # The output is a tensor(0.0000) as expected. + # The distance IoU loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable. diff --git a/llava/model/language_model/__pycache__/llava_llama.cpython-310.pyc b/llava/model/language_model/__pycache__/llava_llama.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..261f7ded1c265971bc62a652e68be4d97f89c3a4 Binary files /dev/null and b/llava/model/language_model/__pycache__/llava_llama.cpython-310.pyc differ diff --git a/llava/model/language_model/__pycache__/llava_llama.cpython-38.pyc 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b/llava/model/language_model/llava_llama.py @@ -0,0 +1,407 @@ +# Copyright 2023 Haotian Liu +# +# 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. + + +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn as nn +from torch.nn import CrossEntropyLoss +import time + +from transformers import ( + AutoConfig, + AutoModelForCausalLM, + LlamaConfig, + LlamaModel, + LlamaForCausalLM, +) + +from transformers.modeling_outputs import CausalLMOutputWithPast + +from llava.constants import GROUND_TOKEN, PROFILE_RUNTIME +from llava.model.iou_3d_loss import distance_box_iou_loss_3d +from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM + + + +from transformers.utils import logging + +logger = logging.get_logger("transformers") + + +class LlavaConfig(LlamaConfig): + model_type = "llava" + + def __init__(self, **kwargs): + self.lm_loss_weight = kwargs.pop("lm_loss_weight", 1.0) + self.use_bbox_iou_loss = kwargs.pop("use_bbox_iou_loss", None) + self.bbox_iou_loss_weight = kwargs.pop("bbox_iou_loss_weight", None) + self.use_bbox_mse_loss = kwargs.pop("use_bbox_mse_loss", None) + self.bbox_mse_loss_weight = kwargs.pop("bbox_mse_loss_weight", None) + self.use_bbox_ce_loss = kwargs.pop("use_bbox_ce_loss", None) + self.bbox_ce_loss_weight = kwargs.pop("bbox_ce_loss_weight", None) + self.num_latents = kwargs.pop("num_latents", None) + self.d_latents = kwargs.pop("d_latents", None) + self.vision_tower = kwargs.pop("vision_tower", None) + super().__init__(**kwargs) + + +class LlavaLlamaModel(LlavaMetaModel, LlamaModel): + config_class = LlavaConfig + + def __init__(self, config: LlamaConfig): + super(LlavaLlamaModel, self).__init__(config) + + +@dataclass +class CausalLMOutputWithPastWithBbox(CausalLMOutputWithPast): + total_loss: Optional[torch.FloatTensor] = None + lm_loss: Optional[torch.FloatTensor] = None + bbox_iou_loss: Optional[torch.FloatTensor] = None + bbox_mse_loss: Optional[torch.FloatTensor] = None + bbox_ce_loss: Optional[torch.FloatTensor] = None + bbox_iou: Optional[torch.FloatTensor] = None + + @classmethod + def ignore_keys_for_eval(cls): + # only keep the losses values for validation during training + # keys left: 0: "total_loss", 1: "lm_loss", 2: "bbox_iou_loss", 3: "bbox_mse_loss", 4: "bbox_iou" + return [ + "logits", + "past_key_values", + "hidden_states", + "attentions", + ] + + +class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM): + config_class = LlavaConfig + + def __init__(self, config, **kwargs): + super(LlamaForCausalLM, self).__init__(config) + self.model = LlavaLlamaModel(config) + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # a MLP bbox regression head + if ( + self.config.use_bbox_iou_loss + or self.config.use_bbox_mse_loss + or self.config.use_bbox_ce_loss + ): + if self.config.vision_tower == "bbox-ground-truth": + self.bbox_head = BBoxHeadForGroundTruthBboxSelectionMLPFusionBoxCoordsAndClassID( + lm_feat_dim_in=config.hidden_size, + vision_feat_dim_in=config.d_latents, + num_vision_feat=config.num_latents, + ) + else: + # self.bbox_head = BBoxHead(lm_feat_dim_in=config.hidden_size, vision_feat_dim_in=d_latents) + self.bbox_head = SimpleBBoxHead( + lm_feat_dim_in=config.hidden_size, + vision_feat_dim_in=config.d_latents, + num_vision_feat=config.num_latents, + ) + + # Initialize weights and apply final processing + self.post_init() + + def get_model(self): + return self.model + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + images: Optional[torch.FloatTensor] = None, + return_dict: Optional[bool] = None, + coords_minknet: Optional[torch.Tensor] = None, + feats_minknet: Optional[torch.Tensor] = None, + inds_reconstruct_minknet: Optional[torch.LongTensor] = None, + bbox_labels: Optional[torch.FloatTensor] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + """ + forward function + + Args: + input_ids (torch.LongTensor, optional): Tensor of token indices to be processed by the model. + attention_mask (Optional[torch.Tensor], optional): Mask to avoid performing attention on padding token indices. + past_key_values (Optional[List[torch.FloatTensor]], optional): List of tensors containing past key values for attention layers. + inputs_embeds (Optional[torch.FloatTensor], optional): Inputs embeddings for model processing. + labels (Optional[torch.LongTensor], optional): Labels for supervised training. + use_cache (Optional[bool], optional): Whether to use caching for faster generation of sequences. + output_attentions (Optional[bool], optional): Whether to return attentions weights. + output_hidden_states (Optional[bool], optional): Whether to return hidden states of the model. + images (Optional[torch.FloatTensor], optional): Tensor for image inputs if the model is configured for vision tasks. + return_dict (Optional[bool], optional): Whether to return a `ModelOutput` instead of a plain tuple. + coords_minknet (Optional[torch.Tensor], optional): Coordinates tensor for Minkowski network, detailing spatial structure. (N, 4) + feats_minknet (Optional[torch.Tensor], optional): Features tensor for Minkowski network, specifying attributes at each coordinate. (N, 3) + inds_reconstruct_minknet (Optional[torch.LongTensor], optional): Index tensor to map Minkowski network outputs back to original point cloud. (N_origin,) + bbox_labels (Optional[torch.FloatTensor], optional): Bounding box labels for supervised training. + + Returns: + Union[Tuple, CausalLMOutputWithPast] + """ + ######################################## + # profile the time cost of each forward pass + start_time_foward = time.time() + + # data preprocessing for MinkowskiEngine + if images is None and coords_minknet is not None: + # this is the input to the model for MinkowskiEngine, + # we need to convert it to SparseTensor and put it into `images` + sparse_tensor_minknet_input = SparseTensor( + features=feats_minknet.to(dtype=torch.float32).squeeze(), + coordinates=coords_minknet.squeeze(), + ) # MinkowskiEngine only supports float32, so we need to convert the input to float32, note that .to() is also differentiable + images = sparse_tensor_minknet_input + ######################################## + output_attentions = ( + output_attentions if output_attentions is not None else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + start_time_prepare_inputs_labels_for_multimodal = time.time() + ( + input_ids, + attention_mask, + past_key_values, + inputs_embeds, + labels, + vision_features_before_mm_projection, # (B, num_latents, d_latents) + ) = self.prepare_inputs_labels_for_multimodal( + input_ids, attention_mask, past_key_values, labels, images + ) + if PROFILE_RUNTIME: + logger.info( + f"prepare_inputs_labels_for_multimodal time: {time.time() - start_time_prepare_inputs_labels_for_multimodal}" + ) + + start_time_llm_forward = time.time() + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + lm_outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + if PROFILE_RUNTIME: + logger.info(f"llm_forward time: {time.time() - start_time_llm_forward}") + + hidden_states = lm_outputs[0] + logits = self.lm_head(hidden_states) # (B, L, V) + + # compute bbox loss + start_time_bbox_loss = time.time() + if ( + self.config.use_bbox_iou_loss + or self.config.use_bbox_mse_loss + or self.config.use_bbox_ce_loss + ): + assert labels is not None and bbox_labels is not None + shifted_hidden_states = hidden_states[ + ..., :-1, : + ] # (B, L-1, D), -1 to remove the last token + shifted_labels = labels[..., 1:] # (B, L-1), -1 to remove the first token + grd_token_pos = shifted_labels.eq( + self.config.added_special_token_to_input_id[GROUND_TOKEN] + ) # (B, L-1) # ground token positions + # Get the hidden states of the ground tokens + grd_token_hidden_states_list = ( + [] + ) # each element contain the hidden states of the ground tokens in one sample + for i in range(shifted_hidden_states.size(0)): # iterate over the batch dimension + grd_token_hidden_states_list.append(shifted_hidden_states[i, grd_token_pos[i]]) + + assert sum([e.shape[0] for e in grd_token_hidden_states_list]) == bbox_labels.shape[0] + + bbox_scores = self.bbox_head( + grd_token_hidden_states_list, + vision_features_before_mm_projection, + ) # (N, num_boxes) + + # calculate CE loss for bbox + # first get which box is the ground truth box + bbox_idx = 0 + gt_bbox_idx_list = [] + bbox_pred_list = [] + for i, hidden_states_in_one_sample in enumerate( + grd_token_hidden_states_list + ): # iterate over the batch dimension + for j in range(hidden_states_in_one_sample.shape[0]): + min_diff, min_idx = torch.min( + (images[i, :, 0:6] - bbox_labels[bbox_idx]).norm(dim=-1), dim=0 + ) + gt_bbox_idx_list.append(min_idx) + assert ( + min_diff < 1e-1 + ), f"min_diff: {min_diff}, min_idx: {min_idx}, bbox_labels[bbox_idx]: {bbox_labels[bbox_idx]}, images[i, :, 0:6]: {images[i, :, 0:6]}" + # get the bbox prediction + bbox_pred_idx = bbox_scores[bbox_idx].argmax() # (1,) + bbox_pred = images[i, bbox_pred_idx][0:6] # (6,) + bbox_pred_list.append(bbox_pred) + bbox_idx += 1 + + gt_bbox_idx = torch.stack(gt_bbox_idx_list) # (N,) + bbox_preds = torch.stack(bbox_pred_list) # (N, 6) + + # then calculate CE loss + bbox_ce_loss_fct = nn.CrossEntropyLoss() + bbox_ce_loss = bbox_ce_loss_fct(bbox_scores, gt_bbox_idx) + + bbox_iou_loss_fct = distance_box_iou_loss_3d + bbox_mse_loss_fct = nn.MSELoss() + assert bbox_preds.shape[0] == bbox_labels.shape[0] + _, bbox_iou = bbox_iou_loss_fct(bbox_preds, bbox_labels, return_iou=True) + bbox_iou_loss = 1 - bbox_iou # range: [0, 1] + bbox_mse_loss = bbox_mse_loss_fct(bbox_preds, bbox_labels) + + # log one bbox prediction for debugging + logger.info(f"DEBUG: bbox_labels[0]: {bbox_labels[0]}") + logger.info(f"DEBUG: bbox_preds[0]: {bbox_preds[0]}") + logger.info(f"DEBUG: bbox_iou for batch: {bbox_iou}") + else: + bbox_iou_loss = None + bbox_iou = None + bbox_mse_loss = None + bbox_ce_loss = None + if PROFILE_RUNTIME: + logger.info(f"bbox_loss time: {time.time() - start_time_bbox_loss}") + + # compute language modeling loss + total_loss = None + lm_loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + lm_loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model/pipeline parallelism + shift_labels = shift_labels.to(shift_logits.device) + lm_loss = lm_loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + lm_outputs[1:] + return (lm_loss,) + output if lm_loss is not None else output + + if lm_loss is not None: + total_loss = lm_loss * self.config.lm_loss_weight + if bbox_iou_loss is not None: + total_loss = total_loss + bbox_iou_loss * self.config.bbox_iou_loss_weight + if bbox_mse_loss is not None: + total_loss = total_loss + bbox_mse_loss * self.config.bbox_mse_loss_weight + if bbox_ce_loss is not None: + total_loss = total_loss + bbox_ce_loss * self.config.bbox_ce_loss_weight + + if PROFILE_RUNTIME: + logger.info(f"foward time: {time.time() - start_time_foward}") + + return CausalLMOutputWithPastWithBbox( + total_loss=total_loss, + lm_loss=lm_loss, + bbox_iou_loss=bbox_iou_loss, + bbox_mse_loss=bbox_mse_loss, + bbox_ce_loss=bbox_ce_loss, + bbox_iou=bbox_iou, + logits=logits, + past_key_values=lm_outputs.past_key_values, + hidden_states=lm_outputs.hidden_states, + attentions=lm_outputs.attentions, + ) + + def predict_bboxes( + self, + input_ids: torch.LongTensor, + lm_hidden_states: torch.FloatTensor, + ) -> dict[str, torch.Tensor]: + """ + predict bounding boxes + + Args: + input_ids (torch.LongTensor): tokenized input, shape (B, L) + lm_hidden_states (torch.FloatTensor): hidden states from the language model, shape (B, L, D) + + Returns: + dict[str, torch.Tensor]: dictionary of tensors: + 1. predicted bounding boxes + 2. number of ground phrases + """ + grd_token_pos = input_ids.eq( + self.self.config.added_special_token_to_input_id[GROUND_TOKEN] + ) # (B, L) + num_grd_phrases = grd_token_pos.sum(dim=1).long() # (B,) + grd_token_hs = lm_hidden_states[grd_token_pos] # (N, D), N is the number of ground tokens + + # compute the bbox predictions + bbox_preds = self.bbox_head(grd_token_hs) # (N, 6) + + ret = { + "bbox_preds": bbox_preds, + "num_grd_phrases": num_grd_phrases, + } + return ret + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + **kwargs, + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + "images": kwargs.get("images", None), + "coords_minknet": kwargs.get("coords_minknet", None), + "feats_minknet": kwargs.get("feats_minknet", None), + "inds_reconstruct_minknet": kwargs.get("inds_reconstruct_minknet", None), + } + ) + return model_inputs + + +AutoConfig.register("llava", LlavaConfig) +AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM) diff --git a/llava/model/language_model/llava_mpt.py b/llava/model/language_model/llava_mpt.py new file mode 100644 index 0000000000000000000000000000000000000000..29e97cacefe4aaa639bbd819b7ecaf2d3845e30b --- /dev/null +++ b/llava/model/language_model/llava_mpt.py @@ -0,0 +1,112 @@ +# Copyright 2023 Haotian Liu +# +# 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. + + +from typing import List, Optional, Tuple +import warnings + +import torch +import torch.nn.functional as F +import math + +from transformers import AutoConfig, AutoModelForCausalLM +from transformers.modeling_outputs import CausalLMOutputWithPast + +from .mpt.modeling_mpt import MPTConfig, MPTForCausalLM, MPTModel +from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM + + +class LlavaMPTConfig(MPTConfig): + model_type = "llava_mpt" + + +class LlavaMPTModel(LlavaMetaModel, MPTModel): + config_class = LlavaMPTConfig + + def __init__(self, config: MPTConfig): + config.hidden_size = config.d_model + super(LlavaMPTModel, self).__init__(config) + + def embed_tokens(self, x): + return self.wte(x) + + +class LlavaMPTForCausalLM(MPTForCausalLM, LlavaMetaForCausalLM): + config_class = LlavaMPTConfig + supports_gradient_checkpointing = True + + def __init__(self, config): + super(MPTForCausalLM, self).__init__(config) + + if not config.tie_word_embeddings: + raise ValueError('MPTForCausalLM only supports tied word embeddings') + self.transformer = LlavaMPTModel(config) + self.logit_scale = None + if config.logit_scale is not None: + logit_scale = config.logit_scale + if isinstance(logit_scale, str): + if logit_scale == 'inv_sqrt_d_model': + logit_scale = 1 / math.sqrt(config.d_model) + else: + raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.") + self.logit_scale = logit_scale + + def get_model(self): + return self.transformer + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, LlavaMPTModel): + module.gradient_checkpointing = value + + def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, images=None): + return_dict = return_dict if return_dict is not None else self.config.return_dict + use_cache = use_cache if use_cache is not None else self.config.use_cache + + input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images) + outputs = self.transformer(input_ids=input_ids, inputs_embeds=inputs_embeds, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache) + logits = F.linear(outputs.last_hidden_state, self.transformer.wte.weight) + if self.logit_scale is not None: + if self.logit_scale == 0: + warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.') + logits *= self.logit_scale + loss = None + if labels is not None: + labels = torch.roll(labels, shifts=-1) + labels[:, -1] = -100 + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1)) + return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states) + + def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): + if inputs_embeds is not None: + raise NotImplementedError('inputs_embeds is not implemented for MPT yet') + attention_mask = kwargs['attention_mask'].bool() + if attention_mask[:, -1].sum() != attention_mask.shape[0]: + raise NotImplementedError('MPT does not support generation with right padding.') + if self.transformer.attn_uses_sequence_id and self.training: + sequence_id = torch.zeros_like(input_ids[:1]) + else: + sequence_id = None + if past_key_values is not None: + input_ids = input_ids[:, -1].unsqueeze(-1) + if self.transformer.prefix_lm: + prefix_mask = torch.ones_like(attention_mask) + if kwargs.get('use_cache') == False: + raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.') + else: + prefix_mask = None + return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True), "images": kwargs.get("images", None)} + + +AutoConfig.register("llava_mpt", LlavaMPTConfig) +AutoModelForCausalLM.register(LlavaMPTConfig, LlavaMPTForCausalLM) diff --git a/llava/model/language_model/mpt/__pycache__/adapt_tokenizer.cpython-310.pyc b/llava/model/language_model/mpt/__pycache__/adapt_tokenizer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1039bbc7da3dd320b595697fac56dd8b092454ce Binary files /dev/null and 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b/llava/model/language_model/mpt/adapt_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..e640c157e8f5581953c518df0611a423225ef598 --- /dev/null +++ b/llava/model/language_model/mpt/adapt_tokenizer.py @@ -0,0 +1,41 @@ +from typing import Union +from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast +Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast] +NUM_SENTINEL_TOKENS: int = 100 + +def adapt_tokenizer_for_denoising(tokenizer: Tokenizer): + """Adds sentinel tokens and padding token (if missing). + + Expands the tokenizer vocabulary to include sentinel tokens + used in mixture-of-denoiser tasks as well as a padding token. + + All added tokens are added as special tokens. No tokens are + added if sentinel tokens and padding token already exist. + """ + sentinels_to_add = [f'' for i in range(NUM_SENTINEL_TOKENS)] + tokenizer.add_tokens(sentinels_to_add, special_tokens=True) + if tokenizer.pad_token is None: + tokenizer.add_tokens('', special_tokens=True) + tokenizer.pad_token = '' + assert tokenizer.pad_token_id is not None + sentinels = ''.join([f'' for i in range(NUM_SENTINEL_TOKENS)]) + _sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids + tokenizer.sentinel_token_ids = _sentinel_token_ids + +class AutoTokenizerForMOD(AutoTokenizer): + """AutoTokenizer + Adaptation for MOD. + + A simple wrapper around AutoTokenizer to make instantiating + an MOD-adapted tokenizer a bit easier. + + MOD-adapted tokenizers have sentinel tokens (e.g., ), + a padding token, and a property to get the token ids of the + sentinel tokens. + """ + + @classmethod + def from_pretrained(cls, *args, **kwargs): + """See `AutoTokenizer.from_pretrained` docstring.""" + tokenizer = super().from_pretrained(*args, **kwargs) + adapt_tokenizer_for_denoising(tokenizer) + return tokenizer \ No newline at end of file diff --git a/llava/model/language_model/mpt/attention.py b/llava/model/language_model/mpt/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c758afa34c534a251fe6d164eb81a6f3a3230b --- /dev/null +++ b/llava/model/language_model/mpt/attention.py @@ -0,0 +1,300 @@ +"""Attention layers.""" +import math +import warnings +from typing import Optional +import torch +import torch.nn as nn +from einops import rearrange +from packaging import version +from torch import nn +from .norm import LPLayerNorm + +def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool): + if original_is_causal and num_query_tokens != num_key_tokens: + if num_query_tokens != 1: + raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.') + else: + return False + return original_is_causal + +def scaled_multihead_dot_product_attention(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False): + q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads) + kv_n_heads = 1 if multiquery else n_heads + k = rearrange(key, 'b s (h d) -> b h d s', h=kv_n_heads) + v = rearrange(value, 'b s (h d) -> b h s d', h=kv_n_heads) + if past_key_value is not None: + if len(past_key_value) != 0: + k = torch.cat([past_key_value[0], k], dim=3) + v = torch.cat([past_key_value[1], v], dim=2) + past_key_value = (k, v) + (b, _, s_q, d) = q.shape + s_k = k.size(-1) + if softmax_scale is None: + softmax_scale = 1 / math.sqrt(d) + attn_weight = q.matmul(k) * softmax_scale + if attn_bias is not None: + _s_q = max(0, attn_bias.size(2) - s_q) + _s_k = max(0, attn_bias.size(3) - s_k) + attn_bias = attn_bias[:, :, _s_q:, _s_k:] + if attn_bias.size(-1) != 1 and attn_bias.size(-1) != s_k or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q): + raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.') + attn_weight = attn_weight + attn_bias + min_val = torch.finfo(q.dtype).min + if key_padding_mask is not None: + if attn_bias is not None: + warnings.warn('Propogating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unneccessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.') + attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val) + if is_causal and (not q.size(2) == 1): + s = max(s_q, s_k) + causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16) + causal_mask = causal_mask.tril() + causal_mask = causal_mask.to(torch.bool) + causal_mask = ~causal_mask + causal_mask = causal_mask[-s_q:, -s_k:] + attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val) + attn_weight = torch.softmax(attn_weight, dim=-1) + if dropout_p: + attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True) + out = attn_weight.to(v.dtype).matmul(v) + out = rearrange(out, 'b h s d -> b s (h d)') + if needs_weights: + return (out, attn_weight, past_key_value) + return (out, None, past_key_value) + +def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]): + for tensor in tensors: + if tensor.dtype not in valid_dtypes: + raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.') + if not tensor.is_cuda: + raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).') + +def flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False): + try: + from flash_attn import bert_padding, flash_attn_interface + except: + raise RuntimeError('Please install flash-attn==1.0.3.post0') + check_valid_inputs(query, key, value) + if past_key_value is not None: + if len(past_key_value) != 0: + key = torch.cat([past_key_value[0], key], dim=1) + value = torch.cat([past_key_value[1], value], dim=1) + past_key_value = (key, value) + if attn_bias is not None: + _s_q = max(0, attn_bias.size(2) - query.size(1)) + _s_k = max(0, attn_bias.size(3) - key.size(1)) + attn_bias = attn_bias[:, :, _s_q:, _s_k:] + if attn_bias is not None: + raise NotImplementedError(f'attn_bias not implemented for flash attn.') + (batch_size, seqlen) = query.shape[:2] + if key_padding_mask is None: + key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool) + query_padding_mask = key_padding_mask[:, -query.size(1):] + (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask) + query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads) + (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask) + key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads) + (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask) + value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads) + if multiquery: + key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1)) + value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1)) + dropout_p = dropout_p if training else 0.0 + reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) + output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights) + output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen) + return (output, None, past_key_value) + +def triton_flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False): + try: + from .flash_attn_triton import flash_attn_func + except: + _installed = False + if version.parse(torch.__version__) < version.parse('2.0.0'): + _installed = True + try: + from flash_attn.flash_attn_triton import flash_attn_func + except: + _installed = False + if not _installed: + raise RuntimeError('Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU and `pip install .[gpu]` if installing from llm-foundry source or `pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). Note: (1) requires you have CMake and PyTorch already installed.') + check_valid_inputs(query, key, value) + if past_key_value is not None: + if len(past_key_value) != 0: + key = torch.cat([past_key_value[0], key], dim=1) + value = torch.cat([past_key_value[1], value], dim=1) + past_key_value = (key, value) + if attn_bias is not None: + _s_q = max(0, attn_bias.size(2) - query.size(1)) + _s_k = max(0, attn_bias.size(3) - key.size(1)) + attn_bias = attn_bias[:, :, _s_q:, _s_k:] + if dropout_p: + raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.') + if needs_weights: + raise NotImplementedError(f'attn_impl: triton cannot return attn weights.') + if key_padding_mask is not None: + warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.') + (b_size, s_k) = key_padding_mask.shape[:2] + if attn_bias is None: + attn_bias = query.new_zeros(b_size, 1, 1, s_k) + attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min) + query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads) + key = rearrange(key, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads) + value = rearrange(value, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads) + if multiquery: + key = key.expand(*key.shape[:2], n_heads, key.size(-1)) + value = value.expand(*value.shape[:2], n_heads, value.size(-1)) + reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) + attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale) + output = attn_output.view(*attn_output.shape[:2], -1) + return (output, None, past_key_value) + +class MultiheadAttention(nn.Module): + """Multi-head self attention. + + Using torch or triton attention implemetation enables user to also use + additive bias. + """ + + def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None): + super().__init__() + self.attn_impl = attn_impl + self.clip_qkv = clip_qkv + self.qk_ln = qk_ln + self.d_model = d_model + self.n_heads = n_heads + self.softmax_scale = softmax_scale + if self.softmax_scale is None: + self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads) + self.attn_dropout_p = attn_pdrop + self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device) + fuse_splits = (d_model, 2 * d_model) + self.Wqkv._fused = (0, fuse_splits) + if self.qk_ln: + layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm + self.q_ln = layernorm_class(self.d_model, device=device) + self.k_ln = layernorm_class(self.d_model, device=device) + if self.attn_impl == 'flash': + self.attn_fn = flash_attn_fn + elif self.attn_impl == 'triton': + self.attn_fn = triton_flash_attn_fn + if verbose: + warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.') + elif self.attn_impl == 'torch': + self.attn_fn = scaled_multihead_dot_product_attention + if torch.cuda.is_available() and verbose: + warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.') + else: + raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') + self.out_proj = nn.Linear(self.d_model, self.d_model, device=device) + self.out_proj._is_residual = True + + def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False): + qkv = self.Wqkv(x) + if self.clip_qkv: + qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) + (query, key, value) = qkv.chunk(3, dim=2) + key_padding_mask = attention_mask + if self.qk_ln: + dtype = query.dtype + query = self.q_ln(query).to(dtype) + key = self.k_ln(key).to(dtype) + (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights) + return (self.out_proj(context), attn_weights, past_key_value) + +class MultiQueryAttention(nn.Module): + """Multi-Query self attention. + + Using torch or triton attention implemetation enables user to also use + additive bias. + """ + + def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None): + super().__init__() + self.attn_impl = attn_impl + self.clip_qkv = clip_qkv + self.qk_ln = qk_ln + self.d_model = d_model + self.n_heads = n_heads + self.head_dim = d_model // n_heads + self.softmax_scale = softmax_scale + if self.softmax_scale is None: + self.softmax_scale = 1 / math.sqrt(self.head_dim) + self.attn_dropout_p = attn_pdrop + self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device) + fuse_splits = (d_model, d_model + self.head_dim) + self.Wqkv._fused = (0, fuse_splits) + if self.qk_ln: + layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm + self.q_ln = layernorm_class(d_model, device=device) + self.k_ln = layernorm_class(self.head_dim, device=device) + if self.attn_impl == 'flash': + self.attn_fn = flash_attn_fn + elif self.attn_impl == 'triton': + self.attn_fn = triton_flash_attn_fn + if verbose: + warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.') + elif self.attn_impl == 'torch': + self.attn_fn = scaled_multihead_dot_product_attention + if torch.cuda.is_available() and verbose: + warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.') + else: + raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') + self.out_proj = nn.Linear(self.d_model, self.d_model, device=device) + self.out_proj._is_residual = True + + def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False): + qkv = self.Wqkv(x) + if self.clip_qkv: + qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) + (query, key, value) = qkv.split([self.d_model, self.head_dim, self.head_dim], dim=2) + key_padding_mask = attention_mask + if self.qk_ln: + dtype = query.dtype + query = self.q_ln(query).to(dtype) + key = self.k_ln(key).to(dtype) + (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights, multiquery=True) + return (self.out_proj(context), attn_weights, past_key_value) + +def attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id): + if attn_impl == 'flash': + return None + elif attn_impl in ['torch', 'triton']: + if alibi: + if (prefix_lm or not causal) or use_sequence_id: + return (1, n_heads, seq_len, seq_len) + return (1, n_heads, 1, seq_len) + elif prefix_lm or use_sequence_id: + return (1, 1, seq_len, seq_len) + return None + else: + raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') + +def build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8): + if attn_impl == 'flash': + return None + elif attn_impl in ['torch', 'triton']: + if alibi: + (device, dtype) = (attn_bias.device, attn_bias.dtype) + attn_bias = attn_bias.add(build_alibi_bias(n_heads, seq_len, full=not causal, alibi_bias_max=alibi_bias_max, device=device, dtype=dtype)) + return attn_bias + else: + raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') + +def gen_slopes(n_heads, alibi_bias_max=8, device=None): + _n_heads = 2 ** math.ceil(math.log2(n_heads)) + m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device) + m = m.mul(alibi_bias_max / _n_heads) + slopes = 1.0 / torch.pow(2, m) + if _n_heads != n_heads: + slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads] + return slopes.view(1, n_heads, 1, 1) + +def build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None): + alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len) + if full: + alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1) + alibi_bias = alibi_bias.abs().mul(-1) + slopes = gen_slopes(n_heads, alibi_bias_max, device=device) + alibi_bias = alibi_bias * slopes + return alibi_bias.to(dtype=dtype) +ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention} \ No newline at end of file diff --git a/llava/model/language_model/mpt/blocks.py b/llava/model/language_model/mpt/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..537e7f9190713bd73332aeb80702efa39320ca60 --- /dev/null +++ b/llava/model/language_model/mpt/blocks.py @@ -0,0 +1,41 @@ +"""GPT Blocks used for the GPT Model.""" +from typing import Dict, Optional, Tuple +import torch +import torch.nn as nn +from .attention import ATTN_CLASS_REGISTRY +from .norm import NORM_CLASS_REGISTRY + +class MPTMLP(nn.Module): + + def __init__(self, d_model: int, expansion_ratio: int, device: Optional[str]=None): + super().__init__() + self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device) + self.act = nn.GELU(approximate='none') + self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device) + self.down_proj._is_residual = True + + def forward(self, x): + return self.down_proj(self.act(self.up_proj(x))) + +class MPTBlock(nn.Module): + + def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Dict={'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', verbose: int=0, device: Optional[str]=None, **kwargs): + del kwargs + super().__init__() + norm_class = NORM_CLASS_REGISTRY[norm_type.lower()] + attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']] + self.norm_1 = norm_class(d_model, device=device) + self.attn = attn_class(attn_impl=attn_config['attn_impl'], clip_qkv=attn_config['clip_qkv'], qk_ln=attn_config['qk_ln'], softmax_scale=attn_config['softmax_scale'], attn_pdrop=attn_config['attn_pdrop'], d_model=d_model, n_heads=n_heads, verbose=verbose, device=device) + self.norm_2 = norm_class(d_model, device=device) + self.ffn = MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, device=device) + self.resid_attn_dropout = nn.Dropout(resid_pdrop) + self.resid_ffn_dropout = nn.Dropout(resid_pdrop) + + def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]: + a = self.norm_1(x) + (b, attn_weights, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=is_causal) + x = x + self.resid_attn_dropout(b) + m = self.norm_2(x) + n = self.ffn(m) + x = x + self.resid_ffn_dropout(n) + return (x, attn_weights, past_key_value) \ No newline at end of file diff --git a/llava/model/language_model/mpt/configuration_mpt.py b/llava/model/language_model/mpt/configuration_mpt.py new file mode 100644 index 0000000000000000000000000000000000000000..e9eb6fc59b50654ddbe19ed56ad8c0abd1b8efef --- /dev/null +++ b/llava/model/language_model/mpt/configuration_mpt.py @@ -0,0 +1,118 @@ +"""A HuggingFace-style model configuration.""" +from typing import Dict, Optional, Union +from transformers import PretrainedConfig +attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8} +init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu', 'init_div_is_residual': True, 'emb_init_std': None, 'emb_init_uniform_lim': None, 'init_std': None, 'init_gain': 0.0} + +class MPTConfig(PretrainedConfig): + model_type = 'mpt' + + def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: int=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, verbose: int=0, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, **kwargs): + """The MPT configuration class. + + Args: + d_model (int): The size of the embedding dimension of the model. + n_heads (int): The number of attention heads. + n_layers (int): The number of layers in the model. + expansion_ratio (int): The ratio of the up/down scale in the MLP. + max_seq_len (int): The maximum sequence length of the model. + vocab_size (int): The size of the vocabulary. + resid_pdrop (float): The dropout probability applied to the attention output before combining with residual. + emb_pdrop (float): The dropout probability for the embedding layer. + learned_pos_emb (bool): Whether to use learned positional embeddings + attn_config (Dict): A dictionary used to configure the model's attention module: + attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention + attn_pdrop (float): The dropout probability for the attention layers. + attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'. + qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer. + clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to + this value. + softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None, + use the default scale of ``1/sqrt(d_keys)``. + prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an + extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix + can attend to one another bi-directionally. Tokens outside the prefix use causal attention. + attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id. + When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates + which sub-sequence each token belongs to. + Defaults to ``False`` meaning any provided `sequence_id` will be ignored. + alibi (bool): Whether to use the alibi bias instead of position embeddings. + alibi_bias_max (int): The maximum value of the alibi bias. + init_device (str): The device to use for parameter initialization. + logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value. + no_bias (bool): Whether to use bias in all layers. + verbose (int): The verbosity level. 0 is silent. + embedding_fraction (float): The fraction to scale the gradients of the embedding layer by. + norm_type (str): choose type of norm to use + multiquery_attention (bool): Whether to use multiquery attention implementation. + use_cache (bool): Whether or not the model should return the last key/values attentions + init_config (Dict): A dictionary used to configure the model initialization: + init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_', + 'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or + 'xavier_normal_'. These mimic the parameter initialization methods in PyTorch. + init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True. + emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer. + emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution + used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``. + init_std (float): The standard deviation of the normal distribution used to initialize the model, + if using the baseline_ parameter initialization scheme. + init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes. + fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes. + init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes. + --- + See llmfoundry.models.utils.param_init_fns.py for info on other param init config options + """ + self.d_model = d_model + self.n_heads = n_heads + self.n_layers = n_layers + self.expansion_ratio = expansion_ratio + self.max_seq_len = max_seq_len + self.vocab_size = vocab_size + self.resid_pdrop = resid_pdrop + self.emb_pdrop = emb_pdrop + self.learned_pos_emb = learned_pos_emb + self.attn_config = attn_config + self.init_device = init_device + self.logit_scale = logit_scale + self.no_bias = no_bias + self.verbose = verbose + self.embedding_fraction = embedding_fraction + self.norm_type = norm_type + self.use_cache = use_cache + self.init_config = init_config + if 'name' in kwargs: + del kwargs['name'] + if 'loss_fn' in kwargs: + del kwargs['loss_fn'] + super().__init__(**kwargs) + self._validate_config() + + def _set_config_defaults(self, config, config_defaults): + for (k, v) in config_defaults.items(): + if k not in config: + config[k] = v + return config + + def _validate_config(self): + self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults) + self.init_config = self._set_config_defaults(self.init_config, init_config_defaults) + if self.d_model % self.n_heads != 0: + raise ValueError('d_model must be divisible by n_heads') + if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])): + raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1") + if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']: + raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}") + if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']: + raise NotImplementedError('prefix_lm only implemented with torch and triton attention.') + if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']: + raise NotImplementedError('alibi only implemented with torch and triton attention.') + if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']: + raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.') + if self.embedding_fraction > 1 or self.embedding_fraction <= 0: + raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!') + if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model': + raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.") + if self.init_config.get('name', None) is None: + raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.") + if not self.learned_pos_emb and (not self.attn_config['alibi']): + raise ValueError(f'Positional information must be provided to the model using either learned_pos_emb or alibi.') \ No newline at end of file diff --git a/llava/model/language_model/mpt/custom_embedding.py b/llava/model/language_model/mpt/custom_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..ab357952c397f47898863e8405c4958bb8de82fd --- /dev/null +++ b/llava/model/language_model/mpt/custom_embedding.py @@ -0,0 +1,11 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +class SharedEmbedding(nn.Embedding): + + def forward(self, input: Tensor, unembed: bool=False) -> Tensor: + if unembed: + return F.linear(input, self.weight) + return super().forward(input) \ No newline at end of file diff --git a/llava/model/language_model/mpt/flash_attn_triton.py b/llava/model/language_model/mpt/flash_attn_triton.py new file mode 100644 index 0000000000000000000000000000000000000000..c0a42186d982283add95b63d99fc118e845bcf9d --- /dev/null +++ b/llava/model/language_model/mpt/flash_attn_triton.py @@ -0,0 +1,484 @@ +""" +Copied from https://github.com/HazyResearch/flash-attention/blob/eff9fe6b8076df59d64d7a3f464696738a3c7c24/flash_attn/flash_attn_triton.py +update imports to use 'triton_pre_mlir' + +*Experimental* implementation of FlashAttention in Triton. +Tested with triton==2.0.0.dev20221202. +Triton 2.0 has a new backend (MLIR) but seems like it doesn't yet work for head dimensions +other than 64: +https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207 +We'll update this implementation with the new Triton backend once this is fixed. + +We use the FlashAttention implementation from Phil Tillet a starting point. +https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py + +Changes: +- Implement both causal and non-causal attention. +- Implement both self-attention and cross-attention. +- Support arbitrary seqlens (not just multiples of 128), for both forward and backward. +- Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward. +- Support attention bias. +- Speed up the forward pass a bit, and only store the LSE instead of m and l. +- Make the backward for d=128 much faster by reducing register spilling. +- Optionally parallelize the backward pass across seqlen_k, to deal with the case of +small batch size * nheads. + +Caution: +- This is an *experimental* implementation. The forward pass should be quite robust but +I'm not 100% sure that the backward pass doesn't have race conditions (due to the Triton compiler). +- This implementation has only been tested on A100. +- If you plan to use headdim other than 64 and 128, you should test for race conditions +(due to the Triton compiler), as done in tests/test_flash_attn.py +"test_flash_attn_triton_race_condition". I've tested and fixed many race conditions +for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident +that there are none left for other head dimensions. + +Differences between this Triton version and the CUDA version: +- Triton version doesn't support dropout. +- Triton forward is generally faster than CUDA forward, while Triton backward is +generally slower than CUDA backward. Overall Triton forward + backward is slightly slower +than CUDA forward + backward. +- Triton version doesn't support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor). +- Triton version supports attention bias, while CUDA version doesn't. +""" +import math +import torch +import triton_pre_mlir as triton +import triton_pre_mlir.language as tl + +@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']}) +@triton.jit +def _fwd_kernel(Q, K, V, Bias, Out, Lse, TMP, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_ob, stride_oh, stride_om, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): + start_m = tl.program_id(0) + off_hb = tl.program_id(1) + off_b = off_hb // nheads + off_h = off_hb % nheads + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_n = tl.arange(0, BLOCK_N) + offs_d = tl.arange(0, BLOCK_HEADDIM) + q_ptrs = Q + off_b * stride_qb + off_h * stride_qh + (offs_m[:, None] * stride_qm + offs_d[None, :]) + k_ptrs = K + off_b * stride_kb + off_h * stride_kh + (offs_n[:, None] * stride_kn + offs_d[None, :]) + v_ptrs = V + off_b * stride_vb + off_h * stride_vh + (offs_n[:, None] * stride_vn + offs_d[None, :]) + if BIAS_TYPE == 'vector': + b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n + elif BIAS_TYPE == 'matrix': + b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + (offs_m[:, None] * stride_bm + offs_n[None, :]) + t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m + lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf') + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf') + acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32) + if EVEN_M & EVEN_N: + if EVEN_HEADDIM: + q = tl.load(q_ptrs) + else: + q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + elif EVEN_HEADDIM: + q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0) + else: + q = tl.load(q_ptrs, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0) + end_n = seqlen_k if not IS_CAUSAL else tl.minimum((start_m + 1) * BLOCK_M, seqlen_k) + for start_n in range(0, end_n, BLOCK_N): + start_n = tl.multiple_of(start_n, BLOCK_N) + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + k = tl.load(k_ptrs + start_n * stride_kn) + else: + k = tl.load(k_ptrs + start_n * stride_kn, mask=offs_d[None, :] < headdim, other=0.0) + elif EVEN_HEADDIM: + k = tl.load(k_ptrs + start_n * stride_kn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0) + else: + k = tl.load(k_ptrs + start_n * stride_kn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + qk += tl.dot(q, k, trans_b=True) + if not EVEN_N: + qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0, float('-inf')) + if IS_CAUSAL: + qk += tl.where(offs_m[:, None] >= (start_n + offs_n)[None, :], 0, float('-inf')) + if BIAS_TYPE != 'none': + if BIAS_TYPE == 'vector': + if EVEN_N: + bias = tl.load(b_ptrs + start_n).to(tl.float32) + else: + bias = tl.load(b_ptrs + start_n, mask=start_n + offs_n < seqlen_k, other=0.0).to(tl.float32) + bias = bias[None, :] + elif BIAS_TYPE == 'matrix': + if EVEN_M & EVEN_N: + bias = tl.load(b_ptrs + start_n).to(tl.float32) + else: + bias = tl.load(b_ptrs + start_n, mask=(offs_m[:, None] < seqlen_q) & ((start_n + offs_n)[None, :] < seqlen_k), other=0.0).to(tl.float32) + qk = qk * softmax_scale + bias + m_ij = tl.maximum(tl.max(qk, 1), lse_i) + p = tl.exp(qk - m_ij[:, None]) + else: + m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i) + p = tl.exp(qk * softmax_scale - m_ij[:, None]) + l_ij = tl.sum(p, 1) + acc_o_scale = tl.exp(m_i - m_ij) + tl.store(t_ptrs, acc_o_scale) + acc_o_scale = tl.load(t_ptrs) + acc_o = acc_o * acc_o_scale[:, None] + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + v = tl.load(v_ptrs + start_n * stride_vn) + else: + v = tl.load(v_ptrs + start_n * stride_vn, mask=offs_d[None, :] < headdim, other=0.0) + elif EVEN_HEADDIM: + v = tl.load(v_ptrs + start_n * stride_vn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0) + else: + v = tl.load(v_ptrs + start_n * stride_vn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + p = p.to(v.dtype) + acc_o += tl.dot(p, v) + m_i = m_ij + l_i_new = tl.exp(lse_i - m_ij) + l_ij + lse_i = m_ij + tl.log(l_i_new) + o_scale = tl.exp(m_i - lse_i) + tl.store(t_ptrs, o_scale) + o_scale = tl.load(t_ptrs) + acc_o = acc_o * o_scale[:, None] + start_m = tl.program_id(0) + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m + tl.store(lse_ptrs, lse_i) + offs_d = tl.arange(0, BLOCK_HEADDIM) + out_ptrs = Out + off_b * stride_ob + off_h * stride_oh + (offs_m[:, None] * stride_om + offs_d[None, :]) + if EVEN_M: + if EVEN_HEADDIM: + tl.store(out_ptrs, acc_o) + else: + tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim) + elif EVEN_HEADDIM: + tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q) + else: + tl.store(out_ptrs, acc_o, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim)) + +@triton.jit +def _bwd_preprocess_do_o_dot(Out, DO, Delta, stride_ob, stride_oh, stride_om, stride_dob, stride_doh, stride_dom, nheads, seqlen_q, seqlen_q_rounded, headdim, BLOCK_M: tl.constexpr, BLOCK_HEADDIM: tl.constexpr): + start_m = tl.program_id(0) + off_hb = tl.program_id(1) + off_b = off_hb // nheads + off_h = off_hb % nheads + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_d = tl.arange(0, BLOCK_HEADDIM) + o = tl.load(Out + off_b * stride_ob + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32) + do = tl.load(DO + off_b * stride_dob + off_h * stride_doh + offs_m[:, None] * stride_dom + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32) + delta = tl.sum(o * do, axis=1) + tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta) + +@triton.jit +def _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr): + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + tl.store(dv_ptrs, dv) + tl.store(dk_ptrs, dk) + else: + tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim) + tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim) + elif EVEN_HEADDIM: + tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k) + tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k) + else: + tl.store(dv_ptrs, dv, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim)) + tl.store(dk_ptrs, dk, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim)) + +@triton.jit +def _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD: tl.constexpr, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): + begin_m = 0 if not IS_CAUSAL else start_n * BLOCK_N // BLOCK_M * BLOCK_M + offs_qm = begin_m + tl.arange(0, BLOCK_M) + offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N) + offs_m = tl.arange(0, BLOCK_M) + offs_d = tl.arange(0, BLOCK_HEADDIM) + q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :]) + k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :]) + v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :]) + do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :]) + dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :]) + if BIAS_TYPE == 'vector': + b_ptrs = Bias + offs_n + elif BIAS_TYPE == 'matrix': + b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :]) + dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32) + dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32) + if begin_m >= seqlen_q: + dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :]) + dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :]) + _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM) + return + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + k = tl.load(k_ptrs) + v = tl.load(v_ptrs) + else: + k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + elif EVEN_HEADDIM: + k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0) + v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0) + else: + k = tl.load(k_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + v = tl.load(v_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + num_block_m = tl.cdiv(seqlen_q, BLOCK_M) + for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M): + start_m = tl.multiple_of(start_m, BLOCK_M) + offs_m_curr = start_m + offs_m + if EVEN_M & EVEN_HEADDIM: + q = tl.load(q_ptrs) + elif EVEN_HEADDIM: + q = tl.load(q_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0) + else: + q = tl.load(q_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0) + qk = tl.dot(q, k, trans_b=True) + if not EVEN_N: + qk = tl.where(offs_n[None, :] < seqlen_k, qk, float('-inf')) + if IS_CAUSAL: + qk = tl.where(offs_m_curr[:, None] >= offs_n[None, :], qk, float('-inf')) + if BIAS_TYPE != 'none': + tl.debug_barrier() + if BIAS_TYPE == 'vector': + if EVEN_N: + bias = tl.load(b_ptrs).to(tl.float32) + else: + bias = tl.load(b_ptrs, mask=offs_n < seqlen_k, other=0.0).to(tl.float32) + bias = bias[None, :] + elif BIAS_TYPE == 'matrix': + if EVEN_M & EVEN_N: + bias = tl.load(b_ptrs).to(tl.float32) + else: + bias = tl.load(b_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_n[None, :] < seqlen_k), other=0.0).to(tl.float32) + qk = qk * softmax_scale + bias + if not EVEN_M & EVEN_HEADDIM: + tl.debug_barrier() + lse_i = tl.load(LSE + offs_m_curr) + if BIAS_TYPE == 'none': + p = tl.exp(qk * softmax_scale - lse_i[:, None]) + else: + p = tl.exp(qk - lse_i[:, None]) + if EVEN_M & EVEN_HEADDIM: + do = tl.load(do_ptrs) + else: + do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0) + dv += tl.dot(p.to(do.dtype), do, trans_a=True) + if not EVEN_M & EVEN_HEADDIM: + tl.debug_barrier() + dp = tl.dot(do, v, trans_b=True) + if not EVEN_HEADDIM: + tl.debug_barrier() + Di = tl.load(D + offs_m_curr) + ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype) + dk += tl.dot(ds, q, trans_a=True) + if not EVEN_M & EVEN_HEADDIM: + tl.debug_barrier() + if not ATOMIC_ADD: + if EVEN_M & EVEN_HEADDIM: + dq = tl.load(dq_ptrs, eviction_policy='evict_last') + dq += tl.dot(ds, k) + tl.store(dq_ptrs, dq, eviction_policy='evict_last') + elif EVEN_HEADDIM: + dq = tl.load(dq_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0, eviction_policy='evict_last') + dq += tl.dot(ds, k) + tl.store(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q, eviction_policy='evict_last') + else: + dq = tl.load(dq_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0, eviction_policy='evict_last') + dq += tl.dot(ds, k) + tl.store(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), eviction_policy='evict_last') + else: + dq = tl.dot(ds, k) + if EVEN_M & EVEN_HEADDIM: + tl.atomic_add(dq_ptrs, dq) + elif EVEN_HEADDIM: + tl.atomic_add(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q) + else: + tl.atomic_add(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim)) + dq_ptrs += BLOCK_M * stride_dqm + q_ptrs += BLOCK_M * stride_qm + do_ptrs += BLOCK_M * stride_dom + if BIAS_TYPE == 'matrix': + b_ptrs += BLOCK_M * stride_bm + dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :]) + dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :]) + _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM) + +def init_to_zero(name): + return lambda nargs: nargs[name].zero_() + +@triton.autotune(configs=[triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': False}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': True}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ'))], key=['CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL', 'BLOCK_HEADDIM']) +@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']}) +@triton.jit +def _bwd_kernel(Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_dob, stride_doh, stride_dom, stride_dqb, stride_dqh, stride_dqm, stride_dkb, stride_dkh, stride_dkn, stride_dvb, stride_dvh, stride_dvn, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, SEQUENCE_PARALLEL: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): + off_hb = tl.program_id(1) + off_b = off_hb // nheads + off_h = off_hb % nheads + Q += off_b * stride_qb + off_h * stride_qh + K += off_b * stride_kb + off_h * stride_kh + V += off_b * stride_vb + off_h * stride_vh + DO += off_b * stride_dob + off_h * stride_doh + DQ += off_b * stride_dqb + off_h * stride_dqh + DK += off_b * stride_dkb + off_h * stride_dkh + DV += off_b * stride_dvb + off_h * stride_dvh + if BIAS_TYPE != 'none': + Bias += off_b * stride_bb + off_h * stride_bh + D += off_hb * seqlen_q_rounded + LSE += off_hb * seqlen_q_rounded + if not SEQUENCE_PARALLEL: + num_block_n = tl.cdiv(seqlen_k, BLOCK_N) + for start_n in range(0, num_block_n): + _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=False, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N) + else: + start_n = tl.program_id(0) + _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=True, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N) + +def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None): + (batch, seqlen_q, nheads, d) = q.shape + (_, seqlen_k, _, _) = k.shape + assert k.shape == (batch, seqlen_k, nheads, d) + assert v.shape == (batch, seqlen_k, nheads, d) + assert d <= 128, 'FlashAttention only support head dimensions up to 128' + assert q.dtype == k.dtype == v.dtype, 'All tensors must have the same type' + assert q.dtype in [torch.float16, torch.bfloat16], 'Only support fp16 and bf16' + assert q.is_cuda and k.is_cuda and v.is_cuda + softmax_scale = softmax_scale or 1.0 / math.sqrt(d) + has_bias = bias is not None + bias_type = 'none' + if has_bias: + assert bias.dtype in [q.dtype, torch.float] + assert bias.is_cuda + assert bias.dim() == 4 + if bias.stride(-1) != 1: + bias = bias.contiguous() + if bias.shape[2:] == (1, seqlen_k): + bias_type = 'vector' + elif bias.shape[2:] == (seqlen_q, seqlen_k): + bias_type = 'matrix' + else: + raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)') + bias = bias.expand(batch, nheads, seqlen_q, seqlen_k) + bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0) + seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128 + lse = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32) + tmp = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32) + o = torch.empty_like(q) + BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16) + BLOCK = 128 + num_warps = 4 if d <= 64 else 8 + grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads) + _fwd_kernel[grid](q, k, v, bias, o, lse, tmp, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, o.stride(0), o.stride(2), o.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM, BLOCK_M=BLOCK, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1) + return (o, lse, softmax_scale) + +def _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None): + if do.stride(-1) != 1: + do = do.contiguous() + (batch, seqlen_q, nheads, d) = q.shape + (_, seqlen_k, _, _) = k.shape + assert d <= 128 + seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128 + assert lse.shape == (batch, nheads, seqlen_q_rounded) + assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1 + assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1 + softmax_scale = softmax_scale or 1.0 / math.sqrt(d) + dq_accum = torch.empty_like(q, dtype=torch.float32) + delta = torch.empty_like(lse) + BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16) + grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads) + _bwd_preprocess_do_o_dot[grid](o, do, delta, o.stride(0), o.stride(2), o.stride(1), do.stride(0), do.stride(2), do.stride(1), nheads, seqlen_q, seqlen_q_rounded, d, BLOCK_M=128, BLOCK_HEADDIM=BLOCK_HEADDIM) + has_bias = bias is not None + bias_type = 'none' + if has_bias: + assert bias.dtype in [q.dtype, torch.float] + assert bias.is_cuda + assert bias.dim() == 4 + assert bias.stride(-1) == 1 + if bias.shape[2:] == (1, seqlen_k): + bias_type = 'vector' + elif bias.shape[2:] == (seqlen_q, seqlen_k): + bias_type = 'matrix' + else: + raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)') + bias = bias.expand(batch, nheads, seqlen_q, seqlen_k) + bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0) + grid = lambda META: (triton.cdiv(seqlen_k, META['BLOCK_N']) if META['SEQUENCE_PARALLEL'] else 1, batch * nheads) + _bwd_kernel[grid](q, k, v, bias, do, dq_accum, dk, dv, lse, delta, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, do.stride(0), do.stride(2), do.stride(1), dq_accum.stride(0), dq_accum.stride(2), dq_accum.stride(1), dk.stride(0), dk.stride(2), dk.stride(1), dv.stride(0), dv.stride(2), dv.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM) + dq.copy_(dq_accum) + +class FlashAttnQKVPackedFunc(torch.autograd.Function): + + @staticmethod + def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None): + """ + qkv: (batch, seqlen, 3, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen). + ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen) + """ + if qkv.stride(-1) != 1: + qkv = qkv.contiguous() + (o, lse, ctx.softmax_scale) = _flash_attn_forward(qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], bias=bias, causal=causal, softmax_scale=softmax_scale) + ctx.save_for_backward(qkv, o, lse, bias) + ctx.causal = causal + return o + + @staticmethod + def backward(ctx, do): + (qkv, o, lse, bias) = ctx.saved_tensors + assert not ctx.needs_input_grad[1], 'FlashAttention does not support bias gradient yet' + with torch.inference_mode(): + dqkv = torch.empty_like(qkv) + _flash_attn_backward(do, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], o, lse, dqkv[:, :, 0], dqkv[:, :, 1], dqkv[:, :, 2], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale) + return (dqkv, None, None, None) +flash_attn_qkvpacked_func = FlashAttnQKVPackedFunc.apply + +class FlashAttnKVPackedFunc(torch.autograd.Function): + + @staticmethod + def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None): + """ + q: (batch, seqlen_q, nheads, headdim) + kv: (batch, seqlen_k, 2, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). + ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) + """ + (q, kv) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, kv]] + (o, lse, ctx.softmax_scale) = _flash_attn_forward(q, kv[:, :, 0], kv[:, :, 1], bias=bias, causal=causal, softmax_scale=softmax_scale) + ctx.save_for_backward(q, kv, o, lse, bias) + ctx.causal = causal + return o + + @staticmethod + def backward(ctx, do): + (q, kv, o, lse, bias) = ctx.saved_tensors + if len(ctx.needs_input_grad) >= 3: + assert not ctx.needs_input_grad[2], 'FlashAttention does not support bias gradient yet' + with torch.inference_mode(): + dq = torch.empty_like(q) + dkv = torch.empty_like(kv) + _flash_attn_backward(do, q, kv[:, :, 0], kv[:, :, 1], o, lse, dq, dkv[:, :, 0], dkv[:, :, 1], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale) + return (dq, dkv, None, None, None) +flash_attn_kvpacked_func = FlashAttnKVPackedFunc.apply + +class FlashAttnFunc(torch.autograd.Function): + + @staticmethod + def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None): + """ + q: (batch_size, seqlen_q, nheads, headdim) + k, v: (batch_size, seqlen_k, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). + ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) + """ + (q, k, v) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]] + (o, lse, ctx.softmax_scale) = _flash_attn_forward(q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale) + ctx.save_for_backward(q, k, v, o, lse, bias) + ctx.causal = causal + return o + + @staticmethod + def backward(ctx, do): + (q, k, v, o, lse, bias) = ctx.saved_tensors + assert not ctx.needs_input_grad[3], 'FlashAttention does not support bias gradient yet' + with torch.inference_mode(): + dq = torch.empty_like(q) + dk = torch.empty_like(k) + dv = torch.empty_like(v) + _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale) + return (dq, dk, dv, None, None, None) +flash_attn_func = FlashAttnFunc.apply \ No newline at end of file diff --git a/llava/model/language_model/mpt/hf_prefixlm_converter.py b/llava/model/language_model/mpt/hf_prefixlm_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..8c1a6487202a6400a7116a6bd68b493892ef0d14 --- /dev/null +++ b/llava/model/language_model/mpt/hf_prefixlm_converter.py @@ -0,0 +1,415 @@ +"""Converts Huggingface Causal LM to Prefix LM. + +Conversion does lightweight surgery on a HuggingFace +Causal LM to convert it to a Prefix LM. + +Prefix LMs accepts a `bidirectional_mask` input in `forward` +and treat the input prompt as the prefix in `generate`. +""" +import math +import warnings +from types import MethodType +from typing import Any, Dict, List, Optional, Tuple, Union +import torch +from transformers.models.bloom.modeling_bloom import BaseModelOutputWithPastAndCrossAttentions, BloomForCausalLM, BloomModel, CausalLMOutputWithCrossAttentions, CrossEntropyLoss +from transformers.models.bloom.modeling_bloom import _expand_mask as _expand_mask_bloom +from transformers.models.bloom.modeling_bloom import _make_causal_mask as _make_causal_mask_bloom +from transformers.models.bloom.modeling_bloom import logging +from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel +from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM +from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM +from transformers.models.gptj.modeling_gptj import GPTJForCausalLM +from transformers.models.opt.modeling_opt import OPTForCausalLM +from transformers.models.opt.modeling_opt import _expand_mask as _expand_mask_opt +from transformers.models.opt.modeling_opt import _make_causal_mask as _make_causal_mask_opt +logger = logging.get_logger(__name__) +_SUPPORTED_GPT_MODELS = (GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM) +CAUSAL_GPT_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM] + +def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_TYPES: + """Converts a GPT-style Causal LM to a Prefix LM. + + Supported HuggingFace model classes: + - `GPT2LMHeadModel` + - `GPTNeoForCausalLM` + - `GPTNeoXForCausalLM` + - `GPTJForCausalLM` + + See `convert_hf_causal_lm_to_prefix_lm` for more details. + """ + if hasattr(model, '_prefix_lm_converted'): + return model + assert isinstance(model, _SUPPORTED_GPT_MODELS) + assert model.config.add_cross_attention == False, 'Only supports GPT-style decoder-only models' + + def _get_attn_modules(model: CAUSAL_GPT_TYPES) -> List[torch.nn.Module]: + """Helper that gets a list of the model's attention modules. + + Each module has a `bias` buffer used for causal masking. The Prefix LM + conversion adds logic to dynamically manipulate these biases to support + Prefix LM attention masking. + """ + attn_modules = [] + if isinstance(model, GPTNeoXForCausalLM): + blocks = model.gpt_neox.layers + else: + blocks = model.transformer.h + for block in blocks: + if isinstance(model, GPTNeoForCausalLM): + if block.attn.attention_type != 'global': + continue + attn_module = block.attn.attention + elif isinstance(model, GPTNeoXForCausalLM): + attn_module = block.attention + else: + attn_module = block.attn + attn_modules.append(attn_module) + return attn_modules + setattr(model, '_original_forward', getattr(model, 'forward')) + setattr(model, '_original_generate', getattr(model, 'generate')) + + def forward(self: CAUSAL_GPT_TYPES, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]]=None, attention_mask: Optional[torch.FloatTensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None): + """Wraps original forward to enable PrefixLM attention.""" + + def call_og_forward(): + if isinstance(self, GPTNeoXForCausalLM): + return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) + else: + return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) + if bidirectional_mask is None: + return call_og_forward() + assert isinstance(bidirectional_mask, torch.Tensor) + attn_modules = _get_attn_modules(model) + (b, s) = bidirectional_mask.shape + max_length = attn_modules[0].bias.shape[-1] + if s > max_length: + raise ValueError(f'bidirectional_mask sequence length (={s}) exceeds the ' + f'max length allowed by the model ({max_length}).') + assert s <= max_length + if s < max_length: + pad = torch.zeros((int(b), int(max_length - s)), dtype=bidirectional_mask.dtype, device=bidirectional_mask.device) + bidirectional_mask = torch.cat([bidirectional_mask, pad], dim=1) + bidirectional = bidirectional_mask.unsqueeze(1).unsqueeze(1) + for attn_module in attn_modules: + attn_module.bias.data = torch.logical_or(attn_module.bias.data, bidirectional) + output = call_og_forward() + for attn_module in attn_modules: + attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None] + return output + + def generate(self: CAUSAL_GPT_TYPES, *args: tuple, **kwargs: Dict[str, Any]): + """Wraps original generate to enable PrefixLM attention.""" + attn_modules = _get_attn_modules(model) + for attn_module in attn_modules: + attn_module.bias.data[:] = 1 + output = self._original_generate(*args, **kwargs) + for attn_module in attn_modules: + attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None] + return output + setattr(model, 'forward', MethodType(forward, model)) + setattr(model, 'generate', MethodType(generate, model)) + setattr(model, '_prefix_lm_converted', True) + return model + +def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> BloomForCausalLM: + """Converts a BLOOM Causal LM to a Prefix LM. + + Supported HuggingFace model classes: + - `BloomForCausalLM` + + See `convert_hf_causal_lm_to_prefix_lm` for more details. + """ + if hasattr(model, '_prefix_lm_converted'): + return model + assert isinstance(model, BloomForCausalLM) + assert model.config.add_cross_attention == False, 'Only supports BLOOM decoder-only models' + + def _prepare_attn_mask(self: BloomModel, attention_mask: torch.Tensor, bidirectional_mask: Optional[torch.Tensor], input_shape: Tuple[int, int], past_key_values_length: int) -> torch.BoolTensor: + combined_attention_mask = None + device = attention_mask.device + (_, src_length) = input_shape + if src_length > 1: + combined_attention_mask = _make_causal_mask_bloom(input_shape, device=device, past_key_values_length=past_key_values_length) + if bidirectional_mask is not None: + assert attention_mask.shape == bidirectional_mask.shape + expanded_bidirectional_mask = _expand_mask_bloom(bidirectional_mask, tgt_length=src_length) + combined_attention_mask = torch.logical_and(combined_attention_mask, expanded_bidirectional_mask) + expanded_attn_mask = _expand_mask_bloom(attention_mask, tgt_length=src_length) + combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask + return combined_attention_mask + + def _build_alibi_tensor(self: BloomModel, batch_size: int, query_length: int, key_length: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor: + num_heads = self.config.n_head + closest_power_of_2 = 2 ** math.floor(math.log2(num_heads)) + base = torch.tensor(2 ** (-2 ** (-(math.log2(closest_power_of_2) - 3))), device=device, dtype=torch.float32) + powers = torch.arange(1, 1 + closest_power_of_2, device=device, dtype=torch.int32) + slopes = torch.pow(base, powers) + if closest_power_of_2 != num_heads: + extra_base = torch.tensor(2 ** (-2 ** (-(math.log2(2 * closest_power_of_2) - 3))), device=device, dtype=torch.float32) + num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2) + extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32) + slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) + qa = torch.arange(query_length, device=device, dtype=torch.int32).view(-1, 1) + ka = torch.arange(key_length, device=device, dtype=torch.int32).view(1, -1) + diffs = qa - ka + key_length - query_length + diffs = -diffs.abs() + alibi = slopes.view(1, num_heads, 1, 1) * diffs.view(1, 1, query_length, key_length) + alibi = alibi.expand(batch_size, -1, -1, -1).reshape(-1, query_length, key_length) + return alibi.to(dtype) + KeyValueT = Tuple[torch.Tensor, torch.Tensor] + + def forward(self: BloomModel, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: + if deprecated_arguments.pop('position_ids', False) is not False: + warnings.warn('`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. ' + 'You can safely ignore passing `position_ids`.', FutureWarning) + if len(deprecated_arguments) > 0: + raise ValueError(f'Got unexpected arguments: {deprecated_arguments}') + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if input_ids is not None and inputs_embeds is not None: + raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time') + elif input_ids is not None: + (batch_size, seq_length) = input_ids.shape + elif inputs_embeds is not None: + (batch_size, seq_length, _) = inputs_embeds.shape + else: + raise ValueError('You have to specify either input_ids or inputs_embeds') + if past_key_values is None: + past_key_values = tuple([None] * len(self.h)) + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + hidden_states = self.word_embeddings_layernorm(inputs_embeds) + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + seq_length_with_past = seq_length + past_key_values_length = 0 + if past_key_values[0] is not None: + tmp = past_key_values[0][0] + past_key_values_length = tmp.shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + if attention_mask is None: + attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) + else: + attention_mask = attention_mask.to(hidden_states.device) + alibi = self._build_alibi_tensor(batch_size=batch_size, query_length=seq_length, key_length=seq_length_with_past, dtype=hidden_states.dtype, device=hidden_states.device) + causal_mask = self._prepare_attn_mask(attention_mask, bidirectional_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length) + for (i, (block, layer_past)) in enumerate(zip(self.h, past_key_values)): + if output_hidden_states: + hst = (hidden_states,) + all_hidden_states = all_hidden_states + hst + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning('`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...') + use_cache = False + + def create_custom_forward(module): + + def custom_forward(*inputs): + return module(*inputs, use_cache=use_cache, output_attentions=output_attentions) + return custom_forward + outputs = torch.utils.checkpoint.checkpoint(create_custom_forward(block), hidden_states, alibi, causal_mask, head_mask[i]) + else: + outputs = block(hidden_states, layer_past=layer_past, attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi) + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + if output_attentions: + oa = (outputs[2 if use_cache else 1],) + all_self_attentions = all_self_attentions + oa + hidden_states = self.ln_f(hidden_states) + if output_hidden_states: + hst = (hidden_states,) + all_hidden_states = all_hidden_states + hst + if not return_dict: + return tuple((v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)) + return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions) + setattr(model.transformer, '_prepare_attn_mask', MethodType(_prepare_attn_mask, model.transformer)) + setattr(model.transformer, '_build_alibi_tensor', MethodType(_build_alibi_tensor, model.transformer)) + setattr(model.transformer, 'forward', MethodType(forward, model.transformer)) + KeyValueT = Tuple[torch.Tensor, torch.Tensor] + + def forward(self: BloomForCausalLM, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, labels: Optional[torch.Tensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: + """Replacement forward method for BloomCausalLM.""" + if deprecated_arguments.pop('position_ids', False) is not False: + warnings.warn('`position_ids` have no functionality in BLOOM and will be removed ' + 'in v5.0.0. You can safely ignore passing `position_ids`.', FutureWarning) + if len(deprecated_arguments) > 0: + raise ValueError(f'Got unexpected arguments: {deprecated_arguments}') + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + transformer_outputs = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, bidirectional_mask=bidirectional_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) + hidden_states = transformer_outputs[0] + lm_logits = self.lm_head(hidden_states) + loss = None + if labels is not None: + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + (batch_size, seq_length, vocab_size) = shift_logits.shape + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)) + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return (loss,) + output if loss is not None else output + return CausalLMOutputWithCrossAttentions(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions) + + def prepare_inputs_for_generation(self: BloomForCausalLM, input_ids: torch.LongTensor, past: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> dict: + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + bidirectional_mask = None + if past[0][0].shape[0] == input_ids.shape[0]: + past = self._convert_to_bloom_cache(past) + else: + bidirectional_mask = torch.ones_like(input_ids) + return {'input_ids': input_ids, 'past_key_values': past, 'use_cache': True, 'attention_mask': attention_mask, 'bidirectional_mask': bidirectional_mask} + setattr(model, 'forward', MethodType(forward, model)) + setattr(model, 'prepare_inputs_for_generation', MethodType(prepare_inputs_for_generation, model)) + setattr(model, '_prefix_lm_converted', True) + return model + +def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTForCausalLM: + """Converts an OPT Causal LM to a Prefix LM. + + Supported HuggingFace model classes: + - `OPTForCausalLM` + + See `convert_hf_causal_lm_to_prefix_lm` for more details. + """ + if hasattr(model, '_prefix_lm_converted'): + return model + assert isinstance(model, OPTForCausalLM) + assert model.config.add_cross_attention == False, 'Only supports OPT decoder-only models' + setattr(model, '_original_forward', getattr(model, 'forward')) + setattr(model, '_original_generate', getattr(model, 'generate')) + model.model.decoder.bidirectional_mask = None + + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + combined_attention_mask = None + if input_shape[-1] > 1: + if self.bidirectional_mask == 'g': + (bsz, src_length) = input_shape + combined_attention_mask = torch.zeros((bsz, 1, src_length, src_length + past_key_values_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device) + else: + combined_attention_mask = _make_causal_mask_opt(input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length).to(inputs_embeds.device) + if self.bidirectional_mask is not None: + assert attention_mask.shape == self.bidirectional_mask.shape + expanded_bidirectional_mask = _expand_mask_opt(self.bidirectional_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device) + combined_attention_mask = torch.maximum(expanded_bidirectional_mask, combined_attention_mask) + if attention_mask is not None: + expanded_attn_mask = _expand_mask_opt(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device) + combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + return combined_attention_mask + setattr(model.model.decoder, '_prepare_decoder_attention_mask', MethodType(_prepare_decoder_attention_mask, model.model.decoder)) + + def forward(self: OPTForCausalLM, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.ByteTensor]=None, head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[List[torch.FloatTensor]]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None): + + def call_og_forward(): + return self._original_forward(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) + if bidirectional_mask is None: + return call_og_forward() + self.model.decoder.bidirectional_mask = bidirectional_mask + try: + outputs = call_og_forward() + except: + self.model.decoder.bidirectional_mask = None + raise + self.model.decoder.bidirectional_mask = None + return outputs + + def generate(self: OPTForCausalLM, *args: tuple, **kwargs: Dict[str, Any]): + """Wraps original generate to enable PrefixLM-style attention.""" + self.model.decoder.bidirectional_mask = 'g' + try: + output = self._original_generate(*args, **kwargs) + except: + self.model.decoder.bidirectional_mask = None + raise + self.model.decoder.bidirectional_mask = None + return output + setattr(model, 'forward', MethodType(forward, model)) + setattr(model, 'generate', MethodType(generate, model)) + setattr(model, '_prefix_lm_converted', True) + return model +_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS + (BloomForCausalLM, OPTForCausalLM) +CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM, BloomForCausalLM, OPTForCausalLM] + +def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES: + """Converts a HuggingFace Causal LM to a Prefix LM. + + Supported HuggingFace model classes: + - `GPT2LMHeadModel` + - `GPTNeoForCausalLM` + - `GPTNeoXForCausalLM` + - `GPTJForCausalLM` + - `BloomForCausalLM` + - `OPTForCausalLM` + + Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the + `generate` method and/or select underlying methods depending on the model class. + + These changes preserve the model API, but add a new input to `forward`: "bidirectional_mask". + + Notes on training: + To actually train the converted model as a Prefix LM, training batches will need to indicate + the prefix/target structure by including `bidirectional_mask` as part of the batch inputs. + + **This is not a standard input and requires custom layers either within or after your dataloader.** + + In addition to adding `bidirectional_mask` to the batch, this custom code should modify `labels` + such that `batch['labels'][batch['bidirectional_mask'] == 1] == -100`. + That is, the prefix portion of the sequence should not generate any loss. Loss should only be + generated by the target portion of the sequence. + + Notes on `GPTNeoForCausalLM`: + To simplify the implementation, "global" and "local" attention layers are handled differently. + For "global" layers, we handle conversion as described above. For "local" layers, which use a + causal attention mask within a restricted local window, we do not alter the masking. + + Notes on `forward` method conversion: + After conversion, the `forward` method will handle a new input, `bidirectional_mask`, + which should be a [batch_size, seq_length] byte tensor, where 1 indicates token positions + belonging to the prefix (prefix tokens can attend to one another bidirectionally), and + 0 indicates token positions belonging to the target. + + The new `forward` method will incorporate `bidirectional_mask` (if supplied) into the existing + causal mask, call the original `forward` method, and (if the causal mask is a buffer) reset + the causal masks before returning the result. + + Notes on `generate` method conversion: + After conversion, the `generate` method will have the same signature but will internally + convert all causal masks to be purely bidirectional, call the original `generate` method, and + (where appropriate) reset the causal masks before returning the result. + + This works thanks to the logic of the HuggingFace `generate` API, which first encodes the token + "prompt" passed to `generate` (which is treated as the prefix) and then sequentially generates + each new token. Encodings are cached as generation happens, so all prefix tokens can attend to one + another (as expected in a Prefix LM) and generated tokens can only attend to prefix tokens and + previously-generated tokens (also as expected in a Prefix LM). + + To preserve the API, the original methods are renamed to `_original_forward` and + `_original_generate`, and replaced with new `forward` and `generate` methods that wrap + them, respectively. Although implementation details vary by model class. + """ + if isinstance(model, _SUPPORTED_GPT_MODELS): + return _convert_gpt_causal_lm_to_prefix_lm(model) + elif isinstance(model, BloomForCausalLM): + return _convert_bloom_causal_lm_to_prefix_lm(model) + elif isinstance(model, OPTForCausalLM): + return _convert_opt_causal_lm_to_prefix_lm(model) + else: + raise TypeError(f'Cannot convert model to Prefix LM. ' + f'Model does not belong to set of supported HF models:' + f'\n{_SUPPORTED_HF_MODELS}') + +def add_bidirectional_mask_if_missing(batch: Dict[str, Any]): + """Attempts to add bidirectional_mask to batch if missing. + + Raises: + KeyError if bidirectional_mask is missing and can't be inferred + """ + if 'bidirectional_mask' not in batch: + if batch.get('mode', None) == 'icl_task': + batch['bidirectional_mask'] = batch['attention_mask'].clone() + for (i, continuation_indices) in enumerate(batch['continuation_indices']): + batch['bidirectional_mask'][i, continuation_indices] = 0 + elif 'labels' in batch and 'attention_mask' in batch: + batch['bidirectional_mask'] = torch.logical_and(torch.eq(batch['attention_mask'], 1), torch.eq(batch['labels'], -100)).type_as(batch['attention_mask']) + else: + raise KeyError('No bidirectional_mask in batch and not sure how to construct one.') \ No newline at end of file diff --git a/llava/model/language_model/mpt/meta_init_context.py b/llava/model/language_model/mpt/meta_init_context.py new file mode 100644 index 0000000000000000000000000000000000000000..6cba6fff0fe21fe222c7ab38eae44a9784c0be9c --- /dev/null +++ b/llava/model/language_model/mpt/meta_init_context.py @@ -0,0 +1,94 @@ +from contextlib import contextmanager +import torch +import torch.nn as nn + +@contextmanager +def init_empty_weights(include_buffers: bool=False): + """Meta initialization context manager. + + A context manager under which models are initialized with all parameters + on the meta device, therefore creating an empty model. Useful when just + initializing the model would blow the available RAM. + + Args: + include_buffers (`bool`, *optional*, defaults to `False`): Whether or + not to also put all buffers on the meta device while initializing. + + Example: + ```python + import torch.nn as nn + + # Initialize a model with 100 billions parameters in no time and without using any RAM. + with init_empty_weights(): + tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)]) + ``` + + + + Any model created under this context manager has no weights. As such you can't do something like + `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`]. + + + """ + with init_on_device(torch.device('meta'), include_buffers=include_buffers) as f: + yield f + +@contextmanager +def init_on_device(device: torch.device, include_buffers: bool=False): + """Device initialization context manager. + + A context manager under which models are initialized with all parameters + on the specified device. + + Args: + device (`torch.device`): Device to initialize all parameters on. + include_buffers (`bool`, *optional*, defaults to `False`): Whether or + not to also put all buffers on the meta device while initializing. + + Example: + ```python + import torch.nn as nn + + with init_on_device(device=torch.device("cuda")): + tst = nn.Liner(100, 100) # on `cuda` device + ``` + """ + old_register_parameter = nn.Module.register_parameter + if include_buffers: + old_register_buffer = nn.Module.register_buffer + + def register_empty_parameter(module, name, param): + old_register_parameter(module, name, param) + if param is not None: + param_cls = type(module._parameters[name]) + kwargs = module._parameters[name].__dict__ + module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) + + def register_empty_buffer(module, name, buffer): + old_register_buffer(module, name, buffer) + if buffer is not None: + module._buffers[name] = module._buffers[name].to(device) + if include_buffers: + tensor_constructors_to_patch = {torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ['empty', 'zeros', 'ones', 'full']} + else: + tensor_constructors_to_patch = {} + + def patch_tensor_constructor(fn): + + def wrapper(*args, **kwargs): + kwargs['device'] = device + return fn(*args, **kwargs) + return wrapper + try: + nn.Module.register_parameter = register_empty_parameter + if include_buffers: + nn.Module.register_buffer = register_empty_buffer + for torch_function_name in tensor_constructors_to_patch.keys(): + setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name))) + yield + finally: + nn.Module.register_parameter = old_register_parameter + if include_buffers: + nn.Module.register_buffer = old_register_buffer + for (torch_function_name, old_torch_function) in tensor_constructors_to_patch.items(): + setattr(torch, torch_function_name, old_torch_function) \ No newline at end of file diff --git a/llava/model/language_model/mpt/modeling_mpt.py b/llava/model/language_model/mpt/modeling_mpt.py new file mode 100644 index 0000000000000000000000000000000000000000..13313441b13fc7a66cb65fd21b482a5de982e2c8 --- /dev/null +++ b/llava/model/language_model/mpt/modeling_mpt.py @@ -0,0 +1,331 @@ +"""A simple, flexible implementation of a GPT model. + +Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py +""" +import math +import warnings +from typing import List, Optional, Tuple, Union +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from .attention import attn_bias_shape, build_attn_bias +from .blocks import MPTBlock +from .custom_embedding import SharedEmbedding +from .norm import NORM_CLASS_REGISTRY +from .configuration_mpt import MPTConfig +from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising +from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm +from .meta_init_context import init_empty_weights +from .param_init_fns import MODEL_INIT_REGISTRY, generic_param_init_fn_ +try: + from .flash_attn_triton import flash_attn_func +except: + pass +Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast] + +class MPTPreTrainedModel(PreTrainedModel): + config_class = MPTConfig + base_model_prefix = 'model' + _no_split_modules = ['MPTBlock'] + +class MPTModel(MPTPreTrainedModel): + + def __init__(self, config: MPTConfig): + config._validate_config() + super().__init__(config) + self.attn_impl = config.attn_config['attn_impl'] + self.prefix_lm = config.attn_config['prefix_lm'] + self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id'] + self.alibi = config.attn_config['alibi'] + self.alibi_bias_max = config.attn_config['alibi_bias_max'] + if config.init_device == 'mixed': + if dist.get_local_rank() == 0: + config.init_device = 'cpu' + else: + config.init_device = 'meta' + if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys(): + norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys()) + raise NotImplementedError(f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).') + norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()] + self.embedding_fraction = config.embedding_fraction + self.wte = SharedEmbedding(config.vocab_size, config.d_model, device=config.init_device) + if not self.alibi: + self.wpe = torch.nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device) + self.emb_drop = nn.Dropout(config.emb_pdrop) + self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)]) + self.norm_f = norm_class(config.d_model, device=config.init_device) + if config.init_device != 'meta': + print(f'You are using config.init_device={config.init_device!r}, but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.') + self.apply(self.param_init_fn) + self.is_causal = not self.prefix_lm + self._attn_bias_initialized = False + self.attn_bias = None + self.attn_bias_shape = attn_bias_shape(self.attn_impl, config.n_heads, config.max_seq_len, self.alibi, prefix_lm=self.prefix_lm, causal=self.is_causal, use_sequence_id=self.attn_uses_sequence_id) + if config.no_bias: + for module in self.modules(): + if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter): + if config.verbose: + warnings.warn(f'Removing bias ({module.bias}) from {module}.') + module.register_parameter('bias', None) + if config.verbose and config.verbose > 2: + print(self) + if 'verbose' not in self.config.init_config: + self.config.init_config['verbose'] = self.config.verbose + if self.config.init_config['verbose'] > 1: + init_fn_name = self.config.init_config['name'] + warnings.warn(f'Using {init_fn_name} initialization.') + self.gradient_checkpointing = False + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, value): + self.wte = value + + @torch.no_grad() + def _attn_bias(self, device, dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None): + if not self._attn_bias_initialized: + if self.attn_bias_shape: + self.attn_bias = torch.zeros(self.attn_bias_shape, device=device, dtype=dtype) + self.attn_bias = build_attn_bias(self.attn_impl, self.attn_bias, self.config.n_heads, self.config.max_seq_len, causal=self.is_causal, alibi=self.alibi, alibi_bias_max=self.alibi_bias_max) + self._attn_bias_initialized = True + if self.attn_impl == 'flash': + return (self.attn_bias, attention_mask) + if self.attn_bias is not None: + self.attn_bias = self.attn_bias.to(dtype=dtype, device=device) + attn_bias = self.attn_bias + if self.prefix_lm: + assert isinstance(attn_bias, torch.Tensor) + assert isinstance(prefix_mask, torch.Tensor) + attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask) + if self.attn_uses_sequence_id and sequence_id is not None: + assert isinstance(attn_bias, torch.Tensor) + attn_bias = self._apply_sequence_id(attn_bias, sequence_id) + if attention_mask is not None: + s_k = attention_mask.shape[-1] + if attn_bias is None: + attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype) + else: + _s_k = max(0, attn_bias.size(-1) - s_k) + attn_bias = attn_bias[:, :, :, _s_k:] + if prefix_mask is not None and attention_mask.shape != prefix_mask.shape: + raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.') + min_val = torch.finfo(attn_bias.dtype).min + attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val) + return (attn_bias, None) + + def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor): + (s_k, s_q) = attn_bias.shape[-2:] + if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len: + raise ValueError('attn_bias does not match the expected shape. ' + f'The last two dimensions should both be {self.config.max_length} ' + f'but are {s_k} and {s_q}.') + seq_len = prefix_mask.shape[-1] + if seq_len > self.config.max_seq_len: + raise ValueError(f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}') + attn_bias = attn_bias[..., :seq_len, :seq_len] + causal = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)).view(1, 1, seq_len, seq_len) + prefix = prefix_mask.view(-1, 1, 1, seq_len) + cannot_attend = ~torch.logical_or(causal, prefix.bool()) + min_val = torch.finfo(attn_bias.dtype).min + attn_bias = attn_bias.masked_fill(cannot_attend, min_val) + return attn_bias + + def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor): + seq_len = sequence_id.shape[-1] + if seq_len > self.config.max_seq_len: + raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}') + attn_bias = attn_bias[..., :seq_len, :seq_len] + cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1) + min_val = torch.finfo(attn_bias.dtype).min + attn_bias = attn_bias.masked_fill(cannot_attend, min_val) + return attn_bias + + def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None): + return_dict = return_dict if return_dict is not None else self.config.return_dict + use_cache = use_cache if use_cache is not None else self.config.use_cache + if attention_mask is not None: + attention_mask = attention_mask.bool() + if prefix_mask is not None: + prefix_mask = prefix_mask.bool() + if not return_dict: + raise NotImplementedError('return_dict False is not implemented yet for MPT') + if output_attentions: + if self.attn_impl != 'torch': + raise NotImplementedError('output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.') + if attention_mask is not None and attention_mask[:, 0].sum() != attention_mask.shape[0] and self.training: + raise NotImplementedError('MPT does not support training with left padding.') + if self.prefix_lm and prefix_mask is None: + raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.') + if self.training: + if self.attn_uses_sequence_id and sequence_id is None: + raise ValueError('sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True ' + 'and the model is in train mode.') + elif self.attn_uses_sequence_id is False and sequence_id is not None: + warnings.warn('MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. ' + 'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.') + if input_ids is not None: + S = input_ids.size(1) + assert S <= self.config.max_seq_len, f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}' + tok_emb = self.wte(input_ids) + else: + assert inputs_embeds is not None + assert self.alibi, 'inputs_embeds is not implemented for MPT unless for alibi.' + S = inputs_embeds.size(1) + tok_emb = inputs_embeds + if self.alibi: + x = tok_emb + else: + past_position = 0 + if past_key_values is not None: + if len(past_key_values) != self.config.n_layers: + raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).') + past_position = past_key_values[0][0].size(1) + if self.attn_impl == 'torch': + past_position = past_key_values[0][0].size(3) + if S + past_position > self.config.max_seq_len: + raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length {S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.') + pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0) + if attention_mask is not None: + pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0) + pos_emb = self.wpe(pos) + x = tok_emb + pos_emb + if self.embedding_fraction == 1: + x = self.emb_drop(x) + else: + x_shrunk = x * self.embedding_fraction + x.detach() * (1 - self.embedding_fraction) + assert isinstance(self.emb_drop, nn.Module) + x = self.emb_drop(x_shrunk) + (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id) + if use_cache and past_key_values is None: + past_key_values = [() for _ in range(self.config.n_layers)] + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + for (b_idx, block) in enumerate(self.blocks): + if output_hidden_states: + assert all_hidden_states is not None + all_hidden_states = all_hidden_states + (x,) + past_key_value = past_key_values[b_idx] if past_key_values is not None else None + if self.gradient_checkpointing and self.training: + (x, attn_weights, past_key_value) = torch.utils.checkpoint.checkpoint(block, x, past_key_value, attn_bias, attention_mask, self.is_causal) + else: + (x, attn_weights, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal) + if past_key_values is not None: + past_key_values[b_idx] = past_key_value + if output_attentions: + assert all_self_attns is not None + all_self_attns = all_self_attns + (attn_weights,) + x = self.norm_f(x) + if output_hidden_states: + assert all_hidden_states is not None + all_hidden_states = all_hidden_states + (x,) + return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns) + + def param_init_fn(self, module): + init_fn_name = self.config.init_config['name'] + MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config) + + def fsdp_wrap_fn(self, module): + return isinstance(module, MPTBlock) + + def activation_checkpointing_fn(self, module): + return isinstance(module, MPTBlock) + +class MPTForCausalLM(MPTPreTrainedModel): + + def __init__(self, config: MPTConfig): + super().__init__(config) + if not config.tie_word_embeddings: + raise ValueError('MPTForCausalLM only supports tied word embeddings') + print(f'Instantiating an MPTForCausalLM model from {__file__}') + self.transformer = MPTModel(config) + for child in self.transformer.children(): + if isinstance(child, torch.nn.ModuleList): + continue + if isinstance(child, torch.nn.Module): + child._fsdp_wrap = True + self.logit_scale = None + if config.logit_scale is not None: + logit_scale = config.logit_scale + if isinstance(logit_scale, str): + if logit_scale == 'inv_sqrt_d_model': + logit_scale = 1 / math.sqrt(config.d_model) + else: + raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.") + self.logit_scale = logit_scale + + def get_input_embeddings(self): + return self.transformer.wte + + def set_input_embeddings(self, value): + self.transformer.wte = value + + def get_output_embeddings(self): + return self.transformer.wte + + def set_output_embeddings(self, new_embeddings): + self.transformer.wte = new_embeddings + + def set_decoder(self, decoder): + self.transformer = decoder + + def get_decoder(self): + return self.transformer + + def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None): + return_dict = return_dict if return_dict is not None else self.config.return_dict + use_cache = use_cache if use_cache is not None else self.config.use_cache + if inputs_embeds is not None: + raise NotImplementedError('inputs_embeds has to be None (for hf/peft support).') + outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache) + logits = self.transformer.wte(outputs.last_hidden_state.to(self.transformer.wte.weight.device), True) + if self.logit_scale is not None: + if self.logit_scale == 0: + warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.') + logits *= self.logit_scale + loss = None + if labels is not None: + labels = torch.roll(labels, shifts=-1) + labels[:, -1] = -100 + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1)) + return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions) + + def param_init_fn(self, module): + init_fn_name = self.config.init_config['name'] + MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config) + + def fsdp_wrap_fn(self, module): + return isinstance(module, MPTBlock) + + def activation_checkpointing_fn(self, module): + return isinstance(module, MPTBlock) + + def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): + if inputs_embeds is not None: + raise NotImplementedError('inputs_embeds is not implemented for MPT yet') + attention_mask = kwargs['attention_mask'].bool() + if attention_mask[:, -1].sum() != attention_mask.shape[0]: + raise NotImplementedError('MPT does not support generation with right padding.') + if self.transformer.attn_uses_sequence_id and self.training: + sequence_id = torch.zeros_like(input_ids[:1]) + else: + sequence_id = None + if past_key_values is not None: + input_ids = input_ids[:, -1].unsqueeze(-1) + if self.transformer.prefix_lm: + prefix_mask = torch.ones_like(attention_mask) + if kwargs.get('use_cache') == False: + raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.') + else: + prefix_mask = None + return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)} + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + """Used by HuggingFace generate when using beam search with kv-caching. + + See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133 + for an example in transformers. + """ + reordered_past = [] + for layer_past in past_key_values: + reordered_past += [tuple((past_state.index_select(0, beam_idx) for past_state in layer_past))] + return reordered_past \ No newline at end of file diff --git a/llava/model/language_model/mpt/norm.py b/llava/model/language_model/mpt/norm.py new file mode 100644 index 0000000000000000000000000000000000000000..067b6140fae546e5cb49cb2b1e4e6af660ced60d --- /dev/null +++ b/llava/model/language_model/mpt/norm.py @@ -0,0 +1,56 @@ +import torch + +def _cast_if_autocast_enabled(tensor): + if torch.is_autocast_enabled(): + if tensor.device.type == 'cuda': + dtype = torch.get_autocast_gpu_dtype() + elif tensor.device.type == 'cpu': + dtype = torch.get_autocast_cpu_dtype() + else: + raise NotImplementedError() + return tensor.to(dtype=dtype) + return tensor + +class LPLayerNorm(torch.nn.LayerNorm): + + def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None): + super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype) + + def forward(self, x): + module_device = x.device + downcast_x = _cast_if_autocast_enabled(x) + downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight + downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias + with torch.autocast(enabled=False, device_type=module_device.type): + return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps) + +def rms_norm(x, weight=None, eps=1e-05): + output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps) + if weight is not None: + return output * weight + return output + +class RMSNorm(torch.nn.Module): + + def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None): + super().__init__() + self.eps = eps + if weight: + self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device)) + else: + self.register_parameter('weight', None) + + def forward(self, x): + return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype) + +class LPRMSNorm(RMSNorm): + + def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None): + super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device) + + def forward(self, x): + downcast_x = _cast_if_autocast_enabled(x) + downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight + with torch.autocast(enabled=False, device_type=x.device.type): + return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype) +NORM_CLASS_REGISTRY = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm} \ No newline at end of file diff --git a/llava/model/language_model/mpt/param_init_fns.py b/llava/model/language_model/mpt/param_init_fns.py new file mode 100644 index 0000000000000000000000000000000000000000..418b83ca2363288046f4b48b1d706c5607341fb5 --- /dev/null +++ b/llava/model/language_model/mpt/param_init_fns.py @@ -0,0 +1,181 @@ +import math +import warnings +from collections.abc import Sequence +from functools import partial +from typing import Optional, Tuple, Union +import torch +from torch import nn +from .norm import NORM_CLASS_REGISTRY + +def torch_default_param_init_fn_(module: nn.Module, verbose: int=0, **kwargs): + del kwargs + if verbose > 1: + warnings.warn(f"Initializing network using module's reset_parameters attribute") + if hasattr(module, 'reset_parameters'): + module.reset_parameters() + +def fused_init_helper_(module: nn.Module, init_fn_): + _fused = getattr(module, '_fused', None) + if _fused is None: + raise RuntimeError(f'Internal logic error') + (dim, splits) = _fused + splits = (0, *splits, module.weight.size(dim)) + for (s, e) in zip(splits[:-1], splits[1:]): + slice_indices = [slice(None)] * module.weight.ndim + slice_indices[dim] = slice(s, e) + init_fn_(module.weight[slice_indices]) + +def generic_param_init_fn_(module: nn.Module, init_fn_, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + del kwargs + if verbose > 1: + warnings.warn(f'If model has bias parameters they are initialized to 0.') + init_div_is_residual = init_div_is_residual + if init_div_is_residual is False: + div_is_residual = 1.0 + elif init_div_is_residual is True: + div_is_residual = math.sqrt(2 * n_layers) + elif isinstance(init_div_is_residual, float) or isinstance(init_div_is_residual, int): + div_is_residual = init_div_is_residual + elif isinstance(init_div_is_residual, str) and init_div_is_residual.isnumeric(): + div_is_residual = float(init_div_is_residual) + else: + div_is_residual = 1.0 + raise ValueError(f'Expected init_div_is_residual to be boolean or numeric, got {init_div_is_residual}') + if init_div_is_residual is not False: + if verbose > 1: + warnings.warn(f'Initializing _is_residual layers then dividing them by {div_is_residual:.3f}. ' + f'Set `init_div_is_residual: false` in init config to disable this.') + if isinstance(module, nn.Linear): + if hasattr(module, '_fused'): + fused_init_helper_(module, init_fn_) + else: + init_fn_(module.weight) + if module.bias is not None: + torch.nn.init.zeros_(module.bias) + if init_div_is_residual is not False and getattr(module, '_is_residual', False): + with torch.no_grad(): + module.weight.div_(div_is_residual) + elif isinstance(module, nn.Embedding): + if emb_init_std is not None: + std = emb_init_std + if std == 0: + warnings.warn(f'Embedding layer initialized to 0.') + emb_init_fn_ = partial(torch.nn.init.normal_, mean=0.0, std=std) + if verbose > 1: + warnings.warn(f'Embedding layer initialized using normal distribution with mean=0 and std={std!r}.') + elif emb_init_uniform_lim is not None: + lim = emb_init_uniform_lim + if isinstance(lim, Sequence): + if len(lim) > 2: + raise ValueError(f'Uniform init requires a min and a max limit. User input: {lim}.') + if lim[0] == lim[1]: + warnings.warn(f'Embedding layer initialized to {lim[0]}.') + else: + if lim == 0: + warnings.warn(f'Embedding layer initialized to 0.') + lim = [-lim, lim] + (a, b) = lim + emb_init_fn_ = partial(torch.nn.init.uniform_, a=a, b=b) + if verbose > 1: + warnings.warn(f'Embedding layer initialized using uniform distribution in range {lim}.') + else: + emb_init_fn_ = init_fn_ + emb_init_fn_(module.weight) + elif isinstance(module, tuple(set(NORM_CLASS_REGISTRY.values()))): + if verbose > 1: + warnings.warn(f'Norm weights are set to 1. If norm layer has a bias it is initialized to 0.') + if hasattr(module, 'weight') and module.weight is not None: + torch.nn.init.ones_(module.weight) + if hasattr(module, 'bias') and module.bias is not None: + torch.nn.init.zeros_(module.bias) + elif isinstance(module, nn.MultiheadAttention): + if module._qkv_same_embed_dim: + assert module.in_proj_weight is not None + assert module.q_proj_weight is None and module.k_proj_weight is None and (module.v_proj_weight is None) + assert d_model is not None + _d = d_model + splits = (0, _d, 2 * _d, 3 * _d) + for (s, e) in zip(splits[:-1], splits[1:]): + init_fn_(module.in_proj_weight[s:e]) + else: + assert module.q_proj_weight is not None and module.k_proj_weight is not None and (module.v_proj_weight is not None) + assert module.in_proj_weight is None + init_fn_(module.q_proj_weight) + init_fn_(module.k_proj_weight) + init_fn_(module.v_proj_weight) + if module.in_proj_bias is not None: + torch.nn.init.zeros_(module.in_proj_bias) + if module.bias_k is not None: + torch.nn.init.zeros_(module.bias_k) + if module.bias_v is not None: + torch.nn.init.zeros_(module.bias_v) + init_fn_(module.out_proj.weight) + if init_div_is_residual is not False and getattr(module.out_proj, '_is_residual', False): + with torch.no_grad(): + module.out_proj.weight.div_(div_is_residual) + if module.out_proj.bias is not None: + torch.nn.init.zeros_(module.out_proj.bias) + else: + for _ in module.parameters(recurse=False): + raise NotImplementedError(f'{module.__class__.__name__} parameters are not initialized by param_init_fn.') + +def _normal_init_(std, mean=0.0): + return partial(torch.nn.init.normal_, mean=mean, std=std) + +def _normal_param_init_fn_(module: nn.Module, std: float, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + del kwargs + init_fn_ = _normal_init_(std=std) + if verbose > 1: + warnings.warn(f'Using torch.nn.init.normal_ init fn mean=0.0, std={std}') + generic_param_init_fn_(module=module, init_fn_=init_fn_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def baseline_param_init_fn_(module: nn.Module, init_std: float, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + del kwargs + if init_std is None: + raise ValueError("You must set model.init_config['init_std'] to a float value to use the default initialization scheme.") + _normal_param_init_fn_(module=module, std=init_std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def small_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + del kwargs + std = math.sqrt(2 / (5 * d_model)) + _normal_param_init_fn_(module=module, std=std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def neox_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs): + """From section 2.3.1 of GPT-NeoX-20B: + + An Open-Source AutoregressiveLanguage Model — Black et. al. (2022) + see https://github.com/EleutherAI/gpt-neox/blob/9610391ab319403cef079b438edd016a2443af54/megatron/model/init_functions.py#L151 + and https://github.com/EleutherAI/gpt-neox/blob/main/megatron/model/transformer.py + """ + del kwargs + residual_div = n_layers / math.sqrt(10) + if verbose > 1: + warnings.warn(f'setting init_div_is_residual to {residual_div}') + small_param_init_fn_(module=module, d_model=d_model, n_layers=n_layers, init_div_is_residual=residual_div, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def kaiming_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', verbose: int=0, **kwargs): + del kwargs + if verbose > 1: + warnings.warn(f'Using nn.init.kaiming_uniform_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}') + kaiming_uniform_ = partial(nn.init.kaiming_uniform_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity) + generic_param_init_fn_(module=module, init_fn_=kaiming_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def kaiming_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', verbose: int=0, **kwargs): + del kwargs + if verbose > 1: + warnings.warn(f'Using nn.init.kaiming_normal_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}') + kaiming_normal_ = partial(torch.nn.init.kaiming_normal_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity) + generic_param_init_fn_(module=module, init_fn_=kaiming_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def xavier_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, verbose: int=0, **kwargs): + del kwargs + xavier_uniform_ = partial(torch.nn.init.xavier_uniform_, gain=init_gain) + if verbose > 1: + warnings.warn(f'Using torch.nn.init.xavier_uniform_ init fn with parameters: ' + f'gain={init_gain}') + generic_param_init_fn_(module=module, init_fn_=xavier_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) + +def xavier_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, verbose: int=0, **kwargs): + xavier_normal_ = partial(torch.nn.init.xavier_normal_, gain=init_gain) + if verbose > 1: + warnings.warn(f'Using torch.nn.init.xavier_normal_ init fn with parameters: ' + f'gain={init_gain}') + generic_param_init_fn_(module=module, init_fn_=xavier_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose) +MODEL_INIT_REGISTRY = {'default_': torch_default_param_init_fn_, 'baseline_': baseline_param_init_fn_, 'kaiming_uniform_': kaiming_uniform_param_init_fn_, 'kaiming_normal_': kaiming_normal_param_init_fn_, 'neox_init_': neox_param_init_fn_, 'small_init_': small_param_init_fn_, 'xavier_uniform_': xavier_uniform_param_init_fn_, 'xavier_normal_': xavier_normal_param_init_fn_} \ No newline at end of file diff --git a/llava/model/llava_arch.py b/llava/model/llava_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..9448c6aaa7a12ca60467f80accad417c5ab2f6a4 --- /dev/null +++ b/llava/model/llava_arch.py @@ -0,0 +1,452 @@ +# Copyright 2023 Haotian Liu +# +# 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. + + +from abc import ABC, abstractmethod + +import torch +import torch.nn as nn +import transformers + +from llava.model.utils import get_w + +from .multimodal_encoder.builder import build_vision_tower + +from llava.constants import ( + GROUND_TOKEN, + IGNORE_INDEX, + IMAGE_TOKEN_INDEX, + DEFAULT_IMAGE_PATCH_TOKEN, + DEFAULT_IM_START_TOKEN, + DEFAULT_IM_END_TOKEN, + PROFILE_RUNTIME, +) + +import time +from transformers.utils import logging + +logger = logging.get_logger("transformers") + + +class LlavaMetaModel: + def __init__(self, config): + super(LlavaMetaModel, self).__init__(config) + + if hasattr(config, "mm_vision_tower"): + self.vision_tower = build_vision_tower(config, delay_load=True) + if self.vision_tower is not None: + self.mm_projector = nn.Linear( + self.vision_tower.hidden_size, config.hidden_size + ) # placeholder, this will be re-initialized later in initialize_vision_modules() + + def get_vision_tower(self): + vision_tower = getattr(self, "vision_tower", None) + if type(vision_tower) is list: + vision_tower = vision_tower[0] + return vision_tower + + def initialize_vision_modules(self, model_args, fsdp=None): + vision_tower = model_args.vision_tower + mm_vision_select_layer = model_args.mm_vision_select_layer + mm_vision_select_feature = model_args.mm_vision_select_feature + pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter + pretrain_vision_tower = model_args.pretrain_vision_tower + + self.config.mm_vision_tower = vision_tower + + if hasattr(self, "vision_tower"): + del self.vision_tower + torch.cuda.empty_cache() + vision_tower = build_vision_tower(model_args) + + if vision_tower is None: + return + + if fsdp is not None and len(fsdp) > 0: + self.vision_tower = [vision_tower] + else: + self.vision_tower = vision_tower + + # add these model args to HF config so that they can be saved (used for loading checkpoint) + self.config.use_mm_proj = True + self.config.mm_hidden_size = vision_tower.hidden_size + self.config.mm_vision_select_layer = mm_vision_select_layer + self.config.mm_vision_select_feature = mm_vision_select_feature + + self.config.num_points = model_args.num_points + self.config.feature_dim = model_args.feature_dim + self.config.num_latents = model_args.num_latents + self.config.d_latents = model_args.d_latents + self.config.num_cross_attention_heads = model_args.num_cross_attention_heads + self.config.position_encoding_type = model_args.position_encoding_type + + if hasattr(self, "mm_projector"): + del self.mm_projector + torch.cuda.empty_cache() + self.mm_projector = nn.Linear(self.config.mm_hidden_size, self.config.hidden_size) + + if pretrain_mm_mlp_adapter is not None: + mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location="cpu") + + self.mm_projector.load_state_dict(get_w(mm_projector_weights, "mm_projector")) + + torch.cuda.empty_cache() + + +class LlavaMetaForCausalLM(ABC): + @abstractmethod + def get_model(self): + pass + + def get_vision_tower(self): + return self.get_model().get_vision_tower() + + def encode_images(self, images): + vision_features_before_mm_projection = self.get_model().get_vision_tower()( + images + ) # for minkowski, the output of this step will be float32 + vision_features_before_mm_projection = vision_features_before_mm_projection.to( + dtype=self.dtype + ) # convert back to the dtype of the LLM (bfloat16 in most cases), no-op if the dtype is already the same + vision_features = self.get_model().mm_projector( + vision_features_before_mm_projection + ) # vision_features and mm_projector are both float32 + + return vision_features, vision_features_before_mm_projection + + def prepare_inputs_labels_for_multimodal( + self, input_ids, attention_mask, past_key_values, labels, images + ): + vision_tower = self.get_vision_tower() + if vision_tower is None or images is None or input_ids.shape[1] == 1: + if ( + past_key_values is not None + and vision_tower is not None + and images is not None + and input_ids.shape[1] == 1 + ): + attention_mask = torch.ones( + (attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), + dtype=attention_mask.dtype, + device=attention_mask.device, + ) + vision_features_before_mm_projection = images + return ( + input_ids, + attention_mask, + past_key_values, + None, + labels, + vision_features_before_mm_projection, + ) + + start_time_encode_images = time.time() + if not isinstance(images, SparseTensor) and (type(images) is list or images.ndim == 5): + concat_images = torch.cat([image for image in images], dim=0) + vision_features, vision_features_before_mm_projection = self.encode_images( + concat_images + ) + split_sizes = [image.shape[0] for image in images] + vision_features = torch.split(vision_features, split_sizes, dim=0) + vision_features = [x.flatten(0, 1) for x in vision_features] + else: + vision_features, vision_features_before_mm_projection = self.encode_images(images) + if PROFILE_RUNTIME: + logger.info(f"Time to encode images: {time.time() - start_time_encode_images}") + + start_time_for_loop = time.time() + new_input_embeds = [] + new_labels = [] if labels is not None else None + cur_image_idx = 0 + for batch_idx, cur_input_ids in enumerate(input_ids): + if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0: + # multimodal LLM, but the current sample is not multimodal + cur_input_embeds = self.get_model().embed_tokens(cur_input_ids) + cur_input_embeds = ( + cur_input_embeds + + (0.0 * self.get_model().mm_projector(vision_tower.dummy_feature)).sum() + ) + new_input_embeds.append(cur_input_embeds) + if labels is not None: + new_labels.append(labels[batch_idx]) + cur_image_idx += 1 + continue + image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] + cur_new_input_embeds = [] + if labels is not None: + cur_labels = labels[batch_idx] + cur_new_labels = [] + assert cur_labels.shape == cur_input_ids.shape + + # The following while loop looks for all image tokens in the current sentence + # and replace them with the corresponding image features. + while image_token_indices.numel() > 0: + cur_vision_features = vision_features[cur_image_idx] + image_token_start = image_token_indices[0] + if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr( + self.config, "mm_use_im_start_end", False + ): + cur_new_input_embeds.append( + self.get_model() + .embed_tokens(cur_input_ids[: image_token_start - 1]) + .detach() + ) + cur_new_input_embeds.append( + self.get_model().embed_tokens( + cur_input_ids[image_token_start - 1 : image_token_start] + ) + ) + cur_new_input_embeds.append(cur_vision_features) + cur_new_input_embeds.append( + self.get_model().embed_tokens( + cur_input_ids[image_token_start + 1 : image_token_start + 2] + ) + ) + if labels is not None: + cur_new_labels.append(cur_labels[:image_token_start]) + cur_new_labels.append( + torch.full( + (cur_vision_features.shape[0],), + IGNORE_INDEX, + device=labels.device, + dtype=labels.dtype, + ) + ) + cur_new_labels.append(cur_labels[image_token_start : image_token_start + 1]) + cur_labels = cur_labels[image_token_start + 2 :] + else: + cur_new_input_embeds.append( + self.get_model().embed_tokens(cur_input_ids[:image_token_start]) + ) + cur_new_input_embeds.append(cur_vision_features) + if labels is not None: + cur_new_labels.append(cur_labels[:image_token_start]) + cur_new_labels.append( + torch.full( + (cur_vision_features.shape[0],), + IGNORE_INDEX, + device=labels.device, + dtype=labels.dtype, + ) + ) + cur_labels = cur_labels[image_token_start + 1 :] + cur_image_idx += 1 + if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr( + self.config, "mm_use_im_start_end", False + ): + cur_input_ids = cur_input_ids[image_token_start + 2 :] + else: + cur_input_ids = cur_input_ids[image_token_start + 1 :] + image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] + if cur_input_ids.numel() > 0: + if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr( + self.config, "mm_use_im_start_end", False + ): + cur_new_input_embeds.append( + self.get_model().embed_tokens(cur_input_ids).detach() + ) + else: + cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids)) + if labels is not None: + cur_new_labels.append(cur_labels) + cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] + cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) + new_input_embeds.append(cur_new_input_embeds) + if labels is not None: + cur_new_labels = torch.cat(cur_new_labels, dim=0) + new_labels.append(cur_new_labels) + if PROFILE_RUNTIME: + logger.info(f"Time for loop: {time.time() - start_time_for_loop}") + + start_time_paddding = time.time() + # pad all sentences in batch to the same length + if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds): + max_len = max(x.shape[0] for x in new_input_embeds) + + new_input_embeds_align = [] + for cur_new_embed in new_input_embeds: + cur_new_embed = torch.cat( + ( + cur_new_embed, + torch.zeros( + (max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), + dtype=cur_new_embed.dtype, + device=cur_new_embed.device, + ), + ), + dim=0, + ) + new_input_embeds_align.append(cur_new_embed) + new_input_embeds = torch.stack(new_input_embeds_align, dim=0) + + if labels is not None: + new_labels_align = [] + _new_labels = new_labels + for cur_new_label in new_labels: + cur_new_label = torch.cat( + ( + cur_new_label, + torch.full( + (max_len - cur_new_label.shape[0],), + IGNORE_INDEX, + dtype=cur_new_label.dtype, + device=cur_new_label.device, + ), + ), + dim=0, + ) + new_labels_align.append(cur_new_label) + new_labels = torch.stack(new_labels_align, dim=0) + + if attention_mask is not None: + new_attention_mask = [] + for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip( + attention_mask, _new_labels, new_labels + ): + new_attn_mask_pad_left = torch.full( + (cur_new_labels.shape[0] - labels.shape[1],), + True, + dtype=attention_mask.dtype, + device=attention_mask.device, + ) + new_attn_mask_pad_right = torch.full( + (cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), + False, + dtype=attention_mask.dtype, + device=attention_mask.device, + ) + cur_new_attention_mask = torch.cat( + ( + new_attn_mask_pad_left, + cur_attention_mask, + new_attn_mask_pad_right, + ), + dim=0, + ) + new_attention_mask.append(cur_new_attention_mask) + attention_mask = torch.stack(new_attention_mask, dim=0) + assert attention_mask.shape == new_labels.shape + else: + new_input_embeds = torch.stack(new_input_embeds, dim=0) + if labels is not None: + new_labels = torch.stack(new_labels, dim=0) + + if attention_mask is not None: + new_attn_mask_pad_left = torch.full( + ( + attention_mask.shape[0], + new_input_embeds.shape[1] - input_ids.shape[1], + ), + True, + dtype=attention_mask.dtype, + device=attention_mask.device, + ) + attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1) + assert attention_mask.shape == new_input_embeds.shape[:2] + + if PROFILE_RUNTIME: + logger.info(f"Time padding: {time.time() - start_time_paddding}") + return ( + None, + attention_mask, + past_key_values, + new_input_embeds, + new_labels, + vision_features_before_mm_projection, + ) + + def initialize_vision_tokenizer(self, model_args, tokenizer): + if model_args.mm_use_im_patch_token: + tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) + self.resize_token_embeddings(len(tokenizer)) + + if model_args.mm_use_im_start_end: + ( + num_new_tokens, + input_embeddings, + output_embeddings, + ) = self.add_special_tokens_and_resize_embeddings( + special_tokens_list=[DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], + tokenizer=tokenizer, + ) + + if model_args.tune_mm_mlp_adapter: + for p in self.get_input_embeddings().parameters(): + p.requires_grad = True + for p in self.get_output_embeddings().parameters(): + p.requires_grad = False + + if model_args.pretrain_mm_mlp_adapter: + mm_projector_weights = torch.load( + model_args.pretrain_mm_mlp_adapter, map_location="cpu" + ) + embed_tokens_weight = mm_projector_weights["model.embed_tokens.weight"] + assert num_new_tokens == 2 + if input_embeddings.shape == embed_tokens_weight.shape: + input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:] + elif embed_tokens_weight.shape[0] == num_new_tokens: + input_embeddings[-num_new_tokens:] = embed_tokens_weight + else: + raise ValueError( + f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}." + ) + elif model_args.mm_use_im_patch_token: + if model_args.tune_mm_mlp_adapter: + for p in self.get_input_embeddings().parameters(): + p.requires_grad = False + for p in self.get_output_embeddings().parameters(): + p.requires_grad = False + + # add special tokens if bbox_tokenization_type is location_tokens + if model_args.bbox_tokenization_type == "location_tokens": + num_special_tokens = model_args.num_voxels_per_axis_for_location_tokens**3 + self.add_special_tokens_and_resize_embeddings( + special_tokens_list=[f"" for i in range(num_special_tokens)], + tokenizer=tokenizer, + ) + for p in self.get_input_embeddings().parameters(): + p.requires_grad = True + for p in self.get_output_embeddings().parameters(): + p.requires_grad = True + elif model_args.bbox_tokenization_type == "ground_token": + self.add_special_tokens_and_resize_embeddings( + special_tokens_list=[GROUND_TOKEN], tokenizer=tokenizer + ) + for p in self.get_input_embeddings().parameters(): + p.requires_grad = True + for p in self.get_output_embeddings().parameters(): + p.requires_grad = True + + # add special token to input id mapping + self.config.added_special_token_to_input_id = tokenizer.get_added_vocab() + + def add_special_tokens_and_resize_embeddings( + self, + special_tokens_list: list[str], + tokenizer: transformers.PreTrainedTokenizer, + ): + num_new_tokens = tokenizer.add_tokens(special_tokens_list, special_tokens=True) + self.resize_token_embeddings(len(tokenizer)) + + if num_new_tokens > 0: + input_embeddings = self.get_input_embeddings().weight.data + output_embeddings = self.get_output_embeddings().weight.data + + input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) + output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) + + input_embeddings[-num_new_tokens:] = input_embeddings_avg + output_embeddings[-num_new_tokens:] = output_embeddings_avg + + return num_new_tokens, input_embeddings, output_embeddings diff --git a/llava/model/make_delta.py b/llava/model/make_delta.py new file mode 100644 index 0000000000000000000000000000000000000000..a289a25b658295e5f5d08e76b7ab0f6fb4690bde --- /dev/null +++ b/llava/model/make_delta.py @@ -0,0 +1,61 @@ +""" +Usage: +python3 -m llava.model.make_delta --base ~/model_weights/llama-7b --target ~/model_weights/llava-7b --delta ~/model_weights/llava-7b-delta --hub-repo-id liuhaotian/llava-7b-delta +""" +import argparse + +import torch +from tqdm import tqdm +from transformers import AutoTokenizer, AutoModelForCausalLM +from llava.model.utils import auto_upgrade + + +def make_delta(base_model_path, target_model_path, delta_path, hub_repo_id): + print("Loading base model") + base = AutoModelForCausalLM.from_pretrained( + base_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True + ) + + print("Loading target model") + auto_upgrade(target_model_path) + target = AutoModelForCausalLM.from_pretrained( + target_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True + ) + + print("Calculating delta") + for name, param in tqdm(target.state_dict().items(), desc="Calculating delta"): + if name not in base.state_dict(): + assert name in [ + "model.mm_projector.weight", + "model.mm_projector.bias", + ], f"{name} not in base model" + continue + if param.data.shape == base.state_dict()[name].shape: + param.data -= base.state_dict()[name] + else: + assert name in [ + "model.embed_tokens.weight", + "lm_head.weight", + ], f"{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}" + bparam = base.state_dict()[name] + param.data[: bparam.shape[0], : bparam.shape[1]] -= bparam + + print("Saving delta") + if hub_repo_id: + kwargs = {"push_to_hub": True, "repo_id": hub_repo_id} + else: + kwargs = {} + target.save_pretrained(delta_path, **kwargs) + target_tokenizer = AutoTokenizer.from_pretrained(target_model_path) + target_tokenizer.save_pretrained(delta_path, **kwargs) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--base-model-path", type=str, required=True) + parser.add_argument("--target-model-path", type=str, required=True) + parser.add_argument("--delta-path", type=str, required=True) + parser.add_argument("--hub-repo-id", type=str, default=None) + args = parser.parse_args() + + make_delta(args.base_model_path, args.target_model_path, args.delta_path, args.hub_repo_id) diff --git a/llava/model/multimodal_encoder/__pycache__/builder.cpython-310.pyc b/llava/model/multimodal_encoder/__pycache__/builder.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..86b9751b00f8ced3bffe346ba9bd881e0011ffd9 Binary files /dev/null and b/llava/model/multimodal_encoder/__pycache__/builder.cpython-310.pyc differ diff --git a/llava/model/multimodal_encoder/__pycache__/clip_encoder.cpython-310.pyc b/llava/model/multimodal_encoder/__pycache__/clip_encoder.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ccfbc8b51d453daf240a9f1d8f027c7719538222 Binary files /dev/null and b/llava/model/multimodal_encoder/__pycache__/clip_encoder.cpython-310.pyc differ diff --git a/llava/model/multimodal_encoder/builder.py b/llava/model/multimodal_encoder/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..e18bf6b3ef0357a606dcef924b91f5a624c7c950 --- /dev/null +++ b/llava/model/multimodal_encoder/builder.py @@ -0,0 +1,22 @@ +from .clip_encoder import CLIPVisionTower + + + +def build_vision_tower(vision_tower_cfg, **kwargs): + vision_tower = getattr( + vision_tower_cfg, + "mm_vision_tower", + getattr(vision_tower_cfg, "vision_tower", None), + ) + if vision_tower.startswith("openai") or vision_tower.startswith("laion"): + return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs) + elif vision_tower == "pointcloud-perceiver": + return PointCloudPerceiverVisionTower(vision_tower, args=vision_tower_cfg) + elif vision_tower == "pointcloud-minkowski": + return PointCloudMinkowskiVisionTower(vision_tower, args=vision_tower_cfg) + elif vision_tower == "pointcloud-minkowski-mask3d": + return PointCloudMinkowskiMask3DVisionTower(vision_tower, args=vision_tower_cfg) + elif vision_tower == "bbox-ground-truth": + return None # return None so that there is no vision input to the LLM, see prepare_inputs_labels_for_multimodal() in llava_arch.py + + raise ValueError(f"Unknown vision tower: {vision_tower}") diff --git a/llava/model/multimodal_encoder/clip_encoder.py b/llava/model/multimodal_encoder/clip_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..dbb9015b0fc9fa93483ba77cc303b793e86c36fc --- /dev/null +++ b/llava/model/multimodal_encoder/clip_encoder.py @@ -0,0 +1,78 @@ +import torch +import torch.nn as nn + +from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig + + +class CLIPVisionTower(nn.Module): + def __init__(self, vision_tower, args, delay_load=False): + super().__init__() + + self.is_loaded = False + + self.vision_tower_name = vision_tower + self.select_layer = args.mm_vision_select_layer + self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch') + + if not delay_load: + self.load_model() + else: + self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name) + + def load_model(self): + self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name) + self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name) + self.vision_tower.requires_grad_(False) + + self.is_loaded = True + + def feature_select(self, image_forward_outs): + image_features = image_forward_outs.hidden_states[self.select_layer] + if self.select_feature == 'patch': + image_features = image_features[:, 1:] + elif self.select_feature == 'cls_patch': + image_features = image_features + else: + raise ValueError(f'Unexpected select feature: {self.select_feature}') + return image_features + + @torch.no_grad() + def forward(self, images): + if type(images) is list: + image_features = [] + for image in images: + image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True) + image_feature = self.feature_select(image_forward_out).to(image.dtype) + image_features.append(image_feature) + else: + image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True) + image_features = self.feature_select(image_forward_outs).to(images.dtype) + + return image_features + + @property + def dummy_feature(self): + return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) + + @property + def dtype(self): + return self.vision_tower.dtype + + @property + def device(self): + return self.vision_tower.device + + @property + def config(self): + if self.is_loaded: + return self.vision_tower.config + else: + return self.cfg_only + + @property + def hidden_size(self): + return self.config.hidden_size + + @property + def num_patches(self): + return (self.config.image_size // self.config.patch_size) ** 2 diff --git a/llava/model/utils.py b/llava/model/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c19d53ed2e235b247a8829bc5250d4faf538d756 --- /dev/null +++ b/llava/model/utils.py @@ -0,0 +1,32 @@ +from transformers import AutoConfig + + +def auto_upgrade(config): + cfg = AutoConfig.from_pretrained(config) + if "llava" in config and "llava" not in cfg.model_type: + assert cfg.model_type == "llama" + print( + "You are using newer LLaVA code base, while the checkpoint of v0 is from older code base." + ) + print( + "You must upgrade the checkpoint to the new code base (this can be done automatically)." + ) + confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]") + if confirm.lower() in ["y", "yes"]: + print("Upgrading checkpoint...") + assert len(cfg.architectures) == 1 + setattr(cfg.__class__, "model_type", "llava") + cfg.architectures[0] = "LlavaLlamaForCausalLM" + cfg.save_pretrained(config) + print("Checkpoint upgraded.") + else: + print("Checkpoint upgrade aborted.") + exit(1) + + +def get_w(weights, keyword): + return { + k.split(keyword + ".", maxsplit=1)[1]: v + for k, v in weights.items() + if keyword in k + } diff --git a/llava/util/__pycache__/tokenization.cpython-310.pyc b/llava/util/__pycache__/tokenization.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..94b6e73c4ef4186943a2a2aa514f5f92cd56120e Binary files /dev/null and b/llava/util/__pycache__/tokenization.cpython-310.pyc differ diff --git a/llava/util/tokenization.py b/llava/util/tokenization.py new file mode 100644 index 0000000000000000000000000000000000000000..10b95bc06ff1387da7333914825d8fa453483900 --- /dev/null +++ b/llava/util/tokenization.py @@ -0,0 +1,551 @@ +import copy +from typing import Dict, Sequence +import torch +import transformers +from llava.constants import ( + IGNORE_INDEX, + DEFAULT_IMAGE_TOKEN, + DEFAULT_IM_START_TOKEN, + DEFAULT_IM_END_TOKEN, + IMAGE_TOKEN_INDEX, +) +from llava import conversation as conversation_lib +from llava.mm_utils import tokenizer_image_token + + +def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: + """Tokenize a list of strings.""" + tokenized_list = [ + tokenizer( + text, + return_tensors="pt", + padding="longest", + max_length=tokenizer.model_max_length, + truncation=True, + ) + for text in strings + ] + input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] + input_ids_lens = labels_lens = [ + tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list + ] + return dict( + input_ids=input_ids, + labels=labels, + input_ids_lens=input_ids_lens, + labels_lens=labels_lens, + ) + + +def _mask_targets(target, tokenized_lens, speakers): + # cur_idx = 0 + cur_idx = tokenized_lens[0] + tokenized_lens = tokenized_lens[1:] + target[:cur_idx] = IGNORE_INDEX + for tokenized_len, speaker in zip(tokenized_lens, speakers): + if speaker == "human": + target[cur_idx + 2 : cur_idx + tokenized_len] = IGNORE_INDEX + cur_idx += tokenized_len + + +def _add_speaker_and_signal(header, source, get_conversation=True): + """Add speaker and start/end signal on each round.""" + BEGIN_SIGNAL = "### " + END_SIGNAL = "\n" + conversation = header + for sentence in source: + from_str = sentence["from"] + if from_str.lower() == "human": + from_str = conversation_lib.default_conversation.roles[0] + elif from_str.lower() == "gpt": + from_str = conversation_lib.default_conversation.roles[1] + else: + from_str = "unknown" + sentence["value"] = BEGIN_SIGNAL + from_str + ": " + sentence["value"] + END_SIGNAL + if get_conversation: + conversation += sentence["value"] + conversation += BEGIN_SIGNAL + return conversation + + +def preprocess_multimodal( + sources: Sequence[str], is_multimodal: bool, mm_use_im_start_end: bool +) -> Dict: + if not is_multimodal: + return sources + + for source in sources: + for sentence in source: + if DEFAULT_IMAGE_TOKEN in sentence["value"]: + sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, "").strip() + sentence["value"] = DEFAULT_IMAGE_TOKEN + "\n" + sentence["value"] + sentence["value"] = sentence["value"].strip() + if "mmtag" in conversation_lib.default_conversation.version: + sentence["value"] = sentence["value"].replace( + DEFAULT_IMAGE_TOKEN, + "" + DEFAULT_IMAGE_TOKEN + "", + ) + replace_token = DEFAULT_IMAGE_TOKEN + if mm_use_im_start_end: + replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN + sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token) + + return sources + + +def preprocess_llama_2( + sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False +) -> Dict: + conv = conversation_lib.default_conversation.copy() + roles = {"human": conv.roles[0], "gpt": conv.roles[1]} + + # Apply prompt templates + conversations = [] + for i, source in enumerate(sources): + if roles[source[0]["from"]] != conv.roles[0]: + # Skip the first one if it is not from human + source = source[1:] + + conv.messages = [] + for j, sentence in enumerate(source): + role = roles[sentence["from"]] + assert role == conv.roles[j % 2], f"{i}" + conv.append_message(role, sentence["value"]) + conversations.append(conv.get_prompt()) + + # Tokenize conversations + + if has_image: + input_ids = torch.stack( + [ + tokenizer_image_token(prompt, tokenizer, return_tensors="pt") + for prompt in conversations + ], + dim=0, + ) + else: + input_ids = tokenizer( + conversations, + return_tensors="pt", + padding="longest", + max_length=tokenizer.model_max_length, + truncation=True, + ).input_ids + + targets = input_ids.clone() + + assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2 + + # Mask targets + sep = "[/INST] " + for conversation, target in zip(conversations, targets): + total_len = int(target.ne(tokenizer.pad_token_id).sum()) + + rounds = conversation.split(conv.sep2) + cur_len = 1 + target[:cur_len] = IGNORE_INDEX + for i, rou in enumerate(rounds): + if rou == "": + break + + parts = rou.split(sep) + if len(parts) != 2: + break + parts[0] += sep + + if has_image: + round_len = len(tokenizer_image_token(rou, tokenizer)) + instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2 + else: + round_len = len(tokenizer(rou).input_ids) + instruction_len = len(tokenizer(parts[0]).input_ids) - 2 + + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + if cur_len < tokenizer.model_max_length: + if cur_len != total_len: + target[:] = IGNORE_INDEX + print(f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." f" (ignored)") + + return dict( + input_ids=input_ids, + labels=targets, + ) + + +def preprocess_llama_2_obj_identifier( + sources, + tokenizer: transformers.PreTrainedTokenizer, + obj_dict: Dict[str, Dict], + obj_context_feature_type: str, + mode: str, +) -> Dict: + """This function tokenizes the conversation into the following format: + %%%% Object-centric context: : ; : ; ... : ;%%%%" + where is currently placeholered by IMAGE_TOKEN_INDEX + but will later be replaced by the actual feature in vector form. + We mark all string tokens as not trainable, only keep the feature vectors trainable. + + Args: + sources (_type_): the conversation sources + tokenizer (transformers.PreTrainedTokenizer): the tokenizer + obj_dict (Dict[str, Dict]): the object dictionary for the scene + obj_context_feature_type (str): the type of object feature to use for the object context + + Returns: + Dict: the tokenized input_ids and labels + """ + conv = conversation_lib.conv_llava_llama_2_obj_identifier.copy() + roles = {"human": conv.roles[0], "gpt": conv.roles[1]} + + # Apply prompt templates + conversations = [] + for i, source in enumerate(sources): + if roles[source[0]["from"]] != conv.roles[0]: + # Skip the first one if it is not from human + source = source[1:] + + conv.messages = [] + for j, sentence in enumerate(source): + role = roles[sentence["from"]] + assert role == conv.roles[j % 2], f"{i}" + conv.append_message(role, sentence["value"]) + conversations.append(conv.get_prompt()) + + # Tokenize conversations + + input_ids = torch.stack( + [tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations], + dim=0, + ) + + targets = input_ids.clone() + + assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2 + + # Mask targets + sep = "[/INST] " + for conversation, target in zip(conversations, targets): + total_len = int(target.ne(tokenizer.pad_token_id).sum()) + + rounds = conversation.split(conv.sep2) + cur_len = 1 + target[:cur_len] = IGNORE_INDEX + for i, rou in enumerate(rounds): + if rou == "": + break + + parts = rou.split(sep) + if len(parts) != 2: + break + parts[0] += sep + + round_len = len(tokenizer_image_token(rou, tokenizer)) + instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2 + + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + if ( + cur_len < tokenizer.model_max_length and mode != "generate" + ): # check if target is correctly masked. when generating, we don't have any target + if cur_len != total_len: + target[:] = IGNORE_INDEX + print(f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." f" (ignored)") + + assert ( + input_ids.shape[0] == targets.shape[0] == 1 + ), "Only support tokenization for one conversation at a time" + input_id = input_ids[0] + targets = targets[0] + # TODO: replace -200 (IMAGE_TOKEN_INDEX) with object identifier tokens, + # we want the LLM to see: + # %%%% Object-centric context: : ; : ; ... : ;%%%%" + # where will later be replaced by the actual feature in vector form, + + if obj_context_feature_type == "vector": + obj_context = "%%%% Object-centric context:" + for obj_id, obj_info in obj_dict.items(): + obj_context += f" <{obj_id}>: {tokenizer.sep_token};" # use as a placeholder, later it will be replaced by the actual feature vector + obj_context += "%%%%" + + tokenized_obj_context = tokenizer(obj_context).input_ids[ + 1:-1 + ] # strip the bos and eos tokens + tokenized_obj_context = torch.tensor(tokenized_obj_context, dtype=torch.long) + tokenized_obj_context[tokenized_obj_context == tokenizer.sep_token_id] = ( + IMAGE_TOKEN_INDEX # replace with IMAGE_TOKEN_INDEX, so that later we can use -200 to find where the feature vector should be inserted + ) + # mark all string tokens as not trainable, only keep the feature vectors trainable + tokenized_obj_context_target = tokenized_obj_context.clone() + tokenized_obj_context_target[tokenized_obj_context != IMAGE_TOKEN_INDEX] = IGNORE_INDEX + elif obj_context_feature_type == "text": + obj_context = "%%%% Object-centric context:" + for obj_id, obj_info in obj_dict.items(): + obj_context += f" <{obj_id}>: {obj_info};" + obj_context += "%%%%" + + tokenized_obj_context = tokenizer(obj_context).input_ids[ + 1:-1 + ] # strip the bos and eos tokens + tokenized_obj_context = torch.tensor(tokenized_obj_context, dtype=torch.long) + tokenized_obj_context_target = tokenized_obj_context.clone() + tokenized_obj_context_target[:] = IGNORE_INDEX # mark all tokens as not trainable + + # now, insert the object context into input_id and target, where the IMAGE_TOKEN_INDEX is + separation_idx = torch.where(input_id == IMAGE_TOKEN_INDEX)[0] + input_id_with_obj_context = torch.cat( + [input_id[:separation_idx], tokenized_obj_context, input_id[separation_idx + 1 :]] + ) + target_with_obj_context = torch.cat( + [ + targets[:separation_idx], + tokenized_obj_context_target, + targets[separation_idx + 1 :], + ] + ) + + if obj_context_feature_type == "vector": + return dict( + input_ids=input_id_with_obj_context, + labels=target_with_obj_context, + obj_dict=obj_dict, # return the object dictionary so that we can later embed the features + ) + elif obj_context_feature_type == "text": + return dict(input_ids=input_id_with_obj_context, labels=target_with_obj_context) + + +def preprocess_v1( + sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False +) -> Dict: + conv = conversation_lib.default_conversation.copy() + roles = {"human": conv.roles[0], "gpt": conv.roles[1]} + + # Apply prompt templates + conversations = [] + for i, source in enumerate(sources): + if roles[source[0]["from"]] != conv.roles[0]: + # Skip the first one if it is not from human + source = source[1:] + + conv.messages = [] + for j, sentence in enumerate(source): + role = roles[sentence["from"]] + assert role == conv.roles[j % 2], f"{i}" + conv.append_message(role, sentence["value"]) + conversations.append(conv.get_prompt()) + + # Tokenize conversations + + if has_image: + input_ids = torch.stack( + [ + tokenizer_image_token(prompt, tokenizer, return_tensors="pt") + for prompt in conversations + ], + dim=0, + ) + else: + input_ids = tokenizer( + conversations, + return_tensors="pt", + padding="longest", + max_length=tokenizer.model_max_length, + truncation=True, + ).input_ids + + targets = input_ids.clone() + + assert conv.sep_style == conversation_lib.SeparatorStyle.TWO + + # Mask targets + sep = conv.sep + conv.roles[1] + ": " + for conversation, target in zip(conversations, targets): + total_len = int(target.ne(tokenizer.pad_token_id).sum()) + + rounds = conversation.split(conv.sep2) + cur_len = 1 + target[:cur_len] = IGNORE_INDEX + for i, rou in enumerate(rounds): + if rou == "": + break + + parts = rou.split(sep) + if len(parts) != 2: + break + parts[0] += sep + + if has_image: + round_len = len(tokenizer_image_token(rou, tokenizer)) + instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2 + else: + round_len = len(tokenizer(rou).input_ids) + instruction_len = len(tokenizer(parts[0]).input_ids) - 2 + + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + if cur_len < tokenizer.model_max_length: + if cur_len != total_len: + target[:] = IGNORE_INDEX + print(f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." f" (ignored)") + + return dict( + input_ids=input_ids, + labels=targets, + ) + + +def preprocess_mpt( + sources, + tokenizer: transformers.PreTrainedTokenizer, +) -> Dict: + conv = conversation_lib.default_conversation.copy() + roles = {"human": conv.roles[0], "gpt": conv.roles[1]} + + # Apply prompt templates + conversations = [] + for i, source in enumerate(sources): + if roles[source[0]["from"]] != conv.roles[0]: + # Skip the first one if it is not from human + source = source[1:] + + conv.messages = [] + for j, sentence in enumerate(source): + role = roles[sentence["from"]] + assert role == conv.roles[j % 2], f"{i}" + conv.append_message(role, sentence["value"]) + conversations.append(conv.get_prompt()) + + # Tokenize conversations + input_ids = torch.stack( + [tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations], + dim=0, + ) + targets = input_ids.clone() + assert conv.sep_style == conversation_lib.SeparatorStyle.MPT + + # Mask targets + sep = conv.sep + conv.roles[1] + for conversation, target in zip(conversations, targets): + total_len = int(target.ne(tokenizer.pad_token_id).sum()) + + rounds = conversation.split(conv.sep) + re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt + for conv_idx in range(3, len(rounds), 2): + re_rounds.append(conv.sep.join(rounds[conv_idx : conv_idx + 2])) # user + gpt + cur_len = 0 + target[:cur_len] = IGNORE_INDEX + for i, rou in enumerate(re_rounds): + if rou == "": + break + + parts = rou.split(sep) + if len(parts) != 2: + break + parts[0] += sep + round_len = len(tokenizer_image_token(rou, tokenizer)) + len( + tokenizer_image_token(conv.sep, tokenizer) + ) + instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) + target[cur_len : cur_len + instruction_len] = IGNORE_INDEX + + cur_len += round_len + target[cur_len:] = IGNORE_INDEX + + if cur_len < tokenizer.model_max_length: + if cur_len != total_len: + target[:] = IGNORE_INDEX + print(f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." f" (ignored)") + + return dict( + input_ids=input_ids, + labels=targets, + ) + + +def preprocess_plain( + sources: Sequence[str], + tokenizer: transformers.PreTrainedTokenizer, +) -> Dict: + # add end signal and concatenate together + conversations = [] + for source in sources: + assert len(source) == 2 + assert DEFAULT_IMAGE_TOKEN in source[0]["value"] + source[0]["value"] = DEFAULT_IMAGE_TOKEN + conversation = ( + source[0]["value"] + source[1]["value"] + conversation_lib.default_conversation.sep + ) + conversations.append(conversation) + # tokenize conversations + input_ids = [ + tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations + ] + targets = copy.deepcopy(input_ids) + for target, source in zip(targets, sources): + tokenized_len = len(tokenizer_image_token(source[0]["value"], tokenizer)) + target[:tokenized_len] = IGNORE_INDEX + + return dict(input_ids=input_ids, labels=targets) + + +def preprocess( + sources: Sequence[str], + tokenizer: transformers.PreTrainedTokenizer, + has_image: bool = False, +) -> Dict: + """ + Given a list of sources, each is a conversation list. This transform: + 1. Add signal '### ' at the beginning each sentence, with end signal '\n'; + 2. Concatenate conversations together; + 3. Tokenize the concatenated conversation; + 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX. + """ + if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN: + return preprocess_plain(sources, tokenizer) + if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_2: + return preprocess_llama_2(sources, tokenizer, has_image=has_image) + if conversation_lib.default_conversation.version.startswith("v1"): + return preprocess_v1(sources, tokenizer, has_image=has_image) + if conversation_lib.default_conversation.version == "mpt": + return preprocess_mpt(sources, tokenizer) + # add end signal and concatenate together + conversations = [] + for source in sources: + header = f"{conversation_lib.default_conversation.system}\n\n" + conversation = _add_speaker_and_signal(header, source) + conversations.append(conversation) + + # tokenize conversations + def get_tokenize_len(prompts): + return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts] + + if has_image: + input_ids = [ + tokenizer_image_token(prompt, tokenizer, return_tensors="pt") + for prompt in conversations + ] + else: + conversations_tokenized = _tokenize_fn(conversations, tokenizer) + input_ids = conversations_tokenized["input_ids"] + + targets = copy.deepcopy(input_ids) + for target, source in zip(targets, sources): + if has_image: + tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source]) + else: + tokenized_lens = _tokenize_fn([header] + [s["value"] for s in source], tokenizer)[ + "input_ids_lens" + ] + speakers = [sentence["from"] for sentence in source] + _mask_targets(target, tokenized_lens, speakers) + + return dict(input_ids=input_ids, labels=targets) diff --git a/llava/util/visualization.py b/llava/util/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..f89efd0ea78f39f184b13b6196f7bee063809fd3 --- /dev/null +++ b/llava/util/visualization.py @@ -0,0 +1,50 @@ +from sklearn.decomposition import PCA +import plotly.graph_objects as go +import torch + + +def visualize_features_PCA( + xyz: torch.Tensor, + features: torch.Tensor, +): + """visualize the features using PCA + + Args: + xyz (torch.Tensor): [num_points, 3] + features (torch.Tensor): [num_points, feature_dim] + """ + + xyz = xyz.cpu().numpy() + features = features.cpu().numpy() + + pca = PCA(n_components=3) + pca_result = pca.fit_transform(features) # (N, channel) + + # Normalize the results between 0 and 1 for color mapping + pca_result -= pca_result.min(axis=0) + pca_result /= pca_result.max(axis=0) + + # Scale to 0-255 for RGB + pca_result = (pca_result * 255).astype(int) + + # Convert to a format Plotly understands + color = ["rgb({},{},{})".format(r, g, b) for r, g, b in pca_result] + + # Create interactive 3D scatter plot + fig = go.Figure( + data=[ + go.Scatter3d( + x=xyz[:, 0], + y=xyz[:, 1], + z=xyz[:, 2], + mode="markers", + marker=dict( + size=5, + color=color, # set color to an array/list of desired values + opacity=0.8, + ), + ) + ] + ) + + fig.show() diff --git a/llava/utils.py b/llava/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..18667d0abc6a88577215766b47934f3b37f8e687 --- /dev/null +++ b/llava/utils.py @@ -0,0 +1,129 @@ +import datetime +import logging +import logging.handlers +import os +import sys + +import requests + +from llava.constants import LOGDIR + +server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**" +moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN." + +handler = None + + +def build_logger(logger_name, logger_filename): + global handler + + formatter = logging.Formatter( + fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + # Set the format of root handlers + if not logging.getLogger().handlers: + logging.basicConfig(level=logging.INFO) + logging.getLogger().handlers[0].setFormatter(formatter) + + # Redirect stdout and stderr to loggers + stdout_logger = logging.getLogger("stdout") + stdout_logger.setLevel(logging.INFO) + sl = StreamToLogger(stdout_logger, logging.INFO) + sys.stdout = sl + + stderr_logger = logging.getLogger("stderr") + stderr_logger.setLevel(logging.ERROR) + sl = StreamToLogger(stderr_logger, logging.ERROR) + sys.stderr = sl + + # Get logger + logger = logging.getLogger(logger_name) + logger.setLevel(logging.INFO) + + # Add a file handler for all loggers + if handler is None: + os.makedirs(LOGDIR, exist_ok=True) + filename = os.path.join(LOGDIR, logger_filename) + handler = logging.handlers.TimedRotatingFileHandler(filename, when="D", utc=True) + handler.setFormatter(formatter) + + for name, item in logging.root.manager.loggerDict.items(): + if isinstance(item, logging.Logger): + item.addHandler(handler) + + return logger + + +class StreamToLogger(object): + """ + Fake file-like stream object that redirects writes to a logger instance. + """ + + def __init__(self, logger, log_level=logging.INFO): + self.terminal = sys.stdout + self.logger = logger + self.log_level = log_level + self.linebuf = "" + + def __getattr__(self, attr): + return getattr(self.terminal, attr) + + def write(self, buf): + temp_linebuf = self.linebuf + buf + self.linebuf = "" + for line in temp_linebuf.splitlines(True): + # From the io.TextIOWrapper docs: + # On output, if newline is None, any '\n' characters written + # are translated to the system default line separator. + # By default sys.stdout.write() expects '\n' newlines and then + # translates them so this is still cross platform. + if line[-1] == "\n": + self.logger.log(self.log_level, line.rstrip()) + else: + self.linebuf += line + + def flush(self): + if self.linebuf != "": + self.logger.log(self.log_level, self.linebuf.rstrip()) + self.linebuf = "" + + +def disable_torch_init(): + """ + Disable the redundant torch default initialization to accelerate model creation. + """ + import torch + + setattr(torch.nn.Linear, "reset_parameters", lambda self: None) + setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) + + +def violates_moderation(text): + """ + Check whether the text violates OpenAI moderation API. + """ + url = "https://api.openai.com/v1/moderations" + headers = { + "Content-Type": "application/json", + "Authorization": "Bearer " + os.environ["OPENAI_API_KEY"], + } + text = text.replace("\n", "") + data = "{" + '"input": ' + f'"{text}"' + "}" + data = data.encode("utf-8") + try: + ret = requests.post(url, headers=headers, data=data, timeout=5) + flagged = ret.json()["results"][0]["flagged"] + except requests.exceptions.RequestException as e: + flagged = False + except KeyError as e: + flagged = False + + return flagged + + +def pretty_print_semaphore(semaphore): + if semaphore is None: + return "None" + return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})" diff --git a/model.py b/model.py new file mode 100644 index 0000000000000000000000000000000000000000..be92a0a3a7896f0261e21f2700d00ab044f62e44 --- /dev/null +++ b/model.py @@ -0,0 +1,69 @@ +# model.py +import json +import torch +from torch.utils.data import DataLoader + +from llava.dataset.obj_identifier_dataset import ( + ObjIdentifierDataset, + DataCollatorForBatchDecodingObjIdentifierDataset, +) +from llava.mm_utils import get_model_name_from_path +from llava.model.builder import load_pretrained_model +from llava.utils import disable_torch_init + + +def load_model_and_dataloader(model_path, model_base, scene_to_obj_mapping, obj_context_feature_type="text", load_8bit=False, load_4bit=False, load_bf16=False): + disable_torch_init() + + model_name = get_model_name_from_path(model_path) + + tokenizer, model, image_processor, context_len = load_pretrained_model( + model_path=model_path, + model_base=model_base, + model_name=model_name, + load_8bit=load_8bit, + load_4bit=load_4bit, + load_bf16=load_bf16, + ) + + dataset = ObjIdentifierDataset( + tokenizer=tokenizer, + data_path=[], + scene_to_obj_mapping=scene_to_obj_mapping, + obj_context_feature_type=obj_context_feature_type, + mode="generate", + ) + collator = DataCollatorForBatchDecodingObjIdentifierDataset(tokenizer=tokenizer) + data_loader = DataLoader( + dataset, collate_fn=collator, batch_size=1, num_workers=0, shuffle=False + ) + + return tokenizer, model, data_loader + + +def get_model_response(model, tokenizer, data_loader, scene_id, user_input, max_new_tokens=50, temperature=0.2, top_p=0.9): + input_data = [ + { + "id": f"interactive@{scene_id}@input", + "scene_id": scene_id, + "conversations": [{"from": "human", "value": f"Ground these sentences: {user_input}\n"}], + "ground_truth": "User provided description for interactive session.", + } + ] + + data_loader.dataset.update_data(input_data) + + for batch in data_loader: + input_ids = batch["input_ids"].squeeze(dim=1).cuda() + with torch.inference_mode(): + output_ids = model.generate( + input_ids, + max_new_tokens=max_new_tokens, + temperature=temperature, + top_p=top_p, + use_cache=True, + ) + prompt = tokenizer.batch_decode(input_ids)[0] + outputs = tokenizer.batch_decode(output_ids[:, input_ids.shape[-1]:], skip_special_tokens=True) + + return prompt, outputs[0] diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..51e436150a63b7995cac38f335880a99e35a9f23 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,39 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "3d-grand" +version = "1.0.1" +readme = "README.md" +requires-python = ">=3.10" +classifiers = ["Programming Language :: Python :: 3"] +dependencies = [ + "gradio==4.36.1", + "numpy", + "tokenizers>=0.12.1", + "torch==2.0.1", + "torchvision==0.15.2", + "shortuuid", + "peft==0.4.0", + "transformers==4.31.0", + "accelerate==0.21.0", + "bitsandbytes==0.41.0", + "scikit-learn==1.2.2", + "sentencepiece==0.1.99", + "einops==0.6.1", + "einops-exts==0.0.4", + "open3d", + "bs4", + "attrs", + "cattrs", +] + +[project.urls] +"Homepage" = "https://3d-grand.github.io/" + +[tool.setuptools.packages.find] +exclude = ["checkpoints*", "data*"] + +[tool.wheel] +exclude = ["checkpoints*", "data*"] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..ad16f362bce3f79bb308fc51dae972e898408a17 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,2 @@ +# Install the current directory +-e . diff --git a/session.py b/session.py new file mode 100644 index 0000000000000000000000000000000000000000..504b0029ab089fa9ce24cb9de94a8d76a66c670d --- /dev/null +++ b/session.py @@ -0,0 +1,85 @@ +import datetime +import json +import os +import uuid +import numpy as np +import cattrs +from attrs import define, field +import logging +import tempfile + +from settings import Settings + +logger = logging.getLogger(__name__) + +@define +class Session: + """A class to store the all information about a session, including the chat + history and the current scene.""" + + session_id: str + start_time: str + scene: str + chat_history_for_display: list[tuple] + chat_counter: int + output_dir: str + + @classmethod + def create(cls): + return cls.create_for_scene(Settings.default_scene) + + @classmethod + def create_for_scene(cls, scene: str): + output_dir = tempfile.mkdtemp() # Create a temporary directory + session = cls( + session_id=str(uuid.uuid4()), + start_time=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + scene=scene, + chat_history_for_display=[(None, Settings.INITIAL_MSG_FOR_DISPLAY)], + chat_counter=0, + output_dir=output_dir + ) + logger.info( + f"Creating a new session {session.session_id} with scene {session.scene} and output dir {session.output_dir}." + ) + return session + + def convert_float32(self, obj): + """Convert all np.float32 values in the given object to Python float.""" + if isinstance(obj, np.float32): + return float(obj) + + if isinstance(obj, list): + return [self.convert_float32(item) for item in obj] + + if isinstance(obj, tuple): + return tuple(self.convert_float32(item) for item in obj) + + if isinstance(obj, dict): + return {key: self.convert_float32(value) for key, value in obj.items()} + + return obj + + def get_session_output_dir(self): + # Create the directory and any parent directories if they don't exist + os.makedirs(os.path.join(self.output_dir, self.scene, self.session_id), exist_ok=True) + return os.path.join(self.output_dir, self.scene, self.session_id) + + def save(self) -> None: + """Save the session as a json file.""" + logger.info(f"Saving session {self.session_id} to disk.") + + structured_data = cattrs.unstructure(self) + structured_data.pop("chat_history_for_display", None) + # Convert all np.float32 to float + converted_data = self.convert_float32(structured_data) + + with open( + os.path.join( + self.get_session_output_dir(), f"{self.session_id}.json" + ), + "w", + encoding="utf-8", + ) as file: + json.dump(converted_data, file, indent=4) + logger.info(f"Session {self.session_id} saved to {self.get_session_output_dir()}.") diff --git a/settings.py b/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..b7572fae23b37795148fd4e3a2f3b6e7607f04ce --- /dev/null +++ b/settings.py @@ -0,0 +1,9 @@ +class GRAND3D_Settings: + default_scene: str = "scene0426_00" + data_path: str = "data" + system_msg: str = "system message" + INITIAL_MSG_FOR_DISPLAY = "Hi there! I can help you find objects in this room via referring expressions. You can say things like 'the desk next to the window' or 'the TV on the drawer, opposing the bed'. What would you like to find?" + + + +Settings = GRAND3D_Settings() diff --git a/utils.py b/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ba983a4eb9eccf6f83f86bccdfa2d8d617b960d9 --- /dev/null +++ b/utils.py @@ -0,0 +1,58 @@ +import os + +import numpy as np +from requests import Response + + +def list_dirs(root_dir): + dirs_to_return = [] + with os.scandir(root_dir) as entries: + for entry in entries: + if entry.is_dir(): + dirs_to_return.append(entry.name) + return dirs_to_return + + +def rotate_x(angle_degrees: int, c2w: np.ndarray) -> np.ndarray: + angle_radians = np.radians(angle_degrees) + rotation_matrix = np.array( + [ + [1, 0, 0, 0], + [0, np.cos(angle_radians), -np.sin(angle_radians), 0], + [0, np.sin(angle_radians), np.cos(angle_radians), 0], + [0, 0, 0, 1], + ] + ) + return c2w @ rotation_matrix + + +def rotate_y(angle_degrees: int, c2w: np.ndarray) -> np.ndarray: + angle_radians = np.radians(angle_degrees) + rotation_matrix = np.array( + [ + [np.cos(angle_radians), 0, np.sin(angle_radians), 0], + [0, 1, 0, 0], + [-np.sin(angle_radians), 0, np.cos(angle_radians), 0], + [0, 0, 0, 1], + ] + ) + return c2w @ rotation_matrix + + +def rotate_z(angle_degrees: int, c2w: np.ndarray) -> np.ndarray: + angle_radians = np.radians(angle_degrees) + rotation_matrix = np.array( + [ + [np.cos(angle_radians), -np.sin(angle_radians), 0, 0], + [np.sin(angle_radians), np.cos(angle_radians), 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + ] + ) + return c2w @ rotation_matrix + + +def get_status_code_and_reason(response: Response | None) -> str: + if response is None: + return "" + return f"{response.status_code} - {response.reason}"