mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-31 09:17:43 +00:00
XianBW
914 lines
37 KiB
Python
914 lines
37 KiB
Python
import hashlib
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import json
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import pickle
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import random
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import re
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from collections import defaultdict
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from datetime import time, timedelta
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from pathlib import Path
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import pandas as pd
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import plotly.express as px
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import streamlit as st
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from litellm import get_valid_models
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from streamlit import session_state as state
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from rdagent.app.data_science.loop import DataScienceRDLoop
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from rdagent.log.storage import FileStorage
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from rdagent.log.ui.conf import UI_SETTING
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from rdagent.log.ui.utils import curve_figure, load_times, trace_figure
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from rdagent.log.utils import (
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LogColors,
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extract_evoid,
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extract_json,
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extract_loopid_func_name,
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is_valid_session,
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)
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from rdagent.oai.backend.litellm import LITELLM_SETTINGS
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from rdagent.oai.llm_utils import APIBackend
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from rdagent.utils.agent.tpl import T
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from rdagent.utils.repo.diff import generate_diff_from_dict
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if "show_stdout" not in state:
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state.show_stdout = False
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if "show_llm_log" not in state:
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state.show_llm_log = False
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if "data" not in state:
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state.data = defaultdict(lambda: defaultdict(dict))
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if "llm_data" not in state:
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state.llm_data = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
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if "log_path" not in state:
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state.log_path = None
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if "log_folder" not in state:
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state.log_folder = Path("./log")
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available_models = get_valid_models()
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LITELLM_SETTINGS.dump_chat_cache = False
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LITELLM_SETTINGS.dump_embedding_cache = False
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LITELLM_SETTINGS.use_chat_cache = False
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LITELLM_SETTINGS.use_embedding_cache = False
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def convert_defaultdict_to_dict(d):
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if isinstance(d, defaultdict):
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d = {k: convert_defaultdict_to_dict(v) for k, v in d.items()}
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return d
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def load_data(log_path: Path):
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data = defaultdict(lambda: defaultdict(dict))
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llm_data = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
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token_costs = defaultdict(list)
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for msg in FileStorage(log_path).iter_msg():
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if not msg.tag:
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continue
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li, fn = extract_loopid_func_name(msg.tag)
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ei = extract_evoid(msg.tag)
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if li:
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li = int(li)
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if ei is not None:
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ei = int(ei)
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if "debug_" in msg.tag:
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if ei is not None:
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llm_data[li][fn][ei].append(
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{
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"tag": msg.tag,
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"obj": msg.content,
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}
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)
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else:
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llm_data[li][fn]["no_tag"].append(
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{
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"tag": msg.tag,
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"obj": msg.content,
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}
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)
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elif "token_cost" in msg.tag:
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token_costs[li].append(msg)
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elif "llm" not in msg.tag and "session" not in msg.tag and "batch embedding" not in msg.tag:
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if msg.tag == "competition":
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data["competition"] = msg.content
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continue
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if "SETTINGS" in msg.tag:
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data["settings"][msg.tag] = msg.content
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continue
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msg.tag = re.sub(r"\.evo_loop_\d+", "", msg.tag)
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msg.tag = re.sub(r"Loop_\d+\.[^.]+\.?", "", msg.tag)
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msg.tag = msg.tag.strip()
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if ei is not None:
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if ei not in data[li][fn]:
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data[li][fn][ei] = {}
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data[li][fn][ei][msg.tag] = msg.content
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else:
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if msg.tag:
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data[li][fn][msg.tag] = msg.content
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else:
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if not isinstance(msg.content, str):
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data[li][fn]["no_tag"] = msg.content
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# To be compatible with old version log trace, keep this
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llm_log_p = log_path / "debug_llm.pkl"
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if llm_log_p.exists():
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try:
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rd = pickle.loads(llm_log_p.read_bytes())
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except:
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rd = []
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for d in rd:
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t = d["tag"]
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if "debug_exp_gen" in t:
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continue
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if "debug_tpl" in t and "filter_" in d["obj"]["uri"]:
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continue
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lid, fn = extract_loopid_func_name(t)
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ei = extract_evoid(t)
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if lid:
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lid = int(lid)
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if ei is not None:
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ei = int(ei)
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if ei is not None:
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llm_data[lid][fn][ei].append(d)
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else:
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llm_data[lid][fn]["no_tag"].append(d)
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return (
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convert_defaultdict_to_dict(data),
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convert_defaultdict_to_dict(llm_data),
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convert_defaultdict_to_dict(token_costs),
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)
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if UI_SETTING.enable_cache:
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load_data = st.cache_data(persist=True)(load_data)
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def load_stdout(stdout_path: Path):
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if stdout_path.exists():
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stdout = stdout_path.read_text()
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else:
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stdout = f"Please Set: {stdout_path}"
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return stdout
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# UI windows
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def task_win(task):
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with st.container(border=True):
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st.markdown(f"**:violet[{task.name}]**")
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st.markdown(task.description)
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if hasattr(task, "architecture"): # model task
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st.markdown(
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f"""
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| Model_type | Architecture | hyperparameters |
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|------------|--------------|-----------------|
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| {task.model_type} | {task.architecture} | {task.hyperparameters} |
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"""
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)
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def workspace_win(workspace, cmp_workspace=None, cmp_name="last code."):
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show_files = {k: v for k, v in workspace.file_dict.items() if "test" not in k}
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if len(show_files) > 0:
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if cmp_workspace:
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diff = generate_diff_from_dict(cmp_workspace.file_dict, show_files, "main.py")
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with st.expander(f":violet[**Diff with {cmp_name}**]"):
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st.code("".join(diff), language="diff", wrap_lines=True, line_numbers=True)
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with st.expander(f"Files in :blue[{replace_ep_path(workspace.workspace_path)}]"):
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code_tabs = st.tabs(show_files.keys())
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for ct, codename in zip(code_tabs, show_files.keys()):
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with ct:
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st.code(
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show_files[codename],
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language=("python" if codename.endswith(".py") else "markdown"),
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wrap_lines=True,
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line_numbers=True,
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)
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if state.show_save_input:
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st.markdown("### Save All Files to Folder")
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unique_key = hashlib.md5("".join(show_files.values()).encode()).hexdigest() + str(
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random.randint(0, 10000)
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)
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target_folder = st.text_input("Enter target folder path:", key=unique_key)
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if st.button("Save Files", key=f"save_files_button_{unique_key}"):
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if target_folder.strip() == "":
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st.warning("Please enter a valid folder path.")
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else:
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target_folder_path = Path(target_folder)
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target_folder_path.mkdir(parents=True, exist_ok=True)
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for filename, content in workspace.file_dict.items():
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save_path = target_folder_path / filename
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save_path.parent.mkdir(parents=True, exist_ok=True)
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save_path.write_text(content, encoding="utf-8")
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st.success(f"All files saved to: {target_folder}")
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else:
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st.markdown(f"No files in :blue[{replace_ep_path(workspace.workspace_path)}]")
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# Helper functions
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def show_text(text, lang=None):
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"""显示文本代码块"""
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if lang:
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st.code(text, language=lang, wrap_lines=True, line_numbers=True)
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elif "\n" in text:
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st.code(text, language="python", wrap_lines=True, line_numbers=True)
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else:
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st.code(text, language="html", wrap_lines=True)
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def highlight_prompts_uri(uri):
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"""高亮 URI 的格式"""
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parts = uri.split(":")
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if len(parts) > 1:
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return f"**{parts[0]}:**:green[**{parts[1]}**]"
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return f"**{uri}**"
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def llm_log_win(llm_d: list):
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for d in llm_d:
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if "debug_tpl" in d["tag"]:
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uri = d["obj"]["uri"]
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if "filter_redundant_text" in uri:
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continue
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tpl = d["obj"]["template"]
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cxt = d["obj"]["context"]
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rd = d["obj"]["rendered"]
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with st.popover(highlight_prompts_uri(uri), icon="⚙️", use_container_width=True):
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t1, t2, t3 = st.tabs([":green[**Rendered**]", ":blue[**Template**]", ":orange[**Context**]"])
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with t1:
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show_text(rd)
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with t2:
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show_text(tpl, lang="django")
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with t3:
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st.json(cxt)
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elif "debug_llm" in d["tag"]:
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system = d["obj"].get("system", None)
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user = d["obj"]["user"]
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resp = d["obj"]["resp"]
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with st.expander(f"**LLM**", icon="🤖", expanded=False):
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t1, t2, t3, t4 = st.tabs(
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[":green[**Response**]", ":blue[**User**]", ":orange[**System**]", ":violet[**ChatBot**]"]
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)
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with t1:
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try:
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rdict = json.loads(resp)
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showed_keys = []
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for k, v in rdict.items():
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if k.endswith(".py") or k.endswith(".md"):
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st.markdown(f":red[**{k}**]")
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st.code(v, language="python", wrap_lines=True, line_numbers=True)
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showed_keys.append(k)
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for k in showed_keys:
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rdict.pop(k)
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if len(showed_keys) > 0:
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st.write(":red[**Other parts (except for the code or spec) in response dict:**]")
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st.json(rdict)
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except:
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show_text(resp)
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with t2:
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show_text(user)
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with t3:
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show_text(system or "No system prompt available")
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with t4:
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input_c, resp_c = st.columns(2)
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key = hashlib.md5(resp.encode()).hexdigest()
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with input_c:
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btc1, btc2 = st.columns(2)
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trace_model = (
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state.data.get("settings", {})
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.get("LITELLM_SETTINGS", {})
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.get("chat_model", available_models[0])
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)
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trace_reasoning_effort = (
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state.data.get("settings", {}).get("LITELLM_SETTINGS", {}).get("reasoning_effort", None)
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)
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LITELLM_SETTINGS.chat_model = btc1.selectbox(
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"Chat Model",
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options=available_models,
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index=available_models.index(trace_model),
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key=key + "_chat_model",
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)
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LITELLM_SETTINGS.reasoning_effort = btc2.selectbox(
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"Reasoning Effort",
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options=[None, "low", "medium", "high"],
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index=[None, "low", "medium", "high"].index(trace_reasoning_effort),
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key=key + "_reasoning_effort",
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)
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json_mode = st.checkbox("JSON Mode", value=False, key=key + "_json_mode")
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sys_p = input_c.text_area(label="system", value=system, height="content", key=key + "_system")
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user_p = input_c.text_area(label="user", value=user, height="content", key=key + "_user")
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with resp_c:
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if st.button("Call LLM", key=key + "_call_llm"):
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with st.spinner("Calling LLM..."):
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try:
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resp_new = APIBackend().build_messages_and_create_chat_completion(
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user_prompt=user_p,
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system_prompt=sys_p,
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json_mode=json_mode,
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)
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except Exception as e:
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resp_new = f"Error: {e}"
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try:
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rdict = json.loads(resp_new)
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st.json(rdict)
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except:
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st.code(resp_new, wrap_lines=True, line_numbers=True)
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def hypothesis_win(hypo):
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try:
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st.code(str(hypo).replace("\n", "\n\n"), wrap_lines=True)
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except Exception as e:
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st.write(hypo.__dict__)
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def exp_gen_win(exp_gen_data, llm_data=None):
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st.header("Exp Gen", divider="blue", anchor="exp-gen")
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if state.show_llm_log and llm_data is not None:
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llm_log_win(llm_data["no_tag"])
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st.subheader("Hypothesis")
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hypothesis_win(exp_gen_data["no_tag"].hypothesis)
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st.subheader("pending_tasks")
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for tasks in exp_gen_data["no_tag"].pending_tasks_list:
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task_win(tasks[0])
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st.subheader("Exp Workspace")
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workspace_win(exp_gen_data["no_tag"].experiment_workspace)
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def evolving_win(data, key, llm_data=None, base_workspace=None):
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with st.container(border=True):
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if len(data) > 1:
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evo_id = st.slider("Evolving", 0, len(data) - 1, 0, key=key)
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elif len(data) == 1:
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evo_id = 0
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else:
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st.markdown("No evolving.")
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return
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if evo_id in data:
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if state.show_llm_log and llm_data is not None:
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llm_log_win(llm_data[evo_id])
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if data[evo_id]["evolving code"][0] is not None:
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st.subheader("codes")
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workspace_win(
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data[evo_id]["evolving code"][0],
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cmp_workspace=data[evo_id - 1]["evolving code"][0] if evo_id > 0 else base_workspace,
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cmp_name="last evolving code" if evo_id > 0 else "base workspace",
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)
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fb = data[evo_id]["evolving feedback"][0]
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st.subheader("evolving feedback" + ("✅" if bool(fb) else "❌"))
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f1, f2, f3 = st.tabs(["execution", "return_checking", "code"])
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f1.code(fb.execution, wrap_lines=True)
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f2.code(fb.return_checking, wrap_lines=True)
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f3.code(fb.code, wrap_lines=True)
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else:
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st.write("data[evo_id]['evolving code'][0] is None.")
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st.write(data[evo_id])
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else:
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st.markdown("No evolving.")
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|
|
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def coding_win(data, base_exp, llm_data: dict | None = None):
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st.header("Coding", divider="blue", anchor="coding")
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if llm_data is not None:
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common_llm_data = llm_data.pop("no_tag", [])
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evolving_data = {k: v for k, v in data.items() if isinstance(k, int)}
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task_set = set()
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for v in evolving_data.values():
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for t in v:
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if "Task" in t.split(".")[0]:
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task_set.add(t.split(".")[0])
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if task_set:
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# 新版存Task tag的Trace
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for task in task_set:
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st.subheader(task)
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task_data = {k: {a.split(".")[1]: b for a, b in v.items() if task in a} for k, v in evolving_data.items()}
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evolving_win(
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task_data,
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key=task,
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llm_data=llm_data if llm_data else None,
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base_workspace=base_exp.experiment_workspace,
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)
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else:
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# 旧版未存Task tag的Trace
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evolving_win(
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evolving_data,
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key="coding",
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llm_data=llm_data if llm_data else None,
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base_workspace=base_exp.experiment_workspace,
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)
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if state.show_llm_log:
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llm_log_win(common_llm_data)
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if "no_tag" in data:
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st.subheader("Exp Workspace (coding final)")
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workspace_win(data["no_tag"].experiment_workspace)
|
|
|
|
|
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def running_win(data, base_exp, llm_data=None, sota_exp=None):
|
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st.header("Running", divider="blue", anchor="running")
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if llm_data is not None:
|
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common_llm_data = llm_data.pop("no_tag", [])
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evolving_win(
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{k: v for k, v in data.items() if isinstance(k, int)},
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key="running",
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llm_data=llm_data if llm_data else None,
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base_workspace=base_exp.experiment_workspace,
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)
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if state.show_llm_log and llm_data is not None:
|
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llm_log_win(common_llm_data)
|
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if "no_tag" in data:
|
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st.subheader("Exp Workspace (running final)")
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workspace_win(
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data["no_tag"].experiment_workspace,
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cmp_workspace=sota_exp.experiment_workspace if sota_exp else None,
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cmp_name="last SOTA",
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)
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st.subheader("Result")
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try:
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st.write(data["no_tag"].result)
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except AttributeError as e: # Compatible with old versions
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st.write(data["no_tag"].__dict__["result"])
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mle_score_text = data.get("mle_score", "no submission to score")
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mle_score = extract_json(mle_score_text)
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st.subheader(
|
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"MLE Submission Score"
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+ ("✅" if (isinstance(mle_score, dict) and mle_score["score"] is not None) else "❌")
|
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)
|
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if isinstance(mle_score, dict):
|
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st.json(mle_score)
|
|
else:
|
|
st.code(mle_score_text, wrap_lines=True)
|
|
|
|
|
|
def feedback_win(fb_data, llm_data=None):
|
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fb = fb_data["no_tag"]
|
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st.header("Feedback" + ("✅" if bool(fb) else "❌"), divider="orange", anchor="feedback")
|
|
if state.show_llm_log and llm_data is not None:
|
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llm_log_win(llm_data["no_tag"])
|
|
try:
|
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st.code(str(fb).replace("\n", "\n\n"), wrap_lines=True)
|
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except Exception as e:
|
|
st.write(fb.__dict__)
|
|
if fb.exception is not None:
|
|
st.markdown(f"**:red[Exception]**: {fb.exception}")
|
|
|
|
|
|
def sota_win(sota_exp, trace):
|
|
st.header("SOTA Experiment", divider="rainbow", anchor="sota-exp")
|
|
if hasattr(trace, "sota_exp_to_submit") and trace.sota_exp_to_submit is not None:
|
|
st.markdown(":orange[trace.**sota_exp_to_submit**]")
|
|
sota_exp = trace.sota_exp_to_submit
|
|
else:
|
|
st.markdown(":orange[trace.**sota_experiment()**]")
|
|
|
|
if sota_exp:
|
|
st.markdown(f"**SOTA Exp Hypothesis**")
|
|
hypothesis_win(sota_exp.hypothesis)
|
|
st.markdown("**Exp Workspace**")
|
|
workspace_win(sota_exp.experiment_workspace)
|
|
else:
|
|
st.markdown("No SOTA experiment.")
|
|
|
|
|
|
def main_win(loop_id, llm_data=None):
|
|
loop_data = state.data[loop_id]
|
|
exp_gen_win(loop_data["direct_exp_gen"], llm_data["direct_exp_gen"] if llm_data else None)
|
|
if "coding" in loop_data:
|
|
coding_win(
|
|
loop_data["coding"],
|
|
base_exp=loop_data["direct_exp_gen"]["no_tag"],
|
|
llm_data=llm_data["coding"] if llm_data else None,
|
|
)
|
|
if "running" in loop_data:
|
|
running_win(
|
|
loop_data["running"],
|
|
base_exp=loop_data["coding"]["no_tag"],
|
|
llm_data=llm_data["running"] if llm_data else None,
|
|
sota_exp=(
|
|
state.data[loop_id - 1].get("record", {}).get("SOTA experiment", None)
|
|
if (loop_id - 1) in state.data
|
|
else None
|
|
),
|
|
)
|
|
if "feedback" in loop_data:
|
|
feedback_win(loop_data["feedback"], llm_data.get("feedback", None) if llm_data else None)
|
|
if "record" in loop_data and "SOTA experiment" in loop_data["record"]:
|
|
sota_win(loop_data["record"]["SOTA experiment"], loop_data["record"]["trace"])
|
|
|
|
|
|
def replace_ep_path(p: Path):
|
|
# 替换workspace path为对应ep机器mount在ep03的path
|
|
# TODO: FIXME: 使用配置项来处理
|
|
match = re.search(r"ep\d+", str(state.log_folder))
|
|
if match:
|
|
ep = match.group(0)
|
|
return Path(
|
|
str(p).replace("repos/RD-Agent-Exp", f"repos/batch_ctrl/all_projects/{ep}").replace("/Data", "/data")
|
|
)
|
|
return p
|
|
|
|
|
|
def get_llm_call_stats(llm_data: dict) -> tuple[int, int]:
|
|
total_llm_call = 0
|
|
total_filter_call = 0
|
|
filter_sys_prompt = T("rdagent.utils.prompts:filter_redundant_text.system").r()
|
|
for li, loop_d in llm_data.items():
|
|
for fn, loop_fn_d in loop_d.items():
|
|
for k, v in loop_fn_d.items():
|
|
for d in v:
|
|
if "debug_llm" in d["tag"]:
|
|
total_llm_call += 1
|
|
if filter_sys_prompt == d["obj"]["system"]:
|
|
total_filter_call += 1
|
|
return total_llm_call, total_filter_call
|
|
|
|
|
|
def timedelta_to_str(td: timedelta | None) -> str:
|
|
if isinstance(td, timedelta):
|
|
total_seconds = int(td.total_seconds())
|
|
hours = total_seconds // 3600
|
|
minutes = (total_seconds % 3600) // 60
|
|
seconds = total_seconds % 60
|
|
return f"{hours:02d}:{minutes:02d}:{seconds:02d}"
|
|
return td
|
|
|
|
|
|
def summarize_win():
|
|
st.header("Summary", divider="rainbow")
|
|
with st.container(border=True):
|
|
min_id, max_id = get_state_data_range(state.data)
|
|
info0, info1, info2, info3, info4, info5 = st.columns([1, 1, 1, 1, 1, 1])
|
|
show_trace_dag = info0.toggle("Show trace DAG", key="show_trace_dag")
|
|
only_success = info0.toggle("Only Success", key="only_success")
|
|
with info1.popover("LITELLM", icon="⚙️"):
|
|
st.write(state.data.get("settings", {}).get("LITELLM_SETTINGS", "No settings found."))
|
|
with info2.popover("RD_AGENT", icon="⚙️"):
|
|
st.write(state.data.get("settings", {}).get("RD_AGENT_SETTINGS", "No settings found."))
|
|
with info3.popover("RDLOOP", icon="⚙️"):
|
|
st.write(state.data.get("settings", {}).get("RDLOOP_SETTINGS", "No settings found."))
|
|
|
|
llm_call, llm_filter_call = get_llm_call_stats(state.llm_data)
|
|
info4.metric("LLM Calls", llm_call)
|
|
info5.metric("LLM Filter Calls", f"{llm_filter_call}({round(llm_filter_call / llm_call * 100, 2)}%)")
|
|
if show_trace_dag:
|
|
st.markdown("### Trace DAG")
|
|
final_trace_loop_id = max_id
|
|
while "record" not in state.data[final_trace_loop_id]:
|
|
final_trace_loop_id -= 1
|
|
st.pyplot(trace_figure(state.data[final_trace_loop_id]["record"]["trace"]))
|
|
df = pd.DataFrame(
|
|
columns=[
|
|
"Component",
|
|
"Hypothesis",
|
|
"Reason",
|
|
"Others",
|
|
"Running Score (valid)",
|
|
"Running Score (test)",
|
|
"Feedback",
|
|
"e-loops(coding)",
|
|
"COST($)",
|
|
"Time",
|
|
"Exp Gen",
|
|
"Coding",
|
|
"Running",
|
|
],
|
|
index=range(min_id, max_id + 1),
|
|
)
|
|
|
|
valid_results = {}
|
|
for loop in range(min_id, max_id + 1):
|
|
loop_data = state.data[loop]
|
|
df.loc[loop, "Component"] = loop_data["direct_exp_gen"]["no_tag"].hypothesis.component
|
|
df.loc[loop, "Hypothesis"] = loop_data["direct_exp_gen"]["no_tag"].hypothesis.hypothesis
|
|
df.loc[loop, "Reason"] = loop_data["direct_exp_gen"]["no_tag"].hypothesis.reason
|
|
df.at[loop, "Others"] = {
|
|
k: v
|
|
for k, v in loop_data["direct_exp_gen"]["no_tag"].hypothesis.__dict__.items()
|
|
if k not in ["component", "hypothesis", "reason"]
|
|
}
|
|
df.loc[loop, "COST($)"] = sum(tc.content["cost"] for tc in state.token_costs[loop])
|
|
|
|
# Time Stats
|
|
if loop in state.times and state.times[loop]:
|
|
exp_gen_time = coding_time = running_time = None
|
|
all_steps_time = timedelta()
|
|
for lpt in state.times[loop]:
|
|
all_steps_time += lpt.end - lpt.start
|
|
if lpt.step_idx == 0:
|
|
exp_gen_time = lpt.end - lpt.start
|
|
elif lpt.step_idx == 1:
|
|
coding_time = lpt.end - lpt.start
|
|
elif lpt.step_idx == 2:
|
|
running_time = lpt.end - lpt.start
|
|
df.loc[loop, "Time"] = timedelta_to_str(all_steps_time)
|
|
df.loc[loop, "Exp Gen"] = timedelta_to_str(exp_gen_time)
|
|
df.loc[loop, "Coding"] = timedelta_to_str(coding_time)
|
|
df.loc[loop, "Running"] = timedelta_to_str(running_time)
|
|
|
|
if "running" in loop_data and "no_tag" in loop_data["running"]:
|
|
try:
|
|
try:
|
|
running_result = loop_data["running"]["no_tag"].result
|
|
except AttributeError as e: # Compatible with old versions
|
|
running_result = loop_data["running"]["no_tag"].__dict__["result"]
|
|
df.loc[loop, "Running Score (valid)"] = str(round(running_result.loc["ensemble"].iloc[0], 5))
|
|
valid_results[loop] = running_result
|
|
except:
|
|
df.loc[loop, "Running Score (valid)"] = "❌"
|
|
if "mle_score" not in state.data[loop]:
|
|
if "mle_score" in loop_data["running"]:
|
|
mle_score_txt = loop_data["running"]["mle_score"]
|
|
state.data[loop]["mle_score"] = extract_json(mle_score_txt)
|
|
if (
|
|
state.data[loop]["mle_score"] is not None
|
|
and state.data[loop]["mle_score"]["score"] is not None
|
|
):
|
|
df.loc[loop, "Running Score (test)"] = str(state.data[loop]["mle_score"]["score"])
|
|
else:
|
|
state.data[loop]["mle_score"] = mle_score_txt
|
|
df.loc[loop, "Running Score (test)"] = "❌"
|
|
else:
|
|
mle_score_path = (
|
|
replace_ep_path(loop_data["running"]["no_tag"].experiment_workspace.workspace_path)
|
|
/ "mle_score.txt"
|
|
)
|
|
try:
|
|
mle_score_txt = mle_score_path.read_text()
|
|
state.data[loop]["mle_score"] = extract_json(mle_score_txt)
|
|
if state.data[loop]["mle_score"]["score"] is not None:
|
|
df.loc[loop, "Running Score (test)"] = str(state.data[loop]["mle_score"]["score"])
|
|
else:
|
|
state.data[loop]["mle_score"] = mle_score_txt
|
|
df.loc[loop, "Running Score (test)"] = "❌"
|
|
except Exception as e:
|
|
state.data[loop]["mle_score"] = str(e)
|
|
df.loc[loop, "Running Score (test)"] = "❌"
|
|
else:
|
|
if isinstance(state.data[loop]["mle_score"], dict):
|
|
df.loc[loop, "Running Score (test)"] = str(state.data[loop]["mle_score"]["score"])
|
|
else:
|
|
df.loc[loop, "Running Score (test)"] = "❌"
|
|
|
|
else:
|
|
df.loc[loop, "Running Score (valid)"] = "N/A"
|
|
df.loc[loop, "Running Score (test)"] = "N/A"
|
|
|
|
if "coding" in loop_data:
|
|
if len([i for i in loop_data["coding"].keys() if isinstance(i, int)]) == 0:
|
|
df.loc[loop, "e-loops(coding)"] = 0
|
|
else:
|
|
df.loc[loop, "e-loops(coding)"] = (
|
|
max(i for i in loop_data["coding"].keys() if isinstance(i, int)) + 1
|
|
)
|
|
if "feedback" in loop_data:
|
|
df.loc[loop, "Feedback"] = "✅" if bool(loop_data["feedback"]["no_tag"]) else "❌"
|
|
else:
|
|
df.loc[loop, "Feedback"] = "N/A"
|
|
|
|
# COST curve
|
|
costs = df["COST($)"].astype(float)
|
|
costs.index = [f"L{i}" for i in costs.index]
|
|
cumulative_costs = costs.cumsum()
|
|
with st.popover("COST Curve", icon="💰", use_container_width=True):
|
|
fig = px.line(
|
|
x=costs.index,
|
|
y=[costs.values, cumulative_costs.values],
|
|
labels={"x": "Loop", "value": "COST($)"},
|
|
title="COST($) per Loop & Cumulative COST($)",
|
|
markers=True,
|
|
)
|
|
fig.update_traces(mode="lines+markers")
|
|
fig.data[0].name = "COST($) per Loop"
|
|
fig.data[1].name = "Cumulative COST($)"
|
|
st.plotly_chart(fig)
|
|
|
|
if only_success:
|
|
df = df[df["Feedback"] == "✅"]
|
|
st.dataframe(df[df.columns[~df.columns.isin(["Hypothesis", "Reason", "Others"])]])
|
|
|
|
# scores curve
|
|
vscores = {}
|
|
for k, vs in valid_results.items():
|
|
if not vs.index.is_unique:
|
|
st.warning(f"Loop {k}'s valid scores index are not unique, only the last one will be kept to show.")
|
|
st.write(vs)
|
|
vscores[k] = vs[~vs.index.duplicated(keep="last")].iloc[:, 0]
|
|
if len(vscores) > 0:
|
|
metric_name = list(vscores.values())[0].name
|
|
else:
|
|
metric_name = "None"
|
|
vscores = pd.DataFrame(vscores)
|
|
if "ensemble" in vscores.index:
|
|
ensemble_row = vscores.loc[["ensemble"]]
|
|
vscores = pd.concat([ensemble_row, vscores.drop("ensemble")])
|
|
vscores = vscores.T
|
|
vscores["test"] = df["Running Score (test)"]
|
|
vscores.index = [f"L{i}" for i in vscores.index]
|
|
vscores.columns.name = metric_name
|
|
with st.popover("Scores Curve", icon="📈", use_container_width=True):
|
|
st.plotly_chart(curve_figure(vscores))
|
|
|
|
st.markdown("### Hypotheses Table")
|
|
st.dataframe(
|
|
df.iloc[:, :8],
|
|
row_height=100,
|
|
column_config={
|
|
"Others": st.column_config.JsonColumn(width="medium"),
|
|
"Reason": st.column_config.TextColumn(width="medium"),
|
|
"Hypothesis": st.column_config.TextColumn(width="large"),
|
|
},
|
|
)
|
|
|
|
def comp_stat_func(x: pd.DataFrame):
|
|
total_num = x.shape[0]
|
|
valid_num = x[x["Running Score (test)"] != "N/A"].shape[0]
|
|
success_num = x[x["Feedback"] == "✅"].shape[0]
|
|
avg_e_loops = x["e-loops(coding)"].mean()
|
|
return pd.Series(
|
|
{
|
|
"Loop Num": total_num,
|
|
"Valid Loop": valid_num,
|
|
"Success Loop": success_num,
|
|
"Valid Rate": round(valid_num / total_num * 100, 2),
|
|
"Success Rate": round(success_num / total_num * 100, 2),
|
|
"Avg e-loops(coding)": round(avg_e_loops, 2),
|
|
}
|
|
)
|
|
|
|
st1, st2 = st.columns([1, 1])
|
|
|
|
# component statistics
|
|
comp_df = (
|
|
df.loc[:, ["Component", "Running Score (test)", "Feedback", "e-loops(coding)"]]
|
|
.groupby("Component")
|
|
.apply(comp_stat_func, include_groups=False)
|
|
)
|
|
comp_df.loc["Total"] = comp_df.sum()
|
|
comp_df.loc["Total", "Valid Rate"] = round(
|
|
comp_df.loc["Total", "Valid Loop"] / comp_df.loc["Total", "Loop Num"] * 100, 2
|
|
)
|
|
comp_df.loc["Total", "Success Rate"] = round(
|
|
comp_df.loc["Total", "Success Loop"] / comp_df.loc["Total", "Loop Num"] * 100, 2
|
|
)
|
|
comp_df["Valid Rate"] = comp_df["Valid Rate"].apply(lambda x: f"{x}%")
|
|
comp_df["Success Rate"] = comp_df["Success Rate"].apply(lambda x: f"{x}%")
|
|
comp_df.loc["Total", "Avg e-loops(coding)"] = round(df["e-loops(coding)"].mean(), 2)
|
|
st2.markdown("### Component Statistics")
|
|
st2.dataframe(comp_df)
|
|
|
|
# component time statistics
|
|
time_df = df.loc[:, ["Component", "Time", "Exp Gen", "Coding", "Running"]]
|
|
time_df = time_df.astype(
|
|
{
|
|
"Time": "timedelta64[ns]",
|
|
"Exp Gen": "timedelta64[ns]",
|
|
"Coding": "timedelta64[ns]",
|
|
"Running": "timedelta64[ns]",
|
|
}
|
|
)
|
|
st1.markdown("### Time Statistics")
|
|
time_stat_df = time_df.groupby("Component").sum()
|
|
time_stat_df.loc["Total"] = time_stat_df.sum()
|
|
time_stat_df.loc[:, "Exp Gen(%)"] = (time_stat_df["Exp Gen"] / time_stat_df["Time"] * 100).round(2)
|
|
time_stat_df.loc[:, "Coding(%)"] = (time_stat_df["Coding"] / time_stat_df["Time"] * 100).round(2)
|
|
time_stat_df.loc[:, "Running(%)"] = (time_stat_df["Running"] / time_stat_df["Time"] * 100).round(2)
|
|
for col in ["Time", "Exp Gen", "Coding", "Running"]:
|
|
time_stat_df[col] = time_stat_df[col].map(timedelta_to_str)
|
|
st1.dataframe(time_stat_df)
|
|
|
|
|
|
def stdout_win(loop_id: int):
|
|
stdout = load_stdout(state.log_folder / f"{state.log_path}.stdout")
|
|
if stdout.startswith("Please Set"):
|
|
st.toast(stdout, icon="🟡")
|
|
return
|
|
start_index = stdout.find(f"Start Loop {loop_id}")
|
|
end_index = stdout.find(f"Start Loop {loop_id + 1}")
|
|
loop_stdout = LogColors.remove_ansi_codes(stdout[start_index:end_index])
|
|
with st.container(border=True):
|
|
st.subheader(f"Loop {loop_id} stdout")
|
|
pattern = f"Start Loop {loop_id}, " + r"Step \d+: \w+"
|
|
matches = re.finditer(pattern, loop_stdout)
|
|
step_stdouts = {}
|
|
for match in matches:
|
|
step = match.group(0)
|
|
si = match.start()
|
|
ei = loop_stdout.find(f"Start Loop {loop_id}", match.end())
|
|
step_stdouts[step] = loop_stdout[si:ei].strip()
|
|
|
|
for k, v in step_stdouts.items():
|
|
with st.expander(k, expanded=False):
|
|
st.code(v, language="log", wrap_lines=True)
|
|
|
|
|
|
def get_folders_sorted(log_path, sort_by_time=False):
|
|
"""
|
|
Cache and return the sorted list of folders, with progress printing.
|
|
:param log_path: Log path
|
|
:param sort_by_time: Whether to sort by time, default False (sort by name)
|
|
"""
|
|
if not log_path.exists():
|
|
st.toast(f"Path {log_path} does not exist!")
|
|
return []
|
|
with st.spinner("Loading folder list..."):
|
|
folders = [folder for folder in log_path.iterdir() if is_valid_session(folder)]
|
|
if sort_by_time:
|
|
folders = sorted(folders, key=lambda folder: folder.stat().st_mtime, reverse=True)
|
|
else:
|
|
folders = sorted(folders, key=lambda folder: folder.name)
|
|
return [folder.name for folder in folders]
|
|
|
|
|
|
# UI - Sidebar
|
|
with st.sidebar:
|
|
# TODO: 只是临时的功能
|
|
if any("log.srv" in folder for folder in state.log_folders):
|
|
day_map = {"srv": "最近(srv)", "srv2": "上一批(srv2)", "srv3": "上上批(srv3)"}
|
|
day_srv = st.radio("选择批次", ["srv", "srv2", "srv3"], format_func=lambda x: day_map[x], horizontal=True)
|
|
if day_srv == "srv":
|
|
state.log_folders = [re.sub(r"log\.srv\d*", "log.srv", folder) for folder in state.log_folders]
|
|
elif day_srv == "srv2":
|
|
state.log_folders = [re.sub(r"log\.srv\d*", "log.srv2", folder) for folder in state.log_folders]
|
|
elif day_srv == "srv3":
|
|
state.log_folders = [re.sub(r"log\.srv\d*", "log.srv3", folder) for folder in state.log_folders]
|
|
|
|
if "log_folder" in st.query_params:
|
|
state.log_folder = Path(st.query_params["log_folder"])
|
|
state.log_folders = [str(state.log_folder)]
|
|
else:
|
|
state.log_folder = Path(
|
|
st.radio(
|
|
f"Select :blue[**one log folder**]",
|
|
state.log_folders,
|
|
format_func=lambda x: x[x.rfind("amlt") + 5 :].split("/")[0] if "amlt" in x else x,
|
|
)
|
|
)
|
|
if not state.log_folder.exists():
|
|
st.warning(f"Path {state.log_folder} does not exist!")
|
|
else:
|
|
folders = get_folders_sorted(state.log_folder, sort_by_time=False)
|
|
if "selection" in st.query_params:
|
|
default_index = (
|
|
folders.index(st.query_params["selection"]) if st.query_params["selection"] in folders else 0
|
|
)
|
|
else:
|
|
default_index = 0
|
|
state.log_path = st.selectbox(
|
|
f"Select from :blue[**{state.log_folder.absolute()}**]", folders, index=default_index
|
|
)
|
|
|
|
if st.button("Refresh Data"):
|
|
if state.log_path is None:
|
|
st.toast("Please select a log path first!", icon="🟡")
|
|
st.stop()
|
|
|
|
state.times = load_times(state.log_folder / state.log_path)
|
|
state.data, state.llm_data, state.token_costs = load_data(state.log_folder / state.log_path)
|
|
st.rerun()
|
|
st.toggle("**Show LLM Log**", key="show_llm_log")
|
|
st.toggle("*Show stdout*", key="show_stdout")
|
|
st.toggle("*Show save workspace*", key="show_save_input")
|
|
st.markdown(
|
|
f"""
|
|
- [Summary](#summary)
|
|
- [Exp Gen](#exp-gen)
|
|
- [Coding](#coding)
|
|
- [Running](#running)
|
|
- [Feedback](#feedback)
|
|
- [SOTA Experiment](#sota-exp)
|
|
"""
|
|
)
|
|
|
|
|
|
def get_state_data_range(state_data):
|
|
# we have a "competition" key in state_data
|
|
# like dict_keys(['competition', 10, 11, 12, 13, 14])
|
|
keys = [
|
|
k
|
|
for k in state_data.keys()
|
|
if isinstance(k, int) and "direct_exp_gen" in state_data[k] and "no_tag" in state_data[k]["direct_exp_gen"]
|
|
]
|
|
return min(keys), max(keys)
|
|
|
|
|
|
# UI - Main
|
|
if "competition" in state.data:
|
|
st.title(
|
|
state.data["competition"]
|
|
+ f" ([share_link](/ds_trace?log_folder={state.log_folder}&selection={state.log_path}))"
|
|
)
|
|
summarize_win()
|
|
min_id, max_id = get_state_data_range(state.data)
|
|
if max_id > min_id:
|
|
loop_id = st.slider("Loop", min_id, max_id, min_id)
|
|
else:
|
|
loop_id = min_id
|
|
if state.show_stdout:
|
|
stdout_win(loop_id)
|
|
main_win(loop_id, state.llm_data[loop_id] if loop_id in state.llm_data else None)
|