mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-01 09:27:43 +00:00
XianBW
67ee1a90bd
* split dsapp to pages & add llm_logs showing * some changes * small bug * add time info * do not test UI in test_import * cache stdout
600 lines
24 KiB
Python
600 lines
24 KiB
Python
import hashlib
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import json
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import pickle
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import re
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from collections import defaultdict
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from datetime import timedelta
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from pathlib import Path
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import pandas as pd
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import streamlit as st
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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.mle_summary import extract_mle_json, is_valid_session
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from rdagent.log.storage import FileStorage
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from rdagent.utils import remove_ansi_codes
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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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def extract_loopid_func_name(tag):
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"""提取 Loop ID 和函数名称"""
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match = re.search(r"Loop_(\d+)\.([^.]+)", tag)
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return match.groups() if match else (None, None)
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def extract_evoid(tag):
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"""提取 EVO ID"""
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match = re.search(r"\.evo_loop_(\d+)\.", tag)
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return match.group(1) if match else None
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def load_times(log_path: Path):
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"""加载时间数据"""
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state.times = defaultdict(lambda: defaultdict(dict))
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for msg in FileStorage(log_path).iter_msg():
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if msg.tag and "llm" not in msg.tag and "session" not in msg.tag:
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li, fn = extract_loopid_func_name(msg.tag)
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if li:
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li = int(li)
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# read times
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loop_obj_path = log_path / "__session__" / f"{li}" / "4_record"
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if loop_obj_path.exists():
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try:
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state.times[li] = DataScienceRDLoop.load(loop_obj_path, do_truncate=False).loop_trace[li]
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except Exception as e:
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pass
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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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@st.cache_data
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def load_data(log_path: Path):
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data = defaultdict(lambda: defaultdict(dict))
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for msg in FileStorage(log_path).iter_msg():
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if msg.tag and "llm" not in msg.tag and "session" 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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li, fn = extract_loopid_func_name(msg.tag)
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if li:
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li = int(li)
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ei = extract_evoid(msg.tag)
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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:
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if int(ei) not in data[li][fn]:
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data[li][fn][int(ei)] = {}
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data[li][fn][int(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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# debug_llm data
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llm_data = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
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llm_log_p = log_path / "debug_llm.pkl"
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with st.spinner("正在加载 debug_llm.pkl..."):
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rd = pickle.loads(llm_log_p.read_bytes())
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for i, d in enumerate(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:
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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 convert_defaultdict_to_dict(data), convert_defaultdict_to_dict(llm_data)
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@st.cache_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(data):
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with st.container(border=True):
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st.markdown(f"**:violet[{data.name}]**")
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st.markdown(data.description)
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if hasattr(data, "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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| {data.model_type} | {data.architecture} | {data.hyperparameters} |
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"""
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)
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def workspace_win(data, instance_id=None):
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show_files = {k: v for k, v in data.file_dict.items() if "test" not in k}
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base_key = str(data.workspace_path)
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if instance_id is not None:
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base_key += f"_{instance_id}"
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unique_key = hashlib.md5(base_key.encode()).hexdigest()
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if len(show_files) > 0:
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with st.expander(f"Files in :blue[{replace_ep_path(data.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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st.markdown("### Save All Files to Folder")
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target_folder = st.text_input("Enter target folder path:", key=f"save_folder_path_input_{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 data.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(data.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)
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elif "\n" in text:
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st.code(text, language="python", wrap_lines=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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return f"**{parts[0]}:**:green[**{parts[1]}**]"
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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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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.expander(highlight_prompts_uri(uri), expanded=False, icon="⚙️"):
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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**", expanded=False, icon="🤖"):
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t1, t2, t3 = st.tabs([":green[**Response**]", ":blue[**User**]", ":orange[**System**]"])
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with t1:
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try:
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rdict = json.loads(resp)
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if "code" in rdict:
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code = rdict["code"]
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st.markdown(":red[**Code in response dict:**]")
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st.code(code, language="python", wrap_lines=True, line_numbers=True)
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rdict.pop("code")
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elif "spec" in rdict:
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spec = rdict["spec"]
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st.markdown(":red[**Spec in response dict:**]")
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st.markdown(spec)
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rdict.pop("spec")
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else:
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# show model codes
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showed_keys = []
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for k, v in rdict.items():
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if k.startswith("model_") and k.endswith(".py"):
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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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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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st.json(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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def hypothesis_win(data):
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st.code(str(data).replace("\n", "\n\n"), wrap_lines=True)
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def exp_gen_win(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:
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llm_log_win(llm_data["no_tag"])
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st.subheader("Hypothesis")
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hypothesis_win(data["no_tag"].hypothesis)
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st.subheader("pending_tasks")
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for tasks in 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(data["no_tag"].experiment_workspace)
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def evolving_win(data, key, llm_data=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:
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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(data[evo_id]["evolving code"][0], instance_id=key)
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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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def coding_win(data, 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(task_data, key=task, llm_data=llm_data if llm_data else None)
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else:
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# 旧版未存Task tag的Trace
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evolving_win(evolving_data, key="coding", llm_data=llm_data if llm_data else None)
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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, instance_id="coding_dump")
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def running_win(data, mle_score, llm_data=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)}, key="running", llm_data=llm_data if llm_data else None
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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 (running final)")
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workspace_win(data["no_tag"].experiment_workspace, instance_id="running_dump")
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st.subheader("Result")
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st.write(data["no_tag"].result)
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st.subheader("MLE Submission Score" + ("✅" if (isinstance(mle_score, dict) and mle_score["score"]) else "❌"))
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if isinstance(mle_score, dict):
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st.json(mle_score)
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else:
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st.code(mle_score, wrap_lines=True)
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def feedback_win(data, llm_data=None):
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data = data["no_tag"]
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st.header("Feedback" + ("✅" if bool(data) else "❌"), divider="orange", anchor="feedback")
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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.code(str(data).replace("\n", "\n\n"), wrap_lines=True)
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if data.exception is not None:
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st.markdown(f"**:red[Exception]**: {data.exception}")
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def sota_win(data):
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st.header("SOTA Experiment", divider="rainbow", anchor="sota-exp")
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if data:
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st.markdown(f"**SOTA Exp Hypothesis**")
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hypothesis_win(data.hypothesis)
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st.markdown("**Exp Workspace**")
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workspace_win(data.experiment_workspace, instance_id="sota")
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else:
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st.markdown("No SOTA experiment.")
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def main_win(data, llm_data=None):
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exp_gen_win(data["direct_exp_gen"], llm_data["direct_exp_gen"] if llm_data else None)
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if "coding" in data:
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coding_win(data["coding"], llm_data["coding"] if llm_data else None)
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if "running" in data:
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running_win(
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data["running"],
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data.get("mle_score", "no submission to score"),
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llm_data=llm_data["running"] if llm_data else None,
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)
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if "feedback" in data:
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feedback_win(data["feedback"], llm_data.get("feedback", None) if llm_data else None)
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if "record" in data and "SOTA experiment" in data["record"]:
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sota_win(data["record"]["SOTA experiment"])
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def replace_ep_path(p: Path):
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# 替换workspace path为对应ep机器mount在ep03的path
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# TODO: FIXME: 使用配置项来处理
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match = re.search(r"ep\d+", str(state.log_folder))
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if match:
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ep = match.group(0)
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return Path(
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str(p).replace("repos/RD-Agent-Exp", f"repos/batch_ctrl/all_projects/{ep}").replace("/Data", "/data")
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)
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return p
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def summarize_data():
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st.header("Summary", divider="rainbow")
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with st.container(border=True):
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df = pd.DataFrame(
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columns=[
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"Component",
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"Running Score",
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"Feedback",
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"e-loops",
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"Time",
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"Coding",
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"Running",
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"Start Time (UTC+8)",
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"End Time (UTC+8)",
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],
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index=range(len(state.data) - 1),
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)
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for loop in range(len(state.data) - 1):
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loop_data = state.data[loop]
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df.loc[loop, "Component"] = loop_data["direct_exp_gen"]["no_tag"].hypothesis.component
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if state.times[loop]:
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df.loc[loop, "Time"] = str(sum((i.end - i.start for i in state.times[loop]), timedelta())).split(".")[0]
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coding_time = state.times[loop][1].end - state.times[loop][1].start
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df.loc[loop, "Coding"] = str(coding_time).split(".")[0]
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if len(state.times[loop]) > 2:
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running_time = state.times[loop][2].end - state.times[loop][2].start
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df.loc[loop, "Running"] = str(running_time).split(".")[0]
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df.loc[loop, "Start Time (UTC+8)"] = state.times[loop][0].start + timedelta(hours=8)
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df.loc[loop, "End Time (UTC+8)"] = state.times[loop][-1].end + timedelta(hours=8)
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if "running" in loop_data and "no_tag" in loop_data["running"]:
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if "mle_score" not in state.data[loop]:
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if "mle_score" in loop_data["running"]:
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mle_score_txt = loop_data["running"]["mle_score"]
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state.data[loop]["mle_score"] = extract_mle_json(mle_score_txt)
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if state.data[loop]["mle_score"]["score"] is not None:
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df.loc[loop, "Running Score"] = str(state.data[loop]["mle_score"]["score"])
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else:
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state.data[loop]["mle_score"] = mle_score_txt
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df.loc[loop, "Running Score"] = "❌"
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else:
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mle_score_path = (
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replace_ep_path(loop_data["running"]["no_tag"].experiment_workspace.workspace_path)
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/ "mle_score.txt"
|
|
)
|
|
try:
|
|
mle_score_txt = mle_score_path.read_text()
|
|
state.data[loop]["mle_score"] = extract_mle_json(mle_score_txt)
|
|
if state.data[loop]["mle_score"]["score"] is not None:
|
|
df.loc[loop, "Running Score"] = str(state.data[loop]["mle_score"]["score"])
|
|
else:
|
|
state.data[loop]["mle_score"] = mle_score_txt
|
|
df.loc[loop, "Running Score"] = "❌"
|
|
except Exception as e:
|
|
state.data[loop]["mle_score"] = str(e)
|
|
df.loc[loop, "Running Score"] = "❌"
|
|
else:
|
|
if isinstance(state.data[loop]["mle_score"], dict):
|
|
df.loc[loop, "Running Score"] = str(state.data[loop]["mle_score"]["score"])
|
|
else:
|
|
df.loc[loop, "Running Score"] = "❌"
|
|
|
|
else:
|
|
df.loc[loop, "Running Score"] = "N/A"
|
|
|
|
if "coding" in loop_data:
|
|
df.loc[loop, "e-loops"] = 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"
|
|
st.dataframe(df)
|
|
|
|
def comp_stat_func(x: pd.DataFrame):
|
|
total_num = x.shape[0]
|
|
valid_num = x[x["Running Score"] != "N/A"].shape[0]
|
|
avg_e_loops = x["e-loops"].mean()
|
|
return pd.Series(
|
|
{
|
|
"Loop Num": total_num,
|
|
"Valid Loop": valid_num,
|
|
"Valid Rate": round(valid_num / total_num * 100, 2),
|
|
"Avg e-loops": round(avg_e_loops, 2),
|
|
}
|
|
)
|
|
|
|
st1, st2 = st.columns([1, 1])
|
|
|
|
# component statistics
|
|
comp_df = df.loc[:, ["Component", "Running Score", "e-loops"]].groupby("Component").apply(comp_stat_func)
|
|
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["Valid Rate"] = comp_df["Valid Rate"].apply(lambda x: f"{x}%")
|
|
comp_df.loc["Total", "Avg e-loops"] = round(df["e-loops"].mean(), 2)
|
|
st2.markdown("### Component Statistics")
|
|
st2.dataframe(comp_df)
|
|
|
|
# component time statistics
|
|
time_df = df.loc[:, ["Component", "Time", "Coding", "Running"]]
|
|
time_df = time_df.astype({"Time": "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[:, "Coding(%)"] = time_stat_df["Coding"] / time_stat_df["Time"] * 100
|
|
time_stat_df.loc[:, "Running(%)"] = time_stat_df["Running"] / time_stat_df["Time"] * 100
|
|
time_stat_df = time_stat_df.map(lambda x: str(x).split(".")[0] if pd.notnull(x) else "0:00:00")
|
|
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 = 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):
|
|
"""缓存并返回排序后的文件夹列表，并加入进度打印"""
|
|
if not log_path.exists():
|
|
st.toast(f"Path {log_path} does not exist!")
|
|
return []
|
|
with st.spinner("正在加载文件夹列表..."):
|
|
folders = sorted(
|
|
(folder for folder in log_path.iterdir() if is_valid_session(folder)),
|
|
key=lambda folder: folder.stat().st_mtime,
|
|
reverse=True,
|
|
)
|
|
st.write(f"找到 {len(folders)} 个文件夹")
|
|
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]
|
|
|
|
state.log_folder = Path(st.radio(f"Select :blue[**one log folder**]", state.log_folders))
|
|
if not state.log_folder.exists():
|
|
st.warning(f"Path {state.log_folder} does not exist!")
|
|
else:
|
|
folders = get_folders_sorted(state.log_folder)
|
|
st.selectbox(f"Select from :blue[**{state.log_folder.absolute()}**]", folders, key="log_path")
|
|
|
|
if st.button("Refresh Data"):
|
|
if state.log_path is None:
|
|
st.toast("Please select a log path first!", icon="🟡")
|
|
st.stop()
|
|
|
|
load_times(state.log_folder / state.log_path)
|
|
state.data, state.llm_data = 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.markdown(
|
|
f"""
|
|
- [Exp Gen](#exp-gen)
|
|
- [Coding](#coding)
|
|
- [Running](#running)
|
|
- [Feedback](#feedback)
|
|
- [SOTA Experiment](#sota-exp)
|
|
"""
|
|
)
|
|
|
|
# UI - Main
|
|
if state.data["competition"]:
|
|
st.title(state.data["competition"])
|
|
summarize_data()
|
|
if len(state.data) > 2:
|
|
loop_id = st.slider("Loop", 0, len(state.data) - 2, 0)
|
|
else:
|
|
loop_id = 0
|
|
if state.show_stdout:
|
|
stdout_win(loop_id)
|
|
main_win(state.data[loop_id], state.llm_data[loop_id])
|