Create index.py

index.py

@@ -0,0 +1,395 @@
+import gradio as gr
+import bittensor as bt
+import typing
+from bittensor.extrinsics.serving import get_metadata
+from dataclasses import dataclass
+import requests
+import wandb
+import math
+import os
+import datetime
+import time
+import functools
+import multiprocessing
+from dotenv import load_dotenv
+from huggingface_hub import HfApi
+from apscheduler.schedulers.background import BackgroundScheduler
+from tqdm import tqdm
+
+load_dotenv()
+
+FONT = """<link href="https://fonts.cdnfonts.com/css/jmh-typewriter" rel="stylesheet">"""
+TITLE = """<h1 align="center" id="space-title" class="typewriter">Subnet 6 Leaderboard</h1>"""
+IMAGE = """<a href="https://discord.gg/jqVphNsB4H" target="_blank"><img src="https://i.ibb.co/88wyVQ7/nousgirl.png" alt="nousgirl" style="margin: auto; width: 20%; border: 0;" /></a>"""
+HEADER = """<h2 align="center" class="typewriter"><a href="https://github.com/PlixAI/pixel-subnet-leaderboard" target="_blank">Subnet 17</a> is a <a href="https://bittensor.com/" target="_blank">Bittensor</a> subnet that incentivizes the creation of the best image open models by evaluating submissions on a constant stream of newly generated synthetic MidJourney v6 data. The models with the best <a href="https://github.com/PlixAI/pixel-subnet-leaderboard/blob/master/docs/validator.md" target="_blank">head-to-head loss</a> on the evaluation data receive a steady emission of TAO.</h3>"""
+EVALUATION_DETAILS = """<b>Name</b> is the 🤗 Hugging Face model name (click to go to the model card). <b>Rewards / Day</b> are the expected rewards per day for each model. <b>Perplexity</b> is represents the loss on all of the evaluation data for the model as calculated by the validator (lower is better). <b>UID</b> is the Bittensor user id of the submitter. <b>Block</b> is the Bittensor block that the model was submitted in. More stats on <a href="https://taostats.io/subnets/netuid-17/" target="_blank">taostats</a>."""
+EVALUATION_HEADER = """<h3 align="center">Shows the latest internal evaluation statistics as calculated by a validator run by Nous Research</h3>"""
+VALIDATOR_WANDB_PROJECT = os.environ["VALIDATOR_WANDB_PROJECT"]
+H4_TOKEN = os.environ.get("H4_TOKEN", None)
+API = HfApi(token=H4_TOKEN)
+REPO_ID = "PlixAI/pixel-subnet-leaderboard"
+METAGRAPH_RETRIES = 10
+METAGRAPH_DELAY_SECS = 30
+METADATA_TTL = 10
+NETUID = 17
+SUBNET_START_BLOCK = 2225782
+SECONDS_PER_BLOCK = 12
+SUBTENSOR = os.environ.get("SUBTENSOR")
+
+@dataclass
+class Competition:
+    id: str
+    name: str
+
+COMPETITIONS = [Competition(id="m6_xl", name="midjourney-6-sdxl")]
+DEFAULT_COMPETITION_ID = "m1"
+
+def run_in_subprocess(func: functools.partial, ttl: int) -> typing.Any:
+    """Runs the provided function on a subprocess with 'ttl' seconds to complete.
+    Args:
+        func (functools.partial): Function to be run.
+        ttl (int): How long to try for in seconds.
+    Returns:
+        Any: The value returned by 'func'
+    """
+
+    def wrapped_func(func: functools.partial, queue: multiprocessing.Queue):
+        try:
+            result = func()
+            queue.put(result)
+        except (Exception, BaseException) as e:
+            # Catch exceptions here to add them to the queue.
+            queue.put(e)
+
+    # Use "fork" (the default on all POSIX except macOS), because pickling doesn't seem
+    # to work on "spawn".
+    ctx = multiprocessing.get_context("fork")
+    queue = ctx.Queue()
+    process = ctx.Process(target=wrapped_func, args=[func, queue])
+
+    process.start()
+
+    process.join(timeout=ttl)
+
+    if process.is_alive():
+        process.terminate()
+        process.join()
+        raise TimeoutError(f"Failed to {func.func.__name__} after {ttl} seconds")
+
+    # Raises an error if the queue is empty. This is fine. It means our subprocess timed out.
+    result = queue.get(block=False)
+
+    # If we put an exception on the queue then raise instead of returning.
+    if isinstance(result, Exception):
+        raise result
+    if isinstance(result, BaseException):
+        raise Exception(f"BaseException raised in subprocess: {str(result)}")
+
+    return result
+
+
+def get_subtensor_and_metagraph() -> typing.Tuple[bt.subtensor, bt.metagraph]:
+    for i in range(0, METAGRAPH_RETRIES):
+        try:
+            print("Connecting to subtensor...")
+            subtensor: bt.subtensor = bt.subtensor(SUBTENSOR)
+            print("Pulling metagraph...")
+            metagraph: bt.metagraph = subtensor.metagraph(NETUID, lite=False)
+            return subtensor, metagraph
+        except Exception as e:
+            print(e)
+            if i == METAGRAPH_RETRIES - 1:
+                raise
+            print(f"Error connecting to subtensor or pulling metagraph, retry {i + 1} of {METAGRAPH_RETRIES} in {METAGRAPH_DELAY_SECS} seconds...")
+            time.sleep(METAGRAPH_DELAY_SECS)
+    raise RuntimeError()
+
+@dataclass
+class ModelData:
+    uid: int
+    hotkey: str
+    namespace: str
+    name: str
+    commit: str
+    hash: str
+    block: int
+    incentive: float
+    emission: float
+    competition: str
+
+    @classmethod
+    def from_compressed_str(cls, uid: int, hotkey: str, cs: str, block: int, incentive: float, emission: float):
+        """Returns an instance of this class from a compressed string representation"""
+        tokens = cs.split(":")
+        return ModelData(
+            uid=uid,
+            hotkey=hotkey,
+            namespace=tokens[0],
+            name=tokens[1],
+            commit=tokens[2] if tokens[2] != "None" else "",
+            hash=tokens[3] if tokens[3] != "None" else "",
+            competition=tokens[4] if len(tokens) > 4 and tokens[4] != "None" else DEFAULT_COMPETITION_ID,
+            block=block,
+            incentive=incentive,
+            emission=emission
+        )
+
+def get_tao_price() -> float:
+    for i in range(0, METAGRAPH_RETRIES):
+        try:
+            return float(requests.get("https://api.mexc.com/api/v3/avgPrice?symbol=TAOUSDT").json()["price"])
+        except Exception as e:
+            print(e)
+            if i == METAGRAPH_RETRIES - 1:
+                raise
+            time.sleep(METAGRAPH_DELAY_SECS)
+        raise RuntimeError()
+
+def get_validator_weights(metagraph: bt.metagraph) -> typing.Dict[int, typing.Tuple[float, int, typing.Dict[int, float]]]:
+    ret = {}
+    for uid in metagraph.uids.tolist():
+        vtrust = metagraph.validator_trust[uid].item()
+        if vtrust > 0:
+            ret[uid] = (vtrust, metagraph.S[uid].item(), {})
+            for ouid in metagraph.uids.tolist():
+                if ouid == uid:
+                    continue
+                weight = round(metagraph.weights[uid][ouid].item(), 4)
+                if weight > 0:
+                    ret[uid][-1][ouid] = weight
+    return ret
+
+def get_subnet_data(subtensor: bt.subtensor, metagraph: bt.metagraph) -> typing.List[ModelData]:
+    result = []
+    for uid in tqdm(metagraph.uids.tolist(), desc="Metadata for hotkeys"):
+        hotkey = metagraph.hotkeys[uid]
+        try:
+            # Wrap calls to the subtensor in a subprocess with a timeout to handle potential hangs.
+            partial = functools.partial(get_metadata, subtensor, metagraph.netuid, hotkey)
+            metadata = run_in_subprocess(partial, METADATA_TTL)
+        except KeyboardInterrupt:
+            raise
+        except:
+            metadata = None
+        if not metadata:
+            continue
+
+        commitment = metadata["info"]["fields"][0]
+        hex_data = commitment[list(commitment.keys())[0]][2:]
+        chain_str = bytes.fromhex(hex_data).decode()
+        block = metadata["block"]
+        incentive = metagraph.incentive[uid].nan_to_num().item()
+        emission = metagraph.emission[uid].nan_to_num().item() * 20 # convert to daily TAO
+
+        model_data = None
+        try:
+            model_data = ModelData.from_compressed_str(uid, hotkey, chain_str, block, incentive, emission)    
+        except:
+            continue
+
+        result.append(model_data)
+    return result
+
+def floatable(x) -> bool:
+    return (isinstance(x, float) and not math.isnan(x) and not math.isinf(x)) or isinstance(x, int)
+    
+def get_float_score(key: str, history, competition_id: str) -> typing.Tuple[typing.Optional[float], bool]:
+    if key in history and "competition_id" in history:
+        data = list(history[key])
+        if len(data) > 0:
+            competitions = list(history["competition_id"])
+            while True:
+                if competitions.pop() != competition_id:
+                    data.pop()
+                    continue
+                if floatable(data[-1]):
+                    return float(data[-1]), True
+                else:
+                    data = [float(x) for x in data if floatable(x)]
+                    if len(data) > 0:
+                        return float(data[-1]), False
+                break
+    return None, False
+
+def get_sample(uid, history, competition_id: str) -> typing.Optional[typing.Tuple[str, str, str]]:
+    prompt_key = f"sample_prompt_data.{uid}"
+    response_key = f"sample_response_data.{uid}"
+    truth_key = f"sample_truth_data.{uid}"
+    if prompt_key in history and response_key in history and truth_key in history and "competition_id" in history:
+        competitions = list(history["competition_id"])
+        prompts = list(history[prompt_key])
+        responses = list(history[response_key])
+        truths = list(history[truth_key])
+        while True:
+            prompt = prompts.pop()
+            response = responses.pop()
+            truth = truths.pop()
+            if competitions.pop() != competition_id:
+                continue
+            if isinstance(prompt, str) and isinstance(response, str) and isinstance(truth, str):
+                return prompt, response, truth
+            break
+    return None
+
+def get_scores(uids: typing.List[int], competition_id: str) -> typing.Dict[int, typing.Dict[str, typing.Optional[float | str]]]:
+    api = wandb.Api()
+    runs = list(api.runs(VALIDATOR_WANDB_PROJECT))
+
+    result = {}
+    for run in runs:
+        history = run.history()
+        for uid in uids:
+            if uid in result.keys():
+                continue
+            perplexity, perplexity_fresh = get_float_score(f"perplexity_data.{uid}", history, competition_id)
+            win_rate, win_rate_fresh = get_float_score(f"win_rate_data.{uid}", history, competition_id)
+            win_total, win_total_fresh = get_float_score(f"win_total_data.{uid}", history, competition_id)
+            weight, weight_fresh = get_float_score(f"weight_data.{uid}", history, competition_id)
+            sample = get_sample(uid, history, competition_id)
+            result[uid] = {
+                "perplexity": perplexity,
+                "win_rate": win_rate,
+                "win_total": win_total,
+                "weight": weight,
+                "sample": sample,
+                "fresh": perplexity_fresh and win_rate_fresh and win_total_fresh
+            }
+        if len(result.keys()) == len(uids):
+            break
+    return result
+
+def format_score(uid, scores, key) -> typing.Optional[float]:
+    if uid in scores:
+        if key in scores[uid]:
+            point = scores[uid][key]
+            if floatable(point):
+                return round(scores[uid][key], 4)
+    return None
+
+def next_tempo(start_block, tempo, block):
+    start_num = start_block + tempo
+    intervals = (block - start_num) // tempo
+    nearest_num = start_num + ((intervals + 1) * tempo)
+    return nearest_num
+
+subtensor, metagraph = get_subtensor_and_metagraph()
+
+tao_price = get_tao_price()
+
+leaderboard_df = get_subnet_data(subtensor, metagraph)
+leaderboard_df.sort(key=lambda x: x.incentive, reverse=True)
+
+competition_scores = {
+    y.id: get_scores([x.uid for x in leaderboard_df if x.competition == y.id], y.id)
+    for y in COMPETITIONS
+}
+    
+current_block = metagraph.block.item()
+next_update = next_tempo(
+    SUBNET_START_BLOCK,
+    subtensor.get_subnet_hyperparameters(NETUID).tempo,
+    current_block
+)
+blocks_to_go = next_update - current_block
+current_time = datetime.datetime.now()
+next_update_time = current_time + datetime.timedelta(seconds=blocks_to_go * SECONDS_PER_BLOCK)
+
+validator_df = get_validator_weights(metagraph)
+weight_keys = set()
+for uid, stats in validator_df.items():
+    weight_keys.update(stats[-1].keys())
+
+def get_next_update():
+    now = datetime.datetime.now()
+    delta = next_update_time - now
+    return f"""<div align="center" style="font-size: larger;">Next reward update: <b>{blocks_to_go}</b> blocks (~{int(delta.total_seconds() // 60)} minutes)</div>"""
+
+def leaderboard_data(show_stale: bool, scores: typing.Dict[int, typing.Dict[str, typing.Optional[float | str]]], competition_id: str):
+    value = [
+        [
+            f'[{c.namespace}/{c.name} ({c.commit[0:8]}, UID={c.uid})](https://huggingface.co/{c.namespace}/{c.name}/commit/{c.commit})',
+            format_score(c.uid, scores, "win_rate"),
+            format_score(c.uid, scores, "perplexity"),
+            format_score(c.uid, scores, "weight"),
+            c.uid,
+            c.block
+        ] for c in leaderboard_df if c.competition == competition_id and (scores[c.uid]["fresh"] or show_stale)
+    ]
+    return value
+
+demo = gr.Blocks(css=".typewriter {font-family: 'JMH Typewriter', sans-serif;}")
+with demo:
+    gr.HTML(FONT)
+    gr.HTML(TITLE)
+    gr.HTML(IMAGE)
+    gr.HTML(HEADER)
+
+    gr.HTML(value=get_next_update())
+
+    with gr.Tabs():
+        for competition in COMPETITIONS:
+            with gr.Tab(competition.name):
+                scores = competition_scores[competition.id]
+                print(scores)
+
+                class_denominator = sum(leaderboard_df[i].incentive for i in range(0, 10) if leaderboard_df[i].incentive and leaderboard_df[i].competition == competition.id)
+
+                class_values = {
+                    f"{leaderboard_df[i].namespace}/{leaderboard_df[i].name} ({leaderboard_df[i].commit[0:8]}, UID={leaderboard_df[i].uid}) · ${round(leaderboard_df[i].emission * tao_price, 2):,} (τ{round(leaderboard_df[i].emission, 2):,})": \
+                        leaderboard_df[i].incentive / class_denominator for i in range(0, 10) if leaderboard_df[i].incentive and leaderboard_df[i].competition == competition.id
+                }
+
+                gr.Label(
+                    value=class_values,
+                    num_top_classes=10,
+                )
+                
+                with gr.Accordion("Evaluation Stats"):
+                    gr.HTML(EVALUATION_HEADER)
+
+                    with gr.Tabs():
+                        for entry in leaderboard_df:
+                            if entry.competition == competition.id:
+                                sample = scores[entry.uid]["sample"]
+                                if sample is not None:
+                                    name = f"{entry.namespace}/{entry.name} ({entry.commit[0:8]}, UID={entry.uid})"
+                                    with gr.Tab(name):
+                                        gr.Chatbot([(sample[0], sample[1])])
+                                        # gr.Chatbot([(sample[0], f"*{name}*: {sample[1]}"), (None, f"*GPT-4*: {sample[2]}")])
+
+                    show_stale = gr.Checkbox(label="Show Stale", interactive=True)
+                    leaderboard_table = gr.components.Dataframe(
+                        value=leaderboard_data(show_stale.value, scores, competition.id),
+                        headers=["Name", "Win Rate", "Perplexity", "Weight", "UID", "Block"],
+                        datatype=["markdown", "number", "number", "number", "number", "number"],
+                        elem_id="leaderboard-table",
+                        interactive=False,
+                        visible=True,
+                    )
+                    gr.HTML(EVALUATION_DETAILS)
+                    show_stale.change(lambda x: leaderboard_data(x, scores, competition.id), [show_stale], leaderboard_table)
+
+    with gr.Accordion("Validator Stats"):
+        validator_table = gr.components.Dataframe(
+            value=[
+                [uid, int(validator_df[uid][1]), round(validator_df[uid][0], 4)] + [validator_df[uid][-1].get(c.uid) for c in leaderboard_df if c.incentive]
+                for uid, _ in sorted(
+                        zip(validator_df.keys(), [validator_df[x][1] for x in validator_df.keys()]),
+                        key=lambda x: x[1],
+                        reverse=True
+                    )
+            ],
+            headers=["UID", "Stake (τ)", "V-Trust"] + [f"{c.namespace}/{c.name} ({c.commit[0:8]}, UID={c.uid})" for c in leaderboard_df if c.incentive],
+            datatype=["number", "number", "number"] + ["number" for c in leaderboard_df if c.incentive],
+            interactive=False,
+            visible=True,
+        )
+
+def restart_space():
+    API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=60 * 15) # restart every 15 minutes
+scheduler.start()
+
+demo.launch()