{error}
" + + +def styled_warning(warn): + return f"{warn}
" + + +def styled_message(message): + return f"{message}
" + + +def has_no_nan_values(df, columns): + return df[columns].notna().all(axis=1) + + +def has_nan_values(df, columns): + return df[columns].isna().any(axis=1) diff --git a/backend/original_src/display/utils.py b/backend/original_src/display/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7cccff2abadb57cd12a5148335f65ccac3676834 --- /dev/null +++ b/backend/original_src/display/utils.py @@ -0,0 +1,131 @@ +from dataclasses import dataclass, make_dataclass +from enum import Enum + +import pandas as pd + +from about import Tasks + +def fields(raw_class): + return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"] + + +# These classes are for user facing column names, +# to avoid having to change them all around the code +# when a modif is needed +@dataclass +class ColumnContent: + name: str + type: str + displayed_by_default: bool + hidden: bool = False + never_hidden: bool = False + +## Leaderboard columns +auto_eval_column_dict = [] +# Init +auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)]) +auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)]) +#Scores +auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)]) +for task in Tasks: + auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)]) +# Model information +auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)]) +auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)]) +auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)]) +auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)]) +auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)]) +auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)]) +auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)]) +auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)]) + +# We use make dataclass to dynamically fill the scores from Tasks +# Convert to proper format for make_dataclass (without default values) +auto_eval_fields = [(name, type(default_val)) for name, type_cls, default_val in auto_eval_column_dict] +AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_fields, frozen=True) + +# Manually set the values as class attributes +for name, type_cls, default_val in auto_eval_column_dict: + setattr(AutoEvalColumn, name, default_val) + +## For the queue columns in the submission tab +@dataclass(frozen=True) +class EvalQueueColumn: # Queue column + model = ColumnContent("model", "markdown", True) + revision = ColumnContent("revision", "str", True) + private = ColumnContent("private", "bool", True) + precision = ColumnContent("precision", "str", True) + status = ColumnContent("status", "str", True) + +## All the model information that we might need +@dataclass +class ModelDetails: + name: str + display_name: str = "" + symbol: str = "" # emoji + + +class ModelAPI(Enum): + hf = ModelDetails(name="hf") + openai = ModelDetails(name="openai-chat-completions") + anthropic = ModelDetails(name="anthropic-chat-completions") + + +class ModelType(Enum): + PT = ModelDetails(name="pretrained", symbol="🟢") + FT = ModelDetails(name="fine-tuned", symbol="🔶") + IFT = ModelDetails(name="instruction-tuned", symbol="⭕") + RL = ModelDetails(name="RL-tuned", symbol="🟦") + Unknown = ModelDetails(name="", symbol="?") + + def to_str(self, separator=" "): + return f"{self.value.symbol}{separator}{self.value.name}" + + @staticmethod + def from_str(type): + if "fine-tuned" in type or "🔶" in type: + return ModelType.FT + if "pretrained" in type or "🟢" in type: + return ModelType.PT + if "RL-tuned" in type or "🟦" in type: + return ModelType.RL + if "instruction-tuned" in type or "⭕" in type: + return ModelType.IFT + return ModelType.Unknown + +class Precision(Enum): + float16 = ModelDetails("float16") + bfloat16 = ModelDetails("bfloat16") + float32 = ModelDetails("float32") + Unknown = ModelDetails("?") + + def from_str(precision): + if precision in ["torch.float16", "float16"]: + return Precision.float16 + if precision in ["torch.bfloat16", "bfloat16"]: + return Precision.bfloat16 + if precision in ["float32"]: + return Precision.float32 + return Precision.Unknown + +# Column selection +COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden] +TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden] +COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden] +TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden] + +EVAL_COLS = [c.name for c in fields(EvalQueueColumn)] +EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)] + +BENCHMARK_COLS = [t.value.col_name for t in Tasks] + +NUMERIC_INTERVALS = { + "?": pd.Interval(-1, 0, closed="right"), + "~1.5": pd.Interval(0, 2, closed="right"), + "~3": pd.Interval(2, 4, closed="right"), + "~7": pd.Interval(4, 9, closed="right"), + "~13": pd.Interval(9, 20, closed="right"), + "~35": pd.Interval(20, 45, closed="right"), + "~60": pd.Interval(45, 70, closed="right"), + "70+": pd.Interval(70, 10000, closed="right"), +} diff --git a/backend/original_src/envs.py b/backend/original_src/envs.py new file mode 100644 index 0000000000000000000000000000000000000000..34138dca6b387204f0048af73df819659d6fc070 --- /dev/null +++ b/backend/original_src/envs.py @@ -0,0 +1,25 @@ +import os + +from huggingface_hub import HfApi + +# Info to change for your repository +# ---------------------------------- +TOKEN = os.environ.get("HF_TOKEN") # A read/write token for your org + +OWNER = "mideind" # Change to your org - don't forget to create a results and request dataset, with the correct format! +# ---------------------------------- + +REPO_ID = f"{OWNER}/icelandic-llm-leaderboard" +QUEUE_REPO = f"{OWNER}/icelandic-llm-leaderboard-requests" +RESULTS_REPO = f"{OWNER}/icelandic-llm-leaderboard-results" + +# If you setup a cache later, just change HF_HOME +CACHE_PATH=os.getenv("HF_HOME", ".") + +# Local caches +EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue") +EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results") +EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk") +EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk") + +API = HfApi(token=TOKEN) diff --git a/backend/original_src/leaderboard/__pycache__/read_evals.cpython-310.pyc b/backend/original_src/leaderboard/__pycache__/read_evals.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f3cf4f342289a96e7c9b2e3d58e6c11190fb9db1 Binary files /dev/null and b/backend/original_src/leaderboard/__pycache__/read_evals.cpython-310.pyc differ diff --git a/backend/original_src/leaderboard/__pycache__/read_evals.cpython-311.pyc b/backend/original_src/leaderboard/__pycache__/read_evals.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4b83d01aa68a6e0626630fae0460b0c938a490df Binary files /dev/null and b/backend/original_src/leaderboard/__pycache__/read_evals.cpython-311.pyc differ diff --git a/backend/original_src/leaderboard/read_evals.py b/backend/original_src/leaderboard/read_evals.py new file mode 100644 index 0000000000000000000000000000000000000000..2085b71a6481f572f7d7676f669bea6edf46cb84 --- /dev/null +++ b/backend/original_src/leaderboard/read_evals.py @@ -0,0 +1,194 @@ +import glob +import json +import math +import os +from dataclasses import dataclass + +import dateutil +import numpy as np + +from display.formatting import make_clickable_model +from display.utils import AutoEvalColumn, ModelType, Tasks, Precision +from submission.check_validity import is_model_on_hub + + +@dataclass +class EvalResult: + """Represents one full evaluation. Built from a combination of the result and request file for a given run. + """ + eval_name: str # org_model_precision (uid) + full_model: str # org/model (path on hub) + org: str + model: str + revision: str # commit hash, "" if main + results: dict + precision: Precision = Precision.Unknown + model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ... + architecture: str = "Unknown" + license: str = "?" + likes: int = 0 + num_params: int = 0 + date: str = "" # submission date of request file + still_on_hub: bool = False + + @classmethod + def init_from_json_file(self, json_filepath): + """Inits the result from the specific model result file""" + with open(json_filepath) as fp: + data = json.load(fp) + + config = data.get("config") + + # Precision + precision = Precision.from_str(config.get("model_dtype")) + + # Get model and org + org_and_model = config.get("model_name", config.get("model_args", None)) + org_and_model = org_and_model.split("/", 1) + + if len(org_and_model) == 1: + org = None + model = org_and_model[0] + result_key = f"{model}_{precision.value.name}" + else: + org = org_and_model[0] + model = org_and_model[1] + result_key = f"{org}_{model}_{precision.value.name}" + full_model = "/".join(org_and_model) + + still_on_hub, _, model_config = is_model_on_hub( + full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False + ) + architecture = "?" + if model_config is not None: + architectures = getattr(model_config, "architectures", None) + if architectures: + architecture = ";".join(architectures) + + # Extract results available in this file (some results are split in several files) + results = {} + for task in Tasks: + task = task.value + + # We average all scores of a given metric (not all metrics are present in all files) + accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark == k]) + if accs.size == 0 or any([acc is None for acc in accs]): + continue + + mean_acc = np.mean(accs) * 100.0 + results[task.benchmark] = mean_acc + + return self( + eval_name=result_key, + full_model=full_model, + org=org, + model=model, + results=results, + precision=precision, + revision= config.get("model_sha", ""), + still_on_hub=still_on_hub, + architecture=architecture + ) + + def update_with_request_file(self, requests_path): + """Finds the relevant request file for the current model and updates info with it""" + request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name) + + try: + with open(request_file, "r") as f: + request = json.load(f) + self.model_type = ModelType.from_str(request.get("model_type", "")) + self.license = request.get("license", "?") + self.likes = request.get("likes", 0) + self.num_params = request.get("params", 0) + self.date = request.get("submitted_time", "") + except FileNotFoundError: + print(f"Could not find request file for {self.org}/{self.model} with precision {self.precision.value.name}") + + def to_dict(self): + """Converts the Eval Result to a dict compatible with our dataframe display""" + average = sum([v for v in self.results.values() if v is not None]) / len(Tasks) + data_dict = { + "eval_name": self.eval_name, # not a column, just a save name, + AutoEvalColumn.precision.name: self.precision.value.name, + AutoEvalColumn.model_type.name: self.model_type.value.name, + AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol, + AutoEvalColumn.architecture.name: self.architecture, + AutoEvalColumn.model.name: make_clickable_model(self.full_model), + AutoEvalColumn.revision.name: self.revision, + AutoEvalColumn.average.name: average, + AutoEvalColumn.license.name: self.license, + AutoEvalColumn.likes.name: self.likes, + AutoEvalColumn.params.name: self.num_params, + AutoEvalColumn.still_on_hub.name: self.still_on_hub, + } + + for task in Tasks: + if task.value.benchmark in self.results.keys(): + data_dict[task.value.col_name] = self.results[task.value.benchmark] + else: + data_dict[task.value.col_name] = None + + return data_dict + + +def get_request_file_for_model(requests_path, model_name, precision): + """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED""" + request_files = os.path.join( + requests_path, + f"{model_name}_eval_request_*.json", + ) + request_files = glob.glob(request_files) + + # Select correct request file (precision) + request_file = "" + request_files = sorted(request_files, reverse=True) + for tmp_request_file in request_files: + with open(tmp_request_file, "r") as f: + req_content = json.load(f) + if req_content["precision"] == precision.split(".")[-1] or req_content["precision"] is None: + request_file = tmp_request_file + return request_file + + +def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]: + """From the path of the results folder root, extract all needed info for results""" + model_result_filepaths = [] + + for root, _, files in os.walk(results_path): + # We should only have json files in model results + if len(files) == 0 or any([not f.endswith(".json") for f in files]): + continue + + # Sort the files by date + try: + files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7]) + except dateutil.parser._parser.ParserError: + files = [files[-1]] + + for file in files: + model_result_filepaths.append(os.path.join(root, file)) + + + eval_results = {} + for model_result_filepath in model_result_filepaths: + # Creation of result + eval_result = EvalResult.init_from_json_file(model_result_filepath) + eval_result.update_with_request_file(requests_path) + + # Store results of same eval together + eval_name = eval_result.eval_name + if eval_name in eval_results.keys(): + eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None}) + else: + eval_results[eval_name] = eval_result + + results = [] + for v in eval_results.values(): + try: + v.to_dict() # we test if the dict version is complete + results.append(v) + except KeyError: # not all eval values present + continue + + return results diff --git a/backend/original_src/populate.py b/backend/original_src/populate.py new file mode 100644 index 0000000000000000000000000000000000000000..c3972bba930b062b4466834f620b545904888eaf --- /dev/null +++ b/backend/original_src/populate.py @@ -0,0 +1,18 @@ +import pandas as pd + +from display.utils import AutoEvalColumn +from leaderboard.read_evals import get_raw_eval_results, EvalResult + + +def get_leaderboard_df( + results_path: str, requests_path: str, cols: list, benchmark_cols: list +) -> tuple[list[EvalResult], pd.DataFrame]: + """Creates a dataframe from all the individual experiment results""" + raw_data = get_raw_eval_results(results_path, requests_path) + all_data_json = [v.to_dict() for v in raw_data] + + df = pd.DataFrame.from_records(all_data_json) + df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) + df = df[cols].round(decimals=2) + + return raw_data, df diff --git a/backend/original_src/submission/__pycache__/check_validity.cpython-310.pyc b/backend/original_src/submission/__pycache__/check_validity.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3fab564b1cde8be0b08f7a8e0e58bea102734530 Binary files /dev/null and b/backend/original_src/submission/__pycache__/check_validity.cpython-310.pyc differ diff --git a/backend/original_src/submission/__pycache__/check_validity.cpython-311.pyc b/backend/original_src/submission/__pycache__/check_validity.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b85ba2340ab1cc90caeb26e662aa342977d3c902 Binary files /dev/null and b/backend/original_src/submission/__pycache__/check_validity.cpython-311.pyc differ diff --git a/backend/original_src/submission/check_validity.py b/backend/original_src/submission/check_validity.py new file mode 100644 index 0000000000000000000000000000000000000000..d084d832be9e92ffcf55d3dbd6ba03071114d7c7 --- /dev/null +++ b/backend/original_src/submission/check_validity.py @@ -0,0 +1,36 @@ +from transformers import AutoConfig +from transformers.models.auto.tokenization_auto import AutoTokenizer + + +def is_model_on_hub( + model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False +) -> tuple[bool, str]: + """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses.""" + try: + config = AutoConfig.from_pretrained( + model_name, revision=revision, trust_remote_code=trust_remote_code, token=token + ) + if test_tokenizer: + try: + tk = AutoTokenizer.from_pretrained( + model_name, revision=revision, trust_remote_code=trust_remote_code, token=token + ) + except ValueError as e: + return (False, f"uses a tokenizer which is not in a transformers release: {e}", None) + except Exception as e: + return ( + False, + "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", + None, + ) + return True, None, config + + except ValueError: + return ( + False, + "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.", + None, + ) + + except Exception as e: + return False, "was not found on hub!", None diff --git a/backend/poetry.lock b/backend/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..5eb071287e56ac4c4593f776af433c3018140590 --- /dev/null +++ b/backend/poetry.lock @@ -0,0 +1,2578 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiofiles" +version = "24.1.0" +description = "File support for asyncio." +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"}, + {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"}, +] + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +description = "Happy Eyeballs for 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