Update src/leaderboard/read_evals.py

app.py

@@ -1,5 +1,5 @@
-import subprocess
 import gradio as gr
+from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
 from huggingface_hub import snapshot_download
@@ -18,8 +18,6 @@ from src.display.utils import (
     COLS,
     EVAL_COLS,
     EVAL_TYPES,
-    NUMERIC_INTERVALS,
-    TYPES,
     AutoEvalColumn,
     ModelType,
     fields,
@@ -34,6 +32,7 @@ from src.submission.submit import add_new_eval
 def restart_space():
     API.restart_space(repo_id=REPO_ID)
 
+### Space initialisation
 try:
     print(EVAL_REQUESTS_PATH)
     snapshot_download(
@@ -50,8 +49,7 @@ except Exception:
     restart_space()
 
 
-raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
-leaderboard_df = original_df.copy()
+LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
 
 (
     finished_eval_queue_df,
@@ -59,77 +57,36 @@ leaderboard_df = original_df.copy()
     pending_eval_queue_df,
 ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
 
-
-# Searching and filtering
-def update_table(
-    hidden_df: pd.DataFrame,
-    columns: list,
-    type_query: list,
-    precision_query: str,
-    size_query: list,
-    show_deleted: bool,
-    query: str,
-):
-    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
-    filtered_df = filter_queries(query, filtered_df)
-    df = select_columns(filtered_df, columns)
-    return df
-
-
-def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
-    return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))]
-
-
-def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
-    always_here_cols = [
-        AutoEvalColumn.model_type_symbol.name,
-        AutoEvalColumn.model.name,
-    ]
-    # We use COLS to maintain sorting
-    filtered_df = df[
-        always_here_cols + [c for c in COLS if c in df.columns and c in columns] 
-    ]
-    return filtered_df
-
-
-def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
-    final_df = []
-    if query != "":
-        queries = [q.strip() for q in query.split(";")]
-        for _q in queries:
-            _q = _q.strip()
-            if _q != "":
-                temp_filtered_df = search_table(filtered_df, _q)
-                if len(temp_filtered_df) > 0:
-                    final_df.append(temp_filtered_df)
-        if len(final_df) > 0:
-            filtered_df = pd.concat(final_df)
-            filtered_df = filtered_df.drop_duplicates(
-                subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
-            )
-
-    return filtered_df
-
-
-def filter_models(
-    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
-) -> pd.DataFrame:
-    # Show all models
-    if show_deleted:
-        filtered_df = df
-    else:  # Show only still on the hub models
-        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
-
-    type_emoji = [t[0] for t in type_query]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
-
-    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
-    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
-    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
-    filtered_df = filtered_df.loc[mask]
-
-    return filtered_df
+def init_leaderboard(dataframe):
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+    return Leaderboard(
+        value=dataframe,
+        datatype=[c.type for c in fields(AutoEvalColumn)],
+        select_columns=SelectColumns(
+            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
+            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
+            label="Select Columns to Display:",
+        ),
+        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
+        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
+        filter_columns=[
+            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
+            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
+            ColumnFilter(
+                AutoEvalColumn.params.name,
+                type="slider",
+                min=0,
+                max=2000,
+                label="Select the number of parameters (M)",
+            ),
+            # ColumnFilter(
+            #     AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
+            # ),
+        ],
+        # bool_checkboxgroup_label="Hide models",
+        # interactive=False,
+    )
 
 
 demo = gr.Blocks(css=custom_css)
@@ -139,105 +96,7 @@ with demo:
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        search_bar = gr.Textbox(
-                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
-                            show_label=False,
-                            elem_id="search-bar",
-                        )
-                    with gr.Row():
-                        shown_columns = gr.CheckboxGroup(
-                            choices=[
-                                c.name
-                                for c in fields(AutoEvalColumn)
-                                if not c.hidden and not c.never_hidden
-                            ],
-                            value=[
-                                c.name
-                                for c in fields(AutoEvalColumn)
-                                if c.displayed_by_default and not c.hidden and not c.never_hidden
-                            ],
-                            label="Select columns to show",
-                            elem_id="column-select",
-                            interactive=True,
-                        )
-                    with gr.Row():
-                        deleted_models_visibility = gr.Checkbox(
-                            value=False, label="Show gated/private/deleted models", interactive=True
-                        )
-                with gr.Column(min_width=320):
-                    #with gr.Box(elem_id="box-filter"):
-                    filter_columns_type = gr.CheckboxGroup(
-                        label="Model types",
-                        choices=[t.to_str() for t in ModelType],
-                        value=[t.to_str() for t in ModelType],
-                        interactive=True,
-                        elem_id="filter-columns-type",
-                    )
-                    filter_columns_precision = gr.CheckboxGroup(
-                        label="Precision",
-                        choices=[i.value.name for i in Precision],
-                        value=[i.value.name for i in Precision],
-                        interactive=True,
-                        elem_id="filter-columns-precision",
-                    )
-                    filter_columns_size = gr.CheckboxGroup(
-                        label="Model sizes (in billions of parameters)",
-                        choices=list(NUMERIC_INTERVALS.keys()),
-                        value=list(NUMERIC_INTERVALS.keys()),
-                        interactive=True,
-                        elem_id="filter-columns-size",
-                    )
-
-            leaderboard_table = gr.components.Dataframe(
-                value=leaderboard_df[
-                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
-                    + shown_columns.value
-                ],
-                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
-                datatype=TYPES,
-                elem_id="leaderboard-table",
-                interactive=False,
-                visible=True,
-            )
-
-            # Dummy leaderboard for handling the case when the user uses backspace key
-            hidden_leaderboard_table_for_search = gr.components.Dataframe(
-                value=original_df[COLS],
-                headers=COLS,
-                datatype=TYPES,
-                visible=False,
-            )
-            search_bar.submit(
-                update_table,
-                [
-                    hidden_leaderboard_table_for_search,
-                    shown_columns,
-                    filter_columns_type,
-                    filter_columns_precision,
-                    filter_columns_size,
-                    deleted_models_visibility,
-                    search_bar,
-                ],
-                leaderboard_table,
-            )
-            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
-                selector.change(
-                    update_table,
-                    [
-                        hidden_leaderboard_table_for_search,
-                        shown_columns,
-                        filter_columns_type,
-                        filter_columns_precision,
-                        filter_columns_size,
-                        deleted_models_visibility,
-                        search_bar,
-                    ],
-                    leaderboard_table,
-                    queue=True,
-                )
+            leaderboard = init_leaderboard(LEADERBOARD_DF)
 
         with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

requirements.txt

@@ -15,4 +15,5 @@ transformers==4.35.2
 tokenizers>=0.15.0
 git+https://github.com/EleutherAI/lm-evaluation-harness.git@b281b0921b636bc36ad05c0b0b0763bd6dd43463#egg=lm-eval
 accelerate==0.24.1
-sentencepiece
+sentencepiece
+gradio_leaderboard

src/display/utils.py

@@ -26,7 +26,7 @@ auto_eval_column_dict = []
 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)])
+auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Rank", "number", True)])
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
 # Model information
@@ -91,10 +91,6 @@ class WeightType(Enum):
 class Precision(Enum):
     float16 = ModelDetails("float16")
     bfloat16 = ModelDetails("bfloat16")
-    float32 = ModelDetails("float32")
-    #qt_8bit = ModelDetails("8bit")
-    #qt_4bit = ModelDetails("4bit")
-    #qt_GPTQ = ModelDetails("GPTQ")
     Unknown = ModelDetails("?")
 
     def from_str(precision):
@@ -102,34 +98,13 @@ class Precision(Enum):
             return Precision.float16
         if precision in ["torch.bfloat16", "bfloat16"]:
             return Precision.bfloat16
-        if precision in ["float32"]:
-            return Precision.float32
-        #if precision in ["8bit"]:
-        #    return Precision.qt_8bit
-        #if precision in ["4bit"]:
-        #    return Precision.qt_4bit
-        #if precision in ["GPTQ", "None"]:
-        #    return Precision.qt_GPTQ
         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"),
-}

src/envs.py

@@ -6,7 +6,7 @@ from huggingface_hub import HfApi
 # ----------------------------------
 TOKEN = os.environ.get("TOKEN") # A read/write token for your org
 
-OWNER = "demo-leaderboard-backend" # Change to your org - don't forget to create a results and request dataset, with the correct format!
+OWNER = "yangheng" # Change to your org - don't forget to create a results and request dataset, with the correct format!
 # ----------------------------------
 
 REPO_ID = f"{OWNER}/leaderboard"

src/leaderboard/read_evals.py

@@ -60,6 +60,7 @@ class EvalResult:
         still_on_hub, _, model_config = is_model_on_hub(
             full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
         )
+        print("Is model on hub? \n", _)
         architecture = "?"
         if model_config is not None:
             architectures = getattr(model_config, "architectures", None)
@@ -70,13 +71,15 @@ class EvalResult:
         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
+            if task.benchmark == "mRNA":
+                # Keep RMSE at original value
+                mean_acc = np.mean(accs)
+            else:
+                mean_acc = np.mean(accs) * 100.0
             results[task.benchmark] = mean_acc
 
         return self(
@@ -93,8 +96,8 @@ class EvalResult:
 
     def update_with_request_file(self, requests_path):
         """Finds the relevant request file for the current model and updates info with it"""
+        # print("Requests Path: ", requests_path)
         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)
@@ -107,9 +110,11 @@ class EvalResult:
         except Exception:
             print(f"Could not find request file for {self.org}/{self.model} with precision {self.precision.value.name}")
 
-    def to_dict(self):
+    def to_dict(self, rank):
         """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)
+        average = rank
+        # average = sorted(average, reverse=True)
+        # rank = [rank+1 for rank, value in enumerate(average)]
         data_dict = {
             "eval_name": self.eval_name,  # not a column, just a save name,
             AutoEvalColumn.precision.name: self.precision.value.name,
@@ -138,6 +143,7 @@ def get_request_file_for_model(requests_path, model_name, precision):
         requests_path,
         f"{model_name}_eval_request_*.json",
     )
+    # print("Request Files: ", request_files)
     request_files = glob.glob(request_files)
 
     # Select correct request file (precision)
@@ -146,6 +152,8 @@ def get_request_file_for_model(requests_path, model_name, precision):
     for tmp_request_file in request_files:
         with open(tmp_request_file, "r") as f:
             req_content = json.load(f)
+            # print("Request File: ", tmp_request_file)
+            # print("Req Content: ", req_content)
             if (
                 req_content["status"] in ["FINISHED"]
                 and req_content["precision"] == precision.split(".")[-1]
@@ -186,9 +194,13 @@ def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResu
             eval_results[eval_name] = eval_result
 
     results = []
-    for v in eval_results.values():
+    for result in eval_results.values():
+        result.average = np.mean(list(result.results.values()))
+    sorted_results = sorted(eval_results.values(), key=lambda r: r.average, reverse=True)
+    print(f"SORTED RESULTS HERE: \n{sorted_results}")
+    for i,v in enumerate(sorted_results):
         try:
-            v.to_dict() # we test if the dict version is complete
+            v.to_dict(i) # we test if the dict version is complete
             results.append(v)
         except KeyError:  # not all eval values present
             continue

src/populate.py

@@ -1,8 +1,9 @@
 import json
 import os
-
+import numpy as np
 import pandas as pd
 
+
 from src.display.formatting import has_no_nan_values, make_clickable_model
 from src.display.utils import AutoEvalColumn, EvalQueueColumn
 from src.leaderboard.read_evals import get_raw_eval_results
@@ -11,15 +12,22 @@ from src.leaderboard.read_evals import get_raw_eval_results
 def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> 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]
+    for result in raw_data:
+        result.average = np.mean(list(result.results.values()))
+    sorted_results = sorted(raw_data, key=lambda r: r.average, reverse=True)
+    print(sorted_results)
+    # ranks = [rank+1 for rank, value in enumerate(sorted_results)]
+    # rank = [rank+1 for rank, value in enumerate(average)]
+    all_data_json = [v.to_dict(i+1) for i, v in enumerate(raw_data)]
 
     df = pd.DataFrame.from_records(all_data_json)
-    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
+    # df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
     df = df[cols].round(decimals=2)
 
     # filter out if any of the benchmarks have not been produced
     df = df[has_no_nan_values(df, benchmark_cols)]
-    return raw_data, df
+    print(df)
+    return df
 
 
 def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
@@ -55,4 +63,4 @@ def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
     df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
     df_running = pd.DataFrame.from_records(running_list, columns=cols)
     df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
-    return df_finished[cols], df_running[cols], df_pending[cols]
+    return df_finished[cols], df_running[cols], df_pending[cols]