display button unification for benchmarks (#28)

- add button unification (670054face4e2bee73a77f83e6141c1a99e09a7c)

app.py

@@ -89,6 +89,17 @@ def init_space():
         EVAL_REQUESTS_PATH, EVAL_COLS
     )
     return dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
+    
+    
+def add_benchmark_columns(shown_columns):
+    benchmark_columns = []
+    for benchmark in BENCHMARK_COLS:
+        if benchmark in shown_columns:
+            for c in COLS:
+                if benchmark in c and benchmark != c:
+                    benchmark_columns.append(c)
+    return benchmark_columns
+
 
 # Searching and filtering
 def update_table(
@@ -96,7 +107,8 @@ def update_table(
 ):
     filtered_df = filter_models(hidden_df, type_query, size_query, precision_query)
     filtered_df = filter_queries(query, filtered_df)
-    df = select_columns(filtered_df, columns)
+    benchmark_columns = add_benchmark_columns(columns)
+    df = select_columns(filtered_df, columns + benchmark_columns)
     return df
 
 
@@ -270,18 +282,19 @@ with demo:
                     # )
 
             # breakpoint()
-
+            benchmark_columns = add_benchmark_columns(shown_columns.value)
             leaderboard_table = gr.components.Dataframe(
                 value=(
                     leaderboard_df[
                         [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                         + shown_columns.value
+                        + benchmark_columns
                         + [AutoEvalColumn.dummy.name]
                     ]
                     if leaderboard_df.empty is False
                     else leaderboard_df
                 ),
-                headers=[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 + benchmark_columns,
                 datatype=TYPES,
                 elem_id="leaderboard-table",
                 interactive=False,
@@ -313,7 +326,7 @@ with demo:
             demo.load(load_query, inputs=[], outputs=[search_bar])
 
             for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size]:
-                selector.select(
+                selector.change(
                     update_table,
                     [
                         hidden_leaderboard_table_for_search,

src/display/utils.py

@@ -104,16 +104,16 @@ auto_eval_column_dict.append(["inference_framework", ColumnContent, ColumnConten
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
     # System performance metrics
-    auto_eval_column_dict.append([f"{task.name}_end_to_end_time", ColumnContent, ColumnContent(f"{task.value.col_name} {E2Es}", "number", True)])
-    auto_eval_column_dict.append([f"{task.name}_batch_size", ColumnContent, ColumnContent(f"{task.value.col_name} {BATCH_SIZE}", "number", True)])
-    # auto_eval_column_dict.append([f"{task.name}_precision", ColumnContent, ColumnContent(f"{task.value.col_name} {PRECISION}", "str", True)])
-    auto_eval_column_dict.append([f"{task.name}_gpu_mem", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Mem}", "number", True)])
-    auto_eval_column_dict.append([f"{task.name}_gpu", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Name}", "str", True)])
-    auto_eval_column_dict.append([f"{task.name}_gpu_util", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Util}", "number", True)])
+    auto_eval_column_dict.append([f"{task.name}_end_to_end_time", ColumnContent, ColumnContent(f"{task.value.col_name} {E2Es}", "number", True, hidden=True)])
+    auto_eval_column_dict.append([f"{task.name}_batch_size", ColumnContent, ColumnContent(f"{task.value.col_name} {BATCH_SIZE}", "number", True, hidden=True)])
+    # auto_eval_column_dict.append([f"{task.name}_precision", ColumnContent, ColumnContent(f"{task.value.col_name} {PRECISION}", "str", True, hidden=True)])
+    auto_eval_column_dict.append([f"{task.name}_gpu_mem", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Mem}", "number", True, hidden=True)])
+    auto_eval_column_dict.append([f"{task.name}_gpu", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Name}", "str", True, hidden=True)])
+    auto_eval_column_dict.append([f"{task.name}_gpu_util", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Util}", "number", True, hidden=True)])
     if task.value.benchmark in MULTIPLE_CHOICEs:
         continue
-    # auto_eval_column_dict.append([f"{task.name}_prefilling_time", ColumnContent, ColumnContent(f"{task.value.col_name} {PREs}", "number", False)])
-    auto_eval_column_dict.append([f"{task.name}_decoding_throughput", ColumnContent, ColumnContent(f"{task.value.col_name} {TS}", "number", True)])
+    # auto_eval_column_dict.append([f"{task.name}_prefilling_time", ColumnContent, ColumnContent(f"{task.value.col_name} {PREs}", "number", False, hidden=True)])
+    auto_eval_column_dict.append([f"{task.name}_decoding_throughput", ColumnContent, ColumnContent(f"{task.value.col_name} {TS}", "number", True, hidden=True)])
 
 
 # Model information
@@ -242,8 +242,8 @@ class Precision(Enum):
 
 
 # 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 = [c.name for c in fields(AutoEvalColumn)]
+TYPES = [c.type for c in fields(AutoEvalColumn)]
 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]