Update sweep bench (depracating .jsonl support) (#289)

* Update sweep bench (depracating .jsonl support)

* Fix README.md

1 files changed, 43 insertions, 25 deletions

diff --git a/examples/sweep-bench/sweep-bench-plot.py b/examples/sweep-bench/sweep-bench-plot.py
index 0d3c81db..481a604c 100755
--- a/examples/sweep-bench/sweep-bench-plot.py
+++ b/examples/sweep-bench/sweep-bench-plot.py
@@ -9,27 +9,54 @@ args = parser.parse_args()
 
 df = None
 
-for jsonl_file in args.file:
-    # Read JSONL file into DataFrame
-    df_part = pd.read_json(jsonl_file, lines=True)
-    df_part['label'] = jsonl_file
-    if df is None:
-        df = df_part
-    else:
-        df = pd.concat([df, df_part])
-
-# Group by model and n_kv, calculate mean and std for both speed metrics
+#for jsonl_file in args.file:
+#    # Read JSONL file into DataFrame
+#    df_part = pd.read_json(jsonl_file, lines=True)
+#    df_part['label'] = jsonl_file
+#    if df is None:
+#        df = df_part
+#    else:
+#        df = pd.concat([df, df_part])
+#
+
+
+
+for md_file in args.file:
+    # Read markdown table file into DataFrame
+    df_part = pd.read_csv(md_file, sep=r'\s*\|\s*', engine='python', 
+                         header=0, skiprows=[1])
+    
+    # Clean up columns (remove empty columns from markdown formatting)
+    df_part = df_part.iloc[:, 1:-1]
+    df_part.columns = [col.strip() for col in df_part.columns]
+    
+    # Rename columns to match expected names
+    df_part = df_part.rename(columns={
+        'N_KV': 'n_kv',
+        'S_PP t/s': 'speed_pp',
+        'S_TG t/s': 'speed_tg'
+    })
+    
+    # Convert to numeric types
+    df_part['n_kv'] = pd.to_numeric(df_part['n_kv'])
+    df_part['speed_pp'] = pd.to_numeric(df_part['speed_pp'])
+    df_part['speed_tg'] = pd.to_numeric(df_part['speed_tg'])
+    
+    # Add label and append to main DataFrame
+    df_part['label'] = md_file
+    df = pd.concat([df, df_part]) if df is not None else df_part
+
+# Group by label and n_kv, calculate mean and std for both speed metrics
 df_grouped = df.groupby(['label', 'n_kv']).agg({
     'speed_pp': ['mean', 'std'],
     'speed_tg': ['mean', 'std']
 }).reset_index()
 
 # Flatten multi-index columns
-df_grouped.columns = ['label', 'n_kv', 'speed_pp_mean', 'speed_pp_std', 
+df_grouped.columns = ['label', 'n_kv', 'speed_pp_mean', 'speed_pp_std',
                       'speed_tg_mean', 'speed_tg_std']
 
 # Replace NaN with 0 (std for a single sample is NaN)
-
 df_grouped['speed_pp_std'] = df_grouped['speed_pp_std'].fillna(0)
 df_grouped['speed_tg_std'] = df_grouped['speed_tg_std'].fillna(0)
 
@@ -45,25 +72,20 @@ colors = plt.cm.rainbow(np.linspace(0, 1, len(labels)))
 # Create prompt processing plot
 plt.figure(figsize=(10, 6))
 ax1 = plt.gca()
-
 plt.grid()
-
 ax1.set_xticks(x_ticks)
 
 # Plot each label's data
 for label, color in zip(labels, colors):
     label_data = df_grouped[df_grouped['label'] == label].sort_values('n_kv')
-    
-    # Plot prompt processing
-    pp = ax1.errorbar(label_data['n_kv'], label_data['speed_pp_mean'], 
-                     yerr=label_data['speed_pp_std'], color=color, 
+    pp = ax1.errorbar(label_data['n_kv'], label_data['speed_pp_mean'],
+                     yerr=label_data['speed_pp_std'], color=color,
                      marker='o', linestyle='-', label=label)
-    
+
 # Add labels and title
 ax1.set_xlabel('Context Length (tokens)')
 ax1.set_ylabel('Prompt Processing Rate (t/s)')
 plt.title('Prompt Processing Performance Comparison')
-
 ax1.legend(loc='upper right')
 
 # Adjust layout and save
@@ -74,24 +96,20 @@ plt.close()
 # Create token generation plot
 plt.figure(figsize=(10, 6))
 ax1 = plt.gca()
-
 plt.grid()
 ax1.set_xticks(x_ticks)
 
 # Plot each model's data
 for label, color in zip(labels, colors):
     label_data = df_grouped[df_grouped['label'] == label].sort_values('n_kv')
-    
-    # Plot token generation
     tg = ax1.errorbar(label_data['n_kv'], label_data['speed_tg_mean'],
-                     yerr=label_data['speed_tg_std'], color=color, 
+                     yerr=label_data['speed_tg_std'], color=color,
                      marker='s', linestyle='-', label=label)
 
 # Add labels and title
 ax1.set_xlabel('Context Length (n_kv)')
 ax1.set_ylabel('Token Generation Rate (t/s)')
 plt.title('Token Generation Performance Comparison')
-
 ax1.legend(loc='upper right')
 
 # Adjust layout and save