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

* Update sweep bench (depracating .jsonl support)

* Fix README.md

3 files changed, 58 insertions, 39 deletions

diff --git a/examples/sweep-bench/README.md b/examples/sweep-bench/README.md
index 608fd104..d92740de 100644
--- a/examples/sweep-bench/README.md
+++ b/examples/sweep-bench/README.md
@@ -7,6 +7,7 @@ in each ubatch-sized window. Only a single token sequence is used.
 The benchmark steps are:
 
 for each ubatch-sized window in context:
+
     1. generate ubatch/4 tokens (not the whole window to save some time)
     2. measure generation performance
     3. remove generated tokens from KV cache
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
diff --git a/examples/sweep-bench/sweep-bench.cpp b/examples/sweep-bench/sweep-bench.cpp
index 4e594de5..27510687 100644
--- a/examples/sweep-bench/sweep-bench.cpp
+++ b/examples/sweep-bench/sweep-bench.cpp
@@ -18,9 +18,9 @@
 #include <vector>
 
 static void print_usage(int, char ** argv) {
-    LOG("\nexample usage:\n");
-    LOG("\n    %s -m model.gguf -c 8192 -b 2048 -ub 512\n", argv[0]);
-    LOG("\n");
+    LOG_TEE("\nexample usage:\n");
+    LOG_TEE("\n    %s -m model.gguf -c 8192 -b 2048 -ub 512\n", argv[0]);
+    LOG_TEE("\n");
 }
 
 int main(int argc, char ** argv) {
@@ -83,7 +83,7 @@ int main(int argc, char ** argv) {
 
             const int ret = llama_decode(ctx, batch_view);
             if (ret != 0) {
-                LOG("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
+                LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
                 return false;
             }
 
@@ -97,11 +97,11 @@ int main(int argc, char ** argv) {
     const unsigned int tg = params.n_ubatch / 4;
 
     if (!params.sweep_bench_output_jsonl) {
-        LOG("\n");
-        LOG("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
-        LOG("\n");
-        LOG("|%6s | %6s | %6s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s");
-        LOG("|%6s-|-%6s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "------", "--------", "--------", "--------", "--------");
+        LOG_TEE("\n");
+        LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
+        LOG_TEE("\n");
+        LOG_TEE("|%6s | %6s | %6s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s");
+        LOG_TEE("|%6s-|-%6s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "------", "--------", "--------", "--------", "--------");
     }
 
     llama_batch batch = llama_batch_init(n_kv_max, 0, 1);
@@ -111,7 +111,7 @@ int main(int argc, char ** argv) {
         llama_batch_add(batch, bos, 0, { 0 }, false);
 
         if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
-            LOG("%s: llama_decode() failed\n", __func__);
+            LOG_TEE("%s: llama_decode() failed\n", __func__);
             return 1;
         }
     }
@@ -131,7 +131,7 @@ int main(int argc, char ** argv) {
             llama_batch_add(batch, std::rand() % n_vocab, n_kv + i, { 0 }, true);
 
             if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
-                LOG("%s: llama_decode() failed\n", __func__);
+                LOG_TEE("%s: llama_decode() failed\n", __func__);
                 return 1;
             }
         }
@@ -153,7 +153,7 @@ int main(int argc, char ** argv) {
         const auto t_pp_start = ggml_time_us();
 
         if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
-            LOG("%s: llama_decode() failed\n", __func__);
+            LOG_TEE("%s: llama_decode() failed\n", __func__);
             return 1;
         }
 
@@ -167,14 +167,14 @@ int main(int argc, char ** argv) {
         const float speed_tg = tg / t_tg;
 
         if(params.sweep_bench_output_jsonl) {
-            LOG(
+            LOG_TEE(
                 "{\"n_kv_max\": %d, \"n_batch\": %d, \"n_ubatch\": %d, \"flash_attn\": %d, \"n_gpu_layers\": %d, \"n_threads\": %u, \"n_threads_batch\": %u, "
                 "\"pp\": %d, \"tg\": %d, \"n_kv\": %d, \"t_pp\": %f, \"speed_pp\": %f, \"t_tg\": %f, \"speed_tg\": %f }\n",
                 n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch,
                 pp, tg, n_kv, t_pp, speed_pp, t_tg, speed_tg
             );
         } else {
-            LOG("|%6d | %6d | %6d | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, n_kv, t_pp, speed_pp, t_tg, speed_tg);
+            LOG_TEE("|%6d | %6d | %6d | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, n_kv, t_pp, speed_pp, t_tg, speed_tg);
         }
     }