update generation type names

app.py

@@ -11,7 +11,7 @@ import pandas as pd
 
 # benchmark order: pytorch, tf eager, tf xla; units = ms
 BENCHMARK_DATA = {
-    "Greedy Search": {
+    "Greedy Decoding": {
         "DistilGPT2": {
             "T4": [336.22, 3976.23, 115.84],
             "3090": [158.38, 1835.82, 46.56],
@@ -53,7 +53,7 @@ BENCHMARK_DATA = {
             "A100": [1801.68, 16707.71, 411.93],
         },
     },
-    "Sample": {
+    "Sampling": {
         "DistilGPT2": {
             "T4": [617.40, 6078.81, 221.65],
             "3090": [310.37, 2843.73, 85.44],
@@ -184,8 +184,8 @@ with demo:
         """
     )
     with gr.Tabs():
-        with gr.TabItem("Greedy Search"):
-            plot_fn = functools.partial(get_plot, generate_type="Greedy Search")
+        with gr.TabItem("Greedy Decoding"):
+            plot_fn = functools.partial(get_plot, generate_type="Greedy Decoding")
             with gr.Row():
                 with gr.Column():
                     model_selector = gr.Dropdown(
@@ -202,7 +202,7 @@ with demo:
                     )
                     gr.Markdown(
                         """
-                        ### Greedy Search benchmark parameters
+                        ### Greedy Decoding benchmark parameters
                         - `max_new_tokens = 64`;
                         - `pad_to_multiple_of = 64` for Tensorflow XLA models. Others do not pad (input prompts between 2 and 33 tokens).
                         """
@@ -210,8 +210,8 @@ with demo:
                 plot = gr.Image(value=plot_fn("T5 Small", "Yes"))  # Show plot when the gradio app is initialized
             model_selector.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
             eager_enabler.change(fn=plot_fn, inputs=[model_selector, eager_enabler], outputs=plot)
-        with gr.TabItem("Sample"):
-            plot_fn = functools.partial(get_plot, generate_type="Sample")
+        with gr.TabItem("Sampling"):
+            plot_fn = functools.partial(get_plot, generate_type="Sampling")
             with gr.Row():
                 with gr.Column():
                     model_selector = gr.Dropdown(
@@ -228,7 +228,7 @@ with demo:
                     )
                     gr.Markdown(
                         """
-                        ### Sample benchmark parameters
+                        ### Sampling benchmark parameters
                         - `max_new_tokens = 128`;
                         - `temperature = 2.0`;
                         - `top_k = 50`;