Add dynamo for optimization

app.py

@@ -1,13 +1,30 @@
+import time
 import torch
 import gradio as gr
+import torch._dynamo as dynamo
 
 
 model = torch.load("GPT2Model.pt")
+optimized_model = dynamo.optimize("inductor")(model)
 tokenizer = torch.load("GPT2Tokenizer.pt")
+
+def timed(fn):
+    start = time.time()
+    result = fn()
+    end = time.time() - start
+    return result, float("{:.5f}".format(end))
+
 def gpt2(prompt):
     input_ids = tokenizer(prompt, return_tensors="pt").input_ids
-    outputs = model.generate(input_ids, do_sample=False, max_length=30)
-    return tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    eager_outputs, eager_time = timed(lambda: model.generate(input_ids, do_sample=False, max_length=30))
+    dynamo_outputs, dynamo_time = timed(lambda: optimized_model.generate(input_ids, do_sample=False, max_length=30))
+    if torch.allclose(eager_outputs, dynamo_outputs):
+        actual_output = tokenizer.batch_decode(dynamo_outputs, skip_special_tokens=True)[0]
+    else:
+        actual_output = "Result is not correct between dynamo and eager!"
+    expect_output = f"Torch eager takes: {eager_time} \nDynamo takes: {dynamo_time} \nSpeedup: "
+    expect_output += "{:.2f}".format(eager_time/dynamo_time) + f"x \nOutput: {actual_output}"
+    return expect_output
 
 demo = gr.Interface(fn=gpt2, inputs="text", outputs="text")