add bert example

examples/dynamo/engine_caching_bert_example.py

@@ -0,0 +1,64 @@
+import numpy as np
+import torch
+import torch_tensorrt
+from engine_caching_example import remove_timing_cache
+from transformers import BertModel
+
+np.random.seed(0)
+torch.manual_seed(0)
+
+model = BertModel.from_pretrained("bert-base-uncased", return_dict=False).cuda().eval()
+inputs = [
+    torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+    torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+]
+
+
+def compile_bert(iterations=3):
+    times = []
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+
+    # The 1st iteration is to measure the compilation time without engine caching
+    # The 2nd and 3rd iterations are to measure the compilation time with engine caching.
+    # Since the 2nd iteration needs to compile and save the engine, it will be slower than the 1st iteration.
+    # The 3rd iteration should be faster than the 1st iteration because it loads the cached engine.
+    for i in range(iterations):
+        # remove timing cache and reset dynamo for engine caching messurement
+        remove_timing_cache()
+        torch._dynamo.reset()
+
+        if i == 0:
+            save_engine_cache = False
+            load_engine_cache = False
+        else:
+            save_engine_cache = True
+            load_engine_cache = True
+
+        start.record()
+        compilation_kwargs = {
+            "use_python_runtime": False,
+            "enabled_precisions": {torch.float},
+            "truncate_double": True,
+            "debug": True,
+            "min_block_size": 1,
+            "make_refitable": True,
+            "save_engine_cache": save_engine_cache,
+            "load_engine_cache": load_engine_cache,
+            "engine_cache_size": 1 << 30,  # 1GB
+        }
+        optimized_model = torch.compile(
+            model,
+            backend="torch_tensorrt",
+            options=compilation_kwargs,
+        )
+        optimized_model(*inputs)
+        end.record()
+        torch.cuda.synchronize()
+        times.append(start.elapsed_time(end))
+
+    print("-----compile bert-----> compilation time:", times, "milliseconds")
+
+
+if __name__ == "__main__":
+    compile_bert()