Fixed the comment and added engine cache example

examples/dynamo/mutable_torchtrt_module_example.py

@@ -11,12 +11,13 @@
 The Mutable Torch TensorRT Module is designed to address these challenges, making interaction with the Torch-TensorRT module easier than ever.
 
 In this tutorial, we are going to walk through
-1. Sample workflow of Mutable Torch TensorRT Module with ResNet 18
-2. Save a Mutable Torch TensorRT Module
-3. Integration with Huggingface pipeline in LoRA use case
-4. Usage of dynamic shape with Mutable Torch TensorRT Module
+    1. Sample workflow of Mutable Torch TensorRT Module with ResNet 18
+    2. Save a Mutable Torch TensorRT Module
+    3. Integration with Huggingface pipeline in LoRA use case
+    4. Usage of dynamic shape with Mutable Torch TensorRT Module
 """
 
+# %%
 import numpy as np
 import torch
 import torch_tensorrt as torch_trt
@@ -144,6 +145,12 @@
 # %%
 # Use Mutable Torch TensorRT module with dynamic shape
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# When adding dynamic shape hint to MutableTorchTensorRTModule, The shape hint should EXACTLY follow the semantics of arg_inputs and kwarg_inputs passed to the forward function
+# and should not omit any entries (except None in the kwarg_inputs). If there is a nested dict/list in the input, the dynamic shape for that entry should also be an nested dict/list.
+# If the dynamic shape is not required for an input, an empty dictionary should be given as the shape hint for that input.
+# Note that you should exclude keyword arguments with value None as those will be filtered out.
+
+
 class Model(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -167,7 +174,10 @@ def forward(self, a, b, c={}):
 dim_2 = torch.export.Dim("dim2", min=1, max=50)
 args_dynamic_shapes = ({1: dim_1}, {0: dim_0})
 kwarg_dynamic_shapes = {
-    "c": {"a": {}, "b": {0: dim_2}},
+    "c": {
+        "a": {},
+        "b": {0: dim_2},
+    },  # a's shape does not change so we give it an empty dict
 }
 # Export the model first with custom dynamic shape constraints
 model = torch_trt.MutableTorchTensorRTModule(model, debug=True, min_block_size=1)
@@ -181,3 +191,59 @@ def forward(self, a, b, c={}):
 }
 # Run without recompiling
 model(*inputs_2, **kwargs_2)
+
+# %%
+# Use Mutable Torch TensorRT module with persistent cache
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Leveraging engine caching, we are able to shortcut the engine compilation and save much time.
+import os
+
+from torch_tensorrt.dynamo._defaults import TIMING_CACHE_PATH
+
+model = models.resnet18(pretrained=True).eval().to("cuda")
+enabled_precisions = {torch.float}
+debug = False
+min_block_size = 1
+use_python_runtime = True
+
+times = []
+start = torch.cuda.Event(enable_timing=True)
+end = torch.cuda.Event(enable_timing=True)
+
+
+example_inputs = (torch.randn((100, 3, 224, 224)).to("cuda"),)
+# Mark the dim0 of inputs as dynamic
+model = torch_trt.MutableTorchTensorRTModule(
+    model,
+    use_python_runtime=use_python_runtime,
+    enabled_precisions=enabled_precisions,
+    debug=debug,
+    min_block_size=min_block_size,
+    immutable_weights=False,
+    cache_built_engines=True,
+    reuse_cached_engines=True,
+    engine_cache_size=1 << 30,  # 1GB
+)
+
+
+def remove_timing_cache(path=TIMING_CACHE_PATH):
+    if os.path.exists(path):
+        os.remove(path)
+
+
+remove_timing_cache()
+
+for i in range(4):
+    inputs = [torch.rand((100 + i, 3, 224, 224)).to("cuda")]
+
+    start.record()
+    model(*inputs)  # Recompile
+    end.record()
+    torch.cuda.synchronize()
+    times.append(start.elapsed_time(end))
+
+print("----------------dynamo_compile----------------")
+print("Without engine caching, used:", times[0], "ms")
+print("With engine caching used:", times[1], "ms")
+print("With engine caching used:", times[2], "ms")
+print("With engine caching used:", times[3], "ms")