examples: Add example usage script for Function/Module-level acceleration

docsrc/index.rst

@@ -73,6 +73,7 @@ Tutorials
    tutorials/_rendered_examples/dynamo/torch_compile_resnet_example
    tutorials/_rendered_examples/dynamo/torch_compile_transformers_example
    tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage
+   tutorials/_rendered_examples/dynamo/torch_compile_module_level_acceleration
 
 Python API Documenation
 ------------------------

examples/dynamo/README.rst

@@ -9,3 +9,4 @@ a number of ways you can leverage this backend to accelerate inference.
 * :ref:`torch_compile_resnet`: Compiling a ResNet model using the Torch Compile Frontend for ``torch_tensorrt.compile``
 * :ref:`torch_compile_transformer`: Compiling a Transformer model using ``torch.compile``
 * :ref:`torch_compile_advanced_usage`: Advanced usage including making a custom backend to use directly with the ``torch.compile`` API
+* :ref:`torch_compile_module_level_acceleration`: Accelerate a specific ``torch.nn.Module`` or function by excluding it from decomposition

examples/dynamo/torch_compile_module_level_acceleration.py

@@ -0,0 +1,162 @@
+"""
+.. _torch_compile_module_level_acceleration:
+
+Dynamo Module Level Acceleration Tutorial
+=========================
+
+This interactive script is intended as an overview of the process by which module-level acceleration for `torch_tensorrt.dynamo.compile` works, and how it can be used to accelerate built-in or custom `torch.nn` modules by excluding them from AOT tracing. This script shows the process for `torch.nn.MaxPool1d`"""
+
+# %%
+# 1. The Placeholder
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Specify the schema and namespace of the operator, as well as a placeholder function
+# representing the schema. The schema should be in torch JIT syntax, indicating input and output
+# types. The namespace, such as tensorrt, will cause the op to be registered as `torch.ops.tensorrt.your_op`
+# Then, create a placeholder function with no operations, but having the same schema and naming as that
+# used in the decorator
+
+# %%
+
+from torch._custom_op.impl import custom_op
+
+
+@custom_op(
+    qualname="tensorrt::maxpool1d",
+    manual_schema="(Tensor x, int[1] kernel_size, int[1] stride, int[1] padding, int[1] dilation, bool ceil_mode) -> Tensor",
+)
+def maxpool1d(x, kernel_size, stride, padding, dilation, ceil_mode):
+    # Defines operator schema, name, namespace, and function header
+    ...
+
+
+# %%
+# 2. The Generic Implementation
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Define the default implementation of the operator in torch syntax. This is used for autograd
+# and other tracing functionality. Generally, the `torch.nn.functional` analog of the operator to replace
+# is desirable. If the operator to replace is a custom module you've written, then add its Torch
+# implementation here. Note that the function header to the generic function can have specific arguments
+# as in the above placeholder
+
+# %%
+import torch
+
+
+@maxpool1d.impl("cpu")
+@maxpool1d.impl("cuda")
+@maxpool1d.impl_abstract()
+def maxpool1d_generic(
+    *args,
+    **kwargs,
+):
+    # Defines an implementation for AOT Autograd to use for shape analysis/propagation
+    return torch.nn.functional.max_pool1d(
+        *args,
+        **kwargs,
+    )
+
+
+# %%
+# 3. The Module Substitution Function
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Define a function which can intercept a node of the kind to be replaced, extract
+# the relevant data from that node/submodule, and then re-package the information
+# for use by an accelerated implementation (to be implemented in step 4). This function
+# should use the operator defined in step 1 (for example `torch.ops.tensorrt.maxpool1d`).
+# It should refactor the args and kwargs as is needed by the accelerated implementation.
+
+# %%
+
+from torch_tensorrt.dynamo.backend.lowering import register_substitution
+
+
+@register_substitution(torch.nn.MaxPool1d, torch.ops.tensorrt.maxpool1d)
+def maxpool1d_insertion_fn(
+    gm: torch.fx.GraphModule,
+    node: torch.fx.Node,
+    submodule: torch.nn.Module,
+) -> torch.fx.Node:
+    # Defines insertion function for new node
+    new_node = gm.graph.call_function(
+        torch.ops.tensorrt.maxpool1d,
+        args=node.args,
+        kwargs={
+            "kernel_size": submodule.kernel_size,
+            "stride": submodule.stride,
+            "padding": submodule.padding,
+            "dilation": submodule.dilation,
+            "ceil_mode": submodule.ceil_mode,
+        },
+    )
+
+    return new_node
+
+
+# %%
+# If the submodule has weights or other Tensor fields which the accelerated implementation
+# needs, the function should insert the necessary nodes to access those weights. For example,
+# if the weight Tensor of a submodule is needed, one could write::
+#
+#
+#       weights = gm.graph.get_attr(n.target + ".weight", torch.Tensor)
+#       bias = gm.graph.get_attr(n.target + ".bias", torch.Tensor)
+#
+#       ...
+#
+#       kwargs={"weight": weights,
+#               "bias": bias,
+#               ...
+#              }
+
+# %%
+# 4. The Accelerated Implementation
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Define an accelerated implementation of the operator, and register it as necessary.
+# This accelerated implementation should consume the args/kwargs specified in step 3.
+# One should expect that torch.compile will compress all kwargs into the args field in
+# the order specified in the schema written in step 1.
+
+# %%
+
+from typing import Dict, Tuple
+from torch.fx.node import Argument, Target
+from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
+from torch_tensorrt.fx.converter_registry import tensorrt_converter
+from torch_tensorrt.fx.converters import acc_ops_converters
+
+
+@tensorrt_converter(torch.ops.tensorrt.maxpool1d.default)
+def tensorrt_maxpool1d(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> TRTTensor:
+    # Defines converter replacing the default operator for this function
+    kwargs_new = {
+        "input": args[0],
+        "kernel_size": args[1],
+        "stride": args[2],
+        "padding": args[3],
+        "dilation": args[4],
+        "ceil_mode": False if len(args) < 6 else args[5],
+    }
+
+    return acc_ops_converters.acc_ops_max_pool1d(
+        network, target, None, kwargs_new, name
+    )
+
+
+# %%
+# 5. Add Imports
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Add your accelerated module file to the `__init__.py` in the
+# `py/torch_tensorrt/dynamo/lowering/substitutions` directory, to ensure
+# all registrations are run. For instance, if the new module file is called `new_mod.py`,
+# one should add `from .new_mod import *` to the `__init__.py`