Added TRTWrapper

Dockerfile

@@ -56,4 +56,5 @@ RUN apt-get update \
   && rm -rf /var/lib/apt/lists/*
 # append /opt/tools to runtime path for NGC CLI to be accessible from all file system locations
 ENV PATH=${PATH}:/opt/tools
+ENV POLYGRAPHY_AUTOINSTALL_DEPS=1
 WORKDIR /opt/monai

docs/source/config_syntax.md

@@ -16,6 +16,7 @@ Content:
   - [`$` to evaluate as Python expressions](#to-evaluate-as-python-expressions)
   - [`%` to textually replace configuration elements](#to-textually-replace-configuration-elements)
   - [`_target_` (`_disabled_`, `_desc_`, `_requires_`, `_mode_`) to instantiate a Python object](#instantiate-a-python-object)
+  - [`+` to alter semantics of merging config keys from multiple configuration files](#multiple-config-files)
 - [The command line interface](#the-command-line-interface)
 - [Recommendations](#recommendations)
 
@@ -175,6 +176,47 @@ _Description:_ `_requires_`, `_disabled_`, `_desc_`, and `_mode_` are optional k
   - `"debug"` -- execute with debug prompt and return the return value of ``pdb.runcall(_target_, **kwargs)``,
     see also [`pdb.runcall`](https://docs.python.org/3/library/pdb.html#pdb.runcall).
 
+## Multiple config files
+
+_Description:_ Multiple config files may be specified on the command line.
+The content of those config files is being merged. When same keys are specifiled in more than one config file,
+the value associated with the key is being overridden, in the order config files are specified.
+If the desired behaviour is to merge values from both files, the key in second config file should be prefixed with `+`.
+The value types for the merged contents must match and be both of `dict` or both of `list` type.
+`dict` values will be merged via update(), `list` values - concatenated via extend().
+Here's an example. In this case, "amp" value will be overridden by extra_config.json.
+`imports` and `preprocessing#transforms` lists will be merged. An error would be thrown if the value type in `"+imports"` is not `list`:
+
+config.json:
+```json
+{
+    "amp": "$True"
+    "imports": [
+	"$import torch"
+    ],
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+	  "$@t1",
+	  "$@t2"
+        ]
+    },
+}
+```
+
+extra_config.json:
+```json
+{
+    "amp": "$False"
+    "+imports": [
+	"$from monai.networks import trt_compile"
+    ],
+    "+preprocessing#transforms": [
+        "$@t3"
+    ]
+}
+```
+
 ## The command line interface
 
 In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle.

monai/bundle/config_parser.py

@@ -20,7 +20,7 @@
 
 from monai.bundle.config_item import ComponentLocator, ConfigComponent, ConfigExpression, ConfigItem
 from monai.bundle.reference_resolver import ReferenceResolver
-from monai.bundle.utils import ID_REF_KEY, ID_SEP_KEY, MACRO_KEY
+from monai.bundle.utils import ID_REF_KEY, ID_SEP_KEY, MACRO_KEY, merge_kv
 from monai.config import PathLike
 from monai.utils import ensure_tuple, look_up_option, optional_import
 from monai.utils.misc import CheckKeyDuplicatesYamlLoader, check_key_duplicates
@@ -423,8 +423,10 @@ def load_config_files(cls, files: PathLike | Sequence[PathLike] | dict, **kwargs
         if isinstance(files, str) and not Path(files).is_file() and "," in files:
             files = files.split(",")
         for i in ensure_tuple(files):
-            for k, v in (cls.load_config_file(i, **kwargs)).items():
-                parser[k] = v
+            config_dict = cls.load_config_file(i, **kwargs)
+            for k, v in config_dict.items():
+                merge_kv(parser, k, v)
+
         return parser.get()  # type: ignore
 
     @classmethod

monai/bundle/scripts.py

@@ -32,7 +32,7 @@
 from monai.apps.utils import _basename, download_url, extractall, get_logger
 from monai.bundle.config_item import ConfigComponent
 from monai.bundle.config_parser import ConfigParser
-from monai.bundle.utils import DEFAULT_INFERENCE, DEFAULT_METADATA
+from monai.bundle.utils import DEFAULT_INFERENCE, DEFAULT_METADATA, merge_kv
 from monai.bundle.workflows import BundleWorkflow, ConfigWorkflow
 from monai.config import IgniteInfo, PathLike
 from monai.data import load_net_with_metadata, save_net_with_metadata
@@ -105,7 +105,7 @@ def update_kwargs(args: str | dict | None = None, ignore_none: bool = True, **kw
         if isinstance(v, dict) and isinstance(args_.get(k), dict):
             args_[k] = update_kwargs(args_[k], ignore_none, **v)
         else:
-            args_[k] = v
+            merge_kv(args_, k, v)
     return args_
 
 

monai/bundle/utils.py

@@ -13,6 +13,7 @@
 
 import json
 import os
+import warnings
 import zipfile
 from typing import Any
 
@@ -21,12 +22,21 @@
 
 yaml, _ = optional_import("yaml")
 
-__all__ = ["ID_REF_KEY", "ID_SEP_KEY", "EXPR_KEY", "MACRO_KEY", "DEFAULT_MLFLOW_SETTINGS", "DEFAULT_EXP_MGMT_SETTINGS"]
+__all__ = [
+    "ID_REF_KEY",
+    "ID_SEP_KEY",
+    "EXPR_KEY",
+    "MACRO_KEY",
+    "MERGE_KEY",
+    "DEFAULT_MLFLOW_SETTINGS",
+    "DEFAULT_EXP_MGMT_SETTINGS",
+]
 
 ID_REF_KEY = "@"  # start of a reference to a ConfigItem
 ID_SEP_KEY = "::"  # separator for the ID of a ConfigItem
 EXPR_KEY = "$"  # start of a ConfigExpression
 MACRO_KEY = "%"  # start of a macro of a config
+MERGE_KEY = "+"  # prefix indicating merge instead of override in case of multiple configs.
 
 _conf_values = get_config_values()
 
@@ -233,3 +243,27 @@ def load_bundle_config(bundle_path: str, *config_names: str, **load_kw_args: Any
             parser.read_config(f=cdata)
 
     return parser
+
+
+def merge_kv(args: dict | Any, k: str, v: Any) -> None:
+    """
+    Update the `args` dict-like object with the key/value pair `k` and `v`.
+    """
+    if k.startswith(MERGE_KEY):
+        """
+        Both values associated with `+`-prefixed key pair must be of `dict` or `list` type.
+        `dict` values will be merged, `list` values - concatenated.
+        """
+        id = k[1:]
+        if id in args:
+            if isinstance(v, dict) and isinstance(args[id], dict):
+                args[id].update(v)
+            elif isinstance(v, list) and isinstance(args[id], list):
+                args[id].extend(v)
+            else:
+                raise ValueError(ValueError(f"config must be dict or list for key `{k}`, but got {type(v)}: {v}."))
+        else:
+            warnings.warn(f"Can't merge entry ['{k}'], '{id}' is not in target dict - copying instead.")
+            args[id] = v
+    else:
+        args[k] = v

monai/handlers/__init__.py

@@ -40,5 +40,6 @@
 from .stats_handler import StatsHandler
 from .surface_distance import SurfaceDistance
 from .tensorboard_handlers import TensorBoardHandler, TensorBoardImageHandler, TensorBoardStatsHandler
+from .trt_handler import TrtHandler
 from .utils import from_engine, ignore_data, stopping_fn_from_loss, stopping_fn_from_metric, write_metrics_reports
 from .validation_handler import ValidationHandler

monai/handlers/trt_handler.py

@@ -0,0 +1,61 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from monai.config import IgniteInfo
+from monai.networks import trt_compile
+from monai.utils import min_version, optional_import
+
+Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events")
+if TYPE_CHECKING:
+    from ignite.engine import Engine
+else:
+    Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine")
+
+
+class TrtHandler:
+    """
+    TrtHandler acts as an Ignite handler to apply TRT acceleration to the model.
+    Usage example::
+        handler = TrtHandler(model=model, base_path="/test/checkpoint.pt", args={"precision": "fp16"})
+        handler.attach(engine)
+        engine.run()
+    """
+
+    def __init__(self, model, base_path, args=None, submodule=None):
+        """
+        Args:
+            base_path: TRT path basename. TRT plan(s) saved to "base_path[.submodule].plan"
+            args: passed to trt_compile(). See trt_compile() for details.
+            submodule : Hierarchical ids of submodules to convert, e.g. 'image_decoder.decoder'
+        """
+        self.model = model
+        self.base_path = base_path
+        self.args = args
+        self.submodule = submodule
+
+    def attach(self, engine: Engine) -> None:
+        """
+        Args:
+            engine: Ignite Engine, it can be a trainer, validator or evaluator.
+        """
+        self.logger = engine.logger
+        engine.add_event_handler(Events.STARTED, self)
+
+    def __call__(self, engine: Engine) -> None:
+        """
+        Args:
+            engine: Ignite Engine, it can be a trainer, validator or evaluator.
+        """
+        trt_compile(self.model, self.base_path, args=self.args, submodule=self.submodule, logger=self.logger)

monai/networks/__init__.py

@@ -11,7 +11,9 @@
 
 from __future__ import annotations
 
+from .trt_compiler import trt_compile
 from .utils import (
+    add_casts_around_norms,
     convert_to_onnx,
     convert_to_torchscript,
     convert_to_trt,

monai/networks/nets/swin_unetr.py

@@ -320,7 +320,7 @@ def _check_input_size(self, spatial_shape):
             )
 
     def forward(self, x_in):
-        if not torch.jit.is_scripting():
+        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
             self._check_input_size(x_in.shape[2:])
         hidden_states_out = self.swinViT(x_in, self.normalize)
         enc0 = self.encoder1(x_in)
@@ -1046,14 +1046,14 @@ def __init__(
 
     def proj_out(self, x, normalize=False):
         if normalize:
-            x_shape = x.size()
+            x_shape = x.shape
+            # Force trace() to generate a constant by casting to int
+            ch = int(x_shape[1])
             if len(x_shape) == 5:
-                n, ch, d, h, w = x_shape
                 x = rearrange(x, "n c d h w -> n d h w c")
                 x = F.layer_norm(x, [ch])
                 x = rearrange(x, "n d h w c -> n c d h w")
             elif len(x_shape) == 4:
-                n, ch, h, w = x_shape
                 x = rearrange(x, "n c h w -> n h w c")
                 x = F.layer_norm(x, [ch])
                 x = rearrange(x, "n h w c -> n c h w")