Rename runtime-config to executor-config and add documentation for Model Library Format

docs/dev/index.rst

@@ -423,3 +423,4 @@ microTVM
    :maxdepth: 1
 
    microtvm_design
+   model_library_format

docs/dev/model_library_format.rst

@@ -0,0 +1,167 @@
+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you 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.
+
+Model Library Format
+====================
+
+About Model Library Format
+--------------------------
+
+TVM traditionally exports generated libraries as Dynamic Shared Objects (e.g. DLLs (Windows) or .so
+(linux)). Inferences can be performed using those libraries by loading them into an executable using
+``libtvm_runtime.so``. This process is very dependent on services provided by traditional OS.
+
+For deployment to unconventional platforms (e.g. those lacking traditional OS), the microTVM project
+can be used to export a generated library in pieces. In this case, microTVM provides another output
+format, Model Library Format. Model Library Format is a tarball containing a file for each part of
+the TVM compiler output.
+
+What can be Exported
+--------------------
+
+At the time of writing, export is limited to full models built with ``tvm.relay.build``.
+
+Directory Layout
+----------------
+
+Model Library Format is traditionally contained within a tarball. All paths are relative to the root
+of the tarball:
+
+- ``/`` - Root of the tarball
+
+  - ``codegen`` - Root directory for all generated device code
+
+    - (see `codegen`_ section)
+
+  - ``executor-config/`` - Configuration for the executor which drives model inference
+
+    - ``graph/`` - Root directory containing configuration for the GraphExecutor
+
+      - ``graph.json`` - GraphExecutor JSON configuration
+
+  -  ``metadata.json`` - Machine-parseable metadata for this model
+
+  - ``parameters/`` - Root directory where simplified parameters are placed
+
+    - ``<model_name>.params`` - Parameters for the model tvm.relay._save_params format
+
+  - ``src/`` - Root directory for all source code consumed by TVM
+
+    - ``relay.txt`` - Relay source code for the generated model
+
+Description of Sub-directories
+------------------------------
+
+.. _subdir_codegen:
+
+``codegen``
+^^^^^^^^^^^
+
+All TVM-generated code is placed in this directory. At the time of writing, there is 1 file per
+Module in the generated Module tree, though this restriction may change in the future. Files in
+this directory should have filenames of the form ``<target>/(lib|src)/<unique_name>.<format>``.
+
+These components are described below:
+
+ * ``<target>`` - Identifies the TVM target on which the code should run. Currently, only ``host``
+   is supported.
+ * ``<unique_name>`` - A unique slug identifying this file. Currently ``lib<n>``, with ``<n>>`` an
+   auto-incrementing integer.
+ * ``<format>`` - Suffix identifying the filename format. Currently ``c`` or ``o``.
+
+An example directory tree for a CPU-only model is shown below:
+
+- ``codegen/`` - Codegen directory
+
+  - ``host/`` - Generated code for ``target_host``
+
+    -  ``lib/`` - Generated binary object files
+
+      - ``lib0.o`` - LLVM module (if ``llvm`` target is used)
+      - ``lib1.o`` - LLVM CRT Metadata Module (if ``llvm`` target is used)
+
+    - ``src/`` - Generated C source
+
+      - ``lib0.c`` - C module (if ``c`` target is used)
+      - ``lib1.c`` - C CRT Metadata module (if ``c`` target is used)
+
+``executor-config``
+^^^^^^^^^^^^^^^^^^^
+
+Contains machine-parsable configuration for executors which can drive model inference. Currently,
+only the GraphExecutor produces configuration for this directory, in ``graph/graph.json``. This
+file should be read in and the resulting string supplied to the ``GraphExecutor()`` constructor for
+parsing.
+
+``parameters``
+^^^^^^^^^^^^^^
+
+Contains machine-parseable parameters. A variety of formats may be provided, but at present, only
+the format produced by ``tvm.relay._save_params`` is supplied. When building with
+``tvm.relay.build``,  the ``name`` parameter is considered to be the model name. A single file is
+created in this directory ``<model_name>.json``.
+
+``src``
+^^^^^^^
+
+Contains source code parsed by TVM. Currently, just the Relay source code is created in
+``src/relay.txt``.
+
+Metadata
+--------
+
+Machine-parseable metadata is placed in a file ``metadata.json`` at the root of the tarball.
+Metadata is a dictionary with these keys:
+
+- ``export_datetime``: Timestamp when this Model Library Format was generated, in
+  `strftime <https://docs.python.org/3/library/datetime.html#strftime-strptime-behavior>`_
+  format ``"%Y-%M-%d %H:%M:%SZ",``.
+- ``memory``: A summary of the memory usage of each generated function. Documented in
+  `Memory Usage Summary`_.
+- ``model_name``: The name of this model (e.g. the ``name`` parameter supplied to
+  ``tvm.relay.build``).
+- ``executors``: A list of executors supported by this model. Currently, this list is always
+  ``["graph"]``.
+- ``target``: A dictionary mapping ``device_type`` (the underlying integer, as a string) to the
+  sub-target which describes that relay backend used for that ``device_type``.
+- ``version``: A numeric version number that identifies the format used in this Model Library
+  Format. This number is incremented when the metadata structure or on-disk structure changes.
+  This document reflects version ``3``.
+
+Memory Usage Summary
+^^^^^^^^^^^^^^^^^^^^
+
+A dictionary with these sub-keys:
+
+ - ``"main"``: ``list[MainFunctionWorkspaceUsage]``. A list summarizing memory usage for each
+   workspace used by the main function and all sub-functions invoked.
+ - ``"operator_functions"``: ``map[string, list[FunctionWorkspaceUsage]]``. Maps operator function
+   name to a list summarizing memory usage for each workpace used by the function.
+
+A ``MainFunctionWorkspaceUsage`` is a dict with these keys:
+
+- ``"device"``: ``int``. The ``device_type`` associated with this workspace.
+- ``"workspace_size_bytes"``: ``int``. Number of bytes needed in this workspace by this function
+  and all sub-functions invoked.
+- ``"constants_size_bytes"``: ``int``. Size of the constants used by the main function.
+- ``"io_size_bytes"``: ``int``. Sum of the sizes of the buffers used from this workspace by this
+  function and sub-functions.
+
+A ``FunctionWorkspaceUsage`` is a dict with these keys:
+
+- ``"device"``: ``int``. The ``device_type`` associated with this workspace.
+- ``"workspace_size_bytes"``: ``int``. Number of bytes needed in this workspace by this function.

python/tvm/driver/tvmc/model.py

@@ -336,8 +336,8 @@ def import_package(self, package_path: str):
             with open(temp.relpath("metadata.json")) as metadata_json:
                 metadata = json.load(metadata_json)
 
-            is_graph_runtime = "graph" in metadata["runtimes"]
-            graph = temp.relpath("runtime-config/graph/graph.json") if is_graph_runtime else None
+            has_graph_executor = "graph" in metadata["executors"]
+            graph = temp.relpath("executor-config/graph/graph.json") if has_graph_executor else None
             params = temp.relpath("parameters/default.params")
 
             self.type = "mlf"

python/tvm/micro/model_library_format.py

@@ -225,7 +225,7 @@ def reset(tarinfo):
         tar_f.add(str(source_dir), arcname=".", filter=reset)
 
 
-_GENERATED_VERSION = 4
+_GENERATED_VERSION = 5
 
 
 def _export_graph_model_library_format(
@@ -241,15 +241,15 @@ def _export_graph_model_library_format(
         Temporary directory to populate with Model Library Format contents.
     """
     is_aot = isinstance(mod, executor_factory.AOTExecutorFactoryModule)
-    runtime = ["aot"] if is_aot else ["graph"]
+    executor = ["aot"] if is_aot else ["graph"]
 
     metadata = {
         "version": _GENERATED_VERSION,
         "model_name": mod.libmod_name,
         "export_datetime": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%SZ"),
         "memory": _build_memory_map(mod),
         "target": {int(k): str(v) for k, v in mod.target.items()},
-        "runtimes": runtime,
+        "executors": executor,
         "style": "full-model",
     }
 
@@ -272,7 +272,7 @@ def _export_graph_model_library_format(
         f.write(str(mod.ir_mod))
 
     if not is_aot:
-        graph_config_dir = tempdir / "runtime-config" / "graph"
+        graph_config_dir = tempdir / "executor-config" / "graph"
         graph_config_dir.mkdir(parents=True)
         with open(graph_config_dir / "graph.json", "w") as f:
             f.write(mod.get_executor_config())
@@ -363,7 +363,7 @@ def _export_operator_model_library_format(mod: build_module.OperatorModule, temp
         "export_datetime": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%SZ"),
         "memory": memory_map,
         "target": {k: str(v) for k, v in targets.items()},
-        "runtimes": [],
+        "executors": [],
         "style": "operator",
     }
     with open(tempdir / "metadata.json", "w") as metadata_f:

tests/python/driver/tvmc/test_mlf.py

@@ -18,6 +18,7 @@
 import pytest
 import os
 import shlex
+import sys
 
 import tvm
 from tvm.driver import tvmc
@@ -130,3 +131,7 @@ def test_tvmc_import_package_mlf_aot(tflite_mobilenet_v1_1_quant, tflite_compile
     assert tvmc_package.graph is None, ".graph must not be set in the MLF archive for AOT executor."
     assert tvmc_package.params is not None, ".params must be set in the MLF archive."
     assert tvmc_package.type == "mlf", ".type must be set to 'mlf' in the MLF format."
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__] + sys.argv[1:]))

tests/python/unittest/test_micro_model_library_format.py

@@ -56,7 +56,7 @@ def test_export_operator_model_library_format():
 
     with open(os.path.join(extract_dir, "metadata.json")) as json_f:
         metadata = json.load(json_f)
-        assert metadata["version"] == 4
+        assert metadata["version"] == 5
         assert metadata["model_name"] == "add"
         export_datetime = datetime.datetime.strptime(
             metadata["export_datetime"], "%Y-%m-%d %H:%M:%SZ"
@@ -89,7 +89,7 @@ def test_export_operator_model_library_format():
 
 
 def validate_graph_json(extract_dir, factory):
-    with open(os.path.join(extract_dir, "runtime-config", "graph", "graph.json")) as graph_f:
+    with open(os.path.join(extract_dir, "executor-config", "graph", "graph.json")) as graph_f:
         graph_json = graph_f.read()
         assert graph_json == factory.graph_json
 
@@ -141,7 +141,7 @@ def @main(%a : Tensor[(1, 2), uint8], %b : Tensor[(1, 2), float32], %c : Tensor[
 
         with open(os.path.join(extract_dir, "metadata.json")) as json_f:
             metadata = json.load(json_f)
-            assert metadata["version"] == 4
+            assert metadata["version"] == 5
             assert metadata["model_name"] == "add"
             export_datetime = datetime.datetime.strptime(
                 metadata["export_datetime"], "%Y-%m-%d %H:%M:%SZ"
@@ -221,7 +221,7 @@ def @main(%a : Tensor[(1, 2), uint8], %b : Tensor[(1, 2), float32], %c : Tensor[
 
         with open(os.path.join(extract_dir, "metadata.json")) as json_f:
             metadata = json.load(json_f)
-            assert metadata["version"] == 4
+            assert metadata["version"] == 5
             assert metadata["model_name"] == "add"
             export_datetime = datetime.datetime.strptime(
                 metadata["export_datetime"], "%Y-%m-%d %H:%M:%SZ"
@@ -300,7 +300,7 @@ def @main(%p0: Tensor[(1, 56, 56, 128), int16], %p1: Tensor[(3, 3, 128, 1), int1
 
     with open(os.path.join(extract_dir, "metadata.json")) as json_f:
         metadata = json.load(json_f)
-        assert metadata["version"] == 4
+        assert metadata["version"] == 5
         assert metadata["model_name"] == "qnn_conv2d"
         export_datetime = datetime.datetime.strptime(
             metadata["export_datetime"], "%Y-%m-%d %H:%M:%SZ"