export and delegate example fix, typos

.ci/scripts/gather_test_models.py

@@ -64,6 +64,8 @@ def export_models_for_ci() -> None:
                     "quantization": quantization,
                     "xnnpack_delegation": xnnpack_delegation,
                     "runner": RUNNERS.get(name, DEFAULT_RUNNER),
+                    # demo_backend_delegation test only supports add_mul model
+                    "demo_backend_delegation": name == "add_mul",
                 }
             )
     set_output("models", json.dumps(models))

.ci/scripts/test.sh

@@ -32,6 +32,11 @@ if [[ -z "${XNNPACK_DELEGATION:-}" ]]; then
   XNNPACK_DELEGATION=false
 fi
 
+DEMO_BACKEND_DELEGATION=$5
+if [[ -z "${DEMO_BACKEND_DELEGATION:-}" ]]; then
+  DEMO_BACKEND_DELEGATION=false
+fi
+
 which "${PYTHON_EXECUTABLE}"
 # Just set this variable here, it's cheap even if we use buck2
 CMAKE_OUTPUT_DIR=cmake-out
@@ -106,6 +111,30 @@ test_model_with_xnnpack() {
   fi
 }
 
+test_demo_backend_delegation() {
+  echo "Testing demo backend delegation on AddMul"
+  "${PYTHON_EXECUTABLE}" -m examples.export.export_and_delegate  --option "composite"
+  "${PYTHON_EXECUTABLE}" -m examples.export.export_and_delegate  --option "partition"
+  "${PYTHON_EXECUTABLE}" -m examples.export.export_and_delegate  --option "whole"
+
+  # Run test model
+  if [[ "${BUILD_TOOL}" == "buck2" ]]; then
+    buck2 run //examples/executor_runner:executor_runner -- --model_path "./composite_model.pte"
+    buck2 run //examples/executor_runner:executor_runner -- --model_path "./partition_lowered_model.pte"
+    buck2 run //examples/executor_runner:executor_runner -- --model_path "./whole.pte"
+  elif [[ "${BUILD_TOOL}" == "cmake" ]]; then
+    if [[ ! -f ${CMAKE_OUTPUT_DIR}/executor_runner ]]; then
+      build_cmake_executor_runner
+    fi
+    ./${CMAKE_OUTPUT_DIR}/executor_runner --model_path "./composite_model.pte"
+    ./${CMAKE_OUTPUT_DIR}/executor_runner --model_path "./partition_lowered_model.pte"
+    ./${CMAKE_OUTPUT_DIR}/executor_runner --model_path "./whole.pte"
+  else
+    echo "Invalid build tool ${BUILD_TOOL}. Only buck2 and cmake are supported atm"
+    exit 1
+  fi
+}
+
 echo "Testing ${MODEL_NAME} (fp32, quantized, xnnpack) with ${BUILD_TOOL}..."
 # Test the select model without XNNPACK or quantization
 test_model
@@ -128,3 +157,8 @@ fi
 if [[ "${XNNPACK_DELEGATION}" == true ]] && [[ "${QUANTIZATION}" == true ]]; then
   test_model_with_xnnpack true
 fi
+
+# Test demo backend delegation
+if [[ "${DEMO_BACKEND_DELEGATION}" == true ]]; then
+  test_demo_backend_delegation
+fi

.github/workflows/pull.yml

@@ -59,10 +59,11 @@ jobs:
         BUILD_TOOL=${{ matrix.build-tool }}
         QUANTIZATION=${{ matrix.quantization }}
         XNNPACK_DELEGATION=${{ matrix.xnnpack_delegation }}
+        DEMO_BACKEND_DELEGATION=${{ matrix.demo_backend_delegation }}
 
         PYTHON_EXECUTABLE=python bash .ci/scripts/setup-linux.sh "${BUILD_TOOL}"
         # Build and test Executorch
-        PYTHON_EXECUTABLE=python bash .ci/scripts/test.sh "${MODEL_NAME}" "${BUILD_TOOL}" "${QUANTIZATION}" "${XNNPACK_DELEGATION}"
+        PYTHON_EXECUTABLE=python bash .ci/scripts/test.sh "${MODEL_NAME}" "${BUILD_TOOL}" "${QUANTIZATION}" "${XNNPACK_DELEGATION}" "${DEMO_BACKEND_DELEGATION}"
 
   test-models-macos:
     name: test-models-macos
@@ -84,11 +85,12 @@ jobs:
         BUILD_TOOL=${{ matrix.build-tool }}
         QUANTIZATION=${{ matrix.quantization }}
         XNNPACK_DELEGATION=${{ matrix.xnnpack_delegation }}
+        DEMO_BACKEND_DELEGATION=${{ matrix.demo_backend_delegation }}
 
         # Setup MacOS dependencies as there is no Docker support on MacOS atm
         PYTHON_EXECUTABLE=python bash .ci/scripts/setup-macos.sh "${BUILD_TOOL}"
         # Build and test Executorch
-        PYTHON_EXECUTABLE=python bash .ci/scripts/test.sh "${MODEL_NAME}" "${BUILD_TOOL}" "${QUANTIZATION}" "${XNNPACK_DELEGATION}"
+        PYTHON_EXECUTABLE=python bash .ci/scripts/test.sh "${MODEL_NAME}" "${BUILD_TOOL}" "${QUANTIZATION}" "${XNNPACK_DELEGATION}" "${DEMO_BACKEND_DELEGATION}"
         popd
 
   test-custom-ops-linux:

docs/website/docs/ir_spec/00_exir.md

@@ -70,7 +70,7 @@ The `ExportedProgram` has the following attributes:
   containing the parameters and buffers.
 * `range constraints (Dict[sympy.Symbol, RangeConstraint])`: For programs that
   are exported with data dependent behavior, the metadata on each node will
-  contain symbolic shapes (hich look like `s0`, `i0`). This attribute maps the
+  contain symbolic shapes (which look like `s0`, `i0`). This attribute maps the
   symbolic shapes to their lower/upper ranges.
 * `equality_constraints (List[Tuple[InputDim, InputDim]])`: A list of nodes in
   the graph and dimensions that have the same shape.
@@ -196,7 +196,7 @@ A `call_function` node represents a call to an operator.
 
 * **Functional:** We say a callable is “functional” if it satisfy all following requirements:
   * Non-aliasing, ie output tensors do not share data storage with each other or with inputs of the operator
-  * Non-mutating, ie the operator does not mutate value of it’s input (for tensors, this includes both metadata and data)
+  * Non-mutating, ie the operator does not mutate value of its input (for tensors, this includes both metadata and data)
   * No side effects, ie the operator does not mutate states that are visible from outside, like changing values of module parameters.
 
 * **Operator:** is a functional callable with a predefined schema. Examples of
@@ -208,7 +208,7 @@ A `call_function` node represents a call to an operator.
 %name = call_function[target = operator](args = (%x, %y, …), kwargs = {})
 ```
 
-#### Differences from vanila FX call_function
+#### Differences from vanilla FX call_function
 
 1. In FX graph, a call_function can refer to any callable, in EXIR, we restrict
 this to only Canonical ATen operators (a select subset of PyTorch ATen operator

docs/website/docs/tutorials/exporting_to_executorch.md

@@ -55,7 +55,7 @@ dynamic and impose ranges on them. To learn more about constraints, you can look
 at [these docs](../export/constraint_apis.md)
 
 The output of `torch._export.capture_pre_autograd_graph` is a fully flattened graph (meaning the graph does
-not contain any module heirachy, except in the case of control flow operators).
+not contain any module hierachy, except in the case of control flow operators).
 Furthermore, the captured graph is in ATen dialect with ATen opset which is autograd safe, i.e. safe for eager mode training.
 This is important for quantization as noted in https://github.com/pytorch/executorch/issues/290.
 

examples/export/export_and_delegate.py

@@ -18,8 +18,9 @@
 from executorch.exir.backend.test.op_partitioner_demo import AddMulPartitionerDemo
 
 from ..models import MODEL_NAME_TO_MODEL
+from ..models.model_factory import EagerModelFactory
 
-from ..utils import export_to_edge
+from .utils import export_to_edge
 
 
 FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
@@ -37,7 +38,7 @@
 """
 
 
-def export_compsite_module_with_lower_graph():
+def export_composite_module_with_lower_graph():
     """
 
     AddMulModule:
@@ -57,9 +58,10 @@ def export_compsite_module_with_lower_graph():
     logging.info(
         "Running the example to export a composite module with lowered graph..."
     )
-    m, m_inputs = MODEL_NAME_TO_MODEL.get("add_mul")()
-    m = m.eval()
-    m_inputs = m.get_example_inputs()
+
+    m, m_inputs = EagerModelFactory.create_model(*MODEL_NAME_TO_MODEL["add_mul"])
+    m_compile_spec = m.get_compile_spec()
+
     # pre-autograd export. eventually this will become torch.export
     m = export.capture_pre_autograd_graph(m, m_inputs)
     edge = export_to_edge(m, m_inputs)
@@ -68,7 +70,7 @@ def export_compsite_module_with_lower_graph():
     # Lower AddMulModule to the demo backend
     logging.info("Lowering to the demo backend...")
     lowered_graph = to_backend(
-        BackendWithCompilerDemo.__name__, edge.exported_program, m.get_compile_spec()
+        BackendWithCompilerDemo.__name__, edge.exported_program, m_compile_spec
     )
 
     # Composite the lower graph with other module
@@ -166,8 +168,9 @@ def export_and_lower_the_whole_graph():
     """
     logging.info("Running the example to export and lower the whole graph...")
 
-    m, m_inputs = MODEL_NAME_TO_MODEL.get("add_mul")()
-    m = m.eval()
+    m, m_inputs = EagerModelFactory.create_model(*MODEL_NAME_TO_MODEL["add_mul"])
+    m_compile_spec = m.get_compile_spec()
+
     m_inputs = m.get_example_inputs()
     # pre-autograd export. eventually this will become torch.export
     m = export.capture_pre_autograd_graph(m, m_inputs)
@@ -177,7 +180,7 @@ def export_and_lower_the_whole_graph():
     # Lower AddMulModule to the demo backend
     logging.info("Lowering to the demo backend...")
     lowered_module = to_backend(
-        BackendWithCompilerDemo.__name__, edge, m.get_compile_spec()
+        BackendWithCompilerDemo.__name__, edge.exported_program, m_compile_spec
     )
 
     buffer = lowered_module.buffer()
@@ -190,7 +193,7 @@ def export_and_lower_the_whole_graph():
 
 
 OPTIONS_TO_LOWER = {
-    "composite": export_compsite_module_with_lower_graph,
+    "composite": export_composite_module_with_lower_graph,
     "partition": export_and_lower_partitioned_graph,
     "whole": export_and_lower_the_whole_graph,
 }