[pybinding] Add mapping from C++ program::verification to Python

extension/pybindings/portable_lib.py

@@ -45,6 +45,7 @@
     _reset_profile_results,  # noqa: F401
     BundledModule,  # noqa: F401
     ExecuTorchModule,  # noqa: F401
+    Verification,  # noqa: F401
 )
 
 # Clean up so that `dir(portable_lib)` is the same as `dir(_portable_lib)`

extension/pybindings/pybindings.cpp

@@ -157,13 +157,15 @@ class Module final {
   explicit Module(
       std::unique_ptr<DataLoader> loader,
       std::unique_ptr<ETDumpGen> tracer = nullptr,
-      size_t debug_buffer_size = 0)
+      size_t debug_buffer_size = 0,
+      Program::Verification program_verification =
+          Program::Verification::InternalConsistency)
       : loader_(std::move(loader)),
         event_tracer_(std::move(tracer)),
         debug_buffer_size_(debug_buffer_size) {
     ::executorch::runtime::runtime_init();
-    Result<Program> program = Program::load(
-        loader_.get(), Program::Verification::InternalConsistency);
+    Result<Program> program =
+        Program::load(loader_.get(), program_verification);
     THROW_IF_ERROR(
         program.error(),
         "loading program failed with error: 0x%" PRIx32,
@@ -375,19 +377,22 @@ inline std::unique_ptr<Module> load_module_from_buffer(
     const void* ptr,
     size_t ptr_len,
     bool enable_etdump,
-    size_t debug_buffer_size) {
+    size_t debug_buffer_size,
+    Program::Verification program_verification) {
   EXECUTORCH_SCOPE_PROF("load_module_from_buffer");
   auto loader = std::make_unique<BufferDataLoader>(ptr, ptr_len);
   return std::make_unique<Module>(
       std::move(loader),
       enable_etdump ? std::make_unique<torch::executor::ETDumpGen>() : nullptr,
-      debug_buffer_size);
+      debug_buffer_size,
+      program_verification);
 }
 
 inline std::unique_ptr<Module> load_module_from_file(
     const std::string& path,
     bool enable_etdump,
-    size_t debug_buffer_size) {
+    size_t debug_buffer_size,
+    Program::Verification program_verification) {
   EXECUTORCH_SCOPE_PROF("load_module_from_file");
 
   Result<MmapDataLoader> res = MmapDataLoader::from(
@@ -402,7 +407,8 @@ inline std::unique_ptr<Module> load_module_from_file(
   return std::make_unique<Module>(
       std::move(loader),
       enable_etdump ? std::make_unique<torch::executor::ETDumpGen>() : nullptr,
-      debug_buffer_size);
+      debug_buffer_size,
+      program_verification);
 }
 
 static constexpr size_t kDEFAULT_BUNDLED_INPUT_POOL_SIZE = 16 * 1024U;
@@ -452,30 +458,41 @@ struct PyModule final {
   explicit PyModule(
       const py::bytes& buffer,
       bool enable_etdump,
-      size_t debug_buffer_size = 0)
+      size_t debug_buffer_size = 0,
+      Program::Verification program_verification =
+          Program::Verification::InternalConsistency)
       : module_(load_module_from_buffer(
             buffer.cast<std::string_view>().data(),
             py::len(buffer),
             enable_etdump,
-            debug_buffer_size)) {}
+            debug_buffer_size,
+            program_verification)) {}
 
   explicit PyModule(
       const void* ptr,
       size_t ptr_len,
       bool enable_etdump,
-      size_t debug_buffer_size = 0)
+      size_t debug_buffer_size = 0,
+      Program::Verification program_verification =
+          Program::Verification::InternalConsistency)
       : module_(load_module_from_buffer(
             ptr,
             ptr_len,
             enable_etdump,
-            debug_buffer_size)) {}
+            debug_buffer_size,
+            program_verification)) {}
 
   explicit PyModule(
       const std::string& path,
       bool enable_etdump,
-      size_t debug_buffer_size = 0)
-      : module_(load_module_from_file(path, enable_etdump, debug_buffer_size)) {
-  }
+      size_t debug_buffer_size = 0,
+      Program::Verification program_verification =
+          Program::Verification::InternalConsistency)
+      : module_(load_module_from_file(
+            path,
+            enable_etdump,
+            debug_buffer_size,
+            program_verification)) {}
 
   PyModule(const PyModule&) = delete;
   PyModule& operator=(const PyModule&) = delete;
@@ -486,14 +503,20 @@ struct PyModule final {
   static std::unique_ptr<PyModule> load_from_buffer(
       const py::bytes& buffer,
       bool enable_etdump,
-      size_t debug_buffer_size = 0) {
-    return std::make_unique<PyModule>(buffer, enable_etdump, debug_buffer_size);
+      size_t debug_buffer_size = 0,
+      Program::Verification program_verification =
+          Program::Verification::InternalConsistency) {
+    return std::make_unique<PyModule>(
+        buffer, enable_etdump, debug_buffer_size, program_verification);
   }
   static std::unique_ptr<PyModule> load_from_file(
       const std::string& path,
       bool enable_etdump,
-      size_t debug_buffer_size = 0) {
-    return std::make_unique<PyModule>(path, enable_etdump, debug_buffer_size);
+      size_t debug_buffer_size = 0,
+      Program::Verification program_verification =
+          Program::Verification::InternalConsistency) {
+    return std::make_unique<PyModule>(
+        path, enable_etdump, debug_buffer_size, program_verification);
   }
 
   static std::unique_ptr<PyModule> load_from_bundled_program(
@@ -805,19 +828,29 @@ PYBIND11_MODULE(EXECUTORCH_PYTHON_MODULE_NAME, m) {
   // Redirects cout and cerr for function calls this guards to the python env.
   auto call_guard = py::
       call_guard<py::scoped_ostream_redirect, py::scoped_estream_redirect>();
+
+  // Bind the verification enum to python.
+  py::enum_<Program::Verification>(m, "Verification")
+      .value("Minimal", Program::Verification::Minimal)
+      .value("InternalConsistency", Program::Verification::InternalConsistency);
+
   m.def(
       "_load_for_executorch",
       PyModule::load_from_file,
       py::arg("path"),
       py::arg("enable_etdump") = false,
       py::arg("debug_buffer_size") = 0,
+      py::arg("program_verification") =
+          Program::Verification::InternalConsistency,
       call_guard);
   m.def(
       "_load_for_executorch_from_buffer",
       &PyModule::load_from_buffer,
       py::arg("buffer"),
       py::arg("enable_etdump") = false,
       py::arg("debug_buffer_size") = 0,
+      py::arg("program_verification") =
+          Program::Verification::InternalConsistency,
       call_guard);
   m.def(
       "_load_for_executorch_from_bundled_program",

extension/pybindings/pybindings.pyi

@@ -5,10 +5,24 @@
 # LICENSE file in the root directory of this source tree.
 
 # pyre-strict
-from typing import Any, Dict, List, Optional, Sequence, Tuple
+from __future__ import annotations
+
+from typing import Any, Dict, Enum, List, Optional, Sequence, Tuple
 
 from executorch.exir._warnings import experimental
 
+@experimental("This API is experimental and subject to change without notice.")
+class Verification(Enum):
+    """Verification maps C++ Program::Verification to Python.
+
+    .. warning::
+
+        This API is experimental and subject to change without notice.
+    """
+
+    Minimal: ...
+    InternalConsistency: ...
+
 @experimental("This API is experimental and subject to change without notice.")
 class ExecuTorchModule:
     """ExecuTorchModule is a Python wrapper around a C++ ExecuTorch program.
@@ -56,7 +70,10 @@ class BundledModule:
 
 @experimental("This API is experimental and subject to change without notice.")
 def _load_for_executorch(
-    path: str, enable_etdump: bool = False, debug_buffer_size: int = 0
+    path: str,
+    enable_etdump: bool = False,
+    debug_buffer_size: int = 0,
+    program_verification: Verification = Verification.InternalConsistency,
 ) -> ExecuTorchModule:
     """Load an ExecuTorch Program from a file.
 
@@ -79,7 +96,10 @@ def _load_for_executorch(
 
 @experimental("This API is experimental and subject to change without notice.")
 def _load_for_executorch_from_buffer(
-    buffer: bytes, enable_etdump: bool = False, debug_buffer_size: int = 0
+    buffer: bytes,
+    enable_etdump: bool = False,
+    debug_buffer_size: int = 0,
+    program_verification: Verification = Verification.InternalConsistency,
 ) -> ExecuTorchModule:
     """Same as _load_for_executorch, but takes a byte buffer instead of a file path.
 

extension/pybindings/test/make_test.py

@@ -7,7 +7,8 @@
 # pyre-unsafe
 
 import unittest
-from typing import Any, Callable, Tuple
+from types import ModuleType
+from typing import Any, Callable, Optional, Tuple
 
 import torch
 from executorch.exir import ExecutorchProgramManager, to_edge
@@ -16,7 +17,7 @@
 
 def make_test(  # noqa: C901
     tester: unittest.TestCase,
-    load_fn: Callable,
+    runtime: ModuleType,
 ) -> Callable[[unittest.TestCase], None]:
     """
     Returns a function that operates as a test case within a unittest.TestCase class.
@@ -25,6 +26,7 @@ def make_test(  # noqa: C901
     which will all have different load functions. In this case each individual test case is a
     subfunction of wrapper.
     """
+    load_fn: Callable = runtime._load_for_executorch_from_buffer
 
     def wrapper(tester: unittest.TestCase) -> None:
         class ModuleAdd(torch.nn.Module):
@@ -251,12 +253,123 @@ def test_quantized_ops(tester):
             expected = example_inputs[0] + example_inputs[1]
             tester.assertEqual(str(expected), str(executorch_output))
 
+        def test_constant_output_not_memory_planned(tester):
+            # Create an ExecuTorch program from ModuleAdd.
+            exported_program, inputs = create_program(
+                ModuleAddConstReturn(),
+                et_config=ExecutorchBackendConfig(
+                    memory_planning_pass=MemoryPlanningPass(alloc_graph_output=False)
+                ),
+            )
+
+            exported_program.dump_executorch_program(verbose=True)
+
+            # Use pybindings to load and execute the program.
+            executorch_module = load_fn(exported_program.buffer)
+            # Invoke the callable on executorch_module instead of calling module.forward.
+            # Use only one input to test this case.
+            executorch_output = executorch_module((torch.ones(2, 2),))
+            print(executorch_output)
+
+            # The test module adds the input to torch.ones(2,2), so its output should be the same
+            # as adding them directly.
+            expected = torch.ones(2, 2) + torch.ones(2, 2)
+            tester.assertEqual(str(expected), str(executorch_output[0]))
+
+            # The test module returns the state. Check that its value is correct.
+            tester.assertEqual(str(torch.ones(2, 2)), str(executorch_output[1]))
+
+        def test_method_meta(tester) -> None:
+            # pyre-fixme[16]: Callable `make_test` has no attribute `wrapper`.
+            exported_program, inputs = create_program(ModuleAdd())
+
+            # Use pybindings to load the program and query its metadata.
+            executorch_module = load_fn(exported_program.buffer)
+            meta = executorch_module.method_meta("forward")
+
+            # Ensure that all these APIs work even if the module object is destroyed.
+            del executorch_module
+            tester.assertEqual(meta.name(), "forward")
+            tester.assertEqual(meta.num_inputs(), 2)
+            tester.assertEqual(meta.num_outputs(), 1)
+            # Common string for all these tensors.
+            tensor_info = "TensorInfo(sizes=[2, 2], dtype=Float, is_memory_planned=True, nbytes=16)"
+            float_dtype = 6
+            tester.assertEqual(
+                str(meta),
+                "MethodMeta(name='forward', num_inputs=2, "
+                f"input_tensor_meta=['{tensor_info}', '{tensor_info}'], "
+                f"num_outputs=1, output_tensor_meta=['{tensor_info}'])",
+            )
+
+            input_tensors = [meta.input_tensor_meta(i) for i in range(2)]
+            output_tensor = meta.output_tensor_meta(0)
+            # Check that accessing out of bounds raises IndexError.
+            with tester.assertRaises(IndexError):
+                meta.input_tensor_meta(2)
+            # Test that tensor metadata can outlive method metadata.
+            del meta
+            tester.assertEqual([t.sizes() for t in input_tensors], [(2, 2), (2, 2)])
+            tester.assertEqual(
+                [t.dtype() for t in input_tensors], [float_dtype, float_dtype]
+            )
+            tester.assertEqual(
+                [t.is_memory_planned() for t in input_tensors], [True, True]
+            )
+            tester.assertEqual([t.nbytes() for t in input_tensors], [16, 16])
+            tester.assertEqual(str(input_tensors), f"[{tensor_info}, {tensor_info}]")
+
+            tester.assertEqual(output_tensor.sizes(), (2, 2))
+            tester.assertEqual(output_tensor.dtype(), float_dtype)
+            tester.assertEqual(output_tensor.is_memory_planned(), True)
+            tester.assertEqual(output_tensor.nbytes(), 16)
+            tester.assertEqual(str(output_tensor), tensor_info)
+
+        def test_bad_name(tester) -> None:
+            # Create an ExecuTorch program from ModuleAdd.
+            # pyre-fixme[16]: Callable `make_test` has no attribute `wrapper`.
+            exported_program, inputs = create_program(ModuleAdd())
+
+            # Use pybindings to load and execute the program.
+            executorch_module = load_fn(exported_program.buffer)
+            # Invoke the callable on executorch_module instead of calling module.forward.
+            with tester.assertRaises(RuntimeError):
+                executorch_module.run_method("not_a_real_method", inputs)
+
+        def test_verification_config(tester) -> None:
+            # Create an ExecuTorch program from ModuleAdd.
+            # pyre-fixme[16]: Callable `make_test` has no attribute `wrapper`.
+            exported_program, inputs = create_program(ModuleAdd())
+            Verification = runtime.Verification
+
+            # Use pybindings to load and execute the program.
+            for config in [Verification.Minimal, Verification.InternalConsistency]:
+                executorch_module = load_fn(
+                    exported_program.buffer,
+                    enable_etdump=False,
+                    debug_buffer_size=0,
+                    program_verification=config,
+                )
+
+                executorch_output = executorch_module.forward(inputs)[0]
+
+                # The test module adds the two inputs, so its output should be the same
+                # as adding them directly.
+                expected = inputs[0] + inputs[1]
+
+                tester.assertEqual(str(expected), str(executorch_output))
+
+        ######### RUN TEST CASES #########
         test_e2e(tester)
         test_multiple_entry(tester)
         test_output_lifespan(tester)
         test_module_callable(tester)
         test_module_single_input(tester)
         test_stderr_redirect(tester)
         test_quantized_ops(tester)
+        test_constant_output_not_memory_planned(tester)
+        test_method_meta(tester)
+        test_bad_name(tester)
+        test_verification_config(tester)
 
     return wrapper