add unit test for op_add

backends/cadence/aot/TARGETS

@@ -50,6 +50,26 @@ python_library(
     ],
 )
 
+python_library(
+    name = "export_example",
+    srcs = [
+        "export_example.py",
+    ],
+    deps = [
+        ":passes",
+        ":utils",
+        ":ops_registrations",
+        ":replace_ops",
+        "//caffe2:torch",
+        "//executorch/backends/cadence/aot/quantizer:fusion_pass",
+        "//executorch/backends/cadence/runtime:runtime",
+        "//executorch/backends/cadence/aot/quantizer:quantizer",
+        "//executorch/backends/transforms:decompose_sdpa",
+        "//executorch/backends/transforms:remove_clone_ops",
+        "//executorch/exir:lib",
+        "//executorch/devtools:lib",
+    ],
+)
 
 python_library(
     name = "pass_utils",

backends/cadence/aot/export_example.py

@@ -60,6 +60,7 @@ def export_model(
     model: nn.Module,
     example_inputs: Tuple[Any, ...],
     file_name: str = "CadenceDemoModel",
+    run_and_compare: bool = True,
 ):
     # create work directory for outputs and model binary
     working_dir = tempfile.mkdtemp(dir="/tmp")
@@ -112,9 +113,10 @@ def export_model(
     )
 
     # TODO: move to test infra
-    runtime.run_and_compare(
-        executorch_prog=exec_prog,
-        inputs=example_inputs,
-        ref_outputs=ref_outputs,
-        working_dir=working_dir,
-    )
+    if run_and_compare:
+        runtime.run_and_compare(
+            executorch_prog=exec_prog,
+            inputs=example_inputs,
+            ref_outputs=ref_outputs,
+            working_dir=working_dir,
+        )

backends/cadence/aot/utils.py

@@ -162,7 +162,8 @@ def print_ops_info(
 
     # Print the final ops and their counts in a tabular format
     logging.info(
-        tabulate(
+        "\n"
+        + tabulate(
             sorted_ops_count,
             headers=[
                 "Final Operators                                    ",  # one character longer than the longest op name

backends/cadence/runtime/TARGETS

@@ -7,6 +7,8 @@ python_library(
     srcs = [
         "__init__.py",
         "executor.py",
+        "runtime.py",
+        "utils.py"
     ] + glob([
         "xtsc-cfg/**/*",
     ]),

examples/cadence/operators/TARGETS

@@ -0,0 +1,26 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+load("@fbcode_macros//build_defs:python_unittest.bzl", "python_unittest")
+
+oncall("odai_jarvis")
+
+
+python_unittest(
+    name = "test_add_op",
+    srcs = [
+        "test_add_op.py",
+    ],
+    typing = True,
+    supports_static_listing = False,
+    deps = [
+        "fbsource//third-party/pypi/parameterized:parameterized",
+        "//caffe2:torch",
+        "//executorch/backends/cadence/aot:ops_registrations",
+        "//executorch/backends/cadence/aot:export_example",
+        "//executorch/backends/cadence/aot:compiler",
+    ],
+)

examples/cadence/operators/test_add_op.py

@@ -0,0 +1,115 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+import unittest
+from typing import Tuple
+
+from parameterized import parameterized
+
+from executorch.backends.cadence.aot.ops_registrations import *  # noqa
+
+import torch
+import torch.nn as nn
+from executorch.backends.cadence.aot.export_example import export_model
+
+
+class ATenOpTestCases(unittest.TestCase):
+    @parameterized.expand(
+        [
+            [(7, 5, 6), (7, 5, 6)],
+            [(7, 5, 6), (1)],
+            [(1), (7, 5, 6)],
+            [(1), (7, 5, 6), 2.23],
+            [(1), (7, 5, 6), -1.0],
+            [(1), (7, 5, 6), -2.23],
+            [(7, 5, 6), (7, 5, 6), 1.23],
+            [(6, 7), (6, 7)],
+            [(6, 7), (6, 7), 2],
+            # Broadcast tests (should be optimized on G3)
+            [(1, 32, 64), (1, 1, 64)],
+            [(1, 32, 64), (64)],
+            [(1, 1, 32), (32)],
+            [(16, 1, 16), (1, 1, 16)],
+            [(16, 1, 16), (16)],
+            [(1, 4, 8, 8), (1, 1, 8, 8)],
+            [(1, 4, 8, 8), (8, 8)],
+            # Broadcast tests (should go to portable ops)
+            [(1, 10, 1, 8), (4, 1, 4, 1)],
+            [(1, 1, 16), (1, 8, 1), 2.5],
+            # # aten.upsample_nearest2d tests
+            [(5, 6, 6, 8), (5, 6, 6, 8)],
+            [(1, 1, 12, 16), (1, 1, 12, 16)],
+        ]
+    )
+    def test_aten_add_out(
+        self, Xshape: Tuple[int], Yshape: Tuple[int], alpha: float = 1
+    ) -> None:
+        class AddTensor(nn.Module):
+            def __init__(self, alpha: float):
+                super().__init__()
+                self.alpha = alpha
+
+            def forward(self, x: torch.Tensor, y: torch.Tensor):
+                return torch.add(x, y, alpha=self.alpha)
+
+        model = AddTensor(alpha)
+
+        X = torch.randn(Xshape)
+        Y = torch.randn(Yshape)
+
+        model.eval()
+        export_model(
+            model, (X, Y), file_name=self._testMethodName, run_and_compare=False
+        )
+
+    @parameterized.expand(
+        [
+            [(7, 5, 6), (7, 5, 6)],
+            [(7, 5, 6), (1)],
+            [(1), (7, 5, 6)],
+            [(1), (7, 5, 6), 2.23],
+            [(1), (7, 5, 6), -1.0],
+            [(1), (7, 5, 6), -2.23],
+            [(7, 5, 6), (7, 5, 6), 1.23],
+            [(6, 7), (6, 7)],
+            [(6, 7), (6, 7), 2],
+            # Broadcast tests (should be optimized on G3)
+            [(1, 32, 64), (1, 1, 64)],
+            [(1, 32, 64), (64)],
+            [(1, 1, 32), (32)],
+            [(16, 1, 16), (1, 1, 16)],
+            [(16, 1, 16), (16)],
+            [(1, 4, 8, 8), (1, 1, 8, 8)],
+            [(1, 4, 8, 8), (8, 8)],
+            # Broadcast tests (should go to portable ops)
+            [(1, 10, 1, 8), (4, 1, 4, 1)],
+            [(1, 1, 16), (1, 8, 1), 2.5],
+            # # aten.upsample_nearest2d tests
+            [(5, 6, 6, 8), (5, 6, 6, 8)],
+            [(1, 1, 12, 16), (1, 1, 12, 16)],
+        ]
+    )
+    def test_aten_add_scalar_out(
+        self, Xshape: Tuple[int], Yshape: Tuple[int], alpha: float = 1
+    ) -> None:
+        # Tensor-Scalar addition
+        class AddScalar(nn.Module):
+            def __init__(self, alpha: float):
+                super().__init__()
+                self.alpha = alpha
+
+            def forward(self, x: torch.Tensor, y: float):
+                return torch.add(x, y, alpha=self.alpha)
+
+        model = AddScalar(alpha)
+
+        X = torch.randn(Xshape)
+        Y = 2.34
+
+        model.eval()
+        export_model(
+            model, (X, Y), file_name=self._testMethodName, run_and_compare=False
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()