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zonglinpengzonglinpeng
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add unit test for op_add (#7087)
add op_add shapes to generate as binaries (#7087) Summary: generates the add model pte’s for cadence to execute on. will use graph builder in later diffs Test Plan: Imported from GitHub, without a `Test Plan:` line. {F1968254537} Reviewed By: hsharma35 Differential Revision: D66510372 Pulled By: zonglinpeng
1 parent dedf77b commit 5785fc3

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6 files changed

+173
-7
lines changed

6 files changed

+173
-7
lines changed

‎backends/cadence/aot/TARGETS‎

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

‎backends/cadence/aot/export_example.py‎

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

‎backends/cadence/aot/utils.py‎

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

‎backends/cadence/runtime/TARGETS‎

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

‎examples/cadence/operators/TARGETS‎

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

‎examples/cadence/operators/test_add_op.py‎

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

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