Split neutron backend test based on executor dependency

backends/nxp/tests/test_neutron_backend.py

@@ -3,27 +3,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-import numpy as np
-import torch
-
-from executorch.backends.nxp.backend.edge_program_converter import (
-    EdgeProgramToIRConverter,
-)
-from executorch.backends.nxp.backend.ir.lib.tflite.BuiltinOptions import BuiltinOptions
-from executorch.backends.nxp.backend.ir.lib.tflite.Model import Model
 from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
-from executorch.backends.nxp.tests.executors import (
-    convert_run_compare,
-    EdgeProgramExecutor,
-    TFLiteExecutor,
-    ToNHWCPreprocess,
-)
-from executorch.backends.nxp.tests.models import (
-    Conv2dModule,
-    ConvFCSoftmaxModule,
-    LinearSoftmaxModule,
-)
-from torch.export import ExportedProgram
+from executorch.backends.nxp.tests.models import Conv2dModule, LinearSoftmaxModule
 
 
 def test_neutron_backend__single_conv_model():
@@ -66,91 +47,3 @@ def test_neutron_backend__linear_softmax_model__payload_header_formatless():
     assert payload[3] == 0x0  # Formatless
     assert all(byte == 0x0 for byte in payload[4:16])  # Aligned to 16 bytes
     assert payload[17] != 0x0  # Followed by non-zero content
-
-
-def test_lowered_program_and_tflite_output_match__conv2d__no_bias(mocker):
-    converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
-
-    model = Conv2dModule(bias=False)
-    input_shape = (1, 4, 32, 32)
-
-    # Run conversion
-    to_quantized_edge_program(model, input_shape)
-
-    # Capture generated model
-    tflite_flatbuffers_model, io_formats = converter_spy.spy_return
-
-    tflite_model = Model.GetRootAs(tflite_flatbuffers_model)
-    sub_graph = tflite_model.Subgraphs(0)
-
-    assert sub_graph.OperatorsLength() == 1
-    assert sub_graph.Operators(0).BuiltinOptionsType() == BuiltinOptions.Conv2DOptions
-
-    # Capture converted program
-    exported_program: ExportedProgram = converter_spy.call_args.args[1]
-
-    input_data = (
-        (torch.randn(input_shape, dtype=torch.float32) * 50)
-        .type(torch.int8)
-        .detach()
-        .numpy()
-    )
-    input_data_tflite = np.transpose(input_data, [0, 2, 3, 1])
-
-    # Execute program and TFLite model
-    program_executor = EdgeProgramExecutor(exported_program)
-    tflite_executor = TFLiteExecutor(model_content=tflite_flatbuffers_model)
-
-    output_edge = program_executor.inference(input_data)
-    output_tflite = tflite_executor.inference(input_data_tflite)
-
-    output_tflite = np.transpose(output_tflite, [0, 3, 1, 2])
-
-    # Outputs difference is smaller than 1 (rounding error in quantization)
-    assert np.max(np.abs(output_edge - output_tflite)) <= 1
-
-
-def test_conv_fc__lowered_program_and_tflite_output_match(mocker):
-    converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
-
-    model = ConvFCSoftmaxModule()
-    input_shape = (1, 4, 5, 5)
-
-    # Run conversion
-    _ = to_quantized_edge_program(model, input_shape)
-
-    # Capture converted program
-    exported_program: ExportedProgram = converter_spy.call_args.args[1]
-
-    # Capture generated model
-    tflite_flatbuffers_model, _ = converter_spy.spy_return
-
-    # No Transpose ops in produced TFLite model
-    tflite_subgraph = Model.GetRootAs(tflite_flatbuffers_model).Subgraphs(0)
-
-    assert tflite_subgraph.OperatorsLength() == 3
-    assert (
-        tflite_subgraph.Operators(0).BuiltinOptionsType()
-        == BuiltinOptions.Conv2DOptions
-    )
-    assert (
-        tflite_subgraph.Operators(1).BuiltinOptionsType()
-        == BuiltinOptions.ReshapeOptions
-    )
-    assert (
-        tflite_subgraph.Operators(2).BuiltinOptionsType()
-        == BuiltinOptions.FullyConnectedOptions
-    )
-
-    # Verify outputs of program and TFLite model
-    input_data = (
-        (torch.randn(input_shape, dtype=torch.float32))
-        .type(torch.int8)
-        .detach()
-        .numpy()
-    )
-    convert_run_compare(
-        exported_program,
-        input_data=input_data,
-        tflite_input_preprocess=ToNHWCPreprocess(),
-    )

backends/nxp/tests/test_neutron_backend_executor.py

@@ -0,0 +1,110 @@
+# Copyright 2024 NXP
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+
+from executorch.backends.nxp.backend.edge_program_converter import (
+    EdgeProgramToIRConverter,
+)
+from executorch.backends.nxp.backend.ir.lib.tflite.BuiltinOptions import BuiltinOptions
+from executorch.backends.nxp.backend.ir.lib.tflite.Model import Model
+from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
+from executorch.backends.nxp.tests.executors import (
+    convert_run_compare,
+    EdgeProgramExecutor,
+    TFLiteExecutor,
+    ToNHWCPreprocess,
+)
+from executorch.backends.nxp.tests.models import Conv2dModule, ConvFCSoftmaxModule
+from torch.export import ExportedProgram
+
+
+def test_lowered_program_and_tflite_output_match__conv2d__no_bias(mocker):
+    converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
+
+    model = Conv2dModule(bias=False)
+    input_shape = (1, 4, 32, 32)
+
+    # Run conversion
+    to_quantized_edge_program(model, input_shape)
+
+    # Capture generated model
+    tflite_flatbuffers_model, io_formats = converter_spy.spy_return
+
+    tflite_model = Model.GetRootAs(tflite_flatbuffers_model)
+    sub_graph = tflite_model.Subgraphs(0)
+
+    assert sub_graph.OperatorsLength() == 1
+    assert sub_graph.Operators(0).BuiltinOptionsType() == BuiltinOptions.Conv2DOptions
+
+    # Capture converted program
+    exported_program: ExportedProgram = converter_spy.call_args.args[1]
+
+    input_data = (
+        (torch.randn(input_shape, dtype=torch.float32) * 50)
+        .type(torch.int8)
+        .detach()
+        .numpy()
+    )
+    input_data_tflite = np.transpose(input_data, [0, 2, 3, 1])
+
+    # Execute program and TFLite model
+    program_executor = EdgeProgramExecutor(exported_program)
+    tflite_executor = TFLiteExecutor(model_content=tflite_flatbuffers_model)
+
+    output_edge = program_executor.inference(input_data)
+    output_tflite = tflite_executor.inference(input_data_tflite)
+
+    output_tflite = np.transpose(output_tflite, [0, 3, 1, 2])
+
+    # Outputs difference is smaller than 1 (rounding error in quantization)
+    assert np.max(np.abs(output_edge - output_tflite)) <= 1
+
+
+def test_conv_fc__lowered_program_and_tflite_output_match(mocker):
+    converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
+
+    model = ConvFCSoftmaxModule()
+    input_shape = (1, 4, 5, 5)
+
+    # Run conversion
+    _ = to_quantized_edge_program(model, input_shape)
+
+    # Capture converted program
+    exported_program: ExportedProgram = converter_spy.call_args.args[1]
+
+    # Capture generated model
+    tflite_flatbuffers_model, _ = converter_spy.spy_return
+
+    # No Transpose ops in produced TFLite model
+    tflite_subgraph = Model.GetRootAs(tflite_flatbuffers_model).Subgraphs(0)
+
+    assert tflite_subgraph.OperatorsLength() == 3
+    assert (
+        tflite_subgraph.Operators(0).BuiltinOptionsType()
+        == BuiltinOptions.Conv2DOptions
+    )
+    assert (
+        tflite_subgraph.Operators(1).BuiltinOptionsType()
+        == BuiltinOptions.ReshapeOptions
+    )
+    assert (
+        tflite_subgraph.Operators(2).BuiltinOptionsType()
+        == BuiltinOptions.FullyConnectedOptions
+    )
+
+    # Verify outputs of program and TFLite model
+    input_data = (
+        (torch.randn(input_shape, dtype=torch.float32))
+        .type(torch.int8)
+        .detach()
+        .numpy()
+    )
+    convert_run_compare(
+        exported_program,
+        input_data=input_data,
+        tflite_input_preprocess=ToNHWCPreprocess(),
+    )