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Split neutron backend test based on executor dependency (#11934)
Summary: Pull Request resolved: #11934 The current test_neutron_backend has a hard requirement on tflite due to the Executor comparing the output. Not all systems have tflite, but we would like to have a sanity check on the backend still. To achieve this, this PR splits the existing test into two files test_neutron_backend contains the tests that do not use any Executor logic (ie, no end to end comparison of outputs) test_neutron_backend_executor contains the rest of the tests. Differential Revision: D77248751
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-108
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‎backends/nxp/tests/test_neutron_backend.py

Lines changed: 1 addition & 108 deletions
Original file line numberDiff line numberDiff line change
@@ -3,27 +3,8 @@
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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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import numpy as np
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import torch
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from executorch.backends.nxp.backend.edge_program_converter import (
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EdgeProgramToIRConverter,
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)
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from executorch.backends.nxp.backend.ir.lib.tflite.BuiltinOptions import BuiltinOptions
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from executorch.backends.nxp.backend.ir.lib.tflite.Model import Model
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from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
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from executorch.backends.nxp.tests.executors import (
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convert_run_compare,
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EdgeProgramExecutor,
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TFLiteExecutor,
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ToNHWCPreprocess,
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)
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from executorch.backends.nxp.tests.models import (
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Conv2dModule,
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ConvFCSoftmaxModule,
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LinearSoftmaxModule,
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)
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from torch.export import ExportedProgram
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from executorch.backends.nxp.tests.models import Conv2dModule, LinearSoftmaxModule
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def test_neutron_backend__single_conv_model():
@@ -66,91 +47,3 @@ def test_neutron_backend__linear_softmax_model__payload_header_formatless():
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assert payload[3] == 0x0 # Formatless
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assert all(byte == 0x0 for byte in payload[4:16]) # Aligned to 16 bytes
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assert payload[17] != 0x0 # Followed by non-zero content
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def test_lowered_program_and_tflite_output_match__conv2d__no_bias(mocker):
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converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
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model = Conv2dModule(bias=False)
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input_shape = (1, 4, 32, 32)
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# Run conversion
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to_quantized_edge_program(model, input_shape)
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# Capture generated model
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tflite_flatbuffers_model, io_formats = converter_spy.spy_return
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tflite_model = Model.GetRootAs(tflite_flatbuffers_model)
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sub_graph = tflite_model.Subgraphs(0)
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assert sub_graph.OperatorsLength() == 1
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assert sub_graph.Operators(0).BuiltinOptionsType() == BuiltinOptions.Conv2DOptions
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# Capture converted program
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exported_program: ExportedProgram = converter_spy.call_args.args[1]
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input_data = (
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(torch.randn(input_shape, dtype=torch.float32) * 50)
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.type(torch.int8)
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.detach()
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.numpy()
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)
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input_data_tflite = np.transpose(input_data, [0, 2, 3, 1])
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# Execute program and TFLite model
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program_executor = EdgeProgramExecutor(exported_program)
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tflite_executor = TFLiteExecutor(model_content=tflite_flatbuffers_model)
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output_edge = program_executor.inference(input_data)
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output_tflite = tflite_executor.inference(input_data_tflite)
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output_tflite = np.transpose(output_tflite, [0, 3, 1, 2])
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# Outputs difference is smaller than 1 (rounding error in quantization)
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assert np.max(np.abs(output_edge - output_tflite)) <= 1
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def test_conv_fc__lowered_program_and_tflite_output_match(mocker):
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converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
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model = ConvFCSoftmaxModule()
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input_shape = (1, 4, 5, 5)
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# Run conversion
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_ = to_quantized_edge_program(model, input_shape)
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# Capture converted program
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exported_program: ExportedProgram = converter_spy.call_args.args[1]
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# Capture generated model
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tflite_flatbuffers_model, _ = converter_spy.spy_return
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# No Transpose ops in produced TFLite model
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tflite_subgraph = Model.GetRootAs(tflite_flatbuffers_model).Subgraphs(0)
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assert tflite_subgraph.OperatorsLength() == 3
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assert (
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tflite_subgraph.Operators(0).BuiltinOptionsType()
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== BuiltinOptions.Conv2DOptions
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)
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assert (
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tflite_subgraph.Operators(1).BuiltinOptionsType()
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== BuiltinOptions.ReshapeOptions
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)
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assert (
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tflite_subgraph.Operators(2).BuiltinOptionsType()
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== BuiltinOptions.FullyConnectedOptions
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)
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# Verify outputs of program and TFLite model
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input_data = (
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(torch.randn(input_shape, dtype=torch.float32))
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.type(torch.int8)
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.detach()
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.numpy()
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)
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convert_run_compare(
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exported_program,
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input_data=input_data,
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tflite_input_preprocess=ToNHWCPreprocess(),
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)

‎backends/nxp/tests/test_neutron_backend_executor.py

Lines changed: 110 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,110 @@
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# Copyright 2024 NXP
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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.
5+
6+
import numpy as np
7+
import torch
8+
9+
from executorch.backends.nxp.backend.edge_program_converter import (
10+
EdgeProgramToIRConverter,
11+
)
12+
from executorch.backends.nxp.backend.ir.lib.tflite.BuiltinOptions import BuiltinOptions
13+
from executorch.backends.nxp.backend.ir.lib.tflite.Model import Model
14+
from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
15+
from executorch.backends.nxp.tests.executors import (
16+
convert_run_compare,
17+
EdgeProgramExecutor,
18+
TFLiteExecutor,
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ToNHWCPreprocess,
20+
)
21+
from executorch.backends.nxp.tests.models import Conv2dModule, ConvFCSoftmaxModule
22+
from torch.export import ExportedProgram
23+
24+
25+
def test_lowered_program_and_tflite_output_match__conv2d__no_bias(mocker):
26+
converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
27+
28+
model = Conv2dModule(bias=False)
29+
input_shape = (1, 4, 32, 32)
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31+
# Run conversion
32+
to_quantized_edge_program(model, input_shape)
33+
34+
# Capture generated model
35+
tflite_flatbuffers_model, io_formats = converter_spy.spy_return
36+
37+
tflite_model = Model.GetRootAs(tflite_flatbuffers_model)
38+
sub_graph = tflite_model.Subgraphs(0)
39+
40+
assert sub_graph.OperatorsLength() == 1
41+
assert sub_graph.Operators(0).BuiltinOptionsType() == BuiltinOptions.Conv2DOptions
42+
43+
# Capture converted program
44+
exported_program: ExportedProgram = converter_spy.call_args.args[1]
45+
46+
input_data = (
47+
(torch.randn(input_shape, dtype=torch.float32) * 50)
48+
.type(torch.int8)
49+
.detach()
50+
.numpy()
51+
)
52+
input_data_tflite = np.transpose(input_data, [0, 2, 3, 1])
53+
54+
# Execute program and TFLite model
55+
program_executor = EdgeProgramExecutor(exported_program)
56+
tflite_executor = TFLiteExecutor(model_content=tflite_flatbuffers_model)
57+
58+
output_edge = program_executor.inference(input_data)
59+
output_tflite = tflite_executor.inference(input_data_tflite)
60+
61+
output_tflite = np.transpose(output_tflite, [0, 3, 1, 2])
62+
63+
# Outputs difference is smaller than 1 (rounding error in quantization)
64+
assert np.max(np.abs(output_edge - output_tflite)) <= 1
65+
66+
67+
def test_conv_fc__lowered_program_and_tflite_output_match(mocker):
68+
converter_spy = mocker.spy(EdgeProgramToIRConverter, "convert_program")
69+
70+
model = ConvFCSoftmaxModule()
71+
input_shape = (1, 4, 5, 5)
72+
73+
# Run conversion
74+
_ = to_quantized_edge_program(model, input_shape)
75+
76+
# Capture converted program
77+
exported_program: ExportedProgram = converter_spy.call_args.args[1]
78+
79+
# Capture generated model
80+
tflite_flatbuffers_model, _ = converter_spy.spy_return
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82+
# No Transpose ops in produced TFLite model
83+
tflite_subgraph = Model.GetRootAs(tflite_flatbuffers_model).Subgraphs(0)
84+
85+
assert tflite_subgraph.OperatorsLength() == 3
86+
assert (
87+
tflite_subgraph.Operators(0).BuiltinOptionsType()
88+
== BuiltinOptions.Conv2DOptions
89+
)
90+
assert (
91+
tflite_subgraph.Operators(1).BuiltinOptionsType()
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== BuiltinOptions.ReshapeOptions
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)
94+
assert (
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tflite_subgraph.Operators(2).BuiltinOptionsType()
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== BuiltinOptions.FullyConnectedOptions
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)
98+
99+
# Verify outputs of program and TFLite model
100+
input_data = (
101+
(torch.randn(input_shape, dtype=torch.float32))
102+
.type(torch.int8)
103+
.detach()
104+
.numpy()
105+
)
106+
convert_run_compare(
107+
exported_program,
108+
input_data=input_data,
109+
tflite_input_preprocess=ToNHWCPreprocess(),
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)

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