Making CMSIS-NN tests pylint compliant

tests/lint/pylint.sh

@@ -20,3 +20,5 @@ set -euxo pipefail
 python3 -m pylint python/tvm --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint vta/python/vta --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/unittest/test_tvmscript_type.py --rcfile="$(dirname "$0")"/pylintrc
+python3 -m pylint tests/python/contrib/test_cmsisnn --rcfile="$(dirname "$0")"/pylintrc
+

tests/python/contrib/test_cmsisnn/__init__.py

@@ -0,0 +1,17 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Infrastructure and tests for CMSIS-NN"""

tests/python/contrib/test_cmsisnn/test_binary_ops.py

@@ -18,29 +18,26 @@
 """CMSIS-NN integration tests: binary ops"""
 
 import itertools
-import sys
 
 import numpy as np
-from enum import Enum
 import pytest
 
 import tvm
 from tvm import relay
 from tvm.relay.op.contrib import cmsisnn
+from tvm.testing.aot import generate_ref_data, AOTTestModel, compile_and_run
+from tvm.micro.testing.aot_test_utils import (
+    AOT_USMP_CORSTONE300_RUNNER,
+)
 
-from utils import (
+from .utils import (
     skip_if_no_reference_system,
     make_module,
     make_qnn_relu,
     get_range_for_dtype_str,
     assert_partitioned_function,
     assert_no_external_function,
 )
-from tvm.testing.aot import generate_ref_data, AOTTestModel, compile_and_run
-from tvm.micro.testing.aot_test_utils import (
-    AOT_CORSTONE300_RUNNER,
-    AOT_USMP_CORSTONE300_RUNNER,
-)
 
 
 def generate_tensor_constant():
@@ -104,6 +101,7 @@ def make_model(
 def test_op_int8(
     op, relu_type, input_0_scale, input_0_zero_point, input_1_scale, input_1_zero_point
 ):
+    """Tests QNN Conv2D operator for CMSIS-NN"""
     interface_api = "c"
     use_unpacked_api = True
     test_runner = AOT_USMP_CORSTONE300_RUNNER
@@ -147,8 +145,10 @@ def test_op_int8(
     )
 
 
-# At least one of the inputs is a constant, both can't be variables, both can't be scalars
 def parameterize_for_constant_inputs(test):
+    """Generates parameters in such a way so that at least one of the inputs is a constant,
+    both can't be variables, both can't be scalars.
+    """
     op = [relay.qnn.op.mul, relay.qnn.op.add]
     input_0 = [generate_variable("input_0"), generate_tensor_constant(), generate_scalar_constant()]
     input_1 = [generate_variable("input_1"), generate_tensor_constant(), generate_scalar_constant()]
@@ -178,6 +178,7 @@ def parameterize_for_constant_inputs(test):
 @tvm.testing.requires_cmsisnn
 @parameterize_for_constant_inputs
 def test_constant_input_int8(op, input_0, input_1):
+    """Tests binary ops where one of the operands is a constant"""
     interface_api = "c"
     use_unpacked_api = True
     test_runner = AOT_USMP_CORSTONE300_RUNNER
@@ -231,9 +232,9 @@ def test_constant_input_int8(op, input_0, input_1):
 def test_both_scalar_inputs_int8(
     op,
 ):
+    """Tests binary ops where both operands are scalars"""
     input_scale = 0.256
     input_zero_point = 33
-    dtype = "int8"
     model = make_model(
         op,
         generate_scalar_constant(),
@@ -257,6 +258,7 @@ def test_invalid_parameters(
     op,
     input_dtype,
 ):
+    """Tests binary ops for non int8 dtypes"""
     input_scale = 0.256
     input_zero_point = 33
     model = make_model(

tests/python/contrib/test_cmsisnn/test_conv2d.py

@@ -26,8 +26,7 @@
 from tvm.testing.aot import generate_ref_data, AOTTestModel, compile_models, compile_and_run
 
 from tvm.micro.testing.aot_test_utils import AOT_USMP_CORSTONE300_RUNNER
-from utils import (
-    skip_if_no_reference_system,
+from .utils import (
     make_module,
     get_range_for_dtype_str,
     get_same_padding,
@@ -76,15 +75,15 @@ def make_model(
         shape = (shape[0], shape[1] + p[0] + p[2], shape[2] + p[1] + p[3], shape[3])
 
     rng = np.random.default_rng(12321)
-    w = tvm.nd.array(
+    weight = tvm.nd.array(
         rng.integers(
             np.iinfo(kernel_dtype).min,
             high=np.iinfo(kernel_dtype).max,
             size=kernel_shape,
             dtype=kernel_dtype,
         )
     )
-    weight_const = relay.const(w, kernel_dtype)
+    weight_const = relay.const(weight, kernel_dtype)
     conv = relay.qnn.op.conv2d(
         invar,
         weight_const,
@@ -102,8 +101,8 @@ def make_model(
         padding=p,
         out_dtype="int32",
     )
-    b = tvm.nd.array(rng.integers(0, high=10, size=(out_channels,), dtype="int32"))
-    bias_const = relay.const(b, "int32")
+    bias = tvm.nd.array(rng.integers(0, high=10, size=(out_channels,), dtype="int32"))
+    bias_const = relay.const(bias, "int32")
     last_op = relay.nn.bias_add(conv, bias_const, axis=3) if enable_bias else conv
     requant_input_sc = [sc * input_scale for sc in kernel_scale]
     last_op = relay.qnn.op.requantize(
@@ -115,7 +114,7 @@ def make_model(
         out_dtype=dtype,
     )
     last_op = make_qnn_relu(last_op, relu_type, output_scale, output_zero_point, dtype)
-    params = {"w": w, "b": b}
+    params = {"w": weight, "b": bias}
     return last_op, params
 
 
@@ -134,9 +133,9 @@ def test_conv2d_number_primfunc_args(
     kernel_scale,
     out_channels,
 ):
+    """Tests number of arguments in Conv2D primfunc"""
     interface_api = "c"
     use_unpacked_api = True
-    test_runner = AOT_USMP_CORSTONE300_RUNNER
 
     ifm_shape = (1, 64, 100, 4)
     kernel_size = (3, 3)
@@ -204,7 +203,7 @@ def test_conv2d_number_primfunc_args(
     expected_num_params = 6 if enable_bias else 5
     cmsisnn_tir_mod = None
     for target, mod in compiled_models[0].executor_factory.lowered_ir_mods.items():
-        if "cmsis-nn" == target.kind.name:
+        if target.kind.name == "cmsis-nn":
             cmsisnn_tir_mod = mod
 
     cmsisnn_func = cmsisnn_tir_mod["tvmgen_default_cmsis_nn_main_0"]
@@ -230,6 +229,7 @@ def test_conv2d_symmetric_padding_int8(
     kernel_scale,
     out_channels,
 ):
+    """Tests QNN Conv2D where the padding is symmetric on both sides of input"""
     interface_api = "c"
     use_unpacked_api = True
     test_runner = AOT_USMP_CORSTONE300_RUNNER
@@ -319,6 +319,7 @@ def test_conv2d_asymmetric_padding_int8(
     kernel_scale,
     out_channels,
 ):
+    """Tests QNN Conv2D where the padding is asymmetric on different sides of input"""
     interface_api = "c"
     use_unpacked_api = True
     test_runner = AOT_USMP_CORSTONE300_RUNNER
@@ -390,13 +391,15 @@ def test_conv2d_asymmetric_padding_int8(
     )
 
 
+# pylint: disable=import-outside-toplevel
 @tvm.testing.requires_cmsisnn
 @pytest.mark.parametrize("ifm_shape", [(1, 55, 55, 3)])
 @pytest.mark.parametrize("kernel_shape", [(3, 2), (1, 3)])
 @pytest.mark.parametrize("strides, dilation", [((3, 2), (1, 1))])
 @pytest.mark.parametrize("padding", ["SAME", "VALID"])
 @pytest.mark.parametrize("activation", ["NONE", "RELU"])
 def test_conv2d_int8_tflite(ifm_shape, kernel_shape, strides, dilation, padding, activation):
+    """Compares TVM output against TFLite output"""
     interface_api = "c"
     use_unpacked_api = True
     test_runner = AOT_USMP_CORSTONE300_RUNNER
@@ -460,6 +463,7 @@ def test_depthwise_int8(
     out_channels,
     depth_multiplier,
 ):
+    """Tests QNN Depthwise int8 op via CMSIS-NN"""
     interface_api = "c"
     use_unpacked_api = True
     test_runner = AOT_USMP_CORSTONE300_RUNNER
@@ -537,6 +541,7 @@ def test_depthwise_int8(
 
 
 def parameterize_for_invalid_model(test):
+    """Generates non int8 inputs"""
     in_dtype = ["uint8", "int8"]
     kernel_dtype = ["uint8", "int8"]
     kernel_zero_point = [-33, 10, 0]
@@ -560,12 +565,12 @@ def test_invalid_parameters(
     kernel_dtype,
     kernel_zero_point,
 ):
+    """Tests Depthwise op for non int8 inputs"""
     ifm_shape = (1, 28, 28, 12)
     out_channels = 2
     input_scale = 1
     input_zero_point = 24
     kernel_scale = [0.11, 0.0237]
-    in_min, in_max = get_range_for_dtype_str(in_dtype)
 
     kernel_layout = "HWIO"
     kernel_shape = [3, 3, ifm_shape[3], out_channels]