1 /*
2 * Copyright (c) 2022-2023 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24
25 #include "tests/AssetsLibrary.h"
26 #include "tests/CL/CLAccessor.h"
27 #include "tests/framework/Fixture.h"
28 #include "tests/framework/Macros.h"
29 #include "tests/framework/datasets/Datasets.h"
30 #include "tests/validation/Validation.h"
31 #include "tests/validation/reference/ConvolutionLayer.h"
32
33 #include "tests/datasets/SmallConvolutionLayerDataset.h"
34 #include "tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h"
35
36 namespace arm_compute
37 {
38 namespace test
39 {
40 namespace validation
41 {
42 namespace
43 {
44 /** Tolerances from tests/validation/CL/DirectConvolutionLayer.cpp
45 */
46 RelativeTolerance<float> tolerance_f32(0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
47 RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
48 constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/
49 constexpr float tolerance_num = 0.07f; /**< Tolerance number */
50 } // namespace
51
52 TEST_SUITE(CL)
53 TEST_SUITE(DYNAMIC_FUSION)
54 /** Synced with tests/validation/CL/ConvolutionLayer.cpp
55 *
56 * Difference | Why the difference
57 * f32 tolerance here is smaller | To use the same tolerance as that of DirectConv2d; lowering tolerance is safe
58 * No quantized tests | Not supported yet
59 * No grouped CNN tests | Not supported yet
60 * No mixed layout tests | Not needed; only NHWC is supported
61 * No activation/post op tests | Not needed in fusion
62 * No ValidateConvolutionMethod | Only a single method (direct conv2d) is supported
63 * No ReshapeWeights = true tests | Not applicable yet. This parameter only concerns gemm-based conv2d
64 * No RunSmallWithPadding tests | Padding is removed
65 *
66 */
67 TEST_SUITE(CONV2D)
68
69 template <typename T>
70 using DynamicFusionGpuConv2dFixture = DynamicFusionGpuConv2dValidationFixture<CLTensor, CLAccessor, GpuConv2d, T>;
71 TEST_SUITE(FP32)
72 FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuConv2dFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
73 framework::dataset::make("DataType", DataType::F32)),
74 framework::dataset::make("DataLayout", { DataLayout::NHWC })),
75 framework::dataset::make("QuantizationInfo", QuantizationInfo())))
76 {
77 // Validate output
78 validate(CLAccessor(_target), _reference, tolerance_f32);
79 }
80 TEST_SUITE_END() // FP32
81
TEST_SUITE(FP16)82 TEST_SUITE(FP16)
83 FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuConv2dFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
84 framework::dataset::make("DataType", DataType::F16)),
85 framework::dataset::make("DataLayout", { DataLayout::NHWC })),
86 framework::dataset::make("QuantizationInfo", QuantizationInfo())))
87 {
88 // Validate output
89 validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
90 }
91 TEST_SUITE_END() // FP16
92
93 // Tests for specific conv2d methods
94 /** Synced with tests/validation/CL/DirectConvolutionLayer.cpp
95 *
96 * Difference | Why the difference
97 * No quantized tests | Not supported yet
98 * No Invalid output size test | Not applicable. Output is removed from the interface
99 * No mixed layout/NCHW tests | Not needed; only NHWC is supported
100 * No activation tests | Not needed in fusion
101 */
TEST_SUITE(DIRECT_CONV2D)102 TEST_SUITE(DIRECT_CONV2D)
103
104 // *INDENT-OFF*
105 // clang-format off
106 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(
107 framework::dataset::make("InputInfo", { TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid: Mismatching data type input/weights
108 TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid: Mismatching input feature maps
109 TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid weights dimensions
110 TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Unsupported biases size
111 TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Unsupported biases dimensions
112 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Unsupported data layout: NCHW
113 TensorInfo(TensorShape(2U, 32U, 16U), 1, DataType::QASYMM8, DataLayout::NHWC), // Unsupported data type: quantized
114 TensorInfo(TensorShape(2U, 32U, 16U), 1, DataType::F32, DataLayout::NHWC),
115 TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Arbitrary weight sizes for NHWC are supported
116 TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Non-rectangular weights dimensions for NHWC are supported
117 TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Strides > 2 for any kernel sizes for NHWC are supported
118 }),
119 framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F16, DataLayout::NHWC),
120 TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
121 TensorInfo(TensorShape(2U, 3U, 3U, 4U, 3U), 1, DataType::F32, DataLayout::NHWC),
122 TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
123 TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
124 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW),
125 TensorInfo(TensorShape(2U, 1U, 1U, 4U), 1, DataType::QASYMM8, DataLayout::NHWC),
126 TensorInfo(TensorShape(2U, 1U, 1U, 4U), 1, DataType::F32, DataLayout::NHWC),
127 TensorInfo(TensorShape(2U, 13U, 13U, 4U), 1, DataType::F32, DataLayout::NHWC),
128 TensorInfo(TensorShape(2U, 5U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
129 TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
130 })),
131 framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
132 TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
133 TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
134 TensorInfo(TensorShape(3U), 1, DataType::F32, DataLayout::NHWC),
135 TensorInfo(TensorShape(4U, 2U), 1, DataType::F32, DataLayout::NHWC),
136 TensorInfo(TensorShape(25U), 1, DataType::F32, DataLayout::NCHW),
137 TensorInfo(TensorShape(4U), 1, DataType::QASYMM8, DataLayout::NHWC),
138 TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
139 TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
140 TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
141 TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
142 })),
143 framework::dataset::make("Conv2dAttributes", {
144 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
145 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
146 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
147 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
148 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
149 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
150 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
151 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
152 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
153 Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}),
154 Conv2dAttributes().stride({3, 3}).pad({0, 0, 0, 0}),
155 })),
156 framework::dataset::make("Expected", { false, false, false, false, false, false, false, true, true, true, true })),
157 input_info, weights_info, biases_info, conv2d_attrs, expected)
158 {
159 auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
160 auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx };
161 GpuWorkloadSketch sketch{ &gpu_ctx };
162
163 const TensorInfo sketch_input_info = sketch.create_tensor_info(input_info);
164 const TensorInfo sketch_weights_info = sketch.create_tensor_info(weights_info);
165 const TensorInfo sketch_biases_info = sketch.create_tensor_info(biases_info);
166 bool is_valid = bool(GpuConv2d::validate_op(sketch, &sketch_input_info, &sketch_weights_info, &sketch_biases_info, conv2d_attrs));
167 ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
168 }
169 template <typename T>
170 using DynamicFusionGpuDirectConv2dFixture = DynamicFusionDirectConv2dValidationFixture<CLTensor, CLAccessor, GpuConv2d, T>;
171
172 TEST_SUITE(FP16)
173 FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDirectConv2dFixture<half>, framework::DatasetMode::PRECOMMIT,
174 combine(combine(combine(zip(zip(zip(zip(zip(
175 framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
176 TensorShape(19U, 5U, 16U, 4U),
177 TensorShape(13U, 5U, 17U, 2U),
178 TensorShape(32U, 37U, 13U) } ),
179 framework::dataset::make("StrideX", { 1, 3, 1, 1 })),
180 framework::dataset::make("StrideY", { 1, 3, 2, 1 })),
181 framework::dataset::make("PadX", { 1, 3, 0, 4 })),
182 framework::dataset::make("PadY", { 1, 3, 0, 4 })),
183 framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),
184 framework::dataset::make("NumKernels", { 17, 3, 1, 19 })),
185 framework::dataset::make("DataType", DataType::F16)),
186 framework::dataset::make("DataLayout", DataLayout::NHWC)))
187 {
188 validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
189 }
190
191 FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDirectConv2dFixture<half>, framework::DatasetMode::NIGHTLY,
192 combine(combine(combine(zip(zip(zip(zip(zip(
193 framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),
194 framework::dataset::make("StrideX", { 1 })),
195 framework::dataset::make("StrideY", { 1 })),
196 framework::dataset::make("PadX", { 1 })),
197 framework::dataset::make("PadY", { 1 })),
198 framework::dataset::make("KernelSize", { 9 })),
199 framework::dataset::make("NumKernels", { 3 })),
200 framework::dataset::make("DataType", DataType::F16)),
201 framework::dataset::make("DataLayout", DataLayout::NHWC)))
202 {
203 validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
204 }
205
206 TEST_SUITE_END() // FP16
207
TEST_SUITE(FP32)208 TEST_SUITE(FP32)
209 FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDirectConv2dFixture<float>, framework::DatasetMode::PRECOMMIT,
210 combine(combine(combine(zip(zip(zip(zip(zip(
211 framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
212 TensorShape(19U, 5U, 16U, 4U),
213 TensorShape(13U, 5U, 17U, 2U),
214 TensorShape(32U, 37U, 13U) } ),
215 framework::dataset::make("StrideX", { 1, 3, 1, 1 })),
216 framework::dataset::make("StrideY", { 1, 3, 2, 1 })),
217 framework::dataset::make("PadX", { 1, 3, 0, 4 })),
218 framework::dataset::make("PadY", { 1, 3, 0, 4 })),
219 framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),
220 framework::dataset::make("NumKernels", { 17, 3, 1, 19 })),
221 framework::dataset::make("DataType", DataType::F32)),
222 framework::dataset::make("DataLayout", DataLayout::NHWC)))
223 {
224 validate(CLAccessor(_target), _reference, tolerance_f32, 0.0, abs_tolerance_f32);
225 }
226
227 FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDirectConv2dFixture<float>, framework::DatasetMode::NIGHTLY,
228 combine(combine(combine(zip(zip(zip(zip(zip(
229 framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),
230 framework::dataset::make("StrideX", { 1 })),
231 framework::dataset::make("StrideY", { 1 })),
232 framework::dataset::make("PadX", { 1 })),
233 framework::dataset::make("PadY", { 1 })),
234 framework::dataset::make("KernelSize", { 9 })),
235 framework::dataset::make("NumKernels", { 3 })),
236 framework::dataset::make("DataType", DataType::F32)),
237 framework::dataset::make("DataLayout", DataLayout::NHWC)))
238 {
239 validate(CLAccessor(_target), _reference, tolerance_f32, 0.0, abs_tolerance_f32);
240 }
241 // clang-format on
242 // *INDENT-ON*
243
244 TEST_SUITE_END() // FP32
245 TEST_SUITE_END() // DIRECT_CONV2D
246 TEST_SUITE_END() // CONV2D
247 TEST_SUITE_END() // DYNAMIC_FUSION
248 TEST_SUITE_END() // CL
249 } // namespace validation
250 } // namespace test
251 } // namespace arm_compute
252