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SiCong Lif44bbc52022-08-29 18:25:51 +01001/*
2 * Copyright (c) 2022 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 "arm_compute/core/CL/CLKernelLibrary.h"
26#include "arm_compute/core/TensorInfo.h"
27#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
28#include "arm_compute/dynamic_fusion/sketch/OperatorAttributes.h"
29#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
30#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h"
SiCong Lif44bbc52022-08-29 18:25:51 +010031
32#include "tests/CL/CLAccessor.h"
SiCong Lif44bbc52022-08-29 18:25:51 +010033#include "tests/framework/Macros.h"
SiCong Lif44bbc52022-08-29 18:25:51 +010034#include "tests/validation/Validation.h"
SiCong Li31df05a2022-11-09 15:57:48 +000035#include "tests/validation/dynamic_fusion/Utils.h"
SiCong Lif44bbc52022-08-29 18:25:51 +010036#include "tests/validation/reference/ConvolutionLayer.h"
SiCong Lif44bbc52022-08-29 18:25:51 +010037#include "tests/validation/reference/Permute.h"
38
SiCong Lif44bbc52022-08-29 18:25:51 +010039using namespace arm_compute::experimental::dynamic_fusion;
40using namespace arm_compute::test::validation::utils;
41
42namespace arm_compute
43{
44namespace test
45{
46namespace validation
47{
48TEST_SUITE(CL)
49TEST_SUITE(INTEGRATION)
50TEST_SUITE(DYNAMIC_FUSION)
51TEST_CASE(Conv2d, framework::DatasetMode::ALL)
52{
53 /* Computation:
54 * out = conv2d1x1(direct_conv)(input, weights, bias)
55 */
56 CLScheduler::get().default_reinit();
57
58 const auto data_type = DataType::F32;
59 const auto data_layout = DataLayout::NHWC;
60 const auto t_input_shape = TensorShape(384, 12, 12);
61 const auto t_weight_shape = TensorShape(384, 1, 1, 16);
62 const auto t_dst_shape = TensorShape(16, 12, 12);
63
64 // Create a new workload sketch
65 auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
66 auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx };
67 GpuWorkloadSketch sketch{ &gpu_ctx };
68
69 // Fuse conv2d
70 Conv2dAttributes conv2d_attr{};
71 auto input_info = sketch.create_tensor_info(t_input_shape, 1, data_type, data_layout);
72 auto weight_info = sketch.create_tensor_info(TensorInfo(t_weight_shape, 1, data_type, data_layout));
73 auto dst_info = sketch.create_tensor_info();
74 GpuConv2d::create_op(sketch, &input_info, &weight_info, nullptr, &dst_info, conv2d_attr);
75
76 // Configure runtime
77 ClWorkloadRuntime runtime;
78 runtime.configure(sketch);
79
80 // (Important) Allocate auxiliary tensor memory if there are any
81 // Instead of using ACL allocated memory, the user can choose to import memory into the tensors
82 for(auto &data : runtime.get_auxiliary_tensors())
83 {
84 CLTensor *tensor = data.first;
85 AuxMemoryInfo aux_mem_req = data.second;
86 tensor->allocator()->init(*data.first->info(), aux_mem_req.alignment);
87 tensor->allocator()->allocate(); // Use ACL allocated memory
88 // auto buf = cl::Buffer();
89 // tensor->allocator()->import_memory(buf); // Or, import external memory
90 }
91
92 // Construct user tensors
93 CLTensor t_input{};
94 CLTensor t_weight{};
95 CLTensor t_dst{};
96
97 // Initialize user tensors
98 t_input.allocator()->init(input_info);
99 t_weight.allocator()->init(weight_info);
100 t_dst.allocator()->init(dst_info);
101
102 // Allocate and fill user tensors
103 // Instead of using ACL allocator, the user can choose to import memory into the tensors
104 t_input.allocator()->allocate();
105 t_weight.allocator()->allocate();
106 t_dst.allocator()->allocate();
107 fill<float>(CLAccessor(t_input), 0, library.get());
108 fill<float>(CLAccessor(t_weight), 1, library.get());
109
110 // Run runtime
111 runtime.run({ &t_input, &t_weight, &t_dst });
112
113 // Create reference
114 SimpleTensor<float> ref_t_input{ t_input_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
115 SimpleTensor<float> ref_t_weight{ t_weight_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
116 SimpleTensor<float> ref_t_bias_placeholder{ t_dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
117
118 // Fill reference
119 fill<float>(ref_t_input, 0, library.get());
120 fill<float>(ref_t_weight, 1, library.get());
121
122 auto ref_t_input_nchw = reference::permute(ref_t_input, PermutationVector(1U, 2U, 0U));
123 auto ref_t_weight_nchw = reference::permute(ref_t_weight, PermutationVector(1U, 2U, 0U));
124 auto ref_t_bias_placeholder_nchw = reference::permute(ref_t_bias_placeholder, PermutationVector(1U, 2U, 0U));
125 auto t_dst_shape_nchw = t_dst_shape;
126 permute(t_dst_shape_nchw, PermutationVector(1U, 2U, 0U));
127
128 PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left, conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom,
129 DimensionRoundingType{});
130 auto ref_t_dst_nchw = reference::convolution_layer(ref_t_input_nchw, ref_t_weight_nchw, ref_t_bias_placeholder_nchw, t_dst_shape_nchw, legacy_pad_stride, conv2d_attr.dilation());
131 const auto ref_t_dst = reference::permute(ref_t_dst_nchw, PermutationVector(2U, 0U, 1U));
132
133 RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
134 validate(CLAccessor(t_dst), ref_t_dst_nchw, tolerance_f32);
135}
136TEST_SUITE(Invalid_Fusion_Should_Fail)
137TEST_CASE(Multiple_Complex_Ops_0, framework::DatasetMode::ALL)
138{
139 /* Computation:
140 * out = conv2d(conv2d(l0_input, l0_weight), l1_weight)
141 */
142 CLScheduler::get().default_reinit();
143
144 const auto data_type = DataType::F32;
145 const auto data_layout = DataLayout::NHWC;
146 const auto t_input_shape = TensorShape(384, 12, 12);
147 const auto t_weight_shape = TensorShape(384, 1, 1, 16);
SiCong Lif44bbc52022-08-29 18:25:51 +0100148 auto t_input_info = TensorInfo(t_input_shape, 1, data_type, data_layout);
149 auto t_weight_info = TensorInfo(t_weight_shape, 1, data_type, data_layout);
150 auto t_dst_info = TensorInfo();
151
152 Conv2dAttributes conv2d_attr{};
153
154 // Create a new workload sketch
155 auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
156 auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx };
157 GpuWorkloadSketch sketch{ &gpu_ctx };
158
159 // Create tensor infos
160 auto input_info = sketch.create_tensor_info(t_input_shape, 1, data_type, data_layout);
161 auto weight_info = sketch.create_tensor_info(TensorInfo(t_weight_shape, 1, data_type, data_layout));
162 auto dst_info = sketch.create_tensor_info();
163
164 // Fuse conv2d into the workload
165 {
166 // Validate operator
167 const auto success = GpuConv2d::validate_op(sketch, &input_info, &weight_info, nullptr, &dst_info, conv2d_attr);
168 ARM_COMPUTE_EXPECT(bool(success), framework::LogLevel::ERRORS);
169
170 GpuConv2d::create_op(sketch, &input_info, &weight_info, nullptr, &dst_info, conv2d_attr);
171 }
172
173 // Create tensor infos
174 auto weight_info_2 = sketch.create_tensor_info(t_weight_info);
175 auto dst_info_2 = sketch.create_tensor_info();
176
177 // Fuse conv2d into the workload
178 {
179 // Validate operator, should fail
180 const auto success = GpuConv2d::validate_op(sketch, &dst_info, &weight_info_2, nullptr, &dst_info_2, conv2d_attr);
181 ARM_COMPUTE_EXPECT(!bool(success), framework::LogLevel::ERRORS);
182 }
183}
184TEST_SUITE_END() // Invalid_Fusion_Should_Fail
185TEST_SUITE_END() // DYNAMIC_FUSION
186TEST_SUITE_END() // INTEGRATION
187TEST_SUITE_END() // CL
188} // namespace validation
189} // namespace test
190} // namespace arm_compute