KleidiAI Coverage Report

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Functions: 96.2% 25 / 0 / 26
Branches: 43.3% 119 / 0 / 275
test/tests/dwconv_test.cpp
Line Branch Exec Source
1 //
2 // SPDX-FileCopyrightText: Copyright 2025 Arm Limited and/or its affiliates <open-source-office@arm.com>
3 //
4 // SPDX-License-Identifier: Apache-2.0
5 //
6
7 #include "test/reference/dwconv.hpp"
8
9 #include <gtest/gtest.h>
10
11 #include <array>
12 #include <cstddef>
13 #include <iostream>
14 #include <string_view>
15 #include <tuple>
16 #include <unordered_map>
17
18 #include "kai/ukernels/dwconv/dwconv_f32_f32_f32p/kai_dwconv_clamp_f32_f32_f32p1vlx1b_3x3_s1_4xc_sme2_mla.h"
19 #include "kai/ukernels/dwconv/dwconv_f32_f32_f32p/kai_dwconv_clamp_f32_f32_f32p_interface.h"
20 #include "kai/ukernels/dwconv/pack/kai_rhs_dwconv_pack_x32p1vlx1b_x32_x32_sme.h"
21 #include "test/common/abi_checker.hpp"
22 #include "test/common/buffer.hpp"
23 #include "test/common/compare.hpp"
24 #include "test/common/cpu_info.hpp"
25 #include "test/common/matmul_test_common.hpp"
26 #include "test/common/matrix_portion.hpp"
27 #include "test/common/seed.hpp"
28 #include "test/reference/clamp.hpp"
29 #include "test/reference/fill.hpp"
30
31 namespace kai::test {
32
33 namespace {
34
35 /// Interface for depthwise kernel.
36
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 906 struct DepthwisePlanarKernel {
37 std::function<size_t(size_t m, size_t n, size_t k)> get_dst_size;
38 std::function<size_t(size_t m, size_t n)> get_dst_offset;
39 std::function<size_t(void)> get_m_step;
40 std::function<void(
41 const void* inptr, const void* packed_rhs, void* outptr_start, size_t stride_in_row, size_t stride_in_col,
42 size_t dst_stride_row, size_t dst_stride_col, unsigned int valid_input_rows, unsigned int valid_out_rows,
43 unsigned int pad_left, unsigned int pad_top, float pad_value, float clamp_min, float clamp_max)>
44 conv;
45 };
46
47 // Rhs packing micro-kernel.
48 906 struct RhsPackDepthwiseKernel {
49 std::function<size_t(size_t fh, size_t fw, size_t nc)> get_rhs_packed_size;
50 std::function<void(
51 size_t filter_height, size_t filter_width, size_t height, size_t width, size_t num_channels, const void* rhs,
52 const void* bias, void* rhs_packed)>
53 pack;
54 };
55
56 /// Description of a Depthwise kernel set
57
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 906 struct Depthwise {
58 std::string_view name;
59 std::function<bool(void)> is_supported;
60 std::pair<unsigned int, unsigned int> filter;
61 DataType data_type;
62 DataType acc_type;
63 RhsPackDepthwiseKernel rhs;
64 DepthwisePlanarKernel depthwise;
65 };
66
67 /// Convenience types for testing.
68 using DepthwiseArray = std::array<Depthwise, 1>;
69 using DepthwiseParamsParams = std::tuple<Depthwise, MatMulShape, Padding2D, float>;
70 using DepthwisePlanarTest = testing::TestWithParam<DepthwiseParamsParams>;
71
72 /// Use interface for depthwise kernel
73 3 const kai_dwconv_clamp_f32_f32_f32p_planar_ukernel& get_dwconv_clamp_f32_f32_f32p1vlx1b_3x3_s1_4xc_sme2_mla() {
74 static kai_dwconv_clamp_f32_f32_f32p_planar_ukernel ukernel;
75 3 ukernel.get_m_step = kai_get_m_step_dwconv_clamp_f32_f32_f32p1vlx1b_3x3_s1_4xc_sme2_mla;
76 3 ukernel.get_dst_offset = kai_get_dst_offset_dwconv_clamp_f32_f32_f32p1vlx1b_3x3_s1_4xc_sme2_mla;
77 3 ukernel.get_dst_size = kai_get_dst_size_dwconv_clamp_f32_f32_f32p1vlx1b_3x3_s1_4xc_sme2_mla;
78 3 ukernel.run_dwconv = kai_run_dwconv_clamp_f32_f32_f32p1vlx1b_3x3_s1_4xc_sme2_mla;
79 3 return ukernel;
80 }
81
82 3 const DepthwiseArray& get_depthwise_methods() {
83 // FP32 kernels with 3x3 filter.
84
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 3 static DepthwiseArray depthwise_methods{};
85 3 depthwise_methods[0].name = "dwconv_clamp_f32_f32_f32p1vlx1b_3x3_s1_4xc_sme2_mla";
86 3 depthwise_methods[0].rhs.get_rhs_packed_size = kai_rhs_get_dst_size_dwconv_pack_x32p1vlx1b_x32_x32_sme;
87 3 depthwise_methods[0].rhs.pack = kai_run_rhs_dwconv_pack_x32p1vlx1b_x32_x32_sme;
88 3 depthwise_methods[0].is_supported = cpu_has_sme2;
89 3 depthwise_methods[0].filter = {3, 3};
90
91 6 const kai_dwconv_clamp_f32_f32_f32p_planar_ukernel& ukernel_f32 =
92 3 get_dwconv_clamp_f32_f32_f32p1vlx1b_3x3_s1_4xc_sme2_mla();
93 3 depthwise_methods[0].data_type = DataType::FP32;
94 3 depthwise_methods[0].acc_type = DataType::FP32;
95 3 depthwise_methods[0].depthwise.get_m_step = ukernel_f32.get_m_step;
96 3 depthwise_methods[0].depthwise.get_dst_size = ukernel_f32.get_dst_size;
97 3 depthwise_methods[0].depthwise.get_dst_offset = ukernel_f32.get_dst_offset;
98 3 depthwise_methods[0].depthwise.conv = ukernel_f32.run_dwconv;
99 3 return depthwise_methods;
100 3 }
101
102 /// Test reference identification.
103 struct TestDataId {
104 using DT = std::underlying_type_t<DataType>;
105 MatMulShape in_shape;
106 MatMulShape rhs_shape;
107 Padding2D pad;
108 DataType dt;
109 DataType dt_acc;
110 float clamp_keep_ratio;
111
112 struct Hash {
113 180 size_t operator()(const TestDataId& test_id) const {
114 180 return //
115 360 (MatMulShape::Hash{}(test_id.in_shape) << 0) ^ //
116 360 (MatMulShape::Hash{}(test_id.rhs_shape) << 1) ^ //
117 360 (Padding2D::Hash{}(test_id.pad) << 2) ^ //
118 360 (std::hash<DT>{}(static_cast<DT>(test_id.dt)) << 3) ^ //
119 360 (std::hash<DT>{}(static_cast<DT>(test_id.dt_acc)) << 4) ^ //
120 180 (std::hash<float>{}(test_id.clamp_keep_ratio) << 5); //
121 }
122 };
123
124 private:
125 ✗ friend bool operator==(const TestDataId& lhs, const TestDataId& rhs) {
126 ✗ return //
127 ✗ lhs.in_shape == rhs.in_shape && //
128 ✗ lhs.rhs_shape == rhs.rhs_shape && //
129 ✗ lhs.pad == rhs.pad && //
130 ✗ lhs.dt == rhs.dt && //
131 ✗ lhs.dt_acc == rhs.dt_acc && //
132 ✗ lhs.clamp_keep_ratio == rhs.clamp_keep_ratio; //
133 }
134 };
135
136 /// Test reference data
137 struct TestData {
138 Buffer lhs; ///< LHS input matrix
139 Buffer rhs; ///< RHS input matrix
140 Buffer bias; ///< Bias vector
141 Buffer out; ///< Reference depthwise result
142 Buffer padding; ///< Padding buffer
143 Range<float> clamp_range; ///< Clamp range
144 };
145
146 /// Generate reference data, caches it.
147 struct ReferenceGenerator {
148 /// Retrieve reference data for the provided test identification
149 60 static const TestData& get_test_reference(const TestDataId test_id, const MatMulShape& out_shape) {
150
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 60 static std::unordered_map<TestDataId, TestData, TestDataId::Hash> m_data;
151
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 60 if (const auto itr = m_data.find(test_id); itr != end(m_data)) {
152 ✗ return itr->second;
153 }
154
155
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 60 return m_data[test_id] = generate_reference(test_id, out_shape);
156 60 }
157
158 private:
159 /// Generate reference data.
160 // NOTE : This block is currently FP32 specific - it is not datatype generic
161 60 static TestData generate_reference(const TestDataId& test_id, const MatMulShape& out_shape) {
162 960 const auto& [in_shape, rhs_shape, pad, dt, acc_dt, clamp_keep_ratio] = test_id;
163
164 // Stable key derived from the cache identifier.
165 60 const auto key_hash = static_cast<std::uint32_t>(TestDataId::Hash{}(test_id));
166
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 60 const auto key = std::string("dwconv_cache:") + std::to_string(key_hash);
167
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 60 auto& feed = seed_stream(key);
168
169 // Generate random input data
170
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 300 Buffer lhs = fill_matrix_random(in_shape.m, in_shape.n * in_shape.k, DataFormat(dt), feed());
171
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 300 Buffer rhs = fill_matrix_random(rhs_shape.m, rhs_shape.n * rhs_shape.k, DataFormat(dt), feed());
172
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 120 Buffer bias = fill_matrix_random(1, out_shape.k, DataFormat(dt), feed());
173
174 // Call reference function
175
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 120 Buffer out = depthwise_reference<float>(
176
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 300 1, in_shape.m, in_shape.n, in_shape.k, rhs_shape.m, rhs_shape.n, lhs.data(), rhs.data(), bias.data(), pad);
177
178 120 const auto [min, max] =
179
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 60 find_clamp_range(dt, out.data(), out_shape.m * out_shape.n * out_shape.k, clamp_keep_ratio);
180
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 120 Buffer out_clamped = clamp(dt, out.data(), out_shape.m * out_shape.n * out_shape.k, min, max);
181
182 // Populate reference data
183 60 TestData test_reference;
184 60 test_reference.lhs = std::move(lhs);
185 60 test_reference.rhs = std::move(rhs);
186 60 test_reference.bias = std::move(bias);
187 60 test_reference.out = std::move(out_clamped);
188 180 test_reference.clamp_range = {min, max};
189 60 return test_reference;
190
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 60 };
191 };
192
193 /// Perform RHS packing for depthwise
194 60 Buffer pack_rhs(const RhsPackDepthwiseKernel& kernel, const MatMulShape& shape, const TestData& reference) {
195 // Calculate size, and allocate buffer
196 60 const size_t dst_size = kernel.get_rhs_packed_size(shape.m, shape.n, shape.k);
197 60 Buffer dst(dst_size);
198
199 // RHS Pack API is subject to change.
200
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 60 abi_check(
201
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 60 kernel.pack, shape.m, shape.n, shape.m, shape.n, shape.k, reference.rhs.data(), reference.bias.data(),
202
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 60 dst.data());
203 60 return dst;
204 60 }
205
206 /// Perform Depthwise Operation using main kernel.
207 60 Buffer dwconv(
208 const DepthwisePlanarKernel& kernel, const Rect& portion, const MatMulShape& in_shape, const MatMulShape& out_shape,
209 const Padding2D pad, const TestData& reference, const Buffer& rhs_packed, Range<float> clamp_range, DataType type) {
210 60 const size_t dst_size = kernel.get_dst_size(out_shape.m, out_shape.n, out_shape.k);
211 60 Buffer dst(dst_size);
212
213
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 60 const size_t dt_size_bytes = data_type_size_in_bits(type) / 8;
214 60 const size_t stride_in_row = in_shape.n * in_shape.k * dt_size_bytes;
215 60 const size_t dst_stride_row = out_shape.n * out_shape.k * dt_size_bytes;
216 60 const size_t stride_col = out_shape.k * dt_size_bytes;
217
218 // Loop the following. M-Step rows are handled at a time.
219
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 1098 for (size_t out_row = portion.start_row(); out_row < portion.end_row(); out_row += kernel.get_m_step()) {
220 1038 const int start_in_row = out_row - pad.top;
221
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 1038 const size_t pad_top = (start_in_row < 0) ? (-start_in_row) : 0;
222
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 1038 const size_t in_row = (start_in_row < 0) ? 0 : start_in_row;
223
224
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 1038 const size_t valid_input_rows = (in_row < in_shape.m) ? (in_shape.m - in_row) : 0;
225 1038 const size_t valid_out_rows = (out_shape.m - out_row);
226
227
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 1038 abi_check(
228
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 1038 kernel.conv, reference.lhs.data() + (in_row * stride_in_row), rhs_packed.data(),
229
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 1038 dst.data() + (out_row * dst_stride_row), stride_in_row, stride_col, dst_stride_row, stride_col,
230 1038 valid_input_rows, valid_out_rows, pad.left, pad_top, 0.f, clamp_range.min, clamp_range.max);
231 1038 }
232
233 60 return dst;
234 60 }
235 } // namespace
236
237 /// End-to-end test for depthwise kernels
238
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 306 TEST_P(DepthwisePlanarTest, Output) {
239 1020 const auto& [method, in_shape, padding, clamp_keep_ratio] = GetParam();
240
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 120 if (not method.is_supported()) {
241
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 60 GTEST_SKIP() << "Unsupported CPU feature";
242 }
243
244 // Calculate Shapes.
245 300 const int out_height = (in_shape.m + padding.top + padding.bottom + 1 - method.filter.first);
246 300 const int out_width = (in_shape.n + padding.left + padding.right + 1 - method.filter.second);
247
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 60 ASSERT_TRUE(out_height > 0 && out_width > 0);
248
249 120 const size_t dt_size_bytes = data_type_size_in_bits(method.data_type) / 8;
250 240 MatMulShape rhs_shape = {method.filter.first, method.filter.second, in_shape.k};
251 120 MatMulShape out_shape = {static_cast<size_t>(out_height), static_cast<size_t>(out_width), (in_shape.k)};
252
253 // 1. Calculate reference.
254 120 const TestData& test_data = ReferenceGenerator::get_test_reference(
255 360 {in_shape, rhs_shape, padding, method.data_type, method.acc_type, clamp_keep_ratio}, out_shape);
256
257 // 2. Pack RHS (Weights+Bias)
258 120 Buffer rhs_packed = pack_rhs(method.rhs, rhs_shape, test_data);
259
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 60 const MatrixPortion out_portion{0, 0, 1, 1};
260
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 120 const Rect portion = out_portion.compute_portion(
261
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 120 out_shape.m, out_shape.n * out_shape.k, method.depthwise.get_m_step(), (rhs_packed.size() / dt_size_bytes));
262
263 // 3. Run Depthwise Kernel.
264
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 240 Buffer out = dwconv(
265 180 method.depthwise, portion, in_shape, out_shape, padding, test_data, rhs_packed, test_data.clamp_range,
266 60 method.data_type);
267
268 // 4. Compare with reference result.
269
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 60 DefaultMismatchHandler handler(0, 0.0001, 0, 0.001);
270
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 120 const auto success = compare(
271
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 60 out.data(), test_data.out.data(), DataType::FP32, out_shape.m, out_shape.n * out_shape.k, portion, handler);
272
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 60 ASSERT_TRUE(success);
273 120 }
274
275 /// Name generator for test case
276 360 [[maybe_unused]] static void PrintTo(const DepthwiseParamsParams& param, std::ostream* os) {
277 1800 const auto& [method, shape, padding, clamp_keep_ratio] = param;
278 720 *os << method.name << "__";
279 360 PrintTo(shape, os);
280 360 *os << "__";
281 360 PrintTo(padding, os);
282 360 *os << "__";
283 720 *os << "__clamp_keep_ratio_" << static_cast<int>(clamp_keep_ratio * 100);
284 360 }
285
286 /// Test parameter listing
287
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 186 INSTANTIATE_TEST_SUITE_P(
288 Depthwise, DepthwisePlanarTest,
289 testing::Combine(
290 testing::ValuesIn(get_depthwise_methods()), //
291 testing::ValuesIn({
292 // clang-format off
293 // IN_HEIGHT, IN_WIDTH, IN_CHANNELS
294 MatMulShape{ 4, 4, 1}, //
295 MatMulShape{ 8, 4, 16}, //
296 MatMulShape{ 96, 33, 37}, //
297 MatMulShape{ 99, 22, 51}, //
298 MatMulShape{ 127, 127, 127}, //
299 // clang-format on
300 }),
301 testing::ValuesIn({
302 // clang-format off
303 // pad_left, pad_right, pad_top, pad_bottom
304 Padding2D{0, 0, 0, 0},
305 Padding2D{0, 1, 0, 1},
306 Padding2D{1, 1, 1, 1},
307 Padding2D{5, 11, 7, 3},
308 // clang-format on
309 }),
310 testing::ValuesIn(std::initializer_list<float>({1.0f, 0.9f, 0.5f}))), // clamp_keep_ratio
311 testing::PrintToStringParamName());
312
313 } // namespace kai::test
314