1 /*
2 * Copyright (c) 2019-2020 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 #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h"
25
26 #include "arm_compute/core/CL/CLHelpers.h"
27 #include "arm_compute/core/CL/CLKernelLibrary.h"
28 #include "arm_compute/core/CL/ICLTensor.h"
29 #include "arm_compute/core/CL/OpenCL.h"
30 #include "arm_compute/core/Helpers.h"
31 #include "arm_compute/core/TensorInfo.h"
32 #include "arm_compute/core/Utils.h"
33 #include "arm_compute/core/Validate.h"
34 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
35 #include "src/core/AccessWindowStatic.h"
36 #include "src/core/helpers/AutoConfiguration.h"
37 #include "src/core/helpers/WindowHelpers.h"
38 #include "support/StringSupport.h"
39
40 #include <cstddef>
41 #include <cstdint>
42 #include <tuple>
43
44 using namespace arm_compute::misc::shape_calculator;
45
46 namespace arm_compute
47 {
48 namespace
49 {
50 using ElementsProcessed = Steps;
51
validate_arguments(const ITensorInfo * input0,const ITensorInfo * input1,const ITensorInfo * output,const GEMMKernelInfo & gemm_info,const ITensorInfo * vector_sum_col,const ITensorInfo * vector_sum_row,const ITensorInfo * bias,const ITensorInfo * output_multipliers,const ITensorInfo * output_shifts)52 Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMKernelInfo &gemm_info,
53 const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
54 const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
55 {
56 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
57 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
58 if(input0->data_type() == DataType::QASYMM8)
59 {
60 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
61 }
62 else
63 {
64 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::QASYMM8, DataType::QSYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL);
65 }
66 ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
67 ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
68
69 const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info;
70 const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info;
71 const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
72
73 ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3) || (rhs_info.k0 > 16)), "Only 2,3,4,8,16 are supported for k0");
74 ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8);
75 ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3) || rhs_info.n0 > 16), "Only 2,3,4,8,16 are supported for n0");
76 ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for quantized GEMM");
77
78 const int m = gemm_info.m;
79 const int n = gemm_info.n;
80 const int k = gemm_info.k;
81
82 TensorShape tensor_shape1{ input1->tensor_shape() };
83 tensor_shape1.set(0, n);
84 tensor_shape1.set(1, k);
85
86 const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
87 const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
88
89 ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != static_cast<unsigned int>(k));
90 if(gemm_info.reinterpret_input_as_3d)
91 {
92 ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != static_cast<unsigned int>(m));
93 }
94 else
95 {
96 ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != static_cast<unsigned int>(m));
97 }
98 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
99
100 const TensorShape expected_output_shape = compute_mm_shape(*input0, *input1, gemm_info);
101 if(output->total_size() != 0)
102 {
103 const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(expected_output_shape);
104 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
105 if(output_stage.type == GEMMLowpOutputStageType::NONE)
106 {
107 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
108 }
109 else
110 {
111 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
112 }
113 }
114
115 if(bias != nullptr)
116 {
117 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
118 ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
119 ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != bias->dimension(0));
120 }
121
122 ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN) || (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT),
123 "Only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT is supported");
124
125 // Checks performed if the output stage needs to be fused
126 if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
127 {
128 // If a_offset == 0, vector_sum_col can be a nullptr
129 if(gemm_info.a_offset != 0)
130 {
131 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
132 ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != expected_output_shape[0]);
133 }
134
135 // If b_offset == 0, vector_sum_row can be a nullptr
136 if(gemm_info.b_offset != 0)
137 {
138 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
139
140 // Check if mm result is a 3D reinterpretation
141 const bool reinterpret_as_3d = expected_output_shape.num_dimensions() > 1 && expected_output_shape.y() != vector_sum_row->tensor_shape().x();
142
143 // Validate input
144 ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (expected_output_shape[1] * expected_output_shape[2]));
145 ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != expected_output_shape[1]);
146
147 if(expected_output_shape.num_dimensions() > 1)
148 {
149 const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;
150
151 TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
152 vector_sum_row_shape.collapse_from(1);
153 TensorShape collapsed_output_shape(expected_output_shape);
154 collapsed_output_shape.collapse_from(output_batch_idx);
155
156 ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != collapsed_output_shape[output_batch_idx],
157 "vector_sum_row must have the same number of batches of output tensor");
158
159 if(gemm_info.a_offset != 0)
160 {
161 TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
162 vector_sum_col_shape.collapse_from(1);
163
164 ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
165 "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
166 }
167 }
168 }
169
170 if(output->total_size() != 0)
171 {
172 ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != output->data_type());
173 }
174 ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
175
176 if(output_multipliers != nullptr && output_shifts != nullptr)
177 {
178 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32);
179 ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1);
180 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32);
181 ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1);
182 if(output_stage.is_quantized_per_channel)
183 {
184 ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != output_shifts->dimension(0));
185 ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != output_multipliers->dimension(0));
186 }
187 }
188 }
189 return Status{};
190 }
191
validate_and_configure_window(ITensorInfo * input0,ITensorInfo * input1,ITensorInfo * output,const GEMMKernelInfo & gemm_info,ITensorInfo * vector_sum_col,ITensorInfo * vector_sum_row,ITensorInfo * bias,ITensorInfo * output_multipliers,ITensorInfo * output_shifts,ElementsProcessed & num_elements_processed)192 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMKernelInfo &gemm_info,
193 ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, ITensorInfo *bias,
194 ITensorInfo *output_multipliers, ITensorInfo *output_shifts, ElementsProcessed &num_elements_processed)
195 {
196 const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
197
198 unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
199 unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
200 bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
201 bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d != 0);
202
203 Window win{};
204 Window win_out{};
205 bool window_changed = false;
206
207 // In case both input and output have to be reinterpreted as 3D tensors,
208 // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
209 if(reinterpret_input_as_3d == reinterpret_output_as_3d)
210 {
211 reinterpret_output_as_3d = false;
212 }
213
214 // Output tensor auto initialization if not yet initialized
215 const TensorShape expected_output_shape = compute_mm_shape(*input0, *input1, gemm_info);
216 if(output_stage.type != GEMMLowpOutputStageType::NONE)
217 {
218 auto_init_if_empty(*output, input0->clone()->set_tensor_shape(expected_output_shape).set_data_type(output_stage.output_data_type));
219 }
220 else
221 {
222 auto_init_if_empty(*output, input0->clone()->set_tensor_shape(expected_output_shape).set_data_type(DataType::S32));
223 }
224
225 TensorInfo tmp_info(*output);
226
227 if(reinterpret_output_as_3d)
228 {
229 // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
230 // the window needs to be constructed on the 2D collapsed version of the tensor
231 TensorShape tmp_shape(output->tensor_shape());
232 tmp_shape.collapse(2U, 1U);
233 tmp_info.set_tensor_shape(tmp_shape);
234 }
235
236 // Configure kernel window
237 num_elems_processed_per_iteration_x = gemm_info.rhs_info.n0;
238 num_elems_processed_per_iteration_y = gemm_info.lhs_info.m0;
239
240 win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
241 win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
242
243 if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
244 {
245 if(gemm_info.a_offset != 0)
246 {
247 AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration_x);
248 window_changed = window_changed || update_window_and_padding(win_out, vector_sum_col_access);
249 }
250 // No access window needed for vector_sum_row
251 ARM_COMPUTE_UNUSED(vector_sum_row);
252
253 if(bias != nullptr)
254 {
255 AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration_x);
256 window_changed = window_changed || update_window_and_padding(win_out, bias_access);
257 }
258
259 if(output_multipliers != nullptr && output_multipliers->dimension(0) > 1)
260 {
261 AccessWindowHorizontal output_multipliers_access(output_multipliers, 0, num_elems_processed_per_iteration_x);
262 AccessWindowHorizontal output_shifts_access(output_shifts, 0, num_elems_processed_per_iteration_x);
263 window_changed = window_changed || update_window_and_padding(win_out, output_multipliers_access, output_shifts_access);
264 }
265 }
266
267 // Collapse along the Z direction
268 // This collapse needs to be here in order to tune the Z dimension of LWS
269 Window collapsed = win;
270 const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);
271 collapsed = win.collapse(win, dimension_to_collapse);
272
273 Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
274 return std::make_pair(err, collapsed);
275 }
276 } // namespace
277
CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel()278 CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel()
279 : _input0(nullptr),
280 _input1(nullptr),
281 _output(nullptr),
282 _vector_sum_col(nullptr),
283 _vector_sum_row(nullptr),
284 _bias(nullptr),
285 _output_multipliers(nullptr),
286 _output_shifts(nullptr),
287 _slide_matrix_b(true),
288 _reinterpret_input_as_3d(false),
289 _reinterpret_output_as_3d(false),
290 _use_dummy_work_items(false),
291 _is_quantized_per_channel(false),
292 _fuse_output_stage(false)
293 {
294 }
295
configure(const ICLTensor * input0,const ICLTensor * input1,ICLTensor * output,const GEMMKernelInfo & gemm_info,const ICLTensor * vector_sum_col,const ICLTensor * vector_sum_row,const ICLTensor * bias,const ICLTensor * output_multipliers,const ICLTensor * output_shifts)296 void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMKernelInfo &gemm_info,
297 const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias,
298 const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
299 {
300 configure(CLKernelLibrary::get().get_compile_context(), input0, input1, output, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts);
301 }
302
configure(const CLCompileContext & compile_context,const ICLTensor * input0,const ICLTensor * input1,ICLTensor * output,const GEMMKernelInfo & gemm_info,const ICLTensor * vector_sum_col,const ICLTensor * vector_sum_row,const ICLTensor * bias,const ICLTensor * output_multipliers,const ICLTensor * output_shifts)303 void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output,
304 const GEMMKernelInfo &gemm_info,
305 const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias,
306 const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
307 {
308 ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
309 ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(),
310 input1->info(),
311 output->info(),
312 gemm_info,
313 vector_sum_col != nullptr ? vector_sum_col->info() : nullptr,
314 vector_sum_row != nullptr ? vector_sum_row->info() : nullptr,
315 bias != nullptr ? bias->info() : nullptr,
316 output_multipliers != nullptr ? output_multipliers->info() : nullptr,
317 output_shifts != nullptr ? output_shifts->info() : nullptr));
318
319 auto padding_info = get_padding_info({ input0, input1, output, vector_sum_row });
320 const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info;
321 const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info;
322 const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
323 const int32_t a_offset = gemm_info.a_offset;
324 const int32_t b_offset = gemm_info.b_offset;
325
326 _input0 = input0;
327 _input1 = input1;
328 _output = output;
329 _vector_sum_col = vector_sum_col;
330 _vector_sum_row = vector_sum_row;
331 _bias = bias;
332 _output_multipliers = output_multipliers;
333 _output_shifts = output_shifts;
334 _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
335 _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d != 0);
336 _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
337 _is_quantized_per_channel = output_stage.is_quantized_per_channel;
338
339 // In case both input and output have to be reinterpreted as 3D tensors,
340 // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
341 if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
342 {
343 _reinterpret_input_as_3d = false;
344 _reinterpret_output_as_3d = false;
345 }
346
347 // Check if we need to slide the matrix B
348 const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions();
349 _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0);
350
351 ElementsProcessed num_elements_processed{};
352
353 // Configure kernel window
354 auto win_config = validate_and_configure_window(input0->info(),
355 input1->info(),
356 output->info(),
357 gemm_info,
358 vector_sum_col != nullptr ? vector_sum_col->info() : nullptr,
359 vector_sum_row != nullptr ? vector_sum_row->info() : nullptr,
360 bias != nullptr ? bias->info() : nullptr,
361 output_multipliers != nullptr ? output_multipliers->info() : nullptr,
362 output_shifts != nullptr ? output_shifts->info() : nullptr,
363 num_elements_processed);
364 ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
365 ICLKernel::configure_internal(win_config.second);
366
367 // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true,
368 // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel.
369 // This means that the actual m used by the kernel is given by output->info()->dimension(1) and not by gemm_info.m
370 const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : output->info()->dimension(1);
371
372 // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads.
373 // NOTE: This might have implications on heuristics and performance
374 const unsigned int internal_m0 = std::min(internal_m, lhs_info.m0);
375
376 // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding.
377 const unsigned int partial_store_m0 = internal_m % internal_m0;
378 const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
379
380 // Create build options
381 CLBuildOptions build_opts;
382 build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
383 build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
384 build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
385 build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));
386 build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
387 build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
388 build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
389 build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m));
390 build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n));
391 build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k));
392 build_opts.add_option("-DM0=" + support::cpp11::to_string(internal_m0));
393 build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
394 build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
395 build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
396 build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
397 build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
398 build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()));
399 build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_dot8_acc_type_from_data_type(input0->info()->data_type()));
400
401 std::string kernel_name("gemmlowp_mm_reshaped_only_rhs_");
402 kernel_name += rhs_info.transpose ? "t" : "nt";
403
404 if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
405 {
406 kernel_name += "_fused_output_stage_fixedpoint";
407 _fuse_output_stage = true;
408 // If a_offset == 0, vector_sum_col can be a nullptr
409 if(a_offset != 0)
410 {
411 build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset));
412 build_opts.add_option_if(vector_sum_col->info()->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES");
413 }
414 // If b_offset == 0, vector_sum_row can be a nullptr
415 build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset));
416 build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * input0->info()->dimension(0)));
417 build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");
418 build_opts.add_option("-DRESULT_OFFSET=" + support::cpp11::to_string(output_stage.gemmlowp_offset));
419 build_opts.add_option("-DRESULT_MULTIPLIER=" + support::cpp11::to_string(output_stage.gemmlowp_multipliers[0]));
420 build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage.gemmlowp_shifts[0]));
421 build_opts.add_option_if(_is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION");
422
423 const int min = output_stage.gemmlowp_min_bound;
424 const int max = output_stage.gemmlowp_max_bound;
425
426 PixelValue min_val{};
427 PixelValue max_val{};
428 std::tie(min_val, max_val) = get_min_max(output->info()->data_type());
429 build_opts.add_option_if(min != min_val.get<int32_t>(), "-DMIN_BOUND=" + support::cpp11::to_string(min));
430 build_opts.add_option_if(max != max_val.get<int32_t>(), "-DMAX_BOUND=" + support::cpp11::to_string(max));
431 }
432
433 // Create kernel
434 _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
435
436 // Set config_id for enabling LWS tuning
437 _config_id = kernel_name;
438 _config_id += "_";
439 _config_id += dot8_supported(CLKernelLibrary::get().get_device()) ? "_dot8" : "";
440 _config_id += "_";
441 _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
442 _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
443 _config_id += support::cpp11::to_string(output->info()->dimension(1));
444 _config_id += "_";
445 _config_id += support::cpp11::to_string(output->info()->dimension(0));
446 _config_id += "_";
447 _config_id += support::cpp11::to_string(gemm_info.k);
448 _config_id += "_";
449 _config_id += support::cpp11::to_string(output->info()->dimension(2));
450 _config_id += "_";
451 _config_id += support::cpp11::to_string(lhs_info.m0);
452 _config_id += "_";
453 _config_id += support::cpp11::to_string(rhs_info.n0);
454 _config_id += "_";
455 _config_id += support::cpp11::to_string(rhs_info.k0);
456 _config_id += "_";
457 _config_id += support::cpp11::to_string(rhs_info.h0);
458 _config_id += "_";
459 _config_id += support::cpp11::to_string(rhs_info.interleave);
460 ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
461 }
462
validate(const ITensorInfo * input0,const ITensorInfo * input1,const ITensorInfo * output,const GEMMKernelInfo & gemm_info,const ITensorInfo * vector_sum_col,const ITensorInfo * vector_sum_row,const ITensorInfo * bias,const ITensorInfo * output_multipliers,const ITensorInfo * output_shifts)463 Status CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMKernelInfo &gemm_info,
464 const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
465 const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
466 {
467 ElementsProcessed num_elements_processed{};
468 ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts));
469 ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
470 input1->clone().get(),
471 output->clone().get(),
472 gemm_info,
473 vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr,
474 vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
475 bias != nullptr ? bias->clone().get() : nullptr,
476 output_multipliers != nullptr ? output_multipliers->clone().get() : nullptr,
477 output_shifts != nullptr ? output_shifts->clone().get() : nullptr,
478 num_elements_processed)
479 .first);
480
481 return Status{};
482 }
483
run(const Window & window,cl::CommandQueue & queue)484 void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl::CommandQueue &queue)
485 {
486 ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
487 ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
488
489 if(_input1->info()->num_dimensions() < 3)
490 {
491 // The stride_z for matrix B must be zero if we do not slice
492 ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0);
493 }
494
495 Window slice = window.first_slice_window_3D();
496 Window slice_matrix_b = slice;
497
498 slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
499 slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
500
501 if(_reinterpret_input_as_3d)
502 {
503 // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
504 const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3;
505 const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom;
506 _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
507 }
508
509 if(_reinterpret_output_as_3d)
510 {
511 // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor
512 const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0);
513 const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;
514 _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
515 }
516
517 // Set window for vector_sum_col
518 Window win_vector_sum_col = slice;
519 win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
520 win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
521
522 // Set window for vector_sum_row
523 Window win_vector_sum_row = slice;
524 win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
525 win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
526 win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
527
528 Window biases_slice = slice;
529 biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1));
530 biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1));
531
532 do
533 {
534 Window slice_b = slice;
535 // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
536 // This scenario can happen when the matrix multiplication is used to perform a convolution operation
537 if(!_slide_matrix_b)
538 {
539 slice_b = slice_matrix_b;
540 }
541
542 unsigned int idx = 0;
543 add_2D_tensor_argument(idx, _input0, slice);
544 add_2D_tensor_argument(idx, _input1, slice_b);
545 add_2D_tensor_argument(idx, _output, slice);
546 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
547 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
548 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
549 if(_reinterpret_input_as_3d)
550 {
551 // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
552 idx++;
553 }
554
555 if(_reinterpret_output_as_3d)
556 {
557 // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor
558 idx++;
559 }
560
561 if(_fuse_output_stage)
562 {
563 add_2D_tensor_argument_if((_vector_sum_col != nullptr), idx, _vector_sum_col, win_vector_sum_col);
564 add_2D_tensor_argument_if((_vector_sum_row != nullptr), idx, _vector_sum_row, win_vector_sum_row);
565 add_1D_tensor_argument_if((_bias != nullptr), idx, _bias, biases_slice);
566 add_1D_tensor_argument_if(_is_quantized_per_channel, idx, _output_multipliers, biases_slice);
567 add_1D_tensor_argument_if(_is_quantized_per_channel, idx, _output_shifts, biases_slice);
568 }
569 enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
570 }
571 while(window.slide_window_slice_3D(slice));
572 }
573 } // namespace arm_compute
574