1 /*
2 * Copyright (c) 2017-2021 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/gpu/cl/kernels/ClIm2ColKernel.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/Validate.h"
33 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
34 #include "src/core/AccessWindowStatic.h"
35 #include "src/core/CL/CLValidate.h"
36 #include "src/core/helpers/AutoConfiguration.h"
37 #include "src/core/helpers/WindowHelpers.h"
38 #include "support/Cast.h"
39 #include "support/StringSupport.h"
40
41 #include <cmath>
42 #include <tuple>
43 #include <utility>
44
45 namespace arm_compute
46 {
47 using namespace misc::shape_calculator;
48 namespace opencl
49 {
50 namespace kernels
51 {
52 namespace
53 {
54 struct Im2ColConfiguration
55 {
56 std::string kernel_name{};
57 std::set<std::string> build_options{};
58 unsigned int num_elems_processed_per_iteration{};
59 bool is_padding_required_nchw{};
60 };
61
validate_arguments(const ITensorInfo * src,const ITensorInfo * dst,const Size2D & kernel_dims,const PadStrideInfo & conv_info,bool has_bias,const Size2D & dilation,unsigned int num_groups)62 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
63 unsigned int num_groups)
64 {
65 const unsigned int channel_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::CHANNEL);
66
67 ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
68 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
69 ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized(src->data_type()) && has_bias);
70 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(dst);
71 ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
72 ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN);
73 ARM_COMPUTE_RETURN_ERROR_ON(num_groups == 0);
74 ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::NHWC && num_groups > 1);
75 ARM_COMPUTE_RETURN_ERROR_ON((src->dimension(channel_idx) % num_groups) != 0);
76
77 // Since there's no implicit padding added, check the total input spatial dimensions (with conv paddings) are big enough for the kernel dimensions
78 const unsigned int width_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
79 const unsigned int height_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);
80 const unsigned total_width = src->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right();
81 const unsigned total_height = src->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom();
82 ARM_COMPUTE_RETURN_ERROR_ON((total_width < kernel_dims.width) || (total_height < kernel_dims.height));
83
84 if(dst->total_size() > 0)
85 {
86 const TensorInfo tensor_info_output = dst->clone()->set_tensor_shape(compute_im2col_conv_shape(src, kernel_dims, conv_info, has_bias, dilation, num_groups == 1, num_groups));
87 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_output);
88 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
89 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(src, dst);
90 }
91
92 return Status{};
93 }
94
validate_and_configure_window(ITensorInfo * src,ITensorInfo * dst,const Size2D & kernel_dims,const PadStrideInfo & conv_info,bool has_bias,const Size2D & dilation,unsigned int num_elems_processed_per_iteration,bool is_padding_required_nchw,unsigned int num_groups)95 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
96 unsigned int num_elems_processed_per_iteration, bool is_padding_required_nchw, unsigned int num_groups)
97 {
98 ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
99
100 // Output tensor auto initialization if not yet initialized
101 TensorShape expected_output_shape = compute_im2col_conv_shape(src, kernel_dims, conv_info, has_bias, dilation, num_groups == 1, num_groups);
102
103 auto_init_if_empty(*dst, src->clone()->set_tensor_shape(expected_output_shape));
104
105 const DataLayout data_layout = src->data_layout();
106 const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
107 const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
108 const unsigned int input_width = src->dimension(width_idx);
109 const unsigned int input_height = src->dimension(height_idx);
110
111 // Configure the execute window based on the selected optimal OpenCL kernel
112 bool window_changed = false;
113 Window win;
114
115 if(data_layout == DataLayout::NHWC)
116 {
117 win = calculate_max_window(*src, Steps(num_elems_processed_per_iteration));
118 }
119 else
120 {
121 if(is_padding_required_nchw)
122 {
123 const BorderSize border(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
124 win = calculate_max_window(*src,
125 Steps(num_elems_processed_per_iteration * conv_info.stride().first, conv_info.stride().second));
126 AccessWindowStatic input_access(src,
127 -border.left,
128 -border.top,
129 ceil_to_multiple(input_width + border.right, kernel_dims.width * num_elems_processed_per_iteration),
130 input_height + border.bottom);
131 window_changed = window_changed || update_window_and_padding(win, input_access);
132 }
133 else
134 {
135 // For the generic case, CLIm2ColKernel doesn't need padding (we do not read out-of-bounds elements) so
136 // update_window_and_padding() can be skipped
137 win = calculate_max_window(*src, Steps());
138 }
139 }
140
141 // set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
142 win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
143
144 Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
145 return std::make_pair(err, win);
146 }
147
configure_opencl_kernel(const ITensorInfo * src,const Size2D & kernel_dims,const PadStrideInfo & conv_info,bool has_bias,const Size2D & dilation,unsigned int num_groups)148 Im2ColConfiguration configure_opencl_kernel(const ITensorInfo *src, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, unsigned int num_groups)
149 {
150 const DataLayout data_layout = src->data_layout();
151 const DataType data_type = src->data_type();
152 const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
153 const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
154 const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
155 const unsigned int input_width = src->dimension(width_idx);
156 const unsigned int input_height = src->dimension(height_idx);
157 const unsigned int input_channel = src->dimension(channel_idx);
158
159 const std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
160
161 // Im2Col configuration
162 std::string kernel_name = "im2col_generic_";
163 CLBuildOptions build_opts;
164 unsigned int num_elems_processed_per_iteration = 1;
165 bool is_padding_required_nchw = false;
166 const UniformQuantizationInfo qinfo = src->quantization_info().uniform();
167
168 build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
169 build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(src->element_size()));
170 build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
171 build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
172 build_opts.add_option("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(convolved_dims.first));
173 build_opts.add_option("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(convolved_dims.second));
174 build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
175 build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
176 build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
177 build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
178 build_opts.add_option("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right()));
179 build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()));
180 build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width));
181 build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height));
182 build_opts.add_option("-DSRC_DEPTH=" + support::cpp11::to_string(input_channel));
183 build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
184 build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
185 build_opts.add_option_if(num_groups > 1, "-DNUM_GROUPS=" + support::cpp11::to_string(num_groups));
186 build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(qinfo.offset), "-DPAD_VALUE=0");
187 build_opts.add_option_if(has_bias, "-DHAS_BIAS");
188
189 if(data_layout == DataLayout::NHWC)
190 {
191 num_elems_processed_per_iteration = std::min(2U, input_channel);
192 is_padding_required_nchw = false;
193
194 // Only the 3x3 and 9x9 cases are optimized for NHWC
195 if(kernel_dims == Size2D(3U, 3U))
196 {
197 kernel_name = "im2col3x3_";
198 build_opts.add_option("-DIM2COL_3X3");
199 }
200 else if(kernel_dims == Size2D(9U, 9U))
201 {
202 kernel_name = "im2col9x9_";
203 build_opts.add_option("-DIM2COL_9X9");
204 }
205 else
206 {
207 build_opts.add_option("-DIM2COL_GENERIC");
208 }
209
210 // Get boundary vector (the first/last vector with potentially a partial vector size) size
211 // If input_channel is a multiple of num_elems_processed_per_iteration, the boundary vec size is the (full) vector size
212 // otherwise, the boundary vec size is the (partial) remainder vector size
213 const unsigned int vec_size = num_elems_processed_per_iteration;
214 const unsigned int partial_vec_size = input_channel % vec_size;
215 const unsigned int boundary_vec_size = vec_size - ((vec_size - partial_vec_size) % vec_size);
216 build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vec_size));
217 build_opts.add_option("-DBOUNDARY_VECTOR_SIZE=" + support::cpp11::to_string(boundary_vec_size));
218 }
219 else
220 {
221 if(dilation == Size2D(1U, 1U))
222 {
223 const bool squared_im2col = kernel_dims.width == kernel_dims.height;
224 if(squared_im2col)
225 {
226 // Check if we can run an optimized im2col for NCHW
227 switch(kernel_dims.width)
228 {
229 case 1:
230 // Optimized im2col1x1 if stride_x = 1 and conv_info.has_padding() = false
231 if(conv_info.stride().first == 1 && !conv_info.has_padding())
232 {
233 kernel_name = "im2col1x1_stridex1_";
234 num_elems_processed_per_iteration = 4;
235 is_padding_required_nchw = true;
236 }
237 break;
238 case 3:
239 kernel_name = "im2col3x3_";
240 num_elems_processed_per_iteration = 1;
241 is_padding_required_nchw = true;
242 break;
243 case 5:
244 kernel_name = "im2col5x5_";
245 num_elems_processed_per_iteration = 1;
246 is_padding_required_nchw = true;
247 break;
248 case 11:
249 // Optimized im2col11x11 if pad_x = pad_y = 0
250 if(!conv_info.has_padding())
251 {
252 kernel_name = "im2col11x11_padx0_pady0_";
253 num_elems_processed_per_iteration = 1;
254 is_padding_required_nchw = true;
255 }
256 break;
257 default:
258 kernel_name = "im2col_generic_";
259 num_elems_processed_per_iteration = 1;
260 is_padding_required_nchw = false;
261 break;
262 }
263 }
264 else if(kernel_dims.width > 1 && !conv_info.has_padding())
265 {
266 kernel_name = "im2col_generic_padx0_pady0_";
267 num_elems_processed_per_iteration = 1;
268 is_padding_required_nchw = false;
269
270 // Optimized im2col is performed using one or more vector operations with the specified vector size
271 // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
272 // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
273 // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
274 // Using the vector size of 8, however, may be faster.
275 // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
276 // is used instead.)
277 const size_t vector_size = std::min(static_cast<size_t>(4), kernel_dims.width);
278 const size_t width_mod_vector_size = kernel_dims.width % vector_size;
279 build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
280 build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
281 }
282 }
283 }
284
285 // Append the data layout to the kernel_name
286 kernel_name += lower_string(string_from_data_layout(data_layout));
287
288 Im2ColConfiguration im2col_config;
289 im2col_config.kernel_name = kernel_name;
290 im2col_config.build_options = build_opts.options();
291 im2col_config.num_elems_processed_per_iteration = num_elems_processed_per_iteration;
292 im2col_config.is_padding_required_nchw = is_padding_required_nchw;
293
294 return im2col_config;
295 }
296 } // namespace
297
ClIm2ColKernel()298 ClIm2ColKernel::ClIm2ColKernel()
299 : _data_layout(DataLayout::UNKNOWN), _convolved_dims(), _num_elems_processed_per_iteration(1), _kernel_dims(), _conv_info(), _num_groups()
300 {
301 _type = CLKernelType::ELEMENTWISE;
302 }
303
configure(const ClCompileContext & compile_context,ITensorInfo * src,ITensorInfo * dst,const Size2D & kernel_dims,const PadStrideInfo & conv_info,bool has_bias,const Size2D & dilation,unsigned int num_groups)304 void ClIm2ColKernel::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias,
305 const Size2D &dilation,
306 unsigned int num_groups)
307 {
308 ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
309 ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups));
310
311 auto padding_info = get_padding_info({ src, dst });
312 _data_layout = src->data_layout();
313
314 const unsigned int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
315 const unsigned int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
316 const unsigned int input_width = src->dimension(width_idx);
317 const unsigned int input_height = src->dimension(height_idx);
318
319 // Select and configure the optimal OpenCL kernel to run.
320 // This function returns the OpenCL kernel's name, the arguments to pass at compile time, the number of elements processed per iteration
321 // and the padding requirement flag
322 Im2ColConfiguration im2col_config = configure_opencl_kernel(src, kernel_dims, conv_info, has_bias, dilation, num_groups);
323
324 // Create kernel
325 _kernel = create_kernel(compile_context, im2col_config.kernel_name, im2col_config.build_options);
326
327 _convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
328 _num_elems_processed_per_iteration = im2col_config.num_elems_processed_per_iteration;
329 _kernel_dims = kernel_dims; // Only needed by the Tuner
330 _conv_info = conv_info; // Only needed by the Tuner
331 _num_groups = num_groups;
332
333 // Configure kernel window
334 auto win_config = validate_and_configure_window(src, dst, kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration,
335 im2col_config.is_padding_required_nchw, num_groups);
336 ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
337 IClKernel::configure_internal(win_config.second);
338
339 // Set config_id for enabling LWS tuning
340 _config_id = im2col_config.kernel_name;
341 _config_id += "_";
342 _config_id += lower_string(string_from_data_type(src->data_type()));
343 _config_id += "_";
344 _config_id += support::cpp11::to_string(num_groups);
345 _config_id += "_";
346 _config_id += support::cpp11::to_string(dst->dimension(0));
347 _config_id += "_";
348 _config_id += support::cpp11::to_string(dst->dimension(1));
349 _config_id += "_";
350 _config_id += lower_string(string_from_data_layout(_data_layout));
351
352 ARM_COMPUTE_ERROR_ON(src->data_layout() == DataLayout::NHWC && has_padding_changed(padding_info));
353 }
354
validate(const ITensorInfo * src,const ITensorInfo * dst,const Size2D & kernel_dims,const PadStrideInfo & conv_info,bool has_bias,const Size2D & dilation,unsigned int num_groups)355 Status ClIm2ColKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
356 unsigned int num_groups)
357 {
358 ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups));
359 Im2ColConfiguration im2col_config = configure_opencl_kernel(src, kernel_dims, conv_info, has_bias, dilation, num_groups);
360 ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration,
361 im2col_config.is_padding_required_nchw, num_groups)
362 .first);
363 return Status{};
364 }
365
run_op(ITensorPack & tensors,const Window & window,cl::CommandQueue & queue)366 void ClIm2ColKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
367 {
368 ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
369 ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(IClKernel::window(), window);
370 ARM_COMPUTE_ERROR_ON(tensors.empty());
371
372 // Get initial windows
373 // Collapse in order to have (SRC_DEPTH * BATCH_SIZE) on the 3rd dimension
374 Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
375 window_collapsed.set_dimension_step(Window::DimZ, 1);
376
377 auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
378 auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
379 ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
380
381 Window window_output;
382 window_output.use_tensor_dimensions(dst->info()->tensor_shape());
383
384 const Window first_slice_3d = window_collapsed.first_slice_window_3D();
385
386 Window slice = first_slice_3d;
387 Window slice_in = first_slice_3d;
388 Window slice_out = window_output.first_slice_window_2D();
389
390 if(_data_layout == DataLayout::NHWC)
391 {
392 const Window tmp_win = window.collapse_if_possible(ICLKernel::window(), 3);
393 const int num_batches = tmp_win[3].end();
394
395 slice.set(1, Window::Dimension(0, static_cast<int>(dst->info()->tensor_shape()[1]), 1));
396 slice.set(2, Window::Dimension(0, static_cast<int>(num_batches), 1));
397 }
398 else
399 {
400 slice.set(0, Window::Dimension(0, static_cast<int>(ceil_to_multiple(_convolved_dims.first, _num_elems_processed_per_iteration)), _num_elems_processed_per_iteration));
401 slice.set(1, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
402 // Note: In case of NCHW the 3rd dimension is already set collapsing the input window
403 }
404
405 // Setup input slice
406 // The dimensions of the input are increased within the OpenCL kernel
407 slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
408 slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
409 slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
410
411 // Setup output slice
412 // The dimensions of the output are increased within the OpenCL kernel
413 slice_out.set(Window::DimX, Window::Dimension(0, 0, 0));
414 slice_out.set(Window::DimY, Window::Dimension(0, 0, 0));
415
416 unsigned int idx = num_arguments_per_3D_tensor() + (_num_groups == 1 ? num_arguments_per_2D_tensor() : num_arguments_per_3D_tensor());
417 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src->info()->strides_in_bytes()[3]));
418 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[((_num_groups == 1) ? 2 : 3)]));
419 do
420 {
421 unsigned int idx = 0;
422 add_3D_tensor_argument(idx, src, slice_in);
423 if(_num_groups == 1)
424 {
425 add_2D_tensor_argument(idx, dst, slice_out);
426 }
427 else
428 {
429 add_3D_tensor_argument(idx, dst, slice_out);
430 }
431 enqueue(queue, *this, slice, lws_hint());
432 }
433 while(window_collapsed.slide_window_slice_3D(slice) && window_output.slide_window_slice_2D(slice_out) && window_collapsed.slide_window_slice_3D(slice_in));
434 }
435 } // namespace kernels
436 } // namespace opencl
437 } // namespace arm_compute
438