1 /*
2 * Copyright (c) 2019-2023 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 #include "src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h"
25
26 #include "arm_compute/core/CL/ICLTensor.h"
27 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
28 #include "src/core/CL/CLUtils.h"
29 #include "src/core/CL/CLValidate.h"
30 #include "src/core/experimental/PostOpUtils.h"
31 #include "src/core/helpers/AutoConfiguration.h"
32 #include "src/core/helpers/WindowHelpers.h"
33 #include "src/core/utils/helpers/float_ops.h"
34 #include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h"
35 #include "support/Cast.h"
36 #include "support/StringSupport.h"
37
38 namespace arm_compute
39 {
40 namespace opencl
41 {
42 namespace kernels
43 {
44 namespace
45 {
46 using ElementsProcessed = Steps;
47
48 const auto post_op_utils = experimental::PostOpCLKernelUtils(
49 {
50 // PostOp sequence -> {Kernel Postfix, PostOp Slots}
51 { {}, { "", {} } },
52 { { experimental::PostOpType::Activation }, { "", { 1 } } },
53
54 { { experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 2 } } },
55 { { experimental::PostOpType::Eltwise_PRelu }, { "_post_act_eltwise_op_act", { 2 } } },
56
57 { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 1, 2 } } },
58 { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_PRelu }, { "_post_act_eltwise_op_act", { 1, 2 } } },
59
60 { { experimental::PostOpType::Eltwise_Add, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 2, 3 } } },
61 { { experimental::PostOpType::Eltwise_PRelu, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 2, 3 } } },
62
63 { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_Add, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 1, 2, 3 } } },
64 { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_PRelu, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 1, 2, 3 } } }
65 });
66
validate_arguments(const ITensorInfo * src0,const ITensorInfo * src1,const ITensorInfo * src2,const ITensorInfo * dst,float alpha,float beta,const GEMMLHSMatrixInfo & lhs_info,const GEMMRHSMatrixInfo & rhs_info,const GEMMKernelInfo & gemm_info)67 Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
68 const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
69 {
70 ARM_COMPUTE_UNUSED(alpha);
71 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
72 ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src0);
73 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32);
74 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1);
75 ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
76 ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
77 ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_info.m0 < 1 || lhs_info.m0 > 8, "Only 1,2,3,4,5,6,7,8 are supported for m0");
78 ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16 || rhs_info.k0 < 2);
79 ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0");
80 ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.n0 > 16 || rhs_info.n0 < 2);
81 ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0");
82 ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (src2 != nullptr)
83 && (!gemm_info.broadcast_bias),
84 "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D");
85 ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported");
86 ARM_COMPUTE_RETURN_ON_ERROR(gemm::validate_image2d_support_on_rhs(*src1, rhs_info));
87 ARM_COMPUTE_RETURN_ERROR_ON_MSG(!post_op_utils.is_post_op_sequence_supported(gemm_info.post_ops), "The sequence of Post Ops is not supported");
88
89 const unsigned int m = gemm_info.m;
90 const unsigned int n = gemm_info.n;
91 const unsigned int k = gemm_info.k;
92
93 TensorShape tensor_shape1{ src1->tensor_shape() };
94 tensor_shape1.set(0, n);
95 tensor_shape1.set(1, k);
96
97 if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
98 {
99 const unsigned int src2_dim0 = src2->dimension(0);
100 const unsigned int src2_dim1 = src2->dimension(1);
101
102 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src0);
103 if(gemm_info.broadcast_bias)
104 {
105 ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
106 }
107 else
108 {
109 ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
110 }
111 }
112
113 const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1);
114
115 const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info));
116
117 ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != k);
118 if(gemm_info.reinterpret_input_as_3d)
119 {
120 ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != m);
121 }
122 else
123 {
124 ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != m);
125 }
126 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
127
128 if(dst->total_size() != 0)
129 {
130 const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info));
131 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
132 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst);
133 ARM_COMPUTE_RETURN_ERROR_ON_MSG(!post_op_utils.are_post_op_shapes_compliant(dst, gemm_info.post_ops), "The Post Op shapes are not compliant");
134 }
135
136 return Status{};
137 }
138
validate_and_configure_window(ITensorInfo * src0,ITensorInfo * src1,ITensorInfo * src2,ITensorInfo * dst,const GEMMLHSMatrixInfo & lhs_info,const GEMMRHSMatrixInfo & rhs_info,const GEMMKernelInfo & gemm_info,ElementsProcessed & num_elements_processed)139 Window validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info,
140 const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed)
141 {
142 ARM_COMPUTE_UNUSED(src0, src1, src2);
143 unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
144 unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
145 bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
146 bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
147
148 // In case both input and dst have to be reinterpreted as 3D tensors,
149 // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
150 // This approach should only be used when the input/dst tensors have pad on the y direction
151 if((reinterpret_input_as_3d == reinterpret_output_as_3d) && gemm_info.has_pad_y)
152 {
153 reinterpret_output_as_3d = false;
154 }
155
156 TensorInfo tmp_info(*dst);
157
158 if(reinterpret_output_as_3d)
159 {
160 // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
161 // the window needs to be constructed on the 2D collapsed version of the tensor
162 TensorShape tmp_shape(dst->tensor_shape());
163 tmp_shape.collapse(2U, 1U);
164 tmp_info.set_tensor_shape(tmp_shape);
165 }
166
167 // Configure kernel window
168 num_elems_processed_per_iteration_x = rhs_info.n0;
169 num_elems_processed_per_iteration_y = lhs_info.m0;
170
171 Window win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
172
173 // Collapse along the Z direction
174 // This collapse needs to be here in order to tune the Z dimension of LWS
175 const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
176 Window collapsed = win.collapse(win, dimension_to_collapse);
177
178 return collapsed;
179 }
180 } // namespace
181
ClGemmMatrixMultiplyReshapedOnlyRhsKernel()182 ClGemmMatrixMultiplyReshapedOnlyRhsKernel::ClGemmMatrixMultiplyReshapedOnlyRhsKernel()
183 {
184 _type = CLKernelType::GEMM;
185 }
186
configure(const CLCompileContext & compile_context,const ITensorInfo * src0,const ITensorInfo * src1,const ITensorInfo * src2,ITensorInfo * dst,float alpha,float beta,const GEMMLHSMatrixInfo & lhs_info,const GEMMRHSMatrixInfo & rhs_info,const GEMMKernelInfo & gemm_info)187 void ClGemmMatrixMultiplyReshapedOnlyRhsKernel::configure(const CLCompileContext &compile_context,
188 const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta,
189 const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
190 {
191 ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
192
193 // dst tensor auto initialization if not yet initialized
194 auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)));
195
196 ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
197
198 _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
199 _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
200 _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
201 _add_bias = src2 != nullptr;
202 _export_to_cl_image = rhs_info.export_to_cl_image;
203 _has_pad_y = gemm_info.has_pad_y;
204 _num_post_op_args = gemm_info.post_ops.total_num_arguments();
205
206 auto padding_info = get_padding_info({ src0, src1, src2, dst });
207
208 // In case both input and dst have to be reinterpreted as 3D tensors,
209 // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
210 if((_reinterpret_input_as_3d == _reinterpret_output_as_3d) && _has_pad_y)
211 {
212 _reinterpret_input_as_3d = false;
213 _reinterpret_output_as_3d = false;
214 }
215
216 // Check if we need to slide the matrix B
217 const unsigned int num_dimensions_src0 = src0->num_dimensions();
218 _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0);
219
220 ElementsProcessed num_elements_processed{};
221
222 // Configure kernel window
223 Window win = validate_and_configure_window(src0->clone().get(), src1->clone().get(), (src2 != nullptr) ? src2->clone().get() : nullptr, dst->clone().get(), lhs_info, rhs_info, gemm_info,
224 num_elements_processed);
225 ICLKernel::configure_internal(win);
226
227 // If _reinterpret_input_as_3d = reinterpret_output_as_3d = true,
228 // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel.
229 // This means that the actual m used by the kernel is given by dst->dimension(1) and not by gemm_info.m
230 const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1);
231
232 // These variables are used only if gemm_info.has_pad_y == true
233 const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1);
234 const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2);
235
236 // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads.
237 // NOTE: This might have implications on heuristics and performance
238 const unsigned int internal_m0 = std::min(internal_m, lhs_info.m0);
239
240 // 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.
241 const unsigned int partial_store_m0 = internal_m % internal_m0;
242 const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
243 _m = internal_m;
244 _n = gemm_info.n;
245 _k = gemm_info.k;
246 // Create build options
247 CLBuildOptions build_opts;
248 build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type()));
249 build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
250 build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
251 build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
252 build_opts.add_option_if(gemm_info.broadcast_bias, "-DBROADCAST_BIAS");
253 build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2)));
254 build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
255 build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
256 build_opts.add_option_if(rhs_info.export_to_cl_image, "-DOPENCL_IMAGE_SUPPORT");
257 build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(src1->dimension(1)));
258 build_opts.add_option("-DM0=" + support::cpp11::to_string(internal_m0));
259 build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
260 build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
261 build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
262 build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
263 build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
264 if(_has_pad_y)
265 {
266 build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
267 build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
268 build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d));
269 build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d));
270 }
271 // If post_ops are used, then we disable the use of gemm_info.activation_info
272 if(gemm_info.post_ops.size() > 0)
273 {
274 post_op_utils.set_post_ops_cl_build_options(build_opts, gemm_info.post_ops);
275 }
276 else
277 {
278 build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
279 build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
280 build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
281 }
282
283 std::string kernel_name("gemm_mm_reshaped_only_rhs_");
284 kernel_name += rhs_info.transpose ? "t" : "nt";
285 kernel_name += rhs_info.export_to_cl_image ? "_texture" : "";
286 post_op_utils.set_post_ops_cl_kernel_name(kernel_name, gemm_info.post_ops);
287
288 // A macro guard to compile ONLY the kernel of interest
289 build_opts.add_option("-D" + upper_string(kernel_name));
290
291 // Create kernel
292 _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
293
294 // Set config_id for enabling LWS tuning
295 _config_id = kernel_name;
296 _config_id += "_";
297 _config_id += (_has_pad_y ? "" : "no_pad_y_");
298 _config_id += (_add_bias ? "add_bias_" : "");
299 _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : "");
300 _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
301 _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
302 _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : "");
303 _config_id += lower_string(string_from_data_type(src0->data_type()));
304 _config_id += "_";
305 _config_id += support::cpp11::to_string(dst->dimension(1));
306 _config_id += "_";
307 _config_id += support::cpp11::to_string(dst->dimension(0));
308 _config_id += "_";
309 _config_id += support::cpp11::to_string(gemm_info.k);
310 _config_id += "_";
311 _config_id += support::cpp11::to_string(dst->dimension(2));
312 _config_id += "_";
313 _config_id += support::cpp11::to_string(lhs_info.m0);
314 _config_id += "_";
315 _config_id += support::cpp11::to_string(rhs_info.n0);
316 _config_id += "_";
317 _config_id += support::cpp11::to_string(rhs_info.k0);
318 _config_id += "_";
319 _config_id += support::cpp11::to_string(rhs_info.h0);
320 _config_id += "_";
321 _config_id += support::cpp11::to_string(rhs_info.interleave);
322
323 ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
324 }
325
validate(const ITensorInfo * src0,const ITensorInfo * src1,const ITensorInfo * src2,const ITensorInfo * dst,float alpha,float beta,const GEMMLHSMatrixInfo & lhs_info,const GEMMRHSMatrixInfo & rhs_info,const GEMMKernelInfo & gemm_info)326 Status ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
327 const GEMMLHSMatrixInfo &lhs_info,
328 const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
329 {
330 ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
331 return Status{};
332 }
333
run_op(ITensorPack & tensors,const Window & window,cl::CommandQueue & queue)334 void ClGemmMatrixMultiplyReshapedOnlyRhsKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
335 {
336 ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
337 ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
338
339 const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
340 const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
341 const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
342 auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
343
344 ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
345 ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr);
346
347 if(src1->info()->num_dimensions() < 3)
348 {
349 // The stride_z for matrix B must be zero if we do not slice
350 ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
351 }
352
353 const size_t lhs_idx_batch_size = _reinterpret_input_as_3d && !_has_pad_y ? 3u : 2u;
354 const size_t rhs_idx_batch_size = 2u;
355 const size_t bia_idx_batch_size = 2u;
356 const size_t out_idx_batch_size = _reinterpret_output_as_3d && !_has_pad_y ? 3u : 2u;
357
358 Window slice = window.first_slice_window_3D();
359 Window slice_matrix_b = slice;
360
361 slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
362 slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
363
364 // Get cross plane pads
365 const unsigned int total_cross_plane_pad_lhs = src0->info()->padding().top + src0->info()->padding().bottom;
366 const unsigned int total_cross_plane_pad_out = dst->info()->padding().top + dst->info()->padding().bottom;
367
368 // The execution should fail if we try to run with has_pad_y = false but we have padding in either the LHS or DST tensor
369 ARM_COMPUTE_ERROR_ON(!_has_pad_y && ((total_cross_plane_pad_lhs != 0) || (total_cross_plane_pad_out != 0)));
370
371 cl::Image2D src1_image2d;
372
373 if(_export_to_cl_image)
374 {
375 const TensorShape shape2d(src1->info()->dimension(0) / 4, src1->info()->dimension(1) * src1->info()->dimension(2));
376 const size_t image_row_pitch = src1->info()->strides_in_bytes()[1];
377
378 src1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), src1->cl_buffer(), shape2d, src1->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
379 }
380
381 do
382 {
383 Window slice_b = slice;
384 // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
385 // This scenario can happen when the matrix multiplication is used to perform a convolution operation
386 if(!_slide_matrix_b)
387 {
388 slice_b = slice_matrix_b;
389 }
390
391 unsigned int idx = 0;
392
393 // LHS buffer
394 add_2D_tensor_argument(idx, src0, slice);
395
396 // RHS buffer or RHS OpenCL image (_export_to_cl_image == true)
397 if(_export_to_cl_image)
398 {
399 _kernel.setArg(idx++, src1_image2d);
400 }
401 else
402 {
403 add_2D_tensor_argument(idx, src1, slice_b);
404 }
405
406 // Bias buffer (_add_bias == true)
407 add_2D_tensor_argument_if(_add_bias, idx, src2, slice);
408
409 // dst buffer
410 add_2D_tensor_argument(idx, dst, slice);
411
412 // post op argument buffers
413 for(size_t i = 0; i < _num_post_op_args; ++i)
414 {
415 const auto post_op_arg = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(experimental::get_post_op_arg_type(i)));
416 add_2D_tensor_argument(idx, post_op_arg, slice);
417 }
418
419 // LHS stride_z
420 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[lhs_idx_batch_size]));
421
422 // RHS stride_z (not used if _export_to_cl_image == true)
423 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[rhs_idx_batch_size]));
424
425 // Bias stride_z (if _add_bias == true)
426 if(_add_bias)
427 {
428 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[bia_idx_batch_size]));
429 }
430
431 // dst stride_z
432 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[out_idx_batch_size]));
433 // post op argument stride_z
434 for(size_t i = 0; i < _num_post_op_args; ++i)
435 {
436 const auto post_op_arg = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(experimental::get_post_op_arg_type(i)));
437 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(post_op_arg->info()->strides_in_bytes()[2]));
438 }
439
440 // Cross-plan padding (if _reinterpret_input_as_3d = true)
441 if(_reinterpret_input_as_3d && _has_pad_y)
442 {
443 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad_lhs));
444 }
445
446 // Cross-plan padding (if reinterpret_output_as_3d = true)
447 if(_reinterpret_output_as_3d && _has_pad_y)
448 {
449 _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad_out));
450 }
451
452 // Pass m, n and k at runtime as signed ints, to ensure results of any subractions they could be operand in, would still be signed.
453 _kernel.setArg<cl_int>(idx++, _m);
454 _kernel.setArg<cl_int>(idx++, _n);
455 _kernel.setArg<cl_int>(idx++, _k);
456
457 enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
458 }
459 while(window.slide_window_slice_3D(slice));
460 }
461 } // namespace kernels
462 } // namespace opencl
463 } // namespace arm_compute
464