Compute Library
 22.05
ClGemmMatrixMultiplyReshapedOnlyRhsKernel.cpp
Go to the documentation of this file.
1 /*
2  * Copyright (c) 2019-2022 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  */
25 
28 #include "src/core/CL/CLUtils.h"
29 #include "src/core/CL/CLValidate.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 
65 });
66 
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);
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");
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 
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);
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 
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 
183 {
184  _type = CLKernelType::GEMM;
185 }
186 
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 
324 }
325 
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 
334 void ClGemmMatrixMultiplyReshapedOnlyRhsKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
335 {
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 
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);
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
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
bool is_one(float a, float epsilon=0.00001f)
Checks if the input floating point number is 1.0f checking if the difference is within a range define...
Definition: float_ops.h:97
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
#define ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(tensor)
Definition: CLValidate.h:35
const Window & window() const
The maximum window the kernel can be executed on.
Definition: IKernel.cpp:28
Shape of a tensor.
Definition: TensorShape.h:39
Descriptor used by the GEMM kernels.
void add_2D_tensor_argument_if(bool cond, unsigned int &idx, const ICLTensor *tensor, const Window &window)
Add the passed 2D tensor&#39;s parameters to the object&#39;s kernel&#39;s arguments starting from the index idx ...
Definition: ICLKernel.h:214
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
void enqueue(cl::CommandQueue &queue, ICLKernel &kernel, const Window &window, const cl::NDRange &lws_hint=CLKernelLibrary::get().default_ndrange(), bool use_dummy_work_items=false)
Add the kernel to the command queue with the given window.
Definition: ICLKernel.cpp:32
const StringSet & options() const
Gets the current options list set.
unsigned int depth_output_gemm3d
Depth of the output tensor in case is reinterpreted as 3D.
cl::NDRange lws_hint() const
Return the Local-Workgroup-Size hint.
Definition: ICLKernel.h:384
bool preferred_dummy_work_items_support(const cl::Device &device)
Helper function to check if "dummy work-items" are preferred to have a power of two NDRange In case d...
Definition: CLHelpers.cpp:363
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
std::string to_string(T &&value)
Convert integer and float values to string.
virtual DataType data_type() const =0
Data type used for each element of the tensor.
TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo &input1, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
Calculate the matrix multiplication output shape of two tensors.
1 channel, 1 F32 per channel
#define ARM_COMPUTE_ERROR_ON(cond)
If the condition is true then an error message is printed and an exception thrown.
Definition: Error.h:466
const std::string & string_from_activation_func(ActivationLayerInfo::ActivationFunction act)
Translates a given activation function to a string.
Definition: Utils.cpp:163
static CLKernelLibrary & get()
Access the KernelLibrary singleton.
GEMM LHS (Left Hand Side) matrix information.
Definition: Types.h:2054
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override
Enqueue the OpenCL kernel to process the given window on the passed OpenCL command queue...
#define ARM_COMPUTE_ERROR_THROW_ON(status)
Definition: Error.h:455
Describe one of the image&#39;s dimensions with a start, end and step.
Definition: Window.h:79
Status class.
Definition: Error.h:52
std::string lower_string(const std::string &val)
Lower a given string.
Definition: Utils.cpp:351
Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const PadStrideInfo &conv_info)
#define ARM_COMPUTE_RETURN_ERROR_ON(cond)
If the condition is true, an error is returned.
Definition: Error.h:296
bool export_to_cl_image
True if the reshaped rhs has to be exported to cl_image.
Definition: Types.h:2081
Copyright (c) 2017-2022 Arm Limited.
1 channel, 1 F16 per channel
TensorType get_post_op_arg_type(size_t index)
Get post op argument TensorType from post op argument index in a flattened, ordered post op argument ...
Definition: PostOpUtils.h:79
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:159
void add_option(std::string option)
Adds option to the existing build option list.
const ITensor * get_const_tensor(int id) const
Get constant tensor of a given id.
Definition: ITensorPack.cpp:54
std::string upper_string(const std::string &val)
Raise a given string to upper case.
Definition: Utils.cpp:358
static Status 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)
Static function to check if given info will lead to a valid configuration.
cl::Kernel create_kernel(const CLCompileContext &ctx, const std::string &kernel_name, const std::set< std::string > &build_opts=std::set< std::string >())
Creates an opencl kernel using a compile context.
Definition: CLHelpers.cpp:391
const std::string & string_from_data_type(DataType dt)
Convert a data type identity into a string.
Definition: Utils.cpp:135
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
#define ARM_COMPUTE_UNUSED(...)
To avoid unused variables warnings.
Definition: Error.h:152
Status validate_image2d_support_on_rhs(const ITensorInfo &tensor_reshaped_info, const GEMMRHSMatrixInfo &rhs_info)
Utility function to validate the image2d OpenCL object support on the RHS reshaped matrix...
GEMM RHS (Right Hand Side) matrix information.
Definition: Types.h:2069
std::string float_to_string_with_full_precision(float val)
Create a string with the float in full precision.
Definition: Utils.h:1124
std::string get_cl_type_from_data_type(const DataType &dt)
Translates a tensor data type to the appropriate OpenCL type.
Definition: CLHelpers.cpp:39
TensorShape compute_rhs_reshaped_shape(const ITensorInfo &a, const GEMMRHSMatrixInfo &rhs_info)
Calculate the Right Hand Side matrix reshaped shape.
bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())
Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...
bool reinterpret_input_as_3d
Flag used to reinterpret the input as 3D.
virtual std::unique_ptr< T > clone() const =0
Provide a clone of the current object of class T.
void add_option_if(bool cond, std::string option)
Adds option if a given condition is true;.
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
bool slide_window_slice_3D(Window &slice) const
Slide the passed 3D window slice.
Definition: Window.h:348
void 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)
Initialise the kernel&#39;s input and output.
#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)
Definition: Validate.h:915
bool has_padding_changed(const std::unordered_map< const ITensorInfo *, PaddingSize > &padding_map)
Check if the previously stored padding info has changed after configuring a kernel.
Definition: Utils.cpp:601
CLCompileContext class.
bool has_pad_y
Flag used to indicate if the input/output tensors have internal pad on the y direction.
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
void add_2D_tensor_argument(unsigned int &idx, const ICLTensor *tensor, const Window &window)
Add the passed 2D tensor&#39;s parameters to the object&#39;s kernel&#39;s arguments starting from the index idx...
Definition: ICLKernel.h:203
std::pair< Status, Window > validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst)
ITensor * get_tensor(int id)
Get tensor of a given id from the pac.
Definition: ITensorPack.cpp:64
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)
Definition: Validate.h:439
#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:541
#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:788
std::unordered_map< const ITensorInfo *, PaddingSize > get_padding_info(std::initializer_list< const ITensorInfo *> infos)
Stores padding information before configuring a kernel.
Definition: Utils.cpp:586
bool is_zero(float a, float epsilon=0.00001f)
Checks if the input floating point number is 0.0f checking if the difference is within a range define...
Definition: float_ops.h:109
experimental::PostOpList< ITensorInfo * > post_ops
(EXPERIMENTAL_POST_OPS) Specifies a list of post ops to be fused after the main op.
#define ARM_COMPUTE_RETURN_ERROR_ON_MSG(cond, msg)
If the condition is true, an error is returned.
Definition: Error.h:244
Tensor packing service.
Definition: ITensorPack.h:39
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:157
cl::Image2D create_image2d_from_buffer(const cl::Context &ctx, const cl::Buffer &buffer, const TensorShape &shape2d, DataType data_type, size_t image_row_pitch)
Create a cl::Image2D object from an OpenCL buffer.
Definition: CLUtils.cpp:35
unsigned int m0
Number of rows processed by the matrix multiplication.
Definition: Types.h:2061
Window first_slice_window_3D() const
First 3D slice of the window.
Definition: Window.h:304
std::string kernel_name
Describe a multidimensional execution window.
Definition: Window.h:39
Convolution using GEMM.
#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)
Definition: Validate.h:201
SimpleTensor< T > slice(const SimpleTensor< T > &src, Coordinates starts, Coordinates ends)