Compute Library
 22.11
ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.cpp
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25 
32 #include "arm_compute/core/Utils.h"
35 
39 
40 #include "support/Cast.h"
41 #include "support/StringSupport.h"
42 
43 #include <tuple>
44 
45 namespace arm_compute
46 {
47 namespace opencl
48 {
49 namespace kernels
50 {
51 using namespace misc::shape_calculator;
52 
53 namespace
54 {
55 using ElementsProcessed = Steps;
56 
57 Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info,
58  const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
59  const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
60 {
61  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
63  if(src0->data_type() == DataType::QASYMM8)
64  {
66  }
67  else
68  {
70  }
71  ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
72  ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
73 
74  const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info;
75  const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info;
76  const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
77 
78  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");
79  ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8);
80  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");
81  ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for quantized GEMM");
82 
83  const int m = gemm_info.m;
84  const int n = gemm_info.n;
85  const int k = gemm_info.k;
86 
87  TensorShape tensor_shape1{ src1->tensor_shape() };
88  tensor_shape1.set(0, n);
89  tensor_shape1.set(1, k);
90 
91  const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1);
92  const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
93 
94  ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != static_cast<unsigned int>(k));
95  if(gemm_info.reinterpret_input_as_3d)
96  {
97  ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != static_cast<unsigned int>(m));
98  }
99  else
100  {
101  ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != static_cast<unsigned int>(m));
102  }
103  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
104 
105  const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info);
106  if(dst->total_size() != 0)
107  {
108  const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_dst_shape);
109  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
110  if(output_stage.type == GEMMLowpOutputStageType::NONE)
111  {
113  }
114  else
115  {
117  }
118  }
119 
120  if(bias != nullptr)
121  {
123  ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
124  ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != bias->dimension(0));
125  }
126 
128  "Only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT is supported");
129 
130  // Checks performed if the dst stage needs to be fused
132  {
133  // If a_offset == 0, vector_sum_col can be a nullptr
134  if(gemm_info.a_offset != 0)
135  {
137  ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != expected_dst_shape[0]);
138  }
139 
140  // If b_offset == 0, vector_sum_row can be a nullptr
141  if(gemm_info.b_offset != 0)
142  {
144 
145  // Check if mm result is a 3D reinterpretation
146  const bool reinterpret_as_3d = expected_dst_shape.num_dimensions() > 1 && expected_dst_shape.y() != vector_sum_row->tensor_shape().x();
147 
148  // Validate input
149  ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (expected_dst_shape[1] * expected_dst_shape[2]));
150  ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != expected_dst_shape[1]);
151 
152  if(expected_dst_shape.num_dimensions() > 1)
153  {
154  const unsigned int dst_batch_idx = reinterpret_as_3d ? 3 : 2;
155 
156  TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
157  vector_sum_row_shape.collapse_from(1);
158  TensorShape collapsed_dst_shape(expected_dst_shape);
159  collapsed_dst_shape.collapse_from(dst_batch_idx);
160 
161  ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != collapsed_dst_shape[dst_batch_idx],
162  "vector_sum_row must have the same number of batches of dst tensor");
163 
164  if(gemm_info.a_offset != 0)
165  {
166  TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
167  vector_sum_col_shape.collapse_from(1);
168 
169  ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
170  "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");
171  }
172  }
173  }
174 
175  if(dst->total_size() != 0)
176  {
177  ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != dst->data_type());
178  }
179  ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
180 
181  if(output_multipliers != nullptr && output_shifts != nullptr)
182  {
184  ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1);
186  ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1);
187  if(output_stage.is_quantized_per_channel)
188  {
189  ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != output_shifts->dimension(0));
190  ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != output_multipliers->dimension(0));
191  }
192  }
193  }
194  return Status{};
195 }
196 
197 std::pair<Status, Window> validate_and_configure_window(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, const GEMMKernelInfo &gemm_info,
198  ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias,
199  ITensorInfo *output_multipliers, ITensorInfo *output_shifts, ElementsProcessed &num_elements_processed)
200 {
201  const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
202 
203  unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
204  unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
205  bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
206  bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d != 0);
207 
208  Window win{};
209  Window win_out{};
210  bool window_changed = false;
211 
212  // In case both input and dst have to be reinterpreted as 3D tensors,
213  // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
214  if(reinterpret_input_as_3d == reinterpret_output_as_3d)
215  {
216  reinterpret_output_as_3d = false;
217  }
218 
219  // dst tensor auto initialization if not yet initialized
220  const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info);
221  if(output_stage.type != GEMMLowpOutputStageType::NONE)
222  {
223  auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(output_stage.output_data_type));
224  }
225  else
226  {
227  auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(DataType::S32));
228  }
229 
230  TensorInfo tmp_info(*dst);
231 
232  if(reinterpret_output_as_3d)
233  {
234  // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
235  // the window needs to be constructed on the 2D collapsed version of the tensor
236  TensorShape tmp_shape(dst->tensor_shape());
237  tmp_shape.collapse(2U, 1U);
238  tmp_info.set_tensor_shape(tmp_shape);
239  }
240 
241  // Configure kernel window
242  num_elems_processed_per_iteration_x = gemm_info.rhs_info.n0;
243  num_elems_processed_per_iteration_y = gemm_info.lhs_info.m0;
244 
245  win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
246  win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
247 
249  {
250  if(gemm_info.a_offset != 0)
251  {
252  AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration_x);
253  window_changed = window_changed || update_window_and_padding(win_out, vector_sum_col_access);
254  }
255  // No access window needed for vector_sum_row
256  ARM_COMPUTE_UNUSED(vector_sum_row);
257 
258  if(bias != nullptr)
259  {
260  AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration_x);
261  window_changed = window_changed || update_window_and_padding(win_out, bias_access);
262  }
263 
264  if(output_multipliers != nullptr && output_stage.is_quantized_per_channel)
265  {
266  AccessWindowHorizontal output_multipliers_access(output_multipliers, 0, num_elems_processed_per_iteration_x);
267  AccessWindowHorizontal output_shifts_access(output_shifts, 0, num_elems_processed_per_iteration_x);
268  window_changed = window_changed || update_window_and_padding(win_out, output_multipliers_access, output_shifts_access);
269  }
270  }
271 
272  // Collapse along the Z direction
273  // This collapse needs to be here in order to tune the Z dimension of LWS
274  Window collapsed = win;
275  const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
276  collapsed = win.collapse(win, dimension_to_collapse);
277 
278  Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
279  return std::make_pair(err, collapsed);
280 }
281 } // namespace
282 
284 {
285  _type = CLKernelType::GEMM;
286 }
287 
289  const GEMMKernelInfo &gemm_info,
290  ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias,
291  ITensorInfo *output_multipliers, ITensorInfo *output_shifts)
292 {
293  ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
294  ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts));
295 
296  auto padding_info = get_padding_info({ src0, src1, dst, vector_sum_row });
297  const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info;
298  const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info;
299  const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
300  const int32_t a_offset = gemm_info.a_offset;
301  const int32_t b_offset = gemm_info.b_offset;
302 
303  _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
304  _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d != 0);
305  _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
306  _is_quantized_per_channel = output_stage.is_quantized_per_channel;
307 
308  // In case both input and dst have to be reinterpreted as 3D tensors,
309  // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
310  if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
311  {
312  _reinterpret_input_as_3d = false;
313  _reinterpret_output_as_3d = false;
314  }
315 
316  // Check if we need to slide the matrix B
317  const unsigned int num_dimensions_src0 = src0->num_dimensions();
318  _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0);
319 
320  ElementsProcessed num_elements_processed{};
321 
322  // Configure kernel window
323  auto win_config = validate_and_configure_window(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts, num_elements_processed);
324  ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
325  ICLKernel::configure_internal(win_config.second);
326 
327  // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true,
328  // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel.
329  // This means that the actual m used by the kernel is given by dst->dimension(1) and not by gemm_info.m
330  const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1);
331 
332  // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads.
333  // NOTE: This might have implications on heuristics and performance
334  const unsigned int internal_m0 = std::min(internal_m, lhs_info.m0);
335 
336  // 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.
337  const unsigned int partial_store_m0 = internal_m % internal_m0;
338  const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
339 
340  // Create build options
341  CLBuildOptions build_opts;
342  build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
343  build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
344  build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(dst->dimension(1)));
345  build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(dst->dimension(2)));
346  build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2)));
347  build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
348  build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
349  build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m));
350  build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n));
351  build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k));
352  build_opts.add_option("-DM0=" + support::cpp11::to_string(internal_m0));
353  build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
354  build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
355  build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0));
356  build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
357  build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
358  build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type()));
359  build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_dot8_acc_type_from_data_type(src0->data_type()));
360 
361  std::string kernel_name("gemmlowp_mm_reshaped_only_rhs_");
362  kernel_name += rhs_info.transpose ? "t" : "nt";
363 
365  {
366  kernel_name += "_fused_output_stage_fixedpoint";
367  _fuse_output_stage = true;
368  // If a_offset == 0, vector_sum_col can be a nullptr
369  if(a_offset != 0 && vector_sum_col != nullptr)
370  {
371  build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset));
372  build_opts.add_option_if(vector_sum_col->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES");
373  }
374  // If b_offset == 0, vector_sum_row can be a nullptr
375  build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset));
376  build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * src0->dimension(0)));
377  build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");
378  build_opts.add_option("-DRESULT_OFFSET=" + support::cpp11::to_string(output_stage.gemmlowp_offset));
379  // In case of _is_quantized_per_channel, RESULT_MULTIPLIER and RESULT_SHIFT are not utilized, but they are passed as a part of T_QUANTIZE8 macro.
380  if(!_is_quantized_per_channel)
381  {
382  build_opts.add_option("-DRESULT_MULTIPLIER=" + support::cpp11::to_string(output_stage.gemmlowp_multipliers[0]));
383  build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage.gemmlowp_shifts[0]));
384  }
385  else
386  {
387  build_opts.add_option("-DRESULT_MULTIPLIER=0");
388  build_opts.add_option("-DRESULT_SHIFT=0");
389  }
390  build_opts.add_option_if(_is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION");
391 
392  const int min = output_stage.gemmlowp_min_bound;
393  const int max = output_stage.gemmlowp_max_bound;
394 
395  PixelValue min_val{};
396  PixelValue max_val{};
397  std::tie(min_val, max_val) = get_min_max(dst->data_type());
398  build_opts.add_option_if(min != min_val.get<int32_t>(), "-DMIN_BOUND=" + support::cpp11::to_string(min));
399  build_opts.add_option_if(max != max_val.get<int32_t>(), "-DMAX_BOUND=" + support::cpp11::to_string(max));
400  }
401 
402  // A macro guard to compile ONLY the kernel of interest
403  build_opts.add_option("-D" + upper_string(kernel_name));
404 
405  // Create kernel
406  _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
407 
408  // Set config_id for enabling LWS tuning
409  _config_id = kernel_name;
410  _config_id += "_";
411  _config_id += dot8_supported(CLKernelLibrary::get().get_device()) ? "_dot8" : "";
412  _config_id += "_";
413  _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
414  _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
415  _config_id += support::cpp11::to_string(dst->dimension(1));
416  _config_id += "_";
417  _config_id += support::cpp11::to_string(dst->dimension(0));
418  _config_id += "_";
419  _config_id += support::cpp11::to_string(gemm_info.k);
420  _config_id += "_";
421  _config_id += support::cpp11::to_string(dst->dimension(2));
422  _config_id += "_";
423  _config_id += support::cpp11::to_string(lhs_info.m0);
424  _config_id += "_";
425  _config_id += support::cpp11::to_string(rhs_info.n0);
426  _config_id += "_";
427  _config_id += support::cpp11::to_string(rhs_info.k0);
428  _config_id += "_";
429  _config_id += support::cpp11::to_string(rhs_info.h0);
430  _config_id += "_";
431  _config_id += support::cpp11::to_string(rhs_info.interleave);
433 }
434 
436  const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
437  const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
438 {
439  ElementsProcessed num_elements_processed{};
440  ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts));
442  src1->clone().get(),
443  dst->clone().get(),
444  gemm_info,
445  vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr,
446  vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
447  bias != nullptr ? bias->clone().get() : nullptr,
448  output_multipliers != nullptr ? output_multipliers->clone().get() : nullptr,
449  output_shifts != nullptr ? output_shifts->clone().get() : nullptr,
450  num_elements_processed)
451  .first);
452 
453  return Status{};
454 }
455 
456 void ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
457 {
460 
461  const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
462  const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
463  const auto bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_BIAS));
464  const auto vector_sum_col = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM));
465  const auto vector_sum_row = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM));
466  const auto output_shifts = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SHIFTS));
467  const auto output_multipliers = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_MULTIPLIERS));
468  auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
469 
470  if(src1->info()->num_dimensions() < 3)
471  {
472  // The stride_z for matrix B must be zero if we do not slice
473  ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
474  }
475 
477  Window slice_matrix_b = slice;
478 
479  slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
480  slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
481 
482  if(_reinterpret_input_as_3d)
483  {
484  // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
485  const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3;
486  const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom;
487  _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
488  }
489 
490  if(_reinterpret_output_as_3d)
491  {
492  // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor
493  const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0);
494  const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom;
495  _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
496  }
497 
498  // Set window for vector_sum_col
499  Window win_vector_sum_col = slice;
500  win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
501  win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
502 
503  // Set window for vector_sum_row
504  Window win_vector_sum_row = slice;
505  win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
506  win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
507  win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
508 
509  Window biases_slice = slice;
510  biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1));
511  biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1));
512 
513  do
514  {
515  Window slice_b = slice;
516  // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
517  // This scenario can happen when the matrix multiplication is used to perform a convolution operation
518  if(!_slide_matrix_b)
519  {
520  slice_b = slice_matrix_b;
521  }
522 
523  unsigned int idx = 0;
524  add_2D_tensor_argument(idx, src0, slice);
525  add_2D_tensor_argument(idx, src1, slice_b);
526  add_2D_tensor_argument(idx, dst, slice);
527  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2]));
528  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2]));
529  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2]));
530  if(_reinterpret_input_as_3d)
531  {
532  // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
533  idx++;
534  }
535 
536  if(_reinterpret_output_as_3d)
537  {
538  // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor
539  idx++;
540  }
541 
542  if(_fuse_output_stage)
543  {
544  add_2D_tensor_argument_if((vector_sum_col != nullptr), idx, vector_sum_col, win_vector_sum_col);
545  add_2D_tensor_argument_if((vector_sum_row != nullptr), idx, vector_sum_row, win_vector_sum_row);
546  add_1D_tensor_argument_if((bias != nullptr), idx, bias, biases_slice);
547  add_1D_tensor_argument_if(_is_quantized_per_channel, idx, output_multipliers, biases_slice);
548  add_1D_tensor_argument_if(_is_quantized_per_channel, idx, output_shifts, biases_slice);
549  }
550  enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
551  }
552  while(window.slide_window_slice_3D(slice));
553 }
554 } // namespace kernels
555 } // namespace opencl
556 } // namespace arm_compute
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
Class describing the value of a pixel for any image format.
Definition: PixelValue.h:34
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
const Window & window() const
The maximum window the kernel can be executed on.
Definition: IKernel.cpp:28
Quantize using a fixed point multiplication.
bool dot8_supported(const cl::Device &device)
Helper function to check whether the cl_arm_integer_dot_product_int8 extension is supported...
Definition: CLHelpers.cpp:241
Descriptor used by the GEMM kernels.
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.
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:367
std::string get_cl_dot8_acc_type_from_data_type(const DataType &dt)
Translates a tensor data type to the appropriate OpenCL dot8 accumulator type.
Definition: CLHelpers.cpp:175
#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.
void configure(const CLCompileContext &compile_context, const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, const GEMMKernelInfo &gemm_info, ITensorInfo *vector_sum_col=nullptr, const ITensorInfo *vector_sum_row=nullptr, ITensorInfo *bias=nullptr, ITensorInfo *output_multipliers=nullptr, ITensorInfo *output_shifts=nullptr)
Initialise the kernel&#39;s source and destination.
#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
static CLKernelLibrary & get()
Access the KernelLibrary singleton.
GEMM LHS (Left Hand Side) matrix information.
Definition: Types.h:2303
Store the tensor&#39;s metadata.
Definition: ITensorInfo.h:40
#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
Manages all the OpenCL kernels compilation and caching, provides accessors for the OpenCL Context...
int32_t gemmlowp_offset
GEMMLowp output stage offset used for quantizing to QASYMM8.
Definition: Types.h:2290
Status class.
Definition: Error.h:52
int32_t gemmlowp_max_bound
GEMMLowp max value used to saturate down the output result before converting back to QASYMM8...
Definition: Types.h:2294
static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info, const ITensorInfo *vector_sum_col=nullptr, const ITensorInfo *vector_sum_row=nullptr, const ITensorInfo *bias=nullptr, const ITensorInfo *output_multipliers=nullptr, const ITensorInfo *output_shifts=nullptr)
Static function to check if given info will lead to a valid configuration.
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
GEMMLowpOutputStageType type
GEMMLowp output stage type.
Definition: Types.h:2289
GEMMLHSMatrixInfo lhs_info
LHS matrix information used to retrieve the number of rows processed by each thread.
Copyright (c) 2017-2022 Arm Limited.
bool is_quantized_per_channel
GEMMLowp quantized per-channel flag.
Definition: Types.h:2298
std::vector< int32_t > gemmlowp_shifts
GEMMLowp output stage multiplier used for quantizing to QASYMM8.
Definition: Types.h:2296
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:159
1 channel, 1 S32 per channel
void add_option(std::string option)
Adds option to the existing build option list.
const OutputStage & output_stage
const ITensor * get_const_tensor(int id) const
Get constant tensor of a given id.
Definition: ITensorPack.cpp:54
unsigned int m
Number of LHS rows.
std::string upper_string(const std::string &val)
Raise a given string to upper case.
Definition: Utils.cpp:360
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:404
unsigned int n
Number of RHS columns.
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
bool update_window_and_padding(Window &win, Ts &&... patterns)
Update window and padding size for each of the access patterns.
Definition: WindowHelpers.h:46
#define ARM_COMPUTE_UNUSED(...)
To avoid unused variables warnings.
Definition: Error.h:152
GEMM RHS (Right Hand Side) matrix information.
Definition: Types.h:2318
int32_t b_offset
Offset to be added to each element of the matrix B.
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
quantized, asymmetric fixed-point 8-bit number unsigned
std::vector< int32_t > gemmlowp_multipliers
GEMMLowp output stage multiplier used for quantizing to QASYMM8.
Definition: Types.h:2295
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
GEMMLowpOutputStageInfo output_stage
GEMMLowp output stage information.
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.
GEMMLowp output stage info.
Definition: Types.h:2287
Quantize using a floating point multiplication.
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:349
Quantize using an integer multiplication.
#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:603
quantized, symmetric fixed-point 8-bit number
CLCompileContext class.
quantized, symmetric per channel fixed-point 8-bit number
int32_t a_offset
Offset to be added to each element of the matrix A.
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
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
GEMMRHSMatrixInfo rhs_info
RHS matrix information used for reshaping the RHS matrix.
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_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)
Definition: Validate.h:439
#define ARM_COMPUTE_CREATE_ERROR(error_code, msg)
Creates an error with a given message.
Definition: Error.h:159
static constexpr size_t DimZ
Alias for dimension 2 also known as Z dimension.
Definition: Window.h:47
unsigned int num_dimensions() const
Returns the effective dimensionality of the tensor.
Definition: Dimensions.h:143
#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:588
Wrapper to configure the Khronos OpenCL C++ header.
unsigned int k
Number of LHS columns or RHS rows.
#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
unsigned int m0
Number of rows processed by the matrix multiplication.
Definition: Types.h:2310
quantized, asymmetric fixed-point 8-bit number signed
int32_t gemmlowp_min_bound
GEMMLowp min value used to saturate down the output result before converting back to QASYMM8...
Definition: Types.h:2293
Window first_slice_window_3D() const
First 3D slice of the window.
Definition: Window.h:305
std::string kernel_name
std::tuple< PixelValue, PixelValue > get_min_max(DataType dt)
Compute the mininum and maximum values a data type can take.
Definition: Utils.h:564
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)
const int32_t * bias