Compute Library
 22.05
ClGemmMatrixMultiplyNativeKernel.cpp
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25 
32 #include "arm_compute/core/Utils.h"
36 #include "src/core/CL/CLUtils.h"
41 #include "support/Cast.h"
42 #include "support/StringSupport.h"
43 
44 namespace arm_compute
45 {
46 namespace opencl
47 {
48 namespace kernels
49 {
50 namespace
51 {
52 using ElementsProcessed = Steps;
53 
54 const auto post_op_utils = experimental::PostOpCLKernelUtils(
55 {
56  // PostOp sequence -> {Kernel Postfix, PostOp Slots}
57  { {}, { "", {} } },
58  { { experimental::PostOpType::Activation }, { "", { 1 } } },
59 
60  { { experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 2 } } },
61  { { experimental::PostOpType::Eltwise_PRelu }, { "_post_act_eltwise_op_act", { 2 } } },
62 
63  { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 1, 2 } } },
64  { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_PRelu }, { "_post_act_eltwise_op_act", { 1, 2 } } },
65 
66  { { experimental::PostOpType::Eltwise_Add, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 2, 3 } } },
67  { { experimental::PostOpType::Eltwise_PRelu, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 2, 3 } } },
68 
71 });
72 
73 Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info,
74  const GEMMRHSMatrixInfo &rhs_info,
75  const GEMMKernelInfo &gemm_info)
76 {
77  ARM_COMPUTE_UNUSED(alpha);
78  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
81  ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
82  ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
83  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");
84  ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16);
85  ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8);
86  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");
87  ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (src2 != nullptr)
88  && (!gemm_info.broadcast_bias),
89  "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D");
90  ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported");
91  ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for GEMM native");
92  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");
93 
94  const unsigned int m = gemm_info.m;
95  const unsigned int n = gemm_info.n;
96  const unsigned int k = gemm_info.k;
97 
101 
102  ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != k);
103  ARM_COMPUTE_RETURN_ERROR_ON(src1->dimension(0) != n);
104  ARM_COMPUTE_RETURN_ERROR_ON(src1->dimension(1) != k);
105  if(gemm_info.reinterpret_input_as_3d)
106  {
107  ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != m);
108  }
109  else
110  {
111  ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != m);
112  }
113 
114  if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
115  {
116  const unsigned int src2_dim0 = src2->dimension(0);
117  const unsigned int src2_dim1 = src2->dimension(1);
118 
120  if(gemm_info.broadcast_bias)
121  {
122  ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
123  }
124  else
125  {
126  ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
127  }
128  }
129 
130  if(dst->total_size() != 0)
131  {
132  const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info));
133  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
135  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");
136  }
137 
138  return Status{};
139 }
140 
141 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info,
142  const GEMMRHSMatrixInfo &rhs_info,
143  const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed)
144 {
145  unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
146  unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
147  bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
148  bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
149 
150  Window win{};
151  Window win_out{};
152  bool window_changed = false;
153 
154  // In case both input and dst have to be reinterpreted as 3D tensors,
155  // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
156  if(reinterpret_input_as_3d == reinterpret_output_as_3d)
157  {
158  reinterpret_output_as_3d = false;
159  }
160 
161  // dst tensor auto initialization if not yet initialized
162  auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)));
163 
164  TensorInfo tmp_info(*dst);
165 
166  if(reinterpret_output_as_3d)
167  {
168  // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
169  // the window needs to be constructed on the 2D collapsed version of the tensor
170  TensorShape tmp_shape(dst->tensor_shape());
171  tmp_shape.collapse(2U, 1U);
172  tmp_info.set_tensor_shape(tmp_shape);
173  }
174 
175  // Configure kernel window
176  num_elems_processed_per_iteration_x = rhs_info.n0;
177  num_elems_processed_per_iteration_y = lhs_info.m0;
178 
179  win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
180  win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
181 
182  AccessWindowStatic src0_access(src0, 0, 0,
183  src0->dimension(0),
184  src0->dimension(1));
185  AccessWindowStatic src1_access(src1, 0, 0,
186  ceil_to_multiple(src1->dimension(0), num_elems_processed_per_iteration_x),
187  src1->dimension(1));
188  AccessWindowStatic dst_access(dst, 0, 0,
189  dst->dimension(0),
190  dst->dimension(1));
191 
192  if(src2 != nullptr)
193  {
194  const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
195 
196  AccessWindowStatic src2_access(src2, 0, 0,
197  ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x),
198  src2->dimension(1));
199 
200  window_changed = update_window_and_padding(win, src0_access, src1_access, src2_access) || // window used by the execute_window_loop
201  update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor
202  }
203  else
204  {
205  window_changed = update_window_and_padding(win, src0_access, src1_access) || // window used by the execute_window_loop
206  update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor
207  }
208 
209  // Collapse along the Z direction
210  // This collapse needs to be here in order to tune the Z dimension of LWS
211  Window collapsed = win;
212  const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
213  collapsed = win.collapse(win, dimension_to_collapse);
214 
215  Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
216  return std::make_pair(err, collapsed);
217 }
218 } // namespace
219 
221 {
222  _type = CLKernelType::GEMM;
223 }
224 
225 void ClGemmMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha,
226  float beta,
227  const GEMMLHSMatrixInfo &lhs_info,
228  const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
229 {
230  ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
231 
232  // dst tensor auto initialization if not yet initialized
233  auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)));
234 
235  ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
236 
237  auto padding_info = get_padding_info({ src0, dst });
238  _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
239  _reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0;
240  _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
241  _add_bias = src2 != nullptr;
242  _num_post_op_args = gemm_info.post_ops.total_num_arguments();
243 
244  // In case both input and dst have to be reinterpreted as 3D tensors,
245  // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
246  if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
247  {
248  _reinterpret_input_as_3d = false;
249  _reinterpret_output_as_3d = false;
250  }
251 
252  // Check if we need to slide the matrix B
253  const unsigned int num_dimensions_src0 = src0->num_dimensions();
254  _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0);
255 
256  ElementsProcessed num_elements_processed{};
257 
258  // Configure kernel window
259  auto win_config = validate_and_configure_window(src0, src1, src2 != nullptr ? src2 : nullptr, dst, lhs_info, rhs_info, gemm_info, num_elements_processed);
260  ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
261  IClKernel::configure_internal(win_config.second);
262 
263  // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true,
264  // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel.
265  // This means that the actual m used by the kernel is given by dst->dimension(1) and not by gemm_info.m
266  const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1);
267 
268  const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1);
269  const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2);
270 
271  // 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.
272  const unsigned int partial_store_m0 = internal_m % lhs_info.m0;
273  const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0;
274 
275  // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads.
276  // NOTE: This might have implications on heuristics and performance
277  const unsigned int internal_m0 = std::min(internal_m, lhs_info.m0);
278  _m = internal_m;
279  _n = gemm_info.n;
280  _k = gemm_info.k;
281 
282  // Create build options
283  CLBuildOptions build_opts;
284  build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type()));
285  build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
286  build_opts.add_option_if(src2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta));
287  build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA");
288  build_opts.add_option_if(gemm_info.broadcast_bias, "-DBROADCAST_BIAS");
289  build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
290  build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
291  build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d));
292  build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d));
293  build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2)));
294  build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
295  build_opts.add_option("-DM0=" + support::cpp11::to_string(internal_m0));
296  build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
297  build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
298  build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
299  build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
300  // If post_ops are used, then we disable the use of gemm_info.activation_info
301  if(gemm_info.post_ops.size() > 0)
302  {
303  post_op_utils.set_post_ops_cl_build_options(build_opts, gemm_info.post_ops);
304  }
305  else
306  {
307  build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation())));
308  build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a()));
309  build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b()));
310  }
311 
312  std::string kernel_name("gemm_mm_native");
313  post_op_utils.set_post_ops_cl_kernel_name(kernel_name, gemm_info.post_ops);
314 
315  // A macro guard to compile ONLY the kernel of interest
316  build_opts.add_option("-D" + upper_string(kernel_name));
317 
318  // Create kernel
319  _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
320 
321  // Set config_id for enabling LWS tuning
322  _config_id = kernel_name;
323  _config_id += "_";
324  _config_id += (_add_bias ? "add_bias_" : "");
325  _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : "");
326  _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
327  _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
328  _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : "");
329  _config_id += lower_string(string_from_data_type(src0->data_type()));
330  _config_id += "_";
331  _config_id += support::cpp11::to_string(dst->dimension(1));
332  _config_id += "_";
333  _config_id += support::cpp11::to_string(dst->dimension(0));
334  _config_id += "_";
335  _config_id += support::cpp11::to_string(gemm_info.k);
336  _config_id += "_";
337  _config_id += support::cpp11::to_string(dst->dimension(2));
338  _config_id += "_";
339  _config_id += support::cpp11::to_string(lhs_info.m0);
340  _config_id += "_";
341  _config_id += support::cpp11::to_string(rhs_info.n0);
342  _config_id += "_";
343  _config_id += support::cpp11::to_string(rhs_info.k0);
344 
346 }
347 
348 Status ClGemmMatrixMultiplyNativeKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
349  const GEMMLHSMatrixInfo &lhs_info,
350  const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info)
351 {
352  ElementsProcessed num_elements_processed{};
353  ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info));
355  src1->clone().get(),
356  src2 != nullptr ? src2->clone().get() : nullptr,
357  dst->clone().get(),
358  lhs_info,
359  rhs_info,
360  gemm_info,
361  num_elements_processed)
362  .first);
363 
364  return Status{};
365 }
366 
367 void ClGemmMatrixMultiplyNativeKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
368 {
371 
372  const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
373  const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
374  const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
375  auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
376 
377  ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
378  ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr);
379 
380  if(src1->info()->num_dimensions() < 3)
381  {
382  // The stride_z for matrix B must be zero if we do not slice
383  ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
384  }
385 
387  Window slice_matrix_b = slice;
388 
389  slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
390  slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
391 
392  if(_reinterpret_input_as_3d)
393  {
394  // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
395  unsigned int idx0;
396  if(_add_bias)
397  {
398  idx0 = (4 + _num_post_op_args) * num_arguments_per_2D_tensor() + (7 + _num_post_op_args);
399  }
400  else
401  {
402  idx0 = (3 + _num_post_op_args) * num_arguments_per_2D_tensor() + (6 + _num_post_op_args);
403  }
404  const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom;
405  _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
406  }
407 
408  if(_reinterpret_output_as_3d)
409  {
410  // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor
411  unsigned int idx0;
412  if(_add_bias)
413  {
414  idx0 = (4 + _num_post_op_args) * num_arguments_per_2D_tensor() + 7 + (_reinterpret_input_as_3d ? 1 : 0) + _num_post_op_args;
415  }
416  else
417  {
418  idx0 = (3 + _num_post_op_args) * num_arguments_per_2D_tensor() + 6 + (_reinterpret_input_as_3d ? 1 : 0) + _num_post_op_args;
419  }
420  const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom;
421  _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
422  }
423 
424  do
425  {
426  Window slice_b = slice;
427  // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
428  // This scenario can happen when the matrix multiplication is used to perform a convolution operation
429  if(!_slide_matrix_b)
430  {
431  slice_b = slice_matrix_b;
432  }
433 
434  unsigned int idx = 0;
435  add_2D_tensor_argument(idx, src0, slice);
436  add_2D_tensor_argument(idx, src1, slice_b);
437  if(_add_bias)
438  {
439  add_2D_tensor_argument(idx, src2, slice);
440  }
441  add_2D_tensor_argument(idx, dst, slice);
442  // post op argument buffers
443  for(size_t i = 0; i < _num_post_op_args; ++i)
444  {
445  const auto post_op_arg = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(experimental::get_post_op_arg_type(i)));
446  add_2D_tensor_argument(idx, post_op_arg, slice);
447  }
448  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2]));
449  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2]));
450  if(_add_bias)
451  {
452  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2]));
453  }
454  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2]));
455  // post op argument stride_z
456  for(size_t i = 0; i < _num_post_op_args; ++i)
457  {
458  const auto post_op_arg = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(experimental::get_post_op_arg_type(i)));
459  _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(post_op_arg->info()->strides_in_bytes()[2]));
460  }
461 
462  // Pass m, n and k at runtime
463  _kernel.setArg<cl_int>(idx++, _m);
464  _kernel.setArg<cl_int>(idx++, _n);
465  _kernel.setArg<cl_int>(idx++, _k);
466 
467  enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
468  }
469  while(window.slide_window_slice_3D(slice));
470 }
471 } // namespace kernels
472 } // namespace opencl
473 } // 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
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.
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
bool broadcast_bias
Flag used to broadcast the bias addition.
const Window & window() const
The maximum window the kernel can be executed on.
Definition: IKernel.cpp:28
Descriptor used by the GEMM kernels.
bool enabled() const
Check if initialised.
Definition: Types.h:1675
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
float a() const
Get the alpha value.
Definition: Types.h:1665
#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
#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...
Status class.
Definition: Error.h:52
ActivationLayerInfo activation_info
Activation function to perform after the matrix multiplication.
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
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
unsigned int k0
Number of partial accumulations performed by the matrix multiplication.
Definition: Types.h:2077
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:358
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
unsigned int n
Number of RHS columns.
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
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
void configure(const ClCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, 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 dst.
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
auto ceil_to_multiple(S value, T divisor) -> decltype(((value+divisor - 1)/divisor) *divisor)
Computes the smallest number larger or equal to value that is a multiple of divisor.
Definition: Utils.h:71
unsigned int n0
Number of columns processed by the matrix multiplication.
Definition: Types.h:2076
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
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
static constexpr unsigned int num_arguments_per_2D_tensor()
Returns the number of arguments enqueued per 2D tensor object.
Definition: ICLKernel.h:306
bool slide_window_slice_3D(Window &slice) const
Slide the passed 3D window slice.
Definition: Window.h:348
#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.
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_CREATE_ERROR(error_code, msg)
Creates an error with a given message.
Definition: Error.h:159
#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
Wrapper to configure the Khronos OpenCL C++ header.
unsigned int k
Number of LHS columns or RHS rows.
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
unsigned int m0
Number of rows processed by the matrix multiplication.
Definition: Types.h:2061
ActivationFunction activation() const
Get the type of activation function.
Definition: Types.h:1660
float b() const
Get the beta value.
Definition: Types.h:1670
Window first_slice_window_3D() const
First 3D slice of the window.
Definition: Window.h:304
std::string kernel_name
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...
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)