Compute Library
 22.11
CpuWinogradConv2d.cpp
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25 #include "arm_compute/core/Error.h"
26 #include "arm_compute/core/Utils.h"
32 #include "src/common/utils/Log.h"
33 #include "src/core/CPP/Validate.h"
34 #include "src/core/NEON/kernels/assembly/winograd.hpp"
35 #include "src/core/NEON/kernels/convolution/common/tensor.hpp"
36 #include "src/core/NEON/kernels/convolution/common/utils.hpp"
45 #include "support/Cast.h"
46 
47 namespace arm_compute
48 {
49 namespace cpu
50 {
51 using namespace arm_compute::experimental;
52 using namespace arm_compute::utils::cast;
53 
54 namespace
55 {
56 inline Tensor4DShape internal_get_shape(const ITensorInfo *in)
57 {
58  const DataLayout data_layout = in->data_layout();
59  const int in_width = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
60  const int in_height = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
61  const int in_channels = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
62  const int in_batches = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES));
63 
64  return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
65 }
66 
67 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info)
68 {
69  ARM_COMPUTE_UNUSED(dst, weights);
71 
72  ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
73  if(biases != nullptr)
74  {
76  ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
77  }
80  return Status{};
81 }
82 
83 bool get_winograd_kernel_implementation(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst,
84  const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math,
85  arm_conv::winograd::WinogradImpl *winograd_impl, std::unique_ptr<arm_conv::ConvolutionArgs> &conv_args)
86 {
87  arm_conv::winograd::WinogradConfig winograd_cfg;
89 
90  const DataType data_type = src->data_type();
91  Tensor4DShape in_shape{ internal_get_shape(src) };
92  Tensor4DShape out_shape{ internal_get_shape(dst) };
93  Tensor4DShape kernel_shape{ internal_get_shape(weights) };
94  uint32_t nthreads = NEScheduler::get().num_threads();
95  // Get configuration arguments for Winograd
96  winograd_cfg.output_rows = 0;
97  winograd_cfg.output_cols = 0;
98  conv_args = std::make_unique<arm_conv::ConvolutionArgs>(
99  in_shape.n_batches,
100  arm_conv::Shape2D{ static_cast<uint32_t>(in_shape.n_rows), static_cast<uint32_t>(in_shape.n_cols) },
101  in_shape.n_channels,
102  conv_info.pad_top(),
103  conv_info.pad_left(),
104  arm_conv::Shape2D{ static_cast<uint32_t>(out_shape.n_rows), static_cast<uint32_t>(out_shape.n_cols) },
105  out_shape.n_channels,
106  arm_conv::Shape2D{ static_cast<uint32_t>(kernel_shape.n_rows), static_cast<uint32_t>(kernel_shape.n_cols) },
108 
109  bool success = false;
110  if(data_type == DataType::F32)
111  {
112  success = arm_conv::winograd::get_implementation<float>(
113  *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
114  }
115 #if defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
116  else if(data_type == DataType::F16)
117  {
118  success = arm_conv::winograd::get_implementation<__fp16>(
119  *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
120  }
121 #endif // defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
122  else
123  {
124  success = false;
125  }
126  return success;
127 }
128 inline bool fuse_function_supported(const ActivationLayerInfo &act_info)
129 {
130  return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU;
131 }
132 } // namespace
133 
135 
136  : _gemm_function(std::make_unique<CpuGemm>()),
137  _activation_func(std::make_unique<CpuActivation>()),
138  _transform_input_kernel(nullptr),
139  _transform_output_kernel(nullptr),
140  _permute_input(std::make_unique<CpuPermute>()),
141  _permute_output(std::make_unique<CpuPermute>()),
142  _permute_weights(std::make_unique<CpuPermute>()),
143  _aux_mem(AuxTensorIdx::Count),
144  _conv_args{ nullptr },
145  _winograd_impl{},
146  _data_layout(),
147  _winograd_transformed_input{},
148  _winograd_transformed_output{},
149  _winograd_transformed_weights{},
150  _input_workspace(),
151  _output_workspace(),
152  _weights_hwio(),
153  _input_nhwc(),
154  _output_nhwc(),
155  _is_prepared{ false },
156  _run_activation{ false }
157 {
158 }
159 
161 
162 void CpuWinogradConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst,
163  const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
164 {
165  ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
166  ARM_COMPUTE_ERROR_THROW_ON(validate(src, weights, biases, dst, conv_info, act_info, enable_fast_math));
167  ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);
168  ARM_COMPUTE_UNUSED(biases);
169  const DataType data_type = src->data_type();
170  uint32_t nthreads = NEScheduler::get().num_threads();
171  _data_layout = src->data_layout();
172  const Tensor4DShape kernel_shape{ internal_get_shape(weights) };
173 
174  bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &_winograd_impl, _conv_args);
175 
176  ARM_COMPUTE_EXIT_ON_MSG_VAR(!success, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
177  ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
178  ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
179  ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
180 
181  const bool has_impl = ((_winograd_impl.input_transform != nullptr) && (_winograd_impl.output_transform != nullptr) && (_winograd_impl.gemm_args != nullptr));
182  if(has_impl)
183  {
184  // Determine how much working space is required, allocate it.
185  const size_t input_workspace_size = _winograd_impl.input_transform->get_working_space_size(*_conv_args, nthreads);
186  const size_t output_workspace_size = _winograd_impl.output_transform->get_working_space_size(*_conv_args, nthreads);
187 
188  TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
189  TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
190  _input_workspace = input_workspace_info;
191  _output_workspace = output_workspace_info;
192 
193  const auto &wds = _winograd_impl.winograd_spec;
194 
195  // Preparing winograd transformed input tensor
196  const size_t data_type_size = src->element_size();
197  const uint32_t m = _winograd_impl.gemm_args->_Msize; // Total number of tiles
198  const uint32_t k = _winograd_impl.gemm_args->_Ksize; // Input channels
199  const uint32_t n = _winograd_impl.gemm_args->_Nsize; // Output channels
200  const uint32_t n_gemms = _winograd_impl.gemm_args->_nmulti;
201  const uint32_t n_batches = _winograd_impl.gemm_args->_nbatches;
202  constexpr size_t storage_alignment = 64;
203 
204  const TensorShape a_shape(k, m, n_batches, n_gemms);
205  Strides a_strides(data_type_size);
206  a_strides.set(1, data_type_size * _winograd_impl.winograd_spec.input_ld_row);
207  a_strides.set(2, data_type_size * _winograd_impl.winograd_spec.input_ld_batch);
208  a_strides.set(3, data_type_size * _winograd_impl.winograd_spec.input_ld_matrix);
209 
210  const TensorShape b_shape(n, k, n_gemms);
211  Strides b_strides(data_type_size);
212  b_strides.set(1, data_type_size * _winograd_impl.winograd_spec.weight_ld_row);
213  b_strides.set(2, data_type_size * _winograd_impl.winograd_spec.weight_ld_matrix);
214 
215  const TensorShape d_shape(n, m, n_batches, n_gemms);
216  Strides d_strides(data_type_size);
217  d_strides.set(1, data_type_size * _winograd_impl.winograd_spec.output_ld_row);
218  d_strides.set(2, data_type_size * _winograd_impl.winograd_spec.output_ld_batch);
219  d_strides.set(3, data_type_size * _winograd_impl.winograd_spec.output_ld_matrix);
220 
221  TensorInfo a_info{};
222  TensorInfo b_info{};
223  TensorInfo d_info{};
224  a_info.init(a_shape, 1, data_type, a_strides, 0, wds.input_matrix_size_bytes);
225  b_info.init(b_shape, 1, data_type, b_strides, 0, wds.weight_matrix_size_bytes);
226  d_info.init(d_shape, 1, data_type, d_strides, 0, wds.output_matrix_size_bytes);
227 
228  _winograd_transformed_input = a_info;
229  _winograd_transformed_weights = b_info;
230  _winograd_transformed_output = d_info;
231 
232  PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);
233 
234  // Configure the kernel to transform the input tensor from NCHW -> NHWC
235  if(_data_layout == DataLayout::NCHW)
236  {
237  _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
238  weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
239  }
240 
241  // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
242  _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
243 
244  // Reorder the convoluted output to ACL's ordering NCHW
245  if(_data_layout == DataLayout::NCHW)
246  {
247  // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
248  TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
249  dst->dimension(1), dst->dimension(3)),
250  1, dst->data_type());
251  _output_nhwc = info;
252  _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
253  }
254 
255  // Configure input transform kernel
256  _transform_input_kernel = std::make_unique<CpuWinogradConv2dTransformInputKernel>(_winograd_impl, *_conv_args, nthreads);
257 
258  // Configure GEMM function
259  _gemm_function->configure(&_winograd_transformed_input, &_winograd_transformed_weights, nullptr, &_winograd_transformed_output, 1.0f, 0.f);
260 
261  // Configure output transform kernel
262  _transform_output_kernel = std::make_unique<CpuWinogradConv2dTransformOutputKernel>(_winograd_impl, *_conv_args, nthreads);
263 
264  //Configure Activation Layer
265  _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
266  if(_run_activation)
267  {
268  _activation_func->configure(dst, nullptr, act_info);
269  }
270 
271  auto asm_mem_req = _gemm_function->workspace();
272  _aux_mem[GemmWorkspace] = asm_mem_req[GemmWorkspace];
273  _aux_mem[Pretranspose] = asm_mem_req[Pretranspose];
274  _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
275  _aux_mem[TransposedRHS] = asm_mem_req[TransposedRHS];
276  _aux_mem[TempResult] = asm_mem_req[TempResult];
277 
278  // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
279  _aux_mem[TransformedInput] = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, wds.input_matrix_size_bytes, storage_alignment);
280  _aux_mem[TransformedOutput] = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, wds.output_matrix_size_bytes, storage_alignment);
281  _aux_mem[WorkspaceIO] = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
282  _aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
283  _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, wds.weight_matrix_size_bytes, storage_alignment);
284  if(_data_layout == DataLayout::NCHW)
285  {
286  _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
287  _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
288  }
289  }
290 }
291 Status CpuWinogradConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
292  const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
293 {
294  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
295  ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));
296 
297  // Disable winograd for fp16 if fast math is false.
298  if(!enable_fast_math)
299  {
301  }
302 
303  const Tensor4DShape kernel_shape{ internal_get_shape(weights) };
304  arm_conv::winograd::WinogradImpl winograd_impl{};
305 
306  std::unique_ptr<arm_conv::ConvolutionArgs> conv_args;
307  const bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &winograd_impl, conv_args);
308 
309  ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(success == false, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
310  ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", winograd_impl.input_transform->get_name().c_str());
311  ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", winograd_impl.input_transform->get_name().c_str());
312  ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", winograd_impl.input_transform->get_name().c_str());
313  return Status{};
314 }
315 
317 {
318  prepare(tensors);
319  auto src = tensors.get_const_tensor(ACL_SRC_0);
320  auto biases = tensors.get_const_tensor(ACL_SRC_2);
321  auto output = tensors.get_tensor(ACL_DST);
322  Window win;
323 
324  const uint32_t nthreads = NEScheduler::get().num_threads();
325 
326  // The Winograd transform implementation does fine-grain threading inside the transforms. Just pass thread_id and nthreads.
327  win.set(Window::DimX, Window::Dimension(0, nthreads, 1));
328 
329  // Wrap the winograd-domain tensorInfos created in configuration in tensors and allocate the required memory.
330  CpuAuxTensorHandler input_nhwc(offset_int_vec(PermutedInput), _input_nhwc, tensors, true);
331  CpuAuxTensorHandler winograd_input_transformed(offset_int_vec(TransformedInput), _winograd_transformed_input, tensors, true);
332  CpuAuxTensorHandler input_workspace(offset_int_vec(WorkspaceIO), _input_workspace, tensors, true);
333  const bool is_nchw = _data_layout == DataLayout::NCHW;
334  if(is_nchw)
335  {
336  //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
337  ITensorPack pack{ { ACL_SRC, src }, { ACL_DST, input_nhwc.get() } };
338  _permute_input->run(pack);
339  }
340 
341  CpuAuxTensorHandler winograd_output_transformed(offset_int_vec(TransformedOutput), _winograd_transformed_output, tensors, true);
342  CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
343  CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
344 
345  ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : src }, { ACL_DST, winograd_input_transformed.get() }, { ACL_INT, input_workspace.get() } };
346  NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, win, transform_input_pack);
347 
348  CpuAuxTensorHandler winograd_weights_transformed(offset_int_vec(TransformedWeights), _winograd_transformed_weights, tensors, true);
349 
350  // Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
351  ITensorPack gemm_pack = tensors;
352  gemm_pack.add_const_tensor(ACL_SRC, winograd_input_transformed.get());
353  gemm_pack.add_const_tensor(ACL_SRC_1, winograd_weights_transformed.get());
354  gemm_pack.add_const_tensor(ACL_BIAS, nullptr);
355  gemm_pack.add_tensor(ACL_DST, winograd_output_transformed.get());
356  _gemm_function->run(gemm_pack);
357 
358  // Output transform
359  ITensorPack transform_output_pack{ { ACL_SRC_0, winograd_output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : output }, { ACL_SRC_1, biases }, { ACL_INT, output_workspace.get() } };
360  NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, win, transform_output_pack);
361  if(is_nchw)
362  {
363  // Reorder the convoluted output to ACL's ordering NCHW
364  ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, output } };
365  _permute_output->run(pack);
366  }
367  if(_run_activation)
368  {
369  ITensorPack pack{ { ACL_SRC, output }, { ACL_DST, output } };
370  _activation_func->run(pack);
371  }
372 }
373 
375 {
376  if(!_is_prepared)
377  {
378  const ITensor *weights = tensors.get_const_tensor(ACL_SRC_1);
379  ITensor *weights_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(PermutedWeights)));
380 
381  CpuAuxTensorHandler permuted_weights(_weights_hwio, *weights_aux);
382  ITensorPack permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } };
383  _permute_weights->run(permute_tensors);
384  const int element_size_in_bytes = permuted_weights.get()->info()->element_size();
385  // Weights were in OHWI format, before being permuted "permuted_weights" to be in HWIO format.
386  const unsigned int height_idx = 3; // H in HWIO
387  const unsigned int width_idx = 2; // W in HWIO
388  const unsigned int channel_idx = 1; // I in HWIO
389 
390  const int permuted_weight_row_stride = permuted_weights.get()->info()->strides_in_bytes()[height_idx] / element_size_in_bytes;
391  const int permuted_weight_col_stride = permuted_weights.get()->info()->strides_in_bytes()[width_idx] / element_size_in_bytes;
392  const int permuted_weight_channel_stride = permuted_weights.get()->info()->strides_in_bytes()[channel_idx] / element_size_in_bytes;
393 
394  // Wrap the winograd-domain transformed weight TensorInfo in Auxiliary tensor and allocate the required memory.
395  ITensor *weights_transf = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransformedWeights)));
396  ARM_COMPUTE_ERROR_ON_NULLPTR(weights_transf);
397  CpuAuxTensorHandler winograd_transformed_weights(_winograd_transformed_weights, *weights_transf);
398 
399  const void *permuted_weights_ptr;
400  void *win_wght_transf_ptr;
401 
402  permuted_weights_ptr = reinterpret_cast<const void *>(permuted_weights.get()->buffer() + permuted_weights.get()->info()->offset_first_element_in_bytes());
403  win_wght_transf_ptr = reinterpret_cast<void *>(winograd_transformed_weights.get()->buffer() + winograd_transformed_weights.get()->info()->offset_first_element_in_bytes());
404 
405  // Prepare Weights
406  _winograd_impl.weight_transform->execute(
407  *_conv_args,
408  permuted_weights_ptr,
409  permuted_weight_row_stride,
410  permuted_weight_col_stride,
411  permuted_weight_channel_stride,
412  win_wght_transf_ptr,
413  _winograd_impl.winograd_spec,
414  0, 1 // Thread 1 of 1
415  );
416  ITensorPack gemm_pack = tensors;
417  gemm_pack.add_const_tensor(ACL_SRC_1, winograd_transformed_weights.get());
418  _gemm_function->prepare(gemm_pack);
419  _is_prepared = 1;
420  }
421 }
423 {
424  return _aux_mem;
425 }
426 
427 } // namespace cpu
428 } // namespace arm_compute
void set(size_t dimension, T value, bool increase_dim_unit=true)
Accessor to set the value of one of the dimensions.
Definition: Dimensions.h:76
Basic function to run kernels::CpuActivationKernel.
Definition: CpuActivation.h:34
Shape of a tensor.
Definition: TensorShape.h:39
#define ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(tensor)
Definition: Validate.h:115
bool enabled() const
Check if initialised.
Definition: Types.h:1694
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
void add_const_tensor(int id, const ITensor *tensor)
Add const tensor to the pack.
Definition: ITensorPack.cpp:49
bool merge(int slot, size_t new_size, size_t new_alignment=0) noexcept
Definition: Types.h:115
#define ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(cond, msg,...)
If the condition is true, an error is returned.
Definition: Error.h:227
1 channel, 1 U8 per channel
#define ARM_COMPUTE_EXIT_ON_MSG_VAR(cond, msg,...)
If the condition is true, the given message is printed and program exits.
Definition: Error.h:395
#define ARM_COMPUTE_RETURN_ON_ERROR(status)
Checks if a status contains an error and returns it.
Definition: Error.h:204
virtual DataType data_type() const =0
Data type used for each element of the tensor.
virtual void schedule_op(ICPPKernel *kernel, const Hints &hints, const Window &window, ITensorPack &tensors)=0
Runs the kernel in the same thread as the caller synchronously.
#define ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(log_level, fmt,...)
Log a message with format to the logger.
Definition: Log.h:66
1 channel, 1 F32 per channel
experimental::MemoryRequirements workspace() const override
Return the memory requirements required by the workspace.
void configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info=ActivationLayerInfo(), bool enable_fast_math=false)
Set the input and output tensors.
Strides PermutationVector
Permutation vector.
Definition: Types.h:51
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
void run(ITensorPack &tensors) override
Run the kernels contained in the function.
Status class.
Definition: Error.h:52
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
Activation Layer Information class.
Definition: Types.h:1639
Interface for CPU tensor.
Definition: ITensor.h:36
SimpleTensor< float > src
Definition: DFT.cpp:155
Copyright (c) 2017-2022 Arm Limited.
std::vector< MemoryInfo > MemoryRequirements
Definition: Types.h:134
1 channel, 1 F16 per channel
#define ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(...)
Definition: Validate.h:159
const ITensor * get_const_tensor(int id) const
Get constant tensor of a given id.
Definition: ITensorPack.cpp:54
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
size_t total_size() const override
Returns the total size of the tensor in bytes.
Definition: TensorInfo.h:250
virtual uint8_t * buffer() const =0
Interface to be implemented by the child class to return a pointer to CPU memory. ...
Basic function to run kernels::CpuPermuteKernel.
Definition: CpuPermute.h:34
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
Padding and stride information class.
Definition: Types.h:669
virtual size_t element_size() const =0
Element size in bytes calculated as data_size() * num_channels()
Basic function to execute GEMM.
Definition: CpuGemm.h:62
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
void prepare(ITensorPack &constants) override
Prepare the function for executing.
Num samples, channels, height, width.
void init(Format format)
Initialize the tensor info with just a format.
Definition: TensorInfo.cpp:123
Strides of an item in bytes.
Definition: Strides.h:37
virtual size_t offset_first_element_in_bytes() const =0
The offset from the beginning of the memory allocation to the first element of the tensor...
static Status validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info=ActivationLayerInfo(), bool enable_fast_math=false)
Static function to check if given info will lead to a valid configuration of CpuWinogradConv2d.
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false)
ITensor * get_tensor(int id)
Get tensor of a given id from the pac.
Definition: ITensorPack.cpp:64
virtual size_t total_size() const =0
Returns the total size of the tensor in bytes.
Target polymorphic_cast(Source *v)
Polymorphic cast between two types.
Definition: Cast.h:47
size_t get_data_layout_dimension_index(const DataLayout &data_layout, const DataLayoutDimension &data_layout_dimension)
Get the index of the given dimension.
Definition: Helpers.inl:193
#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
#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_LOG_PARAMS(...)
#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)
Definition: Validate.h:157
Store the tensor&#39;s metadata.
Definition: TensorInfo.h:43
int offset_int_vec(int offset)
Definition: MemoryHelpers.h:38
arm_gemm::Activation map_to_arm_gemm_activation(const ActivationLayerInfo &act)
Performs a mapping between Compute Library ActivationLayerInfo and the assembly Activation structure...
static CPUInfo & get()
Access the KernelLibrary singleton.
Definition: CPPTypes.cpp:40
virtual unsigned int num_threads() const =0
Returns the number of threads that the SingleThreadScheduler has in its pool.
DataType
Available data types.
Definition: Types.h:79
DataLayout
[DataLayout enum definition]
Definition: Types.h:113
Describe a multidimensional execution window.
Definition: Window.h:39
void add_tensor(int id, ITensor *tensor)
Add tensor to the pack.
Definition: ITensorPack.cpp:39
virtual DataLayout data_layout() const =0
Get the data layout of the tensor.
static IScheduler & get()
Access the scheduler singleton.
Definition: Scheduler.cpp:94