Compute Library
 21.02
GCDepthwiseConvolutionLayer3x3Kernel.cpp
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1 /*
2  * Copyright (c) 2017-2020 Arm Limited.
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25 
26 #include "arm_compute/core/Error.h"
33 #include "arm_compute/core/Types.h"
34 #include "arm_compute/core/Utils.h"
39 #include "support/StringSupport.h"
40 
41 using namespace arm_compute;
43 
45  : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_left(0), _conv_pad_top(0), _lws(gles::NDRange(1U, 1U, 1U))
46 {
47 }
48 
50 {
51  return _border_size;
52 }
53 
55  unsigned int depth_multiplier)
56 {
59  ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3);
60 
61  if(biases != nullptr)
62  {
64  ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(2));
65  ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
66  }
67 
68  // Get convolved dimensions
69  const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier);
70 
71  // Output auto inizialitation if not yet initialized
72  auto_init_if_empty(*output->info(),
74  1,
75  input->info()->data_type());
76 
78  ARM_COMPUTE_ERROR_ON(output->info()->dimension(2) != weights->info()->dimension(2));
79 
80  _input = input;
81  _output = output;
82  _weights = weights;
83  _biases = biases;
84  _conv_stride_x = conv_info.stride().first;
85  _conv_stride_y = conv_info.stride().second;
86  _conv_pad_left = conv_info.pad_left();
87  _conv_pad_top = conv_info.pad_top();
88  _border_size = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left);
89 
90  // Set build options
91  ARM_COMPUTE_ERROR_ON(_conv_stride_x < 1 || _conv_stride_x > 3);
92  std::set<std::string> options;
93 
94  options.emplace("#define DEPTH_MULTIPLIER " + support::cpp11::to_string(depth_multiplier));
95  options.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(_lws[0]));
96  options.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(_lws[1]));
97  options.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(_lws[2]));
98  options.emplace("#define STRIDE_X " + support::cpp11::to_string(_conv_stride_x));
99  options.emplace("#define STRIDE_Y " + support::cpp11::to_string(_conv_stride_y));
100 
101  std::string dt_name = (input->info()->data_type() == DataType::F32) ? "DATA_TYPE_FP32" : "DATA_TYPE_FP16";
102  options.emplace(("#define " + dt_name));
103 
104  unsigned int num_elems_read_per_iteration_x = 8;
105  unsigned int num_elems_read_per_iteration_y = 1;
106  unsigned int num_elems_written_per_iteration_x = 4;
107  unsigned int num_elems_written_per_iteration_y = 1;
108  unsigned int num_elems_written_per_iteration_z = 1;
109 
110  if((_conv_stride_x == 1) && (_conv_stride_y == 1))
111  {
112  switch(input->info()->data_type())
113  {
114 #define PROCESS_4X_3Y_1Z
115 
116  case DataType::F16:
117 #if defined(PROCESS_4X_3Y_1Z)
118  options.emplace("#define PROCESS_4X_3Y_1Z");
119  num_elems_read_per_iteration_y = 5;
120  num_elems_written_per_iteration_y = 3;
121 #endif /* PROCESS_4X_3Y_1Z */
122 #undef PROCESS_4X_3Y_1Z
123  break;
124 
125  default:
126  ARM_COMPUTE_ERROR("Current data type is not supported");
127  break;
128  }
129  }
130  else
131  {
132  switch(input->info()->data_type())
133  {
134  case DataType::F16:
135  options.emplace("#define PROCESS_4X_1Y_1Z");
136  break;
137 
138  default:
139  ARM_COMPUTE_ERROR("Current data type is not supported");
140  break;
141  }
142  }
143 
144  if(_biases != nullptr)
145  {
146  options.emplace("#define BIAS");
147  }
148 
149  // Create kernel
150  std::string kernel_name = "depthwise_convolution_3x3";
151  _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name, options));
152 
153  // Calculate output right and bottom border
154  const int output_width = output->info()->dimension(0);
155  const int output_height = output->info()->dimension(1);
156  const int output_padding_right = ceil_to_multiple(output_width, num_elems_written_per_iteration_x * _lws[0]) - output_width;
157  const int output_padding_bottom = ceil_to_multiple(output_height, num_elems_written_per_iteration_y * _lws[1]) - output_height;
158 
159  // Calculate input right and bottom border
160  const int input_width = input->info()->dimension(0);
161  const int input_height = input->info()->dimension(1);
162 
163  const int input_total_width = std::max(int(input->info()->padding().left), int(_conv_pad_left)) + input_width + std::max(int(input->info()->padding().right), int(_conv_pad_left));
164  const int input_total_height = std::max(int(input->info()->padding().top), int(_conv_pad_top)) + input_height + std::max(int(input->info()->padding().bottom), int(_conv_pad_top));
165 
166  const int input_padding_right = ceil_to_multiple(input_total_width, num_elems_read_per_iteration_x * _lws[0]) - input_width - _conv_pad_left;
167  const int input_padding_bottom = ceil_to_multiple(input_total_height, num_elems_read_per_iteration_y * _lws[1]) - input_height - _conv_pad_top;
168 
169  BorderSize border = BorderSize(0, output_padding_right, output_padding_bottom, 0);
170 
171  Window win = calculate_max_enlarged_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y, num_elems_written_per_iteration_z), border);
172 
173  AccessWindowStatic input_access(input->info(), -_conv_pad_left, -_conv_pad_top, input_width + input_padding_right, input_height + input_padding_bottom);
174  AccessWindowStatic weights_access = AccessWindowStatic(nullptr, 0, 0, 0, 0);
175  AccessWindowStatic bias_access = AccessWindowStatic(nullptr, 0, 0, 0, 1);
176 
177  switch(weights->info()->data_type())
178  {
179  case DataType::F16:
180  weights_access = AccessWindowStatic(weights->info(), 0, 0, 4, 3);
181  if(_biases != nullptr)
182  {
183  bias_access = AccessWindowStatic(_biases->info(), 0, 0, _biases->info()->dimension(0) + 1, 1);
184  }
185  break;
186 
187  default:
188  ARM_COMPUTE_ERROR("Current data type is not supported");
189  break;
190  }
191 
192  AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom);
193 
194  if(_biases != nullptr)
195  {
196  update_window_and_padding(win, input_access, weights_access, bias_access, output_access);
197  }
198  else
199  {
200  update_window_and_padding(win, input_access, weights_access, output_access);
201  }
202 
203  output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
204 
205  IGCKernel::configure(win);
206 }
207 
209 {
212 
213  _kernel.use();
214 
215  _output->set_needs_shifting(true);
216 
217  // Create input window and adjust
218  Window win_in = window;
219  win_in.adjust(Window::DimX, -_conv_pad_left, true);
220  win_in.adjust(Window::DimY, -_conv_pad_top, true);
221  win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x);
222  win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y);
223 
224  Window slice_in = win_in.first_slice_window_3D();
225  Window slice_out = window.first_slice_window_3D();
226  Window slice_weights = window.first_slice_window_3D();
227  slice_weights.set_dimension_step(Window::DimX, 0);
228  slice_weights.set_dimension_step(Window::DimY, 0);
229 
230  // Set biases
231  if(_biases != nullptr)
232  {
233  unsigned int idx = 3 * num_arguments_per_3D_tensor();
234  Window slice_biases;
235  slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
236  add_1D_tensor_argument(idx, _biases, 4, slice_biases);
237  }
238 
239  slice_out.shift(Window::DimX, -(_output->info()->padding()).left);
240 
241  do
242  {
243  unsigned int idx = 0;
244  add_3D_tensor_argument(idx, _input, 1, slice_in);
245  add_3D_tensor_argument(idx, _output, 2, slice_out);
246  add_3D_tensor_argument(idx, _weights, 3, slice_weights);
247 
248  _kernel.update_shader_params();
249  enqueue(*this, slice_out, _lws);
250  }
251  while(window.slide_window_slice_3D(slice_out) && win_in.slide_window_slice_3D(slice_in));
252 }
unsigned int top
top of the border
Definition: Types.h:375
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
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
void add_3D_tensor_argument(unsigned int &idx, const IGCTensor *tensor, const unsigned int binding_point, const Window &window)
Add the passed 3D tensor&#39;s parameters to the object&#39;s kernel&#39;s arguments starting from the index idx...
Definition: IGCKernel.cpp:132
void enqueue(IGCKernel &kernel, const Window &window, const gles::NDRange &lws=gles::NDRange(1U, 1U, 1U))
Add the kernel to the command queue with the given window.
Definition: IGCKernel.cpp:41
TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info, unsigned int depth_multiplier, const Size2D &dilation=Size2D(1U, 1U))
Calculate the depthwise convolution output shape of a tensor.
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
void shift(size_t dimension, int shift_value)
Shift the values of a given dimension by the given shift_value.
Definition: Window.inl:133
Container for 2D border size.
Definition: Types.h:273
#define ARM_COMPUTE_ERROR(msg)
Print the given message then throw an std::runtime_error.
Definition: Error.h:352
constexpr int step() const
Return the step of the dimension.
Definition: Window.h:104
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.
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
Interface for GLES Compute tensor.
Definition: IGCTensor.h:35
unsigned int bottom
bottom of the border
Definition: Types.h:377
GCKernel class.
unsigned int pad_top() const
Get the top padding.
Definition: Types.h:806
unsigned int num_arguments_per_3D_tensor() const
Returns the number of arguments enqueued per 3D tensor object.
Definition: IGCKernel.cpp:147
void use_tensor_dimensions(const TensorShape &shape, size_t first_dimension=Window::DimX)
Use the tensor&#39;s dimensions to fill the window dimensions.
Definition: Window.inl:276
Copyright (c) 2017-2021 Arm Limited.
1 channel, 1 F16 per channel
Implementation of a static rectangular access pattern.
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
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:543
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
Manages all the GLES kernels compilation and caching, provides accessors for the GLES Context...
Class to describe a number of elements in each dimension.
Definition: Steps.h:40
void set_needs_shifting(bool needs_shifting)
Set the flag indicating whether or not a tensor needs shifting.
Definition: IGCTensor.cpp:61
Coordinates of an item.
Definition: Coordinates.h:37
std::pair< unsigned int, unsigned int > stride() const
Get the stride.
Definition: Types.h:770
std::string kernel_name
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...
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
unsigned int pad_right() const
Get the right padding.
Definition: Types.h:801
Padding and stride information class.
Definition: Types.h:722
virtual PaddingSize padding() const =0
Padding of tensor.
unsigned int left
left of the border
Definition: Types.h:378
bool slide_window_slice_3D(Window &slice) const
Slide the passed 3D window slice.
Definition: Window.h:335
unsigned int right
right of the border
Definition: Types.h:376
#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)
Definition: Validate.h:941
#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:790
static constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
static GCKernelLibrary & get()
Get the static instance of GCKernelLibrary.
void set_dimension_step(size_t dimension, int step)
Set the step of a given dimension.
Definition: Window.inl:167
#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(...)
Definition: Validate.h:286
constexpr const Dimension & y() const
Alias to access the second dimension of the window.
Definition: Window.h:154
void configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier=1)
Initialize the function&#39;s source, destination, conv and border_size.
Window calculate_max_enlarged_window(const ValidRegion &valid_region, const Steps &steps, BorderSize border_size)
GCKernel create_kernel(const std::string &shader_name, const StringSet &build_options_set={}) const
Creates a kernel from the kernel library.
void run(const Window &window) override
Enqueue the OpenGL ES shader to process the given window.
BorderSize border_size() const override
The size of the border for that kernel.
Container for valid region of a window.
Definition: Types.h:188
void adjust(size_t dimension, int adjust_value, bool is_at_start)
Adjust the start or end of a given dimension by the given value.
Definition: Window.inl:140
unsigned int pad_bottom() const
Get the bottom padding.
Definition: Types.h:811
Window first_slice_window_3D() const
First 3D slice of the window.
Definition: Window.h:291
unsigned int pad_left() const
Get the left padding.
Definition: Types.h:796
Describe a multidimensional execution window.
Definition: Window.h:39
void add_1D_tensor_argument(unsigned int &idx, const IGCTensor *tensor, const unsigned int binding_point, const Window &window)
Add the passed 1D tensor&#39;s parameters to the object&#39;s kernel&#39;s arguments starting from the index idx...
Definition: IGCKernel.cpp:122
#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)
Definition: Validate.h:205
constexpr const Dimension & x() const
Alias to access the first dimension of the window.
Definition: Window.h:145