Compute Library
 22.05
impl.cpp
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2  * Copyright (c) 2018-2022 Arm Limited.
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24 
26 
29 
30 #include "arm_compute/core/Error.h"
34 #include "arm_compute/core/Types.h"
35 #include "arm_compute/core/Utils.h"
37 
38 #include <algorithm>
39 
40 using namespace arm_compute::detail;
41 
42 namespace arm_compute
43 {
44 namespace cpu
45 {
46 namespace kernels
47 {
48 namespace
49 {
50 bool have_zero_x_internal_padding(ITensorInfo *src, const ITensorInfo *weights)
51 {
52  return (src->padding().left == 0 && weights->padding().left == 0 && src->padding().right == 0 && weights->padding().right == 0);
53 }
54 }
55 
56 template <typename T>
57 void convolve_nhwc(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
58 {
59  // Declare useful types
61  using vector_type = typename vtype::type;
62  using tag_type = typename vtype::tag_type;
63 
64  // Scalar quantities
65  const int element_size = src->info()->element_size();
66  const int input_stride_w = src->info()->strides_in_bytes().y() / element_size;
67  const int input_stride_h = src->info()->strides_in_bytes().z() / element_size;
68  const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size;
69  const int input_dim_w = src->info()->dimension(1);
70  const int input_dim_h = src->info()->dimension(2);
71 
72  const int output_stride_c = dst->info()->strides_in_bytes().x();
73 
74  const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().y() / element_size;
75  const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().z() / element_size;
76  const int kernel_dim_w = weights->info()->dimension(1);
77  const int kernel_dim_h = weights->info()->dimension(2);
78 
79  const int conv_pad_top = conv_info.pad_top();
80  const int conv_pad_left = conv_info.pad_left();
81  const int conv_stride_w = std::get<0>(conv_info.stride());
82  const int conv_stride_h = std::get<1>(conv_info.stride());
83 
84  // Setup input window for the output iterator
85  Window window_out = window;
86  window_out.set(Window::DimX, Window::Dimension(0, 1, 1));
87 
88  // Setup input window for the weights iterator
89  Window window_w = calculate_max_window(*weights->info(), Steps());
90  window_w.set(Window::DimX, Window::Dimension(0, 1, 1));
91  window_w.set(Window::DimY, Window::Dimension(0, 1, 1));
92  window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));
93 
94  Iterator out(dst, window_out);
95  Iterator wei(weights, window_w);
96 
97  constexpr int num_elems_read_per_iteration = 16 / sizeof(T);
98 
99  // nhwc optimized
100  if(have_zero_x_internal_padding(src->info(), weights->info()))
101  {
102  // This function assumes that input and weights have not padding in channel
103 
104  /*
105  * This implementation parallelize the full WC plane of input and weights by
106  * treating them as series of elements. So for example, a 3x3 weights and
107  * floating point vector operations of 4 elements per time, the first 3
108  * channel elements of the first row would be taken and additionally the first
109  * element of the second row. The 9 elements in each single WC weight plane
110  * would require 2 4-element vector operations and a last single element operation.
111  *
112  * This works since when we create the input vector to multiply with the weights,
113  * the exact required elements are loaded in the same order. Therefore the
114  * multiplication works on the correct input/weight elements.
115  */
117  window_out, [&](const Coordinates & id)
118  {
119  /*
120  * In here we create theoretical indexes which then we validate for both
121  * inputs and weights.
122  * As a reminder, this loop take each output point in NHW, C is treated
123  * in the weights loop.
124  */
125  // We are computing the theoretical starting input starting points
126  const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
127  const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
128  const int in_w_end_t = in_w_start_t + kernel_dim_w;
129  const int in_h_end_t = in_h_start_t + kernel_dim_h;
130 
131  // We are computing the valid initial and ending input points by checking the borders
132  const int in_w_start = std::max(in_w_start_t, 0);
133  const int in_h_start = std::max(in_h_start_t, 0);
134  const int in_w_end = std::min(in_w_end_t, input_dim_w);
135  const int in_h_end = std::min(in_h_end_t, input_dim_h);
136 
137  // We use the input points to select the valid weight points to use
138  const int index_wc_start = (in_w_start - in_w_start_t) * kernel_stride_w;
139  const int index_h_start = in_h_start - in_h_start_t;
140  const int index_wc_end = (kernel_dim_w - (in_w_end_t - in_w_end)) * kernel_stride_w;
141  const int index_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
142 
144  window_w, [&](const Coordinates & id_w)
145  {
146  /*
147  * This is the loop in the weights, and it goes along N (the batches)
148  * As a reminder, the batches of the weights are translated into the
149  * channels of the output
150  */
151  const T *in_ptr_row = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes())
152  + id[3] * input_stride_n + in_w_start * input_stride_w + in_h_start * input_stride_h;
153  const T *weights_ptr_row = reinterpret_cast<const T *>(wei.ptr()) + index_h_start * kernel_stride_h;
154  uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c;
155 
156  T out_temp = static_cast<T>(0);
157  for(int index_h = index_h_start; index_h < index_h_end; ++index_h, in_ptr_row += input_stride_h, weights_ptr_row += kernel_stride_h)
158  {
159  const T *in_ptr_mover = in_ptr_row;
160  int index_wc = index_wc_start;
161  vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
162  for(; index_wc <= index_wc_end - num_elems_read_per_iteration; index_wc += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration)
163  {
164  const auto src_vec = wrapper::vloadq(in_ptr_mover);
165  const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wc);
166  out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
167  }
168  out_temp += vreduce(out_temp_vec);
169  for(; index_wc < index_wc_end; ++index_wc, ++in_ptr_mover)
170  {
171  const auto src_val = *(in_ptr_mover);
172  const auto w_val = *(weights_ptr_row + index_wc);
173  out_temp += src_val * w_val;
174  }
175  }
176  *(reinterpret_cast<T *>(out_ptr)) = out_temp;
177  },
178  wei);
179  },
180  out);
181  }
182  else // nhwc non optimized
183  {
185  window_out, [&](const Coordinates & id)
186  {
187  // We are computing the theoretical starting input starting points
188  const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left;
189  const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top;
190  const int in_w_end_t = in_w_start_t + kernel_dim_w;
191  const int in_h_end_t = in_h_start_t + kernel_dim_h;
192 
193  // We are computing the valid initial and ending input points by checking the borders
194  const int in_w_start = std::max(in_w_start_t, 0);
195  const int in_h_start = std::max(in_h_start_t, 0);
196  const int in_w_end = std::min(in_w_end_t, input_dim_w);
197  const int in_h_end = std::min(in_h_end_t, input_dim_h);
198 
199  // We use the input points to select the valid weight points to use
200  const int wei_w_start = in_w_start - in_w_start_t;
201  const int wei_h_start = in_h_start - in_h_start_t;
202  const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end);
203  const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
204 
205  const int index_c_end = weights->info()->dimension(0);
206  const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[3] * input_stride_n;
207 
209  window_w, [&](const Coordinates & id_w)
210  {
211  const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
212  uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c;
213 
214  T out_temp = static_cast<T>(0);
215  for(int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; ++index_wei_h, ++index_in_h)
216  {
217  const T *const in_ptr_row = in_ptr_start + index_in_h * input_stride_h;
218  const T *const weights_ptr_row = weights_ptr_start + index_wei_h * kernel_stride_h;
219  for(int index_wei_w = wei_w_start, index_in_w = in_w_start; index_wei_w < wei_w_end; ++index_wei_w, ++index_in_w)
220  {
221  const T *in_ptr_mover = in_ptr_row + index_in_w * input_stride_w;
222  const T *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w;
223  int index_c = 0;
224  vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
225  for(; index_c <= index_c_end - num_elems_read_per_iteration; index_c += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration, weights_ptr_mover += num_elems_read_per_iteration)
226  {
227  const auto src_vec = wrapper::vloadq(in_ptr_mover);
228  const auto w_vec = wrapper::vloadq(weights_ptr_mover);
229  out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
230  }
231  out_temp += vreduce(out_temp_vec);
232  for(; index_c < index_c_end; ++index_c, ++in_ptr_mover, ++weights_ptr_mover)
233  {
234  const auto src_val = *(in_ptr_mover);
235  const auto w_val = *(weights_ptr_mover);
236  out_temp += src_val * w_val;
237  }
238  }
239  }
240  *(reinterpret_cast<T *>(out_ptr)) = out_temp;
241  },
242  wei);
243  },
244  out);
245  }
246 }
247 
248 template void convolve_nhwc<float>(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info);
249 
250 } // namespace kernels
251 } // namespace cpu
252 } // namespace arm_compute
Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps, bool skip_border, BorderSize border_size)
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
uint8x16_t vloadq(const uint8_t *ptr)
Definition: load.h:58
Describe one of the image&#39;s dimensions with a start, end and step.
Definition: Window.h:79
unsigned int pad_top() const
Get the top padding.
Definition: Types.h:753
const size_t conv_pad_top
Definition: impl.cpp:60
decltype(strategy::transforms) typedef type
Interface for CPU tensor.
Definition: ITensor.h:36
SimpleTensor< float > src
Definition: DFT.cpp:155
Copyright (c) 2017-2022 Arm Limited.
const size_t conv_pad_left
Definition: impl.cpp:59
T x() const
Alias to access the size of the first dimension.
Definition: Dimensions.h:87
static constexpr size_t DimX
Alias for dimension 0 also known as X dimension.
Definition: Window.h:43
template void convolve_nhwc< float >(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
Class to describe a number of elements in each dimension.
Definition: Steps.h:40
T z() const
Alias to access the size of the third dimension.
Definition: Dimensions.h:97
Coordinates of an item.
Definition: Coordinates.h:37
virtual uint8_t * buffer() const =0
Interface to be implemented by the child class to return a pointer to CPU memory. ...
std::pair< unsigned int, unsigned int > stride() const
Get the stride.
Definition: Types.h:717
Create the appropriate SIMD vector given its type and size in terms of bits.
Definition: traits.h:92
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor&#39;s metadata.
constexpr uint8_t * ptr() const
Return a pointer to the current pixel.
Definition: Helpers.inl:139
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()
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
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 constexpr size_t DimY
Alias for dimension 1 also known as Y dimension.
Definition: Window.h:45
float vreduce(const float32x4_t &v)
Reduce a vector to be a scalar by accumulating all lanes in the vector.
Definition: NEMath.inl:421
static constexpr size_t DimZ
Alias for dimension 2 also known as Z dimension.
Definition: Window.h:47
uint8x8_t vdup_n(uint8_t value, traits::vector_64_tag)
Definition: dup_n.h:41
void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators)
Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...
Definition: Helpers.inl:77
T y() const
Alias to access the size of the second dimension.
Definition: Dimensions.h:92
Includes all wrapper headers at once.
virtual const Strides & strides_in_bytes() const =0
The strides in bytes for accessing each dimension of the tensor.
uint8x8_t vmla(const uint8x8_t &a, const uint8x8_t &b, const uint8x8_t &c)
Definition: mla.h:46
Iterator updated by execute_window_loop for each window element.
Definition: Helpers.h:46
unsigned int pad_left() const
Get the left padding.
Definition: Types.h:743
Describe a multidimensional execution window.
Definition: Window.h:39
void convolve_nhwc(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
Definition: impl.cpp:57