Compute Library
 22.11
all.cpp
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1 /*
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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 namespace arm_compute
41 {
42 namespace cpu
43 {
44 namespace kernels
45 {
46 template <typename T>
47 void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info);
48 
49 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
50 void neon_fp16_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
51 {
52  convolve_nchw<float16_t>(window, src, weights, dst, conv_info);
53 }
54 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
55 
56 void neon_fp32_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
57 {
58  convolve_nchw<float>(window, src, weights, dst, conv_info);
59 }
60 
61 template <typename T>
62 void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
63 {
64  ARM_COMPUTE_UNUSED(conv_info);
65 
66  // Declare useful types
68  using vector_type = typename vtype::type;
69  using tag_type = typename vtype::tag_type;
70 
71  // Scalar quantities
72  const int element_size = src->info()->element_size();
73  const int input_stride_w = src->info()->strides_in_bytes()[0] / element_size;
74  const int input_stride_h = src->info()->strides_in_bytes()[1] / element_size;
75  const int input_stride_c = src->info()->strides_in_bytes()[2] / element_size;
76  const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size;
77 
78  const int input_dim_w = src->info()->dimension(0);
79  const int input_dim_h = src->info()->dimension(1);
80 
81  const int output_stride_c = dst->info()->strides_in_bytes()[2];
82 
83  const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().x() / element_size;
84  const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().y() / element_size;
85  const unsigned int kernel_stride_c = weights->info()->strides_in_bytes().z() / element_size;
86 
87  const int kernel_dim_w = weights->info()->dimension(0);
88  const int kernel_dim_h = weights->info()->dimension(1);
89 
90  const int conv_pad_top = conv_info.pad_top();
91  const int conv_pad_left = conv_info.pad_left();
92  const int conv_stride_w = std::get<0>(conv_info.stride());
93  const int conv_stride_h = std::get<1>(conv_info.stride());
94 
95  // Setup input window for the output iterator
96  Window window_out = window;
97  window_out.set(Window::DimZ, Window::Dimension(0, 1, 1));
98 
99  // Setup input window for the weights iterator
100  Window window_w = calculate_max_window(*weights->info(), Steps());
101  window_w.set(Window::DimX, Window::Dimension(0, 1, 1));
102  window_w.set(Window::DimY, Window::Dimension(0, 1, 1));
103  window_w.set(Window::DimZ, Window::Dimension(0, 1, 1));
104 
105  Iterator out(dst, window_out);
106  Iterator wei(weights, window_w);
107 
108  constexpr int num_elems_read_per_iteration = 16 / sizeof(T);
109 
110  execute_window_loop(window_out, [&](const Coordinates & id)
111  {
112  // We are computing the theoretical starting input starting points
113  const int in_w_start_t = static_cast<int>(id.x()) * conv_stride_w - conv_pad_left;
114  const int in_h_start_t = static_cast<int>(id.y()) * conv_stride_h - conv_pad_top;
115  const int in_w_end_t = in_w_start_t + kernel_dim_w;
116  const int in_h_end_t = in_h_start_t + kernel_dim_h;
117 
118  // We are computing the valid initial and ending input points by checking the borders
119  const int in_w_start = std::max(in_w_start_t, 0);
120  const int in_h_start = std::max(in_h_start_t, 0);
121  const int in_w_end = std::min(in_w_end_t, input_dim_w);
122  const int in_h_end = std::min(in_h_end_t, input_dim_h);
123 
124  // We use the input points to select the valid weight points to use
125  const int wei_w_start = in_w_start - in_w_start_t;
126  const int wei_h_start = in_h_start - in_h_start_t;
127  const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end);
128 
129  const int index_c_end = weights->info()->dimension(2);
130  const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[3] * input_stride_n;
131  execute_window_loop(window_w, [&](const Coordinates & id_w)
132  {
133  const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr());
134  uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c;
135  T out_temp = static_cast<T>(0);
136 
137  for(int index_wei_c = 0, index_in_c = 0; index_wei_c < index_c_end; ++index_wei_c, ++index_in_c)
138  {
139  const T *const in_ptr_row_0 = in_ptr_start + index_in_c * input_stride_c;
140  const T *const weights_ptr_row_0 = weights_ptr_start + index_wei_c * kernel_stride_c;
141  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)
142  {
143  const T *in_ptr_row = in_ptr_row_0 + index_in_h * input_stride_h;
144  const T *weights_ptr_row = weights_ptr_row_0 + index_wei_h * kernel_stride_h;
145  int index_w = in_w_start;
146  int index_wei_w = wei_w_start;
147  vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type());
148  for(; index_w <= ((in_w_end - num_elems_read_per_iteration)); index_w += num_elems_read_per_iteration, index_wei_w += num_elems_read_per_iteration)
149  {
150  const auto src_vec = wrapper::vloadq(in_ptr_row + index_w * input_stride_w);
151  const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wei_w * kernel_stride_w);
152  out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec);
153  }
154  out_temp += vreduce(out_temp_vec);
155  for(; index_w < in_w_end; ++index_w, ++index_wei_w)
156  {
157  const auto src_val = *(in_ptr_row + index_w * input_stride_w);
158  const auto w_val = *(weights_ptr_row + index_wei_w * kernel_stride_w);
159  out_temp += src_val * w_val;
160  }
161  }
162  }
163  *(reinterpret_cast<T *>(out_ptr)) = out_temp;
164 
165  },
166  wei);
167  },
168  out);
169 }
170 
171 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
172 template void convolve_nchw<float16_t>(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info);
173 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
174 
175 template void convolve_nchw<float>(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info);
176 
177 } // namespace kernels
178 } // namespace cpu
179 } // 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.
void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
Definition: all.cpp:62
void neon_fp16_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
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.
template void convolve_nchw< float >(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
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
#define ARM_COMPUTE_UNUSED(...)
To avoid unused variables warnings.
Definition: Error.h:152
void neon_fp32_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info)
Definition: all.cpp:56
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:458
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