Compute Library
 22.05
qsymm16.cpp
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24 
27 #include "arm_compute/core/Types.h"
29 #include "src/core/NEON/SVEMath.h"
31 #include <arm_sve.h>
32 
33 namespace arm_compute
34 {
35 namespace cpu
36 {
37 void add_qsymm16_sve2(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
38 {
39  ARM_COMPUTE_UNUSED(policy);
40 
41  // Create input windows
42  Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
43  Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
44 
45  // Clear X Dimension on execution window as we handle manually
46  Window win = window;
47  win.set(Window::DimX, Window::Dimension(0, 1, 1));
48 
49  const auto window_start_x = static_cast<int>(window.x().start());
50  const auto window_end_x = static_cast<int>(window.x().end());
51  const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x();
52 
53  const UniformQuantizationInfo iq1_info = src0->info()->quantization_info().uniform();
54  const UniformQuantizationInfo iq2_info = src1->info()->quantization_info().uniform();
55  const UniformQuantizationInfo oq_info = dst->info()->quantization_info().uniform();
56 
57  const auto vscale1 = svdup_n_f32(iq1_info.scale);
58  const auto vscale2 = svdup_n_f32(iq2_info.scale);
59  const auto invvscaleo = svdup_n_f32(1.f / oq_info.scale);
60  const auto all_true_pg = svptrue_b16();
61 
62  if(is_broadcast_across_x)
63  {
64  const bool is_broadcast_input_2 = input2_win.x().step() == 0;
65  Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
66  Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
67  const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0;
68  const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0;
69 
70  // Clear X Dimension on execution window as we handle manually
71  non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
72 
73  Iterator broadcast_input(broadcast_tensor, broadcast_win);
74  Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
75  Iterator output(dst, win);
76 
77  execute_window_loop(win, [&](const Coordinates &)
78  {
79  const auto non_broadcast_input_ptr = reinterpret_cast<const int16_t *>(non_broadcast_input.ptr());
80  const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());
81 
82  const int16_t broadcast_value = *reinterpret_cast<const int16_t *>(broadcast_input.ptr());
83  const auto broadcast_value_vec = svdup_n_s16(broadcast_value);
84 
85  int x = window_start_x;
86  svbool_t pg = svwhilelt_b16(x, window_end_x);
87 
88  const auto bf_0 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlb_s32(broadcast_value_vec)), vscale2);
89  const auto bf_1 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlt_s32(broadcast_value_vec)), vscale2);
90 
91  do
92  {
93  const auto a = svld1_s16(pg, non_broadcast_input_ptr + x);
94  const auto af_0 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlb_s32(a)), vscale1);
95  const auto af_1 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlt_s32(a)), vscale1);
96 
97  const auto rf_0 = svcvt_s32_f32_z(pg, svmul_f32_z(pg, svadd_f32_z(pg, af_0, bf_0), invvscaleo));
98  const auto rf_1 = svcvt_s32_f32_z(pg, svmul_f32_z(pg, svadd_f32_z(pg, af_1, bf_1), invvscaleo));
99 
100  const auto res = svqxtnt_s32(svqxtnb_s32(rf_0), rf_1);
101 
102  svst1_s16(pg, output_ptr + x, res);
103 
104  x += svcnth();
105  pg = svwhilelt_b16(x, window_end_x);
106  }
107  while(svptest_any(all_true_pg, pg));
108  },
109  broadcast_input, non_broadcast_input, output);
110  }
111  else
112  {
113  // Clear X Dimension on execution window as we handle manually
114  input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
115  input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
116 
117  Iterator input1(src0, input1_win);
118  Iterator input2(src1, input2_win);
119  Iterator output(dst, win);
120 
121  execute_window_loop(win, [&](const Coordinates &)
122  {
123  const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr());
124  const auto input2_ptr = reinterpret_cast<const int16_t *>(input2.ptr());
125  const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());
126 
127  int x = window_start_x;
128  svbool_t pg = svwhilelt_b16(x, window_end_x);
129  do
130  {
131  auto a = svld1_s16(pg, input1_ptr + x);
132  auto b = svld1_s16(pg, input2_ptr + x);
133 
134  const auto af_0 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlb_s32(a)), vscale1);
135  const auto af_1 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlt_s32(a)), vscale1);
136 
137  const auto bf_0 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlb_s32(b)), vscale2);
138  const auto bf_1 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlt_s32(b)), vscale2);
139 
140  const auto rf_0 = svcvt_s32_f32_z(pg, svmul_f32_z(pg, svadd_f32_z(pg, af_0, bf_0), invvscaleo));
141  const auto rf_1 = svcvt_s32_f32_z(pg, svmul_f32_z(pg, svadd_f32_z(pg, af_1, bf_1), invvscaleo));
142 
143  const auto res = svqxtnt_s32(svqxtnb_s32(rf_0), rf_1);
144  svst1_s16(pg, output_ptr + x, res);
145 
146  x += svcnth();
147  pg = svwhilelt_b16(x, window_end_x);
148  }
149  while(svptest_any(all_true_pg, pg));
150  },
151  input1, input2, output);
152  }
153 }
154 } // namespace cpu
155 } // namespace arm_compute
SimpleTensor< float > b
Definition: DFT.cpp:157
constexpr int step() const
Return the step of the dimension.
Definition: Window.h:106
void add_qsymm16_sve2(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
Definition: qsymm16.cpp:37
Quantization info when assuming per layer quantization.
Describe one of the image&#39;s dimensions with a start, end and step.
Definition: Window.h:79
Interface for CPU tensor.
Definition: ITensor.h:36
Copyright (c) 2017-2022 Arm Limited.
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
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
Coordinates of an item.
Definition: Coordinates.h:37
UniformQuantizationInfo uniform() const
Return per layer quantization info.
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
void set(size_t dimension, const Dimension &dim)
Set the values of a given dimension.
Definition: Window.inl:49
Window broadcast_if_dimension_le_one(const TensorShape &shape) const
Don&#39;t advance in the dimension where shape is less equal to 1.
Definition: Window.inl:120
virtual QuantizationInfo quantization_info() const =0
Get the quantization settings (scale and offset) of the tensor.
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
constexpr int end() const
Return the end of the dimension.
Definition: Window.h:101
Iterator updated by execute_window_loop for each window element.
Definition: Helpers.h:46
constexpr int start() const
Return the start of the dimension.
Definition: Window.h:96
Describe a multidimensional execution window.
Definition: Window.h:39
ConvertPolicy
Policy to handle integer overflow.
Definition: Types.h:404
constexpr const Dimension & x() const
Alias to access the first dimension of the window.
Definition: Window.h:158