1 /*
2 * Copyright (c) 2019-2021 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "src/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h"
25
26 #include "arm_compute/core/Error.h"
27 #include "arm_compute/core/Helpers.h"
28 #include "arm_compute/core/ITensor.h"
29 #include "arm_compute/core/TensorInfo.h"
30 #include "arm_compute/core/Types.h"
31 #include "arm_compute/core/Utils.h"
32 #include "arm_compute/core/Validate.h"
33 #include "arm_compute/core/Window.h"
34 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
35 #include "src/core/NEON/NESymm.h"
36 #include "src/core/helpers/AutoConfiguration.h"
37 #include "src/core/helpers/WindowHelpers.h"
38
39 #include <arm_neon.h>
40
41 namespace arm_compute
42 {
43 namespace cpu
44 {
45 namespace kernels
46 {
47 namespace
48 {
validate_arguments(const ITensorInfo * src,const ITensorInfo * bias,const ITensorInfo * dst,int min,int max)49 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *bias, const ITensorInfo *dst, int min, int max)
50 {
51 ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
52 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::S32);
53 ARM_COMPUTE_RETURN_ERROR_ON(min > max);
54
55 // Check biases if exist
56 if(bias != nullptr)
57 {
58 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, bias);
59 ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
60 ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != bias->dimension(0));
61 }
62
63 if(dst->total_size() != 0)
64 {
65 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::QSYMM16);
66 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, src);
67 }
68
69 return Status{};
70 }
71 } // namespace
72
73 template <bool is_bounded_relu>
run_internal(const ITensor * src,const ITensor * bias,ITensor * dst,const Window & window)74 void CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_internal(const ITensor *src, const ITensor *bias, ITensor *dst, const Window &window)
75 {
76 const int16x8_t min_s16 = vdupq_n_s16(static_cast<int16_t>(_min));
77 const int16x8_t max_s16 = vdupq_n_s16(static_cast<int16_t>(_max));
78
79 ARM_COMPUTE_UNUSED(min_s16);
80 ARM_COMPUTE_UNUSED(max_s16);
81
82 const int window_step_x = 8;
83 const auto window_start_x = static_cast<int>(window.x().start());
84 const auto window_end_x = static_cast<int>(window.x().end());
85
86 Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
87 win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
88
89 Iterator in(src, win_collapsed);
90 Iterator out(dst, win_collapsed);
91 if(bias != nullptr)
92 {
93 Window win_biases;
94 win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
95 win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
96
97 Iterator bias_i(bias, win_biases);
98 execute_window_loop(win_collapsed, [&](const Coordinates &)
99 {
100 // Compute 16 elements per iteration
101 int x = window_start_x;
102 for(; x <= (window_end_x - window_step_x); x += window_step_x)
103 {
104 int32x4x2_t in_s32 =
105 {
106 {
107 vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
108 vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4)
109 }
110 };
111
112 const int32x4x2_t bias_s32 =
113 {
114 {
115 vld1q_s32(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x + 0),
116 vld1q_s32(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x + 4)
117 }
118 };
119
120 // Add the bias to GEMM's result
121 in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
122 in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
123
124 vst1q_s16(reinterpret_cast<int16_t *>(out.ptr()) + x, finalize_quantization_int16<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, min_s16, max_s16));
125 }
126
127 // Compute left-over elements
128 for(; x < window_end_x; ++x)
129 {
130 const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x);
131 int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
132
133 // Add bias
134 in_value += bias_value;
135 // Finalize and store the result
136 *(reinterpret_cast<int16_t *>(out.ptr()) + x) = finalize_quantization_int16<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, static_cast<int16_t>(_min),
137 static_cast<int16_t>(_max));
138 }
139 },
140 in, out, bias_i);
141 }
142 else
143 {
144 execute_window_loop(win_collapsed, [&](const Coordinates &)
145 {
146 // Compute 16 elements per iteration
147 int x = window_start_x;
148 for(; x <= (window_end_x - window_step_x); x += window_step_x)
149 {
150 int32x4x2_t in_s32 =
151 {
152 {
153 vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
154 vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4)
155 }
156 };
157
158 vst1q_s16(reinterpret_cast<int16_t *>(out.ptr()) + x, finalize_quantization_int16<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, min_s16, max_s16));
159 }
160
161 // Compute left-over elements
162 for(; x < window_end_x; ++x)
163 {
164 const int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
165 ARM_COMPUTE_UNUSED(in_value);
166 // Finalize and store the result
167 *(reinterpret_cast<int16_t *>(out.ptr()) + x) = finalize_quantization_int16<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, static_cast<int16_t>(_min),
168 static_cast<int16_t>(_max));
169 }
170 },
171 in, out);
172 }
173 }
174
configure(ITensorInfo * src,ITensorInfo * bias,ITensorInfo * dst,int result_fixedpoint_multiplier,int result_shift,int min,int max)175 void CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::configure(ITensorInfo *src, ITensorInfo *bias, ITensorInfo *dst, int result_fixedpoint_multiplier, int result_shift,
176 int min, int max)
177 {
178 // Perform validate step
179 ARM_COMPUTE_UNUSED(bias, dst);
180 ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
181 ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, bias, dst, min, max));
182
183 _result_fixedpoint_multiplier = result_fixedpoint_multiplier;
184 _result_shift = result_shift;
185 _min = min;
186 _max = max;
187
188 // Output auto inizialitation if not yet initialized
189 auto_init_if_empty(*src, src->clone()->set_data_type(DataType::QSYMM16));
190 // Configure kernel window
191 Window win_config = calculate_max_window(*src, Steps());
192 ICpuKernel::configure(win_config);
193
194 // Check if we need to clamp the result using min and max
195 const bool is_bounded_relu = !(min <= -32768 && max >= 32767);
196 _func = is_bounded_relu ? &CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_internal<true> :
197 &CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_internal<false>;
198 }
199
validate(const ITensorInfo * input,const ITensorInfo * bias,const ITensorInfo * output,int min,int max)200 Status CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
201 {
202 ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
203 ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
204 return Status{};
205 }
206
run_op(ITensorPack & tensors,const Window & window,const ThreadInfo & info)207 void CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
208 {
209 ARM_COMPUTE_UNUSED(info);
210 ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
211 ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
212 ARM_COMPUTE_ERROR_ON_MSG(tensors.empty(), "No inputs provided");
213
214 auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
215 auto bias = tensors.get_const_tensor(TensorType::ACL_BIAS);
216 auto dst = tensors.get_tensor(TensorType::ACL_DST);
217
218 (this->*_func)(src, bias, dst, window);
219 }
220
name() const221 const char *CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::name() const
222 {
223 return "CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel";
224 }
225 } // namespace kernels
226 } // namespace cpu
227 } // namespace arm_compute
228