aboutsummaryrefslogtreecommitdiff
path: root/arm_compute/runtime/CL/functions/CLLSTMLayerQuantized.h
blob: 12040a91548d6eecb27fdaa93463d7bf708479db (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
/*
 * Copyright (c) 2019 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#ifndef __ARM_COMPUTE_CLLSTMLAYERQUANTIZED_H__
#define __ARM_COMPUTE_CLLSTMLAYERQUANTIZED_H__

#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
#include "arm_compute/runtime/CL/functions/CLConcatenateLayer.h"
#include "arm_compute/runtime/CL/functions/CLDequantizationLayer.h"
#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
#include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h"
#include "arm_compute/runtime/CL/functions/CLQuantizationLayer.h"
#include "arm_compute/runtime/CL/functions/CLSlice.h"
#include "arm_compute/runtime/CL/functions/CLTranspose.h"

#include "arm_compute/runtime/common/LSTMParams.h"

namespace arm_compute
{
// Forward declarations
class ICLTensor;

/** Basic function to run @ref CLLSTMLayerQuantized
 *
 * This function calls the following CL functions/kernels:
 *
 * -# @ref CLGEMMLowpMatrixMultiplyCore                          Quantized matrix multiplication core. Accumulators are 32-bit integers
 * -# @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint   Convert 32-bit integers into QSYMM16
 * -# @ref CLTranspose                                           Matrix transpose
 * -# @ref CLConcatenateLayer                                    Tensor concatenation
 * -# @ref CLActivationLayer                                     Activation functions (tanh and logistic)
 * -# @ref CLArithmeticAddition                                  Elementwise addition
 * -# @ref CLPixelWiseMultiplication                             Elementwise multiplication
 * -# @ref CLSlice                                               Tensor slicing
 * -# @ref CLDequantizationLayer                                 Dequantize into float
 * -# @ref CLQuantizationLayer                                   Quantize from float
 * */
class CLLSTMLayerQuantized : public IFunction
{
public:
    /** Default constructor */
    CLLSTMLayerQuantized(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLLSTMLayerQuantized(const CLLSTMLayerQuantized &) = delete;
    /** Default move constructor */
    CLLSTMLayerQuantized(CLLSTMLayerQuantized &&) = default;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLLSTMLayerQuantized &operator=(const CLLSTMLayerQuantized &) = delete;
    /** Default move assignment operator */
    CLLSTMLayerQuantized &operator=(CLLSTMLayerQuantized &&) = default;
    /** Initialize function's tensors.
     *
     * @param[in]  input                       Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: QASYMM8.
     * @param[in]  input_to_input_weights      2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  input_to_forget_weights     2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  input_to_cell_weights       2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  input_to_output_weights     2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  recurrent_to_input_weights  2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  recurrent_to_cell_weights   2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  recurrent_to_output_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  input_gate_bias             1D weights tensor with dimensions [output_size]. Data type supported: S32.
     * @param[in]  forget_gate_bias            1D weights tensor with dimensions [output_size]. Data type supported: S32.
     * @param[in]  cell_bias                   1D weights tensor with dimensions [output_size]. Data type supported: S32.
     * @param[in]  output_gate_bias            1D weights tensor with dimensions [output_size]. Data type supported: S32.
     * @param[in]  cell_state_in               2D tensor with dimensions [output_size, batch_size]. Data type supported:  QSYMM16.
     * @param[in]  output_state_in             2D tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
     * @param[out] cell_state_out              Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size]. Data type supported:  QSYMM16.
     * @param[out] output_state_out            Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as @p input.
     */
    void configure(const ICLTensor *input,
                   const ICLTensor *input_to_input_weights, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
                   const ICLTensor *recurrent_to_input_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
                   const ICLTensor *input_gate_bias, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
                   ICLTensor *cell_state_in, const ICLTensor *output_state_in,
                   ICLTensor *cell_state_out, ICLTensor *output_state_out);

    /** Static function to check if given info will lead to a valid configuration of @ref CLLSTMLayerQuantized
     *
     * @param[in]  input                       Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8.
     * @param[in]  input_to_input_weights      2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  input_to_forget_weights     2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  input_to_cell_weights       2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  input_to_output_weights     2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  recurrent_to_input_weights  2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  recurrent_to_cell_weights   2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input.
     * @param[in]  input_gate_bias             1D weights tensor info with dimensions [output_size]. Data type supported: S32.
     * @param[in]  forget_gate_bias            1D weights tensor info with dimensions [output_size]. Data type supported: S32.
     * @param[in]  cell_bias                   1D weights tensor info with dimensions [output_size]. Data type supported: S32.
     * @param[in]  output_gate_bias            1D weights tensor info with dimensions [output_size]. Data type supported: S32.
     * @param[in]  cell_state_in               2D tensor info with dimensions [output_size, batch_size]. Data type supported:  QSYMM16.
     * @param[in]  output_state_in             2D tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
     * @param[out] cell_state_out              Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size]. Data type supported:  QSYMM16.
     * @param[out] output_state_out            Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input.
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input,
                           const ITensorInfo *input_to_input_weights, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
                           const ITensorInfo *recurrent_to_input_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
                           const ITensorInfo *input_gate_bias, const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
                           const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in,
                           const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out);

    // Inherited methods overridden:
    void run() override;
    void prepare() override;

private:
    MemoryGroup _memory_group;

    // Functions used
    CLGEMMLowpMatrixMultiplyCore                        _gemmlowp;
    CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint _output_stage;
    CLTranspose                                         _transpose_weights;
    CLConcatenateLayer                                  _concat_input_weights;
    CLConcatenateLayer                                  _concat_recurrent_weights;
    CLConcatenateLayer                                  _concat_weights;
    CLConcatenateLayer                                  _concat_inputs;
    CLConcatenateLayer                                  _concat_bias;
    CLActivationLayer                                   _sigmoid_forget_gate;
    CLActivationLayer                                   _sigmoid_input_gate;
    CLActivationLayer                                   _sigmoid_output_gate;
    CLActivationLayer                                   _tanh_modulation_gate;
    CLActivationLayer                                   _tanh_output_state;
    CLArithmeticAddition                                _add_cell_state_tmps;
    CLArithmeticAddition                                _add2;
    CLPixelWiseMultiplication                           _mul_forget_gate_cell_state;
    CLPixelWiseMultiplication                           _mul_input_gate_input_mod_gate;
    CLPixelWiseMultiplication                           _mul_output_state_tmp_output_gate;
    CLSlice                                             _slice_input_tensor;
    CLSlice                                             _slice_forget_tensor;
    CLSlice                                             _slice_cell_tensor;
    CLSlice                                             _slice_output_tensor;
    CLDequantizationLayer                               _dequantize;
    CLQuantizationLayer                                 _quantize;

    // Tensor pointers
    const ICLTensor *_input_to_input_weights;
    const ICLTensor *_input_to_forget_weights;
    const ICLTensor *_input_to_cell_weights;
    const ICLTensor *_input_to_output_weights;
    const ICLTensor *_recurrent_to_input_weights;
    const ICLTensor *_recurrent_to_forget_weights;
    const ICLTensor *_recurrent_to_cell_weights;
    const ICLTensor *_recurrent_to_output_weights;
    const ICLTensor *_input_gate_bias;
    const ICLTensor *_forget_gate_bias;
    const ICLTensor *_cell_bias;
    const ICLTensor *_output_gate_bias;

    // Temporary tensors
    CLTensor _recurrent_weights;
    CLTensor _input_weights;
    CLTensor _weights;
    CLTensor _input;
    CLTensor _weights_transposed;
    CLTensor _output_highp;
    CLTensor _output_lowp;
    CLTensor _bias;
    CLTensor _forget_gate_input;
    CLTensor _input_gate_input;
    CLTensor _output_gate_input;
    CLTensor _input_modulation_gate_input;
    CLTensor _forget_gate_output;
    CLTensor _input_gate_output;
    CLTensor _output_gate_output;
    CLTensor _input_modulation_gate_output;
    CLTensor _cell_state_tmp1;
    CLTensor _cell_state_tmp2;
    CLTensor _output_state_tmp;
    CLTensor _output_state_out_symm;
    CLTensor _output_state_out_f32;

    bool _is_prepared;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_CLLSTMLAYERQUANTIZED_H__ */