1 /*
2 * SPDX-FileCopyrightText: Copyright 2024, Arm Limited and/or its affiliates <open-source-office@arm.com>
3 *
4 * SPDX-License-Identifier: Apache-2.0
5 *
6 * Licensed under the Apache License, Version 2.0 (the License); you may
7 * not use this file except in compliance with the License.
8 * You may obtain a copy of the License at
9 *
10 * www.apache.org/licenses/LICENSE-2.0
11 *
12 * Unless required by applicable law or agreed to in writing, software
13 * distributed under the License is distributed on an AS IS BASIS, WITHOUT
14 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15 * See the License for the specific language governing permissions and
16 * limitations under the License.
17 */
18
19 /* ----------------------------------------------------------------------
20 * Project: CMSIS NN Library
21 * Title: arm_lstm_unidirectional_s8.c
22 * Description: S8 LSTM function with S16 gate output
23 *
24 * $Date: 08 February 2024
25 * $Revision: V.1.1.0
26 *
27 * Target Processor: Cortex-M processors
28 *
29 * -------------------------------------------------------------------- */
30
31 #include "arm_nnfunctions.h"
32 #include "arm_nnsupportfunctions.h"
33 /**
34 * @ingroup Public
35 */
36
37 /**
38 * @addtogroup LSTM
39 * @{
40 */
41
42 /*
43 * S8 LSTM function for TensorFlow Lite with S16 gate output
44 *
45 * Refer to header file for details.
46 *
47 */
48
arm_lstm_unidirectional_s8(const int8_t * input,int8_t * output,const cmsis_nn_lstm_params * params,cmsis_nn_lstm_context * buffers)49 arm_cmsis_nn_status arm_lstm_unidirectional_s8(const int8_t *input,
50 int8_t *output,
51 const cmsis_nn_lstm_params *params,
52 cmsis_nn_lstm_context *buffers)
53 {
54
55 int8_t *hidden_in = NULL;
56 memset(buffers->cell_state, 0, params->batch_size * params->hidden_size * sizeof(int16_t));
57 if (params->time_major)
58 {
59 // First dimension is time, input/output for each time step is stored continously in memory
60 for (int t = 0; t < params->time_steps; t++)
61 {
62 const int8_t *data_in = input + (t * params->batch_size * params->input_size);
63 int8_t *hidden_out = output + (t * params->batch_size * params->hidden_size);
64 arm_cmsis_nn_status status = arm_nn_lstm_step_s8(data_in, hidden_in, hidden_out, params, buffers, 1);
65 if (status != ARM_CMSIS_NN_SUCCESS)
66 {
67 return status;
68 }
69 // Output is used as recurrent input/hidden state for the next timestep.
70 hidden_in = &hidden_out[0];
71 }
72 }
73 else
74 {
75 // First dimension is time, add batch_offset to jump in memory for each batch
76 for (int t = 0; t < params->time_steps; t++)
77 {
78 const int8_t *data_in = input + (t * params->input_size);
79 int8_t *hidden_out = output + (t * params->hidden_size);
80 arm_cmsis_nn_status status =
81 arm_nn_lstm_step_s8(data_in, hidden_in, hidden_out, params, buffers, params->time_steps);
82 if (status != ARM_CMSIS_NN_SUCCESS)
83 {
84 return status;
85 }
86 // Output is used as recurrent input/hidden state for the next timestep.
87 hidden_in = &hidden_out[0];
88 }
89 }
90 return ARM_CMSIS_NN_SUCCESS;
91 }
92
93 /**
94 * @} end of LSTM group
95 */
96
