1 /*
2 * Copyright (c) Facebook, Inc. and its affiliates.
3 * All rights reserved.
4 *
5 * This source code is licensed under the BSD-style license found in the
6 * LICENSE file in the root directory of this source tree.
7 */
8
9 #include <assert.h>
10 #include <math.h>
11 #include <stddef.h>
12 #include <stdint.h>
13 #include <stdlib.h>
14
15 #include <pytorch_qnnpack.h>
16 #include <qnnpack/log.h>
17 #include <qnnpack/operator.h>
18
pytorch_qnnp_create_hardswish_nc_q8(size_t channels,uint8_t input_zero_point,float input_scale,uint8_t output_zero_point,float output_scale,uint8_t output_min,uint8_t output_max,uint32_t flags,pytorch_qnnp_operator_t * hardswish_out)19 enum pytorch_qnnp_status pytorch_qnnp_create_hardswish_nc_q8(
20 size_t channels,
21 uint8_t input_zero_point,
22 float input_scale,
23 uint8_t output_zero_point,
24 float output_scale,
25 uint8_t output_min,
26 uint8_t output_max,
27 uint32_t flags,
28 pytorch_qnnp_operator_t* hardswish_out) {
29 pytorch_qnnp_operator_t hardswish_op = NULL;
30 enum pytorch_qnnp_status status = pytorch_qnnp_status_uninitialized;
31
32 if (!pytorch_qnnp_params.initialized) {
33 pytorch_qnnp_log_error(
34 "pytorch_qnnp_create_hardswish_nc_q8 failed because QNNPACK is not properly initialized");
35 goto error;
36 }
37
38 status = pytorch_qnnp_status_invalid_parameter;
39
40 if (channels == 0) {
41 pytorch_qnnp_log_error(
42 "failed to create Hardswish operator with %zu channels: number of channels must be non-zero",
43 channels);
44 goto error;
45 }
46
47 if (input_scale <= 0.0f || !isnormal(input_scale)) {
48 pytorch_qnnp_log_error(
49 "failed to create Hardswish operator with %.7g input scale: scale must be finite and positive",
50 input_scale);
51 goto error;
52 }
53
54 if (output_scale <= 0.0f || !isnormal(output_scale)) {
55 pytorch_qnnp_log_error(
56 "failed to create Hardswish operator with %.7g output scale: scale must be finite and positive",
57 output_scale);
58 goto error;
59 }
60
61 if (output_min >= output_max) {
62 pytorch_qnnp_log_error(
63 "failed to create Hardswish operator with [%" PRIu8 ", %" PRIu8
64 "] output range: range min must be below range max",
65 output_min,
66 output_max);
67 goto error;
68 }
69
70 status = pytorch_qnnp_status_out_of_memory;
71
72 hardswish_op = calloc(1, sizeof(struct pytorch_qnnp_operator));
73 if (hardswish_op == NULL) {
74 pytorch_qnnp_log_error(
75 "failed to allocate %zu bytes for pytorch_qnnp_operator structure",
76 sizeof(struct pytorch_qnnp_operator));
77 goto error;
78 }
79
80 hardswish_op->lookup_table = malloc(256 * sizeof(uint8_t));
81 if (hardswish_op->lookup_table == NULL) {
82 pytorch_qnnp_log_error(
83 "failed to allocate 256 bytes for Hardswish lookup table");
84 goto error;
85 }
86
87 uint8_t* lookup_table = hardswish_op->lookup_table;
88 const float scaled_min = (float)(int32_t)output_min;
89 const float scaled_max = (float)(int32_t)output_max;
90 const float inv_output_scale = 1.0f / output_scale;
91 for (int32_t i = 0; i < 256; i++) {
92 float x =
93 input_scale * (float)(i - (int32_t)(uint32_t)input_zero_point);
94 // hardswish, no min/max functions in C
95 float x2 = x + 3.0f;
96 x2 = x2 > 0.0f ? x2 : 0.0f;
97 x2 = x2 < 6.0f ? x2 : 6.0f;
98 x2 = x * x2 / 6.0f;
99 float scaled_hardswish_x = inv_output_scale * x2 + output_zero_point;
100 if (scaled_hardswish_x < scaled_min) {
101 scaled_hardswish_x = scaled_min;
102 }
103 if (scaled_hardswish_x > scaled_max) {
104 scaled_hardswish_x = scaled_max;
105 }
106 lookup_table[(uint32_t)i] = (uint8_t)lrintf(scaled_hardswish_x);
107 }
108
109 hardswish_op->channels = channels;
110
111 hardswish_op->ukernel_type = pytorch_qnnp_ukernel_type_lut;
112 hardswish_op->format = pytorch_qnnp_format_quint8;
113
114 *hardswish_out = hardswish_op;
115 return pytorch_qnnp_status_success;
116
117 error:
118 pytorch_qnnp_delete_operator(hardswish_op);
119 return status;
120 }
121
pytorch_qnnp_setup_hardswish_nc_q8(pytorch_qnnp_operator_t hardswish,size_t batch_size,const uint8_t * input,size_t input_stride,uint8_t * output,size_t output_stride)122 enum pytorch_qnnp_status pytorch_qnnp_setup_hardswish_nc_q8(
123 pytorch_qnnp_operator_t hardswish,
124 size_t batch_size,
125 const uint8_t* input,
126 size_t input_stride,
127 uint8_t* output,
128 size_t output_stride) {
129 if (!pytorch_qnnp_params.initialized) {
130 pytorch_qnnp_log_error(
131 "pytorch_qnnp_setup_hardswish_nc_q8 failed because QNNPACK is not properly initialized");
132 return pytorch_qnnp_status_uninitialized;
133 }
134
135 if (batch_size == 0) {
136 hardswish->batch_size = 0;
137 return pytorch_qnnp_status_success;
138 }
139
140 hardswish->batch_size = batch_size;
141 hardswish->input = input;
142 hardswish->input_pixel_stride = input_stride;
143 hardswish->output = output;
144 hardswish->output_pixel_stride = output_stride;
145
146 return pytorch_qnnp_status_success;
147 }
148