1 // Copyright 2020 Google LLC
2 //
3 // This source code is licensed under the BSD-style license found in the
4 // LICENSE file in the root directory of this source tree.
5
6 #include <math.h>
7 #include <stddef.h>
8 #include <stdint.h>
9
10 #include <xnnpack.h>
11 #include <xnnpack/log.h>
12 #include <xnnpack/params.h>
13 #include <xnnpack/subgraph.h>
14
15
xnn_define_depthwise_convolution_2d(xnn_subgraph_t subgraph,uint32_t input_padding_top,uint32_t input_padding_right,uint32_t input_padding_bottom,uint32_t input_padding_left,uint32_t kernel_height,uint32_t kernel_width,uint32_t subsampling_height,uint32_t subsampling_width,uint32_t dilation_height,uint32_t dilation_width,uint32_t depth_multiplier,size_t input_channels,float output_min,float output_max,uint32_t input_id,uint32_t filter_id,uint32_t bias_id,uint32_t output_id,uint32_t flags)16 enum xnn_status xnn_define_depthwise_convolution_2d(
17 xnn_subgraph_t subgraph,
18 uint32_t input_padding_top,
19 uint32_t input_padding_right,
20 uint32_t input_padding_bottom,
21 uint32_t input_padding_left,
22 uint32_t kernel_height,
23 uint32_t kernel_width,
24 uint32_t subsampling_height,
25 uint32_t subsampling_width,
26 uint32_t dilation_height,
27 uint32_t dilation_width,
28 uint32_t depth_multiplier,
29 size_t input_channels,
30 float output_min,
31 float output_max,
32 uint32_t input_id,
33 uint32_t filter_id,
34 uint32_t bias_id,
35 uint32_t output_id,
36 uint32_t flags)
37 {
38 if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
39 xnn_log_error("failed to define %s operator: XNNPACK is not initialized",
40 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d));
41 return xnn_status_uninitialized;
42 }
43
44 if (kernel_width == 0 || kernel_height == 0) {
45 xnn_log_error(
46 "failed to define %s operator with %" PRIu32 "x%" PRIu32 " kernel: kernel dimensions must be non-zero",
47 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), kernel_width, kernel_height);
48 return xnn_status_invalid_parameter;
49 }
50
51 if (subsampling_width == 0 || subsampling_height == 0) {
52 xnn_log_error(
53 "failed to define %s operator with %" PRIu32 "x%" PRIu32 " subsampling: subsampling dimensions must be non-zero",
54 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), subsampling_width, subsampling_height);
55 return xnn_status_invalid_parameter;
56 }
57
58 if (dilation_width == 0 || dilation_height == 0) {
59 xnn_log_error(
60 "failed to define %s operator with %" PRIu32 "x%" PRIu32 " dilation: dilation dimensions must be non-zero",
61 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), dilation_width, dilation_height);
62 return xnn_status_invalid_parameter;
63 }
64
65 if (depth_multiplier == 0) {
66 xnn_log_error(
67 "failed to define %s operator with %" PRIu32 " depth multiplier: depth multiplier must be non-zero",
68 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), depth_multiplier);
69 return xnn_status_invalid_parameter;
70 }
71
72 if (input_channels == 0) {
73 xnn_log_error(
74 "failed to define %s operator with %zu input channels: number of channels must be non-zero",
75 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), input_channels);
76 return xnn_status_invalid_parameter;
77 }
78
79 if (isnan(output_min)) {
80 xnn_log_error(
81 "failed to define %s operator with NaN output lower bound: lower bound must be non-NaN",
82 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d));
83 return xnn_status_invalid_parameter;
84 }
85
86 if (isnan(output_max)) {
87 xnn_log_error(
88 "failed to define %s operator with NaN output upper bound: upper bound must be non-NaN",
89 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d));
90 return xnn_status_invalid_parameter;
91 }
92
93 if (output_min >= output_max) {
94 xnn_log_error(
95 "failed to define %s operator with [%.7g, %.7g] output range: lower bound must be below upper bound",
96 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), output_min, output_max);
97 return xnn_status_invalid_parameter;
98 }
99
100 const uint32_t supported_flags = XNN_FLAG_TENSORFLOW_SAME_PADDING;
101 const uint32_t invalid_flags = flags & ~supported_flags;
102 if (invalid_flags != 0) {
103 xnn_log_error(
104 "failed to define %s operator with 0x%08" PRIx32 " flags: invalid flags 0x%08" PRIx32,
105 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), flags, invalid_flags);
106 return xnn_status_invalid_parameter;
107 }
108
109 const bool any_padding = (input_padding_left | input_padding_top | input_padding_right | input_padding_bottom) != 0;
110 if ((flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0 && any_padding) {
111 xnn_log_error(
112 "failed to define %s operator with %" PRIu32 "+%" PRIu32 "x%" PRIu32 "+%" PRIu32" padding: "
113 "TensorFlow SAME padding can't be combined with explicit padding specification",
114 xnn_node_type_to_string(xnn_node_type_convolution_2d),
115 input_padding_top, input_padding_left, input_padding_bottom, input_padding_right);
116 return xnn_status_invalid_parameter;
117 }
118
119 // Convert TensorFlow SAME padding to explicit padding specification whenever possible
120 if ((flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) != 0 && (subsampling_height | subsampling_width) == 1) {
121 flags &= ~XNN_FLAG_TENSORFLOW_SAME_PADDING;
122 const uint32_t padding_height = (kernel_height - 1) * dilation_height;
123 const uint32_t padding_width = (kernel_width - 1) * dilation_width;
124 input_padding_left = padding_width / 2;
125 input_padding_top = padding_height / 2;
126 input_padding_right = padding_width - input_padding_left;
127 input_padding_bottom = padding_height - input_padding_top;
128 }
129
130 if (input_id >= subgraph->num_values) {
131 xnn_log_error(
132 "failed to define %s operator with input ID #%" PRIu32 ": invalid Value ID",
133 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), input_id);
134 return xnn_status_invalid_parameter;
135 }
136
137 if (filter_id >= subgraph->num_values) {
138 xnn_log_error(
139 "failed to define %s operator with filter ID #%" PRIu32 ": invalid Value ID",
140 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), filter_id);
141 return xnn_status_invalid_parameter;
142 }
143
144 if (bias_id >= subgraph->num_values) {
145 xnn_log_error(
146 "failed to define %s operator with bias ID #%" PRIu32 ": invalid Value ID",
147 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), bias_id);
148 return xnn_status_invalid_parameter;
149 }
150
151 if (output_id >= subgraph->num_values) {
152 xnn_log_error(
153 "failed to define %s operator with output ID #%" PRIu32 ": invalid Value ID",
154 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), output_id);
155 return xnn_status_invalid_parameter;
156 }
157
158 struct xnn_node* node = xnn_subgraph_new_node(subgraph);
159 if (node == NULL) {
160 return xnn_status_out_of_memory;
161 }
162
163 node->type = xnn_node_type_depthwise_convolution_2d;
164 node->params.depthwise_convolution_2d.input_padding_top = input_padding_top;
165 node->params.depthwise_convolution_2d.input_padding_right = input_padding_right;
166 node->params.depthwise_convolution_2d.input_padding_bottom = input_padding_bottom;
167 node->params.depthwise_convolution_2d.input_padding_left = input_padding_left;
168 node->params.depthwise_convolution_2d.kernel_height = kernel_height;
169 node->params.depthwise_convolution_2d.kernel_width = kernel_width;
170 node->params.depthwise_convolution_2d.subsampling_height = subsampling_height;
171 node->params.depthwise_convolution_2d.subsampling_width = subsampling_width;
172 node->params.depthwise_convolution_2d.dilation_height = dilation_height;
173 node->params.depthwise_convolution_2d.dilation_width = dilation_width;
174 node->params.depthwise_convolution_2d.depth_multiplier = depth_multiplier;
175 node->params.depthwise_convolution_2d.input_channels = input_channels;
176 node->activation.output_min = output_min;
177 node->activation.output_max = output_max;
178 node->num_inputs = 3;
179 node->inputs[0] = input_id;
180 node->inputs[1] = filter_id;
181 node->inputs[2] = bias_id;
182 node->num_outputs = 1;
183 node->outputs[0] = output_id;
184 node->flags = flags;
185
186 return xnn_status_success;
187 };
188