forked from tensorflow/tflite-micro
-
Notifications
You must be signed in to change notification settings - Fork 0
/
depthwise_conv_hifi.cc
190 lines (166 loc) · 8.71 KB
/
depthwise_conv_hifi.cc
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
/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h"
#include "tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h"
#include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/padding.h"
#include "tensorflow/lite/micro/kernels/depthwise_conv.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h"
#if defined(HIFI4) || defined(HIFI5)
namespace tflite {
TfLiteStatus DepthwiseConvPrepareHifi(TfLiteContext* context,
TfLiteNode* node) {
XtensaDepthwiseConvOpData* data =
static_cast<XtensaDepthwiseConvOpData*>(node->user_data);
const auto& params =
*(static_cast<const TfLiteDepthwiseConvParams*>(node->builtin_data));
MicroContext* micro_context = GetMicroContext(context);
// Calculate scratch memory requirements and request scratch buffer
TfLiteTensor* output =
micro_context->AllocateTempOutputTensor(node, kConvOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
TfLiteTensor* input =
micro_context->AllocateTempInputTensor(node, kConvInputTensor);
TF_LITE_ENSURE(context, input != nullptr);
TfLiteTensor* filter =
micro_context->AllocateTempInputTensor(node, kConvWeightsTensor);
TF_LITE_ENSURE(context, filter != nullptr);
TF_LITE_ENSURE_EQ(context, input->type, kTfLiteInt8);
const RuntimeShape& input_shape = GetTensorShape(input);
const RuntimeShape& filter_shape = GetTensorShape(filter);
const RuntimeShape& output_shape = GetTensorShape(output);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int input_depth = input_shape.Dims(3);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int depth_multiplier = params.depth_multiplier;
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
const int pad_width = data->reference_op_data.padding.width;
const int pad_height = data->reference_op_data.padding.height;
int required_scratch = 0;
// Dilation is currently not supported on HiFi 4 NN Library
if ((params.dilation_width_factor == 1) &&
(params.dilation_height_factor == 1)) {
required_scratch = xa_nn_conv2d_depthwise_getsize(
input_height, input_width, input_depth, filter_height, filter_width,
depth_multiplier, stride_width, stride_height, pad_width, pad_height,
output_height, output_width, PREC_ASYM8S, 0 /* NHWC */);
TF_LITE_ENSURE(context, required_scratch > 0);
}
TF_LITE_ENSURE_OK(
context, context->RequestScratchBufferInArena(
context, required_scratch, &data->scratch_tensor_index));
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(filter);
micro_context->DeallocateTempTfLiteTensor(output);
return kTfLiteOk;
}
TfLiteStatus DepthwiseConvEvalHifi(TfLiteContext* context, TfLiteNode* node,
const TfLiteDepthwiseConvParams& params,
const XtensaDepthwiseConvOpData& data,
const TfLiteEvalTensor* input,
const TfLiteEvalTensor* filter,
const TfLiteEvalTensor* bias,
TfLiteEvalTensor* output) {
// If dilation is not required use the optimized NN Library kernel.
// Otherwise call the reference implementation.
if ((params.dilation_width_factor == 1) &&
(params.dilation_height_factor == 1)) {
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int pad_width = data.reference_op_data.padding.width;
const int pad_height = data.reference_op_data.padding.height;
const int depth_multiplier = params.depth_multiplier;
const int32_t output_activation_min =
data.reference_op_data.output_activation_min;
const int32_t output_activation_max =
data.reference_op_data.output_activation_max;
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
const RuntimeShape& filter_shape = tflite::micro::GetTensorShape(filter);
const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
const RuntimeShape& bias_shape = tflite::micro::GetTensorShape(bias);
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int input_depth = input_shape.Dims(3);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier);
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
const int8_t* input_data = tflite::micro::GetTensorData<int8_t>(input);
const int8_t* filter_data = tflite::micro::GetTensorData<int8_t>(filter);
const int32_t* bias_data = tflite::micro::GetTensorData<int32_t>(bias);
int8_t* output_data = tflite::micro::GetTensorData<int8_t>(output);
int32_t input_data_format = 0;
int32_t output_data_format = 0;
uint8_t* p_scratch = static_cast<uint8_t*>(
context->GetScratchBuffer(context, data.scratch_tensor_index));
for (int i = 0; i < batches; i++) {
TF_LITE_ENSURE_EQ(
context,
xa_nn_conv2d_depthwise_per_chan_sym8sxasym8s(
&output_data[i * output_height * output_width * output_depth],
filter_data,
&input_data[i * input_height * input_width * input_depth],
bias_data, input_height, input_width, input_depth, filter_height,
filter_width, depth_multiplier, stride_width, stride_height,
pad_width, pad_height, output_height, output_width,
-data.reference_op_data.input_zero_point,
data.reference_op_data.per_channel_output_multiplier,
data.reference_op_data.per_channel_output_shift,
data.reference_op_data.output_zero_point, input_data_format,
output_data_format, p_scratch),
0);
}
int out_length = batches * output_height * output_width * output_depth;
TF_LITE_ENSURE_EQ(context,
xa_nn_vec_activation_min_max_8_8(
output_data, output_data, output_activation_min,
output_activation_max, out_length),
0);
return kTfLiteOk;
}
reference_integer_ops::DepthwiseConvPerChannel(
DepthwiseConvParamsQuantized(params, data.reference_op_data),
data.reference_op_data.per_channel_output_multiplier,
data.reference_op_data.per_channel_output_shift,
tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int8_t>(input),
tflite::micro::GetTensorShape(filter),
tflite::micro::GetTensorData<int8_t>(filter),
tflite::micro::GetTensorShape(bias),
tflite::micro::GetTensorData<int32_t>(bias),
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
return kTfLiteOk;
}
} // namespace tflite
#endif // defined(HIFI4) || defined(HIFI5)