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
|
/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (c) 2017-2018 ARM Limited.
*
* 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 "arm_compute/core/CL/kernels/CLReduceOperationKernel.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibraryEx.h"
#include "arm_compute/core/CL/ICLTensor.h"
using namespace arm_compute;
namespace
{
// NOTE This is necessary because it is not guaranteed that the axis positions of input and output
// are the same.
const TensorShape inferOutputShape(const TensorShape &input_shape, const uint32_t axis)
{
TensorShape out_shape{input_shape};
out_shape.set(axis, 1);
return out_shape;
}
} // namespace
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const uint32_t axis,
ReduceOperation op)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
if (output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
}
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16,
DataType::F32, DataType::S32);
if (op == ReduceOperation::SUM)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QASYMM8,
"Not support QASYMM8, yet");
}
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->tensor_shape().total_size() == 0,
"Inputs are not broadcast compatible");
const auto num_dimensions = input->tensor_shape().num_dimensions();
ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= num_dimensions, "axis must be less than (input's rank).");
const TensorShape output_shape = inferOutputShape(input->tensor_shape(), axis);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_shape.total_size() != output->tensor_shape().total_size(),
"output shape's size does not match axis");
return Status{};
}
} // namespace
CLReduceOperationKernel::CLReduceOperationKernel() : _input(nullptr), _output(nullptr), _axis() {}
void CLReduceOperationKernel::configure(const ICLTensor *input, ICLTensor *output,
const uint32_t axis, ReduceOperation op)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op));
_input = input;
_output = output;
_axis = axis;
std::unique_ptr<ITensorInfo> output_info = output->info()->clone();
output_info->set_tensor_shape(inferOutputShape(input->info()->tensor_shape(), axis));
// Construct kernel name
std::string kernel_name;
int op_code = 0;
if (op == ReduceOperation::MAX)
{
kernel_name = "reduce_min_max";
op_code = 1;
}
else if (op == ReduceOperation::MIN)
{
kernel_name = "reduce_min_max";
op_code = 2;
}
else if (op == ReduceOperation::SUM)
{
kernel_name = "reduce_sum_mean";
op_code = 3;
}
else if (op == ReduceOperation::MEAN)
{
kernel_name = "reduce_sum_mean";
op_code = 4;
}
else
throw std::runtime_error("Operation not supported, yet");
// Set kernel build options
std::set<std::string> build_opts;
build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(output_info->data_type()));
build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output_info->dimension(2)));
build_opts.emplace("-DOP_CODE=" + support::cpp11::to_string(op_code));
// Create kernel
_kernel =
static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts));
// Configure kernel window
Window win = calculate_max_window(*output_info, Steps());
Coordinates coord;
coord.set_num_dimensions(output_info->num_dimensions());
output->info()->set_valid_region(ValidRegion(coord, output_info->tensor_shape()));
ICLKernel::configure_internal(win);
}
Status CLReduceOperationKernel::validate(const ITensorInfo *input, const ITensorInfo *output,
const uint32_t axis, ReduceOperation op)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, axis, op));
return Status{};
}
void CLReduceOperationKernel::run(const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
const TensorShape &shape_in = _input->info()->tensor_shape();
unsigned int idx = 2 * num_arguments_per_4D_tensor(); // Skip the input and output parameters
_kernel.setArg<cl_int>(idx++, _axis);
_kernel.setArg<cl_int>(idx++, shape_in[_axis]);
// Support dimensions up to 4
Window slice_out = window.collapse(ICLKernel::window(), 2, 4);
// Setup input slice
Window slice_in(slice_out);
slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));
slice_in.set(3, Window::Dimension(0, 0, 0));
// Copy output's shape in order to use for recovering at end of this method
// TODO Remove changing and recovering output's shape if it is guaranteed that the axis positions
// of input and output are the same
const TensorShape shape_out = _output->info()->tensor_shape();
_output->info()->set_tensor_shape(inferOutputShape(shape_in, _axis));
idx = 0;
add_4D_tensor_argument(idx, _input, slice_in);
add_4D_tensor_argument(idx, _output, slice_out);
enqueue(queue, *this, slice_out, lws_hint());
// Recover output's shape of output tensor
_output->info()->set_tensor_shape(shape_out);
}
|
