aboutsummaryrefslogtreecommitdiff
path: root/src/core/CL/kernels/CLElementwiseOperationKernel.cpp
blob: 7cc6fb38b11e60d9722f77af2811f71e3b9b750d (plain)
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
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
/*
 * Copyright (c) 2018-2020 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"

#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLValidate.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/utils/misc/Cast.h"
#include "support/StringSupport.h"
#include <map>

namespace arm_compute
{
namespace
{
constexpr unsigned int num_elems_processed_per_iteration = 16;

std::map<ArithmeticOperation, std::string> supported_arithmetic_ops =
{
    { ArithmeticOperation::ADD, "ADD" },
    { ArithmeticOperation::SUB, "SUB" },
    { ArithmeticOperation::DIV, "DIV" },
    { ArithmeticOperation::SQUARED_DIFF, "SQUARED_DIFF" },
    { ArithmeticOperation::MIN, "MIN" },
    { ArithmeticOperation::MAX, "MAX" },
    { ArithmeticOperation::POWER, "POWER" },
    { ArithmeticOperation::PRELU, "PRELU" },
};

std::map<ArithmeticOperation, std::string> supported_sat_arithmetic_ops =
{
    { ArithmeticOperation::ADD, "ADD" },
    { ArithmeticOperation::SUB, "SUB" },
};

std::string generate_id_for_tuning_common(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
{
    std::string config_id;
    // Set config_id for enabling LWS tuning
    config_id = kernel_name;
    config_id += "_";
    config_id += lower_string(string_from_data_type(input1.data_type()));
    config_id += "_";
    config_id += support::cpp11::to_string(output.dimension(0));
    config_id += "_";
    config_id += support::cpp11::to_string(output.dimension(1));
    return config_id;
}

Status validate_arguments_with_float_only_supported_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(&input1, &input2, &output);
    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::F16, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);

    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());

    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");

    // Validate in case of configured output
    if(output.total_size() > 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::F16, DataType::F32);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
                                        "Wrong shape for output");
    }

    return Status{};
}

Status validate_arguments_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
{
    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                         DataType::S16, DataType::QSYMM16, DataType::F16,
                                                         DataType::S32, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                         DataType::S16, DataType::QSYMM16, DataType::F16,
                                                         DataType::S32, DataType::F32);

    const bool is_quantized = is_data_type_quantized(input1.data_type()) || is_data_type_quantized(input2.data_type());
    if(is_quantized)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);

        if(is_data_type_quantized_symmetric(input1.data_type()))
        {
            const int32_t in1_offset = input1.quantization_info().uniform().offset;
            const int32_t in2_offset = input2.quantization_info().uniform().offset;
            ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_offset != 0, "For quantized symmetric, offset must be zero");
            ARM_COMPUTE_RETURN_ERROR_ON_MSG(in2_offset != 0, "For quantized symmetric, offset must be zero");
        }
    }

    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());

    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");

    // Validate in case of configured output
    if(output.total_size() > 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                             DataType::S16, DataType::QSYMM16, DataType::F16,
                                                             DataType::S32, DataType::F32);
        ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)),
                                        "Output can only be U8 if both inputs are U8");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
                                        "Wrong shape for output");

        if(is_quantized)
        {
            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);

            if(is_data_type_quantized_symmetric(output.data_type()))
            {
                const int32_t offset = output.quantization_info().uniform().offset;
                ARM_COMPUTE_RETURN_ERROR_ON_MSG(offset != 0, "For quantized symmetric, offset must be zero");
            }
        }
    }
    return Status{};
}

CLBuildOptions generate_build_options_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, const std::string &operation_string)
{
    CLBuildOptions build_opts;

    build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1.data_type()));
    build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2.data_type()));
    build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output.data_type()));
    build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
    build_opts.add_option("-DOP=" + operation_string);
    if(is_data_type_quantized(input1.data_type()))
    {
        const UniformQuantizationInfo iq1info = input1.quantization_info().uniform();
        const UniformQuantizationInfo iq2info = input2.quantization_info().uniform();
        const UniformQuantizationInfo oqinfo  = output.quantization_info().uniform();

        build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(iq1info.offset));
        build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(iq2info.offset));
        build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(oqinfo.offset));
        build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq1info.scale));
        build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(iq2info.scale));
        build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oqinfo.scale));
    }
    return build_opts;
}

std::pair<Status, Window> configure_window_arithmetic_common(const ValidRegion &valid_region, ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
{
    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
    Window win_input1 = win.broadcast_if_dimension_le_one(input1);
    Window win_input2 = win.broadcast_if_dimension_le_one(input2);

    AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
    AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
    AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration);

    bool window_changed = update_window_and_padding(win_input1, input1_access)
                          || update_window_and_padding(win_input2, input2_access)
                          || update_window_and_padding(win, output_access);

    output_access.set_valid_region(win, valid_region);

    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
    return std::make_pair(err, win);
}

std::pair<Status, Window> validate_and_configure_window_for_arithmetic_operators(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
{
    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
    const TensorShape &out_shape    = broadcast_pair.first;
    const ValidRegion &valid_region = broadcast_pair.second;

    set_shape_if_empty(output, out_shape);

    if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16)
    {
        set_format_if_unknown(output, Format::S16);
    }
    else if(input1.data_type() == DataType::F16 || input2.data_type() == DataType::F16)
    {
        set_format_if_unknown(output, Format::F16);
    }
    else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32)
    {
        set_format_if_unknown(output, Format::F32);
    }
    else if(input1.data_type() == DataType::QASYMM8 || input2.data_type() == DataType::QASYMM8)
    {
        set_data_type_if_unknown(output, DataType::QASYMM8);
    }
    else if(input1.data_type() == DataType::QASYMM8_SIGNED || input2.data_type() == DataType::QASYMM8_SIGNED)
    {
        set_data_type_if_unknown(output, DataType::QASYMM8_SIGNED);
    }
    else if(input1.data_type() == DataType::QSYMM16 || input2.data_type() == DataType::QSYMM16)
    {
        set_data_type_if_unknown(output, DataType::QSYMM16);
    }

    return configure_window_arithmetic_common(valid_region, input1, input2, output);
}

std::pair<Status, Window> validate_and_configure_window_for_division(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
{
    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
    const TensorShape &out_shape    = broadcast_pair.first;
    const ValidRegion &valid_region = broadcast_pair.second;
    auto_init_if_empty(output, out_shape, 1, input1.data_type());
    return configure_window_arithmetic_common(valid_region, input1, input2, output);
}
} // namespace

CLElementwiseOperationKernel::CLElementwiseOperationKernel()
    : _act_info(), _input1(nullptr), _input2(nullptr), _output(nullptr)
{
}

void CLElementwiseOperationKernel::configure_common(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
{
    configure_common(CLKernelLibrary::get().get_compile_context(), input1, input2, output);
}

void CLElementwiseOperationKernel::configure_common(const CLCompileContext &compile_context, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
{
    // Configure kernel window
    auto win_config = validate_and_configure_window(*input1, *input2, *output);
    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

    _input1 = input1;
    _input2 = input2;
    _output = output;

    std::string kernel_name = "elementwise_operation_" + name();
    if(is_data_type_quantized(input1->data_type()))
    {
        kernel_name += "_quantized";
    }

    // Set kernel build options
    CLBuildOptions build_opts = generate_build_options(*input1, *input2, *output);
    if(_act_info.enabled())
    {
        build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(_act_info.activation())));
        build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(_act_info.a()));
        build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(_act_info.b()));
    }

    // Create kernel
    _kernel = create_kernel(compile_context, kernel_name, build_opts.options());

    ICLKernel::configure_internal(win_config.second);

    _config_id = generate_id_for_tuning(kernel_name, *input1, *output);
}

void CLElementwiseOperationKernel::run_op(const InputTensorMap &inputs, const OutputTensorMap &outputs, const Window &window, cl::CommandQueue &queue)
{
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

    const auto src_0 = utils::cast::polymorphic_downcast<const ICLTensor *>(inputs.at(TensorType::ACL_SRC_0));
    const auto src_1 = utils::cast::polymorphic_downcast<const ICLTensor *>(inputs.at(TensorType::ACL_SRC_1));
    auto       dst   = utils::cast::polymorphic_downcast<ICLTensor *>(outputs.at(TensorType::ACL_DST));

    const TensorShape &in_shape1 = src_0->info()->tensor_shape();
    const TensorShape &in_shape2 = src_1->info()->tensor_shape();
    const TensorShape &out_shape = dst->info()->tensor_shape();

    bool       can_collapse = true;
    const bool is_vector    = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1;
    if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector)
    {
        can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
        for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
        {
            can_collapse = (in_shape1[d] == in_shape2[d]);
        }
    }

    bool   has_collapsed = false;
    Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;

    const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
    const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;

    Window slice        = collapsed.first_slice_window_3D();
    Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
    Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);

    do
    {
        unsigned int idx = 0;

        add_3D_tensor_argument(idx, src_0, slice_input1);
        add_3D_tensor_argument(idx, src_1, slice_input2);
        add_3D_tensor_argument(idx, dst, slice);

        enqueue(queue, *this, slice, lws_hint());

        ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
        ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
    }
    while(collapsed.slide_window_slice_3D(slice));
}

BorderSize CLElementwiseOperationKernel::border_size() const
{
    const unsigned int replicateSize = _output->dimension(0) - std::min(_input1->dimension(0), _input2->dimension(0));
    const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
    return BorderSize{ 0, border, 0, 0 };
}

/** Arithmetic operations with saturation*/

void CLSaturatedArithmeticOperationKernel::configure(ArithmeticOperation op, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const ConvertPolicy &policy,
                                                     const ActivationLayerInfo &act_info)
{
    configure(CLKernelLibrary::get().get_compile_context(), op, input1, input2, output, policy, act_info);
}

void CLSaturatedArithmeticOperationKernel::configure(const CLCompileContext &compile_context, ArithmeticOperation op, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output,
                                                     const ConvertPolicy       &policy,
                                                     const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_ERROR_THROW_ON(CLSaturatedArithmeticOperationKernel::validate(op, input1, input2, output, policy, act_info));

    _policy   = policy;
    _op       = op;
    _act_info = act_info;
    configure_common(compile_context, input1, input2, output);
}

Status CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ConvertPolicy &policy,
                                                      const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_UNUSED(op, policy);
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output));
    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first);
    ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(output->data_type()));

    return Status{};
}

std::pair<Status, Window> CLSaturatedArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
{
    return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
}

CLBuildOptions CLSaturatedArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
{
    const bool has_float_out = is_data_type_float(output.data_type());
    auto       build_options = generate_build_options_with_arithmetic_rules(input1, input2, output, name());
    build_options.add_option((_policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE");
    return build_options;
}
std::string CLSaturatedArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
{
    auto config_id = generate_id_for_tuning_common(kernel_name, input1, output);
    config_id += (_policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_";
    config_id += lower_string(string_from_data_layout(input1.data_layout()));
    return config_id;
}

std::string CLSaturatedArithmeticOperationKernel::name()
{
    return supported_sat_arithmetic_ops[_op];
}

/** Arithmetic operations*/

void CLArithmeticOperationKernel::configure(ArithmeticOperation op, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const ActivationLayerInfo &act_info)
{
    configure(CLKernelLibrary::get().get_compile_context(), op, input1, input2, output, act_info);
}

void CLArithmeticOperationKernel::configure(const CLCompileContext &compile_context, ArithmeticOperation op, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output,
                                            const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
    ARM_COMPUTE_ERROR_THROW_ON(CLArithmeticOperationKernel::validate(op, input1, input2, output, act_info));

    _op       = op;
    _act_info = act_info;
    configure_common(compile_context, input1, input2, output);
}

Status CLArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
    if(op == ArithmeticOperation::DIV || op == ArithmeticOperation::POWER)
    {
        // Division and Power operators don't support integer arithmetic
        ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_float_only_supported_rules(*input1, *input2, *output));
        ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_division(*input1->clone(), *input2->clone(), *output->clone()).first);
    }
    else
    {
        ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output));
        ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first);
    }
    ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(output->data_type()));

    return Status{};
}
std::pair<Status, Window> CLArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
{
    if(_op == ArithmeticOperation::DIV || _op == ArithmeticOperation::POWER)
    {
        // Division and Power operators don't support integer arithmetic
        return validate_and_configure_window_for_division(input1, input2, output);
    }
    else
    {
        return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
    }
}

CLBuildOptions CLArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
{
    return generate_build_options_with_arithmetic_rules(input1, input2, output, name());
}
std::string CLArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
{
    return generate_id_for_tuning_common(kernel_name, input1, output);
}

std::string CLArithmeticOperationKernel::name()
{
    return supported_arithmetic_ops[_op];
}
} // namespace arm_compute