aboutsummaryrefslogtreecommitdiff
path: root/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
blob: e699ad1815a138e176ea961f17efd87f6aaa9ba1 (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
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
/*
 * Copyright (c) 2017-2019 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/runtime/NEON/functions/NEWinogradConvolutionLayer.h"

#include "arm_compute/core/Error.h"
#include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h"
#include "support/ToolchainSupport.h"

#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd.hpp"

namespace arm_compute
{
namespace
{
inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
    if(input_dims.width > 4 && input_dims.height > 4)
    {
        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, input0, winograd_info)));
        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
    }
    else
    {
        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, input0, winograd_info)));
        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
    }

    if(act_info.enabled())
    {
        NEActivationLayer::validate(output, nullptr, act_info);
    }
    return Status{};
}

inline Status validate_kernel_5x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, input0, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, output, winograd_info)));
    if(act_info.enabled())
    {
        NEActivationLayer::validate(output, nullptr, act_info);
    }
    return Status{};
}

inline Status validate_kernel_3x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>::validate(input, input0, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, output, winograd_info)));
    if(act_info.enabled())
    {
        NEActivationLayer::validate(output, nullptr, act_info);
    }
    return Status{};
}

inline Status validate_kernel_1x3(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>::validate(input, input0, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, output, winograd_info)));

    if(act_info.enabled())
    {
        NEActivationLayer::validate(output, nullptr, act_info);
    }
    return Status{};
}

inline Status validate_kernel_5x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>::validate(input, input0, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, output, winograd_info)));
    if(act_info.enabled())
    {
        NEActivationLayer::validate(output, nullptr, act_info);
    }
    return Status{};
}
inline Status validate_kernel_1x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>::validate(input, input0, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, output, winograd_info)));
    if(act_info.enabled())
    {
        NEActivationLayer::validate(output, nullptr, act_info);
    }
    return Status{};
}

inline Status validate_kernel_7x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>::validate(input, input0, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, output, winograd_info)));
    if(act_info.enabled())
    {
        NEActivationLayer::validate(output, nullptr, act_info);
    }
    return Status{};
}

inline Status validate_kernel_1x7(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
{
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>::validate(input, input0, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, output, winograd_info)));

    if(act_info.enabled())
    {
        NEActivationLayer::validate(output, nullptr, act_info);
    }
    return Status{};
}

inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
{
    const DataLayout data_layout = input->info()->data_layout();
    const int        in_width    = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
    const int        in_height   = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
    const int        in_channels = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
    const int        in_batches  = input->info()->dimension(3);

    return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
}

Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
{
    ARM_COMPUTE_UNUSED(output);
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
    if(biases != nullptr)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
    }
    return INEWinogradLayerTransformWeightsKernel<float>::validate(input, weights);
}

Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims)
{
    Size2D output_tile = Size2D{};
    if(kernel_dims == Size2D(3U, 3U))
    {
        output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
    }
    else if(kernel_dims == Size2D(5U, 5U))
    {
        output_tile = Size2D(2U, 2U);
    }
    else if(kernel_dims == Size2D(1U, 3U))
    {
        output_tile = Size2D(1U, 6U);
    }
    else if(kernel_dims == Size2D(3U, 1U))
    {
        output_tile = Size2D(6U, 1U);
    }
    else if(kernel_dims == Size2D(1U, 5U))
    {
        output_tile = Size2D(1U, 4U);
    }
    else if(kernel_dims == Size2D(5U, 1U))
    {
        output_tile = Size2D(4U, 1U);
    }
    else if(kernel_dims == Size2D(7U, 1U))
    {
        output_tile = Size2D(2U, 1U);
    }
    else if(kernel_dims == Size2D(1U, 7U))
    {
        output_tile = Size2D(1U, 2U);
    }
    return output_tile;
}

bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size)
{
    // Check if we want to configure a Winograd configuration which requires fast math
    using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;

    const std::vector<WinogradConfiguration> fast_math_winograd =
    {
        WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)),
        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
    };

    auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
                            std::pair<int, int>(kernel_size.width, kernel_size.height));

    return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end();
}

} //namespace

NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
    : _memory_group(memory_manager), _gemm_function(memory_manager), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), _activationlayer_function(),
      _permute_input(), _permute_weights(), _permute_output(), _input_transformed(), _output_transformed(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(),
      _weights_hwio(), _input(), _weights(), _output(), _is_prepared(false), _is_activationlayer_enabled(false)
{
}

void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info,
                                           bool enable_fast_math)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info));

    // Get indices for the width and height
    const DataLayout   data_layout = input->info()->data_layout();
    const unsigned int width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
    const unsigned int height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
    const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);

    const Size2D input_dims  = Size2D(input->info()->dimension(width_idx), input->info()->dimension(height_idx));
    const Size2D kernel_size = Size2D(weights->info()->dimension(width_idx), weights->info()->dimension(height_idx));
    const Size2D output_tile = winograd_output_tile(input_dims, kernel_size);

    // Check if the Winograd configuration requires fast math
    if(!enable_fast_math)
    {
        ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true");
    }

    _weights     = weights;
    _input       = input;
    _output      = output;
    _is_prepared = false;

    std::unique_ptr<INEWinogradLayerTransformInputKernel<float>>   transform_input_kernel;
    std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel;
    std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>>  transform_output_kernel;

    int n_gemms = 0;
    int N_BLOCK = 0; // Size of block used by GEMM.

    if(kernel_size == Size2D(3, 3))
    {
        if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4)
        {
            using config             = NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3>;
            transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
            transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
            transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
            n_gemms                  = config::WinogradBase::N_GEMMS;
            N_BLOCK                  = config::WinogradConv::N_BLOCK;
        }
        else
        {
            using config             = NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3>;
            transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
            transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
            transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
            n_gemms                  = config::WinogradBase::N_GEMMS;
            N_BLOCK                  = config::WinogradConv::N_BLOCK;
        }
    }
    else if(kernel_size == Size2D(5, 5))
    {
        using config             = NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5>;
        transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
        transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
        transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
        n_gemms                  = config::WinogradBase::N_GEMMS;
        N_BLOCK                  = config::WinogradConv::N_BLOCK;
    }
    else if(kernel_size == Size2D(1, 3))
    {
        using config             = NEWinogradLayerConfiguration<float, float, 6, 1, 3, 1>;
        transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
        transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
        transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
        n_gemms                  = config::WinogradBase::N_GEMMS;
        N_BLOCK                  = config::WinogradConv::N_BLOCK;
    }
    else if(kernel_size == Size2D(3, 1))
    {
        using config             = NEWinogradLayerConfiguration<float, float, 1, 6, 1, 3>;
        transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
        transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
        transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
        n_gemms                  = config::WinogradBase::N_GEMMS;
        N_BLOCK                  = config::WinogradConv::N_BLOCK;
    }
    else if(kernel_size == Size2D(1, 5))
    {
        using config             = NEWinogradLayerConfiguration<float, float, 4, 1, 5, 1>;
        transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
        transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
        transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
        n_gemms                  = config::WinogradBase::N_GEMMS;
        N_BLOCK                  = config::WinogradConv::N_BLOCK;
    }
    else if(kernel_size == Size2D(5, 1))
    {
        using config             = NEWinogradLayerConfiguration<float, float, 1, 4, 1, 5>;
        transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
        transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
        transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
        n_gemms                  = config::WinogradBase::N_GEMMS;
        N_BLOCK                  = config::WinogradConv::N_BLOCK;
    }
    else if(kernel_size == Size2D(1, 7))
    {
        using config             = NEWinogradLayerConfiguration<float, float, 2, 1, 7, 1>;
        transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
        transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
        transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
        n_gemms                  = config::WinogradBase::N_GEMMS;
        N_BLOCK                  = config::WinogradConv::N_BLOCK;
    }
    else if(kernel_size == Size2D(7, 1))
    {
        using config             = NEWinogradLayerConfiguration<float, float, 1, 2, 1, 7>;
        transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
        transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
        transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
        n_gemms                  = config::WinogradBase::N_GEMMS;
        N_BLOCK                  = config::WinogradConv::N_BLOCK;
    }
    else
    {
        ARM_COMPUTE_ERROR("Not supported.");
    }

    const PaddingType use_padding_type = (conv_info.pad_top() != 0u || conv_info.pad_left() != 0) ? PADDING_SAME : PADDING_VALID;
    const bool        use_same_padding = use_padding_type == PADDING_SAME;

    // Get convolved dimensions
    const int in_channels  = input->info()->dimension(channel_idx);
    const int out_channels = output->info()->dimension(channel_idx);

    const Tensor4DShape in_shape(internal_get_input_shape(input));
    const DataType      data_type      = input->info()->data_type();
    const size_t        data_type_size = input->info()->element_size();
    // Get the memory required to instantiate a new Winograd operator.
    constexpr size_t storage_alignment = 64;

    // Kernel Storage
    const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels,
                                                                                         in_channels)
                                       * data_type_size;

    // Input storage
    const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols,
                                                                                     use_same_padding)
                                      * data_type_size;

    // Output storage
    const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels,
                                                                                        use_same_padding)
                                       * data_type_size;
    ;
    const KernelShape kernel_shape({ out_channels, static_cast<int>(kernel_size.height), static_cast<int>(kernel_size.width), in_channels });
    const int         kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(kernel_shape);

    const int  output_matrix_stride = transform_output_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type);
    const auto output_shape(transform_output_kernel->get_output_shape(kernel_shape, in_shape, use_padding_type));

    const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type);

    // Configure GEMM
    const int tile_rows                = iceildiv(output_shape.n_rows, output_tile.height);
    const int tile_cols                = iceildiv(output_shape.n_cols, output_tile.width);
    const int m                        = in_shape.n_batches * tile_rows * tile_cols;
    const int k                        = in_shape.n_channels;
    const int n                        = out_channels;
    const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
    const int output_matrix_row_stride = kernel_matrix_row_stride;

    TensorShape a_shape(k, m, 1, n_gemms);
    Strides     a_strides(data_type_size);
    a_strides.set(1, a_strides[0] * k);
    //a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
    a_strides.set(2, 0);
    a_strides.set(3, data_type_size * input_matrix_stride);

    TensorShape b_shape(n, k, n_gemms);
    Strides     b_strides(data_type_size);
    b_strides.set(1, data_type_size * kernel_matrix_row_stride);
    b_strides.set(2, data_type_size * kernel_matrix_stride);

    TensorShape d_shape(n, m, 1, n_gemms);
    Strides     d_strides(data_type_size);
    d_strides.set(1, data_type_size * output_matrix_row_stride);
    //d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
    d_strides.set(2, 0);
    d_strides.set(3, data_type_size * output_matrix_stride);

    TensorInfo a_info{};
    TensorInfo b_info{};
    TensorInfo d_info{};
    a_info.init(a_shape, 1, data_type, a_strides, 0, input_storage_size);
    b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size);
    d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size);

    _input_transformed.allocator()->init(a_info, storage_alignment);
    _kernel_storage.allocator()->init(b_info, storage_alignment);
    _output_transformed.allocator()->init(d_info, storage_alignment);

    // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
    TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0),
                                _output->info()->dimension(1), _output->info()->dimension(3)),
                    1, _output->info()->data_type());
    _output_nhwc.allocator()->init(info);

    const ITensor     *input_to_use  = _input;
    ITensor           *output_to_use = _output;
    PermutationVector  weights_permutation_vector(3U, 0U, 1U, 2U);
    const unsigned int max_num_threads = NEScheduler::get().num_threads();

    // Configure the kernel to transform the input tensor from NCHW -> NHWC
    if(data_layout == DataLayout::NCHW)
    {
        _memory_group.manage(&_input_nhwc);
        _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U));
        input_to_use               = &_input_nhwc;
        weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
    }

    // Configure input transform kernel
    _memory_group.manage(&_input_transformed);
    _memory_group.manage(&_input_workspace);
    transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
                                      &_input_transformed, input_matrix_stride, &_input_workspace);
    const size_t input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);
    TensorInfo   input_workspace_info(TensorShape(input_workspace_size), 1, _input->info()->data_type());
    _input_workspace.allocator()->init(input_workspace_info);
    _input_workspace.allocator()->allocate();
    if(data_layout == DataLayout::NCHW)
    {
        _input_nhwc.allocator()->allocate();
    }

    // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
    _permute_weights.configure(weights, &_weights_hwio, weights_permutation_vector);
    transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);

    // Configure GEMM function
    _memory_group.manage(&_output_transformed);
    _gemm_function.configure(&_input_transformed, &_kernel_storage, nullptr, &_output_transformed, 1.0f, 0.f);
    _input_transformed.allocator()->allocate();

    // Configure output transform function
    // The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
    if(data_layout == DataLayout::NCHW)
    {
        _memory_group.manage(&_output_nhwc);
        output_to_use = &_output_nhwc;
    }
    transform_output_kernel->configure(biases, &_output_transformed,
                                       output_matrix_stride, output_to_use,
                                       in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels, &_output_workspace);
    const size_t output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);
    TensorInfo   output_workspace_info(TensorShape(output_workspace_size), 1, _output->info()->data_type());
    _output_workspace.allocator()->init(output_workspace_info);
    _output_workspace.allocator()->allocate();
    _output_transformed.allocator()->allocate();

    // Reorder the convoluted output to ACL's ordering NCHW
    if(data_layout == DataLayout::NCHW)
    {
        _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U));
        _output_nhwc.allocator()->allocate();
    }

    _transform_input_kernel   = std::move(transform_input_kernel);
    _transform_weights_kernel = std::move(transform_weights_kernel);
    _transform_output_kernel  = std::move(transform_output_kernel);

    //Configure Activation Layer
    _is_activationlayer_enabled = act_info.enabled();
    if(_is_activationlayer_enabled)
    {
        _activationlayer_function.configure(_output, nullptr, act_info);
    }
}

void NEWinogradConvolutionLayer::run()
{
    const DataLayout data_layout = _input->info()->data_layout();

    prepare();

    MemoryGroupResourceScope scope_mg(_memory_group);

    if(data_layout == DataLayout::NCHW)
    {
        //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
        _permute_input.run();
    }

    // Transform input tensor to the winograd domain
    NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX);

    //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
    _gemm_function.run();

    // Transform output tensor to the spatial domain
    NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX);

    if(data_layout == DataLayout::NCHW)
    {
        // Reorder the convoluted output to ACL's ordering NCHW
        _permute_output.run();
    }

    if(_is_activationlayer_enabled)
    {
        _activationlayer_function.run();
    }
}

Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                                            const ActivationLayerInfo &act_info, bool enable_fast_math)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info));

    // Get indices for the width and height
    const size_t idx_width  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
    const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);

    // Input shape, kernel size and output tile
    const Size2D input_dims  = Size2D(input->dimension(idx_width), input->dimension(idx_height));
    const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
    const Size2D output_tile = winograd_output_tile(input_dims, kernel_size);

    // Check if the Winograd configuration requires fast math
    if(!enable_fast_math)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true");
    }

    const WinogradInfo winograd_info = WinogradInfo(output_tile,
                                                    kernel_size,
                                                    input_dims,
                                                    conv_info,
                                                    input->data_layout());

    // Validate input transform
    const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
    const TensorInfo  input0       = input->clone()->set_tensor_shape(input0_shape);
    // Validate filter transform
    const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
    const TensorInfo  input1       = weights->clone()->set_tensor_shape(input1_shape);
    // Validate batched matrix multiply
    TensorShape batched_mm_output_shape = input0.tensor_shape();
    batched_mm_output_shape[0]          = input1.tensor_shape()[0];
    const TensorInfo batched_mm_output  = input0.clone()->set_tensor_shape(batched_mm_output_shape);

    if(kernel_size == Size2D(3, 3))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
        return validate_kernel_3x3(input_dims, input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
    }
    else if(kernel_size == Size2D(5, 5))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
        return validate_kernel_5x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
    }
    if(kernel_size == Size2D(3, 1))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
        return validate_kernel_3x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
    }
    else if(kernel_size == Size2D(1, 3))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
        return validate_kernel_1x3(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
    }
    else if(kernel_size == Size2D(5, 1))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
        return validate_kernel_5x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
    }
    else if(kernel_size == Size2D(1, 5))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
        return validate_kernel_1x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
    }
    else if(kernel_size == Size2D(7, 1))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 3, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 3, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
        return validate_kernel_7x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
    }
    else if(kernel_size == Size2D(1, 7))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 3, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 3, "Only SAME or VALID padding supported");
        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
        return validate_kernel_1x7(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
    }
    else
    {
        ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported");
    }
}

void NEWinogradConvolutionLayer::prepare()
{
    if(!_is_prepared)
    {
        // Permute weights
        _weights_hwio.allocator()->allocate();
        _permute_weights.run();
        _weights->mark_as_unused();

        // Transform weights
        _kernel_storage.allocator()->allocate();
        NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX);

        _weights_hwio.allocator()->free();
        _is_prepared = true;
    }
}

} // namespace arm_compute