onednn-src 0.1.13

Source of oneAPI Deep Neural Network Library (oneDNN)
Documentation
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
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
/*******************************************************************************
* Copyright 2021 Intel Corporation
* Copyright 2021-2024 FUJITSU LIMITED
* Copyright 2025 Arm Ltd. and affiliates
*
* 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.
*******************************************************************************/

#ifndef CPU_AARCH64_JIT_SVE_1X1_CONVOLUTION_HPP
#define CPU_AARCH64_JIT_SVE_1X1_CONVOLUTION_HPP

#include "common/c_types_map.hpp"
#include "common/dnnl_thread.hpp"
#include "common/memory_tracking.hpp"
#include "common/primitive.hpp"
#include "common/primitive_hashing.hpp"
#include "common/utils.hpp"

#include "cpu/cpu_convolution_pd.hpp"
#include "cpu/dw_convolution_utils.hpp"
#include "cpu/platform.hpp"

#include "cpu/aarch64/cpu_reducer.hpp"
#include "cpu/aarch64/jit_sve_1x1_conv_kernel.hpp"
#include "cpu/aarch64/jit_uni_1x1_conv_utils.hpp"
#include "cpu/aarch64/jit_uni_dw_convolution.hpp"

namespace dnnl {
namespace impl {
namespace cpu {
namespace aarch64 {

template <impl::data_type_t src_type, impl::data_type_t wei_type = src_type,
        impl::data_type_t dst_type = src_type, cpu_isa_t isa_ = isa_undef>
struct jit_sve_1x1_convolution_fwd_t : public primitive_t {
    struct pd_t : public cpu_convolution_fwd_pd_t {
        using cpu_convolution_fwd_pd_t::cpu_convolution_fwd_pd_t;

        pd_t(const pd_t &other) : cpu_convolution_fwd_pd_t(other) {
            if (copy(other) != status::success) is_initialized_ = false;
        }

        DECLARE_COMMON_PD_T(JIT_IMPL_NAME_HELPER("jit_1x1:", isa_, ""),
                jit_sve_1x1_convolution_fwd_t);

        status_t init(engine_t *engine) {
            using namespace utils;
#if defined(DNNL_AARCH64_USE_ACL)
            if (attr()->fpmath_.mode_ == fpmath_mode::bf16) {
                // prefer ACL to jit for fpmath_mode::bf16 if available
                // since it supports lower precision calculation
                return status::unimplemented;
            }
#endif

            bool ok = true && is_fwd()
                    && set_default_alg_kind(alg_kind::convolution_direct)
                    && expect_data_types(src_type, wei_type, dst_type, dst_type,
                            data_type::undef)
                    && attr()->has_default_values(
                            primitive_attr_t::skip_mask_t::post_ops, dst_type)
                    && !has_zero_dim_memory() && set_default_formats()
                    && attr_.set_default_formats(dst_md(0)) == status::success;
            if (!ok) { return status::unimplemented; }

            const convolution_desc_t *conv_d = desc();
            const memory_desc_t *src_d = src_md();
            rtus_prepare(this, conv_d, src_d, dst_md());

            CHECK(jit_sve_1x1_conv_kernel_t<isa_>::init_conf(jcp_, *conv_d,
                    *src_d, *weights_md(), *dst_md(), *attr(),
                    dnnl_get_max_threads(), rtus_.reduce_src_));
            if (jcp_.with_dw_conv) CHECK(depthwise_po_init(engine));

            auto scratchpad = scratchpad_registry().registrar();
            jit_sve_1x1_conv_kernel_t<isa_>::init_scratchpad(scratchpad, jcp_);

            rtus_prepare_space_info(this, scratchpad, jcp_.nthr);

            return status::success;
        }

        const memory_desc_t *dst_md() const { return dst_md(0, false); }
        const memory_desc_t *dst_md(int index) const {
            return dst_md(index, false);
        }
        const memory_desc_t *dst_md(int index, bool user_input) const override {
            return jcp_.with_dw_conv
                    ? dw_conv_pd_->dst_md(index, user_input)
                    : cpu_convolution_fwd_pd_t::dst_md(index, user_input);
        }

        const memory_desc_t *arg_md(int arg) const {
            return arg_md(arg, false);
        }
        const memory_desc_t *arg_md(int arg, bool user_input) const override {
            if (jcp_.with_dw_conv) {
                switch (arg) {
                    case DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_SRC:
                        return cpu_convolution_fwd_pd_t::dst_md(0, user_input);
                    case DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_WEIGHTS:
                        return dw_conv_pd_->weights_md(0);
                    case DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_BIAS:
                        return dw_conv_pd_->weights_md(1);
                    default: break;
                }
            }
            return convolution_fwd_pd_t::arg_md(arg, user_input);
        }

        arg_usage_t arg_usage(int arg) const override {
            if (arg == (DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_WEIGHTS))
                return arg_usage_t::input;

            if (arg == (DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_BIAS)
                    && attr_post_op_dw_inputs() > 1)
                return arg_usage_t::input;

            return convolution_fwd_pd_t::arg_usage(arg);
        }

        jit_1x1_conv_conf_t jcp_ = utils::zero<decltype(jcp_)>();
        reduce_to_unit_stride_t rtus_ = utils::zero<decltype(rtus_)>();
        using dw_pd_t = jit_sve_512_dw_convolution_fwd_t::pd_t;
        std::unique_ptr<dw_pd_t> dw_conv_pd_;

    protected:
        bool set_default_formats() {
            using namespace format_tag;

            const memory_desc_wrapper src_d(&src_md_);
            const memory_desc_wrapper dst_d(&dst_md_);

            const auto dat_tag_nxc = utils::pick(ndims() - 3, nwc, nhwc, ndhwc);
            format_tag_t dat_tag, wei_tag;

            switch (isa_) {
                case sve_512: {
                    const auto dat_tag_nCx16c = utils::pick(
                            ndims() - 3, nCw16c, nChw16c, nCdhw16c);
                    const auto curr_src_tag = src_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx16c);
                    const auto curr_dst_tag = dst_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx16c);
                    const auto is_data_layout_nxc
                            = IMPLICATION(curr_src_tag != dat_tag_nxc,
                                      src_d.format_kind() == format_kind::any)
                            && IMPLICATION(curr_dst_tag != dat_tag_nxc,
                                    dst_d.format_kind() == format_kind::any)
                            && utils::one_of(
                                    dat_tag_nxc, curr_src_tag, curr_dst_tag);
                    dat_tag = is_data_layout_nxc ? dat_tag_nxc : dat_tag_nCx16c;
                    wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
                            OIw16i16o, gOIw16i16o, OIhw16i16o, gOIhw16i16o,
                            OIdhw16i16o, gOIdhw16i16o);
                    break;
                }
                case sve_256: {
                    const auto dat_tag_nCx8c
                            = utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
                    const auto curr_src_tag = src_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx8c);
                    const auto curr_dst_tag = dst_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx8c);
                    const auto is_data_layout_nxc
                            = IMPLICATION(curr_src_tag != dat_tag_nxc,
                                      src_d.format_kind() == format_kind::any)
                            && IMPLICATION(curr_dst_tag != dat_tag_nxc,
                                    dst_d.format_kind() == format_kind::any)
                            && utils::one_of(
                                    dat_tag_nxc, curr_src_tag, curr_dst_tag);
                    dat_tag = is_data_layout_nxc ? dat_tag_nxc : dat_tag_nCx8c;
                    wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
                            OIw8i8o, gOIw8i8o, OIhw8i8o, gOIhw8i8o, OIdhw8i8o,
                            gOIdhw8i8o);
                    break;
                }
                case sve_128: {
                    const auto dat_tag_nCx4c
                            = utils::pick(ndims() - 3, nCw4c, nChw4c, nCdhw4c);
                    const auto curr_src_tag = src_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx4c);
                    const auto curr_dst_tag = dst_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx4c);
                    const auto is_data_layout_nxc
                            = IMPLICATION(curr_src_tag != dat_tag_nxc,
                                      src_d.format_kind() == format_kind::any)
                            && IMPLICATION(curr_dst_tag != dat_tag_nxc,
                                    dst_d.format_kind() == format_kind::any)
                            && utils::one_of(
                                    dat_tag_nxc, curr_src_tag, curr_dst_tag);
                    dat_tag = is_data_layout_nxc ? dat_tag_nxc : dat_tag_nCx4c;
                    wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
                            OIw4i4o, gOIw4i4o, OIhw4i4o, gOIhw4i4o, OIdhw4i4o,
                            gOIdhw4i4o);
                    break;
                }
                default: return status::unimplemented;
            }
            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
        }
        status_t copy(const pd_t &other) {
            jcp_ = other.jcp_;
            rtus_ = other.rtus_;
            if (other.dw_conv_pd_) {
                dw_conv_pd_.reset(other.dw_conv_pd_->clone());
                if (!dw_conv_pd_) return status::out_of_memory;
            }
            return status::success;
        }

        status_t depthwise_po_init(engine_t *engine) {

            using namespace memory_tracking;
            auto &jcp_1x1 = jcp_;
            primitive_attr_t attr_1x1(*attr());
            if (!attr_1x1.is_initialized()) return status::out_of_memory;
            const auto &src_md = dst_md_;
            const memory_desc_wrapper src_d(src_md);
            const auto nthr = dnnl_get_max_threads();
            auto l2_cache = platform::get_per_core_cache_size(2) * nthr;

            // Note: A robust fusion implementation would be to check if both
            // 1x1 conv and dw conv that are considered here for fusion are
            // optimal independently. This would require creating a new
            // primitive_desc through primitive_iterator & check if they match.
            // Due to concern that these creations and/or checks could be heavy,
            // for 1x1: Check that no better ISA is available.
            // for dw: Always fuse with same ISA.
            // Caveat: May be a better dw conv exists.

            // TODO: Add a check if better ISA exists following above note.
            bool ok = true
                    && (attr_1x1.post_ops_.find(primitive_kind::sum) == -1)
                    // TODO: Below may be further tuned.
                    && (l2_cache * 2 < src_d.size())
                    // load_grp_count check can be redundant due to l2 check
                    // above. Adding it explicitly as the current driver doesn't
                    // work if this condition fails.
                    && (jcp_1x1.load_grp_count < 2);
            if (!ok) return status::unimplemented;

            int dw_po_index
                    = attr_1x1.post_ops_.find(primitive_kind::convolution);
            convolution_desc_t cd_dw;
            primitive_attr_t attr_dw;
            CHECK(get_depthwise_conv_desc(
                    cd_dw, src_md, attr_1x1, attr_dw, dw_po_index));

            // The code below doesn't work because currently it requires `jcp_`
            // member which is not available from the common interface. In turn,
            // this means the common pd creation interface through an iterator
            // can't be used and a specific convolution implementation's pd is
            // required here. It restricts the usage of inherited
            // `convolution_pd_t` constructor.
            // ANCHOR: USING_INHERITED_IS_IMPOSSIBLE.
            //
            // ```cpp
            // primitive_desc_iterator_t it(
            //         engine, (op_desc_t *)&cd_dw, &attr_dw, nullptr);
            // if (!it.is_initialized()) return status::out_of_memory;
            // while (++it != it.end()) {
            //     dw_conv_pd_ = *it;
            //     break;
            // }
            // VDISPATCH_CONV_IC(dw_conv_pd_, "dw_conv_pd hasn't been created");
            // ```
            //
            // ```compiler output
            // error: ‘using element_type = struct dnnl::impl::primitive_desc_t’
            // {aka ‘struct dnnl::impl::primitive_desc_t’} has no member named
            // ‘jcp_’
            // auto &jcp_dw = dw_conv_pd_->jcp_;
            //                             ^~~~
            // ```
            //
            // TODO: figure out the way to initialize fused conv through a
            // normal interface without hacks accessing specific members.
            CHECK(safe_ptr_assign(
                    dw_conv_pd_, new dw_pd_t(&cd_dw, &attr_dw, nullptr)));
            CHECK(dw_conv_pd_->init(engine));
            auto &jcp_dw = dw_conv_pd_->jcp_;

            ok = true
                    && (dnnl_memory_desc_equal(&src_md, dw_conv_pd_->src_md(0)))
                    && (jcp_1x1.oc_without_padding % jcp_1x1.oc_block == 0)
                    && IMPLICATION(
                            jcp_dw.ow_block, jcp_dw.ow_block == jcp_dw.ow);
            if (!ok) return status::unimplemented;

            assert(dw_conv_pd_->dst_md(0)->format_kind != format_kind::any);
            assert(dw_conv_pd_->weights_md(0)->format_kind != format_kind::any);
            assert(IMPLICATION(
                    dw_conv_pd_->weights_md(1)->data_type != data_type::undef,
                    dw_conv_pd_->weights_md(1)->format_kind
                            != format_kind::any));

            jcp_dw.is_fused_conv = true;
            // TODO: Support/experiment arbitary oc_work in dw conv.
            // Until then we keep oc_work perfectly divisible.
            while (jcp_1x1.nb_load % jcp_1x1.nb_load_blocking != 0)
                --jcp_1x1.nb_load_blocking;
            jcp_1x1.nb_load_blocking_max = jcp_1x1.nb_load_blocking;

            while (jcp_1x1.nb_load_blocking % jcp_dw.nb_ch_blocking != 0)
                --jcp_dw.nb_ch_blocking;

            jcp_dw.dw_conv_buffer_oc
                    = jcp_1x1.nb_load_blocking * jcp_1x1.oc_block;

            const auto dat_tag_nxc = utils::pick(ndims() - 3, format_tag::nwc,
                    format_tag::nhwc, format_tag::ndhwc);
            const bool is_data_nxc = utils::everyone_is(
                    dat_tag_nxc, jcp_1x1.src_tag, jcp_1x1.dst_tag);
            if (!is_data_nxc)
                jcp_1x1.bcast_loop_output_step = jcp_1x1.ur * jcp_1x1.load_block
                        * jcp_1x1.typesize_out;

            registrar_t scratchpad(scratchpad_registry_);
            registrar_t dw_scratchpad(scratchpad, names::prefix_fusion);

            size_t dw_conv_buffer_size_ = (size_t)nthr * jcp_dw.kh * jcp_dw.iw
                    * jcp_dw.dw_conv_buffer_oc;
            assert(dw_conv_buffer_size_);
            dw_scratchpad.book(memory_tracking::names::key_fusion_inout_buffer,
                    dw_conv_buffer_size_,
                    types::data_type_size(dw_conv_pd_->src_md()->data_type));

            jit_uni_dw_conv_fwd_kernel_t<isa_, data_type::f32>::init_scratchpad(
                    dw_scratchpad, jcp_dw);

            return status::success;
        }
    };

    template <cpu_isa_t isa, typename conv_t>
    friend status_t init_rtus_driver(conv_t *self);

    jit_sve_1x1_convolution_fwd_t(const pd_t *apd) : primitive_t(apd) {}

    using src_data_t = typename prec_traits_t<src_type>::type;
    using wei_data_t = typename prec_traits_t<wei_type>::type;
    using dst_data_t = typename prec_traits_t<dst_type>::type;

    status_t init(engine_t *engine) override {
        CHECK(safe_ptr_assign(kernel_,
                new jit_sve_1x1_conv_kernel_t<isa_>(
                        pd()->jcp_, *pd()->attr(), *pd()->dst_md(0))));
        CHECK(kernel_->create_kernel());

        if (pd()->jcp_.with_dw_conv) {
            CHECK(safe_ptr_assign(
                    kernel_dw_, new dw_conv_kernel_t(pd()->dw_conv_pd_->jcp_)));
            CHECK(kernel_dw_->create_kernel());
        }
        CHECK(init_rtus_driver<isa_>(this));
        return status::success;
    }

    status_t execute(const exec_ctx_t &ctx) const override {
        execute_forward(ctx);
        return status::success;
    }

private:
    void execute_forward(const exec_ctx_t &ctx) const;
    void execute_forward_thr(const int ithr, const int nthr,
            const src_data_t *src, const wei_data_t *weights,
            const dst_data_t *bias, const wei_data_t *weights_dw,
            const dst_data_t *bias_dw, dst_data_t *dst,
            const memory_tracking::grantor_t &scratchpad,
            const void *post_ops_binary_rhs_arg_vec,
            const void *post_ops_binary_rhs_arg_vec_dw) const;
    const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }

    std::unique_ptr<jit_sve_1x1_conv_kernel_t<isa_>> kernel_;
    std::unique_ptr<rtus_driver_t<isa_>> rtus_driver_;
    using dw_conv_kernel_t = jit_uni_dw_conv_fwd_kernel_f32_t<isa_>;
    std::unique_ptr<dw_conv_kernel_t> kernel_dw_;
};

using jit_sve_128_1x1_convolution_fwd_f32_t
        = jit_sve_1x1_convolution_fwd_t<data_type::f32, data_type::f32,
                data_type::f32, sve_128>;
using jit_sve_256_1x1_convolution_fwd_f32_t
        = jit_sve_1x1_convolution_fwd_t<data_type::f32, data_type::f32,
                data_type::f32, sve_256>;
using jit_sve_512_1x1_convolution_fwd_f32_t
        = jit_sve_1x1_convolution_fwd_t<data_type::f32, data_type::f32,
                data_type::f32, sve_512>;

template <impl::data_type_t diff_dst_type,
        impl::data_type_t wei_type = diff_dst_type,
        impl::data_type_t diff_src_type = diff_dst_type,
        cpu_isa_t isa_ = isa_undef>
struct jit_sve_1x1_convolution_bwd_data_t : public primitive_t {
    struct pd_t : public cpu_convolution_bwd_data_pd_t {
        using cpu_convolution_bwd_data_pd_t::cpu_convolution_bwd_data_pd_t;

        DECLARE_COMMON_PD_T(JIT_IMPL_NAME_HELPER("jit_1x1:", isa_, ""),
                jit_sve_1x1_convolution_bwd_data_t);

        status_t init(engine_t *engine) {
            bool ok = true && desc()->prop_kind == prop_kind::backward_data
                    && set_default_alg_kind(alg_kind::convolution_direct)
                    && expect_data_types(diff_src_type, wei_type,
                            data_type::undef, diff_dst_type, data_type::undef)
                    && attr()->has_default_values() && !has_zero_dim_memory()
                    && set_default_formats();
            if (!ok) return status::unimplemented;

            const convolution_desc_t *conv_d = desc();
            const memory_desc_t *diff_src_d = diff_src_md();
            rtus_prepare(this, conv_d, diff_src_d, diff_dst_md());

            status_t status = jit_sve_1x1_conv_kernel_t<isa_>::init_conf(jcp_,
                    *conv_d, *diff_src_d, *weights_md(), *diff_dst_md(),
                    *attr(), dnnl_get_max_threads(), rtus_.reduce_src_);
            if (status != status::success) return status;

            auto scratchpad = scratchpad_registry().registrar();
            jit_sve_1x1_conv_kernel_t<isa_>::init_scratchpad(scratchpad, jcp_);

            rtus_prepare_space_info(this, scratchpad, jcp_.nthr);

            return status::success;
        }

        // TODO (Roma): structs conf header cleanup
        jit_1x1_conv_conf_t jcp_ = utils::zero<decltype(jcp_)>();
        reduce_to_unit_stride_t rtus_ = utils::zero<decltype(rtus_)>();

    protected:
        bool set_default_formats() {
            using namespace format_tag;

            const memory_desc_wrapper diff_src_d(&diff_src_md_);
            const memory_desc_wrapper diff_dst_d(&diff_dst_md_);

            const auto dat_tag_nxc = utils::pick(ndims() - 3, nwc, nhwc, ndhwc);
            format_tag_t dat_tag, wei_tag;

            switch (isa_) {
                case sve_512: {
                    const auto dat_tag_nCx16c = utils::pick(
                            ndims() - 3, nCw16c, nChw16c, nCdhw16c);
                    const auto curr_src_tag = diff_src_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx16c);
                    const auto curr_dst_tag = diff_dst_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx16c);
                    const auto is_data_layout_nxc
                            = IMPLICATION(curr_src_tag != dat_tag_nxc,
                                      diff_src_d.format_kind()
                                              == format_kind::any)
                            && IMPLICATION(curr_dst_tag != dat_tag_nxc,
                                    diff_dst_d.format_kind()
                                            == format_kind::any)
                            && utils::one_of(
                                    dat_tag_nxc, curr_src_tag, curr_dst_tag);
                    dat_tag = is_data_layout_nxc ? dat_tag_nxc : dat_tag_nCx16c;
                    wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
                            IOw16o16i, gIOw16o16i, IOhw16o16i, gIOhw16o16i,
                            IOdhw16o16i, gIOdhw16o16i);
                    break;
                }
                case sve_256: {
                    const auto dat_tag_nCx8c
                            = utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
                    const auto curr_src_tag = diff_src_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx8c);
                    const auto curr_dst_tag = diff_dst_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx8c);
                    const auto is_data_layout_nxc
                            = IMPLICATION(curr_src_tag != dat_tag_nxc,
                                      diff_src_d.format_kind()
                                              == format_kind::any)
                            && IMPLICATION(curr_dst_tag != dat_tag_nxc,
                                    diff_dst_d.format_kind()
                                            == format_kind::any)
                            && utils::one_of(
                                    dat_tag_nxc, curr_src_tag, curr_dst_tag);
                    dat_tag = is_data_layout_nxc ? dat_tag_nxc : dat_tag_nCx8c;
                    wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
                            IOw8o8i, gIOw8o8i, IOhw8o8i, gIOhw8o8i, IOdhw8o8i,
                            gIOdhw8o8i);
                    break;
                }
                default: break;
            }
            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
        }
    };

    template <cpu_isa_t isa, typename conv_t>
    friend status_t init_rtus_driver(conv_t *self);

    jit_sve_1x1_convolution_bwd_data_t(const pd_t *apd) : primitive_t(apd) {}

    using diff_dst_data_t = typename prec_traits_t<diff_dst_type>::type;
    using wei_data_t = typename prec_traits_t<wei_type>::type;
    using diff_src_data_t = typename prec_traits_t<diff_src_type>::type;

    status_t init(engine_t *engine) override {
        CHECK(safe_ptr_assign(kernel_,
                new jit_sve_1x1_conv_kernel_t<isa_>(
                        pd()->jcp_, *pd()->attr(), *pd()->dst_md(0))));
        CHECK(kernel_->create_kernel());
        CHECK(init_rtus_driver<isa_>(this));
        return status::success;
    }

    status_t execute(const exec_ctx_t &ctx) const override {
        execute_backward_data(ctx);
        return status::success;
    }

private:
    void execute_backward_data(const exec_ctx_t &ctx) const;
    const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
    std::unique_ptr<jit_sve_1x1_conv_kernel_t<isa_>> kernel_;
    std::unique_ptr<rtus_driver_t<isa_>> rtus_driver_;
};
using jit_sve_256_1x1_convolution_bwd_data_f32_t
        = jit_sve_1x1_convolution_bwd_data_t<data_type::f32, data_type::f32,
                data_type::f32, sve_256>;

using jit_sve_512_1x1_convolution_bwd_data_f32_t
        = jit_sve_1x1_convolution_bwd_data_t<data_type::f32, data_type::f32,
                data_type::f32, sve_512>;

/* Backward weight */
template <impl::data_type_t diff_dst_type,
        impl::data_type_t wei_type = diff_dst_type,
        impl::data_type_t diff_src_type = diff_dst_type,
        cpu_isa_t isa_ = isa_undef>
struct jit_sve_1x1_convolution_bwd_weights_t : public primitive_t {
    struct pd_t : public cpu_convolution_bwd_weights_pd_t {
        using cpu_convolution_bwd_weights_pd_t::
                cpu_convolution_bwd_weights_pd_t;

        DECLARE_COMMON_PD_T(JIT_IMPL_NAME_HELPER("jit_1x1:", isa_, ""),
                jit_sve_1x1_convolution_bwd_weights_t);

        status_t init(engine_t *engine) {
            bool ok = true && desc()->prop_kind == prop_kind::backward_weights
                    && set_default_alg_kind(alg_kind::convolution_direct)
                    && expect_data_types(data_type::f32, data_type::f32,
                            data_type::f32, data_type::f32, data_type::f32)
                    && attr()->has_default_values() && !has_zero_dim_memory()
                    && set_default_formats();
            if (!ok) { return status::unimplemented; }

            const convolution_desc_t *conv_d = desc();
            const memory_desc_t *src_d = src_md();
            rtus_prepare(this, conv_d, src_d, diff_dst_md());

            status_t status = jit_sve_1x1_conv_kernel_t<isa_>::init_conf(jcp_,
                    *conv_d, *src_d, *diff_weights_md(), *diff_dst_md(),
                    *attr(), dnnl_get_max_threads(), rtus_.reduce_src_);
            if (status != status::success) return status;

            init_balancers();

            auto scratchpad = scratchpad_registry().registrar();
            jit_sve_1x1_conv_kernel_t<isa_>::init_scratchpad(scratchpad, jcp_);

            auto reducer_bia_scratchpad = memory_tracking::registrar_t(
                    scratchpad, memory_tracking::names::prefix_reducer_bia);
            reducer_bia_conf_.init_scratchpad(reducer_bia_scratchpad);
            rtus_prepare_space_info(this, scratchpad, jcp_.nthr);

            return status::success;
        }

        // TODO (Roma): structs conf header cleanup
        jit_1x1_conv_conf_t jcp_ = utils::zero<decltype(jcp_)>();
        typename cpu_reducer_t<data_type::f32, isa_>::conf_t reducer_bia_conf_;
        reduce_to_unit_stride_t rtus_ = utils::zero<decltype(rtus_)>();

    protected:
        bool set_default_formats() {
            using namespace format_tag;

            const memory_desc_wrapper src_d(&src_md_);
            const memory_desc_wrapper diff_dst_d(&diff_dst_md_);

            const auto dat_tag_nxc = utils::pick(ndims() - 3, nwc, nhwc, ndhwc);

            format_tag_t dat_tag, wei_tag;

            switch (isa_) {
                case sve_512: {
                    auto dat_tag_nCx16c = utils::pick(
                            ndims() - 3, nCw16c, nChw16c, nCdhw16c);
                    const auto curr_src_tag = src_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx16c);
                    const auto curr_dst_tag = diff_dst_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx16c);
                    const auto is_data_layout_nxc
                            = IMPLICATION(curr_src_tag != dat_tag_nxc,
                                      src_d.format_kind() == format_kind::any)
                            && IMPLICATION(curr_dst_tag != dat_tag_nxc,
                                    diff_dst_d.format_kind()
                                            == format_kind::any)
                            && utils::one_of(
                                    dat_tag_nxc, curr_src_tag, curr_dst_tag);

                    dat_tag = is_data_layout_nxc ? dat_tag_nxc : dat_tag_nCx16c;
                    wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
                            OIw16i16o, gOIw16i16o, OIhw16i16o, gOIhw16i16o,
                            OIdhw16i16o, gOIdhw16i16o);
                    break;
                }
                case sve_256: {
                    const auto dat_tag_nCx8c
                            = utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
                    const auto curr_src_tag = src_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx8c);
                    const auto curr_dst_tag = diff_dst_d.matches_one_of_tag(
                            dat_tag_nxc, dat_tag_nCx8c);
                    const auto is_data_layout_nxc
                            = IMPLICATION(curr_src_tag != dat_tag_nxc,
                                      src_d.format_kind() == format_kind::any)
                            && IMPLICATION(curr_dst_tag != dat_tag_nxc,
                                    diff_dst_d.format_kind()
                                            == format_kind::any)
                            && utils::one_of(
                                    dat_tag_nxc, curr_src_tag, curr_dst_tag);

                    dat_tag = is_data_layout_nxc ? dat_tag_nxc : dat_tag_nCx8c;
                    wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
                            OIw8i8o, gOIw8i8o, OIhw8i8o, gOIhw8i8o, OIdhw8i8o,
                            gOIdhw8i8o);
                    break;
                }
                default: break;
            }
            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
        }

    private:
        void init_balancers() {
            const size_t max_buffer_size = jcp_.nthr * 3 * 5 * 5 * 16 * 16;
            if (with_bias()) {
                reducer_bia_conf_.init(reduce_balancer_t(jcp_.nthr,
                        jcp_.oc_block, jcp_.ngroups * jcp_.nb_load, jcp_.mb,
                        max_buffer_size, true));
            }
        }
    };

    template <cpu_isa_t isa, typename conv_t>
    friend status_t init_rtus_driver(conv_t *self);

    jit_sve_1x1_convolution_bwd_weights_t(const pd_t *apd) : primitive_t(apd) {}

    using data_t = typename prec_traits_t<data_type::f32>::type;

    status_t init(engine_t *engine) override;

    status_t execute(const exec_ctx_t &ctx) const override {
        execute_backward_weights(ctx);
        return status::success;
    }

private:
    void execute_backward_weights(const exec_ctx_t &ctx) const;
    const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }

    std::unique_ptr<jit_sve_1x1_conv_kernel_t<isa_>> kernel_;
    std::unique_ptr<cpu_accumulator_1d_t<data_type::f32, isa_>> acc_ker_;
    std::unique_ptr<cpu_reducer_t<data_type::f32, isa_>> reducer_bias_;
    // std::unique_ptr<jit_transpose4x16_src> trans_kernel_;
    std::unique_ptr<rtus_driver_t<isa_>> rtus_driver_;
};

using jit_sve_256_1x1_convolution_bwd_weights_t
        = jit_sve_1x1_convolution_bwd_weights_t<data_type::f32, data_type::f32,
                data_type::f32, sve_256>;

using jit_sve_512_1x1_convolution_bwd_weights_t
        = jit_sve_1x1_convolution_bwd_weights_t<data_type::f32, data_type::f32,
                data_type::f32, sve_512>;

} // namespace aarch64
} // namespace cpu
} // namespace impl
} // namespace dnnl

#endif