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
/*******************************************************************************
* Copyright 2023 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*     http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/

#include "gpu/intel/pool/jit/ir_builder.hpp"

#include <algorithm>
#include <utility>
#include <vector>

#include "common/c_types_map.hpp"
#include "common/utils.hpp"
#include "gemmstone/../../dsl/ir/ir.hpp"
#include "gemmstone/../../dsl/ir/pass/trace.hpp"
#include "gpu/intel/jit/ir/epilogue.hpp"
#include "gpu/intel/jit/ir/gemm_schedule.hpp"
#include "gpu/intel/jit/ir/post_ops.hpp"
#include "gpu/intel/jit/ir/send_builder.hpp"
#include "gpu/intel/jit/ir/tensor_config.hpp"
#include "gpu/intel/jit/pass/pass.hpp"

namespace dnnl {
namespace impl {
namespace gpu {
namespace intel {
namespace pool {
namespace jit {

class post_op_view_mapper_t : public intel::jit::post_op_view_mapper_t {
    using base_t = intel::jit::post_op_view_mapper_t;

public:
    post_op_view_mapper_t(const view_t &cp_view, const int ndims)
        : base_t(cp_view), ndims_(ndims) {}

    view_t create_view(const dsl::type_t &type, uint32_t mask) const override {
        return base_t::create_view(type, normalize_mask(mask));
    }

    view_t create_view(const memory_desc_t &md) const override {
        dim_idx_t cp_ndims = cp_view().nvdims();
        gpu_assert(cp_ndims >= 3);
        layout_t layout = make_layout(md);
        std::vector<dim_t> dims(md.dims, md.dims + md.ndims);
        std::vector<dim_t> pad_dims(md.padded_dims, md.padded_dims + md.ndims);
        maybe_reshape_dims(ndims_, layout, dims, pad_dims);
        layout = spatials_to_3d(layout, false, {0, 1, 2});
        auto tile = dims_to_3d(dims);
        auto pad_tile = dims_to_3d(pad_dims);
        gpu_assert(layout.ndims() == cp_ndims) << "Incompatible dimensions.";
        uint32_t bound_check_mask = 0;
        for (dim_idx_t i = 0; i < cp_ndims; i++) {
            if (tile[i] == 1) continue; // Broadcast, no bound check needed.
            if (pad_tile[i] != cp_view().tlayout().elems(i)) {
                bound_check_mask |= (1 << i);
            } else if (cp_view().has_tmask(i)) {
                bound_check_mask |= (1 << i);
            }
        }
        return view_t(layout, cp_view().vvars(), tile, bound_check_mask);
    }

    bool need_to_restore_zero_padding() const override { return true; }

private:
    static void maybe_reshape_dims(dim_idx_t ndims, layout_t &layout,
            std::vector<dim_t> &dims, std::vector<dim_t> &padded_dims) {
        gpu_assert(layout.ndims() == dims.size());
        if (layout.ndims() < ndims) {
            layout = layout_t(layout.type(), layout.blocks(), layout.offset(),
                    ndims, /*do_normalize=*/false);
            dims.resize(ndims, 1);
            padded_dims.resize(ndims, 1);
        }
    }

    static tile_t dims_to_3d(const std::vector<dim_t> &dims) {
        layout_t dummy_layout(dsl::type_t::u8(), dims);
        return spatials_to_3d(dummy_layout, false, {0, 1, 2}).tile();
    }

    uint32_t normalize_mask(uint32_t orig_mask) const {
        dim_idx_t cp_ndims = cp_view().nvdims();
        gpu_assert(cp_ndims >= 3);
        // Number of dimensions before normalization.
        dim_idx_t orig_ndims = 2 + ndims_;
        std::vector<dim_t> dummy_dims(orig_ndims, 1);
        dim_t mask_set_value = 2;
        for (dim_idx_t i = 0; i < orig_ndims; i++) {
            if ((orig_mask & (1 << i)) != 0) dummy_dims[i] = mask_set_value;
        }
        auto cvt_dims = dims_to_3d(dummy_dims);
        gpu_assert(cvt_dims.size() == cp_ndims);

        uint32_t mask = 0;
        for (dim_idx_t i = 0; i < cp_ndims; i++) {
            if (cvt_dims[i] == mask_set_value) mask = mask | (1 << i);
        }
        return mask;
    }

    const int ndims_;
};

class loop_bound_counter_t : public ir_mutator_t {
public:
    int count(const expr_t &e) {
        const auto retn = simplify(mutate(e));
        gpu_assert(retn.is<int_imm_t>());
        return to_cpp<int>(retn);
    }
    loop_bound_counter_t(const gemm_schedule_t &s) : schedule_(s) {}

private:
    object_t _mutate(const var_t &v) override {
        return expr_t(schedule_.var_bound(v) - 1);
    }
    const gemm_schedule_t &schedule_;
};

stmt_t builder_t::try_build(builder_t &pb, const kernel_info_t &ki,
        const config_t &cfg, const primitive_desc_t &pd) {
    const auto &exec = cfg.options();
    const auto &prb = cfg.problem();
    const auto &src_layout = cfg.src_layout().user();
    const auto &dst_layout = cfg.dst_layout().user();

    const bool is_xe2_or_xe3_small_kdhw = !(prb.kd * prb.kh * prb.kw == 1)
            && (prb.kh * prb.kw <= 9)
            && (exec.hw().ngen_hw() == ngen::HW::Xe2
                    || exec.hw().ngen_hw() == ngen::HW::Xe3);

    gpu_assert(src_layout.ndims() == dst_layout.ndims());

    // Create loop variables.
    auto mb = var_t::make(dsl::type_t::s32(), "mb");
    auto oc = var_t::make(dsl::type_t::s32(), "oc");

    auto od = var_t::make(dsl::type_t::s32(), "od");
    auto oh = var_t::make(dsl::type_t::s32(), "oh");
    auto ow = var_t::make(dsl::type_t::s32(), "ow");

    auto kd = var_t::make(dsl::type_t::s32(), "kd");
    auto kh = var_t::make(dsl::type_t::s32(), "kh");
    auto kw = var_t::make(dsl::type_t::s32(), "kw");

    // Initialize masks.
    const bool check_iw = utils::need_src_or_dst_check(!prb.is_backward, prb.ow,
            prb.iw, prb.kw, prb.l_pad, prb.stride_w, prb.dw);
    const bool check_ih = utils::need_src_or_dst_check(!prb.is_backward, prb.oh,
            prb.ih, prb.kh, prb.t_pad, prb.stride_h, prb.dh);
    const bool check_id = utils::need_src_or_dst_check(!prb.is_backward, prb.od,
            prb.id, prb.kd, prb.f_pad, prb.stride_d, prb.dd);
    const bool check_idhw = check_id || check_ih || check_iw;

    auto &x = view_t::placeholder_var();

    expr_t id_mask, ih_mask, iw_mask;
    if (check_id) id_mask = (x >= 0) & (x < prb.id);
    if (check_ih) ih_mask = (x >= 0) & (x < prb.ih);
    if (check_iw) iw_mask = (x >= 0) & (x < prb.iw);

    const int simd = exec.simd();
    const auto &lg = cfg.loop_grid();
    const auto &kg = cfg.kernel_grid();
    const auto &tg = cfg.thread_group_grid();
    const auto &padded_dims = cfg.dims_padded();
    gpu_assert(padded_dims.size() == 5);
    std::vector<dim_t> dims {padded_dims[0], src_layout.elems(1),
            padded_dims[2], padded_dims[3], padded_dims[4]};

    // Source.
    auto src_view = view_t({mb, oc, od, oh, ow, kd, kh, kw}, 5);
    src_view.set_vdim(mb, (!is_xe2_or_xe3_small_kdhw) ? dims[0] : prb.mb);
    src_view.set_vdim(oc, dims[1]);
    src_view.set_vdim(od, dims[2]);
    src_view.set_vdim(oh, dims[3]);
    src_view.set_vdim(ow, dims[4]);
    src_view.set_vdim(kd, prb.kd);
    src_view.set_vdim(kh, prb.kh);
    src_view.set_vdim(kw, prb.kw);
    src_view.set_tdim(0, mb);
    src_view.set_tdim(1, oc);
    src_view.set_tdim(
            2, od * prb.stride_d - prb.f_pad + kd * (1 + prb.dd), id_mask);
    src_view.set_tdim(
            3, oh * prb.stride_h - prb.t_pad + kh * (1 + prb.dh), ih_mask);
    src_view.set_tdim(
            4, ow * prb.stride_w - prb.l_pad + kw * (1 + prb.dw), iw_mask);
    src_view.set_tlayout(src_layout);
    src_view.set_tmasks(padded_dims);

    // Destination.
    auto dst_view = view_t({mb, oc, od, oh, ow}, 5);
    dst_view.set_vdim(mb, (!is_xe2_or_xe3_small_kdhw) ? dims[0] : prb.mb);
    dst_view.set_vdim(oc, dims[1]);
    dst_view.set_vdim(od, dims[2]);
    dst_view.set_vdim(oh, dims[3]);
    dst_view.set_vdim(ow, dims[4]);
    dst_view.set_tdim(0, mb);
    dst_view.set_tdim(1, oc);
    dst_view.set_tdim(2, od);
    dst_view.set_tdim(3, oh);
    dst_view.set_tdim(4, ow);
    dst_view.set_tlayout(dst_layout);
    dst_view.set_tmasks(padded_dims);

    constraint_set_t init_cset;
    std::vector<stmt_t> init_stmts;
    pb.init_kernel_grid(kg, tg, simd, init_cset, init_stmts);

    gemm_schedule_t schedule(init_cset, kg, tg);
    schedule.set_view(src_view);
    schedule.set_view(dst_view);
    schedule.set_var_bound(mb, dims[0]);

    auto kg_bind = [&](const std::vector<expr_t> &fuse, int idx) {
        if (fuse.size() > 1)
            schedule.bind(schedule.fuse(fuse), kg.idx(idx));
        else if (fuse.size() == 1)
            schedule.bind(fuse[0], kg.idx(idx));
    };
    auto odhw_to_schedule = [&](expr_t s1, expr_t ns, expr_t s0) {
        dim_idx_t s0_idx
                = (s0.is_empty()) ? dim_idx::invalid : src_view.vvar_index(s0);
        dim_idx_t s1_idx = src_view.vvar_index(s1);
        dim_idx_t ns_idx = src_view.vvar_index(ns);
        gpu_assert((s0_idx <= 4 || s0_idx == dim_idx::invalid) && (s1_idx <= 4)
                && (ns_idx <= 4));

        // s1 and ns may swap sides, which affects their fusing order: it has
        // to strictly replicate that of the arguments passed to this lambda!
        const bool need_swap = (s1_idx <= 1);
        // 2 spatials and 2 non-spatials disallowed; only 1 of each or bust
        gpu_assert(need_swap != (ns_idx <= 1));
        if (need_swap) {
            std::swap(s1_idx, ns_idx);
            std::swap(s1, ns);
        }

        const dim_t s1_tlg_unroll = lg[s1_idx];
        const dim_t s1_unroll = s1_tlg_unroll * tg[s1_idx - 2];
        const auto ps1 = s1.str();

        std::vector<expr_t> s0_fuse, s1_fuse;

        expr_t s1_kg, s1_tlg, s1_tg, s1_lg;
        schedule.split(s1, s1_unroll, s1_kg, s1_tlg, ps1 + "_kg", ps1 + "_tlg");
        schedule.split(
                s1_tlg, s1_tlg_unroll, s1_tg, s1_lg, ps1 + "_tg", ps1 + "_lg");

        schedule.tensorize(s1_lg);
        schedule.bind(s1_tg, tg.idx(s1_idx - 2));
        s1_fuse.emplace_back(s1_kg);

        if (s0_idx != dim_idx::invalid) {
            gpu_assert(s0_idx == s1_idx + 1);
            const dim_t s0_tlg_unroll = lg[s0_idx];
            const dim_t s0_unroll = s0_tlg_unroll * tg[s0_idx - 2];
            const dim_t s0_full = s0_unroll * kg[s0_idx - 2];
            const auto ps0 = s0.str();

            if (dims[s0_idx] > s0_full) {
                expr_t s0_split, s0_ktlg; // part of kg[s0] is in kg[s1]
                schedule.split(s0, s0_full, s0_split, s0_ktlg, ps0 + "_split",
                        ps0 + "_ktlg");
                s1_fuse.emplace_back(s0_split);
                s0 = std::move(s0_ktlg);
            } else if (dims[s0_idx] <= utils::div_up(s0_full, 2)) {
                expr_t s1_split, s1_ktlg; // part of kg[s1] is in kg[s0]
                const dim_t s1_ext = utils::div_up(s0_full, dims[s0_idx]);
                schedule.split(s1_fuse[0], s1_ext, s1_ktlg, s1_split,
                        ps1 + "_ktlg", ps1 + "_split");
                s1_fuse[0] = std::move(s1_ktlg);
                s0_fuse.emplace_back(s1_split);
            }

            expr_t s0_kg, s0_tlg, s0_tg, s0_lg;
            schedule.split(
                    s0, s0_unroll, s0_kg, s0_tlg, ps0 + "_kg", ps0 + "_tlg");
            schedule.split(s0_tlg, s0_tlg_unroll, s0_tg, s0_lg, ps0 + "_tg",
                    ps0 + "_lg");

            schedule.tensorize(s0_lg);
            schedule.bind(s0_tg, tg.idx(s0_idx - 2));
            s0_fuse.emplace_back(s0_kg);
        }

        const dim_t ns_unroll = lg[ns_idx];
        const auto pns = ns.str();

        expr_t ns_kg, ns_lg;
        schedule.split(ns, ns_unroll, ns_kg, ns_lg, pns + "_kg", pns + "_lg");
        if (need_swap)
            s1_fuse.emplace(s1_fuse.begin(), ns_kg);
        else
            s1_fuse.emplace_back(ns_kg);
        schedule.tensorize(ns_lg);

        kg_bind(s0_fuse, s0_idx - 2);
        kg_bind(s1_fuse, s1_idx - 2);
    };
    odhw_to_schedule(oc, od, expr_t());
    if (cfg.is_blocked_by_mb())
        odhw_to_schedule(oh, mb, ow);
    else
        odhw_to_schedule(mb, oh, ow);

    auto kdhw_to_schedule = [&](const expr_t &k) {
        const int k_idx = src_view.vvar_index(k);
        gpu_assert((k_idx >= 5) && (k_idx <= 7));
        const dim_t k_dim = lg[k_idx];
        if (k_dim == schedule.var_bound(k)) {
            schedule.tensorize(k);
        } else if (k_dim < schedule.var_bound(k)) {
            if (k_dim > 1) { // otherwise it'll just waste a variable
                expr_t k_lg, k_tnz;
                schedule.split(k, k_dim, k_lg, k_tnz, k.str() + "_lg",
                        k.str() + "_tnz");
                schedule.tensorize(k_tnz);
            }
        } else {
            gpu_error_not_expected() << "k_dim > var_bound; this is wrong";
        }
    };
    kdhw_to_schedule(kd);
    kdhw_to_schedule(kh);
    kdhw_to_schedule(kw);

    schedule.finalize();

    const auto expand_loop_kinds = loop_kind_t::serial
            | loop_kind_t::kernel_grid | loop_kind_t::tg_grid;
    mb = schedule.expand(mb, true, expand_loop_kinds);
    oc = schedule.expand(oc, true, expand_loop_kinds);
    od = schedule.expand(od, true, expand_loop_kinds);
    oh = schedule.expand(oh, true, expand_loop_kinds);
    ow = schedule.expand(ow, true, expand_loop_kinds);

    auto src_thr_tile_coord
            = schedule.thr_view_tile_coord(src_view, /*is_relative=*/false);
    auto src_thr_view = src_view.create_sub_view(src_thr_tile_coord);

    auto dst_thr_tile_coord
            = schedule.thr_view_tile_coord(dst_view, /*is_relative=*/false);
    auto dst_thr_view = dst_view.create_sub_view(dst_thr_tile_coord);

    const auto &src_buf = ki.arg_var(0);
    const auto &dst_buf = ki.arg_var(1);

    std::vector<stmt_t> allocs;
    for (int i = 0; i < ki.nargs(); i++) {
        auto &var = ki.arg_var(i);
        if (!var.type().is_ptr()) continue;
        allocs.push_back(alloc_t::make(var, 0, alloc_kind_t::global));
    }

    ir_context_t ir_ctx(exec, init_cset);

    auto acc_type = cfg.acc_type(simd);
    auto acc_buf = ir_ctx.create_tmp_var(
            dsl::type_t::byte(dsl::type::attr_t::ptr), "acc");
    const auto acc_sc_size = acc_type.base().size();
    const auto acc_size = acc_sc_size * lg[4] * lg[3] * lg[2] * lg[1] * lg[0];

    auto read_buf = ir_ctx.create_tmp_var(
            dsl::type_t::byte(dsl::type::attr_t::ptr), "read");
    auto read_params = get_send_params(
            exec, send_op_t::load, send_address_t::a64, src_thr_view);
    read_params.try_legacy = false;
    auto read = make_access_builder(ir_ctx, src_thr_view, src_buf, read_buf,
            read_params, /*zero_out=*/false);
    std::vector<stmt_t> read_alloc
            = {alloc_t::make(read_buf, read.reg_buf_size(), alloc_kind_t::grf)};
    const auto &read_layout = read.reg_layout();

    // shall only get used on empty mb's; for all else there's epilogue builder
    auto write_params = get_send_params(
            exec, send_op_t::store, send_address_t::a64, dst_thr_view);
    write_params.try_legacy = false;
    auto write = make_access_builder(
            ir_ctx, dst_thr_view, dst_buf, acc_buf, write_params);
    const auto &write_layout = write.reg_layout();
    auto write_stmt = write.stmt();

    tile_t src_tile(read_layout.max_subtile(simd));
    tile_t dst_tile(write_layout.max_subtile(simd));
    gpu_assert(src_tile.elems() == simd);
    gpu_assert(dst_tile.elems() == simd);

    const bool is_identity(prb.kd * prb.kh * prb.kw <= 1);

    const dsl::type_t read_type(read_layout.type()[simd]);

    stmt_t stmt;

    auto gen_fill_values = [](int simd, bool isneg, dsl::type_t type) {
        gpu_assert(type.base().size() <= 4);
        const int mult = 4 / type.base().size();
        expr_t v = 0;
        if (isneg) {
            v = type.is_f32()        ? 0xFF7FFFFF
                    : type.is_bf16() ? 0xFF7FFF7F
                    : type.is_f16()  ? 0xFBFFFBFF
                    : (mult > 1)     ? (mult > 2) ? 0x80808080 : 0x80008000
                                     : 0x80000000;
        }
        v = cast_t::make(dsl::type_t::s32(), v);
        auto v_long = shuffle_t::make_broadcast(v, simd);
        auto v_short = shuffle_t::make_broadcast(v, simd / mult);
        return std::make_pair(v_short, v_long);
    };
    auto gen_zero_out = [&](int simd, bool isneg, const expr_t &buf,
                                const tile_t &tile, const layout_t &layout) {
        stmt_t retn;
        const auto values = gen_fill_values(simd, isneg, layout.type());
        for (auto &s : layout.iter(tile)) {
            const dim_t off = layout.offset<dim_t>(s) * layout.type().size();
            if (off >= utils::rnd_dn(size_bytes(layout), simd * 4))
                retn = retn.append(store_t::make(buf, off, values.first));
            else if (off % (simd * 4) == 0)
                retn = retn.append(store_t::make(buf, off, values.second));
        }
        return retn;
    };
    const bool is_neg
            = cfg.is_max() && (!read_type.is_int() || read_type.is_signed());
    if (is_identity) {
        allocs.emplace_back(read_alloc[0]);
        write_stmt = substitute(write_stmt, acc_buf, read_buf);
        acc_buf = std::move(read_buf);
        acc_type = read_type;
        stmt = (check_idhw)
                ? gen_zero_out(simd, is_neg, acc_buf, dst_tile, write_layout)
                : stmt_t();
        stmt = stmt.append(read.stmt());
    } else {
        gpu_assert(acc_size % simd == 0);
        auto acc_buf_size = std::max(into<int>(acc_size), write.reg_buf_size());
        allocs.push_back(
                alloc_t::make(acc_buf, acc_buf_size, alloc_kind_t::grf));

        stmt_t fill_stmt, compute_stmt = read.stmt();
        stmt = stmt_t();

        const auto a_fv = gen_fill_values(simd, is_neg, acc_type);
        for (int i = 0; i < acc_size; i += simd * 4)
            stmt = stmt.append(store_t::make(acc_buf, i,
                    (acc_size - i < simd * 4) ? a_fv.first : a_fv.second));
        fill_stmt = gen_zero_out(simd, is_neg, read_buf, src_tile, read_layout);

        for (auto &s : read_layout.iter(src_tile)) {
            const dim_t off_l
                    = read_layout.offset<dim_t>(s) * read_layout.type().size();
            const dim_t off_a = (s.get(0) * lg[1] + s.get(1)) * acc_sc_size;

            auto load = cast_t::make(
                    acc_type, load_t::make(read_type, read_buf, off_l));
            auto acc = load_t::make(acc_type, acc_buf, off_a);
            auto op_kind = (cfg.is_max()) ? op_kind_t::_max : op_kind_t::_add;
            auto op = binary_op_t::make(op_kind, acc, load);
            compute_stmt
                    = compute_stmt.append(store_t::make(acc_buf, off_a, op));
        }

        stmt = stmt.append(schedule.create_loop_nest(
                (check_idhw) ? fill_stmt.append(compute_stmt) : compute_stmt));

        if (!cfg.is_max()) {
            expr_t filter(prb.kd * prb.kh * prb.kw);
            if (!cfg.is_padded() && check_idhw) {
                auto dim = [](const expr_t &o, dim_t s, dim_t p, dim_t k,
                                   dim_t i) {
                    if (k <= 1) return expr_t(1);
                    return binary_op_t::make(op_kind_t::_min, o * s - p + k, i)
                            - binary_op_t::make(op_kind_t::_max, o * s - p, 0);
                };
                auto dhw = dim(od, prb.stride_d, prb.f_pad, prb.kd, prb.id)
                        * dim(oh, prb.stride_h, prb.t_pad, prb.kh, prb.ih)
                        * dim(ow, prb.stride_w, prb.l_pad, prb.kw, prb.iw);
                filter = cast_t::make(dsl::type_t::f32(), dhw);
            }
            filter = shuffle_t::make_broadcast(filter, simd);
            for (int i = 0; i < acc_size; i += simd * acc_sc_size) {
                auto acc = cast_t::make(dsl::type_t::f32(simd),
                        load_t::make(acc_type, acc_buf, i));
                stmt = stmt.append(store_t::make(acc_buf, i, acc / filter));
            }
            acc_type = dsl::type_t::f32(simd);
        }
        stmt = inject_alloc_stmts(stmt, read_alloc);
    }

    if (is_identity && pd.attr()->post_ops_.has_default_values()) {
        stmt = stmt.append(write_stmt);
    } else {
        int buf_size = 0;
        post_op_view_mapper_t view_mapper(dst_view, prb.ndims);
        post_op_context_t post_op_ctx(*pd.attr(), cfg.zp_cfg(), schedule, ki,
                *pd.invariant_dst_md(), *pd.invariant_dst_md(), view_mapper);
        stmt = stmt.append(create_epilogue_stmt(exec, ir_ctx, schedule,
                /*force_c_reorder=*/false, post_op_ctx, dst_thr_tile_coord,
                write_layout.with(acc_type.base()), dst_buf, acc_buf,
                buf_size));
        for (auto &alloc : allocs) {
            auto &a = alloc.as<alloc_t>();
            if (a.buf.is_same(acc_buf))
                alloc = alloc_t::make(a.buf,
                        std::max(a.size, uint32_t(buf_size)), a.kind, a.attrs,
                        a.body);
        }
    }

    loop_bound_counter_t lbc(schedule);
    auto exit_cond = (lbc.count(ow) >= prb.ow) ? (ow < prb.ow) : expr_t();
    if (lbc.count(oh) >= prb.oh)
        exit_cond = exit_cond ? (oh < prb.oh) & exit_cond : (oh < prb.oh);
    if (lbc.count(od) >= prb.od)
        exit_cond = exit_cond ? (od < prb.od) & exit_cond : (od < prb.od);
    if (exit_cond)
        stmt = if_t::make(shuffle_t::make_broadcast(exit_cond, simd), stmt);

    if (!is_xe2_or_xe3_small_kdhw && ((dims[0] - prb.mb) / lg[0] >= 1)) {
        auto stop = gen_zero_out(simd, false, acc_buf, dst_tile, write_layout);
        stmt = if_t::make(shuffle_t::make_broadcast(mb >= prb.mb, simd),
                stop.append(write_stmt), stmt);
    }

    stmt = schedule.create_bind_stmt(stmt);
    stmt = inject_let_stmts(stmt, init_stmts);
    stmt = inject_alloc_stmts(stmt, allocs);
    stmt = inject_external_var_let(stmt, ir_ctx);

    stmt = simplify(stmt, ir_ctx);
    stmt = lift_buffer_offsets_in_send(stmt, ir_ctx);
    stmt = inject_send(stmt, ir_ctx);
    stmt = split_wide_stores(stmt, ir_ctx);
    stmt = fix_int32_overflow(stmt, ir_ctx);
    stmt = eliminate_common_subexprs(
            stmt, ir_ctx, exec.regs() * exec.grf_size());
    stmt = simplify(stmt, ir_ctx);
    stmt = optimize_alloc_let(stmt, ir_ctx);
    stmt = stmt_group_t::make(stmt_label_t::kernel(), stmt);

    const int regs = get_peak_regs(stmt, exec.grf_size());

    gpu_debug() << "Pooling kernel body:\n" << stmt;
    gpu_debug() << "Pooling cfg (~" << regs << " regs):\n" << cfg;

    return (regs > exec.regs()) ? stmt_t() : std::move(stmt);
}

} // namespace jit
} // namespace pool
} // namespace intel
} // namespace gpu
} // namespace impl
} // namespace dnnl