onednn-src 0.1.13

/*******************************************************************************
* Copyright 2020-2025 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_ACL_WINOGRAD_CONVOLUTION_HPP
#define CPU_AARCH64_ACL_WINOGRAD_CONVOLUTION_HPP

#include "cpu/cpu_convolution_pd.hpp"

#include "cpu/aarch64/acl_convolution_utils.hpp"

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

struct acl_wino_resource_t : public resource_t {
    acl_wino_resource_t()
        : acl_wino_obj_(utils::make_unique<
                  acl_obj_t<arm_compute::NEWinogradConvolutionLayer>>()) {}

    status_t configure(const acl_conv_conf_t &acp) {
        if (!acl_wino_obj_) return status::out_of_memory;

        // Init Compute Library tensors based on info from descriptor
        acl_wino_obj_->src_tensor.allocator()->init(acp.src_tensor_info);
        acl_wino_obj_->wei_tensor.allocator()->init(acp.wei_tensor_info);
        acl_wino_obj_->dst_tensor.allocator()->init(acp.dst_tensor_info);
        acl_wino_obj_->bia_tensor.allocator()->init(acp.bia_tensor_info);

        // clang-format off
        acl_wino_obj_->conv.configure(
            &acl_wino_obj_->src_tensor,
            &acl_wino_obj_->wei_tensor,
            acp.with_bias ? &acl_wino_obj_->bia_tensor : nullptr,
            &acl_wino_obj_->dst_tensor,
            acp.padstride_info,
            acp.act_info,
            true); // to support 5x5, 7x7 filter shapes in addition to 3x3
        // clang-format on

        return status::success;
    }

    acl_obj_t<arm_compute::NEWinogradConvolutionLayer> &get_acl_obj() const {
        return *acl_wino_obj_;
    }

    DNNL_DISALLOW_COPY_AND_ASSIGN(acl_wino_resource_t);

private:
    std::unique_ptr<acl_obj_t<arm_compute::NEWinogradConvolutionLayer>>
            acl_wino_obj_;
}; // acl_wino_resource_t

struct acl_wino_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;

        DECLARE_COMMON_PD_T(
                "wino:acl", acl_wino_convolution_fwd_t, USE_GLOBAL_SCRATCHPAD);

        status_t init(engine_t *engine) {
            using namespace data_type;
            const bool is_fp16_ok = expect_data_types(f16, f16, f16, f16, undef)
                    && attr()->has_default_values(
                            primitive_attr_t::skip_mask_t::post_ops, f16);
            const bool is_fp32_ok = expect_data_types(f32, f32, f32, f32, undef)
                    && attr()->has_default_values(
                            primitive_attr_t::skip_mask_t::post_ops, f32);
            bool ok = is_fwd()
                    && utils::one_of(desc()->alg_kind,
                            alg_kind::convolution_auto,
                            alg_kind::convolution_winograd)
                    && utils::one_of(true, is_fp16_ok, is_fp32_ok)
                    && !has_zero_dim_memory();

            ok = ok && DNNL_CPU_THREADING_RUNTIME != DNNL_RUNTIME_THREADPOOL;
            if (!ok) return status::unimplemented;

            CHECK(acl_convolution_utils::init_conf_wino(acp_, src_md_,
                    weights_md_, dst_md_, bias_md_, *desc(), *attr()));

            set_default_alg_kind(alg_kind::convolution_winograd);

            CHECK(post_ops.init(
                    engine, attr_.post_ops_, dst_md_, acp_.act_info));
            acp_.use_dst_acc_for_sum = post_ops.has_sum();

            if (acp_.use_dst_acc_for_sum) {
                const memory_desc_wrapper dst_d(&dst_md_);
                auto scratchpad = scratchpad_registry().registrar();
                scratchpad.book(memory_tracking::names::key_generic_acc,
                        dst_d.nelems(), dst_d.data_type_size());
            }

            return status::success;
        }

        acl_conv_conf_t acp_ = utils::zero<decltype(acp_)>();
        acl_post_ops_t post_ops;
    };

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

    status_t create_resource(
            engine_t *engine, resource_mapper_t &mapper) const override {
        if (mapper.has_resource(this)) return status::success;

        auto r = utils::make_unique<acl_wino_resource_t>();
        if (!r) return status::out_of_memory;

        // Configure the resource based on information from primitive descriptor
        CHECK(r->configure(pd()->acp_));
        mapper.add(this, std::move(r));

        return status::success;
    }

    ~acl_wino_convolution_fwd_t() override = default;

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

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

private:
    // To guard the const execute_forward(), the mutex must be 'mutable'
    mutable std::mutex mtx;
    status_t execute_forward(const exec_ctx_t &ctx) const;
    const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
}; // acl_wino_convolution_fwd_t

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

#endif // CPU_AARCH64_ACL_WINOGRAD_CONVOLUTION_HPP