onednn-src 0.1.13

Source of oneAPI Deep Neural Network Library (oneDNN)
Documentation 
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/*******************************************************************************
* Copyright 2020 Intel Corporation
* Copyright 2020 Codeplay Software Limited
*
* 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 GPU_AMD_STREAM_HPP
#define GPU_AMD_STREAM_HPP

#include <hip/hip_runtime.h>
#include <miopen/miopen.h>
#include <rocblas/rocblas.h>

#include "common/engine.hpp"

#include "xpu/sycl/stream_impl.hpp"

#include "gpu/gpu_stream.hpp"

namespace dnnl {
namespace impl {
namespace gpu {
namespace amd {

class stream_t : public gpu::stream_t {
public:
    miopenHandle_t &get_miopen_handle(HIPstream hip_stream = nullptr);
    rocblas_handle &get_rocblas_handle(HIPstream hip_stream = nullptr);

    static status_t create_stream(impl::stream_t **stream,
            impl::engine_t *engine, impl::stream_impl_t *stream_impl) {
        std::unique_ptr<amd::stream_t> s(
                new amd::stream_t(engine, stream_impl));
        if (!s) return status::out_of_memory;

        status_t status = s->init();
        if (status != status::success) {
            // Stream owns stream_impl only if it's created successfully (including initialization).
            s->impl_.release();
            return status;
        }

        *stream = s.release();
        return status::success;
    }

    ::sycl::queue &queue() const { return *impl()->queue(); }

    status_t wait() override {
        queue().wait_and_throw();
        return status::success;
    }

    status_t copy(const memory_storage_t &src, const memory_storage_t &dst,
            size_t size, const xpu::event_t &deps,
            xpu::event_t &out_dep) override {
        return impl()->copy(this, src, dst, size, deps, out_dep);
    }

    status_t fill(const memory_storage_t &dst, uint8_t pattern, size_t size,
            const xpu::event_t &deps, xpu::event_t &out_dep) override {
        return impl()->fill(dst, pattern, size, deps, out_dep);
    }

    const xpu::sycl::context_t &sycl_ctx() const { return impl()->sycl_ctx(); }
    xpu::sycl::context_t &sycl_ctx() { return impl()->sycl_ctx(); }

    xpu::context_t &ctx() override { return impl()->sycl_ctx(); }
    const xpu::context_t &ctx() const override { return impl()->sycl_ctx(); }

    ::sycl::event get_output_event() const {
        return impl()->get_output_event();
    }

    void register_deps(::sycl::handler &cgh) const {
        return impl()->register_deps(cgh);
    }

    status_t interop_task(std::function<void(::sycl::handler &)>);
    hipStream_t get_underlying_stream();
    hipCtx_t get_underlying_context();
    hipDevice_t get_underlying_device();

private:
    xpu::sycl::stream_impl_t *impl() const {
        return (xpu::sycl::stream_impl_t *)stream_t::impl_.get();
    }

    status_t init();
    stream_t(impl::engine_t *engine, impl::stream_impl_t *stream_impl)
        : gpu::stream_t(engine, stream_impl) {}
};

} // namespace amd
} // namespace gpu
} // namespace impl
} // namespace dnnl

#endif