text: /*******************************************************************************
  * Copyright 2019-2021 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.
  *******************************************************************************/
  
  #ifndef GPU_OCL_REF_CONVOLUTION_HPP
  #define GPU_OCL_REF_CONVOLUTION_HPP
  
  #include "common/c_types_map.hpp"
  #include "common/primitive.hpp"
  #include "gpu/gpu_primitive.hpp"
  
  #include "gpu/compute/compute.hpp"
  #include "gpu/gpu_convolution_pd.hpp"
  #include "gpu/gpu_resource.hpp"
  #include "gpu/ocl/ocl_stream.hpp"
  #include "gpu/primitive_conf.hpp"
  
  namespace dnnl {
  namespace impl {
  namespace gpu {
  namespace ocl {
  
  struct ref_convolution_fwd_t : public gpu_primitive_t {
      using gpu_primitive_t::gpu_primitive_t;
      struct pd_t : public gpu_convolution_fwd_pd_t {
          using gpu_convolution_fwd_pd_t::gpu_convolution_fwd_pd_t;
  
          DECLARE_COMMON_PD_T("ocl:ref:any", ref_convolution_fwd_t);
  
          status_t init(engine_t *engine) {
              using namespace data_type;
  
              const auto *compute_engine
                      = utils::downcast<compute::compute_engine_t *>(engine);
  
              const auto attr_skip_mask
                      = primitive_attr_t::skip_mask_t::oscale_runtime
                      | primitive_attr_t::skip_mask_t::zero_points_runtime
                      | primitive_attr_t::skip_mask_t::post_ops
                      | primitive_attr_t::skip_mask_t::sum_dt;
  
              bool ok = set_default_alg_kind(alg_kind::convolution_direct)
                      && utils::one_of(desc()->prop_kind,
                              prop_kind::forward_training,
                              prop_kind::forward_inference)
                      && desc()->alg_kind == alg_kind::convolution_direct
                      && IMPLICATION(
                              utils::one_of(f16, src_md_.data_type,
                                      weights_md_.data_type, dst_md_.data_type),
                              compute_engine->mayiuse(
                                      compute::device_ext_t::khr_fp16))
                      && this->set_default_formats()
                      && attr()->has_default_values(
                              attr_skip_mask, dst_md_.data_type)
                      && attr_.set_default_formats(dst_md(0)) == status::success
                      && post_ops_with_binary_ok(attr(), dst_md()->data_type, 5)
                      && zero_points_ok(attr())
                      && IMPLICATION(!attr()->output_scales_.has_default_values(),
                              utils::one_of(src_md_.data_type, s8, u8)
                                      && utils::one_of(
                                              attr()->output_scales_.mask_, 0,
                                              1 << 1));
              if (!ok) return status::unimplemented;
  
              return init_conf(engine);
          }
  
          status_t init_conf(engine_t *engine);
          status_t init_kernel_ctx(compute::kernel_ctx_t &kernel_ctx) const;
  
          const memory_desc_t *scales_md() const { return &scales_md_; }
  
          conv_conf_t conf;
          offsets_t off;
  
      private:
          bool set_default_formats() {
              using namespace format_tag;
              auto dat_tag = utils::pick(ndims() - 3, ncw, nchw, ncdhw);
              auto wei_tag = with_groups()
                      ? utils::pick(ndims() - 3, goiw, goihw, goidhw)
                      : utils::pick(ndims() - 3, oiw, oihw, oidhw);
              return set_default_formats_common(dat_tag, wei_tag, dat_tag);
          }
  
          status_t init_scales_md() {
              if (conf.attr_info.with_per_oc_oscales
                      && !conf.attr_info.with_runtime_oscales) {
                  scales_md_.data_type = data_type::f32;
                  scales_md_.ndims = 1;
                  scales_md_.dims[0] = attr()->output_scales_.count_;
                  return memory_desc_init_by_tag(scales_md_, format_tag::x);
              }
  
              return status::success;
          }
  
          memory_desc_t scales_md_;
      };
  
      status_t init(engine_t *engine) override {
          compute::kernel_ctx_t kernel_ctx;
  
          auto status = pd()->init_kernel_ctx(kernel_ctx);
          if (status != status::success) return status;
  
          create_kernel(engine, &kernel_, "ref_convolution_fwd", kernel_ctx);
          if (!kernel_) return status::runtime_error;
  
          return status::success;
      }
  
      status_t execute(const exec_ctx_t &ctx) const override {
          return execute_forward(ctx);
      }
  
  protected:
      status_t init_res_storage(
              engine_t *engine, gpu_resource_t *r) const override {
          if (!pd()->conf.attr_info.with_per_oc_oscales
                  || pd()->conf.attr_info.with_runtime_oscales)
              return status::success;
          memory_desc_wrapper scales_mdw(pd()->scales_md());
          memory_storage_t *tmp_mem_storage_ptr;
          CHECK(engine->create_memory_storage(
                  &tmp_mem_storage_ptr, scales_mdw.nelems() * sizeof(float)));
  
          std::unique_ptr<memory_storage_t> tmp_mem_storage(tmp_mem_storage_ptr);
          void *scales_ptr = nullptr;
          CHECK(tmp_mem_storage->map_data(&scales_ptr, nullptr,
                  sizeof(float) * pd()->attr()->output_scales_.count_));
          utils::array_copy((float *)scales_ptr,
                  pd()->attr()->output_scales_.scales_,
                  pd()->attr()->output_scales_.count_);
          CHECK(tmp_mem_storage->unmap_data(scales_ptr, nullptr));
          r->add_memory_storage(SCALES_, std::move(tmp_mem_storage));
          return status::success;
      }
  
  private:
      status_t execute_forward(const exec_ctx_t &ctx) const;
      const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
      compute::kernel_t kernel_;
      enum { SCALES_ = 0 };
  };
  
  struct ref_convolution_bwd_data_t : public gpu_primitive_t {
      using gpu_primitive_t::gpu_primitive_t;
      struct pd_t : public gpu_convolution_bwd_data_pd_t {
          using gpu_convolution_bwd_data_pd_t::gpu_convolution_bwd_data_pd_t;
  
          DECLARE_COMMON_PD_T("ocl:ref:any", ref_convolution_bwd_data_t);
  
          status_t init(engine_t *engine) {
              const auto attr_skip_mask = primitive_attr_t::skip_mask_t::post_ops;
  
              bool ok = set_default_alg_kind(alg_kind::convolution_direct)
                      && desc()->prop_kind == prop_kind::backward_data
                      && desc()->alg_kind == alg_kind::convolution_direct
                      && this->set_default_formats()
                      && attr()->has_default_values(attr_skip_mask)
                      && post_ops_with_binary_ok(attr(), dst_md()->data_type)
                      && attr_.set_default_formats(diff_src_md(0))
                              == status::success;
              if (!ok) return status::unimplemented;
  
              return init_conf(engine);
          }
  
          status_t init_conf(engine_t *engine);
          status_t init_kernel_ctx(compute::kernel_ctx_t &kernel_ctx) const;
  
          conv_conf_t conf;
          offsets_t off;
  
      private:
          bool set_default_formats() {
              using namespace format_tag;
              auto dat_tag = utils::pick(ndims() - 3, ncw, nchw, ncdhw);
              auto wei_tag = with_groups()
                      ? utils::pick(ndims() - 3, goiw, goihw, goidhw)
                      : utils::pick(ndims() - 3, oiw, oihw, oidhw);
              return set_default_formats_common(dat_tag, wei_tag, dat_tag);
          }
      };
  
      status_t init(engine_t *engine) override {
          compute::kernel_ctx_t kernel_ctx;
  
          auto status = pd()->init_kernel_ctx(kernel_ctx);
          if (status != status::success) return status;
  
          create_kernel(engine, &kernel_, "ref_convolution_bwd_data", kernel_ctx);
          if (!kernel_) return status::runtime_error;
  
          return status::success;
      }
  
      status_t execute(const exec_ctx_t &ctx) const override {
          return execute_backward_data(ctx);
      }
  
  private:
      status_t execute_backward_data(const exec_ctx_t &ctx) const;
      const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
      compute::kernel_t kernel_;
  };
  
  struct ref_convolution_bwd_weights_t : public gpu_primitive_t {
      using gpu_primitive_t::gpu_primitive_t;
      struct pd_t : public gpu_convolution_bwd_weights_pd_t {
          using gpu_convolution_bwd_weights_pd_t::
                  gpu_convolution_bwd_weights_pd_t;
  
          DECLARE_COMMON_PD_T("ocl:ref:any", ref_convolution_bwd_weights_t);
  
          status_t init(engine_t *engine) {
              bool ok = set_default_alg_kind(alg_kind::convolution_direct)
                      && desc()->prop_kind == prop_kind::backward_weights
                      && desc()->alg_kind == alg_kind::convolution_direct
                      && this->set_default_formats()
                      && attr()->has_default_values();
              if (!ok) return status::unimplemented;
  
              return init_conf(engine);
          }
  
          status_t init_conf(engine_t *engine);
          status_t init_kernel_ctx(compute::kernel_ctx_t &kernel_ctx) const;
  
          conv_conf_t conf;
          offsets_t off;
  
      private:
          bool set_default_formats() {
              using namespace format_tag;
              auto dat_tag = utils::pick(ndims() - 3, ncw, nchw, ncdhw);
              auto wei_tag = with_groups()
                      ? utils::pick(ndims() - 3, goiw, goihw, goidhw)
                      : utils::pick(ndims() - 3, oiw, oihw, oidhw);
              return set_default_formats_common(dat_tag, wei_tag, dat_tag);
          }
      };
  
      status_t init(engine_t *engine) override {
          compute::kernel_ctx_t kernel_ctx;
  
          auto status = pd()->init_kernel_ctx(kernel_ctx);
          if (status != status::success) return status;
  
          create_kernel(
                  engine, &kernel_, "ref_convolution_bwd_weights", kernel_ctx);
          if (!kernel_) return status::runtime_error;
  
          return status::success;
      }
  
      status_t execute(const exec_ctx_t &ctx) const override {
          return execute_backward_weights(ctx);
      }
  
  private:
      status_t execute_backward_weights(const exec_ctx_t &ctx) const;
      const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
      compute::kernel_t kernel_;
  };
  
  } // namespace ocl
  } // namespace gpu
  } // namespace impl
  } // namespace dnnl
  #endif
  