text: /*******************************************************************************
  * Copyright 2019-2020 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 MATMUL_HPP
  #define MATMUL_HPP
  
  #include <algorithm>
  #include <bitset>
  #include <iostream>
  #include <numeric>
  
  #include "dnnl.h"
  
  #include "common.hpp"
  #include "dnnl_common.hpp"
  #include "dnnl_memory.hpp"
  #include "perf_report.hpp"
  
  namespace matmul {
  
  typedef struct dt_conf_t {
      dnnl_data_type_t dt;
      double min, max; /* representative */
      double f_min, f_max; /* fill range */
      int f_base; /* fill base, use 0 */
      double f_sparsity; /* amount of non-zeros, default 0.25 */
      double f_scale; /* fill scale, scaling factor for integer generated data */
      double eps; /* acceptable error */
  } _dt_conf_t[DAT_TOTAL];
  
  typedef std::bitset<DNNL_MAX_NDIMS> dims_mask_t;
  extern const _dt_conf_t conf_f32;
  
  const int64_t LD_GOOD = INT64_MAX;
  const int64_t LD_NONE = INT64_MAX - 1;
  
  struct desc_t {
      desc_t() : is_legacy_desc(false), name(nullptr) {}
      std::vector<dims_t> sdims;
      bool is_legacy_desc = false;
      const char *name = nullptr;
  };
  int str2desc(desc_t *desc, const char *str);
  std::ostream &operator<<(std::ostream &s, const desc_t &d);
  
  struct settings_t {
      settings_t() = default;
  
      // ctor to save certain fields from resetting
      settings_t(const char *perf_template) : settings_t() {
          this->perf_template = perf_template;
      }
  
      desc_t desc {};
  
      std::vector<const dt_conf_t *> cfg {conf_f32};
      std::vector<std::string> stag {tag::abx}, wtag {tag::abx}, dtag {tag::abx};
      std::vector<int64_t> ld_src {LD_NONE}, ld_wei {LD_NONE}, ld_dst {LD_NONE};
      std::vector<bool> runtime_mb {false}, runtime_m {false}, runtime_n {false},
              runtime_k {false};
      std::vector<dnnl_data_type_t> bia_dt {dnnl_data_type_undef};
      std::vector<int> bia_mask {2};
      std::vector<std::vector<dims_mask_t>> rt_dims_masks {{}};
      std::vector<attr_t::scale_t> oscale {attr_t::scale_t()};
      std::vector<attr_t::zero_points_t> zero_points {attr_t::zero_points_t()};
      std::vector<attr_t::post_ops_t> post_ops {attr_t::post_ops_t()};
      std::vector<dnnl_scratchpad_mode_t> scratchpad_mode {
              dnnl_scratchpad_mode_library};
      attr_t attr = {};
  
      const char *perf_template_csv
              = "perf,%engine%,%impl%,%name%,%cfg%,%attr%,%DESC%,"
                "%Gops%,%Gfreq%,%-time%,%-Gflops%,%0time%,%0Gflops%";
      const char *perf_template_def
              = "perf,%engine%,%impl%,%name%,%prb%,%Gops%,%Gfreq%,%-time%,%-"
                "Gflops%,%0time%,%0Gflops%";
      const char *perf_template = perf_template_def;
  
      void reset() { *this = settings_t(perf_template); }
  };
  
  struct prb_t : public desc_t {
      prb_t(const desc_t &desc, const dt_conf_t *cfg, const std::string &stag,
              const std::string &wtag, const std::string &dtag, int64_t ld_src,
              int64_t ld_wei, int64_t ld_dst, bool runtime_mb, bool runtime_m,
              bool runtime_n, bool runtime_k, dnnl_data_type_t bia_dt,
              int bia_mask, const std::vector<dims_mask_t> &rt_dims_masks,
              const attr_t &attr)
          : desc_t(desc)
          , cfg(cfg)
          , stag(stag)
          , wtag(wtag)
          , dtag(dtag)
          , ld_src(ld_src)
          , ld_wei(ld_wei)
          , ld_dst(ld_dst)
          , runtime_mb(runtime_mb)
          , runtime_m(runtime_m)
          , runtime_n(runtime_n)
          , runtime_k(runtime_k)
          , bia_dt(bia_dt)
          , bia_mask(bia_mask)
          , rt_dims_masks(rt_dims_masks)
          , attr(attr)
          , scales(NULL) {
  
          this->rt_dims_masks.resize(2);
          if (IMPLICATION(src_runtime_dim_mask().none()
                              && weights_runtime_dim_mask().none(),
                      runtime_mb || runtime_m || runtime_n || runtime_k)) {
              // legacy desc_t
              set_runtime_dims_masks();
          }
  
          const auto &srcdims = src_dims();
          const auto &weidims = weights_dims();
          ndims = (int)srcdims.size();
          m = srcdims[ndims - 2];
          k = srcdims.back();
          n = weidims.back();
  
          init_dst();
          const auto &dstdims = dst_dims();
          const auto nelems = std::accumulate(dstdims.begin(), dstdims.end(),
                  (dnnl_dim_t)1, std::multiplies<dnnl_dim_t>());
          ops = 2. * nelems * k;
  
          generate_oscales();
      }
      ~prb_t() {
          if (scales) zfree(scales);
      }
  
      int m, n, k;
      const dt_conf_t *cfg;
      int ndims;
      std::string stag, wtag, dtag;
      int64_t ld_src, ld_wei, ld_dst;
      bool runtime_mb, runtime_m, runtime_n, runtime_k;
      dnnl_data_type_t bia_dt;
      int bia_mask;
      std::vector<dims_mask_t> rt_dims_masks;
  
      attr_t attr;
  
      double ops;
      float *scales;
  
      const dims_t &src_dims() const { return sdims[0]; }
      const dims_t &weights_dims() const { return sdims[1]; }
      const dims_t &dst_dims() const { return sdims[2]; }
  
      const dims_mask_t &src_runtime_dim_mask() const { return rt_dims_masks[0]; }
      const dims_mask_t &weights_runtime_dim_mask() const {
          return rt_dims_masks[1];
      }
      const dims_mask_t &dst_runtime_dim_mask() const { return rt_dims_masks[2]; }
  
      int src_broadcast_mask() const { return get_broadcast_mask(src_dims()); }
  
      int weights_broadcast_mask() const {
          return get_broadcast_mask(weights_dims());
      }
  
      int bias_broadcast_mask() const { return bia_mask; }
  
      void generate_oscales();
  
      BENCHDNN_DISALLOW_COPY_AND_ASSIGN(prb_t);
  
  private:
      int get_broadcast_mask(const dims_t &dims_idx) const {
          const dims_t &dims_dst = this->dst_dims();
  
          int broadcast_mask = 0;
          for (int d = 0; d < ndims; ++d)
              broadcast_mask += dims_dst[d] == dims_idx[d] ? (1 << d) : 0;
  
          return broadcast_mask;
      }
  
      void init_dst_dims() {
          if (sdims.size() > 2) return;
          sdims.resize(3);
          auto &dst_dims = sdims.back();
          dst_dims.resize(ndims);
  
          for (int i = 0; i < ndims - 2; ++i) {
              sdims.back()[i] = MAX2(sdims[0][i], sdims[1][i]);
          }
          sdims.back()[ndims - 2] = m;
          sdims.back()[ndims - 1] = n;
      }
  
      void init_dst_rt_dims_mask() {
          if (rt_dims_masks.size() > 2) return;
  
          const auto &src_rt_dim_mask = src_runtime_dim_mask();
          const auto &wei_rt_dim_mask = weights_runtime_dim_mask();
          dims_mask_t dst_rt_dim_mask;
  
          for (int i = 0; i < ndims - 2; ++i) {
              dst_rt_dim_mask[i] = src_rt_dim_mask[i] | wei_rt_dim_mask[i];
          }
  
          // m, n mask
          dst_rt_dim_mask[ndims - 2] = src_rt_dim_mask[ndims - 2];
          dst_rt_dim_mask[ndims - 1] = wei_rt_dim_mask[ndims - 1];
  
          rt_dims_masks.push_back(dst_rt_dim_mask);
      }
  
      void init_dst() {
          init_dst_dims();
          init_dst_rt_dims_mask();
      }
  
      // used only for legacy desc support
      void set_runtime_dims_masks() {
          // here we only set src and wei masks. dst mask is computed in init_dst
          const auto ndims = sdims[0].size();
          auto &src_mask = rt_dims_masks[0];
          auto &wei_mask = rt_dims_masks[1];
          if (runtime_mb && ndims == 3) { // else silently ignore
              src_mask[0] = true;
              wei_mask[0] = true;
          }
  
          if (runtime_m) src_mask[ndims - 2] = true;
          if (runtime_n) wei_mask[ndims - 1] = true;
          if (runtime_k) {
              src_mask[ndims - 1] = true;
              wei_mask[ndims - 2] = true;
          }
      }
  };
  std::ostream &operator<<(std::ostream &s, const prb_t &prb);
  
  /* some extra control parameters which shouldn't be placed in prb_t */
  
  const dt_conf_t *str2cfg(const char *str);
  std::ostream &operator<<(std::ostream &s, const dt_conf_t *cfg);
  
  struct perf_report_t : public base_perf_report_t {
      using base_perf_report_t::base_perf_report_t;
  
      void report(const prb_t *prb, const res_t *res, const char *prb_str) {
          p_ = prb;
          stag_ = {normalize_tag(p_->stag, p_->ndims)};
          wtag_ = normalize_tag(p_->wtag, p_->ndims);
          dtag_ = normalize_tag(p_->dtag, p_->ndims);
          base_report(res, prb_str);
      }
  
      void dump_cfg(std::ostream &s) const override { s << p_->cfg; }
  
      void dump_desc(std::ostream &s) const override {
          s << static_cast<const desc_t &>(*p_);
      }
  
      void dump_desc_csv(std::ostream &s) const override {
          s << static_cast<const desc_t &>(*p_);
      }
  
      double ops() const override { return p_->ops; }
      const attr_t *attr() const override { return &p_->attr; }
      const char *name() const override { return p_->name; }
      const std::vector<std::string> *stag() const override { return &stag_; }
      const std::string *wtag() const override { return &wtag_; }
      const std::string *dtag() const override { return &dtag_; }
  
  private:
      const prb_t *p_ = NULL;
      std::vector<std::string> stag_;
      std::string wtag_, dtag_;
  };
  
  inline int64_t src_off_f(const prb_t *prb, int64_t mb, int64_t m, int64_t k) {
      return (mb * prb->m + m) * prb->k + k;
  }
  
  inline int64_t wei_off_f(const prb_t *prb, int64_t mb, int64_t k, int64_t n) {
      return (mb * prb->k + k) * prb->n + n;
  }
  
  inline int64_t dst_off_f(const prb_t *prb, int64_t mb, int64_t m, int64_t n) {
      return (mb * prb->m + m) * prb->n + n;
  }
  
  void compute_ref(const engine_t &engine_tgt, const prb_t *prb, dnn_mem_t &src_m,
          dnn_mem_t &wei_m, dnn_mem_t &bia_m,
          const std::vector<dnn_mem_t> &binary_po, dnn_mem_t &dst_m);
  
  int doit(const prb_t *prb, res_t *res);
  
  int bench(int argc, char **argv);
  
  } // namespace matmul
  
  #endif
  