blas.h 2.4 KB
Newer Older
T
tensor-tang 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 *
 * 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. */

#pragma once

#include <string>
T
tensor-tang 已提交
18
#include "paddle/fluid/operators/jit/gen/jitcode.h"
T
tensor-tang 已提交
19 20 21

namespace paddle {
namespace operators {
T
tensor-tang 已提交
22 23
namespace jit {
namespace gen {
T
tensor-tang 已提交
24 25 26 27

// function: vec = Operand(vec(or scalar), vec(or scalar)) (maybe with relu)
class VXXJitCode : public JitCode {
 public:
T
tensor-tang 已提交
28 29 30 31 32 33 34 35 36 37 38 39
  explicit VXXJitCode(int d, operand_type type, int scalar_index,
                      bool with_relu, size_t code_size = 256 * 1024,
                      void* code_ptr = nullptr)
      : JitCode(code_size, code_ptr),
        num_(d),
        type_(type),
        scalar_index_(scalar_index),
        with_relu_(with_relu) {
    this->genCode();
  }

  virtual const char* name() const {
T
tensor-tang 已提交
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
    std::string base = "VXXJitCode";
    if (scalar_index_ == 1) {
      base += "_Scalar";
    } else {
      base += "_Vec";
    }
    if (type_ == operand_type::mul) {
      base += "_Mul";
    } else if (type_ == operand_type::add) {
      base += "_Add";
    }
    if (scalar_index_ == 2) {
      base += "_Scalar";
    } else {
      base += "_Vec";
    }
    base += (with_relu_ ? "_Relu" : "");
    return base.c_str();
  }
  void genCode() override;

 private:
  int num_;
  operand_type type_;
  int scalar_index_;
  bool with_relu_;
  reg64_t param1{abi_param1};
  reg64_t param2{abi_param2};
  reg64_t param3{abi_param3};

  xmm_t xmm_src1 = xmm_t(0);
  xmm_t xmm_src2 = xmm_t(1);
  xmm_t xmm_dst = xmm_t(2);
  xmm_t xmm_zero = xmm_t(3);

  ymm_t ymm_src1 = ymm_t(0);
  ymm_t ymm_src2 = ymm_t(1);
  ymm_t ymm_dst = ymm_t(2);
  ymm_t ymm_zero = ymm_t(3);
};

class VMulJitCode : public VXXJitCode {
 public:
  explicit VMulJitCode(int d, size_t code_size, void* code_ptr = nullptr)
      : VXXJitCode(d, operand_type::mul, 0, false, code_size, code_ptr) {}
};

T
tensor-tang 已提交
87 88
}  // namespace gen
}  // namespace jit
T
tensor-tang 已提交
89 90
}  // namespace operators
}  // namespace paddle