jit_code.h 2.5 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
/* 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

T
tensor-tang 已提交
17
#include <string>
18 19 20 21 22 23 24 25 26 27 28 29 30 31
#include "paddle/fluid/operators/math/jit_gen.h"
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace gen {

using reg64_t = const Xbyak::Reg64;
using reg32_t = const Xbyak::Reg32;
using xmm_t = const Xbyak::Xmm;
using ymm_t = const Xbyak::Ymm;
using zmm_t = const Xbyak::Zmm;
using Label = Xbyak::Label;

T
tensor-tang 已提交
32
typedef enum { mul = 0, add } operand_type;
33

T
tensor-tang 已提交
34
// function: vec = Operand(vec(or scalar), vec(or scalar)) (maybe with relu)
T
tensor-tang 已提交
35
class VXXJitCode : public JitCode {
T
tensor-tang 已提交
36
 public:
T
tensor-tang 已提交
37
  const char* name() const override {
T
tensor-tang 已提交
38
    std::string base = "VXXJitCode";
T
tensor-tang 已提交
39 40 41 42 43
    if (scalar_index_ == 1) {
      base += "_Scalar";
    } else {
      base += "_Vec";
    }
T
tensor-tang 已提交
44 45 46 47 48
    if (type_ == operand_type::mul) {
      base += "_Mul";
    } else if (type_ == operand_type::add) {
      base += "_Add";
    }
T
tensor-tang 已提交
49 50 51 52 53
    if (scalar_index_ == 2) {
      base += "_Scalar";
    } else {
      base += "_Vec";
    }
T
tensor-tang 已提交
54
    base += (with_relu_ ? "_Relu" : "");
T
tensor-tang 已提交
55 56
    return base.c_str();
  }
T
tensor-tang 已提交
57 58 59
  explicit VXXJitCode(int d, operand_type type, int scalar_index,
                      bool with_relu, size_t code_size = 256 * 1024,
                      void* code_ptr = nullptr)
T
tensor-tang 已提交
60 61 62
      : JitCode(code_size, code_ptr),
        num_(d),
        type_(type),
T
tensor-tang 已提交
63
        scalar_index_(scalar_index),
T
tensor-tang 已提交
64
        with_relu_(with_relu) {}
T
tensor-tang 已提交
65
  static bool init(int d, int scalar_index = 0);
T
tensor-tang 已提交
66 67 68 69
  void generate() override;

 private:
  int num_;
T
tensor-tang 已提交
70
  operand_type type_;
T
tensor-tang 已提交
71
  int scalar_index_;
T
tensor-tang 已提交
72
  bool with_relu_;
T
tensor-tang 已提交
73 74 75 76 77 78
  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);
T
tensor-tang 已提交
79 80
  xmm_t xmm_dst = xmm_t(2);
  xmm_t xmm_zero = xmm_t(3);
T
tensor-tang 已提交
81 82 83

  ymm_t ymm_src1 = ymm_t(0);
  ymm_t ymm_src2 = ymm_t(1);
T
tensor-tang 已提交
84 85
  ymm_t ymm_dst = ymm_t(2);
  ymm_t ymm_zero = ymm_t(3);
T
tensor-tang 已提交
86 87
};

88 89 90 91 92
}  // namespace gen
}  // namespace jitkernel
}  // namespace math
}  // namespace operators
}  // namespace paddle