lstm_unit_op.cu 6.4 KB
Newer Older
Z
zchen0211 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

   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. */

15 16 17 18
/* Acknowledgement: the following code is strongly inspired by
https://github.com/caffe2/caffe2/blob/master/caffe2/operators/lstm_unit_op_gpu.cu
*/

Z
zchen0211 已提交
19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
#include "paddle/framework/op_registry.h"
#include "paddle/operators/cross_entropy_op.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/hostdevice.h"

namespace paddle {
namespace operators {

#define CUDA_1D_KERNEL_LOOP(i, n)                              \
  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
       i += blockDim.x * gridDim.x)

template <typename Dtype>
__device__ Dtype cuda_sigmoid(const Dtype x) {
  return Dtype(1) / (Dtype(1) + exp(-x));
}

template <typename Dtype>
__device__ Dtype cuda_tanh(const Dtype x) {
  return Dtype(1 - exp(-2. * x)) / (Dtype(1) + exp(-2. * x));
}

template <typename T>
Z
zchen0211 已提交
42
__global__ void LSTMUnitKernel(const int nthreads, const int dim,
Z
zchen0211 已提交
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 87 88 89 90 91 92 93 94 95
                               const T* C_prev, const T* X, T* C, T* H,
                               const T forget_bias) {
  CUDA_1D_KERNEL_LOOP(index, nthreads) {
    const int n = index / dim;
    const int d = index % dim;

    const T* X_offset = X + 4 * dim * n;
    const T i = cuda_sigmoid(X_offset[d]);
    const T f = cuda_sigmoid(X_offset[1 * dim + d] + forget_bias);
    const T o = cuda_sigmoid(X_offset[2 * dim + d]);
    const T g = cuda_tanh(X_offset[3 * dim + d]);
    const T c_prev = C_prev[index];
    const T c = f * c_prev + i * g;
    C[index] = c;
    const T tanh_c = cuda_tanh(c);
    H[index] = o * tanh_c;
  }
}

template <typename T>
__global__ void LSTMUnitGradientKernel(const int nthreads, const int dim,
                                       const T* C_prev, const T* X, const T* C,
                                       const T* H, const T* C_diff,
                                       const T* H_diff, T* C_prev_diff,
                                       T* X_diff, const T forget_bias) {
  CUDA_1D_KERNEL_LOOP(index, nthreads) {
    const int n = index / dim;
    const int d = index % dim;
    const T* X_offset = X + 4 * dim * n;
    T* c_prev_diff = C_prev_diff + index;
    T* X_diff_offset = X_diff + 4 * dim * n;
    T* i_diff = X_diff_offset + d;
    T* f_diff = X_diff_offset + 1 * dim + d;
    T* o_diff = X_diff_offset + 2 * dim + d;
    T* g_diff = X_diff_offset + 3 * dim + d;

    const T i = cuda_sigmoid(X_offset[d]);
    const T f = cuda_sigmoid(X_offset[1 * dim + d] + forget_bias);
    const T o = cuda_sigmoid(X_offset[2 * dim + d]);
    const T g = cuda_tanh(X_offset[3 * dim + d]);
    const T c_prev = C_prev[index];
    const T c = C[index];
    const T tanh_c = cuda_tanh(c);
    const T c_term_diff =
        C_diff[index] + H_diff[index] * o * (1 - tanh_c * tanh_c);
    *c_prev_diff = c_term_diff * f;
    *i_diff = c_term_diff * g * i * (1 - i);
    *f_diff = c_term_diff * c_prev * f * (1 - f);
    *o_diff = H_diff[index] * tanh_c * o * (1 - o);
    *g_diff = c_term_diff * i * (1 - g * g);
  }
}

96
template <typename T>
Y
Yu Yang 已提交
97
class LstmUnitOpCUDAKernel : public framework::OpKernel<T> {
Z
zchen0211 已提交
98 99 100 101 102 103 104 105 106 107
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                   "It must use GPUPlace.");

    auto* x_tensor = ctx.Input<framework::Tensor>("X");
    auto* c_prev_tensor = ctx.Input<framework::Tensor>("C_prev");
    auto* c_tensor = ctx.Output<framework::Tensor>("C");
    auto* h_tensor = ctx.Output<framework::Tensor>("H");

108
    auto forget_bias = static_cast<T>(ctx.Attr<float>("forget_bias"));
Z
zchen0211 已提交
109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126

    int b_size = c_tensor->dims()[0];
    int D = c_tensor->dims()[1];

    const T* X = x_tensor->data<T>();
    const T* C_prev = c_prev_tensor->data<T>();

    T* C = c_tensor->mutable_data<T>(ctx.GetPlace());
    T* H = h_tensor->mutable_data<T>(ctx.GetPlace());

    int block = 512;
    int n = b_size * D;
    int grid = (n + block - 1) / block;

    LSTMUnitKernel<T><<<grid, block>>>(n, D, C_prev, X, C, H, forget_bias);
  }
};

127
template <typename T>
Y
Yu Yang 已提交
128
class LstmUnitGradOpCUDAKernel : public framework::OpKernel<T> {
Z
zchen0211 已提交
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                   "It must use GPUPlace.");

    auto x_tensor = ctx.Input<Tensor>("X");
    auto c_prev_tensor = ctx.Input<Tensor>("C_prev");
    auto c_tensor = ctx.Input<Tensor>("C");
    auto h_tensor = ctx.Input<Tensor>("H");

    auto hdiff_tensor = ctx.Input<Tensor>(framework::GradVarName("H"));
    auto cdiff_tensor = ctx.Input<Tensor>(framework::GradVarName("C"));

    auto xdiff_tensor = ctx.Output<Tensor>(framework::GradVarName("X"));
    auto c_prev_diff_tensor =
        ctx.Output<Tensor>(framework::GradVarName("C_prev"));

    auto* X = x_tensor->data<T>();
    auto* C_prev = c_prev_tensor->data<T>();
    auto* C = c_tensor->data<T>();
    auto* H = h_tensor->data<T>();

    auto* H_diff = hdiff_tensor->data<T>();
    auto* C_diff = cdiff_tensor->data<T>();

    auto* C_prev_diff = c_prev_diff_tensor->mutable_data<T>(ctx.GetPlace());
    auto* X_diff = xdiff_tensor->mutable_data<T>(ctx.GetPlace());

    int N = c_tensor->dims()[0];
    int D = c_tensor->dims()[1];

160
    auto forget_bias = static_cast<T>(ctx.Attr<float>("forget_bias"));
Z
zchen0211 已提交
161 162 163 164 165

    int block = 512;
    int n = N * D;
    int grid = (n + block - 1) / block;

Z
zchen0211 已提交
166 167 168
    LSTMUnitGradientKernel<T><<<grid, block>>>(n, D, C_prev, X, C, H, C_diff,
                                               H_diff, C_prev_diff, X_diff,
                                               forget_bias);
Z
zchen0211 已提交
169 170 171 172 173 174 175
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
176 177 178 179
REGISTER_OP_GPU_KERNEL(lstm_unit, ops::LstmUnitOpCUDAKernel<float>,
                       ops::LstmUnitOpCUDAKernel<double>);
REGISTER_OP_GPU_KERNEL(lstm_unit_grad, ops::LstmUnitGradOpCUDAKernel<float>,
                       ops::LstmUnitGradOpCUDAKernel<double>);