adam_kernel.cc 8.5 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
// Copyright (c) 2022 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.

#include "paddle/phi/kernels/selected_rows/adam_kernel.h"

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
19
#include "paddle/phi/core/tensor_utils.h"
20
#include "paddle/phi/core/threadpool.h"
21
#include "paddle/phi/kernels/funcs/adam_functors.h"
22
#include "paddle/phi/kernels/funcs/selected_rows_functor.h"
23 24 25 26 27 28 29 30 31 32 33 34 35 36

namespace phi {
namespace sr {

template <typename T, typename Context>
void AdamDenseParamSparseGradKernel(
    const Context& dev_ctx,
    const DenseTensor& param,
    const SelectedRows& grad,
    const DenseTensor& learning_rate,
    const DenseTensor& moment1,
    const DenseTensor& moment2,
    const DenseTensor& beta1_pow,
    const DenseTensor& beta2_pow,
37 38
    const paddle::optional<DenseTensor>& master_param,
    const paddle::optional<DenseTensor>& skip_update,
39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
    const Scalar& beta1,
    const Scalar& beta2,
    const Scalar& epsilon,
    bool lazy_mode,
    int64_t min_row_size_to_use_multithread,
    bool multi_precision,
    bool use_global_beta_pow,
    DenseTensor* param_out,
    DenseTensor* moment1_out,
    DenseTensor* moment2_out,
    DenseTensor* beta1_pow_out,
    DenseTensor* beta2_pow_out,
    DenseTensor* master_param_outs) {
  VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow;

  bool skip_update_ = false;
  if (skip_update.is_initialized()) {
    PADDLE_ENFORCE_EQ(
        skip_update->numel(),
        1,
        errors::InvalidArgument("Input(SkipUpdate) size must be 1, but get %d",
                                skip_update->numel()));
    std::vector<bool> skip_update_vec;
62
    phi::TensorToVector(*skip_update, dev_ctx, &skip_update_vec);
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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
    skip_update_ = skip_update_vec[0];
  }
  // skip_update=true, just copy input to output, and TensorCopy will call
  // mutable_data
  if (skip_update_) {
    VLOG(4) << "Adam skip update";
    phi::Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out);
    phi::Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out);
    phi::Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out);
    phi::Copy(dev_ctx, beta1_pow, dev_ctx.GetPlace(), false, beta1_pow_out);
    phi::Copy(dev_ctx, beta2_pow, dev_ctx.GetPlace(), false, beta2_pow_out);
    return;
  }

  T beta1_ = beta1.to<T>();
  T beta2_ = beta2.to<T>();
  T epsilon_ = epsilon.to<T>();

  VLOG(3) << "beta1_pow.numel() : " << beta1_pow.numel();
  VLOG(3) << "beta2_pow.numel() : " << beta2_pow.numel();
  VLOG(3) << "param.numel(): " << param.numel();

  PADDLE_ENFORCE_EQ(
      beta1_pow_out->numel(),
      1,
      errors::InvalidArgument("beta1 pow output size should be 1, but received "
                              "value is:%d.",
                              beta1_pow_out->numel()));

  PADDLE_ENFORCE_EQ(
      beta2_pow_out->numel(),
      1,
      errors::InvalidArgument("beta2 pow output size should be 1, but received "
                              "value is:%d.",
                              beta2_pow_out->numel()));

  if (grad.rows().size() == 0) {
    VLOG(3) << "grad row size is 0!!";
    return;
  }

  std::vector<int64_t> cpu_rows(grad.rows().begin(), grad.rows().end());
  bool is_strict_sorted = true;
  for (size_t i = 1; i < cpu_rows.size(); ++i) {
    if (cpu_rows[i - 1] >= cpu_rows[i]) {
      is_strict_sorted = false;
      break;
    }
  }

  phi::SelectedRows tmp_grad_merge;
  const phi::SelectedRows* grad_merge_ptr;
  if (is_strict_sorted) {
    grad_merge_ptr = &grad;
  } else {
    // merge duplicated rows if any.
    // The rows of grad_merge have been sorted inside MergeAdd functor
120
    phi::funcs::scatter::MergeAdd<Context, T> merge_func;
121 122 123 124 125 126 127 128
    merge_func(dev_ctx, grad, &tmp_grad_merge, true);
    grad_merge_ptr = &tmp_grad_merge;
  }

  auto& grad_merge = *grad_merge_ptr;
  auto& grad_tensor = grad_merge.value();
  const T* grad_data = grad_tensor.template data<T>();
  auto* grad_merge_rows = &grad_merge.rows();
H
Huang Jiyi 已提交
129
  phi::MixVector<int64_t> mixv_grad_merge_rows(grad_merge_rows);
130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202
  const int64_t* rows = mixv_grad_merge_rows.Data(dev_ctx.GetPlace());
  auto row_numel = grad_tensor.numel() / grad_merge.rows().size();

  funcs::SparseAdamFunctor<T, funcs::CPUAdam> functor(
      beta1_,
      beta2_,
      epsilon_,
      beta1_pow.data<T>(),
      beta2_pow.data<T>(),
      moment1.data<T>(),
      dev_ctx.template Alloc<T>(moment1_out),
      moment2.data<T>(),
      dev_ctx.template Alloc<T>(moment2_out),
      learning_rate.data<T>(),
      grad_data,
      param.data<T>(),
      dev_ctx.template Alloc<T>(param_out),
      rows,
      row_numel,
      grad_merge.rows().size(),
      lazy_mode);
  // update beta1 and beta2
  if (!use_global_beta_pow) {
    dev_ctx.template Alloc<T>(beta1_pow_out)[0] =
        beta1_ * beta1_pow.data<T>()[0];
    dev_ctx.template Alloc<T>(beta2_pow_out)[0] =
        beta2_ * beta2_pow.data<T>()[0];
  }
  if (lazy_mode) {
    VLOG(3) << "run cpu lazy mode";
    size_t row_count = grad_merge.rows().size();
    std::vector<int64_t> cpu_rows(grad_merge.rows());
    for (size_t row_index = 0; row_index < row_count; ++row_index) {
      for (size_t offset = 0; offset < row_numel; ++offset) {
        size_t i = cpu_rows[row_index] * row_numel + offset;
        functor.adam_update(i, grad_data[row_index * row_numel + offset]);
      }
    }
  }
#ifndef _WIN32
  else if (FLAGS_inner_op_parallelism > 1 &&  // NOLINT
           min_row_size_to_use_multithread > 0 &&
           param.dims()[0] > min_row_size_to_use_multithread) {
    VLOG(3) << "use multi thread, inner_op_parallelism="
            << FLAGS_inner_op_parallelism << " min_row_size_to_use_multithread="
            << min_row_size_to_use_multithread;
    if (FLAGS_inner_op_parallelism > 10) {
      VLOG(1) << "FLAGS_inner_op_parallelism " << FLAGS_inner_op_parallelism
              << " is two large!";
    }
    auto& grad_rows = grad_merge.rows();
    std::unordered_map<size_t, int> row_id_to_grad_row_offset;
    size_t param_row_count = param.numel() / row_numel;
    if (param_row_count < 1000) {
      VLOG(1) << "param_row_count should be larger then 1000 to use "
                 "multi thread, currently "
              << param_row_count;
    }
    for (size_t i = 0; i < grad_rows.size(); ++i) {
      row_id_to_grad_row_offset[grad_rows[i]] = i;
    }
    std::vector<std::future<void>> fs;
    int64_t line_in_each_thread =
        param_row_count / FLAGS_inner_op_parallelism + 1;
    for (int i = 0; i < FLAGS_inner_op_parallelism; ++i) {
      int64_t start = i * line_in_each_thread;
      int64_t end = (i + 1) * line_in_each_thread;
      if (start >= static_cast<int64_t>(param_row_count)) {
        break;
      }
      if (end > static_cast<int64_t>(param_row_count)) {
        end = static_cast<int64_t>(param_row_count);
      }
203 204 205 206 207 208
      fs.push_back(phi::Async([&functor,
                               &row_id_to_grad_row_offset,
                               &grad_data,
                               row_numel,
                               start,
                               end]() {
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241
        for (int64_t row_id = start; row_id < end; ++row_id) {
          auto iter = row_id_to_grad_row_offset.find(row_id);
          if (iter != row_id_to_grad_row_offset.end()) {
            for (size_t row_offset = 0U; row_offset < row_numel; ++row_offset) {
              functor.adam_update(
                  row_id * row_numel + row_offset,
                  grad_data[iter->second * row_numel + row_offset]);
            }
          } else {
            for (size_t row_offset = 0U; row_offset < row_numel; ++row_offset) {
              functor.adam_update(row_id * row_numel + row_offset, 0);
            }
          }
        }
      }));
    }
    for (size_t i = 0; i < fs.size(); ++i) fs[i].wait();
  }
#endif    // !_WIN32
  else {  // NOLINT
    functor(param.numel());
  }
}

}  // namespace sr
}  // namespace phi

PD_REGISTER_KERNEL(adam_dense_param_sparse_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::sr::AdamDenseParamSparseGradKernel,
                   float,
                   double) {}