chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/adam_kernel.h"
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#include <vector>
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#include "glog/logging.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/adam_functors.h"
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#include "paddle/phi/kernels/funcs/jit/kernels.h"
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PD_DECLARE_int32(inner_op_parallelism);
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namespace phi {
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template <typename T, typename Context>
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PADDLE_API void AdamDenseKernel(const Context& dev_ctx,
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const DenseTensor& param,
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const DenseTensor& grad,
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const DenseTensor& learning_rate,
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const DenseTensor& moment1,
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const DenseTensor& moment2,
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const optional<DenseTensor>& moment2_max,
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const DenseTensor& beta1_pow,
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const DenseTensor& beta2_pow,
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const optional<DenseTensor>& master_param,
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const optional<DenseTensor>& skip_update,
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const Scalar& beta1,
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const Scalar& beta2,
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const Scalar& epsilon,
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bool lazy_mode,
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int64_t min_row_size_to_use_multithread,
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bool multi_precision,
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bool use_global_beta_pow,
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bool amsgrad,
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DenseTensor* param_out,
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DenseTensor* moment1_out,
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DenseTensor* moment2_out,
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DenseTensor* moment2_max_out,
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DenseTensor* beta1_pow_out,
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DenseTensor* beta2_pow_out,
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DenseTensor* master_param_outs) {
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VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow;
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bool skip_update_ = false;
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if (skip_update.is_initialized()) {
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PADDLE_ENFORCE_EQ(
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skip_update->numel(),
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1,
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errors::InvalidArgument("Input(SkipUpdate) size must be 1, but get %d",
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skip_update->numel()));
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std::vector<bool> skip_update_vec;
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TensorToVector(*skip_update, dev_ctx, &skip_update_vec);
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skip_update_ = skip_update_vec[0];
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}
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// skip_update=true, just copy input to output, and TensorCopy will call
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// mutable_data
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if (skip_update_) {
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VLOG(4) << "Adam skip update";
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Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out);
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Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out);
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Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out);
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if (amsgrad) {
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Copy(dev_ctx,
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moment2_max.get(),
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dev_ctx.GetPlace(),
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false,
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moment2_max_out);
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}
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if (!use_global_beta_pow) {
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Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, beta1_pow_out);
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Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, beta2_pow_out);
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}
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return;
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}
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T beta1_ = beta1.to<T>();
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T beta2_ = beta2.to<T>();
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T epsilon_ = epsilon.to<T>();
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VLOG(3) << "beta1_pow.numel() : " << beta1_pow.numel();
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VLOG(3) << "beta2_pow.numel() : " << beta2_pow.numel();
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VLOG(3) << "param.numel(): " << param.numel();
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PADDLE_ENFORCE_EQ(
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beta1_pow_out->numel(),
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1,
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errors::InvalidArgument("beta1 pow output size should be 1, but received "
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"value is:%d.",
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beta1_pow_out->numel()));
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PADDLE_ENFORCE_EQ(
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beta2_pow_out->numel(),
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1,
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errors::InvalidArgument("beta2 pow output size should be 1, but received "
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"value is:%d.",
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beta2_pow_out->numel()));
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T beta1_p = beta1_pow.data<T>()[0];
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T beta2_p = beta2_pow.data<T>()[0];
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if (!use_global_beta_pow) {
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dev_ctx.template Alloc<T>(beta1_pow_out)[0] = beta1_ * beta1_p;
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dev_ctx.template Alloc<T>(beta2_pow_out)[0] = beta2_ * beta2_p;
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}
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T* param_out_ptr = dev_ctx.template Alloc<T>(param_out);
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T* mom1_out_ptr = dev_ctx.template Alloc<T>(moment1_out);
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T* mom2_out_ptr = dev_ctx.template Alloc<T>(moment2_out);
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T* mom2_max_out_ptr =
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amsgrad ? dev_ctx.template Alloc<T>(moment2_max_out) : nullptr;
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T learning_rate_ = static_cast<T>(learning_rate.data<double>()[0]) *
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(sqrt(1 - beta2_p) / (1 - beta1_p));
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T eps = epsilon_ * sqrt(1 - beta2_p);
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jit::adam_attr_t attr(beta1_, beta2_, amsgrad);
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int64_t numel = param.numel();
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const T* param_ptr = param.data<T>();
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const T* mom1_ptr = moment1.data<T>();
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const T* mom2_ptr = moment2.data<T>();
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const T* mom2_max_ptr = amsgrad ? moment2_max.get().data<T>() : nullptr;
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const T* grad_ptr = grad.data<T>();
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auto adam = jit::KernelFuncs<jit::AdamTuple<T>, CPUPlace>::Cache().At(attr);
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static constexpr int64_t chunk_size = 512;
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#ifdef PADDLE_WITH_MKLML
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#pragma omp parallel for
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#endif
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for (int64_t i = 0; i < numel / chunk_size; ++i) {
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const int64_t offset = i * chunk_size;
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const T* mom2_max_in_data = amsgrad ? mom2_max_ptr + offset : nullptr;
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T* mom2_max_out_data = amsgrad ? mom2_max_out_ptr + offset : nullptr;
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adam(beta1_,
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beta2_,
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-learning_rate_,
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eps,
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chunk_size,
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grad_ptr + offset,
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mom1_ptr + offset,
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mom2_ptr + offset,
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mom2_max_in_data,
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param_ptr + offset,
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mom1_out_ptr + offset,
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mom2_out_ptr + offset,
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mom2_max_out_data,
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param_out_ptr + offset,
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amsgrad);
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}
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if (numel % chunk_size != 0) {
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const int64_t offset = (numel / chunk_size) * chunk_size;
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const int64_t tail_numel = numel % chunk_size;
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const T* mom2_max_in_data = amsgrad ? mom2_max_ptr + offset : nullptr;
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T* mom2_max_out_data = amsgrad ? mom2_max_out_ptr + offset : nullptr;
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adam(beta1_,
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beta2_,
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-learning_rate_,
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eps,
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tail_numel,
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grad_ptr + offset,
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mom1_ptr + offset,
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mom2_ptr + offset,
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mom2_max_in_data,
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param_ptr + offset,
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mom1_out_ptr + offset,
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mom2_out_ptr + offset,
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mom2_max_out_data,
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param_out_ptr + offset,
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amsgrad);
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}
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}
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template <typename T, typename Context>
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void MergedAdamKernel(
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const Context& dev_ctx,
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const std::vector<const DenseTensor*>& param,
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const std::vector<const DenseTensor*>& grad,
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const std::vector<const DenseTensor*>& learning_rate,
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const std::vector<const DenseTensor*>& moment1,
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const std::vector<const DenseTensor*>& moment2,
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const optional<std::vector<const DenseTensor*>>& moment2_max,
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const std::vector<const DenseTensor*>& beta1_pow,
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const std::vector<const DenseTensor*>& beta2_pow,
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const optional<std::vector<const DenseTensor*>>& master_param,
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const Scalar& beta1,
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const Scalar& beta2,
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const Scalar& epsilon,
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bool multi_precision,
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bool use_global_beta_pow,
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bool amsgrad,
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std::vector<DenseTensor*> param_out,
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std::vector<DenseTensor*> moment1_out,
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std::vector<DenseTensor*> moment2_out,
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std::vector<DenseTensor*> moment2_max_out,
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std::vector<DenseTensor*> beta1_pow_out,
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std::vector<DenseTensor*> beta2_pow_out,
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std::vector<DenseTensor*> master_param_out) {
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size_t param_num = param.size();
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PADDLE_ENFORCE_EQ(
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param_num,
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grad.size(),
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errors::InvalidArgument("The size of Input(grad) must be equal to "
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"Input(param), but got the size of Input(grad) "
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"is %d, the size of Input(param) is %d.",
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grad.size(),
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param_num));
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PADDLE_ENFORCE_EQ(
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param_num,
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learning_rate.size(),
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errors::InvalidArgument(
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"The size of Input(learning_rate) must be equal to "
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"Input(param), but got the size of Input(learning_rate) "
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"is %d, the size of Input(param) is %d.",
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learning_rate.size(),
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param_num));
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PADDLE_ENFORCE_EQ(param_num,
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moment1.size(),
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errors::InvalidArgument(
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"The size of Input(moment1) must be equal to "
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"Input(param), but got the size of Input(moment1) "
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"is %d, the size of Input(param) is %d.",
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moment1.size(),
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param_num));
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PADDLE_ENFORCE_EQ(param_num,
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moment2.size(),
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errors::InvalidArgument(
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"The size of Input(moment2) must be equal to "
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"Input(param), but got the size of Input(moment2) "
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"is %d, the size of Input(param) is %d.",
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moment2.size(),
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param_num));
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PADDLE_ENFORCE_EQ(param_num,
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beta1_pow.size(),
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errors::InvalidArgument(
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"The size of Input(beta1_pow) must be equal to "
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"Input(param), but got the size of Input(beta1_pow) "
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"is %d, the size of Input(param) is %d.",
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beta1_pow.size(),
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param_num));
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PADDLE_ENFORCE_EQ(param_num,
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beta2_pow.size(),
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errors::InvalidArgument(
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"The size of Input(beta2_pow) must be equal to "
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"Input(param), but got the size of Input(beta2_pow) "
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"is %d, the size of Input(param) is %d.",
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beta2_pow.size(),
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param_num));
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T beta1_ = beta1.to<T>();
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T beta2_ = beta2.to<T>();
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T epsilon_ = epsilon.to<T>();
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for (size_t idx = 0; idx < param_num; idx++) {
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const T* mom2_max_in_data =
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amsgrad ? moment2_max.get()[idx]->data<T>() : nullptr;
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T* mom2_max_out_data =
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amsgrad ? dev_ctx.template Alloc<T>(moment2_max_out[idx]) : nullptr;
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const T lr_val = static_cast<T>(learning_rate[idx]->data<double>()[0]);
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funcs::AdamFunctor<T, funcs::CPUAdam> functor(
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beta1_,
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beta2_,
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epsilon_,
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beta1_pow[idx]->data<T>(),
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beta2_pow[idx]->data<T>(),
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moment1[idx]->data<T>(),
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dev_ctx.template Alloc<T>(moment1_out[idx]),
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moment2[idx]->data<T>(),
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dev_ctx.template Alloc<T>(moment2_out[idx]),
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mom2_max_in_data,
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mom2_max_out_data,
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&lr_val,
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grad[idx]->data<T>(),
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param[idx]->data<T>(),
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dev_ctx.template Alloc<T>(param_out[idx]),
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amsgrad);
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functor(param[idx]->numel());
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if (!use_global_beta_pow) {
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dev_ctx.template Alloc<T>(beta1_pow_out[idx])[0] =
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beta1_ * beta1_pow[idx]->data<T>()[0];
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dev_ctx.template Alloc<T>(beta2_pow_out[idx])[0] =
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beta2_ * beta2_pow[idx]->data<T>()[0];
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(adam, CPU, ALL_LAYOUT, phi::AdamDenseKernel, float, double) {
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kernel->InputAt(2).SetDataType(phi::DataType::FLOAT64);
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}
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PD_REGISTER_KERNEL(
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merged_adam, CPU, ALL_LAYOUT, phi::MergedAdamKernel, float, double) {
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kernel->InputAt(2).SetDataType(phi::DataType::FLOAT64);
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}
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