wehub-resource-sync
536 lines
21 KiB
C++
536 lines
21 KiB
C++
// 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 "glog/logging.h"
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#include "paddle/phi/backends/xpu/xpu_context.h"
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#include "paddle/phi/core/enforce.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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namespace phi {
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template <typename T, typename Context>
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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, // UNUSED
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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, // UNUSED
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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, // UNUSED
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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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PADDLE_ENFORCE_NE(
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amsgrad,
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true,
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common::errors::Unimplemented("Operation amsgrad is not supported yet."));
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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float* param_ptr = nullptr;
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funcs::GetDataPointer<Context, float>(
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param, ¶m_ptr, dev_ctx, &RAII_GUARD);
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float* mom1_ptr = nullptr;
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funcs::GetDataPointer<Context, float>(
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moment1, &mom1_ptr, dev_ctx, &RAII_GUARD);
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float* mom2_ptr = nullptr;
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funcs::GetDataPointer<Context, float>(
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moment2, &mom2_ptr, dev_ctx, &RAII_GUARD);
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float* lr_ptr = nullptr;
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float* lr_cast_buf = nullptr;
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if (learning_rate.dtype() == DataType::FLOAT64) {
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lr_cast_buf = RAII_GUARD.alloc_l3_or_gm<float>(learning_rate.numel());
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PADDLE_ENFORCE_XDNN_NOT_NULL(lr_cast_buf);
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int r_lr = xpu::cast<double, float>(dev_ctx.x_context(),
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learning_rate.template data<double>(),
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lr_cast_buf,
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learning_rate.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r_lr, "cast lr from float64 to float32");
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lr_ptr = lr_cast_buf;
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} else {
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funcs::GetDataPointer<Context, float>(
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learning_rate, &lr_ptr, dev_ctx, &RAII_GUARD);
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}
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float* beta1_pow_ptr = nullptr;
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const float* beta1_const_pow_ptr = nullptr;
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DenseTensor xpu_beta1_pow;
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if (beta1_pow.place() == CPUPlace()) {
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Copy(dev_ctx, beta1_pow, dev_ctx.GetPlace(), false, &xpu_beta1_pow);
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if (xpu_beta1_pow.dtype() == DataType::FLOAT16)
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funcs::GetDataPointer<Context, float>(
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xpu_beta1_pow, &beta1_pow_ptr, dev_ctx, &RAII_GUARD);
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else
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beta1_const_pow_ptr = xpu_beta1_pow.template data<float>();
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} else {
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if (beta1_pow.dtype() == DataType::FLOAT16)
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funcs::GetDataPointer<Context, float>(
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beta1_pow, &beta1_pow_ptr, dev_ctx, &RAII_GUARD);
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else
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beta1_const_pow_ptr = beta1_pow.template data<float>();
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}
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float* beta2_pow_ptr = nullptr;
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const float* beta2_const_pow_ptr = nullptr;
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DenseTensor xpu_beta2_pow;
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if (beta2_pow.place() == CPUPlace()) {
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Copy(dev_ctx, beta2_pow, dev_ctx.GetPlace(), false, &xpu_beta2_pow);
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if (xpu_beta2_pow.dtype() == DataType::FLOAT16)
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funcs::GetDataPointer<Context, float>(
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xpu_beta2_pow, &beta2_pow_ptr, dev_ctx, &RAII_GUARD);
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else
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beta2_const_pow_ptr = xpu_beta2_pow.template data<float>();
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} else {
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if (beta2_pow.dtype() == DataType::FLOAT16)
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funcs::GetDataPointer<Context, float>(
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beta2_pow, &beta2_pow_ptr, dev_ctx, &RAII_GUARD);
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else
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beta2_const_pow_ptr = beta2_pow.template data<float>();
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}
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DenseTensor xpu_param_out;
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float* param_out_ptr = nullptr;
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const DenseTensorMeta meta_param(DataType::FLOAT32, param_out->dims());
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xpu_param_out.set_meta(meta_param);
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funcs::GetOutDataPointer<Context, float>(
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param_out, &xpu_param_out, ¶m_out_ptr, dev_ctx);
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DenseTensor xpu_mom1_out;
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float* mom1_out_ptr = nullptr;
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const DenseTensorMeta meta_mom1(DataType::FLOAT32, moment1_out->dims());
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xpu_mom1_out.set_meta(meta_mom1);
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funcs::GetOutDataPointer<Context, float>(
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moment1_out, &xpu_mom1_out, &mom1_out_ptr, dev_ctx);
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DenseTensor xpu_mom2_out;
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float* mom2_out_ptr = nullptr;
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const DenseTensorMeta meta_mom2(DataType::FLOAT32, moment2_out->dims());
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xpu_mom2_out.set_meta(meta_mom2);
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funcs::GetOutDataPointer<Context, float>(
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moment2_out, &xpu_mom2_out, &mom2_out_ptr, dev_ctx);
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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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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 (!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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PADDLE_ENFORCE_EQ(
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beta1_pow_out->numel(),
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1,
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errors::InvalidArgument("Tensor holds the wrong size, Expected beta1 pow "
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"output size is 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("Tensor holds the wrong size, Expected beta2 pow "
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"output size is 1, but received "
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"value is:%d.",
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beta2_pow_out->numel()));
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VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow;
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auto beta1_ = beta1.to<float>();
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auto beta2_ = beta2.to<float>();
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auto epsilon_ = epsilon.to<float>();
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float* grad_c = nullptr;
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funcs::GetDataPointer<Context, float>(grad, &grad_c, dev_ctx, &RAII_GUARD);
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int r = xpu::adam(
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dev_ctx.x_context(),
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grad_c != nullptr ? grad_c : grad.template data<float>(),
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mom1_ptr != nullptr ? mom1_ptr : moment1.template data<float>(),
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mom2_ptr != nullptr ? mom2_ptr : moment2.template data<float>(),
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param_ptr != nullptr ? param_ptr : param.template data<float>(),
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beta1_pow_ptr != nullptr ? beta1_pow_ptr : beta1_const_pow_ptr,
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beta2_pow_ptr != nullptr ? beta2_pow_ptr : beta2_const_pow_ptr,
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lr_ptr != nullptr ? lr_ptr : learning_rate.template data<float>(),
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mom1_out_ptr,
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mom2_out_ptr,
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param_out_ptr,
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beta1_,
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beta2_,
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epsilon_,
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param.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
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funcs::CopyOutData<Context, float>(
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xpu_mom1_out, moment1_out, dev_ctx, &RAII_GUARD);
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funcs::CopyOutData<Context, float>(
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xpu_mom2_out, moment2_out, dev_ctx, &RAII_GUARD);
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funcs::CopyOutData<Context, float>(
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xpu_param_out, param_out, dev_ctx, &RAII_GUARD);
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if (!use_global_beta_pow) {
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// update in cpu and then copy to xpu
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if (beta1_pow.place() == CPUPlace() && beta2_pow.place() == CPUPlace()) {
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funcs::SetBetaData<Context, float>(
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beta1_pow, beta1_pow_out, beta1_, dev_ctx);
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funcs::SetBetaData<Context, float>(
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beta2_pow, beta2_pow_out, beta2_, dev_ctx);
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} else {
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float* beta1_pow_out_p1 = nullptr;
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if (beta1_pow_out->dtype() == DataType::FLOAT16) {
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funcs::Scale<Context, float>(beta1_pow_out,
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beta1_pow,
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beta1_pow_ptr,
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beta1_,
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dev_ctx,
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&RAII_GUARD);
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} else {
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const float* beta1_pow_data = beta1_pow.template data<float>();
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beta1_pow_out_p1 = dev_ctx.template Alloc<float>(beta1_pow_out);
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r = xpu::scale(dev_ctx.x_context(),
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beta1_pow_data,
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beta1_pow_out_p1,
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beta1_pow.numel(),
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false,
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beta1_,
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0.0f);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
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}
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float* beta2_pow_out_p1 = nullptr;
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if (beta2_pow_out->dtype() == DataType::FLOAT16) {
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funcs::Scale<Context, float>(beta2_pow_out,
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beta2_pow,
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beta2_pow_ptr,
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beta2_,
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dev_ctx,
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&RAII_GUARD);
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} else {
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const float* beta2_pow_data = beta2_pow.template data<float>();
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beta2_pow_out_p1 = dev_ctx.template Alloc<float>(beta2_pow_out);
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r = xpu::scale(dev_ctx.x_context(),
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beta2_pow_data,
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beta2_pow_out_p1,
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beta2_pow.numel(),
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false,
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beta2_,
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0.0f);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
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}
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}
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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, // UNUSED
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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, // UNUSED
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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, // UNUSED
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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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PADDLE_ENFORCE_NE(
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amsgrad,
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true,
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common::errors::Unimplemented("Operation amsgrad is not supported yet."));
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VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow;
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auto beta1_ = beta1.to<float>();
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auto beta2_ = beta2.to<float>();
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auto epsilon_ = epsilon.to<float>();
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int64_t step_ = 0;
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int64_t mode_ = 2;
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int64_t bias_correction_ = 1;
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float weight_decay_ = 0.0;
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float lr_;
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{
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DenseTensor lr_host;
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lr_host.Resize(learning_rate[0]->dims());
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if (learning_rate[0]->dtype() == DataType::FLOAT64) {
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dev_ctx.template HostAlloc<double>(&lr_host);
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Copy(dev_ctx, *learning_rate[0], CPUPlace(), false, &lr_host);
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lr_ = static_cast<float>(*(lr_host.template data<double>()));
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} else {
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dev_ctx.template HostAlloc<float>(&lr_host);
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Copy(dev_ctx, *learning_rate[0], CPUPlace(), false, &lr_host);
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lr_ = *(lr_host.template data<float>());
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}
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}
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float beta1_pow_data;
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if (beta1_pow[0]->place() == CPUPlace()) {
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beta1_pow_data = *(beta1_pow[0]->data<float>());
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} else {
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DenseTensor beta1_pow_host;
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beta1_pow_host.Resize(beta1_pow[0]->dims());
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dev_ctx.template HostAlloc<float>(&beta1_pow_host);
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Copy(dev_ctx, *beta1_pow[0], CPUPlace(), false, &beta1_pow_host);
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beta1_pow_data = *(beta1_pow_host.template data<float>());
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}
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float beta2_pow_data;
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if (beta2_pow[0]->place() == CPUPlace()) {
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beta2_pow_data = *(beta2_pow[0]->data<float>());
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} else {
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DenseTensor beta2_pow_host;
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beta2_pow_host.Resize(beta2_pow[0]->dims());
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dev_ctx.template HostAlloc<float>(&beta2_pow_host);
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Copy(dev_ctx, *beta2_pow[0], CPUPlace(), false, &beta2_pow_host);
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beta2_pow_data = *(beta2_pow_host.template data<float>());
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}
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int param_num = param.size();
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PADDLE_ENFORCE_EQ(param_num,
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param_out.size(),
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errors::InvalidArgument(
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"The size of Output(ParamOut) must be equal to "
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"Input(Param), but got the size of Output(ParamOut) "
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"is %d, the size of Input(Param) is %d.",
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param_out.size(),
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param_num));
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PADDLE_ENFORCE_EQ(
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param_num,
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moment1_out.size(),
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errors::InvalidArgument(
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"The size of Input(Moment1) must be equal to Input(Param), but got "
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"the size of Input(Moment1) 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(
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param_num,
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moment2_out.size(),
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errors::InvalidArgument(
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"The size of Input(Moment1) must be equal to Input(Param), but got "
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"the size of Input(Moment1) 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_out.size(),
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errors::InvalidArgument(
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"The size of Output(Beta1PowOut) must be equal to "
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"Input(Param), but got the size of Output(Beta1PowOut) "
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"is %d, the size of Input(Param) is %d.",
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beta1_pow_out.size(),
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param_num));
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PADDLE_ENFORCE_EQ(param_num,
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beta2_pow_out.size(),
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errors::InvalidArgument(
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"The size of Output(Beta2PowOut) must be equal to "
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"Input(Param), but got the size of Output(Beta2PowOut) "
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"is %d, the size of Input(Param) is %d.",
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beta2_pow_out.size(),
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param_num));
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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(
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"The size of Input(Grad) must be equal to Input(Param), but got "
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"the size of Input(Grad) 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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moment1.size(),
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errors::InvalidArgument(
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"The size of Input(Moment1) must be equal to Input(Param), but got "
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"the size of Input(Moment1) 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(
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param_num,
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moment2.size(),
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errors::InvalidArgument(
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"The size of Input(Moment1) must be equal to Input(Param), but got "
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"the size of Input(Moment1) 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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std::vector<float*> param_list(param_num);
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std::vector<float*> grad_list(param_num);
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std::vector<float*> moment1_list(param_num);
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std::vector<float*> moment2_list(param_num);
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std::vector<int64_t> shape_list(param_num);
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for (int j = 0; j < param_num; j++) {
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param_list[j] = const_cast<float*>(param[j]->data<float>());
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grad_list[j] = const_cast<float*>(grad[j]->data<float>());
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moment1_list[j] = const_cast<float*>(moment1[j]->data<float>());
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moment2_list[j] = const_cast<float*>(moment2[j]->data<float>());
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shape_list[j] = param[j]->numel();
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PADDLE_ENFORCE_EQ(
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param[j],
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param_out[j],
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errors::InvalidArgument("The size of Input(Param) and Output(ParamOut) "
|
|
"must be the same Tensors."));
|
|
PADDLE_ENFORCE_EQ(
|
|
moment1[j],
|
|
moment1_out[j],
|
|
errors::InvalidArgument("The size of Input(Param) and Output(ParamOut) "
|
|
"must be the same Tensors."));
|
|
PADDLE_ENFORCE_EQ(
|
|
moment2[j],
|
|
moment2_out[j],
|
|
errors::InvalidArgument("The size of Input(Param) and Output(ParamOut) "
|
|
"must be the same Tensors."));
|
|
|
|
dev_ctx.template Alloc<float>(param_out[j]);
|
|
dev_ctx.template Alloc<float>(moment1_out[j]);
|
|
dev_ctx.template Alloc<float>(moment2_out[j]);
|
|
}
|
|
|
|
int r = xpu::multi_tensor_adam(dev_ctx.x_context(),
|
|
grad_list,
|
|
param_list,
|
|
moment1_list,
|
|
moment2_list,
|
|
shape_list,
|
|
lr_,
|
|
beta1_,
|
|
beta2_,
|
|
epsilon_,
|
|
step_,
|
|
mode_,
|
|
bias_correction_,
|
|
weight_decay_,
|
|
beta1_pow_data,
|
|
beta2_pow_data);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "merged_adam");
|
|
|
|
// update param, moment1, moment2
|
|
for (int i = 0; i < param_num; i++) {
|
|
Copy(dev_ctx, *param[i], dev_ctx.GetPlace(), false, param_out[i]);
|
|
Copy(dev_ctx, *moment1[i], dev_ctx.GetPlace(), false, moment1_out[i]);
|
|
Copy(dev_ctx, *moment2[i], dev_ctx.GetPlace(), false, moment2_out[i]);
|
|
}
|
|
|
|
if (!use_global_beta_pow) {
|
|
for (int i = 0; i < param_num; i++) {
|
|
if (beta1_pow[i]->place() == CPUPlace() &&
|
|
beta2_pow[i]->place() == CPUPlace()) {
|
|
funcs::SetBetaData<Context, float>(
|
|
*beta1_pow[i], beta1_pow_out[i], beta1_, dev_ctx);
|
|
|
|
funcs::SetBetaData<Context, float>(
|
|
*beta2_pow[i], beta2_pow_out[i], beta2_, dev_ctx);
|
|
} else {
|
|
float* beta1_pow_out_ptr = nullptr;
|
|
const float* beta1_pow_data = beta1_pow[i]->data<float>();
|
|
beta1_pow_out_ptr = dev_ctx.template Alloc<float>(beta1_pow_out[i]);
|
|
r = xpu::scale(dev_ctx.x_context(),
|
|
beta1_pow_data,
|
|
beta1_pow_out_ptr,
|
|
beta1_pow[i]->numel(),
|
|
false,
|
|
beta1_,
|
|
0.0f);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "merged_adam");
|
|
|
|
float* beta2_pow_out_ptr = nullptr;
|
|
const float* beta2_pow_data = beta2_pow[i]->data<float>();
|
|
beta2_pow_out_ptr = dev_ctx.template Alloc<float>(beta2_pow_out[i]);
|
|
r = xpu::scale(dev_ctx.x_context(),
|
|
beta2_pow_data,
|
|
beta2_pow_out_ptr,
|
|
beta2_pow[i]->numel(),
|
|
false,
|
|
beta2_,
|
|
0.0f);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "merged_adam");
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(
|
|
adam, XPU, ALL_LAYOUT, phi::AdamDenseKernel, float, phi::float16) {
|
|
kernel->InputAt(2).SetDataType(phi::DataType::FLOAT64);
|
|
// Skip beta1_pow, beta2_pow, skip_update data transform
|
|
kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->InputAt(9).SetBackend(phi::Backend::ALL_BACKEND);
|
|
|
|
kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED);
|
|
kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED);
|
|
}
|
|
|
|
PD_REGISTER_KERNEL(merged_adam, XPU, ALL_LAYOUT, phi::MergedAdamKernel, float) {
|
|
kernel->InputAt(2).SetDataType(phi::DataType::FLOAT64);
|
|
// Skip beta1_pow, beta2_pow data transform
|
|
kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED);
|
|
kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED);
|
|
}
|