wehub-resource-sync
363 lines
13 KiB
C++
363 lines
13 KiB
C++
// 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 "glog/logging.h"
|
|
|
|
#include "paddle/phi/backends/xpu/xpu_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/core/tensor_utils.h"
|
|
#include "paddle/phi/kernels/funcs/adam_functors.h"
|
|
#include "paddle/phi/kernels/funcs/selected_rows_functor.h"
|
|
|
|
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 optional<DenseTensor>& moment2_max, // UNUSED
|
|
const DenseTensor& beta1_pow,
|
|
const DenseTensor& beta2_pow,
|
|
const optional<DenseTensor>& master_param,
|
|
const optional<DenseTensor>& skip_update,
|
|
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,
|
|
bool amsgrad, // UNUSED
|
|
DenseTensor* param_out,
|
|
DenseTensor* moment1_out,
|
|
DenseTensor* moment2_out,
|
|
DenseTensor* moment2_max_out, // UNUSED
|
|
DenseTensor* beta1_pow_out,
|
|
DenseTensor* beta2_pow_out,
|
|
DenseTensor* master_param_outs) {
|
|
PADDLE_ENFORCE_NE(
|
|
amsgrad,
|
|
true,
|
|
common::errors::Unimplemented("Operation amsgrad is not supported yet."));
|
|
|
|
using XPUType = typename XPUTypeTrait<T>::Type;
|
|
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
|
|
float* param_ptr = nullptr;
|
|
funcs::GetDataPointer<Context, float>(
|
|
param, ¶m_ptr, dev_ctx, &RAII_GUARD);
|
|
|
|
float* mom1_ptr = nullptr;
|
|
funcs::GetDataPointer<Context, float>(
|
|
moment1, &mom1_ptr, dev_ctx, &RAII_GUARD);
|
|
|
|
float* mom2_ptr = nullptr;
|
|
funcs::GetDataPointer<Context, float>(
|
|
moment2, &mom2_ptr, dev_ctx, &RAII_GUARD);
|
|
|
|
float* lr_ptr = nullptr;
|
|
funcs::GetDataPointer<Context, float>(
|
|
learning_rate, &lr_ptr, dev_ctx, &RAII_GUARD);
|
|
|
|
float* beta1_pow_ptr = nullptr;
|
|
const float* beta1_const_pow_ptr = nullptr;
|
|
|
|
if (beta1_pow.place() == CPUPlace()) {
|
|
if (beta1_pow.dtype() == DataType::FLOAT16) {
|
|
XPUType* beta1_pow_t =
|
|
RAII_GUARD.alloc_l3_or_gm<XPUType>(beta1_pow.numel());
|
|
memory_utils::Copy(param.place(),
|
|
beta1_pow_t,
|
|
beta1_pow.place(),
|
|
beta1_pow.data<T>(),
|
|
sizeof(T) * beta1_pow.numel());
|
|
|
|
int r = xpu::cast<XPUType, float>(
|
|
dev_ctx.x_context(), beta1_pow_t, beta1_pow_ptr, beta1_pow.numel());
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
|
|
} else {
|
|
beta1_pow_ptr = RAII_GUARD.alloc_l3_or_gm<float>(beta1_pow.numel());
|
|
memory_utils::Copy(param.place(),
|
|
beta1_pow_ptr,
|
|
beta1_pow.place(),
|
|
beta1_pow.data<T>(),
|
|
sizeof(T) * beta1_pow.numel());
|
|
}
|
|
|
|
} else {
|
|
if (beta1_pow.dtype() == DataType::FLOAT16)
|
|
funcs::GetDataPointer<Context, float>(
|
|
beta1_pow, &beta1_pow_ptr, dev_ctx, &RAII_GUARD);
|
|
else
|
|
beta1_const_pow_ptr = beta1_pow.template data<float>();
|
|
}
|
|
|
|
float* beta2_pow_ptr = nullptr;
|
|
const float* beta2_const_pow_ptr = nullptr;
|
|
|
|
if (beta2_pow.place() == CPUPlace()) {
|
|
if (beta2_pow.dtype() == DataType::FLOAT16) {
|
|
XPUType* beta2_pow_t =
|
|
RAII_GUARD.alloc_l3_or_gm<XPUType>(beta2_pow.numel());
|
|
memory_utils::Copy(param.place(),
|
|
beta2_pow_t,
|
|
beta2_pow.place(),
|
|
beta2_pow.data<T>(),
|
|
sizeof(T) * beta2_pow.numel());
|
|
|
|
int r = xpu::cast<XPUType, float>(
|
|
dev_ctx.x_context(), beta2_pow_t, beta2_pow_ptr, beta2_pow.numel());
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
|
|
} else {
|
|
beta2_pow_ptr = RAII_GUARD.alloc_l3_or_gm<float>(beta2_pow.numel());
|
|
memory_utils::Copy(param.place(),
|
|
beta2_pow_ptr,
|
|
beta2_pow.place(),
|
|
beta2_pow.data<T>(),
|
|
sizeof(T) * beta2_pow.numel());
|
|
}
|
|
} else {
|
|
if (beta2_pow.dtype() == DataType::FLOAT16)
|
|
funcs::GetDataPointer<Context, float>(
|
|
beta2_pow, &beta2_pow_ptr, dev_ctx, &RAII_GUARD);
|
|
else
|
|
beta2_const_pow_ptr = beta2_pow.template data<float>();
|
|
}
|
|
|
|
DenseTensor xpu_param_out;
|
|
float* param_out_ptr = nullptr;
|
|
const DenseTensorMeta meta_param(DataType::FLOAT32, param_out->dims());
|
|
xpu_param_out.set_meta(meta_param);
|
|
funcs::GetOutDataPointer<Context, float>(
|
|
param_out, &xpu_param_out, ¶m_out_ptr, dev_ctx);
|
|
|
|
DenseTensor xpu_mom1_out;
|
|
float* mom1_out_ptr = nullptr;
|
|
const DenseTensorMeta meta_mom1(DataType::FLOAT32, moment1_out->dims());
|
|
xpu_mom1_out.set_meta(meta_mom1);
|
|
funcs::GetOutDataPointer<Context, float>(
|
|
moment1_out, &xpu_mom1_out, &mom1_out_ptr, dev_ctx);
|
|
|
|
DenseTensor xpu_mom2_out;
|
|
float* mom2_out_ptr = nullptr;
|
|
const DenseTensorMeta meta_mom2(DataType::FLOAT32, moment2_out->dims());
|
|
xpu_mom2_out.set_meta(meta_mom2);
|
|
funcs::GetOutDataPointer<Context, float>(
|
|
moment2_out, &xpu_mom2_out, &mom2_out_ptr, dev_ctx);
|
|
|
|
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;
|
|
TensorToVector(*skip_update, dev_ctx, &skip_update_vec);
|
|
skip_update_ = skip_update_vec[0];
|
|
}
|
|
|
|
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);
|
|
if (!use_global_beta_pow) {
|
|
phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, beta1_pow_out);
|
|
phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, beta2_pow_out);
|
|
}
|
|
return;
|
|
}
|
|
|
|
PADDLE_ENFORCE_EQ(
|
|
beta1_pow_out->numel(),
|
|
1,
|
|
errors::InvalidArgument("Tensor holds the wrong size, Expected beta1 pow "
|
|
"output size is 1, but received "
|
|
"value is:%d.",
|
|
beta1_pow_out->numel()));
|
|
|
|
PADDLE_ENFORCE_EQ(
|
|
beta2_pow_out->numel(),
|
|
1,
|
|
errors::InvalidArgument("Tensor holds the wrong size, Expected beta2 pow "
|
|
"output size is 1, but received "
|
|
"value is:%d.",
|
|
beta2_pow_out->numel()));
|
|
|
|
VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow;
|
|
|
|
auto beta1_ = beta1.to<float>();
|
|
auto beta2_ = beta2.to<float>();
|
|
auto epsilon_ = epsilon.to<float>();
|
|
|
|
float* grad_c = nullptr;
|
|
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;
|
|
}
|
|
}
|
|
|
|
SelectedRows tmp_grad_merge;
|
|
const SelectedRows* grad_merge_ptr;
|
|
if (is_strict_sorted) {
|
|
grad_merge_ptr = &grad;
|
|
} else {
|
|
funcs::scatter::MergeAdd<Context, float> merge_func;
|
|
merge_func(dev_ctx, grad, &tmp_grad_merge, true);
|
|
|
|
xpu_wait(dev_ctx.x_context()->xpu_stream);
|
|
grad_merge_ptr = &tmp_grad_merge;
|
|
}
|
|
|
|
auto& grad_merge = *grad_merge_ptr;
|
|
auto& grad_tensor = grad_merge.value();
|
|
|
|
funcs::GetDataPointer<Context, float>(
|
|
grad_tensor, &grad_c, dev_ctx, &RAII_GUARD);
|
|
|
|
int row_count = grad_merge.rows().size();
|
|
std::vector<int> rows(row_count);
|
|
int* xpu_rows = RAII_GUARD.alloc_l3_or_gm<int>(row_count);
|
|
std::vector<int64_t> merge_rows(grad_merge.rows().begin(),
|
|
grad_merge.rows().end());
|
|
for (size_t i = 0; i < grad_merge.rows().size(); ++i) {
|
|
rows[i] = static_cast<int>(merge_rows[i]);
|
|
}
|
|
xpu_wait(dev_ctx.x_context()->xpu_stream);
|
|
memory_utils::Copy(dev_ctx.GetPlace(),
|
|
xpu_rows,
|
|
CPUPlace(),
|
|
rows.data(),
|
|
row_count * sizeof(int));
|
|
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
|
|
auto ori_rows = param.numel() / row_numel;
|
|
|
|
int r = xpu::sparse_adam(
|
|
dev_ctx.x_context(),
|
|
grad_c != nullptr ? grad_c : grad_tensor.template data<float>(),
|
|
mom1_ptr != nullptr ? mom1_ptr : moment1.template data<float>(),
|
|
mom2_ptr != nullptr ? mom2_ptr : moment2.template data<float>(),
|
|
param_ptr != nullptr ? param_ptr : param.template data<float>(),
|
|
beta1_pow_ptr != nullptr ? beta1_pow_ptr : beta1_const_pow_ptr,
|
|
beta2_pow_ptr != nullptr ? beta2_pow_ptr : beta2_const_pow_ptr,
|
|
lr_ptr != nullptr ? lr_ptr : learning_rate.template data<float>(),
|
|
mom1_out_ptr,
|
|
mom2_out_ptr,
|
|
param_out_ptr,
|
|
beta1_,
|
|
beta2_,
|
|
epsilon_,
|
|
ori_rows,
|
|
xpu_rows,
|
|
row_numel,
|
|
grad_merge.rows().size(),
|
|
lazy_mode);
|
|
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
|
|
|
|
funcs::CopyOutData<Context, float>(
|
|
xpu_mom1_out, moment1_out, dev_ctx, &RAII_GUARD);
|
|
funcs::CopyOutData<Context, float>(
|
|
xpu_mom2_out, moment1_out, dev_ctx, &RAII_GUARD);
|
|
funcs::CopyOutData<Context, float>(
|
|
xpu_param_out, moment1_out, dev_ctx, &RAII_GUARD);
|
|
|
|
if (!use_global_beta_pow) {
|
|
// update in cpu and then copy to xpu
|
|
if (beta1_pow.place() == CPUPlace() && beta2_pow.place() == CPUPlace()) {
|
|
funcs::SetBetaData<Context, float>(
|
|
beta1_pow, beta1_pow_out, beta1_, dev_ctx);
|
|
|
|
funcs::SetBetaData<Context, float>(
|
|
beta2_pow, beta2_pow_out, beta2_, dev_ctx);
|
|
} else {
|
|
float* beta1_pow_out_p1 = nullptr;
|
|
|
|
if (beta1_pow_out->dtype() == DataType::FLOAT16) {
|
|
funcs::Scale<Context, float>(beta1_pow_out,
|
|
beta1_pow,
|
|
beta1_pow_ptr,
|
|
beta1_,
|
|
dev_ctx,
|
|
&RAII_GUARD);
|
|
} else {
|
|
const float* beta1_pow_data = beta1_pow.template data<float>();
|
|
beta1_pow_out_p1 = dev_ctx.template Alloc<float>(beta1_pow_out);
|
|
r = xpu::scale(dev_ctx.x_context(),
|
|
beta1_pow_data,
|
|
beta1_pow_out_p1,
|
|
beta1_pow.numel(),
|
|
false,
|
|
beta1_,
|
|
0.0f);
|
|
xpu_wait(dev_ctx.x_context()->xpu_stream);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
|
|
}
|
|
|
|
float* beta2_pow_out_p1 = nullptr;
|
|
if (beta2_pow_out->dtype() == DataType::FLOAT16) {
|
|
funcs::Scale<Context, float>(beta2_pow_out,
|
|
beta2_pow,
|
|
beta2_pow_ptr,
|
|
beta2_,
|
|
dev_ctx,
|
|
&RAII_GUARD);
|
|
} else {
|
|
const float* beta2_pow_data = beta2_pow.template data<float>();
|
|
beta2_pow_out_p1 = dev_ctx.template Alloc<float>(beta2_pow_out);
|
|
r = xpu::scale(dev_ctx.x_context(),
|
|
beta2_pow_data,
|
|
beta2_pow_out_p1,
|
|
beta2_pow.numel(),
|
|
false,
|
|
beta2_,
|
|
0.0f);
|
|
xpu_wait(dev_ctx.x_context()->xpu_stream);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} // namespace sr
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(adam_dense_param_sparse_grad,
|
|
XPU,
|
|
ALL_LAYOUT,
|
|
phi::sr::AdamDenseParamSparseGradKernel,
|
|
float,
|
|
phi::float16) {
|
|
// 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);
|
|
}
|