wehub-resource-sync
343 lines
13 KiB
Plaintext
343 lines
13 KiB
Plaintext
// 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/gpu/gpu_context.h"
|
|
#include "paddle/phi/common/amp_type_traits.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/for_range.h"
|
|
#include "paddle/phi/kernels/funcs/selected_rows_functor.h"
|
|
|
|
namespace phi {
|
|
namespace sr {
|
|
|
|
template <typename T>
|
|
__global__ void UpdateBetaPow(T beta1,
|
|
T beta2,
|
|
const T* beta1_pow_,
|
|
const T* beta2_pow_,
|
|
T* beta1_pow_out,
|
|
T* beta2_pow_out) {
|
|
*beta1_pow_out = beta1 * beta1_pow_[0];
|
|
*beta2_pow_out = beta2 * beta2_pow_[0];
|
|
}
|
|
|
|
template <typename T, typename MT>
|
|
__global__ void SparseAdamCUDAKernelREG(MT beta1,
|
|
MT beta2,
|
|
MT epsilon,
|
|
const MT beta1_pow,
|
|
const MT beta2_pow,
|
|
const MT* mom1_,
|
|
MT* mom1_out_,
|
|
const MT* mom2_,
|
|
MT* mom2_out_,
|
|
const MT* mom2_max_,
|
|
MT* mom2_max_out_,
|
|
const double* lr_,
|
|
const T* grad_,
|
|
const T* param_,
|
|
T* param_out_,
|
|
const MT* master_param,
|
|
MT* master_param_out,
|
|
const int64_t* rows_,
|
|
int64_t row_numel,
|
|
int64_t row_count,
|
|
bool lazy_mode,
|
|
int ndim,
|
|
bool amsgrad) {
|
|
int64_t id =
|
|
static_cast<int64_t>(blockIdx.x) * static_cast<int64_t>(blockDim.x) +
|
|
static_cast<int64_t>(threadIdx.x);
|
|
MT lr = static_cast<MT>(*lr_);
|
|
|
|
for (; id < ndim; id += blockDim.x * gridDim.x) {
|
|
auto row_idx =
|
|
funcs::BinarySearch<int64_t>(rows_, row_count, id / row_numel);
|
|
if (lazy_mode && row_idx < 0) {
|
|
return;
|
|
} else {
|
|
MT mom1 = mom1_[id];
|
|
MT mom2 = mom2_[id];
|
|
|
|
MT p = master_param ? master_param[id] : static_cast<MT>(param_[id]);
|
|
MT g = row_idx >= 0
|
|
? static_cast<MT>(grad_[row_idx * row_numel + id % row_numel])
|
|
: static_cast<MT>(0);
|
|
mom1 = beta1 * mom1 + (static_cast<MT>(1.0) - beta1) * g;
|
|
mom2 = beta2 * mom2 + (static_cast<MT>(1.0) - beta2) * g * g;
|
|
|
|
MT denom;
|
|
if (amsgrad) {
|
|
MT mom2_max = mom2_max_[id];
|
|
MT moment2_max_ = std::max(mom2, mom2_max);
|
|
mom2_max_out_[id] = moment2_max_;
|
|
|
|
denom = (sqrt(moment2_max_) / sqrt(static_cast<MT>(1.0) - beta2_pow)) +
|
|
epsilon;
|
|
} else {
|
|
denom = (sqrt(mom2) / sqrt(static_cast<MT>(1.0) - beta2_pow)) + epsilon;
|
|
}
|
|
|
|
p += (mom1 / denom) * (-(lr / (static_cast<MT>(1.0) - beta1_pow)));
|
|
|
|
// Write back to global memory
|
|
mom1_out_[id] = mom1;
|
|
mom2_out_[id] = mom2;
|
|
param_out_[id] = static_cast<T>(p);
|
|
if (master_param_out) {
|
|
master_param_out[id] = p;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
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,
|
|
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,
|
|
DenseTensor* param_out,
|
|
DenseTensor* moment1_out,
|
|
DenseTensor* moment2_out,
|
|
DenseTensor* moment2_max_out,
|
|
DenseTensor* beta1_pow_out,
|
|
DenseTensor* beta2_pow_out,
|
|
DenseTensor* master_param_outs) {
|
|
using MT = typename phi::dtype::MPTypeTrait<T>::Type;
|
|
|
|
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;
|
|
TensorToVector(*skip_update, dev_ctx, &skip_update_vec);
|
|
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);
|
|
if (amsgrad) {
|
|
phi::Copy(dev_ctx,
|
|
moment2_max.get(),
|
|
dev_ctx.GetPlace(),
|
|
false,
|
|
moment2_max_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;
|
|
}
|
|
|
|
MT beta1_ = beta1.to<MT>();
|
|
MT beta2_ = beta2.to<MT>();
|
|
MT epsilon_ = epsilon.to<MT>();
|
|
VLOG(3) << "beta1_pow.numel() : " << beta1_pow.numel()
|
|
<< "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()));
|
|
|
|
const MT* master_in_data =
|
|
multi_precision ? master_param->data<MT>() : nullptr;
|
|
MT* master_out_data =
|
|
multi_precision ? dev_ctx.template Alloc<MT>(master_param_outs) : nullptr;
|
|
|
|
const MT* moment2_max_in_data =
|
|
amsgrad ? moment2_max.get().data<MT>() : nullptr;
|
|
MT* moment2_max_out_data =
|
|
amsgrad ? dev_ctx.template Alloc<MT>(moment2_max_out) : 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 {
|
|
// merge duplicated rows if any.
|
|
// The rows of grad_merge have been sorted inside MergeAdd functor
|
|
funcs::scatter::MergeAdd<Context, T> merge_func;
|
|
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();
|
|
phi::MixVector<int64_t> mixv_grad_merge_rows(grad_merge_rows);
|
|
const int64_t* rows = mixv_grad_merge_rows.Data(dev_ctx.GetPlace());
|
|
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
|
|
|
|
if (beta1_pow.place() == CPUPlace() && beta2_pow.place() == CPUPlace()) {
|
|
int threads = 512;
|
|
int64_t ndim = param.numel();
|
|
int64_t blocks = (ndim + threads - 1) / threads;
|
|
|
|
// NOTE(large-tensor): Kernel launch requires int type for grid dimension
|
|
PADDLE_ENFORCE_LE_INT_MAX(blocks, "blocks");
|
|
SparseAdamCUDAKernelREG<T, MT>
|
|
<<<static_cast<int>(blocks), threads, 0, dev_ctx.stream()>>>(
|
|
beta1_,
|
|
beta2_,
|
|
epsilon_,
|
|
*beta1_pow.data<MT>(),
|
|
*beta2_pow.data<MT>(),
|
|
moment1.data<MT>(),
|
|
dev_ctx.template Alloc<MT>(moment1_out),
|
|
moment2.data<MT>(),
|
|
dev_ctx.template Alloc<MT>(moment2_out),
|
|
moment2_max_in_data,
|
|
moment2_max_out_data,
|
|
learning_rate.data<double>(),
|
|
grad_data,
|
|
param.data<T>(),
|
|
dev_ctx.template Alloc<T>(param_out),
|
|
master_in_data,
|
|
master_out_data,
|
|
rows,
|
|
row_numel,
|
|
grad_merge.rows().size(),
|
|
lazy_mode,
|
|
ndim,
|
|
amsgrad);
|
|
if (!use_global_beta_pow) {
|
|
// Update with cpu
|
|
dev_ctx.template HostAlloc<MT>(beta1_pow_out)[0] =
|
|
beta1_ * beta1_pow.data<MT>()[0];
|
|
dev_ctx.template HostAlloc<MT>(beta2_pow_out)[0] =
|
|
beta2_ * beta2_pow.data<MT>()[0];
|
|
}
|
|
} else {
|
|
funcs::SparseAdamFunctor<T, funcs::GPUAdam, MT> functor(
|
|
beta1_,
|
|
beta2_,
|
|
epsilon_,
|
|
beta1_pow.data<MT>(),
|
|
beta2_pow.data<MT>(),
|
|
moment1.data<MT>(),
|
|
dev_ctx.template Alloc<MT>(moment1_out),
|
|
moment2.data<MT>(),
|
|
dev_ctx.template Alloc<MT>(moment2_out),
|
|
moment2_max_in_data,
|
|
moment2_max_out_data,
|
|
learning_rate.data<double>(),
|
|
grad_data,
|
|
param.data<T>(),
|
|
dev_ctx.template Alloc<T>(param_out),
|
|
master_in_data,
|
|
master_out_data,
|
|
rows,
|
|
row_numel,
|
|
grad_merge.rows().size(),
|
|
lazy_mode,
|
|
amsgrad);
|
|
|
|
// FIXME(minqiyang): remove BinarySearch in GPU later
|
|
funcs::ForRange<Context> for_range(dev_ctx, param.numel());
|
|
for_range(functor);
|
|
if (!use_global_beta_pow) {
|
|
// update beta1 and beta2
|
|
UpdateBetaPow<MT><<<1, 32, 0, dev_ctx.stream()>>>(
|
|
beta1_,
|
|
beta2_,
|
|
beta1_pow.data<MT>(),
|
|
beta2_pow.data<MT>(),
|
|
dev_ctx.template Alloc<MT>(beta1_pow_out),
|
|
dev_ctx.template Alloc<MT>(beta2_pow_out));
|
|
}
|
|
}
|
|
}
|
|
|
|
} // namespace sr
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(adam_dense_param_sparse_grad,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::sr::AdamDenseParamSparseGradKernel,
|
|
float,
|
|
double,
|
|
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);
|
|
|
|
if (kernel_key.dtype() == phi::DataType::FLOAT16) {
|
|
kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);
|
|
kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);
|
|
kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32);
|
|
kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32);
|
|
kernel->OutputAt(5).SetDataType(phi::DataType::FLOAT32);
|
|
kernel->OutputAt(6).SetDataType(phi::DataType::FLOAT32);
|
|
}
|
|
kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED);
|
|
kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED);
|
|
}
|