未验证 提交 ea4bdca8 编写于 作者: Y Yu Yang 提交者: GitHub

Merge pull request #6967 from reyoung/feature/optimize_adam_speed

Use for_range to rewrite adam
...@@ -13,59 +13,113 @@ See the License for the specific language governing permissions and ...@@ -13,59 +13,113 @@ See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#pragma once #pragma once
#include "paddle/framework/eigen.h" #include <math.h> // for sqrt in CPU and CUDA
#include "paddle/framework/op_registry.h" #include "paddle/framework/op_registry.h"
#include "paddle/operators/detail/safe_ref.h"
#include "paddle/platform/for_range.h"
namespace paddle { namespace paddle {
namespace operators { namespace operators {
template <typename T>
struct AdamFunctor {
T beta1_;
T beta2_;
T epsilon_;
const T* beta1_pow_;
const T* beta2_pow_;
const T* moment1_;
T* moment1_out_;
const T* moment2_;
T* moment2_out_;
const T* lr_;
const T* grad_;
const T* param_;
T* param_out_;
AdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
const T* beta2_pow, const T* mom1, T* mom1_out, const T* mom2,
T* mom2_out, const T* lr, const T* grad, const T* param,
T* param_out)
: beta1_(beta1),
beta2_(beta2),
epsilon_(epsilon),
beta1_pow_(beta1_pow),
beta2_pow_(beta2_pow),
moment1_(mom1),
moment1_out_(mom1_out),
moment2_(mom2),
moment2_out_(mom2_out),
lr_(lr),
grad_(grad),
param_(param),
param_out_(param_out) {}
inline HOSTDEVICE void operator()(size_t i) const {
// Merge all memory access together.
T g = grad_[i];
T mom1 = moment1_[i];
T mom2 = moment2_[i];
T lr = *lr_;
T beta1_pow = *beta1_pow_;
T beta2_pow = *beta2_pow_;
T p = param_[i];
// Calculation
lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);
mom1 = beta1_ * mom1 + (1 - beta1_) * g;
mom2 = beta2_ * mom2 + (1 - beta2_) * g * g;
p -= lr * (mom1 / (sqrt(mom2) + epsilon_));
// Write back to global memory
moment1_out_[i] = mom1;
moment2_out_[i] = mom2;
param_out_[i] = p;
}
};
template <typename DeviceContext, typename T> template <typename DeviceContext, typename T>
class AdamOpKernel : public framework::OpKernel<T> { class AdamOpKernel : public framework::OpKernel<T> {
public: public:
void Compute(const framework::ExecutionContext& ctx) const override { void Compute(const framework::ExecutionContext& ctx) const override {
auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut"); using paddle::framework::LoDTensor;
auto moment1_out_tensor = ctx.Output<framework::Tensor>("Moment1Out"); using paddle::operators::detail::Ref;
auto moment2_out_tensor = ctx.Output<framework::Tensor>("Moment2Out");
param_out_tensor->mutable_data<T>(ctx.GetPlace());
moment1_out_tensor->mutable_data<T>(ctx.GetPlace());
moment2_out_tensor->mutable_data<T>(ctx.GetPlace());
T beta1 = static_cast<T>(ctx.Attr<float>("beta1")); T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
T beta2 = static_cast<T>(ctx.Attr<float>("beta2")); T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
T epsilon = static_cast<T>(ctx.Attr<float>("epsilon")); T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
auto& param = Ref(ctx.Input<LoDTensor>("Param"), "Must set Param");
auto& grad = Ref(ctx.Input<LoDTensor>("Grad"), "Must set Grad");
auto& mom1 = Ref(ctx.Input<LoDTensor>("Moment1"), "Must set Moment1");
auto& mom2 = Ref(ctx.Input<LoDTensor>("Moment2"), "Must set Moment2");
auto& lr =
Ref(ctx.Input<LoDTensor>("LearningRate"), "Must set LearningRate");
auto& beta1_pow =
Ref(ctx.Input<LoDTensor>("Beta1Pow"), "Must set Beta1Pow");
auto& beta2_pow =
Ref(ctx.Input<LoDTensor>("Beta2Pow"), "Must set Beta2Pow");
auto& param_out =
Ref(ctx.Output<LoDTensor>("ParamOut"), "Must set ParamOut");
auto& mom1_out =
Ref(ctx.Output<LoDTensor>("Moment1Out"), "Must set Moment1Out");
auto& mom2_out =
Ref(ctx.Output<LoDTensor>("Moment2Out"), "Must set Moment1Out");
auto param = framework::EigenVector<T>::Flatten( AdamFunctor<T> functor(beta1, beta2, epsilon, beta1_pow.template data<T>(),
*ctx.Input<framework::Tensor>("Param")); beta2_pow.template data<T>(),
auto grad = framework::EigenVector<T>::Flatten( mom1.template data<T>(),
*ctx.Input<framework::Tensor>("Grad")); mom1_out.template mutable_data<T>(ctx.GetPlace()),
auto moment1 = framework::EigenVector<T>::Flatten( mom2.template data<T>(),
*ctx.Input<framework::Tensor>("Moment1")); mom2_out.template mutable_data<T>(ctx.GetPlace()),
auto moment2 = framework::EigenVector<T>::Flatten( lr.template data<T>(), grad.template data<T>(),
*ctx.Input<framework::Tensor>("Moment2")); param.template data<T>(),
auto lr = framework::EigenVector<T>::Flatten( param_out.template mutable_data<T>(ctx.GetPlace()));
*ctx.Input<framework::Tensor>("LearningRate")); platform::ForRange<DeviceContext> for_range(
auto beta1_pow = framework::EigenVector<T>::Flatten( static_cast<const DeviceContext&>(ctx.device_context()), param.numel());
*ctx.Input<framework::Tensor>("Beta1Pow")); for_range(functor);
auto beta2_pow = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Beta2Pow"));
auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
auto moment1_out = framework::EigenVector<T>::Flatten(*moment1_out_tensor);
auto moment2_out = framework::EigenVector<T>::Flatten(*moment2_out_tensor);
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
moment1_out.device(*place) = beta1 * moment1 + (1 - beta1) * grad;
moment2_out.device(*place) = beta2 * moment2 + (1 - beta2) * grad.square();
// All of these are tensors of 1 element
auto lr_t = lr * (1 - beta2_pow).sqrt() / (1 - beta1_pow);
// Eigen does not support automatic broadcast
// Get dimensions of moment vector to broadcast lr_t
Eigen::DSizes<int, 1> m_dsize(moment1_out_tensor->numel());
param_out.device(*place) =
param -
lr_t.broadcast(m_dsize) *
(moment1_out / (moment2_out.sqrt() + epsilon));
} }
}; };
......
/* Copyright (c) 2016 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. */
#pragma once
#include "paddle/platform/device_context.h"
namespace paddle {
namespace platform {
template <typename DeviceContext>
struct ForRange {
ForRange(const DeviceContext& dev_ctx, size_t limit);
template <typename Function>
void operator()(Function func) const;
};
template <>
struct ForRange<CPUDeviceContext> {
ForRange(const CPUDeviceContext& dev_ctx, size_t limit) : limit_(limit) {}
template <typename Function>
void operator()(Function func) const {
for (size_t i = 0; i < limit_; ++i) {
func(i);
}
}
size_t limit_;
};
#ifdef __NVCC__
template <typename Function>
__global__ static void ForRangeElemwiseOpGridIsOne(Function func) {
size_t idx = static_cast<size_t>(threadIdx.x);
func(idx);
}
template <typename Function>
__global__ static void ForRangeElemwiseOp(Function func, int limit) {
size_t idx = static_cast<size_t>(blockIdx.x * blockDim.x + threadIdx.x);
if (idx < limit) {
func(idx);
}
}
template <>
struct ForRange<CUDADeviceContext> {
ForRange(const CUDADeviceContext& dev_ctx, size_t limit)
: dev_ctx_(dev_ctx), limit_(static_cast<int>(limit)) {}
template <typename Function>
inline void operator()(Function func) const {
constexpr size_t num_threads = 1024;
int block_size = limit_ <= num_threads ? limit_ : num_threads;
int grid_size = (limit_ + num_threads - 1) / num_threads;
if (grid_size == 1) {
ForRangeElemwiseOpGridIsOne<<<1, block_size, 0, dev_ctx_.stream()>>>(
func);
} else {
ForRangeElemwiseOp<<<grid_size, block_size, 0, dev_ctx_.stream()>>>(
func, limit_);
}
}
const CUDADeviceContext& dev_ctx_;
int limit_;
};
#endif
} // namespace platform
} // namespace paddle
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