未验证 提交 0bd7f97b 编写于 作者: 武毅 提交者: GitHub

Merge pull request #7045 from typhoonzero/adam_selectedrows

Adam selectedrows and scatter functors
......@@ -105,48 +105,18 @@ struct SparseAdagradFunctor<platform::CPUDeviceContext, T> {
const framework::Tensor& learning_rate, T epsilon,
framework::Tensor* moment, framework::Tensor* param) {
// 1. g_m.rows = set(g.rows)
auto grad_rows = grad.rows();
std::set<int64_t> row_set(grad_rows.begin(), grad_rows.end());
std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
auto grad_width = grad.value().dims()[1];
std::unique_ptr<framework::SelectedRows> grad_merge{
new framework::SelectedRows()};
grad_merge->set_rows(merge_rows);
grad_merge->set_height(grad.height());
grad_merge->mutable_value()->mutable_data<T>(
framework::make_ddim(
{static_cast<int64_t>(merge_rows.size()), grad_width}),
context.GetPlace());
math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
constant_functor(context, grad_merge->mutable_value(), 0.0);
auto* grad_merge_data = grad_merge->mutable_value()->data<T>();
auto* grad_data = grad.value().data<T>();
for (size_t i = 0; i < grad_rows.size(); i++) {
size_t grad_merge_i = FindPos(merge_rows, grad_rows[i]);
for (int64_t j = 0; j < grad_width; j++) {
grad_merge_data[grad_merge_i * grad_width + j] +=
grad_data[i * grad_width + j];
}
}
math::scatter::MergeAdd<platform::CPUDeviceContext, T> merge_func;
auto grad_merge = merge_func(context, grad);
auto& merge_rows = grad_merge.rows();
auto* grad_merge_data = grad_merge.mutable_value()->template data<T>();
// 2. m += g_m * g_m
std::unique_ptr<framework::SelectedRows> grad_square{
new framework::SelectedRows()};
grad_square->set_rows(grad_merge->rows());
grad_square->set_height(grad_merge->height());
grad_square->mutable_value()->mutable_data<T>(grad_merge->value().dims(),
context.GetPlace());
auto gs =
framework::EigenVector<T>::Flatten(*(grad_square->mutable_value()));
auto gm = framework::EigenVector<T>::Flatten(grad_merge->value());
gs.device(*context.eigen_device()) = gm * gm;
math::scatter::Mul<platform::CPUDeviceContext, T> sqare_func;
auto grad_square = sqare_func(context, grad_merge, grad_merge);
math::SelectedRowsAddToTensor<platform::CPUDeviceContext, T> functor;
functor(context, *grad_square, moment);
functor(context, grad_square, moment);
// 3. update parameter
auto* lr = learning_rate.data<T>();
......
......@@ -78,62 +78,30 @@ struct SparseAdagradFunctor<platform::CUDADeviceContext, T> {
const framework::Tensor& learning_rate, T epsilon,
framework::Tensor* moment, framework::Tensor* param) {
// 1. g_m.rows = set(g.rows)
auto grad_rows = grad.rows();
std::set<int64_t> row_set(grad_rows.begin(), grad_rows.end());
std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
auto grad_width = grad.value().dims()[1];
std::unique_ptr<framework::SelectedRows> grad_merge{
new framework::SelectedRows()};
grad_merge->set_rows(merge_rows);
grad_merge->set_height(grad.height());
grad_merge->mutable_value()->mutable_data<T>(
framework::make_ddim(
{static_cast<int64_t>(merge_rows.size()), grad_width}),
context.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> constant_functor;
constant_functor(context, grad_merge->mutable_value(), 0.0);
auto* grad_merge_data = grad_merge->mutable_value()->data<T>();
auto* grad_data = grad.value().data<T>();
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid1(1, grad_rows.size());
MergeGradKernel<
T, 256><<<grid1, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(grad_data, grad.rows().data(),
grad_merge_data, grad_merge->rows().data(),
grad_merge->rows().size(), grad_width);
math::scatter::MergeAdd<platform::CUDADeviceContext, T> merge_func;
auto grad_merge = merge_func(context, grad);
auto* grad_merge_data = grad_merge.mutable_value()->template data<T>();
auto& merge_rows = grad_merge.rows();
// 2. m += g_m * g_m
std::unique_ptr<framework::SelectedRows> grad_square{
new framework::SelectedRows()};
grad_square->set_rows(grad_merge->rows());
grad_square->set_height(grad_merge->height());
grad_square->mutable_value()->mutable_data<T>(grad_merge->value().dims(),
context.GetPlace());
auto gs =
framework::EigenVector<T>::Flatten(*(grad_square->mutable_value()));
auto gm = framework::EigenVector<T>::Flatten(grad_merge->value());
gs.device(*context.eigen_device()) = gm * gm;
math::scatter::Mul<platform::CUDADeviceContext, T> sqare_func;
auto grad_square = sqare_func(context, grad_merge, grad_merge);
math::SelectedRowsAddToTensor<platform::CUDADeviceContext, T> functor;
functor(context, *grad_square, moment);
functor(context, grad_square, moment);
// 3. update parameter
auto* lr = learning_rate.data<T>();
auto* param_data = param->data<T>();
auto* moment_data = moment->data<T>();
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid2(1, merge_rows.size());
SparseAdagradFunctorKernel<
T, 256><<<grid2, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(grad_merge_data, grad_merge->rows().data(),
.stream()>>>(grad_merge_data, grad_merge.rows().data(),
lr, param_data, moment_data, grad_width,
epsilon);
}
......
......@@ -16,11 +16,14 @@ limitations under the License. */
#include <math.h> // for sqrt in CPU and CUDA
#include "paddle/framework/op_registry.h"
#include "paddle/operators/detail/safe_ref.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/platform/for_range.h"
namespace paddle {
namespace operators {
namespace scatter = paddle::operators::math::scatter;
template <typename T>
struct AdamFunctor {
T beta1_;
......@@ -79,6 +82,69 @@ struct AdamFunctor {
}
};
template <typename T>
struct SparseAdamFunctor {
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_;
const int64_t* rows_;
int64_t row_numel_;
SparseAdamFunctor(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, const int64_t* rows,
int64_t row_numel)
: 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),
rows_(rows),
row_numel_(row_numel) {}
inline HOSTDEVICE void operator()(size_t i) const {
T beta1_pow = *beta1_pow_;
T beta2_pow = *beta2_pow_;
for (int64_t j = 0; j < row_numel_; ++j) {
T g = grad_[i * row_numel_ + j];
T mom1 = moment1_[rows_[i] * row_numel_ + j];
T mom2 = moment2_[rows_[i] * row_numel_ + j];
T lr = *lr_;
T p = param_[rows_[i] * row_numel_ + j];
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_));
moment1_out_[rows_[i] * row_numel_ + j] = mom1;
moment2_out_[rows_[i] * row_numel_ + j] = mom2;
param_out_[rows_[i] * row_numel_ + j] = p;
} // for col id
}
};
template <typename DeviceContext, typename T>
class AdamOpKernel : public framework::OpKernel<T> {
public:
......@@ -90,7 +156,8 @@ class AdamOpKernel : public framework::OpKernel<T> {
T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
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& grad = Ref(ctx.Input<LoDTensor>("Grad"), "Must set Grad");
auto* grad_var = ctx.InputVar("Grad");
auto& mom1 = Ref(ctx.Input<LoDTensor>("Moment1"), "Must set Moment1");
auto& mom2 = Ref(ctx.Input<LoDTensor>("Moment2"), "Must set Moment2");
auto& lr =
......@@ -108,9 +175,11 @@ class AdamOpKernel : public framework::OpKernel<T> {
auto& mom2_out =
Ref(ctx.Output<LoDTensor>("Moment2Out"), "Must set Moment1Out");
AdamFunctor<T> functor(beta1, beta2, epsilon, beta1_pow.template data<T>(),
beta2_pow.template data<T>(),
mom1.template data<T>(),
if (grad_var->IsType<framework::LoDTensor>()) {
auto& grad = Ref(ctx.Input<LoDTensor>("Grad"), "Must set Grad");
AdamFunctor<T> functor(
beta1, beta2, epsilon, beta1_pow.template data<T>(),
beta2_pow.template data<T>(), mom1.template data<T>(),
mom1_out.template mutable_data<T>(ctx.GetPlace()),
mom2.template data<T>(),
mom2_out.template mutable_data<T>(ctx.GetPlace()),
......@@ -118,8 +187,36 @@ class AdamOpKernel : public framework::OpKernel<T> {
param.template data<T>(),
param_out.template mutable_data<T>(ctx.GetPlace()));
platform::ForRange<DeviceContext> for_range(
static_cast<const DeviceContext&>(ctx.device_context()), param.numel());
static_cast<const DeviceContext&>(ctx.device_context()),
param.numel());
for_range(functor);
} else if (grad_var->IsType<framework::SelectedRows>()) {
auto& grad =
Ref(ctx.Input<framework::SelectedRows>("Grad"), "Must set Grad");
// merge duplicated rows if any.
scatter::MergeAdd<DeviceContext, T> merge_func;
auto grad_merge =
merge_func(ctx.template device_context<DeviceContext>(), grad);
auto& grad_tensor = grad_merge.value();
const T* grad_data = grad_tensor.template data<T>();
auto* rows = grad_merge.rows().data();
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
SparseAdamFunctor<T> functor(
beta1, beta2, epsilon, beta1_pow.template data<T>(),
beta2_pow.template data<T>(), mom1.template data<T>(),
mom1_out.template mutable_data<T>(ctx.GetPlace()),
mom2.template data<T>(),
mom2_out.template mutable_data<T>(ctx.GetPlace()),
lr.template data<T>(), grad_data, param.template data<T>(),
param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel);
platform::ForRange<DeviceContext> for_range(
static_cast<const DeviceContext&>(ctx.device_context()),
grad_merge.rows().size());
for_range(functor);
} else {
PADDLE_THROW("Variable type not supported by adam_op");
}
}
};
......
......@@ -12,8 +12,10 @@ 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/operators/math/selected_rows_functor.h"
#include <set>
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/selected_rows_functor.h"
namespace paddle {
namespace operators {
......@@ -179,6 +181,118 @@ template struct SelectedRowsAddToTensor<platform::CPUDeviceContext, double>;
template struct SelectedRowsAddToTensor<platform::CPUDeviceContext, int>;
template struct SelectedRowsAddToTensor<platform::CPUDeviceContext, int64_t>;
// This is a separated namespace for manipulate SelectedRows typed
// data. Like merge duplicated rows, adding two SelectedRows etc.
//
// Another group of functors is called "scatter updates", which means
// use SelectedRows to update a dense tensor with different Ops, like
// add or mul.
namespace scatter {
size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
return std::find(rows.begin(), rows.end(), value) - rows.begin();
}
template <typename T>
struct MergeAdd<platform::CPUDeviceContext, T> {
framework::SelectedRows operator()(const platform::CPUDeviceContext& context,
const framework::SelectedRows& input) {
framework::SelectedRows out;
auto input_rows = input.rows();
std::set<int64_t> row_set(input_rows.begin(), input_rows.end());
std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
auto input_width = input.value().dims()[1];
out.set_rows(merge_rows);
out.set_height(input.height());
out.mutable_value()->mutable_data<T>(
framework::make_ddim(
{static_cast<int64_t>(merge_rows.size()), input_width}),
context.GetPlace());
math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
constant_functor(context, out.mutable_value(), 0.0);
auto* out_data = out.mutable_value()->data<T>();
auto* input_data = input.value().data<T>();
for (size_t i = 0; i < input_rows.size(); i++) {
size_t out_i = FindPos(merge_rows, input_rows[i]);
for (int64_t j = 0; j < input_width; j++) {
out_data[out_i * input_width + j] += input_data[i * input_width + j];
}
}
return out;
}
};
template struct MergeAdd<platform::CPUDeviceContext, float>;
template struct MergeAdd<platform::CPUDeviceContext, double>;
template struct MergeAdd<platform::CPUDeviceContext, int>;
template struct MergeAdd<platform::CPUDeviceContext, int64_t>;
template <typename T>
struct UpdateToTensor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& context,
const ScatterOps& op, const framework::SelectedRows& input1,
framework::Tensor* input2) {
auto in1_height = input1.height();
auto in2_dims = input2->dims();
PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
auto& in1_value = input1.value();
auto& in1_rows = input1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);
auto* in1_data = in1_value.data<T>();
auto* input2_data = input2->data<T>();
// FIXME(typhoonzero): use macro fix the below messy code.
switch (op) {
case ScatterOps::ASSIGN:
INLINE_FOR2(in1_rows.size(), in1_row_numel)
input2_data[in1_rows[i] * in1_row_numel + j] =
in1_data[i * in1_row_numel + j];
break;
case ScatterOps::ADD:
INLINE_FOR2(in1_rows.size(), in1_row_numel)
input2_data[in1_rows[i] * in1_row_numel + j] +=
in1_data[i * in1_row_numel + j];
break;
case ScatterOps::SUB:
INLINE_FOR2(in1_rows.size(), in1_row_numel)
input2_data[in1_rows[i] * in1_row_numel + j] -=
in1_data[i * in1_row_numel + j];
break;
case ScatterOps::SUBBY:
INLINE_FOR2(in1_rows.size(), in1_row_numel)
input2_data[in1_rows[i] * in1_row_numel + j] =
in1_data[i * in1_row_numel + j] -
input2_data[in1_rows[i] * in1_row_numel + j];
break;
case ScatterOps::MUL:
INLINE_FOR2(in1_rows.size(), in1_row_numel)
input2_data[in1_rows[i] * in1_row_numel + j] *=
in1_data[i * in1_row_numel + j];
break;
case ScatterOps::DIV:
INLINE_FOR2(in1_rows.size(), in1_row_numel)
input2_data[in1_rows[i] * in1_row_numel + j] /=
in1_data[i * in1_row_numel + j];
break;
case ScatterOps::DIVBY:
INLINE_FOR2(in1_rows.size(), in1_row_numel)
input2_data[in1_rows[i] * in1_row_numel + j] =
in1_data[i * in1_row_numel + j] /
input2_data[in1_rows[i] * in1_row_numel + j];
break;
}
}
};
} // namespace scatter
} // namespace math
} // namespace operators
} // namespace paddle
......@@ -12,6 +12,8 @@ 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 <set>
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/platform/cuda_helper.h"
......@@ -222,6 +224,157 @@ template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, float>;
template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, double>;
template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, int>;
template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, int64_t>;
namespace scatter {
template <typename T, int block_size>
__global__ void MergeAddKernel(const T* input, const int64_t* input_rows,
T* out, const int64_t* out_rows,
size_t out_rows_size, int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
__shared__ size_t out_idx;
if (tid == 0) {
for (size_t i = 0; i < out_rows_size; i++) {
if (input_rows[ty] == out_rows[i]) {
out_idx = i;
}
}
}
__syncthreads();
input += ty * row_numel;
out += out_idx * row_numel;
for (int index = tid; index < row_numel; index += block_size) {
paddle::platform::CudaAtomicAdd(out + index, input[index]);
}
}
template <typename T>
struct MergeAdd<platform::CUDADeviceContext, T> {
framework::SelectedRows operator()(const platform::CUDADeviceContext& context,
const framework::SelectedRows& input) {
framework::SelectedRows out;
auto input_rows = input.rows();
std::set<int64_t> row_set(input_rows.begin(), input_rows.end());
std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
auto input_width = input.value().dims()[1];
out.set_rows(merge_rows);
out.set_height(input.height());
out.mutable_value()->mutable_data<T>(
framework::make_ddim(
{static_cast<int64_t>(merge_rows.size()), input_width}),
context.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> constant_functor;
constant_functor(context, out.mutable_value(), 0.0);
auto* out_data = out.mutable_value()->data<T>();
auto* input_data = input.value().data<T>();
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid1(1, input_rows.size());
MergeAddKernel<
T, 256><<<grid1, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(input_data, input.rows().data(), out_data,
out.rows().data(), out.rows().size(),
input_width);
return out;
}
};
template struct MergeAdd<platform::CUDADeviceContext, float>;
template struct MergeAdd<platform::CUDADeviceContext, double>;
template struct MergeAdd<platform::CUDADeviceContext, int>;
template struct MergeAdd<platform::CUDADeviceContext, int64_t>;
template <typename T, int block_size>
__global__ void UpdateToTensorKernel(const T* selected_rows,
const int64_t* rows, const ScatterOps& op,
T* tensor_out, int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
selected_rows += ty * row_numel;
tensor_out += rows[ty] * row_numel;
// FIXME(typhoonzero): use macro fix the below messy code.
switch (op) {
case ScatterOps::ASSIGN:
for (int index = tid; index < row_numel; index += block_size) {
tensor_out[index] = selected_rows[index];
}
break;
case ScatterOps::ADD:
for (int index = tid; index < row_numel; index += block_size) {
tensor_out[index] += selected_rows[index];
}
break;
case ScatterOps::SUB:
for (int index = tid; index < row_numel; index += block_size) {
tensor_out[index] -= selected_rows[index];
}
break;
case ScatterOps::SUBBY:
for (int index = tid; index < row_numel; index += block_size) {
tensor_out[index] = selected_rows[index] - tensor_out[index];
}
break;
case ScatterOps::MUL:
for (int index = tid; index < row_numel; index += block_size) {
tensor_out[index] *= selected_rows[index];
}
break;
case ScatterOps::DIV:
for (int index = tid; index < row_numel; index += block_size) {
tensor_out[index] /= selected_rows[index];
}
break;
case ScatterOps::DIVBY:
for (int index = tid; index < row_numel; index += block_size) {
tensor_out[index] = selected_rows[index] / tensor_out[index];
}
break;
}
}
template <typename T>
struct UpdateToTensor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& context,
const ScatterOps& op, const framework::SelectedRows& input1,
framework::Tensor* input2) {
// NOTE: Use SelectedRowsAddToTensor for better performance
// no additional MergeAdd called.
MergeAdd<platform::CUDADeviceContext, T> merge_func;
auto merged_in1 = merge_func(context, input1);
auto in1_height = merged_in1.height();
auto in2_dims = input2->dims();
PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
auto& in1_value = merged_in1.value();
auto& in1_rows = merged_in1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);
auto* in1_data = in1_value.template data<T>();
auto* in2_data = input2->data<T>();
dim3 threads(platform::PADDLE_CUDA_NUM_THREADS, 1);
dim3 grid(1, in1_rows.size());
UpdateToTensorKernel<T, platform::PADDLE_CUDA_NUM_THREADS><<<
grid, threads, 0, context.stream()>>>(in1_data, in1_rows.data(), op,
in2_data, in1_row_numel);
}
};
} // namespace scatter
} // namespace math
} // namespace operators
} // namespace paddle
......@@ -12,9 +12,14 @@ 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/framework/eigen.h"
#include "paddle/framework/selected_rows.h"
#include "paddle/platform/device_context.h"
#define INLINE_FOR2(sizei, sizej) \
for (int64_t i = 0; i < sizei; i++) \
for (int64_t j = 0; j < sizej; j++)
namespace paddle {
namespace operators {
namespace math {
......@@ -52,6 +57,78 @@ struct SelectedRowsAddToTensor {
framework::Tensor* input2);
};
namespace scatter {
// functors for manuplating SelectedRows data
template <typename DeviceContext, typename T>
struct MergeAdd {
// unary functor, merge by adding duplicated rows in
// the input SelectedRows object.
framework::SelectedRows operator()(const DeviceContext& context,
const framework::SelectedRows& input);
};
template <typename DeviceContext, typename T>
struct Add {
framework::SelectedRows operator()(const DeviceContext& context,
const framework::SelectedRows& input1,
const framework::SelectedRows& input2) {
framework::SelectedRows out;
out.set_rows(input1.rows());
out.set_height(input1.height());
out.mutable_value()->mutable_data<T>(input1.value().dims(),
context.GetPlace());
auto e_out = framework::EigenVector<T>::Flatten(*(out.mutable_value()));
auto e_in1 = framework::EigenVector<T>::Flatten(input1.value());
auto e_in2 = framework::EigenVector<T>::Flatten(input2.value());
e_out.device(*context.eigen_device()) = e_in1 + e_in2;
return out;
}
};
template <typename DeviceContext, typename T>
struct Mul {
// multiply two SelectedRows
framework::SelectedRows operator()(const DeviceContext& context,
const framework::SelectedRows& input1,
const framework::SelectedRows& input2) {
framework::SelectedRows out;
out.set_rows(input1.rows());
out.set_height(input1.height());
out.mutable_value()->mutable_data<T>(input1.value().dims(),
context.GetPlace());
auto e_out = framework::EigenVector<T>::Flatten(*(out.mutable_value()));
auto e_in1 = framework::EigenVector<T>::Flatten(input1.value());
auto e_in2 = framework::EigenVector<T>::Flatten(input2.value());
e_out.device(*context.eigen_device()) = e_in1 * e_in2;
return out;
}
// multiply scalar to SelectedRows
framework::SelectedRows operator()(const DeviceContext& context,
const framework::SelectedRows& input1,
const T input2) {
framework::SelectedRows out;
out.set_rows(input1.rows());
out.set_height(input1.height());
out.mutable_value()->mutable_data<T>(input1.value().dims(),
context.GetPlace());
auto e_out = framework::EigenVector<T>::Flatten(*(out.mutable_value()));
auto e_in1 = framework::EigenVector<T>::Flatten(input1.value());
e_out.device(*context.eigen_device()) = input2 * e_in1;
return out;
}
};
enum class ScatterOps { ASSIGN, ADD, SUB, SUBBY, MUL, DIV, DIVBY };
// out = seleted_rows_in / tensor
template <typename DeviceContext, typename T>
struct UpdateToTensor {
void operator()(const DeviceContext& context, const ScatterOps& op,
const framework::SelectedRows& input1,
framework::Tensor* input2);
};
} // namespace scatter
} // namespace math
} // namespace operators
} // namespace paddle
import unittest
import numpy as np
from op_test import OpTest
from paddle.v2.fluid import core
from paddle.v2.fluid.op import Operator
class TestAdamOp1(OpTest):
......@@ -176,5 +178,124 @@ def adam_step(inputs, attributes):
return param_out, moment1_out, moment2_out
def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
'''
Simulate one step of the adam optimizer
:param inputs: dict of inputs
:param attributes: dict of attributes
:return tuple: tuple of output param, moment1, moment2,
beta1 power accumulator and beta2 power accumulator
'''
param = inputs['Param']
# grad = inputs['Grad']
moment1 = inputs['Moment1']
moment2 = inputs['Moment2']
lr = inputs['LearningRate']
beta1_pow = inputs['Beta1Pow']
beta2_pow = inputs['Beta2Pow']
beta1 = attributes['beta1']
beta2 = attributes['beta2']
epsilon = attributes['epsilon']
moment1_out = np.zeros(shape=[height, row_numel])
moment2_out = np.zeros(shape=[height, row_numel])
param_out = np.zeros(shape=[height, row_numel])
for idx, row_id in enumerate(rows):
moment1_out[row_id] = beta1 * moment1[row_id] + (1 - beta1
) * np_grad[idx]
moment2_out[row_id] = beta2 * moment2[row_id] + (
1 - beta2) * np.square(np_grad[idx])
lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
param_out[row_id] = param[row_id] - lr_t * (moment1_out[row_id] / (
np.sqrt(moment2_out[row_id]) + epsilon))
return param_out, moment1_out, moment2_out
class TestSparseAdamOp(unittest.TestCase):
def setup(self, scope, place):
beta1 = 0.78
beta2 = 0.836
epsilon = 1e-4
height = 10
rows = [0, 4, 7]
self.rows = rows
row_numel = 12
self.row_numel = row_numel
self.dense_inputs = {
"Param": np.full((height, row_numel), 5.0).astype("float32"),
"Moment1": np.full((height, row_numel), 5.0).astype("float32"),
"Moment2": np.full((height, row_numel), 5.0).astype("float32"),
'Beta1Pow': np.array([beta1**10]).astype("float32"),
'Beta2Pow': np.array([beta2**10]).astype("float32"),
"LearningRate": np.full((1), 2.0).astype("float32")
}
self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
grad_selected_rows = scope.var('Grad').get_selected_rows()
grad_selected_rows.set_height(height)
grad_selected_rows.set_rows(rows)
np_array = np.ones((len(rows), row_numel)).astype("float32")
np_array[0, 0] = 2.0
np_array[2, 8] = 4.0
grad_tensor = grad_selected_rows.get_tensor()
grad_tensor.set(np_array, place)
self.sparse_inputs = ["Grad"]
param_out, mom1, mom2 = adam_step_sparse(
self.dense_inputs, self.attrs, height, rows, row_numel, np_array)
self.outputs = {
"ParamOut": param_out,
"Moment1Out": mom1,
"Moment2Out": mom2
}
def check_with_place(self, place):
scope = core.Scope()
self.setup(scope, place)
op_args = dict()
for key, np_array in self.dense_inputs.iteritems():
var = scope.var(key).get_tensor()
var.set(np_array, place)
op_args[key] = key
for s in self.sparse_inputs:
op_args[s] = s
for s in self.outputs:
var = scope.var(s).get_tensor()
var.set(self.outputs[s], place)
op_args[s] = s
for k in self.attrs:
op_args[k] = self.attrs[k]
# create and run sgd operator
adam_op = Operator("adam", **op_args)
adam_op.run(scope, place)
for key, np_array in self.outputs.iteritems():
out_var = scope.var(key).get_tensor()
actual = np.array(out_var)
actual = actual.reshape([actual.size])
np_array = np_array.reshape([np_array.size])
for idx, row_id in enumerate(self.rows):
j = 0
while j < self.row_numel:
pos = row_id * self.row_numel + j
self.assertLess((actual[pos] - np_array[pos]) / actual[pos],
0.00001)
j += 1
def test_sparse_sgd(self):
places = [core.CPUPlace()]
if core.is_compile_gpu():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
if __name__ == "__main__":
unittest.main()
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