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提交 d22f4de7 编写于 作者: W wanghaoshuang
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Refine sum_accumulates_op.

上级 cad4d7f3
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Showing with 69 addition and 36 deletion
paddle/fluid/operators/average_accumulates_op.cc
浏览文件 @ d22f4de7
......@@ -18,7 +18,7 @@ namespace paddle {
namespace operators {
template <>
void getAccumulators<paddle::platform::CPUDeviceContext>(
void GetAccumulators<paddle::platform::CPUDeviceContext>(
const framework::ExecutionContext& ctx, int64_t& num_updates_,
int64_t& num_accumulates_, int64_t& old_num_accumulates_) {
auto* in_old_num_accumulates = ctx.Input<Tensor>("in_old_num_accumulates");
......@@ -31,7 +31,7 @@ void getAccumulators<paddle::platform::CPUDeviceContext>(
}
template <>
void setAccumulators<paddle::platform::CPUDeviceContext>(
void SetAccumulators<paddle::platform::CPUDeviceContext>(
const framework::ExecutionContext& ctx, int64_t num_updates_,
int64_t num_accumulates_, int64_t old_num_accumulates_) {
auto* out_old_num_accumulates = ctx.Output<Tensor>("out_old_num_accumulates");
......@@ -113,60 +113,92 @@ class AverageAccumulatesOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AverageAccumulatesOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("param",
"Input(Tensor or LoDTensor): The parameter to be accumulated.");
AddInput("param", "(Tensor), The parameter to be accumulated.");
AddInput("in_sum_1",
"Input(Tensor or LoDTensor): A tensor used to store the parameter "
"(Tensor), A tensor used to store the parameter "
"sums with the same shape as input(param).");
AddInput("in_sum_2",
"Input(Tensor or LoDTensor): A auxiliary tensor to help "
"(Tensor), A auxiliary tensor to help "
"accumulating sums of parameter values with the same shape as "
"input(param). It is used to avoid loss of precision due to too "
"many sums.");
AddInput("in_sum_3",
"Input(Tensor or LoDTensor): A auxiliary tensor to help "
"(Tensor), A auxiliary tensor to help "
"accumulating sums of parameter values with the same shape as "
"input(param).");
AddInput("in_num_accumulates",
"Input(Tensor): The accumulating times of current window with "
"shape [1].");
AddInput("in_old_num_accumulates",
"Input(Tensor): The accumulating times of previous window with "
"(Tensor<int64_t>), The accumulating times of current window with "
"shape [1].");
AddInput(
"in_old_num_accumulates",
"(Tensor<int64_t>), The accumulating times of previous window with "
"shape [1].");
AddInput("in_num_updates",
"Input(Tensor): The total number of batches used by trainning "
"(Tensor<int64_t>), The total number of batches used by trainning "
"before this batch with shape [1].");
AddOutput("out_sum_1",
"Output(Tensor or LoDTensor): A tensor used to store the "
"(Tensor), A tensor used to store the "
"parameter sums with the same shape as input(param).");
AddOutput("out_sum_2",
"Output(Tensor or LoDTensor): A auxiliary tensor to help "
"(Tensor), A auxiliary tensor to help "
"accumulating sums of parameter values with the same shape as "
"input(param). It is used to avoid loss of precision due to too "
"many sums.");
AddOutput("out_sum_3",
"Output(Tensor or LoDTensor): A auxiliary tensor to help "
"(Tensor), A auxiliary tensor to help "
"accumulating sums of parameter values with the same shape as "
"input(param).");
AddOutput("out_num_accumulates",
"Output(Tensor): The accumulating times of current window with "
"shape [1].");
AddOutput("out_old_num_accumulates",
"Output(Tensor): The accumulating times of previous window with "
"shape [1].");
AddOutput("out_num_updates",
"Output(Tensor): The total number of batches used by trainning "
"before this batch with shape [1].");
AddOutput(
"out_num_accumulates",
"(Tensor<int64_t>), The accumulating times of current window with "
"shape [1].");
AddOutput(
"out_old_num_accumulates",
"(Tensor<int64_t>) The accumulating times of previous window with "
"shape [1].");
AddOutput(
"out_num_updates",
"(Tensor<int64_t>), The total number of batches used by trainning "
"before this batch with shape [1].");
AddAttr<float>("average_window",
"The rate of average window size relative to num_updates.");
AddAttr<int64_t>("max_average_window", "Maximum size of average window.");
AddAttr<int64_t>("min_average_window", "Minimu size of average window.");
"(float, default 0) "
"The rate of average window size relative to num_updates.")
.SetDefault(0);
AddAttr<int64_t>("max_average_window",
"(int64_t) "
"Maximum size of average window. It suggests that the "
"number of mini-batches "
"in one pass is appropriate value to set.");
AddAttr<int64_t>("min_average_window",
"(int64_t, default 10000L) "
"Minimu size of average window.")
.SetDefault(10000L);
AddComment(R"DOC(
AverageAccumulates Operator.
Accumulate the sum of parameter whtin sliding window. The size of sliding window is determined by 'average_window', 'max_average_window' and 'min_average_window'.
Accumulate the sum of parameter whtin sliding window. The size of sliding window is
determined by 'average_window', 'max_average_window' and 'min_average_window'.
Memory was shared by Input(in_sum_1) and Output(out_sum_1) which acts as an accumulator 'sum_1'.
'sum_2', 'sum_3', 'num_accumulates', 'old_num_accumulates' and 'num_updates' were the same as 'sum_1'.
All the accumulators were inited to zero before training.
And for a mini-batch in training, accumulators were computed as below steps:
num_updates += 1
num_accumulates += 1
sum_1 += param
if num_updates % kMaxNumAccumulates == 0:
sum_2 += sum_1
sum_1 = 0
if num_accumulates >= min_average_window && num_accumulates >= min(max_average_window, num_updates * average_window):
sum_3 = sum_1 + sum_2
sum_1 = 0
sum_2 = 0
old_num_accumulates = num_accumulates
num_accumulates = 0
)DOC");
}
};
......
paddle/fluid/operators/average_accumulates_op.cu
浏览文件 @ d22f4de7
......@@ -18,7 +18,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <>
void getAccumulators<paddle::platform::CUDADeviceContext>(
void GetAccumulators<paddle::platform::CUDADeviceContext>(
const framework::ExecutionContext& ctx, int64_t& num_updates_,
int64_t& num_accumulates_, int64_t& old_num_accumulates_) {
auto* in_old_num_accumulates = ctx.Input<Tensor>("in_old_num_accumulates");
......@@ -35,7 +35,7 @@ void getAccumulators<paddle::platform::CUDADeviceContext>(
}
template <>
void setAccumulators<paddle::platform::CUDADeviceContext>(
void SetAccumulators<paddle::platform::CUDADeviceContext>(
const framework::ExecutionContext& ctx, int64_t num_updates_,
int64_t num_accumulates_, int64_t old_num_accumulates_) {
auto stream = ctx.cuda_device_context().stream();
......
paddle/fluid/operators/average_accumulates_op.h
浏览文件 @ d22f4de7
......@@ -28,12 +28,12 @@ template <typename T, int MajorType = Eigen::RowMajor,
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename DeviceContext>
void getAccumulators(const framework::ExecutionContext& ctx,
void GetAccumulators(const framework::ExecutionContext& ctx,
int64_t& num_updates, int64_t& num_accumulates,
int64_t& old_num_accumulates);
template <typename DeviceContext>
void setAccumulators(const framework::ExecutionContext& ctx,
void SetAccumulators(const framework::ExecutionContext& ctx,
int64_t num_updates, int64_t num_accumulates,
int64_t old_num_accumulates);
......@@ -47,7 +47,7 @@ class AverageAccumulatesKernel : public framework::OpKernel<T> {
int64_t num_updates = 0;
int64_t num_accumulates = 0;
int64_t old_num_accumulates = 0;
getAccumulators<DeviceContext>(ctx, num_updates, num_accumulates,
GetAccumulators<DeviceContext>(ctx, num_updates, num_accumulates,
old_num_accumulates);
// Get attrs
......@@ -84,6 +84,8 @@ class AverageAccumulatesKernel : public framework::OpKernel<T> {
out_sum_2_tensor.device(place) = in_sum_2_tensor;
out_sum_3_tensor.device(place) = in_sum_3_tensor;
if (num_updates % kMaxNumAccumulates == 0) {
// Move the sum to a different buffer to avoid loss of precision due to
// too many sums.
out_sum_2_tensor.device(place) = in_sum_2_tensor + in_sum_1_tensor;
constant_functor(ctx.template device_context<DeviceContext>(), out_sum_1,
0.0);
......@@ -91,6 +93,7 @@ class AverageAccumulatesKernel : public framework::OpKernel<T> {
if (num_accumulates >= min_average_window &&
num_accumulates >= std::min<int64_t>(max_average_window,
num_updates * average_window)) {
// Now the average window is too long, discard the old sum.
out_sum_3_tensor.device(place) = in_sum_1_tensor + in_sum_2_tensor;
constant_functor(ctx.template device_context<DeviceContext>(), out_sum_1,
0.0);
......@@ -101,7 +104,7 @@ class AverageAccumulatesKernel : public framework::OpKernel<T> {
}
// Set accumulators to output
setAccumulators<DeviceContext>(ctx, num_updates, num_accumulates,
SetAccumulators<DeviceContext>(ctx, num_updates, num_accumulates,
old_num_accumulates);
}
};
......
python/paddle/fluid/optimizer.py
浏览文件 @ d22f4de7
......@@ -732,7 +732,6 @@ class ModelAverage(Optimizer):
"""Apply average values to parameters of current model.
"""
executor.run(self.apply_program)
print "finish apply"
try:
yield
finally:
......@@ -743,4 +742,3 @@ class ModelAverage(Optimizer):
"""Restore parameter values of current model.
"""
executor.run(self.restore_program)
print "finish restore"
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