Merge branch 'develop' into cmake_speed

paddle/operators/CMakeLists.txt

@@ -183,15 +183,20 @@ set(DEPS_OPS
     array_to_lod_tensor_op
     lstm_op
     tensor_array_read_write_op
-    gru_op)
+    gru_op
+    adagrad_op
+    sgd_op)
+
 
 op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
 op_library(cross_entropy_op DEPS cross_entropy)
 op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
 op_library(softmax_op DEPS softmax)
 op_library(sequence_softmax_op DEPS softmax)
+op_library(sum_op DEPS selected_rows_functor)
+op_library(sgd_op DEPS selected_rows_functor)
+op_library(adagrad_op DEPS selected_rows_functor)
 op_library(conv_op DEPS vol2col)
-op_library(sum_op DEPS net_op selected_rows_functor)
 op_library(pool_op DEPS pooling)
 op_library(pool_with_index_op DEPS pooling)
 op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)

paddle/operators/adagrad_op.cc

@@ -14,6 +14,11 @@ limitations under the License. */
 
 #include "paddle/operators/adagrad_op.h"
 
+#include <cmath>
+
+#include "paddle/operators/math/math_function.h"
+#include "paddle/operators/math/selected_rows_functor.h"
+
 namespace paddle {
 namespace operators {
 
@@ -21,7 +26,7 @@ class AdagradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext *ctx) const override {
+  void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("Param"),
                    "Input(Param) of AdagradOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Grad"),
@@ -54,8 +59,8 @@ class AdagradOp : public framework::OperatorWithKernel {
 
 class AdagradOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  AdagradOpMaker(framework::OpProto *proto,
-                 framework::OpAttrChecker *op_checker)
+  AdagradOpMaker(framework::OpProto* proto,
+                 framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("Param", "(Tensor) Input parameter");
     AddInput("Grad", "(Tensor) Input gradient");
@@ -87,10 +92,85 @@ for numerical stability to avoid the division by zero error.
 )DOC");
   }
 };
+
+namespace {
+size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
+  return std::find(rows.begin(), rows.end(), value) - rows.begin();
+}
+}  // namespace
+
+template <typename T>
+struct SparseAdagradFunctor<platform::CPUPlace, T> {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& grad,
+                  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::CPUPlace, 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];
+      }
+    }
+
+    // 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.GetEigenDevice<platform::CPUPlace>()) = gm * gm;
+
+    math::SelectedRowsAddToTensor<platform::CPUPlace, T> functor;
+    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>();
+
+    for (size_t i = 0; i < merge_rows.size(); i++) {
+      for (int64_t j = 0; j < grad_width; j++) {
+        param_data[merge_rows[i] * grad_width + j] -=
+            lr[0] * grad_merge_data[i * grad_width + j] /
+            (std::sqrt(moment_data[merge_rows[i] * grad_width + j]) + epsilon);
+      }
+    }
+  }
+};
+
+template struct SparseAdagradFunctor<platform::CPUPlace, float>;
+template struct SparseAdagradFunctor<platform::CPUPlace, double>;
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(adagrad, ops::AdagradOp, ops::AdagradOpMaker);
-REGISTER_OP_CPU_KERNEL(adagrad,
-                       ops::AdagradOpKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    adagrad, ops::AdagradOpKernel<paddle::platform::CPUPlace, float>,
+    ops::AdagradOpKernel<paddle::platform::CPUPlace, double>);

paddle/operators/adagrad_op.cu

@@ -14,7 +14,138 @@
 
 #define EIGEN_USE_GPU
 #include "paddle/operators/adagrad_op.h"
+#include "paddle/operators/math/selected_rows_functor.h"
+#include "paddle/operators/math/math_function.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+
+namespace {
+
+template <typename T, int block_size>
+__global__ void MergeGradKernel(const T* grad, const int64_t* grad_rows,
+                                T* grad_merge, const int64_t* grad_merge_rows,
+                                size_t grad_merge_rows_size,
+                                int64_t row_numel) {
+  const int ty = blockIdx.y;
+  int tid = threadIdx.x;
+  __shared__ size_t grad_merge_idx;
+
+  if (tid == 0) {
+    for (size_t i = 0; i < grad_merge_rows_size; i++) {
+      if (grad_rows[ty] == grad_merge_rows[i]) {
+        grad_merge_idx = i;
+      }
+    }
+  }
+
+  __syncthreads();
+
+  grad += ty * row_numel;
+  grad_merge += grad_merge_idx * row_numel;
+  for (int index = tid; index < row_numel; index += block_size) {
+    paddle::platform::CudaAtomicAdd(grad_merge + index, grad[index]);
+  }
+}
+
+template <typename T, int block_size>
+__global__ void SparseAdagradFunctorKernel(const T* grad, const int64_t* rows,
+                                           const T* learning_rate, T* param,
+                                           T* moment, int64_t row_numel,
+                                           T epsilon) {
+  const int ty = blockIdx.y;
+  int tid = threadIdx.x;
+
+  grad += ty * row_numel;
+  param += rows[ty] * row_numel;
+  moment += rows[ty] * row_numel;
+
+  for (int index = tid; index < row_numel; index += block_size) {
+    // Since index in rows of SelectedRows can be duplicate, we have to use
+    // Atomic Operation to avoid concurrent write error.
+    paddle::platform::CudaAtomicAdd(param + index,
+                                    -1.0 * learning_rate[0] * grad[index] /
+                                        (sqrt(moment[index]) + epsilon));
+  }
+}
+}  // namespace
+
+template <typename T>
+struct SparseAdagradFunctor<platform::GPUPlace, T> {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& grad,
+                  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::GPUPlace, 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);
+
+    // 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.GetEigenDevice<platform::GPUPlace>()) = gm * gm;
+
+    math::SelectedRowsAddToTensor<platform::GPUPlace, T> functor;
+    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>();
+
+    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(),
+                                   lr, param_data,
+                                   moment_data, grad_width, epsilon);
+  }
+};
+
+template struct SparseAdagradFunctor<platform::GPUPlace, float>;
+template struct SparseAdagradFunctor<platform::GPUPlace, double>;
+
+}  // namespace operators
+}  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(adagrad,
-                       ops::AdagradOpKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    adagrad, ops::AdagradOpKernel<paddle::platform::GPUPlace, float>,
+    ops::AdagradOpKernel<paddle::platform::GPUPlace, double>);

paddle/operators/adagrad_op.h

@@ -19,35 +19,59 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+template <typename Place, typename T>
+struct SparseAdagradFunctor {
+  void operator()(const platform::DeviceContext& context,
+                  const framework::SelectedRows& grad,
+                  const framework::Tensor& learning_rate, T epsilon,
+                  framework::Tensor* moment, framework::Tensor* param);
+};
+
 template <typename Place, typename T>
 class AdagradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
-    auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
+    auto* param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
+    auto* moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
 
     param_out_tensor->mutable_data<T>(ctx.GetPlace());
     moment_out_tensor->mutable_data<T>(ctx.GetPlace());
 
-    float epsilon = ctx.Attr<float>("epsilon");
-
-    auto param = framework::EigenVector<T>::Flatten(
-        *ctx.Input<framework::Tensor>("Param"));
-    auto grad = framework::EigenVector<T>::Flatten(
-        *ctx.Input<framework::Tensor>("Grad"));
-    auto moment = framework::EigenVector<T>::Flatten(
-        *ctx.Input<framework::Tensor>("Moment"));
-    auto lr = framework::EigenVector<T>::Flatten(
-        *ctx.Input<framework::Tensor>("LearningRate"));
-
-    auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
-    auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
-    auto place = ctx.GetEigenDevice<Place>();
-
-    moment_out.device(place) = moment + grad * grad;
-    Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
-    param_out.device(place) =
-        param - lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon);
+    T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
+
+    auto* grad_var = ctx.InputVar("Grad");
+    if (grad_var->IsType<framework::LoDTensor>()) {
+      auto param = framework::EigenVector<T>::Flatten(
+          *ctx.Input<framework::Tensor>("Param"));
+      auto grad = framework::EigenVector<T>::Flatten(
+          *ctx.Input<framework::Tensor>("Grad"));
+      auto moment = framework::EigenVector<T>::Flatten(
+          *ctx.Input<framework::Tensor>("Moment"));
+      auto lr = framework::EigenVector<T>::Flatten(
+          *ctx.Input<framework::Tensor>("LearningRate"));
+
+      auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
+      auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
+      auto place = ctx.GetEigenDevice<Place>();
+
+      moment_out.device(place) = moment + grad * grad;
+      Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
+      param_out.device(place) =
+          param - lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon);
+    } else if (grad_var->IsType<framework::SelectedRows>()) {
+      auto* param_tensor = ctx.Input<framework::Tensor>("Param");
+      PADDLE_ENFORCE_EQ(param_tensor, param_out_tensor);
+
+      auto* moment_tensor = ctx.Input<framework::Tensor>("Moment");
+      PADDLE_ENFORCE_EQ(moment_tensor, moment_out_tensor);
+
+      SparseAdagradFunctor<Place, T> functor;
+      functor(ctx.device_context(), *ctx.Input<framework::SelectedRows>("Grad"),
+              *ctx.Input<framework::Tensor>("LearningRate"), epsilon,
+              moment_out_tensor, param_out_tensor);
+    } else {
+      PADDLE_THROW("Unsupported Variable Type of Grad");
+    }
   }
 };
 

paddle/operators/sgd_op.cu

@@ -20,11 +20,11 @@ namespace paddle {
 namespace operators {
 
 namespace {
-template <typename T>
+template <typename T, int block_size>
 __global__ void SparseSGDFunctorKernel(const T* selected_rows,
                                        const int64_t* rows,
                                        const T* learning_rate, T* tensor_out,
-                                       int64_t row_numel, int block_size) {
+                                       int64_t row_numel) {
   const int ty = blockIdx.y;
   int tid = threadIdx.x;
 
@@ -59,14 +59,15 @@ struct SparseSGDFunctor<platform::GPUPlace, T> {
     auto* in_data = in_value.data<T>();
     auto* out_data = output->data<T>();
 
-    int block_size = 256;
+    const int block_size = 256;
     dim3 threads(block_size, 1);
     dim3 grid(1, in_rows.size());
     SparseSGDFunctorKernel<
-        T><<<grid, threads, 0,
-             reinterpret_cast<const platform::CUDADeviceContext&>(context)
-                 .stream()>>>(in_data, in_rows.data(), learning_rate.data<T>(),
-                              out_data, in_row_numel, block_size);
+        T, 256><<<grid, threads, 0,
+                  reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                      .stream()>>>(in_data, in_rows.data(),
+                                   learning_rate.data<T>(), out_data,
+                                   in_row_numel);
   }
 };
 

paddle/operators/sum_op.cc

@@ -12,7 +12,6 @@ limitations under the License. */
 #include "paddle/operators/sum_op.h"
 #include <vector>
 #include "paddle/framework/var_type_inference.h"
-#include "paddle/operators/net_op.h"
 
 namespace paddle {
 namespace operators {

python/paddle/v2/fluid/tests/test_adagrad_op.py

 import unittest
 import numpy as np
+import paddle.v2.fluid.core as core
+from paddle.v2.fluid.op import Operator
 from op_test import OpTest
+import math
 
 
 class TestAdagradOp1(OpTest):
@@ -65,5 +68,110 @@ class TestAdagradOp2(OpTest):
         self.check_output()
 
 
+class TestSparseAdagradOp(unittest.TestCase):
+    def check_with_place(self, place):
+        scope = core.Scope()
+
+        # create and initialize Grad Variable   
+        height = 10
+        rows = [0, 4, 7, 4]
+        row_numel = 12
+
+        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)
+
+        # create and initialize Param Variable
+        param = scope.var('Param').get_tensor()
+        param_array = np.full((height, row_numel), 5.0).astype("float32")
+        param.set(param_array, place)
+
+        # create and initialize LeraningRate Variable
+        lr = scope.var('LearningRate').get_tensor()
+        lr_array = np.full((1), 2.0).astype("float32")
+        lr.set(lr_array, place)
+
+        # create and initialize moment Variable
+        moment = scope.var('Moment').get_tensor()
+        moment_np_array = np.full((height, row_numel), 2.0).astype("float32")
+        moment.set(moment_np_array, place)
+
+        # create and run sgd operator
+        adagrad_op = Operator(
+            "adagrad",
+            Param='Param',
+            Grad='Grad',
+            ParamOut='Param',
+            Moment='Moment',
+            MomentOut='Moment',
+            LearningRate='LearningRate',
+            epsilon=2.0)
+
+        ctx = core.DeviceContext.create(place)
+        adagrad_op.run(scope, ctx)
+
+        # get and compare moment result
+        moment_result_array = np.array(moment)
+
+        self.assertAlmostEqual(6.0, moment_result_array[rows[0], 0])
+        self.assertAlmostEqual(3.0, moment_result_array[rows[0], 2])
+        self.assertAlmostEqual(2.0, moment_result_array[1, 0])
+        # 2.0 + (1.0 + 1.0)^2
+        self.assertAlmostEqual(6.0, moment_result_array[rows[1], 10])
+        self.assertAlmostEqual(6.0, moment_result_array[rows[3], 4])
+
+        self.assertAlmostEqual(2.0, moment_result_array[5, 8])
+        self.assertAlmostEqual(3.0, moment_result_array[rows[2], 1])
+        self.assertAlmostEqual(18.0, moment_result_array[rows[2], 8])
+
+        # get and compare param result
+        result_array = np.array(param)
+
+        def get_out(param, lr, grad, m, epsilon):
+            return param - lr * grad / (math.sqrt(m) + epsilon)
+
+        self.assertAlmostEqual(
+            get_out(5.0, 2.0, 2.0, 6.0, 2.0),
+            result_array[rows[0], 0],
+            places=5)
+        self.assertAlmostEqual(
+            get_out(5.0, 2.0, 1.0, 3.0, 2.0),
+            result_array[rows[0], 2],
+            places=5)
+        self.assertAlmostEqual(
+            get_out(5.0, 2.0, 0.0, 2.0, 2.0), result_array[1, 0], places=5)
+
+        # grad_merge = 1.0 + 1.0
+        # m = 6.0
+        self.assertAlmostEqual(
+            get_out(5.0, 2.0, 2.0, 6.0, 2.0),
+            result_array[rows[1], 10],
+            places=5)
+
+        self.assertAlmostEqual(
+            get_out(5.0, 2.0, 0.0, 2.0, 2.0), result_array[5, 8], places=5)
+        self.assertAlmostEqual(
+            get_out(5.0, 2.0, 1.0, 3.0, 2.0),
+            result_array[rows[2], 1],
+            places=5)
+        self.assertAlmostEqual(
+            get_out(5.0, 2.0, 4.0, 18.0, 2.0),
+            result_array[rows[2], 8],
+            places=5)
+
+    def test_sparse_adagrad(self):
+        places = [core.CPUPlace()]
+        if core.is_compile_gpu():
+            places.append(core.GPUPlace(0))
+        for place in places:
+            self.check_with_place(place)
+
+
 if __name__ == "__main__":
     unittest.main()