Make NCE_OP more efficient and support SelectedRows (#14469)

* Fix truncated normal.

* Fix.

* Make nce support more distribution.

* Fix API.spec.

* Fix python API.

* Fix.
test=develop

* Fix API.spec
test=develop

* Fix sampler.

* Fix order of arguments in python API.
test=develop

* NCE add selectedrows support

* NCE update weighted sampling

* fix bugs in nce_op, and assign_value_op optimized

* fix bugs in nce_op, revert assign_value_op

* nce_op optimize

* nce_op optimize

* nce_op optimize

* add selectedRows test later

test=develop

* add selectedRows supported

* add selectedRows supported

test=develop

* add selectedRows supported

* add nce selectedRows supported, test=develop

* add nce selectedRows supported

* add nce selectedRows supported, test=develop

* fix height in nce, test=develop

* add ut

* add ut, test=develop

* make AutoGrownIndex inline
test=develop

* fix tinny error, test=develop

paddle/fluid/API.spec

@@ -97,7 +97,7 @@ paddle.fluid.layers.warpctc ArgSpec(args=['input', 'label', 'blank', 'norm_by_ti
 paddle.fluid.layers.sequence_reshape ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
-paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0))
+paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False))
 paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'name'], varargs=None, keywords=None, defaults=(0, None))
 paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))

paddle/fluid/operators/math/sampler.cc

@@ -60,75 +60,30 @@ float LogUniformSampler::Probability(int64_t value) const {
   return (log((value + 2.0) / (value + 1.0))) / log_range_;
 }
 
-CustomSampler::CustomSampler(int64_t range, const float* probabilities,
+CustomSampler::CustomSampler(int64_t range, const float *probabilities,
+                             const int *alias, const float *alias_probabilities,
                              unsigned int seed)
     : Sampler(range, seed) {
-  random_engine_ = std::make_shared<std::mt19937_64>(seed_);
+  random_engine_ = std::make_shared<std::mt19937>(seed_);
   real_dist_ = std::make_shared<std::uniform_real_distribution<>>(0, 1);
   int_dist_ = std::make_shared<std::uniform_int_distribution<>>(0, range);
-  alias_probs_ = std::make_shared<std::vector<float>>(range + 1);
-  alias_ = std::make_shared<std::vector<int64_t>>(range + 1);
-  probs_ = std::make_shared<std::vector<float>>(range + 1);
-
-  std::queue<std::pair<int64_t, float>> bigs;
-  std::queue<std::pair<int64_t, float>> littles;
-  for (int64_t i = 0; i <= range; ++i) {
-    (*probs_)[i] = probabilities[i];
-    float normal_prob = probabilities[i] * (range + 1);
-    if (normal_prob - 1.0 > 1e-4) {
-      bigs.emplace(i, normal_prob);
-    } else if (1.0 - normal_prob > 1e-4) {
-      littles.emplace(i, normal_prob);
-    } else {
-      (*alias_probs_)[i] = normal_prob;
-      (*alias_)[i] = -1;
-    }
-  }
-
-  while ((!littles.empty()) && (!bigs.empty())) {
-    auto big = bigs.front();
-    auto little = littles.front();
-    bigs.pop();
-    littles.pop();
-    (*alias_probs_)[little.first] = little.second;
-    (*alias_)[little.first] = big.first;
-    auto big_left = big.second - (1 - little.second);
-    if (big_left - 1.0 > 1e-4) {
-      bigs.emplace(big.first, big_left);
-    } else if (1.0 - big_left > 1e-4) {
-      littles.emplace(big.first, big_left);
-    } else {
-      (*alias_probs_)[big.first] = big_left;
-      (*alias_)[big.first] = -1;
-    }
-  }
 
-  if (!littles.empty()) {  // littles.second is close to 1.0
-    auto little = littles.front();
-    (*alias_probs_)[little.first] = 1.0;
-    (*alias_)[little.first] = -1;
-  }
-
-  if (!bigs.empty()) {  // bigs.second is close to 1.0
-    auto big = bigs.front();
-    (*alias_probs_)[big.first] = 1.0;
-    (*alias_)[big.first] = -1;
-  }
+  alias_probs_ = alias_probabilities;
+  probs_ = probabilities;
+  alias_ = alias;
 }
 
 int64_t CustomSampler::Sample() const {
   auto index = (*int_dist_)(*random_engine_);
   auto p = (*real_dist_)(*random_engine_);
-  if (p > (*alias_probs_)[index]) {
-    return (*alias_)[index];
+  if (p > alias_probs_[index]) {
+    return alias_[index];
   } else {
     return index;
   }
 }
 
-float CustomSampler::Probability(int64_t value) const {
-  return (*probs_)[value];
-}
+float CustomSampler::Probability(int64_t value) const { return probs_[value]; }
 
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/sampler.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+
 #include <cstdint>
 #include <memory>
 #include <random>
@@ -38,9 +39,12 @@ class Sampler {
       seed_ = seed;
     }
   }
+
   virtual ~Sampler();
+
   // Sample a single value
   virtual int64_t Sample() const = 0;
+
   // The probability that a single call to Sample() returns the given value.
   virtual float Probability(int64_t value) const = 0;
 
@@ -99,6 +103,7 @@ class LogUniformSampler : public Sampler {
 class CustomSampler : public Sampler {
  public:
   explicit CustomSampler(int64_t range, const float* probabilities,
+                         const int* alias, const float* alias_probabilities,
                          unsigned int seed = 0UL);
 
   ~CustomSampler() override {}
@@ -108,10 +113,10 @@ class CustomSampler : public Sampler {
   float Probability(int64_t value) const override;
 
  private:
-  std::shared_ptr<std::vector<float>> alias_probs_;
-  std::shared_ptr<std::vector<int64_t>> alias_;
-  std::shared_ptr<std::vector<float>> probs_;
-  std::shared_ptr<std::mt19937_64> random_engine_;
+  const float* alias_probs_;
+  const int* alias_;
+  const float* probs_;
+  std::shared_ptr<std::mt19937> random_engine_;
   std::shared_ptr<std::uniform_real_distribution<>> real_dist_;
   std::shared_ptr<std::uniform_int_distribution<>> int_dist_;
 };

paddle/fluid/operators/nce_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/nce_op.h"
 
+#include <string>
 #include <vector>
 
 namespace paddle {
@@ -25,7 +26,7 @@ class NCEOp : 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("Input"));
     PADDLE_ENFORCE(ctx->HasInput("Label"));
     PADDLE_ENFORCE(ctx->HasInput("Weight"));
@@ -67,7 +68,7 @@ class NCEOp : public framework::OperatorWithKernel {
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
+      const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<Tensor>("Input")->type()),
         platform::CPUPlace());
@@ -101,11 +102,24 @@ class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
         .AsDispensable();
 
     AddInput(
-        "CustomDistribution",
+        "CustomDistProbs",
         "(Tensor) It is used in 'CostumDist' sampler. "
         "It is a tensor with shape [num_total_classes]."
         "The i-th element is the probsbility of the i-th class being sampled.")
         .AsDispensable();
+    AddInput(
+        "CustomDistAlias",
+        "(Tensor) It is used in 'CostumDist' sampler. "
+        "It is a tensor with shape [num_total_classes]."
+        "The i-th element is the probsbility of the i-th class being sampled.")
+        .AsDispensable();
+    AddInput(
+        "CustomDistAliasProbs",
+        "(Tensor) It is used in 'CostumDist' sampler. "
+        "It is a tensor with shape [num_total_classes]."
+        "The i-th element is the probsbility of the i-th class being sampled.")
+        .AsDispensable();
+
     AddOutput("Cost",
               "(Tensor) A tensor of shape [batch_size, 1]. Cost of samples.");
     AddOutput("SampleLogits",
@@ -124,21 +138,22 @@ class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
               "kernel to compute grads."
               "")
         .AsIntermediate();
+
     AddAttr<int>("num_total_classes",
                  "Total number of classes in all samples.");
     AddAttr<int>("num_neg_samples",
                  "The number of negative classes. The default value is 10.")
         .SetDefault(10);
-
     AddAttr<int>("sampler",
                  "(int) Which sampler to be used to sample negative class."
                  "0: Uniform; 1: LogUniform; 2: CostumDist.")
         .SetDefault(0);
-
     AddAttr<int>("seed",
                  "(int) The seed used in sampler. If it is 0, "
                  "the sampler will generate a seed randomly.")
         .SetDefault(0);
+    AddAttr<bool>("is_sparse", "(boolean, default false) Sparse update.")
+        .SetDefault(false);
 
     AddAttr<std::vector<int>>("custom_neg_classes",
                               "This attribute only be used in unitest. Classes "
@@ -156,11 +171,19 @@ By default this operator uses a uniform distribution for sampling.
   }
 };
 
+class NCEOpGradDescMaker : public framework::DefaultGradOpDescMaker<true> {
+  using ::paddle::framework::DefaultGradOpDescMaker<
+      true>::DefaultGradOpDescMaker;
+
+ protected:
+  virtual std::string GradOpType() const { return "nce_grad"; }
+};
+
 class NCEOpGrad : 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("Input"));
     PADDLE_ENFORCE(ctx->HasInput("Weight"));
     PADDLE_ENFORCE(ctx->HasInput("Cost"));
@@ -190,20 +213,45 @@ class NCEOpGrad : public framework::OperatorWithKernel {
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
+      const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<Tensor>("Input")->type()),
         platform::CPUPlace());
   }
 };
 
+class NCEOpGradVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {
+    auto weight_grad = op_desc.Output(framework::GradVarName("Weight")).front();
+    auto bias_grad = op_desc.Output(framework::GradVarName("Bias")).front();
+
+    auto attr = op_desc.GetAttr("is_sparse");
+    bool is_sparse = boost::get<bool>(attr);
+    if (is_sparse) {
+      VLOG(30) << "nce_op_grad op " << weight_grad << " and " << bias_grad
+               << " is set to SelectedRows";
+      block->Var(weight_grad)
+          ->SetType(framework::proto::VarType::SELECTED_ROWS);
+      block->Var(bias_grad)->SetType(framework::proto::VarType::SELECTED_ROWS);
+    } else {
+      VLOG(30) << "nce_op_grad op " << weight_grad << " and " << bias_grad
+               << " is set to LoDTensor";
+      block->Var(weight_grad)->SetType(framework::proto::VarType::LOD_TENSOR);
+      block->Var(bias_grad)->SetType(framework::proto::VarType::LOD_TENSOR);
+    }
+    block->Var(weight_grad)->SetDataType(block->Var("Input")->GetDataType());
+    block->Var(bias_grad)->SetDataType(block->Var("Input")->GetDataType());
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(nce, ops::NCEOp, ops::NCEOpMaker,
-                  paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(nce_grad, ops::NCEOpGrad);
+REGISTER_OPERATOR(nce, ops::NCEOp, ops::NCEOpGradDescMaker, ops::NCEOpMaker);
+REGISTER_OPERATOR(nce_grad, ops::NCEOpGrad, ops::NCEOpGradVarTypeInference);
 REGISTER_OP_CPU_KERNEL(nce, ops::NCEKernel<paddle::platform::CPUPlace, float>,
                        ops::NCEKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(nce_grad,

paddle/fluid/operators/nce_op.h

@@ -16,26 +16,32 @@ limitations under the License. */
 
 #include <math.h>
 #include <random>
+#include <set>
 #include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/operators/math/sampler.h"
 #include "unsupported/Eigen/CXX11/Tensor"
+
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using SelectedRows = framework::SelectedRows;
 using Sampler = math::Sampler;
+using DDim = framework::DDim;
 
 template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename DeviceContext, typename T>
-void PrepareSamples(const framework::ExecutionContext& context,
-                    Sampler* sampler) {
+void PrepareSamples(const framework::ExecutionContext &context,
+                    Sampler *sampler) {
   auto label = context.Input<Tensor>("Label");
-  const int64_t* label_data = label->data<int64_t>();
+  const int64_t *label_data = label->data<int64_t>();
   auto label_dims = label->dims();
   //  int num_total_classes = context.Attr<int>("num_total_classes");
   // for unitest
@@ -44,7 +50,7 @@ void PrepareSamples(const framework::ExecutionContext& context,
 
   auto sample_labels = context.Output<Tensor>("SampleLabels");
   auto sample_labels_dims = sample_labels->dims();
-  int64_t* sample_labels_data =
+  int64_t *sample_labels_data =
       sample_labels->mutable_data<int64_t>(context.GetPlace());
 
   int num_label = label_dims.size() == 2 ? label_dims[1] : 1;
@@ -70,13 +76,13 @@ void PrepareSamples(const framework::ExecutionContext& context,
 template <typename DeviceContext, typename T>
 class NCEKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext &context) const override {
     int sampler_type = context.Attr<int>("sampler");
     int seed = context.Attr<int>("seed");
     int num_total_classes = context.Attr<int>("num_total_classes");
     int num_neg_samples = context.Attr<int>("num_neg_samples");
 
-    Sampler* sampler;
+    Sampler *sampler;
     switch (sampler_type) {
       case 0: {
         sampler = new math::UniformSampler(num_total_classes - 1, seed);
@@ -87,11 +93,19 @@ class NCEKernel : public framework::OpKernel<T> {
         break;
       }
       case 2: {
-        auto custom_dist = context.Input<Tensor>("CustomDistribution");
-        const float* custom_dist_data = custom_dist->data<float>();
-        PADDLE_ENFORCE_EQ(custom_dist->numel(), num_total_classes);
-        sampler = new math::CustomSampler(num_total_classes - 1,
-                                          custom_dist_data, seed);
+        auto dist_probs = context.Input<Tensor>("CustomDistProbs");
+        auto dist_alias = context.Input<Tensor>("CustomDistAlias");
+        auto dist_alias_probs = context.Input<Tensor>("CustomDistAliasProbs");
+
+        PADDLE_ENFORCE_EQ(dist_probs->numel(), num_total_classes);
+        PADDLE_ENFORCE_EQ(dist_alias->numel(), num_total_classes);
+        PADDLE_ENFORCE_EQ(dist_alias_probs->numel(), num_total_classes);
+
+        const float *probs_data = dist_probs->data<float>();
+        const int *alias_data = dist_alias->data<int>();
+        const float *alias_probs_data = dist_alias_probs->data<float>();
+        sampler = new math::CustomSampler(num_total_classes - 1, probs_data,
+                                          alias_data, alias_probs_data, seed);
         break;
       }
       default: { PADDLE_THROW("Unsupported SamplerType."); }
@@ -99,17 +113,17 @@ class NCEKernel : public framework::OpKernel<T> {
 
     PrepareSamples<DeviceContext, T>(context, sampler);
     auto sample_labels = context.Output<Tensor>("SampleLabels");
-    const int64_t* sample_labels_data = sample_labels->data<int64_t>();
+    const int64_t *sample_labels_data = sample_labels->data<int64_t>();
     auto sample_out = context.Output<Tensor>("SampleLogits");
-    T* sample_out_data = sample_out->mutable_data<T>(context.GetPlace());
+    T *sample_out_data = sample_out->mutable_data<T>(context.GetPlace());
     auto label = context.Input<Tensor>("Label");
     auto sample_weight = context.Input<Tensor>("SampleWeight");
-    const T* sample_weight_data = nullptr;
+    const T *sample_weight_data = nullptr;
     if (sample_weight != nullptr) {
       sample_weight_data = sample_weight->data<T>();
     }
     auto out = context.Output<Tensor>("Cost");
-    T* out_data = out->mutable_data<T>(context.GetPlace());
+    T *out_data = out->mutable_data<T>(context.GetPlace());
     int64_t num_true_class = 1;
     if (label != nullptr) {
       num_true_class = label->dims()[1];
@@ -119,7 +133,7 @@ class NCEKernel : public framework::OpKernel<T> {
     // forward bias
     auto bias = context.Input<Tensor>("Bias");
     if (bias != nullptr) {
-      const T* bias_data = bias->data<T>();
+      const T *bias_data = bias->data<T>();
       for (int64_t i = 0; i < sample_labels->numel(); ++i) {
         sample_out_data[i] = bias_data[sample_labels_data[i]];
       }
@@ -158,16 +172,16 @@ class NCEKernel : public framework::OpKernel<T> {
 template <typename DeviceContext, typename T>
 class NCEGradKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext &context) const override {
     auto d_out = context.Input<Tensor>(framework::GradVarName("Cost"));
-    const T* d_out_data = d_out->data<T>();
+    const T *d_out_data = d_out->data<T>();
     auto label = context.Input<Tensor>("Label");
     auto sample_out = context.Input<Tensor>("SampleLogits");
-    const T* sample_out_data = sample_out->data<T>();
+    const T *sample_out_data = sample_out->data<T>();
     auto sample_labels = context.Input<Tensor>("SampleLabels");
-    const int64_t* sample_labels_data = sample_labels->data<int64_t>();
+    const int64_t *sample_labels_data = sample_labels->data<int64_t>();
     auto sample_weight = context.Input<Tensor>("SampleWeight");
-    const T* sample_weight_data = nullptr;
+    const T *sample_weight_data = nullptr;
     if (sample_weight != nullptr) {
       sample_weight_data = sample_weight->data<T>();
     }
@@ -180,7 +194,7 @@ class NCEGradKernel : public framework::OpKernel<T> {
 
     int sampler_type = context.Attr<int>("sampler");
     int seed = context.Attr<int>("seed");
-    Sampler* sampler;
+    Sampler *sampler;
     switch (sampler_type) {
       case 0: {
         sampler = new math::UniformSampler(num_total_classes - 1, seed);
@@ -191,11 +205,19 @@ class NCEGradKernel : public framework::OpKernel<T> {
         break;
       }
       case 2: {
-        auto custom_dist = context.Input<Tensor>("CustomDistribution");
-        const float* custom_dist_data = custom_dist->data<float>();
-        PADDLE_ENFORCE_EQ(custom_dist->numel(), num_total_classes);
-        sampler = new math::CustomSampler(num_total_classes - 1,
-                                          custom_dist_data, seed);
+        auto dist_probs = context.Input<Tensor>("CustomDistProbs");
+        auto dist_alias = context.Input<Tensor>("CustomDistAlias");
+        auto dist_alias_probs = context.Input<Tensor>("CustomDistAliasProbs");
+
+        PADDLE_ENFORCE_EQ(dist_probs->numel(), num_total_classes);
+        PADDLE_ENFORCE_EQ(dist_alias->numel(), num_total_classes);
+        PADDLE_ENFORCE_EQ(dist_alias_probs->numel(), num_total_classes);
+
+        const float *probs_data = dist_probs->data<float>();
+        const int *alias_data = dist_alias->data<int>();
+        const float *alias_probs_data = dist_alias_probs->data<float>();
+        sampler = new math::CustomSampler(num_total_classes - 1, probs_data,
+                                          alias_data, alias_probs_data, seed);
         break;
       }
       default: { PADDLE_THROW("Unsupported SamplerType."); }
@@ -203,7 +225,7 @@ class NCEGradKernel : public framework::OpKernel<T> {
 
     //    T b = 1. / num_total_classes * num_neg_samples;
     Tensor sample_grad;  // tmp tensor
-    T* sample_grad_data =
+    T *sample_grad_data =
         sample_grad.mutable_data<T>(sample_labels->dims(), context.GetPlace());
     // backward cost
     for (int64_t i = 0; i < sample_labels->numel(); ++i) {
@@ -217,32 +239,105 @@ class NCEGradKernel : public framework::OpKernel<T> {
                                 : w * (o * (1 - o) / (o + b));
       sample_grad_data[i] *= d_out_data[sample_idx];
     }
-    // get d_bias
-    auto d_bias = context.Output<Tensor>(framework::GradVarName("Bias"));
-    if (d_bias != nullptr) {
-      T* d_bias_data = d_bias->mutable_data<T>(context.GetPlace());
-      std::fill(d_bias_data, d_bias_data + d_bias->numel(), 0.0);
+
+    bool is_sparse = context.Attr<bool>("is_sparse");
+
+    if (!is_sparse) {
+      // get d_bias
+      auto d_bias = context.Output<Tensor>(framework::GradVarName("Bias"));
+      if (d_bias != nullptr) {
+        T *d_bias_data = d_bias->mutable_data<T>(context.GetPlace());
+        std::fill(d_bias_data, d_bias_data + d_bias->numel(), 0.0);
+        for (int64_t i = 0; i < sample_labels->numel(); ++i) {
+          d_bias_data[sample_labels_data[i]] += sample_grad_data[i];
+        }
+      }
+      // get d_w
+      auto d_w = context.Output<Tensor>(framework::GradVarName("Weight"));
+      if (d_w != nullptr) {
+        auto d_w_data = d_w->mutable_data<T>(context.GetPlace());
+        std::fill(d_w_data, d_w_data + d_w->numel(), 0.0);
+        auto d_w_matrix = EigenMatrix<T>::From(*d_w);
+        auto x_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Input")));
+        for (int64_t i = 0; i < sample_labels->numel(); ++i) {
+          d_w_matrix.chip(sample_labels_data[i], 0) +=
+              x_matrix.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) *
+              sample_grad_data[i];
+        }
+      }
+    } else {
+      std::vector<int64_t> labels;
       for (int64_t i = 0; i < sample_labels->numel(); ++i) {
-        d_bias_data[sample_labels_data[i]] += sample_grad_data[i];
+        labels.push_back(sample_labels_data[i]);
       }
-    }
-    // get d_w
-    auto d_w = context.Output<Tensor>(framework::GradVarName("Weight"));
-    if (d_w != nullptr) {
-      auto d_w_data = d_w->mutable_data<T>(context.GetPlace());
-      std::fill(d_w_data, d_w_data + d_w->numel(), 0.0);
-      auto d_w_matrix = EigenMatrix<T>::From(*d_w);
+      std::set<T> st(labels.begin(), labels.end());
+      labels.assign(st.begin(), st.end());
+
+      auto *bias_var = context.InputVar("Bias");
+      DDim bias_dim;
+      if (bias_var->IsType<LoDTensor>()) {
+        bias_dim = context.Input<LoDTensor>("Bias")->dims();
+      } else if (bias_var->IsType<SelectedRows>()) {
+        auto *table_t = context.Input<SelectedRows>("Bias");
+        bias_dim = table_t->value().dims();
+      } else {
+        PADDLE_THROW(
+            "The parameter Bias of a NCE_OP "
+            "must be either LoDTensor or SelectedRows");
+      }
+
+      auto d_bias =
+          context.Output<SelectedRows>(framework::GradVarName("Bias"));
+      d_bias->set_rows(labels);
+      d_bias->set_height(bias_dim[0]);
+
+      d_bias->mutable_value()->Resize(
+          {static_cast<int64_t>(labels.size()), bias_dim[1]});
+      T *d_bias_data =
+          d_bias->mutable_value()->mutable_data<T>(context.GetPlace());
+      std::fill(d_bias_data, d_bias_data + labels.size(), 0.0);
+      for (int64_t i = 0; i < sample_labels->numel(); ++i) {
+        d_bias_data[d_bias->Index(sample_labels_data[i])] +=
+            sample_grad_data[i];
+      }
+
+      auto *table_var = context.InputVar("Weight");
+      DDim table_dim;
+      if (table_var->IsType<LoDTensor>()) {
+        table_dim = context.Input<LoDTensor>("Weight")->dims();
+      } else if (table_var->IsType<SelectedRows>()) {
+        auto *table_t = context.Input<SelectedRows>("Weight");
+        table_dim = table_t->value().dims();
+      } else {
+        PADDLE_THROW(
+            "The parameter Weight of a NCE_OP "
+            "must be either LoDTensor or SelectedRows");
+      }
+
+      auto d_w = context.Output<SelectedRows>(framework::GradVarName("Weight"));
+
+      d_w->set_rows(labels);
+      d_w->set_height(table_dim[0]);
+
+      auto *d_table_value = d_w->mutable_value();
+      d_table_value->Resize(
+          {static_cast<int64_t>(labels.size()), table_dim[1]});
+      auto d_w_data = d_table_value->mutable_data<T>(context.GetPlace());
+      std::fill(d_w_data, d_w_data + d_table_value->numel(), 0.0);
+
+      auto d_w_matrix = EigenMatrix<T>::From(*d_table_value);
       auto x_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Input")));
       for (int64_t i = 0; i < sample_labels->numel(); ++i) {
-        d_w_matrix.chip(sample_labels_data[i], 0) +=
+        d_w_matrix.chip(d_w->Index(sample_labels_data[i]), 0) +=
             x_matrix.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) *
             sample_grad_data[i];
       }
     }
+
     // get d_x
     auto d_x = context.Output<Tensor>(framework::GradVarName("Input"));
     if (d_x != nullptr) {
-      auto* d_x_data = d_x->mutable_data<T>(context.GetPlace());
+      auto *d_x_data = d_x->mutable_data<T>(context.GetPlace());
       std::fill(d_x_data, d_x_data + d_x->numel(), 0.0);
       auto d_x_matrix = EigenMatrix<T>::From(*d_x);
       auto w_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
@@ -251,6 +346,7 @@ class NCEGradKernel : public framework::OpKernel<T> {
             w_matrix.chip(sample_labels_data[i], 0) * sample_grad_data[i];
       }
     }
+
     delete sampler;
   }
 };

python/paddle/fluid/layers/nn.py

@@ -4394,7 +4394,8 @@ def nce(input,
         name=None,
         sampler="uniform",
         custom_dist=None,
-        seed=0):
+        seed=0,
+        is_sparse=False):
     """
     ${comment}
 
@@ -4420,11 +4421,12 @@ def nce(input,
         sampler (str): The sampler used to sample class from negtive classes.
                        It can be 'uniform', 'log_uniform' or 'custom_dist'.
                        default: 'uniform'.
-        custom_dist (Variable): A tensor with shape [num_total_classes].
+        custom_dist (float[]): A float[] with size=num_total_classes.
                        It is used when sampler is set to 'custom_dist'.
                        custom_dist[i] is the probsbility of i-th class to be sampled.
                        default: None.
         seed (int): The seed used in sampler. default: 0.
+        is_sparse(bool): The flag indicating whether to use sparse update, the weight@GRAD and bias@GRAD will be changed to SelectedRows.
 
     Returns:
         Variable: The output nce loss.
@@ -4476,12 +4478,7 @@ def nce(input,
         shape=[num_total_classes, dim],
         is_bias=False,
         dtype=input.dtype)
-    inputs = {
-        'Input': input,
-        'Label': label,
-        'Weight': w,
-        'SampleWeight': sample_weight if sample_weight is not None else []
-    }
+    inputs = {}
     if helper.bias_attr:
         b = helper.create_parameter(
             attr=helper.bias_attr,
@@ -4493,18 +4490,10 @@ def nce(input,
     sample_logits = helper.create_variable_for_type_inference(dtype=input.dtype)
     sample_labels = helper.create_variable_for_type_inference(dtype=label.dtype)
 
-    if num_neg_samples is None:
-        num_neg_samples = 10
-    else:
-        num_neg_samples = int(num_neg_samples)
-
-    inputs = {
-        'Input': input,
-        'Label': label,
-        'Weight': w,
-        'Bias': b,
-        'SampleWeight': sample_weight if sample_weight is not None else []
-    }
+    inputs['Input'] = input
+    inputs['Label'] = label
+    inputs['Weight'] = w
+    inputs['SampleWeight'] = sample_weight if sample_weight is not None else []
 
     if sampler == "uniform":
         sampler = 0
@@ -4512,17 +4501,73 @@ def nce(input,
         sampler = 1
     elif sampler == "custom_dist":
         assert custom_dist is not None
-        assert isinstance(custom_dist, Variable)
-        inputs['CustomDistribution'] = custom_dist
+        # assert isinstance(custom_dist, Variable)
+
+        custom_dist_len = len(custom_dist)
+        alias_probs_ = [0] * custom_dist_len
+        alias_ = [0] * custom_dist_len
+        bigs = []
+        littles = []
+        for i in range(custom_dist_len):
+            normal_prob = custom_dist[i] * custom_dist_len
+            if normal_prob - 1.0 > 1e-4:
+                bigs.append((i, normal_prob))
+            elif 1.0 - normal_prob > 1e-4:
+                littles.append((i, normal_prob))
+            else:
+                alias_probs_[i] = normal_prob
+                alias_[i] = -1
+
+        while len(bigs) and len(littles):
+            big = bigs.pop(0)
+            little = littles.pop(0)
+
+            big_idx = big[0]
+            big_prob = big[1]
+
+            alias_probs_[little[0]] = little[1]
+            alias_[little[0]] = big_idx
+            big_left = big[1] + little[1] - 1
+            if big_left - 1.0 > 1e-4:
+                bigs.append((big_idx, big_left))
+            elif 1.0 - big_left > 1e-4:
+                littles.append((big_idx, big_left))
+            else:
+                alias_probs_[big_idx] = big_left
+                alias_[big_idx] = -1
+
+        if len(bigs):
+            big = bigs.pop(0)
+            alias_probs_[big[0]] = 1.0
+            alias_[big[0]] = -1
+        if len(littles):
+            little = littles.pop(0)
+            alias_probs_[little[0]] = 1.0
+            alias_[little[0]] = -1
+
+        probs = assign(input=np.array(custom_dist).astype('float32'))
+        custom_alias = assign(input=np.array(alias_).astype('int32'))
+        custom_alias_probs = assign(
+            input=np.array(alias_probs_).astype('float32'))
+
+        inputs['CustomDistProbs'] = probs
+        inputs['CustomDistAlias'] = custom_alias
+        inputs['CustomDistAliasProbs'] = custom_alias_probs
         sampler = 2
     else:
         raise Exception("Unsupported sampler type.")
 
+    if num_neg_samples is None:
+        num_neg_samples = 10
+    else:
+        num_neg_samples = int(num_neg_samples)
+
     attrs = {
         'num_total_classes': int(num_total_classes),
         'num_neg_samples': num_neg_samples,
         'seed': seed,
-        'sampler': sampler
+        'sampler': sampler,
+        'is_sparse': is_sparse
     }
 
     helper.append_op(
@@ -6474,7 +6519,7 @@ def crop(x, shape=None, offsets=None, name=None):
     helper = LayerHelper('crop', **locals())
 
     if not (isinstance(shape, list) or isinstance(shape, tuple) or \
-                    isinstance(shape, Variable)):
+            isinstance(shape, Variable)):
         raise ValueError("The shape should be a list, tuple or Variable.")
 
     if offsets is None:
@@ -6596,7 +6641,7 @@ def affine_grid(theta, out_shape, name=None):
     helper = LayerHelper('affine_grid')
 
     if not (isinstance(out_shape, list) or isinstance(out_shape, tuple) or \
-        isinstance(out_shape, Variable)):
+            isinstance(out_shape, Variable)):
         raise ValueError("The out_shape should be a list, tuple or Variable.")
 
     if not isinstance(theta, Variable):

python/paddle/fluid/tests/unittests/test_nce.py

@@ -14,8 +14,12 @@
 
 from __future__ import print_function
 
-import unittest
 import numpy as np
+import unittest
+
+import paddle.fluid as fluid
+import paddle.fluid.initializer as initializer
+
 from op_test import OpTest
 
 
@@ -59,7 +63,7 @@ def nce(input, weight, bias, sample_weight, labels, num_classes,
 
 class TestNCE(OpTest):
     def generate_data(self, dim, batch_size, num_classes, num_true_class,
-                      num_neg_samples):
+                      num_neg_samples, is_sparse):
         input = np.random.randn(batch_size, dim).astype(np.float32)
         weight = np.random.randn(num_classes, dim).astype(np.float32)
         bias = np.random.randn(num_classes).astype(np.float32)
@@ -70,7 +74,8 @@ class TestNCE(OpTest):
             'num_neg_samples': num_neg_samples,
             'custom_neg_classes': list(range(num_neg_samples)),
             'seed': 0,
-            'sampler': 0
+            'sampler': 0,
+            'is_sparse': is_sparse
         }
         self.inputs = {
             'Input': input,
@@ -81,7 +86,7 @@ class TestNCE(OpTest):
         }
 
     def set_data(self):
-        self.generate_data(5, 5, 4, 1, 2)
+        self.generate_data(5, 5, 4, 1, 2, False)
 
     def compute(self):
         out = nce(self.inputs['Input'], self.inputs['Weight'],
@@ -107,9 +112,110 @@ class TestNCE(OpTest):
             ["Input", "Weight", "Bias"], "Cost", max_relative_error=0.02)
 
 
-class TestNCECase1(TestNCE):
+class TestNCECase1Tensor(TestNCE):
     def set_data(self):
-        self.generate_data(10, 20, 10, 2, 5)
+        self.generate_data(10, 20, 10, 2, 5, False)
+
+
+class TestNCECase1SelectedRows(unittest.TestCase):
+    def setUp(self):
+        self.base_lr = 0.0001
+        self.batch_size = 8
+
+    @staticmethod
+    def get_place():
+        place = fluid.core.CPUPlace()
+        return place
+
+    @staticmethod
+    def get_train_data(batch_size):
+        batchs = []
+        for i in range(batch_size):
+            input = np.random.randn(batch_size, 10).astype(np.float32)
+            labels = np.random.randint(0, 20, (batch_size, 1))
+            batchs.append([input, labels])
+        return batchs
+
+    def get_optimizer(self):
+        # SGD optimizer
+        optimizer = fluid.optimizer.SGD(learning_rate=self.base_lr)
+        return optimizer
+
+    def train_network(self, num_total_classes, num_neg_samples, sampler,
+                      custom_dist, is_sparse):
+        input = fluid.layers.data(name="input", shape=[10], dtype="float32")
+        label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+
+        w_param = fluid.default_main_program().global_block().create_parameter(
+            shape=[num_total_classes, 10],
+            dtype='float32',
+            name='nce_w',
+            initializer=initializer.ConstantInitializer())
+        b_param = fluid.default_main_program().global_block().create_parameter(
+            shape=[num_total_classes, 1],
+            dtype='float32',
+            name='nce_b',
+            initializer=initializer.ConstantInitializer())
+
+        cost = fluid.layers.nce(input=input,
+                                label=label,
+                                num_total_classes=num_total_classes,
+                                sampler=sampler,
+                                custom_dist=custom_dist,
+                                sample_weight=None,
+                                param_attr='nce_w',
+                                bias_attr='nce_b',
+                                seed=1,
+                                num_neg_samples=num_neg_samples,
+                                is_sparse=is_sparse)
+        avg_cost = fluid.layers.mean(cost)
+        # optimizer
+        optimizer = self.get_optimizer()
+        optimizer.minimize(avg_cost)
+
+        return [avg_cost, [input, label]]
+
+    def test_input_is_selected_rows(self):
+        place = self.get_place()
+        exe = fluid.Executor(place)
+
+        data = self.get_train_data(self.batch_size)
+        nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32')
+
+        rets = []
+        # for dense
+        dense_scope = fluid.core.Scope()
+        dense_startup_program = fluid.framework.Program()
+        dense_train_program = fluid.framework.Program()
+        with fluid.scope_guard(dense_scope):
+            with fluid.program_guard(dense_train_program,
+                                     dense_startup_program):
+                cost, feeds = self.train_network(20, 5, "custom_dist",
+                                                 nid_freq_arr.tolist(), False)
+                feeder = fluid.DataFeeder(feed_list=feeds, place=place)
+                exe.run(dense_startup_program)
+                loss_val = exe.run(dense_train_program,
+                                   feed=feeder.feed(data),
+                                   fetch_list=[cost.name])
+                rets.append(np.mean(loss_val))
+
+        # for sparse
+        sparse_scope = fluid.core.Scope()
+        sparse_startup_program = fluid.framework.Program()
+        sparse_train_program = fluid.framework.Program()
+        with fluid.scope_guard(sparse_scope):
+            with fluid.program_guard(sparse_train_program,
+                                     sparse_startup_program):
+                cost, feeds = self.train_network(20, 5, "custom_dist",
+                                                 nid_freq_arr.tolist(), True)
+                feeder = fluid.DataFeeder(feed_list=feeds, place=place)
+                exe.run(sparse_startup_program)
+                loss_val = exe.run(sparse_train_program,
+                                   feed=feeder.feed(data),
+                                   fetch_list=[cost.name])
+                rets.append(np.mean(loss_val))
+
+        self.assertEqual(rets[0], rets[1])
 
 
 if __name__ == '__main__':