update bpr_loss op code, test=develop

paddle/fluid/API.spec

@@ -32,6 +32,13 @@ paddle.fluid.BuildStrategy.ReduceStrategy.__init__ __init__(self: paddle.fluid.c
 paddle.fluid.BuildStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy) -> None
 paddle.fluid.create_lod_tensor ArgSpec(args=['data', 'recursive_seq_lens', 'place'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.create_random_int_lodtensor ArgSpec(args=['recursive_seq_lens', 'base_shape', 'place', 'low', 'high'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.DataFeedDesc.__init__ ArgSpec(args=['self', 'proto_file'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.DataFeedDesc.desc ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.DataFeedDesc.set_batch_size ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.DataFeedDesc.set_dense_slots ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.DataFeedDesc.set_use_slots ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.AsyncExecutor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.AsyncExecutor.run ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'debug'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.io.save_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.io.save_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.io.save_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
@@ -70,7 +77,7 @@ paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name']
 paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(True, None))
 paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
 paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
-paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False))
+paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False))
 paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
 paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
@@ -176,7 +183,7 @@ paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None,
 paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
-paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'ignore_index', 'name'], varargs=None, keywords=None, defaults=(-100, None))
 paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
@@ -188,6 +195,9 @@ paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=Non
 paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
 paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None))
+paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
@@ -292,6 +302,7 @@ paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'i
 paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
 paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'class_num', 'ignore_thresh', 'loss_weight_xy', 'loss_weight_wh', 'loss_weight_conf_target', 'loss_weight_conf_notarget', 'loss_weight_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None))
 paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
 paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
 paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))

paddle/fluid/operators/bpr_loss_op.cc

@@ -23,19 +23,18 @@ class BprLossOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Label_Pos"),
-                   "Input(Label_Pos) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("LabelPos"),
+                   "Input(LabelPos) should be not null.");
     PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
 
     auto x_dims = ctx->GetInputDim("X");
-    auto label_Pos_dims = ctx->GetInputDim("Label_Pos");
+    auto label_Pos_dims = ctx->GetInputDim("LabelPos");
     int rank = x_dims.size();
-    PADDLE_ENFORCE_EQ(
-        rank, label_Pos_dims.size(),
-        "Input(X) and Input(Label_Pos) shall have the same rank.");
+    PADDLE_ENFORCE_EQ(rank, label_Pos_dims.size(),
+                      "Input(X) and Input(LabelPos) shall have the same rank.");
     PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
                       framework::slice_ddim(label_Pos_dims, 0, rank - 1),
-                      "Input(X) and Input(Label_Pos) shall have the same shape "
+                      "Input(X) and Input(LabelPos) shall have the same shape "
                       "except the last dimension.");
 
     auto y_dims = x_dims;
@@ -61,25 +60,25 @@ class BprLossGradientOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Label_Pos"),
-                   "Input(Label_Pos) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("LabelPos"),
+                   "Input(LabelPos) should be not null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
                    "Input(Y@GRAD) shoudl be not null.");
     PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
                    "Output(X@GRAD) should be not null.");
 
     auto x_dims = ctx->GetInputDim("X");
-    auto label_pos_dims = ctx->GetInputDim("Label_Pos");
+    auto label_pos_dims = ctx->GetInputDim("LabelPos");
     auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
     int rank = x_dims.size();
     PADDLE_ENFORCE_EQ(dy_dims.size(), rank,
                       "Input(Y@Grad) and Input(X) should have the same rank.");
     PADDLE_ENFORCE_EQ(
         label_pos_dims.size(), rank,
-        "Input(Label_Pos) and Input(X) should have the same rank.");
+        "Input(LabelPos) and Input(X) should have the same rank.");
     PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
                       framework::slice_ddim(label_pos_dims, 0, rank - 1),
-                      "The Input(X) and Input(Label_Pos) should have the same "
+                      "The Input(X) and Input(LabelPos) should have the same "
                       "shape except the last dimension.");
     PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
                       framework::slice_ddim(dy_dims, 0, rank - 1),
@@ -88,7 +87,7 @@ class BprLossGradientOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(dy_dims[rank - 1], 1,
                       "The last dimension of Input(Y@Grad) should be 1.");
     PADDLE_ENFORCE_EQ(label_pos_dims[rank - 1], 1,
-                      " the last dimension of Input(Label_Pos) should be 1.");
+                      " the last dimension of Input(LabelPos) should be 1.");
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
     ctx->ShareLoD("X", framework::GradVarName("X"));
   }
@@ -112,7 +111,7 @@ class BprLossOpMaker : public framework::OpProtoAndCheckerMaker {
              "size is equal to the number of classes. This input is a "
              "real number.");
     AddInput(
-        "Label_Pos",
+        "LabelPos",
         "(Tensor), the tensor which represents the ground truth. It has the "
         "same shape with 'X' except the last dimension. the last dimension "
         "size is 1.");
@@ -121,14 +120,14 @@ class BprLossOpMaker : public framework::OpProtoAndCheckerMaker {
               "with 'X' except that the last dimension size is 1. It "
               "represents the sequence bpr loss.");
     AddComment(R"DOC(
-BprLoss Operator.
+Bayesian Personalized Ranking Loss Operator.
 
-This operator belongs to pairwise ranking loss. Label_pos is the desired item.
-The loss at a given point in one seesion is defined as:
+This operator belongs to pairwise ranking loss. LabelPos is the desired item.
+The loss at a given point in one session is defined as:
 $Y[i] = -\frac{1}{N_{i}} * \sum_{j=0}^{N_{i}}\log(\sigma(X[i, Label[i]]-X[i, j]))$
 
 Learn more details by reading paper <session-based recommendations with recurrent
-neural networks>.
+neural networks>(https://arxiv.org/abs/1511.06939)
 
 )DOC");
   }

paddle/fluid/operators/bpr_loss_op.h

@@ -39,22 +39,22 @@ class BprLossOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* x = ctx.Input<Tensor>("X");
-    auto* labels_Pos = ctx.Input<Tensor>("Label_Pos");
+    auto* label_pos = ctx.Input<Tensor>("LabelPos");
     auto* y = ctx.Output<Tensor>("Y");
     y->mutable_data<T>(ctx.GetPlace());
     int rank = x->dims().size();
 
     Tensor x_2d = framework::ReshapeToMatrix(*x, rank - 1);
-    Tensor labels_Pos_2d = framework::ReshapeToMatrix(*labels_Pos, rank - 1);
+    Tensor labels_Pos_2d = framework::ReshapeToMatrix(*label_pos, rank - 1);
     Tensor y_2d = framework::ReshapeToMatrix(*y, rank - 1);
 
-    const framework::Tensor* prob = &x_2d;
+    const framework::Tensor* logits = &x_2d;
     const framework::Tensor* labels_pos = &labels_Pos_2d;
     framework::Tensor* out = &y_2d;
 
-    const int step_size = prob->dims()[0];
-    const int class_num = prob->dims()[1];
-    const T* prob_data = prob->data<T>();
+    const int step_size = logits->dims()[0];
+    const int class_num = logits->dims()[1];
+    const T* logits_data = logits->data<T>();
     T* loss_data = out->data<T>();
 
     const int64_t* label_pos_data = labels_pos->data<int64_t>();
@@ -68,31 +68,33 @@ class BprLossOpKernel : public framework::OpKernel<T> {
         if (j == lbl_pos) continue;
         int index_neg = i * class_num + j;
         sum += TolerableValue<T>()(-std::log(
-            1.0f + TolerableValue<T>()(
-                       std::exp(prob_data[index_neg] - prob_data[index_pos]))));
+            1.0f + TolerableValue<T>()(std::exp(logits_data[index_neg] -
+                                                logits_data[index_pos]))));
       }
       loss_data[i] = -sum / (class_num - 1);
     }
   }
 };
 
-template <typename T>
-class XeGradFunctor {
+template <typename DeviceContext, typename T>
+class BprLossGradientOpKernel : public framework::OpKernel<T> {
  public:
-  XeGradFunctor(T* dx,
-                const T* dy,               // NOLINT
-                const T* x,                // NOLINT
-                const int64_t* label_pos,  // NOLINT
-                size_t num_classes)
-      : dx_(dx),
-        dy_(dy),
-        x_(x),
-        label_pos_(label_pos),
-        num_classes_(num_classes) {}
-
-  HOSTDEVICE void operator()(size_t sample_id) {
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
+    auto* label_pos = ctx.Input<Tensor>("LabelPos");
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+
+    const int step_size = x->dims()[0];
+    const int num_classes_ = x->dims()[1];
+    T* dx_ = dx->mutable_data<T>(ctx.GetPlace());
+    const T* dy_ = dy->data<T>();
+    const T* x_ = x->data<T>();
+    const int64_t* label_pos_ = label_pos->data<int64_t>();
+
+    for (size_t sample_id = 0; sample_id < step_size; sample_id++) {
       for (size_t x_offset = sample_id * num_classes_;
-         x_offset < (sample_id + 1) * num_classes_; ++x_offset) {
+           x_offset < (sample_id + 1) * num_classes_; x_offset++) {
         dx_[x_offset] = static_cast<T>(0);
       }
       auto p_index = sample_id * num_classes_ + label_pos_[sample_id];
@@ -107,34 +109,6 @@ class XeGradFunctor {
         dx_[n_index] -= grad_;
       }
     }
-
- private:
-  T* dx_;
-  const T* dy_;
-  const T* x_;
-  const int64_t* label_pos_;
-  size_t num_classes_;
-};
-
-template <typename DeviceContext, typename T>
-class BprLossGradientOpKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* x = ctx.Input<Tensor>("X");
-    auto* dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
-    auto* label_pos = ctx.Input<Tensor>("Label_Pos");
-    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-    T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
-
-    int rank = x->dims().size();
-    int64_t class_num = x->dims()[rank - 1];
-    XeGradFunctor<T> functor(dx_data, dy->data<T>(), x->data<T>(),
-                             label_pos->data<int64_t>(),
-                             static_cast<size_t>(class_num));
-    platform::ForRange<DeviceContext> for_range(
-        ctx.template device_context<DeviceContext>(),
-        static_cast<size_t>(dy->numel()));
-    for_range(functor);
   }
 };
 

python/paddle/fluid/layers/nn.py

@@ -1356,7 +1356,7 @@ def bpr_loss(input, label_pos):
     helper.append_op(
         type='bpr_loss',
         inputs={'X': [input],
-                'Label_Pos': [label_pos]},
+                'LabelPos': [label_pos]},
         outputs={'Y': [out]})
     return out
 

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

@@ -39,7 +39,7 @@ class TestBprLossOp1(OpTest):
                 sum += (-np.log(1.0 + np.exp(X[i][j] - X[i][label_pos[i][0]])))
             bpr_loss_result.append(-sum / (class_num - 1))
         bpr_loss = np.asmatrix([[x] for x in bpr_loss_result], dtype="float64")
-        self.inputs = {"X": X, "Label_Pos": label_pos}
+        self.inputs = {"X": X, "LabelPos": label_pos}
         self.outputs = {"Y": bpr_loss}
 
     def test_check_output(self):