refine code

paddle/operators/math/sequence_project.h

@@ -90,8 +90,8 @@ template <typename Place, typename T>
 class SequenceProjectFunctor {
  public:
   void operator()(const platform::DeviceContext& context,
-                  framework::LoDTensor& in, framework::LoDTensor& padding_data,
-                  framework::LoDTensor& col, bool padding_trainable,
+                  framework::LoDTensor& in, framework::Tensor& padding_data,
+                  framework::Tensor& col, bool padding_trainable,
                   int context_start, int context_length, int context_stride,
                   int up_pad, int down_pad, bool gradient, bool input_grad,
                   bool pad_grad) {

paddle/operators/sequence_conv_op.cc

@@ -29,10 +29,6 @@ class SequenceConvOp : public framework::OperatorWithKernel {
                    "Input(Filter) of SequenceConvOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of SequenceConvOp should not be null.");
-    // PaddingData mast be not empty. Otherwise(EnforceNotMet: enforce numel() >
-    // 0 failed, 0 <= 0)
-    PADDLE_ENFORCE(ctx->HasInput("PaddingData"),
-                   "Input(PaddingData) of SequenceConvOp should not be null.");
 
     int context_length = ctx->Attrs().Get<int>("context_length");
     bool padding_trainable = ctx->Attrs().Get<bool>("padding_trainable");
@@ -48,6 +44,9 @@ class SequenceConvOp : public framework::OperatorWithKernel {
         "number_of_input_features).");
 
     if (padding_trainable) {
+      PADDLE_ENFORCE(
+          ctx->HasInput("PaddingData"),
+          "Input(PaddingData) of SequenceConvOp should not be null.");
       framework::DDim padding_dim = ctx->GetInputDim("PaddingData");
       int up_pad = std::max(0, -context_start);
       int down_pad = std::max(0, context_start + context_length - 1);
@@ -106,11 +105,12 @@ class SequenceConvOpMaker : public framework::OpProtoAndCheckerMaker {
              "(A float LoDTensor) the input of SequenceConvOp, a vector of "
              "2-D matrix of size (minibatch, number_of_input_features).");
     AddInput("PaddingData",
-             "(A float LoDTensor) the input of SequenceConvOp, a vector of "
+             "(Tensor) the input of SequenceConvOp, a vector of "
              "2-D matrix of size (up_pad + down_pad, "
-             "number_of_input_features). ");
+             "number_of_input_features). ")
+        .AsDispensable();
     AddInput("Filter",
-             "(A float LoDTensor) the input of SequenceConvOp, a vector of "
+             "(Tensor) the input of SequenceConvOp, a vector of "
              "2-D matrix of size (context_length x number_of_input_features).");
     AddOutput("Out",
               "(A float LoDTensor) the output of SequenceConvOp, a vector "

paddle/operators/sequence_conv_op.h

@@ -36,7 +36,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& context) const override {
     auto* in = context.Input<LoDTensor>("X");
     auto* out = context.Output<LoDTensor>("Out");
-    auto filter = *context.Input<LoDTensor>("Filter");
+    auto filter = *context.Input<Tensor>("Filter");
 
     out->mutable_data<T>(context.GetPlace());
     //  out->set_lod(in->lod());
@@ -50,9 +50,9 @@ class SequenceConvKernel : public framework::OpKernel<T> {
     PADDLE_ENFORCE_EQ(in->lod().size(), 1UL,
                       "Only support one level sequence now.");
 
-    const LoDTensor* padding_data = nullptr;
+    const Tensor* padding_data = nullptr;
     if (padding_trainable) {
-      padding_data = context.Input<LoDTensor>("PaddingData");
+      padding_data = context.Input<Tensor>("PaddingData");
     }
 
     int up_pad = std::max(0, -context_start);
@@ -63,7 +63,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
     // use col_shape in the im2col calculation
     framework::DDim col_shape = {in->dims()[0],
                                  sequence_width * context_length};
-    LoDTensor col;
+    Tensor col;
     col.mutable_data<T>(col_shape, context.GetPlace());
     // Because if padding_trainable is false, padding data should be zeros.
     auto temp = framework::EigenVector<T>::Flatten(col);
@@ -73,7 +73,7 @@ class SequenceConvKernel : public framework::OpKernel<T> {
     paddle::operators::math::SequenceProjectFunctor<Place, T>
         seq_project_functor;
     LoDTensor* input = const_cast<LoDTensor*>(in);
-    LoDTensor* pad_data = const_cast<LoDTensor*>(padding_data);
+    Tensor* pad_data = const_cast<Tensor*>(padding_data);
 
     seq_project_functor(context.device_context(), *input, *pad_data, col,
                         padding_trainable, context_start, context_length,
@@ -91,12 +91,11 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& context) const override {
     auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
     auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
-    auto* filter_g =
-        context.Output<LoDTensor>(framework::GradVarName("Filter"));
+    auto* filter_g = context.Output<Tensor>(framework::GradVarName("Filter"));
     auto* padding_data_g =
-        context.Output<LoDTensor>(framework::GradVarName("PaddingData"));
+        context.Output<Tensor>(framework::GradVarName("PaddingData"));
     auto* in = context.Input<LoDTensor>("X");
-    auto* filter = context.Input<LoDTensor>("Filter");
+    auto* filter = context.Input<Tensor>("Filter");
 
     int context_start = context.Attr<int>("context_start");
     int context_length = context.Attr<int>("context_length");
@@ -115,7 +114,7 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
     // use col_shape in the im2col calculation
     framework::DDim col_shape = {in->dims()[0],
                                  sequence_width * context_length};
-    LoDTensor col;
+    Tensor col;
 
     if (in_g || filter_g || (padding_trainable && padding_data_g)) {
       col.mutable_data<T>(col_shape, context.GetPlace());
@@ -161,17 +160,17 @@ class SequenceConvGradKernel : public framework::OpKernel<T> {
       functor(context.device_context(), filter_g, 0);
 
       Tensor filter_grad_ = *filter_g;
-      Tensor out_grad_ = *out_g;
+      LoDTensor out_grad_ = *out_g;
 
-      const LoDTensor* padding_data = nullptr;
+      const Tensor* padding_data = nullptr;
       if (padding_trainable) {
-        padding_data = context.Input<LoDTensor>("PaddingData");
+        padding_data = context.Input<Tensor>("PaddingData");
       }
 
       sequence_width = static_cast<int>(in->dims()[1]);
 
       LoDTensor* input = const_cast<LoDTensor*>(in);
-      LoDTensor* pad_data = const_cast<LoDTensor*>(padding_data);
+      Tensor* pad_data = const_cast<Tensor*>(padding_data);
 
       seq_project_functor(context.device_context(), *input, *pad_data, col,
                           padding_trainable, context_start, context_length,

python/paddle/v2/framework/tests/test_seq_conv.py

@@ -20,24 +20,29 @@ class TestSeqProject(OpTest):
         # one level, batch size
         x = np.random.uniform(0.1, 1, [self.input_size[0],
                                        self.input_size[1]]).astype('float32')
-
-        self.begin_pad = np.max([0, -self.context_start])
-        self.end_pad = np.max([0, self.context_start + self.context_length - 1])
-        self.total_pad = self.begin_pad + self.end_pad
-        if self.total_pad == 0:
-            self.total_pad = 1
-
-        # PaddingData mast be not empty.
-        # Otherwise(EnforceNotMet: enforce numel() > 0 failed, 0 <= 0)
-        padding_data = np.random.uniform(
-            0.1, 1, [self.total_pad, self.input_size[1]]).astype('float32')
         w = np.random.uniform(
             0.1, 1, [self.context_length, self.input_size[1]]).astype('float32')
+
+        begin_pad = np.max([0, -self.context_start])
+        end_pad = np.max([0, self.context_start + self.context_length - 1])
+        total_pad = begin_pad + end_pad
+        padding_data = np.random.uniform(
+            0.1, 1, [total_pad, self.input_size[1]]).astype('float32')
+        self.pad_data = padding_data
         self.inputs = {
             'X': (x, self.lod),
-            'PaddingData': (padding_data, [[0, self.total_pad]]),
-            'Filter': (w, [[0, self.context_length]])
+            'Filter': w,
         }
+        self.inputs_val = ['X', 'Filter']
+        self.inputs_val_no_x = ['Filter']
+        self.inputs_val_no_f = ['X']
+
+        if total_pad != 0:
+            self.inputs['PaddingData'] = padding_data
+            self.inputs_val = ['X', 'PaddingData', 'Filter']
+            self.inputs_val_no_x = ['PaddingData', 'Filter']
+            self.inputs_val_no_f = ['PaddingData', 'X']
+
         self.attrs = {
             'context_start': self.context_start,
             'context_length': self.context_length,
@@ -51,7 +56,7 @@ class TestSeqProject(OpTest):
     def compute(self):
         x, lod = self.inputs['X']
         filter = self.inputs['Filter']
-        pading_data, _ = self.inputs['PaddingData']
+        pading_data = self.pad_data
         out = np.zeros((self.input_size[0], self.context_length *
                         self.input_size[1])).astype('float32')
         lod = lod[0]
@@ -90,12 +95,12 @@ class TestSeqProject(OpTest):
                 out[out_begin:out_end, j * self.input_size[1]:(j + 1) *
                     self.input_size[1]] += in_sub
 
-        filter_dim = filter[0].shape
+        filter_dim = filter.shape
         output_dim = self.outputs['Out'].shape
-        filter[0].shape = filter_dim[0] * filter_dim[1]
+        filter.shape = filter_dim[0] * filter_dim[1]
         self.outputs['Out'].shape = (output_dim[0], )
-        np.dot(out, filter[0], out=self.outputs['Out'])
-        filter[0].shape = filter_dim
+        np.dot(out, filter, out=self.outputs['Out'])
+        filter.shape = filter_dim
         self.outputs['Out'].shape = output_dim
 
     def test_check_output(self):
@@ -104,16 +109,14 @@ class TestSeqProject(OpTest):
     def test_check_grad(self):
         if self.padding_trainable:
             self.check_grad(
-                set(['X', 'PaddingData', 'Filter']),
-                'Out',
-                max_relative_error=0.05)
+                set(self.inputs_val), 'Out', max_relative_error=0.05)
 
     def test_check_grad_input(self):
         self.check_grad(
             ['X'],
             'Out',
             max_relative_error=0.05,
-            no_grad_set=set(['PaddingData', 'Filter']))
+            no_grad_set=set(self.inputs_val_no_x))
 
     def test_check_grad_padding_data(self):
         if self.padding_trainable:
@@ -128,9 +131,10 @@ class TestSeqProject(OpTest):
             ['Filter'],
             'Out',
             max_relative_error=0.05,
-            no_grad_set=set(['X', 'PaddingData']))
+            no_grad_set=set(self.inputs_val_no_f))
 
     def test_check_grad_input_filter(self):
+        if self.padding_trainable:
             self.check_grad(
                 ['X', 'Filter'],
                 'Out',
@@ -140,7 +144,7 @@ class TestSeqProject(OpTest):
     def test_check_grad_padding_input(self):
         if self.padding_trainable:
             self.check_grad(
-                ['X', 'PaddingData'],
+                self.inputs_val_no_f,
                 'Out',
                 max_relative_error=0.05,
                 no_grad_set=set(['Filter']))
@@ -148,7 +152,7 @@ class TestSeqProject(OpTest):
     def test_check_grad_padding_filter(self):
         if self.padding_trainable:
             self.check_grad(
-                ['PaddingData', 'Filter'],
+                self.inputs_val_no_x,
                 'Out',
                 max_relative_error=0.05,
                 no_grad_set=set(['X']))
@@ -191,69 +195,5 @@ class TestSeqProjectCase2(TestSeqProject):
                     [self.input_size[0]]]
 
 
-'''
-class TestSeqProjectCases(TestSeqProject):
-    def setUp(self):
-        self.init_test_case()
-        self.op_type = 'sequence_project'
-
-        num = 0
-        for context_start in [-5, -3, -1, 0, 3]:
-            for context_length in [1, 2, 5, 7]:
-                for batch_size in [1, 2, 5, 7]:
-                    for padding_trainable in [False, True]:
-
-                        if context_length == 1 and context_start == 0 and padding_trainable:
-                            continue
-
-                        self.context_start = context_start
-                        self.context_length = context_length
-                        self.padding_trainable = padding_trainable
-                        self.input_size = [batch_size, 23]
-                        x = np.random.uniform(0.1, 1,
-                                              self.input_size).astype('float32')
-                        self.lod = [[0, self.input_size[0]]]
-                        if self.input_size[0] > 2:
-                            idx = range(self.input_size[0])
-                            del idx[0]
-                            self.lod = [
-                                [0] + np.sort(random.sample(idx, 2)).tolist() +
-                                [self.input_size[0]]
-                            ]
-
-                        self.begin_pad = np.max([0, -self.context_start])
-                        self.end_pad = np.max([0, self.context_start + self.context_length - 1])
-                        self.total_pad = self.begin_pad + self.end_pad
-                        if self.total_pad == 0:
-                            self.total_pad = 1
-                        # PaddingData mast be not empty. Otherwise(EnforceNotMet: enforce numel() > 0 failed, 0 <= 0)
-                        padding_data = np.random.uniform(
-                            0.1, 1, [self.total_pad, self.input_size[1]]).astype('float32')
-
-                        self.inputs = {
-                            'X': (x, self.lod),
-                            'PaddingData': (padding_data, [[0, self.total_pad]])
-                        }
-                        self.attrs = {
-                            'context_start': self.context_start,
-                            'context_length': self.context_length,
-                            'padding_trainable': self.padding_trainable,
-                            'context_stride': self.context_stride
-                        }
-                        out = np.zeros((self.input_size[0], self.input_size[1] *
-                                        self.context_length)).astype('float32')
-                        self.outputs = {'Out': out}
-                        print num
-                        print self.attrs
-                        print batch_size
-                        print padding_trainable
-                        print "$$$$$$$$$$$$$"
-
-                        self.compute()
-                        self.test_check_output()
-
-                        num += 1
-'''
-
 if __name__ == '__main__':
     unittest.main()