Refine the implementation and add unit test.

pkuyym · pkuyym · commit bea41444d78f · 2018-01-18T20:16:03.000+08:00
diff --git a/paddle/operators/sequence_reshape_op.cc b/paddle/operators/sequence_reshape_op.cc
@@ -27,21 +27,50 @@ class SequenceReshapeOp : public framework::OperatorWithKernel {
                    "Output(Out) of SequenceReshapeOp should not be null.");
     auto x_dims = ctx->GetInputDim("X");
     PADDLE_ENFORCE_EQ(x_dims.size(), 2U, "Rank of Input(X) should be 2.");
-    int dimension = ctx->Attrs().Get<int>("dimension");
-    ctx->SetOutputDim("Out", {{x_dims[0], static_cast<int64_t>(dimension)}});
-    ctx->ShareLoD("X", /*->*/ "Out");
+    int dimension = ctx->Attrs().Get<int>("new_dim");
+    ctx->SetOutputDim("Out", {x_dims[0], static_cast<int64_t>(dimension)});
   }
 };
 
 class SequenceReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SequenceReshapeOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "");
-    AddOutput("Out", "");
-    AddAttr<int>("dimension", "");
-    AddAttr<bool>("is_padding", "Default padding zero.");
-    AddComment(R"DOC()DOC");
+    AddInput("X",
+             "(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor with shape "
+             "being [N, M].");
+    AddOutput("Out",
+              "(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor with "
+              "shape [T, new_dim] where T is calculated based on X.lod, M and "
+              "new_dim.");
+    AddAttr<int>("new_dim", "Sequence dimension of the output LoDTensor.");
+    AddComment(R"DOC(
+Sequence Reshape Operator.
+
+This operator will rearrange the input sequences. The new dimension is set by
+attribute and length of each sequence may change longer or shorter which is
+decided by original length, original dimension and new dimension. The following
+example will help to illustrate the function of this operator:
+
+x is a LoDTensor:
+    x.lod  = [[0, 2, 6]]
+    x.data = [[0.1, 0.2], [0.3, 0.4],
+              [0.5, 0.6], [0.7, 0.8], [0.9, 1.0], [1.1, 1.2]]
+    x.dims = [6, 2]
+
+set new_dim = 4
+
+then out is a LoDTensor:
+    out.lod  = [[0,    1,    3]]
+    out.data = [[0.1, 0.2, 0.3, 0.4],
+                [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]]
+    out.dims = [3, 4]
+
+Currently, only 1-level LoDTensor is supported and please make sure (original
+length * original dimension) can be divided by new_dim with no remainder for
+each sequence.
+
+)DOC");
   }
 };
 
@@ -63,12 +92,29 @@ class SequenceReshapeGradOp : public framework::OperatorWithKernel {
   }
 };
 
+class SequenceReshapeGradOpMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* op_desc_ptr = new framework::OpDesc();
+    op_desc_ptr->SetType("sequence_reshape_grad");
+    op_desc_ptr->SetInput("X", Input("X"));
+    op_desc_ptr->SetInput("Out", Output("Out"));
+    op_desc_ptr->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    op_desc_ptr->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op_desc_ptr->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(op_desc_ptr);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(sequence_reshape, ops::SequenceReshapeOp,
-                  ops::SequenceReshapeOpMaker);
+                  ops::SequenceReshapeOpMaker, ops::SequenceReshapeGradOpMaker);
 REGISTER_OPERATOR(sequence_reshape_grad, ops::SequenceReshapeGradOp);
 REGISTER_OP_CPU_KERNEL(
     sequence_reshape,
diff --git a/paddle/operators/sequence_reshape_op.cu b/paddle/operators/sequence_reshape_op.cu
@@ -0,0 +1,23 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/sequence_reshape_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    sequence_reshape,
+    ops::SequenceReshapeKernel<paddle::platform::CUDADeviceContext, float>);
+REGISTER_OP_CUDA_KERNEL(
+    sequence_reshape_grad,
+    ops::SequenceReshapeGradKernel<paddle::platform::CUDADeviceContext, float>);
diff --git a/paddle/operators/sequence_reshape_op.h b/paddle/operators/sequence_reshape_op.h
@@ -26,53 +26,63 @@ class SequenceReshapeKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& context) const override {
     auto* in = context.Input<LoDTensor>("X");
     auto* out = context.Output<LoDTensor>("Out");
-    int out_width = context.Attr<int>("dimension");
-    bool whether_padding = context.Attr<bool>("whether_padding");
+    int out_width = context.Attr<int>("new_dim");
 
     const T* p_in_data = in->data<T>();
-    T* p_out_data = out->mutable_data<T>(context.GetPlace());
 
-    // compute shape for output
     auto in_dims = in->dims();
     int64_t in_width = in_dims[1];
     auto& in_lod = in->lod();
 
     PADDLE_ENFORCE_EQ(in_lod.size(), 1UL,
                       "Only support one level sequence now.");
-    PADDLE_ENFORCE_GE(
-        in_dims[0],
-        /* batch size = */ static_cast<int64_t>(in_lod[0].size() - 1),
-        "The 1st dimension of Input(X) must be equal or larger than batch "
-        "size.");
+    PADDLE_ENFORCE_EQ(
+        in_dims[0], in_lod[0].back(),
+        "Inconsistent size between X.shape[0] and X.lod()[0].back().");
 
     auto in_lod_l0 = in_lod[0];
     int seq_num = in_lod_l0.size() - 1;
 
     auto& out_lod = *out->mutable_lod();
-    out_lod.push_back(std::vector<size_t>({0}));
-    size_t offset = 0;
+    out_lod.resize(1);
+    out_lod[0].clear();
+    out_lod[0].push_back(0);
     for (int i = 0; i < seq_num; ++i) {
       size_t seq_len = in_lod_l0[i + 1] - in_lod_l0[i];
-      if (whether_padding) {
-        offset += std::ceil((float)(seq_len * in_width) / out_width);
-      } else {
-        offset += (seq_len * in_width) / out_width;
-      }
-      out_lod[0].push_back(offset);
+      size_t offset = 0;
+      offset = (seq_len * in_width) / out_width;
+      PADDLE_ENFORCE_EQ(offset * out_width, seq_len * in_width,
+                        "Please make sure (sequence_length * dimension) can be "
+                        "divided by new_dim with no remainder for each "
+                        "sequence. The %dth sequence is invalid.",
+                        i + 1);
+      PADDLE_ENFORCE_GT(offset, 0,
+                        "Illegal operation, length of the %dth sequence become "
+                        "to 0 after reshaped.",
+                        i + 1);
+      out_lod[0].push_back(out_lod[0].back() + offset);
     }
 
-    out->Resize({{static_cast<int64_t>(out_lod[0].back()), out_width}});
+    out->mutable_data<T>(context.GetPlace());
+    out->Resize({static_cast<int64_t>(out_lod[0].back()), out_width});
+    T* p_out_data = out->mutable_data<T>(context.GetPlace());
     math::set_constant(context.device_context(), out, 0.0f);
 
     for (int i = 0; i < seq_num; ++i) {
       size_t in_offset = in_lod_l0[i] * in_width;
       size_t out_offset = out_lod[0][i] * out_width;
-      size_t bytes = sizeof(T) * (in_lod_l0[i + 1] - in_lod_l0[i]) * in_width;
+      size_t in_count = (in_lod_l0[i + 1] - in_lod_l0[i]) * in_width;
+      size_t out_count = (out_lod[0][i + 1] - out_lod[0][i]) * out_width;
+      size_t bytes = sizeof(T) * std::min(in_count, out_count);
       if (platform::is_cpu_place(context.GetPlace())) {
-        std::memcpy(p_out_data + out_offset, p_in_data + in_offset, bytes);
+        memory::Copy(boost::get<platform::CPUPlace>(context.GetPlace()),
+                     p_out_data + out_offset,
+                     boost::get<platform::CPUPlace>(context.GetPlace()),
+                     p_in_data + in_offset, bytes);
       } else {
 #ifdef PADDLE_WITH_CUDA
-        auto& dev_ctx = context.template device_context<DeviceContext>();
+        auto& dev_ctx =
+            context.template device_context<platform::CUDADeviceContext>();
         memory::Copy(boost::get<platform::CUDAPlace>(context.GetPlace()),
                      p_out_data + out_offset,
                      boost::get<platform::CUDAPlace>(context.GetPlace()),
@@ -103,16 +113,23 @@ class SequenceReshapeGradKernel : public framework::OpKernel<T> {
     auto& out_lod = out_tensor_ptr->lod();
     int out_width = out_tensor_ptr->dims()[1];
 
+    math::set_constant(context.device_context(), x_grad_tensor_ptr, 0.0f);
+
     for (int i = 0; i < seq_num; ++i) {
       size_t src_offset = out_lod[0][i] * out_width;
       size_t dst_offset = x_lod[0][i] * x_width;
-      size_t bytes = sizeof(T) * (x_lod[0][i + 1] - x_lod[0][i]) * x_width;
+      size_t src_count = (out_lod[0][i + 1] - out_lod[0][i]) * out_width;
+      size_t dst_count = (x_lod[0][i + 1] - x_lod[0][i]) * x_width;
+      size_t bytes = sizeof(T) * std::min(src_count, dst_count);
       if (platform::is_cpu_place(context.GetPlace())) {
-        std::memcpy(p_x_grad_data + dst_offset, p_out_grad_data + src_offset,
-                    bytes);
+        memory::Copy(boost::get<platform::CPUPlace>(context.GetPlace()),
+                     p_x_grad_data + dst_offset,
+                     boost::get<platform::CPUPlace>(context.GetPlace()),
+                     p_out_grad_data + src_offset, bytes);
       } else {
 #ifdef PADDLE_WITH_CUDA
-        auto& dev_ctx = context.template device_context<DeviceContext>();
+        auto& dev_ctx =
+            context.template device_context<platform::CUDADeviceContext>();
         memory::Copy(boost::get<platform::CUDAPlace>(context.GetPlace()),
                      p_x_grad_data + dst_offset,
                      boost::get<platform::CUDAPlace>(context.GetPlace()),
diff --git a/python/paddle/v2/fluid/tests/test_sequence_reshape.py b/python/paddle/v2/fluid/tests/test_sequence_reshape.py
@@ -0,0 +1,76 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+import unittest
+import numpy as np
+import math
+from op_test import OpTest
+
+
+class TestSequenceReshape(OpTest):
+    def setUp(self):
+        self.op_type = 'sequence_reshape'
+        dimension = 12
+        x_lod = [[0, 4, 5, 8, 11]]
+        x = np.random.uniform(0.1, 1, [11, 24]).astype('float32')
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'new_dim': dimension}
+
+        out, out_lod = self.compute_output(x, x_lod, dimension)
+
+        self.outputs = {'Out': (out, out_lod)}
+
+    def compute_output(self, x, x_lod, dimension):
+        x_width = x.shape[1]
+        out_lod = [[0]]
+        for i in xrange(len(x_lod[0]) - 1):
+            seq_len = x_lod[0][i + 1] - x_lod[0][i]
+            offset = (seq_len * x_width) / dimension
+            assert int(offset) * dimension == seq_len * x_width
+            out_lod[0].append(out_lod[0][-1] + int(offset))
+        out = np.zeros(shape=(out_lod[0][-1], dimension)).astype('float32')
+        for i in xrange(len(x_lod[0]) - 1):
+            x_offset = x_lod[0][i] * x_width
+            out_offset = out_lod[0][i] * dimension
+            out_count = (out_lod[0][i + 1] - out_lod[0][i]) * dimension
+            x_count = (x_lod[0][i + 1] - x_lod[0][i]) * x_width
+            count = min(out_count, x_count)
+            out.ravel()[out_offset:out_offset + count] = x.ravel()[
+                x_offset:x_offset + count]
+        return out, out_lod
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Out")
+
+
+class TestSequenceReshape_reduce(TestSequenceReshape):
+    def setUp(self):
+        self.op_type = 'sequence_reshape'
+        dimension = 24
+        x_lod = [[0, 4, 6, 8, 12]]
+        x = np.random.uniform(0.1, 1, [12, 12]).astype('float32')
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'new_dim': dimension}
+
+        out, out_lod = self.compute_output(x, x_lod, dimension)
+
+        self.outputs = {'Out': (out, out_lod)}
+
+
+if __name__ == '__main__':
+    unittest.main()
