Use StridedMemCpy in Concat/Split Kernel

paddle/framework/operator.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include <algorithm>
+#include <atomic>
 #include <string>
 #include <unordered_map>
 #include <vector>

paddle/memory/memcpy.cc

@@ -80,6 +80,15 @@ void Copy<platform::GPUPlace, platform::CPUPlace>(platform::GPUPlace dst_place,
   platform::GpuMemcpySync(dst, src, num, cudaMemcpyHostToDevice);
 }
 
+template <>
+void Copy<platform::GPUPlace, platform::GPUPlace>(platform::GPUPlace dst_place,
+                                                  void* dst,
+                                                  platform::GPUPlace src_place,
+                                                  const void* src, size_t num) {
+  platform::SetDeviceId(dst_place.device);
+  platform::GpuMemcpySync(dst, src, num, cudaMemcpyDeviceToDevice);
+}
+
 #endif  // PADDLE_ONLY_CPU
 
 }  // namespace memory

paddle/operators/concat_op.cc

@@ -25,12 +25,14 @@ class ConcatOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContextBase *ctx) const override {
+    PADDLE_ENFORCE_GE(ctx->Inputs("X").size(), 1UL,
+                      "Inputs(X) of ConcatOp should be empty.")
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of ConcatOp should not be null.");
 
     auto ins = ctx->GetInputsDim("X");
     size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
-    size_t n = ins.size();
+    const size_t n = ins.size();
 
     PADDLE_ENFORCE_GT(n, 1, "Input tensors count should > 1.");
 
@@ -72,10 +74,27 @@ class ConcatOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
+class ConcatOpGrad : public framework::OperatorWithKernel {
+ public:
+  ConcatOpGrad(const std::string &type,
+               const framework::VariableNameMap &inputs,
+               const framework::VariableNameMap &outputs,
+               const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+  void InferShape(framework::InferShapeContextBase *ctx) const override {
+    ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(concat, ops::ConcatOp, ops::ConcatOpMaker)
+REGISTER_OP(concat, ops::ConcatOp, ops::ConcatOpMaker, concat_grad,
+            ops::ConcatOpGrad)
 REGISTER_OP_CPU_KERNEL(concat,
                        ops::ConcatKernel<paddle::platform::CPUPlace, float>)
+REGISTER_OP_CPU_KERNEL(concat_grad,
+                       ops::ConcatGradKernel<paddle::platform::CPUPlace, float>)

paddle/operators/concat_op.cu

@@ -0,0 +1,20 @@
+/* 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/concat_op.h"
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(concat,
+                       ops::ConcatKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    concat_grad, ops::ConcatGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/concat_op.h

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include <vector>
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/strided_memcpy.h"
 
 namespace paddle {
 namespace operators {
@@ -27,35 +28,39 @@ class ConcatKernel : public framework::OpKernel {
     auto ins = ctx.MultiInput<framework::Tensor>("X");
     auto* out = ctx.Output<framework::Tensor>("Out");
     int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
-    size_t n = ins.size();
-    size_t output_axis_dim = 0;
-    size_t before = 1, after = 1;
-    for (size_t i = 0; i < n; i++) {
-      output_axis_dim += ins[i]->dims()[axis];
-    }
-    auto& input_zero = ins[0];
-    for (int64_t i = 0; i < input_zero->dims().size(); i++) {
-      if (i == axis) {
-        continue;
-      }
-      if (i < axis) {
-        before *= input_zero->dims()[i];
-      } else {
-        after *= input_zero->dims()[i];
-      }
-    }
+    const size_t n = ins.size();
     size_t output_offset = 0;
+    out->mutable_data<T>(ctx.GetPlace());
+    auto out_stride = framework::stride(out->dims());
     for (size_t i = 0; i < n; i++) {
       auto& in = ins[i];
       auto axis_dim = in->dims()[axis];
-      for (size_t j = 0; j < before; j++) {
-        size_t len = axis_dim * after * sizeof(T);
-        const T* src = in->data<T>() + axis_dim * after * j;
-        T* out_data = out->mutable_data<T>(platform::CPUPlace());
-        T* dest = out_data + output_offset + output_axis_dim * after * j;
-        memcpy(dest, src, len);
-      }
-      output_offset += axis_dim * after;
+      auto in_stride = framework::stride(in->dims());
+      StridedMemcpy<T>(ctx.device_context(), in->data<T>(), in_stride,
+                       in->dims(), out_stride, out->data<T>() + output_offset);
+      output_offset += axis_dim * in_stride[axis];
+    }
+  }
+};
+
+template <typename Place, typename T>
+class ConcatGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* in = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto outs = ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
+    int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
+    const size_t n = outs.size();
+    size_t input_offset = 0;
+    auto in_stride = framework::stride(in->dims());
+    for (size_t i = 0; i < n; i++) {
+      auto& out = outs[i];
+      out->mutable_data<T>(ctx.GetPlace());
+      size_t axis_dim = out->dims()[axis];
+      auto out_stride = framework::stride(out->dims());
+      StridedMemcpy<T>(ctx.device_context(), in->data<T>() + input_offset,
+                       in_stride, out->dims(), out_stride, out->data<T>());
+      input_offset += axis_dim * in_stride[axis];
     }
   }
 };

paddle/operators/split_op.cc

@@ -25,6 +25,10 @@ class SplitOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContextBase *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of SplitOp should not be null.");
+    PADDLE_ENFORCE_GE(ctx->Outputs("Out").size(), 1UL,
+                      "Outputs(Out) of SplitOp should not be empty.");
     auto in_dims = ctx->GetInputDim("X");
     auto outs_names = ctx->Outputs("Out");
     size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
@@ -55,9 +59,6 @@ class SplitOp : public framework::OperatorWithKernel {
         dim[axis] = sections[i];
         outs_dims.push_back(dim);
       }
-    } else {
-      PADDLE_ENFORCE_NOT_NULL(nullptr, "split operator should",
-                              " specify indices or sections.");
     }
     ctx->SetOutputsDim("Out", outs_dims);
   }
@@ -117,4 +118,4 @@ USE_CPU_ONLY_OP(concat);
 REGISTER_OP(split, ops::SplitOp, ops::SplitOpMaker, split_grad,
             ops::SplitOpGrad);
 REGISTER_OP_CPU_KERNEL(split,
-                       ops::SplitKernel<paddle::platform::CPUPlace, float>);
+                       ops::SplitOpKernel<paddle::platform::CPUPlace, float>);

paddle/operators/split_op.cu

@@ -0,0 +1,18 @@
+/* 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/split_op.h"
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(split,
+                       ops::SplitOpKernel<paddle::platform::GPUPlace, float>);

paddle/operators/split_op.h

@@ -16,44 +16,29 @@ limitations under the License. */
 
 #include <vector>
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/strided_memcpy.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename Place, typename T>
-class SplitKernel : public framework::OpKernel {
+class SplitOpKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in = ctx.Input<framework::Tensor>("X");
     auto outs = ctx.MultiOutput<framework::Tensor>("Out");
+    auto in_stride = framework::stride(in->dims());
     int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
-    size_t before = 1, after = 1;
     const size_t n = outs.size();
-    size_t input_axis_dim = in->dims()[axis];
-
-    for (int64_t i = 0; i < in->dims().size(); ++i) {
-      if (i == axis) {
-        continue;
-      }
-      if (i < axis) {
-        before *= in->dims()[i];
-      } else {
-        after *= in->dims()[i];
-      }
-    }
     size_t input_offset = 0;
     for (size_t i = 0; i < n; i++) {
       auto& out = outs[i];
+      out->mutable_data<T>(ctx.GetPlace());
       size_t axis_dim = out->dims()[axis];
-      for (size_t j = 0; j < before; j++) {
-        size_t len = axis_dim * after * sizeof(T);
-        T* dest =
-            out->mutable_data<T>(platform::CPUPlace()) + axis_dim * after * j;
-        const T* src =
-            in->data<T>() + input_offset + input_axis_dim * after * j;
-        memcpy(dest, src, len);
-      }
-      input_offset += axis_dim * after;
+      auto out_stride = framework::stride(out->dims());
+      StridedMemcpy<T>(ctx.device_context(), in->data<T>() + input_offset,
+                       in_stride, out->dims(), out_stride, out->data<T>());
+      input_offset += axis_dim * in_stride[axis];
     }
   }
 };

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

@@ -6,17 +6,20 @@
 class TestConcatOp(OpTest):
     def setUp(self):
         self.op_type = "concat"
-        x0 = np.random.random((2, 3, 2, 5)).astype('float32')
-        x1 = np.random.random((2, 3, 3, 5)).astype('float32')
+        x0 = np.random.random((2, 1, 4, 5)).astype('float32')
+        x1 = np.random.random((2, 2, 4, 5)).astype('float32')
         x2 = np.random.random((2, 3, 4, 5)).astype('float32')
-        axis = 2
+        axis = 1
         self.inputs = {'X': [('x0', x0), ('x1', x1), ('x2', x2)]}
         self.attrs = {'axis': axis}
         self.outputs = {'Out': np.concatenate((x0, x1, x2), axis=axis)}
 
     def test_check_output(self):
         self.check_output()
 
+    def test_check_grad(self):
+        self.check_grad(['x0'], 'Out')
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -7,19 +7,18 @@ class TestSplitOp(OpTest):
     def setUp(self):
         self.op_type = "split"
         axis = 0
-        num = 2
-        x = np.random.random((4, 2)).astype('float32')
-        out = np.split(x, num, axis)
+        x = np.random.random((4, 2, 5)).astype('float32')
+        out = np.split(x, [1, 3], axis)
         self.inputs = {'X': x}
-        self.attrs = {'axis': axis, 'num': num}
+        self.attrs = {'axis': axis, 'sections': [1, 2, 1]}
         self.outputs = {'Out': [('out%d' % i, out[i]) \
             for i in xrange(len(out))]}
 
     def test_check_output(self):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(['X'], ['out0', 'out1'])
+        self.check_grad(['X'], ['out0', 'out1', 'out2'])
 
 
 if __name__ == '__main__':