cumsum operator (#8288)

paddle/framework/grad_op_desc_maker.h

@@ -122,6 +122,11 @@ class GradOpDescMakerBase {
     return it->second;
   }
 
+  template <typename T>
+  inline const T& Attr(const std::string& name) const {
+    return boost::get<T>(GetAttr(name));
+  }
+
   std::string ForwardOpType() const { return this->fwd_op_.Type(); }
 
  private:

paddle/framework/op_registry.h

@@ -143,7 +143,7 @@ class OpKernelRegistrar : public Registrar {
 
 /**
  * Macro to register Operator. When the input is duplicable, you should
- * use REGISTER_OP_EX with deop_empty_grad=false instead.
+ * use REGISTER_OP_EX with drop_empty_grad=false instead.
  */
 #define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \
                     grad_op_class)                                   \

paddle/operators/cum_op.h

+/* 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. */
+
+#pragma once
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/operator.h"
+#include "paddle/operators/detail/safe_ref.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename Functor>
+class CumKernel : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
+ public:
+  using T = typename Functor::ELEMENT_TYPE;
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto& X = detail::Ref(context.Input<framework::Tensor>("X"),
+                          "Cannot get input tensor X, variable name = %s",
+                          context.op().Input("X"));
+
+    auto& Out = detail::Ref(context.Output<framework::Tensor>("Out"),
+                            "Cannot get output tensor Out, variable name = %s",
+                            context.op().Output("Out"));
+    int axis = context.Attr<int>("axis");
+    bool exclusive = context.Attr<bool>("exclusive");
+    bool reverse = context.Attr<bool>("reverse");
+    auto x_dims = X.dims();
+    if (axis == -1) {
+      axis = x_dims.size() - 1;
+    }
+    PADDLE_ENFORCE_LT(
+        axis, x_dims.size(),
+        "axis should be less than the dimensiotn of the input tensor");
+    Out.mutable_data<T>(context.GetPlace());
+
+    int pre = 1;
+    int post = 1;
+    int mid = x_dims[axis];
+    for (int i = 0; i < axis; ++i) {
+      pre *= x_dims[i];
+    }
+    for (int i = axis + 1; i < x_dims.size(); ++i) {
+      post *= x_dims[i];
+    }
+
+    auto x = framework::EigenVector<T>::Flatten(X);
+    auto out = framework::EigenVector<T>::Flatten(Out);
+    auto* place =
+        context.template device_context<DeviceContext>().eigen_device();
+
+    using IndexT = Eigen::DenseIndex;
+    if (pre == 1) {
+      if (post == 1) {
+        ComputeImp(*place, Eigen::DSizes<IndexT, 1>(mid), x, out,
+                   /* axis= */ 0, reverse, exclusive);
+      } else {
+        ComputeImp(*place, Eigen::DSizes<IndexT, 2>(mid, post), x, out,
+                   /* axis= */ 0, reverse, exclusive);
+      }
+    } else {
+      if (post == 1) {
+        ComputeImp(*place, Eigen::DSizes<IndexT, 2>(pre, mid), x, out,
+                   /* axis= */ 1, reverse, exclusive);
+      } else {
+        ComputeImp(*place, Eigen::DSizes<IndexT, 3>(pre, mid, post), x, out,
+                   /* axis= */ 1, reverse, exclusive);
+      }
+    }
+  }
+
+ private:
+  template <typename Device, typename Dim, typename X, typename Out>
+  void ComputeImp(Device d, const Dim& dims, X x, Out out, int axis,
+                  bool reverse, bool exclusive) const {
+    if (!reverse) {
+      out.reshape(dims).device(d) = Functor()(x.reshape(dims), axis, exclusive);
+    } else {
+      std::array<bool, Dim::count> rev;
+      rev.fill(false);
+      rev[axis] = reverse;
+      out.reshape(dims).device(d) =
+          Functor()(x.reshape(dims).reverse(rev), axis, exclusive).reverse(rev);
+    }
+  }
+};
+
+template <typename T>
+struct CumsumFunctor {
+  using ELEMENT_TYPE = T;
+  template <typename X>
+  const typename X::TensorScanSumOp operator()(X x, int axis,
+                                               bool exclusive) const {
+    return x.cumsum(axis, exclusive);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/cumsum_op.cc

+/* 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. */
+
+#include "paddle/operators/cum_op.h"
+
+namespace paddle {
+namespace operators {
+
+class CumOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+class CumsumOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CumsumOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : framework::OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Cumsum operator");
+    AddOutput("Out", "Output of Cumsum operator");
+    AddAttr<int>("axis",
+                 "(int, default -1). The dimenstion to accumulate along. "
+                 "-1 means the last dimenstion")
+        .SetDefault(-1)
+        .EqualGreaterThan(-1);
+    AddAttr<bool>("exclusive",
+                  "bool, default false). Whether to perform exclusive cumsum")
+        .SetDefault(false);
+    AddAttr<bool>("reverse",
+                  "bool, default false). If true, the cumsum is performed in "
+                  "the reversed direction")
+        .SetDefault(false);
+    AddComment(R"DOC(
+The cumulative sum of the elements along a given axis.
+By default, the first element of the result is the same of the first element of
+the input. If exlusive is true, the first element of the result is 0.
+)DOC");
+  }
+};
+
+class CumsumGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto *grad_op = new framework::OpDesc();
+    grad_op->SetType("cumsum");
+    grad_op->SetInput("X", OutputGrad("Out"));
+    grad_op->SetOutput("Out", InputGrad("X"));
+    grad_op->SetAttr("axis", Attr<int>("axis"));
+    grad_op->SetAttr("reverse", !Attr<bool>("reverse"));
+    grad_op->SetAttr("exclusive", Attr<bool>("exclusive"));
+    return std::unique_ptr<framework::OpDesc>(grad_op);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+using CPU = paddle::platform::CPUDeviceContext;
+
+REGISTER_OPERATOR(cumsum, ops::CumOp, ops::CumsumOpMaker, ops::CumsumGradMaker);
+REGISTER_OP_CPU_KERNEL(cumsum, ops::CumKernel<CPU, ops::CumsumFunctor<float>>,
+                       ops::CumKernel<CPU, ops::CumsumFunctor<double>>,
+                       ops::CumKernel<CPU, ops::CumsumFunctor<int>>)

paddle/operators/cumsum_op.cu

+/* 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. */
+
+#include "paddle/operators/cum_op.h"
+
+namespace ops = paddle::operators;
+using CUDA = paddle::platform::CUDADeviceContext;
+
+REGISTER_OP_CUDA_KERNEL(cumsum, ops::CumKernel<CUDA, ops::CumsumFunctor<float>>,
+                        ops::CumKernel<CUDA, ops::CumsumFunctor<double>>,
+                        ops::CumKernel<CUDA, ops::CumsumFunctor<int>>)

python/paddle/v2/fluid/layers/ops.py

@@ -65,6 +65,8 @@ __all__ = [
     'logical_or',
     'logical_xor',
     'logical_not',
+    'uniform_random',
+    'cumsum',
 ] + __activations__
 
 for _OP in set(__all__):

python/paddle/v2/fluid/tests/op_test.py

@@ -326,7 +326,8 @@ class OpTest(unittest.TestCase):
                 self.assertTrue(
                     np.allclose(
                         actual_t, expect_t, atol=atol),
-                    "Output (" + out_name + ") has diff at " + str(place))
+                    "Output (" + out_name + ") has diff at " + str(place) +
+                    str(actual_t) + str(expect_t))
                 if isinstance(expect, tuple):
                     self.assertListEqual(actual.lod(), expect[1],
                                          "Output (" + out_name +

python/paddle/v2/fluid/tests/test_cumsum_op.py

+#   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
+from op_test import OpTest
+
+
+class TestSumOp1(OpTest):
+    def setUp(self):
+        self.op_type = "cumsum"
+        self.attrs = {'axis': 2}
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=2)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestSumOp2(OpTest):
+    def setUp(self):
+        self.op_type = "cumsum"
+        self.attrs = {'axis': -1, 'reverse': True}
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
+        self.outputs = {
+            'Out': np.flip(
+                np.flip(
+                    self.inputs['X'], axis=2).cumsum(axis=2), axis=2)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestSumOp3(OpTest):
+    def setUp(self):
+        self.op_type = "cumsum"
+        self.attrs = {'axis': 1}
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestSumOp4(OpTest):
+    def setUp(self):
+        self.op_type = "cumsum"
+        self.attrs = {'axis': 0}
+        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestSumOp5(OpTest):
+    def setUp(self):
+        self.op_type = "cumsum"
+        self.inputs = {'X': np.random.random((5, 6)).astype("float64")}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestSumOp7(OpTest):
+    def setUp(self):
+        self.op_type = "cumsum"
+        self.inputs = {'X': np.random.random((6)).astype("float64")}
+        self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestSumOp8(OpTest):
+    def setUp(self):
+        self.op_type = "cumsum"
+        self.attrs = {'axis': 2, "exclusive": True}
+        a = np.random.random((5, 6, 3)).astype("float64")
+        self.inputs = {'X': a}
+        self.outputs = {
+            'Out': np.concatenate(
+                (np.zeros(
+                    (5, 6, 1), dtype=np.float64), a[:, :, :-1].cumsum(axis=2)),
+                axis=2)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+if __name__ == '__main__':
+    unittest.main()