Reverse iterator op's input

test=develop

paddle/fluid/imperative/layer.cc

@@ -81,10 +81,6 @@ class TensorAddToFunctor : public boost::static_visitor<> {
 
 }  // namespace detail
 
-template <int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenVector = framework::EigenVector<float, MajorType, IndexType>;
-
 void AddTo(Variable* src, Variable* dst, platform::Place place) {
   framework::Tensor* dst_tensor = dst->GetMutable<framework::LoDTensor>();
   framework::Tensor* src_tensor = src->GetMutable<framework::LoDTensor>();
@@ -99,18 +95,10 @@ void AddTo(Variable* src, Variable* dst, platform::Place place) {
                  "dst_numel %lld vs. src_numel %lld", dst_tensor->numel(),
                  src_tensor->numel());
 
-  auto result = EigenVector<>::Flatten(*dst_tensor);
-  auto in_0_e = EigenVector<>::Flatten(*dst_tensor);
-  auto in_1_e = EigenVector<>::Flatten(*src_tensor);
-  platform::DeviceContext* dev_ctx =
-      platform::DeviceContextPool::Instance().Get(place);
-  platform::CPUDeviceContext* x =
-      reinterpret_cast<platform::CPUDeviceContext*>(dev_ctx);
-  result.device(*x->eigen_device()) = in_0_e + in_1_e;
-  // detail::TensorAddToFunctor<float> func(
-  // src_tensor->numel(), src_tensor->data<float>(),
-  // dst_tensor->mutable_data<float>(place));
-  // boost::apply_visitor(func, place);
+  detail::TensorAddToFunctor<float> func(
+      src_tensor->numel(), src_tensor->data<float>(),
+      dst_tensor->mutable_data<float>(place));
+  boost::apply_visitor(func, place);
 }
 
 class Autograd {
@@ -134,7 +122,7 @@ class Autograd {
       std::map<std::string, std::vector<VarBase*>> input_grads =
           ready_op->ApplyGrad();
 
-      for (auto it : input_grads) {
+      for (auto it = input_grads.rbegin(); it != input_grads.rend(); ++it) {
         const std::vector<VarBase*>& ingrads = it.second;
         for (int64_t i = ingrads.size() - 1; i >= 0; --i) {
           if (!ingrads[i]) continue;

python/paddle/fluid/framework.py

@@ -2716,6 +2716,11 @@ class Program(object):
         # whether the program is optimized by memory_optimize_transpiler
         self.__is_mem_optimized = False
 
+        # if this program has been optimized by distributed optimizer
+        # fleet_opt will be given a value
+        self._fleet_opt = None
+        self._program_config = None
+
     @property
     def _is_mem_optimized(self):
         # if the program is optimized, operator input/outputs

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

@@ -51,22 +51,23 @@ class MyPyLayer(fluid.dygraph.PyLayer):
 class MLP(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(MLP, self).__init__(name_scope)
-        self._fc1 = FC(self.full_name(), 3)
-        #  self._fc2 = FC(self.full_name(),
-        #  4)
-        #  self._fc3 = FC(self.full_name(),
-        #  4)
-        self._fc_list = []
-        for i in range(100):
-            fc3 = FC(self.full_name(), 4)
-            self._fc_list.append(fc3)
+        self._fc1 = FC(self.full_name(),
+                       3,
+                       param_attr=fluid.ParamAttr(
+                           initializer=fluid.initializer.Constant(value=0.1)),
+                       bias_attr=fluid.ParamAttr(
+                           initializer=fluid.initializer.Constant(value=0.1)))
+        self._fc2 = FC(self.full_name(),
+                       4,
+                       param_attr=fluid.ParamAttr(
+                           initializer=fluid.initializer.Constant(value=0.1)),
+                       bias_attr=fluid.ParamAttr(
+                           initializer=fluid.initializer.Constant(value=0.1)))
 
     def forward(self, inputs):
         x = self._fc1(inputs)
-        y1 = self._fc2(x)
-        y2 = self._fc3(x)
-        z = fluid.layers.concat([y1, y2])
-        x = fluid.layers.reduce_sum(z)
+        x = self._fc2(x)
+        x = fluid.layers.reduce_sum(x)
         return x
 
 
@@ -191,215 +192,196 @@ class SimpleRNN(fluid.dygraph.Layer):
 
 
 class TestImperative(unittest.TestCase):
-    #  def test_sum_op(self):
-    #  x = np.ones([2, 2], np.float32)
-    #  with fluid.dygraph.guard():
-    #  inputs = []
-    #  for _ in range(10):
-    #  inputs.append(fluid.dygraph.base.to_variable(x))
-    #  ret = fluid.layers.sums(inputs)
-    #  loss = fluid.layers.reduce_sum(ret)
-    #  loss._backward()
-    #  self.assertTrue(np.allclose(ret._numpy(), x * 10))
-    #  self.assertTrue(np.allclose(inputs[0]._gradient(), x))
-
-    #  def test_layer(self):
-    #  with fluid.dygraph.guard():
-    #  cl = core.Layer()
-    #  cl.forward([])
-    #  l = fluid.dygraph.Layer("l")
-    #  self.assertRaises(NotImplementedError, l.forward, [])
-
-    #  def test_pylayer_func_id(self):
-
-    #  with fluid.dygraph.guard():
-
-    #  class PyLayer1(fluid.dygraph.PyLayer):
-    #  def __init__(self):
-    #  super(PyLayer1, self).__init__()
-
-    #  @staticmethod
-    #  def forward(input):
-    #  return input
-
-    #  @staticmethod
-    #  def backward(input):
-    #  return input
-
-    #  class PyLayer2(fluid.dygraph.PyLayer):
-    #  def __init__(self):
-    #  super(PyLayer2, self).__init__()
-
-    #  @staticmethod
-    #  def forward(input):
-    #  return input
-
-    #  @staticmethod
-    #  def backward(input):
-    #  return input
-
-    #  py_layer_1 = PyLayer1()
-    #  py_layer_2 = PyLayer2()
-    #  py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
-    #  py_layer_2(fluid.dygraph.base.to_variable(np.ones([2, 2])))
-    #  id = py_layer_1.forward_id
-    #  self.assertGreater(id, 0)
-    #  self.assertEqual(py_layer_1.backward_id, id + 1)
-    #  self.assertEqual(py_layer_2.forward_id, id + 2)
-    #  self.assertEqual(py_layer_2.backward_id, id + 3)
-    #  py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
-    #  self.assertEqual(py_layer_1.forward_id, id)
-
-    #  def test_pylayer(self):
-    #  np_inp = np.ones([2, 2], np.float32)
-    #  with fluid.dygraph.guard():
-    #  my_py_layer = MyPyLayer()
-    #  var_inp = fluid.dygraph.base.to_variable(np_inp)
-    #  outs = my_py_layer(var_inp)
-    #  dy_out = np.sum(outs[0]._numpy())
-    #  outs[0]._backward()
-    #  dy_grad = var_inp._gradient()
-
-    #  with new_program_scope():
-    #  inp = fluid.layers.data(
-    #  name="inp", shape=[2, 2], append_batch_size=False)
-    #  # TODO(panyx0718): Paddle doesn't diff against data `inp`.
-    #  x1 = inp * 1
-    #  # TODO(panyx0718): If reduce_sum is skipped, the result is wrong.
-    #  x = fluid.layers.reduce_sum(fluid.layers.tanh(x1))
-    #  param_grads = fluid.backward.append_backward(
-    #  x, parameter_list=[x1.name])[0]
-    #  exe = fluid.Executor(fluid.CPUPlace(
-    #  ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
-
-    #  static_out, static_grad = exe.run(
-    #  feed={inp.name: np_inp},
-    #  fetch_list=[x.name, param_grads[1].name])
-
-    #  self.assertTrue(np.allclose(dy_out, static_out))
-    #  self.assertTrue(np.allclose(dy_grad, static_grad))
-
-    #  def test_layer_in_out(self):
-    #  np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
-    #  with fluid.dygraph.guard():
-    #  var_inp = fluid.dygraph.base.to_variable(np_inp)
-    #  l = MyLayer("my_layer")
-    #  x = l(var_inp)[0]
-    #  self.assertIsNotNone(x)
-    #  dy_out = x._numpy()
-    #  x._backward()
-    #  dy_grad = l._x_for_debug._gradient()
-
-    #  with new_program_scope():
-    #  inp = fluid.layers.data(
-    #  name="inp", shape=[3], append_batch_size=False)
-    #  l = MyLayer("my_layer")
-    #  x = l(inp)[0]
-    #  param_grads = fluid.backward.append_backward(
-    #  x, parameter_list=[l._x_for_debug.name])[0]
-    #  exe = fluid.Executor(fluid.CPUPlace(
-    #  ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
-
-    #  static_out, static_grad = exe.run(
-    #  feed={inp.name: np_inp},
-    #  fetch_list=[x.name, param_grads[1].name])
-
-    #  self.assertTrue(np.allclose(dy_out, static_out))
-    #  self.assertTrue(np.allclose(dy_grad, static_grad))
+    def test_sum_op(self):
+        x = np.ones([2, 2], np.float32)
+        with fluid.dygraph.guard():
+            inputs = []
+            for _ in range(10):
+                inputs.append(fluid.dygraph.base.to_variable(x))
+            ret = fluid.layers.sums(inputs)
+            loss = fluid.layers.reduce_sum(ret)
+            loss._backward()
+            self.assertTrue(np.allclose(ret._numpy(), x * 10))
+            self.assertTrue(np.allclose(inputs[0]._gradient(), x))
+
+    def test_layer(self):
+        with fluid.dygraph.guard():
+            cl = core.Layer()
+            cl.forward([])
+            l = fluid.dygraph.Layer("l")
+            self.assertRaises(NotImplementedError, l.forward, [])
+
+    def test_pylayer_func_id(self):
+
+        with fluid.dygraph.guard():
+
+            class PyLayer1(fluid.dygraph.PyLayer):
+                def __init__(self):
+                    super(PyLayer1, self).__init__()
+
+                @staticmethod
+                def forward(input):
+                    return input
+
+                @staticmethod
+                def backward(input):
+                    return input
+
+            class PyLayer2(fluid.dygraph.PyLayer):
+                def __init__(self):
+                    super(PyLayer2, self).__init__()
+
+                @staticmethod
+                def forward(input):
+                    return input
+
+                @staticmethod
+                def backward(input):
+                    return input
+
+            py_layer_1 = PyLayer1()
+            py_layer_2 = PyLayer2()
+            py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
+            py_layer_2(fluid.dygraph.base.to_variable(np.ones([2, 2])))
+            id = py_layer_1.forward_id
+            self.assertGreater(id, 0)
+            self.assertEqual(py_layer_1.backward_id, id + 1)
+            self.assertEqual(py_layer_2.forward_id, id + 2)
+            self.assertEqual(py_layer_2.backward_id, id + 3)
+            py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
+            self.assertEqual(py_layer_1.forward_id, id)
+
+    def test_pylayer(self):
+        np_inp = np.ones([2, 2], np.float32)
+        with fluid.dygraph.guard():
+            my_py_layer = MyPyLayer()
+            var_inp = fluid.dygraph.base.to_variable(np_inp)
+            outs = my_py_layer(var_inp)
+            dy_out = np.sum(outs[0]._numpy())
+            outs[0]._backward()
+            dy_grad = var_inp._gradient()
+
+        with new_program_scope():
+            inp = fluid.layers.data(
+                name="inp", shape=[2, 2], append_batch_size=False)
+            # TODO(panyx0718): Paddle doesn't diff against data `inp`.
+            x1 = inp * 1
+            # TODO(panyx0718): If reduce_sum is skipped, the result is wrong.
+            x = fluid.layers.reduce_sum(fluid.layers.tanh(x1))
+            param_grads = fluid.backward.append_backward(
+                x, parameter_list=[x1.name])[0]
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
+
+            static_out, static_grad = exe.run(
+                feed={inp.name: np_inp},
+                fetch_list=[x.name, param_grads[1].name])
+
+        self.assertTrue(np.allclose(dy_out, static_out))
+        self.assertTrue(np.allclose(dy_grad, static_grad))
+
+    def test_layer_in_out(self):
+        np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
+        with fluid.dygraph.guard():
+            var_inp = fluid.dygraph.base.to_variable(np_inp)
+            l = MyLayer("my_layer")
+            x = l(var_inp)[0]
+            self.assertIsNotNone(x)
+            dy_out = x._numpy()
+            x._backward()
+            dy_grad = l._x_for_debug._gradient()
+
+        with new_program_scope():
+            inp = fluid.layers.data(
+                name="inp", shape=[3], append_batch_size=False)
+            l = MyLayer("my_layer")
+            x = l(inp)[0]
+            param_grads = fluid.backward.append_backward(
+                x, parameter_list=[l._x_for_debug.name])[0]
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
+
+            static_out, static_grad = exe.run(
+                feed={inp.name: np_inp},
+                fetch_list=[x.name, param_grads[1].name])
+
+        self.assertTrue(np.allclose(dy_out, static_out))
+        self.assertTrue(np.allclose(dy_grad, static_grad))
 
     def test_mlp(self):
-        seed = 90
         np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
-        with fluid.dygraph.guard(place=fluid.CPUPlace()):
-            fluid.default_startup_program().random_seed = seed
-            fluid.default_main_program().random_seed = seed
-
+        with fluid.dygraph.guard():
             var_inp = fluid.dygraph.base.to_variable(np_inp)
             mlp = MLP("mlp")
-            opt = fluid.optimizer.SGDOptimizer(learning_rate=0.001)
-            for i in range(100):
             out = mlp(var_inp)
             dy_out = out._numpy()
             out._backward()
-                opt.minimize(out)
             dy_grad = mlp._fc1._w._gradient()
-                dy_fc0_w0 = mlp._fc1._w._numpy()
-                mlp.clear_gradients()
 
         with new_program_scope():
-            fluid.default_startup_program().random_seed = seed
-            fluid.default_main_program().random_seed = seed
-
             inp = fluid.layers.data(
                 name="inp", shape=[2, 2], append_batch_size=False)
             mlp = MLP("mlp")
             out = mlp(inp)
-            opt = fluid.optimizer.SGDOptimizer(learning_rate=0.001)
-            opt.minimize(out)
-            #  param_grads = fluid.backward.append_backward(
-            #  out, parameter_list=[mlp._fc1._w.name])[0]
-            exe = fluid.Executor(fluid.CPUPlace())
+            param_grads = fluid.backward.append_backward(
+                out, parameter_list=[mlp._fc1._w.name])[0]
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
             exe.run(fluid.default_startup_program())
 
-            for i in range(100):
-                static_out, static_grad, static_fc0_w0 = exe.run(
+            static_out, static_grad = exe.run(
+                feed={inp.name: np_inp},
+                fetch_list=[out.name, param_grads[1].name])
+
+        self.assertTrue(np.allclose(dy_out, static_out))
+        self.assertTrue(np.allclose(dy_grad, static_grad))
+
+        params = mlp.parameters(True)
+        self.assertEqual("mlp/MLP_0/FC_0.w_0", params[0].name)
+        self.assertEqual("mlp/MLP_0/FC_0.b_0", params[1].name)
+        self.assertEqual("mlp/MLP_0/FC_1.w_0", params[2].name)
+        self.assertEqual("mlp/MLP_0/FC_1.b_0", params[3].name)
+        self.assertEqual(len(params), 4)
+
+        sublayers = mlp.sublayers(True)
+        self.assertEqual(mlp._fc1, sublayers[0])
+        self.assertEqual(mlp._fc2, sublayers[1])
+        self.assertEqual(len(sublayers), 2)
+
+    def test_rnn(self):
+        np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
+                           [10.0, 11.0, 12.0]])
+        np_inp = np_inp.reshape((1, 4, 3))
+        np_inp = np_inp.astype(np.float32)
+        with fluid.dygraph.guard():
+            var_inp = fluid.dygraph.base.to_variable(np_inp)
+            var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
+            simple_rnn = SimpleRNN("simple_rnn")
+            outs, pre_hiddens = simple_rnn.forward(var_inp)
+            dy_out = outs[3]._numpy()
+            outs[3]._backward()
+            dy_grad_h2o = simple_rnn._cell._h2o_w._gradient()
+            dy_grad_h2h = simple_rnn._cell._h2h_w._gradient()
+            dy_grad_i2h = simple_rnn._cell._i2h_w._gradient()
+
+        with new_program_scope():
+            inp = fluid.layers.data(
+                name="inp", shape=[1, 4, 3], append_batch_size=False)
+            simple_rnn = SimpleRNN("simple_rnn")
+            outs, pre_hiddens = simple_rnn(inp)
+            param_grads = fluid.backward.append_backward(outs[3])
+            exe = fluid.Executor(fluid.CPUPlace())
+            exe.run(fluid.default_startup_program())
+            static_out, static_grad_h2o, static_grad_h2h, static_grad_i2h = exe.run(
                 feed={inp.name: np_inp},
                 fetch_list=[
-                        out.name, "mlp/MLP_0/FC_0.w_0@GRAD",
-                        "mlp/MLP_0/FC_0.w_0"
+                    outs[3].name, param_grads[0][1].name,
+                    param_grads[1][1].name, param_grads[2][1].name
                 ])
-
-        print(dy_out, static_out)
         self.assertTrue(np.allclose(dy_out, static_out))
-        self.assertTrue(np.array_equal(dy_grad, static_grad))
-
-        print(dy_fc0_w0, static_fc0_w0)
-        #params = mlp.parameters(True)
-        #self.assertEqual("mlp/MLP_0/FC_0.w_0", params[0].name)
-        #self.assertEqual("mlp/MLP_0/FC_0.b_0", params[1].name)
-        #self.assertEqual("mlp/MLP_0/FC_1.w_0", params[2].name)
-        #self.assertEqual("mlp/MLP_0/FC_1.b_0", params[3].name)
-        #self.assertEqual(len(params), 4)
-
-        #sublayers = mlp.sublayers(True)
-        #self.assertEqual(mlp._fc1, sublayers[0])
-        #self.assertEqual(mlp._fc2, sublayers[1])
-        #self.assertEqual(len(sublayers), 2)
-
-    #  def test_rnn(self):
-    #  np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
-    #  [10.0, 11.0, 12.0]])
-    #  np_inp = np_inp.reshape((1, 4, 3))
-    #  np_inp = np_inp.astype(np.float32)
-    #  with fluid.dygraph.guard():
-    #  var_inp = fluid.dygraph.base.to_variable(np_inp)
-    #  var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
-    #  simple_rnn = SimpleRNN("simple_rnn")
-    #  outs, pre_hiddens = simple_rnn.forward(var_inp)
-    #  dy_out = outs[3]._numpy()
-    #  outs[3]._backward()
-    #  dy_grad_h2o = simple_rnn._cell._h2o_w._gradient()
-    #  dy_grad_h2h = simple_rnn._cell._h2h_w._gradient()
-    #  dy_grad_i2h = simple_rnn._cell._i2h_w._gradient()
-
-    #  with new_program_scope():
-    #  inp = fluid.layers.data(
-    #  name="inp", shape=[1, 4, 3], append_batch_size=False)
-    #  simple_rnn = SimpleRNN("simple_rnn")
-    #  outs, pre_hiddens = simple_rnn(inp)
-    #  param_grads = fluid.backward.append_backward(outs[3])
-    #  exe = fluid.Executor(fluid.CPUPlace())
-    #  exe.run(fluid.default_startup_program())
-    #  static_out, static_grad_h2o, static_grad_h2h, static_grad_i2h = exe.run(
-    #  feed={inp.name: np_inp},
-    #  fetch_list=[
-    #  outs[3].name, param_grads[0][1].name,
-    #  param_grads[1][1].name, param_grads[2][1].name
-    #  ])
-    #  self.assertTrue(np.allclose(dy_out, static_out))
-    #  self.assertTrue(np.allclose(dy_grad_h2o, static_grad_h2o))
-    #  self.assertTrue(np.allclose(dy_grad_h2h, static_grad_h2h))
-    #  self.assertTrue(np.allclose(dy_grad_i2h, static_grad_i2h))
+        self.assertTrue(np.allclose(dy_grad_h2o, static_grad_h2o))
+        self.assertTrue(np.allclose(dy_grad_h2h, static_grad_h2h))
+        self.assertTrue(np.allclose(dy_grad_i2h, static_grad_i2h))
 
 
 if __name__ == '__main__':