Stable elemwise mul

paddle/pybind/pybind.cc

@@ -77,20 +77,18 @@ PYBIND11_PLUGIN(core) {
            })
       .def("set", PyCPUTensorSetFromArray<float>)
       .def("set", PyCPUTensorSetFromArray<int>)
+      .def("set", PyCPUTensorSetFromArray<double>)
 #ifndef PADDLE_ONLY_CPU
       .def("set", PyCUDATensorSetFromArray<float>)
       .def("set", PyCUDATensorSetFromArray<int>)
+      .def("set", PyCUDATensorSetFromArray<double>)
 #endif
       .def("shape", [](Tensor &self) { return vectorize(self.dims()); })
-      .def("set_float_element",
-           [](Tensor &self, size_t offset, float f) {
-             // TODO(yuyang18): Only support GPU now.
-             self.data<float>()[offset] = f;
-           })
-      .def("get_float_element", [](Tensor &self, size_t offset) -> float {
-        // TODO(yuyang18): Only support GPU now.
-        return self.data<float>()[offset];
-      });
+      .def("set_float_element", TensorSetElement<float>)
+      .def("get_float_element", TensorGetElement<float>)
+      .def("set_double_element", TensorSetElement<double>)
+      .def("get_double_element", TensorGetElement<double>)
+      .def("dtype", [](Tensor &self) { return ToDataType(self.type()); });
 
   py::class_<LoDTensor, Tensor>(m, "LoDTensor")
       .def_buffer(

paddle/pybind/tensor_py.h

@@ -73,10 +73,23 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
 };
 }  // namespace details
 inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
-  auto buffer_info = details::CastToPyBufferImpl<true, 0, float, int>()(tensor);
+  auto buffer_info =
+      details::CastToPyBufferImpl<true, 0, float, int, double>()(tensor);
   return buffer_info;
 }
 
+template <typename T>
+T TensorGetElement(framework::Tensor &self, size_t offset) {
+  PADDLE_ENFORCE(platform::is_cpu_place(self.place()));
+  return self.data<T>()[offset];
+}
+
+template <typename T>
+void TensorSetElement(framework::Tensor &self, size_t offset, T elem) {
+  PADDLE_ENFORCE(platform::is_cpu_place(self.place()));
+  self.data<T>()[offset] = elem;
+}
+
 template <typename T>
 void PyCPUTensorSetFromArray(
     framework::Tensor &self,

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

@@ -12,29 +12,29 @@ def grad_var_name(var_name):
 def create_op(scope, op_type, inputs, outputs, attrs):
     kwargs = dict()
 
+    def __create_var__(name, var_name):
+        scope.new_var(var_name)
+        kwargs[name].append(var_name)
+
     for in_name, in_dup in Operator.get_op_inputs(op_type):
         if in_name in inputs:
             kwargs[in_name] = []
             if in_dup:
                 sub_in = inputs[in_name]
                 for sub_in_name, _ in sub_in:
-                    var = scope.new_var(sub_in_name)
-                    kwargs[in_name].append(sub_in_name)
+                    __create_var__(in_name, sub_in_name)
             else:
-                var = scope.new_var(in_name)
-                kwargs[in_name].append(in_name)
+                __create_var__(in_name, in_name)
 
     for out_name, out_dup in Operator.get_op_outputs(op_type):
         if out_name in outputs:
             kwargs[out_name] = []
             if out_dup:
                 sub_out = outputs[out_name]
                 for sub_out_name, _ in sub_out:
-                    var = scope.new_var(sub_out_name)
-                    kwargs[out_name].append(sub_out_name)
+                    __create_var__(out_name, sub_out_name)
             else:
-                var = scope.new_var(out_name)
-                kwargs[out_name].append(out_name)
+                __create_var__(out_name, out_name)
 
     for attr_name in Operator.get_op_attr_names(op_type):
         if attr_name in attrs:
@@ -44,49 +44,46 @@ def create_op(scope, op_type, inputs, outputs, attrs):
 
 
 def set_input(scope, op, inputs, place):
+    def __set_input__(var_name, var):
+        tensor = scope.find_var(var_name).get_tensor()
+        if isinstance(var, tuple):
+            tensor.set_lod(var[1])
+            var = var[0]
+        tensor.set_dims(var.shape)
+        tensor.set(var, place)
+
     for in_name, in_dup in Operator.get_op_inputs(op.type()):
         if in_name in inputs:
             if in_dup:
                 sub_in = inputs[in_name]
                 for sub_in_name, sub_in_val in sub_in:
-                    var = scope.find_var(sub_in_name)
-                    tensor = var.get_tensor()
-                    sub_in_array = sub_in_val[0] \
-                        if isinstance(sub_in_val, tuple) else sub_in_val
-                    tensor.set_dims(sub_in_array.shape)
-                    tensor.set(sub_in_array, place)
-                    if isinstance(sub_in_val, tuple):
-                        tensor.set_lod(sub_in_val[1])
+                    __set_input__(sub_in_name, sub_in_val)
             else:
-                var = scope.find_var(in_name)
-                tensor = var.get_tensor()
-                in_val = inputs[in_name]
-                in_array = in_val[0] if isinstance(in_val, tuple) else in_val
-                tensor.set_dims(in_array.shape)
-                tensor.set(in_array, place)
-                if isinstance(in_val, tuple):
-                    tensor.set_lod(in_val[1])
+                __set_input__(in_name, inputs[in_name])
 
 
 def set_output_grad(scope, op, outputs, place):
+    def __set_tensor__(name):
+        out_tensor = scope.find_var(name).get_tensor()
+        grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
+        out_dtype = out_tensor.dtype()
+        if out_dtype == core.DataType.FP64:
+            data = np.ones(out_tensor.shape(), dtype=np.float64)
+        elif out_dtype == core.DataType.FP32:
+            data = np.ones(out_tensor.shape(), dtype=np.float32)
+        else:
+            raise ValueError("Not supported data type " + str(out_dtype))
+
+        grad_tensor.set(data, place)
+
     for out_name, out_dup in Operator.get_op_outputs(op.type()):
         if out_name in outputs:
             if out_dup:
                 sub_out = outputs[out_name]
                 for sub_out_name, _ in sub_out:
-                    out_tensor = scope.find_var(sub_out_name).get_tensor()
-                    grad_tensor = scope.new_var(grad_var_name(
-                        sub_out_name)).get_tensor()
-                    grad_tensor.set_dims(out_tensor.shape())
-                    data = np.ones(out_tensor.shape(), dtype=np.float32)
-                    grad_tensor.set(data, place)
+                    __set_tensor__(sub_out_name)
             else:
-                out_tensor = scope.find_var(out_name).get_tensor()
-                grad_tensor = scope.new_var(grad_var_name(out_name)).get_tensor(
-                )
-                grad_tensor.set_dims(out_tensor.shape())
-                data = np.ones(out_tensor.shape(), dtype=np.float32)
-                grad_tensor.set(data, place)
+                __set_tensor__(out_name)
 
 
 def get_numeric_gradient(scope,
@@ -96,7 +93,6 @@ def get_numeric_gradient(scope,
                          output_names,
                          delta=0.005,
                          in_place=False):
-
     set_input(scope, op, inputs, core.CPUPlace())
 
     tensor_to_check = scope.find_var(input_to_check).get_tensor()
@@ -115,28 +111,50 @@ def get_output():
 
     tensor_to_check = scope.find_var(input_to_check).get_tensor()
     tensor_size = product(tensor_to_check.get_dims())
-    gradient_flat = np.zeros(shape=(tensor_size, ), dtype='float32')
+    tensor_to_check_dtype = tensor_to_check.dtype()
+    if tensor_to_check_dtype == core.DataType.FP32:
+        tensor_to_check_dtype = np.float32
+    elif tensor_to_check_dtype == core.DataType.FP64:
+        tensor_to_check_dtype = np.float64
+    else:
+        raise ValueError("Not supported data type " + str(
+            tensor_to_check_dtype))
+
+    gradient_flat = np.zeros(shape=(tensor_size, ), dtype=tensor_to_check_dtype)
+
+    def __get_elem__(tensor, i):
+        if tensor_to_check_dtype == np.float32:
+            return tensor.get_float_element(i)
+        else:
+            return tensor.get_double_element(i)
+
+    def __set_elem__(tensor, i, e):
+        if tensor_to_check_dtype == np.float32:
+            tensor.set_float_element(i, e)
+        else:
+            tensor.set_double_element(i, e)
+
     # we only compute gradient of one element each time.
     # we use a for loop to compute the gradient of every element.
     for i in xrange(tensor_size):
         if in_place:
             set_input(scope, op, inputs, core.CPUPlace())
 
         # get one input element throw it's index i.
-        origin = tensor_to_check.get_float_element(i)
+        origin = __get_elem__(tensor_to_check, i)
         # add delta to it, run op and then get the sum of the result tensor.
         x_pos = origin + delta
-        tensor_to_check.set_float_element(i, x_pos)
+        __set_elem__(tensor_to_check, i, x_pos)
         y_pos = get_output()
 
         if in_place:
             set_input(scope, op, inputs, core.CPUPlace())
 
         x_neg = origin - delta
-        tensor_to_check.set_float_element(i, x_neg)
+        __set_elem__(tensor_to_check, i, x_neg)
         y_neg = get_output()
 
-        tensor_to_check.set_float_element(i, origin)
+        __set_elem__(tensor_to_check, i, origin)
         gradient_flat[i] = (y_pos - y_neg) / delta / 2
 
     return gradient_flat.reshape(tensor_to_check.get_dims())

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

@@ -80,7 +80,7 @@ def setUp(self):
         cross_entropy2 = (-label * np.log(X)).sum(
             axis=1, keepdims=True).astype("float32")
 
-        self.inputs = {"X": X, "Label": label}
+        self.inputs = {"X": X, "Label": label.astype(np.float32)}
         self.outputs = {"Y": cross_entropy}
         self.attrs = {"softLabel": True}
 

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

@@ -7,32 +7,30 @@ class ElementwiseMulOp(OpTest):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
-            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
-            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float64"),
+            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float64")
         }
         self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
 
     def test_check_output(self):
         self.check_output()
 
     def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+        self.check_grad(['X', 'Y'], 'Out')
 
     def test_check_grad_ingore_x(self):
-        self.check_grad(
-            ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+        self.check_grad(['Y'], 'Out', no_grad_set=set("X"))
 
     def test_check_grad_ingore_y(self):
-        self.check_grad(
-            ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+        self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
 
 
 class TestElementwiseMulOp_Vector(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
-            'X': np.random.random((32, )).astype("float32"),
-            'Y': np.random.random((32, )).astype("float32")
+            'X': np.random.random((32, )).astype("float64"),
+            'Y': np.random.random((32, )).astype("float64")
         }
         self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
 
@@ -41,8 +39,8 @@ class TestElementwiseMulOp_broadcast_0(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float32),
-            'Y': np.random.rand(2).astype(np.float32)
+            'X': np.random.rand(2, 3, 4).astype(np.float64),
+            'Y': np.random.rand(2).astype(np.float64)
         }
 
         self.attrs = {'axis': 0}
@@ -55,8 +53,8 @@ class TestElementwiseMulOp_broadcast_1(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float32),
-            'Y': np.random.rand(3).astype(np.float32)
+            'X': np.random.rand(2, 3, 4).astype(np.float64),
+            'Y': np.random.rand(3).astype(np.float64)
         }
 
         self.attrs = {'axis': 1}
@@ -69,8 +67,8 @@ class TestElementwiseMulOp_broadcast_2(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float32),
-            'Y': np.random.rand(4).astype(np.float32)
+            'X': np.random.rand(2, 3, 4).astype(np.float64),
+            'Y': np.random.rand(4).astype(np.float64)
         }
 
         self.outputs = {
@@ -82,8 +80,8 @@ class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.inputs = {
-            'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
-            'Y': np.random.rand(3, 4).astype(np.float32)
+            'X': np.random.rand(2, 3, 4, 5).astype(np.float64),
+            'Y': np.random.rand(3, 4).astype(np.float64)
         }
 
         self.attrs = {'axis': 1}

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

@@ -17,7 +17,7 @@ def setUp(self):
 
         x_np_sign = np.sign(x_np)
         x_np = x_np_sign * np.maximum(x_np, .005)
-        alpha_np = np.array([.1])
+        alpha_np = np.array([.1], dtype="float32")
         self.inputs = {'X': x_np, 'Alpha': alpha_np}
         out_np = np.maximum(self.inputs['X'], 0.)
         out_np = out_np + np.minimum(self.inputs['X'],