modify trace api test=develop (#25397)

paddle/fluid/operators/trace_op.cc

@@ -30,8 +30,8 @@ class TraceOp : public framework::OperatorWithKernel {
         ctx->HasOutput("Out"), true,
         platform::errors::NotFound("Output of TraceOp is not found."));
 
-    int dim1 = ctx->Attrs().Get<int>("dim1");
-    int dim2 = ctx->Attrs().Get<int>("dim2");
+    int dim1 = ctx->Attrs().Get<int>("axis1");
+    int dim2 = ctx->Attrs().Get<int>("axis2");
 
     auto x_dims = ctx->GetInputDim("Input");
 
@@ -84,15 +84,15 @@ class TraceOpMaker : public framework::OpProtoAndCheckerMaker {
         )DOC")
         .SetDefault(0);
     AddAttr<int>(
-        "dim1",
-        R"DOC((int, default 0), the first dim of the 2-D planes from which the diagonals should be taken. 
-        Can be both positive and negative. Default: 0.
+        "axis1",
+        R"DOC((int, default 0), the first axis of the 2-D planes from which the diagonals should be taken. 
+        Can be either positive or negative. Default: 0.
         )DOC")
         .SetDefault(-2);
     AddAttr<int>(
-        "dim2",
-        R"DOC((int, default 1), the second dim of the 2-D planes from which the diagonals should be taken. 
-        Can be both positive and negative. Default: 1.
+        "axis2",
+        R"DOC((int, default 1), the second axis of the 2-D planes from which the diagonals should be taken. 
+        Can be either positive or negative. Default: 1.
         )DOC")
         .SetDefault(-1);
     AddComment(R"DOC(

paddle/fluid/operators/trace_op.cu

@@ -33,8 +33,8 @@ class TraceCUDAKernel : public framework::OpKernel<T> {
     auto* out = context.Output<framework::Tensor>("Out");
 
     const int64_t offset = context.Attr<int>("offset");
-    const int64_t dim1 = context.Attr<int>("dim1");
-    const int64_t dim2 = context.Attr<int>("dim2");
+    const int64_t dim1 = context.Attr<int>("axis1");
+    const int64_t dim2 = context.Attr<int>("axis2");
 
     T* out_data = out->mutable_data<T>(context.GetPlace());
     const framework::Tensor diag =

paddle/fluid/operators/trace_op.h

@@ -174,8 +174,8 @@ class TraceKernel : public framework::OpKernel<T> {
     auto* out = context.Output<framework::Tensor>("Out");
 
     const int64_t offset = context.Attr<int>("offset");
-    const int64_t dim1 = context.Attr<int>("dim1");
-    const int64_t dim2 = context.Attr<int>("dim2");
+    const int64_t dim1 = context.Attr<int>("axis1");
+    const int64_t dim2 = context.Attr<int>("axis2");
 
     auto output_dims = out->dims();
 
@@ -205,8 +205,8 @@ class TraceGradKernel : public framework::OpKernel<T> {
         context.Output<framework::Tensor>(framework::GradVarName("Input"));
 
     int64_t offset = context.Attr<int>("offset");
-    int64_t dim1 = context.Attr<int>("dim1");
-    int64_t dim2 = context.Attr<int>("dim2");
+    int64_t dim1 = context.Attr<int>("axis1");
+    int64_t dim2 = context.Attr<int>("axis2");
 
     auto input_dims = d_x->dims();
     auto input_stride = framework::stride(input_dims);

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

@@ -33,7 +33,7 @@ class TestComplexTraceLayer(unittest.TestCase):
         for place in self._places:
             with dg.guard(place):
                 var_x = dg.to_variable(input)
-                result = cpx.trace(var_x, offset=1, dim1=0, dim2=2).numpy()
+                result = cpx.trace(var_x, offset=1, axis1=0, axis2=2).numpy()
                 target = np.trace(input, offset=1, axis1=0, axis2=2)
                 self.assertTrue(np.allclose(result, target))
 

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

@@ -38,7 +38,7 @@ class TestTraceOp(OpTest):
     def init_config(self):
         self.case = np.random.randn(20, 6).astype('float64')
         self.inputs = {'Input': self.case}
-        self.attrs = {'offset': 0, 'dim1': 0, 'dim2': 1}
+        self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
         self.target = np.trace(self.inputs['Input'])
 
 
@@ -46,24 +46,24 @@ class TestTraceOpCase1(TestTraceOp):
     def init_config(self):
         self.case = np.random.randn(2, 20, 2, 3).astype('float32')
         self.inputs = {'Input': self.case}
-        self.attrs = {'offset': 1, 'dim1': 0, 'dim2': 2}
+        self.attrs = {'offset': 1, 'axis1': 0, 'axis2': 2}
         self.target = np.trace(
             self.inputs['Input'],
             offset=self.attrs['offset'],
-            axis1=self.attrs['dim1'],
-            axis2=self.attrs['dim2'])
+            axis1=self.attrs['axis1'],
+            axis2=self.attrs['axis2'])
 
 
 class TestTraceOpCase2(TestTraceOp):
     def init_config(self):
         self.case = np.random.randn(2, 20, 2, 3).astype('float32')
         self.inputs = {'Input': self.case}
-        self.attrs = {'offset': -5, 'dim1': 1, 'dim2': -1}
+        self.attrs = {'offset': -5, 'axis1': 1, 'axis2': -1}
         self.target = np.trace(
             self.inputs['Input'],
             offset=self.attrs['offset'],
-            axis1=self.attrs['dim1'],
-            axis2=self.attrs['dim2'])
+            axis1=self.attrs['axis1'],
+            axis2=self.attrs['axis2'])
 
 
 class TestTraceAPICase(unittest.TestCase):
@@ -71,7 +71,7 @@ class TestTraceAPICase(unittest.TestCase):
         case = np.random.randn(2, 20, 2, 3).astype('float32')
         data1 = fluid.data(name='data1', shape=[2, 20, 2, 3], dtype='float32')
         out1 = tensor.trace(data1)
-        out2 = tensor.trace(data1, offset=-5, dim1=1, dim2=-1)
+        out2 = tensor.trace(data1, offset=-5, axis1=1, axis2=-1)
 
         place = core.CPUPlace()
         exe = fluid.Executor(place)

python/paddle/incubate/complex/tensor/math.py

@@ -236,39 +236,38 @@ def elementwise_div(x, y, axis=-1, name=None):
         name=name)
 
 
-def trace(input, offset=0, dim1=0, dim2=1, name=None):
+def trace(x, offset=0, axis1=0, axis2=1, name=None):
     """
-    The layer to compute the trace for a complex number tensor. input :attr:`input` must be a ComplexVariable. 
+    The layer to compute the trace for a complex number tensor. x :attr:`x` must be a ComplexVariable. 
     See the detailed description for the function and other arguments 
     in :ref:`api_tensor_math_trace` . 
     
     Args:
-        input(ComplexVariable): The input ComplexVariable. Must be at least 2-dimensional. 
+        x(ComplexVariable): The input ComplexVariable x. Must be at least 2-dimensional. 
             The supported data types include complex64 and complex128.
-        offset(int, optional): Which diagonals in input tensor will be taken. Default: 0 (main diagonals).
-        dim1(int, optional): The first dimension with respect to take diagonal. Default: 0.
-        dim2(int, optional): The second dimension with respect to take diagonal. Default: 1.
+        offset(int, optional): Which diagonals in input tensor x will be taken. Default: 0 (main diagonals).
+        axis1(int, optional): The first axis with respect to take diagonal. Default: 0.
+        axis2(int, optional): The second axis with respect to take diagonal. Default: 1.
         name (str, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None.
     
     Returns:
-        ComplexVariable: The trace result of input tensor, it's data type is the same as input data type.
+        ComplexVariable: The trace result of input tensor x, it's data type is the same as input data type.
 
     Examples:
         .. code-block:: python
 
             import paddle
-            import paddle.fluid.dygraph as dg
             import numpy as np
             
             case1 = np.random.randn(3, 10, 10).astype('float64') + 1j * np.random.randn(3, 10, 10).astype('float64')
             
-            with dg.guard():
-                case1 = dg.to_variable(case1)
-                data1 = paddle.complex.trace(case1, offset=1, dim1=1, dim2=2) # data1.shape = [3]
+            paddle.enable_imperative()
+            case1 = paddle.imperative.to_variable(case1)
+            data1 = paddle.complex.trace(case1, offset=1, axis1=1, axis2=2) # data1.shape = [3]
     """
-    complex_variable_exists([input], "trace")
-    real = math.trace(input.real, offset, dim1, dim2, name)
-    imag = math.trace(input.imag, offset, dim1, dim2, name)
+    complex_variable_exists([x], "trace")
+    real = math.trace(x.real, offset, axis1, axis2, name)
+    imag = math.trace(x.imag, offset, axis1, axis2, name)
 
     return ComplexVariable(real, imag)
 

python/paddle/tensor/math.py

@@ -191,8 +191,8 @@ Examples:
 @templatedoc()
 def pow(input, exponent, out=None, name=None):
     """
-	:alias_main: paddle.pow
-	:alias: paddle.pow,paddle.tensor.pow,paddle.tensor.math.pow
+	:alias_main: paddle.pow
+	:alias: paddle.pow,paddle.tensor.pow,paddle.tensor.math.pow
 
     This is Pow Activation Operator.
 
@@ -262,8 +262,8 @@ def pow(input, exponent, out=None, name=None):
 
 def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, out=None, name=None):
     """
-	:alias_main: paddle.mul
-	:alias: paddle.mul,paddle.tensor.mul,paddle.tensor.math.mul
+	:alias_main: paddle.mul
+	:alias: paddle.mul,paddle.tensor.mul,paddle.tensor.math.mul
 
     Mul Operator.
     This operator is used to perform matrix multiplication for input $x$ and $y$.
@@ -411,8 +411,8 @@ def _elementwise_op(helper):
 
 def add(x, y, alpha=1, out=None, name=None):
     """
-	:alias_main: paddle.add
-	:alias: paddle.add,paddle.tensor.add,paddle.tensor.math.add
+	:alias_main: paddle.add
+	:alias: paddle.add,paddle.tensor.add,paddle.tensor.math.add
 
 Examples:
 
@@ -556,8 +556,8 @@ Examples:
 
 def div(x, y, out=None, name=None):
     """
-	:alias_main: paddle.div
-	:alias: paddle.div,paddle.tensor.div,paddle.tensor.math.div
+	:alias_main: paddle.div
+	:alias: paddle.div,paddle.tensor.div,paddle.tensor.math.div
 
 Examples:
 
@@ -709,8 +709,8 @@ for func in [
 
 def sum(input, dim=None, dtype=None, keep_dim=False, name=None):
     """
-	:alias_main: paddle.sum
-	:alias: paddle.sum,paddle.tensor.sum,paddle.tensor.math.sum
+	:alias_main: paddle.sum
+	:alias: paddle.sum,paddle.tensor.sum,paddle.tensor.math.sum
 
     Computes the sum of tensor elements over the given dimension.
 
@@ -814,8 +814,8 @@ def sum(input, dim=None, dtype=None, keep_dim=False, name=None):
 @templatedoc(op_type="sum")
 def elementwise_sum(inputs, name=None):
     """
-	:alias_main: paddle.elementwise_sum
-	:alias: paddle.elementwise_sum,paddle.tensor.elementwise_sum,paddle.tensor.math.elementwise_sum
+	:alias_main: paddle.elementwise_sum
+	:alias: paddle.elementwise_sum,paddle.tensor.elementwise_sum,paddle.tensor.math.elementwise_sum
 
     ${comment}
     
@@ -912,8 +912,8 @@ def elementwise_sum(inputs, name=None):
 
 def mm(input, mat2, out=None, name=None):
     """
-	:alias_main: paddle.mm
-	:alias: paddle.mm,paddle.tensor.mm,paddle.tensor.math.mm
+	:alias_main: paddle.mm
+	:alias: paddle.mm,paddle.tensor.mm,paddle.tensor.math.mm
 
     Applies matrix multiplication to two tensors.
 
@@ -1017,8 +1017,8 @@ def mm(input, mat2, out=None, name=None):
 
 def addmm(input, x, y, alpha=1.0, beta=1.0, name=None):
     """
-	:alias_main: paddle.addmm
-	:alias: paddle.addmm,paddle.tensor.addmm,paddle.tensor.math.addmm
+	:alias_main: paddle.addmm
+	:alias: paddle.addmm,paddle.tensor.addmm,paddle.tensor.math.addmm
 
     **addmm**
 
@@ -1086,8 +1086,8 @@ def addmm(input, x, y, alpha=1.0, beta=1.0, name=None):
 
 def logsumexp(x, dim=None, keepdim=False, out=None, name=None):
     """
-	:alias_main: paddle.logsumexp
-	:alias: paddle.logsumexp,paddle.tensor.logsumexp,paddle.tensor.math.logsumexp
+	:alias_main: paddle.logsumexp
+	:alias: paddle.logsumexp,paddle.tensor.logsumexp,paddle.tensor.math.logsumexp
 
     This operator calculates the log of the sum of exponentials of the input Tensor.
 
@@ -1157,8 +1157,8 @@ def logsumexp(x, dim=None, keepdim=False, out=None, name=None):
 
 def inverse(input, out=None, name=None):
     """
-	:alias_main: paddle.inverse
-	:alias: paddle.inverse,paddle.tensor.inverse,paddle.tensor.math.inverse
+	:alias_main: paddle.inverse
+	:alias: paddle.inverse,paddle.tensor.inverse,paddle.tensor.math.inverse
 
     Takes the inverse of the square matrix. A square matrix is a matrix with
     the same number of rows and columns. The input can be a square matrix
@@ -1232,8 +1232,8 @@ def inverse(input, out=None, name=None):
 
 def max(input, dim=None, keep_dim=False, out=None, name=None):
     """
-	:alias_main: paddle.max
-	:alias: paddle.max,paddle.tensor.max,paddle.tensor.math.max
+	:alias_main: paddle.max
+	:alias: paddle.max,paddle.tensor.max,paddle.tensor.math.max
 
     Computes the maximum of tensor elements over the given dimension.
 
@@ -1312,8 +1312,8 @@ def max(input, dim=None, keep_dim=False, out=None, name=None):
 
 def min(input, dim=None, keep_dim=False, out=None, name=None):
     """
-	:alias_main: paddle.min
-	:alias: paddle.min,paddle.tensor.min,paddle.tensor.math.min
+	:alias_main: paddle.min
+	:alias: paddle.min,paddle.tensor.min,paddle.tensor.math.min
 
     Computes the minimum of tensor elements over the given dimension.
 
@@ -1391,8 +1391,8 @@ def min(input, dim=None, keep_dim=False, out=None, name=None):
 
 def log1p(x, out=None, name=None):
     """
-	:alias_main: paddle.log1p
-	:alias: paddle.log1p,paddle.tensor.log1p,paddle.tensor.math.log1p
+	:alias_main: paddle.log1p
+	:alias: paddle.log1p,paddle.tensor.log1p,paddle.tensor.math.log1p
 
     Calculates the natural log of the given input tensor, element-wise.
     .. math::
@@ -1437,8 +1437,8 @@ def log1p(x, out=None, name=None):
 
 def addcmul(input, tensor1, tensor2, value=1.0, out=None, name=None):
     """
-	:alias_main: paddle.addcmul
-	:alias: paddle.addcmul,paddle.tensor.addcmul,paddle.tensor.math.addcmul
+	:alias_main: paddle.addcmul
+	:alias: paddle.addcmul,paddle.tensor.addcmul,paddle.tensor.math.addcmul
 
     Calculate the element-wise multiplication of tensor1 and tensor2,
     then multiply the result by value, and add it to input. The shape of input,
@@ -1486,8 +1486,8 @@ def addcmul(input, tensor1, tensor2, value=1.0, out=None, name=None):
 
 def clamp(input, min=None, max=None, output=None, name=None):
     """
-	:alias_main: paddle.clamp
-	:alias: paddle.clamp,paddle.tensor.clamp,paddle.tensor.math.clamp
+	:alias_main: paddle.clamp
+	:alias: paddle.clamp,paddle.tensor.clamp,paddle.tensor.math.clamp
 
     **clampe layer**
 
@@ -1572,30 +1572,30 @@ def clamp(input, min=None, max=None, output=None, name=None):
 
     return output
 
-def trace(input, offset=0, dim1=0, dim2=1, out=None, name=None):
+def trace(x, offset=0, axis1=0, axis2=1, name=None):
     """
-	:alias_main: paddle.trace
-	:alias: paddle.trace,paddle.tensor.trace,paddle.tensor.math.trace
+	:alias_main: paddle.trace
+	:alias: paddle.trace,paddle.tensor.trace,paddle.tensor.math.trace
 
-    This OP computes the sum along diagonals of the input tensor.
+    This OP computes the sum along diagonals of the input tensor x.
     
-    If ``input`` is 2D, returns the sum of diagonal. 
+    If ``x`` is 2D, returns the sum of diagonal. 
 
-    If ``input`` has larger dimensions, then returns an tensor of diagonals sum, diagonals be taken from
-    the 2D planes specified by dim1 and dim2. By default, the 2D planes formed by the first and second dimensions 
-    of the input tensor.
+    If ``x`` has larger dimensions, then returns an tensor of diagonals sum, diagonals be taken from
+    the 2D planes specified by axis1 and axis2. By default, the 2D planes formed by the first and second axes 
+    of the input tensor x.
 
-    The argument ``offset`` determines where diagonals are taken from input tensor:
+    The argument ``offset`` determines where diagonals are taken from input tensor x:
 
     - If offset = 0, it is the main diagonal.
     - If offset > 0, it is above the main diagonal.
     - If offset < 0, it is below the main diagonal.
     
     Args:
-        input(Variable): The input tensor. Must be at least 2-dimensional. The input data type should be float32, float64, int32, int64.
-        offset(int, optional): Which diagonals in input tensor will be taken. Default: 0 (main diagonals).
-        dim1(int, optional): The first dimension with respect to take diagonal. Default: 0.
-        dim2(int, optional): The second dimension with respect to take diagonal. Default: 1.
+        x(Variable): The input tensor x. Must be at least 2-dimensional. The input data type should be float32, float64, int32, int64.
+        offset(int, optional): Which diagonals in input tensor x will be taken. Default: 0 (main diagonals).
+        axis1(int, optional): The first axis with respect to take diagonal. Default: 0.
+        axis2(int, optional): The second axis with respect to take diagonal. Default: 1.
         name (str, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None.
 
     Returns:
@@ -1605,74 +1605,71 @@ def trace(input, offset=0, dim1=0, dim2=1, out=None, name=None):
         .. code-block:: python
 
             import paddle
-            import paddle.fluid.dygraph as dg
             import numpy as np
             
             case1 = np.random.randn(2, 3).astype('float32')
             case2 = np.random.randn(3, 10, 10).astype('float32')
             case3 = np.random.randn(3, 10, 5, 10).astype('float32')
             
-            with dg.guard():
-                case1 = dg.to_variable(case1)
-                case2 = dg.to_variable(case2)
-                case3 = dg.to_variable(case3)
-                data1 = paddle.trace(case1) # data1.shape = [1]
-                data2 = paddle.trace(case2, offset=1, dim1=1, dim2=2) # data2.shape = [3]
-                data3 = paddle.trace(case3, offset=-3, dim1=1, dim2=-1) # data2.shape = [3, 5]
+            paddle.enable_imperative()
+
+            case1 = paddle.imperative.to_variable(case1)
+            case2 = paddle.imperative.to_variable(case2)
+            case3 = paddle.imperative.to_variable(case3)
+            data1 = paddle.trace(case1) # data1.shape = [1]
+            data2 = paddle.trace(case2, offset=1, axis1=1, axis2=2) # data2.shape = [3]
+            data3 = paddle.trace(case3, offset=-3, axis1=1, axis2=-1) # data2.shape = [3, 5]
     """
-    inputs = {'Input': [input]}
-    attrs = {'offset': offset, 'dim1': dim1, 'dim2': dim2}
+    inputs = {'Input': [x]}
+    attrs = {'offset': offset, 'axis1': axis1, 'axis2': axis2}
 
     def __check_input(input, offset, dim1, dim2):
-        check_dtype(input.dtype, 'Input',
+        check_dtype(x.dtype, 'Input',
                     ['int32', 'int64', 'float16', 'float32', 'float64'],
                     'trace')
 
-        input_shape = list(input.shape)
+        input_shape = list(x.shape)
         assert len(input_shape) >= 2,                     \
-                "The input must be at least 2-dimensional, "   \
-                "But received Input's dimensional: %s.\n" %  \
+                "The x must be at least 2-dimensional, "   \
+                "But received Input x's dimensional: %s.\n" %  \
                 len(input_shape)
 
-        dim1_ = dim1 if dim1 >= 0 else len(input_shape) + dim1
-        dim2_ = dim2 if dim2 >= 0 else len(input_shape) + dim2
+        axis1_ = axis1 if axis1 >= 0 else len(input_shape) + axis1
+        axis2_ = axis2 if axis2 >= 0 else len(input_shape) + axis2
 
-        assert dim1_ < len(input_shape),     \
-            "The argument dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n"  \
-            % (-(len(input_shape)), len(input_shape) - 1, dim1)
+        assert axis1_ < len(input_shape),     \
+            "The argument axis1 is out of range (expected to be in range of [%d, %d], but got %d).\n"  \
+            % (-(len(input_shape)), len(input_shape) - 1, axis1)
 
-        assert dim2_ < len(input_shape),   \
-            "The argument dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n"   \
-            % (-(len(input_shape)), len(input_shape) - 1, dim2)
+        assert axis2_ < len(input_shape),   \
+            "The argument axis2 is out of range (expected to be in range of [%d, %d], but got %d).\n"   \
+            % (-(len(input_shape)), len(input_shape) - 1, axis2)
 
 
-        assert  dim1_ != dim2_,   \
-               "dim1 and dim2 cannot be the same dimension." \
-                "But received dim1 = %d, dim2 = %d\n"%(dim1, dim2)
+        assert  axis1_ != axis2_,   \
+               "axis1 and axis2 cannot be the same axis." \
+                "But received axis1 = %d, axis2 = %d\n"%(axis1, axis2)
 
     if not in_dygraph_mode():
-        __check_input(input, offset, dim1, dim2)
+        __check_input(input, offset, axis1, axis2)
     helper = LayerHelper('trace', **locals())
 
-    if out is None:
-        out = helper.create_variable_for_type_inference(dtype=input.dtype)
-    else:
-        check_variable_and_dtype(out, 'out', ['float16', 'float32', 'float64', 'int32', 'int64'], 'trace')
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
 
     helper.append_op(
         type='trace',
-        inputs={'Input': [input]},
+        inputs={'Input': [x]},
         attrs={'offset': offset,
-               'dim1': dim1,
-               'dim2': dim2},
+               'axis1': axis1,
+               'axis2': axis2},
         outputs={'Out': [out]})
     return out
 
 @templatedoc(op_type="kron")
 def kron(x, y, out=None, name=None):
     """
-	:alias_main: paddle.kron
-	:alias: paddle.kron,paddle.tensor.kron,paddle.tensor.math.kron
+	:alias_main: paddle.kron
+	:alias: paddle.kron,paddle.tensor.kron,paddle.tensor.math.kron
 
 ${comment}