Add nn.Tril function

mindspore/nn/layer/basic.py

@@ -35,7 +35,7 @@ from .activation import get_activation
 
 
 __all__ = ['Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'Pad', 'Unfold',
-           'MatrixDiag', 'MatrixDiagPart', 'MatrixSetDiag']
+           'Tril', 'Triu', 'MatrixDiag', 'MatrixDiagPart', 'MatrixSetDiag']
 
 
 class Dropout(Cell):
@@ -547,6 +547,80 @@ class Unfold(Cell):
         return result
 
 
+@constexpr
+def tril(x_shape, x_dtype, k):
+    Validator.check_int(len(x_shape), 1, Rel.GE, "x rank", "tril")
+    Validator.check_is_int(k, "k value", "tril")
+    mask = np.tril(np.ones(x_shape), k)
+    return Tensor(mask, x_dtype)
+
+
+class Tril(Cell):
+    """
+    Returns a tensor with elements above the kth diagonal zeroed.
+
+    Inputs:
+        - **x** (Tensor) - The input tensor.
+        - **k** (Int) - The index of diagonal. Default: 0
+
+    Outputs:
+        Tensor, has the same type as input `x`.
+
+    Examples:
+        >>> x = Tensor(np.array([[1, 2], [3, 4]]))
+        >>> tril = nn.Tril()
+        >>> result = tril(x)
+        >>> print(result)
+        [[1   0]
+         [3   4]]
+    """
+    def __init__(self):
+        super(Tril, self).__init__()
+        self.dtype = P.DType()
+        self.mul = P.Mul()
+
+    def construct(self, x, k=0):
+        assist = tril(x.shape, self.dtype(x), k)
+        return self.mul(x, assist)
+
+
+@constexpr
+def triu(x_shape, x_dtype, k):
+    Validator.check_int(len(x_shape), 1, Rel.GE, "x rank", "triu")
+    Validator.check_is_int(k, "k value", "triu")
+    mask = np.triu(np.ones(x_shape), k)
+    return Tensor(mask, x_dtype)
+
+
+class Triu(Cell):
+    """
+    Returns a tensor with elements below the kth diagonal zeroed.
+
+    Inputs:
+        - **x** (Tensor) - The input tensor.
+        - **k** (Int) - The index of diagonal. Default: 0
+
+    Outputs:
+        Tensor, has the same type as input `x`.
+
+    Examples:
+        >>> x = Tensor(np.array([[1, 2], [3, 4]]))
+        >>> tril = nn.Tril()
+        >>> result = tril(x)
+        >>> print(result)
+        [[1   2]
+         [0   4]]
+    """
+    def __init__(self):
+        super(Triu, self).__init__()
+        self.dtype = P.DType()
+        self.mul = P.Mul()
+
+    def construct(self, x, k=0):
+        assist = triu(x.shape, self.dtype(x), k)
+        return self.mul(x, assist)
+
+
 @constexpr
 def _get_matrix_diag_assist(x_shape, x_dtype):
     Validator.check_int(len(x_shape), 1, Rel.GE, "x rank", "_get_matrix_diag_assist")

tests/ut/python/nn/test_tril.py

@@ -0,0 +1,108 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+test nn.Tril()
+"""
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+def test_tril():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+
+        def construct(self):
+            tril = nn.Tril()
+            return tril(self.value, 0)
+
+    net = Net()
+    out = net()
+    assert np.sum(out.asnumpy()) == 34
+
+
+def test_tril_1():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+
+        def construct(self):
+            tril = nn.Tril()
+            return tril(self.value, 1)
+
+    net = Net()
+    out = net()
+    assert np.sum(out.asnumpy()) == 42
+
+
+def test_tril_2():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+
+        def construct(self):
+            tril = nn.Tril()
+            return tril(self.value, -1)
+
+    net = Net()
+    out = net()
+    assert np.sum(out.asnumpy()) == 19
+
+
+def test_tril_parameter():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            tril = nn.Tril()
+            return tril(x, 0)
+
+    net = Net()
+    net(Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
+
+
+def test_tril_parameter_1():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            tril = nn.Tril()
+            return tril(x, 1)
+
+    net = Net()
+    net(Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
+
+
+def test_tril_parameter_2():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            tril = nn.Tril()
+            return tril(x, -1)
+
+    net = Net()
+    net(Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))

tests/ut/python/nn/test_triu.py

@@ -0,0 +1,108 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+test nn.Triu()
+"""
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+def test_triu():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+
+        def construct(self):
+            triu = nn.Triu()
+            return triu(self.value, 0)
+
+    net = Net()
+    out = net()
+    assert np.sum(out.asnumpy()) == 26
+
+
+def test_triu_1():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+
+        def construct(self):
+            triu = nn.Triu()
+            return triu(self.value, 1)
+
+    net = Net()
+    out = net()
+    assert np.sum(out.asnumpy()) == 11
+
+
+def test_triu_2():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.value = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+
+        def construct(self):
+            triu = nn.Triu()
+            return triu(self.value, -1)
+
+    net = Net()
+    out = net()
+    assert np.sum(out.asnumpy()) == 38
+
+
+def test_triu_parameter():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            triu = nn.Triu()
+            return triu(x, 0)
+
+    net = Net()
+    net(Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
+
+
+def test_triu_parameter_1():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            triu = nn.Triu()
+            return triu(x, 1)
+
+    net = Net()
+    net(Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
+
+
+def test_triu_parameter_2():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            triu = nn.Triu()
+            return triu(x, -1)
+
+    net = Net()
+    net(Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))