!2297 add vm support for operators include MatrixDiag, MatrixDiagPart etc

Merge pull request !2297 from jiangjinsheng/vm_matrixdiag

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -124,7 +124,10 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"a_cos_grad", "acos_grad"},
   {"histogram_fixed_width", "histogram_fixed_width_d"},
   {"broadcast_to", "broadcast_to_d"},
-  {"inplace_update", "inplace_update_d"}};
+  {"inplace_update", "inplace_update_d"},
+  {"matrix_diag", "matrix_diag_d"},
+  {"matrix_diag_part", "matrix_diag_part_d"},
+  {"matrix_set_diag", "matrix_set_diag_d"}};
 
 void TbeAdapter::NormalizeFuncName(std::string *func_name) {
   if (func_name == nullptr) {

mindspore/nn/layer/basic.py

@@ -31,9 +31,12 @@ from mindspore.ops import _selected_ops
 from ..cell import Cell
 from .activation import get_activation
 from ..._checkparam import Validator as validator
+from ..._checkparam import Rel
 
 
-__all__ = ['Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'Pad', 'Unfold']
+__all__ = ['Dropout', 'Flatten', 'Dense', 'ClipByNorm', 'Norm', 'OneHot', 'Pad', 'Unfold',
+           'MatrixDiag', 'MatrixDiagPart', 'MatrixSetDiag']
+
 
 class Dropout(Cell):
     r"""
@@ -527,3 +530,112 @@ class Unfold(Cell):
         ret = self.extract_image_patches(x_transpose)
         ret_transpose = self.transpose(ret, self.format_NCHW)
         return ret_transpose
+
+
+@constexpr
+def _get_matrix_diag_assist(x_shape, x_dtype):
+    validator.check_integer("x rank", len(x_shape), 1, Rel.GE, "_get_matrix_diag_assist")
+    base_eye = np.eye(x_shape[-1], x_shape[-1]).reshape(-1)
+    assist = np.tile(base_eye, x_shape[:-1]).reshape(x_shape + (x_shape[-1],))
+    return Tensor(assist, x_dtype)
+
+
+@constexpr
+def _get_matrix_diag_part_assist(x_shape, x_dtype):
+    validator.check_integer("x rank", len(x_shape), 2, Rel.GE, "_get_matrix_diag_part_assist")
+    base_eye = np.eye(x_shape[-2], x_shape[-1]).reshape(-1)
+    assist = np.tile(base_eye, x_shape[:-2]).reshape(x_shape)
+    return Tensor(assist, x_dtype)
+
+
+class MatrixDiag(Cell):
+    """
+    Returns a batched diagonal tensor with a given batched diagonal values.
+
+    Inputs:
+        - **x** (Tensor) - The diagonal values. It can be of the following data types:
+          float32, float16, int32, int8, uint8.
+
+    Outputs:
+        Tensor, same type as input `x`. The shape should be x.shape + (x.shape[-1], ).
+
+    Examples:
+        >>> x = Tensor(np.array([1, -1]), mstype.float32)
+        >>> matrix_diag = nn.MatrixDiag()
+        >>> result = matrix_diag(x)
+        [[1.   0.]
+         [0.  -1.]]
+    """
+    def __init__(self):
+        super(MatrixDiag, self).__init__()
+        self.matrix_diag = inner.MatrixDiag()
+        self.dtype = P.DType()
+
+    def construct(self, input_x):
+        x_shape = F.shape(input_x)
+        x_dtype = self.dtype(input_x)
+        assist = _get_matrix_diag_assist(x_shape, x_dtype)
+        out_matrix_diag = self.matrix_diag(input_x, assist)
+        return out_matrix_diag
+
+
+class MatrixDiagPart(Cell):
+    r"""
+    Returns the batched diagonal part of a batched tensor.
+
+    Inputs:
+        - **x** (Tensor) - The batched tensor. It can be of the following data types:
+          float32, float16, int32, int8, uint8.
+
+    Outputs:
+        Tensor, same type as input `x`. The shape should be x.shape[:-2] + [min(x.shape[-2:])].
+
+    Examples:
+        >>> x = Tensor([[[-1, 0], [0, 1]], [-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32)
+        >>> matrix_diag_part = nn.MatrixDiagPart()
+        >>> result = matrix_diag_part(x)
+        [[-1., 1.], [-1., 1.], [-1., 1.]]
+    """
+    def __init__(self):
+        super(MatrixDiagPart, self).__init__()
+        self.matrix_diag_part = inner.MatrixDiagPart()
+        self.dtype = P.DType()
+
+    def construct(self, input_x):
+        x_shape = F.shape(input_x)
+        x_dtype = self.dtype(input_x)
+        assist = _get_matrix_diag_part_assist(x_shape, x_dtype)
+        out_matrix_diag_part = self.matrix_diag_part(input_x, assist)
+        return out_matrix_diag_part
+
+
+class MatrixSetDiag(Cell):
+    r"""
+    Modify the batched diagonal part of a batched tensor.
+
+    Inputs:
+        - **x** (Tensor) - The batched tensor. It can be of the following data types:
+          float32, float16, int32, int8, uint8.
+        - **diagonal** (Tensor) - The diagonal values.
+
+    Outputs:
+        Tensor, same type as input `x`. The shape same as `x`.
+
+    Examples:
+        >>> x = Tensor([[[-1, 0], [0, 1]], [-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32)
+        >>> diagonal = Tensor([[-1., 2.], [-1., 1.], [-1., 1.]], mindspore.float32)
+        >>> matrix_set_diag = nn.MatrixSetDiag()
+        >>> result = matrix_set_diag(x, diagonal)
+        [[[-1, 0], [0, 2]], [-1, 0], [0, 1]], [[-1, 0], [0, 1]]]
+    """
+    def __init__(self):
+        super(MatrixSetDiag, self).__init__()
+        self.matrix_set_diag = inner.MatrixSetDiag()
+        self.dtype = P.DType()
+
+    def construct(self, input_x, diagonal):
+        x_shape = F.shape(input_x)
+        x_dtype = self.dtype(input_x)
+        assist = _get_matrix_diag_part_assist(x_shape, x_dtype)
+        out_matrix_set_diag = self.matrix_set_diag(input_x, diagonal, assist)
+        return out_matrix_set_diag

mindspore/ops/_op_impl/tbe/__init__.py

@@ -264,3 +264,6 @@ from .inplace_update import _inplace_update_tbe
 from .splitv import _split_v_tbe
 from .in_top_k import _in_top_k_tbe
 from .lin_space import _lin_space_tbe
+from .matrix_diag import _matrix_diag_tbe
+from .matrix_diag_part import _matrix_diag_part_tbe
+from .matrix_set_diag import _matrix_set_diag_tbe

mindspore/ops/_op_impl/tbe/matrix_diag.py

@@ -0,0 +1,45 @@
+# 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.
+# ============================================================================
+
+"""MatrixDiagD op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+matrix_diag_d_op_info = TBERegOp("MatrixDiag") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("matrix_diag_d.so") \
+    .compute_cost(10) \
+    .kernel_name("matrix_diag_d") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "assist", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .get_op_info()
+
+
+@op_info_register(matrix_diag_d_op_info)
+def _matrix_diag_tbe():
+    """MatrixDiagD TBE register"""
+    return

mindspore/ops/_op_impl/tbe/matrix_diag_part.py

@@ -0,0 +1,45 @@
+# 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.
+# ============================================================================
+
+"""MatrixDiagPartD op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+matrix_diag_part_d_op_info = TBERegOp("MatrixDiagPart") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("matrix_diag_part_d.so") \
+    .compute_cost(10) \
+    .kernel_name("matrix_diag_part_d") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "assist", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .get_op_info()
+
+
+@op_info_register(matrix_diag_part_d_op_info)
+def _matrix_diag_part_tbe():
+    """MatrixDiagPartD TBE register"""
+    return

mindspore/ops/_op_impl/tbe/matrix_set_diag.py

@@ -0,0 +1,46 @@
+# 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.
+# ============================================================================
+
+"""MatrixSetDiagD op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+matrix_diag_d_op_info = TBERegOp("MatrixSetDiag") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("matrix_diag_d.so") \
+    .compute_cost(10) \
+    .kernel_name("matrix_diag_d") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "diagonal", False, "required", "all") \
+    .input(2, "assist", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .get_op_info()
+
+
+@op_info_register(matrix_diag_d_op_info)
+def _matrix_set_diag_tbe():
+    """MatrixSetDiagD TBE register"""
+    return

mindspore/ops/operations/_inner_ops.py

@@ -367,3 +367,144 @@ class LinSpace(PrimitiveWithInfer):
         args = {"assist": assist, "start": start, "stop": stop}
         validator.check_tensor_type_same(args, (mstype.float32,), self.name)
         return assist
+
+
+class MatrixDiag(PrimitiveWithInfer):
+    """
+    Returns a batched diagonal tensor with a given batched diagonal values.
+
+    Inputs:
+        - **x** (Tensor) - A tensor which to be element-wise multi by `assist`. It can be of the following data types:
+          float32, float16, int32, int8, uint8.
+        - **assist** (Tensor) - A eye tensor of the same type as `x`. It's rank must greater than or equal to 2 and
+          it's last dimension must equal to the second to last dimension.
+
+    Outputs:
+        Tensor, has the same type and shape as input `assist`.
+
+    Examples:
+        >>> x = Tensor(np.array([1, -1]), mstype.float32)
+        >>> assist = Tensor(np.arange(-12, 0).reshape(3, 2, 2), mindspore.float32)
+        >>> matrix_diag = P.MatrixDiag()
+        >>> result = matrix_diag(x, assist)
+        [[[-12.   11.]
+          [-10.    9.]]
+         [[ -8.    7.]
+          [ -6.    5.]]
+         [[ -4.    3.]
+          [ -2.    1.]]]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init MatrixDiag"""
+
+    def infer_dtype(self, x_dtype, assist_dtype):
+        valid_type = [mstype.float16, mstype.float32, mstype.int32, mstype.int8, mstype.uint8]
+        args = {"x": x_dtype, "assist": assist_dtype}
+        validator.check_tensor_type_same(args, valid_type, self.name)
+        return x_dtype
+
+    def infer_shape(self, x_shape, assist_shape):
+        validator.check_integer("assist rank", len(assist_shape), 2, Rel.GE, self.name)
+        validator.check('rank of x', len(x_shape)+1,
+                        'rank of assist', len(assist_shape), Rel.LE, self.name)
+        validator.check('assist\'s penultimate dimension', assist_shape[-2], 'assist\'s last dimension',
+                        assist_shape[-1], Rel.EQ, self.name)
+
+        r_end_dim = -len(x_shape)
+        r_idx = -1
+        while r_idx >= r_end_dim:
+            if x_shape[r_idx] != 1:
+                validator.check("reverse x dim %d" % r_idx, x_shape[r_idx], "reverse assist dim %d" %
+                                assist_shape[r_idx-1], assist_shape[r_idx-1], Rel.EQ, self.name)
+            r_idx = r_idx - 1
+
+        return assist_shape
+
+
+class MatrixDiagPart(PrimitiveWithInfer):
+    r"""
+    Returns the batched diagonal part of a batched tensor.
+
+    Inputs:
+        - **x** (Tensor) - The batched tensor. It can be of the following data types:
+          float32, float16, int32, int8, uint8.
+        - **assist** (Tensor) - A eye tensor of the same type as `x`. With shape same as `x`.
+
+    Outputs:
+        Tensor, data type same as input `x`. The shape should be x.shape[:-2] + [min(x.shape[-2:])].
+
+    Examples:
+        >>> x = Tensor([[[-1, 0], [0, 1]], [-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32)
+        >>> assist = Tensor(np.arange(-12, 0).reshape(3, 2, 2), mindspore.float32)
+        >>> matrix_diag_part = P.MatrixDiagPart()
+        >>> result = matrix_diag_part(x, assist)
+        [[12., -9.], [8., -5.], [4., -1.]]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init MatrixDiagPart"""
+
+    def infer_dtype(self, x_dtype, assist_dtype):
+        valid_type = [mstype.float16, mstype.float32, mstype.int32, mstype.int8, mstype.uint8]
+        args = {"x": x_dtype, "assist": assist_dtype}
+        validator.check_tensor_type_same(args, valid_type, self.name)
+        return x_dtype
+
+    def infer_shape(self, x_shape, assist_shape):
+        validator.check_integer("x rank", len(x_shape), 2, Rel.GE, self.name)
+        validator.check("x shape", x_shape, "assist shape", assist_shape, Rel.EQ, self.name)
+
+        if assist_shape[-2] < assist_shape[-1]:
+            out_shape = assist_shape[:-1]
+        else:
+            out_shape = assist_shape[:-2] + assist_shape[-1:]
+        return out_shape
+
+
+class MatrixSetDiag(PrimitiveWithInfer):
+    r"""
+    Modify the batched diagonal part of a batched tensor.
+
+    Inputs:
+        - **x** (Tensor) - The batched tensor. It can be of the following data types:
+          float32, float16, int32, int8, uint8.
+        - **assist** (Tensor) - A eye tensor of the same type as `x`. With shape same as `x`.
+        - **diagonal** (Tensor) - The diagonal values.
+
+    Outputs:
+        Tensor, data type same as input `x`. The shape same as `x`.
+
+    Examples:
+        >>> x = Tensor([[[-1, 0], [0, 1]], [-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32)
+        >>> diagonal = Tensor([[-1., 2.], [-1., 1.], [-1., 1.]], mindspore.float32)
+        >>> matrix_set_diag = P.MatrixSetDiag()
+        >>> result = matrix_set_diag(x, diagonal)
+        [[[-1, 0], [0, 2]], [-1, 0], [0, 1]], [[-1, 0], [0, 1]]]
+
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init MatrixSetDiag"""
+
+    def infer_dtype(self, x_dtype, diagonal_dtype, assist_dtype):
+        valid_type = [mstype.float16, mstype.float32, mstype.int32, mstype.int8, mstype.uint8]
+        args = {"x": x_dtype, "diagonal": diagonal_dtype, "assist": assist_dtype}
+        validator.check_tensor_type_same(args, valid_type, self.name)
+        return x_dtype
+
+    def infer_shape(self, x_shape, diagonal_shape, assist_shape):
+        validator.check_integer("x rank", len(x_shape), 2, Rel.GE, self.name)
+        validator.check("x shape", x_shape, "assist shape", assist_shape, Rel.EQ, self.name)
+
+        if x_shape[-2] < x_shape[-1]:
+            validator.check("x shape excluding the last dimension", x_shape[:-1], "diagnoal shape",
+                            diagonal_shape, Rel.EQ, self.name)
+        else:
+            validator.check("x shape excluding the second to last dimension", x_shape[:-2]+x_shape[-1:],
+                            "diagonal shape", diagonal_shape, Rel.EQ, self.name)
+
+        return assist_shape

tests/ut/python/ops/test_nn_ops.py

@@ -370,6 +370,7 @@ def test_conv2d_same_primitive():
             super(Conv2DSameNet, self).__init__()
             self.conv1 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
             self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
+
         def construct(self, x, y):
             r1 = self.conv1(x)
             r2 = self.conv2(y)
@@ -576,6 +577,22 @@ test_cases = [
                         Tensor(np.ones([1, 3, 4, 4], np.float32)),
                         Tensor(np.ones(3, np.float32))],
     }),
+    ('MatrixDiag', {
+        'block': nn.MatrixDiag(),
+        'desc_inputs': [Tensor(np.array([1, 2, 3]).astype(np.float32))],
+        'skip': ['backward']
+    }),
+    ('MatrixDiagPart', {
+        'block': nn.MatrixDiagPart(),
+        'desc_inputs': [Tensor(np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32))],
+        'skip': ['backward']
+    }),
+    ('MatrixSetDiag', {
+        'block': nn.MatrixSetDiag(),
+        'desc_inputs': [Tensor(np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)),
+                        Tensor(np.array([1, 2]).astype(np.float32))],
+        'skip': ['backward']
+    }),
 ]
 
 test_cases_for_verify_exception = [

tests/ut/python/ops/test_ops.py

@@ -1612,6 +1612,25 @@ test_case_array_ops = [
                         Tensor(5, mstype.int32)],
         'skip': ['backward'],
     }),
+    ('MatrixDiag', {
+        'block': inner.MatrixDiag(),
+        'desc_inputs': [Tensor(np.array([1, -1]), mstype.float32),
+                        Tensor(np.arange(-12, 0).reshape(3, 2, 2), mstype.float32)],
+        'skip': ['backward'],
+    }),
+    ('MatrixDiagPart', {
+        'block': inner.MatrixDiagPart(),
+        'desc_inputs': [Tensor(np.arange(12).reshape(3, 2, 2), mstype.float32),
+                        Tensor(np.arange(-12, 0).reshape(3, 2, 2), mstype.float32)],
+        'skip': ['backward'],
+    }),
+    ('MatrixSetDiag', {
+        'block': inner.MatrixSetDiag(),
+        'desc_inputs': [Tensor(np.arange(12).reshape(3, 2, 2), mstype.float32),
+                        Tensor(np.arange(6).reshape(3, 2), mstype.float32),
+                        Tensor(np.arange(-12, 0).reshape(3, 2, 2), mstype.float32)],
+        'skip': ['backward'],
+    }),
 ]
 
 test_case_other_ops = [