[feat] [assistant] [I40GGH] add new ascend operator Cummin

mindspore/core/base/core_ops.h

@@ -173,6 +173,7 @@ inline const PrimitivePtr kPrimStackPop = std::make_shared<Primitive>("StackPop"
 
 // Arrays
 inline const PrimitivePtr kPrimDynamicBroadcastTo = std::make_shared<Primitive>(kDynamicBroadcastTo);
+inline const PrimitivePtr kPrimCummin = std::make_shared<Primitive>("Cummin");
 inline const PrimitivePtr kPrimBroadcastTo = std::make_shared<Primitive>("BroadcastTo");
 inline const PrimitivePtr kPrimScalarToArray = std::make_shared<Primitive>("scalar_to_array");
 inline const PrimitivePtr kPrimTopK = std::make_shared<Primitive>("TopK");

mindspore/core/ops/cummin.cc

@@ -0,0 +1,63 @@
+/**
+ * Copyright 2021 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.
+ */
+
+#include <algorithm>
+#include <set>
+#include "abstract/primitive_infer_map.h"
+#include "utils/check_convert_utils.h"
+#include "ops/cummin.h"
+
+namespace mindspore {
+namespace ops {
+namespace {
+abstract::TupleShapePtr InferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto prim_name = primitive->name();
+  auto y_shape = CheckAndConvertUtils::GetTensorInputShape(prim_name, input_args, 0);
+  auto x_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->BuildShape())[kShape];
+  auto y_rank = x_shape.size();
+  const int64_t min_dim = 0;
+  (void)CheckAndConvertUtils::CheckInteger("the rank of input", SizeToLong(x_shape.size()), kGreaterThan, min_dim,
+                                           prim_name);
+  int64_t axis = GetValue<int64_t>(primitive->GetAttr("axis"));
+  CheckAndConvertUtils::CheckInRange<int64_t>("axis", axis, kIncludeBoth, {-y_rank, y_rank - 1}, prim_name);
+  return std::make_shared<abstract::TupleShape>(std::vector<abstract::BaseShapePtr>{y_shape, y_shape});
+}
+
+TuplePtr InferType(const PrimitivePtr &prim, const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(prim);
+  auto prim_name = prim->name();
+  MS_EXCEPTION_IF_NULL(input_args[0]);
+  const std::set<TypePtr> valid_types = {kFloat32, kFloat16, kInt32, kInt8, kUInt8};
+  auto x_type = input_args[0]->BuildType();
+  (void)CheckAndConvertUtils::CheckTensorTypeValid("input_x", x_type, valid_types, prim_name);
+  TypePtr argmin_type = kInt32;
+  return std::make_shared<Tuple>(std::vector{x_type, argmin_type});
+}
+}  // namespace
+
+AbstractBasePtr CumminInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                            const std::vector<AbstractBasePtr> &input_args) {
+  auto prim_name = primitive->name();
+  (void)CheckAndConvertUtils::CheckInteger("input numbers", input_args.size(), kEqual, 1, prim_name);
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto type = InferType(primitive, input_args);
+  auto shape = InferShape(primitive, input_args);
+  return abstract::MakeAbstract(shape, type);
+}
+REGISTER_PRIMITIVE_EVAL_IMPL(Cummin, prim::kPrimCummin, CumminInfer, nullptr, true);
+}  // namespace ops
+}  // namespace mindspore

mindspore/core/ops/cummin.h

@@ -0,0 +1,44 @@
+/**
+ * Copyright 2021 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.
+ */
+
+#ifndef MINDSPORE_CORE_OPS_CUMMIN_H_
+#define MINDSPORE_CORE_OPS_CUMMIN_H_
+#include <map>
+#include <vector>
+#include <string>
+#include <memory>
+
+#include "ops/primitive_c.h"
+#include "ops/op_utils.h"
+#include "abstract/abstract_value.h"
+#include "utils/check_convert_utils.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kNameCummin = "kCummin";
+class Cummin : public PrimitiveC {
+ public:
+  Cummin() : PrimitiveC(kNameCummin) { InitIOName({"x"}, {"y"}); }
+  ~Cummin() = default;
+  MS_DECLARE_PARENT(Cummin, PrimitiveC);
+};
+
+AbstractBasePtr CumminInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                            const std::vector<AbstractBasePtr> &input_args);
+using PrimCumminPtr = std::shared_ptr<Cummin>;
+}  // namespace ops
+}  // namespace mindspore
+#endif  // MINDSPORE_CORE_OPS_CUMMIN_H_

mindspore/ops/_op_impl/tbe/__init__.py

@@ -15,6 +15,7 @@
 
 """tbe ops"""
 from .celu import _celu_tbe
+from .cummin import  _cummin_tbe
 from .abs import _abs_tbe
 from .abs_ds import _abs_ds_tbe
 from .inplace_add import _inplace_add_tbe

mindspore/ops/_op_impl/tbe/cummin.py

@@ -0,0 +1,41 @@
+# Copyright 2021 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.
+# ============================================================================
+
+"""Cummin op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+celu_op_info = TBERegOp("Cummin") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("cummin.so") \
+    .compute_cost(10) \
+    .kernel_name("cummin") \
+    .partial_flag(True) \
+    .attr("axis", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .output(0, "argmin", False, "required", "all") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I32_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.I32_Default) \
+    .get_op_info()
+
+
+@op_info_register(celu_op_info)
+def _cummin_tbe():
+    """Cummin TBE register"""
+    return

mindspore/ops/composite/__init__.py

@@ -27,7 +27,7 @@ from .multitype_ops.add_impl import hyper_add
 from .multitype_ops.ones_like_impl import ones_like
 from .multitype_ops.zeros_like_impl import zeros_like
 from .random_ops import normal, laplace, uniform, gamma, poisson, multinomial
-from .math_ops import count_nonzero, tensor_dot, dot, batch_dot, matmul
+from .math_ops import count_nonzero, tensor_dot, dot, batch_dot, matmul, cummin
 from .array_ops import repeat_elements, sequence_mask
 
 
@@ -53,6 +53,7 @@ __all__ = [
     'clip_by_value',
     'clip_by_global_norm',
     'count_nonzero',
+    'cummin',
     'tensor_dot',
     'dot',
     'batch_dot',

mindspore/ops/composite/math_ops.py

@@ -19,12 +19,11 @@ import numpy as np
 from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
 from mindspore.common import dtype as mstype
 from mindspore._checkparam import Validator as validator
+from mindspore.ops.operations import _inner_ops as inner
 from mindspore.ops.primitive import constexpr
 from mindspore.ops import functional as F
 from .. import operations as P
 
-# count_nonzero
-
 
 @constexpr
 def _check_validate_axis(axis, name):
@@ -104,8 +103,6 @@ def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
 
     return nonzero_num
 
-# tensor dot
-
 
 @constexpr
 def _int_to_tuple_conv(axes):
@@ -207,7 +204,7 @@ def _validate_axes(x1_shape, x2_shape, axes, prim_name=None):
     for i in range(len(axes[0])):  # sizes already validated
         if x1_shape[axes[0][i]] != x2_shape[axes[1][i]]:
             invalid_a = True
-        if x1_shape[axes[0][i]] != x2_shape[axes[1][len(axes[0])-1-i]]:
+        if x1_shape[axes[0][i]] != x2_shape[axes[1][len(axes[0]) - 1 - i]]:
             invalid_b = True
     if invalid_a and invalid_b:
         raise ValueError(f"{msg_prefix} 'i' should exist such that 'x1_shape[axes[0][i]]' is equal to "
@@ -836,3 +833,71 @@ def matmul(x1, x2, dtype=None):
     if dtype is not None:
         res = res.astype(dtype)
     return F.reshape(res, shape_out)
+
+
+@constexpr
+def _create_cummin_perm(axis, x_shape):
+    """Insure axis is in [-len(x_shape),len(s_shape)-1]"""
+    len_axis = len(x_shape)
+    if not isinstance(axis, int):
+        raise TypeError(f"The date type of 'axis' should be Int, but got {axis}.")
+    if axis < -len_axis or axis > len_axis:
+        raise ValueError(f"The value of axis should be in [{-len_axis}, {len_axis}], but got {axis}.")
+    prem = [i for i in range(len_axis)]
+    if axis < 0:
+        axis = axis + len_axis
+    prem[0], prem[axis] = axis, 0
+    prem = tuple(prem)
+    return prem
+
+
+def cummin(x, axis):
+    r"""
+    Computation of the cumulative minimum of elements of 'x' in the dimension axis,
+    and the index location of each maximum value found in the dimension 'axis'.
+
+    It returns the cumulative minimum of elements and the index.
+
+    ..math::
+
+        y{i} = min(x{1}, x{2}, ... , x{i})
+
+    Args:
+        x (Tensor): The input tensor, rank of `input_x` > 0.
+        axis (Int): The dimension to do the operation, The axis is in the range from -len(`input_x`.shape)
+          to len(`input_x`.shape) - 1. When it's in the range from 0 to len(`input_x`.shape) - 1, it means starting
+          from the first dimension and counting forwards, When it's less than 0, it means we're counting backwards
+          from the last dimension. for example, -1 means the last dimension.
+
+    Outputs:
+        - **output** (Tensor) - The output tensor of the cumulative minimum of elements.
+        - **indices** (Tensor) - The result tensor of the index of each minimum value been found.
+
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+        TypeError: If 'axis' is not a int.
+        ValueError:If 'axis' is out the range of [-len(`input_x`.shape) to len(`input_x`.shape) - 1]
+
+    Supported Platforms:
+        ``Ascend``
+
+    Examples:
+        >>> a = Tensor([-0.2284, -0.6628,  0.0975,  0.2680, -1.3298, -0.4220], mindspore.float32)
+        >>> output = ops.cummin(a, axis=0)
+        >>> print(output[0])
+        [-0.2284 -0.6628 -0.6628 -0.6628 -1.3298 -1.3298]
+        >>> print(output[1])
+        [0 1 1 1 4 4]
+    """
+    cummin_op = inner.Cummin(axis=0)
+    if axis == 0:
+        out1, out2 = cummin_op(x)
+    else:
+        transpose = P.Transpose()
+        x_shape = P.Shape()(x)
+        prem = _create_cummin_perm(axis, x_shape)
+        x = transpose(x, prem)
+        out1, out2 = cummin_op(x)
+        out1 = transpose(out1, prem)
+        out2 = transpose(out2, prem)
+    return [out1, out2]

mindspore/ops/operations/__init__.py

@@ -59,7 +59,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
                        Reciprocal, CumSum, HistogramFixedWidth, SquaredDifference, Xdivy, Xlogy,
                        Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, TruncateDiv, TruncateMod,
                        Square, Sub, TensorAdd, Add, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps, Tan,
-                       MatrixInverse, IndexAdd, Erfinv, Conj, Real, Imag, Complex, Trunc,)
+                       MatrixInverse, IndexAdd, Erfinv, Conj, Real, Imag, Complex, Trunc)
 
 from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, Poisson, UniformInt, UniformReal,
                          RandomCategorical, StandardLaplace, Multinomial, UniformCandidateSampler,

mindspore/ops/operations/_inner_ops.py

@@ -1429,3 +1429,49 @@ class DynamicBroadcastTo(Primitive):
     def __init__(self):
         """Initialize DynamicBroadcastTo"""
         self.init_prim_io_names(inputs=['x', 'shape'], outputs=['y'])
+
+
+class Cummin(Primitive):
+    r"""
+    Computation of the cumulative minimum of elements of 'input' in the dimension dim,
+    and the index location of each maximum value found in the dimension 'dim'.
+
+    It returns the cumulative minimum of elements and the index.
+
+    ..math::
+
+        y{i} = min(x{1}, x{2}, ... , x{i})
+
+    Args:
+        - **axis** (int) - The dimension to do the operation, The axis is in the range from -len(`input_x`.shape)
+          to len(`input_x`.shape) - 1. When it's in the range from 0 to len(`input_x`.shape) - 1, it means starting
+          from the first dimension and counting forwards, When it's less than 0, it means we're counting backwards
+          from the last dimension. for example, -1 means the last dimension.
+    Inputs:
+        - **input_x** (Tensor) - The input tensor, rank of `input_x` > 0.
+
+    Outputs:
+        - **output** (Tensor) - The output tensor of the cumulative minimum of elements.
+        - **indices** (Tensor) - The result tensor of the index of each minimum value been found.
+
+    Raises:
+        TypeError: If `input_x` is not a Tensor.
+        TypeError: If 'axis' is not a int.
+        ValueError:If 'axis' is out the range from -len(`input_x`.shape) to len(`input_x`.shape) - 1
+
+    Supported Platforms:
+        ``Ascend``
+
+    Examples:
+        >>> a = Tensor([-0.2284, -0.6628,  0.0975,  0.2680, -1.3298, -0.4220], mindspore.float32)
+        >>> output = ops.cummin(a, axis=0)
+        >>> print(output[0])
+        [-0.2284 -0.6628 -0.6628 -0.6628 -1.3298 -1.3298]
+        >>> print(output[1])
+        [0 1 1 1 4 4]
+    """
+
+    @prim_attr_register
+    def __init__(self, axis):
+        """Initialize Cummin"""
+        validator.check_value_type('axis', axis, [int], self.name)

tests/ut/python/ops/test_ops.py

@@ -2180,6 +2180,10 @@ test_case_nn_ops = [
         'desc_const': [0],
         'desc_inputs': [[3, 2]],
         'desc_bprop': [[3, 2]]}),
+    ('Cummin', {
+        'block': inner.Cummin(axis=0),
+        'desc_inputs': [[1, 3, 3, 3]],
+        'skip': ['backward']}),
     ('ApplyFtrl', {
         'block': ApplyFtrlNet(),
         'desc_inputs': [[3, 3]],