add lpnorm

mindspore/core/base/core_ops.h

@@ -65,6 +65,7 @@ constexpr auto kBiasAddGrad = "BiasAddGrad";
 constexpr auto kCos = "Cos";
 constexpr auto kAbs = "Abs";
 constexpr auto kTrunc = "Trunc";
+constexpr auto kLpNorm = "LpNorm";
 constexpr auto kSquare = "Square";
 constexpr auto kReal = "Real";
 constexpr auto kGer = "Ger";
@@ -508,6 +509,7 @@ inline const PrimitivePtr kPrimCumSum = std::make_shared<Primitive>("CumSum");
 inline const PrimitivePtr kPrimCumProd = std::make_shared<Primitive>("CumProd");
 inline const PrimitivePtr kPrimSubscalar = std::make_shared<Primitive>("Subscalar");
 inline const PrimitivePtr kPrimInplaceAdd = std::make_shared<Primitive>("InplaceAdd");
+inline const PrimitivePtr kPrimLpNorm = std::make_shared<Primitive>(kLpNorm);
 inline const PrimitivePtr kPrimInplaceSub = std::make_shared<Primitive>("InplaceSub");
 inline const PrimitivePtr kPrimPow = std::make_shared<Primitive>("Pow");
 inline const PrimitivePtr kPrimPower = std::make_shared<Primitive>("Power");

mindspore/core/ops/lp_norm.cc

@@ -0,0 +1,111 @@
+/**
+ * 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 <map>
+#include <string>
+#include <vector>
+#include <algorithm>
+#include <set>
+#include "ops/lp_norm.h"
+#include "ops/op_utils.h"
+#include "utils/check_convert_utils.h"
+#include "abstract/primitive_infer_map.h"
+
+namespace mindspore {
+namespace ops {
+namespace {
+abstract::ShapePtr InferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto prim_name = primitive->name();
+  const int64_t input_num = 1;
+  (void)CheckAndConvertUtils::CheckInteger("input numbers", SizeToLong(input_args.size()), kEqual, input_num,
+                                           prim_name);
+  for (const auto &item : input_args) {
+    MS_EXCEPTION_IF_NULL(item);
+  }
+  auto input_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->BuildShape())[kShape];
+  auto output_shape = input_shape;
+  auto input_rank = SizeToLong(input_shape.size());
+  auto axis = GetValue<std::vector<int64_t>>(primitive->GetAttr("axis"));
+  auto keep_dims = GetValue<bool>(primitive->GetAttr("keep_dims"));
+  if (input_rank == 0) {
+    CheckAndConvertUtils::CheckInteger("axis size", axis.size(), kEqual, input_rank + 1, prim_name);
+    return std::make_shared<abstract::Shape>(input_shape);
+  } else {
+    CheckAndConvertUtils::CheckInRange("axis size", axis.size(), kIncludeNeither, {0, input_rank + 1}, prim_name);
+  }
+  if (axis.size() > 1) {
+    for (size_t i = 0; i < axis.size(); ++i) {
+      CheckAndConvertUtils::CheckInRange("axis value", axis[i], kIncludeLeft, {-input_rank, input_rank}, prim_name);
+      if (axis[i] < 0) {
+        axis[i] += input_rank;
+      }
+    }
+    for (size_t i = 0; i < axis.size(); ++i) {
+      auto temp = axis;
+      auto idx = std::find(temp.begin(), temp.end(), axis[i]);
+      (void)temp.erase(idx);
+      auto re_idx = std::find(temp.begin(), temp.end(), axis[i]);
+      if (re_idx != temp.end()) {
+        MS_EXCEPTION(ValueError) << "The element of the axis should be different";
+      }
+      if (keep_dims == false) {
+        output_shape[axis[i]] = -1;
+      } else {
+        output_shape[axis[i]] = 1;
+      }
+    }
+    if (keep_dims == false) {
+      for (std::vector<int64_t>::iterator iter = output_shape.begin(); iter != output_shape.end(); ++iter) {
+        if (*iter == -1) {
+          iter = output_shape.erase(iter);
+          iter -= 1;
+        }
+      }
+    }
+  } else {
+    if (axis[0] < 0) {
+      axis[0] += input_rank;
+    }
+    if (keep_dims == false) {
+      (void)output_shape.erase(output_shape.begin() + axis[0]);
+    } else {
+      output_shape[axis[0]] = 1;
+    }
+  }
+  return std::make_shared<abstract::Shape>(output_shape);
+}
+
+TypePtr InferType(const PrimitivePtr &prim, const std::vector<AbstractBasePtr> &input_args) {
+  for (const auto &item : input_args) {
+    MS_EXCEPTION_IF_NULL(item);
+  }
+  auto infer_type = input_args[0]->BuildType();
+  MS_EXCEPTION_IF_NULL(infer_type);
+  const std::set<TypePtr> valid_types = {kFloat16, kFloat32};
+  CheckAndConvertUtils::CheckTensorTypeValid("input", infer_type, valid_types, prim->name());
+  return infer_type;
+}
+}  // namespace
+
+AbstractBasePtr LpNormInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                            const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  return abstract::MakeAbstract(InferShape(primitive, input_args), InferType(primitive, input_args));
+}
+REGISTER_PRIMITIVE_EVAL_IMPL(LpNorm, prim::kPrimLpNorm, LpNormInfer, nullptr, true);
+}  // namespace ops
+}  // namespace mindspore

mindspore/core/ops/lp_norm.h

@@ -0,0 +1,43 @@
+/**
+ * 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_LP_NORM_H_
+#define MINDSPORE_CORE_OPS_LP_NORM_H_
+#include <vector>
+#include <memory>
+
+#include "ops/primitive_c.h"
+#include "abstract/abstract_value.h"
+#include "utils/check_convert_utils.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kNameLpNorm = "LpNorm";
+class LpNorm : public PrimitiveC {
+ public:
+  LpNorm() : PrimitiveC(kNameLpNorm) { InitIOName({"input"}, {"output"}); }
+  ~LpNorm() = default;
+  MS_DECLARE_PARENT(LpNorm, PrimitiveC);
+};
+
+AbstractBasePtr LpNormInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                            const std::vector<AbstractBasePtr> &input_args);
+
+using PrimLpNormPtr = std::shared_ptr<LpNorm>;
+}  // namespace ops
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CORE_OPS_LP_NORM_H_

mindspore/ops/_grad_experimental/grad_math_ops.py

@@ -81,6 +81,41 @@ def get_bprop_index_lerp(self):
     return bprop
 
 
+@bprop_getters.register(P.LpNorm)
+def get_bprop_lp_norm(self):
+    """Grad definition for `LpNorm` operation."""
+    p = self.p
+    keep_dims = self.keep_dims
+    axis = self.axis
+    if isinstance(axis, int):
+        axis = [axis]
+    sign_op = P.Sign()
+    abs_op = P.Abs()
+    zeros_like_op = P.ZerosLike()
+    expand_dims_op = P.ExpandDims()
+    pow_op = P.Pow()
+
+    def bprop(input_x, out, dout):
+        if not keep_dims and input_x.shape != ():
+            for i in axis:
+                dout = expand_dims_op(dout, i)
+                out = expand_dims_op(out, i)
+
+        if p == 0:
+            return (zeros_like_op(input_x),)
+        if p == 1:
+            return (dout * sign_op(input_x),)
+        if p == 2:
+            input_scaled = input_x
+            scale_v = dout / out
+        else:
+            input_scaled = pow_op(abs_op(input_x), (p-2)) * input_x
+            scale_v = dout / pow_op(out, (p-1))
+        return (input_scaled * scale_v,)
+
+    return bprop
+
+
 @bprop_getters.register(P.Erfinv)
 def get_bprop_erfinv(self):
     """Grad definition for `Erfinv` operation."""

mindspore/ops/_op_impl/tbe/__init__.py

@@ -416,6 +416,7 @@ from .cdist_grad import _cdist_grad_tbe
 from .neg_ds import _neg_ds_tbe
 from .not_equal_ds import _not_ds_equal_tbe
 from .reciprocal_ds import _reciprocal_ds_tbe
+from .lp_norm import _lp_norm_tbe
 from .ctc_loss_v2 import _ctc_loss_v2_tbe
 from .ctc_loss_v2_grad import _ctc_loss_v2_grad_tbe
 from .roll import _roll_tbe

mindspore/ops/_op_impl/tbe/lp_norm.py

@@ -0,0 +1,40 @@
+# 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.
+# ============================================================================
+
+"""LpNorm op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+lp_norm_op_info = TBERegOp("LpNorm") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("lp_norm.so") \
+    .compute_cost(10) \
+    .kernel_name("lp_norm") \
+    .partial_flag(True) \
+    .attr("p", "optional", "int", "all", "2") \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all", "false") \
+    .attr("epsilon", "optional", "float", "all", "1e-12") \
+    .input(0, "input", False, "required", "all") \
+    .output(0, "output", False, "required", "all") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .get_op_info()
+
+
+@op_info_register(lp_norm_op_info)
+def _lp_norm_tbe():
+    """LpNorm TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -52,7 +52,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
                        ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd, Cdist, ReduceAny,
                        Cos, Div, DivNoNan, Equal, EqualCount, Exp, Expm1, Erf, Erfc, Floor, FloorDiv, FloorMod, Ceil,
                        Acosh, Greater, GreaterEqual, Lerp, Less, LessEqual, Log, Log1p, LogicalAnd, Mod,
-                       LogicalNot, LogicalOr, MatMul, Maximum, MulNoNan,
+                       LogicalNot, LogicalOr, LpNorm, MatMul, Maximum, MulNoNan,
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,
                        NPUAllocFloatStatus, NPUClearFloatStatus, LinSpace,
                        NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
@@ -406,6 +406,7 @@ __all__ = [
     "Conv2DBackpropInput",
     "ComputeAccidentalHits",
     "Sign",
+    "LpNorm",
     "LARSUpdate",
     "Round",
     "Eps",

mindspore/ops/operations/math_ops.py

@@ -1152,6 +1152,63 @@ class Cdist(Primitive):
         self.init_prim_io_names(inputs=['input_x', 'input_y'], outputs=['output'])
 
 
+class LpNorm(Primitive):
+    """
+    Returns the matrix norm or vector norm of a given tensor.
+
+    .. math::
+        output = sum(abs(input)**p)**(1/p)
+
+    Args:
+        axis(int,list,tuple): Specifies which dimension or dimensions of input to calculate the norm across.
+        p(int): The order of norm.
+        keep_dims(bool): Whether the output tensors have dim retained or not.
+
+    Inputs:
+        - **input** (Tensor) - Input tensor.
+
+    Outputs:
+        Tensor, has the same dtype as `input`, which shape is depend on the args axis.For example, if the size of input
+        is (2, 3, 4), axis is [0, 1], Outputs' shape will be (4,).
+
+    Raises:
+        TypeError: If `input` is not a Tensor.
+        TypeError: If dtype of `input` is not one of: float16, float32.
+        TypeError: If `p` is not an int.
+        TypeError: If `axis` is not an int, a tuple or a list.
+        TypeError: If `axis` is a tuple or a list, but the element of `axis` is not an int.
+        TypeError: If `keep_dims` is not a bool.
+        ValueError: If the element of `axis` is out of the range [-len(input.shape), len(input.shape)).
+        ValueError: If the length of shape of `axis` is bigger than the length of shape of `input`.
+
+    Supported Platforms:
+        ``Ascend``
+
+    Examples:
+        >>> input = Tensor(np.array([[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]]).astype(np.float32))
+        >>> op = ops.LpNorm(axis=[0, 1], p=2, keep_dims=False)
+        >>> output = op(input)
+        >>> print(output)
+        [ 9.165152 10.954452]
+    """
+
+    @prim_attr_register
+    def __init__(self, axis, p=2, keep_dims=False, epsilon=1e-12):
+        """Initialize LpNorm"""
+        validator.check_value_type("p", p, [int], self.name)
+        validator.check_value_type("axis", axis, [int, tuple, list], self.name)
+        validator.check_value_type("keep_dims", keep_dims, [bool], self.name)
+        validator.check_value_type("epsilon", epsilon, [float], self.name)
+        validator.check_non_negative_int(p, "p", self.name)
+        validator.check_non_negative_float(epsilon, "epsilon", self.name)
+        if isinstance(axis, int):
+            self.add_prim_attr('axis', [self.axis])
+        else:
+            for element_of_axis in axis:
+                validator.check_value_type("element_of_axis", element_of_axis, [int], self.name)
+        self.init_prim_io_names(inputs=['input'], outputs=['output'])
+
+
 class MatMul(PrimitiveWithCheck):
     r"""
     Multiplies matrix `x` and matrix `y`.

tests/ut/python/ops/test_ops.py

@@ -1284,6 +1284,10 @@ test_case_math_ops = [
         'block': LaplaceNet((3, 2, 4), 0),
         'desc_inputs': [Tensor(1.0, mstype.float32), Tensor(1.0, mstype.float32)],
         'skip': ['backward']}),
+    ('LpNorm', {
+        'block': P.LpNorm(axis=[0, 1], p=2, keep_dims=False),
+        'desc_inputs': [Tensor([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], mstype.float32)],
+        'desc_bprop': [Tensor([1, 2], mstype.float32)]}),
     ('Gamma', {
         'block': GammaNet((3, 2, 4), 0),
         'desc_inputs': [Tensor(1.0, mstype.float32), Tensor(1.0, mstype.float32)],