[GitHub] [incubator-mxnet] vexilligera commented on a change in pull request #17014: [NumPy] Add NumPy support for norm

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vexilligera commented on a change in pull request #17014: [NumPy] Add NumPy support for norm
URL: https://github.com/apache/incubator-mxnet/pull/17014#discussion_r368790115
 
 

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 File path: src/operator/numpy/linalg/np_norm-inl.h
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_norm-inl.h
+ * \brief norm
+ */
+#ifndef MXNET_OPERATOR_NUMPY_LINALG_NP_NORM_INL_H_
+#define MXNET_OPERATOR_NUMPY_LINALG_NP_NORM_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include <limits>
+#include <cmath>
+#include "../../tensor/la_op.h"
+#include "../../tensor/la_op-inl.h"
+#include "../../tensor/init_op.h"
+#include "./broadcast_reduce_op_customized.h"
+#include "./np_gesvd-inl.h"
+#include "../np_matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+namespace mshadow_op {
+/*! \brief Lp-norm power reducer */
+
+struct nrmlp {
+  double lp;
+  MSHADOW_XINLINE nrmlp(): lp(2) {}
+  MSHADOW_XINLINE nrmlp(double l): lp(l) {}
+
+  /* \brief power for Lp norm */
+  MSHADOW_XINLINE static double lp_power(volatile double src, volatile double p) {
+    if (p != 0.0) {
+      if (src == 0.0) {
+        return src;
+      } else {
+        return power::Map(src, p);
+      }
+    } else {  // 0-norm, sparsity
+      return static_cast<double>(src != 0);
+    }
+  }
+
+  /*! \brief do reduction into dst */
+  template<typename AType, typename DType>
+  MSHADOW_XINLINE void Reduce(volatile AType& sum_of_powers, volatile DType src) { // NOLINT(*)
+    if (src != 0) {
+      sum_of_powers += AType(lp_power(static_cast<double>(src), lp));
+    }
+  }
+
+  /*! \brief do stable reduction into dst */
+  template<typename AType, typename DType>
+  MSHADOW_XINLINE void Reduce(volatile AType& sum_of_powers,  volatile DType src, volatile DType& scale) { // NOLINT(*)
+    if (src != 0) {
+      DType abs = abs::Map(src);
+      if (scale < abs) {
+        sum_of_powers = 1 + sum_of_powers * AType(lp_power(static_cast<double>(scale / abs), lp));
+        scale = abs;
+      } else {
+        sum_of_powers = sum_of_powers + AType(lp_power(static_cast<double>(abs / scale), lp));
+      }
+    }
+  }
+
+  /*! \brief combine the results of two reducers */
+  template<typename DType>
+  MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType& src_val) { // NOLINT(*)
+    dst_val += src_val;
+  }
+
+  /*! \brief combine the results of two reducers */
+  template<typename DType>
+  MSHADOW_XINLINE static void Merge(volatile DType& dst_ssq, volatile DType& dst_scale, volatile DType& src_ssq, volatile DType& src_scale) { // NOLINT(*)
+    if (dst_scale != 0 && dst_scale >= src_scale) {
+      dst_ssq = dst_ssq + src_ssq * DType(lp_power(static_cast<double>(src_scale / dst_scale), 2));
+    } else if (src_scale != 0 && dst_scale < src_scale) {
+      dst_ssq = src_ssq + dst_ssq * DType(lp_power(static_cast<double>(dst_scale / src_scale), 2));
+      dst_scale = src_scale;
+    }
+  }
+
+  /*! \brief finalize reduction result */
+  template<typename DType>
+  MSHADOW_XINLINE void Finalize(volatile DType& sum_of_powers) { // NOLINT(*)
+    if (lp != 0.0) {
+      sum_of_powers = DType(lp_power(static_cast<double>(sum_of_powers), 1.0 / lp));
+    }
+  }
+
+  /*! \brief finalize reduction result */
+  template<typename DType>
+  MSHADOW_XINLINE void Finalize(volatile DType& sum_of_powers, volatile DType& scale) { // NOLINT(*)
+    if (lp != 0.0) {
+      sum_of_powers = scale * DType(lp_power(static_cast<double>(sum_of_powers), 1.0 / lp));
+    }
+  }
+
+  /*!
+   *\brief set the initial value during reduction
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static void SetInitValue(DType &sum_of_powers) { // NOLINT(*)
+    sum_of_powers = 0;
+  }
+
+  /*!
+   *\brief set the initial value during reduction
+   */
+  template<typename DType>
+  MSHADOW_XINLINE static void SetInitValue(DType &sum_of_powers, DType &scale) { // NOLINT(*)
+    SetInitValue(sum_of_powers);
+    scale = 0;
+  }
+};
+
+/*! \brief Elementwise gradient of Lp-norm, does not handle p = 1 */
+struct nrmlp_grad : public mxnet_op::tunable {
+  double lp;
+  MSHADOW_XINLINE nrmlp_grad(): lp(2) {}
+  MSHADOW_XINLINE nrmlp_grad(double l): lp(l) {}
+
+  /* \brief elementwise gradient of lp norm */
+  template<typename DType>
+  MSHADOW_XINLINE DType Map(DType a, DType b) {
+    DType ret;
+    if (lp != 0.0) {  // dx_i = (|x_i| / y) ^ (p - 1) * sgn(x_i)
+      DType abs_a = DType(abs::Map(a));
+      DType sgn_a = DType(sign::Map(a));
+      ret = power::Map(DType(abs_a / b), DType(lp - 1)) * sgn_a;
+    } else {  // L0 norm is elementwise constant and non-differentiable
+      ret = 0;
+    }
+    return ret;
+  }
+};
+
+/*! \brief Gradient for abs-min/max */
+struct abs_grad : public mxnet_op::tunable {
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a, DType b) {
+    DType sgn = DType(sign::Map(a));
+    DType grad = DType(abs::Map(a)) == DType(abs::Map(b)) ?
+                  DType(1.0) : DType(0.0);
+    return sgn * grad;
+  }
+};
+
+/*! \brief Sign */
+struct abs_sign : public mxnet_op::tunable {
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a, DType b) {
+    return DType(sign::Map(a));
+  }
+};
+
+}  // namespace mshadow_op
+
+inline bool NumpyLpNormShape(const nnvm::NodeAttrs& attrs,
+                             mxnet::ShapeVector *in_attrs,
+                             mxnet::ShapeVector *out_attrs);
+
+inline bool NumpyMatrixNormShape(const nnvm::NodeAttrs& attrs,
+                                   mxnet::ShapeVector *in_attrs,
+                                   mxnet::ShapeVector *out_attrs);
+
+inline void assign_svd_empty(mxnet::ShapeVector *out_attrs);
+
+bool NumpyNormShape(const nnvm::NodeAttrs& attrs,
+                    mxnet::ShapeVector *in_attrs,
+                    mxnet::ShapeVector *out_attrs);
+
+TShape swapMatDims(const TShape &shape, const TShape &axis);
+
+TShape inverseTranspose(const TShape &axes);
+
+struct NumpyNormParam : public dmlc::Parameter<NumpyNormParam> {
+  double ord;
+  dmlc::optional<mxnet::TShape> axis;
+  bool keepdims;
+  int flag;
+  DMLC_DECLARE_PARAMETER(NumpyNormParam) {
+    DMLC_DECLARE_FIELD(ord).set_default(2)
+    .describe("Order of the norm. inf means numpy’s inf object.");
+    DMLC_DECLARE_FIELD(axis).set_default(dmlc::optional<mxnet::TShape>())
+    .describe(R"code(If axis is an integer, it specifies the axis of x along
+     which to compute the vector norms. If axis is a 2-tuple,
+     it specifies the axes that hold 2-D matrices, and the matrix norms of
+     these matrices are computed. If axis is None then either a vector norm (when x is 1-D)
+     or a matrix norm (when x is 2-D) is returned.If axis is an integer,
+     it specifies the axis of x along which to compute the vector norms.
+     If axis is a 2-tuple, it specifies the axes that hold 2-D matrices,
+     and the matrix norms of these matrices are computed. If axis is None then either a
+     vector norm (when x is 1-D) or a matrix norm (when x is 2-D) is returned.)code");
+    DMLC_DECLARE_FIELD(keepdims).set_default(false)
+    .describe("If this is set to `True`, the reduced axis is left "
+    "in the result as dimension with size one.");
+    DMLC_DECLARE_FIELD(flag).set_default(-1)
+    .describe("Mapping relations between ord and flag."
+    "ord:  None,  'fro', 'nuc', 'inf'  '-inf'."
+    "flag:  0 ,    1,      2,    3,      4. ");
+  }
+};
+
+TShape swapMatDims(const TShape &shape, const TShape &axis);
+TShape inverseTranspose(const TShape &axes);
+
+bool NumpyNormShape(const nnvm::NodeAttrs& attrs,
+                    mxnet::ShapeVector *in_attrs,
+                    mxnet::ShapeVector *out_attrs);
+
+template<typename xpu>
+void NumpyLpNormCompute(const nnvm::NodeAttrs& attrs,
+                        const OpContext& ctx,
+                        const std::vector<TBlob>& inputs,
+                        const std::vector<OpReqType>& req,
+                        const std::vector<TBlob>& outputs) {
+  const NumpyNormParam& param = nnvm::get<NumpyNormParam>(attrs.parsed);
+  double ord = param.ord;
+
+  if (req[0] == kNullOp) return;
+
+  mxnet::TShape small;
+  mxnet::TShape out_shape = outputs[0].shape_;
+  if (param.keepdims) {
+    small = outputs[0].shape_;
+  } else {
+    small = ReduceAxesShapeImpl(inputs[0].shape_, param.axis, true, false);
+    const_cast<std::vector<TBlob>&>(outputs)[0] = outputs[0].reshape(small);
+  }
+  bool safe_acc = dmlc::GetEnv("MXNET_SAFE_ACCUMULATION", false);
+  if (!safe_acc && inputs[0].type_flag_ == mshadow::kFloat16) {
+    common::LogOnce("MXNET_SAFE_ACCUMULATION=1 is recommended for LpNorm with float16 inputs. "
+                    "See https://mxnet.apache.org/api/faq/env_var "
+                    "for more details.");
+  }
+  if (param.axis.value().ndim() != 2) {  // elementwise Lp-norm
+    if (ord == -std::numeric_limits<double>::infinity()) {  // -inf norm
+      LOG(FATAL) << "Under construction. ";
 
 Review comment:
   The case is actually handled in front-end, these are placeholders in case people want me to move the implementation to the back-end, and would not be reached. I will change the message to "Case handled in front-end" instead.

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