[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI

[GitBox <gi...@apache.org>]

lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI
URL: https://github.com/apache/incubator-mxnet/pull/11477#discussion_r199023126
 
 

 ##########
 File path: scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerNDArray.scala
 ##########
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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.
+ */
+
+package org.apache.mxnet.examples.profiler
+
+import org.kohsuke.args4j.{CmdLineParser, Option}
+import org.slf4j.LoggerFactory
+import scala.collection.JavaConverters._
+import java.io.File
+import org.apache.mxnet.Profiler
+import org.apache.mxnet.Random
+import org.apache.mxnet.Shape
+import org.apache.mxnet.NDArray
+import org.apache.mxnet.Context
+
+/**
+ * @author Depeng Liang
+ */
+object ProfilerNDArray {
+  private val logger = LoggerFactory.getLogger(classOf[ProfilerNDArray])
+
+  def testBroadcast(): Unit = {
+    val sampleNum = 1000
+    def testBroadcastTo(): Unit = {
+      for (i <- 0 until sampleNum) {
+        val nDim = scala.util.Random.nextInt(2) + 1
+        val targetShape = Shape((0 until nDim).map(i => scala.util.Random.nextInt(10) + 1))
+        val shape = targetShape.toArray.map { s =>
+            if (scala.util.Random.nextInt(2) == 1) 1
+            else s
+        }
+        val dat = NDArray.empty(shape: _*)
+        val randomRet = (0 until shape.product)
+          .map(r => scala.util.Random.nextFloat() - 0.5f).toArray
+        dat.set(randomRet)
+        val ndArrayRet = NDArray.broadcast_to(Map("shape" -> targetShape))(dat).get
+        require(ndArrayRet.shape == targetShape)
+        val err = {
+          // implementation of broadcast
+          val ret = {
+            (randomRet /: shape.zipWithIndex.reverse){ (acc, elem) => elem match { case (s, i) =>
+              if (s != targetShape(i)) {
+                acc.grouped(shape.takeRight(shape.length - i).product).map {g =>
+                  (0 until targetShape(i)).map(x => g).flatten
+                }.flatten.toArray
+              } else acc
+            }}
+          }
+          val tmp = ndArrayRet.toArray.zip(ret).map{ case (l, r) => Math.pow(l - r, 2) }
+          tmp.sum / tmp.length
+        }
+        require(err < 1E-8)
+        ndArrayRet.dispose()
+        dat.dispose()
+      }
+    }
+    testBroadcastTo()
+  }
+
+  def randomNDArray(dim: Int): NDArray = {
+    val tmp = Math.pow(1000, 1.0 / dim).toInt
+    val shape = Shape((0 until dim).map(d => scala.util.Random.nextInt(tmp) + 1))
+    Random.uniform(-10f, 10f, shape)
+  }
+
+  def testNDArraySaveload(): Unit = {
+    val maxDim = 5
+    val nRepeat = 10
+    val fileName = s"${System.getProperty("java.io.tmpdir")}/tmpList.bin"
+    for (repeat <- 0 until nRepeat) {
+      try {
+        val data = (0 until 10).map(i => randomNDArray(scala.util.Random.nextInt(4) + 1))
+        NDArray.save(fileName, data)
+        val data2 = NDArray.load2Array(fileName)
+        require(data.length == data2.length)
+        for ((x, y) <- data.zip(data2)) {
+          val tmp = x - y
+          require(tmp.toArray.sum == 0)
+          tmp.dispose()
+        }
+        val dMap = data.zipWithIndex.map { case (arr, i) =>
+          s"NDArray xx $i" -> arr
+        }.toMap
+        NDArray.save(fileName, dMap)
+         val dMap2 = NDArray.load2Map(fileName)
+         require(dMap.size == dMap2.size)
+         for ((k, x) <- dMap) {
+           val y = dMap2(k)
+           val tmp = x - y
+           require(tmp.toArray.sum == 0)
+           tmp.dispose()
+         }
+        data.foreach(_.dispose())
+      } finally {
+        val file = new File(fileName)
+        file.delete()
+      }
+    }
+  }
+
+  def testNDArrayCopy(): Unit = {
+    val c = Random.uniform(-10f, 10f, Shape(10, 10))
+    val d = c.copyTo(Context.cpu(0))
+    val tmp = c - d
+    require(tmp.toArray.map(Math.abs).sum == 0)
+    c.dispose()
+    d.dispose()
+  }
+
+  def reldiff(a: NDArray, b: NDArray): Float = {
+    val diff = NDArray.sum(NDArray.abs(a - b)).toScalar
+    val norm = NDArray.sum(NDArray.abs(a)).toScalar
+    diff / norm
+  }
+
+  def reldiff(a: Array[Float], b: Array[Float]): Float = {
+    val diff =
+      (a zip b).map { case (aElem, bElem) => Math.abs(aElem - bElem) }.sum
+    val norm: Float = a.reduce(Math.abs(_) + Math.abs(_))
+    diff / norm
+  }
+
+  def testNDArrayNegate(): Unit = {
+    val rand = Random.uniform(-10f, 10f, Shape(2, 3, 4))
+    val npy = rand.toArray
+    val arr = NDArray.empty(Shape(2, 3, 4))
+    arr.set(npy)
+    require(reldiff(npy, arr.toArray) < 1e-6f)
+    val negativeArr = -arr
+    require(reldiff(npy.map(_ * -1f), negativeArr.toArray) < 1e-6f)
+    // a final check to make sure the negation (-) is not implemented
+    // as inplace operation, so the contents of arr does not change after
+    // we compute (-arr)
+    require(reldiff(npy, arr.toArray) < 1e-6f)
+    rand.dispose()
+    arr.dispose()
+    negativeArr.dispose()
+  }
+
+  def testNDArrayScalar(): Unit = {
+    val c = NDArray.empty(10, 10)
+    val d = NDArray.empty(10, 10)
+    c.set(0.5f)
+    d.set(1.0f)
+    d -= c * 2f / 3f * 6f
+    c += 0.5f
+    require(c.toArray.sum - 100f < 1e-5f)
+    require(d.toArray.sum + 100f < 1e-5f)
+    c.set(2f)
+    require(c.toArray.sum - 200f < 1e-5f)
+    d.set(-c + 2f)
+    require(d.toArray.sum < 1e-5f)
+    c.dispose()
+    d.dispose()
+  }
+
+  def testClip(): Unit = {
+    val shape = Shape(10)
+    val A = Random.uniform(-10f, 10f, shape)
+    val B = NDArray.clip(A, -2f, 2f)
+    val B1 = B.toArray
+    require(B1.forall { x => x >= -2f && x <= 2f })
+  }
+
+  def testDot(): Unit = {
+    val a = Random.uniform(-3f, 3f, Shape(3, 4))
+    val b = Random.uniform(-3f, 3f, Shape(4, 5))
+    val c = NDArray.dot(a, b)
 
 Review comment:
   +1

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