[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI
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_r199023063
##
File path:
scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerNDArray.scala
##
@@ -0,0 +1,252 @@
+/*
+ * 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
Review comment:
Same in here with `NDArray.api`
This
[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI
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_r199022241
##
File path:
scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerNDArray.scala
##
@@ -0,0 +1,252 @@
+/*
+ * 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
Review comment:
Can you try to use NDArray.api.broadcast_to in here?
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[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI
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_r199023103
##
File path:
scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerNDArray.scala
##
@@ -0,0 +1,252 @@
+/*
+ * 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 {
[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI
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
##
@@ -0,0 +1,252 @@
+/*
+ * 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 {
[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI
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_r199022352
##
File path:
scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerMatMul.scala
##
@@ -0,0 +1,107 @@
+/*
+ * 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 org.apache.mxnet.Context
+import org.apache.mxnet.Profiler
+import java.io.File
+import org.apache.mxnet.Symbol
+import org.apache.mxnet.Shape
+import org.apache.mxnet.Random
+
+/**
+ * @author Depeng Liang
+ */
+object ProfilerMatMul {
+ private val logger = LoggerFactory.getLogger(classOf[ProfilerMatMul])
+
+ def main(args: Array[String]): Unit = {
+val erul = new ProfilerMatMul
+val parser: CmdLineParser = new CmdLineParser(erul)
+try {
+ parser.parseArgument(args.toList.asJava)
+ val ctx = if (erul.gpu >= 0) Context.gpu(erul.gpu) else Context.cpu()
+
+ val path = s"${erul.outputPath}${File.separator}${erul.profilerName}"
+ val kwargs = Map("file_name" -> path, "profile_" + erul.profilerMode ->
"1")
+ Profiler.profilerSetConfig(kwargs)
+ logger.info(s"profile file save to $path")
+
+ val A = Symbol.Variable("A")
+ val B = Symbol.Variable("B")
+ val C = Symbol.dot()(A, B)()
Review comment:
Please use `Symbol.api.dot()`
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