[GitHub] [incubator-mxnet] zachgk commented on a change in pull request #15378: Add Sparse NDArray support for Scala

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

zachgk commented on a change in pull request #15378: Add Sparse NDArray support for Scala
URL: https://github.com/apache/incubator-mxnet/pull/15378#discussion_r298717511
 
 

 ##########
 File path: scala-package/core/src/main/scala/org/apache/mxnet/SparseNDArray.scala
 ##########
 @@ -0,0 +1,201 @@
+/*
+ * 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
+
+import org.apache.mxnet.Base.{NDArrayHandle, NDArrayHandleRef, checkCall, _LIB}
+import org.apache.mxnet.DType.DType
+import org.apache.mxnet.SparseFormat.SparseFormat
+
+object SparseNDArray {
+  /**
+    * Create a Compressed Sparse Row Storage (CSR) Format Matrix
+    * @param data the data to feed
+    * @param indices The indices array stores the column index for each non-zero element in data
+    * @param indptr The indptr array is what will help identify the rows where the data appears
+    * @param shape the shape of CSR NDArray to be created
+    * @param ctx the context of this NDArray
+    * @return SparseNDArray
+    */
+  def csrMatrix(data: Array[Float], indices: Array[Float],
+                indptr: Array[Float], shape: Shape, ctx: Context): SparseNDArray = {
+    val fmt = SparseFormat.CSR
+    val dataND = NDArray.array(data, Shape(data.length), ctx)
+    val indicesND = NDArray.array(indices, Shape(indices.length), ctx).asType(DType.Int64)
+    val indptrND = NDArray.array(indptr, Shape(indptr.length), ctx).asType(DType.Int64)
+    val dTypes = Array(indptrND.dtype, indicesND.dtype)
+    val shapes = Array(indptrND.shape, indicesND.shape)
+    val handle =
+      newAllocHandle(fmt, shape, ctx, false, DType.Float32, dTypes, shapes)
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, dataND.handle, -1))
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, indptrND.handle, 0))
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, indicesND.handle, 1))
+    new SparseNDArray(handle)
+  }
+
+  /**
+    * RowSparseNDArray stores the matrix in row sparse format,
+    * which is designed for arrays of which most row slices are all zeros
+    * @param data Any Array(Array(... Array(Float)))
+    * @param indices the indices to store the data
+    * @param shape shape of the NDArray
+    * @param ctx Context
+    * @return SparseNDArray
+    */
+  def rowSparseArray(data: Array[_], indices: Array[Float],
+                     shape: Shape, ctx: Context): SparseNDArray = {
+    val dataND = NDArray.toNDArray(data)
+    val indicesND = NDArray.array(indices, Shape(indices.length), ctx).asType(DType.Int64)
+    rowSparseArray(dataND, indicesND, shape, ctx)
+  }
+
+  /**
+    * RowSparseNDArray stores the matrix in row sparse format,
+    * which is designed for arrays of which most row slices are all zeros
+    * @param data NDArray input
+    * @param indices in NDArray. Only DType.Int64 supported
+    * @param shape shape of the NDArray
+    * @param ctx Context
+    * @return
+    */
+  def rowSparseArray(data: NDArray, indices: NDArray,
+                     shape: Shape, ctx: Context): SparseNDArray = {
+    val fmt = SparseFormat.ROW_SPARSE
+    val handle = newAllocHandle(fmt, shape, ctx, false,
+      DType.Float32, Array(indices.dtype), Array(indices.shape))
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, data.handle, -1))
+    checkCall(_LIB.mxNDArraySyncCopyFromNDArray(handle, indices.handle, 0))
+    new SparseNDArray(handle)
+  }
+
+  def retain(sparseNDArray: SparseNDArray, indices: Array[Float]): SparseNDArray = {
+    if (sparseNDArray.sparseFormat == SparseFormat.CSR) {
+      throw new IllegalArgumentException("CSR not supported")
+    }
+    NDArray.genericNDArrayFunctionInvoke("_sparse_retain",
+      Seq(sparseNDArray, NDArray.toNDArray(indices))).head.toSparse()
+  }
+
+  private def newAllocHandle(stype : SparseFormat,
+                             shape: Shape,
+                             ctx: Context,
+                             delayAlloc: Boolean,
+                             dtype: DType = DType.Float32,
+                             auxDTypes: Array[DType],
+                             auxShapes: Array[Shape]) : NDArrayHandle = {
+    val hdl = new NDArrayHandleRef
+    checkCall(_LIB.mxNDArrayCreateSparseEx(
+      stype.id,
+      shape.toArray,
+      shape.length,
+      ctx.deviceTypeid,
+      ctx.deviceId,
+      if (delayAlloc) 1 else 0,
+      dtype.id,
+      auxDTypes.length,
+      auxDTypes.map(_.id),
+      auxShapes.map(_.length),
+      auxShapes.map(_.get(0)),
+      hdl)
+    )
+    hdl.value
+  }
+}
+
+/**
+  * Sparse NDArray is the child class of NDArray designed to hold the Sparse format
+  *
+  * <p> Currently, Rowsparse and CSR typed NDArray is supported. Most of the Operators
+  * will convert Sparse NDArray to dense. Basic operators like <code>add</code> will
+  * have optimization for sparse operattions</p>
+  * @param handle The pointer that SparseNDArray holds
+  * @param writable whether the NDArray is writable
+  */
+class SparseNDArray private[mxnet] (override private[mxnet] val handle: NDArrayHandle,
+                                    override val writable: Boolean = true)
+  extends NDArray(handle, writable) {
+
+  private lazy val dense: NDArray = toDense
+
+  override def toString: String = {
+    dense.toString
+  }
+
+  /**
+    * Convert a SparseNDArray to dense NDArray
+    * @return NDArray
+    */
+  def toDense: NDArray = {
+      NDArray.api.cast_storage(this, SparseFormat.DEFAULT.toString).head
+  }
+
+  override def toArray: Array[Float] = {
+    dense.toArray
+  }
+
+  override def at(idx: Int): NDArray = {
+    dense.at(idx)
+  }
+
+  override def slice(start: Int, end: Int): NDArray = {
+    printf(s"\n\nSlice being called!!\n\nstart:$start end:$end")
 
 Review comment:
   Remove debug line

----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
users@infra.apache.org


With regards,
Apache Git Services