diff --git a/dl/src/main/scala/com/intel/analytics/bigdl/dllib/feature/common/MTSampleToMiniBatch.scala b/dl/src/main/scala/com/intel/analytics/bigdl/dllib/feature/common/MTSampleToMiniBatch.scala
new file mode 100644
index 00000000000..0d5b1e386a4
--- /dev/null
+++ b/dl/src/main/scala/com/intel/analytics/bigdl/dllib/feature/common/MTSampleToMiniBatch.scala
@@ -0,0 +1,139 @@
+/*
+ * Copyright 2018 Analytics Zoo Authors.
+ *
+ * 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.
+ */
+package com.intel.analytics.zoo.feature.common
+
+import com.intel.analytics.bigdl.dataset._
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+import com.intel.analytics.zoo.pipeline.api.keras.layers.utils.EngineRef
+
+import scala.reflect.ClassTag
+
+/**
+ * Convert a sequence of [[Sample]] to a sequence of [[MiniBatch]]
+ * through function toMiniBatch using multi thread.
+ */
+class MTSampleToMiniBatch[A: ClassTag, T: ClassTag] (
+          totalBatch: Int,
+          transformer: Transformer[A, Sample[T]],
+          miniBatch: Option[MiniBatch[T]] = None,
+          featurePaddingParam: Option[PaddingParam[T]] = None,
+          labelPaddingParam: Option[PaddingParam[T]] = None,
+          partitionNum: Option[Int] = None)
+      (implicit ev: TensorNumeric[T]) extends Transformer[A, MiniBatch[T]] {
+
+  private val batchPerPartition = Utils.getBatchSize(totalBatch, partitionNum)
+  var miniBatchBuffer = miniBatch.orNull
+  private val batchSize = batchPerPartition
+  private val sampleData = new Array[Sample[T]](batchSize)
+
+  private val parallelism = EngineRef.getCoreNumber()
+
+  private val transformers = (0 until parallelism).map(
+    _ => transformer.cloneTransformer()
+  ).toArray
+
+  private val rawDataCache = new Array[Iterator[A]](batchSize)
+
+  override def apply(prev: Iterator[A]): Iterator[MiniBatch[T]] = {
+    new Iterator[MiniBatch[T]] {
+
+      override def hasNext: Boolean = prev.hasNext
+
+      override def next(): MiniBatch[T] = {
+        if (prev.hasNext) {
+          // prefetch
+          var count = 0
+          while (count < batchSize && prev.hasNext) {
+            val raw = prev.next()
+            rawDataCache(count) = Iterator.single(raw)
+            count += 1
+          }
+
+          // multi thread processing
+          (0 until parallelism).toParArray.foreach{tid =>
+            var j = tid
+            while (j < count) {
+              sampleData(j) = transformers(tid).apply(rawDataCache(j)).next()
+              j += parallelism
+            }
+          }
+
+          if (null == miniBatchBuffer) {
+            val firstSample = sampleData(0)
+            miniBatchBuffer = if (firstSample.isInstanceOf[TensorSample[T]]) {
+              SparseMiniBatch(firstSample.numFeature(), firstSample.numLabel())
+            } else {
+              MiniBatch(firstSample.numFeature(), firstSample.numLabel(),
+                featurePaddingParam, labelPaddingParam)
+            }
+          }
+
+          if (count < batchSize) {
+            miniBatchBuffer.set(sampleData.slice(0, count))
+          } else {
+            miniBatchBuffer.set(sampleData)
+          }
+        } else {
+          null
+        }
+      }
+    }
+  }
+}
+
+object MTSampleToMiniBatch {
+  /**
+   * Apply an MTSampleToMiniBatch transformer.
+   *
+   * @param batchSize           total batch size
+   * @param transformer         transformer who rawData to Sample
+   * @param featurePaddingParam feature padding strategy, see
+   *                            [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details.
+   * @param labelPaddingParam   label padding strategy, see
+   *                            [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details.
+   * @return
+   */
+  def apply[A: ClassTag, T: ClassTag](
+         batchSize : Int,
+         transformer: Transformer[A, Sample[T]],
+         featurePaddingParam: Option[PaddingParam[T]] = None,
+         labelPaddingParam: Option[PaddingParam[T]] = None,
+         partitionNum: Option[Int] = None
+         )(implicit ev: TensorNumeric[T]): MTSampleToMiniBatch[A, T] = {
+    new MTSampleToMiniBatch[A, T](batchSize,
+      transformer,
+      None, featurePaddingParam, labelPaddingParam, partitionNum)
+  }
+
+  /**
+   * Apply an MTSampleToMiniBatch transformer with UDF MiniBatch.
+   *
+   * @param batchSize total batch size
+   * @param miniBatch An User-Defined MiniBatch to construct a mini batch.
+   * @param transformer transformer who rawData to Sample
+   * @return
+   */
+  def apply[A: ClassTag, T: ClassTag](
+        miniBatch: MiniBatch[T],
+        batchSize : Int,
+        transformer: Transformer[A, Sample[T]],
+        partitionNum: Option[Int])
+        (implicit ev: TensorNumeric[T]): MTSampleToMiniBatch[A, T] = {
+    new MTSampleToMiniBatch[A, T](batchSize,
+      transformer,
+      Some(miniBatch), partitionNum = partitionNum)
+  }
+}