MatMul for gemm/gemv calls (#365)

* libnd4j added optional alpha and beta support to matmul

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j typos fixes

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j add optional alpha and beta to matmul_bp

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j one more typo fix

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j added optional alpha and beta to mkl implementation

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* MatMul alpha/beta on java side

Signed-off-by: raver119 <raver119@gmail.com>

* alpha/beta fix in libnd4j

Signed-off-by: raver119 <raver119@gmail.com>

* alpha/beta fix in matmul_bp

Signed-off-by: raver119 <raver119@gmail.com>

* restored view validation

Signed-off-by: raver119 <raver119@gmail.com>

* gemv/gemm now use MatMul op

Signed-off-by: raver119 <raver119@gmail.com>

* few tests fixed

Signed-off-by: raver119 <raver119@gmail.com>

* additional INDArray.mmul signature

Signed-off-by: raver119 <raver119@gmail.com>

* make C order default for INDArray.mmul, unless both A/B have F order

Signed-off-by: raver119 <raver119@gmail.com>

* Nd4j.gemm validation fix

Signed-off-by: raver119 <raver119@gmail.com>

* disable mkldnn matmul for xxf with beta != 0 case

Signed-off-by: raver119 <raver119@gmail.com>

* SimpleRnn workspace fix + timeouts

Signed-off-by: Alex Black <blacka101@gmail.com>

* two more tests + minor fix in matmul platform check

Signed-off-by: raver119 <raver119@gmail.com>

* Flaky test fixes

Signed-off-by: Alex Black <blacka101@gmail.com>

* propagate testresources profile

Signed-off-by: raver119 <raver119@gmail.com>

* Resources fix + flaky test fix

Signed-off-by: Alex Black <blacka101@gmail.com>

Co-authored-by: Oleg <oleg.semeniv@gmail.com>
Co-authored-by: Alex Black <blacka101@gmail.com>

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/TestDataSets.java

@@ -23,6 +23,11 @@ import org.junit.Test;
 
 public class TestDataSets extends BaseDL4JTest {
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 180000L;
+    }
+
     @Test
     public void testTinyImageNetExists() throws Exception {
         //Simple sanity check on extracting

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/optimizer/listener/TestCheckpointListener.java

@@ -44,6 +44,11 @@ import static org.junit.Assert.*;
 
 public class TestCheckpointListener extends BaseDL4JTest {
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 90000L;
+    }
+
     @Rule
     public TemporaryFolder tempDir = new TemporaryFolder();
 
@@ -57,7 +62,7 @@ public class TestCheckpointListener extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        DataSetIterator iter = new IrisDataSetIterator(75,150);
+        DataSetIterator iter = new IrisDataSetIterator(25,50);
 
         return new Pair<>(net, iter);
     }
@@ -178,13 +183,13 @@ public class TestCheckpointListener extends BaseDL4JTest {
 
         CheckpointListener l = new CheckpointListener.Builder(f)
                 .keepLast(3)
-                .saveEvery(3, TimeUnit.SECONDS)
+                .saveEvery(4, TimeUnit.SECONDS)
                 .build();
         net.setListeners(l);
 
         for(int i=0; i<5; i++ ){   //10 iterations total
             net.fit(iter);
-            Thread.sleep(4000);
+            Thread.sleep(5000);
         }
 
         //Expect models saved at iterations: 2, 4, 6, 8 (iterations 0 and 1 shoud happen before first 3 seconds is up)

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100a.java

@@ -54,6 +54,11 @@ import static org.junit.Assert.*;
 @Slf4j
 public class RegressionTest100a extends BaseDL4JTest {
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 90000L;  //Most tests should be fast, but slow download may cause timeout on slow connections
+    }
+
     @Override
     public DataType getDataType(){
         return DataType.FLOAT;

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b3.java

@@ -52,6 +52,11 @@ import static org.junit.Assert.*;
 
 public class RegressionTest100b3 extends BaseDL4JTest {
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 90000L;  //Most tests should be fast, but slow download may cause timeout on slow connections
+    }
+
     @Override
     public DataType getDataType(){
         return DataType.FLOAT;

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b4.java

@@ -69,6 +69,11 @@ import org.nd4j.resources.Resources;
 
 public class RegressionTest100b4 extends BaseDL4JTest {
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 90000L;  //Most tests should be fast, but slow download may cause timeout on slow connections
+    }
+
     @Override
     public DataType getDataType() {
         return DataType.FLOAT;
@@ -123,7 +128,8 @@ public class RegressionTest100b4 extends BaseDL4JTest {
 
             assertEquals(dtype, net.getLayerWiseConfigurations().getDataType());
             assertEquals(dtype, net.params().dataType());
-            assertEquals("Test for dtype: " + dtypeName, outExp, outAct);
+            boolean eq = outExp.equalsWithEps(outAct, 0.01);
+            assertTrue("Test for dtype: " + dtypeName + "\n" + outExp + " vs " + outAct, eq);
         }
     }
 

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b6.java

@@ -56,6 +56,11 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         return DataType.FLOAT;
     }
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 90000L;  //Most tests should be fast, but slow download may cause timeout on slow connections
+    }
+
     @Test
     public void testCustomLayer() throws Exception {
 
@@ -106,7 +111,8 @@ public class RegressionTest100b6 extends BaseDL4JTest {
             assertEquals(dtype, net.getLayerWiseConfigurations().getDataType());
             assertEquals(dtype, net.params().dataType());
             boolean eq = outExp.equalsWithEps(outAct, 0.01);
-            assertTrue(outExp + " vs " + outAct, eq);        }
+            assertTrue(outExp + " vs " + outAct, eq);
+        }
     }
 
 

deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java

@@ -96,6 +96,11 @@ public class KerasModelEndToEndTest extends BaseDL4JTest {
         }
     };
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 90000L;
+    }
+
     @Test(expected = IllegalStateException.class)
     public void fileNotFoundEndToEnd() throws Exception {
         String modelPath = "modelimport/keras/examples/foo/bar.h5";

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/SimpleRnn.java

@@ -72,7 +72,7 @@ public class SimpleRnn extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.lay
         INDArray out = activateHelper(last, training, false, workspaceMgr).getFirst();
         if(storeLastForTBPTT){
             try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()){
-                tBpttStateMap.put(STATE_KEY_PREV_ACTIVATION, out.get(all(), all(), point(out.size(2)-1)));
+                tBpttStateMap.put(STATE_KEY_PREV_ACTIVATION, out.get(all(), all(), point(out.size(2)-1)).dup());
             }
         }
         return out;

deeplearning4j/deeplearning4j-remote/deeplearning4j-json-server/pom.xml

@@ -84,6 +84,13 @@
 
 
   <profiles>
+    <profile>
+      <id>testresources</id>
+      <activation>
+        <activeByDefault>true</activeByDefault>
+      </activation>
+    </profile>
+
     <profile>
       <id>test-nd4j-native</id>
       <activation>

deeplearning4j/deeplearning4j-remote/deeplearning4j-json-server/src/test/java/org/deeplearning4j/remote/BinaryModelServerTest.java

@@ -19,6 +19,7 @@ import org.nd4j.remote.clients.serde.BinarySerializer;
 import org.nd4j.remote.clients.serde.JsonDeserializer;
 import org.nd4j.remote.clients.serde.JsonSerializer;
 import org.nd4j.remote.clients.serde.impl.IntegerSerde;
+import org.nd4j.resources.Resources;
 import org.nd4j.shade.jackson.databind.ObjectMapper;
 
 import javax.imageio.ImageIO;
@@ -65,7 +66,7 @@ public class BinaryModelServerTest extends BaseDL4JTest {
     @Test
     public void testMlnMnist_ImageInput() throws Exception {
 
-        val modelFile = new ClassPathResource("models/mnist/mnist-model.zip").getFile();
+        val modelFile = Resources.asFile("models/mnist/mnist-model.zip");
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(modelFile);
 
         val server = new JsonModelServer.Builder<BufferedImage, Integer>(net)
@@ -129,7 +130,7 @@ public class BinaryModelServerTest extends BaseDL4JTest {
     @Test
     public void testMlnMnist_ImageInput_Async() throws Exception {
 
-        val modelFile = new ClassPathResource("models/mnist/mnist-model.zip").getFile();
+        val modelFile = Resources.asFile("models/mnist/mnist-model.zip");
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(modelFile);
 
         val server = new JsonModelServer.Builder<BufferedImage, Integer>(net)
@@ -198,7 +199,7 @@ public class BinaryModelServerTest extends BaseDL4JTest {
     @Test
     public void testBinaryIn_BinaryOut() throws Exception {
 
-        val modelFile = new ClassPathResource("models/mnist/mnist-model.zip").getFile();
+        val modelFile = Resources.asFile("models/mnist/mnist-model.zip");
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(modelFile);
 
         val server = new JsonModelServer.Builder<BufferedImage, BufferedImage>(net)

deeplearning4j/deeplearning4j-remote/pom.xml

@@ -20,6 +20,9 @@
   <name>deeplearning4j-remote</name>
 
   <profiles>
+    <profile>
+      <id>testresources</id>
+    </profile>
     <profile>
       <id>test-nd4j-native</id>
     </profile>

libnd4j/include/helpers/MmulHelper.h

@@ -60,7 +60,7 @@ namespace sd {
         static sd::NDArray* tensorDot(const sd::NDArray* a, const sd::NDArray* b, const std::vector<std::vector<Nd4jLong>>& modifA, const std::vector<std::vector<Nd4jLong>>& modifB);
 #endif
 
-        static void matmul(const sd::NDArray* x, const sd::NDArray* y, sd::NDArray* z, const bool transX, const bool transY);
+        static void matmul(const sd::NDArray* x, const sd::NDArray* y, sd::NDArray* z, const bool transX, const bool transY, double alpha = 1.0, double beta = 0.0);
     };
 }
 

libnd4j/include/helpers/impl/MmulHelper.cpp

@@ -239,7 +239,7 @@ sd::NDArray* MmulHelper::mmul(const sd::NDArray* A, const sd::NDArray* B, sd::ND
 
 
 //////////////////////////////////////////////////////////////////////////
-    void MmulHelper::matmul(const sd::NDArray* x, const sd::NDArray* y, sd::NDArray* z, const bool transX, const bool transY) {
+    void MmulHelper::matmul(const sd::NDArray* x, const sd::NDArray* y, sd::NDArray* z, const bool transX, const bool transY, double alpha, double beta) {
         int xRank = x->rankOf();
         int yRank = y->rankOf();
 
@@ -276,7 +276,7 @@ sd::NDArray* MmulHelper::mmul(const sd::NDArray* A, const sd::NDArray* B, sd::ND
                 zT = new NDArray(z->reshape(z->ordering(), {1, z->lengthOf()}));
             }
 
-            mmul(xT, yT, zT, 1., 0.);
+            mmul(xT, yT, zT, alpha, beta);
         }
         else {  // rest cases -  batched mmul
 
@@ -292,7 +292,7 @@ sd::NDArray* MmulHelper::mmul(const sd::NDArray* A, const sd::NDArray* B, sd::ND
                 auto xSubArr = (*xT)(i, dimsToExclude);
                 auto ySubArr = (*yT)(i, dimsToExclude);
                 auto zSubArr = (*zT)(i, dimsToExclude);
-                mmul(&xSubArr, &ySubArr, &zSubArr, 1., 0.);
+                mmul(&xSubArr, &ySubArr, &zSubArr, alpha, beta);
             }
         }
 

libnd4j/include/ops/declarable/generic/blas/matmul.cpp

@@ -36,10 +36,14 @@ CUSTOM_OP_IMPL(matmul, 2, 1, false, 0, -2) {
     auto y = INPUT_VARIABLE(1);
     auto z = OUTPUT_VARIABLE(0);
 
-    const int iSize = (int) block.getIArguments()->size();
+    int iSize = (int) block.getIArguments()->size();
     int transX = iSize > 0 ? INT_ARG(0) : 0;
     int transY = iSize > 1 ? INT_ARG(1) : 0;
     const int transZ = iSize > 2 ? INT_ARG(2) : 0;
+    // optional use alpha nad beta
+    iSize = (int)block.getTArguments()->size();
+    double alpha = iSize > 0 ? T_ARG(0) : 1.0;
+    double beta = iSize > 1 ? T_ARG(1) : 0.0;
 
     const int xRank = x->rankOf();
     const int yRank = y->rankOf();
@@ -77,7 +81,7 @@ CUSTOM_OP_IMPL(matmul, 2, 1, false, 0, -2) {
     }
     // ******* end of input validation ******* //
 
-    MmulHelper::matmul(x, y, z, transX, transY);
+    MmulHelper::matmul(x, y, z, transX, transY, alpha, beta);
 
     return Status::OK();
 }
@@ -147,11 +151,17 @@ CUSTOM_OP_IMPL(matmul_bp, 3, 2, false, 0, -2) {
     auto dldx = OUTPUT_VARIABLE(0);
     auto dldy = OUTPUT_VARIABLE(1);
 
-    const int iSize = (int) block.getIArguments()->size();
+    int iSize = (int) block.getIArguments()->size();
     int transX = iSize > 0 ? INT_ARG(0) : 0;
     int transY = iSize > 1 ? INT_ARG(1) : 0;
     const int transZ = iSize > 2 ? INT_ARG(2) : 0;
 
+    // optional use alpha nad beta
+    iSize = (int)block.getTArguments()->size();
+
+    double alpha = iSize > 0 ? T_ARG(0) : 1.0;
+    double beta = iSize > 1 ? T_ARG(1) : 0.0;
+
 /*
 In: x=[a,b], y=[b,c]
 tX  tY  tZ  x       y       z       dz          dLdx                                    dLdy
@@ -164,8 +174,8 @@ F   F   T   [a,b]   [b,c]   [c,a]   [c,a]
 
 
     sd::ops::matmul op;
-    op.execute({eps, y}, {dldx}, {}, {transZ, !transY, transX}, {});
+    op.execute({eps, y}, {dldx}, {alpha, beta}, {transZ, !transY, transX}, {});
-    op.execute({x, eps}, {dldy}, {}, {!transX, transZ, transY}, {});
+    op.execute({x, eps}, {dldy}, {alpha, beta}, {!transX, transZ, transY}, {});
 
     return Status::OK();
 }

libnd4j/include/ops/declarable/platform/mkldnn/matmul.cpp

@@ -32,7 +32,7 @@ namespace ops       {
 namespace platforms {
 
 //////////////////////////////////////////////////////////////////////////
-static void matmulMKLDNN(const NDArray* x, const NDArray* y, NDArray* z, const bool transX, const bool transY) {
+static void matmulMKLDNN(const NDArray* x, const NDArray* y, NDArray* z, const bool transX, const bool transY, float alpha = 1.f, float beta = 0.f) {
 
     // mkl works with following
     // [M,K]     x [K,N]     = [M,N]
@@ -150,6 +150,12 @@ static void matmulMKLDNN(const NDArray* x, const NDArray* y, NDArray* z, const b
 
     // Create attributes (to handle alpha and beta if necessary)
     dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0)
+    if (alpha != 1.f) attr.set_output_scales(0, {alpha});
+    if (beta != 0.f) {
+        dnnl::post_ops po;
+        po.append_sum(beta);
+        attr.set_post_ops(po);
+    }
 
     // operation primitive description
     dnnl::matmul::desc op_desc(x_mkl_md, y_mkl_md, z_mkl_md);
@@ -224,11 +230,16 @@ PLATFORM_IMPL(matmul, ENGINE_CPU) {
     if(x->isEmpty() || y->isEmpty())
         return Status::OK();
 
-    const int iSize = (int) block.getIArguments()->size();
+    int iSize = (int) block.getIArguments()->size();
     int transX = iSize > 0 ? INT_ARG(0) : 0;
     int transY = iSize > 1 ? INT_ARG(1) : 0;
     const int transZ = iSize > 2 ? INT_ARG(2) : 0;
 
+    // optional use alpha nad beta
+    iSize = (int)block.getTArguments()->size();
+    float alpha = iSize > 0 ? T_ARG(0) : 1.0;
+    float beta = iSize > 1 ? T_ARG(1) : 0.0;
+
     const int xRank = x->rankOf();
     const int yRank = y->rankOf();
     const int zRank = z->rankOf();
@@ -265,7 +276,7 @@ PLATFORM_IMPL(matmul, ENGINE_CPU) {
     }
     // ******* end of input validation ******* //
 
-    matmulMKLDNN(x, y, z, transX, transY);
+    matmulMKLDNN(x, y, z, transX, transY, alpha, beta);
 
     return Status::OK();
 }
@@ -276,14 +287,16 @@ PLATFORM_CHECK(matmul, ENGINE_CPU) {
     auto x = INPUT_VARIABLE(0);
     auto y = INPUT_VARIABLE(1);
 
-    auto z = INPUT_VARIABLE(0);
+    auto z = OUTPUT_VARIABLE(0);
 
     const DataType xType = x->dataType();
     const DataType yType = y->dataType();
     const DataType zType = z->dataType();
 
+    float alpha = block.numT() > 0 ? T_ARG(0) : 1.0;
+    float beta = block.numT() > 1 ? T_ARG(1) : 0.0;
 
-    return block.isUseMKLDNN() && x->rankOf() < 3 &&
+    return !(z->ordering() == 'f' && beta != 0.f) && block.isUseMKLDNN() && x->rankOf() < 3 &&
           (
             (xType==DataType::FLOAT32  && yType==DataType::FLOAT32  && zType==DataType::FLOAT32)  ||
             (xType==DataType::HALF     && yType==DataType::HALF     && zType==DataType::FLOAT32)  ||

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests19.cpp

@@ -39,6 +39,97 @@ public:
     }
 };
 
+TEST_F(DeclarableOpsTests19, test_matmul_ccc) {
+    auto x = NDArrayFactory::create<float>('c', {10, 10});
+    auto y = NDArrayFactory::create<float>('c', {10, 10});
+    auto e = NDArrayFactory::create<float>('c', {10, 10});
+    auto z = NDArrayFactory::create<float>('c', {10, 10});
+
+    z.assign(100.f);
+    e.assign(110.f);
+    x.assign(1.0f);
+    y.assign(1.0f);
+
+    sd::ops::matmul op;
+    auto status = op.execute({&x, &y}, {&z}, {1.0, 1.0});
+    ASSERT_EQ(Status::OK(), status);
+
+    ASSERT_EQ(e, z);
+}
+
+TEST_F(DeclarableOpsTests19, test_matmul_fcf) {
+    auto x = NDArrayFactory::create<float>('f', {10, 10});
+    auto y = NDArrayFactory::create<float>('c', {10, 10});
+    auto e = NDArrayFactory::create<float>('f', {10, 10});
+    auto z = NDArrayFactory::create<float>('f', {10, 10});
+
+    z.assign(100.f);
+    e.assign(110.f);
+    x.assign(1.0f);
+    y.assign(1.0f);
+
+    sd::ops::matmul op;
+    auto status = op.execute({&x, &y}, {&z}, {1.0, 1.0});
+    ASSERT_EQ(Status::OK(), status);
+
+    ASSERT_EQ(e, z);
+}
+
+TEST_F(DeclarableOpsTests19, test_matmul_cff) {
+    auto x = NDArrayFactory::create<float>('c', {10, 10});
+    auto y = NDArrayFactory::create<float>('f', {10, 10});
+    auto e = NDArrayFactory::create<float>('f', {10, 10});
+    auto z = NDArrayFactory::create<float>('f', {10, 10});
+
+    z.assign(100.f);
+    e.assign(110.f);
+    x.assign(1.0f);
+    y.assign(1.0f);
+
+    sd::ops::matmul op;
+    auto status = op.execute({&x, &y}, {&z}, {1.0, 1.0});
+    ASSERT_EQ(Status::OK(), status);
+
+    ASSERT_EQ(e, z);
+}
+
+
+TEST_F(DeclarableOpsTests19, test_matmul_ccf) {
+    auto x = NDArrayFactory::create<float>('c', {10, 10});
+    auto y = NDArrayFactory::create<float>('c', {10, 10});
+    auto e = NDArrayFactory::create<float>('f', {10, 10});
+    auto z = NDArrayFactory::create<float>('f', {10, 10});
+
+    z.assign(100.f);
+    e.assign(110.f);
+    x.assign(1.0f);
+    y.assign(1.0f);
+
+    sd::ops::matmul op;
+    auto status = op.execute({&x, &y}, {&z}, {1.0, 1.0});
+    ASSERT_EQ(Status::OK(), status);
+
+    ASSERT_EQ(e, z);
+}
+
+TEST_F(DeclarableOpsTests19, test_matmul_fff) {
+    auto x = NDArrayFactory::create<float>('f', {10, 10});
+    auto y = NDArrayFactory::create<float>('f', {10, 10});
+    auto e = NDArrayFactory::create<float>('f', {10, 10});
+    auto z = NDArrayFactory::create<float>('f', {10, 10});
+
+    z.assign(100.f);
+    e.assign(110.f);
+    x.assign(1.0f);
+    y.assign(1.0f);
+
+    sd::ops::matmul op;
+    auto status = op.execute({&x, &y}, {&z}, {1.0, 1.0});
+    ASSERT_EQ(Status::OK(), status);
+
+    ASSERT_EQ(e, z);
+}
+
 TEST_F(DeclarableOpsTests19, test_conv1d_bp_1) {
     /*
     DynamicCustomOp op = DynamicCustomOp.builder("conv1d_bp")

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel2.java

@@ -19,11 +19,13 @@ package org.nd4j.linalg.api.blas.impl;
 import lombok.val;
 import org.nd4j.linalg.api.blas.Level2;
 import org.nd4j.linalg.api.blas.params.GemvParameters;
+import org.nd4j.linalg.api.blas.params.MMulTranspose;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner;
 import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
 import org.nd4j.linalg.api.ops.executioner.OpExecutionerUtil;
+import org.nd4j.linalg.api.ops.impl.reduce.Mmul;
 import org.nd4j.linalg.exception.ND4JArraySizeException;
 import org.nd4j.linalg.exception.ND4JIllegalStateException;
 import org.nd4j.linalg.factory.Nd4j;
@@ -57,6 +59,10 @@ public abstract class BaseLevel2 extends BaseLevel implements Level2 {
             OpProfiler.getInstance().processBlasCall(false, A, X, Y);
 
         GemvParameters parameters = new GemvParameters(A, X, Y);
+
+        Nd4j.exec(new Mmul(A, X, Y, alpha, beta, MMulTranspose.builder().transposeA(false).build()));
+
+        /*
         if (A.data().dataType() == DataType.DOUBLE) {
             DefaultOpExecutioner.validateDataType(DataType.DOUBLE, parameters.getA(), parameters.getX(),
                             parameters.getY());
@@ -86,7 +92,7 @@ public abstract class BaseLevel2 extends BaseLevel implements Level2 {
         } else {
             throw new ND4JIllegalStateException("Unsupported data type " + A.dataType());
         }
-
+        */
         OpExecutionerUtil.checkForAny(Y);
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel3.java

@@ -19,11 +19,13 @@ package org.nd4j.linalg.api.blas.impl;
 import lombok.extern.slf4j.Slf4j;
 import org.nd4j.linalg.api.blas.Level3;
 import org.nd4j.linalg.api.blas.params.GemmParams;
+import org.nd4j.linalg.api.blas.params.MMulTranspose;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner;
 import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
 import org.nd4j.linalg.api.ops.executioner.OpExecutionerUtil;
+import org.nd4j.linalg.api.ops.impl.reduce.Mmul;
 import org.nd4j.linalg.exception.ND4JArraySizeException;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.profiler.OpProfiler;
@@ -59,6 +61,9 @@ public abstract class BaseLevel3 extends BaseLevel implements Level3 {
 
         GemmParams params = new GemmParams(A, B, C);
 
+        Nd4j.exec(new Mmul(A, B, C, alpha, beta, MMulTranspose.builder().transposeA(false).transposeB(false).build()));
+
+        /*
         int charOder = Order;
         if (A.data().dataType() == DataType.DOUBLE) {
             DefaultOpExecutioner.validateDataType(DataType.DOUBLE, params.getA(), params.getB(), params.getC());
@@ -73,6 +78,7 @@ public abstract class BaseLevel3 extends BaseLevel implements Level3 {
             hgemm(Order, params.getTransA(), params.getTransB(), params.getM(), params.getN(), params.getK(), 1.0f,
                             params.getA(), params.getLda(), params.getB(), params.getLdb(), 0, C, params.getLdc());
         }
+        */
 
         OpExecutionerUtil.checkForAny(C);
     }
@@ -85,6 +91,9 @@ public abstract class BaseLevel3 extends BaseLevel implements Level3 {
         if (Nd4j.getExecutioner().getProfilingMode() == OpExecutioner.ProfilingMode.ALL)
             OpProfiler.getInstance().processBlasCall(true, A, B, C);
 
+        Nd4j.exec(new Mmul(A, B, C, alpha, beta, MMulTranspose.builder().transposeA(transposeA).transposeB(transposeB).build()));
+
+        /*
         GemmParams params = new GemmParams(A, B, C, transposeA, transposeB);
         if (A.data().dataType() == DataType.DOUBLE) {
             DefaultOpExecutioner.validateDataType(DataType.DOUBLE, params.getA(), params.getB(), C);
@@ -102,7 +111,7 @@ public abstract class BaseLevel3 extends BaseLevel implements Level3 {
                             (float) alpha, params.getA(), params.getLda(), params.getB(), params.getLdb(), (float) beta,
                             C, params.getLdc());
         }
-
+*/
         OpExecutionerUtil.checkForAny(C);
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java

@@ -2866,16 +2866,22 @@ public abstract class BaseNDArray implements INDArray, Iterable {
     }
 
     @Override
-    public INDArray mmul(INDArray other) {
+    public INDArray mmul(INDArray other, char resultOrder) {
+        Preconditions.checkArgument(resultOrder == 'c' || resultOrder == 'f', "Order must be either 'c' or 'f', but [" + resultOrder + "] was given");
         Preconditions.checkState(this.dataType() == other.dataType(), "Matrix multiplication: arrays must have same dtype: %s vs. %s", this.dataType(), other.dataType());
-        // FIXME: for 1D case, we probably want vector output here?
+        // FIXME: add support for 3D+ here?
-        long[] shape = {rows(), other.rank() == 1 ? 1 : other.columns()};
+        long[] shape = other.rank() == 1 ? new long[]{rows()} : new long[]{rows(), other.columns()};
-        INDArray result = createUninitialized(this.dataType(), shape, 'f');
+        INDArray result = createUninitialized(this.dataType(), shape, resultOrder);
         if (result.isScalar())
             return Nd4j.scalar(this.dataType(), Nd4j.getBlasWrapper().dot(this, other)).reshape(1, 1);
         return mmuli(other, result);
     }
 
+    @Override
+    public INDArray mmul(INDArray other) {
+        return mmul(other, (this.ordering() == 'f' && other.ordering() == 'f' && other.rank() != 1) ? 'f' : 'c');
+    }
+
     protected INDArray create(int[] shape, char ordering) {
         return Nd4j.create(shape, ordering);
     }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java

@@ -1232,6 +1232,14 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray mmul(INDArray other);
 
+    /**
+     * Perform a copy matrix multiplication
+     * @param other other the other matrix to perform matrix multiply with
+     * @param resultOrder either C or F order for result array
+     * @return the result of the matrix multiplication
+     */
+    INDArray mmul(INDArray other, char resultOrder);
+
     /**
      * Convert this ndarray to a 2d double matrix.
      * Note that THIS SHOULD NOT BE USED FOR SPEED.

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/reduce/Mmul.java

@@ -44,6 +44,8 @@ import java.util.*;
 public class Mmul extends DynamicCustomOp {
 
     protected MMulTranspose mt;
+    protected double alpha = 1.0;
+    protected double beta = 0.0;
 
     /**
      *
@@ -59,6 +61,7 @@ public class Mmul extends DynamicCustomOp {
         super(null,sameDiff,new SDVariable[]{i_v1,i_v2});
         this.mt = mt;
         addIArgument(ArrayUtil.fromBoolean(mt.isTransposeA()), ArrayUtil.fromBoolean(mt.isTransposeB()), ArrayUtil.fromBoolean(mt.isTransposeResult()));
+        addTArgument(alpha, beta);
     }
 
 
@@ -74,6 +77,30 @@ public class Mmul extends DynamicCustomOp {
         this(sameDiff,i_v1,i_v2,MMulTranspose.allFalse());
     }
 
+    public Mmul(INDArray x,
+                INDArray y,
+                INDArray z,
+                double alpha,
+                double beta,
+                MMulTranspose mt) {
+        addInputArgument(x, y);
+
+        if (z != null)
+            addOutputArgument(z);
+
+        if (mt != null) {
+            this.mt = mt;
+            addIArgument(ArrayUtil.fromBoolean(mt.isTransposeA()),
+                    ArrayUtil.fromBoolean(mt.isTransposeB()),
+                    ArrayUtil.fromBoolean(mt.isTransposeResult()));
+        }
+
+        this.alpha = alpha;
+        this.beta = beta;
+
+        addTArgument(alpha, beta);
+    }
+
     /**
      *
      * @param x
@@ -84,25 +111,30 @@ public class Mmul extends DynamicCustomOp {
                 INDArray y,
                 INDArray z,
                 MMulTranspose mt) {
-        super(null, new INDArray[]{x, y}, z == null ? null : new INDArray[]{z});
+        this(x, y, z, 1.0, 0.0, mt);
-        if (mt != null) {
-          this.mt = mt;
-          addIArgument(ArrayUtil.fromBoolean(mt.isTransposeA()),
-                       ArrayUtil.fromBoolean(mt.isTransposeB()),
-                       ArrayUtil.fromBoolean(mt.isTransposeResult()));
-        }
     }
 
     public Mmul(INDArray x, INDArray y, boolean transposeX, boolean transposeY,  boolean transposeZ) {
+        this(x, y, 1.0, 0.0, transposeX, transposeY, transposeZ);
+    }
+
+    public Mmul(INDArray x, INDArray y, double alpha, double beta, boolean transposeX, boolean transposeY,  boolean transposeZ) {
         addInputArgument(x, y);
         addIArgument(ArrayUtil.fromBoolean(transposeX),
                 ArrayUtil.fromBoolean(transposeY),
                 ArrayUtil.fromBoolean(transposeZ));
         mt = MMulTranspose.builder().transposeA(transposeX).transposeB(transposeY).transposeResult(transposeZ).build();
+        addTArgument(alpha, beta);
+        this.alpha = alpha;
+        this.beta = beta;
+    }
+
+    public Mmul(INDArray x, INDArray y, double alpha, double beta) {
+        this(x,y,null, alpha, beta,null);
     }
 
     public Mmul(INDArray x, INDArray y) {
-        this(x,y,null,null);
+        this(x, y, 1.0, 0.0);
     }
 
     public Mmul(SameDiff sameDiff, SDVariable x, SDVariable y, boolean transposeX, boolean transposeY,
@@ -111,6 +143,8 @@ public class Mmul extends DynamicCustomOp {
         addIArgument(ArrayUtil.fromBoolean(transposeX),
                      ArrayUtil.fromBoolean(transposeY),
                      ArrayUtil.fromBoolean(transposeZ));
+
+        addTArgument(alpha, beta);
         mt = MMulTranspose.builder().transposeA(transposeX).transposeB(transposeY).transposeResult(transposeZ).build();
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java

@@ -791,7 +791,7 @@ public class Nd4j {
                                 boolean transposeB) {
         long cRows = (transposeA ? a.columns() : a.rows());
         long cCols = (transposeB ? b.rows() : b.columns());
-        INDArray c = Nd4j.createUninitialized(a.dataType(), new long[] {cRows, cCols}, 'f');
+        INDArray c = Nd4j.createUninitialized(a.dataType(), new long[] {cRows, cCols}, a.ordering() == 'c' && b.ordering() == 'c' ? 'c' : 'f');
         return gemm(a, b, c, transposeA, transposeB, 1.0, 0.0);
     }
 
@@ -817,12 +817,9 @@ public class Nd4j {
                                 boolean transposeB,
                                 double alpha,
                                 double beta) {
-        //Note: some views have non-zero offset but 'default' strides (these are OK). And a 'c' order vector such as [10,1] is OK - same buffer as an 'f' order vector with same shape
+        Preconditions.checkArgument(c.elementWiseStride() == 1, "Nd4j.gemm() C array should NOT be a view");
-        Preconditions.checkState(c.length() == 1 || c.ordering() == 'f' && Shape.hasDefaultStridesForShape(c) ||
+
-                        c.isVectorOrScalar() && c.elementWiseStride() == 1,
+        Nd4j.exec(new Mmul(a, b, c, alpha, beta, MMulTranspose.builder().transposeA(transposeA).transposeB(transposeB).build()));
-                "C (result) array is not F order or is a view. Nd4j.gemm requires the result array to be F order " +
-                        "and not a view. C (result) array: [%ndSInfo]", c);
-        getBlasWrapper().level3().gemm(a, b, c, transposeA, transposeB, alpha, beta);
         return c;
     }
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/listeners/CheckpointListenerTest.java

@@ -40,6 +40,11 @@ public class CheckpointListenerTest extends BaseNd4jTest {
     @Rule
     public TemporaryFolder testDir = new TemporaryFolder();
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 90000L;
+    }
+
     public static SameDiff getModel(){
         Nd4j.getRandom().setSeed(12345);
         SameDiff sd = SameDiff.create();
@@ -151,7 +156,7 @@ public class CheckpointListenerTest extends BaseNd4jTest {
 
         CheckpointListener l = new CheckpointListener.Builder(dir)
                 .keepLast(2)
-                .saveEvery(1, TimeUnit.SECONDS)
+                .saveEvery(4, TimeUnit.SECONDS)
                 .build();
         sd.setListeners(l);
 
@@ -159,7 +164,7 @@ public class CheckpointListenerTest extends BaseNd4jTest {
 
         for(int i=0; i<5; i++ ){   //10 iterations total
             sd.fit(iter, 1);
-            Thread.sleep(1000);
+            Thread.sleep(5000);
         }
 
         //Expect models saved at iterations: 10, 20, 30, 40

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java

@@ -6192,24 +6192,6 @@ public class Nd4jTestsC extends BaseNd4jTest {
         assertEquals(exp, output);
     }
 
-    @Test
-    public void testVectorGemv() {
-        val vectorL = Nd4j.create(new float[]{1, 2, 3}, new long[]{3, 1});
-        val vectorN = Nd4j.create(new float[]{1, 2, 3}, new long[]{3});
-        val matrix = Nd4j.create(new float[]{1, 2, 3, 4, 5, 6, 7, 8, 9}, new long[] {3, 3});
-
-//        log.info("vectorN: {}", vectorN);
-//        log.info("vectorL: {}", vectorL);
-
-        val outN = matrix.mmul(vectorN);
-        val outL = matrix.mmul(vectorL);
-
-        assertEquals(outL, outN.reshape(3,1));
-
-        assertEquals(1, outN.rank());
-    }
-
-
     @Test
     public void testMatrixReshape() {
         val matrix = Nd4j.create(new float[]{1, 2, 3, 4, 5, 6, 7, 8, 9}, new long[] {3, 3});

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/blas/Level3Test.java

@@ -60,7 +60,7 @@ public class Level3Test extends BaseNd4jTest {
         INDArray array1 = Nd4j.linspace(1, 1000, 1000).reshape(10, 100);
         INDArray array2 = Nd4j.linspace(1, 1000, 1000).reshape(100, 10);
 
-        INDArray array3 = array1.mmul(array2);
+        INDArray array3 = array1.mmul(array2, Nd4j.createUninitialized(new long[]{10, 10}, 'f'));
 
 
         //System.out.println("Array3: " + Arrays.toString(array3.data().asFloat()));

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/buffer/DataTypeValidationTests.java

@@ -83,7 +83,7 @@ public class DataTypeValidationTests extends BaseNd4jTest {
     /**
      * Testing level2 blas
      */
-    @Test(expected = ND4JIllegalStateException.class)
+    @Test(expected = RuntimeException.class)
     public void testBlasValidation2() {
         INDArray a = Nd4j.create(100, 10);
         INDArray x = Nd4j.create(100);

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/blas/BlasTests.java

@@ -83,22 +83,7 @@ public class BlasTests extends BaseNd4jTest {
         try {
             Nd4j.gemm(a, b, view, false, false, 1.0, 0.0);
             fail("Expected exception");
-        } catch (IllegalStateException e) {
+        } catch (IllegalArgumentException e) {
-            assertTrue(e.getMessage().contains("view"));
-        }
-    }
-
-    @Test
-    public void testGemmInvalid2() {
-        final INDArray a = Nd4j.rand(4, 3);
-        final INDArray b = Nd4j.rand(4, 5);
-
-        final INDArray target = Nd4j.zeros(3, 5, 'c');
-
-        try {
-            Nd4j.gemm(a, b, target, true, false, 1.0, 0.0);
-            fail("Expected exception");
-        } catch (IllegalStateException e) {
             assertTrue(e.getMessage().contains("view"));
         }
     }
@@ -114,7 +99,7 @@ public class BlasTests extends BaseNd4jTest {
         try {
             Nd4j.gemm(a, b, view, true, false, 1.0, 0.0);
             fail("Expected exception");
-        } catch (IllegalStateException e) {
+        } catch (IllegalArgumentException e) {
             assertTrue(e.getMessage().contains("view"));
         }
     }