diff options
Diffstat (limited to 'tensorflow/core/graph/mkl_layout_pass_test.cc')
| -rw-r--r-- | tensorflow/core/graph/mkl_layout_pass_test.cc | 440 |
1 files changed, 438 insertions, 2 deletions
diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 10671ee2e9..142d60d611 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -18,7 +18,10 @@ limitations under the License. #include "tensorflow/core/graph/mkl_layout_pass.h" #include "tensorflow/core/util/mkl_util.h" +#include <algorithm> +#include <string> #include <vector> + #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/graph/graph.h" @@ -107,10 +110,345 @@ class MklLayoutPassTest : public ::testing::Test { }; REGISTER_OP("Input").Output("o: float").SetIsStateful(); +REGISTER_OP("HalfInput").Output("o: half").SetIsStateful(); +REGISTER_OP("MklInput").Output("o: uint8").SetIsStateful(); +REGISTER_OP("MklInput2").Output("o: uint8").Output("o1: uint8").SetIsStateful(); + +///////////////////////////////////////////////////////////////////// +// Unit tests related to node merge optiimization +///////////////////////////////////////////////////////////////////// + +TEST_F(MklLayoutPassTest, Basic) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'B'] }" + "node { name: 'D' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'B'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Mul);D(Mul)|" + "A->C;A->D;B->C:1;B->D:1"); +} + +// Test set 1: Conv2D + AddBias + +// C=MklConv2D(A,M,B,N); E=BiasAdd(C,D); Z=Sub(E,Y) +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'M' op: 'MklInput'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'N' op: 'MklInput'}" + "node { name: 'C' op: 'MklConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " input: ['A', 'M', 'B', 'N']}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' op: 'BiasAdd'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['C', 'D'] }" + "node { name: 'Y' op: 'Input'}" + "node { name: 'Z' op: 'Sub'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['E', 'Y']}"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);D(Input);DMT/_0(Const);E(MklConv2DWithBias);" + "M(MklInput);N(MklInput);Y(Input);Z(Sub)|A->E;B->E:2;D->E:4;" + "DMT/_0->E:5;E->Z;M->E:1;N->E:3;Y->Z:1"); +} + +// C=MklConv2D(A,M:1,B,N:1); E=BiasAdd(C,D); Z=Sub(E,Y) +// Test for correct output slots selected +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive1) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'M' op: 'MklInput2'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'N' op: 'MklInput2'}" + "node { name: 'C' op: 'MklConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " input: ['A', 'M:1', 'B', 'N:1']}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' op: 'BiasAdd'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['C', 'D'] }" + "node { name: 'Y' op: 'Input'}" + "node { name: 'Z' op: 'Sub'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['E', 'Y']}"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);D(Input);DMT/_0(Const);E(MklConv2DWithBias);" + "M(MklInput2);N(MklInput2);Y(Input);Z(Sub)|A->E;B->E:2;D->E:4;" + "DMT/_0->E:5;E->Z;M:1->E:1;N:1->E:3;Y->Z:1"); +} + +// C=Conv2D(A,B); E=BiasAdd(C,D); Z=Sub(E,Y); +// This is a case of node rewrite followed by node merge. +// We will first rewrite Conv2D to MklConv2D, and then merge MklConv2D +// with BiasAdd to produce MklConv2DWithBias. +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive2) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Conv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " input: ['A', 'B']}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' op: 'BiasAdd'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['C', 'D'] }" + "node { name: 'Y' op: 'Input'}" + "node { name: 'Z' op: 'Sub'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['E', 'Y']}"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);D(Input);DMT/_0(Const);DMT/_1(Const);" + "DMT/_2(Const);E(MklConv2DWithBias);Y(Input);Z(Sub)|" + "A->E;B->E:2;D->E:4;DMT/_0->E:1;DMT/_1->E:3;DMT/_2->E:5;" + "E->Z;Y->Z:1"); +} + +// Graph contains only MklConv2D, no AddBias. +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_NoAddBias) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'M' op: 'MklInput'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'N' op: 'MklInput'}" + "node { name: 'C' op: 'MklConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " input: ['A', 'M', 'B', 'N']}"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(MklConv2D);M(MklInput);N(MklInput)|" + "A->C;B->C:2;M->C:1;N->C:3"); +} + +// MklConv2D output does not go to BiasAdd. +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_Dataflow1) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'M' op: 'MklInput'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'N' op: 'MklInput'}" + "node { name: 'C' op: 'MklConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " input: ['A', 'M', 'B', 'N']}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' op: 'Input'}" + "node { name: 'F' op: 'BiasAdd'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['D', 'E'] }"); // Output of MklConv2D does not go to BiasAdd. + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(MklConv2D);D(Input);E(Input);F(BiasAdd);" + "M(MklInput);N(MklInput)|A->C;B->C:2;D->F;E->F:1;M->C:1;N->C:3"); +} + +// MklConv2D has two outgoing edges: BiasAdd and some other dummy node (Add). +// Merge should not be done in such case. +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_Dataflow2) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'M' op: 'MklInput'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'N' op: 'MklInput'}" + "node { name: 'C' op: 'MklConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " input: ['A', 'M', 'B', 'N']}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' op: 'Input'}" + "node { name: 'F' op: 'BiasAdd'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['D', 'E'] }" // Conv2D has two outputs. + // No merge should happen. + "node { name: 'G' op: 'Add'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'E'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(MklConv2D);D(Input);E(Input);F(BiasAdd);" + "G(Add);M(MklInput);N(MklInput)|A->C;B->C:2;C->G;D->F;" + "E->F:1;E->G:1;M->C:1;N->C:3"); +} + +// data_format attribute value mismatch. Merge should not be done +// in such case. +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_AttrMismatch) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'M' op: 'MklInput'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'N' op: 'MklInput'}" + "node { name: 'C' op: 'MklConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " input: ['A', 'M', 'B', 'N']}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' op: 'BiasAdd'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NHCW' } }" + " input: ['C', 'D'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(MklConv2D);D(Input);E(BiasAdd);M(MklInput);" + "N(MklInput)|A->C;B->C:2;C->E;D->E:1;M->C:1;N->C:3"); +} + +// No MklConv2D in context, but Conv2D in context. +// Only Conv2D would be rewritten to MklConv2D, but no rewrite +// for BiasAddGrad should happen. +// C=MklConv2D(A,M,B,N); D=Sub(C,A); E=BiasAddGrad(D) +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Neg_NoMklConv2DWithBias) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'M' op: 'MklInput'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'N' op: 'MklInput'}" + "node { name: 'C' op: 'MklConv2D'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " input: ['A', 'M', 'B', 'N']}" + "node { name: 'D' op: 'Sub'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'A']}" + "node { name: 'E' op: 'BiasAddGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['D'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(MklConv2D);D(Sub);E(BiasAddGrad);" + "M(MklInput);N(MklInput)|A->C;A->D:1;B->C:2;C->D;D->E;" + "M->C:1;N->C:3"); +} + +// No Conv2D in the context for BiasAddGrad. No rewrite should happen. +// C=Add(A,B); D=Sub(C,A); E=BiasAddGrad(D) +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative_NoConv2D) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Add'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'B']}" + "node { name: 'D' op: 'Sub'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'A']}" + "node { name: 'E' op: 'BiasAddGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['D'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Add);D(Sub);E(BiasAddGrad)|" + "A->C;A->D:1;B->C:1;C->D;D->E"); +} + +// No Conv2D in the context for BiasAddGrad, but MatMul in context. +// Rewrite should happen, but name of BiasAddGrad does not change. +// C=MatMul(A,B); D=Sub(C,A); E=BiasAddGrad(D) +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative_NoConv2D_MatMul) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'MatMul'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'transpose_a' value { b: false } }" + " attr { key: 'transpose_b' value { b: false } }" + " input: ['A', 'B']}" + "node { name: 'D' op: 'Sub'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'A']}" + "node { name: 'E' op: 'BiasAddGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['D'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(MatMul);D(Sub);E(BiasAddGrad)|" + "A->C;A->D:1;B->C:1;C->D;D->E"); +} + +// Test set 3: MatMul..BiasAddGrad -> BiasAddGrad rewrite tests +// C=MatMul(A,B); D=Sub(C,A); E=BiasAddGrad(D) +TEST_F(MklLayoutPassTest, NodeMerge_MatMulBiasAddGrad_Positive) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'MatMul'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'transpose_a' value { b: false } }" + " attr { key: 'transpose_b' value { b: false } }" + " input: ['A', 'B']}" + "node { name: 'D' op: 'Sub'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'A']}" + "node { name: 'E' op: 'BiasAddGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['D'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(MatMul);D(Sub);E(BiasAddGrad)|" + "A->C;A->D:1;B->C:1;C->D;D->E"); +} + +// No MatMul in the context for BiasAddGrad. No rewrite should happen. +// C=Add(A,B); D=Sub(C,A); E=BiasAddGrad(D) +TEST_F(MklLayoutPassTest, NodeMerge_MatMulBiasAddGrad_Negative_NoMatMul) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Add'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'B']}" + "node { name: 'D' op: 'Sub'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'A']}" + "node { name: 'E' op: 'BiasAddGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['D'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Add);D(Sub);E(BiasAddGrad)|" + "A->C;A->D:1;B->C:1;C->D;D->E"); +} + +///////////////////////////////////////////////////////////////////// +// Unit tests related to rewriting node to Mkl node +///////////////////////////////////////////////////////////////////// // Single Conv2D Op; No Mkl layer on the input and on the output. // We will generate dummy Mkl tensor as 2nd input of Conv2D. -TEST_F(MklLayoutPassTest, Conv2D_Basic) { +TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Basic) { InitGraph( "node { name: 'A' op: 'Input'}" "node { name: 'B' op: 'Input'}" @@ -130,7 +468,7 @@ TEST_F(MklLayoutPassTest, Conv2D_Basic) { // 2 Conv2D Ops in sequence. Both should get transformed and 1st Conv2D will // have 2 outputs, both of which will be inputs to next Conv2D. -TEST_F(MklLayoutPassTest, Conv2D_Positive1) { +TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Positive1) { InitGraph( "node { name: 'A' op: 'Input'}" "node { name: 'B' op: 'Input'}" @@ -156,6 +494,104 @@ TEST_F(MklLayoutPassTest, Conv2D_Positive1) { "C:1->D:3;D->E:1;DMT/_0->C:1;DMT/_1->C:3;DMT/_2->D:1"); } +// Conv2D with INT32 which is not supported by Mkl +TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Negative_UnsupportedType) { + InitGraph( + "node { name: 'A' op: 'HalfInput'}" + "node { name: 'B' op: 'HalfInput'}" + "node { name: 'C' op: 'Conv2D'" + " attr { key: 'T' value { type: DT_HALF } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'use_cudnn_on_gpu' value { b: false } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" + " attr { key: 'padding' value { s: 'SAME' } }" + " input: ['A', 'B']}" + "node { name: 'D' op: 'Mul' attr { key: 'T' value { type: DT_HALF } }" + " input: ['B', 'C'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(HalfInput);B(HalfInput);C(Conv2D);D(Mul)|" + "A->C;B->C:1;B->D;C->D:1"); +} + +///////////////////////////////////////////////////////////////////// +// Unit tests related to rewriting node for workspace edges +///////////////////////////////////////////////////////////////////// + +/* Test MaxPool->MaxPoolGrad replacement by workspace+rewrite nodes. */ +TEST_F(MklLayoutPassTest, NodeWorkspace_MaxPool_Positive) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'MaxPool'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'ksize' value { list: {i: 1, i:1, i:3, i:3} } }" + " attr { key: 'padding' value { s: 'VALID' } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:2, i:2} } }" + " input: ['A'] }" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' op: 'MaxPoolGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'ksize' value { list: {i: 1, i:1, i:3, i:3} } }" + " attr { key: 'padding' value { s: 'VALID' } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:2, i:2} } }" + " input: ['C', 'B', 'D'] }" + "node { name: 'F' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'E'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(MklMaxPool);C(Input);D(Input);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);E(MklMaxPoolGrad);F(Mul)|" + "A->B;B->E:2;B:1->E:3;B:2->E:6;B:3->E:7;C->E;C->F;D->E:4;" + "DMT/_0->B:1;DMT/_1->E:1;DMT/_2->E:5;E->F:1"); +} + +// Test MaxPool>MaxPoolGrad replacement when only one of them is present. +// In this case, we will rewrite MaxPool node but workspace edges will not +// be present. +TEST_F(MklLayoutPassTest, NodeWorkspace_MaxPool_Negative1) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'MaxPool'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'ksize' value { list: {i: 1, i:1, i:3, i:3} } }" + " attr { key: 'padding' value { s: 'VALID' } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:2, i:2} } }" + " input: ['A'] }" + "node { name: 'C' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'B'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(MklMaxPool);C(Mul);DMT/_0(Const)|" + "A->B;A->C;B->C:1;DMT/_0->B:1"); +} + +// Test MaxPool->MaxPoolGrad replacement when only one of them is present. +// In this case, we will rewrite MaxPoolGrad and for workspace tensor and +// its Mkl part, we will generate dummy tensor. +TEST_F(MklLayoutPassTest, NodeWorkspace_MaxPool_Negative2) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'MaxPoolGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'ksize' value { list: {i: 1, i:1, i:3, i:3} } }" + " attr { key: 'padding' value { s: 'VALID' } }" + " attr { key: 'strides' value { list: {i: 1, i:1, i:2, i:2} } }" + " input: ['A', 'B', 'C'] }" + "node { name: 'E' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'D'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(MklMaxPoolGrad);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);DMT/_3(Const);DMT/_4(Const);E(Mul)|" + "A->D;A->E;B->D:2;C->D:4;D->E:1;DMT/_0->D:1;DMT/_1->D:3;" + "DMT/_2->D:5;DMT/_3->D:6;DMT/_4->D:7"); +} + +///////////////////////////////////////////////////////////////////// + static void BM_MklLayoutRewritePass(int iters, int op_nodes) { testing::StopTiming(); string s; |