diff options
Diffstat (limited to 'tensorflow/core/graph/mkl_layout_pass_test.cc')
| -rw-r--r-- | tensorflow/core/graph/mkl_layout_pass_test.cc | 561 |
1 files changed, 485 insertions, 76 deletions
diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 142d60d611..6e72baf84e 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -110,9 +110,11 @@ class MklLayoutPassTest : public ::testing::Test { }; REGISTER_OP("Input").Output("o: float").SetIsStateful(); +REGISTER_OP("InputList").Output("o: N * float").Attr("N: int").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(); +REGISTER_OP("Int32Input").Output("o: int32").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 @@ -133,20 +135,22 @@ TEST_F(MklLayoutPassTest, Basic) { // Test set 1: Conv2D + AddBias -// C=MklConv2D(A,M,B,N); E=BiasAdd(C,D); Z=Sub(E,Y) +// C=_MklConv2D(A,M,B,N); E=BiasAdd(C,D); Z=Sub(E,Y) (for interleaved ordering) +// C=_MklConv2D(A,B,M,N); E=BiasAdd(C,D); Z=Sub(E,Y) (for contiguous ordering) TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive) { + CHECK_EQ(kTensorOrdering, MklTfTensorOrdering::TENSORS_CONTIGUOUS); 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'" + "node { name: 'M' op: '_MklInput'}" + "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']}" + " input: ['A', 'B', 'M', 'N']}" "node { name: 'D' op: 'Input'}" "node { name: 'E' op: 'BiasAdd'" " attr { key: 'T' value { type: DT_FLOAT } }" @@ -157,26 +161,28 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive) { " 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"); + "A(Input);B(Input);D(Input);DMT/_0(Const);E(_MklConv2DWithBias);" + "M(_MklInput);N(_MklInput);Y(Input);Z(Sub)|A->E;B->E:1;D->E:2;" + "DMT/_0->E:5;E->Z;M->E:3;N->E:4;Y->Z:1"); } -// C=MklConv2D(A,M:1,B,N:1); E=BiasAdd(C,D); Z=Sub(E,Y) +// C=_MklConv2D(A,M:1,B,N:1); E=BiasAdd(C,D); Z=Sub(E,Y) (for interleaved) +// C=_MklConv2D(A,B,M:1,N:1); E=BiasAdd(C,D); Z=Sub(E,Y) (for contiguous) // Test for correct output slots selected TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive1) { + CHECK_EQ(kTensorOrdering, MklTfTensorOrdering::TENSORS_CONTIGUOUS); 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'" + "node { name: 'M' op: '_MklInput2'}" + "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']}" + " input: ['A', 'B', 'M:1', 'N:1']}" "node { name: 'D' op: 'Input'}" "node { name: 'E' op: 'BiasAdd'" " attr { key: 'T' value { type: DT_FLOAT } }" @@ -187,16 +193,17 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive1) { " 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"); + "A(Input);B(Input);D(Input);DMT/_0(Const);E(_MklConv2DWithBias);" + "M(_MklInput2);N(_MklInput2);Y(Input);Z(Sub)|A->E;B->E:1;D->E:2;" + "DMT/_0->E:5;E->Z;M:1->E:3;N:1->E:4;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. +// We will first rewrite Conv2D to _MklConv2D, and then merge _MklConv2D +// with BiasAdd to produce _MklConv2DWithBias. TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive2) { + CHECK_EQ(kTensorOrdering, MklTfTensorOrdering::TENSORS_CONTIGUOUS); InitGraph( "node { name: 'A' op: 'Input'}" "node { name: 'B' op: 'Input'}" @@ -218,70 +225,70 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Positive2) { " 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;" + "DMT/_2(Const);E(_MklConv2DWithBias);Y(Input);Z(Sub)|" + "A->E;B->E:1;D->E:2;DMT/_0->E:3;DMT/_1->E:4;DMT/_2->E:5;" "E->Z;Y->Z:1"); } -// Graph contains only MklConv2D, no AddBias. +// 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'" + "node { name: 'M' op: '_MklInput'}" + "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']}"); + " input: ['A', 'B', 'M', '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"); + "A(Input);B(Input);C(_MklConv2D);M(_MklInput);N(_MklInput)|" + "A->C;B->C:1;M->C:2;N->C:3"); } -// MklConv2D output does not go to BiasAdd. +// _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'" + "node { name: 'M' op: '_MklInput'}" + "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']}" + " input: ['A', 'B', 'M', '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. + " 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"); + "A(Input);B(Input);C(_MklConv2D);D(Input);E(Input);F(BiasAdd);" + "M(_MklInput);N(_MklInput)|A->C;B->C:1;D->F;E->F:1;M->C:2;N->C:3"); } -// MklConv2D has two outgoing edges: BiasAdd and some other dummy node (Add). +// _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'" + "node { name: 'M' op: '_MklInput'}" + "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']}" + " input: ['A', 'B', 'M', 'N']}" "node { name: 'D' op: 'Input'}" "node { name: 'E' op: 'Input'}" "node { name: 'F' op: 'BiasAdd'" @@ -293,9 +300,9 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_Dataflow2) { " 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"); + "A(Input);B(Input);C(_MklConv2D);D(Input);E(Input);F(BiasAdd);" + "G(Add);M(_MklInput);N(_MklInput)|A->C;B->C:1;C->G;D->F;" + "E->F:1;E->G:1;M->C:2;N->C:3"); } // data_format attribute value mismatch. Merge should not be done @@ -303,43 +310,81 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_Negative_Dataflow2) { 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'" + "node { name: 'M' op: '_MklInput'}" + "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']}" + " input: ['A', 'B', 'M', '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"); + "A(Input);B(Input);C(_MklConv2D);D(Input);E(BiasAdd);M(_MklInput);" + "N(_MklInput)|A->C;B->C:1;C->E;D->E:1;M->C:2;N->C:3"); } -// No MklConv2D in context, but Conv2D in context. -// Only Conv2D would be rewritten to MklConv2D, but no rewrite +// Disabling Conv2DBackpropBias test for now as we have disabled rewrite +// of BiasAddGrad into BackpropBias +#if 0 +// Test set 2: _MklConv2D..BiasAddGrad -> _MklConv2DWithBiasBackpropBias +// rewrite tests + +// D=_MklConv2D(A,M,B,N,C,O); E=Sub(D,A); F=BiasAddGrad(E) +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Positive) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'M' op: '_MklInput'}" + "node { name: 'N' op: '_MklInput'}" + "node { name: 'O' op: '_MklInput'}" + "node { name: 'D' op: '_MklConv2DWithBias'" + " 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', 'C', 'M', 'N', 'O']}" + "node { name: 'E' op: 'Sub'" + " attr {key: 'T' value { type: DT_FLOAT } }" + " input: ['D', 'A']}" + "node { name: 'F' op: 'BiasAddGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " input: ['E'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);DMT/_0(Const);" + "E(Sub);F(_MklConv2DWithBiasBackpropBias);M(_MklInput);N(_MklInput);" + "O(_MklInput)|A->D;A->E:1;B->D:1;C->D:2;D->E;DMT/_0->F:1;E->F;" + "M->D:3;N->D:4;O->D:5"); +} +#endif + +// 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) { +// C=_MklConv2D(A,M,B,N); D=Sub(C,A); E=BiasAddGrad(D) (for interleaved) +// C=_MklConv2D(A,B,M,N); D=Sub(C,A); E=BiasAddGrad(D) (for contiguous) +TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Neg_No_MklConv2DWithBias) { 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'" + "node { name: 'M' op: '_MklInput'}" + "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']}" + " input: ['A', 'B', 'M', 'N']}" "node { name: 'D' op: 'Sub'" " attr {key: 'T' value { type: DT_FLOAT } }" " input: ['C', 'A']}" @@ -348,9 +393,9 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Neg_NoMklConv2DWithBias) { " 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"); + "A(Input);B(Input);C(_MklConv2D);D(Sub);E(BiasAddGrad);" + "M(_MklInput);N(_MklInput)|A->C;A->D:1;B->C:1;C->D;D->E;" + "M->C:2;N->C:3"); } // No Conv2D in the context for BiasAddGrad. No rewrite should happen. @@ -462,8 +507,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Basic) { "node { name: 'D' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" " input: ['B', 'C'] }"); EXPECT_EQ(DoMklLayoutOptimizationPass(), - "A(Input);B(Input);C(MklConv2D);D(Mul);DMT/_0(Const);DMT/_1(Const)|" - "A->C;B->C:2;B->D;C->D:1;DMT/_0->C:1;DMT/_1->C:3"); + "A(Input);B(Input);C(_MklConv2D);D(Mul);DMT/_0(Const);DMT/_1(Const)|" + "A->C;B->C:1;B->D;C->D:1;DMT/_0->C:2;DMT/_1->C:3"); } // 2 Conv2D Ops in sequence. Both should get transformed and 1st Conv2D will @@ -489,9 +534,9 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Positive1) { "node { name: 'E' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" " input: ['C', 'D'] }"); EXPECT_EQ(DoMklLayoutOptimizationPass(), - "A(Input);B(Input);C(MklConv2D);D(MklConv2D);DMT/_0(Const);" - "DMT/_1(Const);DMT/_2(Const);E(Mul)|A->C;A->D;B->C:2;C->D:2;C->E;" - "C:1->D:3;D->E:1;DMT/_0->C:1;DMT/_1->C:3;DMT/_2->D:1"); + "A(Input);B(Input);C(_MklConv2D);D(_MklConv2D);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);E(Mul)|A->C;A->D;B->C:1;C->D:1;C->E;" + "C:1->D:3;D->E:1;DMT/_0->C:2;DMT/_1->C:3;DMT/_2->D:2"); } // Conv2D with INT32 which is not supported by Mkl @@ -513,10 +558,374 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_Negative_UnsupportedType) { "A->C;B->C:1;B->D;C->D:1"); } +// Concat Op test: Concat with no Mkl layer feeding it +TEST_F(MklLayoutPassTest, NodeRewrite_Concat_Basic) { + InitGraph( + "node { name: 'A' op: 'Const' " + " attr { key: 'dtype' value { type: DT_INT32 } }" + " attr { key: 'value' value { " + " tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } " + " int_val: 0 } } } }" + "node { name: 'B' op: 'InputList'" + " attr { key: 'N' value { i: 2 } }}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'Concat'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'N' value { i: 2 } }" + " input: ['A', 'B']}" + "node { name: 'E' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'D'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Const);B(InputList);C(Input);D(_MklConcat);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);E(Mul)|A->D;B->D:1;B->D:2;C->E;" + "D->E:1;DMT/_0->D:3;DMT/_1->D:4;DMT/_2->D:5"); +} + +// Concat with 2 Mkl layers feeding it +TEST_F(MklLayoutPassTest, NodeRewrite_Concat_Input_Mkl) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' 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: 'F' 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: ['C', 'D']}" + "node { name: 'G' op: 'Const' " + " attr { key: 'dtype' value { type: DT_INT32 } }" + " attr { key: 'value' value { " + " tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } " + " int_val: 0 } } } }" + "node { name: 'H' op: 'Concat'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'N' value { i: 2 } }" + " input: ['G', 'E', 'F']}" + "node { name: 'I' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'H'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(Input);DMT/_0(Const);DMT/_1(Const);" + "DMT/_2(Const);DMT/_3(Const);DMT/_4(Const);E(_MklConv2D);" + "F(_MklConv2D);G(Const);H(_MklConcat);I(Mul)|A->E;A->I;B->E:1;C->F;" + "D->F:1;DMT/_0->F:2;DMT/_1->F:3;DMT/_2->E:2;DMT/_3->E:3;" + "DMT/_4->H:3;E->H:1;E:1->H:4;F->H:2;F:1->H:5;G->H;H->I:1"); +} + +// Concat with 1 Mkl and 1 non-Mkl layer feeding it +TEST_F(MklLayoutPassTest, NodeRewrite_Concat_Input_MixedMkl) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' 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: 'F' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'D']}" + "node { name: 'G' op: 'Const' " + " attr { key: 'dtype' value { type: DT_INT32 } }" + " attr { key: 'value' value { " + " tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } " + " int_val: 0 } } } }" + "node { name: 'H' op: 'Concat'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'N' value { i: 2 } }" + " input: ['G', 'E', 'F']}" + "node { name: 'I' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'H'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(Input);DMT/_0(Const);DMT/_1(Const);" + "DMT/_2(Const);DMT/_3(Const);E(_MklConv2D);F(Mul);G(Const);" + "H(_MklConcat);I(Mul)|A->E;A->I;B->E:1;C->F;D->F:1;DMT/_0->E:2;" + "DMT/_1->E:3;DMT/_2->H:3;DMT/_3->H:5;E->H:1;E:1->H:4;F->H:2;" + "G->H;H->I:1"); +} + +#if 0 +// ConcatV2 Op test: ConcatV2 with no Mkl layer feeding it +TEST_F(MklLayoutPassTest, NodeRewrite_ConcatV2_Basic) { + InitGraph( + "node { name: 'A' op: 'Const' " + " attr { key: 'dtype' value { type: DT_INT32 } }" + " attr { key: 'value' value { " + " tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } " + " int_val: 0 } } } }" + "node { name: 'B' op: 'InputList'" + " attr { key: 'N' value { i: 2 } }}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'ConcatV2'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'Tidx' value { type: DT_INT32 } }" + " attr { key: 'N' value { i: 2 } }" + " input: ['B:0', 'B:1', 'A']}" + "node { name: 'E' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'D'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Const);B(InputList);C(Input);D(_MklConcat);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);E(Mul)|A->D:2;B->D;B:1->D:1;C->E;" + "D->E:1;DMT/_0->D:3;DMT/_1->D:4;DMT/_2->D:5"); +} +#endif + +// ConcatV2 with 2 Mkl layers feeding it +TEST_F(MklLayoutPassTest, NodeRewrite_ConcatV2_Input_Mkl) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' 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: 'F' 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: ['C', 'D']}" + "node { name: 'G' op: 'Const' " + " attr { key: 'dtype' value { type: DT_INT32 } }" + " attr { key: 'value' value { " + " tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } " + " int_val: 0 } } } }" + "node { name: 'H' op: 'ConcatV2'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'Tidx' value { type: DT_INT32 } }" + " attr { key: 'N' value { i: 2 } }" + " input: ['E', 'F', 'G']}" + "node { name: 'I' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'H'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(Input);DMT/_0(Const);DMT/_1(Const);" + "DMT/_2(Const);DMT/_3(Const);DMT/_4(Const);E(_MklConv2D);" + "F(_MklConv2D);G(Const);H(_MklConcatV2);I(Mul)|A->E;A->I;B->E:1;C->F;" + "D->F:1;DMT/_0->F:2;DMT/_1->F:3;DMT/_2->E:2;DMT/_3->E:3;" + "DMT/_4->H:5;E->H;E:1->H:3;F->H:1;F:1->H:4;G->H:2;H->I:1"); +} + +// ConcatV2 with 1 Mkl and 1 non-Mkl layer feeding it +TEST_F(MklLayoutPassTest, NodeRewrite_ConcatV2_Input_MixedMkl) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' 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: 'F' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'D']}" + "node { name: 'G' op: 'Const' " + " attr { key: 'dtype' value { type: DT_INT32 } }" + " attr { key: 'value' value { " + " tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } " + " int_val: 0 } } } }" + "node { name: 'H' op: 'ConcatV2'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'Tidx' value { type: DT_INT32 } }" + " attr { key: 'N' value { i: 2 } }" + " input: ['E', 'F', 'G']}" + "node { name: 'I' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['A', 'H'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(Input);C(Input);D(Input);DMT/_0(Const);DMT/_1(Const);" + "DMT/_2(Const);DMT/_3(Const);E(_MklConv2D);F(Mul);G(Const);" + "H(_MklConcatV2);I(Mul)|A->E;A->I;B->E:1;C->F;D->F:1;DMT/_0->E:2;" + "DMT/_1->E:3;DMT/_2->H:4;DMT/_3->H:5;E->H;E:1->H:3;F->H:1;" + "G->H:2;H->I:1"); +} + ///////////////////////////////////////////////////////////////////// // Unit tests related to rewriting node for workspace edges ///////////////////////////////////////////////////////////////////// +/* Test LRN->MaxPool->MaxPoolGrad->LRNGrad replacement by workspace nodes. */ +TEST_F(MklLayoutPassTest, MaxPoolLRN_Positive) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'LRN'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.001 } }" + " attr { key: 'beta' value { f: 0.75 } }" + " attr { key: 'bias' value { f: 1.0 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'depth_radius' value { i: 2 } }" + " input: ['A'] }" + "node { name: 'C' 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: ['B'] }" + "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: ['B', 'C', 'D'] }" + "node { name: 'F' op: 'Input'}" + "node { name: 'G' op: 'LRNGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.001 } }" + " attr { key: 'beta' value { f: 0.75 } }" + " attr { key: 'bias' value { f: 1.0 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'depth_radius' value { i: 2 } }" + " input: ['E', 'F', 'B'] }" + "node { name: 'H' op: 'Input'}" + "node { name: 'I' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['H', 'G'] }"); + EXPECT_EQ( + DoMklLayoutOptimizationPass(), + "A(Input);B(_MklLRN);C(_MklMaxPool);D(Input);DMT/_0(Const);DMT/_1(Const);" + "DMT/_2(Const);E(_MklMaxPoolGrad);F(Input);G(_MklLRNGrad);H(Input);I(Mul)|" + "A->B;B->C;B->E;B->G:2;B:1->G:3;B:2->C:1;B:2->E:4;B:2->G:6;B:3->G:7;" + "C->E:1;C:1->E:3;C:2->E:5;C:3->E:7;D->E:2;DMT/_0->B:1;DMT/_1->E:6;" + "DMT/_2->G:5;E->G;E:1->G:4;F->G:1;G->I:1;H->I"); +} + +/* Test LRN->LRNGrad replacement by workspace nodes. */ +TEST_F(MklLayoutPassTest, LRN_Positive) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'LRN'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.001 } }" + " attr { key: 'beta' value { f: 0.75 } }" + " attr { key: 'bias' value { f: 1.0 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'depth_radius' value { i: 2 } }" + " input: ['A'] }" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' op: 'LRNGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.001 } }" + " attr { key: 'beta' value { f: 0.75 } }" + " attr { key: 'bias' value { f: 1.0 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'depth_radius' value { i: 2 } }" + " input: ['C', 'D', 'B'] }" + "node { name: 'F' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['C', 'E'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(_MklLRN);C(Input);D(Input);DMT/_0(Const);DMT/_1(Const);" + "DMT/_2(Const);E(_MklLRNGrad);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:1;" + "DMT/_0->B:1;DMT/_1->E:4;DMT/_2->E:5;E->F:1"); +} + +/* Test LRN->LRNGrad replacement when only one of them is present. */ +TEST_F(MklLayoutPassTest, LRN_Negative1) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'LRN'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.001 } }" + " attr { key: 'beta' value { f: 0.75 } }" + " attr { key: 'bias' value { f: 1.0 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'depth_radius' value { 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(_MklLRN);C(Mul);DMT/_0(Const)|" + "A->B;A->C;B->C:1;DMT/_0->B:1"); +} + +/* Test LRN->LRNGrad replacement when only one of them is present. */ +TEST_F(MklLayoutPassTest, LRN_Negative2) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'Input'}" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'LRNGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.001 } }" + " attr { key: 'beta' value { f: 0.75 } }" + " attr { key: 'bias' value { f: 1.0 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'depth_radius' value { 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(_MklLRNGrad);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);DMT/_3(Const);DMT/_4(Const);E(Mul)|" + "A->D;A->E;B->D:1;C->D:2;D->E:1;DMT/_0->D:3;DMT/_1->D:7;" + "DMT/_2->D:4;DMT/_3->D:5;DMT/_4->D:6"); +} + +/* Test LRN->LRNGrad negative case, where single LRN feeds + 2 LRNGrad nodes at different slots. */ +TEST_F(MklLayoutPassTest, LRN_Negative3) { + InitGraph( + "node { name: 'A' op: 'Input'}" + "node { name: 'B' op: 'LRN'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.001 } }" + " attr { key: 'beta' value { f: 0.75 } }" + " attr { key: 'bias' value { f: 1.0 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'depth_radius' value { i: 2 } }" + " input: ['A'] }" + "node { name: 'C' op: 'Input'}" + "node { name: 'D' op: 'Input'}" + "node { name: 'E' op: 'LRNGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.001 } }" + " attr { key: 'beta' value { f: 0.75 } }" + " attr { key: 'bias' value { f: 1.0 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'depth_radius' value { i: 2 } }" + " input: ['C', 'D', 'B'] }" + "node { name: 'F' op: 'LRNGrad'" + " attr { key: 'T' value { type: DT_FLOAT } }" + " attr { key: 'alpha' value { f: 0.001 } }" + " attr { key: 'beta' value { f: 0.75 } }" + " attr { key: 'bias' value { f: 1.0 } }" + " attr { key: 'data_format' value { s: 'NCHW' } }" + " attr { key: 'depth_radius' value { i: 2 } }" + " input: ['C', 'B', 'D'] }" + "node { name: 'G' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }" + " input: ['E', 'F'] }"); + EXPECT_EQ(DoMklLayoutOptimizationPass(), + "A(Input);B(_MklLRN);C(Input);D(Input);DMT/_0(Const);DMT/_1(Const);" + "DMT/_2(Const);DMT/_3(Const);DMT/_4(Const);DMT/_5(Const);" + "DMT/_6(Const);E(_MklLRNGrad);F(_MklLRNGrad);G(Mul)|A->B;B->E:2;" + "B->F:1;B:1->E:3;B:2->E:6;B:2->F:5;B:3->E:7;C->E;C->F;D->E:1;" + "D->F:2;DMT/_0->B:1;DMT/_1->F:3;DMT/_2->F:7;DMT/_3->F:4;" + "DMT/_4->F:6;DMT/_5->E:4;DMT/_6->E:5;E->G;F->G:1"); +} + /* Test MaxPool->MaxPoolGrad replacement by workspace+rewrite nodes. */ TEST_F(MklLayoutPassTest, NodeWorkspace_MaxPool_Positive) { InitGraph( @@ -540,10 +949,10 @@ TEST_F(MklLayoutPassTest, NodeWorkspace_MaxPool_Positive) { "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"); + "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:1;B:1->E:3;B:2->E:5;B:3->E:7;C->E;C->F;D->E:2;" + "DMT/_0->B:1;DMT/_1->E:4;DMT/_2->E:6;E->F:1"); } // Test MaxPool>MaxPoolGrad replacement when only one of them is present. @@ -562,11 +971,11 @@ TEST_F(MklLayoutPassTest, NodeWorkspace_MaxPool_Negative1) { "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(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. +// Test 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) { @@ -584,10 +993,10 @@ TEST_F(MklLayoutPassTest, NodeWorkspace_MaxPool_Negative2) { "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);" + "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"); + "A->D;A->E;B->D:1;C->D:2;D->E:1;DMT/_0->D:3;DMT/_1->D:7;" + "DMT/_2->D:4;DMT/_3->D:5;DMT/_4->D:6"); } ///////////////////////////////////////////////////////////////////// |