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Revert "Fuse BatchMatMul with Mul" #26

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Merged
merged 1 commit into from
Apr 30, 2020
Merged

Revert "Fuse BatchMatMul with Mul" #26

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16 changes: 0 additions & 16 deletions tensorflow/core/graph/mkl_layout_pass.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -277,8 +277,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
csinfo_.fused_depthwise_conv2d = "_FusedDepthwiseConv2dNative";
csinfo_.fused_matmul = "_FusedMatMul";
csinfo_.fused_matmul_grad = "_FusedMatMulGrad";
csinfo_.fused_batch_matmul = "_FusedBatchMatMul";
csinfo_.fused_batch_matmul_v2 = "_FusedBatchMatMulV2";
csinfo_.gelu = "Gelu";
csinfo_.gelu_grad = "GeluGrad";
csinfo_.identity = "Identity";
Expand Down Expand Up @@ -306,8 +304,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
csinfo_.mkl_fused_depthwise_conv2d = "_MklFusedDepthwiseConv2dNative";
csinfo_.mkl_fused_matmul = "_MklFusedMatMul";
csinfo_.mkl_fused_matmul_grad = "_MklFusedMatMulGrad";
csinfo_.mkl_fused_batch_matmul = "_MklFusedBatchMatMul";
csinfo_.mkl_fused_batch_matmul_v2 = "_MklFusedBatchMatMulV2";
csinfo_.mkl_pad_with_conv2d = "_MklPadWithConv2D";
csinfo_.mkl_pad_with_fused_conv2d = "_MklPadWithFusedConv2D";
csinfo_.pad = "Pad";
Expand Down Expand Up @@ -506,12 +502,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
CopyAttrsAll, AlwaysRewrite,
kRewriteForLayoutPropagation});

rinfo_.push_back({csinfo_.fused_batch_matmul,
csinfo_.mkl_fused_batch_matmul, CopyAttrsAll,
AlwaysRewrite, kRewriteForOpNameChange});
rinfo_.push_back({csinfo_.fused_batch_matmul_v2,
csinfo_.mkl_fused_batch_matmul_v2, CopyAttrsAll,
AlwaysRewrite, kRewriteForOpNameChange});
rinfo_.push_back({csinfo_.gelu, mkl_op_registry::GetMklOpName(csinfo_.gelu),
CopyAttrsAll, GeluRewrite, kRewriteForLayoutPropagation});
rinfo_.push_back({csinfo_.gelu_grad,
Expand Down Expand Up @@ -964,8 +954,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
string fused_depthwise_conv2d;
string fused_matmul;
string fused_matmul_grad;
string fused_batch_matmul;
string fused_batch_matmul_v2;
string gelu;
string gelu_grad;
string identity;
Expand All @@ -991,8 +979,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
string mkl_fused_depthwise_conv2d;
string mkl_fused_matmul;
string mkl_fused_matmul_grad;
string mkl_fused_batch_matmul;
string mkl_fused_batch_matmul_v2;
string mkl_pad_with_conv2d;
string mkl_pad_with_fused_conv2d;
string mul;
Expand Down Expand Up @@ -3828,8 +3814,6 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const {
n->type_string() != csinfo_.fused_depthwise_conv2d &&
n->type_string() != csinfo_.fused_matmul &&
n->type_string() != csinfo_.fused_matmul_grad &&
n->type_string() != csinfo_.fused_batch_matmul &&
n->type_string() != csinfo_.fused_batch_matmul_v2 &&
!mkl_op_registry::IsMklOp(mkl_op_registry::GetMklOpName(n->type_string()),
T)) {
return nullptr;
Expand Down
25 changes: 0 additions & 25 deletions tensorflow/core/graph/mkl_layout_pass_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -2033,31 +2033,6 @@ REGISTER_TEST_ALL_TYPES(NodeRewrite_FusedMatMul_Negative);
REGISTER_TEST_ALL_TYPES(NodeRewrite_FusedMatMulGrad_Positive);
#undef REGISTER_TEST

// Test set: _FusedBatchMatMul -> MklFusedBatchMatMul rewrite tests
#define REGISTER_TEST(NAME, T, INPUT) \
TEST_F(MklLayoutPassTest, NAME##_##T) { \
InitGraph( \
"node { name: 'A' op: '" #INPUT "'}" \
"node { name: 'B' op: '" #INPUT "'}" \
"node { name: 'C' op: '" #INPUT "'}" \
"node { name: 'D' op: '_FusedBatchMatMul'" \
" attr { key: 'T' value { type:" #T "} }" \
" attr { key: 'adj_x' value { b: false } }" \
" attr { key: 'adj_y' value { b: false } }" \
" attr { key: 'num_args' value { i: 1 } }" \
" attr { key: 'fused_ops' value { list: {s: 'Scale'} } }" \
" input: ['A', 'B', 'C']}" \
"node { name: 'Z' op: 'Zeta'" \
" attr {key: 'T' value { type: " #T " } }" \
" input: ['D', 'C']}"); \
EXPECT_EQ(DoMklLayoutOptimizationPass(), \
"A(" #INPUT ");B(" #INPUT ");C(" #INPUT ");" \
"D(_MklFusedBatchMatMul);Z(Zeta)" \
"|A->D;B->D:1;C->D:2;C->Z:1;D->Z"); \
}
REGISTER_TEST_ALL_TYPES(NodeRewrite_FusedBatchMatMul_Positive)
#undef REGISTER_TEST

// Merge test for PadWithFusedConv2D Op with BiasAdd fusion
// padding is VALID type
// A = input(image), B = input(paddings), C = Pad(A, B) = input of conv2D,
Expand Down
93 changes: 0 additions & 93 deletions tensorflow/core/grappler/optimizers/mkl_remapper_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -765,99 +765,6 @@ TEST_F(MklRemapperTest, FuseBatchNormWithAddAndRelu) {
EXPECT_EQ(found, 3);
}
}

class MklFuseBatchMatMulWithMul : public MklRemapperTest {
public:
void VerifyFused(bool adjx, bool adjy) {
using ::tensorflow::ops::Placeholder;
int b = 2;
int m = 2;
int k = 3;
int n = 4;

tensorflow::Scope s = tensorflow::Scope::NewRootScope();

auto input_shape = ops::Placeholder::Shape({b, m, k});
if (adjx) input_shape = ops::Placeholder::Shape({b, k, m});
auto weight_shape = ops::Placeholder::Shape({b, k, n});
if (adjy) weight_shape = ops::Placeholder::Shape({b, n, k});

auto input = Placeholder(s.WithOpName("input"), DT_FLOAT, input_shape);
auto weight = Placeholder(s.WithOpName("weight"), DT_FLOAT, weight_shape);

auto batchmatmul =
ops::BatchMatMulV2(s.WithOpName("batchmatmul"), input, weight,
ops::BatchMatMulV2::Attrs().AdjX(adjx).AdjY(adjy));
auto scale = ops::Const(s.WithOpName("scale"), {10.0f});
auto mul = ops::Multiply(s.WithOpName("mul"), batchmatmul, scale);

auto fetch_mul = ops::Identity(s.WithOpName("fetch_mul"), mul);

auto input_t = GenerateRandomTensor<DT_FLOAT>({b, m, k});
if (adjx) input_t = GenerateRandomTensor<DT_FLOAT>({b, k, m});
auto weight_t = GenerateRandomTensor<DT_FLOAT>({b, k, n});
if (adjy) weight_t = GenerateRandomTensor<DT_FLOAT>({b, n, k});

GrapplerItem item;
item.fetch = {"fetch_mul"};
item.feed = {{"input", input_t}, {"weight", weight_t}};
TF_CHECK_OK(s.ToGraphDef(&item.graph));

// Place all nodes on CPU.
for (int i = 0; i < item.graph.node_size(); ++i) {
item.graph.mutable_node(i)->set_device("/device:CPU:0");
}

Remapper optimizer(RewriterConfig::ON);
GraphDef output;
TF_CHECK_OK(optimizer.Optimize(nullptr, item, &output));

int found = 0;
for (const NodeDef& node : output.node()) {
if (node.name() == "mul") {
EXPECT_EQ("_FusedBatchMatMulV2", node.op());
EXPECT_EQ("input", node.input(0));
EXPECT_EQ("weight", node.input(1));
EXPECT_EQ("scale", node.input(2));

const auto fused_ops = node.attr().at("fused_ops").list().s();
EXPECT_EQ(1, fused_ops.size());
EXPECT_EQ("Mul", fused_ops[0]);
found++;
}
}
EXPECT_EQ(1, found);

auto tensors_expected = EvaluateNodes(item.graph, item.fetch, item.feed);
auto tensors = EvaluateNodes(output, item.fetch, item.feed);
test::ExpectTensorNear<float>(tensors_expected[0], tensors[0], 1e-6);
}
};

TEST_F(MklFuseBatchMatMulWithMul, a0b0) {
bool adjx = false;
bool adjy = false;
this->VerifyFused(adjx, adjy);
}

TEST_F(MklFuseBatchMatMulWithMul, a1b0) {
bool adjx = true;
bool adjy = false;
this->VerifyFused(adjx, adjy);
}

TEST_F(MklFuseBatchMatMulWithMul, a0b1) {
bool adjx = false;
bool adjy = true;
this->VerifyFused(adjx, adjy);
}

TEST_F(MklFuseBatchMatMulWithMul, a1b1) {
bool adjx = true;
bool adjy = true;
this->VerifyFused(adjx, adjy);
}

#endif // ENABLE_MKLDNN_V1

} // namespace grappler
Expand Down
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