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Add constraints for matmul-add fusion (#67) 

Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
This commit is contained in:
Tung D. Le 2020-02-08 03:51:44 +09:00 committed by GitHub
parent 0bfb660d02
commit 0564c0eaef
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2 changed files with 17 additions and 6 deletions
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3
src/pass/onnx_combine.td
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@ -25,6 +25,7 @@ include "dialect/onnx/onnx.td"
/// >;
def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;
class HasRankOf<int rank> : Constraint<CPred<"$0.getType().isa<ShapedType>() && $0.getType().cast<ShapedType>().getRank() == " # rank>>;
//===----------------------------------------------------------------------===//
// Pattern-Match and Rewrite
@ -38,7 +39,7 @@ def GemmTransB : NativeCodeCall<"$_builder.getI64IntegerAttr(0)">;
// onnx.add(onnx.matmul(%X, %Y), %Z) = onnx.Gemm(%X, %Y, %Z)
def MulAddToGemmOptPattern : Pat<(ONNXAddOp (ONNXMatMulOp:$res $m1, $m2), $m3),
(ONNXGemmOp $m1, $m2, $m3, (GemmAlpha), (GemmBeta), (GemmTransA), (GemmTransB)),
[(HasOneUse $res)]>;
[(HasOneUse $res), (HasRankOf<2> $m1), (HasRankOf<2> $m2)]>;
// ONNX_Op (onnx.Identity (%X)) = ONNX_Op (%X)
def IdentityEliminationPattern : Pat<(ONNXIdentityOp $arg),

20
test/mlir/onnx/onnx_canonicalization.mlir
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@ -1,17 +1,27 @@
// RUN: onnf-opt --canonicalize %s -split-input-file | FileCheck %s
func @test_matmul_add_simplification(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>, %a2: tensor<10x10xf32>) -> tensor<10x10xf32> {
// CHECK-LABEL: test_matmul_add_simplification
// CHECK: %{{[0-9]+}} = "onnx.Gemm"(%{{.*}}, %{{.*}}, %{{.*}}) {alpha = 1.000000e+00 : f32, beta = 1.000000e+00 : f32, transA = 0 : i64, transB = 0 : i64} : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
// CHECK-LABEL: func @test_matmul_add_fused(%{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>) -> tensor<10x10xf32> {
func @test_matmul_add_fused(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>, %a2: tensor<10x10xf32>) -> tensor<10x10xf32> {
// CHECK-NEXT: %{{[0-9]+}} = "onnx.Gemm"(%{{.*}}, %{{.*}}, %{{.*}}) {alpha = 1.000000e+00 : f32, beta = 1.000000e+00 : f32, transA = 0 : i64, transB = 0 : i64} : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
%0 = "onnx.MatMul"(%a0, %a1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
%1 = "onnx.Add"(%0, %a2) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
"std.return"(%1) : (tensor<10x10xf32>) -> ()
}
// onnx.MatMul ops with more than one result uses should not get fused
// onnx.MatMul ops for non 2-D matrices should not get fused because Gemm only supports 2-D matrices.
// CHECK-LABEL: func @test_matmul_add_not_fused(%{{.*}}: tensor<10x10x10xf32>, %{{.*}}: tensor<10x10x10xf32>, %{{.*}}: tensor<10x10x10xf32>) -> tensor<10x10x10xf32> {
func @test_matmul_add_not_fused(%a0: tensor<10x10x10xf32>, %a1: tensor<10x10x10xf32>, %a2: tensor<10x10x10xf32>) -> tensor<10x10x10xf32> {
// CHECK-NEXT: %{{[0-9]+}} = "onnx.MatMul"(%{{.*}}, %{{.*}}) : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
%0 = "onnx.MatMul"(%a0, %a1) : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
%1 = "onnx.Add"(%0, %a2) : (tensor<10x10x10xf32>, tensor<10x10x10xf32>) -> tensor<10x10x10xf32>
"std.return"(%1) : (tensor<10x10x10xf32>) -> ()
}
// onnx.MatMul ops with more than one result uses should not get fused.
// CHECK-LABEL: func @test_sigmoid_add(%{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>) -> tensor<10x10xf32>
func @test_sigmoid_add(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>, %a2: tensor<10x10xf32>) -> tensor<10x10xf32> {
// CHECK: %{{[0-9]+}} = "onnx.MatMul"(%{{.*}}, %{{.*}}) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
// CHECK-NEXT: %{{[0-9]+}} = "onnx.MatMul"(%{{.*}}, %{{.*}}) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
%0 = "onnx.MatMul"(%a0, %a1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
%1 = "onnx.Add"(%0, %a2) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
%2 = "onnx.Add"(%0, %a1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>