Constprop2 (#167)
* initial const prop attempt * added support for broadcast ops * adde all binary broadcast ops into custom builders with precise type * added test example * working * format * fixed suggestion by Tung, start woring on unary * added subtraction and neg the right way, and added elementwise mul too * formatting changes * format * format * added instructions to add new optimizations * added propagation rules that always migrate constants toward the root of the expression, using assoc and commutativity * format comment
This commit is contained in:
Alexandre Eichenberger
GitHub
parent
a7781791e9
commit
742e817722
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@ -85,7 +85,41 @@ def AddConstAssociative1 : Pat<
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// To add(x, add(c1, c2)).
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(ONNXAddOp
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$x,
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(ONNXAddOp $c1, $c2))>;
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(ONNXAddOp $c1, $c2)),
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[(IsNotAConstant:$x)]>;
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def AddConstAssociative2 : Pat<
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// From add(add(x, c), y).
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(ONNXAddOp
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(ONNXAddOp $x,(ONNXConstantOp:$c $_, $_)),
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$y),
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// To add(add(x, y), c).
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(ONNXAddOp
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(ONNXAddOp $x, $y),
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$c),
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[(IsNotAConstant:$x), (IsNotAConstant:$y)]>;
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def AddConstAssociative3 : Pat<
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// From add(x, add(y, c)).
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(ONNXAddOp
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$x,
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(ONNXAddOp $y,(ONNXConstantOp:$c $_, $_))),
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// To add(add(x, y), c).
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(ONNXAddOp
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(ONNXAddOp $x, $y),
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$c),
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[(IsNotAConstant:$x), (IsNotAConstant:$y)]>;
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def AddConstAssociative4 : Pat<
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// From add(add(x, c1), add(y, c2)).
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(ONNXAddOp
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(ONNXAddOp $x,(ONNXConstantOp:$c1 $_, $_)),
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(ONNXAddOp $y,(ONNXConstantOp:$c2 $_, $_))),
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// To add(add(x, y), c1+c2).
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(ONNXAddOp
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(ONNXAddOp $x, $y),
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(ONNXAddOp $c1, $c2)),
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[(IsNotAConstant:$x), (IsNotAConstant:$y)]>;
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// Constant Propagation for Add
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def AddConstProp : Pat<
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@ -150,7 +184,41 @@ def MulConstAssociative1 : Pat<
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// To mul(x, mul(c1, c2)).
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(ONNXMulOp
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$x,
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(ONNXMulOp $c1, $c2))>;
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(ONNXMulOp $c1, $c2)),
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[(IsNotAConstant:$x)]>;
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def MulConstAssociative2 : Pat<
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// From mul(mul(x, c), y).
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(ONNXMulOp
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(ONNXMulOp $x,(ONNXConstantOp:$c $_, $_)),
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$y),
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// To mul(mul(x, y), c).
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(ONNXMulOp
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(ONNXMulOp $x, $y),
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$c),
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[(IsNotAConstant:$x), (IsNotAConstant:$y)]>;
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def MulConstAssociative3 : Pat<
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// From mul(x, mul(y, c)).
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(ONNXMulOp
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$x,
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(ONNXMulOp $y,(ONNXConstantOp:$c $_, $_))),
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// To mul(mul(x, y), c).
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(ONNXMulOp
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(ONNXMulOp $x, $y),
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$c),
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[(IsNotAConstant:$x), (IsNotAConstant:$y)]>;
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def MulConstAssociative4 : Pat<
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// From mul(mul(x, c1), mul(y, c2)).
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(ONNXMulOp
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(ONNXMulOp $x,(ONNXConstantOp:$c1 $_, $_)),
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(ONNXMulOp $y,(ONNXConstantOp:$c2 $_, $_))),
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// To mul(mul(x, y), c1+c2).
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(ONNXMulOp
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(ONNXMulOp $x, $y),
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(ONNXMulOp $c1, $c2)),
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[(IsNotAConstant:$x), (IsNotAConstant:$y)]>;
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// Constant Propagation for Mul
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def MulConstProp : Pat<
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@ -1,10 +1,11 @@
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// RUN: onnx-mlir-opt --constprop-onnx %s -split-input-file | FileCheck %s
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// =============================================================================
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/// MUL tests (same as add, so have only one).
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//===----------------------------------------------------------------------===//
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/// ADD tests
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/// Test ConstantOp assoc for add
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// -----
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// CHECK-LABEL: @test_add_constant_1(%arg0: tensor<3xf32>) -> tensor<3xf32>
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func @test_add_constant_1(%arg0 : tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Constant"() {value = dense<[0.0, 1.0, 2.0]> : tensor<3xf32>} : () -> tensor<3xf32>
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@ -15,6 +16,7 @@ func @test_add_constant_1(%arg0 : tensor<3xf32>) -> tensor<3xf32> {
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}
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/// Test ConstantOp assoc for add
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// -----
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// CHECK-LABEL: @test_add_constant_2(%arg0: tensor<3xf32>) -> tensor<3xf32>
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func @test_add_constant_2(%arg0 : tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Constant"() {value = dense<[0.0, 1.0, 2.0]> : tensor<3xf32>} : () -> tensor<3xf32>
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@ -25,6 +27,7 @@ func @test_add_constant_2(%arg0 : tensor<3xf32>) -> tensor<3xf32> {
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}
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/// Change (x+c1)+c2 to x+(c1+c2)
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// -----
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// CHECK-LABEL: @test_add_constant_3(%arg0: tensor<3xi32>) -> tensor<3xi32>
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func @test_add_constant_3(%arg0 : tensor<3xi32>) -> tensor<3xi32> {
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%0 = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
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@ -37,7 +40,8 @@ func @test_add_constant_3(%arg0 : tensor<3xi32>) -> tensor<3xi32> {
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}
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/// Same test as above, but with a use of an intermediary result
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/// change (x+c1)+c2 + (x+c1) to x+(c1+c2) + (x+c1)
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/// change (x+c1)+c2 + (x+c1) to x+x + (c1+c2+c3)
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// -----
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// CHECK-LABEL: @test_add_constant_4(%arg0: tensor<3xi32>) -> tensor<3xi32>
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func @test_add_constant_4(%arg0 : tensor<3xi32>) -> tensor<3xi32> {
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%0 = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
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@ -46,15 +50,30 @@ func @test_add_constant_4(%arg0 : tensor<3xi32>) -> tensor<3xi32> {
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%3 = "onnx.Add"(%1, %2) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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%4 = "onnx.Add"(%2, %3) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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"std.return"(%4) : (tensor<3xi32>) -> ()
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// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
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// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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// CHECK-NEXT: [[CONST2:%.+]] = "onnx.Constant"() {value = dense<[10, 12, 14]> : tensor<3xi32>} : () -> tensor<3xi32>
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// CHECK-NEXT: [[ADD2:%.+]] = "onnx.Add"(%arg0, [[CONST2]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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// CHECK-NEXT: [[ADD3:%.+]] = "onnx.Add"([[ADD1]], [[ADD2]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, %arg0) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<[10, 13, 16]> : tensor<3xi32>} : () -> tensor<3xi32>
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// CHECK-NEXT: [[ADD2:%.+]] = "onnx.Add"([[ADD1]], [[CONST1]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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}
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/// Change (x+c0)+y + (z+c1) to (x+y)+z + (c1+c2)
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// -----
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// CHECK-LABEL: @test_add_constant_5(%arg0: tensor<3xi32>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>) -> tensor<3xi32>
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func @test_add_constant_5(%arg0 : tensor<3xi32>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>) -> tensor<3xi32> {
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%0 = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
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%1 = "onnx.Constant"() {value = dense<[10, 11, 12]> : tensor<3xi32>} : () -> tensor<3xi32>
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%2 = "onnx.Add"(%0, %arg0) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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%3 = "onnx.Add"(%2, %arg1) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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%4 = "onnx.Add"(%1, %arg2) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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%5 = "onnx.Add"(%3, %4) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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"std.return"(%5) : (tensor<3xi32>) -> ()
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// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, %arg1) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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// CHECK-NEXT: [[ADD2:%.+]] = "onnx.Add"([[ADD1]], %arg2) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<[10, 12, 14]> : tensor<3xi32>} : () -> tensor<3xi32>
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// CHECK-NEXT: [[ADD3:%.+]] = "onnx.Add"([[ADD2]], [[CONST1]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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}
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/// Test broadcast 1 -> 2d
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// -----
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// CHECK-LABEL: @test_broadcast_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
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func @test_broadcast_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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%0 = "onnx.Constant"() {value = dense<[1]> : tensor<1xi32>} : () -> tensor<1xi32>
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@ -67,7 +86,7 @@ func @test_broadcast_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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}
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/// Test broadcast 2d (size one) -> 2d
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// -----
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// CHECK-LABEL: @test_broadcast_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
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func @test_broadcast_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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%0 = "onnx.Constant"() {value = dense<[[1]]> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
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@ -80,7 +99,7 @@ func @test_broadcast_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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}
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/// check 1d -> 2d
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// -----
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// CHECK-LABEL: @test_broadcast_3(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
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func @test_broadcast_3(%arg0 : tensor<3x2xi32>) -> tensor<3x2xi32> {
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%0 = "onnx.Constant"() {value = dense<[[1], [2], [3]]> : tensor<3x1xi32>} : () -> tensor<3x1xi32>
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@ -92,10 +111,12 @@ func @test_broadcast_3(%arg0 : tensor<3x2xi32>) -> tensor<3x2xi32> {
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// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
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}
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// =============================================================================
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/// MUL tests (same as add, so have only one).
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//===----------------------------------------------------------------------===//
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/// MUL tests (same as add, so have only two).
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/// Change (x*c1)*c2 to x*(c1*c2)
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// -----
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// CHECK-LABEL: @test_mul_constant_3(%arg0: tensor<3xi32>) -> tensor<3xi32>
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func @test_mul_constant_3(%arg0 : tensor<3xi32>) -> tensor<3xi32> {
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%0 = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
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@ -107,12 +128,28 @@ func @test_mul_constant_3(%arg0 : tensor<3xi32>) -> tensor<3xi32> {
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// CHECK-NEXT: [[MUL1:%.+]] = "onnx.Mul"(%arg0, [[CONST1]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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}
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// =============================================================================
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/// Change (x*c0)*y * (z*c1) to (x*y)*z * (c1*c2)
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// -----
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// CHECK-LABEL: @test_mul_constant_5(%arg0: tensor<3xi32>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>) -> tensor<3xi32>
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func @test_mul_constant_5(%arg0 : tensor<3xi32>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>) -> tensor<3xi32> {
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%0 = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
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%1 = "onnx.Constant"() {value = dense<[10, 11, 12]> : tensor<3xi32>} : () -> tensor<3xi32>
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%2 = "onnx.Mul"(%0, %arg0) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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%3 = "onnx.Mul"(%2, %arg1) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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%4 = "onnx.Mul"(%1, %arg2) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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%5 = "onnx.Mul"(%3, %4) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
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"std.return"(%5) : (tensor<3xi32>) -> ()
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// CHECK-NEXT: [[MUL1:%.+]] = "onnx.Mul"(%arg0, %arg1) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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// CHECK-NEXT: [[MUL2:%.+]] = "onnx.Mul"([[MUL1]], %arg2) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<[0, 11, 24]> : tensor<3xi32>} : () -> tensor<3xi32>
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// CHECK-NEXT: [[MUL3:%.+]] = "onnx.Mul"([[MUL2]], [[CONST1]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
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}
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//===----------------------------------------------------------------------===//
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/// SUB and NEG tests.
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// check of sub two constants
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// -----
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// CHECK-LABEL: @test_sub_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
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func @test_sub_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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%0 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
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@ -123,7 +160,7 @@ func @test_sub_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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}
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/// check sub to add of negative
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// -----
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// CHECK-LABEL: @test_neg_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
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func @test_neg_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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%0 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
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@ -133,6 +170,7 @@ func @test_neg_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
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}
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// -----
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// CHECK-LABEL: @test_neg_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
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func @test_neg_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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%0 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
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@ -144,6 +182,7 @@ func @test_neg_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
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}
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// -----
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// CHECK-LABEL: @test_neg_3(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
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func @test_neg_3(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
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%0 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
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