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onnx-mlir/test/mlir/onnx/onnx_constprop.mlir

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Constprop (#162) * 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
2020-06-09 03:45:32 +08:00
// RUN: onnx-mlir-opt --constprop-onnx %s -split-input-file | FileCheck %s
// =============================================================================
/// MUL tests (same as add, so have only one).
/// Test ConstantOp assoc for add
// CHECK-LABEL: @test_add_constant_1(%arg0: tensor<3xf32>) -> tensor<3xf32>
func @test_add_constant_1(%arg0 : tensor<3xf32>) -> tensor<3xf32> {
%0 = "onnx.Constant"() {value = dense<[0.0, 1.0, 2.0]> : tensor<3xf32>} : () -> tensor<3xf32>
%1 = "onnx.Add"(%0, %arg0) : (tensor<3xf32> , tensor<3xf32>) -> tensor<3xf32>
"std.return"(%1) : (tensor<3xf32>) -> ()
// CHECK-NEXT: [[CONST:%.+]] = "onnx.Constant"() {value = dense<[0.000000e+00, 1.000000e+00, 2.000000e+00]> : tensor<3xf32>} : () -> tensor<3xf32>
// CHECK-NEXT: [[ADD:%.+]] = "onnx.Add"(%arg0, [[CONST]]) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
}
/// Test ConstantOp assoc for add
// CHECK-LABEL: @test_add_constant_2(%arg0: tensor<3xf32>) -> tensor<3xf32>
func @test_add_constant_2(%arg0 : tensor<3xf32>) -> tensor<3xf32> {
%0 = "onnx.Constant"() {value = dense<[0.0, 1.0, 2.0]> : tensor<3xf32>} : () -> tensor<3xf32>
%1 = "onnx.Add"(%arg0, %0) : (tensor<3xf32> , tensor<3xf32>) -> tensor<3xf32>
"std.return"(%1) : (tensor<3xf32>) -> ()
// CHECK-NEXT: [[CONST:%.+]] = "onnx.Constant"() {value = dense<[0.000000e+00, 1.000000e+00, 2.000000e+00]> : tensor<3xf32>} : () -> tensor<3xf32>
// CHECK-NEXT: [[ADD:%.+]] = "onnx.Add"(%arg0, [[CONST]]) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
}
/// Change (x+c1)+c2 to x+(c1+c2)
// CHECK-LABEL: @test_add_constant_3(%arg0: tensor<3xi32>) -> tensor<3xi32>
func @test_add_constant_3(%arg0 : tensor<3xi32>) -> tensor<3xi32> {
%0 = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
%1 = "onnx.Constant"() {value = dense<[10, 11, 12]> : tensor<3xi32>} : () -> tensor<3xi32>
%2 = "onnx.Add"(%0, %arg0) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
%3 = "onnx.Add"(%1, %2) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
"std.return"(%3) : (tensor<3xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<[10, 12, 14]> : tensor<3xi32>} : () -> tensor<3xi32>
// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
}
/// Same test as above, but with a use of an intermediary result
/// change (x+c1)+c2 + (x+c1) to x+(c1+c2) + (x+c1)
// CHECK-LABEL: @test_add_constant_4(%arg0: tensor<3xi32>) -> tensor<3xi32>
func @test_add_constant_4(%arg0 : tensor<3xi32>) -> tensor<3xi32> {
%0 = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
%1 = "onnx.Constant"() {value = dense<[10, 11, 12]> : tensor<3xi32>} : () -> tensor<3xi32>
%2 = "onnx.Add"(%0, %arg0) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
%3 = "onnx.Add"(%1, %2) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
%4 = "onnx.Add"(%2, %3) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
"std.return"(%4) : (tensor<3xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
// CHECK-NEXT: [[CONST2:%.+]] = "onnx.Constant"() {value = dense<[10, 12, 14]> : tensor<3xi32>} : () -> tensor<3xi32>
// CHECK-NEXT: [[ADD2:%.+]] = "onnx.Add"(%arg0, [[CONST2]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
// CHECK-NEXT: [[ADD3:%.+]] = "onnx.Add"([[ADD1]], [[ADD2]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
}
/// Test broadcast 1 -> 2d
// CHECK-LABEL: @test_broadcast_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
func @test_broadcast_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
%0 = "onnx.Constant"() {value = dense<[1]> : tensor<1xi32>} : () -> tensor<1xi32>
%1 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
%2 = "onnx.Add"(%0, %1) : (tensor<1xi32> , tensor<3x2xi32>) -> tensor<3x2xi32>
%3 = "onnx.Add"(%2, %arg0) : (tensor<3x2xi32> , tensor<3x2xi32>) -> tensor<3x2xi32>
"std.return"(%3) : (tensor<3x2xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<{{.}}[3, 4], [5, 6], [7, 8]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
}
/// Test broadcast 2d (size one) -> 2d
// CHECK-LABEL: @test_broadcast_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
func @test_broadcast_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
%0 = "onnx.Constant"() {value = dense<[[1]]> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
%1 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
%2 = "onnx.Add"(%0, %1) : (tensor<1x1xi32> , tensor<3x2xi32>) -> tensor<3x2xi32>
%3 = "onnx.Add"(%2, %arg0) : (tensor<3x2xi32> , tensor<3x2xi32>) -> tensor<3x2xi32>
"std.return"(%3) : (tensor<3x2xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<{{.}}[3, 4], [5, 6], [7, 8]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
}
/// check 1d -> 2d
// CHECK-LABEL: @test_broadcast_3(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
func @test_broadcast_3(%arg0 : tensor<3x2xi32>) -> tensor<3x2xi32> {
%0 = "onnx.Constant"() {value = dense<[[1], [2], [3]]> : tensor<3x1xi32>} : () -> tensor<3x1xi32>
%1 = "onnx.Constant"() {value = dense<[[10, 11], [21, 22], [31, 32]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
%2 = "onnx.Add"(%0, %1) : (tensor<3x1xi32> , tensor<3x2xi32>) -> tensor<3x2xi32>
%3 = "onnx.Add"(%2, %arg0) : (tensor<3x2xi32> , tensor<3x2xi32>) -> tensor<3x2xi32>
"std.return"(%3) : (tensor<3x2xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<{{.}}[11, 12], [23, 24], [34, 35]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
}
// =============================================================================
/// MUL tests (same as add, so have only one).
/// Change (x*c1)*c2 to x*(c1*c2)
// CHECK-LABEL: @test_mul_constant_3(%arg0: tensor<3xi32>) -> tensor<3xi32>
func @test_mul_constant_3(%arg0 : tensor<3xi32>) -> tensor<3xi32> {
%0 = "onnx.Constant"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
%1 = "onnx.Constant"() {value = dense<[10, 11, 12]> : tensor<3xi32>} : () -> tensor<3xi32>
%2 = "onnx.Mul"(%0, %arg0) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
%3 = "onnx.Mul"(%1, %2) : (tensor<3xi32> , tensor<3xi32>) -> tensor<3xi32>
"std.return"(%3) : (tensor<3xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<[0, 11, 24]> : tensor<3xi32>} : () -> tensor<3xi32>
// CHECK-NEXT: [[MUL1:%.+]] = "onnx.Mul"(%arg0, [[CONST1]]) : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
}
// =============================================================================
/// SUB and NEG tests.
// check of sub two constants
// CHECK-LABEL: @test_sub_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
func @test_sub_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
%0 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
%1 = "onnx.Constant"() {value = dense<[[2]]> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
%2 = "onnx.Sub"(%0, %1) : (tensor<3x2xi32>, tensor<1x1xi32>) -> tensor<3x2xi32>
"std.return"(%2) : (tensor<3x2xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<{{.}}[0, 1], [2, 3], [4, 5]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
}
/// check sub to add of negative
// CHECK-LABEL: @test_neg_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
func @test_neg_1(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
%0 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
%1 = "onnx.Sub"(%arg0, %0) : (tensor<3x2xi32> , tensor<3x2xi32>) -> tensor<3x2xi32>
"std.return"(%1) : (tensor<3x2xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<{{.}}[-2, -3], [-4, -5], [-6, -7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
}
// CHECK-LABEL: @test_neg_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
func @test_neg_2(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
%0 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
%1 = "onnx.Constant"() {value = dense<[[10]]> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
%2 = "onnx.Sub"(%arg0, %0) : (tensor<3x2xi32> , tensor<3x2xi32>) -> tensor<3x2xi32>
%5 = "onnx.Add"(%2, %1) : (tensor<3x2xi32> , tensor<1x1xi32>) -> tensor<3x2xi32>
"std.return"(%5) : (tensor<3x2xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<{{.}}[8, 7], [6, 5], [4, 3]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
}
// CHECK-LABEL: @test_neg_3(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32>
func @test_neg_3(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
%0 = "onnx.Constant"() {value = dense<[[2, 3], [4, 5], [6, 7]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
%1 = "onnx.Constant"() {value = dense<[[10]]> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
%2 = "onnx.Neg"(%0) : (tensor<3x2xi32>) -> tensor<3x2xi32>
%3 = "onnx.Add"(%arg0, %2) : (tensor<3x2xi32> , tensor<3x2xi32>) -> tensor<3x2xi32>
%4 = "onnx.Add"(%3, %1) : (tensor<3x2xi32> , tensor<1x1xi32>) -> tensor<3x2xi32>
"std.return"(%4) : (tensor<3x2xi32>) -> ()
// CHECK-NEXT: [[CONST1:%.+]] = "onnx.Constant"() {value = dense<{{.}}[8, 7], [6, 5], [4, 3]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
// CHECK-NEXT: [[ADD1:%.+]] = "onnx.Add"(%arg0, [[CONST1]]) : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
}