ScalerOp support non-float input using CastOp (#231)
* move scalerop to decompose * change clang format * change clang format * add shape inference for scaler op * fixing generated onnxop * generate onnx.md * redo get onnx.md and onnxop.td.inc using onnx 1.6 * Add shape inference for scaler op * add benefit for scaler decompose and simplify scaler shape inference * add scaler decompose benefit num and simplify shape inference * add cast builder Co-authored-by: chentong319 <chentong@us.ibm.com>
This commit is contained in:
Anh Leu
GitHub
parent
2e8f012195
commit
e631283c71
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@ -494,6 +494,14 @@ def ONNXCastOp:ONNX_Op<"Cast",
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return resultTypes;
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}
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}];
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let builders = [
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OpBuilder<"OpBuilder &builder, OperationState &state, Value input, IntegerAttr to", [{
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auto toAttr = to.getValue().getSExtValue();
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auto resultType = mlir::UnrankedTensorType::get(
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convertONNXTypeToMLIRType(builder, static_cast<onnx::TensorProto_DataType>(toAttr)));
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build(builder, state, resultType, input, to);
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}] >
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];
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}
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def ONNXCeilOp:ONNX_Op<"Ceil",
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@ -648,17 +656,17 @@ def ONNXConstantOp:ONNX_Op<"Constant",
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return resultTypes;
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}
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}];
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let builders = [
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OpBuilder<"OpBuilder &builder, OperationState &state, Attribute sparse_value, Attribute value", [{
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if (value) {
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auto tensorType = value.getType();
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build(builder, state, tensorType, sparse_value, value);
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} else {
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auto tensorType = sparse_value.getType();
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build(builder, state, tensorType, sparse_value, value);
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}
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}]>
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];
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let builders = [
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OpBuilder<"OpBuilder &builder, OperationState &state, Attribute sparse_value, Attribute value", [{
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if (value) {
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auto tensorType = value.getType();
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build(builder, state, tensorType, sparse_value, value);
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} else {
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auto tensorType = sparse_value.getType();
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build(builder, state, tensorType, sparse_value, value);
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}
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}]>
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];
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}
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def ONNXConstantOfShapeOp:ONNX_Op<"ConstantOfShape",
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@ -63,10 +63,6 @@ def AttributeIsNull :
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Constraint<CPred<"! ($_self)">,
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"Attribute is null">;
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def AttributeNotNull :
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Constraint<CPred<" ($_self)">,
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"Attribute exists">;
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def HasFloatType : Constraint<CPred<"(($_self).getType().dyn_cast<ShapedType>().getElementType().isF32())">>;
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def GetNullAttr :
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@ -83,20 +79,30 @@ def ScalerNullPattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(replaceWithValue $x),
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[(HasFloatType:$x), (AttributeIsNull:$a), (AttributeIsNull:$b)],
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(addBenefit 4)>;
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(addBenefit 5)>;
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// No attribute, input x not float type
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def ScalerNullPattern2 : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXCastOp $x, (ScalerT)),
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[(AttributeIsNull:$a), (AttributeIsNull:$b)],
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(addBenefit 3)>;
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(addBenefit 4)>;
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// No scale
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def ScalerNoScalePattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXSubOp $x,
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))),
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[(HasFloatType:$x), (AttributeIsNull:$b)],
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(addBenefit 3)>;
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// No scale, input x not float type
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def ScalerNoScalePattern2 : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXSubOp
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(ONNXCastOp $x, (ScalerT)),
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))
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),
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[(AttributeIsNull:$b)],
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(addBenefit 2)>;
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@ -105,6 +111,15 @@ def ScalerNoOffsetPattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXMulOp $x,
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b))),
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[(HasFloatType:$x), (AttributeIsNull:$a)],
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(addBenefit 3)>;
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// No offset, input x not float type
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def ScalerNoOffsetPattern2 : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXMulOp
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(ONNXCastOp $x, (ScalerT)),
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b))),
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[(AttributeIsNull:$a)],
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(addBenefit 2)>;
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@ -115,7 +130,17 @@ def ScalerPattern : Pat<
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(ONNXSubOp $x,
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))),
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b))),
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[],
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[(HasFloatType:$x)],
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(addBenefit 1)>;
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// Normal ONNXScalerOp, input x not float type
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def ScalerPattern2 : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXMulOp
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(ONNXSubOp (ONNXCastOp $x, (ScalerT)),
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))),
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b))),
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[],
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(addBenefit 0)>;
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#endif // ONNX_DECOMPOSE
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@ -78,8 +78,8 @@ func @test_scaler_null(%arg0: tensor<3xi32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) : (tensor<3xi32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Cast"(%arg0) {to = 1 : i64} : (tensor<3xi32>) -> tensor<3xf32>
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// CHECK-NEXT: return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Cast"(%arg0) {to = 1 : i64} : (tensor<3xi32>) -> tensor<3xf32>
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// CHECK-NEXT: return %0 : tensor<3xf32>
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}
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// -----
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@ -90,9 +90,23 @@ func @test_scaler_no_offset(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Mul"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Mul"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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}
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// -----
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// scaler no offset, int input
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// CHECK-LABEL: func @test_scaler_no_offset2(%{{.*}}: tensor<3xi32>) -> tensor<3xf32> {
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func @test_scaler_no_offset2(%arg0: tensor<3xi32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xi32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Cast"(%arg0) {to = 1 : i64} : (tensor<3xi32>) -> tensor<*xf32>
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// CHECK-NEXT: %1 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Mul"(%0, %1) : (tensor<*xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %2 : tensor<3xf32>
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}
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// -----
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@ -103,9 +117,23 @@ func @test_scaler_no_scale(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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}
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// -----
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// scaler no scale, int input
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// CHECK-LABEL: func @test_scaler_no_scale2(%{{.*}}: tensor<3xi32>) -> tensor<3xf32> {
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func @test_scaler_no_scale2(%arg0: tensor<3xi32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32]} : (tensor<3xi32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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//CHECK-NEXT: %0 = "onnx.Cast"(%arg0) {to = 1 : i64} : (tensor<3xi32>) -> tensor<*xf32>
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//CHECK-NEXT: %1 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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//CHECK-NEXT: %2 = "onnx.Sub"(%0, %1) : (tensor<*xf32>, tensor<3xf32>) -> tensor<3xf32>
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//CHECK-NEXT: return %2 : tensor<3xf32>
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}
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// -----
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@ -116,11 +144,27 @@ func @test_scaler_normal(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32], scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %3 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %3 : tensor<3xf32>
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}
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// -----
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// normal scaler, int input
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// CHECK-LABEL: func @test_scaler_normal2(%{{.*}}: tensor<3xi32>) -> tensor<3xf32> {
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func @test_scaler_normal2(%arg0: tensor<3xi32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32], scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xi32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Cast"(%arg0) {to = 1 : i64} : (tensor<3xi32>) -> tensor<*xf32>
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// CHECK-NEXT: %1 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Sub"(%0, %1) : (tensor<*xf32>, tensor<3xf32>) -> tensor<*xf32>
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// CHECK-NEXT: %3 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %4 = "onnx.Mul"(%2, %3) : (tensor<*xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %4 : tensor<3xf32>
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}
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// -----
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@ -131,9 +175,9 @@ func @test_scaler_constant(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32], scale = [3.125000e-02 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<1986.99939> : tensor<1xf32>} : () -> tensor<1xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<3.125000e-02> : tensor<1xf32>} : () -> tensor<1xf32>
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// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %3 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<1986.99939> : tensor<1xf32>} : () -> tensor<1xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<3.125000e-02> : tensor<1xf32>} : () -> tensor<1xf32>
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// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %3 : tensor<3xf32>
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}
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@ -313,19 +313,27 @@ custom_builder_ops_list = custom_builder_unranked_ops_list + custom_builder_broa
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#a dictionary to add any special definition for an operation
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custom_definition_misc = dict([ ('Constant',
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''' let builders = [
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OpBuilder<"OpBuilder &builder, OperationState &state, Attribute sparse_value, Attribute value", [{
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if (value) {
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auto tensorType = value.getType();
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build(builder, state, tensorType, sparse_value, value);
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} else {
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auto tensorType = sparse_value.getType();
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build(builder, state, tensorType, sparse_value, value);
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}
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}]>
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];'''
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)])
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''' let builders = [
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OpBuilder<"OpBuilder &builder, OperationState &state, Attribute sparse_value, Attribute value", [{
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if (value) {
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auto tensorType = value.getType();
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build(builder, state, tensorType, sparse_value, value);
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} else {
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auto tensorType = sparse_value.getType();
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build(builder, state, tensorType, sparse_value, value);
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}
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}]>
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];'''),
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('Cast',
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''' let builders = [
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OpBuilder<"OpBuilder &builder, OperationState &state, Value input, IntegerAttr to", [{
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auto toAttr = to.getValue().getSExtValue();
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auto resultType = mlir::UnrankedTensorType::get(
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convertONNXTypeToMLIRType(builder, static_cast<onnx::TensorProto_DataType>(toAttr)));
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build(builder, state, resultType, input, to);
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}] >
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];'''
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)])
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onnx_types = (
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'bool', 'int8', 'int16', 'int32', 'int64', 'unkown', 'float16',
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