Add ONNXScalerOp pattern (#220)
* add ONNXScalerOp pattern * move ScalerOp rewrite rule to Rewrite.cpp .td * attempt to fix format issue * fixing format issue * fixing format issue2 * add ONNXScalerOp pattern * move ScalerOp rewrite rule to Rewrite.cpp .td * attempt to fix format issue * fixing format issue * fixing format issue2
This commit is contained in:
Anh Leu
GitHub
parent
13b8591af8
commit
4b33c312d6
|
|
@ -6090,6 +6090,7 @@ def ONNXSVMRegressorOp:ONNX_Op<"SVMRegressor",
|
|||
|
||||
def ONNXScalerOp:ONNX_Op<"Scaler",
|
||||
[NoSideEffect]> {
|
||||
let hasCanonicalizer = 1;
|
||||
let summary = "ONNX Scaler operation";
|
||||
let description = [{
|
||||
"Rescale input data, for example to standardize features by removing the mean and scaling to unit variance."
|
||||
|
|
|
|||
|
|
@ -18,6 +18,23 @@ using namespace mlir;
|
|||
|
||||
namespace {
|
||||
|
||||
// Create an DenseElementsAttr of ArrayAttr.
|
||||
// This function is used to get Value Type for Scaler function.
|
||||
DenseElementsAttr createDenseArrayAttr(
|
||||
PatternRewriter &rewriter, ArrayAttr origAttrs) {
|
||||
mlir::Type elementType = rewriter.getF32Type();
|
||||
int nElements = origAttrs.getValue().size();
|
||||
SmallVector<float, 4> wrapper(nElements, 0);
|
||||
if (origAttrs) {
|
||||
for (int i = 0; i < nElements; ++i) {
|
||||
wrapper[i] = origAttrs.getValue()[i].cast<FloatAttr>().getValueAsDouble();
|
||||
}
|
||||
}
|
||||
return DenseElementsAttr::get(
|
||||
RankedTensorType::get(wrapper.size(), elementType),
|
||||
llvm::makeArrayRef(wrapper));
|
||||
}
|
||||
|
||||
// Check whether an ArrayAttr contains non-zero values or not.
|
||||
bool hasNonZeroInArrayAttr(ArrayAttr attrs) {
|
||||
bool allZeros = true;
|
||||
|
|
@ -92,3 +109,13 @@ void ONNXConvOp::getCanonicalizationPatterns(
|
|||
OwningRewritePatternList &results, MLIRContext *context) {
|
||||
results.insert<ConvOpPaddingPattern>(context);
|
||||
}
|
||||
|
||||
/// on the ONNXScalerOp.
|
||||
void ONNXScalerOp::getCanonicalizationPatterns(
|
||||
OwningRewritePatternList &result, MLIRContext *context) {
|
||||
result.insert<ScalerNullPattern>(context);
|
||||
result.insert<ScalerNullPattern2>(context);
|
||||
result.insert<ScalerNoScalePattern>(context);
|
||||
result.insert<ScalerNoOffsetPattern>(context);
|
||||
result.insert<ScalerPattern>(context);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -100,4 +100,56 @@ def ConvOpPaddingPattern: Pat<
|
|||
[(HasNonZeroInArrayAttr:$pads), (IsNotStringAttrOfValue<"VALID"> $auto_pad)]
|
||||
>;
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
// ONNXScalerOp %X, %Offest, %Scale
|
||||
// x input, a offset, b scale
|
||||
//===----------------------------------------------------------------------===//
|
||||
// Useful test definitions.
|
||||
def AttributeIsNull :
|
||||
Constraint<CPred<"! ($_self)">,
|
||||
"Attribute is null">;
|
||||
|
||||
def HasFloatType : Constraint<CPred<"(($_self).getType().dyn_cast<ShapedType>().getElementType().isF32())">>;
|
||||
|
||||
// Create a DenseElementsAttr from an ArrayAttr.
|
||||
def createDenseArrayAttr:
|
||||
NativeCodeCall<"createDenseArrayAttr($_builder, $0)">;
|
||||
|
||||
def ScalerT : NativeCodeCall<"$_builder.getI64IntegerAttr(0)">;
|
||||
|
||||
// No attribute
|
||||
def ScalerNullPattern : Pat<
|
||||
(ONNXScalerOp $x, $a, $b),
|
||||
(replaceWithValue $x),
|
||||
[(HasFloatType:$x),(AttributeIsNull:$a), (AttributeIsNull:$b)]>;
|
||||
|
||||
// No attribute, input x not float type
|
||||
def ScalerNullPattern2 : Pat<
|
||||
(ONNXScalerOp $x, $a, $b),
|
||||
(ONNXCastOp $x, (ScalerT)),
|
||||
[(AttributeIsNull:$a), (AttributeIsNull:$b)]>;
|
||||
|
||||
// No scale
|
||||
def ScalerNoScalePattern : Pat<
|
||||
(ONNXScalerOp $x, $a, $b),
|
||||
(ONNXSubOp $x,
|
||||
(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))),
|
||||
[(AttributeIsNull:$b)]>;
|
||||
|
||||
// No offset
|
||||
def ScalerNoOffsetPattern : Pat<
|
||||
(ONNXScalerOp $x, $a, $b),
|
||||
(ONNXMulOp $x,
|
||||
(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b))),
|
||||
[(AttributeIsNull:$a)]>;
|
||||
|
||||
// Normal ONNXScalerOp
|
||||
def ScalerPattern : Pat<
|
||||
(ONNXScalerOp $x, $a, $b),
|
||||
(ONNXMulOp
|
||||
(ONNXSubOp $x,
|
||||
(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))),
|
||||
(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b)))>;
|
||||
|
||||
|
||||
#endif // ONNX_REWRITE
|
||||
|
|
|
|||
|
|
@ -96,3 +96,86 @@ func @test_gemm_add_fusion_rank3(%arg0: tensor<128x128x256xf32>, %arg1: tensor<1
|
|||
// CHECK-NEXT: [[GEMM:%.+]] = "onnx.Gemm"(%{{.*}}, %{{.*}}, %{{.*}}) {alpha = 1.000000e+00 : f32, beta = 1.000000e+00 : f32, transA = 0 : i64, transB = 0 : i64} : (tensor<128x128x256xf32>, tensor<128x128x256xf32>, tensor<256xf32>) -> tensor<*xf32>
|
||||
// return [[GEMM]] : tensor<*xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
// Scaler Pattern test
|
||||
// -----
|
||||
|
||||
// null
|
||||
// CHECK-LABEL: func @test_scaler_null_float(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
|
||||
func @test_scaler_null_float(%arg0: tensor<3xf32>) -> tensor<3xf32> {
|
||||
%0 = "onnx.Scaler"(%arg0) : (tensor<3xf32>) -> tensor<3xf32>
|
||||
return %0 : tensor<3xf32>
|
||||
|
||||
// CHECK-NEXT: return %arg0 : tensor<3xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// null not float
|
||||
// CHECK-LABEL: func @test_scaler_null(%{{.*}}: tensor<3xi32>) -> tensor<3xf32> {
|
||||
func @test_scaler_null(%arg0: tensor<3xi32>) -> tensor<3xf32> {
|
||||
%0 = "onnx.Scaler"(%arg0) : (tensor<3xi32>) -> tensor<3xf32>
|
||||
return %0 : tensor<3xf32>
|
||||
|
||||
// CHECK-NEXT: %0 = "onnx.Cast"(%arg0) {to = 0 : i64} : (tensor<3xi32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: return %0 : tensor<3xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// scaler no offset
|
||||
// CHECK-LABEL: func @test_scaler_no_offset(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
|
||||
func @test_scaler_no_offset(%arg0: tensor<3xf32>) -> tensor<3xf32> {
|
||||
%0 = "onnx.Scaler"(%arg0) {scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
|
||||
return %0 : tensor<3xf32>
|
||||
|
||||
|
||||
// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
|
||||
// CHECK-NEXT: %1 = "onnx.Mul"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: return %1 : tensor<3xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// scaler no scale
|
||||
// CHECK-LABEL: func @test_scaler_no_scale(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
|
||||
func @test_scaler_no_scale(%arg0: tensor<3xf32>) -> tensor<3xf32> {
|
||||
%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
|
||||
return %0 : tensor<3xf32>
|
||||
|
||||
// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
|
||||
// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: return %1 : tensor<3xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// normal scaler
|
||||
// CHECK-LABEL: func @test_scaler_normal(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
|
||||
func @test_scaler_normal(%arg0: tensor<3xf32>) -> tensor<3xf32> {
|
||||
%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>
|
||||
return %0 : tensor<3xf32>
|
||||
|
||||
// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
|
||||
// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
|
||||
// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: return %3 : tensor<3xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// normal scaler with constant offset and scale
|
||||
// CHECK-LABEL: func @test_scaler_constant(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
|
||||
func @test_scaler_constant(%arg0: tensor<3xf32>) -> tensor<3xf32> {
|
||||
%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32], scale = [3.125000e-02 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
|
||||
return %0 : tensor<3xf32>
|
||||
|
||||
// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<1986.99939> : tensor<1xf32>} : () -> tensor<1xf32>
|
||||
// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<3.125000e-02> : tensor<1xf32>} : () -> tensor<1xf32>
|
||||
// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: return %3 : tensor<3xf32>
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -256,7 +256,7 @@ OpsWithShapeInference = [
|
|||
]
|
||||
|
||||
# Operations supporting canonicalization.
|
||||
OpsWithCanonicalizer = ['Add', 'Identity', 'Gemm', 'Conv']
|
||||
OpsWithCanonicalizer = ['Add', 'Identity', 'Gemm', 'Conv', 'Scaler']
|
||||
|
||||
# Operations who have operands that, if produced by constant operations, should
|
||||
# be promoted to become an attribute (via attribute promotion).
|
||||
|
|
|
|||
Loading…
Reference in New Issue