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Merge pull request #262 from Xilinx/bump_to_ec6d7aa5
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[AutoBump] Merge with fixes of ec6d7aa (May 08) (29)
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@cferry-AMD
cferry-AMD authored Sep 2, 2024
2 parents c34ae2a + b229224 commit 3998b0d
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152 changes: 152 additions & 0 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2819,4 +2819,156 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
rewriter.replaceOp(binder.op, {loss, logProb});
return success();
});
patterns.onOp(
"Resize", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
llvm::SmallVector<Value> operands;
std::string mode, nearest_mode, coordTfMode;
Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());

if (auto attr = binder.op->getAttr("torch.onnx.antialias")) {
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: support not present for antialias attribute");
}
if (auto attr = binder.op->getAttr("torch.onnx.axes")) {
return rewriter.notifyMatchFailure(
binder.op,
"unimplemented: support not present for axes attribute");
}
if (auto attr = binder.op->getAttr("torch.onnx.exclude_outside")) {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: support not present for "
"exclude_outside attribute");
}
if (auto attr = binder.op->getAttr("torch.onnx.extrapolation_value")) {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: support not present for "
"extrapolation_value attribute");
}
if (auto attr =
binder.op->getAttr("torch.onnx.keep_aspect_ratio_policy")) {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: support not present for "
"keep_aspect_ratio_policy attribute");
}

if (binder.tensorOperandsList(operands) ||
binder.tensorResultType(resultType) ||
binder.customOpNameStringAttr(mode, "mode", "nearest") ||
binder.customOpNameStringAttr(
coordTfMode, "coordinate_transformation_mode", "half_pixel") ||
binder.customOpNameStringAttr(nearest_mode, "nearest_mode", ""))
return failure();

if (mode == "nearest" && nearest_mode != "floor") {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: support not present for nearest_mode "
"except floor");
}

Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));

Value cstFalse =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
Value cstTrue =
rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
Value modeStrValue;

auto extract = [&rewriter, &binder](Value x, Value v) {
auto xTy = x.getType().cast<Torch::ValueTensorType>();
Type extractTy = rewriter.getType<Torch::FloatType>();
if (isa<IntegerType>(xTy.getDtype()))
extractTy = rewriter.getType<Torch::IntType>();

return rewriter.create<Torch::AtenItemOp>(binder.getLoc(), extractTy,
v);
};

auto getValueList = [&](Value operand) {
SmallVector<Value> itemList;
auto sizes =
dyn_cast<Torch::ValueTensorType>(operand.getType()).getSizes();
Torch::BaseTensorType operandType =
operand.getType().cast<Torch::BaseTensorType>();

SmallVector<int64_t> selectSizes;
selectSizes.push_back(1);
Type selectResultType = operandType.getWithSizesAndDtype(
llvm::ArrayRef(selectSizes), operandType.getOptionalDtype());

MLIRContext *context = binder.op->getContext();
for (int i = sizes[0] - 2; i < sizes[0]; i++) {
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value ext = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selectResultType, operand, zero, selectIndex);
Value item = extract(operand, ext);
itemList.push_back(item);
}
auto xTy = operand.getType().cast<Torch::ValueTensorType>();
Value ValueList;
if (isa<IntegerType>(xTy.getDtype())) {
ValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(context)), itemList);
} else {
ValueList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::FloatType::get(context)), itemList);
}
return ValueList;
};

Value scalesValueList = noneVal;
Value sizesValueList = noneVal;
Value alignCorners =
coordTfMode == "align_corners" ? cstTrue : cstFalse;

if (mode == "cubic") {
return rewriter.notifyMatchFailure(binder.op,
"unimplemented: bicubic mode");
}
if (mode == "linear") {
modeStrValue = rewriter.create<Torch::ConstantStrOp>(binder.getLoc(),
"bilinear");
if (operands.size() < 4) {
Value scaleOperand = operands[2];
scalesValueList = getValueList(scaleOperand);
sizesValueList = noneVal;
} else {
Value sizeOperand = operands[3];
scalesValueList = noneVal;
sizesValueList = getValueList(sizeOperand);
}
}
if (mode == "nearest") {
modeStrValue =
rewriter.create<Torch::ConstantStrOp>(binder.getLoc(), "nearest");
if (operands.size() < 4) {
Value scaleOperand = operands[2];
scalesValueList = getValueList(scaleOperand);
sizesValueList = noneVal;
} else {
Value sizesOperand = operands[3];
scalesValueList = noneVal;
sizesValueList = getValueList(sizesOperand);
}
}
if (scalesValueList.getType().isa<Torch::NoneType>() &&
sizesValueList.getType().isa<Torch::NoneType>()) {
return rewriter.notifyMatchFailure(binder.op, "unknown scaling mode");
}
rewriter
.replaceOpWithNewOp<Torch::Aten__InterpolateSizeListScaleListOp>(
binder.op, resultType, operands[0], sizesValueList,
scalesValueList, modeStrValue,
/* AnyTorchOptionalBoolType:$align_corners */ alignCorners,
/* AnyTorchOptionalBoolType:$recompute_scale_factor */ noneVal,
/*Torch_BoolType:$antialias*/ cstFalse);
return success();
});
}
21 changes: 21 additions & 0 deletions test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -2040,3 +2040,24 @@ func.func @test_sce_mean_3d_log_prob(%arg0: !torch.vtensor<[3,5,2],f32>, %arg1:
%0:2 = torch.operator "onnx.SoftmaxCrossEntropyLoss"(%arg0, %arg1) {torch.onnx.reduction = "mean"} : (!torch.vtensor<[3,5,2],f32>, !torch.vtensor<[3,2],si64>) -> (!torch.vtensor<[],f32>, !torch.vtensor<[3,5,2],f32>)
return %0#0, %0#1 : !torch.vtensor<[],f32>, !torch.vtensor<[3,5,2],f32>
}

// -----

// CHECK-LABEL: func.func @test_resize_sizes_nearest
func.func @test_resize_sizes_nearest(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
%none = torch.constant.none
// CHECK: torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
%0 = torch.operator "onnx.Resize"(%arg0, %none, %none, %arg1) {torch.onnx.coordinate_transformation_mode = "asymmetric", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "nearest", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[1,1,2,4],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32>
return %0 : !torch.vtensor<[?,?,?,?],f32>
}

// -----

// CHECK-LABEL: func.func @test_resize_sizes_linear
func.func @test_resize_sizes_linear(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],
f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
%none = torch.constant.none
// CHECK: torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
%0 = torch.operator "onnx.Resize"(%arg0, %none, %none, %arg1) {torch.onnx.mode = "linear"} : (!torch.vtensor<[1,1,2,4],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32>
return %0 : !torch.vtensor<[?,?,?,?],f32>
}

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