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[onnx] Fix expand operation for dynamic shape max (#3001) 

If the broadcast shape is length-1 at a dim while `?` in the input dim
then we need to broadcast to the dynamic dim. This is equivalent to
taking a max of two dimensions.
pull/3003/head
Rob Suderman 2024-03-08 16:23:07 -08:00 committed by GitHub
parent 0723584936
commit bd7f1baa42
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 62 additions and 54 deletions
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36
lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
View File

@ -1495,23 +1495,34 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
patterns.onOp(
"Expand", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
// uses ideas and code from onnx.Reshape
auto loc = binder.getLoc();
Torch::ValueTensorType resultType;
Value data, shape;
if (binder.tensorOperands(data, shape) ||
binder.tensorResultType(resultType))
return failure();
Torch::BaseTensorType shapeType =
shape.getType().cast<Torch::BaseTensorType>();
auto dataType = cast<Torch::BaseTensorType>(data.getType());
auto shapeType = cast<Torch::BaseTensorType>(shape.getType());
if (!dataType.hasSizes() || !shapeType.hasSizes())
return failure();
auto shapeSizes = shapeType.getSizes();
int64_t dataRank = dataType.getSizes().size();
int64_t shapeRank = shapeSizes.size();
if (shapeRank != 1 || shapeSizes[0] == Torch::kUnknownSize)
return failure();
auto rankDifference = dataRank - shapeSizes[0];
SmallVector<int64_t> selectSizes;
Type selectResultType = shapeType.getWithSizesAndDtype(
llvm::ArrayRef(selectSizes), shapeType.getOptionalDtype());
// Variable to store 1-D onnx shape tensor, shapeSizes[0] has the
// dimension size
auto shapeSizes =
dyn_cast<Torch::ValueTensorType>(shape.getType()).getSizes();
// A constant zero value
Value zero = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(0));
loc, rewriter.getI64IntegerAttr(0));
// Variable to store pytorch int list of shape (dimension)
SmallVector<Value> dimList;
@ -1520,12 +1531,21 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
// takes list of int
for (int i = 0; i < shapeSizes[0]; i++) {
Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
loc, rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
Value extract = rewriter.create<Torch::AtenSelectIntOp>(
binder.getLoc(), selectResultType, shape, zero, selectIndex);
loc, selectResultType, shape, zero, selectIndex);
Value dim = rewriter.create<Torch::AtenItemOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
loc, rewriter.getType<Torch::IntType>(), extract);
if (i + rankDifference >= 0) {
Value iv =
rewriter.create<Torch::ConstantIntOp>(loc, i + rankDifference);
auto sz = rewriter.create<Torch::AtenSizeIntOp>(
loc, rewriter.getType<Torch::IntType>(), data, iv);
dim = rewriter.create<Torch::PrimMaxIntOp>(loc, dim, sz);
}
dimList.push_back(dim);
}
Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(

2
projects/pt1/e2e_testing/xfail_sets.py
View File

@ -1507,8 +1507,6 @@ ONNX_XFAIL_SET = {
"ArangeStartOutDtypeModule_basic",
"ArangeStartOutViewModule_basic",
"BroadcastDynamicDimModule_basic",
"BroadcastToModule_basic",
"ExpandModule_basic",
"MoveDimIntNegativeIndexModule_basic",
"ViewSizeFromOtherTensor_basic",

78
test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
View File

@ -1164,15 +1164,21 @@ func.func @test_exp(%arg0: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[3,4],f3
// CHECK-LABEL: @test_expand_dim2_shape2
func.func @test_expand_dim2_shape2(%arg0: !torch.vtensor<[1,4],f32>, %arg1: !torch.vtensor<[2],si32>)
-> !torch.vtensor<[3,4],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[INT0:.+]] = torch.constant.int 0
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
// CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[2],si32>, !torch.int, !torch.int -> !torch.vtensor<[],si32>
// CHECK: torch.aten.item %0 : !torch.vtensor<[],si32> -> !torch.int
// CHECK: %[[INT1:.+]] = torch.constant.int 1
// CHECK: torch.aten.select.int %arg1, %int0, %int1 : !torch.vtensor<[2],si32>, !torch.int, !torch.int -> !torch.vtensor<[],si32>
// CHECK: torch.aten.item %2 : !torch.vtensor<[],si32> -> !torch.int
// CHECK: torch.prim.ListConstruct %1, %3 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: torch.aten.broadcast_to %arg0, %4 : !torch.vtensor<[1,4],f32>, !torch.list<int> -> !torch.vtensor<[3,4],f32>
// CHECK-DAG: %[[INT0:.+]] = torch.constant.int 0
// CHECK-DAG: %[[INT0_0:.+]] = torch.constant.int 0
// CHECK-DAG: %[[SEL0:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[2],si32>, !torch.int, !torch.int -> !torch.vtensor<[],si32>
// CHECK-DAG: %[[ITEM0:.+]] = torch.aten.item %[[SEL0]] : !torch.vtensor<[],si32> -> !torch.int
// CHECK-DAG: %[[I0:.+]] = torch.constant.int 0
// CHECK-DAG: %[[SZ0:.+]] = torch.aten.size.int %arg0, %[[I0]] : !torch.vtensor<[1,4],f32>, !torch.int -> !torch.int
// CHECK-DAG: %[[MX0:.+]] = torch.prim.max.int %[[ITEM0]], %[[SZ0]] : !torch.int, !torch.int -> !torch.int
// CHECK-DAG: %[[INT1:.+]] = torch.constant.int 1
// CHECK-DAG: %[[SEL1:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT1]] : !torch.vtensor<[2],si32>, !torch.int, !torch.int -> !torch.vtensor<[],si32>
// CHECK-DAG: %[[ITEM1:.+]] = torch.aten.item %[[SEL1]] : !torch.vtensor<[],si32> -> !torch.int
// CHECK-DAG: %[[I1:.+]] = torch.constant.int 1
// CHECK-DAG: %[[SZ1:.+]] = torch.aten.size.int %arg0, %[[I1]] : !torch.vtensor<[1,4],f32>, !torch.int -> !torch.int
// CHECK-DAG: %[[MX1:.+]] = torch.prim.max.int %[[ITEM1]], %[[SZ1]] : !torch.int, !torch.int -> !torch.int
// CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[MX0]], %[[MX1]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: torch.aten.broadcast_to %arg0, %[[LIST]] : !torch.vtensor<[1,4],f32>, !torch.list<int> -> !torch.vtensor<[3,4],f32>
%0 = torch.operator "onnx.Expand"(%arg0, %arg1) : (!torch.vtensor<[1,4],f32>, !torch.vtensor<[2],si32>) -> !torch.vtensor<[3,4],f32>
return %0 : !torch.vtensor<[3,4],f32>
}
@ -1181,47 +1187,31 @@ func.func @test_expand_dim2_shape2(%arg0: !torch.vtensor<[1,4],f32>, %arg1: !tor
// CHECK-LABEL: @test_expand_dim2_shape3
func.func @test_expand_dim2_shape3(%arg0: !torch.vtensor<[3,1],f32>, %arg1: !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,3,6],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[INT0:.+]] = torch.constant.int 0
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
// CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
// CHECK: torch.aten.item %0 : !torch.vtensor<[],si64> -> !torch.int
// CHECK: %[[INT1:.+]] = torch.constant.int 1
// CHECK: torch.aten.select.int %arg1, %int0, %int1 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
// CHECK: torch.aten.item %2 : !torch.vtensor<[],si64> -> !torch.int
// CHECK: %[[INT2:.+]] = torch.constant.int 2
// CHECK: torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
// CHECK: torch.aten.item %4 : !torch.vtensor<[],si64> -> !torch.int
// CHECK: torch.prim.ListConstruct %1, %3, %5 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: torch.aten.broadcast_to %arg0, %6 : !torch.vtensor<[3,1],f32>, !torch.list<int> -> !torch.vtensor<[2,3,6],f32>
// CHECK: %[[I0:.+]] = torch.constant.int 0
// CHECK-NEXT: %[[I0_0:.+]] = torch.constant.int 0
// CHECK-NEXT: %[[SEL0:.+]] = torch.aten.select.int %arg1, %[[I0]], %[[I0_0]]
// CHECK-NEXT: %[[ITEM0:.+]] = torch.aten.item %[[SEL0]]
// CHECK-NEXT: %[[I1:.+]] = torch.constant.int 1
// CHECK-NEXT: %[[SEL1:.+]] = torch.aten.select.int %arg1, %[[I0]], %[[I1]]
// CHECK-NEXT: %[[ITEM1:.+]] = torch.aten.item %[[SEL1]]
// CHECK-NEXT: %[[D1:.+]] = torch.constant.int 0
// CHECK-NEXT: %[[SZ1:.+]] = torch.aten.size.int %arg0, %[[D1]]
// CHECK-NEXT: %[[MX1:.+]] = torch.prim.max.int %[[ITEM1]], %[[SZ1]] : !torch.int, !torch.int -> !torch.int
// CHECK-NEXT: %[[I2:.+]] = torch.constant.int 2
// CHECK-NEXT: %[[SEL2:.+]] = torch.aten.select.int %arg1, %[[I0]], %[[I2]]
// CHECK-NEXT: %[[ITEM2:.+]] = torch.aten.item %[[SEL2]]
// CHECK-NEXT: %[[D2:.+]] = torch.constant.int 1
// CHECK-NEXT: %[[SZ2:.+]] = torch.aten.size.int %arg0, %[[D2]]
// CHECK-NEXT: %[[MX2:.+]] = torch.prim.max.int %[[ITEM2]], %[[SZ2]]
// CHECK-NEXT: %[[LIST:.+]] = torch.prim.ListConstruct %[[ITEM0]], %[[MX1]], %[[MX2]]
// CHECK-NEXT: %[[EXPAND:.+]] = torch.aten.broadcast_to %arg0, %[[LIST]]
// CHECK: return %[[EXPAND]]
%0 = torch.operator "onnx.Expand"(%arg0, %arg1) : (!torch.vtensor<[3,1],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,3,6],f32>
return %0 : !torch.vtensor<[2,3,6],f32>
}
// -----
// CHECK-LABEL: @test_expand_dim3_shape4
func.func @test_expand_dim3_shape4(%arg0: !torch.vtensor<[1,3,1],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[3,3,3,3],f32> attributes {torch.onnx_meta.ir_version = 4 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[INT0:.+]] = torch.constant.int 0
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
// CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
// CHECK: torch.aten.item %0 : !torch.vtensor<[],si64> -> !torch.int
// CHECK: %[[INT1:.+]] = torch.constant.int 1
// CHECK: torch.aten.select.int %arg1, %int0, %int1 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
// CHECK: torch.aten.item %2 : !torch.vtensor<[],si64> -> !torch.int
// CHECK: %[[INT2:.+]] = torch.constant.int 2
// CHECK: torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
// CHECK: torch.aten.item %4 : !torch.vtensor<[],si64> -> !torch.int
// CHECK: %[[INT3:.+]] = torch.constant.int 3
// CHECK: torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
// CHECK: torch.aten.item %6 : !torch.vtensor<[],si64> -> !torch.int
// CHECK: torch.prim.ListConstruct %1, %3, %5, %7 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %9 = torch.aten.broadcast_to %arg0, %8 : !torch.vtensor<[1,3,1],f32>, !torch.list<int> -> !torch.vtensor<[3,3,3,3],f32>
%0 = torch.operator "onnx.Expand"(%arg0, %arg1) : (!torch.vtensor<[1,3,1],f32>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[3,3,3,3],f32>
return %0 : !torch.vtensor<[3,3,3,3],f32>
}
// -----
// CHECK-LABEL: @test_dropout
func.func @test_dropout(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: torch.aten.dropout %arg0, %float5.000000e-01, %false : !torch.vtensor<[3],f32>, !torch.float, !torch.bool -> !torch.vtensor<[3],f32