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Add decomposition for aten.roll (#1170) 

* Add decomposition for aten.roll

* add e2e unittest

* refine type of torch.roll

* fix aten::cat output type
pull/1261/head
Tanyo Kwok 2022-08-24 08:36:05 +08:00 committed by GitHub
parent 1106b9aeae
commit 3d0e18bbe7
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
10 changed files with 164 additions and 3 deletions
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2
e2e_testing/torchscript/xfail_sets.py
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@ -127,6 +127,7 @@ MHLO_PASS_SET = {
"ReshapeAliasCollapseModule_basic",
"ReshapeAliasExpandModule_basic",
"ReshapeExpandModule_basic",
"RollModule_basic",
"TestMultipleTensorReturn_basic",
"AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic",
"BaddbmmStaticModule_basic",
@ -447,6 +448,7 @@ LTC_XFAIL_SET = {
"QuantizedMLP_basic",
"RandLikeDtypeModule_basic",
"RandLikeModule_basic",
"RollModule_basic",
"ScalarImplicitFloatModule_basic",
"ScalarImplicitIntModule_basic",
"SliceEndSleStartModule_basic",

25
include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td
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@ -3478,6 +3478,31 @@ def Torch_Aten_ConvolutionDeprecatedOp : Torch_Op<"aten._convolution.deprecated"
}];
}
def Torch_AtenRollOp : Torch_Op<"aten.roll", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::roll : (Tensor, int[], int[]) -> (Tensor)`";
let arguments = (ins
AnyTorchTensorType:$self,
AnyTorchListOfTorchIntType:$shifts,
AnyTorchListOfTorchIntType:$dims
);
let results = (outs
AnyTorchTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenRollOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 3, 1);
}
void AtenRollOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 3, 1);
}
}];
}
def Torch_AtenFlipOp : Torch_Op<"aten.flip", [
AllowsTypeRefinement,
HasValueSemantics,

2
lib/Conversion/TorchToMhlo/Basic.cpp
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@ -979,7 +979,7 @@ LogicalResult ConvertAtenOp<AtenCatOp>::matchAndRewrite(
size_t posDim = toPositiveDim(dim, outType.getRank());
rewriter.replaceOpWithNewOp<mhlo::ConcatenateOp>(
op, ValueRange(builtinTensors), posDim);
op, outType, ValueRange(builtinTensors), posDim);
return success();
}
} // namespace

73
lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
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@ -709,6 +709,77 @@ public:
};
} // namespace
// Decompose aten.roll into aten.slice and aten.cat ops.
// https://pytorch.org/docs/stable/generated/torch.roll.html
namespace {
class DecomposeAtenRollOp : public OpRewritePattern<AtenRollOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(AtenRollOp op,
PatternRewriter &rewriter) const override {
SmallVector<Value> shifts;
if (!getListConstructElements(op.shifts(), shifts))
return rewriter.notifyMatchFailure(
op, "unimplemented: shifts not list of Scalar");
SmallVector<Value> dims;
if (!getListConstructElements(op.dims(), dims))
return rewriter.notifyMatchFailure(
op, "unimplemented: dims not list of Scalar");
if (shifts.size() != dims.size())
return op.emitError("list sizes of shifts and dims are not the same");
auto loc = op.getLoc();
Value constNone = rewriter.create<ConstantNoneOp>(loc);
Value constZero = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(0));
Value constOne = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(1));
auto self = op.self();
auto selfTy = self.getType().cast<BaseTensorType>();
// roll(input, shift, dim) = cat({
// slice(input, dim, -shift, none),
// slice(input, dim, 0, -shift)}, dim)
auto imitateRoll = [&](Value input, Value shift, Value dim,
int64_t cstDim) {
Value negShift = rewriter.create<AtenNegIntOp>(loc, shift);
ArrayRef<int64_t> inputShape = selfTy.getSizes();
SmallVector<int64_t> sizes;
sizes.append(inputShape.begin(), inputShape.end());
sizes[cstDim] = ShapedType::kDynamicSize;
Type sliceTy = selfTy.getWithSizesAndDtype(llvm::makeArrayRef(sizes),
selfTy.getDtype());
Value slice0 = rewriter.create<AtenSliceTensorOp>(
loc, sliceTy, input, dim, negShift, constNone, constOne);
Value slice1 = rewriter.create<AtenSliceTensorOp>(
loc, sliceTy, input, dim, constZero, negShift, constOne);
Type listType = Torch::ListType::get(sliceTy);
Value slices = rewriter.create<PrimListConstructOp>(
loc, listType, llvm::ArrayRef<Value>{slice0, slice1});
return rewriter.create<AtenCatOp>(loc, self.getType(), slices, dim);
};
int rank = getTensorRank(self);
if (rank < 0)
return rewriter.notifyMatchFailure(op, "Unimplemented: unranked tensor");
Value output = self;
auto nShifts = shifts.size();
for (size_t k = 0; k < nShifts; ++k) {
auto dim = dims[k];
int64_t cstDim = -1;
if (!matchPattern(dim, m_TorchConstantInt(&cstDim)))
return rewriter.notifyMatchFailure(
op, "unimplemented: dim must be constant");
cstDim = toPositiveDim(cstDim, rank);
output = imitateRoll(output, shifts[k], dim, cstDim);
}
rewriter.replaceOp(op, output);
return success();
}
};
} // namespace
// Decompose aten.repeat into aten.expand and aten.view ops.
//
// Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.repeat.html
@ -2555,6 +2626,8 @@ public:
patterns.add<DecomposeConstantTensorAllocLikeOp<AtenZerosLikeOp, 0>>(
context);
target.addIllegalOp<AtenZerosLikeOp>();
patterns.add<DecomposeAtenRollOp>(context);
target.addIllegalOp<AtenRollOp>();
patterns.add<DecomposeAtenRepeatOp>(context);
target.addIllegalOp<AtenRepeatOp>();
patterns.add<DecomposeAtenExpandOp>(context);

3
lib/Dialect/Torch/Transforms/RefineTypes.cpp
View File

@ -658,7 +658,8 @@ void TypeAnalysis::visitOperation(Operation *op,
AtenZero_Op, AtenIndexTensorOp, ValsemVariantAtenIndexPutImplOp,
AtenIndexPutOp, ValsemVariantAtenCopyOp, AtenZeroOp,
AtenIndexPutHackedTwinOp, AtenMaskedFillScalarOp, AtenFlipOp,
PrimAbsScalarOp, AtenNumpyTOp, AtenTriuOp, AtenMaskedFillTensorOp>(
PrimAbsScalarOp, AtenNumpyTOp, AtenTriuOp, AtenMaskedFillTensorOp,
AtenRollOp>(
op)) {
return incorporateKnowledge(op->getResult(0), operands[0]->getValue());
}

4
lib/Dialect/Torch/Transforms/ShapeLibrary.cpp
View File

@ -4213,6 +4213,10 @@ module {
}
return %7 : !torch.list<int>
}
func.func @__torch_mlir_shape_fn.aten.roll(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>) -> !torch.list<int> {
%0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>
return %0 : !torch.list<int>
}
func.func @__torch__.torch.jit._shape_functions.expand(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {
%int-1 = torch.constant.int -1
%true = torch.constant.bool true

3
python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/shape_lib_gen.py
View File

@ -635,6 +635,9 @@ def aten〇repeat(self: List[int], repeats: List[int]) -> List[int]:
out.append(self[i] * repeats[i + leading_rank])
return out
def aten〇roll(self: List[int], shifts: List[int], dims: List[int] = ()) -> List[int]:
return upstream_shape_functions.unary(self)
def aten〇expand(self: List[int], size: List[int], implicit: bool = False) -> List[int]:
return upstream_shape_functions.expand(self, size)

1
python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py
View File

@ -338,6 +338,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
emit("aten::convolution_overrideable : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int) -> (Tensor)")
emit("aten::_convolution : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int, bool, bool, bool, bool) -> (Tensor)")
emit("aten::_convolution.deprecated : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int, bool, bool, bool) -> (Tensor)")
emit("aten::roll : (Tensor, int[], int[]) -> (Tensor)"),
emit("aten::flip : (Tensor, int[]) -> (Tensor)")
emit(
"aten::native_batch_norm : (Tensor, Tensor?, Tensor?, Tensor?, Tensor?, bool, float, float) -> (Tensor, Tensor, Tensor)"

22
python/torch_mlir_e2e_test/test_suite/basic.py
View File

@ -1047,6 +1047,27 @@ def BroadcastToModule_basic(module, tu: TestUtils):
# ==============================================================================
class RollModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([3, -1, 2], torch.float32, True),
])
def forward(self, x):
return x.roll([2, -1], [0, 2])
@register_test_case(module_factory=lambda: RollModule())
def RollModule_basic(module, tu: TestUtils):
module.forward(tu.rand(3, 1, 2))
# ==============================================================================
class RepeatModule(torch.nn.Module):
def __init__(self):
@ -1065,7 +1086,6 @@ class RepeatModule(torch.nn.Module):
def RepeatModule_basic(module, tu: TestUtils):
module.forward(tu.rand(3, 1, 2))
# ==============================================================================

32
test/Dialect/Torch/decompose-complex-ops.mlir
View File

@ -1336,6 +1336,7 @@ func.func @torch.aten.std.dim(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vten
}
// -----
// CHECK-LABEL: func.func @torch.aten.flatten.using_ints(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?],f32> {
// CHECK: %[[INT0:.*]] = torch.constant.int 0
@ -1350,3 +1351,34 @@ func.func @torch.aten.flatten.using_ints(%arg0: !torch.vtensor<[?,?,?,?],f32>) -
%1 = torch.aten.flatten.using_ints %arg0, %int0, %int3: !torch.vtensor<[?,?,?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?],f32>
return %1 : !torch.vtensor<[?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.roll(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?],f32>, %[[ARG1:.*]]: !torch.int, %[[ARG2:.*]]: !torch.int) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[T0:.*]] = torch.prim.ListConstruct %[[ARG1]], %[[ARG2]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: %[[INT:.*]]-2 = torch.constant.int -2
// CHECK: %[[T1:.*]] = torch.prim.ListConstruct %[[INT1]], %[[INT]]-2 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[INT0:.*]] = torch.constant.int 0
// CHECK: %[[INT1_0:.*]] = torch.constant.int 1
// CHECK: %[[T2:.*]] = torch.aten.neg.int %[[ARG1]] : !torch.int -> !torch.int
// CHECK: %[[T3:.*]] = torch.aten.slice.Tensor %[[ARG0]], %[[INT1]], %[[T2]], %[[NONE]], %[[INT1]]_0 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int, !torch.none, !torch.int -> !torch.vtensor<[?,?],f32>
// CHECK: %[[T4:.*]] = torch.aten.slice.Tensor %[[ARG0]], %[[INT1]], %[[INT0]], %[[T2]], %[[INT1]]_0 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
// CHECK: %[[T5:.*]] = torch.prim.ListConstruct %[[T3]], %[[T4]] : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>) -> !torch.list<vtensor<[?,?],f32>>
// CHECK: %[[T6:.*]] = torch.aten.cat %[[T5]], %[[INT1]] : !torch.list<vtensor<[?,?],f32>>, !torch.int -> !torch.vtensor<[?,?],f32>
// CHECK: %[[T7:.*]] = torch.aten.neg.int %[[ARG2]] : !torch.int -> !torch.int
// CHECK: %[[T8:.*]] = torch.aten.slice.Tensor %[[T6]], %[[INT]]-2, %[[T7]], %[[NONE]], %[[INT]]1_0 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int, !torch.none, !torch.int -> !torch.vtensor<[?,?],f32>
// CHECK: %[[T9:.*]] = torch.aten.slice.Tensor %[[T6]], %[[INT]]-2, %[[INT]]0, %[[T7]], %[[INT]]1_0 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
// CHECK: %[[T10:.*]] = torch.prim.ListConstruct %[[T8]], %[[T9]] : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>) -> !torch.list<vtensor<[?,?],f32>>
// CHECK: %[[T11:.*]] = torch.aten.cat %[[T10]], %[[INT]]-2 : !torch.list<vtensor<[?,?],f32>>, !torch.int -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T11]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.roll(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.int, %arg2: !torch.int) -> !torch.vtensor<[?,?],f32> {
%0 = torch.prim.ListConstruct %arg1, %arg2: (!torch.int, !torch.int) -> !torch.list<int>
%int1 = torch.constant.int 1
%int-2 = torch.constant.int -2
%1 = torch.prim.ListConstruct %int1, %int-2: (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.aten.roll %arg0, %0, %1 : !torch.vtensor<[?,?],f32>, !torch.list<int>, !torch.list<int> -> !torch.vtensor<[?,?],f32>
return %2 : !torch.vtensor<[?,?],f32>
}