mirror of https://github.com/llvm/torch-mlir
Revert "[torch] AtenSliceOp folder that produces splat results (#2869)"
This reverts commit fc04bc7ee9.
revert-2869-AtenSlice-folder
Xida Ren (Cedar)
GitHub
parent
fc04bc7ee9
commit
1ae0e7dcbb
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@ -2552,52 +2552,22 @@ OpFoldResult AtenBroadcastToOp::fold(FoldAdaptor adaptor) {
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//===----------------------------------------------------------------------===//
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OpFoldResult AtenSliceTensorOp::fold(FoldAdaptor adaptor) {
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DenseElementsAttr input =
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dyn_cast_or_null<DenseElementsAttr>(adaptor.getSelf());
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IntegerAttr start = dyn_cast_or_null<IntegerAttr>(adaptor.getStart());
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IntegerAttr end = dyn_cast_or_null<IntegerAttr>(adaptor.getEnd());
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IntegerAttr step = dyn_cast_or_null<IntegerAttr>(adaptor.getStep());
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IntegerAttr dim = dyn_cast_or_null<IntegerAttr>(adaptor.getDim());
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if (start && end && step && step.getValue().getSExtValue() == 1 &&
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start.getValue().getSExtValue() == 0 &&
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end.getValue().getSExtValue() == std::numeric_limits<int64_t>::max())
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int64_t start, end, step;
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if (matchPattern(getStart(), m_TorchConstantInt(&start)) &&
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matchPattern(getEnd(), m_TorchConstantInt(&end)) &&
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matchPattern(getStep(), m_TorchConstantInt(&step)) && step == 1 &&
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start == 0 && end == std::numeric_limits<int64_t>::max())
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return getOperand(0);
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auto inType = getOperand(0).getType().dyn_cast<ValueTensorType>();
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auto outType = getResult().getType().dyn_cast<ValueTensorType>();
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if (!inType || !outType || !inType.hasSizes() || !outType.hasSizes() ||
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!inType.hasDtype() || !outType.hasDtype() ||
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inType.getDtype() != outType.getDtype())
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auto inType = getOperand(0).getType().dyn_cast<BaseTensorType>();
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auto outType = getResult().getType().dyn_cast<BaseTensorType>();
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if (inType != outType)
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return nullptr;
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if (!inType || !outType || !inType.hasSizes() || !outType.hasSizes())
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return nullptr;
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if (inType.getSizes().size() != outType.getSizes().size() ||
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!inType.areAllSizesKnown() || !outType.areAllSizesKnown())
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return nullptr;
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if (input && input.isSplat())
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return DenseElementsAttr::get(
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outType.toBuiltinTensor().clone(inType.getDtype()),
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input.getSplatValue<Attribute>());
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// If the output is a single value we can index into a constant input and grab
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// that single value:
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if (input && start && dim &&
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llvm::all_of(outType.getSizes(), [](int64_t dim) { return dim == 1; })) {
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bool unaryNonDim = true;
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int64_t dimInt = dim.getValue().getSExtValue();
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for (int i = 0, s = inType.getSizes().size(); i < s; ++i) {
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unaryNonDim &= inType.getSizes()[i] == 1 || i == dimInt;
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}
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if (unaryNonDim) {
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Attribute value =
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input.getValues<Attribute>()[start.getValue().getSExtValue()];
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return DenseElementsAttr::get(
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outType.toBuiltinTensor().clone(inType.getDtype()), value);
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}
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}
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// If the input and output shapes are the same we can just fold:
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for (size_t i = 0; i < inType.getSizes().size(); ++i) {
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if (inType.getSizes()[i] != outType.getSizes()[i])
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return nullptr;
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@ -2104,53 +2104,6 @@ func.func @torch.aten.slice.tensor$no_fold_step(%arg0: !torch.vtensor<[?],f32>,
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return %0 : !torch.vtensor<[?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.slice.tensor$fold_dim_1() -> (!torch.vtensor<[1,1],si64>, !torch.vtensor<[1,1],si64>) {
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// CHECK-NOT: torch.aten.slice.Tensor
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// CHECK: %[[RET_0:.*]] = torch.vtensor.literal(dense<50> : tensor<1x1xsi64>) : !torch.vtensor<[1,1],si64>
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// CHECK-NOT: torch.aten.slice.Tensor
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// CHECK: %[[RET_1:.*]] = torch.vtensor.literal(dense<70> : tensor<1x1xsi64>) : !torch.vtensor<[1,1],si64>
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// CHECK-NOT: torch.aten.slice.Tensor
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// CHECK: return %[[RET_0]], %[[RET_1]]
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func.func @torch.aten.slice.tensor$fold_dim_1() -> (!torch.vtensor<[1, 1],si64>, !torch.vtensor<[1, 1],si64>) {
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%tensor = torch.vtensor.literal(dense<[[10,20,30,40,50,60,70,80,90,100]]> : tensor<1x10xsi64>) : !torch.vtensor<[1, 10],si64>
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%int0 = torch.constant.int 0
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%int1 = torch.constant.int 1
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%int4 = torch.constant.int 4
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%int5 = torch.constant.int 5
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%int6 = torch.constant.int 6
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%int7 = torch.constant.int 7
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%dim = torch.constant.int 1
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%0 = torch.aten.slice.Tensor %tensor, %dim, %int4, %int5, %int1 : !torch.vtensor<[1, 10], si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1, 1], si64>
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%1 = torch.aten.slice.Tensor %tensor, %dim, %int6, %int7, %int1 : !torch.vtensor<[1, 10], si64>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1, 1], si64>
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return %0, %1 : !torch.vtensor<[1,1],si64>, !torch.vtensor<[1,1],si64>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.slice.tensor$fold_dim_0() -> (!torch.vtensor<[1,1],f32>, !torch.vtensor<[1,1],f32>) {
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// CHECK-NOT: torch.aten.slice.Tensor
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// CHECK: %[[RET_0:.*]] = torch.vtensor.literal(dense<1.600000e+01> : tensor<1x1xf32>) : !torch.vtensor<[1,1],f32>
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// CHECK-NOT: torch.aten.slice.Tensor
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// CHECK: %[[RET_1:.*]] = torch.vtensor.literal(dense<6.400000e+01> : tensor<1x1xf32>) : !torch.vtensor<[1,1],f32>
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// CHECK-NOT: torch.aten.slice.Tensor
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// CHECK: return %[[RET_0]], %[[RET_1]] : !torch.vtensor<[1,1],f32>, !torch.vtensor<[1,1],f32>
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func.func @torch.aten.slice.tensor$fold_dim_0() -> (!torch.vtensor<[1, 1],f32>, !torch.vtensor<[1, 1],f32>) {
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%tensor = torch.vtensor.literal(dense<[[2.0],[4.0],[8.0],[16.0],[32.0],[64.0],[128.0],[256.0],[512.0],[1024.0]]> : tensor<10x1xf32>) : !torch.vtensor<[10, 1],f32>
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%int0 = torch.constant.int 0
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%int1 = torch.constant.int 1
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%int3 = torch.constant.int 3
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%int4 = torch.constant.int 4
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%int5 = torch.constant.int 5
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%int6 = torch.constant.int 6
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%dim = torch.constant.int 0
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%0 = torch.aten.slice.Tensor %tensor, %dim, %int3, %int4, %int1 : !torch.vtensor<[10, 1], f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1, 1], f32>
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%1 = torch.aten.slice.Tensor %tensor, %dim, %int5, %int6, %int1 : !torch.vtensor<[10, 1], f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1, 1], f32>
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return %0, %1 : !torch.vtensor<[1, 1],f32>, !torch.vtensor<[1, 1], f32>
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}
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// CHECK-LABEL: func.func @torch.aten.rsub.Scalar$canonicalize_literal_0d() -> !torch.vtensor<[],si64> {
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// CHECK: %int-1 = torch.constant.int -1
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// CHECK: %[[VAL_0:.*]] = torch.prim.NumToTensor.Scalar %int-1 : !torch.int -> !torch.vtensor<[],si64>
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