mirror of https://github.com/llvm/torch-mlir
Additional tests for view lowering (#2584)
The logic for lowering the aten view op to linalg is fairly complex. In this PR I have tried to follow all non-failing paths through the lowering and add unit tests where they're missing. There is 1 logical change to the lowering: redundant tensor.cast ops (same source and destination type) are folded.pull/2585/head
James Newling
GitHub
parent
7b94189e07
commit
647f2f5076
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@ -533,6 +533,10 @@ public:
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}
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}
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auto cast = [&](Location loc, Type t, Value v) -> Value {
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return rewriter.createOrFold<tensor::CastOp>(loc, t, v);
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};
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// Check if the shapes already match up to dynamic sizes. If so, we can just
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// cast as the result type because the previous loop sets up the necessary
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// dim checks in case of dynamic sizes.
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@ -542,7 +546,9 @@ public:
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llvm::all_of(outputAssociations, [](ReassociationIndices indices) {
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return indices.size() == 1;
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})) {
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, input);
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auto castResult = cast(loc, resultType, input);
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rewriter.replaceOp(op, castResult);
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return success();
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}
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@ -551,8 +557,7 @@ public:
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makeShapeLLVMCompatible(outputShape), resultType.getElementType());
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Type adjustedInputType = RankedTensorType::get(
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makeShapeLLVMCompatible(inputShape), resultType.getElementType());
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Value castedInput =
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rewriter.create<tensor::CastOp>(loc, adjustedInputType, input);
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Value castedInput = cast(loc, adjustedInputType, input);
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std::optional<Value> expandedInput;
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std::optional<Value> collapsedInput;
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@ -602,7 +607,8 @@ public:
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Value result = collapsedInput.has_value() ? collapsedInput.value()
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: expandedInput.value();
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, result);
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auto castResult = cast(loc, resultType, result);
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rewriter.replaceOp(op, castResult);
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return success();
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}
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@ -1154,7 +1160,8 @@ public:
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return failure();
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Type resultType = getTypeConverter()->convertType(op.getType());
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, adaptor.getSelf());
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType,
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adaptor.getSelf());
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return success();
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}
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};
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@ -1407,7 +1414,8 @@ public:
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SmallVector<AffineMap> indexingMaps{inputMap, outputMap};
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SmallVector<utils::IteratorType> iteratorTypes(resultType.getRank(), utils::IteratorType::parallel);
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SmallVector<utils::IteratorType> iteratorTypes(
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resultType.getRank(), utils::IteratorType::parallel);
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Value constantZero =
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getConstant(rewriter, loc, 0, mlir::IndexType::get(context));
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@ -1417,7 +1425,6 @@ public:
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loc, outTensor.getType(), input, outTensor, indexingMaps,
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iteratorTypes,
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[&](OpBuilder &b, Location loc, ValueRange args) {
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Value realVal =
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b.create<complex::ReOp>(loc, elementType, args[0]);
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Value imagVal =
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@ -5,11 +5,9 @@
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// CHECK-LABEL: func.func @torch.aten.view$twotothree(
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// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[3,2],f32>) -> !torch.vtensor<[2,3],f32> {
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// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[3,2],f32> -> tensor<3x2xf32>
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// CHECK: %[[CASTED:.*]] = tensor.cast %[[BUILTIN_TENSOR]] : tensor<3x2xf32> to tensor<3x2xf32>
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// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[CASTED]] {{\[\[}}0, 1]] : tensor<3x2xf32> into tensor<6xf32>
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// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0, 1]] : tensor<3x2xf32> into tensor<6xf32>
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// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0, 1]] : tensor<6xf32> into tensor<2x3xf32>
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// CHECK: %[[EXPAND_CAST:.*]] = tensor.cast %[[EXPANDED]] : tensor<2x3xf32> to tensor<2x3xf32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPAND_CAST]] : tensor<2x3xf32> -> !torch.vtensor<[2,3],f32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPANDED]] : tensor<2x3xf32> -> !torch.vtensor<[2,3],f32>
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// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[2,3],f32>
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func.func @torch.aten.view$twotothree(%arg0: !torch.vtensor<[3,2],f32>) -> !torch.vtensor<[2,3],f32> {
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@ -18,13 +16,14 @@ func.func @torch.aten.view$twotothree(%arg0: !torch.vtensor<[3,2],f32>) -> !torc
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%0 = torch.prim.ListConstruct %int2, %int3 : (!torch.int, !torch.int) -> !torch.list<int>
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%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[3,2],f32>, !torch.list<int> -> !torch.vtensor<[2,3],f32>
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return %1 : !torch.vtensor<[2,3],f32>
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}
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.view$dynamictest(
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// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
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// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
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// CHECK: %[[CASTED:.*]] = tensor.cast %[[BUILTIN_TENSOR]] : tensor<?x?xf32> to tensor<?x?xf32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[CASTED]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[BUILTIN_TENSOR]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
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// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[?,?],f32>
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func.func @torch.aten.view$dynamictest(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
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@ -35,7 +34,29 @@ func.func @torch.aten.view$dynamictest(%arg0: !torch.vtensor<[?,?],f32>) -> !tor
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%2 = torch.prim.ListConstruct %0, %1 : (!torch.int, !torch.int) -> !torch.list<int>
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%3 = torch.aten.view %arg0, %2 : !torch.vtensor<[?,?],f32>, !torch.list<int> -> !torch.vtensor<[?,?],f32>
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return %3 : !torch.vtensor<[?,?],f32>
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}
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.view$dynamictest2(
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// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,6,?],f32>) -> !torch.vtensor<[?,2,3,?],f32> {
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// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,6,?],f32> -> tensor<?x6x?xf32>
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// CHECK: %[[EXPAND:.*]] = tensor.expand_shape %[[BUILTIN_TENSOR]] {{\[\[}}0], [1, 2], [3]] : tensor<?x6x?xf32> into tensor<?x2x3x?xf32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPAND]] : tensor<?x2x3x?xf32> -> !torch.vtensor<[?,2,3,?],f32>
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// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[?,2,3,?],f32>
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func.func @torch.aten.view$dynamictest2(%arg0: !torch.vtensor<[?,6,?],f32>) -> !torch.vtensor<[?,2,3,?],f32> {
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%int3 = torch.constant.int 3
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%int2 = torch.constant.int 2
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%int0 = torch.constant.int 0
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%2 = torch.aten.size.int %arg0, %int2 : !torch.vtensor<[?,6,?],f32>, !torch.int -> !torch.int
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%0 = torch.aten.size.int %arg0, %int0 : !torch.vtensor<[?,6,?],f32>, !torch.int -> !torch.int
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%1 = torch.prim.ListConstruct %0, %int2, %int3, %2 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
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%3 = torch.aten.view %arg0, %1 : !torch.vtensor<[?,6,?],f32>, !torch.list<int> -> !torch.vtensor<[?,2,3,?], f32>
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return %3 : !torch.vtensor<[?,2,3,?], f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.view$dynamicVal(
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// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[1,?,128],f32>) -> !torch.vtensor<[16,1,128],f32> {
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@ -43,8 +64,7 @@ func.func @torch.aten.view$dynamictest(%arg0: !torch.vtensor<[?,?],f32>) -> !tor
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// CHECK: %[[CASTED:.*]] = tensor.cast %[[BUILTIN_TENSOR]] : tensor<1x?x128xf32> to tensor<1x16x128xf32>
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// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[CASTED]] {{\[\[}}0, 1], [2]] : tensor<1x16x128xf32> into tensor<16x128xf32>
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// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0], [1, 2]] : tensor<16x128xf32> into tensor<16x1x128xf32>
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// CHECK: %[[EXPAND_CAST:.*]] = tensor.cast %[[EXPANDED]] : tensor<16x1x128xf32> to tensor<16x1x128xf32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPAND_CAST]] : tensor<16x1x128xf32> -> !torch.vtensor<[16,1,128],f32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPANDED]] : tensor<16x1x128xf32> -> !torch.vtensor<[16,1,128],f32>
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// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[16,1,128],f32>
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func.func @torch.aten.view$dynamicVal(%arg0: !torch.vtensor<[1,?,128],f32>) -> !torch.vtensor<[16,1,128],f32> {
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@ -54,16 +74,58 @@ func.func @torch.aten.view$dynamicVal(%arg0: !torch.vtensor<[1,?,128],f32>) -> !
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%0 = torch.prim.ListConstruct %int16, %int1, %int128 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
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%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[1,?,128],f32>, !torch.list<int> -> !torch.vtensor<[16,1,128],f32>
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return %1 : !torch.vtensor<[16,1,128],f32>
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}
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten$dynamicValOutput(
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// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[4,5,6],f32>) -> !torch.vtensor<[8,1,?,1],f32> {
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// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[4,5,6],f32> -> tensor<4x5x6xf32>
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// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0, 1, 2]] : tensor<4x5x6xf32> into tensor<120xf32>
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// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0, 1, 2, 3]] : tensor<120xf32> into tensor<8x1x15x1xf32>
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// CHECK: %[[CAST:.*]] = tensor.cast %[[EXPANDED]] : tensor<8x1x15x1xf32> to tensor<8x1x?x1xf32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[CAST]] : tensor<8x1x?x1xf32> -> !torch.vtensor<[8,1,?,1],f32>
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// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[8,1,?,1],f32>
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func.func @torch.aten$dynamicValOutput(%arg0: !torch.vtensor<[4,5,6],f32>) -> !torch.vtensor<[8,1,?,1],f32> {
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%int8 = torch.constant.int 8
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%int1 = torch.constant.int 1
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%int-1 = torch.constant.int -1
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%0 = torch.prim.ListConstruct %int8, %int1, %int-1, %int1 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
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%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[4,5,6],f32>, !torch.list<int> -> !torch.vtensor<[8,1,?,1],f32>
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return %1 : !torch.vtensor<[8,1,?,1],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten$dynamicValOutput2(
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// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[4,5,6],f32>) -> !torch.vtensor<[2,1,2,3,?],f32> {
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// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[4,5,6],f32> -> tensor<4x5x6xf32>
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// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0], [1, 2]] : tensor<4x5x6xf32> into tensor<4x30xf32>
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// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0, 1, 2], [3, 4]] : tensor<4x30xf32> into tensor<2x1x2x3x10xf32>
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// CHECK: %[[CAST:.*]] = tensor.cast %[[EXPANDED]] : tensor<2x1x2x3x10xf32> to tensor<2x1x2x3x?xf32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[CAST]] : tensor<2x1x2x3x?xf32> -> !torch.vtensor<[2,1,2,3,?],f32>
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// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[2,1,2,3,?],f32>
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// 4 -> [2,1,2] [5,6] -> [3,10].
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func.func @torch.aten$dynamicValOutput2(%arg0: !torch.vtensor<[4,5,6],f32>) -> !torch.vtensor<[2,1,2,3,?],f32> {
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%int2 = torch.constant.int 2
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%int1 = torch.constant.int 1
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%int3 = torch.constant.int 3
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%int-1 = torch.constant.int -1
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%0 = torch.prim.ListConstruct %int2, %int1, %int2, %int3, %int-1 : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
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%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[4,5,6],f32>, !torch.list<int> -> !torch.vtensor<[2,1,2,3,?],f32>
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return %1 : !torch.vtensor<[2,1,2,3,?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.view$expandInferredDim(
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// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[2,6],f32>) -> !torch.vtensor<[3,2,2],f32> {
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// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[2,6],f32> -> tensor<2x6xf32>
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// CHECK: %[[CASTED:.*]] = tensor.cast %[[BUILTIN_TENSOR]] : tensor<2x6xf32> to tensor<2x6xf32>
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// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[CASTED]] {{\[\[}}0, 1]] : tensor<2x6xf32> into tensor<12xf32>
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// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0, 1]] : tensor<2x6xf32> into tensor<12xf32>
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// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[COLLAPSED]] {{\[\[}}0, 1, 2]] : tensor<12xf32> into tensor<3x2x2xf32>
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// CHECK: %[[EXPAND_CAST:.*]] = tensor.cast %[[EXPANDED]] : tensor<3x2x2xf32> to tensor<3x2x2xf32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPAND_CAST]] : tensor<3x2x2xf32> -> !torch.vtensor<[3,2,2],f32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPANDED]] : tensor<3x2x2xf32> -> !torch.vtensor<[3,2,2],f32>
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// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[3,2,2],f32>
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func.func @torch.aten.view$expandInferredDim(%arg0: !torch.vtensor<[2,6],f32>) -> !torch.vtensor<[3,2,2],f32> {
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@ -73,4 +135,123 @@ func.func @torch.aten.view$expandInferredDim(%arg0: !torch.vtensor<[2,6],f32>) -
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%0 = torch.prim.ListConstruct %int3, %int2, %int-1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
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%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[2,6],f32>, !torch.list<int> -> !torch.vtensor<[3,2,2],f32>
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return %1 : !torch.vtensor<[3,2,2],f32>
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}
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.view$singleUnknownMatches0(
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// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[10,3,?,2,3],f32>) -> !torch.vtensor<[2,3,5,?,6],f32> {
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// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[10,3,?,2,3],f32> -> tensor<10x3x?x2x3xf32>
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// CHECK: %[[COLLAPSE:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0, 1], [2], [3, 4]] : tensor<10x3x?x2x3xf32> into tensor<30x?x6xf32>
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// CHECK: %[[EXPAND:.*]] = tensor.expand_shape %[[COLLAPSE]] {{\[\[}}0, 1, 2], [3], [4]] : tensor<30x?x6xf32> into tensor<2x3x5x?x6xf32>
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// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPAND]] : tensor<2x3x5x?x6xf32> -> !torch.vtensor<[2,3,5,?,6],f32>
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// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[2,3,5,?,6],f32>
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// [10,3,?,2,3] -> [30,?,6] -> [2,3,5,?,6]
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// Associations are,
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// -- for collapse, [0,1], [2], [3,4] and
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// -- for expand [0,1,2], [3], [4].
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func.func @torch.aten.view$singleUnknownMatches0(%arg0: !torch.vtensor<[10,3,?,2,3],f32>) -> !torch.vtensor<[2,3,5,?,6],f32> {
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%int3 = torch.constant.int 3
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%int2 = torch.constant.int 2
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%int6 = torch.constant.int 6
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%int5 = torch.constant.int 5
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%int-1 = torch.constant.int -1
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||||
%0 = torch.prim.ListConstruct %int2, %int3, %int5, %int-1, %int6 : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[10,3,?,2,3],f32>, !torch.list<int> -> !torch.vtensor<[2,3,5,?,6],f32>
|
||||
return %1 : !torch.vtensor<[2,3,5,?,6],f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// Multiple aspects of decomposition here:
|
||||
// 1) an expand from (8) to (2,2,2)
|
||||
// 2) a collapse from (2,1,3) to (6)
|
||||
// 3) a single unknown dim matching in the middle.
|
||||
// 4) on either side of the unkown dim (3), another unkown dim,
|
||||
// but one which matches between the input and the output
|
||||
|
||||
// CHECK: func.func @torch.aten.view$combineConcepts(
|
||||
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[8,?,?,?,2,1,3],f32>) -> !torch.vtensor<[2,2,2,?,?,?,6],f32> {
|
||||
// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[8,?,?,?,2,1,3],f32> -> tensor<8x?x?x?x2x1x3xf32>
|
||||
// CHECK: %[[COLLAPSE:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0], [1], [2], [3], [4, 5, 6]] : tensor<8x?x?x?x2x1x3xf32> into tensor<8x?x?x?x6xf32>
|
||||
// CHECK: %[[EXPAND:.*]] = tensor.expand_shape %[[COLLAPSE]] {{\[\[}}0, 1, 2], [3], [4], [5], [6]] : tensor<8x?x?x?x6xf32> into tensor<2x2x2x?x?x?x6xf32>
|
||||
// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPAND]] : tensor<2x2x2x?x?x?x6xf32> -> !torch.vtensor<[2,2,2,?,?,?,6],f32>
|
||||
// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[2,2,2,?,?,?,6],f32>
|
||||
|
||||
func.func @torch.aten.view$combineConcepts(%arg0 : !torch.vtensor<[8,?,?,?,2,1,3], f32>) -> !torch.vtensor<[2,2,2,?,?,?,6], f32> {
|
||||
|
||||
%int1 = torch.constant.int 1
|
||||
%size1 = torch.aten.size.int %arg0, %int1 : !torch.vtensor<[8,?,?,?,2,1,3], f32>, !torch.int -> !torch.int
|
||||
|
||||
%int3 = torch.constant.int 3
|
||||
%size3 = torch.aten.size.int %arg0, %int3 : !torch.vtensor<[8,?,?,?,2,1,3], f32>, !torch.int -> !torch.int
|
||||
|
||||
%int2 = torch.constant.int 2
|
||||
%int6 = torch.constant.int 6
|
||||
%int-1 = torch.constant.int -1
|
||||
%0 = torch.prim.ListConstruct %int2, %int2, %int2, %size1, %int-1, %size3, %int6 : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[8,?,?,?,2,1,3], f32>, !torch.list<int> -> !torch.vtensor<[2,2,2,?,?,?,6], f32>
|
||||
return %1 : !torch.vtensor<[2,2,2,?,?,?,6], f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.view$multiDynamicsInSourceOfCollapse
|
||||
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,2,?,4,?],f32>) -> !torch.vtensor<[?],f32> {
|
||||
// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,2,?,4,?],f32> -> tensor<?x2x?x4x?xf32>
|
||||
// CHECK: %[[COLLAPSE:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0, 1, 2, 3, 4]] : tensor<?x2x?x4x?xf32> into tensor<?xf32>
|
||||
// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[COLLAPSE]] : tensor<?xf32> -> !torch.vtensor<[?],f32>
|
||||
// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[?],f32>
|
||||
func.func @torch.aten.view$multiDynamicsInSourceOfCollapse (%arg0 : !torch.vtensor<[?,2,?,4,?], f32>) -> !torch.vtensor<[?], f32> {
|
||||
%int-1 = torch.constant.int -1
|
||||
%0 = torch.prim.ListConstruct %int-1 : (!torch.int) -> !torch.list<int>
|
||||
%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[?,2,?,4,?], f32>, !torch.list<int> -> !torch.vtensor<[?], f32>
|
||||
return %1 : !torch.vtensor<[?], f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @torch.aten.view$castingView
|
||||
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[3,4,5],f32> {
|
||||
|
||||
// The current lowring only succeeds if the input (arg0) has shape [3,4,5],
|
||||
// determined at runtime. This is a bit limiting, and we'll probably want to
|
||||
// improve that in the future. For now we check that there are 2 runtime
|
||||
// asserts on the sizes of dimensions 0 and 1 (size of dimension 2 implied).
|
||||
|
||||
// CHECK-COUNT-2: cf.assert {{.*}} "mismatching contracting dimension
|
||||
// CHECK: return {{.*}} : !torch.vtensor<[3,4,5],f32>
|
||||
|
||||
func.func @torch.aten.view$castingView (%arg0 : !torch.vtensor<[?,?,?], f32>) -> !torch.vtensor<[3,4,5], f32> {
|
||||
%int3 = torch.constant.int 3
|
||||
%int4 = torch.constant.int 4
|
||||
%int5 = torch.constant.int 5
|
||||
%0 = torch.prim.ListConstruct %int3, %int4, %int5 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[?,?,?], f32>, !torch.list<int> -> !torch.vtensor<[3,4,5], f32>
|
||||
return %1 : !torch.vtensor<[3,4,5], f32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// A function with a torch.view op, going from shape (10,?,2,3) to (2,5,?,6).
|
||||
// We expect this to lower to a collapse with [0], [1], [2,3] followed by
|
||||
// an expand with [0,1], [2], [3]:
|
||||
// CHECK: func.func @torch.aten.view$dynamicInferredSame(
|
||||
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[10,?,2,3],f32>) -> !torch.vtensor<[2,5,?,6],f32> {
|
||||
// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[10,?,2,3],f32> -> tensor<10x?x2x3xf32>
|
||||
// CHECK: %[[COLLAPSE:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0], [1], [2, 3]] : tensor<10x?x2x3xf32> into tensor<10x?x6xf32>
|
||||
// CHECK: %[[EXPAND:.*]] = tensor.expand_shape %[[COLLAPSE]] {{\[\[}}0, 1], [2], [3]] : tensor<10x?x6xf32> into tensor<2x5x?x6xf32>
|
||||
// CHECK: %[[BUILTIN_TENSOR_CAST:.*]] = torch_c.from_builtin_tensor %[[EXPAND]] : tensor<2x5x?x6xf32> -> !torch.vtensor<[2,5,?,6],f32>
|
||||
// CHECK: return %[[BUILTIN_TENSOR_CAST]] : !torch.vtensor<[2,5,?,6],f32>
|
||||
|
||||
func.func @torch.aten.view$dynamicInferredSame(%arg0: !torch.vtensor<[10,?,2,3],f32>) -> !torch.vtensor<[2,5,?,6],f32> {
|
||||
%int2 = torch.constant.int 2
|
||||
%int5 = torch.constant.int 5
|
||||
%int6 = torch.constant.int 6
|
||||
%int-1 = torch.constant.int -1
|
||||
%0 = torch.prim.ListConstruct %int2, %int5, %int-1, %int6 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[10,?,2,3],f32>, !torch.list<int> -> !torch.vtensor<[2,5,?,6],f32>
|
||||
return %1 : !torch.vtensor<[2,5,?,6],f32>
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue