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build: update llvm tag to 2dde4ba6 (#1229) 

Summary of changes:
 - Tensor dialect now sets `emitAccessorPrefix` to prefixed, thus
   requring updates to methods that retrieve arguments
   [https://reviews.llvm.org/D131361]
 - Update MHLO to build with LLVM commit hash 2dde4ba6
 - Replace `AbsOp` with `AbsFOp` [https://reviews.llvm.org/D131325]
 - Replace deprecated `getValue()` with `value()`
   [https://reviews.llvm.org/D131349]
 - Remove `AnalysisState::defaultInitialize()`
   [https://reviews.llvm.org/D131746]
 - Update MHLO MLIR tests to use the updated assembly format
 - Disabled two failing TOSA tests (Github Issue link:
   https://github.com/llvm/torch-mlir/issues/1231)
pull/1232/head snapshot-20220816.566
Ashay Rane 2022-08-15 23:54:45 -07:00 committed by GitHub
parent 3b3cb99ef8
commit 84d345c650
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
18 changed files with 75 additions and 85 deletions
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2
e2e_testing/torchscript/xfail_sets.py
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@ -28,7 +28,6 @@ TOSA_PASS_SET = {
"ElementwiseBinaryModule_basic", "ElementwiseBinaryModule_basic",
"ElementwiseSigmoidModule_basic", "ElementwiseSigmoidModule_basic",
"ElementwiseExpModule_basic", "ElementwiseExpModule_basic",
"ElementwiseReluModule_basic",
"ElementwiseFloorModule_basic", "ElementwiseFloorModule_basic",
"ElementwiseLogModule_basic", "ElementwiseLogModule_basic",
"ElementwiseBinaryStaticShapeModule_basic", "ElementwiseBinaryStaticShapeModule_basic",
@ -103,7 +102,6 @@ TOSA_PASS_SET = {
"ElementwiseFlattenBroadcastModule_basic", "ElementwiseFlattenBroadcastModule_basic",
"SquareModule_basic", "SquareModule_basic",
"MaxPool2dStaticModule_basic", "MaxPool2dStaticModule_basic",
"ResNet18StaticModule_basic",
"NativeLayerNormModule4D_basic", "NativeLayerNormModule4D_basic",
"LayerNormNormalizeOverAllDimsModule_basic", "LayerNormNormalizeOverAllDimsModule_basic",
"PermuteModule_basic", "PermuteModule_basic",

2
externals/llvm-external-projects/torch-mlir-dialects/lib/Dialect/TMTensor/IR/TMTensorOps.cpp vendored
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@ -516,7 +516,7 @@ struct FoldTensorCastOp : public OpInterfaceRewritePattern<TMTensorOp> {
for (OpOperand *opOperand : op.getInputOperands()) { for (OpOperand *opOperand : op.getInputOperands()) {
auto tensorCastOp = opOperand->get().getDefiningOp<tensor::CastOp>(); auto tensorCastOp = opOperand->get().getDefiningOp<tensor::CastOp>();
newOperands.push_back(canFoldIntoConsumerOp(tensorCastOp) newOperands.push_back(canFoldIntoConsumerOp(tensorCastOp)
? tensorCastOp.source() ? tensorCastOp.getSource()
: opOperand->get()); : opOperand->get());
} }
// Init tensors may fold, in which case the resultType must also change. // Init tensors may fold, in which case the resultType must also change.

2
externals/llvm-project vendored

@ -1 +1 @@
Subproject commit 061e0189a3dab6b1831a80d489ff1b15ad93aafb Subproject commit 2dde4ba63974daf59f8ce5c346505f194f920131

2
externals/mlir-hlo vendored

@ -1 +1 @@
Subproject commit 0430519b7ebf11a3f44c469fce8b579561fa6052 Subproject commit 9c49473d80a8667e94232ddb5ed60a1a9d8ad266

4
lib/Conversion/TorchToLinalg/DataMovement.cpp
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@ -576,7 +576,7 @@ public:
rewriter rewriter
.create<tensor::CollapseShapeOp>(loc, intermediateResultType, .create<tensor::CollapseShapeOp>(loc, intermediateResultType,
castedInput, inputAssociations) castedInput, inputAssociations)
.result(); .getResult();
} }
if (llvm::any_of(outputAssociations, [](ReassociationIndices indices) { if (llvm::any_of(outputAssociations, [](ReassociationIndices indices) {
@ -588,7 +588,7 @@ public:
expandedInput.has_value() ? expandedInput.value() expandedInput.has_value() ? expandedInput.value()
: castedInput, : castedInput,
outputAssociations) outputAssociations)
.result(); .getResult();
} }
Value result = collapsedInput.has_value() ? collapsedInput.value() Value result = collapsedInput.has_value() ? collapsedInput.value()

2
lib/Conversion/TorchToLinalg/Reduction.cpp
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@ -239,7 +239,7 @@ static Value createLinalgPayloadForReduceOp(OpBuilder &b, Location loc,
Value elem = payloadArgs[0]; Value elem = payloadArgs[0];
Value result = payloadArgs[1]; Value result = payloadArgs[1];
Value self = convertScalarToDtype(b, loc, elem, resultElementType); Value self = convertScalarToDtype(b, loc, elem, resultElementType);
auto abs = b.create<math::AbsOp>(loc, self); auto abs = b.create<math::AbsFOp>(loc, self);
AtenLinalgVectorNormOp::Adaptor adaptor(operands); AtenLinalgVectorNormOp::Adaptor adaptor(operands);
Value ord = convertScalarToDtype(b, loc, adaptor.ord(), resultElementType); Value ord = convertScalarToDtype(b, loc, adaptor.ord(), resultElementType);
auto pow = b.create<math::PowFOp>(loc, abs, ord); auto pow = b.create<math::PowFOp>(loc, abs, ord);

2
lib/Conversion/TorchToLinalg/Uncategorized.cpp
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@ -210,7 +210,7 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
return b.create<arith::OrIOp>(loc, lhsTest, rhsTest); return b.create<arith::OrIOp>(loc, lhsTest, rhsTest);
} }
if (isa<AtenAbsOp>(op)) if (isa<AtenAbsOp>(op))
return b.create<math::AbsOp>(loc, payloadArgs[0]); return b.create<math::AbsFOp>(loc, payloadArgs[0]);
if (isa<AtenSigmoidOp>(op)) { if (isa<AtenSigmoidOp>(op)) {
auto negate = createCalculationForMathOpWithDtypeConversion<arith::NegFOp>( auto negate = createCalculationForMathOpWithDtypeConversion<arith::NegFOp>(
b, converter, payloadArgs[0], op); b, converter, payloadArgs[0], op);

3
lib/Conversion/TorchToMhlo/Basic.cpp
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@ -1063,9 +1063,6 @@ LogicalResult ConvertAtenOp<ValsemVariantAtenUniformOp>::matchAndRewrite(
op.getLoc(), op.getLoc(),
rewriter.getFloatAttr(inputTy.getElementType(), toDoubleValue)); rewriter.getFloatAttr(inputTy.getElementType(), toDoubleValue));
auto outType = getTypeConverter()
->convertType(op.getType())
.template dyn_cast<TensorType>();
rewriter.replaceOpWithNewOp<mhlo::RngOp>( rewriter.replaceOpWithNewOp<mhlo::RngOp>(
op, inputTy, fromTensor, toTensor, mhloShape, mhlo::RngDistribution::UNIFORM); op, inputTy, fromTensor, toTensor, mhloShape, mhlo::RngDistribution::UNIFORM);
return success(); return success();

2
lib/Conversion/TorchToMhlo/Linear.cpp
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@ -531,7 +531,7 @@ public:
std::copy(outputPadding.begin(), outputPadding.end(), std::copy(outputPadding.begin(), outputPadding.end(),
edgePaddingHighVec.begin() + 2); edgePaddingHighVec.begin() + 2);
Value paddingValue = Value paddingValue =
mhlo::getConstTensor<float>(rewriter, op, {0.0}, {}).getValue(); mhlo::getConstTensor<float>(rewriter, op, {0.0}, {}).value();
paddingValue = mhlo::promoteType(rewriter, paddingValue, inputTy); paddingValue = mhlo::promoteType(rewriter, paddingValue, inputTy);
mlir::DenseIntElementsAttr edgePaddingLow = mlir::DenseIntElementsAttr edgePaddingLow =
rewriter.getI64VectorAttr(edgePaddingLowVec); rewriter.getI64VectorAttr(edgePaddingLowVec);

6
lib/Conversion/TorchToMhlo/Reduction.cpp
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@ -87,11 +87,9 @@ getMaxInDim(ConversionPatternRewriter &rewriter, Operation *op, Value &input,
if (!initValue) return llvm::None; if (!initValue) return llvm::None;
Value initIndex; Value initIndex;
if (mlir::mhlo::kMhloDimSizeBits == 32) { if (mlir::mhlo::kMhloDimSizeBits == 32) {
initIndex = initIndex = mhlo::getConstTensor<int32_t>(rewriter, op, {0}, {}).value();
mhlo::getConstTensor<int32_t>(rewriter, op, {0}, {}).getValue();
} else { } else {
initIndex = initIndex = mhlo::getConstTensor<int64_t>(rewriter, op, {0}, {}).value();
mhlo::getConstTensor<int64_t>(rewriter, op, {0}, {}).getValue();
} }
DenseIntElementsAttr dimensions = DenseIntElementsAttr::get( DenseIntElementsAttr dimensions = DenseIntElementsAttr::get(

9
lib/Dialect/Torch/Transforms/InlineGlobalSlots.cpp
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@ -94,7 +94,9 @@ static bool isUseTreatedWithValueSemantics(OpOperand &use) {
/// unsafe /// unsafe
class InlineGlobalSlotsAnalysisState : public AnalysisState { class InlineGlobalSlotsAnalysisState : public AnalysisState {
public: public:
InlineGlobalSlotsAnalysisState(ProgramPoint point) : AnalysisState(point) {} InlineGlobalSlotsAnalysisState(ProgramPoint point) : AnalysisState(point) {
setSafe();
}
bool isUninitialized() const override { bool isUninitialized() const override {
// We are an optimistic analysis, so we are always default initialized to // We are an optimistic analysis, so we are always default initialized to
@ -102,11 +104,6 @@ public:
return false; return false;
} }
ChangeResult defaultInitialize() override {
// We are an optimistic analysis, so the default state is always "safe".
return setSafe();
}
void print(raw_ostream &os) const override { void print(raw_ostream &os) const override {
os << "InlineGlobalSlotsAnalysisState(" << (isSafe ? "safe" : "unsafe") os << "InlineGlobalSlotsAnalysisState(" << (isSafe ? "safe" : "unsafe")
<< ")"; << ")";

12
test/Conversion/TorchToMhlo/basic.mlir
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@ -7,7 +7,7 @@
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> { // CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32> // CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.none // CHECK: %[[VAL_2:.*]] = torch.constant.none
// CHECK: %[[VAL_3:.*]] = "mhlo.copy"(%[[VAL_1]]) : (tensor<?x?xf32>) -> tensor<?x?xf32> // CHECK: %[[VAL_3:.*]] = mhlo.copy %[[VAL_1]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_4]] : !torch.vtensor<[?,?],f32> // CHECK: return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.clone$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> { func.func @torch.aten.clone$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
@ -47,7 +47,7 @@ func.func @torch.vtensor.literal$signed() -> !torch.vtensor<[2],si64> {
// CHECK: %[[T0:.*]] = torch_c.to_i64 %[[INT1]] // CHECK: %[[T0:.*]] = torch_c.to_i64 %[[INT1]]
// CHECK: %[[T1:.*]] = tensor.from_elements %[[T0]] : tensor<1xi64> // CHECK: %[[T1:.*]] = tensor.from_elements %[[T0]] : tensor<1xi64>
// CHECK: %[[T2:.*]] = mhlo.convert %[[T1]] : tensor<1xi64> // CHECK: %[[T2:.*]] = mhlo.convert %[[T1]] : tensor<1xi64>
// CHECK: %[[T3:.*]] = "mhlo.reshape"(%[[T2]]) : (tensor<1xi64>) -> tensor<i64> // CHECK: %[[T3:.*]] = mhlo.reshape %[[T2]] : (tensor<1xi64>) -> tensor<i64>
// CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<i64> -> !torch.vtensor<[],si64> // CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<i64> -> !torch.vtensor<[],si64>
// CHECK: return %[[T4]] : !torch.vtensor<[],si64> // CHECK: return %[[T4]] : !torch.vtensor<[],si64>
func.func @torch.prim.NumToTensor.Scalar$basic() -> !torch.vtensor<[], si64> { func.func @torch.prim.NumToTensor.Scalar$basic() -> !torch.vtensor<[], si64> {
@ -229,16 +229,16 @@ func.func @torch.aten.batch_norm$no_bias_weight(%arg0: !torch.vtensor<[?,3,?,?],
// CHECK: %true = torch.constant.bool true // CHECK: %true = torch.constant.bool true
// CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int4, %int5 : (!torch.int, !torch.int) -> !torch.list<int> // CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int4, %int5 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_5:.*]] = mhlo.constant dense<[1, 21, 20]> : tensor<3xi64> // CHECK: %[[VAL_5:.*]] = mhlo.constant dense<[1, 21, 20]> : tensor<3xi64>
// CHECK: %[[VAL_6:.*]] = "mhlo.dynamic_reshape"(%[[VAL_1]], %[[VAL_5]]) : (tensor<3x7x4x5xf32>, tensor<3xi64>) -> tensor<1x21x20xf32> // CHECK: %[[VAL_6:.*]] = mhlo.dynamic_reshape %[[VAL_1]], %[[VAL_5]] : (tensor<3x7x4x5xf32>, tensor<3xi64>) -> tensor<1x21x20xf32>
// CHECK: %[[VAL_7:.*]] = mhlo.constant dense<1.000000e+00> : tensor<21xf32> // CHECK: %[[VAL_7:.*]] = mhlo.constant dense<1.000000e+00> : tensor<21xf32>
// CHECK: %[[VAL_8:.*]] = mhlo.constant dense<0.000000e+00> : tensor<21xf32> // CHECK: %[[VAL_8:.*]] = mhlo.constant dense<0.000000e+00> : tensor<21xf32>
// CHECK: %[[VAL_9:.*]], %[[VAL_10:.*]], %[[VAL_11:.*]] = "mhlo.batch_norm_training"(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>) -> (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>) // CHECK: %[[VAL_9:.*]], %[[VAL_10:.*]], %[[VAL_11:.*]] = "mhlo.batch_norm_training"(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>) -> (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>)
// CHECK: %[[VAL_12:.*]] = mhlo.constant dense<[3, 7, 4, 5]> : tensor<4xi64> // CHECK: %[[VAL_12:.*]] = mhlo.constant dense<[3, 7, 4, 5]> : tensor<4xi64>
// CHECK: %[[VAL_13:.*]] = "mhlo.dynamic_reshape"(%[[VAL_9]], %[[VAL_12]]) : (tensor<1x21x20xf32>, tensor<4xi64>) -> tensor<3x7x4x5xf32> // CHECK: %[[VAL_13:.*]] = mhlo.dynamic_reshape %[[VAL_9]], %[[VAL_12]] : (tensor<1x21x20xf32>, tensor<4xi64>) -> tensor<3x7x4x5xf32>
// CHECK: %[[VAL_14:.*]] = mhlo.constant dense<[3, 7, 1, 1]> : tensor<4xi64> // CHECK: %[[VAL_14:.*]] = mhlo.constant dense<[3, 7, 1, 1]> : tensor<4xi64>
// CHECK: %[[VAL_15:.*]] = "mhlo.dynamic_reshape"(%[[VAL_10]], %[[VAL_14]]) : (tensor<21xf32>, tensor<4xi64>) -> tensor<3x7x1x1xf32> // CHECK: %[[VAL_15:.*]] = mhlo.dynamic_reshape %[[VAL_10]], %[[VAL_14]] : (tensor<21xf32>, tensor<4xi64>) -> tensor<3x7x1x1xf32>
// CHECK: %[[VAL_16:.*]] = mhlo.constant dense<[3, 7, 1, 1]> : tensor<4xi64> // CHECK: %[[VAL_16:.*]] = mhlo.constant dense<[3, 7, 1, 1]> : tensor<4xi64>
// CHECK: %[[VAL_17:.*]] = "mhlo.dynamic_reshape"(%[[VAL_11]], %[[VAL_16]]) : (tensor<21xf32>, tensor<4xi64>) -> tensor<3x7x1x1xf32> // CHECK: %[[VAL_17:.*]] = mhlo.dynamic_reshape %[[VAL_11]], %[[VAL_16]] : (tensor<21xf32>, tensor<4xi64>) -> tensor<3x7x1x1xf32>
// CHECK: %[[VAL_18:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<4x5xf32>) -> tensor<3x7x4x5xf32> // CHECK: %[[VAL_18:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<4x5xf32>) -> tensor<3x7x4x5xf32>
// CHECK: %[[VAL_19:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_2]]) {broadcast_dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<4x5xf32>) -> tensor<3x7x4x5xf32> // CHECK: %[[VAL_19:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_2]]) {broadcast_dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<4x5xf32>) -> tensor<3x7x4x5xf32>
// CHECK: %[[VAL_20:.*]] = mhlo.multiply %[[VAL_13]], %[[VAL_18]] : tensor<3x7x4x5xf32> // CHECK: %[[VAL_20:.*]] = mhlo.multiply %[[VAL_13]], %[[VAL_18]] : tensor<3x7x4x5xf32>

6
test/Conversion/TorchToMhlo/dropout.mlir
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@ -10,7 +10,7 @@
// CHECK: %[[CST_2:.*]] = arith.constant 1.000000e+00 : f64 // CHECK: %[[CST_2:.*]] = arith.constant 1.000000e+00 : f64
// CHECK: %[[CST_3:.*]] = arith.subf %[[CST_2]], %[[ARG1]] : f64 // CHECK: %[[CST_3:.*]] = arith.subf %[[CST_2]], %[[ARG1]] : f64
// CHECK: %[[T3:.*]] = tensor.from_elements %[[CST_3]] : tensor<1xf64> // CHECK: %[[T3:.*]] = tensor.from_elements %[[CST_3]] : tensor<1xf64>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf64>) -> tensor<f64> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf64>) -> tensor<f64>
// CHECK: %[[T5:.*]] = mhlo.convert(%[[ARG0]]) : (tensor<?x?xf32>) -> tensor<?x?xf64> // CHECK: %[[T5:.*]] = mhlo.convert(%[[ARG0]]) : (tensor<?x?xf32>) -> tensor<?x?xf64>
// CHECK: %[[DIM_0:.*]] = tensor.dim %[[T5]], %[[CST_1]] : tensor<?x?xf64> // CHECK: %[[DIM_0:.*]] = tensor.dim %[[T5]], %[[CST_1]] : tensor<?x?xf64>
// CHECK: %[[CST_I64_0:.*]] = arith.index_cast %[[DIM_0]] : index to i64 // CHECK: %[[CST_I64_0:.*]] = arith.index_cast %[[DIM_0]] : index to i64
@ -33,7 +33,7 @@
// CHECK: shape.assuming_yield %[[T19]] : tensor<?x?xf32> // CHECK: shape.assuming_yield %[[T19]] : tensor<?x?xf32>
// CHECK: } // CHECK: }
// CHECK: %[[T20:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xf64>) -> tensor<1xf32> // CHECK: %[[T20:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xf64>) -> tensor<1xf32>
// CHECK: %[[T21:.*]] = "mhlo.reshape"(%[[T20]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T21:.*]] = mhlo.reshape %[[T20]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T22:.*]] = shape.shape_of %[[T15]] : tensor<?x?xf32> -> tensor<2xindex> // CHECK: %[[T22:.*]] = shape.shape_of %[[T15]] : tensor<?x?xf32> -> tensor<2xindex>
// CHECK: %[[T23:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T21]], %[[T22]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>, tensor<2xindex>) -> tensor<?x?xf32> // CHECK: %[[T23:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T21]], %[[T22]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>, tensor<2xindex>) -> tensor<?x?xf32>
// CHECK: %[[T24:.*]] = mhlo.multiply %[[T15]], %[[T23]] : tensor<?x?xf32> // CHECK: %[[T24:.*]] = mhlo.multiply %[[T15]], %[[T23]] : tensor<?x?xf32>
@ -44,4 +44,4 @@ func.func @torch.aten.native_dropout.train(%arg0: !torch.vtensor<[?,?],f32>, %ar
%bool_true = torch.constant.bool true %bool_true = torch.constant.bool true
%result0, %result1 = torch.aten.native_dropout %arg0, %arg1, %bool_true: !torch.vtensor<[?,?],f32>, !torch.float, !torch.bool -> !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],i1> %result0, %result1 = torch.aten.native_dropout %arg0, %arg1, %bool_true: !torch.vtensor<[?,?],f32>, !torch.float, !torch.bool -> !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],i1>
return %result0, %result1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],i1> return %result0, %result1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],i1>
} }

34
test/Conversion/TorchToMhlo/elementwise.mlir
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@ -83,7 +83,7 @@ func.func @torch.aten.neg$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vten
// CHECK: %[[INT1:.*]] = torch.constant.int 1 // CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T5:.*]] = chlo.broadcast_add %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T5:.*]] = chlo.broadcast_add %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32> // CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32>
@ -105,10 +105,10 @@ func.func @torch.aten.addscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torc
// CHECK: %[[T2:.*]] = torch_c.to_i64 %[[INT2]] // CHECK: %[[T2:.*]] = torch_c.to_i64 %[[INT2]]
// CHECK: %[[T3:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T3:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T5:.*]] = "mhlo.reshape"(%[[T4]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T5:.*]] = mhlo.reshape %[[T4]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T6:.*]] = tensor.from_elements %[[T2]] : tensor<1xi64> // CHECK: %[[T6:.*]] = tensor.from_elements %[[T2]] : tensor<1xi64>
// CHECK: %[[T7:.*]] = mhlo.convert(%[[T6]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T7:.*]] = mhlo.convert(%[[T6]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T8:.*]] = "mhlo.reshape"(%[[T7]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T8:.*]] = mhlo.reshape %[[T7]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T9:.*]] = chlo.broadcast_multiply %[[T5]], %[[T8]] : (tensor<f32>, tensor<f32>) -> tensor<f32> // CHECK: %[[T9:.*]] = chlo.broadcast_multiply %[[T5]], %[[T8]] : (tensor<f32>, tensor<f32>) -> tensor<f32>
// CHECK: %[[T10:.*]] = chlo.broadcast_add %[[T0]], %[[T9]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T10:.*]] = chlo.broadcast_add %[[T0]], %[[T9]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
@ -146,7 +146,7 @@ func.func @torch.aten.addtensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !
// CHECK: %[[T2:.*]] = torch_c.to_i64 %[[INT2]] // CHECK: %[[T2:.*]] = torch_c.to_i64 %[[INT2]]
// CHECK: %[[T3:.*]] = tensor.from_elements %[[T2]] : tensor<1xi64> // CHECK: %[[T3:.*]] = tensor.from_elements %[[T2]] : tensor<1xi64>
// CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T5:.*]] = "mhlo.reshape"(%[[T4]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T5:.*]] = mhlo.reshape %[[T4]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T6:.*]] = chlo.broadcast_multiply %[[T1]], %[[T5]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T6:.*]] = chlo.broadcast_multiply %[[T1]], %[[T5]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T7:.*]] = chlo.broadcast_add %[[T0]], %[[T6]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32> // CHECK: %[[T7:.*]] = chlo.broadcast_add %[[T0]], %[[T6]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T8:.*]] = torch_c.from_builtin_tensor %[[T7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T8:.*]] = torch_c.from_builtin_tensor %[[T7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
@ -184,7 +184,7 @@ func.func @torch.aten.addtensor$promote(%arg0: !torch.vtensor<[?,?],si32>, %arg1
// CHECK: %[[INT1:.*]] = torch.constant.int 1 // CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T5:.*]] = chlo.broadcast_subtract %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T5:.*]] = chlo.broadcast_subtract %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32> // CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32>
@ -206,10 +206,10 @@ func.func @torch.aten.subscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torc
// CHECK: %[[T2:.*]] = torch_c.to_i64 %[[INT2]] // CHECK: %[[T2:.*]] = torch_c.to_i64 %[[INT2]]
// CHECK: %[[T3:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T3:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T5:.*]] = "mhlo.reshape"(%[[T4]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T5:.*]] = mhlo.reshape %[[T4]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T6:.*]] = tensor.from_elements %[[T2]] : tensor<1xi64> // CHECK: %[[T6:.*]] = tensor.from_elements %[[T2]] : tensor<1xi64>
// CHECK: %[[T7:.*]] = mhlo.convert(%[[T6]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T7:.*]] = mhlo.convert(%[[T6]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T8:.*]] = "mhlo.reshape"(%[[T7]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T8:.*]] = mhlo.reshape %[[T7]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T9:.*]] = chlo.broadcast_multiply %[[T5]], %[[T8]] : (tensor<f32>, tensor<f32>) -> tensor<f32> // CHECK: %[[T9:.*]] = chlo.broadcast_multiply %[[T5]], %[[T8]] : (tensor<f32>, tensor<f32>) -> tensor<f32>
// CHECK: %[[T10:.*]] = chlo.broadcast_subtract %[[T0]], %[[T9]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T10:.*]] = chlo.broadcast_subtract %[[T0]], %[[T9]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
@ -247,7 +247,7 @@ func.func @torch.aten.subtensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !
// CHECK: %[[T2:.*]] = torch_c.to_i64 %[[INT2]] // CHECK: %[[T2:.*]] = torch_c.to_i64 %[[INT2]]
// CHECK: %[[T3:.*]] = tensor.from_elements %[[T2]] : tensor<1xi64> // CHECK: %[[T3:.*]] = tensor.from_elements %[[T2]] : tensor<1xi64>
// CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T5:.*]] = "mhlo.reshape"(%[[T4]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T5:.*]] = mhlo.reshape %[[T4]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T6:.*]] = chlo.broadcast_multiply %[[T1]], %[[T5]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T6:.*]] = chlo.broadcast_multiply %[[T1]], %[[T5]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T7:.*]] = chlo.broadcast_subtract %[[T0]], %[[T6]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32> // CHECK: %[[T7:.*]] = chlo.broadcast_subtract %[[T0]], %[[T6]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T8:.*]] = torch_c.from_builtin_tensor %[[T7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T8:.*]] = torch_c.from_builtin_tensor %[[T7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
@ -284,7 +284,7 @@ func.func @torch.aten.subtensor$promote(%arg0: !torch.vtensor<[?,?],si32>, %arg1
// CHECK: %[[T1:.*]] = torch_c.to_i64 %[[INT9]] // CHECK: %[[T1:.*]] = torch_c.to_i64 %[[INT9]]
// CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T5:.*]] = chlo.broadcast_multiply %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T5:.*]] = chlo.broadcast_multiply %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32> // CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32>
@ -317,7 +317,7 @@ func.func @torch.aten.multensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !
// CHECK: %[[T1:.*]] = torch_c.to_i64 %[[INT9]] // CHECK: %[[T1:.*]] = torch_c.to_i64 %[[INT9]]
// CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T5:.*]] = chlo.broadcast_divide %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T5:.*]] = chlo.broadcast_divide %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32> // CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32>
@ -350,7 +350,7 @@ func.func @torch.aten.divtensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !
// CHECK: %[[T1:.*]] = torch_c.to_i64 %[[INT3]] // CHECK: %[[T1:.*]] = torch_c.to_i64 %[[INT3]]
// CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T5:.*]] = chlo.broadcast_compare %[[T0]], %[[T4]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xi1> // CHECK: %[[T5:.*]] = chlo.broadcast_compare %[[T0]], %[[T4]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xi1>
// CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1> // CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[T6]] : !torch.vtensor<[?,?],i1> // CHECK: return %[[T6]] : !torch.vtensor<[?,?],i1>
@ -457,10 +457,10 @@ func.func @torch.aten.relu(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[
// CHECK: %[[T1:.*]] = torch_c.to_f64 %[[ARG1]] // CHECK: %[[T1:.*]] = torch_c.to_f64 %[[ARG1]]
// CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xf64> // CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xf64>
// CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xf64>) -> tensor<1xf32> // CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xf64>) -> tensor<1xf32>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T5:.*]] = tensor.from_elements %[[T1]] : tensor<1xf64> // CHECK: %[[T5:.*]] = tensor.from_elements %[[T1]] : tensor<1xf64>
// CHECK: %[[T6:.*]] = mhlo.convert(%[[T5]]) : (tensor<1xf64>) -> tensor<1xf32> // CHECK: %[[T6:.*]] = mhlo.convert(%[[T5]]) : (tensor<1xf64>) -> tensor<1xf32>
// CHECK: %[[T7:.*]] = "mhlo.reshape"(%[[T6]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T7:.*]] = mhlo.reshape %[[T6]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T8:.*]] = chlo.broadcast_multiply %[[T4]], %[[T7]] : (tensor<f32>, tensor<f32>) -> tensor<f32> // CHECK: %[[T8:.*]] = chlo.broadcast_multiply %[[T4]], %[[T7]] : (tensor<f32>, tensor<f32>) -> tensor<f32>
// CHECK: %[[T9:.*]] = chlo.broadcast_add %[[T0]], %[[T8]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T9:.*]] = chlo.broadcast_add %[[T0]], %[[T8]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
@ -479,7 +479,7 @@ func.func @torch.aten.addscalar$variable(%arg0: !torch.vtensor<[?,?],f32>, %arg1
// CHECK: %[[T2:.*]] = torch_c.to_f64 %[[ARG2]] // CHECK: %[[T2:.*]] = torch_c.to_f64 %[[ARG2]]
// CHECK: %[[T3:.*]] = tensor.from_elements %[[T2]] : tensor<1xf64> // CHECK: %[[T3:.*]] = tensor.from_elements %[[T2]] : tensor<1xf64>
// CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xf64>) -> tensor<1xf32> // CHECK: %[[T4:.*]] = mhlo.convert(%[[T3]]) : (tensor<1xf64>) -> tensor<1xf32>
// CHECK: %[[T5:.*]] = "mhlo.reshape"(%[[T4]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T5:.*]] = mhlo.reshape %[[T4]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T6:.*]] = chlo.broadcast_multiply %[[T1]], %[[T5]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T6:.*]] = chlo.broadcast_multiply %[[T1]], %[[T5]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T7:.*]] = chlo.broadcast_add %[[T0]], %[[T6]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32> // CHECK: %[[T7:.*]] = chlo.broadcast_add %[[T0]], %[[T6]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T8:.*]] = torch_c.from_builtin_tensor %[[T7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T8:.*]] = torch_c.from_builtin_tensor %[[T7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
@ -497,7 +497,7 @@ func.func @torch.aten.addtensor$variable(%arg0: !torch.vtensor<[?,?],f32>, %arg1
// CHECK: %[[T1:.*]] = torch_c.to_i64 %[[ARG1]] // CHECK: %[[T1:.*]] = torch_c.to_i64 %[[ARG1]]
// CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T5:.*]] = chlo.broadcast_multiply %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T5:.*]] = chlo.broadcast_multiply %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32> // CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32>
@ -514,7 +514,7 @@ func.func @torch.aten.mulscalar$variable(%arg0: !torch.vtensor<[?,?],f32>, %arg1
// CHECK: %[[T1:.*]] = torch_c.to_i64 %[[ARG1]] // CHECK: %[[T1:.*]] = torch_c.to_i64 %[[ARG1]]
// CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T5:.*]] = chlo.broadcast_divide %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32> // CHECK: %[[T5:.*]] = chlo.broadcast_divide %[[T0]], %[[T4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32> // CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32> // CHECK: return %[[T6]] : !torch.vtensor<[?,?],f32>
@ -531,7 +531,7 @@ func.func @torch.aten.divscalar$variable(%arg0: !torch.vtensor<[?,?],f32>, %arg1
// CHECK: %[[T1:.*]] = torch_c.to_i64 %[[ARG1]] // CHECK: %[[T1:.*]] = torch_c.to_i64 %[[ARG1]]
// CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64> // CHECK: %[[T2:.*]] = tensor.from_elements %[[T1]] : tensor<1xi64>
// CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32> // CHECK: %[[T3:.*]] = mhlo.convert(%[[T2]]) : (tensor<1xi64>) -> tensor<1xf32>
// CHECK: %[[T4:.*]] = "mhlo.reshape"(%[[T3]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T4:.*]] = mhlo.reshape %[[T3]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T5:.*]] = chlo.broadcast_compare %[[T0]], %[[T4]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xi1> // CHECK: %[[T5:.*]] = chlo.broadcast_compare %[[T0]], %[[T4]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xi1>
// CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1> // CHECK: %[[T6:.*]] = torch_c.from_builtin_tensor %[[T5]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[T6]] : !torch.vtensor<[?,?],i1> // CHECK: return %[[T6]] : !torch.vtensor<[?,?],i1>

8
test/Conversion/TorchToMhlo/linear.mlir
View File

@ -328,7 +328,7 @@ func.func @torch.aten.convolution(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !
// CHECK: %[[T_10:.*]] = arith.index_cast %[[T_9]] : index to i64 // CHECK: %[[T_10:.*]] = arith.index_cast %[[T_9]] : index to i64
// CHECK: %[[VAL_0:.*]] = arith.constant 1 : i64 // CHECK: %[[VAL_0:.*]] = arith.constant 1 : i64
// CHECK: %[[T_11:.*]] = tensor.from_elements %[[T_10]], %[[VAL_0]], %[[VAL_0]] : tensor<3xi64> // CHECK: %[[T_11:.*]] = tensor.from_elements %[[T_10]], %[[VAL_0]], %[[VAL_0]] : tensor<3xi64>
// CHECK: %[[T_12:.*]] = "mhlo.dynamic_reshape"(%[[T_2]], %[[T_11]]) : (tensor<?xf32>, tensor<3xi64>) -> tensor<?x1x1xf32> // CHECK: %[[T_12:.*]] = mhlo.dynamic_reshape %[[T_2]], %[[T_11]] : (tensor<?xf32>, tensor<3xi64>) -> tensor<?x1x1xf32>
// CHECK: %[[T_13:.*]] = chlo.broadcast_add %[[T_8]], %[[T_12]] : (tensor<?x?x?x?xf32>, tensor<?x1x1xf32>) -> tensor<?x?x?x?xf32> // CHECK: %[[T_13:.*]] = chlo.broadcast_add %[[T_8]], %[[T_12]] : (tensor<?x?x?x?xf32>, tensor<?x1x1xf32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[T_14:.*]] = torch_c.from_builtin_tensor %[[T_13]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32> // CHECK: %[[T_14:.*]] = torch_c.from_builtin_tensor %[[T_13]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
// CHECK: return %[[T_14]] : !torch.vtensor<[?,?,?,?],f32> // CHECK: return %[[T_14]] : !torch.vtensor<[?,?,?,?],f32>
@ -479,10 +479,10 @@ func.func @torch.aten.convolution$transposed_outputpadding(%arg0: !torch.vtensor
// CHECK: %[[T_15:.*]] = arith.divsi %[[T_8]], %[[T_24]] : i64 // CHECK: %[[T_15:.*]] = arith.divsi %[[T_8]], %[[T_24]] : i64
// CHECK: %[[T_16:.*]] = arith.muli %[[T_10]], %[[T_24]] : i64 // CHECK: %[[T_16:.*]] = arith.muli %[[T_10]], %[[T_24]] : i64
// CHECK: %[[T_17:.*]] = tensor.from_elements %[[T_24]], %[[T_15]], %[[T_10]], %[[T_12]], %[[T_14]] : tensor<5xi64> // CHECK: %[[T_17:.*]] = tensor.from_elements %[[T_24]], %[[T_15]], %[[T_10]], %[[T_12]], %[[T_14]] : tensor<5xi64>
// CHECK: %[[T_18:.*]] = "mhlo.dynamic_reshape"(%[[T_6]], %[[T_17]]) : (tensor<2x2x3x3xf32>, tensor<5xi64>) -> tensor<2x1x2x3x3xf32> // CHECK: %[[T_18:.*]] = mhlo.dynamic_reshape %[[T_6]], %[[T_17]] : (tensor<2x2x3x3xf32>, tensor<5xi64>) -> tensor<2x1x2x3x3xf32>
// CHECK: %[[T_19:.*]] = "mhlo.transpose"(%[[T_18]]) {permutation = dense<[1, 0, 2, 3, 4]> : tensor<5xi64>} : (tensor<2x1x2x3x3xf32>) -> tensor<1x2x2x3x3xf32> // CHECK: %[[T_19:.*]] = "mhlo.transpose"(%[[T_18]]) {permutation = dense<[1, 0, 2, 3, 4]> : tensor<5xi64>} : (tensor<2x1x2x3x3xf32>) -> tensor<1x2x2x3x3xf32>
// CHECK: %[[T_20:.*]] = tensor.from_elements %[[T_15]], %[[T_16]], %[[T_12]], %[[T_14]] : tensor<4xi64> // CHECK: %[[T_20:.*]] = tensor.from_elements %[[T_15]], %[[T_16]], %[[T_12]], %[[T_14]] : tensor<4xi64>
// CHECK: %[[T_21:.*]] = "mhlo.dynamic_reshape"(%[[T_19]], %[[T_20]]) : (tensor<1x2x2x3x3xf32>, tensor<4xi64>) -> tensor<1x4x3x3xf32> // CHECK: %[[T_21:.*]] = mhlo.dynamic_reshape %[[T_19]], %[[T_20]] : (tensor<1x2x2x3x3xf32>, tensor<4xi64>) -> tensor<1x4x3x3xf32>
// CHECK: %[[T_22:.*]] = mhlo.convolution(%[[T_0]], %[[T_21]]) // CHECK: %[[T_22:.*]] = mhlo.convolution(%[[T_0]], %[[T_21]])
// CHECK{LITERAL}: dim_numbers = [b, f, 0, 1]x[i, o, 0, 1]->[b, f, 0, 1], window = {stride = [1, 1], pad = [[2, 2], [2, 2]], lhs_dilate = [2, 2], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 2 : i64} : (tensor<1x2x7x7xf32>, tensor<1x4x3x3xf32>) -> tensor<1x4x15x15xf32> // CHECK{LITERAL}: dim_numbers = [b, f, 0, 1]x[i, o, 0, 1]->[b, f, 0, 1], window = {stride = [1, 1], pad = [[2, 2], [2, 2]], lhs_dilate = [2, 2], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 2 : i64} : (tensor<1x2x7x7xf32>, tensor<1x4x3x3xf32>) -> tensor<1x4x15x15xf32>
// CHECK: %[[T_23:.*]] = torch_c.from_builtin_tensor %[[T_22]] : tensor<1x4x15x15xf32> -> !torch.vtensor<[1,4,15,15],f32> // CHECK: %[[T_23:.*]] = torch_c.from_builtin_tensor %[[T_22]] : tensor<1x4x15x15xf32> -> !torch.vtensor<[1,4,15,15],f32>
@ -498,4 +498,4 @@ func.func @torch.aten.convolution$transposed_groups(%arg0: !torch.vtensor<[1,2,7
%2 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int> %2 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%3 = torch.aten.convolution %arg0, %arg1, %none, %2, %0, %1, %true, %0, %int2 : !torch.vtensor<[1,2,7,7],f32>, !torch.vtensor<[2,2,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,4,15,15],f32> %3 = torch.aten.convolution %arg0, %arg1, %none, %2, %0, %1, %true, %0, %int2 : !torch.vtensor<[1,2,7,7],f32>, !torch.vtensor<[2,2,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,4,15,15],f32>
return %3 : !torch.vtensor<[1,4,15,15],f32> return %3 : !torch.vtensor<[1,4,15,15],f32>
} }

14
test/Conversion/TorchToMhlo/pooling.mlir
View File

@ -17,7 +17,7 @@
// CHECK: %[[VAL_7:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_6]]) ({ // CHECK: %[[VAL_7:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_6]]) ({
// CHECK: ^bb0(%[[VAL_8:.*]]: tensor<f32>, %[[VAL_9:.*]]: tensor<f32>): // CHECK: ^bb0(%[[VAL_8:.*]]: tensor<f32>, %[[VAL_9:.*]]: tensor<f32>):
// CHECK: %[[VAL_10:.*]] = mhlo.maximum %[[VAL_8]], %[[VAL_9]] : tensor<f32> // CHECK: %[[VAL_10:.*]] = mhlo.maximum %[[VAL_8]], %[[VAL_9]] : tensor<f32>
// CHECK: "mhlo.return"(%[[VAL_10]]) : (tensor<f32>) -> () // CHECK: mhlo.return %[[VAL_10]] : tensor<f32>
// CHECK: }) {padding = dense<0> : tensor<4x2xi64>, window_dilations = dense<[1, 1, 2, 1]> : tensor<4xi64>, window_dimensions = dense<[1, 1, 2, 2]> : tensor<4xi64>, window_strides = dense<1> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32> // CHECK: }) {padding = dense<0> : tensor<4x2xi64>, window_dilations = dense<[1, 1, 2, 1]> : tensor<4xi64>, window_dimensions = dense<[1, 1, 2, 2]> : tensor<4xi64>, window_strides = dense<1> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_11:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32> // CHECK: %[[VAL_11:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
// CHECK: return %[[VAL_11]] : !torch.vtensor<[?,?,?,?],f32> // CHECK: return %[[VAL_11]] : !torch.vtensor<[?,?,?,?],f32>
@ -49,7 +49,7 @@ func.func @torch.aten.max_pool2d(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch
// CHECK: %[[VAL_6:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) ({ // CHECK: %[[VAL_6:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) ({
// CHECK: ^bb0(%[[VAL_8:.*]]: tensor<f32>, %[[VAL_9:.*]]: tensor<f32>): // CHECK: ^bb0(%[[VAL_8:.*]]: tensor<f32>, %[[VAL_9:.*]]: tensor<f32>):
// CHECK: %[[VAL_10:.*]] = mhlo.maximum %[[VAL_8]], %[[VAL_9]] : tensor<f32> // CHECK: %[[VAL_10:.*]] = mhlo.maximum %[[VAL_8]], %[[VAL_9]] : tensor<f32>
// CHECK: "mhlo.return"(%[[VAL_10]]) : (tensor<f32>) -> () // CHECK: mhlo.return %[[VAL_10]] : tensor<f32>
// CHECK: }) // CHECK: })
// CHECK-SAME{LITERAL}: {padding = dense<[[0, 0], [0, 0], [2, 2], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<[1, 1, 2, 1]> : tensor<4xi64>, window_dimensions = dense<[1, 1, 2, 2]> : tensor<4xi64>, window_strides = dense<1> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32> // CHECK-SAME{LITERAL}: {padding = dense<[[0, 0], [0, 0], [2, 2], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<[1, 1, 2, 1]> : tensor<4xi64>, window_dimensions = dense<[1, 1, 2, 2]> : tensor<4xi64>, window_strides = dense<1> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32> // CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
@ -94,7 +94,7 @@ func.func @torch.aten.max_pool2d$padding(%arg0: !torch.vtensor<[?,?,?,?],f32>) -
// CHECK: %[[VAL_14:.*]] = arith.muli %[[VAL_12]], %[[VAL_10]] : i64 // CHECK: %[[VAL_14:.*]] = arith.muli %[[VAL_12]], %[[VAL_10]] : i64
// CHECK: %[[VAL_15:.*]] = tensor.from_elements %[[VAL_8]], %[[VAL_14]] : tensor<2xi64> // CHECK: %[[VAL_15:.*]] = tensor.from_elements %[[VAL_8]], %[[VAL_14]] : tensor<2xi64>
// CHECK: %[[VAL_16:.*]] = "mhlo.dynamic_iota"(%[[VAL_15]]) {iota_dimension = 1 : i64} : (tensor<2xi64>) -> tensor<?x?xi64> // CHECK: %[[VAL_16:.*]] = "mhlo.dynamic_iota"(%[[VAL_15]]) {iota_dimension = 1 : i64} : (tensor<2xi64>) -> tensor<?x?xi64>
// CHECK: %[[VAL_17:.*]] = "mhlo.dynamic_reshape"(%[[VAL_16]], %[[VAL_13]]) : (tensor<?x?xi64>, tensor<3xi64>) -> tensor<?x?x?xi64> // CHECK: %[[VAL_17:.*]] = mhlo.dynamic_reshape %[[VAL_16]], %[[VAL_13]] : (tensor<?x?xi64>, tensor<3xi64>) -> tensor<?x?x?xi64>
// CHECK: %[[VAL_18:.*]] = mhlo.constant dense<0> : tensor<i64> // CHECK: %[[VAL_18:.*]] = mhlo.constant dense<0> : tensor<i64>
// CHECK: %[[VAL_19:.*]]:2 = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_17]], %[[VAL_6]], %[[VAL_18]]) ({ // CHECK: %[[VAL_19:.*]]:2 = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_17]], %[[VAL_6]], %[[VAL_18]]) ({
// CHECK: ^bb0(%[[IVAL_0:.*]]: tensor<f32>, %[[IVAL_1:.*]]: tensor<i64>, %[[IVAL_2:.*]]: tensor<f32>, %[[IVAL_3:.*]]: tensor<i64>): // CHECK: ^bb0(%[[IVAL_0:.*]]: tensor<f32>, %[[IVAL_1:.*]]: tensor<i64>, %[[IVAL_2:.*]]: tensor<f32>, %[[IVAL_3:.*]]: tensor<i64>):
@ -104,7 +104,7 @@ func.func @torch.aten.max_pool2d$padding(%arg0: !torch.vtensor<[?,?,?,?],f32>) -
// CHECK: %[[IVAL_7:.*]] = mhlo.minimum %[[IVAL_1]], %[[IVAL_3]] : tensor<i64> // CHECK: %[[IVAL_7:.*]] = mhlo.minimum %[[IVAL_1]], %[[IVAL_3]] : tensor<i64>
// CHECK: %[[IVAL_8:.*]] = "mhlo.select"(%[[IVAL_4]], %[[IVAL_1]], %[[IVAL_3]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[IVAL_8:.*]] = "mhlo.select"(%[[IVAL_4]], %[[IVAL_1]], %[[IVAL_3]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: %[[IVAL_9:.*]] = "mhlo.select"(%[[IVAL_6]], %[[IVAL_7]], %[[IVAL_8]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[IVAL_9:.*]] = "mhlo.select"(%[[IVAL_6]], %[[IVAL_7]], %[[IVAL_8]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: "mhlo.return"(%[[IVAL_5]], %[[IVAL_9]]) : (tensor<f32>, tensor<i64>) -> () // CHECK: mhlo.return %[[IVAL_5]], %[[IVAL_9]] : tensor<f32>, tensor<i64>
// CHECK{LITERAL}: }) {padding = dense<0> : tensor<3x2xi64>, window_dilations = dense<1> : tensor<3xi64>, window_dimensions = dense<[1, 3, 3]> : tensor<3xi64>, window_strides = dense<[1, 2, 2]> : tensor<3xi64>} : (tensor<?x?x?xf32>, tensor<?x?x?xi64>, tensor<f32>, tensor<i64>) -> (tensor<?x?x?xf32>, tensor<?x?x?xi64>) // CHECK{LITERAL}: }) {padding = dense<0> : tensor<3x2xi64>, window_dilations = dense<1> : tensor<3xi64>, window_dimensions = dense<[1, 3, 3]> : tensor<3xi64>, window_strides = dense<[1, 2, 2]> : tensor<3xi64>} : (tensor<?x?x?xf32>, tensor<?x?x?xi64>, tensor<f32>, tensor<i64>) -> (tensor<?x?x?xf32>, tensor<?x?x?xi64>)
// CHECK: %[[VAL_20:.*]] = torch_c.from_builtin_tensor %[[VAL_19]]#0 : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32> // CHECK: %[[VAL_20:.*]] = torch_c.from_builtin_tensor %[[VAL_19]]#0 : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK: %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_19]]#1 : tensor<?x?x?xi64> -> !torch.vtensor<[?,?,?],si64> // CHECK: %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_19]]#1 : tensor<?x?x?xi64> -> !torch.vtensor<[?,?,?],si64>
@ -140,7 +140,7 @@ func.func @torch.aten.max_pool2d_with_indices(%arg0: !torch.vtensor<[?,?,?],f32>
// CHECK: %[[VAL_6:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) ({ // CHECK: %[[VAL_6:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) ({
// CHECK: ^bb0(%[[IVAL_0:.*]]: tensor<f32>, %[[IVAL_1:.*]]: tensor<f32>): // CHECK: ^bb0(%[[IVAL_0:.*]]: tensor<f32>, %[[IVAL_1:.*]]: tensor<f32>):
// CHECK: %[[IVAL_2:.*]] = mhlo.add %[[IVAL_0]], %[[IVAL_1]] : tensor<f32> // CHECK: %[[IVAL_2:.*]] = mhlo.add %[[IVAL_0]], %[[IVAL_1]] : tensor<f32>
// CHECK: "mhlo.return"(%[[IVAL_2]]) : (tensor<f32>) -> () // CHECK: mhlo.return %[[IVAL_2]] : tensor<f32>
// CHECK{LITERAL}: }) {padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<1> : tensor<4xi64>, window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>, window_strides = dense<[1, 1, 2, 2]> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32> // CHECK{LITERAL}: }) {padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<1> : tensor<4xi64>, window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>, window_strides = dense<[1, 1, 2, 2]> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_7:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32> // CHECK: %[[VAL_7:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>
// CHECK: %[[IDX_0:.*]] = arith.constant 0 : index // CHECK: %[[IDX_0:.*]] = arith.constant 0 : index
@ -161,7 +161,7 @@ func.func @torch.aten.max_pool2d_with_indices(%arg0: !torch.vtensor<[?,?,?],f32>
// CHECK: %[[VAL_19:.*]] = "mhlo.reduce_window"(%[[VAL_17]], %[[VAL_18]]) ({ // CHECK: %[[VAL_19:.*]] = "mhlo.reduce_window"(%[[VAL_17]], %[[VAL_18]]) ({
// CHECK: ^bb0(%[[IVAL_3:.*]]: tensor<f32>, %[[IVAL_4:.*]]: tensor<f32>): // CHECK: ^bb0(%[[IVAL_3:.*]]: tensor<f32>, %[[IVAL_4:.*]]: tensor<f32>):
// CHECK: %[[IVAL_5:.*]] = mhlo.add %[[IVAL_3]], %[[IVAL_4]] : tensor<f32> // CHECK: %[[IVAL_5:.*]] = mhlo.add %[[IVAL_3]], %[[IVAL_4]] : tensor<f32>
// CHECK: "mhlo.return"(%[[IVAL_5]]) : (tensor<f32>) -> () // CHECK: mhlo.return %[[IVAL_5]] : tensor<f32>
// CHECK{LITERAL}: }) {padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<1> : tensor<4xi64>, window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>, window_strides = dense<[1, 1, 2, 2]> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32> // CHECK{LITERAL}: }) {padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<1> : tensor<4xi64>, window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>, window_strides = dense<[1, 1, 2, 2]> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_20:.*]] = mhlo.divide %[[VAL_6]], %[[VAL_19]] : tensor<?x?x?x?xf32> // CHECK: %[[VAL_20:.*]] = mhlo.divide %[[VAL_6]], %[[VAL_19]] : tensor<?x?x?x?xf32>
// CHECK: %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_20]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32> // CHECK: %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_20]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
@ -196,7 +196,7 @@ func.func @torch.aten.avg_pool2d(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch
// CHECK: %[[VAL_6:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) ({ // CHECK: %[[VAL_6:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) ({
// CHECK: ^bb0(%[[IVAL_0:.*]]: tensor<f32>, %[[IVAL_1:.*]]: tensor<f32>): // CHECK: ^bb0(%[[IVAL_0:.*]]: tensor<f32>, %[[IVAL_1:.*]]: tensor<f32>):
// CHECK: %[[IVAL_2:.*]] = mhlo.add %[[IVAL_0]], %[[IVAL_1]] : tensor<f32> // CHECK: %[[IVAL_2:.*]] = mhlo.add %[[IVAL_0]], %[[IVAL_1]] : tensor<f32>
// CHECK: "mhlo.return"(%[[IVAL_2]]) : (tensor<f32>) -> () // CHECK: mhlo.return %[[IVAL_2]] : tensor<f32>
// CHECK{LITERAL}: }) {padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<1> : tensor<4xi64>, window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>, window_strides = dense<[1, 1, 2, 2]> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32> // CHECK{LITERAL}: }) {padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<1> : tensor<4xi64>, window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>, window_strides = dense<[1, 1, 2, 2]> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_7:.*]] = mhlo.constant dense<9> : tensor<i64> // CHECK: %[[VAL_7:.*]] = mhlo.constant dense<9> : tensor<i64>
// CHECK: %[[VAL_8:.*]] = mhlo.convert(%[[VAL_7]]) : (tensor<i64>) -> tensor<f32> // CHECK: %[[VAL_8:.*]] = mhlo.convert(%[[VAL_7]]) : (tensor<i64>) -> tensor<f32>

16
test/Conversion/TorchToMhlo/reduction.mlir
View File

@ -23,12 +23,12 @@
// CHECK: %[[VAL_18:.*]] = mhlo.minimum %[[VAL_12]], %[[VAL_14]] : tensor<i64> // CHECK: %[[VAL_18:.*]] = mhlo.minimum %[[VAL_12]], %[[VAL_14]] : tensor<i64>
// CHECK: %[[VAL_19:.*]] = "mhlo.select"(%[[VAL_15]], %[[VAL_12]], %[[VAL_14]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[VAL_19:.*]] = "mhlo.select"(%[[VAL_15]], %[[VAL_12]], %[[VAL_14]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: %[[VAL_20:.*]] = "mhlo.select"(%[[VAL_17]], %[[VAL_18]], %[[VAL_19]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[VAL_20:.*]] = "mhlo.select"(%[[VAL_17]], %[[VAL_18]], %[[VAL_19]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: "mhlo.return"(%[[VAL_16]], %[[VAL_20]]) : (tensor<f32>, tensor<i64>) -> () // CHECK: mhlo.return %[[VAL_16]], %[[VAL_20]] : tensor<f32>, tensor<i64>
// CHECK: } // CHECK: }
// CHECK: %[[VAL_21:.*]] = arith.constant 1 : i64 // CHECK: %[[VAL_21:.*]] = arith.constant 1 : i64
// CHECK: %[[VAL_22:.*]] = tensor.from_elements %[[VAL_3]], %[[VAL_21]] : tensor<2xi64> // CHECK: %[[VAL_22:.*]] = tensor.from_elements %[[VAL_3]], %[[VAL_21]] : tensor<2xi64>
// CHECK: %[[VAL_23:.*]] = "mhlo.dynamic_reshape"(%[[VAL_10]]#0, %[[VAL_22]]) : (tensor<?xf32>, tensor<2xi64>) -> tensor<?x1xf32> // CHECK: %[[VAL_23:.*]] = mhlo.dynamic_reshape %[[VAL_10]]#0, %[[VAL_22]] : (tensor<?xf32>, tensor<2xi64>) -> tensor<?x1xf32>
// CHECK: %[[VAL_24:.*]] = "mhlo.dynamic_reshape"(%[[VAL_10]]#1, %[[VAL_22]]) : (tensor<?xi64>, tensor<2xi64>) -> tensor<?x1xi64> // CHECK: %[[VAL_24:.*]] = mhlo.dynamic_reshape %[[VAL_10]]#1, %[[VAL_22]] : (tensor<?xi64>, tensor<2xi64>) -> tensor<?x1xi64>
// CHECK: %[[VAL_25:.*]] = torch_c.from_builtin_tensor %[[VAL_23]] : tensor<?x1xf32> -> !torch.vtensor<[?,1],f32> // CHECK: %[[VAL_25:.*]] = torch_c.from_builtin_tensor %[[VAL_23]] : tensor<?x1xf32> -> !torch.vtensor<[?,1],f32>
// CHECK: %[[VAL_26:.*]] = torch_c.from_builtin_tensor %[[VAL_24]] : tensor<?x1xi64> -> !torch.vtensor<[?,1],si64> // CHECK: %[[VAL_26:.*]] = torch_c.from_builtin_tensor %[[VAL_24]] : tensor<?x1xi64> -> !torch.vtensor<[?,1],si64>
// CHECK: return %[[VAL_25]], %[[VAL_26]] : !torch.vtensor<[?,1],f32>, !torch.vtensor<[?,1],si64> // CHECK: return %[[VAL_25]], %[[VAL_26]] : !torch.vtensor<[?,1],f32>, !torch.vtensor<[?,1],si64>
@ -64,7 +64,7 @@ func.func @torch.aten.max.dim$keepdim(%arg0: !torch.vtensor<[?,?],f32>) -> (!tor
// CHECK: %[[VAL_18:.*]] = mhlo.minimum %[[VAL_12]], %[[VAL_14]] : tensor<i64> // CHECK: %[[VAL_18:.*]] = mhlo.minimum %[[VAL_12]], %[[VAL_14]] : tensor<i64>
// CHECK: %[[VAL_19:.*]] = "mhlo.select"(%[[VAL_15]], %[[VAL_12]], %[[VAL_14]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[VAL_19:.*]] = "mhlo.select"(%[[VAL_15]], %[[VAL_12]], %[[VAL_14]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: %[[VAL_20:.*]] = "mhlo.select"(%[[VAL_17]], %[[VAL_18]], %[[VAL_19]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[VAL_20:.*]] = "mhlo.select"(%[[VAL_17]], %[[VAL_18]], %[[VAL_19]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: "mhlo.return"(%[[VAL_16]], %[[VAL_20]]) : (tensor<f32>, tensor<i64>) -> () // CHECK: mhlo.return %[[VAL_16]], %[[VAL_20]] : tensor<f32>, tensor<i64>
// CHECK: } // CHECK: }
// CHECK: %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_10]]#0 : tensor<?xf32> -> !torch.vtensor<[?],f32> // CHECK: %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_10]]#0 : tensor<?xf32> -> !torch.vtensor<[?],f32>
// CHECK: %[[VAL_22:.*]] = torch_c.from_builtin_tensor %[[VAL_10]]#1 : tensor<?xi64> -> !torch.vtensor<[?],si64> // CHECK: %[[VAL_22:.*]] = torch_c.from_builtin_tensor %[[VAL_10]]#1 : tensor<?xi64> -> !torch.vtensor<[?],si64>
@ -101,11 +101,11 @@ func.func @torch.aten.max.dim(%arg0: !torch.vtensor<[?,?],f32>) -> (!torch.vtens
// CHECK: %[[VAL_18:.*]] = mhlo.minimum %[[VAL_12]], %[[VAL_14]] : tensor<i64> // CHECK: %[[VAL_18:.*]] = mhlo.minimum %[[VAL_12]], %[[VAL_14]] : tensor<i64>
// CHECK: %[[VAL_19:.*]] = "mhlo.select"(%[[VAL_15]], %[[VAL_12]], %[[VAL_14]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[VAL_19:.*]] = "mhlo.select"(%[[VAL_15]], %[[VAL_12]], %[[VAL_14]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: %[[VAL_20:.*]] = "mhlo.select"(%[[VAL_17]], %[[VAL_18]], %[[VAL_19]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[VAL_20:.*]] = "mhlo.select"(%[[VAL_17]], %[[VAL_18]], %[[VAL_19]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: "mhlo.return"(%[[VAL_16]], %[[VAL_20]]) : (tensor<f32>, tensor<i64>) -> () // CHECK: mhlo.return %[[VAL_16]], %[[VAL_20]] : tensor<f32>, tensor<i64>
// CHECK: } // CHECK: }
// CHECK: %[[VAL_21:.*]] = arith.constant 1 : i64 // CHECK: %[[VAL_21:.*]] = arith.constant 1 : i64
// CHECK: %[[VAL_22:.*]] = tensor.from_elements %[[VAL_3]], %[[VAL_21]] : tensor<2xi64> // CHECK: %[[VAL_22:.*]] = tensor.from_elements %[[VAL_3]], %[[VAL_21]] : tensor<2xi64>
// CHECK: %[[VAL_23:.*]] = "mhlo.dynamic_reshape"(%[[VAL_10]]#1, %[[VAL_22]]) : (tensor<?xi64>, tensor<2xi64>) -> tensor<?x1xi64> // CHECK: %[[VAL_23:.*]] = mhlo.dynamic_reshape %[[VAL_10]]#1, %[[VAL_22]] : (tensor<?xi64>, tensor<2xi64>) -> tensor<?x1xi64>
// CHECK: %[[VAL_24:.*]] = torch_c.from_builtin_tensor %[[VAL_23]] : tensor<?x1xi64> -> !torch.vtensor<[?,1],si64> // CHECK: %[[VAL_24:.*]] = torch_c.from_builtin_tensor %[[VAL_23]] : tensor<?x1xi64> -> !torch.vtensor<[?,1],si64>
// CHECK: return %[[VAL_24]] : !torch.vtensor<[?,1],si64> // CHECK: return %[[VAL_24]] : !torch.vtensor<[?,1],si64>
func.func @torch.aten.argmax$keepdim(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,1],si64> { func.func @torch.aten.argmax$keepdim(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,1],si64> {
@ -140,7 +140,7 @@ func.func @torch.aten.argmax$keepdim(%arg0: !torch.vtensor<[?,?],f32>) -> !torch
// CHECK: %[[VAL_18:.*]] = mhlo.minimum %[[VAL_12]], %[[VAL_14]] : tensor<i64> // CHECK: %[[VAL_18:.*]] = mhlo.minimum %[[VAL_12]], %[[VAL_14]] : tensor<i64>
// CHECK: %[[VAL_19:.*]] = "mhlo.select"(%[[VAL_15]], %[[VAL_12]], %[[VAL_14]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[VAL_19:.*]] = "mhlo.select"(%[[VAL_15]], %[[VAL_12]], %[[VAL_14]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: %[[VAL_20:.*]] = "mhlo.select"(%[[VAL_17]], %[[VAL_18]], %[[VAL_19]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64> // CHECK: %[[VAL_20:.*]] = "mhlo.select"(%[[VAL_17]], %[[VAL_18]], %[[VAL_19]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: "mhlo.return"(%[[VAL_16]], %[[VAL_20]]) : (tensor<f32>, tensor<i64>) -> () // CHECK: mhlo.return %[[VAL_16]], %[[VAL_20]] : tensor<f32>, tensor<i64>
// CHECK: } // CHECK: }
// CHECK: %[[VAL_11:.*]] = torch_c.from_builtin_tensor %[[VAL_10]]#1 : tensor<?xi64> -> !torch.vtensor<[?],si64> // CHECK: %[[VAL_11:.*]] = torch_c.from_builtin_tensor %[[VAL_10]]#1 : tensor<?xi64> -> !torch.vtensor<[?],si64>
// CHECK: return %[[VAL_11]] : !torch.vtensor<[?],si64> // CHECK: return %[[VAL_11]] : !torch.vtensor<[?],si64>
@ -174,7 +174,7 @@ func.func @torch.aten.argmax(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor
// CHECK: %[[VAL_10:.*]] = arith.index_cast %[[VAL_9]] : index to i64 // CHECK: %[[VAL_10:.*]] = arith.index_cast %[[VAL_9]] : index to i64
// CHECK: %[[ONE_0:.*]] = arith.constant 1 : i64 // CHECK: %[[ONE_0:.*]] = arith.constant 1 : i64
// CHECK: %[[VAL_11:.*]] = tensor.from_elements %[[ONE_0]], %[[ONE_0]], %[[VAL_10]] : tensor<3xi64> // CHECK: %[[VAL_11:.*]] = tensor.from_elements %[[ONE_0]], %[[ONE_0]], %[[VAL_10]] : tensor<3xi64>
// CHECK: %[[VAL_12:.*]] = "mhlo.dynamic_reshape"(%[[VAL_4]], %[[VAL_11]]) : (tensor<?xf32>, tensor<3xi64>) -> tensor<1x1x?xf32> // CHECK: %[[VAL_12:.*]] = mhlo.dynamic_reshape %[[VAL_4]], %[[VAL_11]] : (tensor<?xf32>, tensor<3xi64>) -> tensor<1x1x?xf32>
// CHECK: %[[VAL_13:.*]] = torch_c.from_builtin_tensor %[[VAL_12]] : tensor<1x1x?xf32> -> !torch.vtensor<[1,1,?],f32> // CHECK: %[[VAL_13:.*]] = torch_c.from_builtin_tensor %[[VAL_12]] : tensor<1x1x?xf32> -> !torch.vtensor<[1,1,?],f32>
// CHECK: return %[[VAL_13]] : !torch.vtensor<[1,1,?],f32> // CHECK: return %[[VAL_13]] : !torch.vtensor<[1,1,?],f32>
func.func @torch.aten.sum.dim_Intlist$keepdim(%arg0: !torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[1,1,?],f32> { func.func @torch.aten.sum.dim_Intlist$keepdim(%arg0: !torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[1,1,?],f32> {

34
test/Conversion/TorchToMhlo/view_like.mlir
View File

@ -42,7 +42,7 @@
// CHECK: %[[T26:.*]] = tensor.from_elements %[[T11]], %[[C0_I64_2]], %[[C0_I64_2]] : tensor<3xi64> // CHECK: %[[T26:.*]] = tensor.from_elements %[[T11]], %[[C0_I64_2]], %[[C0_I64_2]] : tensor<3xi64>
// CHECK: %[[T27:.*]] = tensor.from_elements %[[T25]], %[[T21]], %[[T23]] : tensor<3xi64> // CHECK: %[[T27:.*]] = tensor.from_elements %[[T25]], %[[T21]], %[[T23]] : tensor<3xi64>
// CHECK: %[[T28:.*]] = tensor.from_elements %[[T2]], %[[C1_I64]], %[[C1_I64]] : tensor<3xi64> // CHECK: %[[T28:.*]] = tensor.from_elements %[[T2]], %[[C1_I64]], %[[C1_I64]] : tensor<3xi64>
// CHECK: %[[T29:.*]] = "mhlo.real_dynamic_slice"(%[[T0]], %[[T26]], %[[T27]], %[[T28]]) : (tensor<?x?x?xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<?x?x?xf32> // CHECK: %[[T29:.*]] = mhlo.real_dynamic_slice %[[T0]], %[[T26]], %[[T27]], %[[T28]] : (tensor<?x?x?xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<?x?x?xf32>
// CHECK: %[[T30:.*]] = mhlo.convert %[[T29]] : tensor<?x?x?xf32> // CHECK: %[[T30:.*]] = mhlo.convert %[[T29]] : tensor<?x?x?xf32>
// CHECK: %[[T31:.*]] = torch_c.from_builtin_tensor %[[T30]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32> // CHECK: %[[T31:.*]] = torch_c.from_builtin_tensor %[[T30]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK: return %[[T31]] : !torch.vtensor<[?,?,?],f32> // CHECK: return %[[T31]] : !torch.vtensor<[?,?,?],f32>
@ -96,7 +96,7 @@ func.func @torch.aten.slice.strided$slice_like(%arg0: !torch.vtensor<[?,?,?],f32
// CHECK: %[[T26:.*]] = tensor.from_elements %[[T11]], %[[C0_I64_2]], %[[C0_I64_2]] : tensor<3xi64> // CHECK: %[[T26:.*]] = tensor.from_elements %[[T11]], %[[C0_I64_2]], %[[C0_I64_2]] : tensor<3xi64>
// CHECK: %[[T27:.*]] = tensor.from_elements %[[T25]], %[[T21]], %[[T23]] : tensor<3xi64> // CHECK: %[[T27:.*]] = tensor.from_elements %[[T25]], %[[T21]], %[[T23]] : tensor<3xi64>
// CHECK: %[[T28:.*]] = tensor.from_elements %[[T2]], %[[C1_I64]], %[[C1_I64]] : tensor<3xi64> // CHECK: %[[T28:.*]] = tensor.from_elements %[[T2]], %[[C1_I64]], %[[C1_I64]] : tensor<3xi64>
// CHECK: %[[T29:.*]] = "mhlo.real_dynamic_slice"(%[[T0]], %[[T26]], %[[T27]], %[[T28]]) : (tensor<4x65x256xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<?x65x256xf32> // CHECK: %[[T29:.*]] = mhlo.real_dynamic_slice %[[T0]], %[[T26]], %[[T27]], %[[T28]] : (tensor<4x65x256xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<?x65x256xf32>
// CHECK: %[[T30:.*]] = mhlo.convert(%[[T29]]) : (tensor<?x65x256xf32>) -> tensor<2x65x256xf32> // CHECK: %[[T30:.*]] = mhlo.convert(%[[T29]]) : (tensor<?x65x256xf32>) -> tensor<2x65x256xf32>
// CHECK: %[[T31:.*]] = torch_c.from_builtin_tensor %[[T30]] : tensor<2x65x256xf32> -> !torch.vtensor<[2,65,256],f32> // CHECK: %[[T31:.*]] = torch_c.from_builtin_tensor %[[T30]] : tensor<2x65x256xf32> -> !torch.vtensor<[2,65,256],f32>
// CHECK: return %[[T31]] : !torch.vtensor<[2,65,256],f32> // CHECK: return %[[T31]] : !torch.vtensor<[2,65,256],f32>
@ -151,7 +151,7 @@ func.func @torch.aten.slice.strided.static$slice_like(%arg0: !torch.vtensor<[4,6
// CHECK: %[[T26:.*]] = tensor.from_elements %[[C0_I64_2]], %[[T11]], %[[C0_I64_2]] : tensor<3xi64> // CHECK: %[[T26:.*]] = tensor.from_elements %[[C0_I64_2]], %[[T11]], %[[C0_I64_2]] : tensor<3xi64>
// CHECK: %[[T27:.*]] = tensor.from_elements %[[T19]], %[[T25]], %[[T23]] : tensor<3xi64> // CHECK: %[[T27:.*]] = tensor.from_elements %[[T19]], %[[T25]], %[[T23]] : tensor<3xi64>
// CHECK: %[[T28:.*]] = tensor.from_elements %[[C1_I64]], %[[T2]], %[[C1_I64]] : tensor<3xi64> // CHECK: %[[T28:.*]] = tensor.from_elements %[[C1_I64]], %[[T2]], %[[C1_I64]] : tensor<3xi64>
// CHECK: %[[T29:.*]] = "mhlo.real_dynamic_slice"(%[[T0]], %[[T26]], %[[T27]], %[[T28]]) : (tensor<?x?x?xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<?x?x?xf32> // CHECK: %[[T29:.*]] = mhlo.real_dynamic_slice %[[T0]], %[[T26]], %[[T27]], %[[T28]] : (tensor<?x?x?xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<?x?x?xf32>
// CHECK: %[[T30:.*]] = mhlo.convert(%[[T29]]) : (tensor<?x?x?xf32>) -> tensor<?x1x?xf32> // CHECK: %[[T30:.*]] = mhlo.convert(%[[T29]]) : (tensor<?x?x?xf32>) -> tensor<?x1x?xf32>
// CHECK: %[[T31:.*]] = torch_c.from_builtin_tensor %[[T30]] : tensor<?x1x?xf32> -> !torch.vtensor<[?,1,?],f32> // CHECK: %[[T31:.*]] = torch_c.from_builtin_tensor %[[T30]] : tensor<?x1x?xf32> -> !torch.vtensor<[?,1,?],f32>
// CHECK: return %[[T31]] : !torch.vtensor<[?,1,?],f32> // CHECK: return %[[T31]] : !torch.vtensor<[?,1,?],f32>
@ -206,7 +206,7 @@ func.func @torch.aten.slice.last$slice_like(%arg0: !torch.vtensor<[?,?,?],f32>)
// CHECK: %[[T26:.*]] = tensor.from_elements %[[C0_I64_2]], %[[T11]], %[[C0_I64_2]] : tensor<3xi64> // CHECK: %[[T26:.*]] = tensor.from_elements %[[C0_I64_2]], %[[T11]], %[[C0_I64_2]] : tensor<3xi64>
// CHECK: %[[T27:.*]] = tensor.from_elements %[[T19]], %[[T25]], %[[T23]] : tensor<3xi64> // CHECK: %[[T27:.*]] = tensor.from_elements %[[T19]], %[[T25]], %[[T23]] : tensor<3xi64>
// CHECK: %[[T28:.*]] = tensor.from_elements %[[C1_I64]], %[[T2]], %[[C1_I64]] : tensor<3xi64> // CHECK: %[[T28:.*]] = tensor.from_elements %[[C1_I64]], %[[T2]], %[[C1_I64]] : tensor<3xi64>
// CHECK: %[[T29:.*]] = "mhlo.real_dynamic_slice"(%[[T0]], %[[T26]], %[[T27]], %[[T28]]) : (tensor<4x65x256xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<4x?x256xf32> // CHECK: %[[T29:.*]] = mhlo.real_dynamic_slice %[[T0]], %[[T26]], %[[T27]], %[[T28]] : (tensor<4x65x256xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<4x?x256xf32>
// CHECK: %[[T30:.*]] = mhlo.convert(%[[T29]]) : (tensor<4x?x256xf32>) -> tensor<4x1x256xf32> // CHECK: %[[T30:.*]] = mhlo.convert(%[[T29]]) : (tensor<4x?x256xf32>) -> tensor<4x1x256xf32>
// CHECK: %[[T31:.*]] = torch_c.from_builtin_tensor %[[T30]] : tensor<4x1x256xf32> -> !torch.vtensor<[4,1,256],f32> // CHECK: %[[T31:.*]] = torch_c.from_builtin_tensor %[[T30]] : tensor<4x1x256xf32> -> !torch.vtensor<[4,1,256],f32>
// CHECK: return %[[T31]] : !torch.vtensor<[4,1,256],f32> // CHECK: return %[[T31]] : !torch.vtensor<[4,1,256],f32>
@ -246,7 +246,7 @@ func.func @torch.aten.slice.last.static$slice_like(%arg0: !torch.vtensor<[4,65,2
// CHECK: %[[T12:.*]] = tensor.from_elements %[[C0_I64_1]], %[[C0_I64]], %[[C0_I64_1]] : tensor<3xi64> // CHECK: %[[T12:.*]] = tensor.from_elements %[[C0_I64_1]], %[[C0_I64]], %[[C0_I64_1]] : tensor<3xi64>
// CHECK: %[[T13:.*]] = tensor.from_elements %[[T5]], %[[T11]], %[[T9]] : tensor<3xi64> // CHECK: %[[T13:.*]] = tensor.from_elements %[[T5]], %[[T11]], %[[T9]] : tensor<3xi64>
// CHECK: %[[T14:.*]] = tensor.from_elements %[[C1_I64]], %[[T1]], %[[C1_I64]] : tensor<3xi64> // CHECK: %[[T14:.*]] = tensor.from_elements %[[C1_I64]], %[[T1]], %[[C1_I64]] : tensor<3xi64>
// CHECK: %[[T15:.*]] = "mhlo.real_dynamic_slice"(%[[T0]], %[[T12]], %[[T13]], %[[T14]]) : (tensor<?x?x?xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<?x?x?xf32> // CHECK: %[[T15:.*]] = mhlo.real_dynamic_slice %[[T0]], %[[T12]], %[[T13]], %[[T14]] : (tensor<?x?x?xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<?x?x?xf32>
// CHECK: %[[T16:.*]] = mhlo.convert %[[T15]] : tensor<?x?x?xf32> // CHECK: %[[T16:.*]] = mhlo.convert %[[T15]] : tensor<?x?x?xf32>
// CHECK: %[[T17:.*]] = torch_c.from_builtin_tensor %[[T16]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32> // CHECK: %[[T17:.*]] = torch_c.from_builtin_tensor %[[T16]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK: return %[[T17]] : !torch.vtensor<[?,?,?],f32> // CHECK: return %[[T17]] : !torch.vtensor<[?,?,?],f32>
@ -285,7 +285,7 @@ func.func @torch.aten.slice.none$slice_like(%arg0: !torch.vtensor<[?,?,?],f32>)
// CHECK: %[[T12:.*]] = tensor.from_elements %[[C0_I64_1]], %[[C0_I64]], %[[C0_I64_1]] : tensor<3xi64> // CHECK: %[[T12:.*]] = tensor.from_elements %[[C0_I64_1]], %[[C0_I64]], %[[C0_I64_1]] : tensor<3xi64>
// CHECK: %[[T13:.*]] = tensor.from_elements %[[T5]], %[[T11]], %[[T9]] : tensor<3xi64> // CHECK: %[[T13:.*]] = tensor.from_elements %[[T5]], %[[T11]], %[[T9]] : tensor<3xi64>
// CHECK: %[[T14:.*]] = tensor.from_elements %[[C1_I64]], %[[T1]], %[[C1_I64]] : tensor<3xi64> // CHECK: %[[T14:.*]] = tensor.from_elements %[[C1_I64]], %[[T1]], %[[C1_I64]] : tensor<3xi64>
// CHECK: %[[T15:.*]] = "mhlo.real_dynamic_slice"(%[[T0]], %[[T12]], %[[T13]], %[[T14]]) : (tensor<4x65x256xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<4x?x256xf32> // CHECK: %[[T15:.*]] = mhlo.real_dynamic_slice %[[T0]], %[[T12]], %[[T13]], %[[T14]] : (tensor<4x65x256xf32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<4x?x256xf32>
// CHECK: %[[T16:.*]] = mhlo.convert(%[[T15]]) : (tensor<4x?x256xf32>) -> tensor<4x33x256xf32> // CHECK: %[[T16:.*]] = mhlo.convert(%[[T15]]) : (tensor<4x?x256xf32>) -> tensor<4x33x256xf32>
// CHECK: %[[T17:.*]] = torch_c.from_builtin_tensor %[[T16]] : tensor<4x33x256xf32> -> !torch.vtensor<[4,33,256],f32> // CHECK: %[[T17:.*]] = torch_c.from_builtin_tensor %[[T16]] : tensor<4x33x256xf32> -> !torch.vtensor<[4,33,256],f32>
// CHECK: return %[[T17]] : !torch.vtensor<[4,33,256],f32> // CHECK: return %[[T17]] : !torch.vtensor<[4,33,256],f32>
@ -311,7 +311,7 @@ func.func @torch.aten.slice.none.static$slice_like(%arg0: !torch.vtensor<[4,65,2
// CHECK: %[[T6:.*]] = arith.index_cast %[[T5]] : i64 to index // CHECK: %[[T6:.*]] = arith.index_cast %[[T5]] : i64 to index
// CHECK: %[[T7:.*]] = tensor.from_elements %[[T2]], %[[T3]] : tensor<2xi64> // CHECK: %[[T7:.*]] = tensor.from_elements %[[T2]], %[[T3]] : tensor<2xi64>
// CHECK: %[[T8:.*]] = mhlo.compute_reshape_shape %[[T6]], %[[T7]] : index, tensor<2xi64> -> tensor<2xi64> // CHECK: %[[T8:.*]] = mhlo.compute_reshape_shape %[[T6]], %[[T7]] : index, tensor<2xi64> -> tensor<2xi64>
// CHECK: %[[T9:.*]] = "mhlo.dynamic_reshape"(%[[T0]], %[[T8]]) : (tensor<?x?x?x?xf32>, tensor<2xi64>) -> tensor<?x224xf32> // CHECK: %[[T9:.*]] = mhlo.dynamic_reshape %[[T0]], %[[T8]] : (tensor<?x?x?x?xf32>, tensor<2xi64>) -> tensor<?x224xf32>
// CHECK: %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<?x224xf32> -> !torch.vtensor<[?,224],f32> // CHECK: %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<?x224xf32> -> !torch.vtensor<[?,224],f32>
// CHECK: return %[[T10]] : !torch.vtensor<[?,224],f32> // CHECK: return %[[T10]] : !torch.vtensor<[?,224],f32>
func.func @torch.aten.view$basic(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,224],f32> { func.func @torch.aten.view$basic(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,224],f32> {
@ -342,7 +342,7 @@ func.func @torch.aten.view$basic(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch
// CHECK: %[[T10:.*]] = arith.index_cast %[[T9]] : i64 to index // CHECK: %[[T10:.*]] = arith.index_cast %[[T9]] : i64 to index
// CHECK: %[[T11:.*]] = tensor.from_elements %[[T2]], %[[T3]], %[[T4]], %[[T5]] : tensor<4xi64> // CHECK: %[[T11:.*]] = tensor.from_elements %[[T2]], %[[T3]], %[[T4]], %[[T5]] : tensor<4xi64>
// CHECK: %[[T12:.*]] = mhlo.compute_reshape_shape %[[T10]], %[[T11]] : index, tensor<4xi64> -> tensor<4xi64> // CHECK: %[[T12:.*]] = mhlo.compute_reshape_shape %[[T10]], %[[T11]] : index, tensor<4xi64> -> tensor<4xi64>
// CHECK: %[[T13:.*]] = "mhlo.dynamic_reshape"(%[[T0]], %[[T12]]) : (tensor<?x?x?x?x?xf32>, tensor<4xi64>) -> tensor<?x120x4x64xf32> // CHECK: %[[T13:.*]] = mhlo.dynamic_reshape %[[T0]], %[[T12]] : (tensor<?x?x?x?x?xf32>, tensor<4xi64>) -> tensor<?x120x4x64xf32>
// CHECK: %[[T14:.*]] = torch_c.from_builtin_tensor %[[T13]] : tensor<?x120x4x64xf32> -> !torch.vtensor<[?,120,4,64],f32> // CHECK: %[[T14:.*]] = torch_c.from_builtin_tensor %[[T13]] : tensor<?x120x4x64xf32> -> !torch.vtensor<[?,120,4,64],f32>
// CHECK: return %[[T14]] : !torch.vtensor<[?,120,4,64],f32> // CHECK: return %[[T14]] : !torch.vtensor<[?,120,4,64],f32>
func.func @torch.aten.reshape$basic(%arg0: !torch.vtensor<[?,?,?,?,?],f32>) -> !torch.vtensor<[?,120,4,64],f32> { func.func @torch.aten.reshape$basic(%arg0: !torch.vtensor<[?,?,?,?,?],f32>) -> !torch.vtensor<[?,120,4,64],f32> {
@ -382,7 +382,7 @@ func.func @torch.aten.reshape$basic(%arg0: !torch.vtensor<[?,?,?,?,?],f32>) -> !
// CHECK: %[[T10:.*]] = arith.index_cast %[[T9]] : i64 to index // CHECK: %[[T10:.*]] = arith.index_cast %[[T9]] : i64 to index
// CHECK: %[[T11:.*]] = tensor.from_elements %[[T4]], %[[T5]], %[[T6]] : tensor<3xi64> // CHECK: %[[T11:.*]] = tensor.from_elements %[[T4]], %[[T5]], %[[T6]] : tensor<3xi64>
// CHECK: %[[T12:.*]] = mhlo.compute_reshape_shape %[[T10]], %[[T11]] : index, tensor<3xi64> -> tensor<3xi64> // CHECK: %[[T12:.*]] = mhlo.compute_reshape_shape %[[T10]], %[[T11]] : index, tensor<3xi64> -> tensor<3xi64>
// CHECK: %[[T13:.*]] = "mhlo.dynamic_reshape"(%[[T0]], %[[T12]]) : (tensor<2x3x?x?xf32>, tensor<3xi64>) -> tensor<2x3x?xf32> // CHECK: %[[T13:.*]] = mhlo.dynamic_reshape %[[T0]], %[[T12]] : (tensor<2x3x?x?xf32>, tensor<3xi64>) -> tensor<2x3x?xf32>
// CHECK: %[[T14:.*]] = torch_c.from_builtin_tensor %[[T13]] : tensor<2x3x?xf32> -> !torch.vtensor<[2,3,?],f32> // CHECK: %[[T14:.*]] = torch_c.from_builtin_tensor %[[T13]] : tensor<2x3x?xf32> -> !torch.vtensor<[2,3,?],f32>
// CHECK: return %[[T14]] : !torch.vtensor<[2,3,?],f32> // CHECK: return %[[T14]] : !torch.vtensor<[2,3,?],f32>
func.func @torch.aten.view$minus1(%arg0: !torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[2,3,?],f32> { func.func @torch.aten.view$minus1(%arg0: !torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[2,3,?],f32> {
@ -401,7 +401,7 @@ func.func @torch.aten.view$minus1(%arg0: !torch.vtensor<[2,3,?,?],f32>) -> !torc
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[],f32> -> tensor<f32> // CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[],f32> -> tensor<f32>
// CHECK: %[[INT1:.*]] = torch.constant.int 1 // CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: %[[T1:.*]] = torch.prim.ListConstruct %[[INT1]] : (!torch.int) -> !torch.list<int> // CHECK: %[[T1:.*]] = torch.prim.ListConstruct %[[INT1]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[T2:.*]] = "mhlo.reshape"(%[[T0]]) : (tensor<f32>) -> tensor<1xf32> // CHECK: %[[T2:.*]] = mhlo.reshape %[[T0]] : (tensor<f32>) -> tensor<1xf32>
// CHECK: %[[T3:.*]] = torch_c.from_builtin_tensor %[[T2]] : tensor<1xf32> -> !torch.vtensor<[1],f32> // CHECK: %[[T3:.*]] = torch_c.from_builtin_tensor %[[T2]] : tensor<1xf32> -> !torch.vtensor<[1],f32>
// CHECK: return %[[T3]] : !torch.vtensor<[1],f32> // CHECK: return %[[T3]] : !torch.vtensor<[1],f32>
func.func @torch.aten.view$to_rank1(%arg0: !torch.vtensor<[],f32>) -> !torch.vtensor<[1],f32> { func.func @torch.aten.view$to_rank1(%arg0: !torch.vtensor<[],f32>) -> !torch.vtensor<[1],f32> {
@ -414,7 +414,7 @@ func.func @torch.aten.view$to_rank1(%arg0: !torch.vtensor<[],f32>) -> !torch.vte
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[1],f32>) -> !torch.vtensor<[],f32> { // CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[1],f32>) -> !torch.vtensor<[],f32> {
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[1],f32> -> tensor<1xf32> // CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[1],f32> -> tensor<1xf32>
// CHECK: %[[T1:.*]] = torch.prim.ListConstruct : () -> !torch.list<int> // CHECK: %[[T1:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[T2:.*]] = "mhlo.reshape"(%[[T0]]) : (tensor<1xf32>) -> tensor<f32> // CHECK: %[[T2:.*]] = mhlo.reshape %[[T0]] : (tensor<1xf32>) -> tensor<f32>
// CHECK: %[[T3:.*]] = torch_c.from_builtin_tensor %[[T2]] : tensor<f32> -> !torch.vtensor<[],f32> // CHECK: %[[T3:.*]] = torch_c.from_builtin_tensor %[[T2]] : tensor<f32> -> !torch.vtensor<[],f32>
// CHECK: return %[[T3]] : !torch.vtensor<[],f32> // CHECK: return %[[T3]] : !torch.vtensor<[],f32>
func.func @torch.aten.view$to_rank0(%arg0: !torch.vtensor<[1],f32>) -> !torch.vtensor<[],f32> { func.func @torch.aten.view$to_rank0(%arg0: !torch.vtensor<[1],f32>) -> !torch.vtensor<[],f32> {
@ -451,7 +451,7 @@ func.func @torch.aten.squeeze.dim$0$static(%arg0: !torch.vtensor<[2,1,2,1,2],f32
// CHECK: %[[T7:.*]] = tensor.dim %[[T0]], %[[C4]] : tensor<?x1x?x1x?xf32> // CHECK: %[[T7:.*]] = tensor.dim %[[T0]], %[[C4]] : tensor<?x1x?x1x?xf32>
// CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64 // CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64
// CHECK: %[[T9:.*]] = tensor.from_elements %[[T2]], %[[T4]], %[[T6]], %[[T8]] : tensor<4xi64> // CHECK: %[[T9:.*]] = tensor.from_elements %[[T2]], %[[T4]], %[[T6]], %[[T8]] : tensor<4xi64>
// CHECK: %[[T10:.*]] = "mhlo.dynamic_reshape"(%[[T0]], %[[T9]]) : (tensor<?x1x?x1x?xf32>, tensor<4xi64>) -> tensor<?x?x1x?xf32> // CHECK: %[[T10:.*]] = mhlo.dynamic_reshape %[[T0]], %[[T9]] : (tensor<?x1x?x1x?xf32>, tensor<4xi64>) -> tensor<?x?x1x?xf32>
// CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x?x1x?xf32> -> !torch.vtensor<[?,?,1,?],f32> // CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x?x1x?xf32> -> !torch.vtensor<[?,?,1,?],f32>
// CHECK: return %[[T11]] : !torch.vtensor<[?,?,1,?],f32> // CHECK: return %[[T11]] : !torch.vtensor<[?,?,1,?],f32>
func.func @torch.aten.squeeze.dim$1(%arg0: !torch.vtensor<[?,1,?,1,?],f32>) -> !torch.vtensor<[?,?,1,?],f32> { func.func @torch.aten.squeeze.dim$1(%arg0: !torch.vtensor<[?,1,?,1,?],f32>) -> !torch.vtensor<[?,?,1,?],f32> {
@ -477,7 +477,7 @@ func.func @torch.aten.squeeze.dim$1(%arg0: !torch.vtensor<[?,1,?,1,?],f32>) -> !
// CHECK: %[[T7:.*]] = tensor.dim %[[T0]], %[[C4]] : tensor<?x1x?x1x?xf32> // CHECK: %[[T7:.*]] = tensor.dim %[[T0]], %[[C4]] : tensor<?x1x?x1x?xf32>
// CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64 // CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64
// CHECK: %[[T9:.*]] = tensor.from_elements %[[T2]], %[[T4]], %[[T6]], %[[T8]] : tensor<4xi64> // CHECK: %[[T9:.*]] = tensor.from_elements %[[T2]], %[[T4]], %[[T6]], %[[T8]] : tensor<4xi64>
// CHECK: %[[T10:.*]] = "mhlo.dynamic_reshape"(%[[T0]], %[[T9]]) : (tensor<?x1x?x1x?xf32>, tensor<4xi64>) -> tensor<?x1x?x?xf32> // CHECK: %[[T10:.*]] = mhlo.dynamic_reshape %[[T0]], %[[T9]] : (tensor<?x1x?x1x?xf32>, tensor<4xi64>) -> tensor<?x1x?x?xf32>
// CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x1x?x?xf32> -> !torch.vtensor<[?,1,?,?],f32> // CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x1x?x?xf32> -> !torch.vtensor<[?,1,?,?],f32>
// CHECK: return %[[T11]] : !torch.vtensor<[?,1,?,?],f32> // CHECK: return %[[T11]] : !torch.vtensor<[?,1,?,?],f32>
func.func @torch.aten.squeeze.dim$from_end(%arg0: !torch.vtensor<[?,1,?,1,?],f32>) -> !torch.vtensor<[?,1,?,?],f32> { func.func @torch.aten.squeeze.dim$from_end(%arg0: !torch.vtensor<[?,1,?,1,?],f32>) -> !torch.vtensor<[?,1,?,?],f32> {
@ -499,7 +499,7 @@ func.func @torch.aten.squeeze.dim$from_end(%arg0: !torch.vtensor<[?,1,?,1,?],f32
// CHECK: %[[T5:.*]] = tensor.dim %[[T0]], %[[C4]] : tensor<2x1x2x1x2xf32> // CHECK: %[[T5:.*]] = tensor.dim %[[T0]], %[[C4]] : tensor<2x1x2x1x2xf32>
// CHECK: %[[T6:.*]] = arith.index_cast %[[T5]] : index to i64 // CHECK: %[[T6:.*]] = arith.index_cast %[[T5]] : index to i64
// CHECK: %[[T7:.*]] = tensor.from_elements %[[T2]], %[[T4]], %[[T6]] : tensor<3xi64> // CHECK: %[[T7:.*]] = tensor.from_elements %[[T2]], %[[T4]], %[[T6]] : tensor<3xi64>
// CHECK: %[[T8:.*]] = "mhlo.dynamic_reshape"(%[[T0]], %[[T7]]) : (tensor<2x1x2x1x2xf32>, tensor<3xi64>) -> tensor<2x2x2xf32> // CHECK: %[[T8:.*]] = mhlo.dynamic_reshape %[[T0]], %[[T7]] : (tensor<2x1x2x1x2xf32>, tensor<3xi64>) -> tensor<2x2x2xf32>
// CHECK: %[[T9:.*]] = torch_c.from_builtin_tensor %[[T8]] : tensor<2x2x2xf32> -> !torch.vtensor<[2,2,2],f32> // CHECK: %[[T9:.*]] = torch_c.from_builtin_tensor %[[T8]] : tensor<2x2x2xf32> -> !torch.vtensor<[2,2,2],f32>
// CHECK: return %[[T9]] : !torch.vtensor<[2,2,2],f32> // CHECK: return %[[T9]] : !torch.vtensor<[2,2,2],f32>
func.func @torch.aten.squeeze$static(%arg0: !torch.vtensor<[2,1,2,1,2],f32>) -> !torch.vtensor<[2,2,2],f32> { func.func @torch.aten.squeeze$static(%arg0: !torch.vtensor<[2,1,2,1,2],f32>) -> !torch.vtensor<[2,2,2],f32> {
@ -525,7 +525,7 @@ func.func @torch.aten.squeeze$static(%arg0: !torch.vtensor<[2,1,2,1,2],f32>) ->
// CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64 // CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64
// CHECK: %[[C1_I64:.*]] = arith.constant 1 : i64 // CHECK: %[[C1_I64:.*]] = arith.constant 1 : i64
// CHECK: %[[T9:.*]] = tensor.from_elements %[[C1_I64]], %[[T2]], %[[T4]], %[[T6]], %[[T8]] : tensor<5xi64> // CHECK: %[[T9:.*]] = tensor.from_elements %[[C1_I64]], %[[T2]], %[[T4]], %[[T6]], %[[T8]] : tensor<5xi64>
// CHECK: %[[T10:.*]] = "mhlo.dynamic_reshape"(%[[T0]], %[[T9]]) : (tensor<?x?x?x?xf32>, tensor<5xi64>) -> tensor<1x?x?x?x?xf32> // CHECK: %[[T10:.*]] = mhlo.dynamic_reshape %[[T0]], %[[T9]] : (tensor<?x?x?x?xf32>, tensor<5xi64>) -> tensor<1x?x?x?x?xf32>
// CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<1x?x?x?x?xf32> -> !torch.vtensor<[1,?,?,?,?],f32> // CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<1x?x?x?x?xf32> -> !torch.vtensor<[1,?,?,?,?],f32>
// CHECK: return %[[T11]] : !torch.vtensor<[1,?,?,?,?],f32> // CHECK: return %[[T11]] : !torch.vtensor<[1,?,?,?,?],f32>
func.func @torch.aten.unsqueeze$dim$0(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[1,?,?,?,?],f32> { func.func @torch.aten.unsqueeze$dim$0(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[1,?,?,?,?],f32> {
@ -552,7 +552,7 @@ func.func @torch.aten.unsqueeze$dim$0(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !
// CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64 // CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64
// CHECK: %[[C1_I64:.*]] = arith.constant 1 : i64 // CHECK: %[[C1_I64:.*]] = arith.constant 1 : i64
// CHECK: %[[T9:.*]] = tensor.from_elements %[[T2]], %[[C1_I64]], %[[T4]], %[[T6]], %[[T8]] : tensor<5xi64> // CHECK: %[[T9:.*]] = tensor.from_elements %[[T2]], %[[C1_I64]], %[[T4]], %[[T6]], %[[T8]] : tensor<5xi64>
// CHECK: %[[T10:.*]] = "mhlo.dynamic_reshape"(%[[T0]], %[[T9]]) : (tensor<?x?x?x?xf32>, tensor<5xi64>) -> tensor<?x1x?x?x?xf32> // CHECK: %[[T10:.*]] = mhlo.dynamic_reshape %[[T0]], %[[T9]] : (tensor<?x?x?x?xf32>, tensor<5xi64>) -> tensor<?x1x?x?x?xf32>
// CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x1x?x?x?xf32> -> !torch.vtensor<[?,1,?,?,?],f32> // CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x1x?x?x?xf32> -> !torch.vtensor<[?,1,?,?,?],f32>
// CHECK: return %[[T11]] : !torch.vtensor<[?,1,?,?,?],f32> // CHECK: return %[[T11]] : !torch.vtensor<[?,1,?,?,?],f32>
func.func @torch.aten.unsqueeze$dim$1(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,1,?,?,?],f32> { func.func @torch.aten.unsqueeze$dim$1(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,1,?,?,?],f32> {
@ -579,7 +579,7 @@ func.func @torch.aten.unsqueeze$dim$1(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !
// CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64 // CHECK: %[[T8:.*]] = arith.index_cast %[[T7]] : index to i64
// CHECK: %[[C1_I64:.*]] = arith.constant 1 : i64 // CHECK: %[[C1_I64:.*]] = arith.constant 1 : i64
// CHECK: %[[T9:.*]] = tensor.from_elements %[[T2]], %[[T4]], %[[T6]], %[[C1_I64]], %[[T8]] : tensor<5xi64> // CHECK: %[[T9:.*]] = tensor.from_elements %[[T2]], %[[T4]], %[[T6]], %[[C1_I64]], %[[T8]] : tensor<5xi64>
// CHECK: %[[T10:.*]] = "mhlo.dynamic_reshape"(%[[T0]], %[[T9]]) : (tensor<?x?x?x?xf32>, tensor<5xi64>) -> tensor<?x?x?x1x?xf32> // CHECK: %[[T10:.*]] = mhlo.dynamic_reshape %[[T0]], %[[T9]] : (tensor<?x?x?x?xf32>, tensor<5xi64>) -> tensor<?x?x?x1x?xf32>
// CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x?x?x1x?xf32> -> !torch.vtensor<[?,?,?,1,?],f32> // CHECK: %[[T11:.*]] = torch_c.from_builtin_tensor %[[T10]] : tensor<?x?x?x1x?xf32> -> !torch.vtensor<[?,?,?,1,?],f32>
// CHECK: return %[[T11]] : !torch.vtensor<[?,?,?,1,?],f32> // CHECK: return %[[T11]] : !torch.vtensor<[?,?,?,1,?],f32>
func.func @torch.aten.unsqueeze$from_end(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,1,?],f32> { func.func @torch.aten.unsqueeze$from_end(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,1,?],f32> {