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Fix dynamic shapes type verifications (#1409) 

* Fix dynamic shapes type verifications
pull/1371/head
Tanyo Kwok 2022-09-23 20:50:29 +08:00 committed by GitHub
parent 72e422b589
commit 16dd7e2e5f
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 126 additions and 52 deletions
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17
lib/Conversion/TorchToMhlo/Basic.cpp
View File

@ -878,10 +878,19 @@ LogicalResult ConvertAtenOp<AtenBatchNormOp>::matchAndRewrite(
return success();
} else {
Type outputTy = getTypeConverter()->convertType(op.getType());
rewriter.replaceOpWithNewOp<mhlo::BatchNormInferenceOp>(
op, outputTy, input, weight, bias, runningMean, runningVar,
rewriter.getFloatAttr(inputTy.getElementType(), eps),
rewriter.getI64IntegerAttr(1));
SmallVector<int64_t, 4> castShape{inputTy.getShape().begin(),
inputTy.getShape().end()};
castShape[1] = weightTy.getShape()[0];
auto castTy = RankedTensorType::get(castShape, inputTy.getElementType());
// Feature counts must match among operands of mhlo::BatchNormInferenceOp.
Value inputCasted =
rewriter.create<tensor::CastOp>(op.getLoc(), castTy, input);
Value output = rewriter.create<mhlo::BatchNormInferenceOp>(
op.getLoc(), inputCasted.getType(), inputCasted, weight, bias,
runningMean, runningVar,
// 'epsilon' must satisfy constraint: 32-bit float attribute.
rewriter.getF32FloatAttr(eps), rewriter.getI64IntegerAttr(1));
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, outputTy, output);
return success();
}
}

99
lib/Conversion/TorchToMhlo/Linear.cpp
View File

@ -71,6 +71,63 @@ Value getPermutedTensor(PatternRewriter &rewriter, Operation *op, Value input,
return result.getResult();
}
RankedTensorType castContractingDim(PatternRewriter &rewriter, Operation *op,
Value &lhs, Value &rhs,
int64_t lhsResultDim, int64_t rhsResultDim,
int64_t lhsContractingDim,
int64_t rhsContractingDim) {
auto lhsTy = lhs.getType().dyn_cast<RankedTensorType>();
auto rhsTy = rhs.getType().dyn_cast<RankedTensorType>();
auto oldLhsShape = lhsTy.getShape();
auto oldRhsShape = rhsTy.getShape();
SmallVector<int64_t> lhsShape;
SmallVector<int64_t> rhsShape;
lhsShape.append(oldLhsShape.begin(), oldLhsShape.end());
rhsShape.append(oldRhsShape.begin(), oldRhsShape.end());
auto lhsContractingDimSize = lhsShape[lhsContractingDim];
auto rhsContractingDimSize = rhsShape[rhsContractingDim];
if (lhsContractingDimSize != rhsContractingDimSize) {
if (lhsContractingDimSize == ShapedType::kDynamicSize &&
rhsContractingDimSize >= 0) {
lhsShape[lhsContractingDim] = rhsContractingDimSize;
auto newRankTy = RankedTensorType::get(lhsShape, lhsTy.getElementType());
lhs = rewriter.create<tensor::CastOp>(op->getLoc(), newRankTy, lhs);
} else if (rhsContractingDimSize == ShapedType::kDynamicSize &&
lhsContractingDimSize >= 0) {
rhsShape[rhsContractingDim] = lhsContractingDimSize;
auto newRankTy = RankedTensorType::get(rhsShape, rhsTy.getElementType());
rhs = rewriter.create<tensor::CastOp>(op->getLoc(), newRankTy, rhs);
}
}
SmallVector<int64_t> outShape;
// set batch dims, will skip invalid dimensions
for (size_t k = 0; k < lhsShape.size(); ++k) {
if (k == lhsResultDim || k == lhsContractingDim)
continue;
outShape.push_back(lhsShape[k]);
}
for (size_t k = 0, b = 0; k < rhsShape.size(); ++k) {
if (b >= outShape.size())
break;
if (k == rhsResultDim || k == rhsContractingDim)
continue;
if (outShape[b] == ShapedType::kDynamicSize && rhsShape[k] >= 0) {
outShape[b] = rhsShape[k];
}
b++;
}
// set result dimensions
if (lhsResultDim < lhsShape.size() && lhsResultDim >= 0) {
outShape.push_back(lhsShape[lhsResultDim]);
}
if (rhsResultDim < rhsShape.size() && rhsResultDim >= 0) {
outShape.push_back(rhsShape[rhsResultDim]);
}
return RankedTensorType::get(outShape, lhsTy.getElementType());
}
void getBmmBroadcast(PatternRewriter &rewriter, Operation *op, Value &inpLhs,
Value &inpRhs, int64_t leadingRank,
size_t dimSizeIndexBits) {
@ -183,10 +240,15 @@ public:
options.dimSizeIndexBits);
}
auto batchDims = llvm::to_vector<4>(llvm::seq<int64_t>(0, nBatchDims));
auto lhsResultDim = nBatchDims;
auto rhsResultDim = nBatchDims + 1;
auto lhsContractingDim = nBatchDims + 1;
auto rhsContractingDim = nBatchDims;
if (lhsRank == 1)
if (lhsRank == 1) {
lhsResultDim = nBatchDims + 1;
lhsContractingDim = nBatchDims;
}
mhlo::DotDimensionNumbersAttr dotDimensionNumbers =
mhlo::DotDimensionNumbersAttr::get(
@ -195,15 +257,13 @@ public:
/*rhsBatchingDimensions=*/batchDims,
/*lhsContractingDimensions=*/{lhsContractingDim},
/*rhsContractingDimensions=*/{rhsContractingDim});
auto resultTy = ConvertAtenOp<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>();
auto outTy =
castContractingDim(rewriter, op, lhs, rhs, lhsResultDim, rhsResultDim,
lhsContractingDim, rhsContractingDim);
output = rewriter
.create<mhlo::DotGeneralOp>(op->getLoc(), resultTy, lhs, rhs,
.create<mhlo::DotGeneralOp>(op->getLoc(), outTy, lhs, rhs,
dotDimensionNumbers, nullptr)
.getResult();
return success();
}
@ -221,7 +281,7 @@ public:
if (failed(performMatmul(op, adaptor, rewriter, lhs, rhs, output)))
return op.emitError("failed to perform matmul operation");
rewriter.replaceOpWithNewOp<mhlo::ConvertOp>(
rewriter.replaceOpWithNewOp<tensor::CastOp>(
op,
ConvertAtenOp<AtenOpT>::getTypeConverter()
->convertType(op.getType())
@ -355,9 +415,15 @@ public:
auto resultRank = std::max(lhsTy.getRank(), rhsTy.getRank());
auto nBatchDims = resultRank - 2;
auto batchDims = llvm::to_vector<4>(llvm::seq<int64_t>(0, nBatchDims));
auto lhsResultDim = nBatchDims;
auto rhsResultDim = nBatchDims + 1;
auto lhsContractingDim = nBatchDims + 1;
auto rhsContractingDim = nBatchDims;
auto outTy =
castContractingDim(rewriter, op, lhs, rhs, lhsResultDim, rhsResultDim,
lhsContractingDim, rhsContractingDim);
mhlo::DotDimensionNumbersAttr dotDimensionNumbers =
mhlo::DotDimensionNumbersAttr::get(
rewriter.getContext(),
@ -365,24 +431,21 @@ public:
/*rhsBatchingDimensions=*/batchDims,
/*lhsContractingDimensions=*/{lhsContractingDim},
/*rhsContractingDimensions=*/{rhsContractingDim});
auto resultTy =
ConvertAtenOp<AtenOpT>::getTypeConverter()->convertType(op.getType());
Value matmulOutput = rewriter.create<mhlo::DotGeneralOp>(
op->getLoc(), resultTy, lhs, rhs, dotDimensionNumbers, nullptr);
op->getLoc(), outTy, lhs, rhs, dotDimensionNumbers, nullptr);
Value matmulPlusBias = matmulOutput;
if (!biasTy.template isa<Torch::NoneType>()) {
// Bias addition broadcasts to the matmul output shape.
matmulPlusBias =
rewriter
.create<chlo::BroadcastAddOp>(op->getLoc(), resultTy,
matmulOutput, bias, nullptr)
matmulPlusBias = rewriter
.create<chlo::BroadcastAddOp>(
op->getLoc(), outTy, matmulOutput, bias, nullptr)
.getResult();
}
rewriter.replaceOpWithNewOp<mhlo::ConvertOp>(op, resultTy, matmulPlusBias);
auto resultTy =
ConvertAtenOp<AtenOpT>::getTypeConverter()->convertType(op.getType());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultTy, matmulPlusBias);
return success();
}
};

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

@ -159,22 +159,24 @@ func.func @torch.aten.batch_norm$training(%arg0: !torch.vtensor<[?,3,?,?],f32>)
// -----
// CHECK-LABEL: func.func @torch.aten.batch_norm$training(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,3,?,?],f32>) -> !torch.vtensor<[?,3,?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,3,?,?],f32> -> tensor<?x3x?x?xf32>
// CHECK: %[[VAL_2:.*]] = mhlo.constant dense<0.000000e+00> : tensor<3xf32>
// CHECK: %[[VAL_3:.*]] = mhlo.constant dense<1.000000e+00> : tensor<3xf32>
// CHECK: %true = torch.constant.bool true
// CHECK: %false = torch.constant.bool false
// CHECK: %float1.000000e-01 = torch.constant.float 1.000000e-01
// CHECK: %float1.000000e-05 = torch.constant.float 1.000000e-05
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x3x?x?xf32>
// CHECK: %[[VAL_6:.*]] = tensor.from_elements %[[VAL_5]] : tensor<1xindex>
// CHECK: %[[VAL_7:.*]] = "mhlo.batch_norm_inference"(%[[VAL_1]], %[[VAL_3]], %[[VAL_2]], %[[VAL_2]], %[[VAL_3]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>) -> tensor<?x3x?x?xf32>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x3x?x?xf32> -> !torch.vtensor<[?,3,?,?],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[?,3,?,?],f32>
func.func @torch.aten.batch_norm$training(%arg0: !torch.vtensor<[?,3,?,?],f32>) -> !torch.vtensor<[?,3,?,?],f32> {
// CHECK-LABEL: func.func @torch.aten.batch_norm$inference(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,3,?,?],f32>) -> !torch.vtensor<[?,3,?,?],f32> {
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,3,?,?],f32> -> tensor<?x3x?x?xf32>
// CHECK: %[[T1:.*]] = mhlo.constant dense<0.000000e+00> : tensor<3xf32>
// CHECK: %[[T2:.*]] = mhlo.constant dense<1.000000e+00> : tensor<3xf32>
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: %[[FLOAT1:.*]].000000e-01 = torch.constant.float 1.000000e-01
// CHECK: %[[FLOAT1:.*]].000000e-05 = torch.constant.float 1.000000e-05
// CHECK: %[[C1:.*]] = arith.constant 1 : index
// CHECK: %[[T3:.*]] = tensor.dim %[[T0]], %[[C1]] : tensor<?x3x?x?xf32>
// CHECK: %[[T4:.*]] = tensor.from_elements %[[T3]] : tensor<1xindex>
// CHECK: %[[T5:.*]] = tensor.cast %[[T0]] : tensor<?x3x?x?xf32> to tensor<?x3x?x?xf32>
// CHECK: %[[T6:.*]] = "mhlo.batch_norm_inference"(%[[T5]], %[[T2]], %[[T1]], %[[T1]], %[[T2]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>) -> tensor<?x3x?x?xf32>
// CHECK: %[[T7:.*]] = tensor.cast %[[T6]] : tensor<?x3x?x?xf32> to tensor<?x3x?x?xf32>
// CHECK: %[[T8:.*]] = torch_c.from_builtin_tensor %[[T7]] : tensor<?x3x?x?xf32> -> !torch.vtensor<[?,3,?,?],f32>
// CHECK: return %[[T8]] : !torch.vtensor<[?,3,?,?],f32>
func.func @torch.aten.batch_norm$inference(%arg0: !torch.vtensor<[?,3,?,?],f32>) -> !torch.vtensor<[?,3,?,?],f32> {
%0 = torch.vtensor.literal(dense<0.000000e+00> : tensor<3xf32>) : !torch.vtensor<[3],f32>
%1 = torch.vtensor.literal(dense<1.000000e+00> : tensor<3xf32>) : !torch.vtensor<[3],f32>
%true = torch.constant.bool true

26
test/Conversion/TorchToMhlo/linear.mlir
View File

@ -5,7 +5,7 @@
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[2,3],f32> -> tensor<2x3xf32>
// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[3,3],f32> -> tensor<3x3xf32>
// CHECK: %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<2x3xf32>, tensor<3x3xf32>) -> tensor<2x3xf32>
// CHECK: %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<2x3xf32>
// CHECK: %[[T3:.*]] = tensor.cast %[[T2]] : tensor<2x3xf32> to tensor<2x3xf32>
// CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<2x3xf32> -> !torch.vtensor<[2,3],f32>
// CHECK: return %[[T4]] : !torch.vtensor<[2,3],f32>
func.func @torch.aten.mm$basic$static(%arg0: !torch.vtensor<[2,3],f32>, %arg1: !torch.vtensor<[3,3],f32>) -> !torch.vtensor<[2,3],f32> {
@ -20,7 +20,7 @@ func.func @torch.aten.mm$basic$static(%arg0: !torch.vtensor<[2,3],f32>, %arg1: !
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,3],f32> -> tensor<?x3xf32>
// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[3,?],f32> -> tensor<3x?xf32>
// CHECK: %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<?x3xf32>, tensor<3x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<?x?xf32>
// CHECK: %[[T3:.*]] = tensor.cast %[[T2]] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T4]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.mm$basic$dynamic(%arg0: !torch.vtensor<[?,3],f32>, %arg1: !torch.vtensor<[3,?],f32>) -> !torch.vtensor<[?,?],f32> {
@ -46,7 +46,7 @@ func.func @torch.aten.mm$basic$dynamic(%arg0: !torch.vtensor<[?,3],f32>, %arg1:
// CHECK: %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK: %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<10x4x5xf32>, tensor<3xi64>) -> tensor<10x4x5xf32>
// CHECK: %[[T10:.*]] = "mhlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<10x3x4xf32>, tensor<10x4x5xf32>) -> tensor<10x3x5xf32>
// CHECK: %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<10x3x5xf32>
// CHECK: %[[T11:.*]] = tensor.cast %[[T10]] : tensor<10x3x5xf32> to tensor<10x3x5xf32>
// CHECK: %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<10x3x5xf32> -> !torch.vtensor<[10,3,5],f32>
// CHECK: return %[[T12]] : !torch.vtensor<[10,3,5],f32>
func.func @torch.aten.bmm$basic$static(%arg0: !torch.vtensor<[10,3,4],f32>, %arg1: !torch.vtensor<[10,4,5],f32>) -> !torch.vtensor<[10,3,5],f32> {
@ -72,7 +72,7 @@ func.func @torch.aten.bmm$basic$static(%arg0: !torch.vtensor<[10,3,4],f32>, %arg
// CHECK: %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK: %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<?x4x?xf32>, tensor<3xi64>) -> tensor<?x4x?xf32>
// CHECK: %[[T10:.*]] = "mhlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<?x?x4xf32>, tensor<?x4x?xf32>) -> tensor<?x?x?xf32>
// CHECK: %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<?x?x?xf32>
// CHECK: %[[T11:.*]] = tensor.cast %[[T10]] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK: return %[[T12]] : !torch.vtensor<[?,?,?],f32>
func.func @torch.aten.bmm$basic$dynamic(%arg0: !torch.vtensor<[?,?,4],f32>, %arg1: !torch.vtensor<[?,4,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
@ -98,7 +98,7 @@ func.func @torch.aten.bmm$basic$dynamic(%arg0: !torch.vtensor<[?,?,4],f32>, %arg
// CHECK: %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK: %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T0]], %[[T8]]) {broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<256x120xf32>, tensor<3xi64>) -> tensor<4x256x120xf32>
// CHECK: %[[T10:.*]] = "mhlo.dot_general"(%[[T9]], %[[T1]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<4x256x120xf32>, tensor<4x120x256xf32>) -> tensor<4x256x256xf32>
// CHECK: %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<4x256x256xf32>
// CHECK: %[[T11:.*]] = tensor.cast %[[T10]] : tensor<4x256x256xf32> to tensor<4x256x256xf32>
// CHECK: %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<4x256x256xf32> -> !torch.vtensor<[4,256,256],f32>
// CHECK: return %[[T12]] : !torch.vtensor<[4,256,256],f32>
func.func @torch.aten.matmul$basic$static(%arg0: !torch.vtensor<[256,120],f32>, %arg1: !torch.vtensor<[4,120,256],f32>) -> !torch.vtensor<[4,256,256],f32> {
@ -124,7 +124,7 @@ func.func @torch.aten.matmul$basic$static(%arg0: !torch.vtensor<[256,120],f32>,
// CHECK: %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK: %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<256x?xf32>, tensor<3xi64>) -> tensor<4x256x?xf32>
// CHECK: %[[T10:.*]] = "mhlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<4x?x256xf32>, tensor<4x256x?xf32>) -> tensor<4x?x?xf32>
// CHECK: %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<4x?x?xf32>
// CHECK: %[[T11:.*]] = tensor.cast %[[T10]] : tensor<4x?x?xf32> to tensor<4x?x?xf32>
// CHECK: %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<4x?x?xf32> -> !torch.vtensor<[4,?,?],f32>
// CHECK: return %[[T12]] : !torch.vtensor<[4,?,?],f32>
func.func @torch.aten.matmul$basic$dynamic(%arg0: !torch.vtensor<[4,?,256],f32>, %arg1: !torch.vtensor<[256,?],f32>) -> !torch.vtensor<[4,?,?],f32> {
@ -147,7 +147,7 @@ func.func @torch.aten.matmul$basic$dynamic(%arg0: !torch.vtensor<[4,?,256],f32>,
// CHECK: %[[T6:.*]] = tensor.from_elements %[[T3]], %[[T5]] : tensor<2xi64>
// CHECK: %[[T7:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T6]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<256xf32>, tensor<2xi64>) -> tensor<1x256xf32>
// CHECK: %[[T8:.*]] = "mhlo.dot_general"(%[[T0]], %[[T7]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<1x?x256xf32>, tensor<1x256xf32>) -> tensor<1x?xf32>
// CHECK: %[[T9:.*]] = mhlo.convert %[[T8]] : tensor<1x?xf32>
// CHECK: %[[T9:.*]] = tensor.cast %[[T8]] : tensor<1x?xf32> to tensor<1x?xf32>
// CHECK: %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<1x?xf32> -> !torch.vtensor<[1,?],f32>
// CHECK: return %[[T10]] : !torch.vtensor<[1,?],f32>
func.func @torch.aten.matmul$3dx1d(%arg0: !torch.vtensor<[1,?,256],f32>, %arg1: !torch.vtensor<[256],f32>) -> !torch.vtensor<[1,?],f32> {
@ -170,7 +170,7 @@ func.func @torch.aten.matmul$3dx1d(%arg0: !torch.vtensor<[1,?,256],f32>, %arg1:
// CHECK: %[[T6:.*]] = tensor.from_elements %[[T3]], %[[T5]] : tensor<2xi64>
// CHECK: %[[T7:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T0]], %[[T6]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<256xf32>, tensor<2xi64>) -> tensor<?x256xf32>
// CHECK: %[[T8:.*]] = "mhlo.dot_general"(%[[T7]], %[[T1]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [1], rhs_contracting_dimensions = [1]>} : (tensor<?x256xf32>, tensor<?x256x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T9:.*]] = mhlo.convert %[[T8]] : tensor<?x?xf32>
// CHECK: %[[T9:.*]] = tensor.cast %[[T8]] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T10]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.matmul$1dx3d(%arg0: !torch.vtensor<[256],f32>, %arg1: !torch.vtensor<[?,256,?],f32>) -> !torch.vtensor<[?,?],f32> {
@ -185,7 +185,7 @@ func.func @torch.aten.matmul$1dx3d(%arg0: !torch.vtensor<[256],f32>, %arg1: !tor
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,256],f32> -> tensor<?x256xf32>
// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK: %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<?x256xf32>, tensor<256xf32>) -> tensor<?xf32>
// CHECK: %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<?xf32>
// CHECK: %[[T3:.*]] = tensor.cast %[[T2]] : tensor<?xf32> to tensor<?xf32>
// CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<?xf32> -> !torch.vtensor<[?],f32>
// CHECK: return %[[T4]] : !torch.vtensor<[?],f32>
func.func @torch.aten.matmul$2dx1d(%arg0: !torch.vtensor<[?,256],f32>, %arg1: !torch.vtensor<[256],f32>) -> !torch.vtensor<[?],f32> {
@ -200,7 +200,7 @@ func.func @torch.aten.matmul$2dx1d(%arg0: !torch.vtensor<[?,256],f32>, %arg1: !t
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256,?],f32> -> tensor<256x?xf32>
// CHECK: %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<256xf32>, tensor<256x?xf32>) -> tensor<?xf32>
// CHECK: %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<?xf32>
// CHECK: %[[T3:.*]] = tensor.cast %[[T2]] : tensor<?xf32> to tensor<?xf32>
// CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<?xf32> -> !torch.vtensor<[?],f32>
// CHECK: return %[[T4]] : !torch.vtensor<[?],f32>
func.func @torch.aten.matmul$1dx2d(%arg0: !torch.vtensor<[256],f32>, %arg1: !torch.vtensor<[256,?],f32>) -> !torch.vtensor<[?],f32> {
@ -215,7 +215,7 @@ func.func @torch.aten.matmul$1dx2d(%arg0: !torch.vtensor<[256],f32>, %arg1: !tor
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK: %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<256xf32>, tensor<256xf32>) -> tensor<f32>
// CHECK: %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<f32>
// CHECK: %[[T3:.*]] = tensor.cast %[[T2]] : tensor<f32> to tensor<f32>
// CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<f32> -> !torch.vtensor<[],f32>
// CHECK: return %[[T4]] : !torch.vtensor<[],f32>
func.func @torch.aten.matmul$1dx1d(%arg0: !torch.vtensor<[256],f32>, %arg1: !torch.vtensor<[256],f32>) -> !torch.vtensor<[],f32> {
@ -241,7 +241,7 @@ func.func @torch.aten.matmul$1dx1d(%arg0: !torch.vtensor<[256],f32>, %arg1: !tor
// CHECK: %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK: %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<256x256xf32>, tensor<3xi64>) -> tensor<?x256x256xf32>
// CHECK: %[[T10:.*]] = "mhlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<?x?x256xf32>, tensor<?x256x256xf32>) -> tensor<?x?x256xf32>
// CHECK: %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<?x?x256xf32>
// CHECK: %[[T11:.*]] = tensor.cast %[[T10]] : tensor<?x?x256xf32> to tensor<?x?x256xf32>
// CHECK: %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<?x?x256xf32> -> !torch.vtensor<[?,?,256],f32>
// CHECK: return %[[T12]] : !torch.vtensor<[?,?,256],f32>
func.func @torch.aten.matmul$proj(%arg0: !torch.vtensor<[?,?,256],f32>) -> !torch.vtensor<[?,?,256],f32> {
@ -257,7 +257,7 @@ func.func @torch.aten.matmul$proj(%arg0: !torch.vtensor<[?,?,256],f32>) -> !torc
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,256],f32> -> tensor<?x256xf32>
// CHECK: %[[T1:.*]] = mhlo.constant dense<1.000000e+00> : tensor<256x256xf32>
// CHECK: %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<?x256xf32>, tensor<256x256xf32>) -> tensor<?x256xf32>
// CHECK: %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<?x256xf32>
// CHECK: %[[T3:.*]] = tensor.cast %[[T2]] : tensor<?x256xf32> to tensor<?x256xf32>
// CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<?x256xf32> -> !torch.vtensor<[?,256],f32>
// CHECK: return %[[T4]] : !torch.vtensor<[?,256],f32>
func.func @torch.aten.mm$proj(%arg0: !torch.vtensor<[?,256],f32>) -> !torch.vtensor<[?,256],f32> {